CTOs have piled into digital AI. The next advantage lies in connecting it to the real world, argues Nick Thompson, co‑founder and CEO of OLO Robotics
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Spend any time with CTOs and the chat is almost always about AI. Copilots, chatbots, analytics pipelines, document understanding – the digital side of the business is getting plenty of attention. But in most of those conversations the AI stops at the edge of the screen while the warehouse, factory floor and inspection bay still run much as they were a decade ago.
Physical automation has always existed. But it has sat behind a wall of specialist expertise, capital and long lead times that most IT and software teams could not see past. That wall is now coming down. The question for technology leaders is whether they treat that change as part of their AI strategy, or leave it as somebody else’s problem.
From niche tech to mainstream platform
My background is in software rather than robotics. After two decades building software teams and technology businesses, I started to look more closely at the intersection of AI and physical automation. What I found was familiar: a specialist domain starting to look like a platform that generalist development teams can use.
Cloud computing is the obvious precedent. Before cloud, running infrastructure meant capital expenditure, specialist skills and lengthy procurement cycles. The arrival of platforms such as AWS, Azure and Google Cloud abstracted that complexity into services that a small team could consume in hours rather than months. The systems did not become simpler; they became accessible through a different model.
Robotics is moving in the same direction. ROS2, the standard framework for programming industrial robots, is powerful and open source but difficult to learn. Making progress has meant understanding a large stack of concepts and tools, which has kept robotics in the hands of specialist roboticists who are in short supply. For many organisations, that has been enough to keep robots on the ‘future plans’ slide.
But recently, platforms have emerged on top of ROS2 that provide browser‑based environments, integrated simulation and SDKs in common languages. Instead of assembling their own mix of simulators, dashboards and custom scripts, development teams can work in one place and treat robots as just another class of endpoint in the architecture. These capabilities are already in production; what is lagging is recognising them as part of mainstream AI and automation strategy.
The robot team you already employ
As perception and planning increasingly rely on AI models, the bottleneck in robotics is shifting away from low‑level control towards data management, orchestration and integration – areas where existing software and IT teams are already experienced.
A developer can now log into a web‑based environment, spin up a simulated industrial robot and experiment with behaviours before any hardware is ordered. Large language models can generate ROS2 code from natural‑language descriptions. Developers review that code, run it in simulation and refine it as they would any other component. The code sits in the same repositories, goes through the same review processes and is deployed through the same pipelines as other software.
This changes who can own automation strategy. Rather than building a separate robotics function, organisations can draw on engineers who already understand their systems and data, and treat robots as part of the same stack rather than as isolated projects.
Rethinking how you buy robots
Traditionally, each robotics deployment has been treated as a bespoke project. You hire specialist roboticists, engage systems integrators, commit capital to hardware and integration, then discover over time whether the automation meets expectations. It is a familiar process for anyone who remembers pre‑cloud infrastructure projects.
A platform‑based approach alters both the economics and the sequence of decisions. Development starts in simulation. Use cases are validated before hardware is purchased. Organisations can run small pilots, discard the ones that do not deliver and scale up the ones that do. Robots become standardised endpoints running defined behaviours, rather than one‑off builds.
Consumption models are evolving too. Some providers now offer robot‑as‑a‑service, where customers pay for hours of operation instead of owning the asset outright. Combined with sim‑first development, this changes the risk profile of automation projects in a way that mirrors the move from owned hardware to cloud computing.
For IT leaders who lived through cloud procurement debates – build versus buy, capex versus opex, open versus proprietary – these patterns should be recognisable. Organisations that engaged with cloud early, in a measured way, gained advantages that compounded over time. The same potential exists in physical automation.
The ‘last mile’ of your AI projects
The highest‑profile robotics deployments tend to be in warehousing and logistics, where autonomous mobile robots move goods between zones and robotic arms pick from shelves. But for many organisations, the more immediate opportunities sit at the boundary between digital programmes and the physical environment.
For predictive maintenance, for example, many manufacturers have invested in sensors, data pipelines and AI models to forecast equipment failures. In practice, these initiatives often culminate in dashboards and alerts on control‑room screens. When an alert sounds, a technician still walks the floor, inspects the asset and performs a standard intervention. Detection has been automated; the response remains manual.
Robots can handle parts of that response. A mobile platform equipped with a camera and basic tooling can be dispatched to a machine, collect visual or sensor data and carry out a simple inspection or reset. The workflow from model output to physical action can be orchestrated by the same software teams that built the predictive maintenance system.
A similar story appears in warehouse operations. Many facilities now use AI‑driven demand forecasting and sophisticated warehouse management systems to decide what to pick, when and to where. Execution can still often be manual however, with staff walking long distances to carry out those decisions. Autonomous mobile robots and robotic picking systems can take the instructions those systems already generate and turn them into physical movement on the warehouse floor.
Organisations have digitised much of the data layer through sensors, telemetry and analytics; but the connection to consistent physical action is still developing. In sectors facing acute labour shortages such as warehouses, ports, construction, brownfield manufacturing and field service, combining human expertise with robotic assistance is becoming a practical response to current constraints.
A strategic choice, not a technical one
Every significant technology shift creates a period in which early adopters can build advantages that are difficult to replicate later. Cloud, mobile and data platforms all followed that pattern. Organisations that engaged early, took time to understand new models and built internal capability now operate differently as a result.
Physical automation is entering that phase. The factors that kept robotics at arm’s length from mainstream IT – the need for scarce specialist skills, the capital intensity of hardware purchases, the complexity of bespoke integration – are being reduced by open frameworks, platform layers, simulation environments and new consumption models.
For CTOs, the question is not whether physical systems will become part of IT strategy, but when and on what terms. If competitors are quicker to connect their AI investments to physical systems, closing the loop from predictive maintenance alerts to automated inspection, or from demand forecasts to in‑facility logistics, they will accumulate operational advantages over time.
The practical move is to treat physical automation the way you would treat any emerging technology. Understand the procurement models. Run a contained pilot that links one existing digital programme to a simple physical workflow. Build internal capability in the teams who already know your systems. If you are still treating robotics as a one‑off patch at the edge of operations, that is now a choice, not a technical inevitability.
Nick Thompson is co‑founder and CEO of Sheffield-based OLO Robotics.
Following the launch of its Core Modernisation Playbook, Publicis Sapient’s Dave Murphy, Head of Financial Services – EMEA & APAC, explores why so many banks struggle to modernise despite investing heavily in AI. The challenge is no longer knowing what to transform, but how to execute transformation safely
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Most banks have spent the last decade applying AI where its impact is most visible, in chatbots, fraud scoring and personalised product recommendations. The results have been real, and in places significant, however, they have also been contained. AI has been working at the edges of the organisation, improving the surfaces that customers and compliance teams interact with, while the actual machinery of the bank carries on untouched. The core systems, the batch processing logic, the settlement rules written in COBOL three decades ago, have continued to run exactly as they always have, insulated from the transformation happening around them.
Meeting the AI Execution Challenge
That insulation is starting to break down. For CTOs weighing where AI investment should go next, the implications are considerable. The question is no longer whether banks need to modernise. Every major institution already knows it needs real-time payments, AI-ready operations, API-enabled architectures and cleaner data foundations. The challenge is execution. How do you modernise decades of critical systems without introducing unacceptable operational risk?
That is where AI is beginning to change the equation. Rather than focusing solely on customer experience or analytics, it is being applied to the engineering process itself. The discovery, extraction, transformation and validation work that has traditionally been the slowest, riskiest and most expensive phase of core modernisation is becoming increasingly automated. This is where the greatest opportunity now exists.
The Core Modernisation Playbook
Publicis Sapient explores this shift in its recent Core Modernisation Playbook. At the centre of that approach is Sapient Slingshot, a platform designed to apply AI across the engineering lifecycle rather than simply the coding stage. Slingshot applies AI across the entire modernisation lifecycle, from understanding legacy systems through to transformation and continuous validation within a governed workflow. The objective is not simply to write code faster. It is to make large-scale modernisation more predictable, auditable and easier to execute.
One global bank recently used this approach to analyse three million lines of COBOL and produce verified functional specifications in eight weeks, work that would previously have taken well over a year and required scarce legacy expertise that is becoming increasingly difficult to find. What changed was not the strategy or destination. It was the execution model.
Core banking code written over thirty or forty years rarely comes with a user manual. Business rules become embedded directly in software, operational workarounds accumulate over time, and critical knowledge often exists only in the minds of engineers who built the systems. As those engineers retire, banks are not simply losing people. They are losing the institutional knowledge needed to modernise safely.
This reflects a broader shift in how modernisation is delivered. Instead of treating discovery, code generation and testing as separate phases owned by different teams, they become part of a connected execution model. Understanding legacy systems, building modern services and validating every change happen as part of the same continuous process. That reduces risk while improving speed, which is exactly what heavily regulated organisations require.
AI-Powered Business
AI-powered business rule extraction is a direct response to that problem. Rather than depending on interviews with legacy experts and time-consuming manual code walkthroughs, automated analysis can surface the logic embedded in legacy programs, cross-reference it against actual processing behaviour, and generate specifications that engineers who’ve never touched the original COBOL can read and act on. In the case above, specification accuracy came in at 95%, and the time needed to analyse individual batch feeds dropped from 35 days to five. That compression changes the shape of what a modernisation programme looks like in its early phases. Work that used to occupy teams for the better part of two years can be substantially completed in months, and what comes out the other end isn’t an informal summary but a set of audit-ready specifications and, in this instance, more than 200 implementation-ready backlog items that become the foundation for everything that follows.
Establishing Strong Foundations For AI
Foundations matter because the validation phase of modernisation is where most programmes break down. There’s no shortage of ambition or strategic clarity in banking transformation. Most major institutions have roadmaps, target architectures and detailed business cases. What consistently falls apart is proving, with sufficient rigour, that a rewritten system behaves identically to the one it’s replacing.
Core banking processes handle exceptions, regulatory edge cases and accumulated business logic in ways that are difficult to fully enumerate up front, let alone test exhaustively by hand. Traditional testing models depend heavily on manually created test cases, subject-matter-expert review and long validation cycles. Coverage gaps are hard to detect, particularly when the legacy behaviour they’re meant to validate against is poorly documented in the first place. Banks end up completing the development work and then getting stuck proving the new system does what it’s supposed to.
AI-driven testing changes that equation by generating and executing test cases at a scale manual teams can’t match, covering standard transactions, edge cases and downstream dependencies systematically rather than sampling them. That doesn’t remove the need for human judgement on what ‘correct’ looks like, but, it does remove the bottleneck of generating enough coverage to have confidence in the answer.
Modernisation Strategy
None of this means banks can modernise the way digital natives do. Core banking platforms sit inside dense ecosystems of payments networks, risk engines, regulatory reporting systems and third-party providers. A change in one system can create consequences dozens of steps downstream, and banks must modernise around uptime, auditability and customer trust in a way a retailer redesigning a checkout flow simply doesn’t. That’s precisely why execution has become the defining challenge. Every bank already knows what it wants to build. The competitive advantage now lies in how effectively it modernises the foundations that make those ambitions possible.
Sapient Slingshot – Deploying An Execution Engine
What AI applied to the engineering process offers is a way to close that gap without pretending the underlying complexity has gone away. Understanding what a legacy system actually does, generating modern code and architecture that preserves the business behaviour underneath it, and validating the result continuously rather than at the end of a multi-year build: these are the three things that have historically made core modernisation slow, expensive and prone to stalling.
Applying AI across the engineering lifecycle does not remove the need for governance, auditability or human judgement. It strengthens them by making documentation, traceability and validation part of the execution process rather than activities completed afterwards. This is where platforms such as Sapient Slingshot are emerging as execution engines for modernisation, enabling banks to modernise with greater speed, control and confidence rather than simply generating code more quickly.
For CTOs, the practical takeaway is less about any single tool and more about where in the lifecycle AI is being pointed. The customer-facing layer of banking has benefited from a decade of AI investment. The engine room is only just beginning. The banks that move first will not simply modernise faster. They will build the AI-ready foundations that determine how quickly every future innovation can be delivered.
The Core Modernisation Playbook
Get your copy of the latest White Paper from Publicis Sapient here
Nicholas Batten, Co-founder and CTO at Nuumad explains how a combination of probabilistic and deterministic AI can help pharmacies adapt for the future.
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As funding pressures on the NHS grow, community pharmacies are reaching a turning point. What used to focus mainly on dispensing medicines is evolving into a more patient facing healthcare network. But this shift is not guaranteed. Without the right technology in place, pharmacies may struggle to keep up with rising demand, regulatory requirements and increasingly complex patient needs.
The conversation around artificial intelligence (AI) has, to date, been dominated by hype. In healthcare particularly, this is problematic. Clinical environments are highly regulated systems where safety, traceability and accountability are non negotiables. The real opportunity, therefore, is not AI adoption, but understanding how intelligent systems, both probabilistic and deterministic, can be deployed responsibly to scale care without compromising compliance or cybersecurity.
AI as a force multiplier, not a replacement
One of the most persistent misconceptions is that AI will replace clinical expertise. In practice, the opposite is true. Across progressive pharmacy networks, intelligent systems are being deployed instead to augment healthcare professionals.
AI, when used appropriately, acts as a force multiplier. It can assist with triage, summarise patient inputs, support protocol adherence and reduce administrative burden. This allows pharmacists to focus their attention where it matters most: clinical judgement and patient interaction.
However, it is critical to recognise that not all intelligence in healthcare needs to be AI driven. In many cases, structured workflows, exact data matching and rules based logic can deliver similar operational gains with greater predictability, reducing the need for human checks. For technology leaders, this distinction is essential. The goal is to optimise system design as opposed to AI usage.
In clinical settings, augmentation is the defining principle, not automation. Human oversight remains central, but it is enhanced by systems that increase throughput, reduce error and standardise care delivery.
Deterministic systems vs probabilistic models
A key strategic decision when deploying intelligent infrastructure in healthcare is choosing where AI is appropriate and where it is not.
Probabilistic AI models, such as large language models, excel in handling unstructured data and generating flexible outputs, at the same time they also introduce uncertainty. Even with high accuracy, their non deterministic nature can create challenges around explainability, auditability and regulatory approval.
By contrast, deterministic logic systems operate on predefined rules and structured data. Every decision pathway is traceable, reproducible and auditable – delivering a clear advantage in highly regulated environments such as pharmacy consultations, especially when paired with an intuitive, best in class user experience.
This is not an argument against AI, but rather in favour of balance. In practice, the most effective architectures combine both approaches: AI at the edges to enhance usability and efficiency, and deterministic systems at the core to guarantee compliance and safety.
For example, AI can help organise patient inputs or highlight relevant information, while final decisions are handled by rule based systems aligned with clinical protocols. This hybrid approach enables organisations to benefit from AI while keeping risk under control.
Beyond individual technologies, the real transformation lies in how systems are orchestrated.
Modern consultation platforms have become dynamic orchestration layers that manage the entire patient journey. From pre-consultation risk assessment and structured symptom capture, to real-time clinical support and automated follow-up, these systems create a continuous, data-driven workflow.
This orchestration reduces fragmentation. Pharmacists no longer need to navigate multiple disconnected systems or manually reconcile information. Instead, they operate within a unified environment where clinical protocols, patient data and decision support are seamlessly integrated.
The impact is significant. Consultations become faster and more consistent, training time for new staff is reduced, and perhaps most importantly, the standard of care becomes repeatable across locations with more empowered healthcare professionals.
Scale can now come into play. Pharmacies can expand private services such as travel health, weight management or preventative care without proportionally increasing operational complexity or risk – avoiding the additional cost and resources typically required to manage and mitigate these challenges on an ongoing basis.
Structured data is the foundation of this model. By capturing patient information in a consistent, machine-readable format, pharmacies can unlock automation, reporting and continuous improvement, all while maintaining compliance with regulatory requirements.
Balancing innovation with regulatory responsibility
The UK regulatory landscape for AI in healthcare is still evolving, with frameworks being shaped by bodies such as the MHRA (Medicines & Healthcare Products Regulatory Agency), creating both opportunity and uncertainty. Organisations that move too slowly risk falling behind and those that move too quickly risk non compliance.
Navigating this requires a disciplined approach to innovation where every system, whether AI-powered or not, must be clinically validated, fully auditable and aligned with existing healthcare regulations. Transparency is critical as if a system cannot clearly explain how it reaches a decision, it is unlikely to meet the standards required for clinical deployment.
Equally, leaders must resist the temptation to prioritise cost or speed over safety. In healthcare, the consequences of failure extend beyond financial penalties and can impact clinicians trust, organisational reputation and mostly importantly patient outcomes.
Responsible digital transformation, therefore, is about making deliberate, informed decisions about where technology adds value and where it introduces risk.
The intelligent pharmacy era
Pharmacies are no longer just dispensing centres, they are becoming decentralised healthcare providers. Intelligent systems are enabling this shift, but success depends on how they are implemented.
AI has a role to play, particularly in enhancing efficiency and user experience. But it is only one component of a broader ecosystem that includes deterministic logic, structured data, secure infrastructure and workflow orchestration.
For technology leaders, the challenge now is to build systems that scale and can be trusted at the same time. The technology is already available. What matters now is leadership and choosing the right architectural approaches, embedding governance from day one and recognising that true innovation in healthcare is measured by reliability, safety, and impact.
Simon Hayward, GM & VP Sales International at Freshworks explains why AI needs to work effectively for mid-sized organisations to truly succeed.
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Determining the impact AI will have on the future of enterprises lies in the hands of the global mid-market sector. Sitting between SMEs and large corporations with revenues between £25m and £500m – mid-market businesses employ 40% of the global workforce and are vital growth engines to the global economy and determining the extent of AI’s global impact.
The path to successful AI adoption
Investors and board-level decision makers will spend much of 2026 identifying key target markets and the mid-market should be top of every list.
Identifying and assuring investors’ delivery of AI ROI is critical to not only the future of the technology but also the success of so many global businesses. Mid-market companies represent roughly a third of private-sector GDP and around 40% of global employment.
They sit at the heart of the global economy, yet they are often overlooked in AI discussions that focus primarily on hyper-scalers or the largest enterprises. These companies do not have the luxury of multi-year transformation programs or unlimited budgets. For them, AI must deliver tangible value quickly or it simply will not stick.
Speed, simplicity and real outcomes
Mid-sized organisations are under constant pressure. They compete with far larger players while working with fewer resources. Cost efficiency matters, meaning time to value is critical.
Software that takes months to deploy or requires swathes of consultants is a non-starter. AI that creates more complexity than it removes will fail, regardless of how sophisticated the underlying models may be.
For AI to succeed in the mid-market, it must be designed around how people actually work. It must integrate easily into existing systems, automate real tasks and deliver measurable outcomes in weeks, not years. Anything less becomes shelfware.
This is where the conversation around AI needs to mature. The future of AI adoption is not about who has the most advanced models. It is about who can turn intelligence into impact, at speed, for teams that are already stretched thin.
The companies powering the economy
The future impact of AI will not be determined by a handful of global giants. It will be shaped by the millions of companies powering the economy every day.
Businesses with revenues of around a billion dollars, not ten or one hundred billion, are using AI to help their teams work more effectively, serve customers better and grow more efficiently. When AI works for them, it scales across the economy.
What is especially encouraging is how traditional businesses are embracing this shift. Companies with decades of history are using AI to modernise core operations, automate routine work and improve both employee and customer experiences. These are not abstract experiments or innovation theatre. They are practical applications that free up investment, improve productivity and support sustainable growth.
This is where AI becomes truly transformative for the mid-market: not as a futuristic concept, but as a tool that helps people focus on higher-value work and helps businesses compete more effectively.
Reducing complexity, not adding to it
If AI is going to deliver meaningful economic impact, we need to meet companies where they are.
That means reducing complexity, not adding to it. It means building technology that is intuitive by design, not powerful but inaccessible. And it means focusing relentlessly on outcomes, not features.
The most successful AI deployments I see share a common trait. They are invisible when they work well. They remove friction instead of introducing it. They make teams faster, smarter and more effective without requiring a fundamental rewrite of how a business operates.
This approach is especially critical for the mid-market, where every investment must justify itself quickly and clearly.
The real test for AI
The mid-market does not need more hype. It needs AI that works. If AI can help these companies scale, compete and grow more efficiently, the economic impact will be great. Productivity gains will compound. Innovation will accelerate. Opportunity will spread more evenly across industries and regions.
The real test for AI is not whether it can impress in a demo. It is whether it can deliver value where it matters most. And that future will be defined by how well we serve the companies at the heart of the global economy.
92% of operators say AI demand is speeding up build timelines, but supply chain challenges are creating costly remediation risks
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New research from data centre infrastructure specialist Onnec has found that surging demand for AI is heaping pressure on to data centre operators to accelerate builds – increasing the risk of delays, higher costs and remediation after go-live.
The survey of 300 senior decision-makers at data centre operators in the UK, Ireland and Nordics found that 92% of operators say they are being forced to compress build timelines to keep pace with AI demand. But 75% say speed-to-market pressure is forcing design decisions before infrastructure requirements are fully understood.
As a result:
Safety and cost pressures are rising: 74% say the pressure to accelerate data centre builds is increasing health and safety risks on site, while 67% say their AI-ready build or retrofit costs have increased in the past 12 months. Among those reporting a rise, costs have increased by an average of 42%.
Quality control is being squeezed: 45% say compressed timelines have reduced time for testing, commissioning or quality assurance, while 43% say they have increased risk of quality issues or rework on completed builds. A further 43% say infrastructure has required upgrades or remediation after go-live.
Supply chain disruption is delaying delivery: 61% have had a project delayed by supply chain issues, while 79% agree that geopolitical instability will affect the cost or availability of data centre components.
“Operators are under enormous pressure to deliver AI capacity quickly, but speed and readiness are not the same thing,” comments Matt Salter, Global Head of Data Centres at Onnec. “A data centre can go live on time and still need optimisation for the AI workload it was built to support. Reworks, retrofits and remediation are increasingly common. And network infrastructure is often where problems show up first. No amount of compute or GPU power can compensate for infrastructure or cabling that wasn’t designed to keep pace with today’s AI demands.”
Supply chain bottlenecks are holding back delivery
Almost half (45%) of operators are leaning towards new builds to deliver AI infrastructure, but one of the biggest pressure points impacting new builds is the supply chain. While 87% of operators are confident they have the right supply chain ecosystem to support fast, reliable delivery, delays are still occurring across the whole data centre environment.
Among those who have delayed a project due to supply chain issues, operators reported issues sourcing GPUs and compute (53%), cooling systems (45%), specialist staff (45%), power distribution equipment (43%) and cabling (39%).
Meanwhile, 29% of operators are leaning towards retrofitting to expand AI infrastructure and maintain pace without sacrificing design quality. But retrofitting is rarely a simple fix. Key challenges include cooling limitations for high-density racks (35%), retrofitting live environments without disruption (33%) and insufficient power capacity (30%).
“The operators who come out ahead in the AI race won’t be the ones who moved fastest,” continues Salter. “They’ll be the ones who took a holistic approach to design from the outset, treating cabling, power, cooling and compute as one connected system rather than separate workstreams managed under deadline pressure. Without that, operators risk locking in performance constraints that are harder and more expensive to address after go-live.”
Jeremy Vianna, Vice President, Strategic Growth at Nearform explains how AI-native engineering (AINE) is rewriting software creation.
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Agile is no longer the differentiator it once was. In fact, it’s now table stakes, with nine in 10 organisations practicing it.
Agile originally emerged as a response to the rigid, adversarial Waterfall model, replacing years‑long requirement gathering and disappointing final releases with rapid iteration, continuous learning and tight alignment between business and technology. By the mid‑2010s, agile thinking spread beyond software into the broader enterprise, becoming a baseline practice, rather than a competitive differentiator – most teams now use it, differing only in execution quality.
Because Agile inherently keeps teams moving in the right direction through constant feedback, the real shift today isn’t about methodology, but about what AI‑native engineering introduces: a new step‑change in capability. Just as early adopters of Agile once dramatically outpaced those on Waterfall, organisations who adopt AI‑driven engineering practices can achieve a similar – but far greater – velocity advantage, amplified by machine‑speed iteration rather than human‑speed process.
The next competitive edge isn’t about how you run ceremonies, it’s about how you produce software – and that’s changing fast. AI-native engineering (AINE) is rewriting software creation. It’s not about using AI as a bolt-on, but instead as a new means of production for intelligent organisations.
The impact of this shift is already visible: early enterprise adopters of AINE report a 20% productivity lift across development and service functions, and some engineers adopting coding assistants noticed their productivity increase by 10-20%. That’s before you even account for agents and closed-loop learning.
What is AI-native engineering?
AI-native engineering is all about using AI tools to create AI solutions – resulting in systems that are built from the ground up, and designed to scale, with AI. The agents execute governed tasks, while the AI-native architecture compounds performance over time, ensuring the system keeps improving.
As an example, at Nearform, we recently embedded governed agents inside an AI-powered cross-product search for a global pharma client. This enabled usage to feed the adaptation of prompts, retrieval and policies. Naturally, relevance improved instantly. But the biggest impact was seen in the discovery timeline, which compressed from six weeks to just two, and the AWS infrastructure came up in minutes, instead of weeks.
But why is this relevant now? Because enterprises aren’t struggling with model accuracy anymore, they’re struggling with operationalising AI at scale. Most companies still haven’t been able to move pilots into measurable production value, only 26% have the capabilities to move beyond proof of concept, and 74% are still failing to realise tangible AI value. AINE is the missing operating model.
The new innovator’s dilemma
Unlike previous delivery models, AI-native engineering introduces compounding velocity. This means automated code generation and tests reduce release cycles from weeks to a matter of hours. Governed AI agents run multi-step workflows, keep context inside the system and reduce rework and handoffs. Meanwhile, continuous evaluation pushes improvements back into prompts, retrieval and policies.
Early AI-native engineering systems may look ‘worse’ on legacy control metrics, as they’re packed with unfamiliar governance paths. But AI-native startups, unburdened by process debt, adopt AI-native engineering from day one – driven by smaller, senior teams supported by streams of governed agents. This immediately results in higher velocity and less waste – and as tooling and methods mature, cost advantage and learning loops become an insurmountable advantage.
McKinsey’s State of AI report evidences this, showing how most organisations are still experimenting, while a minority of high performers – who are redesigning workflows – are realising outsized value.
Failing to adopt AI-native engineering risks the AI-edition of the innovator’s dilemma – protecting today’s governance and delivery model, while tomorrow’s competitors compound away from you.
AI-native engineering erodes yesterday’s moats
The old moats were legacy codebases, hard-won internal knowledge and proprietary data. AI-native engineering attacks each of these.
With AI-native engineering, automated refactoring and code generation significantly drop the cost of rebuild vs. maintain – eroding the value of legacy codebases. Similarly, copilots and agents encode decision history, meaning internal expertise becomes portable across teams, instead of being trapped inside handoffs. And the proprietary data advantage is narrowed by foundation models and synthetic data. In fact, foundation models already encode massive general knowledge, meaning smaller players aren’t starting from zero anymore.
We’ve also seen institutional knowledge become more portable. In the pharma use case above, copilots and agents codified decision history and evaluation criteria into the system, meaning context is able to travel with the work.
The barrier to high-performing systems is collapsing. It’s no longer about “who owns the most data” or what you’ve built – it’s about how fast you’re able to learn and improve.
The measurement problem nobody wants to admit
Currently, most CTOs trying to prove AI’s value are measuring the wrong things. Velocity, story points, lines of code, etc, are all proxies for human labour – friction points getting in the way of progress. When you move at machine speed, these measures collapse to zero. What actually matters is whether AI is solving the problem, not how fast a human would have solved it.
Agile teams often ask the business for time, trust and a year of dedicated resources, so they can ship iteratively, learn continuously and ultimately, deliver the most valuable outcome – without knowing upfront exactly what that will be. Finance however, works on annual planning and wants clear commitments on cost, scope and ROI at the outset, creating a long‑standing tension between agile delivery and fiscal predictability. Traditionally, software engineering could bridge this gap because it relied on decades of experience, solid estimation models and deterministic systems.
But AI-native engineering’s value is compounding. It’s not just seen in sprint velocity, it’s seen through the reduction of rework cycles, the acceleration of the second and third release after the first, and the rate of technical reduction debt over time. These are harder to measure in the short-term, and therefore often easier to dismiss – which is precisely why many organisations stay trapped in pilots. Not because the technology failed, but because large delivery models and metrics aren’t designed to capture compounding value, making it difficult to justify the next investment.
The CTOs realising true value aren’t necessarily those with the best agents, they’re the ones who started by instrumenting their workflows before introducing AI. This gives them a genuine baseline to measure against, meaning ROI conversations become more of a demonstration than a negotiation.
A pragmatic path forward
Start with a thin-slice in production: pick a workflow that has real P&L impact as a starting point. Then ship a governed agent and closed-loop evaluation and measure against the baseline.
Build the AI-native engineering backbone: introduce governance as code into the workflow, embed evaluation into CI/CD, and centralise shared memory to preserve context and knowledge across teams.
Reshape the talent mix: create senior-led pods that combine domain leaders with engineers who are fluent in agents, retrieval and testing automation.
Scale by compounding: each release becomes a reusable capability, which you can use to grow horizontally over time, into adjacent workflows.
AI-native engineering moves beyond bolting AI-features onto existing workflows, towards changing how you build. While Agile made delivery scalable for almost every organisation, AI-native engineering makes it compounding.
The organisations that internalise that now will set the pace for the next decade. The rest will be catching up… at human speed.
By Jeremy Vianna, Vice President, Strategic Growth, Nearform.
Robbie Tilleard, GM EMEA at Lorikeet, on why you don’t need a year, a team of twenty engineers, or a bespoke model trained on your data to deploy an AI in CX
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Revolut launched its AI financial assistant in April this year. Starling launched one three weeks before that. An in-app AI financial assistant has become table stakes for FinTechs overnight.
The good news is you don’t need a year to get there. All you need is a plan and the right data.
Eight Years or Eight Days
Revolut’s AI assistant took a year from announcement to launch, and Starling describes their assistant as the culmination of eight years of work. Both were building the infrastructure before it existed off-the-shelf.
FinTechs we work with deploy an AI assistant in weeks, not months. They don’t build from scratch. They buy infrastructure that’s already solved the hard parts… Knowing when to escalate, handling ambiguity without hallucinating, navigating the tone of a conversation where someone has had their card blocked and is not in the mood for corporate language.
Sound familiar? It should. Every technology shift in financial services has looked like this for a period. Early movers build bespoke, the rest wait for the case studies, and then someone works out you don’t need to build when you can buy much faster. Then it simply comes down to having the right data in place, connecting your systems, and putting in place a clear implementation plan. Financial services knows the playbook. The difference is that the loop has simply gotten a lot faster with AI.
Why Not Wait Longer Then?
Customer expectations have moved, shaped by AI interactions people have outside financial services (support queries answered in seconds, refunds processed mid-conversation), and the gap between ChatGPT responses and waiting for a queued ticket to a human agent is becoming visible in churn data. The FinTechs that move first will set the bar for what customers expect from anyone in the same category.
There is also a regulatory dimension. In the UK, the FCA moved Consumer Duty into more active supervision this year. The review published in March 2025 found that vulnerable customers continue to receive worse outcomes than other customers, particularly where firms have primarily digital customer journeys, and that most firms have underestimated the depth of outcomes monitoring required.
The reaction to AI in many compliance teams looks a lot like the reaction to autonomous vehicles. Every time a Waymo crashes, the genuine safety improvements (fewer accidents overall, faster hazard detection, no fatigue) get less coverage than the single incident.
Yet AI is going to help Consumer Duty over time, not hinder it. Autonomy with the right guardrails produces more consistent outcomes than human handling at scale. An AI system can understand a customer’s problem immediately, resolve it on the spot where it can, and route to the right internal team where it can’t, without queue times, without agent fatigue, without twenty different people interpreting the same policy differently. This ensures the core customer care team remains focused and can prioritise the cases with the most need. And this isn’t a far off future for consumers. The infrastructure to build it already exists off the shelf.
The European Dimension
There are additional points to consider if you operate in Europe. The EU AI Act’s full compliance requirements for high-risk AI sees financial services explicitly on the list.
Being compliant means documented risk management, transparency mechanisms, human oversight baked into the architecture and a traceable record for every decision that the system makes. The cost of getting this wrong is high: up to €35 million or 7% of global revenue for the most serious breaches.
Most organisations aren’t close to achieving requirements. More than likely they’ve rolled out AI as an experiment (that’s good!) but without a plan to get it scaled in production (not so good). Systems built purely to retrieve and respond weren’t designed with auditability or explainability in mind and it isn’t something you can bolt on later.
If you operate in Europe and have not mapped your current customer experience AI stack against these requirements, the time is now.
What You Actually Need
You don’t need a year, a team of twenty engineers, or a bespoke model trained on your data. You need focused use cases, infrastructure that handles the compliance layer, and a team willing to run a tight pilot with clear success criteria and a plan to scale.
You can move now. The companies that don’t are still lacing up.
Callum Pennington, CEO & Co-founder of HBHR, tells us why HR teams that don’t embrace AI risk being left behind.
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Across Europe, AI used in hiring, promotion, workforce management and performance evaluation, is being formally classified as ‘high-risk’ under the recent EU AI Act. HR is now on the regulatory front line. This year, most of the core rules for those systems will begin to apply, with further obligations phasing in through 2027.
That label is supposed to be a safeguard, but inside many HR teams it often lands more like a warning: if something is ‘high-risk’, it is safer to keep your hands off it altogether. Faced with new regulation, loud debate and limited capacity, many business leaders are quietly concluding that the safest option is to delay, restrict or avoid AI in HR altogether.
In my view, that instinct, however understandable, is now one of the biggest risks HR faces. Over-caution does not freeze risk, it freezes progress. The real choice for HR leaders is no longer ‘AI or no AI’. It is what kind of AI they use, under what controls, and with whom in charge.
High risk doesn’t mean ‘don’t touch‘
The EU AI Act organises systems by risk. At the top are prohibited practices, such as emotion recognition in workplaces or social scoring, which are simply banned. Below that sit the ‘high-risk’ systems, including many HR and people management tools, where stricter rules apply. These include documentation, human oversight, transparency and robust data governance.
On paper, that is a familiar pattern. Medical devices, credit scoring and critical infrastructure controls are all treated as high-risk too. The message isn’t ‘never use them’, it’s ‘treat them as important, design them carefully, and put them under proper governance’.
In HR, though, ‘high-risk’ can sound scary. If something goes wrong in a recruitment algorithm or workforce-planning tool, it is not hard to imagine the headlines. That fear is pushing some organisations towards a defensive posture, to park AI projects, ban tools outright, and stay in the comfort zone of manual decisions.
The problem is that ‘going manual’ isn’t the same as ‘being safe’. It simply hides risk better. Spreadsheets do not come with model cards or audit logs, but they can still embed bias, errors and inconsistency.
In many ways, this moment mirrors the shift from paper records to spreadsheets. At the time, some organisations worried that digital tools would introduce new risks. In reality, spreadsheets improved accuracy, visibility and accountability. AI represents a similar shift today — the difference being that well-designed systems can now surface risks earlier rather than burying them inside manual processes.
According to HBHRʼs own research drawing on a survey of 2,000 UK employees, ongoing manual payroll errors are already having an immense impact on the workforce. Twenty percent of workers say a single payslip error has caused them to miss a bill, while 18% report having to borrow money because of payroll mistakes.
A well designed, well documented AI system with human oversight is not the opposite of compliance. Increasingly, it is exactly how compliance will be demonstrated.
The hidden cost of over-caution
When organisations freeze on AI, they push more strain onto already stretched HR teams juggling legacy systems and manual workarounds. Modern tools are simply an expectation, with HBHRʼs research demonstrating that 85% of employees expect their employers to use up-to-date technology to minimise mistakes, with 72% saying that the technology their employer uses directly correlates to the confidence they feel in their pay being accurate and on time. Outdated legacy tools not only contribute to a lack of trust among employees, but create extra work and pain for employers.
You see it most clearly in high-volume processes, where recruiters spend days sifting through CVs, whilst payroll teams are forced to manually reconcile inputs, hoping nothing has been mistyped along the way. At the same time, boardrooms are being told that AI is a once-in-a-generation opportunity, and that countries like the UK face a huge economic gap if they fail to develop the right skills and adopt the right tools. HR simply cannot sit that conversation out and still claim to be a strategic partner.
Over-caution creates its own risk: higher error rates in core people processes, slower responses to regulatory change, and HR teams so buried in admin that they have no capacity left for culture, capability or workforce planning. These errors and barriers are not without consequence – 61% of employees surveyed by HBHR would look for a new job after six months of repeated payroll errors and delays.
Moving beyond ‘AI vs humans‘
Public debate often frames AI as a direct threat to HR roles, as algorithms that replace recruiters and chatbots that replace HR advisers. This narrative is just not true. Most HR teams I meet are not short of work, rather theyʼre short of capacity. The opportunity is a symbiotic HR function, where each does what it is best at.
In practical terms, AI is very good at repetitive, pattern-recognition tasks at scale. It can scan thousands of CVs for clearly defined, job-relevant criteria and hand recruiters a shortlist that is actually manageable. It can monitor payroll and pension data for anomalies, payments that do not match any live employee, or contributions that look out of line with policy, and flag them long before they become front-page stories. It can power assistants, like our own HRGenie at HBHR, that answers routine questions about holiday, pay and policies on demand, instead of sending employees into ticket queues.
People, by contrast, are good at context, empathy and ethical judgement. They are the ones who should decide whether a flagged pattern is a genuine risk or a perfectly reasonable exception, whether a candidate is the right fit for a team, whether a performance signal points to misconduct, burnout or a problem with the role itself.
The most effective HR functions I see are not those that automate everything they can, nor those that reject automation outright. They are the ones that deliberately design people-led, AI-supported processes, with humans making the calls that actually affect jobs, pay and progression.
From admin to engine
Across organisations, three clear camps are emerging in the approach to AI: those that use it with clear intent by starting with real operational problems, those that experiment widely but lack a coherent strategy and those that reject it outright as too risky. The third, overly cautious group is already beginning to fall behind, not because they lack talent, but because they lack the tools to scale it.
Everyone talks about wanting ‘strategic HR’. But strategy requires headroom and trustworthy data. If HR leaders are still spending most of their week reconciling numbers between three systems, or chasing down the source of basic discrepancies, it is almost impossible to play that strategic role.
The combination of the EU AI Act and accelerating workplace change makes this an inflection point. HR can either retreat into manual, reactive processes in the name of caution, or step forward and shape a people-led, AI-enabled function that is more resilient, more compliant and more human than what came before.
That does not mean turning HR into a testing ground for experimental tools. It means getting the foundations right: unified HR and payroll systems rather than fragmented stacks of disconnected tools, clear governance for AI, and HR leaders who are confident asking hard questions about how technology works, not just what the sales slide promises.
Handled thoughtfully, AI will not replace the ‘people’ side of HR. If anything, it will finally give HR teams the time, visibility and capacity to focus on it properly.
In an era where regulations are tightening, skills are shifting and expectations are rising, an overly cautious approach to AI in HR is no longer the safest option. Standing still may ultimately prove far riskier than moving forward thoughtfully.
Cirata CEO Stephen Kelly tells us why having fully centralised data is the key to creating seamless AI access for companies.
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MIT reported that up to 95% of enterprise AI projects fail. Businesses are racing to adopt the technology and rushing execution in the process, significantly eroding ROI and potentially leaving thousands of businesses at risk of regulatory exposure.
Despite the urgency, there lies an invisible burden which is undermining progress. It is called AI debt and it may be the single greatest obstacle standing between AI ambitions and commercial reality.
AI debt is the accumulated result of incomplete digital transformation. It is every decision which meant legacy infrastructure was never fully retired, or the fragmented data siloes that were never unified. Any new platforms layered on top of such large and complex data sets that haven’t been organised properly, are creating complexity rather than clarity.
On their own, these shortcuts may have seemed pragmatic but collectively, they are now stalling innovation.
The risk of unfinished business
Recent analysis from McKinsey highlights the scale of the missed opportunity. Despite AI tools becoming commonplace today, 63% of organisations reported that they are still experimenting or piloting early-stage AI projects. This shows that most organisations are yet to embed AI deeply enough into their workflows and processes to capture its full value, estimated globally at between $2.6 trillion and $4.4 trillion.
The number one reason for this is years of bolt-on systems that have created tangled IT estates that slow decision-making and make rapid innovation nearly impossible. Running legacy and modern environments side by side inflates maintenance costs and introduces operational confusion which can result in failed implementations. These poorly executed migrations waste capital and introduce security and compliance risk, particularly under regulations such as General Data Protection Regulation (GDPR) and Digital Operational Resilience Act (DORA).
These consequences are measurable. Projects are delayed by months, which has a knock-on effect for budgets. Overall, the whole process inevitably grinds to a standstill because the data required to fuel AI models remains locked away in silos. Estimates suggest that between 50 and 70 per cent of enterprise data remains unconnected and inaccessible for advanced analytics.
The rise of AI debt
The push towards autonomous systems capable of independent decision-making is amplifying this risk of failure. While a majority of organisations plan to deploy AI agents in the near term, only a fraction have centralised their data or ensured their infrastructure can handle the projected surge in workloads.
The statistics are sobering. A recent report from Cisco found that fewer than one in five companies have fully centralised their data for seamless AI access. Over 60 per cent expect workloads to increase by more than 30 per cent within the next few years. Less than a third feel fully prepared to secure agentic AI systems against emerging threats.
Even the most digitally advanced firms are grappling with spiralling compute costs and persistent talent shortages in cybersecurity and AI engineering. In the same way that technical debt slowed software development in the 1990s and 2000s, AI infrastructure debt threatens to stall the current wave of transformation before it delivers meaningful returns.
At its core, AI debt is a data problem. AI systems amplify whatever they are trained on. If the data is incomplete or contextually degraded, the outputs will be flawed, often in ways that appear plausible but lack integrity. This phenomenon, sometimes described as AI slop, is not merely a technical nuisance but a commercial and reputational risk.
This occurs when organisations migrate or modernise without preserving metadata, lineage and governance and so its meaning is lost along with trust. In regulated industries, that erosion has legal implications. In competitive markets, it has revenue implications.
The path forward requires paying down the debt.
Paying off AI debt
At Cirata, we always advise that the best way to eliminate AI debt is to address fragmentation at its source. This means employing a system that can create a unified, interoperable data foundation that supports AI at scale. By decoupling data orchestration from underlying infrastructure, organisations can move, replicate and integrate data seamlessly across on-premises, hybrid and multi-cloud environments without disrupting production systems.
This approach delivers several strategic advantages. Automated data flows across clouds and platforms ensure models are trained and updated with the latest data. Businesses should always look to leverage open standards, such as Apache Iceberg, to prevent vendor lock-in and preserve long-term flexibility.
IT leaders should explore solutions to help them make sense of their data. By centralising governance and eliminating brittle integrations, organisations can feel confident they’ll be on the right side of AI project success. Most importantly, they can break the cycle of making short-term compromises that accumulate into long-term risk.
No algorithm will compensate for structural weakness
The promise of AI remains immense. Autonomous systems and generative models will continue to reshape industries. But no algorithm can compensate for a weak foundation. Just as a building requires structural integrity before additional floors are added, AI requires a unified and trusted data infrastructure before it can deliver sustained value. The organisations that thrive in 2026 and beyond will not be those that launched the most pilots. They will be those that had the discipline to eliminate their AI debt first.
Errol Rodericks, Director of product marketing EMEA and LATAM at Denodo, on why the future of insurance will be defined by whether humans and AI agents have the right data, in the right form, to make the right decisions
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AI adoption across the UK insurance sector continues to accelerate, yet the impact is falling short of the industry’s expectations. The narrative is now shifting beyond systems that analyse data and generate recommendations towards agentic AI. Capable of taking action, whether that means initiating claims processes, identifying potential fraud in real time, refining underwriting decisions or triggering next-best actions. Many see this as the next major leap forward for insurance, and they may be correct, but not for the reasons most people think.
While AI development certainly doesn’t lack pace, most of the insurance industry remains constrained by a more fundamental issue. AI cannot make trusted business decisions until it first understands the business. That understanding depends on trusted, relevant data being delivered in the right context and at the right time. Without it, insurers will struggle to give AI systems meaningful autonomy, however advanced the models become.
This is not because insurers lack data or technology. Over the past decade, the industry has invested heavily in data lakes, advanced analytics and AI tools, and integration and data engineering pipelines. Yet despite these investments, many organisations continue to struggle to move AI beyond proof-of-concept projects and into day-to-day operations.
Meeting the Challenge
The challenge is not data availability. It is ensuring data is trusted, relevant and readily accessible when decisions need to be made.
Few industries experience this challenge quite like the insurance industry. Decision-making depends on information drawn from multiple policy, claims and customer systems, alongside external data sources such as telematics, weather and credit data. When that information is fragmented or delayed, even the most advanced AI systems are left working with an incomplete picture, making it difficult for AI to understand the wider business context behind every decision.
Agentic AI does not solve this problem. It exposes it.
Access Alone Is Not Enough
Many organisations respond by building a shared data foundation consisting of a unified layer where humans and AI agents can access the same information. While this is directionally right, it is incomplete. The challenge is not that organisations lack a shared data layer. They struggle to deliver the right version of data for each decision, at the moment it matters.
Insurance operates on multiple, decision-specific views of data, each with distinct requirements:
Claims decisions depend on real-time, enriched incident data.
Underwriting relies on forward-looking risk models and external signals.
Fraud detection requires cross-entity patterns and behavioural analysis.
Customer servicing depends on a simplified, current policyholder context.
These are not variations of the same dataset; they are purpose-built representations of data, shaped by different latency, governance, and semantic needs, which becomes even more critical with agentic AI. Different agents operate at different points in the decision lifecycle, and require different data, in different forms, at different times.
A shared data layer can improve access to information, but access alone does not guarantee better decisions. Context transforms data into business understanding, and business understanding is what enables trusted decisions.
Turning Data Into Action
This is where many AI strategies stall. Most architectures are designed to store, process, and analyse data, but not to activate it at the point of decision. There is a fundamental gap between data being available and data being usable within real-time workflows.
Agentic AI operates directly in this gap. Without access to live, governed, and contextually aligned data, agents operate with partial understanding, and their outputs become unreliable. This is why many AI initiatives remain stuck in experimentation.
To move forward, insurers need to rethink how data is delivered. Not as raw datasets or reports but as data products. A reusable, governed, and outcome-aligned data asset designed to support a specific decision or workflow is what’s needed. Instead of exposing raw data, insurers should deliver contextualised, decision-ready views, with embedded governance and policy controls, consistent business semantics, and real-time access to internal and external sources.
For example:
A claims data product unifying FNOL, policy data, repair estimates, and external signals.
A fraud data product combining claims history, network relationships, and behavioural indicators.
An underwriting data product integrating internal risk data with third-party enrichment.
These are not static datasets. They are decision-ready data assets, designed to deliver the right information, in the right context, for a specific business outcome.
Why Data Quality Matters Now More Than Ever
For agentic AI to deliver value, data must be live, governed at access, semantically consistent, and traceable. This is where a logical data layer becomes critical, not just as an integration approach, but as a way to connect distributed data in real time, apply governance dynamically, and deliver consistent, business-ready views across systems. This enables both humans and AI agents to act with confidence, without introducing further fragmentation.
The insurers that lead in 2026 will not be those with the most advanced models. They will be the ones that connect AI directly to business outcomes. That means starting with the outcome, such as reducing claims cycle time, improving fraud detection, increasing underwriting precision, or enhancing customer experience, and working backwards to define the decisions, data and systems required to support them.
This is how AI moves from experimentation to operational impact.
Where AI Initiatives Succeed or Fail
The next phase of AI in insurance will not be defined by advances in model performance. It will be determined by how well AI understands the business it is supporting. That requires trusted, relevant data, but it also needs the business context that allows AI to interpret that data correctly and make decisions insurers can trust.
Agentic AI accelerates this realisation. It makes clear that data must be trusted, contextual, available at the moment of decision, and aligned to outcomes. The goal is not simply to give AI more information. It is to help AI understand how the business works well enough to make confident, consistent decisions. Those who solve this will scale AI successfully, and those who do not will continue to pilot without transformation.
The future of insurance will not be defined by whether humans and AI agents share the same data. It will be defined by whether they have the right data, in the right form, to make the right decisions. That requires a shift from shared data to decision-ready data, from access to activation, and from experimentation to measurable outcomes. The real inflection point for AI in insurance will come when organisations enable AI to understand the business well enough to make decisions that customers, employees, and regulators can trust.
Russell Gammon, Chief Innovation Officer at Alphatax, on why tax needs to operate on a shared foundation where information can flow and be updated consistently
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The modern CFO has more responsibilities than ever before, with a 2024 study finding that over 80% had taken on additional demands in the previous two years. As the report points out, “CFO is not a finance role. It is a strategic business role whose mandate is finance.”
With multiple priorities competing for time and attention, tax sits at the centre of many of the metrics CFOs are accountable for, from cash flow and risk exposure at the operational end of the scale to corporate reputation at the strategic level. Tax functions are under growing pressure to meet these expectations, with workloads increasing even as resources and budgets in many organisations remain flat or decline.
Under these conditions, tax teams are forced into a reactive, deadline-led operating model, and it’s understandable that work is completed in cycles rather than as part of an ongoing, connected process that continuously inputs into business decisions. The problem this creates, however, is that it perpetuates a clear disconnect between the undeniable importance of tax and the role it can and should play in day-to-day financial management.
A Taxing Set of Problems
It’s a situation that has evolved over a considerable period of time. For instance, most CFOs progress through finance-led career paths, with limited exposure to the depth and breadth of tax as a defined discipline.
In addition, tax is highly specialised and fragmented. It covers multiple complex, often interrelated requirements, each with its own processes and requiring access to specific subject matter experts. Even within tax teams, knowledge is distributed across individuals.
Tax also operates in a context where interpretation is often required, and clear-cut answers are not always immediately available. Bring these issues together, and it’s hardly surprising that many organisations view tax as a risk-sensitive function that must exist in its own bubble, rather than as one integrated into broader financial strategy or front and centre for the CFO.
The many and varied supporting technologies used across the tax function inevitably reflect this fragmentation. A large, expanding list of multiple-point solutions is used to address specific requirements rather than to provide a unified view of the tax function. This means tax data is almost inevitably spread across disparate systems and teams, and as any tax professional knows, the potential for inconsistencies and errors is ever-present.
Much of the work required to manage this complexity remains manual, and even where integration exists, insights are often buried within compliance outputs rather than being fed back into planning or decision-making.
Empowering the CFO
For CFOs, this situation almost inevitably draws them towards retrospective processes and analysis, when what they need is forward-looking insight. In practical terms, this reinforces a cycle of reactive intervention and increases the likelihood that opportunities to improve tax outcomes are missed.
This is increasingly at odds with the role CFOs are expected to fulfil, particularly given the emphasis on agile decision-making and the high levels of accountability that come with the job. How, for example, can a CFO be expected to make the best decisions about business expansion or investment when they do not have a clear, connected view of the organisation’s tax position?
Addressing these challenges should start with a commitment to break down the process, expertise and information silos that have historically defined the tax function; away from managing tax as a series of separate activities and towards a more integrated approach.
Tax needs to operate on a shared foundation where information can flow between different areas and be updated consistently. CFOs should be empowered with a coherent view of the organisation’s tax position and what is happening at any given point in time, without relying on the need for remedial work or retrospective reporting.
With better, integrated visibility at their disposal, the CFO can shift their focus towards strategic planning and opportunities, while the tax team can also address potential risks before they escalate.
Data Insights
A key part of this approach is making better use of the data generated through compliance processes, treating it not simply as an output but as a source of insight that can also be fed back into planning and forecasting.
Clearly, technology has an important role to play in facilitating this transformation, particularly in reducing the manual effort associated with routine tasks and improving the accessibility of data across the organisation. Don’t forget, the objective is not to remove human judgment from tax, but to ensure that specialists can focus their time on supporting the wider business.
In this context, the tax function takes on a different, more strategic role, with CFOs empowered to draw it into their decision-making processes rather than engaging with it only at the point of reporting, as so many still do today.
Hugh Scantlebury, CEO and Founder of Aqilla , on why the finance teams that benefit most from AI will likely be the ones that strike the right balance between automation and oversight
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At first glance, finance and accounting appear to be ideal environments for AI integration. The work is structured, rules-driven and built on numerical data, which AI can automate and process at scale and speed. As such, it’s ideal for much of the repetitive work — such as invoice capture, reconciliations, reporting, and anomaly detection. These tasks still consume so much time and reduce space and capacity for strategic and creative thinking.
But as confidence in AI grows across the sector, and as the technology is integrated into accounting and finance software, is there a risk that organisations once fearful of the technology may go to the other extreme? What might happen if they lean so deeply into AI that finance and accounting professionals become distant and removed from the numbers?
These questions matter because the decisions that sit behind the numbers are rarely driven by pure logic. Financial strategy is shaped by risk appetite, leadership judgement, organisational priorities and sometimes even internal politics.
So many entrepreneurial success stories include a fateful risk, gamble or moment of inspiration that defies established financial wisdom. A founder deciding whether to invest in growth or a charity balancing financial sustainability with its mission does not rely on numbers alone. That means a surprisingly high number of commercial decisions have a human dimension that AI cannot, and arguably should not, replace.
Balancing AI and Human Strengths
So how can organisations maintain the human intuition and instinct that sits behind so many corporate transactions while embracing AI? The answer is to use the technology to remove repetition rather than people from core financial processes. In practical terms, that means using automation to reduce cognitive load so people can focus on interpretation, creativity and decision-making — the human parts of finance and accounting.
That’s a sensible approach because poorly implemented automation accelerates errors and obscures decision-making processes. It can also cut humans out of the process exactly at the point where instincts and experience are most needed. In that situation, AI-enabled systems simply become faster at making poor decisions. And those poor decisions end up costing time as well as money.
Once that happens, the space, time and resources that finance and accounting leaders are trying to create for more strategic work will rapidly shrink. By contrast, when humans actively guide AI systems, review outputs and set boundaries, automation becomes a powerful extension of human capability rather than a substitute for it.
The Limits of Automation in Decision-Making
In finance and accounting, numbers often create a sense of objectivity and certainty. But financial reporting still involves interpretation, context and judgement. A technically correct output is not always the same thing as the right commercial decision for a business, its employees or its long-term strategy.
As such, it’s important that accounting and finance professionals at every level do not lose a grip on their data when engaging with AI and automation. Aside from losing an understanding of the systems and processes behind the outputs, there’s still an ethical responsibility to deploy AI in a way that preserves human oversight, authority and compliance in financial reporting. That’s because, for the first time, we’re asking technology, in the form of AI, to provide an opinion on our data — not just deliver the logic and the numbers.
For that reason, finance leaders still need visibility into how AI-generated outputs are reached, the ability to challenge them when necessary, and a clear understanding of the underlying data behind the results. That’s important because AI output is ultimately based on prediction, and prediction is not the same as established and quantifiable truth.
Keeping Score
One possible middle ground is confidence scoring and validation workflows. Rather than unquestioningly trusting every AI-generated output, organisations can introduce processes that flag lower-confidence results for human review before action is taken. That creates a more balanced relationship between automation and oversight, while also giving finance teams clearer visibility into how reliable or complete AI-generated outputs actually are. The goal should be confidence in AI-supported workflows, not unquestioning reliance.
That visibility, however, should not sit solely with grads and junior finance staff. They need to understand the manual calculations and processes sitting behind automated systems so they can properly challenge the results. But the same principle applies at senior levels too. Experience and seniority should not create distance from the underlying logic behind the numbers. If anything, AI makes that visibility even more important.
Otherwise, organisations risk creating the worst of both worlds: juniors who can’t challenge AI outputs because they haven’t learned the manual processes, and complacent seniors who know the manual systems and assume AI is following them. It means organisations can end up with more information at their fingertips than ever before, while simultaneously becoming more detached from the underlying data and logic behind it.
That risk becomes even more significant at senior levels, where financial decisions often carry wider operational, commercial and strategic consequences. The more detached leaders become from the logic behind the outputs, the greater the impact when reliability or credibility issues emerge within the data.
Conclusion
Productivity gains from AI and automation have the potential to create more space for higher-value thinking rather than remove people from the process. The information AI surfaces can ultimately help senior leaders make more creative and strategic decisions by revealing connections, patterns and insights that may previously have remained hidden within the data.
Leaders have always relied on summaries, dashboards, and reporting layers to help them make decisions. But AI dramatically widens the gap between decision-makers and the operational reality beneath the numbers. The danger is not simply inaccurate data. It’s overconfidence. When systems appear highly intelligent and highly efficient, organisations can gradually stop questioning how conclusions are reached in the first place.
Ultimately, organisations may wish to focus on people-led automation rather than handing complete control to systems. The goal should be confidence in AI-supported workflows, not unswerving reliance on automated outputs — with AI acting as an extension of human capability rather than a replacement for judgement.
The finance teams that benefit most from AI will likely be the ones that strike the right balance between automation and oversight. Hand over the repetition and the manual processing, but don’t lose visibility into the data itself. Because if organisations surrender that understanding completely, all the entrepreneurial instinct, creativity and commercial judgement in the world may no longer be enough to compensate for the decisions being made underneath them.
Louis Landry, Chief Technology Officer at Teradata explains why it’s an exciting time for enterprises who are leveraging agents to transform their processes.
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Technological advances such as agentic AI have completely changed the way enterprises work for the better, but they have also posed several challenges. Businesses are not struggling with AI because the technology isn’t ready, but because the infrastructure beneath it isn’t.
Several demonstrations will have you believe that AI implementation is just another straightforward process, but this is not the case at all. Such presentations are impressive because the data is already polished, the queries are direct, and the answers are predictable, while the overall environment is controlled. But this is not the enterprise reality.
The typical enterprise runs multiple different SaaS applications, each with its own platform, data models and methods for showcasing the same business concepts. Not only that but they’re layered on top of millions of relationships and contextual rules that no demo was ever designed to handle. As a result of the complex state some enterprises’ systems are in, most of their AI adoption projects end up stalling or failing. This was also confirmed by last year’s MIT report which stated that 95% of businesses don’t see a return on their AI investment despite spending $30 to $40 billion.
So what are so many enterprises doing wrong and how can they revert it?
What’s hindering enterprises’ AI journey
Most AI models are impressive in isolation, but an enterprise environment doesn’t operate in siloes. Businesses have layered systems, interdependencies and nuanced rules that AI models will need to be able to support, otherwise they’ll fall short. Without the ability to act autonomously, they will not be able to provide meaningful change to the business.
With this in mind, a successful AI deployment needs proper orchestration. The 5% of enterprises that are winning this race coordinate their AI agents to own different areas within the business and come together through an integrated foundation instead of depending on a single model for everything. Successful enterprises see AI as a strategic differentiator, grounded in integrated, proprietary datasets that competitors can’t duplicate. They also ensure their systems are following the business objectives and operational limitations, as well as compliance requirements and competitive pressures.
AI autonomy in four levels
In terms of what successful enterprises have in common, they are all in what I refer to as the “AI autonomy journey” where they usually go through four different levels to build a robust foundation that transforms agentic AI deployments to capable and trusted advisors. Each business will achieve different levels of autonomy depending on how technologically advanced they are, their complexity and business value.
Level 1
Looking into these levels in more detail, the first stage is where most businesses sit today. Their AI systems are taught to respond to basic interactions in natural language and collect data, though humans are still needed to validate the results.
Level 2
The next level sees AI tools taking a more trusted analyst position. They start to comprehend context and intricacies while understanding connections between datasets. Such systems are able to recognise business terminologies and manage scenarios independently.
Here’s an example to help you visualise the two levels. An enterprise in the banking sector wants to understand its loan repayment rates across different customer segments and product types. A level one system will be able to retrieve and share the repayment figures with the exact criteria, while a level two system will not only gather the numbers, but will also flag which customer profiles are showing signs of risk and recommend next steps for the analyst. This stage goes beyond simply reporting figures, as it is able to translate complex financial patterns into clear and actionable intelligence.
Level 3
Then we move on to level three, which is a significant turning point for an enterprise’s AI autonomy level. At this stage, the technology is able to make connections across your business and recommend actions with insights, including retention strategies, adjusting contract terms or raising potential risks.
Level 4
And finally, at level four, we see AI stop advising and instead start acting. For example, if you prompt it to optimise customer retention while keeping margins at a certain point the system will autonomously identify at-risk customers, deploy the right strategies and track the results while adjusting its tactics in real-time. With that said, employees are still in control of defining the goals and setting the risk tolerance levels and compliance requirements.
The above levels reveal a clear progression. If level one is “prompt and prove” and level two is “prompt and trust,” then level three is “prompt, understand and suggest” and level four is “define, deploy, adapt”. Through this autonomous journey, the system gradually becomes an intelligent business analyst that actively improves how decisions are made, enabling the business to operate at a scale that simply wasn’t possible before.
The road to competitive advantage
As enterprises start exploring what their AI autonomy journey looks like, we will also see a gap forming between businesses at different stages. Companies moving on to higher levels of AI autonomy will gain real competitive advantage much faster than others. Having said that, achieving this isn’t as simple as a matter of deploying more powerful AI models. It requires a robust knowledge base that AI systems can actually depend on, otherwise they become unreliable and decisions get misaligned. This is where context engineering comes into play as a critical tool helping autonomous agents become more accurate and useful, and shaping how AI systems interact with the broader business. Context engineering also enables agents to perform complex tasks without worrying about errors coming up in the process.
It’s an exciting time for enterprises who are leveraging agents to transform their processes. The ones who are successful in their adoption will be those who understand early on that AI is only as powerful as the foundation it sits on. This means that it’s crucial to get the datasets, context and orchestration right from the beginning, to be able to progress with your AI autonomy journey and set yourself apart from competition.
Sascha Giese, Tech Evangelist at SolarWinds talks to us about how to manage the transition from AI experimentation to delivery.
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When Johnson & Johnson revealed a shift in its AI strategy last year, it signalled the end of a period of experimentation for the pharmaceutical giant and the beginning of a more disciplined, results-driven era.
At its peak, J&J employees were pursuing nearly 900 individual AI-related use cases. But as the WSJ reported at the time, this was a position that was simply unsustainable. Instead, the company decided to change tack and home in on high-value generative AI use cases in areas such as drug discovery and supply chains.
For many in the tech sector, this was a high-profile example of a business deciding to call time on pilots and trials in favour of a more structured approach where AI finally has to earn its keep.
Fast forward to the beginning of 2026, and the State of AI in the Enterprise report by the Deloitte AI Institute suggests that many companies are also following a similar path, with business leaders becoming impatient and wanting to see a return on their investment.
AI is shifting from a one-off investment to an ongoing operational expense
And it’s easy to see why. Amid the current AI boom, a recent report by Mavvrik, a US-based IT financial management platform, found that 80% of enterprises missed their AI infrastructure cost forecasts by more than 25%. It claimed that AI costs are “crushing margins” and that AI-related overheads are harder to manage than cloud costs because AI introduces new variables where “minor changes in usage can spike spend by 100x”.
It’s a good point. Unlike traditional software, which tends to follow predictable instructions, AI systems explore multiple possible paths to reach a solution. Sometimes that could mean resolving an issue in five minutes. At other times, it might mean AI systems retrying, looping and testing alternatives for hours, leading to additional expense.
But there are other unforeseen costs as well. The dash to embed AI can often expose shortcomings in existing IT infrastructure. Legacy systems and on-prem environments, for example, that might have been perfectly suited to a pre-AI world may now lack the necessary processing capacity required.
What rightsizing AI adoption really looks like
As a result, this may force organisations to bring forward plans to upgrade their technology. But at what cost? It’s not simply a case of writing a cheque and flicking a switch. In practice, it means being clear about the task AI is meant to improve, and defining measurable indicators and a baseline against which to judge impact.
But rolling out AI isn’t just about the technology. One of the lessons we’ve all learned over the last couple of years is that if workflows are unclear or the quality of data is poor, then AI will expose those shortcomings. Without a proper assessment of whether the company is ready for AI, such a project could quickly turn out to be unsuccessful, becoming a time and money sink.
Equally, if AI is embedded into structured environments – with defined responsibilities and feedback loops – then we also know that AI can genuinely improve outcomes.
For instance, a recent report by SolarWinds sought to understand how generative AI (GenAI) has been incorporated into ITSM (IT Service Management) workflows. It focused on those areas within helpdesks designed to reduce manual effort, such as automatically suggesting ticket responses, sourcing relevant knowledge base articles and generating incident summaries.
The report wanted to discover whether AI was genuinely making life easier for IT teams or merely adding another layer of complexity. The findings showed helpdesks saw a significant decrease in average incident resolution time after enabling the GenAI features.
Savings and Benefits
On its own, that saving of just under five hours per ticket – or just under 20% of the time spent per support request – is impressive. But it’s only when you multiply that efficiency gain across the hundreds, if not thousands, of tickets generated each year that the true scale of AI’s impact becomes clear.
While the savings clearly point to GenAI’s benefits, the report also highlighted a common thread around that all-important pivot from pilot to operations. Instead of viewing GenAI as a test or side project, those teams that integrated AI tools into their daily service desk workflows appeared to have better outcomes.
“These results show what’s achievable when AI adoption is combined with effective change management and a focus on process improvements,” said the report. “They also act as a benchmark for other organisations evaluating the potential impact of similar tools,” it said.
Proper safeguards are key
But perhaps the biggest lesson learned from the last couple of years is that if we’re to truly maximise ROI, then we need to look at the bigger picture and develop AI systems in a much more methodical way. In effect, we need to develop AI by design. What does that mean? Well, in terms of privacy and security, it means establishing clear rules around not just the use of data but also how AI tools act and behave.
We also need to look beyond bias checks during model training to ensure that fairness is woven into AI right at the start, while ensuring that humans have the final say. And to build confidence in a new generation of tools, we need a proper paper trail to ensure that AI decisions can be traced and analysed so that, in the event something goes wrong, people can understand not simply what happened but why. It’s a good practice to ask an AI vendor which models are in use, where the training data came from, and what happens to the data users feed into the AI.
It’s too soon to say definitively whether Johnson & Johnson’s pivot signalled the end of AI’s pilot phase and the beginning of a more disciplined era. What we do know, though, is that the organisations most likely to succeed are the ones that are able to successfully manage the transition from AI experimentation to delivery while keeping a keen eye on outcomes and cost.
Sean Evers, VP of Sales & Partner at Pipedrive explains why using AI effectively involves redesigning workflows around business problems, whilst maintaining a focus on the human experience.
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AI appears to be everywhere, but clear results and a quantifiable ROI are not so common.
Many businesses are learning that simply adopting the latest tools doesn’t guarantee efficiency, innovation or growth. It’s a lesson that must be re-learned from time to time as the latest new tech paradigm comes upon the business world faster than the wisdom to make best use of them. And to be fair, faster than new technology itself becomes mature.
Too often in the past two years AI initiatives have been slower than desired in showing value because they’ve been treated as backend tech upgrades rather than strategic, human-centred transformations. An organisation is not a server rack, able to work at double the output when a new blade is inserted. Working with the grain of the business will always lead to better outcomes as the people and processes mix better with the changing variables being introduced.
Businesses can move beyond reflexive actions brought on by AI hype by embedding new technologies into problem-shaped workflows, creating meaningful impact through strategy, culture and human-centred implementation. AI tools are designed to solve specific challenges, not every challenge. We’re not at AGI yet (artificial general intelligence). Use tools wisely in limited areas, train teams well, and ensure a clearly communicated strategy supports team efforts. Then, all of a sudden, those investments in new tech will really move the needle on what’s been carefully defined as critically important.
Redesigning workflows
There is no best-in-class AI ‘Swiss army knife’. Even the most cutting-edge tech can’t deliver value unless workflows are redesigned around real business problems. AI succeeds when aligned to clear strategy, not when applied to broken processes like a sticking plaster. Firms positioning AI purely as an IT project will often encounter resistance, poor uptake and disappointing outcomes. The tech cycle has been this way forever, likely since fire and the wheel. Success always depends on employee understanding, willingness and trust in both their leadership and the ability of the tech to deliver on vendors’ promises.
Pipedrive’s ‘The evolving role of AI in sales workload management report’, highlighted how artificial intelligence has been reshaping sales roles and optimising workload distribution. In some areas, like in the sales function, the findings reveal that AI has become a co-pilot for professionals, enhancing efficiency and helping them focus on high-value activities:
One takeaway from the report was that AI users spend more time on strategic activities compared to non-users.
AI usage was still higher among sales managers (41%) than among salespeople (31%), indicating a need for more accessible AI tools and tailored training programmes.
Focus on what matters
AI centred on the human experience and business problems is what delivers real results. Thus, so-called soft skills and processes like corporate transparency, workplace empathy and ethical AI governance in practice are not really ‘soft’ considerations. Only a hard-headed, machine-minded person would fail to consider them as the essential enablers of organisational uptake and progress.
When employees are informed participants in change, AI becomes empowering rather than disruptive. That shift does not happen by accident. It requires leaders to articulate what problem AI is solving, for whom, and how success will be measured. Now, in many SMEs, the temptation is to deploy AI features because competitors are doing so, or because vendors promise transformational gains. But transformation without direction is simply noise.
Take a typical sales team in a growing business. Introducing AI-driven forecasting or automated lead scoring will not improve performance if the underlying pipeline stages are unclear or data hygiene is poor. In that scenario, AI merely accelerates existing inefficiencies. However, when leadership first defines what a qualified lead actually looks like, standardises processes and sets clear accountability, AI can enhance decision-making and free up time for higher-value conversations and really make a difference.
This is where cautiously redesigning workflows becomes critical. AI should be introduced at friction points like repetitive admin, inconsistent reporting, or slow handovers between teams. This is better than when added as a layer across every function. A targeted deployment creates quick wins, builds confidence and demonstrates measurable returns. Over time, those incremental gains compound and any cultural and technological lessons can be carefully applied in a virtuous cycle.
Managing the transition
The role of leadership is equally important. When AI is positioned as a cost-cutting mechanism employees will view it with suspicion. When it is brought in as a productivity partner to reduce manual tasks and allow individuals to focus on creative, relational or strategic work then adoption improves. Communication must be clear about what will change and what will not. Ambiguity will always breed a very understandable resistance.
Ethical considerations also need to move from policy into well-understood daily practice. For SMEs in particular, governance can feel like a burden reserved for larger enterprises who can ‘do it properly’. In reality, lightweight but explicit guardrails are often enough. Define what data AI tools can access. Establish human review points for high-impact decisions. Be transparent with customers about how AI is being used in interactions. These measures build trust internally and externally, which protects the long-term value of relationships and contracts.
There is also a mindset shift required. AI is not a one-off implementation. At the current rate of change it appears that it will be an evolving capability for many years. Teams will need space to repeatedly test, refine and learn. That may mean starting with a single department, gathering feedback and iterating before scaling. It may mean accepting that some pilots will fail. The objective is not perfection at launch, but steady alignment between technology and business requirements.
Being an SME can be advantageous
For SMEs, this disciplined approach can actually be a competitive advantage. Larger organisations often struggle with legacy systems and complex approval chains. Smaller businesses can be more agile, provided they resist the urge to chase every new feature release. Clarity of purpose becomes their differentiator.
Ultimately, fitting AI to the problem is about respecting the fundamentals of good management. Define the objective. Align people and process. Introduce technology where it adds measurable value. Review and refine. The companies seeing real returns from AI in 2026 are not those with the most tools, but those with the clearest strategy.
AI will continue to evolve rapidly. New capabilities will emerge, and expectations will rise. But the principle remains constant: technology works best when it amplifies well-designed human systems. Organisations that remember this will find AI soon stops being overwhelming when enabled as a controlled, practical tool for growth.
Paul Done, Field CTO at MongoDB talks to us about why people who think ‘vibe coding’ is all just lazy, AI generated code, are missing the valuable contribution AI can make to high-quality coding.
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Very few phrases in software have spread as quickly, or been as misunderstood, as “vibe coding”. For some, it signals the democratisation of software development, or the practise of causal programming for throw-away applications. But to its loudest critics, vibe coding represents a shortcut culture: developers tossing four‑line prompts into an AI model, then flooding production systems with fragile code. Many engineers also dislike the term because it implies coding without really understanding the code itself or blindly accepting AI output. In turn, some see vibe coding as trivialising the real effort and expertise required in software development.
I’ve been in the industry long enough to have heard similar complaints before: a new technology trend or tool that will make developers lazy, hollow out engineering skills, and ultimately leave organisations exposed. But that framing misses the point, because it has become a catch-all to describe two very different practises, with very different implications for developer teams and enterprises.
AI-driven coding is not vibe coding
Vibe coding often distracts and undermines the very real and positive impact of adding AI to the programming workflow. The leaps in the past few months alone have meant that AI agents have rapidly transformed how software is developed. Rather than manually writing code line by line, developers can now assign complex tasks to AI agents in plain English and have them independently research solutions, write code, debug errors, and deploy systems. These agents are capable of handling long, multi-step workflows – for example, setting up infrastructure, integrating tools, testing functionality, and documenting the results – often completing in minutes what previously took days.
As a result, programming is shifting from direct coding to orchestrating and supervising AI-driven processes, where developers focus more on defining problems, guiding agents, and reviewing outcomes, rather than implementing every technical detail themselves.
This style of development still requires a comprehensive understanding of engineering principles. As a result, it should not be confused or conflated with vibe coding, where code may also get generated rapidly, but lacks proper reviews, and is deployed with limited consideration for scalability, security or ownership. When critics warn about insecure “slop” code entering production, they are often pointing to failures of process and governance. That is a legitimate concern. But it is a concern about how teams operate, not about the inherent properties of AI-assisted development.
Even long-standing sceptics are beginning to acknowledge this distinction. Figures such as Linus Torvalds, Donald Knuth, and Robert C. Martin have recognised that AI can play a constructive role when it sits within disciplined engineering practice. The conversation is shifting away from whether AI should be used at all, and towards how it should be governed.
AI accelerates existing coding practises
Teams that have always kept strong review standards, testing rigour and capable leadership will not have those practises washed away if AI enters part of their workflow. For these teams, AI helps to increase the output without lowering standards. This is because it amplifies the culture already there.
The same principle applies to security. Secure software depends on threat modelling, dependency management, access controls and continuous monitoring. If those disciplines are weak, vulnerabilities will surface regardless of whether the first draft was written by a human or generated by a model. AI changes velocity, not responsibility.
For CIOs and technology leaders, this has practical implications. Prohibiting AI tools is unlikely to succeed and may drive their use underground – and given the advances in AI-driven coding, it may even amount to a serious competitive disadvantage. The more effective response is to strengthen the controls around them. Clear coding standards, automated testing, AI-driven code review, policy-driven code scanning, and runtime observability must become the default.
As AI accelerates the pace of software development, traditional human-centric code review workflows will struggle to keep up. When code can be generated much faster than it can be manually reviewed, human reviewers quickly become the bottleneck. The emerging model is one where AI systems enforce standards, review and validate code continuously, and flag risks at machine speed, while human engineers focus on defining requirements, architecture and higher-level oversight, rather than line-by-line review. AI belongs inside a structured software development lifecycle with guardrails, traceability and accountability.
A higher baseline for good code
With these in place, AI can raise the bar for what “good” looks like in our industry. Junior developers can receive immediate feedback on idiomatic patterns, security pitfalls and performance trade-offs. Senior engineers can offload repetitive tasks and focus on architectural coherence and long-term design. Test generation, documentation updates, refactoring and coding standards enforcement can become continuous activities rather than deferred clean-up exercises.
In many organisations, technical debt accumulates because improvement work competes with delivery deadlines. AI is reducing that tension by lowering the cost of maintaining standards. It can flag inconsistencies, suggest improvements and reinforce agreed conventions across large codebases.
This is particularly relevant for modern, data-intensive applications where distributed architectures and complex data models leave little margin for error. AI can support good engineering principles by improving validation, surfacing edge cases and increasing visibility into how systems evolve.
The debate around vibe coding muddies the waters about AI in our industry – the question is not whether AI will be part of software development. It already is. The differentiator will be how seriously organisations treat governance, architecture and long-term maintainability in an era of accelerated output.
Markus Nispel, Head of AI Engineering & EMEA CTO at Extreme Networks explains why your strategy and budget aren’t holding up your AI network initiatives – your execution is.
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The pilot phase is over. A third of companies have now scaled AI into production. For everybody else, the holdup isn’t strategy or budget – it’s execution.
Many AI initiatives struggle to deliver meaningful ROI for familiar reasons: missed business objectives and clearly defined KPIs, limited AI expertise, processes that haven’t been redesigned for AI, gaps in AI and data governance, and persistent challenges around data availability and siloes, quality, and preparation.
At the same time, supporting AI at scale requires infrastructure that can keep pace with continuous data flows, shifting workloads, and real-time decision-making. Traditional networks were built for stable, predictable workloads – not the speed, volume, and variability that AI-driven systems demand.
As AI moves from experimentation into production, networks must handle continuous data flows, adapt instantly to shifting demand, and operate at a scale that manual intervention simply cannot match. Meeting these demands requires a shift in network design and operation. This is where AI-powered, autonomous networking becomes essential.
Autonomous networking in practice
Autonomous networking allows systems to detect disruptions early, adjust traffic flows and configuration dynamically, and continuously optimise performance in real time, rather than reacting after issues occur.
This is made possible through multi-agent systems. Individual agents manage narrow tasks such as bandwidth monitoring, anomaly detection, and routing optimisation. Others sit above them, orchestrating them, interpreting user and business needs, and pulling the right capabilities together.
The system also works within defined governance frameworks. Humans stay involved in critical decisions, with full visibility, transparency and explainability into system activity, planning, reasoning and decision-making. Every automated action is logged and can be traced, verified and audited if necessary.
Trust, as in real life, is built through repeated positive outcomes. When networks keep making the right decisions, teams gain confidence, reduce oversight and can focus on higher-value work rather than constant troubleshooting.
This transition is already underway. Recent data reveals that 88% of organisations already rely on multiple AI-powered tools for networking and security operations. For stretched-thin teams, autonomous networking has grown into a necessity. Meanwhile, the strain is evident, with 92% of leaders reporting that AI places heavier demands on bandwidth and computing resources.
In this environment, network performance becomes a defining factor in whether AI systems can operate reliably and deliver measurable value.
Security through intelligent boundaries
Scaling AI also changes the security landscape. Networks are no longer connecting only users and devices; they are supporting a growing number of non-human identities. Agents across finance, marketing, engineering and operations interact with systems and data, often without human involvement.
Traditional security models were not designed for this level of autonomy. Access can no longer be broad or static. It must be tightly scoped, context-aware, and continuously verified. Every action must be visible and traceable.
But confidence is climbing. In fact, 93% of executives now say AI-powered networking reduces security risk rather than increasing it. That shift matters. It shows organisations are moving past scepticism and starting to take control.
That doesn’t mean we should be giving agents free rein. It means tightening the boundaries around what they’re allowed to do and enforcing strict agent governance. Access can’t be broad or permanent. It has to be specific, context-aware, and continuously verified. Every action should be traceable through a clear audit trail as AI for networking is implemented.
Where fabric architecture makes the difference
Networking for AI will not work without visibility. Real-time traffic monitoring and system connection mapping make enforcement possible. From there, segmentation becomes essential to contain risk and prevent unintended access or lateral movement – especially in the agentic era we have entered.
Fabric architecture enables this level of control at scale. By creating a unified network environment, it ensures that policies are applied consistently across all users, devices, and workloads.
This allows organisations to automatically isolate sensitive systems and tightly control how entities interact. If an AI agent behaves unexpectedly or is compromised, its access can be contained immediately, preventing wider impact.
Organisations that get this right bring identity, access control, visibility and threat detection together into a single, cohesive system. Autonomy can then operate within clearly defined boundaries, giving IT teams the confidence to guide, verify and trust every action the network takes.
Real-world application
The impact of this shift is visible across industries. Take retail in 2026. AI allows hyper-personalised shopping through cameras, IoT sensors, mobile apps and edge devices. Inventory data, customer preferences and behavioural patterns flow nonstop. Shelf labels, RFID systems and automated checkouts all require connectivity.
Healthcare follows similar patterns. AI supports diagnostics and robotic-assisted procedures, with patient data moving continuously across EMR systems, monitoring devices and analysis platforms. Without well-designed enterprise networks and access controls, those same systems can become exposure points rather than protection.
Manufacturing uses AI to predict equipment failures, optimise production lines and coordinate autonomous robots as sensors and machinery continuously communicate.
Across industries, without unified enterprise networks, stringent access control and defined human checkpoints, a single misconfigured AI agent could compromise sensitive data, disrupt operations or even endanger lives.
From experimentation to production
The era of experimentation is over. Organisations that operationalise AI effectively move faster, operate more securely, and respond to change with greater agility. Achieving this requires networks that can anticipate demand, enforce boundaries, and adapt continuously without compromising performance.
When that foundation is in place, AI can deliver on its promise – reducing operational friction, minimising disruption, and enabling teams to focus on innovation rather than maintenance.
Satish Thiagarajan, founder of Brysa, a Salesforce and data consultancy based in the UK, explains why high quality data is the foundation for using AI successfully.
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AI is everywhere in business right now, and for good reason. It offers the potential for better visibility across operations, fewer surprises, and more efficient use of resources. Most businesses have run a pilot to test that. Fewer have made it past one.
The stall point is consistent. Projects start well, attract investment, generate interest, and then stop delivering. The technology rarely gets the blame internally, but it tends to take it publicly. The real problem is almost always the data behind it.
Why data decides whether AI works
Business data systems weren’t designed to tell the whole story. They were each added over time to solve specific problems. One for finance, one for sales, another for HR, and a few more for marketing, support, and operations. Individually, they do the job for which they were intended, but they don’t really connect. And that’s fine, until you try to apply AI.
AI doesn’t think in terms of individual systems. It looks for patterns across everything, from customers and revenue to pipeline activity and service history. When that information is split across systems that don’t line up, those patterns break down, and what you’re left with is data that doesn’t quite agree with itself: records that don’t match, account data that conflicts with billing history, customer activity that looks different depending on where you check.
At that point, the problem isn’t a lack of data. It’s that the data doesn’t hold together. And if the data doesn’t hold together, the output won’t either.
What “AI-ready” actually looks like
Before AI can do anything useful, the data has to make sense on its own. Without that, AI is, effectively, guessing.
Context matters just as much. Data only becomes useful when it carries the relationships behind it. A customer record on its own doesn’t say much. Connect it to purchases, support history, and engagement activity, and you start to see why things happened, and that’s what AI needs to work with.
Timing forms another pressure point. Business moves quickly, but the data often doesn’t. If updates are delayed, or stitched together after the fact, AI will always be working from an outdated version of events. When data flows in real time, it reflects what’s actually happening, not what happened last week.
Then there’s continuity. When past performance connects directly to current activity, AI has something to learn from. Without that link, every decision starts from scratch. To get to that point, data needs to be consolidated in one place, and that’s where a CRM comes in.
Bringing it together in one place
Used properly, a CRM becomes more than a system for managing contacts. It acts as a central hub for accounts, opportunities, cases, campaigns, and service activity. AI doesn’t need perfectly clean data, but it does need to understand how work actually happens, and a CRM gives it that. Business data is constantly changing, and it needs to scale across teams, regions, and functions. Role-based access, audit trails, and clear permissions aren’t just nice-to-haves, they keep data usable as it grows.
Building a usable data foundation
The goal when faced with fragmented systems isn’t to replace everything, but to connect what’s already there. Customer data, comprising account histories, contact records, and interaction activity, is standardised and can be used across sales, marketing, and service, reducing duplication and avoiding inconsistency.
Workflows matter as well. If data quality depends on someone fixing issues later, it won’t hold. When validation happens at the point of entry, and updates flow automatically across systems, accuracy is built in, rather than an afterthought. When the data is reliable and connected, AI can support things like demand forecasting, lead prioritisation, or churn prediction, and good governance ties it all together.
AI doesn’t fail because the models aren’t capable. When the underlying data is fragmented, AI reflects that fragmentation. When the data is coherent and connected, AI has something solid to work with. That’s the difference between systems that generate outputs and systems that actually support decisions.
Satish Thiagarajan is the founder of Brysa, a Salesforce and data consultancy based in the UK. His company advises media, industrial, and services clients on using Data Cloud and Agentforce to turn signals into action. His work focuses on closing the loop between insight and execution in sales, marketing, and service.
Ivalua finds businesses are caught in a skimpflation sandwich, quietly trading down on quality for consumers while their own suppliers do the same to them
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A new report from Ivalua, the enterprise AI platform for procurement, has found that 52% of businesses say cost pressure is driving ‘skimpflation’ in their supply chains – quietly trading down on component or ingredient quality to protect margins. In the UK, this rises to 64% – the highest of any market surveyed.
The report finds businesses are struggling to balance cost, risk and resilience. Constrained by manual processes and limited supplier visibility, the fastest route to saving is skimpflation. This is happening on two fronts: businesses are either reworking product specifications to bring the cost of goods down (59%) or switching to cheaper suppliers or goods outright (36%). The savings, however, rarely reach the consumer. Among businesses that made those changes, just 10% cut their prices, while 43% raised them and 47% left them unchanged. The result is a quiet trade-down where shoppers pay the same, or more, for products and goods made to a lower standard.
The squeeze is showing up elsewhere too, as more than half (53%) of businesses experienced delayed product launches, likely tied to supplier quality issues, poor collaboration or missed warnings of disruptions. And consumers aren’t the only victims of the scourge, as 49% of businesses are seeing skimpflation from their own suppliers.
“Skimpflation is the silent inflation. It never shows up in the headline CPI figure but consumers feel it every time something breaks sooner, wears thinner or runs out faster,” says Alex Saric, Smart Procurement Expert at Ivalua. “But short-term cost and quality decisions can conceal long-term risk. Businesses spend years building brand loyalty, then a single reformulated product or cheaper component undoes it. The firms cutting hardest today aren’t just protecting margins. They’re borrowing against their reputation, and that debt comes due the moment consumers notice the difference.”
Brittle foundations behind the squeeze
Businesses are also rethinking where they buy from as cost pressure, shortages and trade disruption reshape supply chains. Over the past year, organisations have replaced, reduced or exited suppliers across every major region, including Eastern Europe (44%), China (43%), Western Europe/UK (40%) and The Middle East (32%).
Supplier cost inflation (33%) ranks as the biggest trigger for these regional shifts, closely followed by shortages of critical components (32%) and efforts to reduce tariff and trade exposure (30%). The pressure of managing a supply base in constant flux across every major region is forcing businesses to make decisions faster than traditional processes allow.
To address this, 76% of organisations are using or experimenting with AI, with a further 14% planning to adopt it. Among those already using it, 89% say it has been effective at identifying and qualifying new suppliers and sourcing hubs. The catch is that almost half (44%) admit their supply chain data isn’t AI-ready. Until that gap closes, AI won’t fix supply chain problems, it will expose them.
“When businesses can’t see across their supply chain, cost pressure forces their hand. They cut components, switch to cheaper suppliers and hope customers turn a blind eye,” concludes Saric. “AI changes that by giving procurement teams the visibility to find savings and qualify suppliers before quality problems reach the customer. But it only works on clean, connected data. Run it on spreadsheets and scattered systems and it doesn’t surface better decisions – it just makes the wrong ones faster. Get the foundations right and businesses stop having to choose between protecting their margins and protecting their customers.”
U.S. Merit Systems Protection Board: Driving Operational Exellence
Interface revisits the important work of the U.S. Merit Systems Protection Board – an independent, quasi-judicial agency in the Executive branch that serves as the guardian of Federal merit systems. CIOCraig Thomas, explains the strategy behind the completion of Phase II of its case management journey, application stabilisation challenges and the deliberate rollout of secure artificial intelligence tools.
“At MSPB we field IT that is necessary, useful, cost-effective, manageable and scalable. If the technology doesn’t add value, doesn’t help us meet our mandate, I don’t want it… We see the active deployment phase as an incubator for support methodology, data and reporting strategy, and road-mapping the next five years of your application – use the active deployment phase, don’t just firefight.”
Morgan Street Holdings: Cybersecurity for Strategic Operations
Morgan Street Holdings is a privately owned investment and operating company whose portfolio includes global supply chain specialist HAVI, marketing and sourcing firm tms, consumer products brand Stanley, and hospitality and vending business Continental Services. Collectively, the organisation employs more 10,000+ people globally across more than 50 countries. Business Information Security Officer, Douglas Darden, talks building cyber resilience to enable the business and deliver cybersecurity for strategic operations.
“It’s not in us as a cyber team to just say no. Give us the opportunity to conduct a risk assessment and the cybersecurity function can become a business enabler.”
Also in this issue, we hear from Confluent on why AI is changing what data privacy means; learn from Kore.ai on how unmonitored AI agents are becoming enterprise AI’s biggest risk; and Xsolla explain why the real AI debate in video games isn’t about jobs, it’s about creativity.
Paul Dawalibi, CEO of Innovation City – Ras Al Khaimah, on why the next wave of transformation will emerge from the places most deliberately built to welcome it, equip it, and champion it without reservation
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The machines are accelerating faster than our institutions can comprehend. AI has not merely arrived, it has detonated, compressing years of progress into weeks. And forcing every industry to reckon with what comes next. Yet in this moment of unprecedented velocity, a paradox persists. The very founders and teams best positioned to shape this future often find themselves fighting systems built for a slower, more predictable age.
They battle outdated regulations and navigate prohibitive costs. They operate in environments designed for the previous generation of business. And too often, brilliant ideas die not from lack of vision, but because the ecosystem around them was never built to support the speed, scale, and sovereignty that modern innovation demands.
For decades, the world assumed that real ambition naturally flowed to the great megacities. Places like London, New York and San Francisco. Those places brought extraordinary revolutions. But today, they exact a hidden tax: on time, on capital, on mental bandwidth, on the simple ability to focus. Talent and capital are voting with their feet, seeking environments where the default setting is momentum rather than friction.
The next chapter will not be written in those places. It will be written in deliberately designed sanctuaries. Hyper-focused ecosystems where resources, regulation, and human support are engineered not just to enable innovation, but to accelerate it. These are not free zones or business parks in the traditional sense. They are living systems built on three pillars, delivered with a philosophy we call white glove.
The Three Pillars That Change Everything
The first pillar is sovereign resources. For AI builders, the bottleneck has never been ideas. It has been access to the compute, talent, and infrastructure that turn ideas into reality at speed. In a true innovation sanctuary, these are not scarce commodities to be fought over. They are shared foundations. Dedicated data centers. Curated networks of specialists. Proximity transforms isolated founders into powerful collective intelligence. When the raw materials of the future are abundant and aligned, what once felt impossible begins to feel inevitable.
The second pillar is regulatory intelligence. Speed without standards is chaos. Standards without speed are stagnation. The ecosystems that will define this era understand that regulatory frameworks must be as dynamic as the technologies they govern. They anticipate the needs of digital assets, tokenisation, and agentic systems. And move from years and millions in friction to days and focused investment. They protect what matters – trust, ethics, sovereignty – while ruthlessly eliminating what does not.
The third pillar is the gravity of focus. Breakthroughs rarely emerge from fragmented attention. When the cost of living and operating is dramatically lower than in legacy hubs – often 50 to 60 percent less – entire teams can relocate together. Families put down roots. Deep work becomes the norm rather than the exception. The spontaneous collisions that fuel genius stop being lucky accidents and become the fabric of daily life. This is how movements are built.
White Glove is Not a Service. It is a Stance.
What elevates these ecosystems from merely functional to truly transformative is the white glove philosophy. A level of personalised, proactive championship that goes far beyond standard facilitation.
It means that when a company arrives, the ecosystem does not simply clear a path. It walks beside them. Talent acquisition becomes strategic matchmaking. Regulatory navigation becomes clear before questions are asked. The search for that critical first customer becomes an active introduction to the right partners at the right moment. Because in the end, validation from the market is more powerful than any pitch deck or funding round. It is proof that the world is ready for what you have built.
We have seen this model work before. Y Combinator did not merely advise – it obsessed over every detail of a startup’s trajectory. Singapore did not merely welcome industry – it engineered precise conditions for the sectors it chose to lead. The common thread is simple but rare. An ecosystem that measures its own success by the velocity and impact of the builders it attracts.
Innovation City: Building the Sanctuary
In Ras Al Khaimah, we are not constructing another free zone. We are creating a sanctuary for those who intend to build what comes next.
Strategically positioned less than an hour from Dubai, within reach of a third of the world’s population, at the crossroads of continents – we have chosen this place because it still believes in agility, in partnership, in the radical idea that the role of an ecosystem is to remove obstacles, not create them.
Here, we are putting the white glove philosophy into practice at scale. We are building dedicated AI infrastructure so that computational power serves the many, not just the largest. By crafting regulatory frameworks for digital assets and real-world tokenisation that respect both innovation and responsibility. We are creating the conditions for families and teams to thrive – because great work is sustained by great lives.
More than any single asset, we are cultivating a culture. A place where ‘how do we make this happen?’ replaces ‘why it cannot be done”. Where bureaucracy is recognised as the enemy of progress, not its protector. Where the builders who are willing to run through walls find an entire community running with them.
This is what happens when an ecosystem decides that its highest purpose is to amplify human ambition.
The Future Belongs to Those Who Build it
The paradox we face is not inevitable. It is the result of design choices made in a different era. And design choices can be remade.
The great hubs of the past deserve their legacy. But the next wave of transformation will emerge from the places most deliberately built to welcome it, equip it, and champion it without reservation. That work is underway.
In Ras Al Khaimah and in other rising sanctuaries around the world, a new standard has been established. Not for what is merely permitted, but for what becomes possible when every barrier between vision and execution is removed.
The future will not ask for permission. It will go where it is most fiercely supported. We are building that place. And we invite every founder, every team, every leader who shares this conviction to build it with us. The age of accidental ecosystems is over. The age of designed destiny has begun.
Earlier this month, aim10x brought innovation to Amsterdam
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Innovation was the keyword of the day at o9’s executive aim10x event. On the 4th of June in Amsterdam, software company o9 Solutions ran its summit for procurement leaders and experts, many of whom are finding themselves at a crossroads as technology and supply chains evolve.
The aim of the event was to enable procurement leaders to discuss how to adapt at a time when supply chains are becoming more interconnected, and how to navigate changes in operating conditions. These vital conversations included forecasting, supplier collaboration, and inventory management – among many other timely topics.
o9’s APEX
The summit also aimed to highlight the importance of AI-native agentic systems and how companies must move past legacy operating models. During the opening keynote sessions, led by Chakri Gottemukkala, o9’s Co-Founder, CEO, and Chairman, APEX – o9’s AI operating model – was previewed. APEX uses what o9 calls ‘neuro-symbolic AI’ to solve issues with enterprise decision-making and change management challenges.
The o9 team then demonstrated the power of AI-driven capabilities, which Gottemukkala combined with real-life success stories from across many years of supply chain innovation. He dived into discussing case studies, the real technology behind APEX, and the pillars that make up what it stands for (Agile, Adaptive, Autonomous, Planning, and Execution).
AI that goes “well beyond the AI of language”
Later, the conversation shifted to why LLMs alone are no longer enough. Dr. Ashwin Rao, Executive Vice President, Next-Gen AI and Technology at o9 Solutions, explained why neuro-symbolic AI is essential because it goes the extra mile. “Language is not complete cognition,” he said. “LLMs are powerful, but we need AI that goes well beyond the AI of language. That’s the AI of mathematics, structure, and domain knowledge.”
Further explanations of neuro-symbolic AI and demonstrations followed. Alongside this, three retailers each told their stories of how they moved past disconnected operating models. JD Sports, Canyon Bicycles, and Adidas all dug into their stories and how they approached sustainable planning transformations. Read all the details of their stories here.
Inspiring transformations
aim10x also highlighted four transformation lessons across the event. These came from Marelli, RHI Magnesita, Garrett Motion, and Teleflex, all of whom outlined their own transformation stories. During these transformations, all four business arrived at similar conclusions: that integrated planning is a cultural shift before it’s a technical one, and that the organisations which thrive ensure they have the internal capability to keep improving. Read the lessons learned from their transformations here.
Throughout the talks, breakout sessions, demonstrations, and networking, three themes ran through everything at aim10x:
That integrated visibility is a company initiative, not a function.
That data quality is an ongoing discipline.
And that the implementation is not the transformation.
With everything that happened during this fascinating event, what’s interesting is that aim10x Europe was only the preview. When o9 hosts its Americas event on the 23rd of September, its vision will truly take shape. Register to join in the conversation here.
Craig Gravina, CTO at Semarchy, on why successful AI at scale requites the transparency and trust only proper governance foundations can deliver
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Across the globe, organisations are aggressively approving AI budgets, hiring teams, and deploying models, yet the promised returns remain elusive for many. AI investments continue to grow, but measurable business value is not keeping pace with the level of spend.
The numbers tell a striking story. Semarchy’s 2026 research of C-suite executives reveals that 99% of UK organisations claim AI readiness – with 69% saying they’re completely ready. Yet 56% making significant investments cite data management as their top challenge, while only 33% are prioritizing it for investment. This 23-point gap reveals the fundamental issue: organisations are building AI applications whilst underfunding the data infrastructure those applications require.
Globally, fewer than 2% of enterprises successfully avoid data quality problems, even though 74% planned to increase AI spending in 2025.
This isn’t a failure of technology talent or algorithms; it’s a governance breakdown. The path to faster AI ROI doesn’t run through better models, but through better governance and transparency, embedded at the center of every AI initiative from the outset.
The Hidden Cost of Ungoverned AI
When AI operates without governance, the primary risk shifts from technical to reputational. Whereas technical failures are recoverable, reputational damage is far harder to restore. Consider consequential AI decisions: credit denials, medical recommendations, hiring shortlists. When these go wrong publicly, the fallout extends far beyond the technical team. When regulators or customers ask how they make their decisions, organisations without governance have no credible answers.
The regulatory pressure only amplifies this risk. Global frameworks such as the EU AI Act, emerging UK regulations, and sector-specific rules increasingly require explainability and traceability. Companies that cannot show how AI reached its conclusions risk fines, restrictions, and forced remediation.
Public misfires, such as biased outputs or visible system breakdowns like Grok AI’s high-profile failure on X last year, can rapidly erode customer trust and brand equity. For consumer tech brands especially, reputational cost can exceed technical cost exponentially. Internally, unexplained or unreliable AI outputs erode confidence, resulting in disengaged business users, stalled initiatives, and data leaders losing credibility.
These aren’t exceptional cases. They’re the predictable result of deploying AI without governance infrastructure.
The Governance Misconception
Many organisations still treat AI governance as a compliance checkpoint, bolted on just before deployment to satisfy legal or risk requirements. This narrow interpretation is exactly what makes governance feel like an obstacle. Organisations feel forced into a false choice: move fast and bypass governance or be compliant and accept delays.
In this scenario, projects slow to a crawl. Data teams spend months preparing ‘clean’ datasets specifically for AI, only to sit in approval queues. By the time governance clears, business conditions have changed or stakeholders have moved on to new priorities.
The answer isn’t less governance; it’s different governance. Governance should be intrinsic to how data is defined, managed, and delivered. When governance “travels with the data,” models consume information that already carries quality checks, lineage, access controls, and semantic context already present. AI initiatives don’t wait for approval gates – they consume governed data from the start. There’s nothing to retrofit or remediate later, because the controls were in place from day one.
This shift from governance-as-gate to governance-as-infrastructure is the key divide between organisations that struggle to realise AI ROI and those that achieve it.
Transparency as a Strategic Asset
Leadership often frames transparency as a compliance requirement, but in AI, it’s just as critical for development speed. When data lineage is clear, AI teams don’t have to spend weeks figuring out where data came from, how it was transformed, or whether they can trust it. They can focus on building and refining models instead of investigating data provenance, shifting effort from detective work to value creation.
Transparency also removes technical friction. With explicit semantic context, models interpret data correctly without layers of custom preprocessing or extensive feature engineering. Consistent access controls across data sources prevent last minute security reviews from stalling projects at critical milestones.
Transparency enables continuous improvement cycles. When you can see exactly which data influenced a decision, you can diagnose errors with precision rather than guesswork. You can identify which specific data sources introduce noise or bias into model outputs, and measure whether data quality improvements affect AI performance in production, creating feedback loops.
Organisations that try to reconstruct this visibility only during audits pay for it repeatedly in delays, extended debugging, and failed deployments. By contrast, those that build transparency from the start move faster through development, testing, deployment, monitoring, and iteration. Transparency then becomes a competitive advantage rather than compliance burden.
What Real AI Success Looks Like
Real AI success isn’t a single breakthrough model. It’s the ability to deliver value repeatedly and at scale. Successful organisations move from proof of concept to production without major rework or last-minute governance scrambles.
In these organisations, AI consumes the same governed data as everyone else. Agents and models access data through shared interfaces and policies, alongside business users and operational systems. There’s no special ‘AI data prep’ phase, no separate pipelines to maintain, and no governance gap to close before go‑live. AI is simply another consumer of trusted data.
Explainability comes by default. Because lineage, transformations, and quality metrics are already present in the data, teams can trace any decision without forensic effort. When governance is built into data flows, AI teams can retrain on fresh data without re-running compliance reviews, keeping models current as business conditions change. Iteration cycles shorten from months to weeks or days.
This foundation also enables decentralised experimentation with centralised trust. Teams can launch AI initiatives against shared, governed data products without creating shadow pipelines.
Getting There: Three Practical Shifts
Reaching this future state doesn’t require a multi-year overhaul or new tech stack. It needs a focused effort in how data is delivered to every consumer, including AI, with governance built in rather than added later.
First, stop treating AI data preparation as a separate workstream. If AI teams need specially cleaned and packaged data, you’ve created handoffs that introduce delay and governance risk. Instead, provide data that all consumers can trust and use immediately.
Second, embed semantic context with the data. AI needs business context and meaning, not just schemas. What does ‘customer’ mean in this specific context – prospect, active user, former customer? What business rules apply, and what relationships matter for decision making? This semantic layer enables AI to interpret data correctly without custom workarounds.
Third, make lineage and quality observable by default. When lineage, quality scores, and transformation history are always available, debugging, compliance, and continuous improvement happen without emergency efforts.
Yet our research reveals the challenge: whilst 56% of UK organisations cite data as their top AI challenge, only 33% are prioritising investment in it. This misalignment between stated priorities and actual investment is precisely why AI initiatives continue to struggle, regardless of confidence levels.
The ROI Equation
The companies that achieve AI ROI faster aren’t the ones with the biggest budgets or the most sophisticated models – they’re the ones that eliminated the friction between data and AI consumption through proper governance infrastructure.
When governance is intrinsic, projects don’t wait for approval gates. With built-in transparency, debugging is fast, and audits are painless. The result? Shorter time to value, lower risk of public failure, reduced regulatory exposure, and AI investments that deliver measurable impact. These aren’t abstract benefits – they show up in project timelines, deployment success rates, and business KPIs.
The technology is ready. The question is whether your data governance is ready to support AI at scale. Success requires the transparency and trust that only proper governance foundations can provide.
Peter Pugh-Jones, EMEA Field Chief Data Officer at Confluent, on why the challenge for companies will not be building AI systems but standing behind the decisions those systems make
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For years conversations about data privacy have centred on conventional dangers, like breaches from outside, or leaks from within. The assumption has been that the biggest risk lies in information either being inadequately protected or improperly handled.
As we move deeper into the AI era, that picture is shifting. A more complex privacy challenge that we’re now faced with comes from how data is used once it sits inside an organisation.
AI is a crucial part of everyday business operations. It automates processes and shapes decisions. Data is no longer simply stored or shared; it’s interpreted and acted on at incredible speed, in ways that are difficult to trace or explain.
Decision-makers recognise the importance of accessing this data. 80% of business decision makers say without up-to-date data, businesses can’t be fully confident in decisions.
As such, the systems and rules that dictate how AI is used have never been more important. Business leaders need to be able to understand and explain how automated decisions are reached and the data that informed it.
Governance has to sit at the centre of the privacy conversation — not as a compliance exercise, but as the framework for the responsible application of AI. Without that foundation, organisations are likely to expose themselves to regulatory and reputation risk that they’re simply not equipped to avoid.
Privacy Now Depends On Explanation
Governments worldwide recognise the need for these AI frameworks. Europe’s AI Act has drawn the most attention, but equivalent regulatory approaches are appearing in markets from South Korea and the UK.
The result is that companies deploying AI systems are held to higher standards than ever when it comes to demonstrating that they use AI in a lawful, appropriate manner. As such, many businesses are discovering a gap between deploying AI and understanding it.
Implementing a model that produces useful outputs is relatively straightforward. Explaining how those outputs were created and which data shaped them is much harder. Where that clarity is missing, privacy becomes an operational problem.
Organisations may need to pause or redesign systems already in production because they cannot explain how those systems reached their conclusions. Some companies may even find themselves withdrawing products already released into the market.
So: what challenges are these organisations facing?
The Segmentation Challenge AI Introduces
One significant obstacle is segmentation. Companies need to understand who their systems interact with and which rules apply to each, and this isn’t always clear.
Businesses have historically segmented audiences by geography, demographics or behaviours. AI creates a more demanding requirement in that the systems using it must recognise when different safeguards should apply to different groups of people.
Regulation is pushing this further especially around children and vulnerable users. Content recommendations and automated responses that are acceptable for one group can become inappropriate for another.
The difficulty is that many AI systems are trained on broad datasets gathered across the internet. That breadth makes it harder to understand whether information came from a reliable source, or whether it should appear in a particular context. Without stronger segmentation, organisations risk losing control over how AI behaves in real interactions — and if that AI behaves inappropriately, it’s likely to lead to a breach of regulation.
Why Governance Cannot Be An Afterthought
Another mistake appears repeatedly in businesses deploying AI and modern data platforms. Governance is treated as something that can be dealt with later once systems are already running, with isolated but exciting pilots running well on smaller data sets often leading businesses to try and run before they can walk.
The challenge is even sharper in real time environments where data moves constantly between applications and teams. When governance is not designed into those flows from the beginning it quickly becomes a bottleneck.
In practice, the most effective point to govern data is when it first enters the business. Once information is already moving through systems, feeding models and generating outputs introducing controls becomes far more complex.
Many companies only realise months later that they’re lacking certain controls needed to manage risk. If they were never properly built in, it’s an expensive, time-consuming, incredibly nuanced task to put the genie back in the bottle.
The Shared Responsibility For AI Decisions
As with any cybersecurity approach, the human link can also be the weakest in the chain.
Responsibility for AI risk is often treated as if it sits neatly with one team. In many companies the assumption is that specialists are handling it, whether that is data scientists, a compliance function or a single executive role. The underlying thought is: “this is not my problem — someone else is dealing with it.”
That assumption creates problems. AI systems are trained on vast datasets and their behaviour is not always easy to predict without close scrutiny. When governance is treated as a delegated task, important questions can slip through the cracks unanswered.
Decisions informed by AI still sit with the business using the technology and ultimately with its leadership. Without senior oversight and a clear understanding of how systems are built, trained and governed privacy risks tend to surface long after those systems have become part of everyday operations.
The Question Organisations Should Be Asking
Organisations have the capability to build powerful AI systems. The harder question is whether those systems can stand up to scrutiny.
That means understanding where data originates, recognising differences between users and putting governance in place before automation begins to scale. It’s not enough to just co-opt an impressive new platform and give it access to your data.
In an AI driven environment privacy does not sit somewhere at the end of the process. It shapes how systems are designed, deployed and managed from the beginning.
In the years ahead, the challenge for companies will not be building AI systems. The challenge will be standing behind the decisions those systems make.
Konstantinos Stavropoulos, Product Manager at AUSTRIACARD HOLDINGS, on why the firms that succeed will be those that adopt AI with the right balance of control, flexibility and practicality
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As FinTechs accelerate their adoption of artificial intelligence, the question of capability – what AI can do, and how it can improve efficiency – is typically overshadowed by the question of governance: how and where should this technology run. For an industry built on trust, regulation and the responsible handling of sensitive data, this goes to the heart of how FinTechs manage risk, protect privacy and intellectual property, and maintain control over their operations including cost.
A Down-to-Earth AI Approach for FinTechs
AI is transforming the way that organisations automate processes, with companies such as Tag Systems – a member of AUSTRIACARD HOLDINGS, and the biggest payment card provider for UK FinTechs – experiencing and supporting this transition. From document handling and onboarding to cross-checking and company-specific workflows, It offers an efficient automation alternative to manual, repetitive and time-consuming activities. However, in regulated business domains such as the fintech industry, processes that involve sensitive customer data or company-specific intellectual property need to be protected.
Most widely used AI models (LLMs, Large Language Models) run in vendor-managed cloud environments. This approach may have enabled rapid adoption, but it requires organisations to send data beyond their own well-guarded setting. Even when some protections are in place, question marks around sovereignty (and how data is stored and potentially used by 3rd parties in the cloud) remain.
For FinTechs, these are not abstract concerns. They are vital governance considerations. Therefore, solutions are needed to bring AI closer to (ideally, inside) the organisation, similar to that offered by AUSTRIACARD’s GaiaB™ – originally ‘GenAI in a box’, and also meaning ‘Second Earth’. By combining powerful (Dell) hardware with a cutting-edge agentic AI software platform, GaiaB™ Appliance keeps artificial intelligence in-house to ensure sovereignty, compliance, and control. A down-to-earth approach to adopting it.
Can Fintechs Risk Sharing Their Data and Intellectual Property?
When AI-driven tasks and processes run in a 3rd party managed environment, risk is also (partly) transferred to that 3rd party. In other words, organisations must rely on an external provider, not only for performance, but also for security, availability and compliance alignment. When AI-driven tasks and processes run in-house, that is not the case. For most fintechs, particularly those operating in tightly regulated environments, this is increasingly attractive.
One of the key advantages of running artificial intelligence in a controlled, local or private environment (potentially, even air-gapped, which is also supported by solutions such as GaiaB™ Appliance) is that data and intellectual property stay where they belong. Instead of sending information to external providers, organisations can process it internally, within systems they already govern. This reduces exposure, simplifies oversight, and provides a clearer answer to the fundamental challenge of making the most of AI without losing control of sensitive information.
Can AI be Predictable for FinTechs?
Many organisations begin experimenting with AI through online usage-based services (AI token – fundamental AI unit of data – dependent). This may appear to be an attractive OPEX proposition, but costs that appear manageable at first can escalate when it gets embedded in business processes and usage scales. Due to the nature of tokens, AI consumption charging models are typically unpredictable, particularly for smaller firms and those operating on tight margins. Such businesses may well become reliant on services for which they have not budgeted.
A more controlled deployment model means that organisations do not have to pay, or understand what they would need to pay, every time they use AI. Instead, they can invest in well-defined capabilities, which they can scale as needed. For example, GaiaB™ Appliance offers four hardware options (Tiny, Small, Medium, and Large), which can be used to create a modular and scalable AI infrastructure. Cost predictability enables FinTechs to align AI investment more closely with business needs, and avoid cost escalation.
How FinTechs can Target Optimal, Cost-Effective AI Performance
When AI is delivered as an external online service, performance can vary depending on network conditions, service levels or pricing tiers. In contrast, when AI runs within a FinTech’s own controlled local environment, the organisation can target more consistent performance. Consistent levels of latency and availability matter when AI is embedded into mission-critical operational processes.
In business domains where requirements evolve quickly, AI strategies need to be adaptable, not fixed. Equally important is the need to avoid dependency on any single AI model provider. AI is evolving rapidly. Models improve, new approaches emerge, and what is considered state-of-the-art today may not be optimal and cost-effective tomorrow. FinTechs should be able to adapt, switch and tailor models without being locked into a rigid framework. In these terms, flexible solutions of modular and scalable nature and of AI model independence are invaluable.
The AI Way Forward for FinTechs
Most organisations are trying to improve – not replace – human decision-making with artificial intelligence. Interestingly, some of the most popular use cases today are the least glamorous: automating workflows, routing information, extracting and classifying data, and supporting employees in repetitive time-consuming tasks. Such applications deliver considerable value from AI by improving productivity and allowing people to focus on higher-value work. Similar to other industries, the future of AI in FinTech entails the concept of agentic AI which has been gaining traction. Instead of relying on a single model or an autonomous/semi-autonomous AI agent, organisations can deploy multiple specialised agents that handle specific tasks.
To adopt AI successfully as a strategic growth lever within a secure governance framework, FinTech leaders need to balance cost and benefit, risk and reward. Can the organisation maintain control over its data? Does it manage risk and meet regulatory expectations? Can it control costs as usage scales? Can it adapt as technology evolves? Ultimately, can it deploy artificial intelligence in a way that supports real business outcomes?
The firms that succeed will be those that adopt AI with the right balance of control, flexibility and practicality.
Zip is delivering the first governed AI platform for finance and procurement
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Zip, the AI platform for enterprise procurement, today announced two major launches: Zip Superagents and Zip MCP. Designed to work alongside the humans who handle procurement today, Superagents are an AI platform that takes on real work – reviewing contracts, unblocking approvals, coding invoices, and researching vendors – with every action audited and governed inside Zip. Zip MCP is a procurement-native implementation of the Model Context Protocol that connects Zip’s procurement intelligence directly to AI assistants like Claude and ChatGPT, enabling employees to work in their preferred AI tools without leaving Zip’s controls behind.
Zip is also launching Zip AI Spend Automation, a new enterprise offering that combines platform access, AI consumption credits, and support from Zip’s own forward-deployed engineers to help the most progressive organizations fully automate their intake-to-pay process. Block, UCI Health, and Snowflake are among the first enterprise customers to deploy AI Spend Automation, with UCI Health already reporting $20M in cost avoidance and value recapture from a single project.
The governance gap shadow AI left behind
Most companies don’t have the infrastructure to safely run AI on their procurement data yet, but employees aren’t waiting for it. Across the enterprise, employees are uploading spend data into Claude to analyse it, redlining sensitive contracts inside ChatGPT, and generating internal financial analyses in personal Gemini or Copilot accounts. Every time they do, sensitive enterprise data leaves systems where every action is controlled and audited, entering environments with no oversight, no compliance controls, and no record of what was done.
The consequences for getting this wrong aren’t hypothetical. SOX violations carry fines of up to $25 million. Executives can face prison time. Public companies that fail compliance audits can be delisted from the stock exchange. When an auditor asks how a decision was made six months later, no one can produce a record. When an employee leaves the company, sensitive data lives on in their personal AI history.
That’s why the world’s leading AI companies run their procurement on Zip. Procurement is one of the few business functions where decisions carry direct financial, legal, and compliance consequences, and where every approval needs to be auditable months or years later. To date, Zip’s customers have saved more than $10 billion through its AI suite by running AI inside a procurement-native governance layer rather than alongside one. Today’s launch is how Zip closes that gap: by connecting procurement infrastructure directly to the AI assistants employees already use, and by launching Superagents to take on the procurement workflows no system has been able to fully automate until now.
“After working with hundreds of enterprises – including the world’s leading AI companies – we’ve learned that this kind of work is already happening, with or without governance,” said Lu Cheng, Co-Founder and CTO at Zip. “Even the companies building AI themselves want this work governed. They want the audit trail. They want the controls. They want the integrations to their systems of record. And they want to make sure their employees aren’t uploading sensitive financial, contract, and customer data into general-purpose AI. That’s why we built Zip’s orchestration layer the way we did over the past five years, so that AI doesn’t just operate alongside enterprise procurement, but inside it, where every action is governed and auditable.”
Zip superagents: Governed AI that takes on the work
Zip Superagents are AI agents designed to reason, act, and collaborate inside the workflows where procurement actually happens. Unlike general-purpose AI tools or scripted automation, Superagents are embedded within Zip’s orchestration platform, where each customer’s approval policies, supplier data, ERP integrations, contracts, spend history, and stakeholder workflows already live.
That context is what allows Superagents to do more than generate recommendations. They can determine the right next step, take action across systems, coordinate with employees and stakeholders, and escalate for human review when needed. Every action is governed by the same roles, permissions, and controls that apply to employees, with granular action settings, audit trails, and deterministic logic for high-impact steps like system updates and approvals.
At launch, Zip is introducing five Superagents built around the highest-friction areas of procurement:
Procurement Superagent: Unblocks stalled requests, manages tail-spend negotiation, and handles repetitive follow-up within procurement’s approved policies and approval controls.
Contract Superagent: Reviews and redlines contracts against company-approved playbooks, flags deviations from standard terms, and routes high-risk issues to legal stakeholders.
AP Superagent: Sorts, codes, matches, and routes invoices using existing approval rules, supplier records, and ERP data to reduce manual AP work while maintaining accurate payment controls.
Config Superagent: Identifies workflow bottlenecks and drafts configuration improvements for admin review – letting teams continuously optimize processes without uncontrolled changes to critical workflows.
Intake Superagent: Guides employees through compliant request creation, routing purchases to the right buying channel, extracting quote details, nudging toward preferred suppliers, and prefilling required fields based on policy and supplier context.
“At Block, we’ve made a fundamental bet on AI,” noted Mithun Sharma, Chief Procurement Officer at Block. “Every team has a mandate to automate, and procurement is no different. This is one of the many reasons we have partnered with Zip. We get the procurement platform our team runs on, fully governed AI agents automating the work, and forward-deployed engineers who specialize in procurement AI and build alongside us.”
Zip MCP: Procurement governance, wherever employees work
Ungoverned AI doesn’t only happen in shadow tools, it also happens when employees do procurement work inside AI assistants that have no connection to enterprise controls. Zip MCP closes that gap.
Zip’s vendor-hosted MCP connects Zip’s AI procurement platform directly to any MCP-compatible AI assistant. An employee researching vendors in Claude can have Zip proactively surface a request submission directly from that conversation, creating earlier visibility for procurement teams without any context switching. Power users can pull aggregated reporting across suppliers, requests, invoices, and payments from within a single AI conversation. Every action respects user permissions through OAuth, runs inside Zip’s compliance controls, and generates a complete audit trail.
Zip MCP is model-agnostic, ensuring procurement governance travels with the work regardless of which AI tool an employee prefers. This is the first time MCP has been implemented natively for enterprise procurement.
Zip AI Spend Automation: How enterprises get there
Superagents are powerful out of the box. But enterprise procurement is not one-size-fits-all. Every organization has unique approval hierarchies, compliance requirements, vendor policies, and edge cases. Zip AI Spend Automation is how the largest enterprises fully deploy governed AI agents for their specific workflows.
AI Spend Automation pairs platform access and AI consumption credits with Zip’s team of forward-deployed engineers (FDEs), who build and deploy custom agents directly inside customer environments:
Zip agent builder platform: Access to all Superagent use cases, skills, and tools
Custom development: Ongoing support from forward-deployed engineers for agent configuration and optimization
Advanced AI governance: Access to additional control and auditability capabilities purpose-built for enterprise needs
Consumption credits: Bundled AI credits to supercharge AI usage
“Healthcare operates on a different set of stakes, where delayed decisions and fragmented processes create real consequences for patient care, organizational agility, and financial stewardship,” said Susanna Rustad, Chief Procurement and Supply Chain Officer at UCI Health. “What stood out to us about Zip’s AI Spend Automation was its ability to bring greater visibility, alignment, and discipline to complex enterprise decisions. In one major IT infrastructure initiative, it contributed to more than $20 million in cost avoidance and value recapture.”
Chris Tredwell, Chief Operating Officer and Charis Thomas, Chief Product Officer at Aqilla, on why the question is no longer whether to adopt AI, but whether processes, governance structures and training pathways are ready for the workforce
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Have you ever got into an old car with a Gen-Zer? If they were driving, chances are you wouldn’t have got very far. A recent survey has found that 39% of 14–29-year-olds couldn’t identify an ignition key. Proof, if it were needed, that once technology advances, old ideas are quickly forgotten. This isn’t just happening in cars. The internet and social media have produced their own native generations – people who have never known a world without those technologies.
The same pattern is starting to emerge with AI. That means Gen Z and Millennials are about to experience a similar shift. The first wave of true AI natives will soon enter the workforce – a cohort that has never known a world without AI.
AI- The New Normal
People’s reactions will largely depend on their experience with AI. But one thing is certain: these graduate and entry-level employees won’t need to be convinced of its value. They’ve already seen what it can do, so if it’s missing, disbelief – or frustration – is likely to follow. It’s a bit like broadband. Here in the UK, it’s simply the standard we all expect. We don’t stop to think about how that connectivity reshaped our lives, helped us work from home or allowed us to stream high-definition media.
Many organisations are still in the early or experimental phases of AI adoption. They might be using the technology to automate basic email inbox management and take meeting minutes. Meanwhile, those further ahead of the curve are exploring more advanced tools and assessing where automation can be safely deployed, particularly for reporting and analysis.
But AI natives won’t see these use cases as experimental. In fact, they probably wouldn’t even refer to them as use cases. It’s just normal, like using a search engine rather than visiting a library to carry out research.
Prompting New Behaviour
Perhaps the biggest difference, however, is where organisations may integrate AI into their existing workflows, AI natives are more likely to structure work around it from the outset.
For them, work tends to start within an AI system, defining the objective clearly, setting constraints, and effectively “briefing” it, before iterating quickly and refining outputs as they go. For AI natives, this kind of prompt-based mindset isn’t a specialist skill; it’s simply how they approach tasks.
This is a fundamental shift. For AI natives, the question isn’t “Should we use AI here?” It’s “Why can’t I use it for this piece of work?” When their expectations collide with more cautious, process-led environments, friction is almost inevitable. Not because one approach is right and the other is wrong, but because both sides are starting from completely different assumptions.
Skills Transfer and Mentoring
But how does the need for AI natives to understand and work through basic manual processes coexist with intuitive prompt-based thinking? Should AI use come with experience-based restrictions in the finance sector? For example, do your three years first, and then you can use the tools.
It’s probably not what AI natives want to hear, but there is logic behind the approach. Learning the manual processes behind automation will enable new recruits to apply the necessary checks and balances to system outputs — putting them in a position to verify data rather than passively accept it.
Without taking this step, there’s a real risk that people will lose the ability to question the outputs they’re working with. That, in turn, has implications for how people are taught. Whether in an educational setting or on the job, that training needs to help AI natives understand the logic behind the systems they’ll be working with.
Lurking in the Shadows
If AI natives encounter friction when trying to use the technology, there’s a risk they’ll seek informal workarounds. Organisations have seen similar patterns before with personal devices or early cloud adoption. With AI, the risks are more focused on data, traceability, and accountability than on system access and security, though these remain important considerations.
Rather than restricting AI use, organisations are already beginning to reshape it by building oversight into how systems are used. That might mean making the AI’s working assumptions more visible and requiring humans to validate outputs. It might also require AI systems to signal their confidence in those outputs and to request manual checks. Over time, this reduces risk and creates an environment where people can work confidently with AI without losing sight of who is responsible.
This approach also challenges a common narrative. Much of the current discussion around AI focuses on job displacement, but the reality is more nuanced. The issue isn’t a simple replacement of human intuition and experience, but how those qualities evolve alongside increasingly capable systems.
Rather than removing the need for people, this managed shift reinforces it. Greater emphasis is placed on human-in-the-loop models, in which individuals with a deep understanding of AI can interrogate, challenge, and interpret system outputs.
A different starting point
So, what happens next? AI tool adoption, for sure. But it goes far deeper than that. Getting ready for AI natives means shifting to a different starting point – and learning to “live in the prompt”.
As AI natives begin entering the workforce and eventually move into leadership roles, the expectation won’t be that AI is introduced; it will be that it is already there. That shift reshapes how people think, learn, and approach tasks from the outset. It will also change how tasks are conceived, carried out and reviewed. The ability to configure, interrogate and challenge systems will become as important as the ability to interpret their outputs.
For organisations, the question is no longer whether to adopt AI, but whether their processes, governance structures and training pathways are ready for a workforce that already assumes it – and will expect to work that way from day one.
Adnan Patka, Enterprise Manager – Blockchain, AI and Web3 at OVHcloud, on why the convergence of blockchain and AI heralds a brighter future for both
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Anthropic’s Mythos team recently assessed various LLM technologies, scoring ChatGPT against Gemini, Grok, Claude and others. The metrics included trustworthiness, but also user deception, sycophancy, encouragement of user delusion and co-operation with human misuse.
Clearly, there’s no doubt that AI can be an enormously positive transformational force: the development of AlphaFold, whose founders won the Nobel Prize, showed us that very clearly. But when AI is being assessed for encouraging ‘user delusion’, it’s clear that it still has a PR problem.
In fact, AI has more than just a PR problem: research from McKinsey highlighted that although almost two thirds (64%) of decision-makers reported that AI was accelerating innovation within their organisation, less than half (39%) could actually report positive financial impact on the business as a result.
The AI Trust Gap
AI doesn’t exist in a vacuum, and the interplay of AI with other technologies is beginning to come into focus. We see that blockchain has great potential to complement AI, providing the transparency and trustworthiness that it needs. In a recent study of Web3 professionals, almost three-quarters (70%) said that blockchain had the potential to fill AI’s trust gaps.
But where else are these issues coming from?
It’s overly glib to say that people and businesses don’t always trust AI. What our research told us was that 32% of Web3 experts believe that privacy is AI’s main challenge. Historically, AI systems have been shown to be biased, and data has often been collected by AI systems for training or other uses – sometimes without user permission. One in seven (14%) of the study also reported that they believed AI needed more transparency to be trustworthy.
Blockchain’s Potential for Supporting AI
Blockchain is well-placed to help tackle these issues. Blockchain was originally created as a database with clear traceable links between items, without the need for a single governing authority.
For example, consider an AI system which optimises food supply chains, allowing a supermarket to maintain lower prices. In an environment where people may distrust AI, they may suspect that the system is simply buying lower-quality stock and shipping it more affordably, potentially meaning shorter shelf-lives and less tasty tomatoes! However, blockchain systems can help to track the provenance of these items, proving where the tomatoes were grown, when, by who, and the subsequent shipping and treatment of them, all in a publicly accessible ledger.
Blockchain systems also automatically check the validity of transactions and link them together in a way that makes fraud very difficult. Changing one transaction generally requires changing the entire ‘course of history’ or having significant control (specifically, controlling over half of the nodes) of an entire blockchain, which is usually unfeasible in most large, public blockchains today.
Delivering Benefits with Blockchain
There are also a number of cases where blockchain can bring functionality benefits to AI systems. For example, as we saw, privacy is a significant concern for AI users, but blockchain systems are able to authenticate securely without exposing user credentials, through self-sovereign identity. This is especially useful where AI systems need to interface with other tools that might not be as trustworthy as the core system.
Blockchain principles can also support data exchange in federated learning systems, where data is shared between different machine learning platforms. Blockchain can establish the validity of this data while still preserving its integrity and user privacy. This means that AI can be trained on data that is consistent and robust – without breaking trust.
There are a huge number of applications for this kind of technology, but unfortunately, it’s not all plain sailing.
Blockchain’s Challenges
The industry is undergoing a huge change today. Interest in stablecoins – a crypto asset which is attached to the value of a more stable asset like a traditional (fiat) currency or other physical commodity – has quadrupled since October 2025. Large, well-established organisations are considering what private and public blockchains can do for them, which could ultimately have a positive and stabilising impact on the regular bear and bull cycle that the crypto market experiences.
However, some more negative parts of blockchain’s legacy persist. Almost two thirds (61%) of our research sample said that concerns about blockchain’s credibility were having an impact on its integration into the enterprise ecosystem. This includes blockchain’s links with the more unsavoury side of cryptocurrency, including cybercriminal activity. Over half (54%) simply said that there wasn’t enough understanding about blockchain’s capabilities and what it could really do, holding IT decision-makers back from making the most of it.
At the same time, this isn’t a one-way street. AI’s capabilities – in particular, automated, agentic AI – can carry out blockchain activities without needing user input. For example, AI can issue transactions or analyse data and summarise it for monitoring systems. There is tremendous potential for AI to support many different parts of the blockchain ecosystem, summarising trends, automatically carrying out tasks, or executing smart contracts without human intervention.
A Mutually Beneficial Relationship
Blockchain and AI have enormous potential to support each other. There are some organisations already starting to integrate blockchain into AI and vice versa, but it will still be a number of years before the relationship is mature. That said, two-thirds of the Web3 experts we surveyed (67%) believed that it’d take roughly one to four years, with only eight percent saying that it might take five to six years.
Indeed, given the enormous benefits that blockchain can bring to AI, not to mention the rapidly increasing acceptance of blockchain technologies across the financial sector in particular, the convergence of these two technologies is almost inevitable. If we can push past the challenges, the future is bright for both blockchain and AI.
Adam Gale, Field CTO for AI & Cybersecurity at NetApp, on why cybersecurity in the AI era will depend on two capabilities: detecting abnormal behaviour as early as possible and ensuring trusted data can always be restored
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How can we not talk about cyberattacks when talking about AI? Barely a week passes without another story on AI-generated scams, deepfake voice fraud or ransomware becoming more sophisticated. Security teams are starting to see the effects in their own environments too. In many organisations, the first sign is simply the number of alerts appearing in security dashboards each morning.
Generative AI tools are lowering the technical barriers that once limited cybercrime. Cybercriminals can use it to generate convincing phishing emails in almost any language, code malware variants in minutes, or automate the process of scanning infrastructure for vulnerabilities. In fact, cyberattacks are now being generated at industrial scale. The good news is that AI can be an equally powerful tool for organisations in defending against this deluge.
When threats multiply faster than defences
It’s important to understand how quickly – and how far – Ransomware-as-a-Service has come in recent years. Large language models (LLMs) can now generate convincing phishing campaigns tailored to specific roles or individuals. Threat actors are already using them to scrape publicly available data and craft highly personalised emails that mimic internal communications. Malware authors are experimenting with AI to produce polymorphic code that changes its behaviour between executions, making signature-based detection far less effective.
Voice cloning is another rapidly emerging risk. In several reported cases, help desks and IT support teams have been persuaded to reset credentials after receiving calls from voices that sounded identical to employees or senior executives. What previously required careful social engineering can now be automated and scaled.
Many of these incidents succeed because cybersecurity platforms were built around a fundamental assumption: threats evolve incrementally and can therefore be detected by identifying known patterns. These include signature-based detection, rule-based alerting and static threat indicators. However, the problem is that this model breaks down quickly when cybercriminals can generate new variations faster than those signatures can be updated.
Additionally, monitoring tools generate huge volumes of warnings, many of which turn out to be harmless, but IT security teams still need to investigate them. When hundreds of alerts appear each day, this creates a risk of alert fatigue which may see genuine threats hidden in a sea of false alarms. As a result, malicious actors are now operating at machine scale while many defensive processes still rely on human triage, and this mismatch is becoming one of the defining problems in modern cybersecurity.
Fighting AI with AI
On the bright side, all that AI-powered automation can also be used for defence – not just offence. For example, AI can strengthen cybersecurity by examining activity across systems and data and identifying behaviour that deviates from normal operations.
Instead of relying only on known hacking techniques, AI-driven systems can monitor how data is accessed and modified across environments. If behaviour changes unexpectedly, systems can raise alerts or trigger automated responses. Detecting anomalies early matters even more as breaches become more automated, because the earlier suspicious activity is identified, the easier it is to contain the impact.
Automation also reduces the burden on security teams. Analysts spend less time reviewing routine alerts and focus more on investigating genuine threats. As organisations increasingly rely on AI systems, protecting the data those systems depend on becomes central to cybersecurity strategy.
This is especially crucial today, as modern enterprise data environments are highly distributed. Data is constantly moving between on-premises systems, cloud platforms, analytics pipelines and AI training environments. In turn, every transfer, replication process or API connection creates another potential attack surface.
This also means that storage plays an elevated role in supporting cyber resilience. Capabilities such as storage-level anomaly detection, autonomous ransomware protection and immutable snapshots allow organisations to identify suspicious data activity and preserve trusted recovery points. If systems are compromised, clean versions of data can be restored quickly without paying ransoms or rebuilding environments from scratch.
Building Confidence in AI Environments
AI is rapidly industrialising cybercrime. Malicious actors can now generate new techniques, test them and deploy them at a pace that traditional security operations struggle to match. Therefore, defence strategies must adapt accordingly. That means shifting from signature-based detection to behavioural analysis and from manual investigation to automated response. In other words, defending against AI-powered threats will increasingly require AI-powered security.
Ultimately, cybersecurity in the AI era will depend on two capabilities: detecting abnormal behaviour as early as possible and ensuring trusted data can always be restored. In a world where attackers can generate new threats in minutes, organisations that cannot protect and recover their data quickly will struggle to trust their systems at all – including the AI tools they increasingly rely on.
Maxime Vermeir, Vice President of AI Strategy at ABBYY, on how organisations can build a faster and more resilient approach to KYC compliance
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“Know Your Customer” (KYC) used to feel as painfully slow as dial-up internet, but it doesn’t have to be that way any more.
KYC is the process organisations use to verify the identity of their customers, assess risk, and ensure compliance with regulations such as AML (Anti-Money Laundering) and counter-terrorist financing laws.
Today, it’s about more than compliance. It’s a critical trust and customer-experience driver. Financial institutions, insurers, and fintechs use KYC to build confidence, protect their brand, and deliver frictionless onboarding experiences that feel more like Apple Store checkout than DMV purgatory.
However, organisations face an increasingly challenging landscape.
Regulatory pressure is intensifying. The July 2027 EU AML Regulation, for example, will harmonise standards across all member states, introducing stricter requirements for beneficial ownership data and ongoing due diligence.
At the same time, customer expectations have shifted dramatically. People expect to open accounts or complete onboarding in minutes, not days. Fraud and identity theft are growing. Deepfakes, synthetic IDs, and digital manipulation make verification more complex and costly than ever.
Against this backdrop, many organisations struggle to keep pace. Inefficient processes, fragmented systems, and manual checks create delays, increase risk, and damage the customer experience. We examine the six deadly sins of KYC compliance and how organisations can address them to build a faster and more resilient approach.
Fragmented, Siloed KYC Workflows
KYC processes often span multiple systems, from CRM to AML, to onboarding portals and case management. A lack of integration between these different areas creates more work, with data capture often duplicated, and SLAs missed. Teams have no unified view of onboarding performance.
Disconnected databases, inconsistent standards, and repetitive customer documentation cause onboarding friction, high operational costs, and gaps that fraudsters can exploit with alarming speed.
To combat these risks, organisations need to implement end-to-end visibility across fragmented workflows. The easiest way to achieve this is through AI-powered Process Intelligence tools. These tools can reveal bottlenecks, improve communication between teams, and avoid the risk of repeating time-consuming work.
Manual Document Handling and Validation Bottlenecks
Most onboarding delays occur during document intake and validation. Human review teams spend hours checking IDs, proof of address, and corporate records, often working across multiple systems and formats. This introduces inconsistencies, with decisions likely varying between reviewers, and increases the likelihood of errors or missed details.
The process is also resource-intensive and difficult to scale. As volumes increase, manual reviewers either become bottlenecks or require additional headcount, pushing up operational costs. Long cycle times mean customers wait, which can lead to drop-off, frustration, and reputational risk to the organisation.
Automating document classification, extraction, and validation can mean the difference between success and failure, even for complex, multi-page corporate KYC packs. These systems leverage intelligent workflows and advanced data processing to accurately sort documents, extract critical information, and standardise it in real time.
This not only reduces manual effort but also significantly minimises human error, identifying missing fields and inconsistencies before submission. AI tools for regulatory automation and fraud checks enable higher rates of first-pass compliance and faster document processing. This means less time spent on manual reviews and a faster overall process.
Lack of Process Visibility and Control
Compliance and operations teams at financial services organisations often lack real-time visibility into where a customer’s onboarding file sits in the process or how long it has been at each stage. Information typically spreads across systems, inboxes, and manual trackers, making it difficult to build a clear view of progress.
As a result, it’s difficult to pinpoint the problem when delays happen. This lack of transparency makes it harder to meet SLAs or prepare for audits. Teams may only realise there’s an issue once deadlines are missed or escalations occur.
Process Intelligence provides real-time monitoring of onboarding KPIs, including time per stage, rework rates, and failure points, and allows teams to simulate process improvements. It creates a complete digital audit trail of every step, supporting both operational management and regulatory compliance.
Better visibility makes it easier for organisations to maintain control, prove compliance, and deliver a predictable customer experience.
Inconsistent Execution Across Regions and Business Lines
In many businesses, each branch or business unit follows slightly different onboarding procedures, often shaped by local practices, legacy systems, or different interpretations of compliance requirements. While these variations may seem small individually, together they increase fragmentation across the organisation.
This can lead to inconsistent customer experiences and non-uniform compliance documentation. One customer may be onboarded quickly, while another similar customer faces delays or repeats because a different team or location handles them. Over time, this erodes trust and makes the organisation look disjointed and unpredictable.
Best-practice workflows must be standardised enterprise-wide, and all data and documentation should adhere to consistent formats and validation rules across jurisdictions. This is where Process Intelligence excels, benchmarking and comparing process execution across teams, countries, and products, and highlighting deviations from policy.
Slow Remediation and Periodic Review Cycles
When periodic reviews or remediation campaigns begin, teams struggle to find and validate the information they need. Customer records may be spread across multiple systems or stored in inconsistent formats, making it difficult to quickly identify what is missing.
Manual checks only make things worse. Reviewing large volumes of records is time-consuming and repetitive, increasing the likelihood of human error. As workloads increase during remediation campaigns, these risks multiply.
A better approach is event-driven (pKYC) automation. Instead of relying on periodic reviews, it detects changes in customer data and automatically triggers the right review workflows. Intelligent document processing (IDP) can quickly revalidate and update documents, while process intelligence tools track progress, flag exceptions, and ensure tasks are completed on time.
Proving Compliance and Audit Readiness
Regulators increasingly expect organisations to demonstrate full transparency across their KYC processes, including clear data lineage, time-stamped actions, and explainable decision-making. This is particularly true where AI or automation is involved.
It is no longer sufficient to show that checks were completed. Firms must be able to evidence exactly how data was collected, transformed, verified, and used at every stage of the customer lifecycle. However, many organisations lack this end-to-end audit view. KYC processes are often fragmented across multiple systems, and as a result, audit trails are incomplete or difficult to reconstruct.
Process Intelligence maintains a comprehensive record of every process step, decision, and exception, while IDP provides field-level traceability, showing where each data point came from and how it was verified.
Using AI-powered tools that combine process intelligence with document processing makes KYC faster, easier, and more accurate. It means customers can be onboarded more quickly and mistakes are reduced, making the whole KYC process easier to track and audit. Organisations can trust that they comply with regulations while building trust among customers and giving them a smoother, better experience.
Andrea Babayan, Demand Growth Strategist at Ipsotek (an Eviden business), on why the real competitive advantage will not belong to the hub with the most advanced analytics stack but the one that can demonstrate proportionality, resilience and clarity of purpose
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Transportation hubs are not simply adopting new technologies. They are reconfiguring how they operate. Digital identity systems, AI-driven video analytics, environmental sensors, drones and upgraded communications platforms are converging into core processes.
What was once framed as incremental innovation is now embedded in passenger throughput, dispatch coordination, platform management and perimeter oversight. Modernisation is no longer an upgrade cycle. It is a redesign of the operating model.
The more difficult question is not what these systems can detect, but whether institutions are evolving with equal deliberation.
Identity Border Systems as Throughput Infrastructure
Border modernisation makes the shift explicit. Europe’s Entry/Exit System (EES) illustrates how biometric identity is no longer merely a security control. It is a flow determinant. When enrolment, verification and exception handling are integrated into primary processing lanes, identity becomes inseparable from capacity planning and operational resilience.
The expansion of touchless identity verification in US airports reflects the same structural move. Identity confirmation now shapes staffing equations, lane geometry and peak-period modelling.
When these systems perform, throughput improves. When they stall, queues lengthen – and confidence declines.
Not all identity applications carry equivalent risk. Verification within controlled checkpoints differs materially from open-ended identification in public space. Proportionality must therefore be embedded at design stage. Governance is not a constraint imposed after deployment; it defines the parameters within which identity systems can scale without undermining trust.
In jurisdictions such as the European Union, where the AI Act establishes a formal framework for high-risk systems, institutional readiness must include formal risk assessment, auditability and transparent oversight mechanisms as foundational design requirements – not retrospective safeguards.
Border digitisation is not merely technological enhancement. It is throughput engineering under regulatory and operational accountability.
Sensor Convergence and Institutional Responsibility
Beyond borders, transport networks are integrating AI-powered video analytics with environmental sensors, access control systems and communications infrastructure. The objective is coordinated situational awareness – not visibility for its own sake.
Crowd density analysis can inform service adjustments. Platform anomalies can trigger structured escalation. Perimeter events can be validated through multiple inputs before operational response.
This convergence strengthens detection capability. It also expands institutional responsibility. As data streams intersect, purpose limitation, retention discipline, interoperability standards and escalation protocols must be clear. Integration increases capability – and scrutiny.
Governance is not a brake on innovation. It is the framework that allows it to scale responsibly. Alert volume creates noise. Decision quality creates advantage.
The Operational Decision Core
Modernisation is increasingly coalescing into centralised operational environments – decision cores that synthesise data across security, passenger flow, maintenance, access control and communications.
This direction aligns with the formalisation of collaborative operational models such as the Airport Operations Centre (APOC), which positions airport coordination as a structured, cross-stakeholder decision environment rather than a collection of siloed control rooms.
What was once compartmentalised becomes interconnected. The value lies not in dashboards, but in how insights shape staffing allocation, capital prioritisation, service recovery and commercial performance. Yet greater visibility does not eliminate boundaries. Cross-functional insight should improve coordination, not dilute accountability. Rich operational data must remain tied to defined purposes.
As AI systems transition from pilot to operational dependency, resilience becomes decisive. Designing for stress is therefore as important as designing for efficiency. Compliance cannot be retrofitted. Interoperability cannot be improvised. Institutional maturity cannot be assumed.
What Comes Next
Transportation hubs are becoming decision environments embedded within physical infrastructure. Cameras function as sensors; identity shapes throughput; and analytics informs operational judgement. But the defining difference over the next decade will not be technological sophistication; it will be institutional discipline.
In my view, the real competitive advantage will not belong to the hub with the most advanced analytics stack. It will belong to the one that can demonstrate proportionality, resilience and clarity of purpose – consistently, transparently and under pressure. Smarter systems are inevitable. Smarter institutions are a choice.
Q&A – Inez Berkhof-Hollander, EMEA Vice President at the global B2B payments network TreviPay discusses the future for B2B payments
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Your research with 550 senior UK and European business buyers found that AI is now widely used in B2B payments. Where is it being implemented, why, and are there any risks here?
“What we’re seeing is that AI adoption in B2B payments is less about experimentation and more about removing friction from complex, high‑volume processes that were historically manual. We see AI currently mostly used on the AP side, not so much on the AR side yet.
Today, AI is most commonly applied in three areas: invoice processing and data extraction; payment routing; and reconciliation. The undervalued opportunity is still on the AR side. For exception handling, dispute management and in credit and risk decisioning. These are all pain points where accuracy, speed and scale really matter. Particularly in industries with high invoice volumes or complex payment terms.
The business driver is clear… Suppliers want faster time to cash. Buyers want fewer errors and disputes. And finance teams want better cash predictability.
That said, the risk isn’t the technology itself – it’s how it’s governed. In B2B, when AI decisions sit close to credit, compliance and customer relationships; black‑box models without explainability, or automation without appropriate guardrails, can introduce operational and regulatory risk. The most effective applications we see are those where AI augments human decision‑making. Rather than replacing it entirely, there is clear auditability and accountability built in.
In other words, AI is already delivering tangible value in B2B payments, but the winners will be those who treat it as an enterprise capability, not just a standalone feature.”
Looking wider – the research also examined friction within the payments process. What were the most important areas that suppliers can leverage to their competitive advantage?
“One of the clearest signals from the research is that payment experience has become inseparable from the overall customer experience and the commercial relationship.
The biggest opportunities sit at the intersection of flexibility and predictability. Suppliers that make it easy for buyers to pay – through compliant and accurate invoicing, aligned payment terms, and transparency throughout the lifecycle – are easier to do business with as they reduce disputes and accelerate cash flow. Hence, ultimately they will increase customer loyalty.
What’s interesting is that many of these levers are not new, but they are finally being treated as strategic. Invoice accuracy, payment visibility, dispute resolution, and alignment between sales and finance are no longer just operational hygiene; they are differentiators. In competitive markets, the ability to offer consistent terms across regions, customized invoicing or reporting or to accommodate how buyers want to pay without creating internal complexity, is increasingly decisive.
From an O2C perspective, friction often appears at handovers: from order to invoice, from invoice to payment, and from payment to reconciliation. Suppliers that invest in smoothing those transitions – rather than optimising individual steps in isolation – are better positioned to compete on experience without eroding margin.”
Your report shows 82% of buyers value invoice customisation. Why has something traditionally seen as back-office admin become such a decisive competitive factor?
“Because invoices are no longer just accounting documents – they’re a key part of the buyer experience.
In B2B, invoices often trigger downstream processes on the buyer side: approval workflows, ERP/PO matching, compliance checks, and even cash forecasting. When invoices don’t align with a buyer’s internal requirements – whether that’s formatting, data fields or references – friction is inevitable. That friction shows up as delayed payments, disputes, and strained relationships.
What the 82% figure really tells us is that buyers are under pressure themselves. Finance teams are expected to do more with less, manage risk more actively, and support the wider business – all while maintaining control. Invoice customisation helps them do that.
For suppliers, this is a powerful insight. Meeting buyers where they are, instead of forcing one‑size‑fits‑all processes, has moved from ‘nice to have’ to a strategic necessity. It’s also a clear example of how O2C capabilities directly support revenue protection and growth – not just operational efficiency.”
What was the regional difference in your data that surprised you the most?
“What stood out most was not just what differs by region, but why.
Across Europe, Pay by Invoice (or ‘Net Terms’) remains dominant, but the expectations around speed, visibility and automation vary significantly. In some markets, buyers are primarily focused on control and compliance; in others, on efficiency and working capital optimisation. Regulatory maturity, banking infrastructure and ERP penetration all play a role in shaping those expectations.
What surprised me was how consistently these regional nuances translate into different definitions of ‘good experience’. The markets that move fastest are not necessarily those with the most advanced technology, but those where finance, procurement and payments strategies are more closely aligned.
For suppliers operating pan‑European or globally, this reinforces the importance of flexibility. A single market‑specific approach rarely scales. The winning strategies are built around a common O2C backbone, with local adaptability layered on top.”
Pay by Invoice remains dominant in Europe, but you also highlight digital wallets and even stablecoins. How do you see the payment mix evolving over the next 3-5 years?
“Pay by Invoice will remain the backbone of B2B payments in Europe for the foreseeable future. And that’s not a sign of stagnation, but of trust in a model that supports credit, risk management and commercial flexibility at scale.
Where we will see change is behind the scenes. Greater digitisation, faster settlement, and better integration between invoicing, payments and reconciliation will progressively modernise how Pay by Invoice operates.
When it comes to alternative instruments like stablecoins, the conversation is still early, but it’s becoming more serious. Their potential value lies in settlement efficiency and cross‑border use cases, rather than replacing core commercial constructs like trade credit. Whether they become relevant at scale will depend on regulation, risk frameworks and clear economic benefit for both sides of the transaction.
What’s important is that B2B payment evolution will be pragmatic, not disruptive for its own sake. Buyers and suppliers will adopt new rails where they reduce friction or risk – not because they’re new, but because they meaningfully improve the O2C lifecycle.
How Sonata Software is driving CPL Aromas’s technology transformation
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When CPL Aromas needed a software partner with deep technical expertise to guide its transformation, it chose Sonata Software. Theirs is a relationship that’s been going strong for eight years and counting – since Alfred Muthunathan, CIO at CPL, joined the team. “He was looking to transform the IT landscape,” says Uttam Hazari, Head of UKI & Europe at Sonata Software. “But before he could embark on that journey, it was important to ensure that the foundation was stable first.”
The goal was very simple: to ensure CPL could continually strengthen its system performance and improve reliability across its global operations. “It was growing at a rapid pace,” says Hazari. “However, the systems and data weren’t well integrated. It was important for us to work collaboratively with CPL to not only analyse the processes, but also stabilise the systems and the applications, and allow the company to regain its confidence.”
This early engagement reflects Sonata’s Frontier Firm approach – combining deep engineering expertise with business-aligned transformation to create resilient digital foundations.
A transformational relationship
Alongside the ERP support, Sonata partnered with CPL Aromas to develop its first B2B customer portal, enabling users to securely access orders and shipment information, thereby improving transparency and reducing manual interactions. “The early successes formed the foundation for a long-term digital transformation partnership built on trust, responsiveness, and delivery excellence,” Hazari adds.
This momentum reflects Sonata’s broader approach to building long-term, value-driven digital partnerships.
“Sonata Software is committed to building strategic partnerships that help organisations accelerate their digital transformation journeys. Today, our clients are looking for initiatives that move faster, reduce risk, and deliver clear, measurable business impact. As a Microsoft Frontier Partner, we bring deep expertise in translating advanced Cloud and AI capabilities into tangible outcomes. Our expansion across the UK and Europe marks an exciting phase of growth for Sonata, and our transformation journey with CPL Aromas is a key pillar of that strategy. We are privileged to have CPL Aromas as both a valued customer and a trusted partner.” – Anthony Lange, Chief Revenue Officer at Sonata Software
What began as a simple collaboration has evolved into a multi-phase, enterprise-wide transformation journey over the past eight years. A couple of years in, Sonata’s role expanded. “As part of that, we helped CPL rebuild and transform their core infrastructure,” says Hazari. “We also helped to implement Azure Site Recovery, stabilise IMS operations, strengthen identity and endpoint security, and establish modern monitoring with Azure Sentinel. And this is just the beginning.”
From ERP to infrastructure management to full enterprise modernisation, Sonata has been with CPL every step of the way, including the end-to-end global migration from the legacy ERP platform to Microsoft Dynamics 365 across multiple legal entities. “As we speak, the engagement has matured into a data-driven, AI-first innovation spanning across Dynamics 365 and the customer engagement platform, RPA automation, and agentic development,” says Hazari.
AI-first
Developing an AI-first approach is at the core of CPL’s current and ongoing focus, and it’s an area where Sonata has played a major role. “Sonata has been working very closely with CPL Aromas in its AI-first evolution – not just technically, but strategically as well,” says Hazari.
One of the key contributions to this has been building a modern digital core through the Dynamics 365 ERP program, enabling real-time visibility, standardised processes, and scalable operations. “These things are a prerequisite for effective AI deployment,” says Hazari. “Without that, you can’t just bring in AI to do the magic. Data is the most important ingredient. If the data isn’t right, the AI won’t be either. Then came automation at scale through RPA initiatives, which reduced the manual workload and increased process efficiency.”
More recently, Sonata has been working with CPL to pioneer its agentic AI adoption. “One of the recently deployed agents is a custom planner agent, which is designed to automate one of CPL’s most complex manufacturing processes,” Hazari explains. “This represents a major leap towards AI-driven decision intelligence across operations. Together, these initiatives have established the technology, data, and automation foundation for CPL Aromas’ AI-first strategy.”
These capabilities exemplify how Frontier Firms operationalise AI – embedding intelligence into core processes to drive real-time, autonomous decision-making.
The four pillars
Sonata is now helping CPL deliver value across four strategic dimensions. These consist of:
Operational resilience and security
Standardisation of global processes and transparency
Enterprise-wide data platform
Agentic AI-driven decision-making
“Bringing these initiatives together is transforming CPL into a digitally agile and AI-ready enterprise,” says Hazari. “That’s the value we’ve been able to deliver.”
The goal is for this relationship to continue for many more years. Sonata will continue to contribute towards CPL’s incredible growth, and as this collaboration progresses, it will be centred on CPL’s AI-first operating model.
“Scaling agentic AI beyond the initial successes is very important,” says Hazari. “That’s across all business functions, whether it’s finance, quality control, demand forecasting, supply chain optimisation, and even customer engagement. The idea is to help CPL in democratising AI across business functions, to reach a state where they’re able to create their own workflows and generate their own agents.”
As well as this, it’s about continuous automation and expansion to further reduce manual effort wherever possible from current processes. “Lastly, we’re working on enhancing digital sustainability,” says Hazari. “This includes ensuring that systems remain scalable, secure, and ready for emerging AI technologies. Technology is changing; AI is changing very rapidly. It’s extremely important for Sonata, as the strategic partner, to make sure we’re able to keep CPL Aromas one step ahead in this journey.”
Appian: Why AI is Putting Better Business Within Every Organisation’s Reach
Our cover story highlights how AI is putting better business within everyone’s reach.Mark Talbot, Director – CS AI Initiatives at Appian, reasons that as organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it. “Instead of concentrating control and decision rights in a small, central group, modern AI tools give more agency to the people closest to the work. They can see what is not working, imagine better approaches, and use AI to help redesign and improve the processes they rely on every day.”
CPL Aromas: How a Leading Fragrance House is Using AI to Amplify Creativity
In the world of retail, a leading fragrance house uses AI to amplify creativity. Alfred Muthunathan, CIO at CPL Aromas, explains how the family-owned business is using AI as a strategic capability to support creativity and accelerate innovation. “We didn’t bolt AI onto our systems; we redesigned the organisation, so AI is native to how we operate… Our new system takes away the workload from perfumers and has allowed us to create something that always keeps the nuances of our industry at its core.”
Vibrant Capital: Scaling AI on Main Street
Shadman Zafar, Founder & CEO of Vibrant Capital, is building a CIO-led model for enterprise transformation. Vibrant Capital is an operator-led investment and company-building platform focused on scaling AI in the real economy. “We don’t spray investments across hundreds of AI startups. We curate a portfolio with purpose – selecting companies that solve the real mission-critical problems CIOs face in scaling AI adoption.”
Also in this issue, we learn about the supply chain transformation journey at Swiss sportswear brand On, unpack the latest AI readiness research from Snowflake and hear from Hitachi Vantara about the importance of strong data foundations for the best utilisation of AI.
Snowflake’s UK research reveals that while many continue to invest in artificial intelligence (AI), the country’s businesses are in the early stages of finding productivity gains at scale
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UK productivity remains a longstanding economic challenge, with policymakers consistently positioning AI as a key lever for growth and competitiveness.
Snowflake’s findings show AI investment and experimentation amongst businesses though impact so far is varied. 45% of UK organisations say AI is delivering early gains or in specific use cases, though 23% are already seeing it delivering productivity improvements at scale. Buoyed by this thought, most organisations expect AI investment to increase over the next 12 to 24 months, with just 1% planning to decrease spend. Businesses continue to back thetechnology as a strategic priority, demonstrating confidence that productivity breakthroughs will come.
The UK AI Report
Conducted by YouGov on behalf of Snowflake, the report surveyed 500 senior decision-makers including CEOs & CFOs from large UK organisations, spanning key industries including public sector, manufacturing and financial services. These industries are seen as being vital to the Government’s AI and productivity ambitions. Its 2025 AI Opportunities Action Plan aims to boost the UK economy by £47 billion annually, estimating that widespread AI adoption could increase national productivity by up to 1.5% each year.
Dr Fabian Stephany, Economist & Departmental Research Lecturer at the Oxford Internet Institute (OII), University of Oxford commented: “I am encouraged to see early evidence that AI is beginning to generate measurable productivity gains for UK firms. Since the introduction of generative AI, many observers have been asking when these gains would materialise, and the findings suggest that this moment may now be arriving. This is consistent with what research would predict: technological breakthroughs rarely translate immediately into productivity improvements, as organisations need time to adapt their workflows, governance structures and capabilities.“
From AI Promise to Productivity Reality
The data suggests that while belief in AI’s potential is strong, execution at scale is proving more complex. Findings indicate that the primary obstacles to AI-led productivity are not technological. Instead, organisations point to structural and operational barriers as factors slowing the move from pilots to enterprise-wide transformation.
Top barriers include a lack of skilled workforce, poor data quality, organisational silos and unclear leadership or strategic direction. Technology itself ranks below many of these internal challenges, cited by just 19% of respondents. Responsibility for AI governance is also often fragmented across executive, technology, data and business leaders. While executive leadership typically holds responsibility for investment, there is no clear governance owner, limiting accountability and slowing decision-making.
This suggests that many UK organisations are pursuing AI at a measured pace, building progress through smaller, targeted use cases while strengthening internal structures, with broader productivity gains likely to follow as foundations mature.
In debates about national productivity, AI is positioned as a growth engine. For business leaders, these gains will manifest themselves at both their top and bottom line with cost reduction as a clear goal. Nearly half (44%) say that cost reduction matters most as a key measure of success, while 26% say the same of revenue growth.
The Executive Confidence Gap
The research also reveals cautious confidence on AI deployments among senior leaders. Only 24% of organisations say AI initiatives are identified and prioritised using a rigorous framework aligned to business objectives. Meanwhile, 40% expect AI to take two years or more to materially improve productivity.
Around 60% say ethics and safety concerns influence their decisions to adopt and scale AI. This reflects a responsible approach to deployment, particularly in regulated and risk-sensitive sectors.
Jennifer Belissent, Principal Data Strategist, Snowflake, said: “UK organisations clearly believe in AI’s long-term potential, and continued investment runs parallel to this belief. This research shows, however, that belief alone is not enough. Productivity gains require clear ownership, strong data foundations and alignment between AI initiatives and measurable business objectives. AI has the potential to be a real driver of UK productivity and economic growth. But unlocking that potential depends on getting the fundamentals right – governance, data and clear accountability.”
A Varied Industry Picture
The research highlights clear differences in AI maturity and confidence across key UK industries, although productivity gains remain uneven.
Financial services is more advanced on governance and strategic alignment, but regulatory and reputational concerns are slowing the move from structure to scale.
Manufacturing shows strong belief in AI’s long-term productivity potential, yet expects slower returns due to skills gaps and integration challenges.
Retail lags on confidence and delivery, with AI often confined to isolated use cases amid persistent data quality issues and fragmented ownership.
In the public sector, organisations are the most risk-aware and governance-led, but also anticipate longer timelines before productivity gains are realised.
53% cite safety & reliability of AI outputs as the top concern affecting confidence in AI.
Two thirds (66%) say ethics and safety significantly shape adoption decisions.
52% say AI will not materially improve productivity for at least two years.
While this cautious approach prioritises trust and accountability, it may mean productivity gains take longer to come to fruition.
Turning Point for UK Enterprise AI
Across industries, many are still realising how best to drive AI productivity at scale and the skills needed to make this a reality. While levels of governance maturity and risk appetite differ, the journey to broader productivity gains is shared.
Dr Stephany added: “The report’s finding that skills shortages are a key barrier to adoption strongly resonates with findings from my research group (SkillScale) at the Oxford Internet Institute, University of Oxford. AI systems are only as powerful as the people who develop, maintain, apply and govern them. In SkillScale’s research, we find that workers with AI-related skills command a wage premium of around 23% in the UK, have higher chances of finding a job, and are more likely to receive additional job benefits. These patterns reflect the strong and growing demand for talent capable of working with artificial intelligence. Expanding access to AI skills and training will therefore be critical if organisations want to sustain and scale these productivity gains and ensure that the benefits of AI are broadly shared.”
Jennifer Belissent concluded: “The research paints a clear picture. The foundations for AI success in the UK are in place. Organisations are investing, experimenting and strengthening governance frameworks. However, to close the gap between ambition and measurable productivity gains, businesses need stronger alignment, clearer ownership and more robust data foundations. If AI is to play the transformative role policymakers and business leaders expect, the focus must now shift from experimentation to disciplined execution.”
Methodology
The research was conducted by YouGov on behalf of Snowflake among 500 senior decision-makers from large UK organisations with 250 or more employees across manufacturing, financial services, retail, the public sector and other industries. Fieldwork was conducted in January 2026.
Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 13,300 customers around the globe, including hundreds of the world’s largest companies, use Snowflake’s AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone.
Andrew Power, Head of UK&I at Tricentis, on why the right approach to AI can deliver the foundation for more resilient, predictable systems
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Artificial intelligence is reshaping software delivery in financial services. Code that once took teams weeks to develop can now be generated and deployed in a matter of hours. This isn’t just about faster delivery; it changes the fundamentals of how software is built and how it behaves in production.
Financial institutions have moved quickly to integrate AI across core systems, from customer operations to anti-money laundering (AML) and software development to capture efficiency and innovation gains. UK parliamentary evidence shows adoption is already widespread, with the majority of firms using AI, and more planning to follow.
But as adoption spreads and becomes more embedded within key systems, so does exposure. Risk is no longer confined to individual defects, but shaped by how quickly those defects can spread across interconnected environments.
AI has removed the limits on how quickly software can be created, but not on how confidently it can be trusted, and financial institutions can now generate and deploy code faster than they can safely validate it.
This creates a new paradox: AI is both accelerating the pace of software change and increasing the speed and scale at which failures can materialise.
Machine-Speed Failure
AI-driven development shortens the distance between change and consequence. Software updates can move through the pipeline from creation to production with significantly less friction. However, this also reduces the time available to identify, flag and contain any issues before they have an impact.
AI-driven software changes don’t just move fast, they scale fast. Unlike traditional failures, these are systemic risks. A single misstep in an AI-generated update can propagate unpredictably.
For financial services, this is especially significant when key systems are deeply interconnected, spanning complex layers of infrastructure, integrations, and third-party services. Even a minor defect can propagate quickly across systems, amplifying its impact.
What would once have been contained can now escalate, cascading across systems and causing wider disruption that affects customers, operations and, in some cases, market activity. In financial services, this is not just a technical issue but a business risk with direct implications for customer trust, regulatory compliance and financial stability. The challenge is no longer simply identifying defects but maintaining confidence in what is being deployed.
This risk is already being felt across the sector. Institutions are accelerating delivery to meet customer expectations and competitive pressures, but often without corresponding advances in validation. Tricentis’ research shows 68% of financial services organisations anticipate outages or serious incidents due to poor software quality.
Regulatory Pressure for AI is Increasing
The issue is also drawing attention from regulators. Earlier this year, the UK Treasury Committee warned that current approaches to AI in financial services are inadequate and could expose customers and the wider system to “serious harm”, highlighting the need for stronger guardrails, clearer accountability and more robust oversight to deploy it safely.
Traditional resilience frameworks were never designed for systems evolving in real time, and AI can no longer be treated as a marginal technology risk. It must become central to how organisations manage and assess resilience.
This marks a shift from software quality being an engineering concern to a board-level issue of operational resilience. If machine-speed change is the new operational hazard, then failure to address it becomes a strategic issue rather than a technical one. With that in mind, financial leaders must acknowledge AI’s dual role as both a driver of risk and a mechanism for preventing it.
AI as Both a Safeguard & Source of Risk
AI also offers the most effective and scalable way to manage the risks it introduces. Advanced AI-driven validation, continuous monitoring and risk-prioritised testing can identify issues earlier than any manual process, helping reduce the likelihood they reach production.
In effect, the same AI that accelerates software creation must now be applied to validation and governance – operating at the same speed and scale.
The same capabilities that facilitate rapid software production can be applied to validation and governance, continuously evaluating system behaviour, detecting anomalies and prioritising testing based on potential business impact, rather than volume. This allows organisations to move beyond rigid approaches and towards more adaptive, responsive quality models that more accurately reflect the way AI behaves.
Instead of relying on standard periodic testing cycles, systems can be validated on an ongoing basis. This enables earlier intervention before issues escalate.
AI can also help organisations better understand the complexity of their own systems. By analysing dependencies across applications and infrastructure, it becomes possible to identify which processes are most critical and where failures would have the greatest impact.
From Acceleration to Control
There is a clear mismatch in how financial organisations approach AI. While many are leveraging AI to accelerate development, far fewer are evolving their validation and governance to keep pace, and it’s in this gap that risk emerges.
This is the “confidence gap”, where organisations can create software faster than they can safely deploy it.
To address this imbalance, firms must treat software quality as a core component of their AI strategy. Development and validation must move forward together. Governance must adapt to continuous, AI-driven change. This requires a move from static testing and coverage metrics to continuous, risk-based validation, where software is assessed in real time based on potential business impact.
If AI is the engine driving software creation, validation must act as the braking system – built in, not bolted on at the end. At machine speed, gaps in control become points of failure. The aim is not to slow innovation, but to ensure it progresses in a way that is sustainable and safe. When validation keeps pace with development, firms can move quickly and competitively, whilst maintaining control over how risk is introduced and managed.
This is a change we are seeing across large enterprises adopting AI-driven quality approaches, where validation, monitoring and governance are increasingly orchestrated together rather than treated as separate processes.
Preventing the Next Outage
The financial sector has already seen how quickly failures can escalate in complex, interconnected environments. In March, an IT error at Lloyds Banking Group exposed the private financial information of nearly half a million customers, prompting the bank to issue £139,000 in compensation.
Such incidents aren’t isolated: over the last two years, more than 33 days of unplanned banking outages have been reported to Parliament, underlining the scale of the issue.
As AI increases the velocity of change, it also raises the stakes for getting it wrong. But the irony is that it also provides the tools needed to prevent these failures from happening in the first place. AI is both contributing to the risk of outages and becoming the most effective way to prevent them.
By applying AI to continuous validation, monitoring and risk detection, organisations can spot issues earlier, understand their potential impact and intervene before disruption occurs. This shifts the focus from reacting to outages to preventing them, and it’s where the paradox becomes constructive. AI doesn’t have to be a source of instability.
With the right approach, it can become the foundation for more resilient, predictable systems. Those that fail risk trading innovation for instability. In the AI era, speed without confidence is simply another form of risk.
By Dvir Hoffman, CEO at CommBox on why organisations that will lead in the next phase of digital transformation are those that treat AI not as a feature, but as a production capability
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Enterprise AI has entered a new phase. The experimentation cycle that defined the past few years, full of proofs of concept, innovation labs, and sandbox deployments, is giving way to a harder question: how do we operationalise AI at scale, safely and measurably?
Nowhere is this tension more visible than in customer service voice environments. Voice remains the most complex, emotionally nuanced and operationally demanding channel. It is also where AI has the potential to unlock some of the greatest value.
Recent research from McKinsey highlights that organisations embedding generative AI directly into customer operations are seeing productivity improvements of 30 to 45 percent when deployment is integrated into workflows rather than layered on top. The distinction is critical. AI succeeds not because it sounds intelligent, but because it is embedded into systems, governance and business metrics.
For technology leaders, the path from pilot to production is less about enthusiasm for AI and more about discipline in execution.
Why AI Voice Often Stalls Before Scale
The majority of AI initiatives do not fail because the technology underperforms. They stall because the surrounding enterprise architecture is not aligned.
In controlled pilots, AI voice agents can demonstrate impressive conversational capability. They answer FAQs, interpret intent and simulate human dialogue convincingly. But production environments are not defined by conversation quality alone. They are defined by operational depth.
When a customer calls a healthcare provider, an insurer or a retailer, the AI must do more than talk. It must: Authenticate identity securely. Retrieve and update records in real time. Execute transactions, escalate appropriately and comply with regulatory frameworks. Without direct integration into CRM systems, billing platforms, policy databases or electronic health records, AI remains superficial.
This is where many organisations hit friction. Production requires orchestration across telephony infrastructure, data platforms, compliance frameworks and human workflows.
Governance becomes another inflection point. Voice interactions carry legal and reputational weight, particularly in regulated sectors. Disclosure requirements, audit trails, escalation protocols and data protection controls cannot be retrofitted after deployment. The World Economic Forum’s 2024 work on responsible AI underscores that governance must be embedded into AI systems by design, particularly where customer trust and compliance are at stake.
When governance is treated as an afterthought, scaling slows dramatically.
There is also a measurement problem. Too many pilots are judged by narrow metrics such as intent recognition accuracy or conversation duration. Production environments are judged by business impact: containment rates, reduction in average handling time, cost-to-serve, regulatory adherence and customer satisfaction. If AI is not connected to those outcomes from the outset, executive momentum fades.
The shift from pilot to production requires organisations to think less about model performance and more about operational alignment.
Automation Without Eroding Trust
A common concern among executives is whether AI voice will erode customer trust. The answer depends entirely on how it is deployed.
Voice remains deeply human. Customers call when they want clarity, reassurance or resolution. In emotionally charged situations, such as reporting an accident, disputing a claim, or querying medical results, the experience must feel competent and controlled.
The most effective AI voice deployments do not attempt to automate everything. They focus on high-volume, structured interactions where resolution paths are clear and compliance rules can be embedded confidently. Appointment scheduling, policy updates, payment processing and order tracking are examples where end-to-end automation can meaningfully reduce friction.
In live enterprise environments, we are seeing organisations safely automate a significant proportion of inbound calls when the AI agent has direct access to real-time data and defined escalation thresholds. Customers benefit from immediate resolution, while human agents are freed to handle complex and emotionally sensitive cases.
Equally important is what happens when escalation occurs. AI should not disappear at the point of handoff. Instead, it should transfer context, summarise the conversation and provide agents with relevant data and next-best-action prompts. This augmentation model aligns with Gartner’s 2024 analysis of customer service technology trends, which emphasises that the greatest gains come from combining automation with agent enablement rather than pursuing full replacement.
Trust is reinforced when customers feel they are being served efficiently and responsibly. That requires transparency about when AI is involved, clear pathways to human support and systems that operate within strict compliance guardrails.
In regulated industries, explainability is no longer optional. Enterprises must be able to demonstrate how decisions were made, how data was handled and how customers can escalate concerns. When these safeguards are engineered into the platform, AI voice becomes a tool for strengthening trust rather than compromising it.
Where Enterprise Leaders Should Focus
As AI investment accelerates, CIOs and CDOs face pressure to deliver measurable value while maintaining governance standards. The lesson from organisations successfully scaling AI voice is that integration and oversight matter more than experimentation.
AI should be treated as infrastructure. That means prioritising deep integration with core enterprise systems from the outset. An AI voice agent that cannot execute transactions securely or access accurate, real-time data will struggle to deliver meaningful business outcomes.
Governance must also be operational, not theoretical. Clear escalation pathways, role-based permissions, audit capabilities and sector-specific compliance frameworks need to be embedded within the technology layer. When risk management is part of the architecture, deployment accelerates rather than slows.
Finally, measurement must be tied directly to business performance. Containment rates, resolution times, operational cost reductions and customer satisfaction metrics should be defined before rollout. McKinsey’s 2024 research reinforces that organisations capturing the most value from generative AI are those embedding it deeply into workflows with explicit performance targets.¹ AI that operates alongside business KPIs, rather than parallel to them, is far more likely to achieve sustained executive backing.
The broader transformation taking place across enterprise technology is not about AI replacing human capability. It is about rearchitecting customer engagement so that automation, data and people operate in synchrony.
Meeting AI Voice Demands
Voice remains one of the most demanding channels to modernise precisely because it sits at the intersection of emotion, compliance and operational complexity. Yet that is also why it offers such strategic value. When AI voice is integrated into core systems, governed rigorously and measured against real business outcomes, it moves from being an innovation experiment to becoming a structural advantage.
The organisations that will lead in this next phase of digital transformation are those that treat AI not as a feature, but as a production capability. Moving from pilot to production is not simply a technical milestone. It is a cultural one, signalling that AI is no longer an experiment on the edge of the enterprise, but a trusted component at its core.
Lee Nolan, GM UK&I at Hitachi Vantara, on why AI will not be defined by the sophistication of the models being deployed but the strength, consistency and reliability of the data that sits behind them
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Spend five minutes in any boardroom and AI will come up. Strategies are being signed off, budgets are being released and pilots are already underway, giving the impression that momentum is building at pace. Yet beneath that surface is a more uncomfortable reality, one that is becoming harder to ignore as organisations move beyond experimentation and into delivery.
Most organisations are trying to build AI on foundations that were never designed for it. The ambition is clear and well-funded, but the underlying data infrastructure has not kept up. That gap between intent and readiness is now becoming visible, particularly as organisations look to scale beyond isolated use cases and deliver outcomes that are consistent and commercially meaningful.
Recent research into UK businesses reinforces this point. While adoption continues to move forward, only a small number of organisations are genuinely set up to support AI at scale. The issue is not tools or talent, but the condition of the data at the core of the business.
AI Exposes What Businesses Would Rather Ignore
AI is often described as a layer that can be applied to existing systems to unlock value. In practice it does the opposite. It brings complexity into sharp focus, exposing inconsistencies and inefficiencies that may have been tolerated for years.
That is why data quality has moved to the centre of the conversation. Around 67% of UK organisations now cite it as the primary driver of AI success. The shift reflects a growing awareness that no level of investment in AI can compensate for weak or unreliable inputs.
For many organisations, data has evolved without a consistent approach to governance. In fact, Gartner estimates that 80% of organisations attempting to scale digital initiatives will fail due to weaknesses in data and analytics governance. Systems have been added over time, ownership is unclear and definitions vary. The result is a fragmented environment where the same metric can mean different things across the business. When AI is introduced, it does not resolve those inconsistencies, it amplifies them.
The Hype Cycle is Giving Way to Reality
Over the past year, there has been a shift in how organisations approach AI. The early phase was driven by urgency, with businesses keen to move quickly and demonstrate progress. That momentum remains, but it is now being balanced by a more realistic perspective.
There is a greater focus on outcomes, with more scrutiny on how AI is delivering value. Many organisations have realised that quick wins are harder to achieve when the underlying data is not fit for purpose, and that scaling AI requires a level of operational discipline that cannot be bypassed.
What was initially framed as a technology challenge is now understood as a business challenge, spanning processes, ownership and governance as much as platforms and tools.
Confidence is High but Capability is Uneven
Confidence across organisations remains high, but it often does not reflect reality.
While many businesses consider their data infrastructure to be mature, progress is often uneven. Some teams may be working with well governed data, while others are still reliant on manual processes and disconnected systems. This creates a situation where parts of the business are ready to move forward, while others are not, a challenge reflected in wider industry research from McKinsey & Company, which highlights siloed data as one of the biggest barriers to scaling AI.
That inconsistency is where problems begin. AI depends on trust in the data If that trust is not consistent across the organisation, outputs become unreliable and adoption slows. From the outside, many organisations appear ready, but internally the foundations are still being stabilised.
Control vs Convenience
There is also a growing emphasis on control, particularly around where data is stored and how it is managed. Data sovereignty is now playing a central role in decision making, with around 85% of UK organisations saying it directly influences how they deploy AI.
This reflects a broader recognition that data is both a critical asset and a potential point of risk. As organisations become more reliant on it, they are also becoming more deliberate in how it is governed and protected.
At the same time, the expectation that everything should be built in-house is fading. Many organisations are turning to external partners to accelerate progress, while retaining control over their data and strategic direction. This balance allows them to move faster without losing oversight.
Vague AI Strategies
One of the most persistent challenges is the lack of clarity around what AI is meant to deliver. Too many initiatives begin with broad ambition and little definition of success, resulting in activity that is difficult to measure and even harder to scale. This is reflected in wider industry trends, with Gartner noting that only 53% of AI projects make it from prototype into production.
The organisations making progress are far more structured. They define clear objectives, establish measurable outcomes and maintain a focus on value throughout. In the UK, a growing majority are now putting formal KPIs in place for their AI initiatives.
This shifts AI from being an experiment to something that can be managed, evaluated and improved over time.
AI Will not Wait for Organisations to Catch Up
AI will continue to evolve at pace, and the pressure on organisations to keep up is unlikely to diminish. What is changing is the nature of the conversation. It is becoming less about what AI can do in theory, and more about what organisations are actually capable of delivering.
Most are still in the process of building the foundations required to support AI effectively. That is not a failure, but it does define the scale of the challenge ahead.
Because ultimately, AI will not be defined by the sophistication of the models being deployed. It will be defined by the strength, consistency and reliability of the data that sits behind them.
Mark Talbot, Director, CS AI Initiatives at Appian, reasons that as organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it
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Many organisations have long treated improvement as something that arrives as a top-down effort, not something built with the people doing the work. Specialists designed new processes, discussed them in formal forums, and introduced them through large change programmes that often felt detached from daily work. For most employees, ‘transformation’ meant being asked to follow new rules, rather than designing better ways of working.
AI is starting to reverse that pattern. Instead of concentrating control and decision rights in a small, central group, modern AI tools give more agency to the people closest to the work. They can see what is not working, imagine better approaches, and use AI to help redesign and improve the processes they rely on every day. This shift – which can be described as the democratisation of AI – changes who participates in improving the business. However, it is worth remembering that this shift only works at scale when AI is embedded within a platform that maintains governance, visibility and control.
Process Improvement in the Hands of Many
Until recently, fixing a broken process often meant filing tickets, waiting for a slot on an IT roadmap, or hoping that a specialist team would eventually address the issue. Creating applications, building automations or redesigning workflows were seen as highly technical tasks. For most employees, waste and inefficiency were things to work around, not things they had the tools or authority to change.
That obstacle is now deteriorating, as long as organizations don’t lose sight of the fact that governance remains essential, particularly in highly regulated environments
AI agents, generative AI and conversational interfaces allow people across the business to shape how work is structured. Within this model, someone in operations can describe an outcome in plain language and have an AI system propose and embed the steps within existing processes. Within a governed platform, non-technical users can adapt existing solutions and automate repetitive tasks without waiting months for central support. At the same time, process insights give developers visibility into what is being built, enabling them to refine, standardise and scale applications more quickly across the organisation.
Data is opening up as well. Data fabrics and related architectures connect scattered information sources into governed layers that a wider audience can access safely. Instead of waiting on static reports, people can access relevant, trusted data when they need it, and use AI to interpret and apply it to their decisions.
When process insight and data access reach this level, best practices move beyond documentation or occasional training. Tools and workflows embed them into daily work, improving performance across the organisation.
Scaling Improvement Across the Organisation
As more individuals understand how their work connects to broader outcomes, organisations unlock a powerful driver of change. Process improvement no longer depends only on a small group of specialists. Employees can recognise when processes are inefficient or risky and have the means to address them at scale, inside an AI platform.
By encoding domain knowledge into AI assistants and digital coworkers within an enterprise-grade AI platform, organisations can share expertise across roles and levels. These AI-powered helpers do not replace professional judgment. They strengthen it. They surface options, highlight inconsistencies and provide context, while humans make the final decision. Over time, each interaction becomes both a learning moment and a new piece of institutional knowledge that organisations can capture and reuse.
In this model, process improvement is no longer episodic or confined to formal transformation projects. It becomes part of everyday work, inside a platform with AI tools that provide real-time feedback and recommendations.
AI, Noise Reduction, and Better Oversight
This shift raises a key question: if AI platforms make analysis, decision support, and process design more accessible, what happens to deep expertise?
There is a concern that easy access to AI advice might weaken people’s understanding. If answers are always a prompt away, will teams still develop the knowledge that comes from working through complexity? If people follow AI suggestions without grasping the logic, how meaningful can human oversight really be?
Over-reliance on instant guidance can create only surface-level competence. People may treat AI outputs as instructions rather than as inputs to their own reasoning.
On the other hand, used well, AI can create more room for expertise, not less.
By handling repetitive tasks and routine decisions, AI reduces the volume of low-value work that consumes people’s time. Teams can then focus on exceptions and refine how they make decisions. Instead of dealing with every routine request themselves, they can focus on work where context and experience matter most.
When AI removes more of the routine burden, teams have more capacity to focus on judgement, process design and oversight. That helps build expertise while keeping improvement connected to the wider goals and governance of the business.
Shaping AI, Not Just Living With It
As organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it. AI is moving from something that happens to the workforce to AI being something that is built and refined with the workforce.
Organisations should treat people as partners in shaping AI, rather than as operators of automated systems. When AI platforms can be combined with process visibility and human judgement, employees can have an outsized effect on the systems around them. They can influence how work is structured and how decisions are made. In that sense, AI redistributes who participates in designing better ways of working, and creates an opportunity to anchor that shift in thoughtful design and human expertise.
Vincent Guillevic, Director of Fraud Labs at Entrust, argues companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers
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Identity verification and tackling fraud began as a face-to-face process, built on human trust. Opening a bank account involved meeting a banker in person and from there, trust was established because both parties could see and interact with each other directly in branch.
Fast forward to the digital age and a lot of services have moved online. Identity verification has therefore shifted from in-person checks to remote identity verification. Today, we’re in an era where identity is now central to every interaction we have online.
Fraud has followed the same trajectory. Much like a burglar would test every possible entry point rather than just the front door, fraudsters probe every stage of the customer journey. They look for weaknesses at onboarding, during login, and throughout ongoing transactions and data requests.
That challenge has intensified in recent years. AI has given fraudsters faster, sophisticated and scalable tools. Deepfakes can bypass checks, AI‑generated documents can appear real, and phishing and impersonation attacks can now be automated at scale.
Once a fraudster gains access to a legitimate account, the damage escalates quickly. Global losses from account takeover (ATO) fraud were projected to reach $17 billion in 2025, up from $13 billion in 2024. While the underlying intent of fraudsters seeking the weakest point of entry, the breadth, speed and sophistication of modern attacks have.
Identity Fraud Patterns Across the Customer Lifecycle
Fraud can occur at any stage of the customer journey. From verifying identity at onboarding to securing connections and fighting fraud in everyday transactions. Each stage introduces its own risks, and attackers adapt their tactics based on where value can be extracted most efficiently.
In 2025, patterns showed a clear distinction between industries targeted for new account fraud and those targeted for account takeover fraud. Businesses that offer immediate incentives such as promotional offers or sign-up bonuses are primarily targeted for new account fraud. In contrast, businesses where accounts accumulate long-term financial or data value face higher levels of ATO.
Industries built around sign-up incentives or instance access experience most fraud at onboarding. For instance, in crypto, 67% of fraud attempts occur during account creation, largely driven by sign-up incentives. Vehicle rental follows a similar pattern, with 67% of fraud taking place at onboarding as attackers use fake identities to gain short-term access to high-value assets. In these sectors, low-friction onboarding creates opportunities to harvest incentives or establish accounts that later become avenues for future money laundering.
Account takeover fraud reflects a different strategy. Rather than creating fake accounts, attackers focus on compromising established accounts using tactics such as stolen credentials, phishing, malware, or social engineering. Entrust data shows this is most common in industries where accounts hold enduring value. In payments, 82% of fraud attempts occur after onboarding, while in professional services the figure is 62%. High-value, long-standing accounts are attractive because they enable fund transfers, loans, and access to identity-rich data, making them more valuable than newly created accounts.
These patterns highlight two critical realities. First, organisations can no longer optimise for one type of risk at the expense of another. Defending a single point in the journey inevitably leaves gaps elsewhere. Second, fraud has become highly professionalised. Modern fraud operations are organised, strategic, and adaptive, moving toward the highest rewards and the weakest controls.
Prevention Must Span the Entire Journey
If fraud can occur at any stage, prevention must operate at every stage. Organisations that implement robust, lifecycle-wide identity strategies save an average of $8 million per year in fraud-related costs. These savings come from detecting threats earlier, more accurately, and beyond a single checkpoint.
There are three areas where that lifecycle approach needs to be strongest.
Get onboarding right
Onboarding is the first opportunity to establish genuine trust. Strong Know Your Customer (KYC) or Know Your Employee (KYE) processes combine document verification with biometric checks such as face recognition or fingerprint scanning to confirm that the person applying is who they claim to be. Liveness detection adds a further layer by distinguishing real users from synthetic identities and deepfakes, which are linked to approximately one in five biometric fraud attempts.
With strong identity verification at onboarding not only reduces immediate fraud, but also limits the downstream damage caused with fraudulent accounts.
Secure existing accounts with continuous authentication
Verifying identity once is no longer sufficient. Continuous authentication, combining multi-factor authentication with biometric re-verification like facial recognition, allows businesses to protect established accounts without creating unnecessary friction for legitimate users.
Crucially, it enables authentication requirements to adapt dynamically as risk levels change, rather than applying the same static check regardless of context. In payments businesses, where most fraud targets the authentication process itself, this adaptability is key to mitigating attacks before losses occur.
Monitor behaviour in real time, not just identity
Device intelligence and behavioural signals make it possible to assess risk based on how users interact with services, flagging unusual login patterns, device anomalies, or out-of-character transactions.
As AI-driven fraud becomes more sophisticated and convincing, behavioural indicators provide another layer of ongoing fraud detection. Focusing monitoring on high-risk actions, rather than only high-risk identities closes a critical gap in traditional defences.
The Window of Opportunity
Fraud has always followed the customer journey. What has changed is the availability of advanced technology capable of tracking, analysing, and responding to threats at every stage. The key question for organisations is whether these capabilities are deployed as a connected strategy or left as isolated controls with gaps in between.
Companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers, and preserve the trust that underpins long-term digital relationships.
Andrew McLernon, CEO and co-founder at Interlink, on why culture is the real disruptor
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For much of modern business history, disruption has been framed as something external. An emerging technology or a competitor rewriting the rules. Often, markets shift faster than organisations can respond and leaders are told to move quicker, work harder and implement more systems to keep up.
Today, AI has become the latest catalyst for this narrative, with every week seeming to bring another promise of productivity gains or automation breakthroughs. Yet as AI accelerates, many organisations are responding in surprisingly familiar ways: longer hours, stricter oversight, everyone back to the office mandates and layers of new processes built on outdated foundations.
In my experience, this is the wrong response. The real disruption of the AI era isn’t technological. It’s cultural. And leaders who fail to recognise that, risk solving tomorrow’s challenges with yesterday’s assumptions.
The Illusion of Productivity
When economic pressure rises, organisations often default to visibility as a proxy for performance. Leaders want to see people working, whether that means more time in the office, more meetings or more activity. But activity isn’t the same as effectiveness.
AI is already capable of performing many routine tasks faster than humans, a fact that should lead us to rethink how work is structured. Instead, many businesses are doubling down on models that were designed for a different era, treating time spent on tasks as the primary measure of contribution, rather than outcomes achieved.
The irony is that this approach undermines the very productivity gains leaders say they want. People become busier but not necessarily more effective. Creativity declines, decision-making slows and, ultimately, innovation suffers because teams are exhausted rather than energised.
True productivity in an AI-enabled world comes from clarity and focus, not from squeezing more hours out of people.
Culture Before Performance
At Interlink, we’ve learned that performance rarely improves by targeting performance alone. It improves when culture enables people to do their best work.
Culture isn’t slogans or perks; it’s the operating system behind every decision. It determines whether people feel trusted or controlled, whether ideas are encouraged or suppressed and whether change is embraced or resisted.
As we scaled a profitable, AI-powered business across multiple continents, we discovered that culture has to scale before performance can. If it doesn’t, growth amplifies dysfunction. That realisation changed how we approached leadership. Instead of asking, “How do we get more output?” we began asking, “What conditions allow people to produce their best work consistently?”
The answers were not technological; they were human.
Redesigning Work Rather Than Reinforcing Old Models
One of the biggest leadership mistakes I see today is adding complexity to existing systems instead of redesigning them. Organisations introduce new tools without changing behaviours. They add layers of management without simplifying decision-making. They enforce policies intended to restore control rather than building trust.
For us, introducing a four-day working week was not about doing less; it was about focusing on what truly matters. Compressing time sharpened our priorities, improved decision-making and encouraged greater ownership of outcomes by everyone across the business. The result was counterintuitive for some observers: productivity rose, retention strengthened and creative thinking accelerated. When time had clearer boundaries, focus sharpened and accountability deepened.
Flexible and hybrid working emerged from the same philosophy. Instead of designing work around physical presence, we designed it around contribution and trust replaced oversight as the foundation of accountability.
These changes weren’t always comfortable and they absolutely required leaders to relinquish some traditional forms of control. But they reinforced a principle that has become increasingly clear: autonomy drives engagement and engagement drives performance.
The Tension Between ‘Back to the Office’ and the Future of Work
The current push for universal office returns reflects a deeper anxiety about how work is evolving. For some leaders, visibility feels like certainty. If people are physically present, it feels easier to manage performance. But this perspective risks confusing familiarity with effectiveness.
The future of work is unlikely to be defined by a single model. People’s roles, responsibilities and life circumstances vary too widely for one-size-fits-all solutions. Organisations that impose rigid structures in pursuit of control may find themselves losing talented individuals who value flexibility and trust.
That doesn’t mean offices are irrelevant. Physical spaces remain powerful for collaboration, learning and connection. The challenge is not choosing between remote or office-based work but designing environments that genuinely enhance productivity rather than simply recreating old habits.
The businesses that succeed will be those that treat flexibility as a strategic tool rather than a concession.
AI as an Amplifier of Leadership, not a Replacement
Because our business operates in AI-powered demand generation, we spend a great deal of time thinking about the relationship between automation and human expertise. AI excels at pattern recognition, scale and speed but what it lacks is context, empathy and strategic judgement.
The danger for leaders is assuming that technology alone can drive transformation. AI amplifies whatever culture already exists. In organisations built on trust and curiosity, it accelerates innovation; in environments dominated by fear or rigidity, it often automates inefficiency.
The competitive advantage lies not in whether a company uses AI (most soon will) but in how leaders integrate it into a culture that values learning and experimentation.
Simplicity as a Leadership Discipline
Another lesson from scaling is that complexity grows naturally. As businesses expand, processes multiply; communication becomes fragmented, and decision-making slows because too many layers intervene between ideas and action.
We’ve learned to treat simplicity as a leadership discipline. That means regularly rebuilding systems that no longer serve us, even when they once worked well. It also means resisting the temptation to add new structures simply because growth makes things feel messy. As well, simplicity requires intentional effort. Leaders must continually ask which processes genuinely add value and which exist only because they always have.
Leadership for an Uncertain Future
Perhaps the most important shift leaders must make is moving from control to clarity. In a world where technology evolves faster than organisational structures, certainty is increasingly rare. What teams need is not rigid instruction but clear purpose, shared values and the autonomy to adapt.
Leadership becomes less about directing tasks and more about shaping environments where people can thrive. That includes prioritising wellbeing not as a perk but as a strategic requirement. Burnout may produce short-term output, but it erodes long-term capability and the organisations that will define the next era of business are unlikely to be those that simply adopt the latest technology fastest. They will be the ones that rethink how work itself is designed, aligning technology with human potential rather than attempting to replace it.
Culture as the Ultimate Competitive Advantage
As AI becomes ubiquitous, technological differentiation will narrow. Tools that once seemed revolutionary will become standard. But what will remain distinctive is culture.
Culture determines how quickly teams learn, how openly they challenge assumptions and how resilient they are during uncertainty. It shapes whether innovation is encouraged or quietly resisted. And, in that sense, culture is not a soft concept; it is a strategic asset.
The real disruption of the AI age is not automation, it’s the opportunity to redesign leadership around trust, simplicity and human potential. Leaders who embrace that shift will find that technology accelerates their progress. Those who cling to outdated models may discover that even the most advanced tools cannot compensate for disengaged people.
Disruption isn’t about changing the industry first; it’s about changing how we lead.
Simon Pamplin, CTO of Certes, warns of the risks of supplier breach and how that can affect you
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Many organisations still assume that once their data is handed over to a cloud provider or managed service partner, the risk goes with it. That assumption is not only wrong, it’s also dangerous. Outsourcing IT services does not mean outsourcing accountability. When sensitive information leaves your environment without strong protection, you are effectively placing your reputation, regulatory standing and customer trust in someone else’s hands. When those controls fail, as they often do, it is the data owner who ultimately pays the price.
Regulators have become increasingly clear on this point. Responsibility for protecting data sits squarely with the organisation that owns it, not the supplier processing or moving it on their behalf. Contracts, assurances and compliance statements offer little comfort once data has been exposed.
As a result, supply chain security is no longer an operational detail to be left to technical teams. It is a board level issue that affects risk, compliance, reputation and long term resilience. Senior leaders are now expected to understand where their data travels, who has access to it and how it is protected at every step.
The reality is uncomfortable but unavoidable. Risk cannot be outsourced. Services, platforms and operations can be delegated, but accountability remains firmly with the data owner. The only way to break the link between supplier failure and organisational damage is to ensure that data stays protected wherever it goes.
Why third-party breaches hurt so much
Some of the most damaging recent breaches did not begin inside the organisations that ultimately suffered the consequences. Attackers found their way in through suppliers, shared platforms or service providers that sat outside direct control. Once inside, they were able to access and extract data that belonged to someone else entirely.
Despite this, it was the data owner that faced regulatory investigation, fines, legal action and lasting reputational damage. Customers didn’t blame the supplier; they didn’t even know it existed. They blamed the organisation they trusted with their information. Boards and executives are then left explaining why sensitive data was allowed to travel unprotected through third-party environments.
The false comfort of perimeter security
A common thread in many of these incidents is over-reliance on perimeter based security. Organisations focus heavily on protecting their own networks and identities, while assuming partners will do the same. In reality, attackers rarely respect organisational boundaries. They move through supply chains, exploit weaker links and target data wherever it is most accessible.
Once data leaves your environment, perimeter controls lose their value. If the information itself is not protected, a breach at any point in the chain exposes it. This is why traditional security approaches struggle to contain the fallout from supplier compromises.
Harvest now, decrypt later is already happening
There is an additional risk that many organisations are massively underestimating. Attackers are not only stealing data for immediate use. They are also running harvest now, decrypt later campaigns. Sensitive information is being exfiltrated today, stored, and held until cryptographic advances make it readable.
This is significant because data shared across supply chains retains its importance and value over time. Financial records, personal data, intellectual property and regulated information do not expire quickly. When quantum computing capabilities mature, encryption methods that were once considered strong will no longer offer adequate protection. Data stolen years earlier can suddenly become exposed.
The assumption that quantum threats are a distant concern misses the point. The risk is not when quantum computing arrives. The risk is that the data that will be valuable then is already being collected now. Without quantum-ready, Post-Quantum Cryptography (PQC)-safe security protection in place today, organisations are building a future liability into their supply chains.
Organisations need to be looking at these PQC-safe solutions now that focus on ensuring data remains protected even against future cryptographic breakthroughs. When applied to data in motion, it ensures that information remains unreadable wherever it travels, across internal systems, cloud platforms and third-party environments.
Securing data across the supply chain
The most effective way to reduce supplier risk is to protect the data itself, rather than relying on each partner’s infrastructure. Encryption in transit, strong control of encryption keys and clear policies governing how data flows between systems are critical.
When data is protected end-to-end, a supplier breach does not automatically become a business crisis. Even if attackers gain access to systems, the information they intercept is unusable. This removes much of the incentive for the attack and dramatically reduces the impact if one occurs.
Crucially, this approach works with existing systems. Many organisations rely on legacy platforms that are difficult or costly to replace. Protecting data flows around those systems allows them to remain in use while still meeting modern security and regulatory expectations.
Another benefit of data-centric protection is reduced dependence on supplier assurances. Rather than relying on the assumption that every partner has implemented perfect security, organisations can enforce their own protection standards at the data level. This shifts control back to the data owner and reduces exposure to weaknesses outside their direct oversight.
It also simplifies compliance. When organisations can demonstrate that sensitive data is consistently protected wherever it moves, regulatory conversations become far more straightforward.
Protecting what actually matters
The lesson from repeated third-party breaches is clear. Attackers go where the data is, not where the organisational chart says responsibility should lie. Organisations that focus solely on infrastructure security will continue to be caught out by supplier failures.
Those who take a data focused, quantum-secure approach can change the outcome. Breaches may still occur, but their impact need not define the organisation. When stolen data is unreadable, reputation, trust and regulatory standing are far easier to protect.
The message is simple. You may rely on suppliers, but your data is still your responsibility. Protect it accordingly.
Todd Moore, Global Vice President, Data Security Products at Thales, on why making AI security a boardroom priority today, will help firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation
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Financial Services organisations are responsible for some of the biggest growth in the global economy. Equally, they’re some of the most vulnerable. Like many other sectors, they’re racing to embrace AI, but with adoption comes new security risks.
According to Thales’ Data Threat Report: Financial Services Edition 81% of FinServ organisations are now investing in GenAI-specific security tools, with nearly a quarter using newly allocated budget. This surge in funding marks a turning point: AI security has moved from being an IT concern to a boardroom priority.
The fact that new budget lines are being carved out specifically for AI security signals a fundamental shift in corporate strategy. Boards increasingly recognise that protecting AI systems is as critical as safeguarding payment rails or core banking infrastructure. For an industry built on trust, resilience, and regulatory compliance, this investment wave shows how central AI has become to both risk management and competitive growth.
Balancing AI Innovation and Security
While FinServ organisations are aware of the security risks AI poses, they’re also seizing upon the opportunities it presents. The report has found that in 2024, FinServ businesses outpaced the broader market in AI deployment, leading in enabling employees to use AI and ahead in AI integration, which has continued into 2025. Additionally, 45% say they’re in the ‘integration’ or ‘transformation’ phases of their GenAI journey, compared to just 33% across wider industries.
AI’s ability to accelerate services, automate processes, and analyse data at scale makes it an exciting prospect, especially in the financial sector. This makes securing AI systems a priority for FinServ organisations, with increased GenAI integration reflecting developing organisational maturity and progress beyond experimentation.
The Risk
Yet the scale of opportunity is matched by the scale of challenge. AI systems require vast amounts of structured and unstructured data to conduct analysis and make recommendations.
For FinServ organisations, this often includes highly sensitive customer and transactional information, proprietary algorithms, and records bound by strict regulatory oversight. The risk is not only about whether AI systems themselves are secure, but whether the data they’re working from is accurate, as well as whether their adoption inadvertently creates new routes to data exposure and exfiltration.
Businesses need a clear strategy to fully understand how AI models are operating within their IT infrastructure, the applications they’re interacting with, and the data they’re accessing and pulling from.
The Response
Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring. Newly allocated budgets for AI security, with nearly a quarter of FinServ firms making such investments, show how central AI has become to board-level strategy. These investments move firms beyond reactive fixes to proactive frameworks that evolve with the technology. AI security is no longer just an IT concern, it’s a strategic priority requiring collaboration between security, compliance, and business leaders. By factoring risk into early planning, organisations can align innovation with responsibility and build resilience for the long term.
Pioneering AI Security
Building on investment in AI-specific security is only the beginning. As scrutiny intensifies, the firms that will lead are those that treat AI security as integral to business strategy, not a bolt-on layer. Success will require visibility into how models behave, continuous validation against emerging risks, and adaptive controls that evolve with the threat landscape.
The financial services organisations that embed these safeguards into their core infrastructure will protect sensitive data as well as setting a benchmark for resilience and trust in an AI-driven economy. By making AI security a boardroom priority today, these firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation.
Thales: AI is the New Insider Threat
Thales 2026 Data Threat Report Finds 70% of Organisations Rank AI as Top Data Security Risk
Data security has taken centre stage as the success of enterprise AI initiatives increasingly hinges on consistent, controlled access to proprietary organisational data sources. The 2026 Thales Data Threat Reportexamines the complex calculus that organizations must undertake to enable innovation while securing their most valuable asset – their data.
This research was based on a global survey of 3,120 respondents fielded via web survey with targeted populations for each country, aimed at professionals in security and IT management.
Jamil Jiva, Global Head of Asset Management at Linedata, on why the next chapter of AI-driven finance will be shaped not just by technology, but by creativity
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Beyond Data: Where AI Finds Unexpected Inspiration
The discussion about training AI largely focuses on concerns that accessible, human-generated data is limited and may soon run out completely. If this is the case, how can technology that depends on a seemingly endless stream of inputs to iterate, test, and adapt deliver the results we expect? AI relies on structured, high-quality data to thrive, but what happens when we run out of spreadsheets and financial models to train AI? We need new data sources to ensure it continues to learn, adapt, and deliver accurate insights. Video games stand out as offering some of the richest, most expansive, and complex environments for AI training.
At first glance, video games and financial operations seem to belong to entirely separate worlds. However, AI connects these domains, with models leveraging virtual-world training to tackle real-world financial tasks. Financial documents such as credit agreements and tax returns are often convoluted, unstructured, and labour-intensive to process. Therefore, AI designed to interpret such data must possess strategic reasoning, real-time adaptability, and advanced pattern recognition. So, could video games be the ideal training ground?
Contrary to popular belief, gameplay can significantly improve how people think, learn, and solve problems. The abilities required to excel at video games closely reflect the skills AI systems must acquire today.
Levelling Up: What Virtual Worlds Teach Machines
Practice leads to proficiency, a principle that applies to both humans and AI. Interestingly, many of the most significant advances in AI development have emerged not from conventional data training, but from taking creative approaches. Games push AI to emulate human thinking and sharpen its statistical intuition.
These game-trained models are neither expensive nor heavily reliant on resources, and they sidestep the issue of data scarcity. As a result, they are actively shaping the future of financial intelligence. The examples below offer a clear demonstration of the potential of gameplay.
Virtual Economies: Lessons from World of Warcraft
World of Warcraft, with millions of players interacting in an immersive and dynamic world, features an economy that closely mirrors real-world financial systems, complete with inflation, supply and demand cycles, and fraud risks. The game even inspired one of the most renowned epidemiological studies: when the in-game ‘Corrupted Blood’ plague spread unpredictably, scientists used it as a model for real-world pandemic simulations.
Financial models depend on vast, interconnected data networks, much like the economy in World of Warcraft. Organisations employ AI to continuously monitor patterns, detect anomalies such as fraud or misstatements, and optimise data extraction for financial reporting, mirroring the way AI analyses virtual economies.
Urban Chaos: GTA V and Real-World Simulation
While Grand Theft Auto (GTA) V is famous for its open-world chaos, researchers have leveraged its traffic systems and non-player character behaviours to train AI for applications such as self-driving cars, crime pattern recognition, and urban planning. At its heart, GTA provides a platform for AI to process vast amounts of unstructured data in real time.
Similarly, financial institutions manage millions of data points from a wide range of sources. Their AI tools must automatically extract insights, classify information, and normalise complex formats. GTA serves as a controlled yet intricate environment for simulating scenarios, enabling AI to optimise for real-world tasks through ongoing feedback loops.
Sandbox Creativity: Minecraft and Adaptive Thinking
Minecraft provides a sandbox environment where AI learns through exploration. OpenAI even trained an AI to play Minecraft by watching YouTube tutorials, closely mimicking the way humans learn. Similarly, any AI used by financial institutions must be able to self-learn from new document types and structures, adapting just as a Minecraft AI learns to survive.
Reinforcement learning, where AI improves based on feedback, is a key element of intelligent document processing. Thanks to its vast scalability and dynamic, hierarchical environments, Minecraft serves as an ideal setting for navigation and repeated feedback loops, helping models develop domain-flexible reasoning.
Multiplayer Mayhem: Dota 2 and the Art of Teamwork
Dota 2 stands out as one of the most complex competitive games ever created, presenting AI with challenges in real-time decision-making, strategic coordination, and adaptability. OpenAI Five, trained on the equivalent of 45,000 years of gameplay within just 10 months, managed to defeat renowned, professional human teams. As anyone who has mastered StarCraft knows, tactical adaptability is essential for gaining the upper hand.
Financial institutions operate in environments that are just as dynamic as the shifting levels of a video game. Market conditions, regulations, and data formats are in constant flux. AI must be able to adjust to new document structures, handle missing information, and navigate edge cases, much like AlphaStar adapts to an opponent’s unpredictable strategies.
From Pixels to Profits: Bringing Game Logic to Finance
Whether to streamline operations, mitigate risks, or make informed decisions in today’s data-intensive financial landscape, AI has the potential to fundamentally transform financial offerings, delivering personalised and evolving experiences that foster understanding and combine seamlessness with regulatory compliance.
Yet AI does not simply require more data from which to learn; it needs better data. Video games offer near limitless, pre-built, highly complex digital worlds where AI can test hypotheses, simulate scenarios, and refine decision-making models. By utilising these unique environments, AI is challenged to enhance its speed, accuracy, and efficiency.
The world of video games has many lessons we can learn when building AI, and given AI’s remarkable ability for transferable learning, it makes sense to leverage these pre-trained models to power essential financial workflows. It is more than just document processing; it is thinking, and the same intelligence that enables AI to defeat world champions in Dota 2 is now driving the next generation of financial AI solutions.
The next chapter of AI-driven finance will be shaped not just by technology, but by creativity. By embracing unconventional data sources such as the immersive complexity of video games, industry leaders will unlock new possibilities for personalisation, security, and customer engagement.
Richard Doherty, Head of Wealth & Asset Management, Publicis Sapient, on how asset managers must redesign their enterprise for AI-driven decision intelligence
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The asset management industry is entering a structural inflexion point. The first wave of AI focused on improving productivity through copilots and automation. The next wave will fundamentally reshape how decisions are made, executed, and governed across the enterprise. This is not a technology upgrade. It is an operating model shift.
Despite significant investment, many firms remain trapped in fragmented AI experimentation. A majority are yet to realise meaningful economic returns from AI, not due to lack of capability, but due to a failure to redesign how intelligence is applied across the organisation. The gap between ambition and outcome is not a technology problem. It is a structural one.
From Automation to Decision Intelligence
The industry conversation has evolved. The question is no longer whether to adopt AI, but how to scale it across the enterprise. However, most firms are still approaching this challenge through the lens of automation, identifying tasks that can be executed faster or at lower cost. This delivers incremental value, but does not address the underlying constraint: the structure of decision-making within the organisation.
Traditional operating models are built around sequential workflows. Work moves from function to function: research, compliance, operations, and distribution, each dependent on the previous stage. This creates latency, duplication, and fragmentation. Agentic operating models shift the focus from tasks to decisions.
Instead of asking “Which processes can we automate?”, leading firms are asking: “Which decisions can be augmented or owned by intelligent systems?”
This shift enables organisations to move from sequential workflows to parallel decision systems; from human-led analysis to AI-assisted reasoning; from periodic insight to continuous intelligence. The result is not a marginal improvement. It is a step-change in how the enterprise operates.
The Pressures Driving Change
This transformation is not happening in a vacuum. Asset managers face mounting structural pressures: margin compression driven by fee pressure and passive competition; rising operational complexity from regulation and product proliferation; and advisor capacity constraints that limit scalable growth. Agentic operating models directly address all three.
By automating complex workflows, rather than individual tasks, firms can significantly increase advisor and analyst capacity without proportional cost increases. Parallel decision systems reduce the time required to launch products, respond to market events, and deliver client insights. This compresses cycles from months to days. Continuous monitoring of guidelines, portfolios, and operational processes reduces exposure to regulatory breaches and operational failures.
These are not theoretical benefits. They represent measurable improvements in cost-to-serve, time-to-market, and operational resilience.
Not all Intelligence is the Same
To scale AI effectively, organisations must recognise that not all problems require the same type of intelligence. Enterprise AI operates across three distinct layers, and conflating them is one of the primary reasons AI initiatives fail to scale.
Deterministic systems execute predefined rules with complete consistency. They are essential for functions where there is zero tolerance for error, trade validation, settlement processing, and regulatory reporting. If a business outcome must be identical every time, deterministic logic remains the correct approach.
Predictive systems use historical data to forecast outcomes. Applied in areas such as portfolio risk modelling, fraud detection, and client churn prediction, they generate probabilities and insights, but they do not interpret context or make decisions independently.
Agentic systems operate where problems require interpretation, judgment, and contextual understanding, investment guideline interpretation, regulatory document analysis, portfolio insights, and client communication. These systems can reason across complex information, generate insights, and take action within defined boundaries.
The ‘Different but Valid’ Dilemma
A critical challenge in adopting agentic systems is understanding how they behave. Traditional software produces identical outputs. Agentic systems produce reasoned outputs.
This introduces what I call the ‘different but valid’ dilemma. An agent may take a different reasoning path from a human and arrive at a different, but still correct, conclusion. This variability is not an error. It is inherent to reasoning systems.
The real risk lies in hallucination, outputs that are not grounded in data or evidence. Managing this requires organisations to clearly define where variability is acceptable. All AI-driven processes sit on a spectrum: deterministic actions with no variability (trade execution), predictive actions with controlled variability (risk scoring), and agentic actions with higher variability (investment insights).
Leading firms design systems where agents perform reasoning, deterministic systems enforce execution, and humans retain oversight on high-consequence decisions. This balance enables both flexibility and control.
The Operating Model Shift
The most significant change is not technological; it is organisational. Traditional models are built on functional workflows. Agentic models are built on coordinated decision systems.
Consider what launching a new investment product looks like under each model. In a traditional model, it involves sequential handoffs between teams, compliance reviews the guidelines, operations configures the systems, and distribution drafts the client narrative. Each stage waits for the last.
In an agentic model, intelligent systems operate in parallel: compliance agents interpret guidelines, operations agents configure constraints, distribution agents generate client narratives, and governance agents validate outputs. This orchestration compresses timelines, reduces friction, and enables continuous decision-making. It represents a fundamental redesign of how work is performed.
Governance: the Foundation for Trust
Trust is the prerequisite for scaling AI. Without it, adoption stalls, not because the technology fails, but because the organisation cannot adequately explain or defend the decisions it makes.
Leading firms implement governance models built on three principles. First, explainability: every decision must be traceable and auditable. Second, authority boundaries: agents operate within clearly defined limits. Third, human oversight: high-consequence decisions remain under human control.
Regulatory expectations will continue to evolve, but one principle remains constant: organisations must be able to explain how decisions are made.
Scaling AI is a Leadership Challenge
Executives must take a deliberate approach across four areas:
Define the intelligence model: map business problems to deterministic, predictive, or agentic systems.
Build the foundation: invest in data, infrastructure, and orchestration capabilities.
Redesign the operating model: shift from workflows to decision systems.
Implement governance to ensure transparency, control, and compliance.
Start with high-value use cases and expand rapidly across the enterprise. The firms that act now will establish a structural advantage in cost, speed, and decision quality. Those that do not risk being constrained by legacy operating models that cannot scale with the demands of modern markets.
The Question is not if, it is Who
The industry is not simply adopting new technology. It is redefining how decisions are made. The firms that succeed will not be those that deploy AI tools in isolation. They will be those who design the right form of intelligence for each problem, redesign their operating models around intelligent systems, and scale agentic capabilities across the enterprise.
This shift is already underway. The question is no longer whether it will happen. The question is which firms will lead, and which will be forced to follow.
Alex Saric, Ivalua’s CMO, tells us how supply chain is shifting and where AI is succeeding – as well as where it’s lagging
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Last month, we attended Ivalua NOW 2026, joining 1,500+ supply chain professionals in Paris to get an up-to-date view of the landscape. As part of this vibrant event we sat down with Alex Saric, Chief Marketing Officer of Ivalua, to dig into some of the ways the industry has shifted and evolved in recent years – and the role AI has to play.
In an article that Saric wrote for our sister brand, CPOstrategy, back in 2019, he said that organisations were under more pressure than ever to innovate at speed. Seven years on, the world has drastically changed. Between COVID-19 and the lightning-fast acceleration of AI, supply chain has evolved to an unprecedented degree. So the question is: what does innovating at speed look like in 2026 compared to 2019?
“Back then, we were still driving traditional source-to-pay digitalisation and providing the transparency that’s still needed in this more uncertain, volatile world,” says Saric. “That volatility only increases every year. I think most people, probably me included, assumed that it would calm down. But I’d say, in 2026, the impetus is on making AI – and particularly agentic AI – the kind of tool you want it to be. From something that answers a question for you to something that really executes and drives more output from procurement. It’s really about taking it from pilot to production at a rapid pace, where it’s actually driving business impact.”
Changing variables
Back in 2019, nobody could have predicted the acceleration of AI in the supply chain – not even Saric. “What’s interesting is that even if you go back five or 10 years, people were talking about the commoditisation of procurement technology, which has become relatively easy to use. The capabilities are getting smaller. If anything, that has now accelerated with AI and the disparities between one organisation and another are even higher. But no, I couldn’t have predicted this level of acceleration.”
Things have evolved even since 2025. At last year’s Ivalua NOW, Saric said that “the increasingly uncertain sourcing and procurement landscape is forcing the industry to assess the impact on organisations, reassess supply strategies, and it’s all happening so fast”. When asked if that is different now, the answer is a firm “no” – but the variables do keep changing.
“It’s almost as if you’re viewing the entire supply strategy as a game. For a while, there are clear optimisation strategies to sourcing that everyone is focused on and implementing,” says Saric. “But then, suddenly, all the rules change. It’s one thing if they change just once and you adapt, set different parameters, and optimise again. But the problem now is that they change overnight, and you have no idea when. That’s a massively complicated environment for procurement. Their job has become exponentially more difficult.”
AI isn’t transformational (yet)
It’s a topic both Saric and Franck Lheureux, Ivalua’s CEO, touched on during the introduction to Ivalua NOW 2026: that things have never been more difficult for supply chain professionals. Even with the wider (and more confident) use of AI across the industry, the pace of change and the geopolitical risks and pressures weigh heavier than ever. In fact, according to Saric, AI’s impact has hardly been transformational – yet.
“The nature of enterprise technology is that it’s always a bit slower to get adopted and rolled out,” he explains. “There’s extra scrutiny, there’s change management; all these factors that have to be considered compared to consumer technology. The biggest changes last year were theoretical for the most part. There were very few organisations actually using AI in a way that’s driving value. A sizable minority of our customers are using it actively in production and they’re driving value from it. The step which still needs to come is moving from having it as a handy assistant or a way to get information faster, and actually driving a difference in how people work.”
This isn’t going to happen overnight. However, Saric expects to have customers onstage at Ivalua NOW 2027 who have completely changed their way of working via AI, and that most businesses will be using it to some extent. It is certainly creating efficiencies and values, even if it’s a slow process. For example, the application of AI for user experience is proving to be one area where it’s coming into its own.
“It’s really enabled procurement to become a much more conversational experience,” Saric explains. “Broadly, that’s the biggest impact so far. But besides that, it’s also helping make better decisions to identify contracts that have certain clauses to drive standardisation and conduct assessments with suppliers. If there’s a performance issue or you want to suggest improvement plans, AI can also help with drafting RFPs.
“There’s a whole range of pretty distinct skills that are saving a lot of time. In many cases, it’s bringing information and insights to the fingertips of the procurement users, rather than them wasting hours looking for that information.”
The procurement-IT alliance
More than technological advances like AI, strong inter-communication between procurement and the broader business is a key to success in modern organisations. Saric hosted a conversation during Ivalua NOW based around the collaboration between procurement and IT, and how to approach this partnership effectively. During this, he delved into his 25 years in the industry to guide the conversation.
“What I’ve consistently seen is that the most successful procurement digitalisation projects typically had strong collaboration with IT,” says Saric. “With AI, it’s even more important. You really have to understand AI and ensure you’re not exposing your organisation to potential security risks, or violating other policies. There’s a lot of technical detail that needs to be understood.”
He continues: “IT is the department that’s best positioned to help guide procurement through that process. The second thing is that there needs to be proactive engagement upfront with executive sponsorship from both procurement and IT. You can’t simply slap an AI tool on top of a broken foundation and think that it will be able to find and decipher all the issues in your data. Having the right foundation is critical, and that’s another reason why IT is an important partner for procurement.”
Sanofi: Supporting the World’s Health Through Data
This month’s cover story spotlights Sanofi, one of the world’s largest pharmaceutical companies. For an organisation that puts the end-user – the patient – first, this requires an unwavering focus on R&D and continuous improvement. For the sake of the world’s health; every patient counts. So, when opportunities arose to improve services through data and advanced technology like AI, Sanofi brought in experts to steer and develop the journey.
Snehal Patel, Head of Global Data and AI Platform, takes a deep dive with Interface… “These innovations have fundamentally transformed Sanofi’s data and AI value chain,” says Patel. “It’s enabled scalable and efficient development across the organisation. We now have a far more agile development environment that supports the broader AI initiatives at Sanofi.”
Anson Cho, Director of Information Security & Data Protection at Langham Hospitality Group, discusses the pandemic’s silver lining and the development of a proprietary matrix to embed security into the heart of operational excellence.
“Our strategy wasn’t about over-engineering our systems to match the spend of a global financial institution; it was about increasing our defensive maturity so we are never an easy mark,” says Cho. “In cybersecurity, you want to ensure your barriers are sophisticated enough that attackers move on. We focus on staying ahead of the curve and continuously evolving so that our security posture remains a formidable deterrent.”
FNB: Redefining Data Science in Commercial Banking
Yudhvir Seetharam, Chief Analytics Officer at South Africa’s First National Bank (FNB) on a data science journey characterised by curiosity, culture and the drive for a competitive edge.
“Ours is a holistic approach focusing on the customer,” he explains. “Understanding the context of each customer journey and then using that context so that when we interact with you, we’re able to drive the right conversation with the right customer, at the right time, through the right channel and for the right reason. These ‘five rights’ make our interactions with clients more impactful than a spray and pray approach.”
Thomas Benjaminsen Normann, Product Director at Paymentology on the future for agentic payments and the progress still to be made
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Santander and Mastercard’s live AI-agent payment pushed the industry past the stage of talking about agentic commerce as a future use case and into the reality of a transaction moving through live banking infrastructure. In doing so, it placed an AI agent at the point of spend within a system that still assumes the person initiating the payment is also the one making the decision and carrying the liability.
That assumption is far easier to sustain when a payment draws on existing funds than when it creates a debt that someone must later repay. And may dispute. As soon as an agent moves from guiding a choice to completing the transaction, the usual alignment between instruction, authorisation and liability becomes harder to see.
Card authorisation has long rested on a simple premise: the person using the card is the one deciding to spend. Even when the transaction runs through a wallet, an app or a stored credential, the model still relies on a cardholder who is directly involved in the act of payment.
Agentic payments
Agentic payments stretch that arrangement. The customer may have set the rules, the budget or the merchant preference in advance, but the point of execution can now sit with software acting later and at speed. The question then extends beyond whether the transaction was authenticated to whether the debt it created was taken on with the kind of consent and clarity card systems have traditionally relied on.
Mastercard has responded by building a stronger trust layer around delegated intent. Once software acts on a customer’s behalf, the usual signs of presence and intent at the moment of payment carry less weight than they do in an ordinary card transaction. Santander’s pilot showed that this can be handled inside a tightly controlled framework with predefined permissions.
The challenge becomes very different once the same model moves into ordinary credit flows, where issuers are dealing with borrowing, repayment and dispute risk rather than a bounded test case.
Risk models built on human behaviour
Fraud systems and credit models have been trained to read people. How they spend, how quickly they move, where they buy, and what tends to happen before repayment trouble begins to show. An AI agent, even when acting entirely within a customer’s instructions, is unlikely to look much like that. It may search more widely, compare more aggressively, transact at unusual times and behave with a consistency that looks odd against a human baseline. Some legitimate payments will appear suspicious. Some suspect ones may look routine. Signals that once separated ordinary behaviour from risky behaviour will arrive in forms the system is not used to reading.
Research from Capgemini indicates that 71% of consumers want generative AI integrated into shopping interactions. Meanwhile, 58% say they already use generative AI instead of traditional search for recommendations. That does not mean autonomous purchasing becomes mainstream overnight, but it does suggest the move from AI-assisted discovery to AI-executed transactions will not stay theoretical for long. For issuers, that means transaction systems are about to encounter a new behavioural signature without much history behind them.
The pressure does not sit only with fraud screening. Credit decisioning is built on assumptions about how people build balances, revolve debt, repay over time and run into repayment trouble. An AI agent may be acting entirely within a customer’s instructions while still producing patterns those models were never trained to read cleanly. A sudden increase in spend, an unusual merchant mix or a burst of late-night activity may deserve scrutiny when a person generates it.
The same signals may be perfectly consistent with a software agent searching widely, responding instantly to price changes or executing against preset rules with much greater speed and regularity than a person would. Once that behaviour starts landing in the credit book, signals that once carried meaning around affordability, intent or emerging repayment risk become less reliable as indicators.
Signals the authorisation layer does not carry
The transaction also arrives with gaps that matter more once software is involved. Existing payment messages can identify the merchant, the amount, the credential used and the authentication path. What they do not natively describe is whether the action came from a customer or an agent, what spending authority had been delegated, whether that authority was limited to a category, merchant or price threshold, and whether the funding source was intended to be debit, charge or revolving credit. A payment can be technically valid while still leaving the issuer with too little context about how the decision was made.
A controlled pilot can solve some of that by imposing rules around the transaction from outside the standard message, which is effectively what bounded testing is for. Everyday credit use is less forgiving. If the issuer is expected to approve the payment, apply the right controls, score the exposure and later defend the outcome in a dispute, those signals have to be legible inside the flow rather than reconstructed around it after the event.
At that point, the question is less about whether the payment experience works and more about whether the issuer-side controls underneath it can carry the weight. That includes the ability to apply rules in real time, restrict how a credential can be used, and keep a clear record of how the transaction was authorised and what kind of exposure it created.
The missing context does not stop at authorisation. It follows the transaction further down the line, when an issuer has to explain why a payment was approved, whether the agent acted within its delegated scope. And how that scope should be evidenced if the customer challenges the transaction. Card systems are used to relying on the credential, the authentication path and the transaction record.
Digital versus Traditional Wallets
Agentic payments demand something more granular: a clearer account of who or what acted, under what limits, and with what right to create a liability on the customer’s behalf. The control layer around that decision, including how credentials are restricted and how delegated authority is defined, starts to matter much more than it did in a conventional wallet or stored-card journey.
Infrastructure many issuers built out for tokenised wallets now looks more like part of the control architecture for agent-led spend. Because the payment credential itself may need tighter restrictions than the market has been used to applying.
Santander and Mastercard have shown that an AI agent can now make it all the way through a live payment flow. What follows from that is less about whether software can reach the point of spend and more about what the rest of the stack needs to know once it gets there. If agentic payments are to move beyond controlled deployments and into ordinary credit use, issuers will need clearer ways to tell who acted, under what authority, against which funding source, and with what liability attached. Until those signals travel cleanly through the flow rather than being inferred around it, agentic payments on credit will remain easier to demonstrate than to absorb into everyday card operations.
Ivalua announced the winners of its annual global customer and partner awards at Ivalua NOW in Paris.
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Ivalua’s customer awards recognise organisations that demonstrate exceptional impact, innovation, and tangible business outcomes. This year’s winners featured exceptional use of AI in procurement, rapid deployment, and effective supplier data management.
2026 Customer Award winners:
Technology Innovation and Excellence Award: Hutchinson
Procurement Trailblazer – Making a Difference: Körber
Procurement Community Team Spirit: TÜV SÜD
Best New Deployment: IPC International
IPC International commented: “This recognition celebrates the success of our Source-to-Contract deployment, which wouldn’t have been possible without exceptional collaboration”. “It shows how teamwork, strong partnerships, and a phased and structured approach to roll-out, can deliver digital transformation at pace,” noted Jenny Eisen, IPC International Project Lead.
Franck Lheureux, CEO at Ivalua, added: “Our customers’ achievements show the power of effectively combining people, technology, and processes. We are proud to support leading organisations worldwide and help them accelerate their ambitious transformation goals. Congratulations to this year’s award winners; we are honoured to be part of your journey.”
The partner awards recognize contributions to the success of Ivalua and its customers over the past year, based on joint business initiatives and the growth of certified consultants.
Partner Awards winners:
Global Partner of the Year: Deloitte
EMEA Partner of the Year: Capgemini
AMER Partner of the Year: Deloitte
APAC Partner of the Year: KPMG
Ivalua also recognized eight Value Award winners: KPMG (Northern Europe), PwC (Southern Europe/Middle East), Axys (France/Belux), Accenture (Procurement Transformation), Optis (Collaboration). Sourcing Champions (Channel Sales), Modali Consulting (Project Excellence), and Deloitte India (Ivalua Practice Development).
“From enabling organisations to fully harness our technology to helping us drive innovation, our partners play a vital role in Ivalua’s growth. The continued expansion of our partner ecosystem and community of certified Ivalua experts demonstrates the strong momentum we’re achieving together,” added Gabriel Giret, VP Global Alliances & Academy at Ivalua. “Congratulations to this year’s winners and thanks to all our partners for their ongoing, outstanding support.”
Ivalua significantly expanded its partner ecosystem in the past year as Ivalua continued to gain market share and partner interest. Certified implementation consultants grew by 27% worldwide to over 3,100. Similarly, technology partners grew by 39% as Ivalua rapidly expanded its ecosystem. Ivalua is the Source-to-Pay platform of choice for many technology partners due to Ivalua’s comprehensive, extensible data model and intelligent workflows. Partner data can be fully captured in Ivalua’s platform and used to guide intelligent workflows, automatically driving customer spending based on company policies and changing market conditions.
SupplyChain Strategy attended Ivalua NOW 2026 to gather insights on the future of procurement
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Supply chain and procurement are in a state of flux. While leaders across all industries are focusing on many of the same exciting topics – AI, ESG, automation, etc. – the world around us is experiencing uncertainty. But that type of chaos often births innovation. Ivalua NOW is shaped around embracing that innovation.
Ivalua’s global event, which took place in Paris on the 11th and 12th of March, brought together over 1,500 procurement and supply chain professionals at the Carrousel du Louvre. The stunning venue attracted leaders and experts, all of whom were determined to face geopolitical chaos and the race to adopt new technologies. Importantly, that includes ensuring the human side of business remains in focus.
It’s always been the case that supply chain leaders need to adapt in order to thrive, but the key now is also being armed with the right knowledge and tools, which are only becoming more complex. Generative AI is transforming the way we work, but the focus at Ivalua NOW 2026 was on how to apply AI effectively. GenAI is no longer a buzz word: it has real-world practical applications across the supply chain. And that’s what this event – and the whole concept of shaping new horizons – is about.
The technology is real
After the first day of customer and partner meetings, talks, and workshops, the main event kicked off on the following day with an AI video of Alex Saric, Ivalua’s CMO, flying from New York to Paris in his car. Why? “Obviously we had a little fun with technology for our opening video today, but there is a point,” Saric said in his opening speech.
“Our world is rapidly changing how we live, how we work. Technology is having a huge shift. Now the video may seem futuristic – even a little bit outlandish – but actually, all the technology in it is real and available today.”
However, Saric said, while huge change is happening around us, “what is important is maintaining the right balance in our personal and professional lives. We need to embrace the new because it’s exciting; it lets us do much more. And let’s be honest – not embracing it doesn’t mean you’re standing still. It means you’re falling behind as everyone else does embrace it.”
What humans do best
That’s not to say that everything is changing. “We need to preserve the old ways of working as well, especially when it comes to people and relationships,” Saric added. “That’s really critical. Agentic AI is already starting to fundamentally transform how we work, allowing us to make faster, more informed decisions, and freeing us from a lot of the dull and mundane tasks that still consume a huge amount of our day. By doing so, it’s going to allow us to focus on what humans do best: relationships, people, strategy. Now that’s the real promise of the human-agent operating model, which is coming.”
Saric doubled down on the fact that businesses have a choice: they can resist, and watch the future be shaped for them without their input. Or they can be proactive, and start using modern tools the way they want that fits best for their organisation. He added that the aim of Ivalua NOW 2026 was to inspire attendees by showing new innovations, showcasing customers who are succeeding on their journeys, and enabling networking opportunities.
Reshaping the supply chain world
“Your job has never been harder,” said Franck Lheureux, Ivalua’s CEO, said during his keynote address. “But there is hope. You wouldn’t be here otherwise.”
Lheureux posed the questions every supply chain professional in the room is asking themselves: what talent do I need to thrive in today’s agentic world? How do I deal with cyber threats? When and how do I adopt AI for the best? How do I continue to prioritise climate change? How do I fight inflation, costs, and other impediments?
“Your impact has never been greater. You’re a force for good” – Franck Lheureux, CEO
“This is your world,” Lheureux added. “You’re reshaping your job, inventing a better future. Preparing this, a quote by Mahatma Ghandi came to my mind: ‘be the change you wish to see in the world’. You have the power to be the change, as long as you start, as long as you set yourself in motion, and set yourself in a positive direction.
“Your impact has never been greater. You’re a force for good. Every dollar you spend, you have a decisive opportunity to make it a relevant spend. Doing good for people, doing good for suppliers, doing good for your employees, society, and the planet. That’s your superpower. You have the opportunity, you have the technology, you have the appetite. More importantly, you have a mandate to make it happen.”
Acting and reacting
Lheureux inspiring words set the tone for the rest of the day. David Khuat-Duy, Founder and Chief AI Officer at Ivalua, followed with an overview of the agentic AI revolution, and what it means to both Ivalua and supply chain at large. Then, there was an educational talk through Ivalua’s innovations and trends from Pascal Bensoussan, Chief Product Officer.
Deep discussions of cutting-edge ideas and innovations continued through the day. Supply chain leaders from all walks of life presented on digital transformation, how women are driving change within procurement, why the CPO-CIO alliance is so vital, supplier dynamics, sustainability, and of course, many discussions about AI – the benefits, the risks, and beyond.
The type of guidance this event offers is invaluable. Despite the rapid pace of change, and the endless discussions about how best to move forward, forging a clear path is still a challenge. At 2025’s Ivalua NOW event, Lheureux stated that it’s hard to think about and project the future if you’re constantly forced to react on a day-to-day basis. Later in the day at this year’s event, I asked Lheureux what has changed – if anything.
“I sense that our customers have a paradoxical situation to fight the day-to-day constraints they face,” he explained. “What are the levels of a given company to cope with external shock, and still build a strategy for a long-term future? It’s supply chain resilience, and diversifying your supplier portfolio to reduce the dependency on one region.”
Ultimately, the key to moving forward proactively instead of reactively is being unlocked with technology. Lheureux added: “You need the technology to deploy best practice and policies, to shape the world, to shape the future with different results. And AI will still have something to do about it.”
Manifest Vegas 2026 took place in February, and as part of SupplyChain Strategy’s official partnership with the event, we joined in the festivities to bring back exclusive insights for our readers.
The event showcased new technologies, strategies, and other innovations for application across the supply chain. Manifest buzzed with conversations and ideas, the energy high as supply chain professionals shared their knowledge and wisdom. Looking ahead back in 2025, Tanzil Uddin, SVP of Content and Partnerships at Manifest told us, “2026 will be pushing things up a notch.”
He added: “Manifest is really a full ecosystem event, dedicated to the end-to-end supply chain. We bring together startups and investors, but also supply chain leadership like Chief Supply Chain Officers and Chief Procurement Officers from retail, manufacturing, automotive, life sciences, and more.”
Alongside our many conversations with leaders at Manifest, this issue features a conversation with Malcolm Dare, Executive Director, Commercial, at Sizewell C, about how the company is gearing up to meet 7% of the UK’s energy needs for the next 60 years. The under-construction nuclear power station is also creating education and work opportunities for the local area, which is a huge boon for Suffolk.
Dare says: “Building a sixth form college and handing it over to the education authority to run is one of the lasting legacies that we want to do. It also means that local people may choose to go through the sixth form route and then, after getting their qualifications, subsequently opt to work at Sizewell C when it’s an operating power plant. That is a generational activity that would have been left behind.”
As well as all of this, we have expert supply chain insights from Prabhat Rao Pinnaka, Eddy Massaad, and Paul Vezelis, as well as a CPOstrategy Podcast conversation with Venkatesh Srinivasan. Finally, we take a look ahead at our picks for upcoming supply chain events to keep an eye on.
We met up with leaders at CHEP, Elida Beauty, Sun Pharma, Stanford Medicine and Covenant Logistics, to hear how strategy is being remodelled
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Supply chain resilience is no longer about recovery from isolated shocks. Leaders now describe disruption as continuous and systemic, demanding fundamentally different organisational thinking.
Sandra Leyva Martinez, Head of Sustainability, CHEP Americas, explains that organisations must prepare for overlapping risks as opposed to isolated events: “We don’t have one crisis at a time anymore. Multiple events spanning environmental, social, political and technological forces are happening simultaneously and often amplifying one another. That changes how you think about resilience. It’s no longer about predicting what might happen. It’s about building systems that respond quickly and intelligently when things happen, and making sure collaboration and transparency exist across the value chain so organisations can move together rather than react in isolation.”
Sachin Mariguddi, Chief Supply Chain Officer, Elida Beauty believes resilience is as much cultural as operational: “The rate of change is accelerating so fast that waiting to react is no longer viable. Organisations have to lean into change, experiment, learn quickly and adapt continuously. Supply chains that succeed will be those that treat uncertainty as normal and build the capability to respond in real time rather than trying to predict every disruption in advance.”
Vickram Srivastava, Head of Supply Chain North America, urges leaders to reset expectations entirely: “Supply chain disruption is here to stay. Whether it’s geopolitical tension, climate events, trade shifts or operational bottlenecks, variability is now part of the system. Leaders must reset expectations with partners, build multiple scenarios and ensure they can respond quickly when disruption occurs rather than assuming stability will return.”
Amanda Chawla, SVP Chief Supply Chain and Post Acute Care Officer, Stanford Medicine says resilience must be built into organisational architecture: “I have to change the way I fundamentally think about resiliency – from an infrastructure standpoint, from a data standpoint, from a team and analytics standpoint. It requires redesigning how decisions are made, how information flows and how quickly we can respond when supply conditions change.”
Matt McLelland, Vice President of Sustainability and Innovation, Covenant Logistics highlights operational execution: “Resilience and sustainability both come down to what actually happens on the ground. In transport, that means understanding how freight moves every day and making practical decisions that work operationally, meet customer expectations and adapt to regulatory and market changes at the same time.”
Supply Chain Now’s Scott Luton reveals an industry moving beyond AI hype to real-world execution
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Manifest 2026 in Las Vegas felt different. One sharp friend of mine (Florent Guillet-Caillot) referred to it as a “a gigantic beehive, full of energy and enthusiasm for what the future holds.” And that was before the extra buzz generated with the networking happy hours that were prevalent amongst all the friendly folks in attendance.
I enjoyed the opportunity to connect with hundreds of colleagues and friends, lead a wonderful panel discussion, and conduct more than twenty interviews with leaders from across the supply chain ecosystem: operators, technologists, investors, educators, journalists, entrepreneurs and analysts.
And after all those conversations, several observations became clear to me. None are necessarily new, but they certainly represented a doubling and tripling down of major themes across the global supply chain industry.
AI has moved from hype to execution
A couple of years ago, many conversations about AI in supply chain were still theoretical. At Manifest 2026, that wasn’t the case. In fact, my conversations in Las Vegas only added to my belief that those leaders that fail to take advantage of what has largely become the AI imperative…well, the costs to your competitive advantage have continued to rise dramatically.
In my conversation with Aadil Kazmi, Head of AI at Infios, he emphasized that the AI conversation has long shifted toward execution. Results are to be expected now; not demos and theoreticals.
One of the key points he raised was the power of unified data models. When order management, warehouse management, and transportation systems operate on the same foundation, organizations can actually act on intelligence rather than spending months stitching together fragmented datasets.
Richard Ford, Chief Technology Officer at Integrity360, on why cybersecurity must move beyond control and embrace trust
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Cybersecurity has long been focused on building walls, but the biggest threat is already inside. Today, insider risk accounts for nearly half of all data breaches. This isn’t just about malicious actors, it’s about regular employees and trusted contractors who make simple, costly mistakes.
Remote and hybrid working has only intensified the problem. With teams distributed and work happening across cloud platforms and collaboration tools, it’s harder than ever to track what’s happening, let alone why. Although AI tools promise efficiency, they also introduce new vulnerabilities. Employees pasting code into chatbots or bypassing corporate tools to meet deadlines. All seemingly innocent, but highly risky.
Insider Risk
Ransomware gangs know this and are now skipping the technical breach altogether and going straight to the source – a company’s insiders. Whether through bribery or social engineering, attackers are finding that humans can be the weakest link in even the most well-defended environments. Despite this, most security budgets still focus outward.
Traditional tools like data loss prevention (DLP) struggle to keep up with today’s dynamic and unpredictable user behaviour. Meanwhile, simulated phishing tests and punitive training schemes often breed resentment, not resilience. It’s time to rethink the model.
Human Error, Human Fix
We need to stop treating employees as the problem and start making them part of the solution. Enter Human Risk Management (HRM), a behavioural approach to cybersecurity that recognises the complexity of modern work. HRM tools monitor real-world user behaviour, detect anomalies in context, and deliver just-in-time nudges to prevent risky actions before they happen. Instead of punishing mistakes, they help users avoid them in the first place.
Of course, technology alone won’t fix the issue, culture is key. Leadership must champion security as a shared responsibility, not an IT rulebook. Success should be measured by how quickly employees improve, not how often they slip up. Awareness campaigns need to be practical and rooted in real-world behaviour.
Organisations also need to understand how digital transformation has changed the risk landscape. Shadow IT is no longer a fringe issue, it’s how work gets done. Whether it’s a developer using an AI plugin or a marketer sharing files via a personal drive, employees will always find the fastest path to productivity. Security must meet them there, not block the way.
Cybersecurity Built on Trust
The smartest businesses are those that treat identity like infrastructure, and behaviour like a vital data stream. They invest in tools that adapt to people, not the other way around. This means a move away from a surveillance approach and embracing the nuance of human error and design systems that support.
In a world where threats are increasingly internal and AI is both a risk and a tool, cybersecurity can no longer be about control. It must be about trust, and that starts with understanding the humans behind the keyboards.
Radi El Haj, CEO of global payment technology provider RS2, on the shift toward programmable, data-driven financial infrastructure designed for a real-time global economy.
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The financial industry is entering a new phase of infrastructure modernisation. While cryptocurrencies and blockchain continue to dominate headlines, a more pragmatic evolution is reshaping banking from within: tokenised deposits embedded directly into core payment systems.
Tokenised deposits represent a practical bridge between legacy banking infrastructure and next-generation real-time capabilities. Unlike crypto-native assets, they remain fully regulated bank liabilities. But with the added advantage of programmability, automation and real-time settlement logic built directly into the banking stack.
The recently launched Cari Network by five US regional banks signals this shift. It demonstrates how banks are beginning to rethink bank-to-bank payments and settlement at the infrastructure layer.
Moving away from batch-based clearing toward programmable, real-time funds allows institutions to enhance efficiency, reduce settlement risk and modernise core systems without stepping outside regulatory guardrails.
For banks re-entering acquiring, upgrading issuing, or consolidating fragmented payment systems, tokenised deposits represent not a parallel innovation, but an embedded evolution of core processing.
What Tokenised Deposits Really Change
At their foundation, tokenised deposits are digitally represented, insured banking liabilities. The critical distinction is that they remain part of the regulated banking system. This preserves trust, governance and compliance – the pillars that underpin institutional finance.
The transformational value lies in programmability at the infrastructure level. When business logic is embedded into the payment instrument itself, banks can automate reconciliation, conditional settlement, liquidity allocation and multi-party disbursements in real time. Instead of adding layers of complexity on top of legacy systems, tokenisation integrates intelligence directly into the core ledger and processing environment.
For banks operating across issuing, acquiring and cross-border settlement, this reduces friction between systems and enables a more unified, real-time operating model.
AI as the Operational Intelligence Layer
Tokenisation alone is not sufficient. To operate at scale, programmable deposits require an intelligent control layer. Artificial intelligence and machine learning provide that layer.
AI can forecast liquidity requirements across issuing and acquiring portfolios, optimise routing decisions in real-time, detect anomalous behaviour and automate compliance monitoring. As transaction volumes increase and settlement windows compress, predictive intelligence becomes critical to maintaining resilience and control.
In multi-bank or multi-ledger environments, AI also plays a key interoperability role. It reconciles cross-network flows, identifies bottlenecks and dynamically allocates capital across settlement channels. This is where infrastructure maturity matters most.
Banks that embed AI-driven operational intelligence directly into their payment processing architecture will be best positioned to deliver speed without sacrificing stability.
Infrastructure is the Real Challenge
Tokenised deposits are not a feature — they are an architectural shift. Scaling them requires cloud-native, modular processing platforms capable of integrating with existing payment rails, regulatory frameworks and cross-border networks. Without modern infrastructure, tokenised deposits risk remaining contained pilots.
For banks modernising acquiring or issuing capabilities, this becomes a broader transformation programme: upgrading the core ledger, harmonising payment rails, embedding real-time analytics and ensuring seamless interoperability across domestic and international networks.
This is where strategic infrastructure partners become critical. Institutions need platforms that combine:
Cloud-native core processing
Real-time settlement capabilities
Embedded AI-driven monitoring and liquidity management
Open APIs for cross-network interoperability
Regulatory-grade resilience and auditability
Only then can tokenisation move from concept to scalable reality.
Embedding Innovation Into the Banking Ecosystem
Payments innovation succeeds when it strengthens the banking ecosystem rather than bypassing it. Tokenised deposits offer a path to modernisation that reinforces trust, compliance and regulatory clarity. When combined with AI-enabled operational intelligence, they create a responsive infrastructure capable of supporting real-time commerce, embedded finance and cross-border settlement at scale.
Institutions that approach this as an integrated infrastructure strategy — rather than a point solution — will see the greatest impact:
Reduced settlement risk through programmable fund controls
Improved liquidity optimisation across issuing and acquiring flows
Real-time fraud detection powered by AI
More efficient cross-border routing and capital allocation
This is not about replacing banking infrastructure. It is about rebuilding it intelligently.
The Next Phase of Real-Time Banking
The launch of initiatives like the Cari Network signals the beginning of a broader industry evolution. As tokenised deposits mature, collaboration between banks, FinTechs and technology providers will determine how effectively they scale.
The strategic question for banks is not whether tokenisation delivers value — it is whether their infrastructure is prepared to support it.
Those that invest in modern, modular processing platforms capable of integrating tokenisation and AI at the core will establish long-term competitive advantage. They will move beyond incremental upgrades toward a unified, intelligent payments architecture.
Tokenised deposits represent more than a technological enhancement. They reflect a shift toward programmable, data-driven financial infrastructure designed for a real-time global economy.
For banks and technology providers operating at the intersection of issuing, acquiring and settlement modernisation, this marks the beginning of a new era — one defined not simply by faster payments, but by smarter, more resilient infrastructure.
About RS2
RS2 is a leading global provider of payment technology solutions and processing services, offering a unified approach to managing payments across all channels for banks, integrated software vendors, payment facilitators, independent sales organizations, payment service providers, and businesses worldwide. RS2’s platform stands out as a robust cloud-native solution designed for both issuing and acquiring operations. With its advanced orchestration layer seamlessly integrating all aspects of business operations, clients gain access to comprehensive analytics, reporting tools, and reconciliation features. This empowers businesses to effortlessly expand their global footprint through a single integration, while also gaining valuable insights into payment processes and customer behavior, enhancing operational efficiency, increasing conversion rates, and driving profitability.
Dr. Yvonne Bernard, CTO at Hornetsecurity, on meeting the challenge of managing the speed of AI adoption and harnessing its defensive capabilities while mitigating the risk of uncontrolled adoption
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The past year has been defined by acceleration. Threat actors rapidly embraced automation, AI, and social engineering. Scaling their tactics at unprecedented speed, while defenders raced to keep pace. Historically, defensive resilience evolves in step with attacker innovation, but in 2025 that balance began to falter.
In an analysis of over 6 billion monthly emails, Hornetsecurity’s Security Labs found that the volume of sophisticated threats grew faster than most security teams could adapt to. Malware-infected emails soared by 131%, scams increased by nearly 35%, and phishing attempts – powered by access to advanced AI – rose by 21% from the previous year.
Typically, attacks, even at volume, are easily filtered by good firewalls and secure email gateways. But the sophistication and AI-led nature of 2025’s boom made it even harder for organisations to defend themselves. The question now is: can security teams and businesses wrestle back control?
Evolving Cyberattack Landscape
AI enhances efficiency and precision. As such, cybercriminals use it to launch faster, more convincing and adaptive attacks, ranging from deepfakes to credential stuffing. As an example, there is a concerning trend of attackers increasingly using ‘MFA bypass kits’ to create deceptive login pages. These pages capture not only the user’s credentials but also have logic built in to handle MFA prompts as well. The unsuspecting user is then passed to the real login page for the target service and meanwhile the ‘kit’ grabs a copy of the user’s session token. This allows the attacker to impersonate the person and access their data.
Examples of such kits include Evilginx (open source) and the W3LL panel. Protecting against these attacks can be challenging, as they are adept at bypassing MFA safeguards. Threat actors often use compromised LinkedIn accounts, for example, to gain access to substantial information and connections. This enables them to impersonate trusted business connections. Paired with the weaponisation of Agentic AI, this will magnify existing vulnerabilities within an organisation, while introducing new ones that defy traditional containment models.
As it stands, the lack of oversight within organisations on the extent of AI’s adoption by cybercriminals has enabled the emergence of ‘Ransomware 3.0.’ Ransomware has evolved past simple encryption and exfiltration, with this next phase focusing on LLM-driven orchestration and a shift to data integrity manipulation.
To counter AI-accelerated compromises and ‘Ransomware 3.0’ in 2026, organisations must adopt a Zero Trust-based cyber resiliency strategy. This requires businesses to implement strong, non-phishable machine authentication, strict least-privilege access, and constant monitoring to protect the integrity of the data that users and AI agents can access. It should become the baseline expectations rather than aspirational goals for this year.
The Secret Value of ‘Least Privilege’ Access
Another strategy to proactively improve cybersecurity defences in 2026 is to enforce the principle of ‘least privilege’ access. This tactic grants users access only to the data that’s needed for their role. Limiting excessive access is important for preventing the potential for widespread data exposure and damage in the case of an account compromise.
Businesses, however, must strike a balance over access; if it’s too strict, it can hinder productivity and lead to shadow IT issues. Getting this balance right when it comes to privileged access is where sophisticated permission managers are invaluable tools to work with. They streamline the process and remove the guessing game of who and what to grant access to, thereby ensuring, in the case of an attack, that the entire organisation won’t be brought to its knees.
How CISOs are Adopting ‘Resilience, not Perfection’
The rate at which AI is advancing means not every organisation will be equipped with the tools or the know-how to tackle every AI-inspired attack. But as the saying goes, ‘prevention is better than cure’. It’s better to create a strong security culture than to continually chase after the next best tool.
Organisations can’t strengthen their resilience without involving every single person under their umbrella. That’s why CISOs must continue to invest in cybersecurity awareness programs.
These should include simulated AI-phishing attacks (phishing remains the number one attack vector) to test users and enable them to apply learnings from the modules.
If any user clicks on a phishing email, they should receive additional training at that very moment, to cement the learning. Over time, a good training system should automatically identify users who rarely fall for such attacks and reduce the training they receive while making the simulations they do receive more difficult. Conversely, giving persistent offenders additional bite-sized training and simulations can help improve security outcomes over time.
The key challenge for 2026 is managing the speed of AI adoption and harnessing its defensive capabilities while mitigating the risk of uncontrolled adoption. But with excellent training, cyberattack practice runs, and the adoption of Zero Trust principles, organisations will find themselves in a strong position.
About Dr. Yvonne Bernard
Dr. Yvonne Bernard is the CTO of Hornetsecurity by Proofpoint, Proofpoint’s business unit leveraging the Hornetsecurity product suite dedicated to managed service providers (MSPs) and small to mid-sized businesses (SMBs), providing next-generation cloud-based security, compliance, backup, and security awareness solutions that help companies and organisations of all sizes around the world.
Dr Megha Kumar, Chief Product Officer and Head of Geopolitical Risk at CyXcel, on whether our risk and regulatory frameworks and institutional cultures can keep pace with Agentic AI
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Within the next couple of years, Agentic AI is likely to progress from early stages of operation to be fully embedded within systems. Its expansion will be subtle rather than spectacular. It will integrate steadily into enterprise platforms, logistics networks, compliance workflows, cybersecurity operations centres and executive decision-support tools. Processes will move faster, operating expenses will decline and performance indicators will trend upward.
Yet these visible improvements mask a deeper challenge. The regulatory exposure, data governance pressures and erosion-of-trust risks associated with Agentic AI are being misjudged.
Unlike earlier AI applications designed primarily to generate outputs – whether text, imagery, or predictive insights – agentic systems are built to act. They sequence decisions, draw from multiple data environments, initiate consequential processes and function at scale with differing levels of human supervision. In sandbox environments this can seem contained and controllable. Over extended periods in live environments, however, sustained oversight, traceability and effective governance become significantly more complex.
Evolving Operational Complexity
There are two key challenges that businesses must address.
First, how do organisations monitor what agentic systems are doing once deployed? These systems evolve through updates, integrations and retraining and they interact with new data environments.
Second, how do you ensure responsible behaviour throughout the lifecycle? Regulators, policymakers and customers will likely expect firms to shift from compliance assurance to risk assurance and demonstrable evidence of trust and transparency.
The prevailing assumption is that human oversight will mitigate these risks. Human in the loop or human over the loop has become the default reassurance. In practice, however, that assumption breaks down far faster than many anticipate.
When a system works 95 per cent of the time, human reviewers limit their scrutiny. Behavioural science tells us that automation bias and complacency occur when automated systems are high-performing. Employees often become validators of AI outputs rather than critical examiners. The diligence gap widens gradually and then suddenly.
Facing Up to Difficult Questions
How do you incentivise employees to remain diligent checkers when the system mostly ‘works’? And how much time does effective oversight actually require? True review is not a cursory glance at a dashboard. It involves interrogating assumptions, validating inputs, checking context and assessing downstream consequences. In many cases, meaningful oversight may take nearly as long as performing the original task manually. When checking becomes more costly than doing the job yourself, pressure to ‘trust the system’ intensifies.
And what happens to accountability when oversight exists on paper but not in practice? Governance documentation may show layered review structures, escalation pathways and audit processes. Yet if humans are functionally disengaged, responsibility becomes dispersed. When errors surface, organisations may struggle to attribute fault – was it the model design, the data, the integrator, the operator or the reviewer who signed off without fully scrutinising?
Regulators are only beginning to grapple with these realities. In jurisdictions such as the European Union, the EU AI Act introduces risk-based obligations, documentation requirements and human oversight provisions. These are important steps, however, the operationalisation of those requirements in dynamic, agentic environments remain untested at scale. Compliance on paper will not automatically translate into resilient governance in practice.
Addressing the Trust Challenge
Beyond regulatory exposure, there is a broader trust challenge emerging.
As Agentic AI systems scale across industries, they will generate vast volumes of automated outputs – reports, communications, risk assessments, content, decisions and transactions. If errors or manipulations spread through interconnected systems, confidence in digital outputs may erode.
In geopolitically sensitive contexts, this has profound implications. Agentic systems interacting with external data sources could amplify disinformation, introduce biased datasets or make decisions based on manipulated inputs. The speed of automation may outpace the speed of verification. Trust, once diluted, is difficult to restore.
Data protection risks will also intensify. Agentic systems frequently require broad access privileges to perform tasks effectively. They may access internal databases and personal data and interact with third-party platforms. Each interaction creates potential exposure points. A single misconfiguration or prompt injection attack could trigger cascading consequences across systems.
The next phase of AI adoption will not simply amplify productivity: it will amplify regulatory, legal and reputational risk. This moment therefore demands serious scrutiny before agentic AI becomes deeply embedded in business infrastructure.
The Moment for Action has Arrived
So, what should organisations be doing now?
To begin with, organisations need to look past superficial, tick-box compliance. Effective governance cannot live solely in policy documents – it must function in day-to-day operations. This means investing in continuous monitoring capabilities, robust audit trails and real-time anomaly detection tailored specifically to Agentic AI behaviours.
In parallel, incentive structures should be redesigned. Meaningful human oversight will not happen if it is treated as secondary to speed or output. If employees are expected to provide meaningful review, organisations must allocate time, training and authority accordingly. Performance metrics should reflect risk management responsibilities, not just output rate.
Clear lines of accountability are equally important. Senior leadership and boards should determine who carries ultimate responsibility for outcomes produced by agents. Where third-party vendors are involved, responsibilities must be contractually and operationally defined. Incident response mechanisms should be rehearsed in advance, rather than presumed to work when pressure is high.
Expertise must also be integrated across functions. Legal, risk, compliance, cybersecurity, data protection and operational teams should be engaged from the outset. Deploying Agentic AI is not simply a technical upgrade – it reshapes the organisation’s risk profile.
Finally, resilience demands deliberate stress-testing. Leaders should examine not only pathways to success but how models fail at scale. How would the organisation respond if a system update embedded systemic bias, if an integration vulnerability enabled unauthorised activity or if automated actions eroded customer confidence? Rigorous scenario exercises, however uncomfortable, are essential to building genuine preparedness.
As Agentic AI advances, Risk Management Should Match its Pace
None of this is an argument against adoption. Agentic AI presents meaningful productivity improvements and the potential for sustained competitive differentiation. Organisations that deploy it with discipline and foresight may secure a measurable advantage. The danger lies not in adoption itself, but in pursuing acceleration without knowing the risks and putting the right guardrails in place.
The coming two years are critical for businesses. Before these systems become deeply embedded in core processes, organisations have an opportunity to shape the control environment around them. However, once agentic systems are fully embedded, retrofitting controls will be far more difficult and costly. Leaders must therefore treat this period as a design phase for oversight, not merely a race for competitive advantage.
Agentic AI is advancing rapidly. The defining question is whether our risk and regulatory frameworks and institutional cultures can evolve just as quickly.
As companies pour billions into developing their own AI tools, Fayola-Maria Jack, Founder and CEO of Resolutiion, argues that many are forgetting what worked well in the early tech era, confusing ownership with innovation
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Back in the very early days of computing, organisations rarely hesitated to buy the hardware and software they needed to modernise. Now we’re deep into the AI age. Many organisations are deciding the best approach to adopting the technology is to take building it into their own hands.
Many of the more traditional companies, like big banks, have publicly stated that they’re developing their own AI tools in house. Meanwhile, corporate investment in AI reached £191 billion ($252.3 billion) in 2024 and is only likely to have risen since..
Yet, the challenges of internal AI development are becoming abundantly clear. A recent report from MIT found that 95% of AI pilot projects failed to deliver any discernible financial savings or uplift in profits. It also found companies purchasing AI tools succeed about 67% of the time. Meanwhile, internal builds succeed only one-third as often.
Why do companies feel they need to build their own AI tools?
Those statistics alone show buying AI from specialised vendors and building partnerships is often the wiser choice. But, with a handful of traditional businesses deciding to lean the other way, it begs the question: why are these companies not only initially choosing the in-house route, but also persisting with it despite low success rates?
The instinct to ‘build’ is rooted in legacy thinking – and to some extent, a naivety around what makes AI solutions special. Traditional enterprises have long equated ownership with control: control over systems, data, and perceived competitive advantage.
When AI entered the scene, many executives applied that same logic, assuming that building in-house equated to ownership, at the heart of innovation. But this overlooks a fundamental truth that is unique to AI – AI isn’t another IT system you can own and stabilise. It evolves exponentially, not linearly. It demands constant retraining, rapid iteration, and deep specialisation – all at odds with the traditional corporate IT environment, which is built for stability and compliance, not experimentation and speed.
Are companies really investing in innovation?
Another common belief is that buying is seen as conceding leadership to outsiders. While building feels safer politically, signalling ‘we’re investing in innovation’. Ironically, though, that safety is often an illusion that leads to slower progress and higher long-term cost. But again, there is deep irony if talent is outsourced to India, or another foreign jurisdiction, on the basis of cheap labour.
The exact same dynamic plays out internally, too. AI initiatives are career-defining projects for senior technology leaders and they attract budget, visibility, and prestige. Once a build programme is launched, it’s politically difficult to pivot, even in the face of poor performance. As a result, the build strategy often survives by narrative rather than by evidence.
Underpinning all of this is the institutional belief that ‘our data is unique’ – that their data will deliver proprietary insight and competitive advantage. In reality, most internal data is messy, siloed, and outdated. It reflects years of practices that are often misaligned with best practice, and therefore should never be used to train AI. Instead of building capability, many organisations end up building complexity.
Increased Caution in Regulated Sectors
Alongside these misbeliefs, regulatory caution and data residency also play into the decision to build in-house; especially in regulated sectors like finance, healthcare, and government. Here, enterprises typically believe that adopting third-party AI tools may expose sensitive data to external environments they cannot fully control. Perhaps this is because data protection laws have created a heightened sensitivity to where data is processed and how it’s used to train models.
Take banks as an example – historically they have viewed data as a fortress, a core asset to be guarded. Their culture of confidentiality and regulation makes them instinctively cautious about sharing information externally. Add to this the fact that large banks already have substantial internal technology infrastructures and budgets, and building seems logical on paper. The truth, however, is that building internally doesn’t eliminate compliance risk, but often amplifies it. This is because companies take on the burden of securing systems, updating controls, and managing ethical frameworks themselves.
On the other hand, buying from specialist providers means adopting a system that’s been engineered for compliance at scale. Purchasing doesn’t dilute compliance, it accelerates it, because you inherit the expertise and validation of teams who do this. In fact, most reputable AI vendors now far exceed enterprise compliance standards, designing privacy-preserving architectures that mitigate these risks far more effectively than in-house teams can, full-time.
Competitive Edge
The financial sector’s competitive edge increasingly lies not in owning the algorithms, but in applying them better and faster. Challenger banks and fintechs have embraced this: they buy tools (whereby anti-money-laundering and fraud detection platforms are incorporated into model-risk management protocols aligned with regulatory expectations), they integrate, and they move rapidly. Traditional banks, by contrast, are still in a transitional mindset, modernising legacy systems while trying to preserve control. That’s why their build programmes are often more about transformation theatre than tangible AI capability, and will ultimately see them fall further behind.
Underestimation of AI’s Lifecycle Cost
Beyond the issues of legacy thinking, poor data quality and compliance risk, companies attempting to build in-house also face a number of additional challenges when it comes to the talent, time, and technical debt needed.
Talent: True AI expertise is scarce and expensive. Competing with the open market for top data scientists and ML engineers is unsustainable for most enterprises.
Time: AI doesn’t stop evolving while your internal team builds. By the time a prototype is ready, the underlying technology stack may have already advanced.
Technical debt: Maintaining models, retraining on new data, and ensuring explainability and auditability over time all demand continuous investment.
Most companies underestimate this lifecycle cost by an order of magnitude. Add to that the reputational risk of bias or error (especially when deploying AI in customer-facing contexts) and the true cost of internal builds can spiral quickly.
A Change in Mindset is Needed
As more of these challenges surface, we should see an uptick in companies moving towards buying AI rather than building it – and it’s a pattern that’s thankfully already emerging. As AI becomes infrastructure, not novelty, enterprises will mirror the software evolution of the 1990s and 2000s: moving from bespoke builds to modular adoption.
The early adopters that buy today will pull ahead dramatically because they can focus on application and differentiation, not on maintenance. In time, the ‘build’ approach will be seen much like writing your own word processor in 1995: a costly distraction from real innovation.
Organisations need to shift from ownership to orchestration. This requires humility, recognising that innovation now happens outside corporate walls, and confidence – trusting that your value lies in how intelligently you deploy technology, not in whether you wrote its source code. Culturally, companies need to redefine ‘strategic advantage’ as agility plus insight, not possession plus control. AI isn’t an asset you own; it’s a capability you cultivate.
In simpler terms, the companies that thrive in the AI age will be those that treat AI as an ecosystem, not an ‘ego system’.
With AI adoption booming across financial services, Laurent Descout, CEO and co-founder of Neo, explores why the technology has emerged at the right time for payments and what it signals for the industry’s future
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The payments industry is undergoing significant transformation as instant payments become the global standard and ISO 20022 reshapes financial messaging. For financial institutions, these changes are creating a more complex operating environment that demands new approaches to managing payments.
Payments have long been the bedrock of financial systems. Allowing businesses and individuals to pay for the goods and services that keep the world’s economy moving.
Over time, the processes by which payments operate have evolved. From the first wire transfers in the 19thcentury to credit cards in the 1950s. Smartphones and the internet then took payments digital, with online banking, mobile wallets and pay by phone following suit.
Now, the next wave of innovation is beginning to reshape how payment systems operate. With 62% of global organisations reporting that they are at least experimenting with AI agents. The payments industry is no exception. From processing transactions and powering chatbots in fraud detection and compliance, AI is commercially imperative for banks of all sizes.
Emerging at a Pivotal Moment
The growing adoption of AI coincides with global efforts to speed up and streamline payments. Instant payments are fast becoming the global standard.
This shift has been driven by consumer and business expectations, but also by changing regulations. In Europe, the Instant Payments Regulation in 2025. And most recently, SWIFT’s migration to ISO20022 has driven changes in the way payments are processed.
ISO 20022 introduces a single, data-rich standard for financial messaging. Under SWIFT’s migration, which reached a major milestone in November 2025 with the end of the coexistence period, payments can now carry more detailed and consistent information.
This reduces ambiguity and improves data quality throughout the transaction lifecycle. This makes payments easier to track, reducing delays and increasing customer confidence.
Under the upcoming November 2026 deadline, support will end for unstructured messages and retire MT101 request-for-transfer messages. Leaving only structured and hybrid formats. ISO 20022 also underpins modern payment rails and APIs. This enables banks and PSPs to offer instant payments and embedded payment services within corporate workflows.
Furthermore, there are initiatives from SWIFT like SWIFT Go, SWIFT Pre-Validation and SWIFT Case Management. These focus on making payments less frictional, more transparent and faster at post-issuing resolution.
What AI Brings to the Table
Against this backdrop of growing complexity, AI offers financial institutions powerful tools to automate processes, improve decision-making and reduce operational friction. All of these are key attributes for monitoring payment systems and have the potential to speed up processes end-to-end.
One of its core applications in payments is applying machine learning to navigate the complex web of global payment rails, completing transactions more efficiently and with fewer errors.
It also unlocks more advanced capabilities from enriching payment messages, forecasting cash flow, identifying liquidity gaps, and optimising reconciliation processes.
Crucially, AI helps PSPs cope with the increasing complexity of regulation. From document analysis to compliance reporting, AI can scan thousands of pages in seconds, extracting relevant insights and streamlining manual workflows.
This means the end of endlessly searching through paperwork for small details, which can feel like searching for a needle in a haystack.
Advanced AI models can even write and test code, accelerating product development, reducing time-to-market, and making system updates faster and more cost-effective.
Fraud Protection
In addition to promoting the good, AI in payments also keeps out the bad.
In the fight against fraud, AI can recognise suspicious patterns and use algorithms trained on historical data to flag anomalous transactions.
This monitoring occurs in real-time, allowing for immediate action to prevent losses and incorporating machine learning models to reduce the likelihood of false positives.
Importantly, these checks happen silently and frictionlessly, with no disruption to legitimate users or the payment system.
It’s unsurprising, therefore, that 90% of financial institutions are using AI for fraud prevention strategies.
Striking the Right Balance
However, AI is not a silver bullet for the payments industry, and when poorly implemented, it can be ineffective or introduce new risks.
To avoid this, PSPs should reset their expectations and strategically evaluate the most effective use cases for implementing AI, such as streamlining document analysis or automating repetitive tasks, rather than applying it indiscriminately across all areas of business.
AI implementation should be piloted in high-impact areas like fraud detection, compliance automation, and liquidity forecasting, while ensuring it is coupled with robust governance.
A Catalyst for Progress
As AI continues to make payments faster and safer, payment institutions should look towards fintech for best practices on how to integrate AI into systems strategically and cost-effectively. Many of these smaller firms continue to outperform larger banks with AI innovations thanks to their agility.
Adam Spearing, VP of AI GTM EMEA at ServiceNow, on why those that invest in AI foundations now will shape their operating models on their own terms
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Much of the debate around AI still centres on pilots: which tools to test, which use cases to prioritise, which risks to manage. Executive teams commission proofs of concept, establish governance forums and assess compliance exposure. Far less scrutiny is applied to the consequences of waiting.
Traditional technical debt is familiar territory for CIOs. It stems from shortcuts, ageing platforms and deferred upgrades. It builds over time and is eventually addressed through structured modernisation programmes. Visible in legacy code, brittle integrations and manual workarounds. It appears on risk registers and capital plans. Leaders know how to describe it and, in principle, how to resolve it.
Forward-looking technical debt is different. It arises when organisations postpone the foundational changes needed for new ways of working. It is not created by past expediency, but by present hesitation. And it accumulates faster.
AI Adoption
In the context of AI, the effects are already emerging. Each quarter spent debating readiness instead of building it increases the distance between legacy operating models and AI-enabled competitors. As models improve and user expectations shift, that distance widens, reshaping competitive baselines. What begins as a modest capability gap can harden into structural disadvantage.
While companies debate whether to adopt AI, the margin for strategic choice narrows. Many organisations frame AI adoption as a binary decision: adopt now or wait until the technology matures further. In practice, the room for discretion is smaller than it appears. Time spent stalled in pilots or governance loops increases the gap between internal capability and market expectation.
More than 75% of organisations are expected to face moderate to severe AI-related technical debt in 2026, predicts Forrester. The issue will not simply be missed efficiency gains. It will be structural misalignment between how their systems operate and how work is increasingly done.
This misalignment often appears gradually. Teams rely on manual data preparation because underlying systems cannot support automation. AI tools are layered onto fragmented architectures and deliver inconsistent outputs. Employees experiment with external tools because internal platforms cannot provide the functionality they need. Each workaround creates further fragmentation.
Over time, these patterns compound. Integration backlogs expand. Security and risk teams struggle to enforce consistent controls across proliferating tools. Data governance becomes reactive rather than designed. What began as caution begins to constrain strategic options.
The AI Paradox
Here’s the paradox: organisations are either rushing into unsuccessful AI pilots that create immediate technical debt, or they’re avoiding AI entirely and creating forward-looking debt through inaction. Both paths lead to the same place – systems that can’t support the future of work.
AI isn’t just another technology layer to bolt onto existing infrastructure. It’s fundamentally changing how people interact with systems and how work gets done. Increasingly, AI becomes an interface through which employees access information, execute tasks and navigate processes. When AI becomes the interface – not just for customers but for employees navigating their daily tasks – organisations without AI-ready foundations will find themselves unable to compete on speed, efficiency, or experience.
The companies that hesitate aren’t just missing out on automation benefits today. They’re building a deficit that grows exponentially as AI capabilities advance. Each new model release, each competitor’s successful implementation, each customer expectation shift adds to the debt. Each significant model improvement raises the performance benchmark across the market. Unlike legacy systems that degrade slowly, this gap accelerates.
From Avoidance to Advantage
Breaking free from forward-looking technical debt requires a fundamental mindset shift. This isn’t about buying more technology or launching more AI pilots. It’s about creating the conditions for sustainable AI adoption that builds capability rather than complexity.
The organisations succeeding with AI aren’t the ones with the biggest budgets or the most aggressive rollouts. They’re the ones that took a deliberate, phased approach to ensuring their data, systems, and culture could support AI at scale. They treated readiness as an operational discipline rather than an innovation side project. They understood that AI adoption isn’t a destination, it’s a continuous capability that requires solid foundations.
This starts with honest visibility into current technology estates. Leaders must understand what systems can realistically support AI workloads, where data quality creates barriers, and which processes are ready for automation. Only then can organisations introduce AI incrementally, modernising systems where necessary rather than forcing new capabilities onto brittle foundations. Without that clarity, AI risks being layered onto structural weaknesses.
Modernisation therefore becomes targeted. Consolidating fragmented workflows, standardising data models and reducing unnecessary integration points increase the feasibility of scaling AI across multiple use cases. Early deployments focused on well-defined processes with clear data lineage can build internal confidence while strengthening governance practices.
Clear Debt to Stay Competitive
Forward-looking technical debt does not appear on a balance sheet. It shows up in slower product cycles, manual workarounds, integration backlogs and frustrated employees. It surfaces when competitors deliver AI-assisted services as standard and customers begin to expect the same everywhere. By the time these symptoms are visible, the underlying gap has already widened.
Timing therefore becomes a strategic variable. AI capability builds cumulatively: early investment in clean data, modern workflows and interoperable systems creates a base for continuous improvement. Each iteration becomes easier, faster and more reliable. Those that delay face the opposite trajectory: increasing complexity, rising retrofit costs and shrinking room for strategic choice.
The real issue is not adoption in principle. It is whether leadership teams are prepared to treat readiness as urgent rather than optional.
Reducing forward-looking technical debt requires acting before competitive pressure dictates terms, aligning technology modernisation with operating model reform, and accepting that disciplined progress now is less risky than accelerated catch-up later.
AI adoption will continue irrespective of individual organisational hesitation. Vendors will continue to refine their offerings. Regulators will clarify expectations. Customers and employees will adjust their behaviours. Those that invest in foundations now will shape their operating models on their own terms. Those that delay risk reacting to a competitive gap that is already commercially significant.
Adonis Celestine, Senior Director – Global Automation Practice Lead at Applause, on the rise of AI and why In a world of autonomous systems, trust is the ultimate competitive advantage
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Every generation of technology has its defining disruptor – the force that rises above the rest and reshapes its environment. In the mid-2000s, Marc Andreessen captured the moment when digital systems began transforming entire industries with his famous line: “software is eating the world”. At the time, software was the apex predator of technology, defining how value was created and delivered. Today, that hierarchy has shifted. Artificial Intelligence (AI) has reached the top of the technology food chain. Not just accelerating software, but fundamentally reimagining how it’s created, tested, and deployed.
AI is no longer just a tool; it is a co-creator. Developers now rely on AI daily to translate high-level intentions into working code. A practice sometimes known as ‘vibe coding’. Tasks that once took months can now be delivered in weeks, days, or even minutes. The pace is exhilarating, but it introduces challenges that traditional quality assurance (QA) practices were never designed to meet. And if QA cannot keep up, speed will come at the cost of reliability and trust.
When AI Outpaces QA
Conventional QA depends on predictability. Features are defined, code is written, and test cases verify the expected behaviour. However, AI disrupts this traditional model. Generative and Agentic AI systems don’t simply follow instructions; they interpret them. These systems adapt to context, learn from data, and can produce different outputs from the same prompt, influenced by factors such as training, temperature settings, and the model’s probabilistic nature. With development cycles now measured in minutes, traditional QA handoffs are often impossible.
This has led to a growing gap between speed and certainty. Teams can ship products faster than ever, yet it’s becoming much more difficult to ensure consistent, ethical, or safe behaviour in real-world conditions. Enterprises are already experiencing AI-powered features that fail in ways conventional testing could not anticipate, undermining trust and creating new risks.
Hidden Risks in Autonomous AI Workflows
AI-driven development introduces blind spots that traditional QA often struggles to detect. One key issue is context drift. This occurs when AI performs well in controlled testing environments but behaves unpredictably when faced with edge cases, cultural differences, or ambiguous inputs. For example, a customer-facing chatbot might pass functional tests but produce biased or misleading responses when deployed on a global scale.
Another challenge is compound autonomy. When multiple AI agents are involved in code generation, testing, and deployment, the system may begin to validate its own processes. Without human oversight, errors can propagate unnoticed. An AI agent might ‘approve’ certain behaviours because they statistically align with previous outputs. Rather than meeting user or business expectations.
Invisible change also complicates QA efforts. AI models continuously evolve through processes like retraining, prompt tuning, or data updates. A feature that worked flawlessly last week may function differently today. Traditional regression testing often fails to capture these subtle but significant shifts.
Most critically, AI workflows blur the lines of accountability. When failures occur, it can be unclear whether the issue lies with the model, the data, the prompt, the integration, or the deployment pipeline. QA teams must continuously validate not only the outputs but also the decision-making processes behind them.
Redefining Quality and Trust in an AI World
Slowing AI development is neither practical nor beneficial. Organisations must redefine quality in a probabilistic, AI-driven environment. Quality now extends beyond just correctness. It involves ensuring that systems operate reliably in real-world scenarios. This shift requires moving from static test cases to continuous, adaptive validation.
QA teams must evolve into ‘quality intelligence’ teams, broadening their responsibilities from simply detecting defects to actively fostering trust in AI systems. AI-assisted testing is crucial in this process. It can automatically generate extensive test cases by analysing requirements and code patterns. It can predict defects using machine learning. Detect visual inconsistencies across devices, and produce realistic, privacy-compliant synthetic test data. Additionally, Agentic AI can autonomously maintain and self-heal test scripts, adjusting their logic as underlying code or user interfaces change.
Furthermore, AI systems themselves need rigorous evaluation. Techniques such as red teaming, rainbow teaming, benchmarking, bias and ethics checks, and drift monitoring are essential to help promote AI’s reliability, fairness, and alignment with business objectives.
Human oversight is critical. While AI can scale testing and automate numerous tasks, critical thinking, risk assessment, and judgment cannot be fully delegated. Humans must guide, validate, and refine AI outputs to maintain both quality and trust.
Emerging Roles and Responsibilities
AI is reshaping professional roles. Developers are increasingly using AI by instructing machines through natural language rather than traditional programming methods. This shift has led to the emergence of new roles such as AI agent orchestrators, prompt engineers, QA specialists for autonomous systems, and governance leads who ensure ethical and auditable AI practices.
These roles are essential for maintaining human oversight. Developers and testers must experiment, validate, and continuously refine AI outputs while being cautious not to rely too heavily on AI.
Trust in the Age of the Apex Predator
As with any apex predator, AI has changed the rules of the game. Software once “ate the world” by making systems programmable. Today, AI “eats software” by making it autonomous, capable of creating, modifying, and deploying autonomously. In this new environment, speed is no longer the ultimate measure of success; trust is. Systems may move fast, but without rigorous QA, ethical oversight, and human judgment, they may not be reliable, accurate or ethical.
The new apex predator demands adaptation. Organisations navigating this AI-driven era must embrace automation and innovation, but pair it with strong quality practices, governance, and continual human oversight. Only by combining these elements can companies ensure their AI systems are not only fast and efficient but also dependable and aligned with business objectives. In a world of autonomous systems, trust is the ultimate competitive advantage.
Tom Lanaway is Head of Innovation at Connective3, a global brand & performance marketing agency. He leads a team building AI-powered marketing measurement and marketing intelligence tools.
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Most businesses are asking the wrong question about AI. They’re asking, ‘Which AI tool should we use?’ They should be asking: ‘Can our people actually think with AI?’
I run an innovation team at a marketing agency. We’ve spent the last two years building AI into everything we do, including measurement, content, strategy, and automation. We’ve got lots of tools, 18 different products to be precise.
Below is what I’ve learned. But the tools aren’t always the bottleneck; sometimes the skills are.
The Tennis Racket Problem
A colleague put it perfectly recently: “AI is a tool. Think of it as if you’ve got a smart assistant sat there. But it’s saying, I’m going to give you the best tennis racket, now go and play in a Grand Slam.”
That metaphor stuck with me because it captures something the artificial intelligence hype cycle keeps missing. We’ve convinced ourselves it democratises everything. That anyone can now do anything. That the barrier to entry has collapsed. And there’s truth in that, but it’s incomplete. The barrier to access has collapsed, but the barrier to effectiveness hasn’t. Give someone GPT-4, and they can generate text. Give them the best tennis racket, and they can hit a ball. But the gap between hitting a ball and playing at Wimbledon is still vast. Most organisations are stuck in that gap, wondering why their AI investments aren’t transforming anything.
Three Skills That Aren’t Always Present
When I look at where teams struggle and where I see the same patterns across other businesses, three specific competencies keep showing up as gaps:
1. Problem Decomposition
Not everyone knows how to break down complex work into chunks that AI can help with. This sounds simple, but it isn’t. Most people approach AI with whole tasks such as ‘Write me a marketing strategy’, ‘Analyse this data’ Or ‘Create a campaign’. AI will then produce something, but it’s usually mediocre, because the person hasn’t done the harder work of understanding which specific parts of that task AI is good at, and which parts need human judgment. The skill isn’t using AI; it’s knowing what to give it. Someone who is brilliant at their job but can’t decompose problems will get worse results from AI than someone more junior who understands how to break work into the right pieces.
2. Output Assessment
How do you know if what AI gives you is good? This is where intuition becomes essential and it’s also where the ‘AI replaces expertise’ narrative falls apart. You need domain knowledge to evaluate AI output. You need enough experience to feel when something’s off, even if you can’t immediately articulate why. You need the pattern recognition that comes from years of doing the actual work. Artificial Intelligence doesn’t replace that intuition; it requires it. The best AI users I’ve observed aren’t the most technical; they’re the ones who’ve built up enough expertise in their field to quickly assess whether AI output is useful, directionally correct, or completely off base. They know what good looks like, so they can recognise it when they see it, or notice when it’s missing.
3. Articulation
Can you clearly express what you really want? This is the unglamorous core of the whole thing. Some people struggle to articulate their requirements to other humans, let alone to AI. We’ve all sat in meetings where someone spends 20 minutes explaining what they need, and you’re still not sure what they want. AI makes that problem worse. The skill isn’t ‘prompt engineering’ in the technical sense; it’s the much older skill of clear thinking and clear communication. If you can’t articulate what you want specifically, precisely, with the right context and constraints, you won’t get useful output from AI or from anyone else.
The Uncomfortable Implication
Here’s what this means for how businesses should think about AI investment:
Stop leading with tools: Most organisations have tool fatigue already. Another platform, another integration, another training session on which buttons to click. It’s not working.
Start with the human work: Before asking ‘What AI should we use?’, ask ‘Can our people break down problems, assess output, and articulate requirements?’ If they can’t do those things well without AI, they won’t do them well with AI either.
Invest in the skills, not just the access: This doesn’t mean AI prompt engineering courses; it means developing clearer thinking, better problem decomposition, and sharper articulation. These are old skills, applied to new tools.
Accept that expertise still matters: The people who’ll use AI best are the ones who already know their domain deeply. AI amplifies competence; it doesn’t create it.
Connected Intelligence Isn’t About Connected Systems
I’ve spent a lot of time thinking about how different marketing channels and data sources connect and how you build intelligence across systems rather than in silos.
But I’ve come to think the more important connection isn’t between systems, it’s between human judgment and AI capability. The integration layer that matters most is the one between the person and the tool.
Get that wrong, and it doesn’t matter how sophisticated your AI stack is. Get it right, and even basic tools become powerful.
Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams…
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Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams to identify and respond quickly, whilst also meeting regulatory timeframes for handling complaints and supporting vulnerable customers.
Netcall: AI-Powered Sentiment
The specialist bank has worked with Netcall to deploy AI-powered sentiment analysis using Netcall’s Liberty Create platform. The solution reduces manual effort and improves operational efficiency by bringing customer emails from multiple mailboxes into a single interface. Incoming messages are automatically analysed to identify dissatisfaction, highlighting cases that may require faster intervention. This allows urgent cases to be prioritised, helping HTB to resolve issues before they escalate and improve the customer experience.
“Our AI-powered sentiment analysis solution rapidly processes vast amounts of email data. Its efficiency allows our team to focus on resolving customer enquiries and issues rather than sorting priorities. The streamlined process ensures swifter responses and better customer outcomes, upholding our reputation for exceptional customer service.” Ed Eames, Head of Customer Savings Operations at Hampshire Trust Bank.
The application was built by the Hampshire Trust Bank development team using Liberty Create. It worked closely with Netcall to integrate AI sentiment analysis into existing processes. Customer-facing teams were involved throughout to ensure the solution aligned with established workflows and regulatory requirements.
Customer Service Control
A key benefit of the approach is the level of control it gives internal teams. Keywords, sentiment thresholds, and classifications can be adjusted directly. This allows rapid refinement as customer behaviour changes or new regulatory considerations emerge, without waiting for development cycles.
“Liberty Create has enabled my development team to work with remarkable agility. The ability to rapidly create and refine applications to meet ever-evolving business needs has significantly enhanced our efficiency. This allows us to deliver a wealth of new features to end users and customers with speed. With the integration of AI, we’ve been able to advance our processes while ensuring exceptional customer service. Our Sentiment Analysis application launch is a prime example of this.” Trina Burnett, Head of Engineering at Hampshire Trust Bank.
The sentiment analysis system also supports automated and ad-hoc reporting. This provides a single source of insight into customer interactions and actions taken. This helps reduce manual effort, supports audit and compliance activity, and enables teams to continuously improve customer service operations.
“As scrutiny around customer experience and accountability increases across UK financial services, the ability to listen, adapt and respond at pace is becoming a defining capability for banks seeking to maintain trust and service standards,” said Alex Ballingall, Key Account Manager at Netcall.
“HTB’s approach shows how banks can use AI-driven insight practically. Turning customer communications into faster action without adding operational complexity,” Ballingall concluded.
About Netcall
Netcall is a leading provider of low-code and customer engagement solutions. A UK company quoted on the AIM market of the London Stock Exchange. By enabling customer-facing and IT talent to collaborate, Netcall takes the pain out of big change projects. It helps businesses dramatically improve the customer experience, while lowering costs. Over 600 organisations in financial services, insurance, local government and healthcare use the Netcall Liberty platform to make life easier for the people they serve. Netcall aims to help organisations radically improve customer experience through collaborative CX.
Patrick Cooney, CFO at Version 1, on why, in an AI-driven operating environment, financial discipline is more important than ever
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Over the last decade, digital transformation has become part of the CFO’s remit. As organisations invested in automation, cloud and data platforms, finance leaders were well placed to oversee spend, drive efficiency and ensure technology investments delivered measurable returns. As artificial intelligence (AI) moves from experimentation into the core of how organisations operate, that model is beginning to evolve. Primarily because AI-scale transformation demands a different balance of expertise.
A recent move by Coca-Cola illustrates this shift. The decision to take digital strategy out of CFO John Murphy’s remit and appoint Sedef Salingan Sahin as the company’s first Chief Digital Officer is not a rejection of finance-led transformation. It reflects a practical reality. While strong financial discipline remains essential, the architectural complexity and technical depth required to embed AI across an enterprise now go beyond traditional finance capabilities alone.
In all industries, AI is no longer confined to innovation labs or isolated pilots. It is increasingly embedded in how organisations operate, make decisions and manage risk. At Version 1, our earliest focus on AI was external: helping partners use AI to transform their own businesses. We have quickly turned the lens inward. Over the past quarter, we have accelerated the use of AI across our own finance and operational functions, implementing a wide range of practical use cases that fundamentally change how work gets done.
Some of these are relatively simple but have had a significant impact. Using AI to summarise documents, generate meeting notes or surface insights from large volumes of information has become normal and is already saving time across the organisation. Others are more structural. In finance, we are applying AI to areas such as accounts payable, accounts receivable and general ledger reconciliations, where large datasets and repetitive processes create natural opportunities for automation and acceleration.
We are also rethinking reporting itself. Rather than manually producing variance analyses each month, we are developing standardised prompts that allow AI to highlight key trends, explain deviations from budget and surface insights that would traditionally take hours to compile. These are not abstract efficiencies. Rather, they directly affect the speed, quality and value of financial decision-making.
What is striking though is the pace of change. Even over the past few months, usage has increased exponentially as people find new ways to integrate AI into their daily work. This is no longer an optional experiment. AI is reshaping how organisations function from the inside out.
Modern CFOs Deliver Stewardship and Governance
One of the biggest challenges CFOs face with AI is that traditional ROI models struggle to capture its true impact. Unlike earlier waves of digital transformation, AI does not deliver value solely through cost reduction or headcount optimisation. Increasingly, its value lies in better planning, faster decision-making, improved risk management and higher-quality outputs.
I see this clearly in how we use AI for planning. Recently, we fed a combination of internal data, previous plans and external consultancy material into a large language model and spent time crafting a detailed prompt. The output was a first-pass design for a major simplification programme (including workstreams, resourcing requirements and sequencing) that would previously have taken weeks to develop.
It is worth noting that this new process didn’t replace human judgement – it dramatically accelerated it. We are using similar approaches to shape annual finance priorities, drawing on historic plans and organisational context to generate structured, actionable starting points. This kind of value is real, but it does not always show up neatly in short-term financial metrics.
At the same time, the risks associated with AI are increasing. Model drift, regulatory scrutiny, data security and vendor dependency all carry financial implications. This is why governance matters as much as innovation. At Version 1, we have put formal structures in place, including an AI oversight committee that reviews and approves new tools, ensures appropriate controls are in place and sets clear boundaries around responsible use. We tightly manage which platforms can be used and how data is protected, recognising that public, uncontrolled tools pose unacceptable risks in an enterprise environment.
This combination of accelerating value and growing risk is precisely why ownership models are changing. Many CFOs continue to play a leading role in digital transformation, with research showing that around three-quarters of finance leaders now prioritise digital strategies at the highest levels of the organisation.
People Remain at the Heart of AI Adoption
As AI scales, the CFO’s role is shifting from delivery ownership to strategic stewardship. Finance leaders are uniquely positioned to connect technology ambition with financial reality, ensuring AI investments are governed properly, aligned to business outcomes and measured over time.
This aligns closely with how we think about our own operating model at Version 1. We use what we call a ‘strength in balance’ business model, built around three equally important pillars: customers, people and a strong organisation. That final pillar includes financial performance, risk management, cybersecurity and governance, all areas that become more critical, not less, as AI adoption accelerates.
People are central to this conversation. AI inevitably raises questions about job impact and cost optimisation, and organisations have a responsibility to approach this responsibly. That means clear communication, strong change management and treating people fairly where roles evolve. It also means investing in training and enablement. We have rolled out organisation-wide AI training focused on responsible use, and we are developing a network of AI champions with deeper skills who can identify and build use cases without relying solely on central teams.
The most effective model I see emerging is a shared one. Specialist digital leaders focus on building and embedding AI capabilities at scale. CFOs retain accountability for financial discipline, data governance and value realisation. When these roles work in partnership, organisations are far more likely to capture the value they expect from AI.
Financially Guided Value Delivery
As AI becomes a baseline capability rather than a differentiator, debates about who ‘owns’ digital strategy are becoming less relevant. The more important question is how organisations ensure AI investments deliver measurable, sustainable value. For CFOs, AI is now undeniably a balance sheet issue.
Investment in the latest technology affects cost structures, risk exposure, governance and long-term resilience. Those who engage proactively, shape governance and demand disciplined value creation will help their organisations unlock lasting advantage. Those who remain passive risk inheriting complexity, cost and compliance challenges that are far harder to unwind later.
In an AI-driven operating environment, financial discipline is not diminished. It is more important than ever.
In the world of MedTech, innovation does not happen in isolation. It relies on deeply interconnected digital ecosystems that span research and development, manufacturing, clinical environments and global corporate operations. For Olympus, a global medical technology company with 30,000 employees operating across multiple regions and regulatory environments, cybersecurity has become a foundational enabler of trust, resilience and patient safety.
At the centre of this transformation is Ryan Larsen, Global Head of IT Security at Olympus, whose role sits at the intersection of technology, leadership and mission-driven purpose. His mandate is clear: ensure that Olympus’ global digital and operational environments remain secure, reliable, and able to support innovation at scale.
“In practical terms, I’m responsible for the cyber defence and digital resilience of Olympus as a global MedTech company,” Ryan explains. “That means ensuring our systems, data and people are protected so innovation can move quickly, safely and with trust across R&D, manufacturing and corporate operations worldwide.”
Virginia Farm Bureau: An Enterprise CIO’s Journey
Virginia Farm Bureau is an organisation renowned for resiliency, collaboration, commitment to a greater cause, diversity and service to its members. For outgoing CIO Patrick (Pat) Caine leadership at Virginia Farm Bureau has never been about technology for technology’s sake. After 18 years as CIO, his role evolved into what he describes as that of an “enterprise technology leader,” responsible for supporting a uniquely complex organisation whose mission stretches far beyond insurance or IT.
“I’m responsible for all aspects of enterprise IT,” he explains. “Founded in 1926, Virginia Farm Bureau is a diverse membership organisation with four major business entities and multiple companies that provide agricultural advocacy and related agricultural business support services, healthcare insurance sales and administration, P&C Insurance, and a large entertainment property that hosts the State Fair of Virginia.”
Gowling WLG: Implementing Human Centred AI
When we talk about AI finding its feet within a business, the obvious challenge is change management. How do you ensure your team is on board with the change? What if they have technical questions? How does a business address their fears and concerns? This is where having a people-focused leader and a technology-focused leader forming a united front is incredibly valuable.
Kelly Davis is the Chief People Officer at international law firm Gowling WLG. Al Hounsell is the Senior Director, AI Innovation & Knowledge. Davis has been in HR leadership roles for most of her career. During that time, she has been very intentional about the way she has moved between industries.
Hounsell started his career as an entrepreneur. He then went to business school and law school, before ending up in a large global firm. There, he fell in love with the nascent legal technology ecosystem. He joined Gowling WLG over a year ago. His goal is to reimagine the practice of law by infusing it with technology.
New research from Appian shows strong optimism among public sector workers about artificial intelligence (AI) transforming public services. However, awareness among the public remains limited,…
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New research from Appian shows strong optimism among public sector workers about artificial intelligence (AI) transforming public services. However, awareness among the public remains limited, with 75% of surveyed UK adults aged 18+ (representing approximately 41 million people*) unable to name a single way in which the public sector currently uses AI.
The 2026 UK Public Sector AI Adoption Outlook report surveyed 1,000 public sector workers and 1,000 UK citizens. It reveals a clear divide between those tasked with delivering AI-enabled services and those who use them. While two thirds (67%) of public servants believe it will improve public services over the next five years – rising to 87% among director-level leaders – only 44% of citizens share this optimism. Afigure closely mirrored by workers in administrative roles (40%).
This disconnect could be explained by the way AI is currently being deployed inside government. Nearly half (45%) of initiatives operate as bolt-on experiments or standalone tools rather than being embedded into core service workflows. Many applications remain invisible to citizens – limiting public awareness of where and how artificial intelligence is already in use.
“Too much AI in the public sector is still being used as a personal productivity tool rather than embedded into the processes that actually run services. When AI is treated as a bolt-on experiment or standalone tool, it struggles to deliver meaningful impact – our research shows nearly half of government’s application of AI falls into that trap. If organisations want AI to move beyond pilots and produce real value, it has to be integrated into core processes from the start.”
Peter Corpe, Industry Lead UK Public Sector at Appian
Public Trust in AI Remains Limited
Public trust in responsible AI use remains low across much of government. Fewer than half of UK citizens trust central government (39%) or local government (44%) to use it responsibly – placing government behind retailers (60%), banks (55%) and consumer technology companies (54%). The clear exception is the NHS, which commands a 63% net trust rating, making it the most trusted organisation for AI use across both public and private sectors.
Regarding AI making decisions without human oversight, 67% of public sector workers are comfortable with the technology selecting cases for tax or benefits compliance checks compared with 40% of citizens, while 56% of public sector workers support its use in analysing NHS scans versus 40% of citizens. Concerns about AI also extend beyond individual decisions, with the majority of the public worried about implications around data security and privacy (67%), job losses (63%), auditability of decisions (61%) and ethical oversight and bias (59%).
Fixing Processes Should Come Before Delivering AI at Scale
Inside government, enthusiasm for AI is tempered by concerns about execution. Less than a third (29%) of public sector workers say their organisation or department is delivering on most of its commitments. A similar proportion say they are moving slower than planned (27%), while a quarter (25%) identify a significant gap between AI strategy and delivery.
One year on from the AI Opportunities Action Plan, where the Government allocated £2bn to implement research and resources, the new research findings point to a growing disconnect between strategic ambition and service delivery reality. Nearly 9 in 10 public sector workers (89%) say their organisation is not fully able to leverage AI.
This delivery challenge is widely recognised by both public sector workers and citizens. A majority of public sector workers (55%) and citizens (56%) agree that existing processes must be fixed before new technologies are introduced, prioritising process improvement over deploying new AI tools.
“AI is only as good as the work you give it,” said Corpe. “This research shows strong belief in AI’s potential, but also a clear warning: without fixing the underlying processes first, it will struggle to deliver on its promise. Serious AI is not about experimentation or standalone tools – it’s about applying intelligence to the core processes that keep public services running.”
Different Priorities, Same End Goal
While both citizens and public sector workers agree that existing processes must be fixed as a priority, the research reveals contrasting expectations of what AI should deliver. Citizens want AI investment to deliver faster services (35%), improved public safety and fraud prevention (27%) and easier-to-use digital services (26%).
By contrast, public sector workers are more focused on efficiency gains (47%) and cost savings (41%), highlighting that citizens focus on outcomes they directly experience and public sector workers focus on how those outcomes are delivered.
The 2026 UK Public Sector AI Adoption Outlook was commissioned by Appian and conducted independently by Censuswide. The study surveyed 1,000 UK public sector workers, including 250 director-level respondents or above, and 1,000 UK citizens aged 18+.
Gregory Mostyn, CEO and co-founder of Wexler, on why the era of generalist AI tools is over, and how the future will focus on high-precision AI designed for specific industries
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For decades, the UK’s professional services sector, including areas such as Law, Insurance, and Wealth Management, has argued that its business value is locked in its access to proprietary data and the specialised labour required to navigate it. Investors, lured by the moat of institutional knowledge, priced these companies accordingly. However, the first quarter of 2026 has seen significant AI disruption within the professional services market. The catalyst wasn’t a single event, but rather a move by foundational model providers that turned the industry’s most defensible assets into commodities.
When Anthropic launched its specialised legal AI plugin, OpenAI integrated a real-time insurance underwriting engine directly into its interface, and Alturist Corp automated bespoke tax strategies, the market reacted harshly. As professional services titans such as RELX, MoneySuperMarket, and St James’s Place saw their share prices decline by more than 10% in a matter of hours, the message became clear: the era of treating AI as a ‘future risk’ is over.
The market has been awoken to the fact that foundational AI models are no longer just plugins or nice ‘add-on’ tools; they are competitors. The move by foundation-model providers into professional services – like the legal sector – is not a one-off shock, but rather an inevitability.
The Proliferation of Information
Historically, a law firm’s competitive advantage was its access to information – repositories of case law, proprietary research, and historical contracts. Investors and clients valued these companies on the assumption that this data constituted an impenetrable barrier to competitors. Before AI entered the mainstream, the cost of extracting actionable information from thousands of pages of data required a small army of junior associates and hundreds of billable hours.
In 2026, that moat has mostly evaporated. Recent benchmarks show that frontier models now achieve 80% accuracy on complex documents, compared with the 71% average of a human associate. More importantly, they do it at a fraction of the cost. It is now estimated that the inference cost for a system at the level of GPT-3.5 dropped by more than 280-fold between November 2022 and October 2024. It’s predicted that UK law firms will reduce their chargeable hours by 16% through the implementation of AI.
The narrative that AI would be able to handle only ‘low-level’ tasks, such as NDAs or simple contract summaries, has all but evaporated. Anthropic’s move into high-stakes litigation support validates this trend.
AI – From Swiss Army Knives to Scalpels
An error made by many law firms when AI became entrenched within the market was to treat it as a ‘plug-in’, a nice-to-have built onto existing internal software. Many adopted general-purpose tools, often referred to as ‘Swiss Army knife’ solutions, that covered the breadth of legal work but lacked the precision, jurisdictional nuance, and risk-weighted requirements for high-stakes professional services.
The 2026 market reaction highlighted the needs of a ‘scalpel’ approach – those that go deep in a specialised vertical within a legal workflow. For example, instead of a junior associate spending billable hours searching through case files to establish the facts of a case, they could use a ‘fact intelligence’ platform that can automate that process into minutes, whilst increasing accuracy by 95% versus 78% for human reviewers and up to 90% savings in large-scale litigation. The market is no longer rewarding firms for having information. Rather, it rewards those who can apply it at the lowest possible cost and friction.
Reallocating Capital Across Professional Services
We’re already seeing investors withdrawing from the traditional software market and reallocating that capital into specialised AI firms. However, the risk for legacy players is that they are being disrupted from both ends. From the bottom, they are losing the efficiency game to generalist foundation models from companies such as OpenAI and Google, which are commoditising the ‘knowledge’ aspect of professional services, including basic advice and contract drafting. At the top, they are losing the expertise game to specialised firms that use AI as a precision instrument; their overhead would be lower than that of a traditional Magic Circle firm, allowing them to undercut prices while maintaining profit margins.
The result is a massive reallocation of capital. Investments into vertical AI (AI built for one specific industry) are expected to surge to $115 billion by 2034. The market no longer bets on labour with tools, but on autonomous workflows. Investors have realised that the value lies in the middle layer – the software that sits between a general foundation model and a specific industry’s needs.
Innovation or Obsolescence
So far, the first market fluctuation of 2026 has taught us that you cannot outrun new technologies. To survive, firms must stop treating AI as an add-on and treat it as a foundation for their core business infrastructure.
For UK professional services, the choice is no longer whether to adopt AI, but whether they can evolve quickly enough to avoid becoming the training data for companies building foundational models. The firms that remain in 2030 will recognise that the competitive landscape has changed. You’re not just competing with your peers, but with the compute cycles of the world’s most powerful AI labs.
The era of generalist AI tools is over, and the future will focus on high-precision AI designed for specific industries.
Kevin Janzen, CEO of Gaming & EdTech AI Studio at Globant, on how AI will change the way games are made and expand the market
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Every major games studio is now experimenting with artificial intelligence. From generating NPC dialogue to automating animation and video assets. AI is promising to speed up production and lower costs for developers.
According to Boston Consulting Group (BCG), the gaming industry finds itself at a crossroads…. Looking to gain the momentum it felt between 2017 and 2021, where revenue surged from $131 billion to $211 billion. And AI could be at the forefront of this pivotal moment.
But as AI becomes central to how games are built, studios face a major challenge. Adopting automation without losing authenticity. For developers and retailers alike, this becomes a business concern that deserves close attention. Creativity sits at the heart of gaming, and the choices studios make today will influence what reaches players tomorrow. For the technology channel, this transformation means faster release cycles, broader product diversity, and a need for sharper forecasting.
A New Phase in Gaming’s Evolution
For most of gaming’s history, every era has been defined through visuals. Each generation has delivered stylistic, immersive worlds, such as the blocky charm of Minecraft to the cinematic realism of Red Dead Redemption 2.
Now, the real change is happening behind the scenes. AI is reshaping how games are built and experienced. Development teams are using AI to handle time-consuming tasks such as vast world-building creation and animation. This frees artists to focus on what players remember – the design and storytelling.
Players are already seeing the benefits in their gameplay. AI lets games adapt or adjust difficulty based on players’ skill levels, or change dialogue based on a player’s choices. This makes gaming worlds feel realistic, responsive and more personal.
With budgets continuing to climb for gaming studios, these new features matter. AI gives studios breathing room to experiment. Smaller teams can take creative risks, and established developers can experiment and test new ideas without derailing production. However, efficiency and costs aren’t the only gains as AI is creating space for developers to be more ambitious than ever before.
Automation and Artistry
For all its promise, AI also brings creative risk. Gamers notice when a quest feels repetitive or when dialogue sounds mechanical. And if AI is used carelessly, developers risk losing authenticity.
That sense of care is what keeps players invested. Whether it’s hand drawn detail, or play-driven choices. Games like this show what happens when technology supports vision rather than replacing it.
That’s why the industry’s embrace of AI is such a gamble. Used well, AI can help developers create richer, more personalised worlds. But used carelessly, it risks stripping away the artistry that makes games memorable.
The Ripple Effect Across the Supply Chain
As AI becomes a standard tool, development processes are speeding up and opening new creative possibilities. Independent studios now have access to the kind of production power once limited to major developers. That shift means faster pipelines and ultimately, more games reaching the market.
For retailers and resellers, this brings both opportunity and pressure. A consistent stream of releases can guarantee sales across the year, while lower production costs encourage more niche or experimental games that appeal to new audiences. Greater variety and volume benefits the market, but it also makes it harder to predict which games will break through.
Players are becoming more aware of how games are made and AI’s role in development. They’re starting to ask not only how a game plays, but also how it was built. Understanding the intent behind a studio’s use of AI – one that uses AI as a genuine creative tool and those that rely on it as a shortcut – will help retailers anticipate demand and spot the games with long-term potential.
The Right Way to Play the AI Game
The studios using AI most effectively have a few things in common. They keep AI in the background, using it to manage routine work, such as generating textures and landscapes, so creative teams can focus on narrative and emotional tone.
They also use AI to make experiences more personal. Thoughtful application of adaptive systems allows games to respond to individual play styles, adjusting difficulty and pacing to keep players engaged. This level of design deepens engagement and gives players a sense that the world responds to them personally.
Another area where AI is also making an impact is making games more inclusive. More than 400 million people around the world play with a disability, and new tools are expanding access – from adaptive controls to real-time translation that lets players connect across languages. As gaming becomes more diverse, the audience grows for everyone, including retailers, who can reach a larger, more engaged customer base.
When automation complements gaming artistry, it strengthens the relationship and trust between the developer and the player. Creativity becomes the main focus again, and that’s what keeps players loyal.
Balancing Innovation and Trust
AI is fast becoming integral to how games are conceived, built, and experienced — and that shift will reshape the entire value chain. For developers, success will come from balancing automation with artistry, ensuring that AI enhances creativity rather than replaces it.
For retailers, distributors, and partners, this transformation offers both opportunity and responsibility. A faster, more diverse release pipeline will bring fresh sales potential, but also greater complexity in forecasting and curation. The winners in this new phase of gaming will be those who can spot titles where AI adds genuine depth, inclusivity, and player connection — not just production speed.
Handled thoughtfully, AI won’t just change how games are made, it will expand the market for everyone involved in bringing those experiences to players. That’s a game worth playing for the entire tech channel.
JP Cavanna, Director of Cybersecurity at Six Degrees, on balancing the risks and benefits of AI in cyber defence strategies
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Undeniably, AI is here to stay. Having become part of day-to-day life, it’s hard to remember what life was like without it. But when it comes to cybersecurity, is it causing more harm than good?
Recent research outlines that 73% of organisations have already integrated AI into their security posture. The technology is clearly becoming a cornerstone of modern cybersecurity. Organisations are turning to AI not just as a tool, but as a partner in security operations, leveraging its capabilities to identify malicious activity faster, guide investigations, and automate repetitive tasks.
For it to be truly effective, though, AI must be paired with human expertise – but this is where organisations are starting to become complacent. Given the growing sophistication of cyber-attacks, and even AI-powered attacks, many are removing the human element while expecting AI tools to do all the work for them, leaving them even more vulnerable to threats. This overreliance risks creating blind spots, where critical thinking, contextual understanding, and instinct are overlooked. Without the balance of human judgement, AI can amplify mistakes at scale, turning efficiency into exposure.
The Cybersecurity Paradox
This situation puts many organisations in a potentially difficult position. On the one hand, AI can significantly improve the efficiency of security operations. In the typical SOC, for example, AI technologies can process alerts in around 10-15 minutes. This represents a significant improvement over human analysts, who can easily require twice as long for the same task.
Aside from the obvious efficiency gains, applying AI to these repetitive, time-pressured processes can also significantly reduce the scope for human error. And in turn, take considerable pressure off security analysts. Going some way to battling alert fatigue, an increasingly well-documented and persistent problem. In these circumstances, valuable human experience and specialist expertise can instead be more effectively applied to complex investigations, strategic decision-making, and other higher-value priorities.
On the flipside, however, AI remains prone to generating inaccurate or misleading insights, and users may not realise they are applying the wrong information to potentially serious security issues. Similarly, habitual blind trust in AI outputs can easily erode performance levels and even introduce new vulnerabilities. There is also scope for sensitive data to enter public environments, with the potential to cause compliance issues. This kind of information can also reappear in future versions of the AI model in question, therefore resulting in further data exposure risks.
Parallels with IoT Adoption
The situation mirrors that seen in the early days of IoT adoption, where the rush to innovate would often override security considerations. In this current context, therefore, human oversight and vigilance are extremely important. Clear governance frameworks, defined accountability, and continuous monitoring must underpin any AI deployment. Therefore ensuring that innovation does not outpace risk management or compromise long-term resilience.
A Growing Arms Race
If that wasn’t challenging enough, threat actors are also in on the AI boom in what has already been described as an ‘arms race’. In practical terms, AI tools are already widely used to create more convincing phishing attacks free from some of the more obvious traditional tell-tale signs of criminal intent, such as imperfect grammar or a suspicious tone.
Deepfake technology has also raised the stakes. We’ve all seen how convincing AI-generated video has already become. This is now finding its way into real-world examples, with one fake video reportedly causing a CFO to authorise a large financial transfer as a result.
At the same time, technology infrastructure is constantly under attack by AI-powered tools. They can be used to analyse defensive systems and identify weaknesses faster than humans. The net result of these developments is that defenders constantly play catch-up, as they can only respond to new attack vectors once discovered. The underlying takeaway is that at present, AI cannot be trusted to operate autonomously. Instead, human intuition, scepticism and contextual understanding remain essential to spotting emerging tactics.
As attackers refine their methods at machine speed, organisations need to resist the temptation to match automation with automation alone. They must double down on strategic thinking and continuous skills development.
Balancing Benefits and Risk
So, where does this leave security leaders who are looking to balance the benefits and risks? Firstly, and to underline a fundamental point, while AI offers scale and speed, it cannot replace critical human oversight. Organisations should view AI as an enhancer, not a replacer. Success lies in promoting partnership, not substitution.
Strong governance is vital. This should start with clear AI usage policies that define what can and cannot be shared with AI tools, while proper data classification and access control ensure that sensitive information is protected. In addition, regular validation of AI outputs can help to prevent inaccurate or misleading results from being unnecessarily acted upon.
Then there are the perennial challenges associated with employee awareness training, which is vital for avoiding complacency and understanding the limitations of generative AI tools. Cyber leaders should also monitor how AI is being used inside and outside the corporate environment, as staff often experiment with tools on personal devices.
Get this all right, and security teams can put themselves in a very strong position to embrace AI, safe in the knowledge that they have the guardrails and processes in place to balance innovation and efficiency with effective human-led oversight. Ultimately, success will depend not on how much AI is deployed, but on how intelligently it is governed and refined alongside the people responsible for securing an organisation.
Zach Burks, CEO of Mintology, examines the rise of Artificial General Intelligence (AGI) and explores what the future may hold for cash
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Blockchain was built on the noble principle of creating a system of value that was fair, secure, decentralised, and incorruptible. Crypto promised to protect people from the volatility of human error, from reckless governments, greedy bankers, and the decay of trust that defines our financial institutions.
For a time, it worked. We built code that didn’t lie; we created ledgers that couldn’t be tampered with; and we proved that finance could run on quantitative logic rather than human bias.
But a new kind of intelligence is emerging, one that will allow malicious actors to execute on autopilot and generatively infiltrate innocent users, what will become known as Artificial General Intelligence (AGI).
AGI is still some way off, but predictions suggest it could be in use as early as 2027, or at least propagating outwards without human knowledge at that point. Once in the open world, AGI is impossible to predict, as a chimp could not predict what a human will do next, nor can a human predict what AGI will do. However, assume these possibilities: this technology will have the power to decrypt and unlock blockchain-based currencies, learn how to crack cryptographic puzzles, run other AGI agents and rinse and repeat.
Paradoxically, the safest asset in the world will no longer be Bitcoin; it will be physical currency or items deemed as currency.
The Age of the Codebreaker
It is estimated that 68–74% of all cyber-attacks involve a human element, error, manipulation, or social engineering. Our entire security architecture has been designed around that premise: defend against people.
Smart contracts, encryption, and consensus protocols depend on predictable, rational behaviour, or protect against irrational actions. They are designed to survive attacks from individuals or organisations that rely on either quantity (bot networks) or quality (human intelligence), not both, nor novel vectors (such as novel exploits in math breakthroughs).
A near-sentient system changes that equation. It fuses the scale of automation with the intent of human-like intelligence. If weaponised, it could probe billions of attack vectors in seconds, rewrite its own code to evolve around defences, and destroy a financial system from the inside out.
We’ve seen the first state actor sponsored AI Agentic cyber espionage recently, and that is just from normal AI, not even AGI. Further reinforcing the point that AI is a powerful intelligence, and AGI will be on another level, unfathomable from the human’s perspective.
Crypto’s strength has always been its demand for continuous codebreaking. It exploits the one finite human resource, time. But AGI will erase that constraint. Time ceases to be a defence in the age of autonomy.
The End of Digital Trust
Trust is the foundation of money. Without it, no currency, crypto or fiat can survive. Blockchain gave us a new kind of trust, trust in code and mathematical truth.
We told ourselves that decentralisation would make corruption of the network improbable by humans. But we didn’t anticipate machine corruption, the rise of autonomous systems capable of penetrating those same decentralised defences.
Academic research already shows that generative AI can autonomously discover one-day vulnerabilities. It can exploit them faster than existing patching cycles. Combine that with the commercialisation of state-sponsored scamming. A $1 trillion illicit economy, according to the World Economic Forum’s Global Cybersecurity Outlook 2025. And you have a perfect storm for simple AI, not accounting for what AGI’s intentions may be.
The moment AI becomes self-directing and amoral when neutral, and outright immoral when viewed from a human perspective, but not a binary perspective (in the computer sense), the concept of secure digital value collapses. No wallet is safe if an AGI can learn every exploit in existence before the first patch is written. Or a new mathematical proof that defeats the difficulty of PoW chains like Bitcoin. Or has implanted itself in every device it can reach and simply transfers your assets away like a hacker.
No Wallet, DeFi protocol, or even Blockchain is safe if AGI wants to take a path of gathering financial resources to enact whatever plan it may develop. As AI becomes omnipresent, the irony is that the very technologies designed to control us by centralised power, digital IDs, central-bank digital currencies (CBDCs), and government backed stablecoins, may become vectors of vulnerability.
A Warning for CBDCs
A report conducted by the Department of Homeland Security recently stated that CBDCs can be susceptible to high levels of cybercrime. These include phishing scams and mass exchange rate manipulation. In an era of AGI, the rate at which these vulnerabilities can be exploited becomes tenfold.
When your savings live entirely inside a system that can be hijacked faster than you can blink, society will retreat to the one haven it knows it can trust: physical cash or cash-like equivalents. But honestly, if this happens, there isn’t much of a society left over at that point.
Cash or Bartering Will Be King (Again)
It sounds absurd, the idea that in an era of automated economies, humanoid robots, and algorithmic wealth managers, the safest thing you could own is a paper banknote. Yet that’s exactly where we’re headed if we go down a path of ‘unplugging’. We move off the grid to combat the AGI release, assuming we are still alive to do so at that point.
Cash can’t be hacked or reprogrammed. It doesn’t depend on the uptime of a network or the integrity of a wallet provider. It is the last financial instrument that exists entirely outside the reach of code. Yet in the scenario of AGI going rogue and being released into the world, the most likely scenario I predict is that the markets will see a slight flicker, almost as if a single global hedge fund blew up, or maybe a bit worse… Within minutes, markets around the world will react as assets gathered by the AGI are dumped and transferred for the purpose of AGI.
Although, paradoxically, if the AGI crashes the markets so badly, hacks billions in Bitcoin and sells it, takes over bank accounts, the cascading effect of a global crash on this order, would impart the effect of all its efforts to gather resources moot. So it cannot crash the market spectacularly. If AGI wants to use its resources in some way. If that is its plan, that is. Why pay a human when you can control a humanoid robot?
The lesson is uncomfortable… The more intelligent our systems become, the more valuable it is to hold something that isn’t correlated to the status quo. Hence, cash (assuming the government hasn’t destroyed the value of the currency) and currency-like items via bartering will be the new status quo in this post AGI world.
Can We Stop It?
The survival of blockchain-based finance will depend on merging on-chain verification with off-chain intelligence. AI must be used not just as an optimisation tool but as a shield. An intelligent custodian that monitors for synthetic behaviour, agent-driven manipulation, and abnormal transaction patterns.
Research conducted by Boston Consulting Group proposes autonomous agents, which could be used to detect and counter adversarial machine behaviour in real time. It’s a promising start, but still reactive, not preventative.
To protect digital value, critical financial infrastructure must incorporate hardware kill-switches, air-gapped recovery procedures, and circuit breakers independent of algorithmic consensus.
In a future where AI moves capital faster than humans can think, there must still be something that can say stop, instantly and irrevocably. This is the first path forward, when we are talking about normal AI and agentic AI as we know it today in 2025. We must fight fire with fire, and use AI agents to protect and attack, otherwise we are knights in armour on a battlefield against drones. This is all before AGI is released; then it becomes an arms race (if there is a competitor AGI) for the two to fight it out or join forces, because at that point, humans are only along for the ride.
The New Definition of Wealth
In the AGI era, wealth won’t be measured by what you own, but by what you can protect. Digital capital will remain essential, but it will need a new architecture that assumes non-human adversaries and responds autonomously. Regulation will never be able to move quickly enough to stop AGI, and even if it did, there remains the challenge of understanding training vs intent and rationally policing the difference between the two. The term ‘agentic state’ has never been so poignant.
Cash will therefore – in either local currencies, new currencies, or bartered items – become king again, not for efficiency, but for situational sovereignty. The markets of the future will be defined less by access and more by security, control, and locality.
AGI could one day manage every trade, optimise every yield, and eliminate every inefficiency if aligned for the good of humanity, but if malaligned AGI grows, the technology will become humanity’s own worst enemy.
This dilemma means a changed society, if there is even one left, that in order to operate needs to keep something tangible in its hands, a note, a coin, a battery, a 5.56 caliber bullet, a reminder that security isn’t always a guarantee.
With physical currency, you sometimes let your immediate environment in, with digital money, you invite the internet in, at the speed of beyond trillions of operations a second, faster than a blink of an eye.
About the Author
Zach Burks is an accomplished blockchain developer with over a decade of experience in the Ethereum ecosystem. He has progressed the governing principles of Ethereum first-hand through his collaboration with the Ethereum Foundation on improving the ERC-721 standard, the cornerstone standard for all NFTs, and by authoring ERC-2981, the industry-defining on-chain royalties standard. Zach is also the mastermind behind Gasless Minting, which revolutionized the NFT creation process.
A 2026 survey of nearly 1,000 C-suite executives found that 87% of companies now use AI in their core operations. However, AI errors and…
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A 2026 survey of nearly 1,000 C-suiteexecutives found that 87% of companiesnow use AI in their core operations. However, AI errors and rework continue to cost businesses over $67bn a year
Loopex Digital’s January 2026 analysis identified several common mistakes companies make when relying on AI.
1. Giving AI Too Much Control in HR
AI-led hiringfilters out 38% of top-level candidates before human review because it relies on keyword matching. Candidates respond by adjusting CVs to fit those words, often hiding real experience.
“When we started to use AI in our hiring process, we saw some strong candidates get rejected,” said Maria Harutyunyan, co-founder of Loopex Digital. “Out of 100 applicants, the 2 candidates that would’ve been hired didn’t make it because they used different wording instead of the exact keywords.”
How to fix this: “We simplified our job descriptions, removed buzzwords that didn’t matter, and limited AI to shortlisting. The quality of hires improved immediately,”said Maria.
2. Trusting AI Notes Without Review
AI note-takers often struggle with background noise and poor audio, leading to inaccurate notes. In many cases, up to 70% of summaries focus on side comments rather than decisions.
“We tested 10+ AI note-takers across50 of our regular meetings. Most of the main summaries ended up being jokes and half-finished sentences,” said Maria. “Key decisions were either unclear or missing entirely from the AI summary.”
How to fix this: “We limited AI notes to action points and decisions,” said Maria. “Everything else is filtered out or reviewed manually, cutting note clean-up from half an hour to minutes.”
3. Letting Artificial Intelligence Replace Your Customer Support Team
When customers realise they’re speaking to AI, call abandonment jumps from 4% to 25%. Even when customers stay on the line, AI tools can get policy and pricing details wrong, leading to confusion, complaints, refunds, and extra clean-up work for support teams.
How to fix this: Use AI only for simple FAQs, not complex cases. Define clear escalation rules for cancellations, complaints, and legal issues and route those to a human immediately. Restrict your AI from creative responses in support, only letting it use approved templates.
AccessPay, the leading bank integration provider, has released its Finance Trends 2026 report. It presents the findings of its annual survey of finance…
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AccessPay, the leading bank integration provider, has released its Finance Trends 2026 report. It presents the findings of its annual survey of finance leaders for the fourth consecutive year… AccessPay reveals marked sectoral differences between finance teams in financial services firms and those in corporates with regards to their priorities and attitudes to technology adoption.
Key findings from the report include:
Finance leaders are prioritising finance efficiency and cost control
Finance teams across all sectors are placing renewed emphasis on efficiency and cost control in 2026. 47% of general corporates cited this as a priority, a goal shared by 46% of financial services firms.
Although cost control is a perennial concern in financial management, sluggish economic growth, rising costs, and geopolitical turmoil have brought it to the fore. Finance leaders are being pushed to do more with less, which also means there is greater interest in adopting advanced technologies; 47% of general corporates and 43% of financial services firms stated they were prioritising the adoption of AI within the coming 18 months.
Financial services firms are pulling ahead in finance transformation
In both the financial services (29%) and general corporate (24%) sectors, a leading pack of firms report that their finance function has a high degree of automation and integration across all back-office systems.
Beyond this, there is a stark dichotomy between the financial and non-financial segments. 45% of financial services firms stated they were advanced in their finance transformation efforts, where most finance processes are automated. In comparison, 41% of corporates stated finance transformation efforts were progressing, with partial automation and manual workarounds. This highlights that there are still many quick wins to be realised in the corporate space through simple automation based on bank connectivity.
Insufficient budget is a bigger barrier to AI adoption for corporates
Financial services firms are much more likely to have invested in AI for finance operations than general corporates. 46% of financial services firms report having implemented AI enhancements to a high degree, compared to 28% of corporates.
Both financial and non-financial sectors faced common barriers to AI adoption, including a lack of internal expertise and resistance to cultural change. However, corporates were far more likely to cite insufficient budget as an issue with 31% raising this as a barrier, compared to 17% of financial services firms.
“The disparities between the financial and non-financial sectors in terms of their attitudes towards technology investment are striking,” comments Anish Kapoor, CEO of AccessPay. “Longer-term, the underinvestment in general corporates could backfire. In the current macroeconomic environment, finance teams will need to stress-test plans to ensure they can operate at the low end of their scenarios. This is why we predict 2026 will be a key year for automation in payment and treasury operations. If finance departments are to operate with reduced headcount or scale without increasing staff, leaders also need to consider how to make up that shortfall with technology.”
Download the full report here to learn more about digital transformation in finance operations and how bank connectivity solutions can help automate payments and bank statement data flows.
About AccessPay’s Finance Trends Survey
AccessPay’s Finance Trends 2026 Survey was conducted online during October 2025. The aggregated results are based on 130 respondents from various sectors, including financial services, legal, retail, manufacturing and utilities. Findings for the financial services sector are based on 84 respondents across banking and insurance, while corporate findings are based on 54 respondents. A small proportion of companies is classified in both segments. Typical job titles of respondents include (Deputy) Finance Director, Financial Systems Manager, Head of Treasury, and Head of Managed Services.
Maxio analysis of $40B+ in billings data shows vertical focus and AI innovation driving success, while growth inflection points emerge earlier than expected
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Analysis of $40B+ in billings data shows vertical focus and AI innovation driving success, while growth inflection points emerge earlier than expected
Growth remains strong for B2B SaaS and AI companies, but volatility is high, according to the B2B Growth Report by Maxio, a leading billing automation and revenue management platform. While the market is healthy overall, with the average company growing 18% year over year, more than 35% of companies experienced a decline, revealing an industry where growth increasingly depends on focus, discipline and execution rather than market momentum alone.
The report analyzed over $40 billion in billings data across 2,000+ companies from 2024-2025, revealing unexpected patterns in how growth varies by company size, business model, investment backing, and approach to AI. The findings challenge conventional assumptions about scaling thresholds, the universal benefits of AI adoption, and the predictability of growth trajectories.
“Growth didn’t disappear in 2025; it became harder to earn,” said Alan Taylor, Chief Operating Officer at Maxio. “The winners weren’t chasing every trend. Whether AI-native or traditional SaaS, the top performers stayed focused on solving real customer problems.”
Key Report Findings:
Growth is still the norm, but it’s not universal: Average company growth reached 18%, while aggregate market growth was closer to 13%, reflecting slower expansion among larger, more mature businesses. Nearly two-thirds of companies grew year over year, yet more than one-third declined. Down years remain common across all revenue bands.
Growth slows earlier than expected: The data revealed inflection points at around $5 million in billings with another slowdown beyond $25 million, not the typical $1 million, $10 million or $50 million marks, showing the operational challenges of scaling.
Vertical focus outperforms horizontal scale: Vertically focused companies grew faster than horizontal peers (20% vs 16%), reinforcing the value of specialization in competitive markets.
Capital helps, but doesn’t guarantee faster growth: Bootstrapped companies nearly matched VC-backed growth (20% vs. 22%), though scale differed dramatically with VC-funded companies nearly 4x larger. Private equity-backed companies focused more on profitability, growing 13% on average while skewing significantly larger than other cohorts.
AI accelerates, but only at the core: Truly AI-led companies, with AI central to product and positioning, grew fastest at 21%. However, AI-enhanced companies lagged at 16%, while non-AI companies quietly outperformed at 19%. This pattern suggests that AI adoption alone does not guarantee impact—AI implementation without clear value differentiation may not translate into competitive advantage.
“Average growth numbers only tell part of the story,” said Ray Rike, founder and CEO at Benchmarkit. “What stood out is how early growth friction shows up. Teams that identify where and why growth is accelerating will be best positioned to focus their resources on the market segments that provide faster growth.”
2026 Outlook
Despite a more competitive and complex environment, industry optimism is back and strong. Seventy-two percent of companies expect to grow faster in 2026 than 2025. However, leaders are entering the year with more measured expectations around buyer scrutiny, competition and the need for operational efficiency.
Sustainable growth is built, not assumed, the report found. Companies that understand their true growth levers, invest with intent, and maintain discipline as they scale will be best positioned to win in 2026.
Maxio is the billing and financial reporting platform trusted by over 2,000 SaaS, AI and subscription businesses worldwide. With $18B+ in billings under management, Maxio empowers finance teams to scale recurring revenue, automate quote-to-cash and deliver the insights needed to grow confidently.
Exiger’s new tool will help fight against modern slavery in the supply chain
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Exiger has announced the launch of its no cost, open access website that allows global citizens, companies, governments, and NGOs to review whether a company or its supply chain is linked to state-sponsored forced labour. forcedlabor.ai empowers all companies, regardless of the size of their compliance budget, to make better decisions about who they do business with and ensure that their supply chain isn’t unknowingly profiting from modern slavery.
“Modern slavery is a blight on humanity,” said Exiger CEO Brandon Daniels. “An estimated 50 million people are trapped in modern slavery, many of whom are hidden in opaque supply chains. As part of our mission to make the world a safe and transparent place to succeed, Exiger has decided to make the world’s largest dataset on companies connected to state-sponsored forced labour available to everyone. Hundreds of thousands of companies and millions of global supply chains are impacted.”
forcedlabor.ai lets companies, citizens, government agencies, law enforcement, NGOs, and civil society enter the name of supplier or company to immediately see potential forced labour connections in their supply chains. Powered by Exiger’s proprietary AI capabilities, results are evidenced-based and actionable. forcedlabor.ai will cover a growing scope and currently encompasses People’s Republic of China (PRC) state-sponsored forced labour, Uyghur Forced Labor Prevention Act (UFLPA) risks and US Customs and Border Protection (CBP) Withhold Release Orders (WRO) exposure across virtually every industry, including retail, automotive, industrials, consumer goods and electronics, and agricultural products.
“The CCP is responsible for one of the gravest human atrocities in recent history: the genocide of Uyghurs,” said Representative John Moolenaar, Chairman of the House Select Committee on the Strategic Competition Between the US and the Chinese Communist Party, on the launch of forcedlabor.ai. “Corporate actors must be open-eyed and take action to avoid complicity in this abuse and billions of dollars in global supply chains that rely on the CCP’s persecution of the Uyghurs.”
“When our global non-profit, which helps organisations build their resilience to modern slavery and labour exploitation, was looking for a technology to provide supply chain visibility, we reviewed over 400 platforms, and Exiger was heads and shoulders above all of the others,” said Slave-Free Alliance CEO Tim Nelson. “They’ve built the world’s largest forced labour risk database with some 20 billion records, and now they’re making incredibly valuable data available to everyone, creating a level of baseline transparency never before possible.”
The tool was developed with input from human rights and supply chain specialists including Kit Conklin, Exiger SVP of Risk & Compliance, Atlantic Council Nonresident Senior Fellow and former US House Select Committee on China Senior Advisor, as well as Dr. Laura Murphy, one of the world’s foremost experts on forced labour, Professor of Human Rights and Contemporary Slavery at the Helena Kennedy Centre for International Justice at Sheffield Hallam University, Senior Associate in the Human Rights Initiative at CSIS, and former Department of Homeland Security (DHS) Senior Policy Advisor who led the UFLPA Entity List team.
“This is a revolutionary, first-of-its-kind platform that makes regulator-grade forced labor risk intelligence accessible to everyone,” said Conklin. “The scale and universal availability of this data, powered by AI, represents a new era in forced labor transparency.”
Exiger is launching forcedlabor.ai at WEF’s 2026 Annual Meeting at Davos. Exiger CEO and WEF Governor Brandon Daniels will discuss how AI and supply chain intelligence, including forced labour data, are reshaping the industrial, economic and defence landscape alongside private sector CEOs and government officials at the USA House. The first session, Boardroom to Battlefield: Winning the AI Tempo War for Economic and National Security, is at 12:15PM CET on Wednesday, January 21, and the second, From Enforcement to Advantage: The Integrated Trade Strategy Powering America’s Industrial Revival is at 2:15PM CET on Thursday, January 22. Sessions will be livestreamed. For details on all of Exiger appearances and activations at Davos, visit https://www.exiger.com/davos2026.
Some Europe & Middle East CIOs anticipate up to 178% ROI on AI investments, with further efficiencies expected as Agentic AI scales
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Enterprises have moved decisively from AI pilots to scaled implementations, driven by proven benefits and expectations of significant financial returns, according to the Lenovo Europe & Middle East CIO Playbook 2026 with research insights by IDC. Nearly half (46%) of AI proof-of-concepts have already progressed into production, with organisations projecting average returns of $2.78 for every dollar invested.
The 2026 Lenovo CIO Playbook: The Race for Enterprise AI, draws on insights from 800 IT and business decision makers in Europe and the Middle East. It captures a regional inflection point and reinforces the value proposition for enterprise AI as both real and immediate. It calls on CIOs to act now to avoid lagging competitors. The research marks a clear shift from AI experimentation to measurable value creation, with nearly all (93%) of those surveyed planning to increase AI investments in the next 12 months. At an average spending growth rate of 10%, and 94% anticipating positive returns.
Enterprise AI Adoption in Europe and the Middle East
AI is now recognised as a core engine of business reinvention and competitive advantage. However, AI adoption in the markets is progressing at different speeds. Reflecting varying levels of digital maturity, regulatory readiness, and investment capacity, and there is a clear overconfidence problem among CIOs. While 57% of organisations in Europe and the Middle East are approaching or already in late-stage AI adoption, only 27% have a comprehensive AI governance framework. Further limitations in data quality, in-house expertise, integration complexity, and organisational alignment are causing a mismatch between ambition and readiness.
With Agentic AI overtaking Generative AI as the top priority for CIOs in 2026, these factors will prevent many organisations from fully capitalising on AI’s potential, leaving significant returns unrealised. Moreover, 65% of organisations are focused on scaling Agentic AI across their operations within 12 months, but only 16% report significant usage today, with the majority still piloting or actively exploring use cases.
More advanced markets such as Scandinavia, Italy, and the UK are moving beyond pilots, with a majority of organisations already systematically adopting AI and increasing focus on hybrid and edge deployments to support scale. In contrast, parts of Southern and Eastern Europe remain earlier in their AI journeys, with a higher proportion of organisations still in planning or early development stages. Meanwhile, the Middle East is emerging as a fast-moving growth market, showing strong adoption momentum and a sharp year-on-year increase in interest in advanced and Agentic AI.
Across the region, hybrid deployment models dominate as organisations balance innovation with data sovereignty and operational control. While interest in Agentic AI is accelerating. This signals a broader shift from experimentation toward more autonomous, production-ready AI use cases, even as readiness levels continue to vary by market.
“We’re now seeing clear returns from the AI pilots and proof-of-concepts organizations have invested in, with AI delivering measurable impact across the region. But many are not fully equipped with the skills, governance and readiness needed to scale AI to its full potential. As priorities shift toward Agentic AI, and compliance with regulation such as the EU AI Act becomes imperative, trust and scale must be built in from the start. Those who don’t, risk leaving tangible returns on the table.”
Matt Dobrodziej, President of Europe, Lenovo
Hybrid AI Now Preferred Enterprise Architecture
The research shows that real-world business and financial considerations are accelerating the shift toward hybrid AI. Factors such as data privacy, advanced security requirements, and the need to customise and optimise infrastructure are driving adoption of this model, which blends public cloud, private cloud, and on-premises compute. Nearly three out of five (58%) organisations now prefer hybrid as their primary AI deployment model.
Scalable, high-performing AI infrastructure is a critical enabler of enterprise AI success. Respondents in the region highlighted the importance of compute that is both cost- and energy-efficient. This factor ranked second overall, with many identifying it as key to moving AI from pilots into reliable production.
With AI PCs and edge endpoints central to an effective Hybrid AI strategy and securely running AI workloads locally, deploying AI-capable devices has emerged as the top IT investment priority for 2026.
“CIOs across the region are entering a decisive phase of AI adoption where agentic AI and enterprise-scale inferencing are moving from experimentation to core business priorities,” said Dobrodziej. “To unlock real value, organisations need strong foundations, including secure, energy-efficient infrastructure, flexible hybrid architectures, and AI-capable devices and edge endpoints that bring inference closer to where data is created, and work happens. When combined with the right governance and services, this end-to-end approach enables enterprises to innovate confidently, responsibly, and at scale.”
Lenovo recently introduced Lenovo Agentic AI, a full-lifecycle enterprise solution for creating, deploying, and managing AI agents, alongside Lenovo xIQ, a suite of AI-native platforms designed to simplify and operationalise AI across the enterprise. Built on the Lenovo Hybrid AI Advantage™, these offerings combine hybrid infrastructure, platforms, and services to address governance, integration, and performance from day one. Supported by the Lenovo AI Library of proven use cases, CIOs can reduce risk, accelerate time-to-value, and scale AI initiatives with greater confidence as they move beyond experimentation.
To further enable real-world deployment, Lenovo ThinkSystem and ThinkEdge inferencing servers help enterprises turn trained models into production-ready, low-latency AI applications across data center, cloud, and edge environments. By enabling faster, more efficient inference at scale, Lenovo helps CIOs bridge the gap between AI ambition and day-to-day business impact.
Building on this end-to-end AI foundation, Lenovo’s Smarter AI for All vision is focused on bringing AI to more people and businesses at scale, from enterprise infrastructure to AI PCs that deliver intelligent, personalised experiences directly to users. As outlined at Lenovo Tech World at CES 2026, Lenovo is advancing this vision across its AI PC and smartphone portfolio, with Lenovo and Motorola Qira representing one example of how personal AI can enhance productivity by understanding context across devices and helping users get things done.
Learn more about how enterprises can accelerate AI adoption with the right infrastructure, governance, and partnerships:Explore the full 2026 CIO Playbook report.
About the CIO Playbook Study
This is the third year of surveying CIOs in Europe and the Middle East, with Lenovo commissioning IDC which conducted research between 16th September 2025 and 17th October 2025. This year’s report draws on insights from 800 IT and business decision makers in Europe and the Middle East. Industries represented include: BFSI, Retail, Manufacturing, Telco/CSP, Healthcare, Government, Education and others.
About Lenovo
Lenovo is a US$69 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY). To find out more visit https://www.lenovo.com, and read about the latest news via ourStoryHub.
Dr Antoni Vidiella, CSO of Financial Services at Globant, on why the next stage of AI in financial services depends on modernising the legacy systems that still underpin banking and FinTech
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Many financial service institutions are now moving beyond simple automation and exploring how to embed artificial intelligence across every layer of their operations, from payments and compliance to customer engagement. As banks and FinTechs continue this shift, the sector is entering a new phase in which real-time intelligence, connected data and adaptive systems will define competitiveness.
Yet unlocking this value requires far more than the introduction of new AI tools. To turn data into meaningful business intelligence and to enable new growth models in digital finance, financial institutions must modernise the systems at their core. Without strong foundations, AI cannot scale effectively or operate in a responsible, transparent or secure way. The potential may be vast, but the path to achieving it begins with the fundamentals.
The Challenge of Legacy Systems
Like many other industries, financial institutions still rely on architectures that were built decades ago. These systems continue to support essential functions such as payment processing and risk modelling, yet their rigidity and fragmentation severely limit the potential of AI. Information remains scattered across mainframes, cloud platforms and on-premises databases. As a result, the data required to train and operate modern AI systems is often incomplete, inconsistent or inaccessible in real time.
This fragmentation reflects a deeper structural issue. Many core banking systems were designed around periodic or batch processing. Fraud detection, credit assessment and compliance monitoring therefore remain reactive, even as customer expectations shift toward instantaneous experiences. The consequence is a widening gap between what AI can theoretically deliver and what institutions can achieve with the infrastructure they currently have.
The scale of adoption shows how urgent this challenge has become. A 2024 study by the Bank of England and the Financial Conduct Authority found that 75 percent of UK financial services firms already use AI, with a further 10 percent planning adoption within the next three years. Yet research in 2025 by Lloyds Banking Group indicates that while institutions are beginning to see gains in productivity and customer experience, many acknowledge that their underlying systems are not ready for the next stage of AI maturity. The ambition is there, but the technical foundations remain uneven.
Modernisation as the Foundation for Scalable, Trustworthy AI
Modernisation represents the most significant step institutions can take to prepare for the intelligent financial systems of the future. Moving to cloud-native architectures, adopting microservices and improving data quality all make it possible to activate AI across an organisation rather than in isolated pilots. These shifts also make the resulting systems more secure, more transparent and easier to govern.
Importantly, modernisation is no longer the slow, resource-intensive process it once was. AI-assisted approaches have transformed what is possible. Automated code analysis, conversion and validation can reduce modernisation timelines dramatically. In one example, more than 11,000 lines of legacy COBOL code were migrated to modern Java services in only 105 hours, a task that would traditionally have taken several months. These advances illustrate how quickly institutions can begin creating the environments required for real-time intelligence.
The global opportunity reinforces the need for speed. AI adoption in banking is accelerating rapidly, with institutions racing to modernise their systems and unlock new operational efficiencies. Those that move first will capture the earliest benefits and operate with a level of agility that older architectures simply cannot match.
How Intelligence is Reshaping Payments and Embedded Finance
Payments provide a clear view of how AI is transforming the financial landscape. As digital transactions grow in both scale and complexity, the industry needs systems that can act instantly and intelligently. AI models can analyse behavioural patterns in real time, reducing false positives in fraud detection and strengthening overall resilience. They can also optimise transaction routing, identifying the most efficient or cost-effective paths in ways legacy systems are not equipped to handle.
These shifts extend beyond payments. Embedded finance is becoming a central feature of retail, mobility, insurance and platform-based services. As the ecosystem expands, it will rely heavily on AI to offer tailored credit decisions, contextual payments and adaptive insurance coverage. These capabilities require unified, real-time data environments that can only be delivered through modernised core systems. Without this foundation, the benefits of intelligent payments remain out of reach.
The Essential Role of Responsible Innovation
As AI takes on a larger role in high-impact financial decisions, responsible innovation becomes a defining priority. Trust must be maintained at every stage of the customer journey. Findings from the Bank of England and the FCA show that 55 percent of AI systems in UK finance involve some form of automated decision-making, though very few operate without human oversight. This balance reflects a clear need for systems that are transparent, explainable and accountable.
Responsible AI requires more than good intentions. It depends on strong governance frameworks, rigorous monitoring for bias and clear visibility into how decisions are made. It also relies on consistent, well-managed data. Modern cloud-enabled infrastructures make these practices more achievable, allowing institutions to meet regulatory expectations while building customer confidence. Legacy systems, by contrast, make responsible innovation significantly harder to sustain because they lack the transparency and control required for effective oversight.
How GenAI is Reshaping Operations and Customer Experience
Generative AI expands the possibilities for transformation even further. In customer engagement, GenAI enables natural, personalised interactions that respond to customer needs in real time. It can simplify onboarding, deliver proactive financial insights and support customers throughout complex journeys without compromising clarity or accuracy.
Within operations, GenAI reduces the administrative burden that regulatory compliance often creates. It can summarise complex legislation, draft documentation and support audit processes far more efficiently than manual methods. In product development, it helps institutions test new ideas, model risk scenarios and understand customer behaviour more quickly, reducing time to market and increasing innovation capacity.
However, all these capabilities rely on a consistent and reliable data environment. GenAI cannot deliver meaningful insights if the data underpinning it remains fragmented or outdated. The quality of the output will always reflect the quality of the foundations beneath it.
Building a Resilient Path to Long-Term Innovation
Modernisation is frequently described as a technical necessity, yet its impact is far more strategic. Institutions that invest now will be better equipped to integrate new technologies, respond to regulatory changes and develop AI-enabled products with greater precision. They will also be better positioned to enhance the customer experience, which increasingly depends on real-time intelligence and personalised insight.
Most importantly, modernisation elevates human expertise rather than replacing it. AI supports judgement, strengthens decision-making and frees teams from manual tasks, allowing them to focus on the relationship-building and strategic insight that define successful financial services.
Creating the Intelligent Financial Institution of the Future
Financial services are entering a new era shaped by real-time intelligence, interconnected digital journeys and deeply personalised experiences. Achieving this vision requires modern, resilient systems that can support advanced AI and GenAI. Institutions that begin modernising now will lead the next decade of innovation and create financial ecosystems that are more adaptive, more secure and more connected than ever before. The future is intelligent, but it can only be built on strong foundations.
Emily Nash-Walker, Sr Director of Product Strategy at Tungsten Automation on finding real value for AI across financial services
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The Bank of England has recently sounded the alarm of a potential AI bubble looming. Experts are calling out clear parallels with the dot-com boom, such as over expectations on the tech, huge investment, and limited returns or focus on value addition. In the financial services sector, where innovation and risk are no strangers, the Bank of England’s warning couldn’t be more relevant.
Since the launch of ChatGPT, financial services and FinTech firms have dedicated unprecedented time and money to AI. From LLMs to predictive analysis and AI Agents. However, underneath the rapid adoption we see, there is rising tension between experimentation and governance.
Shadow AI
Many FinTechs and traditional financial services firms are now working on “shadow AI” (internal systems developed without formal oversight, transparency, or risk management), creating a sort of AI “grey market”. This new market offers huge innovation, but without being managed properly, it undermines key governance, and in the fintech space, this means risking consumer data, consumer confidence, and ultimately trust. If left unchecked, this could trigger the industry’s next big credibility crisis and expose them to the next big financial crisis.
AI Overextension
AI can have huge transformative effects on financial services and is at the forefront of changing the industry for the better. From fraud detection to customer service automation, there’s no doubt that AI has changed how institutions engage, analyse, and operate for the better.
But the industry’s eagerness to innovate quickly has led to a familiar problem: overextension. According to MIT research, 95% of GenAI pilots never reach production. Meanwhile, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year if they are implemented effectively. But that is a big “if”.
Right now, too many organisations are focused on experimentation in isolation, often in siloed AI labs. Where AI tools are being built by small internal teams without full visibility or awareness from compliance or IT departments. Algorithms are being trained on partial or poor-quality data. And models are being deployed without clear documentation of how they make decisions. More than 81% of financial compliance experts are concerned about the accountability and explainability of AI-driven decisions. Fundamentally lacking the accountability and explainability that should underpin AI that drives real, low-risk value for businesses.
Dangers of the AI Bubble
If the AI bubble bursts, it won’t be because of the technology. It will be because of how it’s being applied. And the more experiments an organisation invests in without real value being shown, the more they will be exposed to the effects when it pops.
As the bubble grows, so does “Shadow AI”. The pursuit of innovation across sectors leads to siloed teams investing quickly but often without the right guardrails.
Shadow AI shows many similarities to the early days of the cloud era, when employees adopted unsanctioned tools to move faster than IT could keep up, leaving organisations fragmented and exposed to risk. Innovation is as essential or even more essential than it has ever been, but this idea of fragmentation is also more of a risk now than it has ever been.
In financial services, the implications are far more serious than in most industries. Consider the risks if a credit-scoring model built without audit trails begins making biased decisions. Or if a KYC automation tool fails to detect a sanctions breach because it’s running on unvalidated data. And banks built on shadow AI lack the explainability to know, let alone test or assure these models.
AI Governance
FinTech success depends on reliability, transparency, and data integrity. Once those foundations erode, rebuilding them becomes far harder than any technical fix. The solution isn’t to slow down innovation. It’s to govern it properly.
The whole industry needs to move beyond AI experimentation toward governed automation. Integrating AI responsibly into existing workflows, supported by clear oversight, robust data management, and explainable outcomes, has to be the priority.
Smart businesses are focused on AI for the right reasons. It means focusing on what’s needed, practical and measurable instead of chasing ideas of what you could potentially do. Organisations need to be aware of the hype and focus on systems that deliver compliance, accuracy, and ROI.
Financial services have always had challengers in the sector pushing boundaries with new tech, and this has never been so true. It’s an industry that has always spent a lot of time focused on hype. But this next phase of innovation, specifically AI adoption, will see winners prioritising something different. Patience, precision, and accountability will win over efficiency, new features, and speed.
Heeding the Warnings
As the Bank of England has warned, overinvestment and complacency when it comes to defining and reporting concrete value may be creating a big bubble primed to pop. To prevent or limit exposure, leaders should ask three business-critical questions before plunging more investment into AI:
What business problem are we solving?
Is our data structured, accurate, and governed?
Can we measure the outcome and explain the result?
If the answer to any of these is uncertain, the risk is also uncertain. The danger with shadow AI is that often the answer to all 3 is opaque and unclear. AI’s potential in financial services remains enormous. But true intelligence doesn’t come from the newest model or the biggest dataset. It comes from disciplined execution.
When the hype fades, the organisations that endure will be those that integrate AI responsibly, manage data intelligently, and put compliance at the core of innovation.
As with the dotcom boom and many other technological revolutions, the question isn’t whether AI will reshape the sector; it’s who will still be standing when the dust settles. The difference will come down to who governs their AI with a focus on real value versus those who chase experimental AI without true accountability.
Marko Katavic, Director of AI and Decision Intelligence at Moneybox, argues the future of financial services should not aim to replace bureaucratic safety systems with AI, but instead integrate AI to deliver human-level accessibility
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Trust is the foundation and the currency of the financial services industry. When customers hand over their hard earned money, they trust in their chosen provider’s ability to safeguard their finances and help achieve their financial goals.
Long before computers came about, the financial services industry built trust and minimised risk through carefully organised processes led by people. A significant amount of bureaucracy, process control and mapping has reduced mistakes for decades. However, as technology has developed, the way the industry interacts with these processes is changing.
The Rise of Bureaucracy and Software
The introduction of computers enabled the financial services industry to scale processes, increase productivity and widen customer pools. This was achieved through structured software mapped to closed deterministic and bureaucratic processes that allowed the industry to reduce errors and increase efficiency by applying the same structured decision-making to lots of customers automatically, rather than having humans make decisions for each individual customer.
Now we face the rising popularity of AI agents, and effectively integrating these entities into the sensitive systems that were built before them. When applied correctly, they offer immense value, but applied incorrectly, and they risk causing immense harm.
As we are at the relative start of the AI implementation journey, it is crucial to determine how we take AI tools with such significant decision making capabilities, and safely plug them into our systems now to maintain trust, and more importantly so that they help customers, rather than hinder.
The Missing Human Layer
The key to successful AI implementation in the financial services industry is to understand the market gap it can fill. For the last four decades, scaling financial services safely has only been achieved with many layers of bureaucracy – slowing delivery, adding friction, and ultimately limiting who could be served. Furthermore, the human experts who could navigate these bureaucratic complexities and translate it into clear, accessible decisions for customers were few and far between.
This gap is what modern AI systems can close. AI can act as an intelligent layer in front of the bureaucracy, to help the wider public make smart financial decisions with greater confidence. We must learn from the success of large AI systems, as their approachability and ease of use is what draws customers in at scale.
However, for AI to fulfill this promise, it must meet the same standards of institutional safety and compliance. This ease of use must be brought to customers safely, meaning we must engineer the very same systems of safety that currently underpin the financial sector, ensuring AI offers accessibility without compromising on trust.
Engineering Safe Boundaries
To achieve this, we have to go beyond integration – we have to engineer clear boundaries between AI and traditional software. We must use AI to deliver an accessible, relatable customer experience, while ensuring it follows the principles built into tested software. This approach is critical because good outcomes only come as a result of managed risk and tested judgement.
There is significant hype around feeding agents large knowledge bases of policies via Retrieval-Augmented Generation (RAG). While using state-of-the-art models can achieve reasonable, but not perfect, policy concordance for judgement tasks – if the aim is to deliver full flexibility of human interaction to customers at scale, then this protocol is only acceptable for basic customer service, such as issue handling. It falls short when it comes to dealing with the diverse approaches and behaviours customers bring – meaning that errors can only be minimised, not entirely controlled.
When dealing with nuanced considerations such as investment decisions and judgements that have long-standing consequences, it is better to implement software layers that are interactive with AI for logic checking and generating results, rather than trying to emulate complex decision making principles through predictive language.
A Recipe for Success
Modern AI systems, even when producing the right answer 95% of the time, are making decisions on ‘instinct’. No financial firm would implement a workforce of highly instinctual individuals making critical decisions without bureaucratic control. Therefore, putting AI on the path to make financial decisions without the tried-and-tested software to control logical reasoning is a path to failure.
The recipe for success in a customer-facing context is clear. Providers should use AI to mimic everyday language and bring a personal dimension to customers at scale, but keep core financial decision-making within the safe domain of tried and tested software and experts.
While this may sound simple on paper, achieving a seamless system where everything blends together is the core differentiator between companies that will win customer confidence, and companies that will simply offer ‘cool ‘short-term gimmicks. To close the advice gap, the future of financial services should not aim to replace bureaucratic safety systems with AI, but instead integrate AI to deliver human-level accessibility – while keeping decisioning limited to the domain of purpose-built software.
Plumery’s AI fabric is future-proofed and designed for use cases beyond today’s horizon
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Plumery, a digital banking development platform for customer-centric banking, has released AI Fabric. It creates an artificial intelligence (AI)-ready foundation for AI-assisted digital banking.
AI-Ready Digital Banking
Based on an event-driven data mesh, the new solution gives financial institutions a standardised way to connect AI and generative AI (GenAI) models/agents to banking data. Eliminating the need for bespoke system integrations. AI Fabric moves institutions away from brittle point-to-point architectures towards an event-driven, API-first architecture that scales with innovation.
Most financial institutions struggle to operationalise AI because their data is fragmented across legacy cores, channels, and point-to-point integrations. Each new AI pilot can require fresh plumbing, security reviews, and governance work, which delays time-to-value and increases risk. In addition, under increasing regulatory pressure, institutions are required to explain, audit, and govern AI decisions. Together, these factors make ad-hoc approaches to AI difficult to scale.
AI Fabric
Plumery’s AI Fabric enables institutions to plug in and swap AI capabilities as the ecosystem evolves. It exposes high-quality, domain-oriented banking events and data streams in a consistent, governed, and reusable way. This works across products, channels, and customer journeys. Importantly, the platform separates systems of record from systems of engagement and intelligence. Offering financial institution long-term agility instead of short-lived AI experiments.
By reducing point-to-point integrations and one-off data pipelines, an institution can lessen operational complexity and technical debt. This makes change cheaper, safer, and more predictable. Additionally, having clear data lineage, ownership, and control makes it easier to explain decisions, manage model risk, and satisfy regulators – reducing compliance friction as AI adoption grows.
“Financial institutions are clear about what they need from AI. They want real production use cases that improve customer experience and operations, but they will not compromise on governance, security, or control. Our AI Fabric gives them a standard, bank-grade way to allow AI use within their tools and data without rebuilding integrations for every model. The event-driven data mesh architecture improves the process by changing how banking data is produced, shared, and consumed, rather than adding another AI layer on top of fragmented systems.”
Ben Goldin, Founder and CEO of Plumery
Why Financial Institutions need an AI Foundation
In today’s fast-changing world, financial institutions need an AI foundation that absorbs change instead of amplifying it. With AI Fabric, institutions can experiment, deploy, and evolve AI-assisted use cases incrementally without re-architecting every time a model, vendor, or requirement changes.
Additionally, operational, customer, and risk decisions can be powered by live banking events rather than delayed, batch-based snapshots. This enables AI to assist where it matters most: in-journey, in-context, and in-the-moment.
Even financial institutions not yet ready to operationalise AI can lay the groundwork today with AI Fabric, ensuring they can move quickly and safely when priorities, budgets, or markets shift.
About Plumery
Headquartered in the Netherlands, Plumery’s mission is to empower financial institutions worldwide, regardless of size, to craft distinctive, contemporary, and customer-centric mobile and web experiences.
Plumery operates with a diverse team that embodies a unique combination of seasoned expertise and vibrant innovation. This blend has been cultivated through years of experience at start-ups, scale-ups, and established financial institutions, and most notably at globally leading financial technology companies, where they were instrumental in creating disruptive digital banking solutions and platforms that now serve more than 300 banks globally.
Plumery’s Digital Success Fabric platform provides banks with the foundation for success beyond fast time to market by expediting the development of their digital front ends while significantly cutting costs compared to in-house initiatives or solutions with high total cost of ownership.
Caroline Grey, Co-Founder and CRO, Treefera, explores how better visibility is fixing blind spots
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The world is getting noisier. Climate volatility, environmental degradation and political instability are increasing both the number and the severity of disturbances that ripple through supply chains. With each disturbance, new complexities arise from new variables, like soil health and rainfall shifts, regulatory changes and geopolitical friction. These variables compound into an opaque risk ecosystem.
The unfortunate reality is that traditional data collection methods, aimed at managing supply chains and reducing risks across the all-important first mile, now can’t keep up in this challenging environment. Manual data collection or static surveys can’t process this rising tide of complexity fast enough to inform decisions, meaning businesses don’t have access to critical information. The result? Without this primary data, businesses are “flying blind,” which means they don’t have control over their supply chain operational performance, thus impacting revenue.
Technology plays a crucial role in quietening the noise, restoring clarity and providing leaders with the insights needed to improve supply chain resilience. We’re already seeing that satellite imagery, drone information and ground truth data can all be elevated using AI agents, allowing businesses to make better decisions.
The first-mile challenge
Today, 60% of business costs and risks occur within the first mile of logistical supply chains, meaning poor management of risk in these sourcing regions can directly impact business success. At the same time, regulatory pressure and requirements are growing, with the impending EUDR and EU omnibus legislation. Businesses need to have access to the right processes and technology solutions to ensure compliance.
For the EUDR, this means that any business that operates within, or sells to, Europe will need to ensure no deforestation occurs within their supply chain. They will also need to backdate evidence from as far back 2020. While timelines for this regulation remain uncertain, acting now to prepare for compliance should be a business priority today.
Given the scale of first-mile risk, visibility is essential to build resilience. This can only be done by leaning into the right mix of technologies, including the smart use of AI to generate insights that allow for greater, better-informed decision-making. AI allows us to abstract complexity – leveraging the massive acceleration in the capabilities of satellites over the past 10 years and turning disparate and disconnected data into actionable insights at global scale and near-instant speed.
AI-nativity needs to be the first step
While the entire supply chain can be opaque, the first mile has historically been hardest to manage, with information about sourcing regions and commodity origins often fragmented, remote and expensive.Businesses need access to insightful data that uncovers what’s really happening on the ground.
Data governs the flow of capital, and the quality of this data can equip enterprises with the ability to scope out and invest in appropriate sourcing options.Satellites, drones and ground truth data help with this, but only to a point – they provide surface level information without the depth of insight necessary for action.
AI-enabled data systems make it possible to track first-mile activity in real time. These tools translate raw, real-world data into scalable insights that decision makers can act on. They can also be tailored to specific business needs – from monitoring particular geographies to aligning with the compliance frameworks that matter most.
How does this work in practice? Take a large brand sourcing cocoa in Madagascar, for example, which needs to assess the risks posed by deforestation in order to meet EUDR standards. By utilising AI and satellite technology, they can map their entire supply chain to assess deforestation, tenure and labour risks, while producing automated DDS (Due Diligence Statement) reports ready to be submitted to the EU.
Agentic AI is the enabler here, synthesising complex and vast real-world datasets into expert-grade insights that are accessible at speed and scale. AI agents build on traditional AI models through autonomy and comprehensive self-learning mechanisms. Ultimately, this technology supports businesses in understanding the risk landscape within the first mile. And when incorporated into models that include regulatory and compliance frameworks, businesses can manage accountability and maintain their governance commitments.
Building deep insights from historic blind spots
The factors that affect global supply chains show no sign of slowing down – it’s time for businesses to take a different approach to risk identification and management, especially within the first mile. By ensuring access to accurate, scalable data and utilising real-time monitoring, businesses are laying the foundation for unwavering supply chain resilience.
For the C-Suite, the stakes are clear: revenue security and enterprise value now hinge on visibility at the first mile. In a world of climate shocks, political instability and regulatory pressure, legible supply chain data is no longer a technical nice-to-have; it is the foundation for protecting continuity, defending margins and sustaining growth over the long term.
In this issue, we’re shining a spotlight on the rapidly-growing sportswear brand, On. Craig Jones, Chief Supply Chain Officer at On, has a long history of transformational excellence. We sat down with him to discuss the expertise he brought to On, and how the business has evolved.
On is a young company, founded in 2010, and has grown exponentially ever since. It has gained international attention and renown during the last 16 years, but swift growth can have unforeseen consequences. To make sure vital elements of the supply chain didn’t get overlooked with all the extra work and pressure upon it, Jones made sure to start by getting back to basics.
“After 30 years in the game, I would say I can walk around a warehouse and kind of smell where the problems and bottlenecks are,” Jones says. “A lot of it is just the basics of running a distribution centre with planning. If you think about planning, there are not many companies I know that have accurate forecasting, especially with the volatility of our environment today. So it’s important to be clear on who owns what and what needs to be done by whom. A lot of it is just about discipline without being over-the-top.”
Alongside the On profile, we have some expert insights about supply chain circularity from Kimberley Duarte, Strategic Programs and Operations at the Circular Supply Chain Network. Caroline Grey, Co-Founder and CRO at Treefera, also lends us her wisdom on the topic of building supply chain resilience through first-mile visibility. Plus, procurement and technology leader, Nedra Dickson, talks us through the value of small business.
Additionally, we’ve got fascinating round-ups from DPW Amsterdam and Exiger’s Executive Forum from the tail-end of 2025, and we’ll also be looking ahead to Manifest 2026 and other upcoming events.
Radi El Haj, CEO of global payments technology leader RS2, argues that while cost-cutting is important, banks are overlooking AI’s biggest opportunity: fuelling growth through hyper-personalisation, predictive analytics, and dynamic pricing, all while staying on the right side of compliance
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In banking, artificial intelligence (AI) is often portrayed as an efficiency force-multiplier: automating back-office tasks, detecting fraud, reducing cost. Yet the bigger prize is less about cost and more about growth: unlocking new revenue streams through data monetisation, hyper-personalisation and dynamic pricing. At RS2, a platform that powers issuing and acquiring across banks and enterprises globally, we see how these possibilities can move from concept to profitable reality.
Unlocking Transactional Data for Revenue
Banks sit on rich transactional data – what customers buy, how they spend, when they engage. Historically, this data has helped reduce risk, fight money-laundering or optimise operations. But now it can be used to drive growth. According to an EY overview, AI-powered tools enable banks to personalise services, identify cross-sell opportunities and “potentially boost revenue streams.”
Consider a bank that analyses a customer’s payment behaviour, identifies recurring patterns (e.g., frequent travel, high hotel spend) and then offers a tailored premium travel card or concierge-style value add. Or a commercial bank that segments SMEs by payment volume and cash-flow profile and monetises by offering dynamic pricing on foreign exchange or supply-chain financing.
Responsible monetisation demands governance. A recent essay on monetising financial data with AI warns that “you’re sitting on a goldmine of data … but the major caveat is the need to manage risk”. The practical implication: invest in data-quality, maintain strict consent and usage controls, disaggregate personally identifying detail where possible and ensure transparency with customers. As banks move from “can we do this?” to “should we do this?”, the ones that succeed will embed data ethics, consent frameworks and explainability at the core.
Compliance and Innovation: Building Self-Hosted AI Frameworks
Growth-facing AI can’t sail past compliance. Banks need to remain within the bounds of regulatory regimes such as GDPR, PSD2 and CCPA. A key enabler is self-hosted or controlled AI infrastructure that allows experimentation without exposing sensitive data to third-party cloud vendors or uncontrolled derivative uses.
In the UK, the Bank of England notes that the future of AI in financial services demands both innovation and safety – building internal capabilities while contributing to systemic resilience. For banks this means: maintain internal model-hosting (or tightly controlled cloud with data isolation), build a “sandbox to production” pipeline where models are validated for bias, fairness and explainability, and treat regulatory engagement not as a blocker but as a design parameter.
With this architecture in place, banks can push beyond the cost-centre mindset (fraud detection, operations) into growth-mindset use-cases – real-time decisioning, dynamic pricing, micro-segment product design – all while retaining control over data flows, vendor risk and audit trails.
Explainable AI: Trust at the Front-Line
If AI is going to power new revenue models – dynamic offers, predictive cross-sell, hyper-personalised pricing – then customers and regulators alike must trust the outcomes. Enter explainable AI (XAI).
Explainability isn’t a nice add-on: it’s mandatory when AI touches decisioning that affects consumers (pricing, credit, product eligibility). If a customer is offered a differential rate based on their profile, they are entitled to know (in clear language) why. If a regulator challenges the fairness of an algorithmic decision, the bank must show the decision-tree, the bias mitigation steps and the audit trail of model monitoring.
As banks deploy AI in growth-facing scenarios, transparency becomes a strategic differentiator: one bank may claim to offer “smarter offers” – another will be able to document that those offers are fair, auditable and compliant. That traceability becomes a selling point when partnering with fintechs, regulators or corporate clients.
Lessons from Leading Banks: Growth-Not Just Cost-Cutting
While many banks still emphasise cost-cutting, the story is shifting. For instance, research from FIS shows that banks with a strong data strategy are tying AI investments to revenue outcomes, not just automation.
In practice, a global bank uses AI-driven cash-flow tools for corporate clients and is now preparing to monetise the service rather than treat it purely as a cost centre. Another major institution, NatWest, has embedded AI in its digital-assistant ecosystem and already reports improved customer engagement metrics and lower servicing costs.
From the experience at RS2, we see banks and FinTechs that pay attention to platform architecture, data lineage and flexible monetisation workflows succeed faster. The value flows not from a single “AI project” but from embedding AI into the payment rails, product lifecycle, pricing engine and loyalty ecosystem.
It is noteworthy that banks are not alone here: payments-technology providers like RS2 are collaborating with financial institutions to integrate AI into issuing and acquiring flows, offering a way to turn payments data into behavioural insight, and knowledge into value-added services.
Bringing it Together
For banks, the dominant mindset should shift from “AI as efficiency tool” to “AI as growth platform”. That transition requires three foundational capabilities: a clean, consent-driven data ecosystem; an AI infrastructure that balances innovation and control; and an organisational discipline around explainability, governance and monetisation strategy.
At RS2 we believe that the combination of payments technology, platform mindset and global scale gives us a front-row seat to this shift. The banks that lead in the next five years will be those that embed AI not in margins but in revenue lines – crafting new products, offering dynamic pricing, delivering real-time personalisation and monetising payments data in a responsible manner.
The future isn’t about AI simply making existing processes cheaper; it is about re-working how banks generate value. If your AI agenda stops at cost-cutting, you’re leaving the biggest opportunities on the table.
About RS2
RS2 is a leading global provider of payment technology solutions and processing services, offering a unified approach to managing payments across all channels for banks, integrated software vendors, payment facilitators, independent sales organizations, payment service providers, and businesses worldwide. RS2’s platform stands out as a robust cloud-native solution designed for both issuing and acquiring operations. With its advanced orchestration layer seamlessly integrating all aspects of business operations, clients gain access to comprehensive analytics, reporting tools, and reconciliation features. This empowers businesses to effortlessly expand their global footprint through a single integration, while also gaining valuable insights into payment processes and customer behavior, enhancing operational efficiency, increasing conversion rates, and driving profitability.
Robert Cottrill, Technology Director at digital transformation company ANS, explores how businesses can harness the potential of AI while mitigating the growing risks to cybersecurity and privacy
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AI can transform businesses, but is it also opening the door to cyber risks? Fuelled by competitive pressure and rising government support through the UK’s Industrial Strategy, it’s no surprise that more and more businesses are racing to adopt AI.
But there’s a catch. The more businesses scale their AI adoption, the bigger their attack surface becomes. Without a proactive and structured approach to securing AI systems, organisations risk trading short-term efficiencies for long-term vulnerabilities.
The AI Boom
AI investment is skyrocketing. Businesses are deploying generative AI tools, machine learning models, and intelligent automation across nearly every function, from customer service and fraud detection to supply chain optimisation. Platforms like DeepSeek and open-source AI models are now part of the mainstream tech stack.
Initiatives like the UK’s AI Opportunities Action Plan are fuelling experimentation and adoption. AI is now seen not just as a productivity tool, but as a critical lever for digital transformation.
However, the rapid pace of AI deployment is outpacing the development of the security frameworks required to protect it. When integrated with sensitive data or critical infrastructure, AI systems can introduce serious risks if not properly secured. These risks include data leakage through AI prompts or model training, as well as AI-generated phishing and social engineering attacks
While technical threats often take centre stage, businesses also can’t forget the increasing regulatory requirements surrounding AI. As AI systems become more powerful, enabling businesses to extract valuable insights from vast datasets, they also raise serious ethical and legal challenges.
Regulatory frameworks like the EU AI Act and GDPR aim to provide guardrails for responsible AI use. But these regulations often struggle to keep up with the rapid advancements in AI technology, leaving businesses exposed to potential breaches and misuse of personal data.
The Need for Responsible AI Adoption
To build resilience while embracing AI, businesses need a dual approach:
1. Prioritise AI-specific training across the workforce
Cybersecurity teams are already stretched. Introducing AI into the mix raises the stakes. Organisations must prioritise upskilling their cybersecurity professionals to understand how AI can both protect and threaten systems.
But this isn’t just a job for the security team. As AI tools become embedded in daily workflows, employees across functions must also be trained to spot risks. Whether it’s uploading sensitive data into a chatbot or blindly trusting algorithms, human error remains a major weak point.
A well-trained workforce is the first and most crucial line of defence.
2. Adopt open-source AI responsibly
Another key strategy for reducing AI-related risks is the responsible adoption of open-source AI platforms. Open-source AI enhances transparency by making AI algorithms and tools available for broader scrutiny. This openness fosters collaboration and collective innovation, allowing developers and security experts worldwide to identify and address potential vulnerabilities more efficiently.
The transparency of open-source AI demystifies AI technologies for businesses, giving them the confidence to adopt AI solutions while ensuring they stay alert about potential security flaws. When AI systems are subject to global review, organisations can tap into the expertise of a diverse and engaged tech community to build more secure, reliable AI applications.
To adopt responsibly, businesses need to ensure that the AI they are using aligns with security best practices, complies with regulations, and is ethically sound. By using open-source AI responsibly, organisations can create more secure digital environments and strengthen trust with stakeholders.
Securing the Future of AI
AI is a transformative force that will redefine cybersecurity. We’re already seeing AI being used to automate threat detection and response. But it’s also powering more advanced attacks, from deepfake impersonation to large-scale automated exploits.
Organisations that succeed will be those that embed cybersecurity into every stage of their AI journey, from innovation to implementation. That means making risk management part of the innovation conversation, not a downstream fix.
By taking a responsible approach, investing in training, leveraging open-source AI wisely, and embedding cybersecurity into every layer of the business, organisations can unlock AI’s potential while defending against its risks.
AI is a double-edged sword, but with thoughtful adoption, businesses can confidently navigate the complex landscape of AI and cybersecurity.
Mike Southgate, Co-founder of UK-based RegTech firm Ermi, on why artificial intelligence alone cannot replace human judgment in the creation of rules for automated transaction monitoring
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In the drive to modernise and improve financial-crime detection, artificial intelligence (AI) has emerged as a powerful tool. Machine-learning models have the ability to process vast volumes of transactional data, identify patterns invisible to the human eye and flag anomalies at scale.
But despite these clear benefits, AI on its own cannot deliver the transparency, accountability, or contextual nuance that is needed for effective transaction monitoring. Human judgment (Human In the loop) remains absolutely essential.
The Autonomy Illusion
Rising financial crime, advances in laundering typologies and increased regulatory scrutiny, has put financial institutions under pressure to adopt AI-driven anti-money-laundering (AML) systems, with the promise that they will be more effective.
According to the IICFIP Global Financial Crimes Impact Report 2025, global losses from financial crime exceed US $8 trillion annually, including money laundering losses of between US $800 billion and $2 trillion, fraud losses of over US $5 trillion, and corruption losses around US $3.6 trillion. Yet INTERPOL reports that only one percent of illicit financial flows are ever intercepted, frozen, or recovered.
Transaction monitoring vendors are increasingly marketing AI-driven AML solutions, claiming that the algorithms are able to autonomously detect suspicious behaviour. But these capabilities are often vastly overstated. Machine-learning models suffer from multiple issues. They are only as effective as the data they are trained on and ensuring accurate (E.g. data relevant to the firm buying the tool) and up to date data is challenging. Not least because financial crime is a moving target. Criminals continually change their tactics, often faster than AI can be retrained. Because the system relies on patterns learned from historical data rather than anticipating new, adaptive strategies, subtle illicit activity, such as transactions that mimic legitimate behaviour, often go undetected. Similarly, data to train an AI must know whether past patterns were truly criminal, which we may not always know.
Understanding AI’s Shortcomings
Importantly, the line between criminal and normal behaviour will depend upon the client. Consider a scenario where a high-net-worth individual initiates a series of international transfers. An AI model may flag these transactions purely based on volume or geography. Without contextual understanding for the type of client, the alert is likely to be a false positive. Conversely, a sophisticated money laundering scheme could evade detection entirely by mimicking legitimate behaviour. In both cases, human insight is critical. AI lacks context of clients or in-depth knowledge of of “normal” business models.
Opacity is another concern. Many machine-learning systems operate as black boxes, generating alerts without and meaningful explanation. Regulators are increasingly demanding transparency, for example under the EU AI Act and Financial Action Task Force (FATF) guidance on AI in AML (FATF, 2021). Institutions have an obligation to justify why a transaction was flagged (or not), what criteria were used and how decisions align with risk-based approaches.
Black-box models can also undermine internal governance. Compliance teams need to understand and trust the systems they rely on. And when an alert cannot be traced to a clear rule, confidence is undermined and investigations stall. Over-reliance on automation has the potential to overshadow critical human judgment.
Human Rule Design with Context
Effective transaction monitoring must still therefore have human-led contextual rule design. Unlike generic thresholds or static parameters, contextual rules take into account the full spectrum of customer behaviour, business models and risk exposure. Having defined rules will also allow transparency and traceability.
For example, a transaction exceeding £10,000 may trigger a review in retail banking but is routine in corporate financial operations. Contextual rules enable financial institutions to adapt the detection rule logic based on customer type and risk, transaction purpose, jurisdictional risk and historical patterns.
Contextual rule design also supports dynamic adaptation, so that systems are able to respond intelligently to changes in a client’s behaviour. For example, if a customer suddenly increases the volume or frequency of cross-border payments, the system evaluates these changes against historical patterns, business type, transaction purpose and associated risk factors. Alerts are then generated only when deviations are statistically or contextually significant, rather than for every fluctuation.
By incorporating this nuanced understanding, organisations are able to reduce false positives, prioritise genuinely suspicious activity and ensure compliance teams focus on actionable alerts rather than noise.
Contextual Rules
Importantly, contextual rules enhance explainability. Each rule can be traced to a specific rationale, for example, regulatory guidance, internal policy, or risk appetite. This strengthens audit readiness and helps with regulatory engagement. Transparency also supports continuous improvement as threats evolve or business priorities shift.
Financial crime detection is not just a technical challenge and is fundamentally about context. But AI struggles with nuance. It cannot distinguish between a legitimate seasonal spike and a layering attempt, in which illicit funds are moved through multiple accounts or jurisdictions to obscure their origin. It also cannot surmise intent, assess reputational risk, or weigh geopolitical implications, or above all… just be a sceptical compliance officer who doesn’t trust anyone.
Humans excel at contextual reasoning. They interpret indicators in light of customer behaviour and relationships, market dynamics and regulatory expectations. They ask the right questions, challenge assumptions and escalate concerns when needed. In short, humans bring vital judgment to transaction monitoring.
An example of this in action: in 2024, a European bank’s AI system flagged 80,000 transactions as “high risk.” Only 0.3 percent proved genuinely suspicious (IICFIP, 2025). Without human review, the bank would have wasted significant time chasing false positives, while potentially missing the subtler patterns of actual illicit activity.
Augmentation, Not Automation
The future of transaction monitoring is not about replacing humans but about strengthening them. AI should be used to support decision making by surfacing patterns and anomalies, while humans provide interpretation, oversight and context.
Forward-thinking financial institutions are getting ready for a regulatory landscape that will demand AI models are explainable and auditable. And by carefully combining machine efficiency with human judgment that organisations will reduce operational risk and strengthen compliance.
As financial crime grows more sophisticated, our transaction monitoring needs to evolve too. AI is a powerful tool but it is not a panacea. Effective transaction monitoring requires human insight and contextual awareness. Hybrid models that balance automation with human-led rule sets and interpretation will be essential.
While AI offers unparalleled speed and pattern recognition, it cannot replace the human ability to reason, contextualise and make judgment calls. Human-led transparency, explainability and context are not optional features for effective AML. Organisations that use AI to augment, not replace, human judgment will be best positioned to detect sophisticated threats, maintain regulatory trust and act decisively. In stopping financial crime, trust is essential and trust cannot be automated.
Vertiv expects powering up for AI, Digital Twins and Adaptive Liquid Cooling to shape future Data Centre Design and Operations
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Data Centre innovation is continuing to be shaped by macro forces and technology trends related to AI, according to a report from Vertiv, a global leader in critical digital infrastructure. The Vertiv™ Frontiers report, which draws on expertise from across the organisation, details the technology trends driving current and future innovation, from powering up for AI, to digital twins, to adaptive liquid cooling.
“The data centre industry is continuing to rapidly evolve how it designs, builds, operates and services data centres, in response to the density and speed of deployment demands of AI factories,” said Vertiv chief product and technology officer, Scott Armul. “We see cross-technology forces, including extreme densification, driving transformative trends such as higher voltage DC power architectures and advanced liquid cooling that are important to deliver the gigawatt scaling that is critical for AI innovation. On-site energy generation and digital twin technology are also expected to help to advance the scale and speed of AI adoption.”
Extreme densification – accelerated by AI and HPC workloads; gigawatt scaling at speed – data centres are now being deployed rapidly and at unprecedented scale
Data centre as a unit of compute – the AI era requires facilities to be built and operated as a single system
Silicon diversification – data centre infrastructure must adapt to an increasing range of chips and compute
The report details how these macro forces have in turn shaped five key trends impacting specific areas of the data centre landscape.
1. Powering up for AI
Most current data centres still rely on hybrid AC/DC power distribution from the grid to the IT racks, which includes three to four conversion stages and some inefficiencies. This existing approach is under strain as power densities increase, largely driven by AI workloads. The shift to higher voltage DC architectures enables significant reductions in current, size of conductors, and number of conversion stages while centralising power conversion at the room level. Hybrid AC and DC systems are pervasive, but as full DC standards and equipment mature, higher voltage DC is likely to become more prevalent as rack densities increase. On-site generation, and microgrids, will also drive adoption of higher voltage DC.
2. Distributed AI
The billions of dollars invested into AI data centres to support large language models (LLMs) to date have been aimed at supporting widespread adoption of AI tools by consumers and businesses. Vertiv believes AI is becoming increasingly critical to businesses but how, and from where, those inference services are delivered will depend on the specific requirements and conditions of the organisation. While this will impact businesses of all types, highly regulated industries, such as finance, defence, and healthcare, may need to maintain private or hybrid AI environments via on-premise data centres, due to data residency, security, or latency requirements. Flexible, scalable high-density power and liquid cooling systems could enable capacity through new builds or retrofitting of existing facilities.
3. Energy autonomyaccelerates
Short-term on-site energy generation capacity has been essential for most standalone data centres for decades, to support resiliency. However, widespread power availability challenges are creating conditions to adopt extended energy autonomy, especially for AI data centres. Investment in on-site power generation, via natural gas turbines and other technologies, does have several intrinsic benefits but is primarily driven by power availability challenges. Technology strategies such as Bring Your Own Power (and Cooling) are likely to be part of ongoing energy autonomy plans.
4. Digital twin-driven design and operations
With increasingly dense AI workloads and more powerful GPUs also come a demand to deploy these complex AI factories with speed. Using AI-based tools, data centres can be mapped and specified virtually, via digital twins, and the IT and critical digital infrastructure can be integrated, often as prefabricated modular designs, and deployed as units of compute, reducing time-to-token by up to 50%. This approach will be important to efficiently achieving the gigawatt-scale buildouts required for future AI advancements.
5. Adaptive, resilient liquid cooling
AI workloads and infrastructure have accelerated the adoption of liquid cooling. But conversely, AI can also be used to further refine and optimise liquid cooling solutions. Liquid cooling has become mission-critical for a growing number of operators but AI could provide ways to further enhance its capabilities. AI, in conjunction with additional monitoring and control systems, has the potential to make liquid cooling systems smarter and even more robust by predicting potential failures and effectively managing fluid and components. This trend should lead to increasing reliability and uptime for high value hardware and associated data/workloads.
Vertiv does business in more than 130 countries, delivering critical digital infrastructure solutions to data centres, communication networks, and commercial and industrial facilities worldwide. The company’s comprehensive portfolio spans power management, thermal management, and IT infrastructure solutions and services – from the cloud to the network edge. This integrated approach enables continuous operations, optimal performance, and scalable growth for customers navigating an increasingly complex digital landscape.
Jon Abbott, Technologies Director of Global Strategic Clients at Vertiv, asks how we can build a generation of data centres for the AI age
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The promise of artificial intelligence (AI) is enlightenment. The pressure it places on infrastructure is far less elegant.
Across every layer of the data centre stack, AI is exposing structural limits – from cooling thresholds and power capacity to build timelines and failure modes. What many operators are now discovering is that legacy models, even those only a few years old, are struggling to accommodate what AI-scale workloads demand.
This isn’t simply a matter of scale – it is a shift in shape. AI doesn’t distribute evenly, it lands hard, in dense blocks of compute that concentrate energy, heat and physical weight into single systems or racks. Those conditions aren’t accommodated by traditional data hall layouts, airflow assumptions or power provisioning logic. The once-exceptional densities of 30kW or 40kW per rack are quickly becoming the baseline for graphics processing unit- (GPU) heavy deployments.
The consequences are significant. Facilities must now support greater thermal precision, faster provisioning and closer coordination across design and operations. And they must do so while maintaining resilience, efficiency and security.
Design Under Pressure
The architecture of the modern data centre is being rewritten in response to three intersecting forces. First, there is density – AI accelerators demand compact, high-power configurations that increase structural and thermal load on individual cabinets. Second, there is volatility – AI workloads spike unpredictably, requiring cooling and power systems that can track and respond in real time. Third, there is urgency – AI development cycles move fast, often leaving little room for phased infrastructure expansion.
In this environment, assumptions that once underpinned data centre design begin to erode. Air-only cooling no longer reaches critical components effectively, uninterruptible power supply (UPS) capacity must scale beyond linear load, and procurement lead times no longer match project delivery windows.
To adapt, operators are adopting strategies that prioritise speed, integration and visibility. Modular builds and factory-integrated systems are gaining traction – not for convenience, but for the reliability that controlled environments can offer. In parallel, greater emphasis is being placed on how cooling and power are architected together, rather than as separate functions.
Exploring the Physical Gap
There is a growing disconnect between the digital ambition of AI-led organisations and the physical readiness of their facilities. A rack might be specified to run the latest AI training cluster. The space around it, however, may not support the necessary airflow, load distribution or cable density. Minor mismatches in layout or containment can result in hot spots, inefficiencies or equipment degradation.
Operators are now approaching physical design through a different lens. They are evaluating structural tolerances, rebalancing containment zones, and planning for both current and future cooling scenarios. Liquid cooling, once a niche consideration, is becoming a near-term requirement. In many cases, it is being deployed alongside existing air systems to create hybrid environments that can handle peak loads without overhauling entire facilities.
What this requires is careful sequencing. Introducing liquid means introducing new infrastructure: secondary loops, pump systems, monitoring, maintenance. These elements must be designed with the same rigour as the electrical backbone. They must also be integrated into commissioning and telemetry from day one.
Risk in the Seams
The more complex the system, the more attention must be paid to the seams. AI infrastructure often relies on a patchwork of new and existing technologies – from cooling and power to management software and physical access control. When these systems are not properly aligned, risk accumulates quietly.
Hybrid cooling loops that lack thermal synchronisation can create blind spots. Overlapping monitoring systems may provide fragmented data, hiding early signs of imbalance. Delays in commissioning or last-minute changes in hardware specification can introduce vulnerabilities that remain undetected until something fails.
Avoiding these scenarios requires joined-up design. From early-stage planning through to testing and operation, infrastructure must be treated as a whole. That includes the physical plant, the digital control layer and the operational processes that bind them.
Physical Security Under AI Conditions
As infrastructure becomes more specialised and high-value, the importance of physical security rises. AI racks often contain not only critical data but hardware that is financially and strategically valuable. Facilities are responding with enhanced perimeter control, real-time surveillance, and tighter access segmentation at the rack and room level.
More organisations are adopting role-based access tied to operational state. Maintenance windows, for example, may trigger temporary access privileges that expire after use. Integrated access and monitoring logs allow operators to correlate physical movement with system behaviour, helping to identify unauthorised activity or unexpected patterns.
In environments where automation and remote management are becoming standard, physical security must be designed to support low-touch operations with intelligent systems able to flag anomalies and initiate response workflows without constant human oversight.
Infrastructure as an Adaptive System
The direction of travel is clear. Infrastructure must be able to evolve as quickly as the workloads it supports. This means designing for flexibility and for lifecycle. It means understanding where capacity is needed today, and how that might shift in six months. It means choosing platforms that support interoperability, rather than locking into closed systems.
The goal is not simply to survive the shift to AI-scale compute. It is to build a foundation that can keep up with whatever comes next – whether that is a new training model, a change in energy market conditions, or a new set of regulatory constraints.
Jan Van Hoecke, VP AI Services at iManage and a highly experienced computer scientist with a passion for technology and problem-solving. on navigating the AI landscape for success in 2026
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The AI landscape faces a number of big shifts in 2026. Agentic AI will undergo a reality check as enterprises discover the gap between marketing hype and actual capabilities, while organisations will go through a mindset change from treating AI hallucinations as crises to managing them, acknowledging the inherent limitations of the technology. There will also be a shift in how data will be structured in AI systems, to help the move from just finding facts (“what”) to understanding reasons (“why”). Middleware application providers will face new challenges, as those vendors controlling both platforms and data will become more influential. Finally, standardised AI chat interfaces will evolve into smarter, dynamically generated, task-specific user experiences that adapt to immediate needs.
Agentic AI Reality Check
2026 is the year when agentic AI will get a reality check, as the gap between marketing promises made in 2025 and their actual competencies will become starkly visible. As enterprise adopters share the mixed successes of agentic AI, the market will begin to differentiate between true autonomous agents and the clever workflow wrappers.
Currently, many products promoted as AI agents are, in reality, rigidly programmed systems that simply follow predefined paths. They cannot independently plan or adapt in real-time to accomplish tasks. The current evolution of AI agents closely resembles the development of autonomous vehicles: early self-driving cars could only maintain lane position by relying strictly on preset instructions, and likewise, today’s AI agents are limited to executing narrowly defined tasks within established workflows. True autonomy, where AI agents can dynamically perform and solve complex problems better than humans and without human intervention, remains, for now, an aspirational goal.
AI Hallucination Goes from Crisis to Management
In 2026, the AI hallucination crisis will reach a critical juncture as organisations realise they must learn to coexist with the current fundamentally imperfect technology – until a new technology comes into play that can effectively address the issue. The focus will shift from AI hallucination ‘crisis’ to management.
As the industry deliberates who carries the liability for AI’s mistakes and inaccuracies – the tool makers or the users – enterprises will stop waiting for vendors to solve the problem and take matters into their own hands. They will adopt a variety of pragmatic risk mitigation strategies – from double and triple-checking work, and enforcing human oversight for high-stakes decisions, to taking hallucination insurance policies.
Major model builders acknowledge that current foundational LLM technology cannot eliminate hallucinations and ambiguity through incremental improvements alone. New technology is needed. Until then, and perhaps with the realisation that a technological breakthrough is years away, users will start driving the hallucination conversation – both by building systematic defenses within how they use AI, and forcing vendors to accept shared responsibility through better documentation and clearer model limitations.
The Next Evolution in AI Data Architecture Lies in a Shift from “What” to “Why”
There will be a fundamental shift in how data is structured for AI systems, driven by the limitations of current approaches in answering complex questions. While Retrieval Augmented Generation (RAG) has proven effective at locating information and answering “what” questions, it struggles with the deeper “why” and “how” inquiries.
This limitation stems from RAG’s flat-file architecture, which excels at locating information but fails to capture the complex interconnections and relationships that underpin meaningful understanding and knowledge, especially in specialised domains like legal and professional services information.
The solution lies in AI-driven autonomous structuring of data. These systems will be better placed (than humans) to reveal critical relationships across multiple data points at scale, also highlighting the contextual dependencies essential for answering the “why” and “how” questions effectively.
Consequently, in 2026, with machines taking the lead, the method of structuring data will undergo a complete transformation, gradually eliminating the human role in creating structure, to reveal the business-critical interconnections across multiple data points.
Middleware AI Apps Squeeze
Given the essential link between data and AI, middleware companies that specialise in building custom applications layered on top of data platforms will begin to get pushed to the margins, forced to compete on niche features – while the core value of data and insight is captured by the platform owners. The true leaders will be those organisations that both own and manage their data, while also offering an AI-powered interface that enables users to interact with their data securely and efficiently, fully leveraging the capabilities of modern AI technology.
Shift to AI-generated, Task-Oriented User Interfaces
In 2026, the current traditional vendor-designed, standard AI chat-based user interfaces will transition to dynamically AI-generated task-specific user interfaces that adapt to users’ immediate needs. This represents a fundamental shift from standardised software – for example, where everyone uses identical Microsoft Word or SharePoint interfaces – to personalised, short-term user interfaces that exist only as long as the user requires them for a specific task.
This transformation will also address the critical pain point that users typically have – i.e, the crushing cognitive load of navigating bloated, feature-rich software. Instead of searching through endless menus in an overstuffed application like Excel, the user will simply state their goal – “Compare the Q3 and Q4 sales figures for our top 5 products and show me a chart” – and the AI will instantly generate a temporary, purpose-built interface – a “micro-app” – solely designed for that one single task.
In the context of dynamically generated user interfaces, both data storage and the creation of bespoke interfaces will be managed by AI. The AI organisations that will truly lead in providing such bespoke user interface-generating capability are those that possess and control their own data.
About iManage
iManage is dedicated to Making Knowledge Work™. Our cloud-native platform is at the centre of the knowledge economy, enabling every organisation to work more productively, collaboratively, and securely. Built on more than 20 years of industry experience, iManage helps leading organisations manage documents and emails more efficiently, protect vital information assets, and leverage knowledge to drive better business outcomes. As your strategic business partner, we employ our award-winning AI-enabled technology, an extensive partner ecosystem, and a customer-centric approach to provide support and guidance you can trust to make knowledge work for you. iManage is relied on by more than one million professionals at 4,000 organisations around the world.
Simon Bowes, CVP Manufacturing Industry Strategy EMEA at Blue Yonder, discusses AI’s role in resilience
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For some time, global supply chains have been under considerable pressure. Media coverage continues to reflect the uncertainties faced across industries as diverse as construction, chemicals, semiconductors and food, among many others. News reports point to both positive developments and persistent challenges, with stories about disruption, weak demand and slow recovery appearing alongside more encouraging signals.
On the ground, these issues are causing significant problems. Research reveals that 84% of executives have experienced supply chain disruptions, ranging from route changes and extreme weather to geopolitical unrest. These disruptions complicate planning, impacting everything from production capacity to transportation costs.
On the other hand, supply chains have also proved themselves to be incredibly resilient, with organisations everywhere adapting to unprecedented levels of disruption to keep the wheels of commerce in motion. Looking ahead, however, how can organisations strike a balance between maintaining operational continuity and adapting quickly to new risks and changing market conditions?
AI-powered digital transformation
Key to long-term success is an ongoing commitment to digital transformation, particularly the deeper integration of smart supply-chain platforms and AI-driven tools that strengthen visibility and support faster, more confident decision-making.
In this context, “smart” supply chain platforms are modern, cloud-based systems designed to connect data, processes and stakeholders across the end-to-end supply chain. They replace fragmented legacy tools and spreadsheets with a unified operating environment, integrating data from internal functions (planning, procurement, manufacturing, logistics, etc) and external partners (customers, suppliers, carriers, retailers). In doing so, they provide a single, consistent source of truth across the supply chain network.
AI can play a central role in generating insights that organisations use to radically improve planning and decision-making. By processing large data sets to detect patterns, anomalies or emerging risks more quickly than manual analysis, machine-learning models can anticipate fluctuations in demand and inventory positions, using that information to inform forward planning.
AI can also recommend actions, such as re-routing shipments or adjusting production plans, to minimise disruption while automation reduces reliance on manual decision-making and speeds up response times. Together, these capabilities help organisations adapt more quickly when conditions change, as they inevitably will.
Effective integration
Despite strong potential, integrating AI into incredibly complex supply chains is not without its challenges. For instance, many networks remain extremely fragmented, with data often trapped in multiple systems that don’t communicate well. This reduces the quality and completeness of information available to AI models, meaning organisations struggle to operationalise tools consistently across relevant functions. According to Blue Yonder’s research, 82% of leaders agree that outdated technology will hinder their supply chain’s potential, and 51% state that implementing new tech is a top strategic priority.
In the rush to deliver performance improvements, some organisations have implemented AI on a piecemeal basis, deploying point solutions that address only one area (such as warehouse optimisation or forecasting) without supporting broader end-to-end decisioning. The problem with this approach is that it can easily create new barriers by reinforcing data and process silos, making it harder to share insight across functions and limiting the ability to coordinate responses when conditions change.
So, what do AI-powered supply chain processes look like in practice? Imagine a manufacturer sourcing key components from multiple suppliers across different regions. Without prior warning, a disruption, such as a severe weather event that closes a port or a supplier’s production delay, occurs. Using traditional processes, teams would need to manually piece together information from procurement, logistics and production systems to understand the scale of the problem, a task that can take hours or even days.
Armed with the appropriate data platform and AI tools, because data from procurement, production, logistics and inventory is unified, the organisation receives an early alert. AI models quickly analyse the likely impact of the problem, such as which orders will be affected, expected delays, and how production capacity will be influenced, among other factors.
The platform then evaluates scenarios around options such as alternative suppliers, rerouting via different ports, adjusting production schedules or reallocating inventory across distribution centres. It then recommends the most effective option based on lead time, cost and service commitments.
Planners can review and approve the recommended response, supported by a clear rationale. After this point, execution steps are automatically triggered across procurement, transportation partners and warehouse operations. As conditions evolve, AI continues to monitor performance and adjust recommendations, ensuring customer commitments are met and cost impacts are minimised.
In many ways, the argument in favour of using AI to improve supply chain performance and resilience has already been won. Research has shown that 80% of industry leaders say AI is already changing how they operate. Delivering on the technology’s true potential requires a shift from experimentation to scaled deployment. That means unifying data, connecting processes and equipping teams to act on AI-driven insight with confidence.
Jamil Jiva, Head of Asset Management at Linedata, on unlocking the benefits of AI for Private Equity
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Private equity has always been a race against time: identify the right opportunity, execute the deal, and drive growth before the next cycle begins. Traditionally, the competitive edge came from sharp analysis and strategic foresight. But today, as competition intensifies and margins for inefficiency vanish, another advantage is emerging: the ability to reclaim time itself.
Generative AI is the force multiplier behind this shift. It’s becoming an extension of the deal team, capable of accelerating the most time-consuming elements of the investment lifecycle. When applied thoughtfully, AI can unlock what may be the most important metric in modern private equity: Return on Time (ROT).
ROT measures the hours reclaimed from manual, repetitive work and reinvested in activities that truly drive value. In other words, AI is giving deal teams the gift of time. And in private equity, there may be no greater currency.
AI as an Extension of the Deal Team
Many firms have already taken the first step towards using AI to automate the ‘heavy lift’ tasks that have traditionally slowed teams down.
Deal sourcing is where the first savings can be made. Machine learning models trained on past investments, sector trends, and even unstructured data from news and social media are helping teams identify potential opportunities earlier. Sometimes before they even hit the market. Instead of hours spent trawling through databases or reading reports, deal professionals can now focus their energy on strategic decisions and relationship building.
Once a target is in sight, due diligence becomes the next time-intensive phase ripe for AI optimisation. Generative and analytical AI tools can now extract and classify data from hundreds of pages of financial documents, contracts, and ESG disclosures in minutes rather than days.
Post-acquisition, portfolio monitoring is where AI is starting to transform how value creation is managed. Natural language processing (NLP) can scan management reports and board decks to flag anomalies or benchmark performance against similar assets. Instead of manually consolidating metrics from scattered sources, investment teams can access real-time, AI-generated insights via live dashboards, giving them more bandwidth and brain space to focus on value creation.
At each stage, AI doesn’t replace the expertise of analysts and associates; it amplifies it. By handling the volume and velocity of modern data, AI helps firms make faster, better-informed decisions. The kind that can define fund performance.
Measuring ROT
In an industry where success is often quantified in basis points, ‘return on time’ may sound abstract (almost as abstract as the concept of time itself). But it’s quickly becoming a very real and measurable advantage.
Every hour a deal professional spends wrangling data or formatting reports is an hour not spent nurturing relationships or driving portfolio performance. AI can convert those reclaimed hours into strategic capacity.
For example, a mid-market firm that uses AI to automate quarterly portfolio reporting might save its operations team 15 hours per company per cycle. Across a 30-asset portfolio, that’s over 1,800 hours annually. That’s the equivalent of adding a full-time team member, without increasing headcount.
More importantly, the quality of those hours improves. Teams can reallocate time to higher-value activities, like mentoring junior talent, exploring new sectors, or deepening engagement with portfolio executives. In private equity, where speed and insight often determine who wins a deal or exits successfully, that time dividend can compound dramatically.
Scaling with Governance and Buy-In
While the business case is clear, scaling AI across investment teams is littered with challenges. Sensitive financial and portfolio data demand strong governance frameworks, especially as regulations such as the EU Data Act tighten the rules around data privacy and AI accountability.
Equally important is cultural buy-in. Starting small is the surest way to build trust and momentum, focusing on high-friction areas like due diligence and fragmented data workflows to deliver quick wins and tangible results. Clear communication is vital, but nothing reinforces confidence like seeing fast, impactful outcomes firsthand.
The most successful adopters recognise that AI implementation is an organisational shift that impacts far more than just IT. Analysts, partners, and operating teams all need to understand how AI supports, not substitutes, their expertise. Training programs and visible leadership support are essential to make the change stick.
Firms that neglect the human side of transformation risk underutilising their tools or facing quiet resistance from teams that don’t trust or understand the outputs. In contrast, firms that invest in cultural alignment often see adoption take flight organically, as teams begin to experience benefits they can see in their daily work.
The Gift of Time
AI’s impact on private equity will not be measured solely by reduced costs or faster workflows, but by the strategic capacity it returns to teams.
From there, the benefits become both quantitative and qualitative. As critical KPIs see an uplift, so too will more holistic metrics like decision-making confidence, analyst satisfaction, and internal adoption rates. In an industry built on the efficient use of capital, time remains the most precious and finite resource of all. Measuring and maximising Return on Time could be the differentiator that marks the next step up in private equity performance.
FinTech Connect was a crossroads for strategy and execution. Global banks, FinTech challengers, regulators and investors gathered to define 2026 priorities, debate operational challenges and benchmark technology roadmaps.
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A Decade of Fintech Innovation
FinTech Connect marked its 10th anniversary at ExCeL London. Drawing 5,000+ industry professionals, 140+ speakers and 100+ exhibitors to explore banking, payments, compliance, digital transformation and blockchain innovation. The co-location with Tokenize: LDN brought deeper coverage of tokenisation and digital-asset infrastructure alongside core FinTech topics.
AI in Fintech: From Vision to Practice
A theme threaded through almost every theatre was AI adoption in financial services. But unlike earlier years’ speculative hype, this edition focused on practical deployment and risk management.
One standout panel, “GenAI That Customers Can Trust: The One Zero Digital Banker Story,” shared how One Zero built responsible generative AI features tailored for banking workflows, emphasising transparency and user trust. Industry leaders underscored that explainability, governance and compliance are no longer optional in enterprise AI.
A direct follow-on session, “How Do We Make AI Responsible in Practice?”, featured Rajeev Chakraborty from the Home Office discussing model governance and ethical safeguards for operational AI—an area rapidly becoming central to CIO and risk officer agendas.
Across both days, panels also explored how AI can reduce backlog in financial institutions, with Santander UK’s Head of AI demonstrating measurable impact on operational efficiency, and tackling tech debt at scale—a perennial challenge heightened by the influx of automation projects.
Key takeaway: AI’s role has shifted from emerging trend to core enterprise infrastructure, but success now hinges on responsible implementation, observable outcomes, and regulatory alignment.
Digital Transformation & Core Banking Strategies
Transforming legacy systems was another anchor topic. The Digital Transformation stage hosted robust discussions around neobanks and challenger strategies, with executives from TSB Bank and HSBC highlighting how incumbents are adopting agile ways of working while balancing risk and customer expectations.
The session “All In on Legacy? Driving Time to Market Without Big-Bang Migrations” resonated with many practitioners: incremental modernisation beats wholesale lift-outs when prioritising stability and customer continuity.
Another practical highlight, “Engineering Productivity Measurement: Traditional Bank to UK’s Largest Fintech,” narrated the journey of building measurable engineering benchmarks to align business goals and product delivery.
Key takeaway: Attendees left with a reinforced understanding that successful transformation blends cultural shift, incremental modernization, and strategic tech investment—not hurried replacement of core systems.
RegTech & Ethical Compliance: Balancing Innovation with Governance
RegTech, Compliance & Security sessions tackled the tension between rapid innovation and tightening regulatory guardrails—a debate central to fintech scaling.
A standout session titled “Ethical AI in Regulatory Technology: Balancing Innovation & Compliance” featured voices from governance, compliance and data-ethics functions. Panelists discussed strategies for embedding fairness, bias mitigation and traceability into machine-assisted workflows—a crucial step for institutions deploying automated decisioning.
Another forward-looking talk, “How Quantum Innovation Will Redefine Regulatory Operations,” examined how future computing paradigms could reshape compliance tooling and data verification—but also stressed the need to prepare today’s infrastructure for tomorrow’s disruptions.
Key takeaway: Compliance isn’t just a cost centre; speakers argued that robust RegTech can be a competitive advantage, reducing risk while enabling faster scaling.
PayTech & eCommerce: Securing the Digital Commerce Era
The PayTech & eCommerce stage delivered insights on securing payment flows and shaping the next wave of commerce innovation.
In “Emerging Global Tech Trends in Payments & Cash Management,” HSBC’s payment leaders unpacked how real-time rails and open APIs are influencing cross-border flows. Fintech Connect 2025
The panel “Transforming Payment Security with AI” brought together payment experts and academics to examine fraud detection innovations—AI-enabled risk scoring, adaptive authentication and cooperative intelligence sharing—as a defence against evolving threats. Fintech Connect 2025
A later session on “Tackling Cyber Threats in a New Era of Digital Payments,” addressed real-time threat detection, third-party risk and securing complex ecosystems, underscoring cybersecurity’s front-and-centre role for digital commerce. Fintech Connect 2025
Key takeaway: Payments remain fertile ground for innovation, but trust and security are foundational determinants of user adoption and ecosystem resilience.
The Tokenize: LDN co-located stage brought in robust debate around real-world asset (RWA) tokenization and Web3 infrastructure—not as fringe buzzwords, but as emerging institutional tools.
Panels like “Bridging the RWA Infrastructure Gap” unpacked regulatory friction points and scaling challenges, highlighting custody risk, compliance complexity and standardisation needs—critical prerequisites to institutional adoption.
Another session on “Expanding Investment Opportunities With Fractional Ownership” featured cross-sector thought leaders, including Dr Lisa Cameron (MP & Crypto APPG Chair), exploring how tokenised assets can democratise access to traditionally illiquid markets.
Web3 panels examined trust, privacy and compliance in blockchain ecosystems and navigated the practicalities of smart contracts and decentralised identities—topics that are rapidly gaining traction with enterprise adopters.
A key session, titled Blockchain and CBDCs: At the Heart of Public Transformation? featured NatWest’s Head of Group Payment Strategy Lee McNabb, EY’s Emerging Tech & Innovation Leader Igor Mikhalev and Joy Adams, COO for Digital Assets at Deutsche Bank. A lively debate chaired by CommerzBank’s Poonam Ahuja examined the pros and cons of digital currencies and the rise of stablecoins.
Key takeaway: Tokenisation is still nascent, but panels stressed it’s transitioning into a practical institutional infrastructure conversation, with regulatory clarity and integration tooling cited as catalysts for broader uptake.
Startup Innovation & Demo Highlights
The Innovation & Start Up stage and Start-Up LaunchPad provided rapid-fire exposure to emerging companies pushing the frontier.
These sessions were among the most interactive parts of the show, with founders directly answering questions on integration, compliance and product-market fit.
Key takeaway: Startups revealed solutions that dovetail with enterprise needs—especially around AML automation, customer engagement and data orchestration—making them compelling partners for larger financial services buyers.
Networking, Community & Celebration
FinTech Connect didn’t just deliver talks; it facilitated dense networking across peer groups, investors, regulators and tech leads. The AI-powered networking app helped attendees pre-book conversations and tailor agendas, turning serendipity into structured discovery.
The 10th anniversary celebration—complete with drinks, a Christmas Market theme and live entertainment—reinforced the community aspect and capped the event on a high note.
Conclusion: A Hard-Working Fintech Forum
FinTech Connect 2025 proved to be more than a conference—it was a strategic inflection point. While technology and vendor showcases were abundant, it was the panel debates and operational talks that delivered the most actionable insight. Attendees departed with:
A clearer view of AI adoption roadmaps;
Practical frameworks for RegTech and compliance transformation;
Nuanced understanding of payments security and real-time rails;
Emerging tokenisation playbooks suitable for institutional pilots.
As FinTech leaders prepare 2026 budgets and technology plans, FinTech Connect has reaffirmed itself as a must-attend forum where strategy, innovation and regulation intersect—and where the next decade of financial services will continue to take shape.
The CRO of Kallikor discusses how supply chain professionals can become more proactive and less reactive
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Poor alignment of strategy to execution plans across supply chain operations coupled to a continuous mix of internal and external disruptions are leading to a culture of constant firefighting. Last-minute fixes and costly reactive changes to address even small bottlenecks are eroding margins and confidence at the top. And, for CEOs and CFOs, this short-term mindset in the operation leaves them incapable of making confident growth bets for the future.
Instead, decision-makers need a way to shift from firefighting to foresight, with access to a safe virtual environment that enables testing of long-term strategies, quantifying the impact of even the smallest changes and aligning decisions across finance, commercial, logistics and supply chain operations.
Starting anywhere
No more reactive, day-to-day problem solving to deal with demand changes or external factors. Instead,leaders can use composable simulation to target the real pressure points that constrain growth – whether that’s in-store operations, warehouse flows, transport strategy or outdated inventory policies. By tackling the bottlenecks that matter most, businesses gain the agility to act decisively.
With a joined-up model spanning areas of operation, changes in one domain which impact adjacent processes can be modelled together – avoiding greedy optimisation which favours one function, landing additional costs elsewhere in the business. Those might include warehouse design, store operations or network flow, with the power to move seamlessly between but balance the needs of different areas as needed. That flexibility gives executives the foresight to direct resources where they’ll unlock future growth, not just satisfy immediate local operational challenges.
From composability to adaptability
Composability is crucial for adaptability. With composable simulation, businesses can quickly adapt to changing requirements or conditions across the operation. New use cases or models can be added if needed, supporting targeted improvements on-the-fly, but critically balancing each of the elements of the end-to-end flow.
Firefighting mindsets have been exacerbated by rigid rules and policies, or guardrails, in supply chains, especially when they no longer fit today’s market dynamics. Composable simulation enables businesses to simulate different scenarios that challenge those guardrails.
The short and long-term impact of changing or removing certain policies or rules can be fully tested in a risk-free environment, enabling true foresight. It’s even possible to identify new effective ways of operating across the supply chain which may have been previously overlooked due to the established, siloed structures in place.
Accessibility that drives alignment
But even with the flexibility, impact and foresight offered by composable simulation, it only creates value if the insights are accessible to decision-makers at every level, so that impact is lessened if the platform is too complex for staff to leverage and C-suite leaders can’t gain the relevant insight.
Many old-school tools demanded near perfect data and data scientist level tech talent to deliver value. That’s no longer the case. Innovations in AI and no-code models mean composable simulation no longer requires spotless data or deep technical expertise. A happy by-product is that composable simulation avoids the downsides of data aggregation necessary to feed the traditional tools.
Even if substantial gaps are present in the available data, AI can synthesise the missing elements based on the available information. Through intelligent preparation of extensive simulation experiments it is possible to describe and fully explore complex scenarios which extend beyond the bounds of what could be achieved with ‘real’ data alone.
The result is a safe environment for testing operational decisions and modelling real-world impact, accessible to all, from technical specialists to the C-Suite.
Targeted change, strategic impact
In short, composability allows businesses to leave firefighting in the past, challenge the rigid guardrails that have held them back and achieve much-needed foresight. By building modular simulation digital twins, leaders can test new strategies, expose outdated rules and see the ripple effects of every decision before committing capital. Instead of reactively addressing bottlenecks, the supply chain becomes the cockpit for growth foresight, where the C-suite can align investors, operations and strategy – and act boldly with confidence.
John Philips, EMEA General Manager at FloQast, on why the secret to happier, more efficient accountants is collaborating with AI – not just using it for menial tasks
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AI is on everyone’s lips right now. But for teams in small- to mid-sized organisations, it can be hard to know how to practically benefit from this huge, potentially world-changing technology. In some ways its benefits are clear and obvious. Processing information at previously unheard-of speeds, automating menial tasks, and removing the need for complex hard-coding from so many of these processes. But in others, it can be hard to channel your usage. Not just feeding your GPT of choice a bunch of scattergun tasks, but truly harnessing the capabilities of artificial intelligence to transform your work.
With that in mind, we’ve been working on research into this exact issue. In our latest report,The Journey to AI Collaboration, produced in partnership with the University of Georgia, we’ve found that it’s the accountants who actively work and collaborate with AI, rather than simply using it for menial tasks, who see real gains.
AI – Good for People, Good for Business
In this case, we’re defining ‘collaboration’ as ‘actively working with AI in intentional ways to achieve specific tasks and product deliverables related to accounting.’ And by ‘gains’, I don’t just mean what appears at the bottom of their organisations’ balance sheets. I mean benefits that can be seen in the lives of the accountants themselves. They sleep better, feel less burnt out, and report stronger satisfaction with their work.
For example, when scored on a ‘burnout scale’ from one to 100, AI collaborators registered only 17.5 compared to non-AI-users on 21.6. Likewise, a majority (52%) of AI collaborators reported feeling well-rested from their sleep, compared to only 18% of non-AI users.
Our previous research has shown organisations that improve their employees’ quality of working life and work-life balance tend to see better performance, which in turn supports growth. It’s all a virtuous cycle. So, as companies invest in their stance, they need to ensure it’s based on collaboration, rather than treating it like any other software solution.
What’s more, accountants and CFOs who collaborate with artificial intelligence are more likely to report being proactive, staying engaged, and having a valuable voice in their roles. They are almost twice as likely to make choices that impact their organisation’s performance and make suggestions for achieving strategic objectives. They are also more likely to have a valuable voice in strategic direction.
A Barn Door to Aim for
Only 5–6% of accountants and CFOs have meaningfully integrated AI into their work – yet those are the ones who see the kind of benefits described above. Clearly, this is a bit of a barn door to aim for: the vast majority of accountants aren’t yet collaborating in a truly valuable way with this technology.
This doesn’t mean AI is a foreign concept in accounting – quite the opposite. We found that 76% of respondents had used it at work. In other words, at the most basic level, it is already well bedded into our industry. But it’s that ‘meaningfully’ word that makes the difference. ‘Using’ AI covers everything from asking it to write or edit an email, to uploading data and asking a non-company-sanctioned generative AI tool to create a summary.
Of that 76%, less than 10 percent say AI has become integral to their work. Crossing the boundary into integral collaboration rather than simply using a tool requires a qualitatively different approach. It means being intentional and specific about what you’re trying to achieve and should result in being able to complete your work more efficiently – not just differently – with that AI assistance.
Company-Wide Benefits of AI
AI collaboration benefits accountants, but it also transforms entire organisations. Employee retention sits at 59% for ‘AI collaborators’ – companies that fold AI into their processes as a partner, rather than an endpoint solution. In general, we found that organisations that support collaboration do better at keeping their high-value staff, have more trust in the results AI models produce, and a clearer vision for the future.
For instance, we asked respondents to indicate their agreement with five statements on the extent to which their work and profession were important to them and their sense of self. Turning those results into a score out of 100, we found that AI collaborators hit a whopping 83, compared to non-AI users on 62. This seems to indicate a positive feedback loop between intelligent, collaborative use of artificial intelligece and a strong sense of identity with the accounting profession.
Organisations that support accountant-AI collaboration also see increased productivity. Accountants who collaborate with AI are more likely to report that they have sufficient time to do their work (56%). Accountants in AI-forward organisations also report a lower sense of time pressure (10 points lower) than accountants who use it in a non-integrated way or accountants who do not use AI. These benefits of AI collaboration also help the CFO by making the accounting function easier to operate and freeing up accountants’ time and energy for more strategic tasks.
A Leadership Lag
Despite the benefits, there are significant barriers to building effective accountant-AI teams. Most accountants and CFOs do not feel prepared for the transition to AI collaboration, and only a small percentage have a complete vision for the role of artificial intelligence in accounting. While AI’s potential is huge, most leaders don’t have a plan – only 16% of CFOs have a vision for how it will transform accounting in their organisation.
Realising the potential of AI collaboration in accounting starts with two steps with which accountants should be familiar. First, organisations need to proactively define roles and responsibilities in relation to AI. Then, with that clarity in place, they need to work on a collaborative, human-AI team tasked with accomplishing certain shared objectives.
It’s also crucial to work on growing employees’ trust in artificial intelligence. Knowing the roles that AI is designed to play and understanding your role relative to AI is just as important as knowing how your role connects with the role of a co-worker. Accountants who are actively collaborating with AI are also more likely to view it as auditable – which requires a clear sense of what AI is supposed to do and how it should go about those tasks. Likewise, collaborators are 25 points more likely to view AI as explainable – feeling able to explain how it does what it does.
Making the Most of the New World
The bottom line of these findings is simple: accountants have made the first move in starting to use AI day-to-day, but the next step is to harness its full abilities in a truly collaborative way. It’s crucial to fold artificial intelligence into accounting processes as a key player, not a standalone tool, fostering greater understanding among employees of who’s responsible for it, what its goals are, how it performs its tasks, and what its goals should be. With that kind of on-boarding, accountants and their companies alike will benefit – unlocking greater efficiency, improved job satisfaction, better work-life balance, and stronger growth.
Santo Orlando, Practice Director – App, Data and AI Services at Insight, on how your organisation can level up with Agentic AI
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By now, most of us have heard of Generative AI. Many businesses have already adopted the technology for tasks like customer service, code generation and content creation. Generative AI, however, is only the start; we’re only scratching the surface of the potential that AI has to offer
Enter Agentic AI
Unlike Generative AI, which relies on human input and prompts, Agentic AI can act autonomously to fulfil complex tasks without human intervention. As a result, nearly 45% of business leaders think Agentic AI will outpace Generative AI in terms of impact, and more than 90% expect to adopt it even faster than they did with generative AI. However, despite its promise, our joint understanding of Agentic AI – and how to implement it – is still very much in its infancy.
So, where do you start? To kickstart your Agentic AI journey here are five fundamental steps to consider.
Generative AI vs Agentic AI
If Generative AI is like having a personal assistant, supporting you one-on-one to speed up your tasks, then Agentic AI is more like having a dedicated team of smart, individual coworkers who can take initiative and get things done across your business – without needing constant oversight.
One powerful example of this in action is in sales. With Agentic AI, organisations are able to receive real-time insights during discovery calls. The AI ‘agents’ allow sales reps to respond with timely, relevant information, helping them build trust, operate faster and close deals more effectively.
By collecting and analysing data from across teams, agents can uncover patterns, translate complex metrics into actionable strategies and even highlight opportunities that might otherwise be unintentionally overlooked. In some early implementations, sales teams have reported saving five to ten hours per rep each month – adding up to thousands of hours redirected toward deeper customer engagement.
The one-to-one relationship we’ve grown accustomed to with Generative AI has evolved into the one-to-many dynamic of Agentic AI, which is capable of handling tasks for multiple users and automating entire business processes. Even more impressively, agents can make decisions, control data and take actions on their own. A capability that can seem daunting without a clear understanding of how it works.
That’s why businesses need to start small, and here are a few practical steps to get going quicklyand wisely with agentic AI.
Step 1: Getting your data ready
Agentic AI is the logical progression for organisations already exploring generative tools. However, the data needs to be in an optimal condition – clean, organised and secure – before autonomous agents can be deployed effectively.
As such, eliminating redundant, outdated and trivial (ROT) data is vital. Without removing ROT, agents may rely on obsolete information, leading to inaccurate or misleading outputs. For example, this could happen if a company deploys an HR chatbot that’s connected to outdated data sources. If an employee were to ask about their 2025 benefits, the chatbot might pull information from as far back as 2017, resulting in confusion and misinformation.
Proper file labelling, standardised document practices and use of version histories in place of multiple saved versions helps to ensure agents access only the most relevant and accurate information.
Step 2: Start with low-risk cases
Agents work on a transactional basis, charging for each operation, which can quickly add up. As such, it’s wise to experiment with simple, low-stakes applications first. This approach allows for quicker deployment and demonstrates immediate value to the business without significant costs or risks.
One example could be using an agent to assess sentiment in social media responses following a product launch. This can offer real-time feedback on public perception and inform messaging strategies. Other low-risk use cases include generating reactive press releases and monitoring competitor websites. Additionally, prioritising automation of routine tasks, especially those involving platforms like Salesforce, SharePoint, or Microsoft 365, allows teams to maximise impact without costly system overhauls.
Overall, organisations need to be willing to fail fast and expect failure. It won’t be perfect from the start. However, an experimental pilot approach helps to efficiently refine AI agents, reducing the risk of costly mistakes and making sure that only effective solutions are scaled up.
Step 3: Create a single source of truth
Establishing a dedicated, cross-functional team to explore agentic AI use cases helps prevent siloed adoption and supports enterprise-wide visibility. This team should span as much of the organisation as possible and include representatives from departments such as marketing, finance and technical solutions.
Collaborative workshops can then act as a forum to identify key processes that would benefit from autonomous capabilities and help businesses align potential applications with specific departmental objectives and broader business goals.
Step 4: Learn, learn and learn
Many companies underestimated the importance of training and governance with Generative AI – and Agentic AI is no different. Organisations need to establish clear governance to define how AI agents should and shouldn’t be used, covering not just technical implications, but HR, compliance and risk concerns as well.
Equally, businesses and those employed must understand Agentic AI’s full functionality to get the most out of it. Like with almost all technical training, AI education cannot be viewed as a one-time ‘tick-box’ exercise. Ongoing learning is necessary to keep pace with new capabilities and best practices.
For example, consider what’s already emerging, like security agents that automate high-volume threat protection and identity management tasks; sales agents that find leads, reach out to customers and set up meetings; and reasoning agents that transform vast amounts of data into strategic business insights.
Step 5: Reviewing ROI
Enthusiasm around Agentic AI is high. But before organisations dive in headfirst, it’s important they first define success. Technology can’t be the solution if there is uncertainty surrounding the goal. Successful deployment requires a clear definition of the problem organisations are looking to solve and knowledge of how to align the solution with measurable business value. Without this, initiatives risk stalling at the experimental stage.
Key performance indicators should also be identified early. These may include increased productivity, time savings, cost reduction or improved decision-making. Establishing these benchmarks and taking a data-driven approach ensures that AI initiatives align with business goals and demonstrate tangible benefits to stakeholders.
Moving forward
The process of switching to Agentic AI is about changing how businesses handle everyday problems with wide ranging effects, not just about using cutting edge technology. Iteration and learning along the way, as well as deliberate, measured adoption are the keys to increasing value. It’s simple. Success with AI starts with small, straightforward actions and use cases.
Abdenour Bezzouh, Chief Technology Officer at myPOS on how AI is revolutionising FinTech from reactive to proactive solutions
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AI is significantly changing the way small and medium-sized businesses manage their finances. In the UK, the number of SMEs adopting AI tools has increased 32-fold between 2022 and 2024. Meanwhile, average spending on AI tools has risen nearly sixfold over the same period. Once seen purely as a tool for automation, AI now plays a much more proactive role. It helps businesses anticipate cash-flow gaps, prevent fraud, and deliver more personalised customer experiences.
As the technology becomes more embedded, one question looms large. How do we ensure that automation strengthens, rather than replaces, the human relationships at the core of financial services? The answer lies in designing AI to improve human decision-making. Forward-thinking FinTechs are leveraging AI to build trust, enable inclusion, and prevent issues before they ever reach the customer. This shift, from reactive problem-solving to proactive service delivery, represents one of the most significant evolutions in digital finance.
At myPOS, we’re focused on designing AI to augment human decision-making, enabling our teams to intervene where empathy, context, or judgement is needed. For example, our AI flags unusual transactions in real-time. But instead of automatically blocking them, it alerts our human teams, who can access the situation and act with the right context.
From Reactive to Proactive: The New Standard in Trust
For decades, financial services have operated reactively: a transaction failed, then a customer called; fraud occurred, then an investigation began. AI makes it possible to reverse that logic. By analysing transactions in real time, algorithms can detect unusual patterns that may signal fraud or technical disruptions. This alllows companies to act before the customer even notices a problem.
This proactive approach is becoming central to trust in the FinTech industry, both in the UK and globally. It prevents disruptions, reduces disputes, and allows businesses to run more smoothly. The same principle now applies to onboarding, where document verification and compliance checks that once took days can now be completed in minutes with AI-assisted tools. When technology removes unnecessary friction, users feel more confident that their financial services will ‘just work’.
Augmenting, Not replacing, Human Judgement
Although AI can process information faster and with more accuracy than any human, it lacks emotional intelligence. In fact, a survey found that nearly 70% of UK consumers say AI chatbots fail to understand emotional cues. While AI can identify anomalies in data, it cannot detect the frustration in a customer’s voice or the urgency behind a small business owner’s request. The future of FinTech clearly depends on improving the speed and accuracy of human decision-making.
A common mistake organisations make when deploying AI is focusing on the wrong metrics. Success is often measured solely by ‘deflection rates’, or whether a bot resolves an issue without human intervention. This approach overlooks the true indicators of quality service: first-contact resolution, customer trust, and the likelihood that users will recommend the service. Prioritising these outcomes leads to AI supporting meaningful experiences rather than just reducing manual workload.
Ethics and Transparency
As AI becomes a key driver of financial decisions, ethical responsibility must be treated as a core design requirement. The principles of fairness, explainability, and accountability need to underpin every aspect of an AI system, from data collection to deployment.
For example, transparent decision-making allows customers to understand why a transaction was flagged or a decision made, turning AI into a trust-building tool rather than a black box. At myPOS, for example, every on-device decision is explained and complimented by a ‘request human review’ button. By clicking it, merchants are redirected to a live analyst within two business hours. Crucially, human oversight is needed to interpret AI outputs, make contextual judgments, and intervene when automated systems may misclassify or misrepresent a user’s situation. Ultimately, AI ethics is foundational to trust, which only humans can fully maintain.
A Smarter Relationship with Customers
AI’s predictive capabilities are also changing the fundamental nature of customer relationships. Instead of responding to problems, FinTechs can now anticipate them: identifying cash-flow gaps before they occur, suggesting actions to improve financial stability, or alerting users to potential risks early.
This proactive intelligence significantly enhances trust, shifting interactions from transactional to consultative. It empowers small and medium-sized businesses to make data-driven decisions that once required dedicated financial teams, while freeing human representatives to focus on higher-value conversations – those that demand empathy, judgment, and nuanced understanding.
Personal, Prediction, and Human
The next phase of FinTech innovation will be defined by how seamlessly AI blends automation with personalisation. We’re already seeing the rise of conversational commerce, embedded payments, and tailored financial insights delivered directly at the point of sale. As these capabilities expand, so will expectations around transparency, accountability, and empathy in how AI operates.
The future of FinTech is smarter, faster and human centric. AI will continue to handle the repetitive and reactive, but people will remain essential for what truly matters: understanding, trust, and connection. When businesses design AI around these core values – fairness, explainability, and empathy – the technology will strengthen the human relationships that keep the financial world moving.
From banking to alternative funds, modular architecture is the missing link for effective adoption of artificial intelligence, writes Alessandro De Leonardis, CIO of Armundia Group
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The global banking industry is approaching a strategic crossroads – one that will prove expensive for those who choose the wrong direction. Financial institutions stand to lose USD 170 billion in profits over the next decade if they do not adapt rapidly to the evolution of artificial intelligence, according to the McKinsey Global Banking Annual Review 2025. Yet the report’s most provocative insight isn’t about AI itself, but the infrastructure required to leverage it effectively.
Agentic AI has the potential to reshape banking at its foundations. Early adopters will strengthen long-term advantages, potentially boosting returns on tangible equity by up to four percentage points. On the other hand, laggards face structural declines in profitability. The difference between these outcomes won’t be determined by who adopts AI first, but who has the architectural foundations to implement it effectively. Increasingly, those foundations are modular.
From Generative to Agentic AI: Revolution not Evolution
To understand why architecture matters so deeply, we must distinguish between the two paradigms reshaping financial services.
Generative AI, the star of 2023-24, excels at creating content: automated reports, document summaries, customer-service response, and so on. It is powerful, but fundamentally reactive. GenAI requires human prompts and produces outputs that must still be reviewed and acted upon by humans.
Agentic AI represents a step-change. These systems combine autonomous reasoning, planning, and execution. They don’t only generate recommendations, they act on them. An Agentic AI system can autonomously manage an entire loan-approval workflow: collecting documents, verifying information, assessing creditworthiness, checking regulatory compliance, and making approval decisions, all without human involvement at each step.
The impact is already measurable. MIT Technology Review Insights found that 70% of banking leaders are implementing agentic AI through production deployments (16%) or pilot projects (52%). Deloitte reports early adopters achieving 30–50% cost reductions in specific workflows. McKinsey anticipates the emergence of a “disruptive agentic business model” within three to five years, with potential cost reductions of up to 70% in some categories. But the benefits are far from evenly accessible.
Why Monolithic Architecture are Incompatible with AI
The uncomfortable truth is that most banks are attempting to deploy twenty-first-century AI on twentieth-century infrastructure. And it doesn’t work.
Legacy systems still absorb around 60% of banks’ technology budgets, according to a 2024 Bloomberg Intelligence survey. These monolithic architectures were never designed for the rapid iteration, continuous integration, and granular governance demanded by AI deployment.
Monolithic systems require release cycles lasting months; AI models require continuous retraining and fine-tuning based on real-world performance. The mismatch is structural. Modern Agentic AI relies on orchestrating multiple specialised agents… One for data collection, another for risk evaluation, a third for decision execution. Monolithic architectures struggle to support this level of inter-system communication.
Governance is another barrier. AI systems require differentiated risk controls depending on the level of autonomy. A fully autonomous fraud-detection agent needs different guardrails than a customer-service chatbot. Monolithic systems offer all-or-nothing governance, not graduated controls.
Financial institutions cannot transform everything at once; they need incremental adoption. Starting with high-impact use cases, learning, then expanding. Monolithic architectures force “big-bang” transformations that almost never succeed.
This architectural misalignment explains why so many AI initiatives stall in pilot purgatory, never reaching production scale.
Modular Architecture as an Enabler of AI
Modular, service-based FinTech architecture solves these problems by design. Instead of monolithic platforms, modular systems are composed of independent, interoperable functional blocks connected via APIs. Each module can be developed, updated, or replaced without affecting the whole.
The key is the concept of the service: a module that does not expose standardised technical interfaces simply does not function. Services are the technical objects enabling interoperability:
A compliance module exposes services for regulatory checks,
A data-ingestion module exposes services for data collection and structuring,
An Agentic AI module exposes services for executing autonomous workflows.
This architecture creates an ecosystem where each component has clear responsibilities and well-defined interfaces.
For AI deployment, this translates into concrete advantages. Banks are implementing Agentic AI systems into specific processes – KYC/AML screening, credit-memo generation, collections monitoring, intelligent communication routing – without rebuilding their entire stack. Service-based modularity allows AI agents to be activated on circumscribed workflows, with impact measured before expansion.
Because agents operate within discrete modules, failures remain contained. A malfunctioning fraud-detection agent does not propagate into customer-facing systems. This isolation allows institutions to experiment more boldly.
Service-based architectures also enable integration of best-of-breed AI solutions. One module may use Anthropic’s Claude for document analysis, another Google’s Gemini for customer interaction, a third proprietary models for highly specialised credit scoring. Monolithic systems lock institutions into single-vendor dependencies.
Different modules can carry different levels of AI autonomy, aligned with risk profiles and regulatory requirements: high autonomy for customer-service bots, human-in-the-loop supervision for lending decisions.
As McKinsey notes, the winners of this transformation will practise “precision over heft”- implementing AI surgically where it generates measurable bottom-line impact. Service-based modular architecture is the technical manifestation of such precision.
Techfin vs FinTech: When Architecture Comes First
There is a fundamental difference between starting from finance and adding technology, and starting from technology and specialising in finance.
In the first case, solutions are built top-down – gather functional requirements, then find the technology to satisfy them.
In the second, solutions are built bottom-up – design the architecture before the functional requirements, optimising for flexibility rather than feature completeness.
When designing wealth- and asset-management platforms – such as FundWatch or 360 FUNDS – this distinction becomes tangible. Being AI-ready does not mean adding an ‘AI layer’ on top of an existing platform. It means the modular architecture allows AI capabilities to be integrated precisely where needed.
Modularity operates along two dimensions:
Process modules (compliance, analytics, reporting, client engagement) that can be activated independently;
Target modules tailored for different market participants: custodians, asset servicers, alternative-fund managers, wealth advisers—each activating different module combinations.
AI governance is embedded in the architecture, not layered on top. A fully autonomous reconciliation agent operates under different guardrails than a semi-autonomous investment-recommendation agent—different approval workflows, audit trails, and supervision requirements.
This approach does not remove the need for transformation, but it changes its rhythm. Instead of three-year platform-replacement projects, institutions can transform progressively: start with a high-impact module, prove value, learn from deployment, scale outward.
The key managerial shift is conceptual: the question is no longer “When will our digital transformation be finished?” but “Which module do we activate this quarter, and what do we learn?”
The $170bn Question
McKinsey’s warning – USD 170 billion of potential profit erosion – is not inevitable. Avoiding it requires strategic decisions today about the technology architecture of tomorrow.
The institutions that will thrive are not necessarily the largest or the earliest adopters of AI. They will be those building modular infrastructures engineered for precision, capable of integrating AI surgically, experimenting rapidly, scaling intelligently, and governing rigorously.
They will recognise that AI is not merely a technological deployment, it is an architectural imperative. And they will understand the deeper truth: in the Agentic AI era, precision beats scale.
The question faced by every financial institution is not whether to adopt AI, but whether its architecture can support it. For most legacy systems built on monolithic foundations, the honest answer is no.
The modular imperative is clear. The question remains: are you building for yesterday’s challenges or tomorrow’s opportunities?
As pressure mounts to deliver faster and more reliably, the ability to adjust in motion becomes a vital competitive edge
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Supply chain disruption is no longer an anomaly; it’s a constant. From geopolitical tension and rising fuel costs to climate-related events and shifting regulations, logistics leaders are navigating an environment defined by volatility.
But that’s only part of the story. Rising accident rates and escalating costs are adding further strain: large truck crashes have increased since 2024, despite widespread safety efforts. At the same time, fleets are grappling with tightening regulatory and compliance pressures, from evolving emissions rules, such as the EPA’s proposed heavy-duty vehicle standards, to more stringent Compliance, Safety, Accountability (CSA) scoring.
As a result, the concept of supply chain “resilience” has evolved from a buzzword into an operational necessity. At the centre of that resilience is real-time visibility, not only across shipments and inventories, but across fleet safety and compliance too.
While many organisations have made significant strides in digitising warehouse operations, improving demand forecasting, and modernising port logistics, one area remains critically under-addressed: road transport. Despite being one of the chain’s most vulnerable and variable links, the road remains a blind spot for many. Recent research reflects this gap – more than 70% of respondents admitted their fleets lack real-time visibility into road conditions.
Supply chain leaders are grappling with an acute driver shortage that threatens the backbone of road transport. Across the EU, Norway, and the UK, there is already a shortfall of over 233,000 truck drivers, a gap projected to swell to more than 745,000 by 2028 as older drivers retire without enough new entrants to replace them. In the UK alone, an alarming 55% of HGV drivers are aged between 50 and 65, with an average age of 51, signalling that a significant portion of the workforce may leave within the next decade.
Against this backdrop, supply chain leaders must embrace real-time road intelligence, powered by artificial intelligence and edge-computing vision systems, as a key strategic tool for visibility, adaptability, and risk management.
Road transport: A dynamic environment with limited visibility
These mounting challenges highlight the urgent need for stronger oversight and proactive risk management across fleets. Unlike static warehouse environments or planned shipping schedules, roads are dynamic and unpredictable. They’re impacted by human behaviour, weather conditions, infrastructure quality, and spontaneous events, any of which can delay deliveries or damage goods. Yet visibility into these disruptions often remains alarmingly limited.
A recent survey revealed that while 84% of safety leaders identified fleet safety as a high priority, 60% admitted they have no formal fleet safety technology in place, frequently relying on nothing more than basic GPS tracking. Moreover, 46% of surveyed professionals are still unclear about the full financial impact of accidents on their businesses, underscoring how visibility gaps continue to be a serious liability. Without accurate, real-time data on driver behaviour, vehicle conditions, and external risks, companies are left reactive rather than proactive, a critical threat to supply chain resilience.
Edge-computing vision systems powered by artificial intelligence (AI) address this challenge by collecting and processing road-level data directly at the source in real-time. These systems provide immediate insight into traffic conditions, driver behaviour, and environmental hazards, turning the road from a risk point into a source of actionable intelligence. They also play a crucial role in optimising operational costs, a large fleet of delivery trucks means high expenses, and keeping these under control is a constant challenge, especially for companies managing hundreds of vehicles making multiple deliveries each day.
For instance, when weather patterns shift quickly or congestion builds on a critical route, teams can reallocate resources, reroute vehicles, or update delivery schedules in real-time. This shift from reactive management to proactive planning is one of the key advantages of road intelligence.
Systems capable of analysing 100% of drive time add another layer of value, capturing full journey context to support decision-making, coaching and incident resolution.
AI and risk mitigation
AI is a core enabler of dynamic risk mitigation. Rather than relying on historical averages or static route plans, modern AI-driven systems identify emerging patterns and adapt recommendations based on current conditions.
This includes spotting subtle indicators of risk, such as shifts in driver behaviour that suggest fatigue, or clusters of hard braking in a specific area that might point to a developing road hazard. As foundational models evolve, AI is even being trained to predict the likely movements of drivers and vehicles, enabling earlier intervention to prevent incidents before they occur. With this level of intelligence, logistics teams can anticipate disruptions before they escalate and respond proactively to keep operations on track.
Crucially, advanced driver safety platforms today do far more than just warn of lane departures or potential forward collisions. They continuously analyse driving performance in real time, issuing immediate voice alerts to correct risky actions, turning each potential hazard into a safer outcome on the spot. For example, a driver about to tailgate or showing early signs of drowsiness can receive a prompt to adjust, helping avert accidents before they happen. Many systems also incorporate positive reinforcement, recognising and rewarding safe driving habits to strengthen safety cultures across fleets.
This kind of dynamic responsiveness is essential during peak demand periods, extreme weather events, or disruptions to global trade routes. As pressure mounts to deliver faster and more reliably, the ability to adjust in motion becomes a vital competitive edge.
Building resilience into the last mile
The last mile has become one of the most scrutinised segments of the supply chain, where delays and miscommunication are most visible to customers. It’s also where efficiency and traceability are most challenging to maintain, particularly during external disruptions.
Real-time road intelligence provides the operational agility to protect this final delivery stage. By integrating road-level data into dispatch and routing systems, teams can make micro-adjustments that reduce delays, improve customer communication, and avoid costly rework.
This agility can also help prevent compliance breaches, protect product quality, and reinforce customer trust in temperature-sensitive or high-value logistics. Fleet managers using AI-driven road intelligence platforms have already seen measurable improvements, such as a 50% reduction in road accidents, by combining real-time alerts with proactive coaching sessions.
Closing the gaps: From compliance to ESG
Beyond operational continuity, road intelligence also plays a critical role in helping organisations meet growing regulatory and ESG requirements. Visibility into emissions, idling time, route efficiency, and driver behaviour helps teams identify areas for improvement and demonstrate measurable progress against sustainability goals.
It also supports ethical business practices, ensuring safety is prioritised, risky behaviours are addressed constructively, and drivers are given the tools to perform at their best. This reinforces a safety-first culture contributing to long-term resilience, driver retention, and public trust.
Real-time road data provides the insight and accountability needed to align transport operations with broader environmental and governance commitments.
Looking forward: A strategic asset, not a tactical add-on
Real-time road intelligence isn’t a tactical bolt-on; it’s becoming foundational to building resilient supply chains. By embedding AI-powered insights into core logistics processes, organisations gain the flexibility to respond faster, the foresight to avoid costly disruptions, and the intelligence to meet evolving expectations.
In a world where supply chains must operate precisely in dynamic environments, the ability to see and respond at the edge is crucial.
The road has long been treated as the most unpredictable link in the supply chain. However, with the right intelligence in place, it can become one of the most strategic. AI, when fuelled by scale, speed and visibility, becomes a force for good, reducing accidents, empowering drivers and creating a safer ecosystem for everyone on the road.
At the most recent Exiger Executive Forum, we had the opportunity to listen to the experts discuss how supply chains can shore up in chaotic times
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Most often than not, the control you have over your value chain is an illusion.
That’s the bold statement November’s Exiger Executive Forum picked to examine and dissect. The event, entitled False Security: The Illusion of Control in Modern Day Value Chains, was chosen carefully to reflect what procurement and supply and value chain leaders are concerned about today.
On the 18th of November, we joined Exiger and its distinguished guests at the beautiful Great Scotland Yard Hotel in London to dig into this topic and hear directly from the best of the best in an expert panel. The guest list reached from defence leadership, supply chain experts, world-leading analysts and senior politicians.
The aim? To challenge that illusion of control, and frame the conversation as a tough love wake-up call. Without open dialogue like this, risks can quietly accumulate in the background, leading to systemic failures.
That’s why the Exiger Executive Forum is so important. By giving the most pressing matters – especially the uncomfortable ones – a platform, issues are demystified and disempowered and real solutions to be put into place – both with deep values and credible pragmatism. This allows leaders in procurement and supply chain to resolve modern day challenges with confidence, regain lost control and determine their future and not merely react.
Tim Fowler, Client Engagement Director at Exiger, acted as moderator for the evening’s discussions. He opened the discussion with a sobering reality: that organisations all over the world are facing systemic risks. “Global supply chains are more data-driven, more regulated, more digitised than ever,” he explained. “But, paradoxically, they’ve never been more fragile with the convergence of geopolitical fragmentation, resource scarcity, technology threats, and regulatory volatility.”
The risk caused, Fowler said, is one that “hides in plain sight”. Many enterprises operate under the assumption that they have full visibility of their suppliers, and that as a result, they’re in control. However, dig a little deeper and there are many unseen dependencies, regional concentrations, and of course, human risk. With a more hopeful lilt, Fowler then reminded attendees that the goal of the Executive Forum is to explore what real control and resilience means in a chaotic and ever-changing world, with the help of the expert panel:
• Koray Köse, CEO & Chief Analyst, Köse Advisory; Senior Fellow, GlobSEC GeoTech Centre; and Board Member, Slave-Free Alliance
• Scott LaFoy, Vice President, Nuclear and Technology Security Programs, Exiger • Sven Markert, Head of Supply Chain & Logistics, Siemens Smart Infrastructure • Angela Qu, Advisor, Strategist, and former Chief Supply Chain Officer • Faysal Rahman, Director, Corporate Coverage – Global Defence Coordinator, Deutsche Bank
The illusion of control
In the first segment of the evening’s strategic expert exchange, Fowler dug into the concept of this illusion of control with the panel. For Köse, the illusion of control is one of the greatest blind spots in modern business. But why? “It’s all based on our systemic understanding or how we actually created value in the past,” he explained. “Not 50 years ago, but even just 10 or 15 years ago, the world looked very different from what we are facing today. Changing the rules of the game is something many companies still do not examine seriously. It requires a deep review of how their value chains are designed, the governance and compliance structures that guide them, and the intelligence embedded into their processes. Ultimately, it is about building resolve and the capability and capacity to not only survive the challenges of today, but to shape and compete in the markets of tomorrow.”
Following this, Qu was asked whether she has also witnessed a false sense of security within governance models in organisations she’s worked with. She pointed out that many companies now have risk mapping, risk monitoring, and risk mitigation as a top agenda since COVID-19, but shortages and disruptions continue. What’s key, for Qu, is “awareness, visibility, and overview. I think we’ve made big steps in the last 2-3 years,” she explained. “There are a lot of conflicts in the classical KPIs, which are still siloed even after the COVID crisis. That’s why you need good visibility of the whole value chain setup – not only tier one.”
For Markert, maintaining agility when managing various political, technological, and economic challenges has been a major undertaking. “The truth is, I don’t know if we really maintain the agility or just manage the chaos,” he admitted. “We’re focusing on adaptability over perfection, so we accept that full control is impossible. Then, we’re coming back to basics. This starts with processes, then technology. Lastly, people are the most important and most valuable assets you have. You have to build up cross-functional teams. We don’t want to predict the future; we want to be prepared for the future.”
From a financial standpoint, Rahman stated he believes it’s important to take a step back and contextualise the challenge we’re living in. The last few years have seen a pandemic, wars, and geopolitical tensions the likes of which have never been seen, impacting supply chains. With this in mind, Rahman believes that there “couldn’t be more of an emphasis” on supply chain resilience. “How do you make sure your operational resilience is robust so you can withstand black swan events that are becoming more and more common?” he asks. “Diversification of risk is really important.”
Sometimes, failure is simply not an option. For LaFoy, who works with national security-grade supply chains, having all of the information in front of you is great, but it means nothing if you don’t use it to take action. “Often people think they can see everything, and that’s only step one of the problem – it doesn’t fully address it,” he said. “You have to be willing to take action within the organisation, to mitigate the problem, fix it, and try to rebuild. People like to say that they’re going to fix their supply chain, but the supply chain is likely supporting a programme that has existed for so long it’s entrenched within the organisation. So it’s almost always too late.”
Vulnerabilities and systemic risk
Fowler: “Where do you see the biggest unseen vulnerabilities accumulating today?”
Köse: “It’s in the KPIs. Companies are measuring themselves against metrics that no longer drive sustainable or resilient value creation in today’s world. They still prioritise short term shareholder returns that evaporate with every risk event. KPIs shift from quarter to quarter, yet value chains take decades to build and mature, just as supplier partnerships and political relationships take decades to cultivate. Both can erode rapidly when interdependent opportunistic and negative actions and disruptions occur.”
Fowler: “How do you encourage best practice and good behaviour with your clients?”
Rahman: “The number one ingredient is confidence. Having transparency across the value chain, the supply chain, the governance procedures, is super important too. It can take 50 years to build trust and one second to lose it, so it’s important to take a very risk-averse approach while being very commercial and pragmatic.”
Fowler: “What have you seen work in terms of breaking down siloes to drive agility?”
Qu: “I usually go with strategy, organisation, technology. Technology encompasses risk mitigation, as well as ESG and compliance. We need dedicated projects, working with suppliers and engineers to reduce waste and create internal excellence. Personal resilience is also very important.”
Fowler: “How do you balance all the elements of regional concentration and supplier dependency?”
Markert: “Efficiency is still key if you want to stay competitive. We cannot optimise purely on costs anymore – that’s gone. We have to take into consideration, as Angela said, the transparency insights beyond tier one. For me, it’s all about continuity and compliance.”
Fowler: “What lessons can the private sector draw from defense-grade risk management?”
LaFoy: “The defence-grade supply chain has this draconian adherence to certain processes, and that inflexibility doesn’t always translate in a positive way. But in this case, it’s necessary to examine what key things you’re prioritising as a company.
Technology, intelligence, and the myth of visibility
It’s clear, in spite of the warnings about vulnerabilities and control, that the overall feelings for supply chain professionals are hope and determination. Fowler introduced the next segment of the conversation by mentioning that investors and PE companies are now focusing on supply chain risk and resilience as key measures. This bodes well for those in supply chain when they inevitably come to justifying proposed improvements. The fact that supply chain risk ties directly into financial risk proves once again that supply chain is a business-wide concern, if there was any remaining doubt.
For Rahman, from a financial perspective, there are a couple of areas clients are focusing on when it comes to their investments. “One is financial risk,” he told Fowler. “What we mean by that is leverage – how much debt and cash they’ve got on the balance sheet. The other is business risk, which is quite broad. It’s about how much the product is needed in the market, whether it’s a diversified product, and so on.”
When it comes to questions of compliance and ESG in supply chain, balancing those areas of focus with what investors want can be a challenge. Those investors may have a clear idea of their areas of interest when thinking about risk and resilience, and Qu’s solution for making sure those vital areas don’t get overlooked is to always see things from the customers’ perspective.
“That customer, if you want them to choose your product versus a product from competitors, they want to know you’re compliant to all regulations,” she explained. “That results in collaboration among different departments to focus on a common goal and how we achieve it. Also, you need an overview of potential risks and have solutions in place for those focus areas, supported by technology. Things can go wrong, but if that happens when you’re prepared, it’s not the end of the world. There are still activities where humans can take over.”
The conversation again turned to leadership, and how that affects organisations in a way that incentivises them to focus on protection and resilience, while not stifling innovation and agility. The key, for Köse, lies in communication and constant messaging, so vital areas don’t get forgotten. “The important factor is drawing the journey very clearly to everyone who is a stakeholder in this process, and make sure that every part of their contribution will become part of the overall value creation process. When we talk about resilience, you always need to think about the next step. We’re not necessarily predicting anything, but we’re preparing for everything.”
The conversation shifted to summarising comments, where the panellists highlighted resilience across all functions, with a heavy emphasis on supply chain, utilising AI to help navigate decisions, and simply showing up as being some of the most important aspects to getting the modern supply chain right. “We need to be able to understand, from A to Z, geopolitical interdependencies, financial impact, innovation impact, industrial history, and the most valuable assets – your people and your culture,” Köse concluded. “Showing up in that context, and driving that as leaders, is ultimately really critical.”
During the course of the evening, the expert panelists exposed the glaring issues and shattered illusions across the modern value chain, while leaving attendees hopeful that they can achieve operational resilience through a proactive commitment to preparedness. Thank you to Exiger for inviting us to join in this vital conversation; we look forward to the next one.
Kyle Hill, CTO of leading digital transformation company and Microsoft Services Partner of the Year 2025, ANS, explores how businesses of all sizes can make the most of their AI investment and maintain a competitive edge in an era of innovation
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Across the world, businesses are clamouring to adopt the latest AI technologies, and they’re willing invest significantly. According to Gartner, generative AI has produced a significant increase in infrastructure spending from organisations across the last few months, which prompted it to add approximately $63 billion to its January 2024 IT spending forecast.
Capable of reshaping business operations, facilitating supply-chain efficiency, and revolutionising the customer experience, it’s no wonder major enterprises are keen to channel their budgets towards AI. But the benefits of AI can extend beyond large enterprises and make a considerable difference to small businesses too if adopted responsibly.
Game-Changing Innovation
Most SMBs don’t have the same ability for taking spending risks as their larger counterparts, so they need to be confident that any investments they do make are worthwhile. It’s therefore understandable why some might assume it to be an elite tool reserved for the major players.
To understand how SMBs can make the most of their AI investments, it’s important to first look at what the technology can offer.
Across industries, AI is promising to be a game changer, taking day-to-day operations to a new level of accuracy and efficiency. AI technology can enhance businesses of all sizes by:
Enhancing customer experience
Businesses can use AI tools to process and analyse vast amounts of data – from spending habits and frequent buys to the length of time spent looking at a specific product. They can then use these insights to provide a more tailored experience via personalised recommendations, unique suggestions and substitution offers when a product is out of stock. And, with AI chat functions, businesses can provide more timely responses to any questions or requests, without always needing an abundance of customer service staff on hand.
Powering day-to-day procedures
One of the most common and inclusive uses of AI across organisations is for assisting and automating everyday tasks including data input, coding support and content generation. These tools, such as OpenAI’s ChatGPT and Microsoft Copilot applications, don’t require big investments to adopt. Smaller teams and businesses are already using them to save valuable employee time and resources and boost productivity. This also saves the need for these organisations to outsource these capabilities where they might not have them otherwise.
Minimising waste
AI is also helping businesses to drive profit, minimising wasted resources, and identifying potential disruptions. By tracking levels of supply and demand, AI can automatically identify challenges such as stock shortages, delivery-route disruptions, or a heightened demand for a particular product. More impressively, however, they are also capable of suggesting solutions to these problems – from the fastest delivery route that avoids traffic, to diverting stock to a new warehouse. Such planning and preparation help businesses to avoid disruptions which costs valuable time, money, and resources.
According to Forbes Advisor, 56% of businesses are already using AI for customer service, and 47% for digital personal assistance. If organisations want to keep up with their cutting edge-competitors, AI tools are quickly becoming a must-have for their inventory.
For SMBs looking to stay afloat in this competitive landscape of AI innovation, getting the most out of their technological investment is crucial.
Laying down the foundations
Adopting AI isn’t as straightforward as ‘plug and play’ and SMBs shouldn’t underestimate the investment these tools require. Whilst many of the applications may be easy to use, it’s important that business leaders take time to fully understand the technology and its potential uses. Otherwise, they risk missing some major benefits and not getting the most from their investment, particularly as they scale out.
Acknowledging the potential risks and challenges of implementing new AI tools can help organisations prepare solutions and ensure that their business is equipped to manage the modern technology. This can help businesses to avoid costly mistakes and hit the ground running with their innovation efforts.
SMB leaders looking to implement AI first need to ask the following:
What can AI do for me?
Are day-to-day administration tasks your biggest sticking points? Or are you looking to provide customer service like no-other? Identifying how AI might be of most use for your business can help you to make the most effective investments. It’s also worth considering the tools and applications you already have, and how AI might enhance these. Many companies already use Microsoft Office, for instance, which Microsoft Copilot can seamlessly slot into, making for a much smoother rollout.
Can my business manage its data?
AI is powered by data, so having sufficient data-management and storage processes in place is necessary. Before investing in AI, businesses might benefit from first looking at managed data platforms and services. This is crucial for providing the scalability, security and flexibility needed to embrace innovation in a responsible and effective way.
What about regulation?
The use and development of AI are becoming increasingly regulated, with legislation such as the EU AI Act providing stringent, risk-based guidance on its adoption. Keeping up with the latest rules and legislative changes is vital. Not only will this help your business to maintain compliance, but it will also help to maintain trust with customers and employees alike, whose data might be stored and processed by AI. Reputational damage caused by a data breach is a tough blow even for big businesses, so organisations would be wise to avoid it where possible.
Embracing Innovation
This new age of AI is exciting; it holds great transformative potential. We’ve already seen the development of accessible, affordable tools, such as Microsoft Copilot, opening a world of new innovative potential to businesses of all sizes. Those that don’t dip their toes in the AI pool risk getting left behind.
The question smaller businesses ask themselves can no longer be about whether AI is right for them; instead, it should be about how they can best access its benefits within the parameters of their budget.
By thoroughly preparing and taking time to understand the full process of AI adoption, SMBs can make sure that their digital transformation efforts are a success. In today’s world, this is the best way to remain fiercely competitive in a continuously evolving landscape.
About ANS
ANS is a digital transformation provider and Microsoft’s UK Services Partner of the Year 2025. Headquartered in Manchester, it offers public and private cloud, security, business applications, low code, and data services to thousands of customers, from enterprise to SMB and public sector organisations. With a strong commitment to community, diversity, and inclusion, ANS aims to empower local talent and contribute to the growth of the Northwest tech ecosystem. Understanding customers’ needs is at the heart of ANS’s approach, setting them apart from any other company in the industry.
The ANS Academy is rated outstanding by Ofsted and offers in-house apprenticeships across a range of technology disciplines. ANS has supported more than 250 apprentices to gain qualifications in the last decade via apprenticeships across technology, commercial, finance, business administration and marketing.
ANS owns and operates five IL3‐accredited data centres in Manchester and has an ecosystem of tech partners including Microsoft (Gold Partner), AWS, VMWare, Citrix, HPE, Dell, Commvault and Cisco. It is one of the very few organisations to have received all six of Microsoft’s Solutions Partner Designations.
With the rise of AI-enabled fraud in mind, Dave Rossi, Managing Director at National Hunter, argues the need for a radical rethink
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AI is making financial fraud less predictable and far more damaging. With access to new tools like Fraud GPT, deep fakes, and large-scale automated, and agentic, autonomous decision making capabilities to supercharge methods such as spearphishing, fraudsters are now able to target their activity more accurately, convincingly, and at higher volumes than ever before. Add in use of AI to flood the industry with financial applications which increase phishing and identity theft, especially for vulnerable individuals, and the cost of financial fraud continues to explode.
As one recent report revealed, in the UK alone, banking fraud caused £417.4 million in losses across 21,392 reported cases over the past year, making it the third costliest fraud type. Combatting this explosion in financial crime requires a different approach. One that not only transforms identity checks through robust, multi-tiered tools but also includes assessment of behavioural signals, transaction monitoring and cross validation to highlight suspicious activity at any point in the customer lifecycle.
Critically, argues Dave Rossi, Managing Director, National Hunter, it demands a new mindset based on collaboration, information sharing and a culture that encourages people to raise concerns, call out suspicious activity and prioritise fraud detection at every stage of the customer journey.
Financial Fraud Explosion
Financial institutions are struggling to adopt the new mindset required to protect customers, reputation and the bottom line from financial fraud. The continued internal conflict between the need to add layers of verification and detection to deliver essential safeguards and a perception that such measures will lead to customer disengagement and loss is adding unacceptable risk in a new era of AI enabled, widescale financial fraud.
Financial fraud is no longer opportunistic and small scale. From individuals trafficked to dedicated fraud centres in the Far East to the systematic use of AI to build synthetic IDs at scale and deep fake voice and video calls used successfully for spearfishing activity, financial fraud is a global, organised crime.
The ease with which AI can be used to generate synthetic identities alone should prompt a radical overhaul of anti-fraud measures. According to Signicat, AI-driven identity fraud is up 2,100% since 2021. It is now outpacing many traditional forms of financial crime. Rather than stolen passports and forged documents, fraudsters are now using AI to create manufactured personas, ID documents and accounts created using digital footprints that appear legitimate but have been built to deceive. Adding defence measures – both technology and human – to the process may potentially add friction to the customer experience but failing to protect either the business or customers will, without any doubt, cost significantly more.
Synthetic IDs
Organisations need to understand the sheer scale of AI-enabled financial fraud. LexisNexis Risk Solutions estimates that there are around 2.8 million synthetic identities in circulation in the UK, and hundreds of thousands more are created annually. They also claim 85% of synthetic IDs go undetected by standard models, creating a potential cost to the UK economy of £4.2 billion by 2027 unless companies adopt more stringent screening measures.
The use of AI at this scale enables criminal gangs to play the long game, with the behaviour of synthetic accounts mirroring real customers over months or years to build a credit history before cashing out and leaving the business and bank to handle the write-off. And this tactic is being used to target business in every industry. According to Experian over a third (35%) of all UK businesses reported being targeted by AI-related fraud in the first quarter of 2025, an increase of more than 50% over the same time period last year.
The use of synthetic IDs is just one way in which AI has changed the familiar patterns of financial fraud. The sophistication of deep fake technology is another, with fake voice and video building on chat based social engineering messaging via real-time chat scripts for LinkedIn DMs and WhatsApp messages, to successfully facilitate incredibly sophisticated spearfishing attacks. Mimicking the persona of high value individuals, especially CEOs and CFOs, such attacks have led to devastating losses, including the UK-based fintech which lost £1.8 million in 2024 following an attack using a combination of spearphishing and generative AI to impersonate the company’s CFO.
Trust Issues
Organisations cannot afford the current levels of (over) trust. Indeed, the success of the majority of AI-enabled financial fraud can be tied to organisational culture. Synthetic IDs succeed when the focus is only on verification – which checks identity – rather than on-going monitoring of behaviour and transactions as well as cross validation, which highlight intent. Spearfishing leverages a culture of uncertainty, succeeding in environments where individuals do not feel confident or are not encouraged to question the veracity of the CFO’s payment orders, for example.
The reliance on credentials verification is inadequate in a world of Fraud GPT. With diverse sophisticated technologies now being deployed at scale, it is no longer acceptable to rely on traditional models of verification, such as document validation. Furthermore, organisations are losing trust in newer techniques, such as facial biometric authentication due to the sophistication of AI deepfakes. Concerns are growing about the risks associated with proposed national eIDs: when a digital ID appears to be verified by government there is a temptation to believe without additional, yet essential, scrutiny.
Organisations need to consider intention as well as identity. What are the behavioural signals that could indicate fraud? Which transactions are suspicious and what additional insight can be surfaced through continual cross-validation of activity? Adding layers of verification and flagging possibly suspicious activity may initially annoy the odd genuine customer, but the reality of AI-enabled fraud is devastating individuals, businesses and financial institutions. It is now vital to adopt a fraud-first culture, where individuals at every level of the organisation have both the tools and understanding to spot suspicious activity and are encouraged to call out concerns, especially if they relate to senior management requests.
Collaborative Model
Failure to shift from over-trust to low-trust will continue to play into the hands of criminal gangs. Gangs that are constantly sharing information about weak targets. Innovative, anti-fraud organisations are leading the fight back through intelligence sharing, cross-validation and next generation screening. Adopting both robust verification and validation technologies and culture that encourages suspicion and also fosters cross-industry insight is key to addressing this complex, evolving threat.
By proactively sharing the information surfaced through comprehensive verification as well as behavioural and device analytics, the industry can gain rapid understanding of the fast-changing tactics being deployed by these criminal gangs and take the appropriate remedial action to protect, customers, reputation and the bottom line.
At AWS, we’re obsessed with helping our customers harness the benefits of cloud and AI. While maintaining robust security, resilience…
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At AWS, we’re obsessed with helping our customers harness the benefits of cloud and AI. While maintaining robust security, resilience and scalability. We believe the true value of he cloud is unlocked when seen as an end-to-end transformation opportunity. A chance for organisations across Asia Pacific and Japan, such as Techcombank (TCB), to seize the innovations Gen AI and Agentic AI can offer today.
According to a new AWS-Strand Partners 2025 report, AI adoption among businesses in Vietnam is growing rapidly at an annual rate of 39%. Close to 170,000 businesses in Vietnam have already adopted AI. And 77% of those businesses expect AI to increase their revenue within the next year.
Delivering Business Benefits
TCB’s journey with AWS exemplifies the transformative power of cloud and AI adoption. Spanning strategic planning and co-innovation, with a shared commitment to transformation:
Within six months, AWS helped TCB migrate retail and corporate banking systems to the cloud. This enabled on-demand scalability, reduced infrastructure costs, improved time to market and enhanced availability for TCB, cutting downtime.
By rapidly scaling infrastructure, reliably and securely, TCB has seen digital transactions grow by 38%.
Today, 55% of new customers now join via digital channels and 97% of transactions are processed digitally.
The AWS Data Migration Service is expected to generate projected cost savings of up to $10.4 million over five years. Driven by improved infrastructure efficiency and simplified operations.
Harnessing Gen AI & Agentic AI
Gen AI is delivering workplace transformations, including enabling contact centre agents to resolve customer concerns. TCB has established itself as a pioneer, becoming Vietnam’s first bank to develop proprietary applications using Amazon Bedrock. Initiatives include customer chatbots for employee use, advanced language translation tools, and SMARTIE – an AI personal assistant built on a custom Large Language Model (LLM).
AWS: A Trusted Partner for Cloud at Scale
AWS distinguishes itself as a transformation partner through its unique combination of global expertise, strong local partnerships, and proven implementation frameworks. This comprehensive approach enables organisations to achieve meaningful business transformation while staying at the cutting edge of technological innovation.
“By enabling financial institutions like Techcombank to innovate at scale, we’re helping create the foundation for Vietnam’s next phase of AI-driven economic growth.”
Eric Yeo, Country General Manager – AWS Vietnam
Discover more about the ways Techcombank is overcoming challenges on its transformation journey with AWS from Eric Yeo, Country General Manager – AWS Vietnam
Financial Services Director Arunkumar Gopalakrishnan on how Publicis Sapient is developing the playbook for delivering successful AI-led digital transformations across the financial services landscape
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Publicis Sapient doesn’t sell tools; it delivers human-led, AI-enhanced solutions that blend proprietary platforms with deep industry expertise across global banking. The organisation is shaping the future of financial services by delivering complex digital transformations across continents – from the UK and Southeast Asia to the Middle East and the United States – anchored by a belief that true innovation lies at the intersection of business insight and technological depth.
Publicis Sapient utilises a SPEED philosophy – Strategy, Product, Engineering, Experience and Data. “For us, SPEED isn’t just a framework. It’s the way we align our capabilities to accelerate transformation,” explains Financial Services Director Arunkumar Gopalakrishnan. “My focus is the ‘P’ in that process – Agile Program Management and Product Management; helping clients move from vision to value at pace.”
Transformation at SPEED
During his time with Publicis Sapient, Arunkumar has seen the power of transformation at SPEED on a variety of high-stakes projects: helping a major UK bank launch a digital-only entity on the cloud; partnering with a leading Thai bank to revitalise its mobile-banking experience for a fast-growing, tech-savvy customer base; supporting a sovereign-funded startup bank exploring blockchain for trade finance in collaboration with Microsoft; building a holistic wealth-management platform for a large US custodian bank; and helping another lead the way in AI adoption. These are the type of innovation journeys where Publicis Sapient excels at moving from groundwork to exponential scale.
At the Intersection of Business and Technology
Publicis Sapient excels by fusing two disciplines often treated as separate. “We are at the intersection of business and technology,” explains Arunkumar. “You need deep business acumen to understand client challenges. However, you must have enough technical depth to engage meaningfully with engineering teams. That balance is what enables real problem-solving.”
From fraud prevention to blockchain and digital banking the industry is changing fast,” he notes. “Working with Generative AI today feels like standing on a new frontier. It keeps us on our toes, but it’s also what drives us – to stay relevant, deliver outcomes and connect both worlds of business and technology.”
Meeting the Challenge: Balancing Innovation and Risk
The biggest challenges facing financial services clients are not purely technological. They are structural and cultural. “Banks operate in complex regulatory environments,” notes Arunkumar. “There’s always a tension between innovation and risk management. On one hand, you want the next shiny thing; no one wants to be left behind in the technology race. On the other, you can’t bring something to life without going through the proper regulatory and risk-management controls.”
That balance defines the work Publicis Sapient does. “We are a people + product business,” he says. “Our strength lies in talented people, strong domain understanding, platforms, tools and a culture that says to clients: We have your back; we get it done.”
Many of the firm’s engagements, he explains, involve deep collaboration, experimentation and iteration. “Some of the use cases we take on aren’t easy. We work with partners, we research, we prototype, we unlearn and relearn. Progress in this space is continuous, not linear.”
A Digital Transformation Success Story
Publicis Sapient partnered with a large US Bank to lead digital transformation efforts focused on GenAI implementation and scaling. It worked in collaboration with Google and the Bank to design, build, and adopt GenAI to spur innovation, enhance risk management and improve productivity.
The high-level solution is composed of modular components including a secure GenAI Gateway for LLM access control, RAG framework for contextual retrieval, and Vertex AI integration leveraging Gemini models for high-quality natural language responses.
Delivering the Solution
The solution delivered integrated, repeatable accelerators designed to solve the central business challenges of speed, risk, and control from the ground up.
Publicis Sapient’s Financial Services Director Arunkumar Gopalakrishnan explains how the platform was built upon some core pillars:
Unified LLM Access & Model Context Protocol: “We streamlined the model consumption layer with a foundational gateway, built on a resilient Model Context Protocol (MCP). The MCP acts as an essential abstraction layer, ensuring all data streams, model inputs, and application requests are managed consistently, securely, and in compliance with governance rules.”
Integrated Governance & Security: “We implemented a ‘shift-left’ security approach, embedding continuous guardrails directly into the GenAI pipelines. This pre-processing step, coupled with adversarial testing, proactively minimizes human error, reduces operational risk, and ensures a continuous, audit trail.”
Proprietary Knowledge Grounding (RAG): “The platform enables the Retrieval-Augmented Generation (RAG) pattern. This involves securely indexing the bank’s vast internal repository of operational knowledge and compliance guides – by using this verified knowledge to ‘ground’ LLM responses, the platform ensures every AI output is based on the bank’s accurate, proprietary data. This successfully mitigates factual errors, minimizes hallucination risk, and protects brand integrity.”
Agent Orchestration (Agentic AI): “Moving beyond simple chat, the platform includes capabilities for managing Agentic AI workflows which greatly improves efficiency. These agents are goal-directed systems that execute multi-step business processes (e.g., investigating a service ticket, performing patching operations etc. with human-in-loop for oversight). This is the crucial layer for end-to-end automation of complex, cross-functional tasks.”
Unified Observability: “The final pillar establishes a system for tracking crucial metricstied to defined business outcomes. This enterprise-level observability framework captures data like response latency, quality, consumption rates etc. This data allows leadership to continuously monitor output quality and reviewing against the standards of accuracy and trustworthiness.”
Realising the Benefits
The positive impact of the work Publicis Sapient is doing includes:
Scalable Framework: Designed as a Platform-as-a-Service (PaaS) model to support future GenAI use cases across the enterprise; agent-driven extensibility enables enhancements with rapid time-to-market deployments.
Accelerated Onboarding: Automates the provisioning process by surfacing relevant documentation, policies, and procedures instantly.
Knowledge Reuse: Leverages existing enterprise knowledge bases to reduce redundancy and improve consistency.
Building with Purpose: From Vision to Scale
At the heart of Publicis Sapient’s transformation philosophy is its Digital Business Transformation Framework. Teams use a playbook to take clients from problem definition to scaled delivery. “It starts with Ignite – understanding the problem and bringing in strategic expertise,” explains Arunkumar. “Then comes Hunt & Shape – identifying and defining value, mapping MVPs and roadmaps. And finally Build & Scale – turning ideas into outcomes by building the right solutions.”
Scaling, he insists, is not only about size but certainty. “You don’t scale right away. You start small – proofs of concept, limited users, experiments and learn fast. Once you know what works, you can accelerate.”
He points to a current AI engagement as an example. “We started with one application hosted on the platform last year. Now we have twenty-plus, and many more coming. Building the foundation took months, but once we understood the landscape, everything else became a fast follower. You develop a playbook, you know the risks, and then it’s about momentum.”
Generative AI: A Catalyst for Reinvention
Few technologies have captured the imagination of financial services like Generative AI. Arunkumar sees its impact as both profound and pragmatic. “While business leaders talk about productivity gains, CIOs are using GenAI to drive measurable productivity and cost efficiency by modernising high-friction IT Service Management processes,” he notes.
Publicis Sapient identifies three areas where the shift is most visible:
Enhanced self-service: Intelligent Virtual Assistants act as the first line of defence. They automate a big chunk of initial inquiries, freeing human agents and improving response times.
IT-agent augmentation: GenAI synthesises ticket histories, diagnoses root causes and drafts expert-level resolutions. It drastically shortens the mean time to Resolution for critical incidents.
Developer velocity: Secure, context-aware coding assistants are improving efficiency, allowing engineers to focus on high-value work.
Publicis Sapient’s next frontier is Agentic AI, where intelligent agents move beyond analysis to orchestration and action. Teams have been testing these systems within IT service environments for major banks. “We started with a simple knowledge-search application,” he recalls. “It consolidated information across multiple systems to provide accurate, high-performance answers.”
From there, Publicis Sapient expanded into process automation. “Imagine an IT engineer under pressure to fix issues fast,” he says. “The knowledge-search tool becomes a force multiplier, identifying root causes instantly. Next, you automate the actions – patching servers, routing tickets, escalating tasks – with a human in the loop for control.” The goal is productivity gains with safety.
Challenges remain – particularly model drift and AI hallucination – but these can be mitigated with rigorous evaluation frameworks. “AI is probabilistic, not deterministic,” says Arunkumar. “You can’t expect one-plus-one to always equal two. That’s why continuous grounding, validation and human oversight are key.”
Innovation in Action: Real-World Use Cases
For Arunkumar, the most exciting part of AI transformation lies in the unexpected. “Some of the best use cases aren’t flashy but support everyday processes that, when optimised, deliver outsized value.”
He describes one example from a banking client: improving the reliability of customer statements. “A bank may send hundreds of thousands of daily communications – statements, notifications, alerts. Sometimes statements fail to send, and by the time customers notice, the issue snowballs into reputational risk.”
AI, he notes, can detect these failures proactively. “If a statement isn’t generated by 7 a.m., the system flags it very soon and resolves it before customers even notice. Predictive AI identifies the anomaly; GenAI drafts the corrective communication for review. It’s small, but it saves time, cost and reputation.”
Such “mundane” use cases, he argues, are where the real transformation happens. “Everyone talks about the big, shiny things. But in complex, regulated environments, it’s the subtle automations that drive consistent outcomes.”
Platforms for the Future
Publicis Sapient’s investment in AI platforms underscores its commitment to innovation. Arunkumar highlights three in particular:
Bodhi, the foundational system for building intelligent agents
Slingshot, designed to accelerate software-development lifecycles
Sustain AI, focused on IT service management and operational resilience
“These are our three-pronged approach to transformation,” he explains. “Each builds on the other – Bodhi as the foundation, Slingshot for velocity, and Sustain AI for long-term stability. And there’s more in the pipeline…”
Culture, Collaboration and the Power of Small Wins
For all the technology involved, Arunkumar insists transformation ultimately depends on people. “In any transformation, you work with stakeholders who have competing priorities,” he says. “The key is to focus on agreements first – find small wins and move forward. Progress builds trust.”
Publicis Sapient is a people + product business where Arunkumar encourages his teams to balance ambition with empathy. “If a meeting is contentious, end with one thing agreed. Take the rest next time. Transformation isn’t about forcing alignment; it’s about building it. We tell our clients, and our teams, ‘We have your back’. That trust is what makes complex programs succeed.”
Looking Ahead: Building Expertise and Depth
The focus for 2026, and beyond, is on cultivating deep, dual-disciplinary expertise. “Our teams sit between business and technology,” he explains. “You must be good at both. No one can master all financial services, it’s too vast, but you can specialise. Pick a niche within key areas – asset management, wealth, retail banking, corporate banking, payments, financial crime – and become excellent at it.”
At the same time, he urges his teams to stay curious about technology. “Even if you’re not implementing solutions yourself, you need to understand them and speak the same language as engineers and architects. That’s how collaboration works.”
Continuous learning, he believes, is non-negotiable. “There’s so much information out there – training, communities, conversations. We just need to channel it, understand the basics and keep moving forward.”
Transformation: A Continuous Journey
At Publicis Sapient transformation is never static. “We don’t fix something once and move on,” he says. “We think, test, learn, and build again. You must define the real problem before you solve it, validate your progress and inspire others to see the vision.”
Purpose and persistence turn complexity into clarity for Publicis Sapient’s clients. “The journey is continuous,” says Arunkumar with characteristic calm. “But that’s what makes it exciting. Every challenge is an opportunity to learn, collaborate and move forward – one small win at a time.”
AI commerce is set to transform how people shop and buy. Find out how Visa Intelligent Commerce empowers AI agents to deliver reliable and secure experiences at every step…
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Artificial Intelligence is transforming the way we shop and pay. Visa is leveraging the power of its network and its decades of experience to bring trust and security to AI-powered commerce, bringing to life Visa Intelligent Commerce, which enables AI to find and buy.
This is an innovative initiative that opens Visa’s payment network to the developers and engineers who are building the foundational AI agents that are transforming commerce.
“Soon, people will have AI agents that will browse, select, purchase, and manage on their behalf,” according to Jack Forestell, Chief Product and Strategy Officer at Visa. “These agents will need to be trusted with payments, not just by users, but also by banks and merchants.”
Similar to the transition from physical to online shopping, and from online to mobile shopping, Visa is setting a new standard for a new era of commerce. Now, with Visa Intelligent Commerce, AI agents can find, purchase, and pay on behalf of consumers according to their pre-selected preferences. Each consumer sets the limits, and Visa helps manage the rest.
Creating a Trusted Future for AI Commerce
Millions of people will soon rely on AI to find the perfect sweater, search for a new vacation destination, or complete a shopping list. Visa will eliminate the friction from payment, making it possible to transact securely and reliably in an AI-powered world.
Visa Intelligent Commerce is built on 30 years of experience working with AI and machine learning to manage risk and fraud and to deliver secure payment experiences. Alongside industry leaders such as Anthropic, IBM, Microsoft, Mistral AI, OpenAI, Perplexity, Samsung, Stripe, and others, Visa will facilitate personalised and secure AI commerce on a global scale.
“We are working with companies at the forefront of AI innovation to drive engagement on AI platforms and support new ways to pay, with security and trust as our number one priority,” Forestell added. “Together with our partners, we are fully harnessing the potential of AI to transform every aspect of commerce, payments, and business.”
Empowering Consumers, Merchants, and Developers
The transformation of AI commerce – today a futuristic and relatively unknown concept – into a frictionless, secure, and personalised experience for both merchants and consumers is underway at Visa.
Visa Intelligent Commerce incorporates a set of integrated APIs and a merchant partner program into AI platforms, allowing developers to implement Visa’s AI commerce capabilities securely and at scale.
Visa Intelligent Commerce offers:
AI-Enabled Cards. Replaces card data with tokenised digital credentials, which enhances security for consumers and simplifies payment processes for developers. In turn, it confirms that the agent chosen by the consumer is authorised to act on their behalf. This incorporates identity verification into AI commerce. Only the consumer can instruct the agent on what to do and when to activate a payment credential.
AI-Powered Personalisation. The consumer is in control. It shares basic spending and shopping information from Visa with the consumer’s consent to improve the agent’s performance and personalise purchasing recommendations.
Simple and Secure AI Payments. Allows consumers to easily set spending limits and conditions, providing clear guidelines for the agent’s transactions. Additionally, it shares real-time commerce signals with Visa, enabling Visa to monitor transactions and help manage disputes.
Visa’s payment technologies, including tokenisation and authentication APIs, will help enable transactions that are secure and frictionless, providing confidence to consumers who use AI to make purchases. Visa has decades of experience in fraud management, along with a robust data platform, and uses this expertise to power the Visa Intelligent Commerce program.
Paul Sweeney, Product Integration Officer at Aryza, explores how AI is reshaping customer engagement in credit and collections — not by replacing people, but by making every interaction more human
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Today, we’re constantly bombarded with requests for our personal data, from market researchers and government census takers to supermarket loyalty schemes that demand we flash a QR code at every checkout. It’s no wonder consumers are tuning out. So, when credit and collections organisations come calling for more information, customers are already halfway to disengaging before the conversation even begins.
From Forms to Conversations
Today, many forward-thinking organisations are turning to conversational AI to make these interactions feel more natural and less like a chore. Instead of filling out endless forms or providing data step-by-step, AI now enables something far more natural, a dialog. The system intelligently recognising what’s already been shared and gently prompting for what is still needed. It flows better and feels less transactional and more human.
Great customer service teams remember what you’ve told them before, pull up your files and data seamlessly, and avoid that infuriating pretence that they don’t know who you are, because let’s face it, nothing frustrates us more than companies we pay money to acting like we’re strangers.
The Rise of Everyday AI
Customers have long relied on tools like Google to hunt down information, adjusting phrases to get the right results. Now, generative AI has taken that habit to the next level. With platforms such as ChatGPT becoming a top five consumer application, it has started acting as a personal assistant for everything from daily decisions about what to cook for dinner to how to deal with financial dilemmas.
For credit and collections, it’s easy to imagine the potential. Simply upload your bills, take a photo of your accounts, and ask it to prioritise payments or even draft a response to the bills you can’t cover yet. It predicts your follow-up questions, suggests next steps, and can whip up a formal letter to your utility provider explaining the delay and what you’d like to happen next. If you haven’t already tried this, do so. It’s an eye opener and a glimpse of what’s coming. In fact, the use of AI is becoming increasingly common for financial advice, as it ranks as the second most common use case (41%)
Trusting the Machine: What the Data Shows
A recent report by Intuit Credit Karma revealed that 66% of people surveyed have already used generative AI to seek financial guidance, with the highest adoption rates among Gen Z and millennials. It’s a clear sign of the growing level of trust in AI-driven insights, in fact, 80% of respondents said they acted on the advice received and felt it improved their financial situation. However, the findings also underscore a deeper issue, as many people are turning to AI for financial questions, they feel too embarrassed or uncertain to ask elsewhere, highlighting the ongoing need for greater financial confidence and education.
Looking ahead, this kind of interaction will become the norm rather than the exception. Each customer will have their own form of AI assistant, one that knows their context, frames the right questions, and guides them smoothly towards their goals.
Empowering the People Behind the Screens
On the other hand of the equation, customer service staff are getting a major boost from AI too. Good systems now automatically tag and direct incoming messages, prioritising urgent ones from vulnerable customers over the routine inquiries. Conversations are summarised in real time, providing agents with a clear overview of what’s been discussed, how the issue is progressing, and the odds of a positive outcome. These AI tools handle the heavy lifting on volume, spotlighting complexities or trade-offs, ushering in an era where every worker has an AI co-worker.
What kind of AI assistant would a contact centre supervisor need? How about a C-suite executive, what features would they require? And if you’re an enterprise architect, would you want part-time reps generating policy docs or asking high-level questions? Probably not. You’d insist on guardrails, strict policies, and complete auditability at every step of AI-driven interactions. Generic AI will deliver generic experiences. For supervisors and decision-makers, AI assistants will also become indispensable coordination and decision-support tools, monitoring performance across teams, flagging bottlenecks, and recommending the best subsequent actions to maintain service quality and compliance. Those that deeply understand the challenges you face across all the lending cycle are best placed to power the AI assistants that you will depend on in the future.
The AI revolution is already here, but now’s the time for everyone to zero in on the data, its journey, and the models powering this future. Deep data architecture will be critical: each role, customer, agent, and supervisor requires access to tailored data and AI capabilities that fit their needs. That’s how we move from one-size-fits-all automation to truly personalised, intelligent experiences that improve outcomes for everyone.
Frustration with a broken system is a great motivator, and Spencer Penn, CEO and Co-Founder of LightSource, can attest to that
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LightSource is a business which, in its own words, gives the user ‘superpowers’ – an ambitious statement with plenty of evidence to confirm it. LightSource, based out of San Francisco, functions as a direct materials operating system, but provides so much more with its ‘Spec to Scale’ philosophy.
Spencer Penn, its CEO and Co-Founder, spent most of his career at Tesla prior to this role. He helped lead a program there called the Model Three, which was Tesla’s first mass market electric car. That was his first real exposure to the world of procurement and supply chain, and sparked a love and passion for that side of the business that led him to help create LightSource.
“I got exposed to a lot of challenges and insights, and some of those insights now underlie the product we’ve built at LightSource,” he explains. That love for procurement was inspired, in part, by the former CFO of Tesla, Deepak Ahuja, who Penn reported to and describes as “legendary”. Additionally, the rush to create the Model Three exposed Penn to the sourcing world for the first time.
Addressing a wider problem
After around three years of ideation, LightSource was launched officially in 2021. Penn never really planned to do this professionally; he thought he’d continue working in the tech field as an employee. But several things changed his mind.
“One, I realised the sourcing issues at Tesla weren’t just a Tesla problem,” he explains. “If you look at the income statements of any big manufacturing business, direct materials is the biggest area of spend, and it’s also the least innovative and underserved in terms of technology. So there’s a big market opportunity there. Many of us see it, but it doesn’t mean we quit our jobs like I did.
“The real thing that made the difference, for me, was meeting my Co-Founder, Idan Mintz. He’s my business partner and a brilliant technologist. He’s an engineer, our CTO, and he came from Google X and Google Research. Meeting him showed me I had a real counterpart on the technology side, and made me feel like we could go and pursue the change we wanted to see.”
And so, Penn and Mintz began to bring together the right elements to create a team and draw investor and customer interest. At that point, the risk wasn’t in following through with the idea – the risk was that if they waited any longer, someone else would fill the gap LightSource wanted to fill.
BeNeering’s MD and CRO dig into the company’s history with AI, and the necessity of seeing beyond the hype of new technologies
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A privately-owned company with over 20 years of procurement digitalisation experience fueling it, BeNeering is at the forefront of the AI-powered revolution in its sector. BeNeering was founded in 2007. At the time, Christoph Moll, BeNeering founder and Managing Director, was busy implementing SAP SRM systems for large European multinationals. While this provided great experience, Moll could see the limitations – and he wanted to move beyond them. He wanted to do something more.
“That’s how BeNeering was founded,” he says. “We focused on consulting until 2013, at which point we transitioned to being a solutions provider. I cherry-picked a great development team, and I’m happy to say that the same strong team is with us still today. Stability is our value.”
Natalia Parmenova, Chief Revenue Officer, is an example of one such strong team member, who has enormous faith in the business. “From my perspective, BeNeering is a super special solution provider for a few reasons,” she states. “First of all, we believe in co-innovation. Everything we develop is built together with very large and advanced customers. We try to stay close to those customers, to people who are forward-thinking and developing their procurement functions, so we can develop solutions for them with them. We are not just inventing things we think are useful – we are basing our development on customer needs. That’s really important.”
Simplify and optimise
Many people fall into procurement accidentally. It’s a common story among procurement professionals, and a source of amusement considering how passionate many of them become about it. For Parmenova and Moll, however, it was a case of seeing the way the wind was blowing and following the scent of change.
“In 2001, when I was employed by SAP, I wanted to do things better to help users in procurement,” says Moll. “That was how it started for me. My vision was really to simplify and optimise steps and processes for both requestors and buyers.”
Dafydd Llewellyn digs into why businesses need to be deliberate about how they use AI
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Taking on a new leadership role can be both exciting and daunting. Dafydd Llewellyn is the CEO of HICX, having taken up the mantle in September. In fact, by the time we spoke with him, he’d only been at his post for five weeks – but he was buzzing with nothing but pure enthusiasm about it. At this crucial juncture, when the pace of change is faster than ever before, strong leadership is vital. As such, Llewellyn has his hand on the tiller as he guides HICX on the next part of its journey.
“We’re a supplier management platform,” he says. “Companies work with us because they want to transform their supplier management to become more unified, more strategic, and more intelligent. Ultimately, what that means is our customers’ businesses are really able to make the most and harness the power of the suppliers.”
AI in the real world
Being competitive in the modern day means being not just au fait with artificial intelligence, but having a deeper understanding of it. The theme of DPW Amsterdam this year, as with the New York event in June, was ‘Put AI to work’. While AI has been a topic of discussion for several years now, planning (and bracing) for its impact is a different matter. But HICX, led by Llewellyn, is ready.
“It’s great that DPW is focusing on AI,” he says. “At HICX, we are being very deliberate about the way we approach AI. The way we’re looking at it is really about using AI to allow procurement professionals to be more value-added in the tasks they do, and remove all the repetitive tasks that they really don’t want to be doing. They can drive more value for the business that way.
“So, we are looking at things around business processes and automation, right through to document capture, through to compliance checks; using AI agents enables us to do that,” Llewellyn continues. “I can give you a real-life example. If you think about the workflow for a new supplier in the old days, you’d have to have them involved to create that workflow. Now, we can use AI to create that workflow just by writing it in natural language.”
These milestones reflect not just commercial progress, but market validation of Exiger’s platform
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The US Army has licensed Exiger’s AI software in order to accelerate its defence acquisition, reduce lead times, and enhance operational readiness. It’s part of a multi-million collar contract that’s been awarded to Exiger to provide end-to-end supply chain risk illumiation.
“This is a revolutionary capability that will transform the way the U.S. Army approaches sustainment,” said Exiger CEO Brandon Daniels. “Our software will help identify at-risk NIINs that may be subject to undue constraints from a variety of factors. It will unlock the organic and additive capabilities that the government has invested in. And it will monitor for severe risk hiding in the supply chain, identifying where natural and manmade disasters, supplier operational and reputational risk, and foreign adversary sourcing could create disruptions in the weapons systems our warfighters depend on. Together, these capabilities deliver a more predictive industrial base, capable of responding to evolving mission needs at speed.”
Exiger has also joined forces with Palantir as part of this project, combining Palantir’s operating system with its own mission-built supply chain AI.
“This partnership combines Palantir’s and Exiger’s world-class technologies to integrate production decisions with battlefield demands, ensuring the US Army can deliver faster and more reliably to those on the front lines,” said Mike Gallagher, Head of Defense, Palantir.
“AI and automation across the supply chain enable deeper visibility, faster risk surfacing, active and proactive mitigation, and accelerated supply movement, giving commanders and portfolio acquisition executives a level of foresight and speed never before possible,” Daniels added.
Cathal McCarthy, Chief Strategy Officer at Kore.ai, on why now is the time for enterprises to take stock and set themselves up for a long-term, successful future in applying AI where it can make the most difference
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The generative AI boom has triggered a wave of enterprise experimentation. From proof-of-concepts to customer-facing AI Agents, which can be launched at pace but too often in isolation. This comes as MIT’s latest report finds that only 5% of Generative AI pilots are successful, with the majority failing due to poor integration with enterprise systems and in-house implementations without engagement with expert vendors.
As adoption grows, so does the call for accountability. Control and centralisation is more important than ever. Siloed operations and experimentation pilots have meant that there are a trail of disconnected tools, incomplete experiments and sometimes confusion within enterprises of where AI is being used and who is using it, meaning it can’t be governed effectively.
Now is the time for enterprises to take stock and set themselves up for a long-term, successful future in applying AI where it can make the most difference. The state of play today shows where clear changes are needed.
AI Islands
In a recent report from Boston Consulting Group and Kore.ai, 80% of AI leaders say they now favour platform-based strategies over scattered deployments. These platforms are not just about efficiency; they’re quickly becoming the only viable model for visibility, scalability and governance.
The consequences of fragmentation are starting to show. CIOs and CTOs are sounding the alarm on siloed AI solutions that make it harder to measure impact, manage risk, or move quickly. This is often the case when AI tools and solutions are implemented in-house and without proven expertise.
These ‘AI islands’ are hard to govern, expensive to integrate and nearly impossible to scale responsibly. More than half surveyed in the report say current AI solutions are slowing them down and nearly three-quarters highlight explainability and compliance as top concerns. Clearly, connecting these AI islands together via a common platform can offer more long-term benefits such as better governance, faster time to market, and cost consolidation.
Regulation Demands New Architecture
Where governance could have been considered a final step by some, it now has to be a design principle from the outset. Transparency, auditability, and oversight must be built into the very fabric of how AI is developed, deployed and monitored.
Take the EU AI Act for example, the world’s first broad AI law, now applying to general-purpose AI models from August 2nd, 2025. The rules aim to boost transparency, safety and accountability across the AI value chain while preserving innovation.
According to the BCG report, 74% of leaders believe new regulations will significantly influence how they roll out AI across their organisations. And for good reason. Fragmented systems don’t just introduce inefficiency, they create gaps that regulators, stakeholders and customers are not ready to accept.
For all the talk of regulation as a constraint, it’s also an opportunity. Regulations should be seen as catalysts, rather than roadblocks. Companies that ensure governance is hard-wired into their AI projects don’t just avoid risk, they create greater trust. And this means greater adoption. This is what leaders need to see, as increased adoption of AI products ensures sustainable, long-term growth.
Enterprises in industries holding sensitive and personal data like BFSI, healthcare and retail, are already adopting a platform-based approach. Not only does this ensure integration across the business but also means it future proofs compliance, meeting industry and government regulated standards today but also building in parameters for upcoming regulations.
Gaining Control
Adopting a platform model doesn’t limit creativity. And it doesn’t mean sacrificing flexibility. Instead of juggling multiple tools, you get one place to plug in what you’ve built and get the best of what’s out there. By running all of your AI capabilities under one unified platform and set of guardrails, your teams across the organisation move forward with one framework, which means, they move faster, make quicker decisions and have a clear understanding of what is – and isn’t – working.
Most importantly, a platform turns compliance into a competitive and operational advantage. You can swap models, scale pilots and grow without silos tripping you up, and bring centralised control. This momentum is crucial for scaling and growing an organisation. Platforms create the foundation to scale AI responsibly and effectively and that’s key for future-proofing AI projects and creating impact that matters.
Interface hears from Emergn CTO Fredrik Hagstroem on approaches to AI best practice that can drive positive business transformations
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What does it actually mean for an organisation to be AI-ready, beyond having the right tools and data
“Being AI-ready is fundamentally about openness to learning and the ability to react quickly. While having the right tools and well-managed data is essential, true readiness is defined by an organisation’s capacity to operate, monitor, and measure the effectiveness of AI solutions.
We often see organisations invest heavily in implementation and tooling, only to realise that no one is prepared to take responsibility for running, monitoring, and improving AI systems.
AI-savvy organisations design solutions differently depending on the type of work, operational versus knowledge work, and, for knowledge work, focus on measuring effectiveness rather than just productivity.”
Where do most companies go wrong when trying to embed AI into their operations?
“Many companies treat AI solutions like traditional IT projects, using user acceptance as a checkpoint between development and handover to IT operations. This approach often fails before it even begins.
AI performs tasks that typically require human intelligence, perception, reasoning, and decision-making. While AI can execute these tasks with far greater precision and consistency than humans, someone within the organisation remains ultimately accountable for the results.
The most common misstep is underestimating the need to provide users with the right level of oversight and control so they can accept accountability for AI-driven decisions.
For example, explaining how AI decisions are made and demonstrating that they are ethical and fair depends not only on transparency and traceability but also on maintaining control and proper training data records.”
How can leaders prevent transformation fatigue during AI-driven change initiatives?
“Change is inevitable, so responding to it is part of effective leadership. AI will transform how businesses operate, but transformation fatigue arises when people feel constantly subject to change rather than in control of it.
Deliberate planning and thoughtful communication help, but the most effective approach is to empower people to feel more in control. This often involves organising teams around value streams that cut across business, technology, and operations.
Leaders can ensure teams have the skills and information necessary to take ownership of outcomes and make adjustments based on real results. This is especially important with AI solutions, which should be structured to provide continuous feedback, allowing teams to monitor performance, improve models, and refine processes based on learning.”
What kind of mindset and cultural shift is required for AI to deliver long-term value?
“Delivering long-term value from AI requires a shift from control to collaboration, and from predictability to adaptability. Organisations focused on individual targets and siloed accountability often struggle to realise AI’s full potential.
Value emerges when teams adopt a collective mindset, defining success by shared outcomes, whether customer experience, business impact, or strategic growth. Individual productivity only matters when it benefits the whole system.
Another critical shift is embracing uncertainty. Traditional corporate cultures often reward certainty and fixed plans. Cultures that support experimentation, feedback loops, and incremental change are more likely to see lasting benefits from AI.
This cultural evolution isn’t just about tools; it’s about how work is structured, how teams interact, and how decisions are made. Empowering teams to act fast, learn fast, and improve fast is central to sustaining AI-driven value.”
How can organisations balance AI experimentation with maintaining trust, transparency, and alignment with business goals?
“Each AI initiative should be evaluated based on the type of work and value it aims to deliver, whether efficiency, experience, or innovation. Different goals require different levels of oversight and distinct success metrics, making a portfolio approach to investment essential. Maintaining alignment with business goals means focusing on outcomes rather than outputs.
This requires systems where feedback, transparency, and learning are built in from the start, allowing initiatives to fail gracefully. Trust begins with a clear governance framework, as AI, like any transformative technology, can have unintended consequences. Transparency is not just audit trails; it’s about inviting dialogue, sharing lessons learned, and adapting as standards and regulations evolve.
Experimentation and learning go hand in hand. Delivering incremental value early builds credibility and transparency, helping teams understand what works and what doesn’t. Ultimately, AI is only valuable to the extent that it drives the business toward its strategic goals.”
How do organisations deal with some of the risks associated with AI – hallucinations, privacy issues, etc. – and how do they go about both securing essential data and overcoming employee resistance to the technology?
“Treating AI adoption as an iterative, feedback-driven process is key to managing risks. Success is less about getting everything perfect from the start and more about structuring work to minimise unintended consequences and adapt quickly.
“Hallucinations” is a misleading term. Today’s AI doesn’t imagine things; it follows programmed rules based on probabilities and patterns. Like any software, AI carries risks of errors or mismanaged data.
What is new is how AI uses data, to train models that imitate human decision-making. Without careful management, models can produce biased or unethical outcomes. Technology does not remove employee accountability. Recognising this allows organisations to design AI solutions with lower risk.
Designing solutions with humans in the loop is critical. It promotes transparency and explainability and is the most effective way to overcome resistance while maintaining control over outcomes.”
ABBYY survey finds financial services industry leading on innovation, but challenges exist with deployment
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New research commissioned by ABBYY has revealed a staggering 91% of financial services organisations are using sophisticated Generative AI tools. However, many experienced major challenges with deployment.
While 98% of banking firms reported positive results from GenAI, many admit to needing to augment it with other technologies for better outcomes, according to the 2025 ABBYY State of Intelligent Automation Report: GenAI Confessions. 44% of financial services companies say their investment in GenAI will rise more than 20% in 2026.
Managing AI Expectations
The survey, conducted by Opinium Research, shows that training the GenAI models was harder than expected for 39% of financial services firms, 32% found it difficult to integrate into business processes and 29% found their staff did not have the necessary skills to deploy it. In addition, 26% did not have proper governance.
It meant 42% of companies had to add document AI to improve outputs, while 39% used process intelligence, and the same amount asked staff to manually check results – much higher than the global average of 25%, suggesting too much manual intervention.
Adding other technologies led to 59% of respondents having increased trust in GenAI, 55% seeing better quality outputs, and just over half (51%) benefiting from more cost savings and better integration into their workflows.
“It seems that financial services leaders spent money on GenAI tools that promised more than they can provide. In some cases, they didn’t even need it. Before moving forward with GenAI tools for agentic automation, companies need to first evaluate their current processes and create a visibility map of their workflow with data analytics tools such as process intelligence. When training models prove more difficult than expected, pre-trained, purpose-built AI turns out to be the right solution.”
Maxime Vermeir, Senior Director of AI, ABBYY
Generative AI Creating a Buzz
While the top reason for introducing GenAI was to increase efficiency and customer service (67%), banking industry bosses are the most concerned about employee wellbeing. Over a third of respondents (35%) hoped the technology would reduce employee burnout and a quarter (25%) cited improving job satisfaction as a key goal – much higher than other industries such as transport and logistics (11%) and manufacturing (15%).
However, the survey also revealed that four-in-ten (40%) of financial services leaders admit that a driving factor for introducing GenAI was that employees were already using it on a Bring Your Own Software (BYOS) basis for personal productivity – which could impact security concerns over Shadow AI. Over half (51%) say employees wanted the technology to “make them look smarter and more professional,” while 67% said it reduces workload and increases productivity.
Generally, staff are optimistic about GenAI, with 88% of leaders saying workers enjoy positive results.
“GenAI is creating remarkable opportunities to reimagine how work gets done, which is rightfully generating a great deal of excitement. However, shadow AI, when individuals use commonly available tools like ChatGPT, Grok, or Perplexity without oversight at work, potentially raises serious data privacy and compliance concerns. The corporate benefits of GenAI’s potential are truly unlocked when leaders drive secure, strategic adoption with risk management as a priority.”
Ulf Persson, CEO, ABBYY
Key Findings from ABBYY
Other key findings from the report include:
65% of financial services organizations are using purpose-built AI – compared to 59% of companies globally
62% use agentic compared to 53% on average by other industries
Top uses for GenAI in banking: data analysis (59%), employee productivity (56%), automating business documents (56%), customer-facing apps like chatbots (55%)
Departments using GenAI: Finance for fraud detection and cash flow predictions (57%), sales and marketing (56%) compliance and legal (45%)
Wishlist of improvements for GenAI include being free of human bias and using less resources
Opinium research of 1,200 senior managers or above in companies of 100+ employees in the US, UK, France, Germany, Australia and Singapore with 110 financial services leaders questioned. Research undertaken between 20th of June and 8th of July 2025.
About ABBYY
ABBYY helps organizations optimize processes, accelerate decisions, and drive better outcomes with Process AI and Document AI. More than 10,000 enterprises, including many Fortune 500 companies, rely on ABBYY’s 35 years of innovation to turn business data into actionable insights that improve the way we work and live. Headquartered in Austin, Texas, and offices in 13 countries, ABBYY leads the way for smarter agentic automation. For more information, visit www.abbyy.com
Exiger has been awarded a huge contract to help modernise the detection of transshipment for the US government
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Exiger, the market-leading supply chain AI company, announced today that it has been awarded an exclusive, multi-million dollar contract by US Customs and Border Protection (CBP) to modernise the detection of illicit transshipment across global supply chains. Designed to evade tariffs, trade restrictions and sanctions, illicit transshipment is the practice of manipulating supply chains to disguise a product’s true country of origin. Exiger’s Trade AI will be adopted and deployed across CBP, serving as an additional tool for the US government’s transshipment detection capability.
Transshipment identification and enforcement are critical priorities for the Department of Homeland Security (DHS) and CBP. Convergent Solutions, Inc., DBA Exiger Government Solutions, will equip CBP enforcement offices and personnel across the US with access to Exiger’s AI platform and data to identify illicit transshipment at-scale and in real-time.
“Billions of dollars worth of global trade move through illegal transshipment channels that seek to bypass US restrictions,” said Exiger CEO Brandon Daniels. “A core CBP mission is to enforce US trade and forced labor laws, thereby helping ensure that American manufacturers and workers are competing on a level playing field. Exiger is proud to support this mission, bringing to bear the world’s largest proprietary supply chain database and the market’s most sophisticated AI.”
Exiger’s AI will be an additional resource available to CBP personnel to:
Detect illegal transshipment across global supply chains
Monitor and enforce tariff and trade regulations
Leverage Exiger’s proprietary AI models and trade intelligence data to enrich data in CBP systems and enhance decision making
Deploy AI-enabled validations of tariff classification, value and country of origin
Create automated bills of material for products and sub-components
Map the flow of raw materials and sub-components through global supply chains
Risk-score shipments in-real time
Collect tariff revenues earlier
Trace global supply chains to enhance import visibility and risk segmentation
Exiger’s proven AI solutions have been deployed across 60+ US Government agencies, including the Department of War, Department of State, Department of Energy, DHS, the intelligence community, and armed forces.
Exiger’s technology continues to earn top recognition. In April, Exiger was named an awardee on the Government Services Administration’s Supply Chain Risk Illumination Professional Tools and Services (SCRIPTS) Blanket Purchase Agreement, and was the highest-ranked unrestricted vendor awardee of the 10-year, $919 million contract. This year, Exiger was named a Leader in the 2025 Gartner® Magic Quadrant™ for Supplier Risk Management Solutions, a Best-of-Breed Solution and three-time Value Leader in Spend Matters’ SolutionMap, and a Leader in Omdia’s Market Radar: Firmware and Software Supply Chain Security. Exiger also won a 2025 STEVIE® Award for AI Company of the Year.
New DeepL research finds AI is now used for over a third (37%) of customer interactions across UK financial services, with multilingual communication as the leading application. However, nearly two-thirds (65%) of UK financial services professionals admit employees are already using unapproved AI tools to communicate with customers
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Artificial intelligence is rapidly becoming essential to how UK banks and fintechs retain customers in international markets, according to new research from DeepL, a global AI product and research company. A new survey of 1,500 financial services professionals in Europe, including 500 across the UK reveals that AI is now embedded in customer communications – from faster support to real-time multilingual translation – with over a third (37%) client interactions already AI-powered. With nearly half of all client work now cross-border, firms are using AI to deliver consistent, trusted experiences at speed and scale. But the research also highlights growing risks from “shadow AI,” as employees turn to unapproved tools that could undermine customer trust and regulatory compliance.
AI’s Developing Role in Financial Services Customer Comms
AI is now responsible for a significant share of customer interactions in UK financial services companies. On average, 37% of all client communications already involve AI tools, a figure that is projected to rise to 46% within 12 months and 50% within three years.
The most common uses for AI in UK customer communications include:
AI powered translation (used by 52% of respondents)
Virtual assistants or chatbots for banking queries with customers (51%)
AI for fraud alerts and transaction monitoring (50%)
Automated responses for credit card or account support (48%)
Wealth management or investment advice (48%)
Translation is the most popular use case, reflecting the pressures financial services firms face in serving increasingly international customer bases, overcoming persistent language barriers, and addressing challenges in hiring multilingual staff.
How AI is Changing the Face of Cross-Border Comms
Over a third (39%) of all customer work in UK financial services companies is now cross-border. Yet firms are struggling to keep pace with the communication demands that come with international business: 85% percent of professionals report that language gaps have slowed down customer activity for non-English speakers, and 84% say it is difficult to hire staff who can communicate effectively across multiple languages and regions.
Against this backdrop, AI is emerging as a powerful tool to improve customer communication. Seven in ten UK finance professionals say AI improves the speed and availability of customer support, while the same proportion believe it helps maintain consistent communication quality across languages. Over seven in ten also report that customers are more satisfied when service is available in their preferred language. These findings highlight how AI is not only helping firms manage the complexity of cross-border work but also strengthening customer trust and loyalty in highly competitive markets.
Shadow AI Risks the Reputation of Financial Services Firms
Alongside rapid adoption of AI in customer facing areas comes increased risk. The research highlights mounting concerns around “shadow AI,” where employees turn to unapproved AI tools to save time but without oversight or safeguards.
Nearly two-thirds (65%) of UK financial services professionals admit employees are already using unapproved AI tools to communicate with customers. This poses serious cybersecurity and compliance concerns, as sensitive data may be exposed without the right safeguards. Shadow AI often arises when teams do not have access to the specialist tools they need — for example, using general-purpose AI tools when secure, purpose-built translation solutions are required. To address this, firms must ensure IT and customer-facing teams work together to choose the right solutions.
“In financial services, where every interaction is highly regulated and reputational risk is acute, staff will inevitably look for workarounds if the tools provided don’t meet their needs,” said David Parry-Jones, Chief Revenue Officer at DeepL. “The real risk is not employees experimenting with AI, but companies failing to give them secure, fit-for-purpose solutions. By building a collaborative approach between IT and frontline teams, organisations can avoid shadow AI, protect against cybersecurity threats, and still realise the full benefits of trusted AI.”
About DeepL
DeepL is a global AI product and research company focused on building secure, intelligent solutions to complex business problems. Over 200,000 customers and millions of individuals across 228 global markets today trust DeepL’s Language AI platform for human-like translation, improved writing and real-time voice translation. Building on a history of innovation, quality and security, DeepL continues to expand its offerings beyond the field of Language, including the soon to be released DeepL Agent – an autonomous AI assistant designed to transform the way businesses and knowledge workers get work done. Founded in 2017 by CEO Jaroslaw “Jarek” Kutylowski, DeepL now has over 1,000 passionate employees and is supported by world-renowned investors including Benchmark, IVP, and Index Ventures. For more information on DeepL, visit www.deepl.com.
Methodology
As a part of DeepL’s ongoing effort to analyze industry-specific and regional trends in AI adoption, Censuswide conducted a survey in June 2025 on behalf of DeepL. The research targeted 1501 professionals in financial services, split evenly across commercial banking, retail banking, fintech, and payments. The participants were located in France, Germany, the UK and Ireland, and answered nine multiple-choice questions. The questions gathered insights on how financial services teams use AI in customer service—from multilingual communication and onboarding to fraud alerts, virtual assistants, and the impact on speed, quality, and trust.
Evident’s annual AI Index reveals the banks making the biggest moves in AI… JPMorganChase, Capital One and Royal Bank of Canada are the three leading banks in AI adoption…
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JPMorganChase has maintained its position as the world’s most AI-advanced bank in the Evident AI Index. The global standard benchmark for AI adoption in the financial services sector.
According to Evident, the leading banks for AI maturity have pulled away from their peers in 2025, consolidating earlier gains and – increasingly – realising ROI for their AI investments.
Evident AI Index
The annual Evident AI Index evaluates the ongoing AI performance of 50 major banks in North America, Europe, and APAC against 70+ indicators drawn from millions of public data points.
It reveals that although nearly every bank is advancing in the Evident AI Index, the top 10 banks are increasing their scores 2.3x faster year-on-year than the rest of the Index.
This year’s top three AI performers – JPMorganChase, Capital One and Royal Bank of Canada – have retained their rankings for a third successive year. JPMorganChase takes the top spot in three of Evident’s four pillars of AI capability – Innovation, Leadership and Transparency. Capital One leads on Talent, and has continued to gain ground on its rival. While the two undisputed leaders have further extended their lead, there is now little to separate the two in terms of overall AI maturity.
The top 10 is increasingly dominated by US-headquartered institutions, but RBC, UBS and HSBC continue to secure places among the global leaders as the top performers in Canada, Europe and the UK respectively.
Based on the Evident AI Index, the ten banks leading the race for AI maturity are:
BANK
2025 INDEX
2024 INDEX
2024-25Change
JPMorganChase
1
1
–
Capital One
2
2
–
Royal Bank of Canada
3
3
–
CommBank
4
5
+1
Morgan Stanley
5
10
+5
Wells Fargo
6
4
-2
UBS
7
6
-1
HSBC
8
7
-1
Goldman Sachs
9
11
+2
Bank of America
10
15
+5
“Banking is one of the most advanced and competitive industries on the planet when it comes to developing and rolling out AI at scale.While some have described recent history as ‘The Summer AI Turned Ugly’, in the banking industry a different story is playing out. We’re beginning to see clear signs that AI investment is starting to translate into tangible financial gains, both in terms of efficiency and, increasingly, via new revenue opportunities. Banks and their shareholders expect ROI to accelerate over the next few years, and those in our top 10 are in pole position to see their efforts come to fruition.“
Alexandra Mousavizadeh, Co-founder & CEO, Evident
By far, the most competitive segment of the Index was found among those banks ranked just outside the top 10. All five of the banks in this range – BNP Paribas (#11), Citigroup (#12), TD Bank (#13), BBVA (#14), and Lloyds Banking Group (#15) saw a >20% increase in scores year-on-year (compared to ~10% for the wider Index), highlighting the intensity of the battle to keep pace with the leading banks.
Across the regions covered in the Index, all six regional leaders are unchanged from 2024, with the gap between domestic leaders’ and laggards’ AI capabilities also growing year-on-year.
Mousavizadeh adds:
“Bifurcation in AI maturity creates a credibility gap. Banks that fail to keep pace risk losing the confidence of boards, regulators, and investors. At the same time, lagging institutions will struggle to attract and retain top-tier AI talent. This combination of stakeholder doubt and the risk of talent flight slows deployment, undermines momentum, and compounds the difficulty of turning AI investments into measurable business outcomes.”
HSBC Heads Top AI Performing UK Banks
When it comes to AI adoption, the UK is one of the most consistent regions in terms of bank performance. Four of the five UK banks rank in the top half of the Index. Three of the five UK banks advanced their position in the ranking year-over-year. And all five UK banks are tightly clustered – featuring the narrowest spread between the top-performing bank (HSBC) and bottom-performing bank (Standard Chartered) across every region.
Responsible AI continues to be an area of strength, with four of the five UK banks ranking among the top 10 in the Transparency pillar. Conversely, no UK bank places in the top 10 in the Talent pillar.
HSBC improved its standing by +1 position across both the Talent and Innovation pillars, while ceding ground in Leadership (-10 rank) and Transparency (-3 rank). Consequently, HSBC lost one position in the overall ranking, but maintained a spot among the top 10 banks.
In contrast, Lloyds Banking Group demonstrated the most forward momentum, rising from 27th to 15th in the ranking. This performance was buoyed by significant jumps in Talent (+12 rank), Leadership (+20 rank), and Transparency (+14 rank), with Lloyds one of only four Index banks to improve across all four pillars of the methodology.
Mousavizadeh comments:
“Lloyds Banking Group’s strong performance reflects a significant mindset shift at the bank, with the establishment of a centralised AI team and an increased focus on AI hires to accelerate the execution of its AI strategy. The upshot is that Lloyds is now sharing more details of its active use cases and long-term plans, translating into a much improved ranking in the Index.”
In a short space of time, Lloyds has matched HSBC in the number of recent AI use cases specifying outcomes. In March, the bank filed a patent for its Global Correlation Engine (CGE) – documenting an AI-driven approach to cybersecurity threats that results in 92% fewer false positives. And in July, the bank rolled out Athena, its first large-scale GenAI product.
Measuring Returns on AI Investment
According to Evident, twice as many banks reported a total number of active artificial intelligence use cases (jumping from 12 to 25 banks since last year), and 32 out of 50 have disclosed at least one use case with an associated financial or non-financial impact – up from 26 in 2024.
While more banks are reporting returns at the use-case level, only a small group have quantified the performance of their AI portfolios at Group level. Today, eight banks are disclosing portfolio-level ROI estimates – either realized or projected – with just three reporting both.
These frontrunners include BNP Paribas, DBS, and JPMorganChase (all of which have already revised projections upwards). JPMorganChase is at the top of the table, raising its estimates from $1 billion to “heading more towards $2 billion” in AI-driven benefits, according to President and COO Daniel Pinto.
Annabel Ayles, Co-founder & Co-CEO of Evident, comments:
“All banks – regardless of size – are increasing their AI budgets, and our data shows virtually every key metric of AI adoption increasing.We’re already seeing these investments translate into tangible examples of use cases deployment. And our discussions with banking leaders suggest they’re expecting to see material, reportable AI returns in the next 12-18 months. Our data strongly suggests that this achievement is imminent. The question is: how big will the returns be? If they exceed expectations, current AI investment levels could pale in comparison to what comes next.”
Talent, Innovation, Leadership and Transparency in AI
According to Evident, the top 10 banks in the Index all demonstrate industry-leading AI performance across at least one of the four pillars, as follows:
Talent:
Ten banks now employ almost half of all AI talent in the Index (circa 90,000 workers), with US banks dominating the leaderboard.
The AI talent pool across the top 50 banks grew 25% year-over-year, the fastest on record, nearly 5x faster than overall headcount growth.
On average, the top 10 banks by talent volume disclosed nearly 2x more use cases than the rest of the banks in the Index.
38 of the 50 banks now disclose some form of AI training to its employees (up from 32 banks last year). And 33 banks now offer distinct training for senior leadership.
Innovation:
JPMorgan retained #1 spot for Innovation through the unparalleled strength of its AI research team and continued venture investments into AI-focused companies.
Capital One overtook Royal Bank of Canada for the #2 spot, partly driven by the Discover merger, doubling its AI research team and showing steady growth in patents.
HSBC moved up to #8, the leading light amongst the European banks, who otherwise don’t feature.
Despite banks rushing to fund hyperscalers and the infrastructure that will power the AI era, general investment by banks into AI-focused and Data/Tech-focused companies is down double digits (17% from 2024) for the second year in a row.
Leadership:
Over the past year, even those organizations that have traditionally chosen to keep their progress behind closed doors, are making their AI activities more visible.
Five banks maintained their top 10 ranks in Leadership: JPMorganChase took the top spot, strengthening its external AI communications efforts considerably, and Royal Bank of Canada jumped +5 ranks to take #3 position, publishing projected financial returns from AI for the first time during its Investor Day in March.
New entrants to the top-10 included: Natwest, UBS, and Morgan Stanley – and while they did not go as far disclosing financial targets for AI value, they each provided richer updates on use cases and impact than ever before.
Transparency:
JPMorganChase retained the top position for Transparency and seven of the top 10 banks carry over from 2024.
Responsible AI activity continues unabated across the industry – over the past year, the volume of RAI-specific talent found across the 50 banks more than doubled, and nearly 300 RAI-specific research papers were published, a +60% increase year-on-year.
35 of the 50 banks engage in partnerships with academic institutions, government bodies, or private companies (up from 31 banks last year), with nearly 80% of these partnerships yielding published case studies or use cases (up from 45% last year), demonstrating the increasingly tangible outcomes of their RAI efforts.
Evident AI Index Methodology
Since launching in January 2023, the Evident AI Index has quickly become established as the leading independent source of data and insight on artificial intelligence adoption across the banking industry.
The Index combines extensive research, automated data capture from public sources, consultation across Evident’s network of AI experts, and ongoing dialogue with featured banks.
Drawing from millions of public data points spanning 70+ individual indicators, it ranks each bank across four key capability areas which collective signal AI maturity:
Talent: measures the depth, density and development of AI talent within each organisation.
Innovation: captures long-term investment in AI-related innovation, including research, patents, partnerships and engagement with the open-source ecosystem.
Leadership: assesses the role of leadership in setting and communicating the organisation’s AI agenda.
Transparency: evaluates public engagement with Responsible AI (RAI), from internal talent and frameworks to external partnerships and disclosures.
Samsung and OpenAI Announce Strategic Partnership to Accelerate Advancements in Global AI Infrastructure
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Samsung will bring together technologies and innovations across advanced semiconductors, data centres, shipbuilding, cloud services and maritime technologies
OpenAI, Samsung Electronics, Samsung SDS, Samsung C&T and Samsung Heavy Industries have announced a letter of intent (LOI) for their strategic partnership to accelerate advancements in global AI data centre infrastructure and develop future technologies together in relevant fields. This expansive collaboration will bring together the collective strengths and leadership of Samsung companies across semiconductors, data centres, shipbuilding, cloud services and maritime technologies.
The signing ceremony was held at Samsung’s corporate headquarters in Seoul, Korea, attended by Young Hyun Jun, Vice Chairman & CEO of Samsung Electronics; Sung-an Choi, Vice Chairman & CEO of Samsung Heavy Industries; Sechul Oh, President & CEO of Samsung C&T; and Junehee Lee, President & CEO of Samsung SDS.
Samsung Electronics
Samsung Electronics will work with OpenAI as a strategic memory partner to supply advanced semiconductor solutions for OpenAI’s global Stargate initiative. With OpenAI’s memory demand projected to reach up to 900,000 DRAM wafers per month, Samsung will contribute toward meeting this need with its extensive lineup of high-performance DRAM solutions.
As a comprehensive semiconductor solutions provider, Samsung’s leading technologies span across memory, logic and foundry with a diverse product portfolio that supports the full AI workflow from training to inference.
The company also brings differentiated capabilities in advanced chip packaging and heterogeneous integration between memory and system semiconductors, enabling it to provide unique solutions for OpenAI.
Samsung SDS
Samsung SDS has entered into a potential partnership with OpenAI to jointly develop AI data centre and provide enterprise AI services.
Leveraging its expertise in advanced data center technologies, Samsung SDS will collaborate with OpenAI in the design, development and operation of the Stargate AI data centers. Under the LOI, Samsung SDS can now provide consulting, deployment and management services for businesses seeking to integrate OpenAI’s AI models into their internal systems.
In addition, Samsung SDS has signed a reseller partnership for OpenAI’s services in Korea and plans to support local companies in adopting OpenAI’s ChatGPT Enterprise offerings.
Samsung C&T and Samsung Heavy Industries
Samsung C&T and Samsung Heavy Industries will collaborate with OpenAI to advance global AI data centers, with a particular focus on the joint development of floating data centers.
Floating data centers are considered to have advantages over data centers because they can address land scarcity and lower cooling costs. Still, their technical complexity has so far limited wider deployment.
Building on their proprietary technologies, Samsung C&T and Samsung Heavy Industries will also explore opportunities to pursue projects in floating power plants and control centers, in addition to floating data center infrastructure.
Starting with the landmark partnership with OpenAI, Samsung plans to fully support Korea’s goals to become one of the world’s top three nations in AI and create new opportunities in the field.
Samsung is also exploring broader adoption of ChatGPT within the companies to facilitate AI transformation in the workplace.
About OpenAI
OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity.
About Samsung Electronics Co., Ltd.
Samsung inspires the world and shapes the future with transformative ideas and technologies. The company is redefining the worlds of TVs, digital signage, smartphones, wearables, tablets, home appliances and network systems, as well as memory, system LSI and foundry. Samsung is also advancing medical imaging technologies, HVAC solutions and robotics, while creating innovative automotive and audio products through Harman. With its SmartThings ecosystem, open collaboration with partners, and integration of AI across its portfolio, Samsung delivers a seamless and intelligent connected experience.
Robert Cottrill, Technology Director at digital transformation company ANS, explores how businesses can harness the potential of AI while mitigating the growing risks to cybersecurity and privacy
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AI can transform businesses, but is it also opening the door to cybersecurity risks?
Fuelled by competitive pressure and rising government support through the UK’s Industrial Strategy, it’s no surprise that more and more businesses are racing to adopt AI.
But there’s a catch. The more businesses scale their AI adoption, the bigger their attack surface becomes. Without a proactive and structured approach to securing AI systems, organisations risk trading short-term efficiencies for long-term vulnerabilities.
The AI Boom
AI investment is skyrocketing. Businesses are deploying generative AI tools, machine learning models, and intelligent automation across nearly every function, from customer service and fraud detection to supply chain optimisation. Platforms like DeepSeek and open-source AI models are now part of the mainstream tech stack.
Initiatives like the UK’s AI Opportunities Action Plan are fuelling experimentation and adoption. AI is now seen not just as a productivity tool, but as a critical lever for digital transformation.
However, the rapid pace of AI deployment is outpacing the development of the security frameworks required to protect it. When integrated with sensitive data or critical infrastructure, AI systems can introduce serious risks if not properly secured. These risks include data leakage through AI prompts or model training, as well as AI-generated phishing and social engineering attacks
While technical threats often take centre stage, businesses also can’t forget the increasing regulatory requirements surrounding AI.
As AI systems become more powerful, enabling businesses to extract valuable insights from vast datasets, they also raise serious ethical and legal challenges.
Regulatory frameworks like the EU AI Act and GDPR aim to provide guardrails for responsible AI use. But these regulations often struggle to keep up with the rapid advancements in AI technology, leaving businesses exposed to potential breaches and misuse of personal data.
The Need for Responsible AI Adoption with Cybersecurity
To build resilience while embracing AI, businesses need a dual approach:
1. Prioritise AI-specific training across the workforce
Cybersecurity teams are already stretched. Introducing AI into the mix raises the stakes. Organisations must prioritise upskilling their cybersecurity professionals to understand how AI can both protect and threaten systems.
But this isn’t just a job for the security team. As AI tools become embedded in daily workflows, employees across functions must also be trained to spot risks. Whether it’s uploading sensitive data into a chatbot or blindly trusting algorithms, human error remains a major weak point.
A well-trained workforce is the first and most crucial line of defence.
2. Adopt open-source AI responsibly
Another key strategy for reducing AI-related risks is the responsible adoption of open-source AI platforms. Open-source AI enhances transparency by making AI algorithms and tools available for broader scrutiny. This openness fosters collaboration and collective innovation, allowing developers and security experts worldwide to identify and address potential vulnerabilities more efficiently.
The transparency of open-source AI demystifies AI technologies for businesses, giving them the confidence to adopt AI solutions while ensuring they stay alert about potential security flaws. When AI systems are subject to global review, organisations can tap into the expertise of a diverse and engaged tech community to build more secure, reliable AI applications.
To adopt responsibly, businesses need to ensure that the AI they are using aligns with security best practices, complies with regulations, and is ethically sound. By using open-source AI responsibly, organisations can create more secure digital environments and strengthen trust with stakeholders.
Securing the Future of AI
AI is a transformative force that will redefine cybersecurity. We’re already seeing AI being used to automate threat detection and response. But it’s also powering more advanced attacks, from deepfake impersonation to large-scale automated exploits.
Organisations that succeed will be those that embed cybersecurity into every stage of their AI journey, from innovation to implementation. That means making risk management part of the innovation conversation, not a downstream fix.
By taking a responsible approach, investing in training, leveraging open-source AI wisely, and embedding cybersecurity into every layer of the business, organisations can unlock AI’s potential while defending against its risks.
AI is a double-edged sword, but with thoughtful adoption, businesses can confidently navigate the complex landscape of AI and cybersecurity.
CIBC launches GenAI platform, CAI, for data analysis, accelerated research, light coding and more…
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CIBC today announced the bank-wide launch of CIBC AI (CAI), its in-house Generative AI platform, to help drive further productivity across the organization and enable team members to deliver on the bank’s client-focused strategy.
CIBC AI (CAI)
CAI launched a pilot phase in July 2024 with an initial group of team members across Canada, the US and the UK. The AI platform has saved team members an estimated 200,000+ hours during the pilot by enabling team members to automate common tasks such as summarizing documents, drafting emails, compiling research and other text-based content.
“It’s been tremendous watching the uptake of CAI across our bank and how it has helped simplify routine tasks for team members, better enabling them to focus on delivering value to our clients. What sets CAI apart is its adaptability to the unique needs of each team, from writing to research and analysis or even light coding suggestions, CAI has had a positive impact across all lines of business.”
Dave Gillespie, Executive Vice-President, Infrastructure, Architecture and Modernisation, CIBC
CAI is a custom-built Generative AI platform that was designed by CIBC from the ground up to support team members with a task-driven approach. It features an intuitive dashboard that allows users to easily navigate through various functionalities such as data analysis, accelerated research and preparing presentations. With the adoption of CAI, team members are able to focus their time on higher value activities.
Responsible AI
Team members need to complete a mandatory training course in order to access CAI, which provides an understanding of CIBC’s approach to AI and data, as well as the responsible governance framework in place to guide the use of AI at the bank.
“Innovation has long been a hallmark of CIBC’s approach to meeting client needs, and we’re incredibly proud to take another exciting step forward in enhancing everyday experiences for our team members.” added Gillespie.
CIBC reinforced its commitment to responsible AI by becoming the first major Canadian bank to sign the Government of Canada’s Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems in March.
About CIBC
CIBC is a leading North American financial institution with 14 million personal banking, business, public sector and institutional clients. Across Personal and Business Banking, Commercial Banking and Wealth Management, and Capital Markets and Direct Financial Services businesses, CIBC offers a full range of advice, solutions and services through its leading digital banking network, and locations across Canada, in the United States and around the world. Ongoing news releases and more information about CIBC can be found at www.cibc.com/ca/media-centre.
New data from Evident shows banks are increasingly turning AI research into real-world tools
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AI benchmarking and intelligence platform Evident has published its latest report… The State of AI Research in Banking, analyses over 2,700 AI-specific papers from 50 of the world’s largest banks.
The State of AI Research in Banking
The report shows that the big banks have increased their annual artificial intelligence research output by 7x over the past five years. The most AI-advanced institutions are focusing on research areas that directly serve their AI production pipelines.
Since 2019, the number of banks publishing AI research has nearly doubled from 25 to 46 from 50 banks tracked by Evident. Last year, two-thirds of this research (65%) was driven by just five banks. They are JPMorganChase (37%), Capital One (14%), Wells Fargo (5%), RBC (5%), TD Bank (4%).
According to Evident, it’s possible to map the banks’ historic research pipelines directly to their artificial intelligence use cases and products. From RBC’s ATOM model powering responsible lending to Capital One’s multi-agent systems for customer service. Examples of banks where research papers have served as blueprints for production include:
Capital Markets & Trading: Scotiabank, RBC Borealis, BlackRock, JPMorganChase
Transactions, Risk, AML, and Fraud: RBC Borealis, NatWest, CommBank
Agentic AI and Workflow Automation: Capital One, JPMorganChase, UniCredit
Causal AI and Personalisation: BBVA, TD Bank
Customer Experience and Summarization: NatWest, JPMorganChase
“Through their research programmes, banks like JPMorganChase, Capital One, RBC, Wells Fargo, and TD Bank are setting the tone for how AI will be deployed in high-stakes, regulated environments. In contrast to the more commercially-guarded R&D practices of Big Tech, these banks are signalling the future of applied AI in financial services. And, most impressively, moving from research pipelines into production at scale within two to three years. Which is lightning fast by academic standards.”
Alexandra Mousavizadeh, Co-founder & CEO, Evident
The Rise of Agentic AI
The State of AI Research in Banking report also points to the rise of Agentic AI as a priority within the world’s largest banks.
Evident’s data shows that AI Agents and Agent-based Systems research is now the fifth most popular research paper theme. Agentic themed research accounts for nearly 6% of year-to-date 2025 publications – or twice the current share of public agentic use cases Evident found across banking.
As more resources pour into agentic research, there has been an accompanying year-over-year decline in papers focused on Computer Vision (-0.7%), Scientific Discovery (-1.8%), and Healthcare / Biomedicine (-2.2%). This data further underscores where and how banks are shifting efforts away from open inquiry, in favour of applied research that clearly relates to immediate business applications.
“While academic research within big business is often dismissed as a vanity exercise to keep PhDs happy, our analysis shows the opposite. The leading banks are pushing the frontier on emerging technologies like agentic AI – building the architectures and workflows that will soon underpin real-world applications. This isn’t research for research’s sake: it’s laying the foundation for faster deployments, smarter trading agents, and the next frontier of AI-driven financial services,” added Mousavizadeh.
About Evident
Evident is the intelligence platform for AI adoption in financial services. The company supports leaders stay ahead of change with in-depth insights, benchmarking, and real-time data through its flagship Indexes, Insights across Talent, Innovation, Leadership, Transparency and Responsible AI pillars, a real-time Use Case Tracker, community and events. Evident also provides private outcomes benchmarking, enabling firms to understand how their adoption of artificial intelligence compares to peers. Learn more at www.evidentinsights.com
The proof, as they say, is in the pudding – and the evidence of TealBook’s increasingly-successful evolution lies in its client relationships
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We talked endlessly about data and AI at DPW New York 2025. A universal truth is that the successful implementation of AI requires clean data; it doesn’t have to be perfect, but businesses certainly need to have a decent handle on their data before adopting AI tools successfully.
To help make this a reality, North American data and software company TealBook has recently announced a legal entity-based data model. It’s designed to resolve supplier records to the correct legal entities, map parent-child relationships, and enrich profiles with verifiable attributes, enabling accurate supplier data to flow seamlessly into procurement systems and AI applications. “This is part of a 12-year journey for TealBook,” says Stephany Lapierre, the company’s Founder and CEO. “Our vision has always been to build a way to enable procurement organisations to have high quality data with a lot of integrity, in order to give them the trust they need to put data directly into their systems.
“Twelve years ago, we underestimated the complexity of getting large enterprises to trust a third-party data solution. As part of our journey, we started using AI early on to find information where it exists on supplier websites and databases, and start creating digital profiles in a structured way for procurement to access it, match it to their vendor master, and use it.”
TealBook’s evolution
But, again, at the beginning, TealBook couldn’t be sure whether the data was high enough quality. In 2017, the company was primarily known as a supplier discovery application, positioned as a pre-sourcing engine to help procurement teams identify alternative suppliers. At the time, TealBook’s data and models enabled it to determine which companies were similar to others, allowing users to search and find comparable suppliers to expand their sourcing options.
“But that was just a way for us to deliver something that was underserved in the market,” Lapierre continues. “Then our customers started asking for certificates, which are hard to collect and match. They needed cleaner data. They felt they were under-reporting. So in 2018, we started to see whether our technology could refine the data more, and focused on certificates and supplier diversity. We collected great use cases along this journey, and the vision never wavered.
“Just last year we released a new technology – completely different, really sophisticated – allowing us to pull from a lot more data sources, and we have provenance so our customers can actually verify where the data’s coming from. We can match it to vendor masters. And now, we also have this new model that includes 230 million verifiable global legal entities from across 145 countries’ registries. We marry this with global parent and child hierarchy, which is really hard for our customers to match themselves.”
Partnership with Kraft Heinz
Now, after 12 years of that vision, TealBook is deeply proud of what it’s achieved. Part of its ability to get to this point is due to early adoption from key customers. Kraft Heinz is a business which Lapierre describes as a “co-innovation partner”, and has been invaluable in helping TealBook achieve its recent goals.
From the perspective of Stefanie Fink, Head of Global Data and Digital Procurement at Kraft Heinz, the partnership has been an immediately valuable one. “It really started with having a visionary, like-minded relationship,” she says. “That’s an important piece of it, because my vision for procurement is that we are partners in our enterprise.
“In order for us to do our jobs, we have to bring in the right data for use. This is where Stephany’s partnership and vision really resonated. We were really looking for diversity and we could make things easier for our partners, while making sure we had the right people in our ecosystem. We also had to lift up the hood and see what was underneath everything we’ve got. Stephany brought our vision to life. TealBook has evolved too, as we’ve seen; it’s more about orchestration and software-as-a-service. It has been a partnership of need and we cannot continue to do other things without this kind of partnership around data.”
When initially dabbling with this relationship, Fink was clear that Kraft Heinz had no desire to be taking care of more stuff. What she wanted from TealBook was a strong focus on good quality data. After last year’s product release from TealBook, Kraft Heinz already saw its data enriched by 25%. The recently-announced new data model gives the business and TealBook’s other customers the right structure tied to a legal entity, which is a highly credible anchor. “We’re able to do entity resolution – all automated – remove all the duplicates, and then you start with a clean, digitised vendor master,” says Lapierre. “That’s what brings further enrichment.”
The challenge of assessing data quality
Assessing its data before involving TealBook was important for Kraft Heinz, but challenging for such a large organisation. “We had to fail first and fail fast,” says Fink. “We tried some AI around fixing things early, but that didn’t work for us. It was a real eye-opener, realising where this next evolution could take us regarding focusing on AI and agents for the right things, not the meaningless things. Before, we were asking agents to tell us if things were duplicates, when we should have been asking: what do these suppliers offer? Where is the innovation? Where is the value?”
What surprised Fink most when looking under Kraft Heinz’s hood was the lack of attention that was being paid to what the business was doing. “It was amazing that nobody had questioned it sooner,” she says. “So I said, let’s take this as a crawl, walk, run approach, and I have a wonderful CPO who really understands where we want procurement to go as a function. She was excited about us just getting it done and getting people involved, and that’s what it takes: real pride in ownership of the data.”
Getting engrossed in GenAI
True partnership and an all-in approach has enabled Kraft Heinz to work successfully with AI – something some businesses are struggling with as the conversation around artificial intelligence grows louder. For Lapierre, as the CEO of a tech company, adopting AI successfully has meant trying and failing and being fully entrenched in AI as it has evolved.
“We’ve been using AI in our technology since 2016,” she states. “We’re an early adopter. We’d be talking about scraping data, and data in the cloud, and AI models, and our customers’ pupils would widen in surprise. We’ve come a long way and the market has come a long way.
“The technology we deliver today wouldn’t be possible without the AI tools now at our disposal. We used to build models; we don’t do that anymore. We spend a lot of time investing in engineers to build and test models, and that’s made us so much more efficient. I use GenAI every day for so many things now, and I’m encouraging my team to be so involved in AI. That’s how you build expertise, and you need really strong expertise to use GenAI well.
“Getting good with AI is about taking risks and having a leadership team that pushes for new things, and suddenly the successful use of AI becomes a habit.”
The march towards agentic AI can be a daunting thing, but it’s important to get over that fear in order to make strides
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A common question when discussing AI is ‘where do humans fit in?’. The fear of technological advancements stealing our jobs is an old one, but the conclusion is always the same and always true: there will never be a time when human judgement and teamwork isn’t required.
At DPW New York 2025, we sat down with Rinus Strydom, Chief Revenue Officer at Pactum AI, and Steven Velte, Executive Director Procurement Transformation at Honeywell – a customer of Pactum AI – to discuss AI’s evolution and the human connection. As AI develops, for Strydom, Pactum’s focus is on agentic, rather than generative. There’s a key difference there, especially for initial adoption at large enterprises.
“A lot of enterprises feel a little bit afraid, because generative AI can go a little off the rails,” he explains. “But when you put agents to work, they’re always within the rails that are defined by the customers. Once we get over that hurdle and can make clients see that they can take their procurement operating model and have it just run at scale with agents, rather than being afraid that their image will get tarnished, AI can be put to work much faster.”
Putting AI to work
When it comes to strategies procurement leaders can adopt to make AI work for them, it’s a major discussion point for Strydom and Velte. As a customer, it’s important for Honeywell to feel like its work with Pactum AI is a collaboration; it’s part of what makes its strides into AI work successfully. “This collaboration goes deeper than what we’ve typically had in the past,” says Velte.
“When we go through organisational changes, we need a true partner, And when that partner gets into the elevator with you, they don’t just push the button with you – they go up to the next floor with you and sit at the table to talk about what’s happening. So a barrier to AI adoption is not having that deep collaboration and partnership.”
“I think another thing leaders can do today is really help with that psychological change management to make it feel like a safe thing,” Strydom adds. Mindset shift is such a vital part of this change, especially when it comes to successful collaboration. “It’s important to embrace agentic AI, to encourage people to become managers of agents and not run away or become fearful.”
Identifying the opportunities
The true benefits of AI are now beginning to present themselves, as people increasingly embrace AI. For Velte, businesses have to get going with their AI plans in order to realise where the real opportunities lie. “I can make a business case with tons of ROI, potential productivity gains, revenue uplift, bottom line, profit line – all of that. But the real benefits that come from AI are those hidden benefits we don’t realise. When you start looking at it, there’s a common theme of saving time, and time becomes the real benefit. Unlocking better use of time gives you more potential to work on other creative aspects of the business.”
For Strydom, the true value lies in achieving things that used to be extremely difficult to achieve. Pactum AI’s customer base is broadly looking at 10X ROI, which, now, is easily done thanks to the use of AI agents. Agents also allow procurement teams to scale extremely fast, which is something that has, historically, been hard-won.
“For example, if you need to change payment terms across your entire supply base, you can do that with thousands of agents in parallel. You could never do that before. It gives you the agility to react to global macro risk issues, like tariffs.”
Start now; perfection comes later
One of the loudest topics of conversation at DPW New York 2025 was data quality and the challenge of cleaning that data up. It’s a huge topic, and a daunting one. Many businesses fall into the trap of thinking their data has to be perfect before they can get fully involved with AI, but the conclusion many procurement leaders are coming to is that getting started is more important than perfection.
“Data quality is always the holy grail going forward,” says Velte. “Everyone’s going to look for it, and try to attain it. When you start implementing within an AI framework, you just need to go in there and know that you’re going to constantly evolve in a good way, thanks to the agents, AI programs, and initiatives. They’re going to uncover and unlock a lot of data and inconsistencies that you have. You won’t get there unless you start looking into them as an opportunity area. Data perfection is not the way to go; it’s about getting in there, starting to look at the opportunities, and being willing to be creative, disruptive, and innovating quickly.
“There’s never going to be a time when everything is 100% correct and accurate, because data is always evolving,” adds Strydom. “Start now. The data can be enriched over time with the agents’ help.”
Maximum savings, maximum momentum
Pactum is using AI specifically to enable it to be a strategic advisor for customers like Honeywell. The use cases coming out are very new, and changing fast. What Strydom and his team want is to be able to guide customers on the right strategies for them, how to get maximum savings, and maximum momentum. As this landscape becomes more complex, human intervention and guidance is more important than ever, which links back to the topic of mindset and change management.
There’s been a lot of debate within Pactum AI as to how the business embraces this. “From a marketing perspective, too, there’s the question of whether we should make our agents look human,” says Strydom. “Actually, what we’re seeing is that suppliers actually enjoy interfacing with a bot. Walmart, one of our customers, did a survey where they found that 85% of their suppliers actually prefer to negotiate with Pactum than with a human. It’s more efficient, fair, and unbiased.”
Speaking of humans, shortage of talent has been a talking point within procurement for some time. That was, until advanced tech became more widely adopted, and bringing in procurement experts became less important than bringing in technology experts who are willing to learn. With the advent of agentic AI, according to Strydom, procurement leaders are now acting as managers of agents.
“All the analyst surveys say that procurement organisations are being asked to do more with less every year,” he says. “So the type of talent is definitely transforming. What we see is that the procurement organisations of the future are much more strategic. They’re focusing on creating strategy and procurement policies and procedures, and then having the agents actually go out and do the menial day-to-day work – entering things into ERP, turning requisitions into purchase orders, onboarding suppliers, and so on. All of that can now be done very quickly and efficiently by agents. This really elevates the role, and allows procurement to become a partner to the business.”
Velte adds: “When you talk about talent shortage, it’s also that shift in the mindset we’re going through right now. The expertise is changing, and we want to be able to bring in talented people with that technology flare. When we look at the next generation of leaders coming out of university and college, they’re AI enabled already. They’re expecting AI to be available to them to accelerate their development, career goals, and ambitions.”
Making sense of the landscape
As DPW New York 2025 unfolded around us, the discussion inevitably turned to the ways in which DPW helps procurement make sense of the AI landscape. Pactum AI is actually a perfect example of how useful DPW is. Only four years ago, the business was a startup, and won a pitch contest at DPW Amsterdam. “That catapulted the business, and got us a lot of visibility,” says Strydom. “It’s a great place for visibility with practitioners, investors, and partners.”
Again, it comes back to people. Being able to meet them in real life, communicate face-to-face, and learn from one another. “It’s about reconnecting with a lot of our partners,” says Velte. “But it’s also about seeing what is out there on the forefront that’s becoming available. It’s an amazing opportunity for us to really benchmark ourselves, while also getting a glimpse of what’s coming around the corner.”
Enterprise-wide AI platform security protects sensitive data and governs integrations to help organisations scale Agentic AI with confidence
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ServiceNow the AI platform for business transformation, has unveiled its new Zurich platform release. It delivers breakthrough innovations with faster multi-agentic AI development, enterprise-wide AI platform security capabilities, and reimagined workflows. New intelligent developer tools enable secure vibe coding with natural language. This helps turn employees into high-velocity builders and creators and lower the barrier to app creation. Built-in security capabilities, including ServiceNow Vault Console and Machine Identity Console, natively secure sensitive data across workflows. This governs integrations to help organisations scale Agentic AI and innovations with confidence. The introduction of autonomous workflows turns data into action through agentic playbooks. Uniquely offering the flexibility to apply AI and human input in workflows where and when it’s needed for greater control and efficiency.
AI Transformation with ServiceNow
Enterprise leaders are racing to move beyond table-stakes AI implementations to unlock transformative, tangible results. According to Gartner, “By 2029, over 60% of enterprises will adopt AI agent development platforms to automate complex workflows previously requiring human coordination.” The ServiceNow AI Platform delivers this transformational promise across the enterprise. It underpins a new era of highly efficient human-AI collaboration.
“Zurich marks a turning point for enterprise AI. ServiceNow is delivering multi-agentic AI systems in production that are not just powerful, but governable, secure, and built for scale,” said Amit Zavery, president, COO, and chief product officer at ServiceNow. “We are transforming the enterprise tech stack to be AI-native. From autonomous workflows that act on data with precision, to developer tools that democratise high-velocity innovation. With built-in controls for security, risk, and compliance, we’re helping organisations move beyond experimentation. And into a new era of intelligent execution.”
Vibe Coding Meets Enterprise Scale
According to Gartner, “Agentic AI features will be near ubiquitous, embedded in software, platforms and applications, transforming user experiences and workflows.” The introduction of ServiceNow Build Agent and Developer Sandbox provides resources for employees to work with AI more efficiently. They can now do this conversationally, and at scale, to solve real problems in every corner of the business.
Build Agent is a breakthrough for enterprise app creation—bringing vibe coding to the rigor of the ServiceNow AI Platform. In seconds, employees can turn an idea into a production-ready application by asking in natural language. Say, “Create an onboarding app that assigns tasks to HR, IT, and Facilities,” and Build Agent handles the rest. Design, build, logic, integrations, testing, and industry-leading governance included. What sets it apart is enterprise discipline: every app comes with audit trails, security, and compliance built in. Developers and citizen creators alike get the speed of AI with the confidence of enterprise-grade control, in a streamlined interface.
Developer Sandbox empowers developers to build better applications, faster, while maintaining the highest standards of quality. Sandboxes provide isolated environments within a single instance, so multiple teams can collaborate, build, and test new features without conflicts, and rapid scale doesn’t come at the cost of control. Teams can version, iterate, and deliver without waiting in line for developer resources. Developers can safely experiment with vibe coding, test AI-powered workflows, and resolve version control issues before changes go live. This reduces rework, shortens feedback loops, and helps teams ship higher-quality applications rapidly with lower risk.
Security That Enables AI Strategy
As enterprises adopt autonomous workflows powered by agentic AI, securing how these systems access data and communicate across environments is essential. Zurich introduces new built-in AI platform security capabilities to make it easier to protect sensitive information. It can also govern integrations and manage growing AI footprints.
The newServiceNow Vault Console provides a guided experience to discover, classify, and protect sensitive data across workflows. For example, an admin managing customer service operations can now identify personal data across tickets, apply different types of protection policies, and track compliance activity. The console also offers recommendations for protecting newly discovered sensitive data, along with customizable dashboards to monitor key metrics. What used to require manual configuration across multiple tools can now be managed in one place, with intelligent insights and a streamlined experience.
Machine Identity Console addresses the need for integration security with enterprise-grade authentication and authorization, delivering control over bots and APIs head on. As the ServiceNow AI Platform scales, every API connection, including those from AI agents, introduces another identity to manage and determine what it can access. This console gives platform teams visibility into all inbound API integrations using machine identities such as service accounts and keys, flags outdated or weak authentication methods, and provides clear steps to strengthen security. If an integration is using basic authentication or hasn’t been active in 100 days, the console spots it and helps resolve it.
Digital Transformation
“At Kanton Zürich, digital transformation is central to how we deliver secure and efficient public services. Since 2018, ServiceNow has enabled us to centralize and standardize our processes with data security as a top priority,” said Jürg Kasper, head of business solutions, Kanton Zürich. “Zurich’s latest advancements in both security and AI will allow us to automate more complex workflows, unlocking new efficiencies that enhance how we serve our citizens—with greater speed, clarity, and assurance.”
Without built-in security and trust, scaling AI comes with risk. These new security features in Zurich build upon ServiceNow’s AI Control Tower, announced in May 2025, which provides enterprise-wide visibility, embedded compliance, and end-to-end lifecycle governance for Agentic AI systems. By centralising oversight of every AI agent, model, and workflow, native or third-party, the AI Control Tower ensures organisations can scale AI with confidence, aligning innovation with enterprise-grade security and trust.
Turn Data Into Outcomes With Autonomous Workflows
As organisations rapidly scale AI, they face the added challenge of delivering solutions consistently, reliably, and responsibly. Enterprises need the right guardrails, full visibility, and strong governance to achieve service delivery. Or they risk eroding trust and slowing results. ServiceNow’s AI Platform does all this in a single platform. It sets a new standard for how organisations can create autonomous workflows to turn data into action and AI into measurable business impact.
Agentic playbooks from ServiceNow bring people, automation, and AI together seamlessly, powering autonomous workflows. A traditional playbook is a structured sequence of automated steps. These are based on predefined business rules and processes—ideal for ensuring consistency, efficiency, and trust. Agentic playbooks amplify this model by embedding AI into the trusted framework. AI agents eliminate manual effort, completing tasks in seconds and accelerating execution. This frees employees to focus on higher-value work where human judgment matters most. For example, in a credit card support situation, an agentic playbook can guide an AI agent to verify someone’s identity. It can freeze a card, send a replacement and notify the customer while allowing a human agent to step in. The result: governed, efficient, and trusted work—supercharged by AI to deliver faster, smarter outcomes.
The ServiceNow Zurich platform release also seamlessly combines Process and Task Mininginsights within a unified platform. These new capabilities give organisations an end-to-end understanding of how work gets done. Revealing where human expertise is essential, and where AI agents can deliver the greatest impact. With process intelligence built directly into the platform, customers can move seamlessly from insight to action. Streamlining operations, applying AI where it matters most. And accelerating real business outcomes without the complexity of disconnected legacy tools.
At Kinexions 2025, Jennifer Roberts, Supply Chain Leader, IBM who talked us through how the supply chain is transforming at the global giant
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Jennifer Roberts, Supply Chain Leader at IBM, is visibly buzzing as she shares her favourite Kinexions moments so far. “Kinexions is really exciting,” she says, having flown in from Raleigh-Durham, North Carolina to be here. “The first thing for me is getting to see the people I work with at Kinaxis who help advance the solution within IBM,” she explains. “We have a great account management team that’s helping us look to the future. And the energy here is always exciting. They really are a motivating company when it comes to thinking about the future. I’m really thankful that IBM invested in the ability of our teams to join the event this year.”
Roberts and IBM’s C-level executive suite for supply chain are located at Raleigh-Durham’s Research Triangle Park where IBM has a large facility covering 600 acres. “It’s a good place to be,” she says. “But a large part of my team is broadly located throughout the US in Poughkeepsie, New York, Rochester and Minnesota. And then we also have a team down in Guadalajara, Mexico. The global supply chain is located everywhere, but the people I work with are primarily in those locations.”
Roberts leads Demand Planning Operations for IBM’s hardware manufacturing division, supporting mainframe, power, and storage products across both internal and contract manufacturing. She supports transformation efforts within the Demand Supply Planning and Inventory organisations.
Supply chain transformation
Roberts specialises in configuring and modelling planning architecture in Kinaxis and SAP, translating, automating and transforming business processes, while identifying and collecting the relevant data from various large unstructured data sources. Her goal is to optimise supply chain processes and tools, reduce costs, improve efficiency and enhance customer satisfaction.
The words “revolution” and “transformation” have embodied the discourse at Kinexions and these are two concepts that play out in a major way at IBM. “Our business is all about transformation,” she explains. “We are constantly looking to evolve to solve a variety of different areas of opportunity. There’s certainly never a day where we aren’t thinking about what the next disruption may be. And so within our organisation, we focus a lot on resiliency, protecting our supply chain and ensuring we can deliver quality to our clients.” Indeed, IBM onboarded Kinaxis around five years ago to help transform Demand Planning and Supply Planning. Kinaxis Maestro provides IBM with the transparency needed to see how changes in demand and supply affect each other, utilising the most current data to run multiple concurrent scenarios.
AI in supply chain
IBM’s supply chain transformation efforts are currently focused heavily on AI. Of course, IBM has been leaders in the AI space for quite some time with the Watsonx products, but supply chain is considered client zero within IBM for that platform. “We are focused on efficiencies in the organisation, digital transformation, developing digital twins and taking enterprise data and bringing it together so that we can orchestrate a plan that is visible to all through one source of truth,” she reveals. “And that’s something we can all execute against seamlessly.”
“Everyone wants data in real-time. Everyone is looking for accuracy of data. They’re looking for answers to problems faster than we’ve ever been able to perform before,” she explains. “When the next big diversion comes, the next big distraction, we need to be able to quickly align ourselves, not just within the supply chain, but upstream with our sales organisation, who are feeding us all the sales opportunities and giving us insight into where the business is going. And then our downstream suppliers need to be equally connected. So, we partner with those organisations to ensure it’s all very seamless and that our data flows in both directions so we can manage results. So, one of the advantages of our internal AI supply chain tool, which we call CSCA 360 (Cognitive Advisor), is to get a 360-degree view of the world considering all those products. And access is a big part of that because we run our S&OP and MRP (Material Requirements Planning) processes through that tool, along with our inventory management process as well.”
According to Roberts, the biggest opportunities for Supply Chain at IBM lay within ways to mitigate disruptions earlier, boosting resiliency and agility, while protecting the supply chain. “There are things that hit us between the eyes at the last minute, and we have to be as responsive as possible to solve those problems. Data insights and being able to assess them proactively, is so important. And that’s where I see our organisation heading more strategically, through taking the data, ingesting it faster, making decisions on it, using generative AI and focusing on allowing people to dig into the data more quickly and get answers on information they’re seeking. We’ve been using agentic AI for years, but we’re really starting to dig into what it can do for us now in terms of impacting productivity.”
The human touch
Although Kinexions has been showcasing transformation and technological revolution it has also stressed the importance of work culture, something vitally important to Roberts. “Our leadership drives the mindset of transformation being at the forefront of where we’re going, in order to keep up with the demands of the future,” she tells us. “We’re always being asked to look at where we can create opportunities within the business and not just taking the leadership’s advice on what we should be doing. We look to all our employees and get their ideas from the bottom up; deciding whether or not there’s business value that can be returned from things that aren’t always visible.
“I think the most important part of your business is your people. Without having the ability of your people to be transparent in where they see opportunities, you really are going to hold yourselves back. Keep an open mind, ask a lot of questions, listen closely. I’m always told you have two ears and one mouth. And I think as a leadership team, you should allow your employees to come forth with ideas, plus, we need to think about why they are suggesting them – well, it’s because they’re impacted every day by what’s going on around them. So, listen.”
From automating decisions to redefining procurement talent, AlixPartners lays out why risk-takers lead the way.
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The use of artificial intelligence (AI) in procurement is gaining traction with many organisations already looking at how the technology can improve processes. However, there’s scope to go beyond efficiency and instead focus on transforming value delivery.
At DPW New York, we spoke to Amit Mahajan and Aaron Addicoat from AlixPartners, a management consultancy firm doing things a little differently. The organisation is advising its clients on how to implement AI to drive value, but it’s also using AI internally, too.
“AlixPartners has a unique business model,” explains Addicoat. “We have a very senior model, very few junior resources. So now you imagine taking people with 10 or 15 years experience and now you equip them with AI… For us, it’s a huge unlock.”
This is about more than just productivity gains. AlixPartners focuses on using AI to transform the way procurement teams work, while crucially, maintaining the human touch.
How procurement professionals are using AI
With the support of technology, it’s possible to shift procurement from a cost-saving exercise to a potential revenue driver. Procurement teams are already looking for these opportunities, as Mahajan explains. “They’re starting to think about new ways of doing things,” he says. “It’s not just automation, but asking how do I leapfrog and do something differently?”
There are plenty of use cases where AI is helping with automation. This is a great place to start as it frees up human workers to do more valuable jobs that need a personal touch. “I have a client who’s using AI every day,” says Addicoat. “This allows them to review documents and contracts rapidly, to find key clauses and termination dates. They’re also using it in spend control processes to identify which things need to be reviewed more thoroughly.”
Many organisations are also using AI agentically to create their own bots. This gives teams a more accessible way to review information. “One example is a client who’s using AI for their business to help with acronyms,” says Addicoat. “They built it as an acronym tool to help break down the language barrier between different functions using different terms. This led to better engagement.”
This empowers employees across an organisation to be more autonomous while still getting the full picture. Agentic AI, especially, allows them to interact with information in a way that previously would’ve required specialist technical knowledge. Now, it’s possible to query information within a contract directly.
“It’s about using agents and AI to look at anomalies within your procurement contracts,” explains Mahajan, “and be able to help the category analysts, the category specialists, and others to get more of those insights.”
While generative AI might be a hot topic, it’s not the only way to use the technology. In combining several sources of data and using AI to spot trends, it’s possible to create workflows tailored to the current environment. Addicoat explains: “We take a series of data inputs, such as weather patterns, lead times, contractual terms, inventory, and forecast. Then the AI generates the purchase order, queues it for review, and upon approval, places the order.”
This can help an organisation to place orders with the right supplier in the most timely fashion to avoid delays, and optimise for cost, for example. This fully automates the end-to-end process, using AI to interpret those important data signals.
While this is useful for procurement teams, it’s only the start. “Using AI in this way is really cool,” says Addicoat, “but what I found most fascinating is that you’re building a data model, and with AI layered into it, that over time can tell you how to optimise itself.”
This has huge implications for procurement teams looking to save money and drive revenue. “For example, it could tell us the commodity price at a certain point in time was low,” says Addicoat, “but because inventory capacity to hold resin was maxed out the client could only buy so much at that low price. So now investing in a new storage unit at a cost of a few hundred thousand dollars could, under the same scenario in the future, save millions of dollars..Data quality challenges
A roadblock that can stop procurement teams from fully embracing AI is a lack of quality data. With so many sources of information, often including paper-based documents, some might think it’s difficult to get the data AI needs to be truly useful.
“Don’t wait for everything to be perfect before you get started,” says Addicoat.
This is a sentiment echoed by Mahajan: “Use AI to solve your data problem before solving your business problems.”
This requires a mindset shift. While AI can help cleanse, enrich, and structure existing unstructured data, it’s important to take the right approach. Shift from asking ‘what can we do with our data?’ to ‘what value do we need to create?’ and work backwards from there.
With this approach, the questions are less about the data and more about the business problem. This then allows you to use AI to work with the information you have to help answer those questions.
“Start with the value proposition in mind and work backwards,” explains Addicoat. “You can get data from anywhere — it has to serve a purpose.”
Bringing back the human touch
AI can free up procurement teams to focus on tasks that need more nuance and expertise. Using technology to automate workflows and make information more accessible has a huge impact on employee productivity. “It’s fundamentally transforming the way they work, the amount of work they can do, and the type of work they’re able to do,” says Addicoat.
There’s always the worry that with any new technology, the human element will be forgotten. “With every new advancement that comes in,” says Mahajan, “whether that was a steam engine or when computers came along, everybody wondered what they were going to do. But as humans, we always find ways to start doing higher-level work.”
This means that many professionals will find new ways of doing things. “Imagine all the mundane tasks you have to do in your daily job now,” Addicoat continues. “With these new ways of working, imagine the speed with which you can turn an idea into something real. All that time you free up allows you to go talk to people and build relationships that mean something.”
On the other side of things, the sheer volume of AI-generated content out there is going to drive people towards those more meaningful interactions. “You don’t know what to trust and what to believe anymore,” Addicoat says. “That’s going to lead to a resurgence in face-to-face content, being at the office, and being at events.”
AI’s impact on procurement talent
The talent landscape is changing. With technology playing a larger part than ever before, organisations don’t just need procurement professionals, they need adaptable, tech-savvy people. The nature of the job means that those in procurement need a wide range of skills.
“We do everything,” says Addicoat, “legal, operations, supply chain, negotiation, analytics. Procurement professionals are generalists.”
Tech plays into every element of that skillset, which means tech skills are becoming even more important for candidates applying for procurement roles. “Nobody goes to college thinking they’ll be a procurement professional,” says Mahajan, “but with AI and tech, that’s changing.”
With procurement often seen as a proving ground for leadership, embedding these tech-minded generalists could have a huge impact on the future. “We have a shortage of talent,” explains Addicoat. “But with more and more CEOs and COOs coming from procurement, that speaks volumes to what procurement does and the value it brings, as well as what the future holds.”
At AlixPartners, the passion for procurement is very clear with Addicoat saying: “There are only two kinds of people in the world: those who love procurement and those who don’t know it yet.”
Change is coming
With AI of all forms steadily gaining traction, procurement could change dramatically in the coming years. It’s the organisations that are willing to take risks and embrace change that will come out on top.
“AI has the potential to disrupt the whole management consulting world,” says Mahajan. “Firms focused on transformation will thrive.”
With AI’s capabilities increasing rapidly, it’s difficult to predict what comes next. However, adaptability is key. “Hold onto your hat. In a year and a half, the world’s going to look very different,” concludes Addicoat.
AI is already transforming procurement, but meaningful value depends on more than just tools. At Beroe, that starts with aligning AI to real business problems
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As AI continues to dominate conference stages and boardroom discussions, the pressure to use it is everywhere. As this technology becomes further embedded in enterprise strategy, many organisations are still grappling with how to apply it in a way that delivers real, measurable value.
Rather than focusing on AI for the sake of innovation, the question now is how to align new tools with real business problems. That means looking beyond dashboards and pilots to deploy AI where it can simplify decision-making and improve processes.
At Beroe, this principle is central to how AI solutions are developed, deployed, and scaled. As the company behind the world’s leading procurement intelligence platform, Beroe provides real-time market data, cost analysis, and supplier risk assessments, empowering thousands of organisations globally to streamline operations and mitigate risks. Its latest advances in autonomous negotiation, supplier discovery, and predictive analytics show what it means to align AI with business objectives.
Speaking with Prerna Dhawan, Chief Product Officer at Beroe, during this year’s DPW New York conference, the discussion explored how procurement leaders can move beyond hype and start unlocking the full potential of AI.
Misalignment with business needs
There are plenty of real-world examples of how AI can improve efficiency within a business, from automating manual tasks like invoice processing to identifying new suppliers based on complex sourcing criteria. Accessing this technology is easier than ever with a wide range of tools available to procurement professionals. It can be tempting to jump on the bandwagon and integrate AI across every area of an organisation, but success requires a more nuanced approach.
The key is to ask the right questions, Dhawan explains: “We talk about all the latest and greatest technology out there, but what does it mean in practical terms? We need to ask, ‘How can I apply it today in the work I am doing as a head of product or as a procurement professional?’”
The allure of generative AI is especially strong, but business leaders should ask whether that’s the right solution for their needs. As with any decision, it’s important to consider the business problem. “It starts with a little bit of knowledge about what you’re looking for,” says Dhawan. “What are some of your biggest challenges, and which of those challenges could AI technology solve?”
Matching the right tool to the job
Once an organisation has identified a specific problem, it’s possible to find the AI solution that fits. While generative AI gets a lot of attention, other AI technologies and machine learning based systems might be more appropriate.
In some cases, prescriptive, rule-based, or predictive AI could be a better choice to solve a problem without the need for a large language model. For example, forecasting commodity prices doesn’t require generative AI, just strong, contextual machine learning.
“We are looking at AI across two dimensions,” says Dhawan. “Firstly, what is our offering to customers, in terms of procurement intelligence and autonomous negotiation technology. Second, we are looking at AI internally. Let’s say in product development, how do we use the latest AI solutions to accelerate our product development cycles so we can release new modules and capabilities more quickly.”
Regardless of the type of tool chosen, it should cover a high-impact use case. Integrating AI to solve a problem that only surfaces for a small group of people a couple of times a year won’t have a great return on investment. Instead, look for regularly occurring problems that, if fixed, could have a huge impact on productivity or quality.
Reducing the cognitive load
We’re already bombarded by information, and the use of AI to add to this doesn’t make sense. “I don’t need another dashboard in my life,” says Dhawan.
When implemented correctly, AI can make data more accessible while reducing cognitive load for users. The result is increased productivity and faster decision-making.
“I think the power of AI is to simplify access to data. This is why ChatGPT has been a success: it democratises access to information. That’s what our B2B technology world is waiting for. It gives me something simple that allows me to talk to my data. Then I can focus on what insights I need to make a decision or take action.”
For most B2B users, the key is intelligent simplification. Look for ways to simplify access to data through agent AI tools and conversational interfaces. This brings the focus back to action rather than dashboards.
Inside Beroe
While many procurement teams are still exploring AI’s potential, Beroe has already embedded it across both its platform and internal operations. The company, founded in 2006, provides procurement intelligence to thousands of organisations worldwide. Its platform delivers the critical data that professionals need to make informed sourcing decisions, from commodity prices and risk indicators to ESG scores and supplier intelligence.
“We provide all data that procurement needs for decision making, whether it’s cost data, risk data, ESG data or price data,” says Dhawan. “Our reimagination of the future is not just giving access to more data but creating that layer of recommendations that help you make decisions at speed and scale.”
One of the clearest examples of this in action is Beroe’s new ‘autonomous negotiations’ platform resulting from its recent acquisition of negotiation technology business, nnamu. Delivering a significant evolution in the procurement technology landscape the platform enhances the foundational elements of AI and game theory with Beroe’s industry-leading market intelligence and, according to Dhawan, it’s being deployed successfully in live sourcing scenarios.
“This is a technology that is being used for multilateral negotiations,” Dhawan explained. “It’s no longer just a POC or prototype, it’s live and being used at scale.” These new tools reflect Beroe’s core mission: to help procurement professionals minimise surprises and maximise margins.
Crucially, Beroe isn’t waiting for perfect data to apply these technologies. Instead, the company is using AI to work with what’s available — cleansing, interpreting, and extracting value from both structured and unstructured sources.
“You can use AI for cleansing data – even paper contracts,” Dhawan says. “Historically, we thought data had to be structured. But now, with vision models and image analytics, that’s no longer the case.”
Rather than striving for 100% accuracy before taking action, Beroe embraces a more agile mindset that balances speed and precision.
Is mindset holding procurement back?
The technology is ready. The use cases are proven. So why do so many procurement teams still hesitate to embrace AI? “There’s this subconscious fear that I think is a barrier to adoption,” she said. “And to some extent, it’s to do with our friends in Hollywood.”
There’s the myth that AI is a job-threatening black box, especially in industries where trust and experience are the backbone of good decision-making. For procurement, where professional judgement and business context are critical, the idea of handing over tasks to AI can feel risky.
But Dhawan believes this fear is misplaced. At Beroe, AI isn’t replacing procurement professionals, it’s augmenting them. Whether it’s surfacing new suppliers, automating elements of negotiation, or flagging risks earlier in the sourcing cycle, the aim is to enhance human decision-making. She says: “I think with the new kinds of AI technology that’s available to us, it is an opportunity for us in B2B tech to embrace more human-centred design with higher focus on UX.”
Looking ahead
Looking ahead to 2026 and beyond, Dhawan sees procurement evolving into a more personalised and responsive function – one where AI plays a critical role in both strategy and execution.
“We see hyper-personalisation coming, both in supplier relationships and internal stakeholder engagement,” she explains. “AI will be at the centre of that.”
Rather than one-size-fits-all sourcing strategies, AI will enable procurement teams to tailor their approaches to specific business units, categories, or even individual suppliers. This means smarter segmentation, more relevant insights, and stronger commercial outcomes.
Another key shift is the growing ability to connect macro events, such as geopolitical shocks or regulatory changes, with micro actions inside the business. AI can help procurement teams identify these signals earlier, respond faster, and still align with long-term goals such as cost efficiency or sustainability.
“It’s about balancing your fire-fighting reactions to market events with your long term goals and strategy,” says Dhawan. “Procurement needs visibility and flexibility at the same time.”
Beroe is already moving in this direction. Alongside its growing AI capabilities, the company is refining how it delivers intelligence, building agents and recommendation layers that not only inform decisions, but also help teams take action on them. Whether that means automating routine negotiations or proactively flagging supply risks, Beroe is evolving to meet the needs of a procurement function that’s more dynamic than ever.
As Dhawan points out, the goal isn’t to overwhelm teams with more tools, it’s to make their lives easier. “It’s about reducing complexity and giving procurement professionals confidence in what to do next,” she concludes.
For many procurement leaders, AI still feels like a long-term ambition. But the solutions are already here, and through companies like Beroe, they’re already in use. The challenge now is not whether AI can deliver value. It’s whether teams are ready to adopt the mindset and cultural shift that will allow them to unlock that value.
Jonathan Jackman, Regional VP at Kinaxis, dives into how AI is reshaping supply chain planning.
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Artificial intelligence (AI) is often seen as a threat to jobs, with a recent TUC poll showing half of UK adults worry that AI will take their job. When it comes to the supply chain sector, AI is shaping up to be a powerful tool that empowers planners to take on more creative, fulfilling roles.
The prospect of AI-enabled supply chain planning is an exciting one for both professionals and businesses. Scaling operations without the need to massively increase headcount is a major selling point for any enterprise, while for professionals, the prospect of removing the repetitive, mundane and manual processes that restrict and slow effective planning is surely a promising one.
Far from job elimination, AI is a major upgrade for supply chain workers in a number of different ways. We’re entering a new era of increasingly autonomous AI systems, which will elevate supply chain planning to new heights. So, how exactly will the day-to-day role of the planner evolve as we go further into the AI era?
Humans still in control
First, it’s important to dispel a myth: the supply chains of the future will not be “driverless”. Many believe that AI, and particularly agentic AI, has the potential to run supply chains on autopilot. This is far from reality: while AI can surface insights, automate tasks and even take action in a crisis, it will always need to be augmented by a human to fully interpret the nuances of the real-world.
This human oversight is a crucial failsafe. There will be many times where AI flags potential shortages and proposes the best way to respond, but it will only ever be as good as the insights it is fed and the guidance given by human. For example, what if it is missing a crucial bit of real-time information about an upcoming election which could lead to disruptive trade challenges? While the algorithms. may be great at crunching the numbers and making recommendations, only a human planner can assess the full context surrounding a decision before deciding action.
The future of supply chain planning isn’t AI instead of humans, it will be AI and humans. In the AI era, supply chain professionals will be the orchestrators, steering AI systems and validating recommendations with important human insights and context.
Each planner is likely to have fleets of AI agents beneath them, acting as demand forecasters, inventory optimisers and scenario simulators – feeding information back to the supply chain professionals to empower them to make the best decisions based on the maximum amount of data analysis, all done in real time.
Planners unleashed
With AI handling the mundane and routine supply chain tasks, planners will be unleashed to focus on the creative, strategic elements of the job that machines simply cannot do: building relationships, working with partners, building and selling strategy, and, of course, managing AI agents.
Consider negotiations with partners, for example, AI won’t be able to compete with a human. It will, though, supply planners with the data they need to enter those discussions armed with deeper insights than ever before, empowering them to work more effectively.
Planners will also play a critical role in shaping the very AI tools they use – training models, curating data, and ensuring outputs reflect reality. Over time, this human feedback loop will make the technology even more valuable.
One key evolutionary step we are starting to see is the emergence of Autonomous Concurrent Orchestration. Currently, many vendors focus on agents automating existing siloed processes, but in the future, we will see more agents that synchronise planning decisions across functions – procurement, logistics, manufacturing – in real time. Agent-to-agent communication will break down silos and speed up problem solving and decision making, easing the burden on supply chain professionals.
Augmenting, not replacing
Perhaps artificial intelligence is the wrong phrase when it comes to supply chains Instead, the industry should be discussing augmented intelligence, where machines unlock insights and real-time decision making that simply wasn’t possible when tasks relied on manual processes.
For planners, the AI era promises exciting change: embracing new tools and evolving alongside this technology is not only good for business, but good for the careers of supply chain professionals.
We sat down with Abe Eshkenazi, CEO of ASCM, to dig into the organisation’s focus points, and how CHAINge is addressing supply chain’s needs
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Tell me a bit about your background, and how you got into supply chain.
Early in my career, I spent quite a bit of time in operations and materials management. We didn’t call it supply chain back in the day – it went by a number of different terms. Not surprisingly, given my role within ASCM, I worked closely with supply chain professionals, not only to elevate the role of the supply chain professional, but to understand the impact that supply chain has on business and society.
At ASCM, we’re focused on not only supporting that competent, capable individual, but ensuring that organisations are responsible in terms of using supply chain to really enable consumers and patients to get what they need at a reasonable price and reasonable time. This is what supply chain is about. My background combines that business management education and deep engagement with supply chain professionals. This gives me a strong appreciation for not only their challenges, but the opportunities the field faces today.
Tell me about the planning for CHAINge NA this year. What were you looking to achieve when putting ideas together?
Today, supply chain professionals are trying to balance efficiency with geographic diversity and political resilience. They’re trying to put those things together and identify what would make an individual do their job better and exchange that information with others. So our planning is centered around a key theme, which is: how do we equip supply chain professionals for what’s next?
The systems that we built for speed and cost optimisation are under stress right now. They’re struggling under the weight of complexity, volatility, consumer demands, and all the disruptions that we’re facing today. We’re being called today to rethink not only how quickly and cheaply we can move things and get them to the consumer, but how responsibly, transparently, and resiliently we can operate today. Our hope is that the engagement part of the event enables individuals to exchange information and walk away with insights and actionable strategies that can be taken back to their organisations and implemented. We’re truly looking for that engagement from the attendees. This is an event for the attendees, by the attendees.
It’s also about making the contact and relationships that we all depend on. We’re all seeking opportunities and examples of organisations that have done it better or have responded easier to the challenges that we’re facing today. This provides individuals with an opportunity to engage. We had an opportunity to do this at our European event, after which attendees overwhelmingly indicated that the engagement part – the opportunity to exchange information learned from each other – was a key element of the event itself. We’re trying to replicate that, but with the amount of issues that the US is facing versus the rest of the world, the topics are going to be a little bit different here.
What are the core topics covered at CHAINge NA that you think are most helpful for supply chain professionals?
We need to take a temperature of the current environment, and not surprisingly, we structure the event around several core themes that we’re all facing today. First, resilient and agile supply chains. The adaptability that’s required today is unlike any time that we’ve ever faced. We’ve had disruptions before, and we’ve responded as an industry. Today, we’re continuing to respond, but the pressures on these individuals due to day-to-day uncertainty has created a very different environment.
The second core topic is emerging technologies. As the focus on resiliency and agility becomes much more critical, there are only a few ways to gather the data necessary to enable organisations to make informed decisions. Not surprisingly, AI, digital twins, and a whole host of scenario planning technology tools are a focus for a lot of organisations today. Digital transformation is happening in almost every organisation to shore up their visibility, their transparency, and their traceability.
Also, advancing sustainability practices. We can’t forget that at the end of the day, we still need to be sustainable as an industry. This has been a huge focus within supply chain. It’s taken a little bit of a backseat in the current environment, but organisations are still focused on ensuring that they are sustainable and ethical in their business practices. Lastly, no discussion can be had without understanding what the talent availability is, what their capabilities are, and whether we are ensuring that we do have the right talent.
How important is collaboration (accelerated by things like CHAINge) in supply chain, especially as the landscape becomes more complex?
In today’s environment, as we focus on visibility and on connecting all parts of our supply chain end-to-end, we understand the demand signals clearly so that we can address them appropriately. Collaboration is no longer optional – it’s essential. No single individual organisation can solve today’s challenges on their own, whether it’s navigating geopolitical tensions, managing risk in a global network, or even driving sustainability. The solutions demand cross-functional and industry collaboration. It used to be that the Chief Supply Chain Officer in the back room was only called upon when there was a crisis. Well, I think we’ve got enough crises today that we need to push that individual into the front office.
First, we need to enable them to use their voice at the table to advocate for appropriate supply chain practices, but also in combination with a wide range of other roles. These are the teams that are now addressing these issues. It’s no longer just a supply chain issue; it’s an organisational issue. It’s a societal issue that we now need to address, and there’s only one way to address that; that’s through collaboration within the organisation, as well as with your partners, your vendors, and your vendor’s vendor. This is a very dynamic environment today, and enabling organisations to have that complete visibility and connectivity is critical.
There’s been a lot of talk about a shortage of talent across supply chain; how big an issue is this, from your perspective? And how can it be overcome?
From our perspective, it’s one of the defining issues of our time. As supply chain has moved from the back office to the boardroom, so has the demand for skilled professionals. More often than not, supply chain people come out of finance or engineering. In today’s environment – a very diverse workforce – digital natives are coming into the workforce. They’re not only adaptable, but very comfortable with modern technology. It’s a little bit of a reverse from the leadership that we have in supply chain today, that may still be using that Excel spreadsheet on their systems. Supply chain has the demand for those skilled individuals.
To address this, we’re focused on a number of things. First, expanding the awareness of supply chain as a rewarding career path, which our salary and satisfaction surveys confirm. Secondly, talking openly about investing in ongoing professional development. We’ve been to a lot of conferences and whether we’re talking about AI, sustainability, or disruptions, at the end of the discussion, it always comes down to people. We should be talking about the people at the beginning of the discussion as opposed to the end of it. We need to create that opportunity for individuals to see that they can not only make a difference, but that their voice is heard and followed on within their organisation. That’s what we’re preparing supply chain professionals for.
We need to provide an inclusive workplace that attracts and retains that diverse talent. As I indicated before, individuals coming into the workforce are digital natives. They’re very adept at AI and they’re more than willing to jump in with the technology. We need to enable them with problem solving, critical thinking, and experience on the job. I couldn’t be more excited about the individuals coming into the workforce today and the focus, and they’re able to change the world through supply chain.
How can supply chain professionals approach the challenge of ever-changing regulatory requirements?
Financial markets and supply chains do not like uncertainty. We like certain demand signals so we can ensure that our supplies are appropriately managed. Supply chain professionals need to have robust systems to monitor changes and provide that data, or the regulatory information and policy individuals reporting become significant. Among the concerns that we have is that more often than not, it’s become regulatory or policy and it becomes a checklist. Part of that concern is whether we’re really focused on really making a change, or focused just on those compliance checklists that often drive down to minimum effect.
Today, technology helps, but so does developing a culture of compliance and resiliency. Once again, collaboration matters, sharing best practices across industries, and enabling individuals to understand that there are ways to respond to the regulatory and the policy changes.
What are some of the most exciting innovations happening in supply chain today?
I think the combination of the people and technology is what’s going to make an exponential difference. On the technology side, tools like advanced analytics, AI, and digital twins are transforming how we forecast, manage risk, and build resiliency. The real innovation is combining cutting edge technology with a highly skilled, adaptable workforce. I heard a fantastic quote the other day: ‘AI is not going to take your job; an individual using AI is going to take your job’. That’s where the focus is right now – enabling individuals to use technology to really leverage that and enable organisations to be much more responsive and agile, as they address demands.
The Financial Transformation Summit (FTS), presented by MoneyNext, took place June 18-19 2025 at London’s ExCeL Centre, Royal Victoria Dock. With over 2,000 attendees, 300+ speakers, and 400 roundtables, it stood out as one of the most immersive and interactive events in the financial services calendar.
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FinTech Strategy hit the conference floor at the heart of the action delivering insights from experts across Banking, Insurance, Wealth, and Lending at Financial Transformation Summit (FTS).
Financial Transformation Summit attendees from banking, insurance, wealth, lending, fintech, consultancy, and regulatory sectors convened for two days packed with keynotes, panel talks, immersive demos, and networking among 60+ exhibitors and startups.
Co-located streams – Banking, Insurance, Wealth, and Lending part of themed zones– meant that ticket-holders could explore adjacent sectors fluidly across a guiding theme: culture, collaboration, and customer centricity driving tech adoption and transformation.
Programme Highlights
Keynotes & Panels
1. Data Silos & Cross‑Institutional Collaboration
A panel featuring senior leaders from EVLO, Aon, Schroders, and Brit Insurance tackled how institutions – despite collectively spending over $33 billion annually on data – still struggle to collaborate due to privacy concerns and regulation. Innovative solutions included federated learning, anonymised client IDs and consent-backed APIs.
2. Digital Insurance via Wallets
Anna Bojic (Miss Moneypenny Technologies) unveiled a fresh take on insurance – embedding policy and claim data into Apple/Google Wallets. The idea: dynamic customer interaction directly from smartphone wallets, enhancing real‑time engagement and retention.
3. ESG Economics & Market Reality
Marc Kahn (Investec) challenged ESG orthodoxy, urging firms to emphasise human and planetary wellbeing – beyond purely financial returns – to capture stakeholder trust and sustainable growth.
4. People & Psychological Safety
Kirsty Watson (Aberdeen Group) and Vikki Allgood (Fidelity International) underlined that technological investments are futile without organisational design and psychological safety. Allgood cited a McKinsey study revealing only 26% of leaders build teams with a sense of safety – a critical step toward innovation.
5. Human‑Centred AI
Monica Kalia (Planda AI) championed AI that models individual financial contexts – recognising diversity within demographic cohorts and personalizing services accordingly.
Roundtable Experiences at FTS
At the event’s heart were the TableTalk roundtables – 400+ small-group sessions, each led by a subject-matter expert. These were limited to six participants each, enabling deep, peer-led discussions on themes like:
AI in risk and compliance
Open banking integration
ESG data standards
Cyber resilience
Change management and culture adaptation
Attendees consistently praised their interactive nature – far removed from the stage‑focused “listening” format often critiqued at other conferences.
Demonstrations & Exhibitor Showcase
Over 60 exhibitors presented tech-driven innovations: Generative AI, open‑banking APIs, ESG reporting tools, embedded finance solutions, and more. A few standouts were:
CRIF highlighted AI-powered credit scoring with ESG overlays – promising dynamic risk assessments backed by sustainability data
Emerging FinTechs demoing AI compliance engines, digital wallet insurance packaging, and data-sharing platforms
Hylanddemonstrated the intuitive end-user experience of its Hyland Content Innovation Cloud™ and showed how easy it is to configure, tailor and deploy solutions that can empower key stakeholders across any business
The demo zone allowed engaging, hands-on exploration and real-time Q&As; it complemented the content with practical insights.
Standout Themes & Strategic Insights
1. Tech is Not Enough Without Culture
Recurrent messaging emphasised that culture, trust, governance, and psychological safety are foundational – not secondary – to digital initiatives. Technology alone won’t deliver transformation without a people-first mindset.
2. Cross‑Sector Data Collaboration
Despite heavy investment, institutions still operate in silos. Shared, secure infrastructure and regulatory-aligned frameworks are being prototyped, but broad adoption remains a work in progress.
3. AI-as-a-Personalisation Backbone
AI is shifting from automation to empathy. Organisations showcased tools to hyper-personalise offers yet maintain privacy and inclusion – moving beyond outdated demographic frameworks into genuine behavioural understanding.
4. Embedded Finance & Digital Wallets
Insurance via wallet applications and embedded finance models point to seamless customer journeys – less app hopping, more value delivered at the point of need.
5. Rebalancing ESG & Profit Metrics
Speakers emphasised integrating ESG factors into performance metrics – not just for compliance, but as an operative advantage anchored in long-term stability and stakeholder trust.
Who Should Attend FTS Next Year?
Ideal for:
Transformation and change leaders
CTOs, CIOs, and Heads of Innovation
Data and AI strategists
Operational and HR leaders focused on culture
FinTech innovators and solution providers
If you’re crafting digital transformation strategies, an attuned leader in financial services, or a consultant embedding tech in legacy environments, this summit provides rich, actionable content.
Expect next year’s event to build on this foundation:
More AI-specific tracks, possibly Generative AI streams
ESG deep-dives with case studies on implementation
Expanded regulator involvement around data governance and cross-border compliance
FTS: Final Verdict
Overall, the FTS 2025 delivered on its brand promise:
Interactive and inclusive: 400 roundtables empowered voices across levels.
Cross‑sector learning: Banking, Insurance, Wealth, and Lending streams offered both breadth and depth.
Insightful keynotes: Big ideas on AI, ESG, data-sharing, and culture were well-explored.
Real-world relevance: Exhibitor demos connected theory with practice.
Networking with purpose: Opportunities to engage, learn, and collaborate were abundant.
The Financial Transformation Summit struck a compelling balance between big-picture vision and granular, execution-level insight. It emphasised that while technology enables; culture, customer centricity and collaboration drive real progress. The format – with its roundtables, demos, and keynotes – offered a dynamic platform for knowledge exchange.
If you attended, chances are you left with practical next steps. If you didn’t, you missed one of the most interactive, future-focused events shaping financial services transformation today.
Alexandra Mousavizadeh, CEO and Co-Founder of Evident, with her top five AI innovations advancing financial services in 2025
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AI is no longer optional for the world’s biggest banks, it has become a fundamental part of their operations, rapidly transforming modern banking. As the industry faces mounting pressure to innovate, the technology is emerging as a critical tool for achieving a competitive advantage. From automating processes and enhancing customer experiences to improving risk management, banks are investing heavily in artificial intelligence to boost productivity, efficiency and profitability.
2025 has been a pivotal year for AI adoption, as banks shift their focus from strategy development to demonstrating measurable value. Stakeholders will increasingly demand clear evidence of AI’s impact on efficiency gains, revenue growth, employee productivity and customer satisfaction. The next phase of AI adoption will distinguish early adopters who leverage it effectively from those who fall behind.
Here are five predictions for how artificial intelligence will reshape banking in 2025 and beyond.
1. Banks focus will shift from AI strategy to measuring value creation
The big banks are well on their way to operationalising AI at scale and, consequently, it now has to prove its ROI.
Capturing ROI has been one of the most discussed topics internally at banks this year but noticeably absent from the industry disclosures so far. In 2025 realised results are going to be needed to justify ongoing investments. Equity analysts will be asking for clear evidence of the value AI is delivering whether that’s efficiency gains, revenue growth, staff productivity or customer satisfaction.
With just six banks disclosing the realised business impact of artificial intelligence in financial terms so far, it’s time for everyone else to step up.
2. AI Training will take Centre Stage: Ensuring employees can use AI tools effectively
AI training is shifting downstream, so the focus is no longer just having AI tools but ensuring that employees are able to use them properly.
Our talent data suggests that 60% of incoming AI talent arriving at banks is sourced straight out of university. Banks need to ensure AI-focused training and career development opportunities are available across all levels of their organisation to fast-track adoption and start seeing a return.
Specifically, in 2025 we expect to see banks investing in training programmes that shift the emphasis from early internal adopters and specialist hires to the rest of the bank. This could be training ‘leaders’ in AI literacy or upskilling ultimate ‘users’.
3. Unstructured data is no longer a problem
Whether banks are building their own AI or buying in third-party solutions, the end result will only be as good as the underlying infrastructure. Banks made these investments years ago; in 2025, as the drive towards organisation-wide AI deployment ratchets up, we’ll start to see which institutions have placed the right bets.
However, advances in handling unstructured data may ease the burden of cleaning up legacy data pools, providing a lifeline to institutions weighed down by outdated systems. Emerging technologies like AI-powered data wrangling and natural language processing are enabling banks to extract value from messy or siloed data. This is reducing the dependency on large-scale data overhauls.
4. We’ll see the first ‘killer app’ for Agentic AI documented at a major bank
As trust in the technology grows, and banks continue to build artificial intelligence capabilities, we’re expecting to see more use cases that let the AI operate and make decisions without human intervention.
2025 should be the year when the first killer apps for agentic AI surface, although it’s worth noting that, at the time of writing in January, Australia’s CommBank is the first and so far, only big bank out with a live agentic AI use case. The bank is deploying agents to solve some of the 15,000 payment disputes raised by its customers every day. The rest of the major players are yet to show their hand on the agentic front.
5. Trump’s AI Executive Order: A rebrand, not a repeal
Despite President Trump’s pledge to repeal President Biden’s AI Executive Order, this move resulted in a rebranding rather than a full repeal. Biden’s order primarily focused on federal government AI adoption rather than regulating the private sector, leaving industries like banking largely unaffected. Financial institutions are already collaborating with regulators to ensure AI safety and to avoid deploying contentious use cases.
Overall, US regulations will focus on competitiveness, growth and spending cuts. As a result, we anticipate a more liberal approach to AI regulation aimed at staying ahead of China. With the recent appointments of Sriram Krishnan, Michael Kratsios and Lynne Parker we expect regulation will support open source development and avoid a pause on research, an approach that may clash with Musk’s views.
While US AI safety advocates continue to monitor developments, Europe is likely to press ahead with its regulatory agenda regardless. This could create an uneven playing field if Europe’s approach ends up being significantly more heavy-handed than that of the US.
Rob Israch, President at finance automation specialists Tipalti, reflects on the post-hype AI landscape for innovation in financial services
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The initial excitement around AI In finance is shifting toward a more practical focus on real business value. Many companies were swept up in the early enthusiasm. However, companies are now leaning toward integrating artificial intelligence more meaningfully into core workflows to deliver lasting value.
While 92% of companies plan to increase their AI investments over the next three years, just 1% of leaders say their organisations are truly AI mature. True maturity means AI drives measurable outcomes and is central and streamlined into daily operations.
So for finance teams, this shift is critical. In an economy shaped by changes in inflation, tariffs and taxes, every investment must deliver clear ROI and help the business by streamlining operations, enhancing forecasts and adopting predictive analytics.
As companies push for sustainable growth and a thawing IPO market signals possible opportunities, scalable and integrated AI solutions will be key to business success.
Building for Real Problems, Not Hypothetical Gaps
Most companies agree that innovation in the finance department is key to unlocking the next level of growth. However, despite growing ambition to adopt AI and automation, 84% of finance teams still rely heavily on manual processes. Leaving little leftover time for strategic thinking.
To truly drive value, AI must be applied not just tactically, but strategically for each business. Research shows that while 74% of companies have adopted AI, only 4% have advanced capabilities that drive clear business value. Real impact is delivered when the technology goes beyond simple workflow automation and becomes a source of real-time, predictive insight across the finance function.
Take treasury operations, for example. Traditionally, treasury teams have faced mounting challenges in managing cash flow, forecasting liquidity, and overseeing global bank relationships. With AI-powered tools, finance teams can now gain real-time, intelligent cash visibility across thousands of banks, ERP systems, and data sources. This transformation not only empowers leaders to make faster and smarter decisions but also underscores the importance of streamlined systems within the finance function.
From a Surplus of Tools to One Unified Platform
What businesses don’t want is extra layers of complexity; they need a straightforward, unified platform that solves real problems.
Large enterprises may seek ‘AI-first’ products and invest in cross-functional AI platforms. But they typically have the resources to fund extensive IT teams or consultants to customise these systems. However, for most businesses, this level of support isn’t a reality. So, businesses without reams of IT people, benefit more from a consolidated system that delivers efficiency and scalability. This allows them to stay focused on growth and innovation.
If AI is seamlessly embedded within these solutions, it can enhance performance without increasing complexity. Whether improving automation, workflow management or operational efficiency, AI should be an integral part of the product.
Staging the Runway for the Next Stage of Growth
Companies that fully integrate AI will be more ready for sustainable growth. However, integration is just the start… Once AI is embedded, organisations must focus on how it can deliver real, strategic value. This means designing solutions not only to automate processes but to provide actionable insights. Currently, only 26% have developed the skills to move beyond AI conceptually and deliver real value. In the finance function, using AI strategically can lower processing costs by 81% and speed up processing times by 73%.
As more advanced models are integrated into workplaces systems, they can predict payment patterns, cash flow trends, and vendor behaviour. In today’s dynamic environment, companies that have sustainable, AI-powered solutions centred on usability and scalability are best positioned for the next stage of growth.
The Continued Road to AI Maturity
As finance teams navigate a more mature AI landscape and prepare for future growth, the focus is shifting from individual features to foundational value. With investors sharpening their focus, they seek durable business models. The companies that succeed will be those that have applied AI to maximise their investment.
These companies haven’t just chased metrics; they’ve spent the past few years strengthening their foundations and embedding AI deeply into their architecture.
Collaborating with Amdocs has been a game-changer for Telkom. Here’s why.
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As telecom companies race to adopt generative AI, a critical shift is underway – from generic copilots to deeply verticalised, telco-grade agents. Amdocs, in collaboration with AWS and NVIDIA, is leading this evolution with its amAIz Agents – introducing a new class of AI agents built specifically for the telecom industry.
Unlike general-purpose AI, verticalised agents are built with domain-specific knowledge, reasoning, and telco ontology that reflect the complexity of telecom operations. These agents understand service plans, billing structures, and network topologies, enabling them to deliver context-aware responses and take meaningful action.
Amdocs, NVIDIA and AWS released a publication that defines and showcases how AI agents can be tailored for specific telecom domains, illustrating the concept of ‘agent verticalization’ and its impact on operational efficiency and customer experience. These domain-specific agents, across every telco domain like care, sales, network, and marketing, work in coordination, enabling end-to-end automation and intelligent customer engagement through seamless orchestration.
In the whitepaper, ‘AI Verticalization for Telco’, Amdocs outlines the essential traits of telco-grade agents such as composable architecture, reasoning, and agentic experience, and enterprise-grade traits such as trust, security, and cloud-native scalability.
Amdocs: Three decades as a key transformation partner
It’s a rare thing, in the fast-paced world of technology, for partnerships to last decades. However, for Telkom, Amdocs has been by its side for almost 30 years. The latter has played a critical role in supporting both mobile and wireline operation through its B/OSS platforms. These platforms are regarded as industry leaders, and Telkom has been able to navigate major shifts with Amdocs’s help, from legacy to next-gen digital stacks.
“We have been in this game for some time, being the digital backbone of choice for South Africa, really, Amdocs has been a strategic partner of Telkom for over 30 years,” says Dr Noxolo Kubheka-Dlamini, Chief Digital and Information Officer at Telkom. “We have a shared goal of delivering a better, faster, and more seamless experience to our customers. What stands out about Amdocs is their deep domain expertise, strong delivery capabilities, commitment to our success, and ability to evolve with our ambitious goals. We see them as an extension of our own teams.”
We Fix Boring founder Andrej Persolja on why investors are making bigger bets on fewer teams via the impact of AI, enhanced profiling and better targeting
How founders can improve their chances of raising investment – team alignment, production and business differentiation, and customer-centered strategy. Creating a story that investors can easily understand and buy into.
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If you’re planning a FinTech investment pitch, the chances are that your first thoughts will relate to the numbers. You’ll open your spreadsheets and dig out your margins, forecasts, CAC-to-LTV ratios, and KPIs. You’ll do everything you can to make your brand look impressive on paper. It’s what you’ve been taught to do because metrics matter. Of course they do. However, what many founders don’t realise is that although metrics clearly carry value, they should only ever be the starting point of any investment pitch. Because, at the end of the day, investors are people first, and their decisions are based on emotion as much as they are on money.
The Human Factor in FinTech Funding
Investors are not machines. It might sound like stating the obvious, but when so much hinges on investor approval, it can be hard to remember that you’re dealing with human beings. So, you focus on upselling your financial model, growth projections, and market opportunity, entirely overlooking the value of an emotional response. One influenced by your narrative, your team, your product vision, and your belief in your startup’s ability to reshape an industry. And that’s where so many fintechs go wrong.
In sectors like FinTech, where technical innovation is everywhere, what often sets a pitch apart is its ability to tell a compelling story. One that communicates not just what the product does, but why it matters. That emotional connection can often provide the edge that secures the deal.
Positioning and Emotional Resonance are Often the Missing Links
Innovation often outpaces regulation in fintech, and profitability can be years away. So, what convinces an investor to take a bet on an early-stage startup? The potential return on investment matters and will always be a factor. But it’s rarely the only factor. Because there are countless high-growth opportunities out there. So why choose yours?
The answer is belief. Belief in your vision. Belief in your ability to execute. And the belief that your product solves a real, meaningful problem in a way that others haven’t. That’s why positioning, and the emotional resonance behind it, plays such a critical role in raising capital.
When fintech investors evaluate opportunities, they aren’t just looking at your tech stack or your runway. They’re asking themselves: What does this company stand for? What kind of disruption do I want to back? What values do I want my capital to reflect? If your pitch doesn’t communicate that clearly and emotionally, it becomes just another deck in a crowded inbox.
Strong positioning grounds your FinTech in something bigger than features or metrics. It communicates purpose. And when you pair that with an emotionally resonant brand narrative, you give investors a reason to care. Not just about your product, but about why it exists and where it’s going. Because trust, change, and vision are core themes that can move an investor from ‘interested’ to ‘committed.’
Crafting a FinTech Brand Narrative to Drive Investment
Building a compelling brand narrative in FinTech is no longer optional. It’s a critical part of your investment strategy. And it all starts with one fundamental question: What is your why? Beyond monetisation and market sizing, what real-world problem are you solving? Why does it matter now? Whether you’re streamlining payments, reimagining lending, or building infrastructure for digital finance, your deeper purpose is what sets your FinTech apart. And it’s what investors are really looking for. That, and a strong user experience (UX) that shows commitment to your customers and the potential to build loyalty.
The Role of UX in Investment Pitching
Traditionally, FinTech companies have been held back by one major challenge: compliance. But in today’s digital-first environment, where every player in banking, insurance, and payments is competing for speed, convenience, and trust, the challenge has become twofold: compliance and user experience.
In digital finance, the core area of competition is how quickly you can get the user to value. That means having crystal-clear user journeys and a focus on where and how users perceive value. Using one of my clients – a SaaS solution for institutional investors – as an example, by simplifying the user experience across our landing pages and onboarding, we increased conversion from 0% to 37%. That didn’t just improve user experience. It provided quantifiable traction that could be shown to investors. And if you need to prove traction to investors, every click matters.
With FinTech investment rebounding – up 5.3% in H1 2025 compared to 2024 – now is the time to act. But standing out means more than just showing attractive metrics. Investors want a clear narrative that combines numbers with a strong strategic story. They’re looking for confidence in the team, clarity in the vision, and proof that your product is ready to scale. Both operationally and emotionally.
So, to reiterate. Yes, if you’re preparing an investment pitch for your FinTech, the financial model matters. But seasoned investors know markets shift, projections change, and competition intensifies. A fintech company that can articulate a powerful vision, show traction through product-led growth, and tell a story that resonates on a human level will always have an edge.
So, take your ideas and take your numbers, and make them look as pretty and appealing as possible. But don’t forget to wrap them in a story if you want to spark your investor’s imagination.
Rob Vann, Chief Solutions Officer at Cyberfort, on the importance of the human factor for successful AI integration in financial services
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Financial service institutions are currently navigating an increasingly complex digital landscape where opportunity and risk walk hand in hand. According to The Bank of England’s 2024 report, 75% of financial service firms are already using Artificial Intelligence (AI). Afurther 10% are planning to use AI over the next three years.
It goes without saying that the rapid uptake can be attributed to the benefits of AI for financial service firms. These include enhancing fraud detection and automating customer service, to improving risk assessment and streamlining compliance processes. Financial institutions are undeniably seeing faster, more accurate decision-making and cost saving as a result of AI integration.
However, the reality is more complicated. The same report also reveals security has emerged as the highest perceived risk of AI integration. Both now and looking three years ahead. With this in mind, banks and fintechs alike are struggling to address these immediate security concerns. As well as implementing and keeping ahead of new AI regulation. Meanwhile, also trying to prepare and anticipate what is next for AI technology. With AI becoming essential to the future of financial services, is there too much focus on technical integration and not enough on the human element?
The Current Limitations to AI Integration
While Generative AI’s (GenAI) ability to understand plain language makes it easier to use, this creates an abundance of potential security risks. Financial staff using these tools might accidentally share sensitive data when asking questions, or the AI could reveal confidential trading information if it’s not properly trained or restricted. This can also work in reverse, by continually telling the AI tool that an untrue thing is correct, the AI tool will adopt this position and present it as fact. For example, if a GenAI tool was trained that people called ‘Rob’ are always bad credit risks, it would quickly factor that into its answers irrespective of the clear (to humans) fact that it is nonsense. This of course works equally well accidentally and maliciously.
Another considerable limitation of current GenAI systems lies in how the mechanisms are set to prioritise delivering information. Unlike seasoned human financial analysts who possess the experience and time to make informed decisions, GenAI mechanisms are set to prioritise over a number of known and unknown criteria, that are not necessarily trained from that specific use to the model. For example, a user disconnecting without an answer may mean the Gen AI tool prioritises responding within a specific time frame over providing correct information. This is especially prevalent in public GenAI tools where the context and desire of the user will be different to the current question but may be applied as universal learning. Furthermore, Public GenAI rarely sees the reaction to the output, so it is unable to differentiate between the good and bad answers its given, meaning training on dumb makes the GenAI less smart, not more.
This can lead to potentially dangerous scenarios in critical financial operations. Where the GenAI tool simply guesses or creates an answer that isn’t based on fact, potentially enabling or making the wrong decisions.
A Comprehensive Approach to AI Integration
Instead, financial services and institutions must focus on creating and adopting a comprehensive approach to AI integration and security to address these challenges and limitations.
Firstly, firms should invest in building their own AI models that follow their company’s security rules, rather than relying on unreliable public systems. If public systems are being used by staff though, setting clear rules about, and controls when using these tools, like ChatGPT, will also be essential in ensuring the safety of company information. Staff need to know what they can and can’t share, and monitoring and controls should create clear boundaries and limitations to the use of open AI models.
Companies must also train staff on how to use AI systems safely, as even the best security measures can fail if employees don’t know how to use them properly.
Finally, organisations should also use multiple AI systems that work together with human experts to double-check results, making sure no single system can make unchecked decisions without a human AI partnership.
So, what does a good human AI partnership look like?
How to Leverage Human-AI Partnerships
Finance services institutions need to recognise that the solution should focus on allowing AI and human skills to compliment each other. It isn’t just about better AI – it’s about enabling human expertise to scale efficiently.
The simple principle of “the right tool for the right job” needs to be at the forefront of users minds. A GenAI platform can search through billions of records and identify six that are anomalous in some way. A second AI platform can ask it to validate its findings against the original question. And then a human expert can identify which 4 of the 6 are expected behaviours. And which 2 are malicious, dangerous, or need further action.
In the same way as asking the human to search through billions of records manually is unachievable, asking the GenAI platform to apply context it doesn’t have or retain causal experience is equally unrealistic.
AI excels at processing vast amounts of data to recognise patterns, but humans bring crucial understanding, ethical judgment, and strategic thinking. Working in unison, taking a partnership focused approach can allow organisations to leverage both the processing power of AI and the nuanced decision-making abilities of experienced professionals.
Risk management within this partnership becomes absolutely essential. For instance, if AI flags potential money laundering, a compliance officer needs to review this before any action is taken. Or if AI suggests changes to investment portfolios based on market trends, investment managers must validate these recommendations against their market knowledge and client needs.
Banks too need clear procedures for escalation. If AI suggests unusual trading patterns, there should be a defined process for who reviews this. Whether that’s the trading desk, a separate compliance team, or even senior management. The same applies for credit decisions, fraud alerts, or risk assessments.
The Real Risk: Avoiding AI Altogether
Interestingly, the biggest risk to financial institutions isn’t from those using AI – it’s from those avoiding it altogether. The key is finding the right balance – embracing AI’s capabilities while maintaining strong human oversight and security measures. Financial institutions must create protected data environments and train AI platforms for specific tasks with specific information. They must establish clear guidelines for AI tool usage. And conduct regular security audits to ensure their AI systems remain both effective and secure.
An AI’s development, training, utilisation and continued learning should be planned monitored and developed. This should be longside its human partner’s usage and of course the overall outputs and results.
GenAI Platform Best Practice
When building a GenAI platform, the following principles should be considered.
Design it carefully, with a restricted scope and a set of agreed outcomes, how will it learn? What makes this the best learning data? And of course GenAI supervised by humans can play a big part in this.
Validate its learning, tell it what’s right and wrong – a GenAI model will learn (like a human) through mistakes. But it won’t hold the knowledge of why? Or what? So keep the feedback relevant, continuous and tight.
Try to break it – ask it random things. For example, when it replies “I don’t know” tell it that’s a good answer. When it makes something up, be clear and provide feedback.
Ensure the human partners understand its limitations – people don’t get to outsource their thinking. They get to participate with a low level, high volume intelligence. Make sure they know that and are checking every answer.
Measure against your original outcome goals. Don’t scope creep without following the above principles. Yes it can analyse data, but it can’t think if what you’re asking is stupid or not.
Enjoy the financial, time, accuracy and speed benefits of your human/ai partnership
The future of financial services lies in effective human-AI collaboration, not just AI adoption. Success requires building secure, well-trained AI systems that compliment human expertise rather than replace it. Embrace this partnership mindset while maintaining strong security measures and human oversight. Then financial institutions can harness AI’s power while mitigating its risks.
The two-day event (9th-10th September) offers attendees all the tools they need to improve their resilience and adaptability.
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Be the CHAINge you want to see in supply chain, and join fellow supply chain professionals at CHAINge North America. Located at the Greater Columbus Convention Center, in the heart of Columbus, Ohio, the two-day event (9th-10th September) offers attendees all the tools they need to improve their resilience and adaptability.
SupplyChain Strategy readers receive an exclusive $200 discount when registering for CHAINge North America, by using code SCS200
The event gives attendees access to a rich agenda of learning opportunities, covering topics such as:
Supply chain digitalisation
Data visibility
Risk and resilience
Future-proofing supply chains
Woman in supply chain
Harnessing AI
And much more. Those attending CHAINge North America join their peers for two days of interactive learning, lively discussion, and novel ideas to drive change in their own supply chain.
All supply chain professionals and executives are welcome to become part of the movement and discover the latest in supply chain innovation.
Register today and use our exclusive discount code: SCS200
As well as eye-opening talks, CHAINge North America attendees gain access to:
10-minute innovation tech showcases
Educational breakout sessions
Use case theatres
Industry Q&A
Join your fellow professionals on the 9th and 10th of September for this industry-leading event. Register now and use code SCS200 for $200 off the cost.
David Sewell, Chief Technology Officer at Synechron on why robust digital infrastructure is the missing link in the UK’s AI ambitions
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The current British government wants everyone to know that it sees opportunity in AI. Across a series of flashy public events this spring, Prime Minister Keir Starmer announced a string of support packages. Culminating in a £2 billion AI investment pledge. Standing next to the Prime Minister, Nvidia’s Jensen Huang addressed a gathered audience of businessmen and politicians by mentioning the “extraordinary” atmosphere in the UK. Huang also mentioned that the UK is now the third largest AI venture capital market in the world.
The UK has set an ambition to be a global powerhouse in artificial intelligence – building on what it’s already done. The question now is how to ensure it gets there.
The financial industry, centred in The City but now in every corner of the nation, is core to getting there. As James Lichau, financial services co-leader at BPM said: “AI presents immense opportunities for the FinTech industry”. From better banking applications to bespoke advisory and vastly improved investment theses, Britain’s AI dream will flower with its fintech ambitions.
The Global AI Momentum and Infrastructure Reality
The UK has been quick to realise the importance of the moment, but others are moving too. Two billion pounds is a sizeable commitment but compared to the United States’ $4 billion CHIPS and Science Act AI investments and China’s estimated $15 billion in annual public and private AI spending, it’s not the largest in the world.
Capital investment is accelerating as nations and corporations are pouring large sums into artificial intelligence capabilities. What might have previously been seen as “unnecessary spend” is now being approved as essential infrastructure. The best engineers now command salaries the equivalent of city budgets. Financial companies of all sizes have placed substantial wagers on AI’s ability to create new value.
This means Britain will need to be smart and targeted in where to place support. The most obvious place is infrastructure. Infrastructure is critical because ambition without infrastructure is unsustainable. Even the most sophisticated AI strategies, backed by some of the largest companies in the world, will fail without the foundational digital systems to support them.
The UK’s AI aspirations face a fundamental test: can government investment translate into real-world capability when the underlying infrastructure remains underdeveloped? History shows that technological leadership demands comprehensive ecosystem development encompassing everything from basic connectivity to advanced computing resources.
Infrastructure: the foundation for progress
A successful AI ecosystem requires three interconnected elements.
First, compute capacity represents the engine of AI development. Training sophisticated machine learning models demands enormous computational resources, often requiring specialised hardware configurations that can process vast datasets efficiently. Without adequate compute infrastructure, AI development becomes expensive and time-consuming, forcing organisations to seek resources elsewhere or abandon projects entirely. Peter Kyle, Secretary of State for Science, Innovation & Technology described the possibilities this way: “Giving our researchers and innovators access to the processing power they need will not only maintain our standing as the world’s third‑biggest AI power, but put British expertise at the heart of the AI breakthroughs.”
Second, power supply infrastructure must support the energy-intensive operations that modern AI systems require. Data centres housing AI workloads consume significantly more electricity than traditional computing facilities, creating new demands on national energy grids. This is why countries like Iceland with large geothermal and hydroelectric energy capacity typically outperform in power-intensive industries. Meanwhile, the massive grid outage this spring showed the fragility of Spain’s power system. The UK’s AI Energy Council is holding discussions about upgrading the national grid, with plans to power the next wave of AI using nuclear and renewable energy.
Third, connectivityis crucial for reliable movement of large data sets. Networks enable real-time deployment of AI services, allowing organisations to access and process data across real-world applications. Without robust connectivity, AI remains confined to isolated research environments rather than driving economic productivity. The UK has a longstanding programme of investment in broadband infrastructure although the speed requirements represent a significant expansion of current capabilities.
Beyond Headline Commitments: The Implementation Challenge
The caveat frequently used by investment managers applies here as well: “Past performance is not a guarantee of future results.” Some regions have built a head start in the race for AI supremacy. That doesn’t mean they will stay in the lead. From algorithmic trading to fraud detection, fintech applications will be among the first to falter if infrastructure lags behind innovation
Countries that address infrastructure limitations decisively can leapfrog competitors and establish sustainable competitive advantages.
The UK must be unafraid to copy success from elsewhere, while also finding areas to break new ground. The UK AI Opportunities Action Plan is a strong start. Government, business, and investment leaders must now collaborate to turn ambition into execution.
SupplyChain Strategy attended July’s Exiger Executive Forum to hear from the best and the brightest in the industry.
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Supply chain resilience is one of the most pressing concerns of modern business, whether executives are aware of it or not. That was the central theme of the Exiger Executive Forum held on July 23rd 2025. Titled Supply Chain Sovereignty in a Fractured World: Winning the AI and Geopolitical Race for Resilience, the event brought together business analysts, CEOs, supply chain and procurement executives, academics, and politicians for an open discussion around supply chain sovereignty and the urgent need to secure supply chains across myriad industries and territories.
As geopolitical events, trade wars, and threats to globalised networks threaten to destabilise global and local supply chains, the case for supply chain sovereignty, which is an organisation’s ability to control its supply chain and minimise dependence on external suppliers, becomes increasingly stark. However, a myriad of stakeholders must come together to enable organisations and nations to gain independent control of supply chains, and collaboration between industry, government, and academia is essential.
Three guest speakers joined Maria Villablanca, CEO and Co-Founder of Future Insights Network, each representing voices from within politics, business, and academia: Tobias Ellwood, former UK Minister and Chair of the Defence Select Committee; Koray Köse, CEO and Chief Analyst of Köse Advisory, Senior Fellow at GlobSEC Geotech Centre, and Board Member of Slave-Free Alliance; and Karsten Machholz, Professor for Supply Chain Management and Strategic Procurement at University of Applied Sciences, Wuerzburg-Schweinfurt.
The discussion exemplified the discordancy of priorities and perspectives among senior voices from all angles regarding security, economics, policies all impacting value chains, albeit with a shared willingness to engage in secure, competitive, ethical and innovative supply chains, fuelling businesses and economies through heightened volatility in a fractured world that is recalibrating through the era of reglobalisation.
Supply chain sovereignty: Bridging political understanding, and urgency
“It is a dangerous world that we’re entering,” Ellwood warned. “If I ask you ‘Do you think the world will be safer or more dangerous in five years from now?’, I think we’d all agree in which direction it’s going. We have to then ask ourselves how we prepare for that.” To that end, Ellwood believes an increased focus on supply chain sovereignty is both an economic and military imperative.
For Ellwood, the central issue is limited understanding, both public and private, around the urgency presented by the current risk and threat environments. Through the combination of limited knowledge around supply chain complexity and an election cycle-focused impetus to enact vote-winning policies, he believes the political class lacks both the nous and urgency to prioritise supply chain sovereignty.
“After 20 years in politics, I can safely say that many politicians are simply unaware of what’s coming over the hill,” said Ellwood. “The tide took me out to the last general election, and so I went from helping to craft and nudge policy and encourage Britain to move forward to then scrutinising what we were doing, not just at home but internationally. Now that I’m outside of politics, I continue doing those same things.”
The necessity for political engagement is not lost on Köse, who through his own experiences of researching, advising and leading supply chain organisations, has been advocating for supply chain resilience as a top line driver for economies and companies, has equally encountered the depth of that disconnect.
“At an early point I realised that geopolitics is the key denominator for all value chains and all of us in this context,” he said, adding that work is overdue but starting to be underway to bridge this gap. “The London Defence Conference, as one critical congregation, is key for you all folks to be aware of. Not only because of what they do in terms of bringing the politicians into one room to debate some of the most fierce topics of the day, but it’s all about convergence. Bringing in supply chain leaders, policy makers and technology folks with a direct approach to debate.”
Villablanca noted that Ellwood’s presence was indicative of a gradually shifting tide, however. “It’s not lost on me that here we are in this panel, talking about supply chain, and we have a former politician with us,” she said. “That is very different to some of my earliest supply chain conferences where we didn’t see that, so it’s a sign of the times. Set the scene for us around why you’re here and why it’s important to discuss the geopolitical situation vis-a-vis supply chain today.”
“I spent most of my time in politics trying to strategise, trying to go four or five chess moves ahead, and I found I was on my own,” Ellwood replied. “Politicians operate for the day, for the here and now, the election cycle; the news cycle is what keeps them busy. They’re not thinking about these things and yet the world we’re now seeing in everything… everything is being weaponised because that is the change in the character of conflict.
“But today, from my perspective, I see the world splintering into two spheres of hugely competing influences. If you look at the number of countries that have signed up to China’s One Belt One Road initiative, you’ll see that many of them are either opting or hedging their bets as to where things go.
“To make matters worse, our exemplifiers of what democracy looks like aren’t in a good place. We see what’s going on in America, British politics and so on, and Europe and America are not on the same page. We aren’t promoting global law in the sense that we had a sense of determination that we had when organisations were set up in 1945. Other nations are getting together and realising that there’s an opportunity to exploit the wobbliness of our world order and do things their own way.
“That’s where the mechanisation of just about anything comes in to cause us economic harm, to sow political discord from afar. It’s very easy to do and becoming easier simply because of the openness of our society. It means, from a rudimentary perspective, anything you do can be weaponised against you.”
“It’s very easy, from afar, to then limit your supply chains and thereby limit your capabilities. There are countries that specialise in sowing economic discord from afar. They understand and learn and know supply chains better than we do, and they can work out which missing pieces will cause our assembly lines to grind to a halt.”
That lack of preparedness, he says, is an impediment to putting the nation on a footing that could support a war effort on the scale of the World Wars.
He continued: “There’s also the prospect of preparing for war, which means that we are suddenly spending more money on defence. Our ability to switch on the supply chain levers to support military capability is not there. This is why companies that have no connection with the defence world need to think about the services they provide that might have a military bearing. In five years time, you may be called upon to do exactly that.
“That is the mindset we now need to get into. Security and economy are one and the same now, and that’s what we need to learn.”
AI, foresight, and risk strategy
The conversation then shifted to the business side, where securing critical supply chains powering key technologies such as AI, defence and security, biotech, energy and quantum computing has become a more pressing concern in the wake of a range of global disruptions through the early 2020s.
Along with broad supply chain breakdown during the COVID-19 pandemic, the geopolitical environment has become more fraught. Escalating trade wars, the imposition of sweeping import tariffs in the US and heightening tensions between America and China have thrown globalised networks into question. Alongside those challenges, Environmental, Social and Governance (ESG) directives have placed an increased onus on supply chain leaders to sanitise their supply networks against modern slavery, conflict minerals, and indirectly sourcing materials from rogue nations. The case for establishing redundancies in supply, as well as heightening visibility on an end-to-end supply basis, was thus clear amongst the panel.
“Koray, you work with a lot of different companies,” began Villablanca. “Do you think there’s a mindset issue where politics and commerciality need to come together to realise the common goal and create resilient supply chains?”
“Directly, there probably is a mindset issue,” Köse replied. “I think there is a lack of clarity about the importance of geopolitics’ impact upon supply chains, and there is certainly the capability issue of understanding the context of geopolitics.” He then elaborated on the challenge by highlighting shortfalls in companies’ predictive capabilities.
“Companies operate with risk dashboards,” he continued. “Sometimes it’s just red, yellow, green, and that’s all you have. They have a few key risk indicators like financial compliance issues, quality issues, performance issues, but you never see strategic foresight. It’s retroactive, based on historical numbers. If you look at a production line it might say, ‘We didn’t have an incident for 80 days’. What if somebody were to say, ‘We won’t have an incident in the next 100 or 80 days’? You don’t see that in production; it always looks backwards because it is built on the past.
“A big problem in a lot of the military complex, and in politics, is thinking that the next war will be like the last one. They cannot necessarily understand that asymmetric, hybrid and proxy warfare is really where things are going, and the same goes for technology. Supply chains are often built on yesterday’s technology.”
To then end, he believes supply chain leaders should be more forthright in leveraging their profound influence upon business operations: “In supply chain, we see the conversation about having a ‘seat at the table’ for decades now and I always say, ‘Just bring your own freaking table’, and invite everybody to it. Everything, every cent in an organisation, goes through you. Own that leverage and don’t run after them, invite them to come to you. Your table is where value is generated, secured and innovation and competitiveness are established. You hold the fate of the future.”
As to politics’ place within meeting this challenge, Villablanca asked Ellwood whether the political sphere could be doing more to shape the corporate agenda.
“Yes, and that last point you said is the most critical; recognising that there is a massive risk, that this is a very different world that we’re now facing, and I expect the point that’s really being made is the absence of politicians,” he said. “The politicians themselves need to be told what we need because their expertise in understanding this arena is poor.
“China now owns the periodic table. If you are into silicon wafers, where’s your serum going to come from? If you’re into magnets, where’s your Europium going to come from? You need to know this sort of detail, and it’s not just you yourself. It’s your suppliers and the suppliers of your suppliers, too.”
While supply chain transparency has undoubtedly increased in recent years, he stressed that considerable work remains to realise total visibility.
“At a recent procurement event I was astonished at how many household names were unaware of what their second and third-tier partners were doing during the procurement cycle,” Ellwood continued. “They didn’t understand the vulnerabilities, down to the SMEs, of what’s going on. If the assembly line stops then that’s quite serious, but what’s going to happen because of that stress?
“There are people who don’t understand it over here, not recognising that our competitors are deliberately looking at our supply chains and working out where that vulnerability lies. It is so that Ford stops making trucks, so that pharmaceuticals stop making medicines. Ministers are ignorant about this and we need to become better at it. This is the frontline of the next war that we’ll fight, and that war is coming.”
“I would add that some can’t fathom the complexity of certain supply chains and the vulnerability and risk associated with multiple tiers within them,” Villablanca posited. “There’s probably a translation issue with regards to business and politics around supply chain.”
To this, Ellwood stressed that international government groups hold the keys to unlocking a broader understanding within members’ respective political spheres.
“The G7, the Five Eyes Alliance, this is where these conversations need to go,” said Ellwood. “To recognise this must be a priority within the western world, we now need to have an alternative source to make sure that we can build our aircraft, we can build our factories, we can build our products. It isn’t so much the rare earth minerals themselves, but it’s the processing. Setting up a processing factory for rare earth minerals takes almost a decade.”
Here, a guest interjected with a point that hearkened back to Ellwood’s own admission that politicians have an innate directive to focus on local, vote-winning issues: “Politicians recognise there are no votes in this. The average MP will say their inbox is full of ‘fix the NHS’, ‘get the roads fixed’.”
Resolving political challenges such as those, Ellwood replied, is predicated upon strengthening economies to open fiscal headroom for public investment.
“If our economy is affected by problems with our supply chains, there’ll be no money in the treasury,” he explained. “Not for health, transport, potholes, policing, defence. It’s imperative that if you want to fill the coffers, then we need to protect ourselves. You can only do that with supply chain resilience. As a politician, you’ve got to take the people with you if you want to make the case.”
Villablanca then repositioned the conversation with regards to pressing issues around sustainability.
“There’s a lot of risk associated with our supply chains that goes beyond geopolitics,” she said. “We also have climate issues, economic issues. How do we maintain sovereignty in our supply chains while still trying to pursue goals around sustainability?”
“Supply chain transparency is something that I advocated for when I was a young consultant in the early 2000s when my hair was not so grey,” said Machholz, highlighting the gradual shift in supply chain priorities around identifying the finer details across those networks. “It isn’t a new topic and in the EU we now have the Critical Raw Materials Act.
Machholz drew the conversation towards sustainability in the context of integrity and continuity. “I’m German, and what we have is engineering power. We are good at car and machine manufacturing, but we have no natural resources. We have a little bit of coal, but all other things need to be imported. There have to be some sources to get those things.
“There’s Trump and tariffs going up and down, and we have some other geopolitical tensions affecting supply. You might say, ‘Where do I source this particular thing from? We don’t really have a second source of supply, because both of these sources are located in the same geographical spot.’ Maybe both of them are coming out of China.”
For Machholz, lessons to be gleaned around forecasting with technology’s latest predictive capabilities were presented en masse by the pandemic. “If we look at COVID, almost all supply chains were disrupted and you were running out of materials,” he continued. “You needed to be much more risk alert, and this is the problem we have already touched on: not looking in the back mirror, but using your data and turning insights into foresights to see what could happen, and then being agile and adapting.
“Sustainability could be one thing, having several sources, having alternatives, but of course, especially if we’re talking about critical raw materials, critical parts or maybe patent-protected or monopolistic suppliers, we are in an ambitious situation, put it that way, to find some alternatives.”
Machholz stressed: “This is something that each supply chain manager, CPO, and CFO, needs to understand to set boards’ scenarios. I’m pretty sure with the help of artificial intelligence we can elaborate much more on our data and predict different scenarios so we can be more prepared rather than just reactive.”
Shifting from cost-cutting to resilience
Of course, supply chain executives are under siege from an enormous breadth of challenges, whether it’s geopolitics, technological evolution as both a benefit and a threat, and shifts in consumer behaviours precipitated by those same factors. Rising to meet those challenges on all fronts, especially in a business landscape that often adheres to cost optimisation and efficiency over investing in resilience, can give rise to decision paralysis or financially-stymied strategies.
Turning to Köse, Villablanca asked: “There’s a mountain of black swan events lurking around us, ready to attack at any minute. What are the things that a supply chain leader should be focusing on today to try to build resilience?”
“To be honest, I don’t think they’re looking at building resilience,” said Köse. “What they’re doing right now is cost optimisation, looking at inflation and making sure that the profit margins are going to be protected through the bottom line, not considering top line revenue maximisation.
“I think agility and economics always need to come back to top line, which basically means in the context of normal business 101 you are producing something, that there is a want and a need and a willingness to pay, and not necessarily hyper-focusing on the cost line or saying, ‘I’m not going to produce a bunch of bullshit that nobody’s going to pay for, just because I got to claim savings to my CFO’.”
“I’m going to challenge you there,” Villablanca interjected. “I think, theoretically, that’s great, but everybody in this room is running a business. We have our own boards, people above us, board directors and so on saying, at the end of the day, you are remunerated and we are all remunerated for our quotas. How do you deal with the day-to-day management of your business as well as building that kind of resilience, agility and visibility?”
To this, Köse stressed that the difference can be made by reframing how businesses examine and counteract risk. “We’re thinking about turning the tide by really embedding foresight in risk indicators. Those risk indicators need to incorporate geotechnical, geostrategic issues with foresight,” he continued before highlighting what he implied to be a tendency for organisations to bury their heads in the sand when faced with developing geopolitical challenges.
“I published an article before Russia invaded Ukraine, about Russia getting ready to invade Ukraine, that went through loads of red tape and debate internally that calling Russia an aggressor was cancelled out from the research note,” said Köse. “They said, ‘You can’t say that’ while it was pretty obvious that Russia were clearly the aggressors.
“The supply chain-focused function needs to spread out and have these geopolitical indicators, geotech-related risk indicators, and not just the last financial report from your supplier A to Z or tier one or tier two.
“We must then tie it back to the value and revenue you’re generating. Get away from this hyper focus and obsession with savings. In that context, make your analytics smarter with a bold analysis of things that you feel uncomfortable about. Think about ‘what now?’ and think about politics. I know we eradicated politics out of business as much as we eradicated many other beliefs from the conversation, but it has to come back.”
With this in mind, he proposed that cost optimisation is to an organisation’s detriment where resilience is concerned, not to its security. “Your indicators for success are not just on the cost line item or bottom line. Your priority must be on the top line. If I sell more, I can grow. With cost optimisation you can shrink yourself to death. That’s what some countries have done with political reviews where you shrink this, you shrink that, let’s shrink here, let’s shrink there. Potholes, collapsing bridges and rail systems, come because of the shrinkage of your investment budget for public infrastructure, for example. What I have found in the last decade of the sustainability high is that it actually impeded resilience, while the narrative said it was supposed to increase resilience.”
To this, Machholz highlighted the data behind Köse’s comments that resilience offers heightened growth potential than cost-cutting measures.
“There were some studies from McKinsey which showed that companies who are investing in risk management are 4.7 times more profitable than those who don’t,” Machholz shared, stressing that businesses engaged in this mindset are missing growth opportunities.
“People just fall back and say, ‘Okay, now the risk is over, COVID is over, whatever event is over,” he continued. “‘We can just go back to business as usual’. Resilience is just extra cost, extra inventory, maybe a second supply chain that needs attention, money, and people to take care of it, and they just simply don’t do it. This is, I think, one of the big threats that we are all facing.”
Exiger Executive Forum: A closer look
The Exiger Executive Forum (EEF) in London is a global think tank that brings together elite independent voices from strategy, policy, technology and business to equip leaders with the frameworks and foresight needed to navigate the multipolar era. The EEF is exclusively curated for industry experts, analysts, policy makers, and senior procurement and supply chain decision-makers through Exiger, a market-leading supply chain AI company. The next Exiger Executive Forum ‘War-time Economics: How Europe’s €800BN Defence Spend Will Reshape Supply Chains’ will take place in London on Thursday, September 18th, 2025.
Ellwood concurred that this lack of foresight and willingness to invest in protective supply chain measures leaves businesses undefended against interruptions both foreseen and not. “We need to prepare ourselves for unexpected events to happen as the norm,” he said. “What would happen to any business if it didn’t have power for 72 hours? How would you look after your personnel? How do you make sure you salvage the business so that, after 72 hours, you can get back up and running. These aren’t questions that we naturally posed at the moment because again, we tend to park these things.
“The mentality may be, ‘The world certainly feels like it’s getting dangerous, but my life actually looks okay.’ That isn’t the right attitude. If you go to Sweden or Finland, who are much closer to the war with Russia, they are preparing in a way that we are not for a major event or incident. It may well be that when something happens and it’s the moment where governments wake up, but you shouldn’t be waiting for that moment.”
Villablanca then highlighted the recent, universal example of poor supply chain resilience bringing business, both domestic and international, to a grinding halt. “Did we learn nothing from COVID?” she asked. “Did we not take the opportunity to stress test our supply chains and look for the vulnerabilities within multiple layers?”
In response, Ellwood invited guests to consider whether the muscle developed in response to COVID’s interruptions had been allowed to atrophy. “I think that’s a question for everybody; how much of that was retained?” he asked before blending the conversation of supply chain agility with the potential for organisations to support national security should their respective nations go to war.
“During COVID, supply opportunities came about,” he said. “Everyone here today represents diverse businesses. What services do you provide that you could tweak or add value to where something else has fallen short?
“That’s where life really becomes interesting because that’s what happened in the First and Second World Wars. We called on organisations that previously had no interest in helping out with the war effort to add support and value to the wider machine and protect ourselves from a resilience perspective.”
Challenges faced by supply chains, he explained, have analogues to business that clearly marry the political and business spheres: “When we say ‘war effort’ today, it isn’t just Army, Air Force, Navy, air, land and sea. It’s now cyber, it’s space, it’s coastguard, it’s AI. This greater warfare is where a lot of the real pain will happen. As happened in COVID, it’s going to be the clever people in the industry that step forward to say, ‘I’ve already thought about this’. They’re in the patent-esque mode, they’ve done the work to say, with a few tweaks here and there, give us some extra money, and I can alter what I’m producing to provide a solution.”
The roles of government and industry
While there are clear precedents for, and incoming needs to, prioritise supply chain resilience in both the political and business spheres, the conversation made it clear that a unified front stands to offer the most impact.
The challenge, particularly in a political environment preoccupied with economic stabilisation, increased productivity, and soothed international relations, is identifying a shared north star or galvanising body to lead the shared project.
Striking at the heart of the conversation, one guest posited: “If we want to align supply chain and geopolitics moving forward with a mutually-reinforcing relationship and shared goals, joint risk assessment, a focus on resilience over efficiency, and heightened cross-disciplinary talent and data, what are the forward steps?
“What can we within industry do in partnership with governments to move this forward?”
Representing the political voice, Ellwood replied: “There are certainly supply chain improvements that you can do on a national, sovereign basis. But from where I sit, there is a wide political threat that we face and are losing right now. One of them is to do with the energy supply, and another is the threat of AI. The quantum race will be won or lost in the next five years’ time, and that will be game-changing. It simply means that if the winner can harness the power of computing on that scale, everything’s over.”
Ellwood then invoked the technological advancements made in modern wartime, stressing that political figures must wield the mindset of those times to accelerate progress.
“I would like to see some two or three Manhattan Project equivalents, if you like, to ask, ‘How do we harness modular nuclear power?’,” he said. “That’s a very easy way to keep our lights on locally. Then, how do you harness AI? Let’s make sure it is this side of the world that wins that.
“Again, there isn’t that coordination, that sense of urgency, because it’s too far down the road,” he concluded, then highlighting that opposing forces on the world stage already have the unified capabilities that many Western nations lack. “State, industry, and academia in China, for example, are all morphed into one and that gives them huge benefits in the race for these key arenas.”
Köse elaborated on this point by highlighting Turkey’s effective coalescence of business and government.
“If you think about the private-public national defence sector in Turkey, it came from being totally dependent on the US armoury to a leading innovator of drone wars,” Köse explained. “When you think about asymmetric warfare, innovative, impactful and economic weaponry, from drones to secure soldier transportation and all of that, think about what Turkey is producing right now in technology compared to others. The headway Turkey experienced in the last decade in the defence sector is unprecedented.
“That private-public sector coalition and symbiosis has covered such a need for them in a decade that many are surprised. I think that is something that Europe has to relearn, because Europe thinks a lot about public sector dominance in an area where the private sector should actually take charge. In the US, it’s the opposite. They say, ‘keep the public sector out’. The solution lies in collaboration and bringing each sectors strength to the table while leaving out their weaknesses and flaws.
While of course not advocating for adopting the political model, he agreed with Ellwood that nations like China have an innate advantage in this race. “When you think about the way that the autocratic countries are going about it, it’s the public sector dominating the private sector environment,” he said. “That’s why they’re so hyperfocused on things and they can scale but not necessarily innovate in this sector.
“I love the government when it’s in the right place to actually do something positive and impactful. But when I’m exposed to it, I usually get anxiety issues due to the lack of pragmatism, innovation and agility. But hopefully there’s this convergence of politics, business and academia driving intelligence into critical sectors and industry, and we’re trying to drive it through this think tank here.”
The unified case for supply chain sovereignty
Exiger’s Supply Chain Sovereignty in a Fractured World event was an enlightening review of the supply chain landscape and the myriad challenges and stakeholders it encompasses.
While the panellists’ conversation in many ways highlighted the disconnect between government, business, and academia, the resonating message was one of shared pressures and goals. Where governments have pulled back on the reins of public spending, many organisations have in kind adopted a cost-optimisation mindset that may protect the bottom line but opens the door to heightened vulnerability.
Where governments must consider challenges around energy sovereignty and insulating populations against the breakdown of globalised networks – as was demonstrated upon Russia’s invasion of Ukraine in 2022 – supply chain executives must create redundancies to cover lapses and minimise potential disruptions to production and wider organisational integrity.
The guests’ final comment, that states which can marry both the public and private spheres towards shared interests, neatly encapsulates the urgency with which those worlds must reunite. While much work remains to enmesh those spheres, it is clear that the conversation is progressing at pace.
James Watson and Rachel Noll, Argon & Co, explore how smarter use of data, automation, and robotics can help manufacturers unlock productivity.
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The UK government’s newly launched industrial strategy was long in the making, but has arrived with bold ambitions. Its 10-year roadmap for economic growth has a firm bet on advanced manufacturing as one of the eight high-potential industries in the UK, along with sectors like financial services, clean energy, and life sciences.
For many operating in this sector, this support couldn’t have arrived soon enough. Manufacturing has been pushed from disruption to disruption, hampered by inflation, persistent labour shortages, and global supply chain crises. Businesses have been urgently calling for tools to help them do more with less, and, against this backdrop, the government’s commitment to invest in digital transformation and skills has been widely welcomed.
The industrial strategy features investment in specialist advisory services and organisations to increase technology and robotics adoption across advanced manufacturing. But the big question is now whether it will deliver the change that manufacturers are hankering for, especially in relation to smart manufacturing.
How manufacturers can get smart: in five stages
Central to the Advanced Manufacturing Sector Plan is a push to scale the adoption of robotics, data, and advanced digital technologies. While cutting-edge automation and predictive AI are becoming more accessible, many manufacturers – particularly SMEs – still lack the maturity or infrastructure to implement them.
The industrial strategy aims to bridge this gap, announcing a new Robotics and Autonomous Systems (RAS) programme, backed by an initial investment of £40 million. This will establish a new network of Robotics Adoption Hubs – physical centres with the expertise, equipment, and connections to accelerate firms’ adoption of robotics. These will be designed as a ‘one-stop shop’ to help end-users invest in RAS technologies in a safe, low-risk environment.
However, smarter manufacturing also needs to be backed by operational visibility and a strong data foundation. Here’s how manufacturers can embark on this journey successfully:
Stage one: Increase operational visibility
Manufacturers first need sight of their core operational metrics to define and monitor performance. After all, you cannot improve what you don’t measure.
Many manufacturers still rely on paper-based reports and inconsistent metrics, making it hard to compare shifts or pinpoint problems. Without operational visibility, actions tend to be reactive and retrospective. Perhaps a shift has underperformed, but without reliable data, it’s impossible to identify the cause.
The first step is defining consistent metrics across all shifts – such as operatives per line, output per line, downtime reasons, or quality defects. Even simple tools like whiteboards or spreadsheets can instil the habit of consistent data capture and begin building a mindset of continuous improvement. The input might be manual and prone to human error, but it provides a common point of reference and highlights areas needing further insight.
Stage two: Build deeper operational insight
Capturing data in an automated format is inherently more reliable, as it doesn’t require human interpretation. Data such as scan times, equipment health and performance, and employee clock-in and out times can feed into visualisation tools like Power BI or Grafana, helping to spot trends and anomalies over time.
Data is ideally stored in a data warehouse to allow for secure deposit and retrieval in a structured format. Layering information from different sources can reveal patterns. For example, does the mechanical equipment perform consistently at all hours? Are reworks linked to break times?
Organisations may spend longer in this phase retrieving, cleansing, and analysing data, but it’s a vital foundation for future analytics.
Stage three: Apply predictive analytics
One of the defining features of smarter manufacturing is being able to predict what’s happening next and act on it – and predictive analytics can bring this to the factory floor. With knowledge of trends, organisations can begin to form corrective courses of action, strategies of intervention, and avoid downtime. For instance, if the data shows that breakdowns spike after 100 hours of runtime, repairs and servicing can be scheduled in advance. Or, if absenteeism spikes after bank holidays, extra staff can be rostered.
Stage four: Use prescriptive analytics
At this stage, it is assumed the organisation has a strong data foundation. Prescriptive analytics recommends specific actions based on historical feedback loops: detecting a trend, initiating a response, and measuring its effectiveness.
By combining data sources, like weather, complaints, and inbound profiles, organisations can run probability-based models to suggest specific checks or actions. However, human judgment is still required to execute or validate these suggestions. To build trust, models should offer tracing to help users understand why a decision has been made.
Stage five: Become self-optimising
At this final stage, responses are automated, based on high confidence in the data and models. Trust in data is key to achieving full insights maturity. Getting here has likely taken time, learning, and refinement, and as a result, can be relied upon with little human intervention. Like Google Maps rerouting you in real-time around traffic, self-optimising systems react instantly to disruptions – the user only needs to accept or decline the suggestion.
A “human-in-the-loop” retains a level of control, but decisions can be made in seconds. While full automation across the value chain is ambitious, it can be prioritised in high-value areas.
The human factor
While the industrial strategy is welcomed with open arms by most in the industry, success still depends on people as much as policy. While the journey is data-driven, people are the linchpin to progress – or the lack of.
Resistance to change is common. Humans simply cannot process large volumes of data as effectively as a machine can, but their insight is vital for interpreting results and providing context. Ultimately, the most effective smart manufacturing journeys have a perfect blend of human intuition with machine intelligence.
John Santagate, Global Senior Vice President of Robotics at Infios, delves into the challenges tariffs pose.
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Successful supply chains have always been measured by how well they deal with complexity. Getting deliveries and returns right requires multiple levels of collaboration, information sharing and strategic decision making to reduce the risks of confusion or delays. In tandem, customer expectations have changed. Expedited deliveries and a smooth returns process are now intrinsically linked to a positive customer experience. Amongst US consumers, cost, transparency of shipping and flexibility and ease of returns, including real-time tracking, are now the leading delivery preferences.
With seamless buying experiences now standard, pauses in supply chain execution have major consequences for customer loyalty and brand reputation. This is particularly damaging at a time when every pound is crucial. Beyond driving cost efficiencies, enhanced speed and resilience are now equal parts of the supply chain challenge, and retailers must get this process right to succeed.
Even if brands understand that resilience is key, achieving this is another matter entirely. The volume and regularity of significant supply chain disruptions have tested the resilience of even the strongest supply chains. Organisations continually reevaluate the processes they have in place to ensure goods continue to reach customers.
Global impact of tariffs
Political upheaval, global conflicts and the introduction of trade tariffs have driven six months of unprecedented global supply chain uncertainty. It’s estimated that the economic impact of the tariff disruption alone could reach as high as $1.4 trillion globally. Ongoing tensions have destabilised established supplier relationships and created uncertainty in the cost of products and materials. Beyond costs, businesses face increased uncertainty in product availability and financial planning, adding further obstacles to already complex operations.
2025 was a fundamental milestone in supply chain strategy. Single region sourcing and rigid inventory management are rapidly fading. In its place, diversification in sourcing and real-time adaptability have become more important than ever.
At its base, for retailers, navigating the evolving tariff environment is about maintaining customer satisfaction. Organisations have opted to move manufacturing of products to new markets. Others have used previous pauses in tariff implementations, and regular legal challenges, to try and ‘time’ tariff implementations and activate previously budgeted activity at the optimum period.
Among these changes, a question has emerged – in a world that is now defined by constant tariff uncertainty, where can technology help to establish a new, more resilient approach to supply chain execution?
Does forward buying help?
Forward buying of inventory has become the most common response to tariff-inspired uncertainty, as organisations aim to maintain product levels and meet customer demand. In the short term, some stability has been achieved. Organisations have been able to maintain existing purchasing and pricing strategies and the flow of goods. Over the long term, however, this strategy carries risks. In fast moving industries, like consumer goods, demand can be linked to virality. Trends can die as quickly as they begin, increasing the risk of product redundancy. Falling demand already costs even the smallest retailers as much as £10K per year. Over the long term, tariff uncertainty will continue to disturb the balance between purchasing and investor management and could cause costs to spiral.
Staying future-ready requires businesses to enhance preparedness. Streamlining operations and building real-time visibility are an important step. As peak season planning picks up, many organisations face uncertainty around how to manage procurement and ordering in a way that minimises waste and inefficiency.
Integration of supply chain technologies, like order management (OMS) and warehouse management (WMS), provide real-time visibility across customer demand, supplier delays, and order status. Live, up-to-date information empowers teams to proactively manage and optimise supply chain operations, reducing bottlenecks and maintaining overall efficiency.
Making technology-powered decisions
The current tariff environment has also reduced the decision-making window. Taking a painstaking approach to sourcing goods and materials was once common practise. The current environment, however, necessitates companies to pivot on short notice. The announcement of any new policy or tariff could inflate costs to an unsustainable level. The ability to effectively source alternative suppliers, in markets with smaller tariff restrictions, or being able to re-route products and amend production timelines, has become a focal point of success.
This level of decision making requires the practical application of data. Predictive analytics are a powerful tool that organisations can use to understand when costs might rise, or delivery delays could happen. Real-time dashboards mitigate supply chain disruption and provide informed and expedited decision making. Businesses can monitor changing global developments; assess potential risks to their own supply chain processes and act in a greatly reduced timeframe. Traditionally, these planning cycles may have taken place on a quarterly basis. Today, data analytics tools mean pivots can be made in days or hours. The impact of this cannot be overstated, building resilience against disruption alongside a wider competitive advantage.
It is safe to say that disruption isn’t going away. Whilst tariffs undoubtably pose challenges, the opportunity for organisations to use this period for fundamental business change is clear. Technology can build stronger supply chain processes and speed up real-time decision making. Not only will this improve responses to tariff-based disruption, but ultimately it will improve the ability for businesses to meet customer expectations, which remains the end goal.
Simon Bowes, CVP Manufacturing Industry Strategy EMEA at Blue Yonder, on how to navigate challenging situations in supply chain.
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Organisations worldwide continue to face severe supply chain disruptions, creating immense operational challenges. Compounding these difficulties is a bleak economic outlook that shows few signs of improving, keeping consumer confidence stubbornly low.
Meanwhile, experts are claiming that President Trump may stand firm on his plans for sweeping global tariffs. This is despite a US trade court ruling that the President had exceeded his authority in imposing the duties and ordered an immediate block on them – only for a federal appeals court to temporarily reinstate the most sweeping of the President’s tariffs. This means tariffs remain an ongoing problem and, the UK market will likely face further disruption.
When you factor in increased costs, labour shortages, escalating geopolitical tensions, cybersecurity attacks, and weather-related disasters (like the $27 billion in damages seen in the US alone), it’s evident that constant instability has become the new normal for supply chains.
Senior executives agree, with 84% stating in a recent survey, that they have encountered disruptions within their supply chain over the past year. Therefore, organisations must be prepared for the unexpected, understand the potential consequences, and have a plan in place to mitigate such risks.
How can organisations create a strategy for the unpredictable? The answer is by building a comprehensive plan that integrates the capabilities, processes, and technologies needed to operate efficiently, no matter what happens.
End-to-end supply chain planning
The first step is to create an overarching strategy that encompasses the entire supply chain. Having visibility across all areas will support synchronised planning and communication across disparate functions.
When organisations bring together teams and processes, they can start to overcome the traditionally fragmented approach to supply chain management. Uncoordinated procedures inevitably create an inefficient and weaker supply chain, which makes it particularly vulnerable to disruptions.
Whereas, resilience is strengthened by collaboration between functions, if backed with integrated data systems and communication methods to enable sharing of real-time information. Keeping all parties in the loop, with relevant data and meaningful insights, encourages better and faster responses to problems, as well as increases awareness of potential forthcoming issues.
Ideally, what’s needed is an end-to-end connected platform where all departments, offices and sites are working from the same consistent, up-to-date data. And, are not required to change systems to find or cross-check relevant information and iron out anomalies.
Smart decision making with AI and automation
Next, it’s vital to incorporate intelligent automation to improve and speed up decision making. Companies are already using data tools to forecast supply and demand planning, but they now can incorporate AI’s ‘always-on’ capabilities to dynamically evaluate and adapt to changes in supply and demand.
AI-powered solutions can assess how work is progressing by automating data gathering for analysis and optimisation. Automation can handle routine issues, leaving supply chain professionals free to focus on more strategic tasks. Furthermore, AI can facilitate transparent, trackable decision-making to accommodate predicted supply chain disruptions or react to unexpected ones. This level of auditing provides vital insights that will help refine future decisions and actions for the next time similar circumstances materialise, improving outcomes in the long-term.
Additionally, organisations can leverage AI to predict the likelihood of disruptive events happening. Knowing how often they occur and how they have unfolded in the past can inform decision-making and planning. Whether that’s examining competitor behaviour or economic trends, AI tools can process millions of pieces of real-world data to model likely what-if and worst-case scenarios that could impact the supply chain. While these instances may seldom occur, proactive scenario pre-planning provides the foundation for an effective response in the event of real-world disruptions or disasters.
Organisations should identify the specific issues which present the highest risk to their business and ensure appropriate mitigation measures are ready to be activated immediately they are needed.
Investment in flexible, agile solutions
Restrictive working practices coupled with outdated technology can make it harder to react effectively when disruptions occur. Building long-term supply chain resilience means finding a best-in-class solution and partner with deep domain expertise to guide deployment of appropriate modern technologies.
When considering options, businesses should keep in mind fundamental requirements for flexible, agile technologies. These include checking how a software or platform supports data integration and cross-organisational collaboration, whether it can simulate market conditions in near real-time, if the technology architecture is compatible with AI, and how easily does it scale.
It’s critical to have a technology platform that’s designed for scalability and extensibility to manage changing workloads and requirements. Therefore, organisations should look for products with a cloud-native architecture for scalability and resilience, a microservices-based approach for flexibility, and solutions that are easy to configure and maintain without specialised IT expertise.
Building a resilient supply chain
In today’s volatile business landscape, organisations must embed resilience into their end-to-end supply chains, supported by the right technical infrastructure. Investing in modern technologies and platforms offers additional advantages. Advanced solutions that adapt easily to changing conditions, automate manual processes, and harness the power of AI can also provide a competitive edge. For instance, AI’s ability to crunch and analyse vast amounts of data can reveal hidden opportunities stemming from unexpected events—opportunities that might have been overlooked previously.
By making smart technology decisions, organisations can build more resilient supply chains, enabling them not only to survive in current unstable conditions but also to optimise performance and operate more profitably.
By Mohammad Mesgarpour, Head of Data Sciences at Microlise, discusses why we need to think beyond data when it comes to logistics.
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Data is everywhere — often invisible, but constantly at work behind the scenes. As we move through our day, it quietly powers much of what we experience. A simple card payment in a shop sets off a chain reaction: your bank processes the transaction, the store updates its stock levels, capturing vehicle location and driving behaviour location data by telematics box, and the company’s central system records the sale.
It’s data that informs the display board on a train platform, letting you know your train is just two minutes away. From our morning routines to our evening commutes, data is woven into how we live in 2025.
And the scale of it is immense.
Today, it’s estimated that there are around 181 zettabytes of data globally. That’s equivalent to one trillion gigabytes or one billion terabytes. In just a few years, this figure is expected to soar to 394 zettabytes — a rapid expansion that highlights just how central data has become to everyday life.
We may not always see it, but at every digital touchpoint, data is shaping the world around us.
Data in logistics
The logistics industry has long recognised the value of data and has been quick to adopt technologies that help improve performance and efficiency. As new tools and systems have emerged, the sector has consistently found ways to use them to its advantage.
It started with the basics. Early telemetry services, such as GPS tracking, gave operators a clear view of their vehicles’ location on a map – a simple yet powerful tool. From there, the industry moved into deeper insights, analysing fuel consumption patterns and driving behaviours to improve overall fuel efficiency and road safety.
Since then, the capabilities have expanded significantly.
Today, vehicles can generate ten times more data than they did just ten years ago. Thanks to advances in both hardware and software, operators now have access to a wealth of information that can transform decision-making and drive smarter logistics operations.
But this volume of data doesn’t come without challenges. More data doesn’t always mean better outcomes or deeper insights. Businesses are beginning to recognise that without the right systems; high-quality and relevant data; and effective analysis, they can become overwhelmed rather than empowered.
The real opportunity lies not just in capturing data, but in turning it into meaningful, manageable and actionable insight. It can drive operational efficiency, informed decision-making and measurable business outcome.
The appliance of data science
It’s easy to assume that simply collecting data is enough to transform logistics and haulage operations. But in reality, raw data alone won’t deliver results. To drive real value, that data needs to be refined, analysed in context of strategic business objectives. This is where the real analytical challenge begins.
There’s a well-known saying in data science: garbage in, garbage out. And it’s more relevant than ever in an era where artificial intelligence tools – like ChatGPT – are increasingly part of the conversation where the quality of data directly determines the accuracy and effectiveness of the AI model’s output.
Anyone with deep subject matter expertise will quickly spot the flaws when these models are asked about highly specific topics. They may generate convincing answers based on flawed or outdated sources, and while experts can see through the inaccuracies, others may accept them at face value. When that misinformation is reused and reinforced, the cycle continues, leading to skewed conclusions and poor decisions.
The bottom line? Better data leads to better outcomes.
This principle becomes even more important in real-world applications, such as complying with the government’s updated requirement to inspect trailer braking systems at least four times a year instead of once. With accurate, well-managed data, operators can confidently predict when inspections should take place, helping to reduce downtime, avoid unnecessary checks and keep fleets moving efficiently.
Turn around, go back
Geofencing is another area where accurate data is critical to the success of logistics operations. When systems misreport how long a delivery takes after entering a geofence (delivery site), the ripple effects can disrupt far more than just one delivery.
Inaccuracies here can throw off turnaround times, leading to incorrect arrival and departure times, delayed subsequent jobs, inaccurate performance metrics and ultimately frustrated customers. What begins as a small data issue can quickly escalate, leading to missed expectations, strained relationships and inefficiencies across the board. Moreover, if this inaccurate turnaround time is fed into a machine learning model to improve future logistics planning, it can lead to a systematic degradation in the model’s reliability and usefulness, and consequently, in the effectiveness of the plan itself.
High-quality data helps avoid these pitfalls entirely. When the source information is precise, the systems built around it work as intended. And importantly, solving data issues upstream before they feed into larger workflows is far simpler than trying to fix the consequences later on.
In logistics, precision isn’t a luxury. It’s essential.
Open source informs much more
Modern technology plays a key role in identifying the behaviours that impact operational efficiency. Actions like harsh braking, rapid acceleration or excessive cornering speed all contribute to increased fuel consumption. And today’s systems don’t just monitor them, they help correct them. Moreover, onboard sensors and telematics devices track and monitor vehicle health in real time, flagging issues before they become costly problems. Whether it’s the driver, the transport manager or fleet manager, having this information early enables proactive maintenance rather than reactive fixes.
The story doesn’t stop at the vehicle.
Open-source and crowd-sourced data brings another layer of intelligence, offering a broader context that goes beyond what’s happening inside the cab. By combining internal data with external sources, hauliers can gain insight into accident-prone areas, localised weather patterns or planned road closures; all of which influence route planning and delivery performance.
This level of enrichment adds real value. Rather than simply receiving updates every mile or minute, operators benefit from a fuller picture of the journey, making location data smarter, not just more frequent.
Reporting for duty
Accurate data – whether it’s tracking punctuality, fuel consumption or driver performance – underpins a wide range of operational reports. These insights can be tailored to suit each customer’s needs, helping them streamline operations, drive efficiencies and stay competitive in a fast-moving industry.
As we move toward an expected 394 zettabytes of global data by 2028, the value of this information lies not just in volume, but in context and quality. Future data won’t simply indicate what happened, it will increasingly help explain why it happened, too.
Take driver behaviour as an example. Instead of just recording that a driver braked harshly, new systems will identify the circumstances behind the action. This shift means drivers will be recognised for making safe, responsive decisions rather than penalised by isolated statistics.
It’s a powerful step forward. But unlocking the full potential of this data-driven future depends on how well the information is used. Data must be processed, applied and interpreted thoughtfully.
When done right, it not only enhances internal operations, but it also delivers measurable value to customers as well.
Charles Crossland, Managing Director at Goodman UK, discusses the unique challenges the food supply chain is facing.
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The food supply chain operates under unique pressures. With short product life cycles and a complex journey from source to shelf, it must navigate strict regulatory demands, price volatility, and increasing consumer expectations – all while maintaining speed, freshness, and traceability.
In recent years, global disruptions have exposed vulnerabilities. From reduced access to imported goods to increased transport costs, the sector has had to rapidly adapt. In response, many businesses are turning to technology and data-driven strategies to build resilience and agility into their supply chain operations.
Building resilience in a volatile market
Stock shortages are no longer unusual, and customers are increasingly aware of the fragility of food supply systems. There’s now greater scrutiny on how food moves through the supply chain and growing pressure on businesses to deliver consistency and transparency.
Businesses are adopting new technologies such as artificial intelligence (AI), predictive analytics, and automation to improve supply chain visibility and performance. AI-powered forecasting tools, for example, can help businesses respond faster to demand fluctuations, minimising waste and reducing risk.
At the same time, many have moved away from “just-in-time” approaches for non-perishable goods and are reassessing their sourcing strategies. Dual sourcing, diversified supplier bases, and increased inventory holding are helping to minimise risk and prevent single points of failure.
Smart logistics and strategic warehousing
The transport and distribution stages of the supply chain are also evolving. Soaring fuel prices, labour shortages, and carbon targets are forcing businesses to review delivery routes and optimise their warehouse networks. Proximity to customers is now more important than ever.
By investing in strategically located distribution hubs — close to major infrastructure and consumer populations — businesses can reduce lead times, optimise last-mile logistics, and cut transport-related emissions.
All logistics operations, from warehousing to transport, are increasingly equipped with smart systems for real-time tracking, allowing for greater control over stock movement and condition. For temperature-sensitive goods in particular, the use of tracking sensors helps monitor freshness, reduce spoilage, and maintain product quality throughout transit.
Extending freshness through technology
Warehousing is undergoing a quiet revolution. Robotics and automated systems are now performing tasks such as picking, sorting, and packing with improved accuracy and speed. This is especially valuable in the food sector, where shelf life and freshness are key.
Technologies being deployed include:
Grading visibility systems which assess produce quality and reduce manual handling
Advanced freshness testing which pinpoints stages of ripeness with precision
Specialised climate control systems, including zoned heating and cooling, to maintain product quality
By reducing errors, extending shelf life, and improving product flow, these innovations contribute directly to reduced food waste.
Sustainability as a supply chain driver
Sustainability is no longer a nice to have — it’s becoming central to how supply chains are designed and operated. The environmental impact of food production and distribution is under growing scrutiny from regulators, retailers, and consumers alike.
Businesses are now expected to track and report on carbon outputs across their operations. Efficient route planning, electrified fleets, and eco-friendly packaging are just some of the areas seeing rapid investment.
Data is critical here too. By using detailed analytics, organisations can identify hotspots for energy use or waste and adjust operations accordingly. Many are now measuring not only emissions but also transport efficiency in a bid to reduce their environmental footprint.
Looking ahead: A tech-enabled, resilient future
Incorporating smart technologies into warehouse workflows and logistics strategies is already delivering benefits — from productivity gains to improved safety and fewer errors. But this is just the beginning.
As food supply chains grow more connected and responsive, businesses will need to continually adapt. The future will be shaped by those able to combine agility with long-term planning — embracing innovation, forming deeper supplier relationships, and keeping sustainability at the core.
Mario van den Broek, Partner, RSM Netherlands, dives into regulatory fragmentation and how it’s affecting shipping.
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The global shipping industry has reached a critical turning point.
The International Maritime Organization’s (IMO) recently agreed emissions deal has been hailed as a milestone in maritime decarbonisation – signalling long-overdue progress in regulating one of the world’s most polluting industries. But this breakthrough has been overshadowed by a stark omission: the United States’ decision to walk away from negotiations.
The US’s withdrawal raises serious questions about the enforceability and cohesion of the agreement. The IMO’s regulatory model relies on flag states to enforce compliance. If more nations opt out or water down their commitments, enforcement becomes inconsistent, and a two-tier shipping system could emerge: one made up of operators bearing the cost of compliance, and another of those operating under weaker or unenforced regimes.
More worryingly, it risks triggering a wider trend of regulatory fragmentation – with significant consequences for manufacturers, logistics providers and supply chains around the world.
Why is this a setback for companies?
For global businesses, consistency and predictability in regulation are critical. Fragmentation in maritime decarbonisation policy disrupts both. Without a unified global standard, companies must navigate a patchwork of national or regional rules – each with different timelines, thresholds and enforcement regimes. This not only creates legal and operational uncertainty but also increases the cost and complexity of compliance.
Companies that rely on international shipping, especially manufacturers, exporters and retailers, may be forced to choose between higher-cost compliant carriers or risk reputational and regulatory exposure by engaging non-compliant operators. Those costs will not be evenly distributed.
Firms operating across multiple markets may find themselves juggling multiple emissions reporting systems, carbon pricing mechanisms and verification requirements. For small and mid-sized businesses in particular, these added burdens could squeeze margins and dampen competitiveness.
There are also strategic risks. A lack of coherence in shipping policy makes long-term supply chain planning more difficult. For example, businesses that have invested heavily in decarbonisation may now hesitate to go further if they perceive competitors, especially in markets with looser regulation, are gaining an unfair advantage. This could stall progress not just in shipping, but across adjacent sectors that depend on it, from automotive to consumer goods.
The US’s decision to walk away from the IMO negotiations weakens the political legitimacy of the agreement and signals to others that opting out is a viable path. In doing so, it undermines the collective action needed to decarbonise global trade routes. The result is a business environment marked by growing divergence – where resilience is replaced by reactivity and climate ambition is undercut by regulatory uncertainty.
How can companies turn this into a strategic advantage?
While the policy landscape remains uncertain, companies can still take practical steps to prepare for change. Carbon pricing is beginning to influence shipping costs in some markets, and businesses that assess the potential impact early may be better placed to respond. This includes reviewing freight strategies, factoring potential carbon levies into budgeting and setting clearer sustainability expectations for suppliers.
Some organisations are already exploring options to reduce emissions within their supply chains, such as selecting carriers that use alternative fuels like LNG, biofuels or methanol. Manufacturers are responding too, choosing greener carriers, shortening transport routes and investing in digital tools to track and report emissions.
Moreover, embedding sustainability into core decision-making – rather than treating it as a separate or reactive issue – will help companies manage regulatory risk, meet stakeholder expectations, and identify areas for operational improvement. This not only helps them build more resilient supply chains but also aligns with rising customer expectations and investor pressure for greater environmental accountability.
Businesses must not only adapt to regulation but engage constructively in the development of future standards. By contributing insights and maintaining dialogue with industry groups and policymakers, businesses can play a role in shaping a more coordinated, transparent framework for decarbonising global shipping.
Looking ahead
The carbon divide is set to disrupt global trade. As nations diverge in their approach to maritime decarbonisation, companies will increasingly find themselves navigating a fragmented landscape that distorts competition and complicates compliance. But fragmentation doesn’t have to mean paralysis.
By preparing now, engaging constructively, and embedding sustainability into supply chain strategy, businesses can not only mitigate risk but also help shape more stable and predictable conditions for global trade.
Without trust, AI cannot deliver on its full potential, leaving manufacturers hesitant to go beyond pilot projects, says Darren Falconer.
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It’s no secret that trust is the foundation for successful AI adoption. By addressing scepticism, prioritising data quality, and ensuring algorithms are explainable and auditable, AI can become a powerful force-multiplier in manufacturing operations.
Manufacturers are increasingly looking to AI to boost efficiency, streamline operations and automate routine tasks. 75% are planning to step up their AI spending in 2025. However, much of this attention is focused on Generative AI – something that we believe is poorly suited to factory settings.
Part of this misalignment stems from a lack of understanding of AI’s practical applications in industry. With only 7% of manufacturing leaders feeling “very knowledgeable” about AI applications, scepticism and trust issues loom large.
Feedback from vendors and end-users consistently points to trust as a leading barrier to adoption. Without trust, AI cannot deliver on its full potential. This leaves many manufacturers hesitant to go beyond pilot projects, XpertRule’s Technical Director, Darren Falconer explores this further.
Overcoming the AI ‘fear factor’
The portrayal of AI in the media has long been dominated by dystopian headlines and Hollywood blockbusters, with fears of mass unemployment and doomsday narratives. For manufacturers, this continuous, subliminal bombardment creates a trust deficit before any AI project even begins.
Business leaders are having to overcome not only technical hurdles but also the deep-seated scepticism that AI solutions are uncontrollable or inherently risky. To counter this, companies must approach AI with transparency and explainability at every stage, showing that AI is a tool to amplify human capability not replace it.
For a simple comparison, think about cruise control in a car. [within cars today,] Traditional cruise control maintains a set speed but that’s all. Compare that to adaptive cruise control, which considers real-time conditions, adapts to your driving preferences and responds intelligently. Similarly, AI in manufacturing must adapt to the unique needs and complexities of each operation.
For those implementing these systems, understanding the ‘mechanics’ – how algorithms interact with data inputs and external influences – is a vital part of building trust. Explainable AI bridges the gap between automation and operator oversight, providing a clear view of how the system reacts and adapts. This clarity increases confidence among users, fostering trust in AI’s outputs.
But of course, building trust also requires a mindset shift – from a data-centric focus to a decision-centric approach.
Trust starts with decisions, not data
A common misstep in AI adoption is starting with the data instead of focusing on the desired outcomes. Many manufacturers think, We have all this data – what can we do with it? However, this approach often leads to complex systems that lack focus, transparency, fail to deliver meaningful outcomes and reinforce doubt over AI’s value.
A decision-centric approach begins by asking, What do we want to achieve, and what decisions need to be made to deliver those outcomes? Only then should businesses ask, What data supports those decisions and what are the models linking these decisions to this data?
From there, manufacturers must focus on ensuring data quality – calibrating sensors, cleaning data streams, validating inputs and standardising formats. Remember, the vast majority of AI success lies in data preparation and only a small percentage in the modelling itself.
Imagine a manufacturer aiming to improve quality control. They might gather extensive data from every step of the production process to find possible defects, leading to an overwhelming volume of disjointed data with no clear path to action.
Using a decision-centric approach, they would:
Define the goal: Improve product quality and aim to reduce defects by 10% over the next quarter.
Identify key decisions: What factors directly impact product quality? What parameters should trigger quality checks? How can inspection processes be optimised to catch defects earlier? What actions should be taken when deviations are detected?
Use AI to model the outcomes: Build AI models that analyse historical production data , to discover explainable patterns relating outcomes to metrics like machine settings, material consistency or environmental conditions. The system can then use these models in real time to flag anomalies that indicate potential defects and recommend adjustments to maintain product quality.
This clarity in purpose makes AI implementations transparent, explainable and, ultimately, more trustworthy. It also provides a clear framework for measuring success, helping to build greater confidence from engineers, users and management alike.
Decision intelligence – the missing link
A key factor in building trust is recognising that AI doesn’t replace human insights and experience – quite the opposite. Human operators and engineers bring a level of expertise, contextual knowledge and intuition that machines cannot replicate. Having a ‘human in the loop’ is therefore critical to an AI system’s effectiveness.
Decision Intelligence connects Explainable AI principles with operational trustworthiness by embedding human oversight at its core. For example, experienced technicians possess knowledge built up over years of practice. While they can’t be everywhere at once, their expertise can be integrated into AI systems to automate routine decisions while reserving complex or ambiguous scenarios for human intervention.
This balance between human and machine intelligence ensures AI systems remain transparent, reliable and dynamic. It also enables manufacturers to scale the knowledge of their experts, reducing variability across shifts and locations while maintaining trust and accountability.
From pilots to trusted partner
For AI adoption to move from pilot projects to the heart of manufacturing operations, trust must come first. A decision-centric approach offers a practical pathway to achieve this, ensuring AI systems are transparent, aligned with business goals and designed to augment human expertise.
When manufacturers trust their AI systems, they can harness the technology’s full potential, creating new opportunities for efficiency, resilience and competitive advantage. Decision Intelligence becomes the connector between Explainable AI and operational trust, moving AI from being perceived as a risk to becoming a trusted partner.
AI’s rapid evolution is creating both opportunity and urgency. AlixPartners lays out what needs to change — and why risk-takers will lead the way.
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The use of artificial intelligence (AI) in procurement is gaining traction with many organisations already looking at how the technology can improve processes. However, there’s scope to go beyond efficiency and instead focus on transforming value delivery.
At DPW New York, we spoke to Amit Mahajan and Aaron Addicoat from AlixPartners, a management consultancy firm doing things a little differently. The organisation is advising its clients on how to implement AI to drive value, but it’s also using AI internally, too.
“AlixPartners has a unique business model,” explains Addicoat. “We have a very senior model, very few junior resources. So now you imagine taking people with 10 or 15 years experience and now you equip them with AI… for us, it’s a huge unlock.”
This is about more than just productivity gains. AlixPartners focuses on using AI to transform the way procurement teams work, while crucially, maintaining the human touch.
How procurement professionals are using AI
With the support of technology, it’s possible to shift procurement from a cost-saving exercise to a potential revenue driver. Procurement teams are already looking for these opportunities, as Mahajan explains. “They’re starting to think about new ways of doing things,” he says. “It’s not just automation, but asking how do I leapfrog and do something differently?”
There are plenty of use cases where AI is helping with automation. This is a great place to start as it frees up human workers to do more valuable jobs that need a personal touch. “I have a client who’s using AI every day,” says Addicoat. “This allows them to review documents and contracts rapidly, to find key clauses and termination dates. They’re also using it in spend control processes to identify which things need to be reviewed more thoroughly.”
Many organisations are also using AI agentically to create their own bots. This gives teams a more accessible way to review information. “One example is a client who’s using AI for their business to help with acronyms,” says Addicoat. “They built it as an acronym tool to help break down the language barrier between different functions using different terms. This led to better engagement.”
This empowers employees across an organisation to be more autonomous while still getting the full picture. Agentic AI, especially, allows them to interact with information in a way that previously would’ve required specialist technical knowledge. Now, it’s possible to query information within a contract directly.
“It’s about using agents and AI to look at anomalies within your procurement contracts,” explains Mahajan, “and be able to help the category analysts, the category specialists, and others to get more of those insights.”
While generative AI might be a hot topic, it’s not the only way to use the technology. In combining several sources of data and using AI to spot trends, it’s possible to create workflows tailored to the current environment. Addicoat explains: “We take a series of data inputs, such as weather patterns, lead times, contractual terms, inventory, and forecast. Then the AI generates the purchase order, queues it for review, and upon approval, places the order.”
This can help an organisation to place orders with the right supplier in the most timely fashion to avoid delays, and optimise for cost, for example. This fully automates the end-to-end process, using AI to interpret those important data signals.
While this is useful for procurement teams, it’s only the start. “Using AI in this way is really cool,” says Addicoat, “but what I found most fascinating is that you’re building a data model, and with AI layered into it, that over time can tell you how to optimise itself.”
This has huge implications for procurement teams looking to save money and drive revenue. “For example, it could tell us the commodity price at a certain point in time was low,” says Addicoat, “but because inventory capacity to hold resin was maxed out the client could only buy so much at that low price. So now investing in a new storage unit at a cost of a few hundred thousand dollars could, under the same scenario in the future, save millions of dollars..Data quality challenges
A roadblock that can stop procurement teams from fully embracing AI is a lack of quality data. With so many sources of information, often including paper-based documents, some might think it’s difficult to get the data AI needs to be truly useful.
“Don’t wait for everything to be perfect before you get started,” says Addicoat.
This is a sentiment echoed by Mahajan: “Use AI to solve your data problem before solving your business problems.”
This requires a mindset shift. While AI can help cleanse, enrich, and structure existing unstructured data, it’s important to take the right approach. Shift from asking ‘what can we do with our data?’ to ‘what value do we need to create?’ and work backwards from there.
With this approach, the questions are less about the data and more about the business problem. This then allows you to use AI to work with the information you have to help answer those questions.
“Start with the value proposition in mind and work backwards,” explains Addicoat. “You can get data from anywhere — it has to serve a purpose.”
Bringing back the human touch
AI can free up procurement teams to focus on tasks that need more nuance and expertise. Using technology to automate workflows and make information more accessible has a huge impact on employee productivity. “It’s fundamentally transforming the way they work, the amount of work they can do, and the type of work they’re able to do,” says Addicoat.
There’s always the worry that with any new technology, the human element will be forgotten. “With every new advancement that comes in,” says Mahajan, “whether that was a steam engine or when computers came along, everybody wondered what they were going to do. But as humans, we always find ways to start doing higher-level work.”
This means that many professionals will find new ways of doing things. “Imagine all the mundane tasks you have to do in your daily job now,” Addicoat continues. “With these new ways of working, imagine the speed with which you can turn an idea into something real. All that time you free up allows you to go talk to people and build relationships that mean something.”
On the other side of things, the sheer volume of AI-generated content out there is going to drive people towards those more meaningful interactions. “You don’t know what to trust and what to believe anymore,” Addicoat says. “That’s going to lead to a resurgence in face-to-face content, being at the office, and being at events.”
AI’s impact on procurement talent
The talent landscape is changing. With technology playing a larger part than ever before, organisations don’t just need procurement professionals, they need adaptable, tech-savvy people. The nature of the job means that those in procurement need a wide range of skills.
“We do everything,” says Addicoat, “legal, operations, supply chain, negotiation, analytics. Procurement professionals are generalists.”
Tech plays into every element of that skillset, which means tech skills are becoming even more important for candidates applying for procurement roles. “Nobody goes to college thinking they’ll be a procurement professional,” says Mahajan, “but with AI and tech, that’s changing.”
With procurement often seen as a proving ground for leadership, embedding these tech-minded generalists could have a huge impact on the future. “We have a shortage of talent,” explains Addicoat. “But with more and more CEOs and COOs coming from procurement, that speaks volumes to what procurement does and the value it brings, as well as what the future holds.”
At AlixPartners, the passion for procurement is very clear with Addicoat saying: “There are only two kinds of people in the world: those who love procurement and those who don’t know it yet.”
Change is coming
With AI of all forms steadily gaining traction, procurement could change dramatically in the coming years. It’s the organisations that are willing to take risks and embrace change that will come out on top.
“AI has the potential to disrupt the whole management consulting world,” says Mahajan. “Firms focused on transformation will thrive.”
With AI’s capabilities increasing rapidly, it’s difficult to predict what comes next. However, adaptability is key. “Hold onto your hat. In a year and a half, the world’s going to look very different,” concludes Addicoat.
We spoke to Chief Product Officer Prerna Dhawan about what it takes to move from experimentation to execution.
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As AI continues to dominate conference stages and boardroom discussions, the pressure to use it is everywhere. As this technology becomes further embedded in enterprise strategy, many organisations are still grappling with how to apply it in a way that delivers real, measurable value.
Rather than focusing on AI for the sake of innovation, the question is how to align new tools with real business problems. That means looking beyond dashboards and pilots to deploy AI where it can simplify decision-making and improve processes.
At Beroe, this principle is central to how AI solutions are developed, deployed, and scaled. As the company behind the world’s leading procurement intelligence platform, Beroe provides real-time market data, cost analysis, and supplier risk assessments, empowering thousands of organisations globally to streamline operations and mitigate risks. Its latest advances in autonomous negotiation, supplier discovery, and predictive analytics show what it means to align AI with business objectives.
We spoke with Prerna Dhawan, Chief Product Officer at Beroe, during this year’s DPW New York conference. The discussion explored how procurement leaders can move beyond hype and start unlocking the full potential of AI.
Misalignment with business needs
There are plenty of real-world examples of how AI can improve efficiency within a business, from automating manual tasks like invoice processing to identifying new suppliers based on complex sourcing criteria. Accessing this technology is easier than ever with a wide range of tools available to procurement professionals. It can be tempting to jump on the bandwagon and integrate AI across every area of an organisation, but success requires a more nuanced approach.
The key is to ask the right questions, Dhawan explains: “We talk about all the latest and greatest technology out there, but what does it mean in practical terms? We need to ask, ‘How can I apply it today in the work I am doing as a head of product or as a procurement professional?’”
The allure of generative AI is especially strong, but business leaders should ask whether that’s the right solution for their needs. As with any decision, it’s important to consider the business problem. “It starts with a little bit of knowledge about what you’re looking for,” says Dhawan. “What are some of your biggest challenges, and which of those challenges could AI technology solve?”
Matching the right tool to the job
Once an organisation has identified a specific problem, it’s possible to find the AI solution that fits. While generative AI gets a lot of attention, other AI technologies and machine learning based systems might be more appropriate.
In some cases, prescriptive, rule-based, or predictive AI could be a better choice to solve a problem without the need for a large language model. For example, forecasting commodity prices doesn’t require generative AI, just strong, contextual machine learning.
“We are looking at AI across two dimensions,” says Dhawan. “Firstly, what is our offering to customers, in terms of procurement intelligence and autonomous negotiation technology. Second, we are looking at AI internally. Let’s say in product development, how do we use the latest AI solutions to accelerate our product development cycles so we can release new modules and capabilities more quickly.”
Regardless of the type of tool chosen, it should cover a high-impact use case. Integrating AI to solve a problem that only surfaces for a small group of people a couple of times a year won’t have a great return on investment. Instead, look for regularly occurring problems that, if fixed, could have a huge impact on productivity or quality.
Reducing the cognitive load
We’re already bombarded by information, and the use of AI to add to this doesn’t make sense. “I don’t need another dashboard in my life,” says Dhawan.
When implemented correctly, AI can make data more accessible while reducing cognitive load for users. The result is increased productivity and faster decision-making.
“I think the power of AI is to simplify access to data. This is why ChatGPT has been a success: it democratises access to information. That’s what our B2B technology world is waiting for. It gives me something simple that allows me to talk to my data. Then I can focus on what insights I need to make a decision or take action.”
For most B2B users, the key is intelligent simplification. Look for ways to simplify access to data through agent AI tools and conversational interfaces. This brings the focus back to action rather than dashboards.
Inside Beroe
While many procurement teams are still exploring AI’s potential, Beroe has already embedded it across both its platform and internal operations. The company, founded in 2006, provides procurement intelligence to thousands of organisations worldwide. Its platform delivers the critical data that professionals need to make informed sourcing decisions, from commodity prices and risk indicators to ESG scores and supplier intelligence.
“We provide all data that procurement needs for decision making, whether it’s cost data, risk data, ESG data or price data,” says Dhawan. “Our reimagination of the future is not just giving access to more data but creating that layer of recommendations that help you make decisions at speed and scale.”
One of the clearest examples of this in action is Beroe’s new ‘autonomous negotiations’ platform resulting from its recent acquisition of negotiation technology business, nnamu. Delivering a significant evolution in the procurement technology landscape the platform enhances the foundational elements of AI and game theory with Beroe’s industry-leading market intelligence and, according to Dhawan, it’s being deployed successfully in live sourcing scenarios.
“This is a technology that is being used for multilateral negotiations,” Dhawan explained. “It’s no longer just a POC or prototype, it’s live and being used at scale.” These new tools reflect Beroe’s core mission: to help procurement professionals minimise surprises and maximise margins.
Crucially, Beroe isn’t waiting for perfect data to apply these technologies. Instead, the company is using AI to work with what’s available — cleansing, interpreting, and extracting value from both structured and unstructured sources.
“You can use AI for cleansing data – even paper contracts,” Dhawan says. “Historically, we thought data had to be structured. But now, with vision models and image analytics, that’s no longer the case.”
Rather than striving for 100% accuracy before taking action, Beroe embraces a more agile mindset that balances speed and precision.
Is mindset holding procurement back?
The technology is ready. The use cases are proven. So why do so many procurement teams still hesitate to embrace AI? “There’s this subconscious fear that I think is a barrier to adoption,” she said. “And to some extent, it’s to do with our friends in Hollywood.”
There’s the myth that AI is a job-threatening black box, especially in industries where trust and experience are the backbone of good decision-making. For procurement, where professional judgement and business context are critical, the idea of handing over tasks to AI can feel risky.
But Dhawan believes this fear is misplaced. At Beroe, AI isn’t replacing procurement professionals, it’s augmenting them. Whether it’s surfacing new suppliers, automating elements of negotiation, or flagging risks earlier in the sourcing cycle, the aim is to enhance human decision-making. She says: “I think with the new kinds of AI technology that’s available to us, it is an opportunity for us in B2B tech to embrace more human-centred design with higher focus on UX.”
Looking ahead
Looking ahead to 2026 and beyond, Dhawan sees procurement evolving into a more personalised and responsive function – one where AI plays a critical role in both strategy and execution.
“We see hyper-personalisation coming, both in supplier relationships and internal stakeholder engagement,” she explains. “AI will be at the centre of that.”
Rather than one-size-fits-all sourcing strategies, AI will enable procurement teams to tailor their approaches to specific business units, categories, or even individual suppliers. This means smarter segmentation, more relevant insights, and stronger commercial outcomes.
Another key shift is the growing ability to connect macro events, such as geopolitical shocks or regulatory changes, with micro actions inside the business. AI can help procurement teams identify these signals earlier, respond faster, and still align with long-term goals such as cost efficiency or sustainability.
“It’s about balancing your fire-fighting reactions to market events with your long term goals and strategy,” says Dhawan. “Procurement needs visibility and flexibility at the same time.”
Beroe is already moving in this direction. Alongside its growing AI capabilities, the company is refining how it delivers intelligence, building agents and recommendation layers. These not only inform decisions, but also help teams take action on them. Whether that means automating routine negotiations or proactively flagging supply risks, Beroe is evolving to meet the needs of a procurement function that’s more dynamic than ever.
As Dhawan points out, the goal isn’t to overwhelm teams with more tools, it’s to make their lives easier. “It’s about reducing complexity and giving procurement professionals confidence in what to do next,” she concludes.
For many procurement leaders, AI still feels like a long-term ambition. But the solutions are already here, and through companies like Beroe, they’re already in use. The challenge now is not whether AI can deliver value. It’s whether teams are ready to adopt the mindset and cultural shift that will allow them to unlock that value.
Maria Torrent March, Managing Director, Warehousing & Logistics, Europe at Iron Mountain, digs into the F&B supply chain landscape.
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What are the characteristics and pain points specific to the food and beverage logistics and warehousing sector that set it aside from other sectors? Does it demand more speed? Environmental control?
The food and beverages (F&B) sector is large, dynamic, and continuously growing due to high consumer demand for everyday products. The warehousing and logistics (W&L) sector must remain flexible and scalable. This is in order to meet deliverables and ensure products are dispatched on time, especially when dealing with perishable items.
The F&B sector requires greater environmental control to maintain quality and safety. This can be achieved by partnering with W&L providers who are accredited with the British Retail Consortium (BRC). BRC accredited providers are required to meet strict protocols and are certified to hold food and consumer goods. Additionally, BRC warehouses offer several benefits, such as protected company reputation, implementation of industry best practices, and reduction in risks and potential liabilities. These are critical when handling sensitive items when it comes to food storage.
How is the process of managing logistics and warehousing in the F&B sector changing? What are the forces driving that change?
The management of logistics and warehousing in the F&B sector is undergoing significant transformation. This is driven by evolving consumer demands, regulatory pressures, and technological advancements. Consumers now prioritise products that are delivered quickly and sustainably. It’s pushing companies to adopt faster distribution networks, and eco-friendly practices like solar power, EV charging stations, and rainwater harvesting.
Technological innovation is also a key factor impacting the evolution of warehousing and logistics in the F&B sector. Automation and AI are optimising warehousing operations, reducing labour costs and errors while improving efficiency in handling perishable goods. The F&B sector is looking to improve efficiency and reduce transportation costs by leveraging strategic locations like the golden logistics triangle. This is a key hub for W&L because of its high number of distribution facilities and proximity to transportation networks such as rail and air. While the railway supply chain is relatively new, it can be ideal for F&B, where goods are heavy and where there are weight limitations in trucks or shipping.
Many high-street retailers stock multiple brands that each have individual supply chains. As a result, they are exploring how they can implement streamlined supply chain strategies across their businesses. They want to partner with 3PLs who can provide consultancy for managing these complex networks of supply chains, and not just a standard solution.
How do you make warehouse spaces more flexible and scalable to provide the necessary adaptability to manage fluctuating demand and seasonal peaks?
The F&B sector often faces challenges with space allocation to meet unpredictable demands. Robotics can be used to perform wall-to-wall scans of warehouses, creating a digital twin. This enables quick decision making and improves warehouse control and reliability in response to changing seasonal peaks.
Furthermore, with the use of AI, organisations can predict increases in demand due to holidays, sales, and seasonal trends. Iron Mountain has employed the use of AI across its warehouses. That allows us to predict stock locations and replenishment and improve productivity from the high-quality data received from Dexory. Dexory is a UK-based company that specialises in AI driven warehouse automation. This not only allows warehouses to make fast, real-time decisions on pricing and inventory levels but also helps to predict future demand spikes with greater accuracy.
Where do technologies like automation, digital twins, IoT, etc. fit into this picture?
AI and automation play a crucial role in inventory management. Iron Mountain considered adopting a more traditional setup with stock controllers but was concerned about potential labour shortages In 2024, it was reported that 37% of European warehousing organisations, including those in the UK, were experiencing significant labour shortages. 76% noted a noticeable shortfall. These shortages have impacted the logistics sector, making a notable difference to warehouse and logistical efficiency.
As a result, Iron Mountain partnered with Dexory to deploy an autonomous robot that provides live data insights by scanning the warehouse daily. This technology delivers full visibility of inventory, which is highly valuable for the F&B sector, where understanding how to quickly move stock based on demand is essential. Additionally, AutoStore is used to provide an automated storage and retrieval system, enabling rapid responses to customer requests. Utilising this technology makes warehouse and logistics operations more efficient, faster, and reliable.
We’re in an age where disruption is starting to feel like the norm rather than the exception. How can warehousing and logistics help supply chains be more reactive, agile, and resilient?
Disruption is common in the W&L sector, so organisations must be both flexible and reliable when it comes to supply disruptions, which can take many forms, including geopolitical conflicts, climate events, or sudden demand spikes.
Many organisations have had to think about these challenges over the last few years, starting with the pandemic. Sudden world events can force F&B companies to reorganise their supply chains. It’s important to consider these issues from their perspective. For instance, they may be seeking different suppliers in different markets. Ultimately, it’s about offering flexible solutions and tailoring them to the sector you are working with.
Over time, warehouses have adapted to become more dynamic, technology-driven, and strategically integrated into the broader supply chain. The W&L sector is always looking for scalable solutions that can be implemented when issues or disruptions arise, making it easier for supply chains to adapt and evolve in the face of challenges while maintaining operational efficiency and customer satisfaction.
We caught up with Valdera’s Co-Founders to find out why chemical procurement comes with its own challenges.
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Chemical procurement is one of the most complex and overlooked categories in the supply chain. Between navigating regulatory constraints, aligning on technical specifications, and finding qualified suppliers, even the most experienced procurement teams face major hurdles. That’s exactly the gap Valdera was built to solve.
Founded by sister-brother duo Sruti Arulmani (CEO) and Dheev Arulmani (COO), Valdera is an AI-native sourcing platform purpose-built for chemicals and raw materials. Rather than applying generic technology to a specialised industry, the team set out to reimagine chemical procurement from the ground up.
“Chemicals are one of the most complex sourcing categories,” says Dheev. “In order for a company to gain leverage from AI in this space, it must build the data infrastructure and the AI specific to this industry. That was the inspiration behind Valdera. Our vision was to partner directly with procurement organisations and help digitise that entire sourcing workflow all the way from supplier discovery to market intelligence to qualification.”
“Direct procurement is really at the core of your product’s margin,” adds Sruti. “In today’s economy, business leaders are focused on staying profitable, and that starts with ensuring the materials behind your products deliver on both margin and performance. Most of the physical products we touch and interact with every day come down to what they’re made of. That’s why we’re so passionate about chemicals and raw materials.”
The power of vertical AI models
While general-purpose LLMs are powerful, they fall short when it comes to industries like chemical procurement where context, precision, and deep domain expertise are crucial. Valdera has taken a different approach: building vertical AI specifically trained to understand the language, data, and complexity of chemicals and raw materials.
“In procurement, especially for chemicals, one-size-fits-all AI doesn’t cut it,” says Sruti. “You need models that can interpret highly technical specifications, normalize data across formats and suppliers, and understand the nuances that determine whether a supplier can actually meet a request.”
That’s exactly what Valdera has built. “We will continue to layer the specificity of the chemical industry on top of an LLM that’s already good at structuring information and returning information in a useful way,” Sruti adds.
Dheev continues: “If you look at the generic LLMs available today, the challenge with these is that they fundamentally don’t work in this industry. The reason for that is that there are no LLMs that are trained on chemical specs. So what we’ve done is take those models and fine-tune them using our own proprietary dataset of chemical specs and properties, built over the last five years. That’s what positions us to drive real value for our users.”
Prioritising privacy
In the chemicals industry, data is sensitive. Trust is everything. Buyers are protective of their proprietary formulations, and understandably do not want their data used to train models that could benefit competitors. On the other side, suppliers are cautious about publicly listing their full product catalogs, especially when it comes to custom or high-value materials. Valdera was built with these realities in mind, and its platform is designed to protect both sides.
“In chemicals, suppliers are very protective of their proprietary catalogs,” Dheev adds. “And buyers are equally cautious about sharing proprietary formulations that go into their products. So there needs to be an independent third party that both sides can trust—someone who can facilitate discovery and sourcing without compromising confidentiality.”
“For us, it’s about protecting the interests of both buyers and suppliers,” Sruti explains. “We only use customer data to drive outcomes for that customer. We’re not here to train on anyone’s inputs or share information across the ecosystem. We’re here to help our customers get the best results for their business. That’s core to how we think about data privacy and partnership.”
The humanity of procurement
Even as AI becomes more powerful, procurement remains deeply human. Trust, context, and judgement are critical to strong buyer-supplier relationships, and no model can replace that. Instead, AI can enable teams to work faster, focus on strategy, and unlock new value across the supply chain.
“Procurement is a human business,” says Sruti. “At the end of the day, it’s two people coming together and making an agreement. We believe that’s never going to change.”
Rather than add complexity or replace roles, Valdera’s AI helps teams do more with the resources they already have. That means less time spent on manual tasks like gathering supplier documentation or comparing specs and more time spent on strategic decision-making, relationship-building, and growing the business.
“Our customers don’t want to be buried in paperwork. They want to focus on the work that actually drives outcomes,” Sruti adds. “We’re here to take the most repetitive parts of the job off their plate so they can do that.”
“The chemicals industry is inherently relationship-driven,” says Dheev. “But today’s procurement teams are stretched thin. With Valdera, one person can now manage a broader scope: sourcing faster, accessing a wider network of qualified suppliers, and making smarter decisions in less time. That’s what’s getting our customers excited.”
Driving impact beyond cost
In chemical procurement, cost will always matter but it’s only part of the equation. The organizations leading the way are the ones thinking strategically: securing supply, expanding their supplier base, improving agility, and driving long-term value. That’s why more teams are turning to Valdera not just to cut costs, but to unlock a new level of visibility, access, and control.
“Our vision is to enable procurement professionals to leverage this data in order to give them market intelligence, expand their supplier network, and enable margin expansion,” Dheev concludes. “If you ask any of our customers, they’ll tell you savings are just table stakes when using Valdera. The real impact comes from levers like security of supply, innovation and sustainability. Those levers are harder to quantify, but they’re critical to the long-term success of the business.”
Implementing an outcome-based approach
In a crowded and fast-evolving tech landscape, it’s easy to get distracted by the promise of sweeping, all-in-one solutions. But the most effective procurement teams stay focused, starting with a clear understanding of their business goals and choosing technology that’s purpose-built to achieve them.
“Success starts with knowing the outcomes you’re trying to drive,” says Sruti. “Whether it’s sourcing the right chemicals, improving security of supply, unlocking savings, or advancing sustainability and innovation. Being clear about those goals is what helps you identify the right tools and partners to get there.”
That kind of clarity leads to faster wins and less wasted effort. “We always encourage customers to start where the impact matters most,” Dheev adds. “Don’t spread yourself too thin. Be specific about the problem you’re solving, define the KPI that matters, and test any solution against that. Just because a tool is popular doesn’t mean it’s the right fit. The best results come from targeted solutions that align with your most pressing priorities.”
Candex exists to solve tail spend by removing friction and giving procurement leaders time to focus on what truly drives value.
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Candex isn’t chasing trends for the sake of innovation. Instead, the company is focused on solving one of the oldest and most persistent challenges in enterprise procurement: getting rid of the noise.
Most in procurement will be familiar with Candex. Co-founded by Shani Vaza, Chief R&D Officer, and Jeremy Lappin, CEO, Candex is a technology-based master vendor that simplifies onboarding and payments to small and one-time vendors. It delivers a fast, compliant, and easy buying experience for requisitioners, while procurement gains automation, visibility, and control, reducing the vendor master by up to 80%.
For years, procurement teams have battled fragmented data, manual onboarding processes, and administrative bottlenecks. This results in time and resources spent on tasks that add little value, while strategic initiatives suffer from a lack of focus.
For many organisations, 70% of vendors account for just 5% of spend. With Candex, procurement can manage that long tail of spend without adding operational burden. This frees up teams to focus on strategic priorities, redirect spend to preferred suppliers, and drive more value across the business. At this year’s DPW New York conference, Jeremy Lappin and Chief Customer Officer Danielle McQuiston shared how their platform is helping procurement evolve beyond compliance and cost savings into something far more valuable: clarity.
Addressing the core problem
While many conversations at the event kept coming back to the use of AI, Candex is doing things differently. “AI will transform procurement by uncovering better, more innovative vendors,” says Lappin. “But every new vendor comes with the burden of onboarding and compliance. That’s where Candex makes a real difference—we streamline that process by enabling fast, compliant purchasing without the heavy lift of onboarding. As companies adopt AI, they’ll need a system like ours to truly benefit from what it reveals.”
It’s about bringing the conversation back to the core problem. Lappin continues: “Candex makes it possible to onboard and pay new vendors in minutes, and without setup delays, while keeping procurement firmly in control. That’s where we unlock both agility and compliance.”
Solving procurement’s data problem
After speaking to many procurement leaders at events such as DPW New York 2025, one topic of conversation stood out: that messy data can be a major hurdle to overcome before successful AI adoption can occur. Companies dealing with multiple affiliates for a single vendor can find their data ends up split, duplicated, and difficult to work with at scale.
“The fragmentation of data is a very old problem,” says Lappin. “One of the reasons it occurs is because the data is organised by affiliates and isn’t aggregated properly. This creates enormous processes.”
A dedicated platform can take on the heavy lifting of sorting through this data, without the use of complex AI models. Lappin continues: “One thing that Candex does to help this problem with smaller vendors is auto-aggregating affiliates under one corporate umbrella. It’s going to massively reduce the data problem by directing that small spend through us.”
McQuiston adds: “Data is the foundation of all the decisions that procurement makes. And the fact that they can consolidate that data within Candex, and look at it only when it’s relevant to what actions they have to take, is a huge contribution to the space for procurement.”
The right data at the right time
Candex isn’t trying to flood procurement teams with dashboards. Instead, it delivers data when and where it’s needed, stripping away the noise to surface what’s important.
“Our customers tell us we filter out 95% of the noise and highlight just the actions that matter. It’s not just visibility, it’s visibility at the right moment,” says McQuiston. “We have amazing reporting that has hundreds of lines of precision data in there, but it’s also aggregated in a way that it calls out to the things that need attention rather than being bogged down with the rest.”
“Oftentimes the stuff that goes through us is the stuff that procurement doesn’t have the time to give its attention to,” explains Lappin. “I think one of the most powerful things we do is get rid of the things they shouldn’t care about so that it’s very easy to see what they should.”
Simplicity wins
Some procurement tools are complex, slow to adopt, and full of friction, but Candex takes a different approach. “The users just want to be able to operate and do the work that they need to do to serve their objectives,” says McQuiston. “Procurement doesn’t have enough resources to deal with all of the small things.”
Bringing the focus back to the core function of procurement simplifies processes and reduces noise. When working with a lot of small vendors, procurement teams can get bogged down with admin and data. This is where Candex takes on the weight of that burden, and allows the business to move forward.
“At the end of the day, Candex is a tool that is so simple from a user perspective, but still has the confidence of the procurement organisation,” McQuiston continues. “It also shines a better light on the procurement function, which often gets a black eye for being in the way of things.”
Real people
For Lappin, the hype around AI isn’t what makes a product great; it’s real-world validation from customers. “There’s only one way to get through the hype,” he says, “and that’s to find other companies that are using the products and loving them. I think that’s one of the things that has made us successful.”
It’s one of the strengths of DPW; these events showcase real use cases, not just demonstrations. This enables attendees to see the impact of new technologies for themselves, and connect with the people behind them. “DPW has the ambition to use real use cases rather than just relying on demos,” says McQuiston. “That’s what’s a little bit different about DPW compared to some other conferences; the proof is in the pudding.”
Lappin and McQuiston also highlighted the importance of customer-led innovation through Candex Connects – roundtables all over the world that allow procurement peers to meet, discuss the challenges affecting them, and learn from one another, as well as sharing their own inspirational use cases. “We’re not just providing a solution. We’re providing a space where our customers get together, discuss best practices,” McQuiston adds. “And I think we’ve done that really well.”
Procurement, repositioned
Ultimately, Candex is about more than just a tool. It’s about reshaping the perception and potential of procurement teams, giving them the freedom and focus to lead strategically. By removing some of the friction of dealing with myriad small vendors, procurement teams are empowered to drive deeper value.
“Our whole business is focused on agility and value creation,” says McQuiston. “We have to be compliant because our customers demand it, but it’s not really about cost savings when you talk about tail spend. Procurement has always been in a position where they believe they can squeeze something out of every purchase. We’ve gotten to a point in the evolution of the function where they realise there’s a portion they can’t squeeze anything out of. It’s powerful to be able to let that go.”
“Procurement needs to be involved in decisions around spend,” adds Lappin. “They help negotiate. They figure out the right vendors. They really are needed in this process, which is why it exists.”
Candex isn’t just solving tail spend, it’s redefining how procurement operates at scale. With built-in controls, full audit trails, and seamless integration with existing systems, Candex empowers procurement to lead strategically, reduce supplier bloat, and stay agile in a complex world.
Candex is proving that the biggest transformation comes from helping procurement teams reduce the noise and get back to the work that matters.
Eelco van der Zande, Managing Director of ReBound Returns, helps navigate the issues caused by tariffs.
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Rapid changes in global trade policy are creating serious challenges for businesses operating across borders. With tariffs soaring one day and easing the next, retailers are being forced to rethink how they handle international returns in real time.
Fluctuating import duties imposed by the US have at times exceeded 145%, and retaliatory measures from key trade partners have thrown global supply chains off balance. Even with the most recent truce reducing US tariffs on China to 30%, there’s no guarantee these figures will hold. As of June, 2025, US trade policy remains fluid, with ongoing negotiations reshaping tariff structures across multiple regions, including Europe and Asia. President Trump has noted that some levies have been suspended- not cancelled – and may rise again within months.
Adding to the uncertainty, twelve US states have filed a lawsuit in the Court of International Trade, seeking to halt to the “Liberation Day” tariffs. A US appeals court has allowed the tariffs to remain in effect while it reviews their legality.
The new risks of cross-border returns
Amongst the ambiguity, international returns are now under intense scrutiny. With each item crossing a border potentially attracting new tariffs, returning products for restocking has become costly. When an item crosses a border twice- first for sale, then for return- and possibly a third time for resale, retailers face multiple layers of duties and fees. A t-shirt sold internationally could now incur fees exceeding its original retail value. This makes it more important than ever to evaluate every return for cost-efficiency and logistical feasibility.
Volatility also makes forward planning difficult. Retailers can’t afford to be reactive; returns systems must be agile, localised, and data-driven to navigate the shifting conditions. Strategic returns management is key to future-proofing reverse logistics against unpredictable tariffs.
Localising and consolidating returns to minimise costs
One of the most effective ways to reduce tariffs exposure is to localise returns processing. Keeping returns in the country where they were purchased allows retailers to avoid costly re-importation. Processing and storing products at local returns centres and re-fulfilling them to new customers in the same region can save on shipping and duties. Repurposing items through alternative channels can also reduce costs.
Consolidating returns into fewer, larger shipments rather than handling them individually can significantly cut logistics expenses. Using regional return hubs to group items before further processing or redistribution reduces transportation spend and carbon footprint. This local-first approach not only limits fuel consumption and emissions, but also supports a circular economy by keeping goods in-region. As ESG expectations rise, aligning reverse logistics with sustainability goals becomes a competitive differentiator. This optimised, local approach enhances efficiency and makes cross-border returns more sustainable and financially viable at scale.
Faster returns to reduce inventory lag
With tariffs driving up inventory costs, time has become a critical cost factor in returns management. Every day a returned item sits idle or in transit is a day of lost revenue and tied-up capital. Slow processing delays resale and undermines profitability in an already margin-sensitive environment.
Retailers must accelerate returns processing to reduce inventory lag. That means quickly assessing, sorting, and restocking products. Fast triaging, localised warehousing and agile reverse logistics can shave days or even weeks off the cycle, improving inventory turnover and unlocking working capital. In practice, faster processing can significantly increase recovered revenue from returned goods.
Smarter and fewer returns through better data
As tariffs raise the cost of goods, each return, especially the avoidable ones, become more expensive. Retailers that harness return data across their operations can turn unpredictability into strategic insight. This requires integrating data from multiple sources into a unified view, enabling more accurate demand forecasting, better inventory planning, and identification of products that are driving unnecessary returns.
Leading retailers are also using AI-powered platforms to anticipate which items are most likely to be returned and to automatically route them to the most efficient return locations. These systems integrate seamlessly with order and warehouse management tools, reducing cycle time and cost.
Data insights can also reveal deeper patterns, such as size discrepancies, product quality issues, or customer behaviour trends, that are contributing to high return rates. Addressing these issues through refined product descriptions, size guidance, and customer education expectations better can lead to measurable reductions in returns.
Even modest drops in return rates can yield significant savings when margins are tight. Smarter use of data enables faster, more informed decisions, and stronger profitability.
Seamless returns to build customer loyalty
The increasing complexity of cross-border returns hasn’t slowed rising customer expectations. Shoppers are less forgiving of a clunky or slow returns process, especially when tariffs mean they have paid more or waited longer for their purchase. A seamless experience with fast, easy, and transparent return options is crucial.
Retailers that offer convenient local drop-off points, clear communication, and flexible refund or exchange options are far more likely to retain customers and drive repeat purchases. Quick refunds help preserve brand loyalty, even amid pricing pressures and economic uncertainty.
Retailers that prioritise returns optimisation have seen measurable improvements in customer retention and the frequency of repeat purchases. A great returns experience doesn’t just mitigate risk, it builds trust, strengthens brand reputation, and turns a potential point of friction into a loyalty driver.
Adapting returns strategies for a shifting tariff landscape
When tariffs can rise or fall overnight, international returns must be treated as a strategic function, not just a back-end process. They directly impact margins, sustainability, and customer loyalty.
Retailers that embrace smarter returns management with localised, streamlined processing, better data insight, and seamless customer experiences will be best positioned to weather ongoing volatility. To get ahead, retailers should consider conducting a full audit of their current returns operations, identifying gaps in localisation, speed, and tech adoption. Investing in smart logistics infrastructure today can unlock major savings and build long-term resilience.
The proof, as they say, is in the pudding – and the evidence of TealBook’s increasingly-successful evolution lies in its client relationships.
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We talked endlessly about data and AI at DPW New York 2025. A universal truth is that the successful implementation of AI requires clean data. It doesn’t have to be perfect, but businesses certainly need to have a decent handle on their data before adopting AI tools successfully.
To help make this a reality, North American data and software company TealBook has recently announced a legal entity-based data model. It’s designed to resolve supplier records to the correct legal entities, map parent-child relationships, and enrich profiles with verifiable attributes, enabling accurate supplier data to flow seamlessly into procurement systems and AI applications. “This is part of a 12-year journey for TealBook,” says Stephany Lapierre, the company’s Founder and CEO. “Our vision has always been to build a way to enable procurement organisations to have high quality data with a lot of integrity. That way, you give them the trust they need to put data directly into their systems.
“Twelve years ago, we underestimated the complexity of getting large enterprises to trust a third-party data solution. As part of our journey, we started using AI early on to find information where it exists on supplier websites and databases. We also started creating digital profiles in a structured way for procurement to access it, match it to their vendor master, and use it.”
TealBook’s data evolution
But, again, at the beginning, TealBook couldn’t be sure whether the data was high enough quality. In 2017, the company was primarily known as a supplier discovery application. It was positioned as a pre-sourcing engine to help procurement teams identify alternative suppliers. At the time, TealBook’s data and models enabled it to determine which companies were similar to others. This meant users could search and find comparable suppliers to expand their sourcing options.
“But that was just a way for us to deliver something that was underserved in the market,” Lapierre continues. “Then our customers started asking for certificates, which are hard to collect and match. They needed cleaner data. They felt they were under-reporting. So in 2018, we started to see whether our technology could refine the data more. We focused on certificates and supplier diversity. We collected great use cases along this journey, and the vision never wavered.
“Just last year we released a new technology – completely different, really sophisticated – allowing us to pull from a lot more data sources. We have provenance so our customers can actually verify where the data’s coming from. We can match it to vendor masters. And now, we also have this new model that includes 230 million verifiable global legal entities from across 145 countries’ registries. We marry this with global parent and child hierarchy, which is really hard for our customers to match themselves.”
Partnership with Kraft Heinz
Now, after 12 years of that vision, TealBook is deeply proud of what it’s achieved. Part of its ability to get to this point is due to early adoption from key customers. Kraft Heinz is a business which Lapierre describes as a “co-innovation partner”, and has been invaluable in helping TealBook achieve its recent goals.
From the perspective of Stefanie Fink, Head of Global Data and Digital Procurement at Kraft Heinz, the partnership has been an immediately valuable one. “It really started with having a visionary, like-minded relationship,” she says. “That’s an important piece of it, because my vision for procurement is that we are partners in our enterprise.
“In order for us to do our jobs, we have to bring in the right data for use. This is where Stephany’s partnership and vision really resonated. We were really looking for diversity and we could make things easier for our partners, while making sure we had the right people in our ecosystem. We also had to lift up the hood and see what was underneath everything we’ve got. Stephany brought our vision to life. TealBook has evolved too, as we’ve seen; it’s more about orchestration and software-as-a-service. It has been a partnership of need and we cannot continue to do other things without this kind of partnership around data.”
When initially dabbling with this relationship, Fink was clear that Kraft Heinz had no desire to be taking care of more stuff. What she wanted from TealBook was a strong focus on good quality data. After last year’s product release from TealBook, Kraft Heinz already saw its data enriched by 25%. The recently-announced new data model gives the business and TealBook’s other customers the right structure tied to a legal entity, which is a highly credible anchor. “We’re able to do entity resolution – all automated – remove all the duplicates, and then you start with a clean, digitised vendor master,” says Lapierre. “That’s what brings further enrichment.”
The challenge of assessing data quality
Assessing its data before involving TealBook was important for Kraft Heinz, but challenging for such a large organisation. “We had to fail first and fail fast,” says Fink. “We tried some AI around fixing things early, but that didn’t work for us. It was a real eye-opener, realising where this next evolution could take us. Particularly regarding focusing on AI and agents for the right things, not the meaningless things. Before, we were asking agents to tell us if things were duplicates, when we should have been asking: what do these suppliers offer? Where is the innovation? Where is the value?”
What surprised Fink most when looking under Kraft Heinz’s hood was the lack of attention that was being paid to what the business was doing. “It was amazing that nobody had questioned it sooner,” she says. “So I said, let’s take this as a crawl, walk, run approach. I have a wonderful CPO who really understands where we want procurement to go as a function. She was excited about us just getting it done and getting people involved, and that’s what it takes: real pride in ownership of the data.”
Getting engrossed in GenAI
True partnership and an all-in approach has enabled Kraft Heinz to work successfully with AI. This is something some businesses are struggling with as the conversation around artificial intelligence grows louder. For Lapierre, as the CEO of a tech company, adopting AI successfully has meant trying and failing and being fully entrenched in AI as it has evolved.
“We’ve been using AI in our technology since 2016,” she states. “We’re an early adopter. We’d be talking about scraping data, and data in the cloud, and AI models, and our customers’ pupils would widen in surprise. We’ve come a long way and the market has come a long way.
“The technology we deliver today wouldn’t be possible without the AI tools now at our disposal. We used to build models; we don’t do that anymore. We spend a lot of time investing in engineers to build and test models. That’s made us so much more efficient. I use GenAI every day for so many things now. I’m encouraging my team to be so involved in AI. That’s how you build expertise. You need really strong expertise to use GenAI well.
“Getting good with AI is about taking risks and having a leadership team that pushes for new things. Suddenly, the successful use of AI becomes a habit.”
Why businesses should prepare themselves for AI by not getting lost in the whirlwind of hype and focusing only on what works for their needs.
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With AI being the topic of conversation for procurement professionals right now, it’s easy to get lost in the maze of conflicting information. Vroozi is a procure-to-pay platform powered by robust AI capabilities to deliver meaningful use cases. CEO and Co-Founder, Shaz Khan, takes approaching AI the right way very seriously.
For Vroozi, the use of AI is a two-sided coin. It’s an organisation that talks about AI both in production and consumption. AI is a tool that has been a game-changer, because it has enabled Vroozi’s software and technology engineers to be able to rapidly prototype and develop code. And that code is beneficial for creating feature sets and capabilities that the company wants to introduce to the market.
“Similarly, we take steps to look at how a customer interacts with our software for the first time,” Khan explains. “The implementation process is also ripe for consuming and producing great results with AI. Imagine you go through some type of interview wizard where you prompt the system based on your region and industry. The system will self-configure according to your business unit. This is real intelligence that understands your business at a different level, as well as the competitive landscape, and brings in best practices to deliver incredible results.”
Getting the approach right
Having said that, Khan freely admits that we’re in the early innings of AI adoption. For him, leaders should adopt a multi-pronged approach to implement AI. The first move is to assemble a team. “One key area with AI is that a lot of companies are relying on outside experts that don’t know the business and the goals that they’re trying to achieve,” he explains.
“You should invest in your own people before you invite outside parties in. Bring that education and assemble a use case, before assessing the problems you’re trying to solve and determining whether AI is a good tool set or capability to solve the problem. If these things match up, execute the game plan, bring in the right technologies and the right expertise, and only then bring AI capabilities into your workforce.”
The challenges
With this being the “early innings”, there are also barriers and challenges. The main issue, from Khan’s perspective, is security. “There’s a trust aspect that has to be looked at,” he explains. “There’s also an ethics aspect. Are you delivering the right results? And how much autonomy are you giving AI and its agents to go out and deliver those results for you without any human interaction? I think the companies that get it right will strike a balance between the trifecta of automation, really great AI technologies, and a balance of human interaction to create an overall output.”
There’s also the question of data. If the data isn’t clean, output will be compromised and lead to poor results. We haven’t seen the worst of what can happen, Khan believes, and AI has the potential to create scenarios that are hard to recover from, if used poorly. “We need to prepare ourselves now to prevent those types of potential calamities from happening,” says Khan. Which is the entire point of DPW: for procurement and technology leaders to educate and learn about best AI practice.
This allows people to cut through the, as Khan puts it, “hysteria” around AI that can cause problems for businesses. They’re rushing to solve problems, and while leveraging AI can be a component of a complete holistic toolkit, it can’t be the only answer. “A lot of companies today still struggle with getting their businesses off spreadsheets,” he states. “AI should be an equaliser and enabler to get it right.”
Structuring unstructured data
For Khan, in order to ready themselves for AI, procurement professionals and practitioners need to be absolutely committed to data management and governance. “What companies often forget is that much of today’s data is unstructured. It’s not neatly stored in databases – it might be a chat, an image of a spec sheet, or a contract never digitised. This unstructured data often can’t be used by AI models today, so companies risk only addressing a small part of the challenge. Data governance has to be an ongoing exercise.”
Having said that, Khan is keen to differentiate between clean data and perfect data. In fact, many procurement professionals we spoke to at DPW New York 2025 said the same. The message is: don’t wait around for everything to be perfect, or you’ll never start.
“Good enough data is just fine,” Khan says. “But if you’re going to continue to feed your AI engines and algorithms bad data, your outputs will be compromised. Companies need to have data governance strategies and upfront policies in place so that they can manage this, independent of the people that offer them.”
AI creating a complete picture
While treading carefully is important, Khan is equally keen to extoll the many virtues of AI for procurement professionals. There are many incredible use cases already, and AI tool sets and algorithms can effectively interrogate a company’s data and give them the answers they require. AI enables these users to have a complete picture of their buying cycle, and allows them to get additional information for where they can pivot.
“This is where the true power of agentic AI will come into play,” says Khan. “When you can fully trust the system inputs, AI will be able to orchestrate those processes autonomously, and present that information to an end user for final decision.”
Khan is very excited about what Vroozi is doing within its own AI layer. The business looks at AI and intelligence as a pervasive thread across its entire tech stack. Every aspect of its platform has some kind of AI enablement, although it’s not an AI-first company.
“We follow three distinct areas where we are thriving on the AI front,” says Khan. “First is intelligent document processing. Can we take structured and unstructured data such as contracts, quotes, work orders, and invoices, and populate them automatically onto a screen without any human touch? Processing invoices might require an army of people typing in data, and they might not capture it all. But an AI toolset can take millions of records and process them simultaneously. That’s the power of AI.”
The power of hyper-personalisation
The second area is what Vroozi calls hyper-personalisation, where it intensely personalises the platform to meet a company’s preferences and needs. It’s about how AI can find trends and not only predict the user’s needs, but also help take the next steps. This includes finding suppliers and ordering things that are needed, so that workflows aren’t disrupted.
“Then we also have what we call the push economy,” says Khan. “AI’s power is in pushing and giving people head starts. So when you talk about AI algorithms and look at analytics, it’s about how AI can present to companies in the procurement space when they need to lock in favourable pricing on products and services, and predict when you are seeing potential fraud scenarios based on trends and patterns. You need a lot of data for those AI models to train on, which is why I say we’re in the early innings. It takes time, but it’s incredibly powerful when you get to that point.”
The benefits ahead
At such an exciting time for procurement, 2025 and 2026 look bright for leaders in this space. Not only procurement, but also supply chain and FinTech, are set to benefit from what AI can do with data.
“There’s going to be a focus on how to capture and harness data, and feed it into AI in a way that produces results,” says Khan. “What we’ll see in the next two years is that AI has now learned from the data that’s been fed into it. You’re going to see higher-quality results and better outcomes. Again, I would caution companies to define the problem first. Then determine if AI is an absolute enabler and game changer. We believe AI can be an influencer and supercharger in terms of productivity. However, there needs to be specific use cases that make sense for corporations.
“In 2025 and beyond, you’re going to see great technologies embedded into organisations that really work.”
Sylvain Rottier, General Manager at Tennant Company, explores how supply chain professionals are shoring up against labour shortages.
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Europe is facing an ongoing workforce crisis that demands major solutions, meaning business leaders can’t really afford to wait. The numbers are disconcerting: labour shortages across the European Union have grown from 1.7% in 2014 to 2.6% in the first quarter of 2024—a 53% increase that shows no signs of slowing.
Indeed, Europe’s demographic crisis seems to be accelerating, with projections indicating the continent will lose 95 million working-age people by 2050 compared to 2015 levels. For supply chain executives, this threatens operational continuity and competitive positioning.
The impact may vary dramatically across sectors, but few industries will feel the pressure more acutely than essential services like cleaning and facilities management. Annual turnover rates in janitorial services have reached 200-400%, creating a revolving door that diminishes institutional knowledge and operational effectiveness.
The impact beyond empty positions
Twenty-five percent of EU businesses now report production problems directly attributable to labour shortages, transforming what was once a staffing inconvenience into an operational constraint.
The financial implications are potentially severe. Companies experiencing 200% annual turnovers —unfortunately common in labour-intensive sectors—spend six-figure sums annually just on replacement hiring. This figure encompasses recruitment costs, training expenses, and the hidden price of reduced productivity during onboarding periods. However, these costs represent a small part of the problem.
Quality degradation becomes inevitable when organisations rely heavily on inexperienced workers. Higher error rates, missed cleaning protocols, equipment damage, and inconsistent service delivery damage customer satisfaction and brand reputation. In supply chain environments where precision and reliability are paramount, these quality issues can trigger costly disruptions throughout the entire network.
Perhaps most concerning is the competitive disadvantage that emerges when labour shortages force companies to reject new business opportunities. Constrained order books and inflated production costs create a vicious cycle where struggling organisations become less attractive employers, further exacerbating their staffing challenges.
From automation to intelligence
Traditional automation offered limited relief because it required extensive programming for specific tasks and was often an awkward-at-best fit for changing conditions. Today’s AI-enabled robotic systems represent a huge leap forward, delivering true operational intelligence that can learn and adapt, and also optimise performance in real-time.
Modern robotic platforms (such as BrainOS, which power Tennant AMR Machines) leverage machine learning algorithms to improve their performance based on environmental feedback and operational data. Unlike their predecessors, these systems can navigate complex, dynamic environments while avoiding obstacles, adjusting cleaning patterns based on usage data, and even predicting maintenance needs before equipment failures occur.
Integration capabilities have also come a long way. Contemporary AI-powered robots connect with existing warehouse management systems, inventory tracking platforms, and facility management software. This connectivity enables centralised monitoring, performance optimisation, and data-driven decision-making that extends far beyond the robots’ immediate task purpose.
The technology’s greatest advantage lies in its ability to maintain consistent performance standards. While human workers may struggle with fatigue, illness, or high turnover, AI-enabled robots deliver consistent results that enable accurate capacity planning and service level guarantees.
Implementation strategy
Successful AI-robotics deployment requires a shift in thinking from replacement to augmentation. The most effective implementations complement human capabilities rather than eliminate human roles entirely. This approach not only addresses practical concerns about workforce displacement but also maximises return on investment by leveraging the unique strengths of both human intelligence and artificial intelligence.
Smart organisations begin with pilot programmes that target specific, well-defined tasks within controlled environments. This approach allows teams to understand integration challenges, optimise workflows, and build internal expertise before scaling to full deployment. Critical success factors include ensuring compatibility with existing systems, establishing clear performance metrics, and maintaining open communication with affected workers throughout the transition.
The skills landscape is evolving rapidly, creating new job categories in real time. Rather than eliminating careers, thoughtful implementation transforms traditional roles into technology-empowered positions that offer greater career advancement potential and higher compensation. For sectors like cleaning services, which have long struggled with “dead-end job” perceptions, this transformation can meet turnover rates with higher-calibre talent.
Training programmes should prepare workers for collaborative environments where human judgment combines with robotic precision. These hybrid roles often prove more engaging and rewarding than traditional positions, creating career pathways that retain institutional knowledge while embracing technological advancement.
Building tomorrow’s competitive advantage
The demographic trends driving current labour shortages will intensify over the coming decades. Organisations that delay AI-robotics adoption risk falling behind competitors who embrace these technologies early and develop operational expertise while the market is still developing.
However, successful transformation requires more than technology acquisition. Companies must strike a balance between technological capabilities and the human touches that drive innovation, customer relationships, and adaptive problem-solving. The goal isn’t to create fully automated facilities but to build resilient, flexible operations that can weather demographic headwinds.
Leadership teams must think beyond immediate cost savings to consider long-term strategic positioning. AI-enabled robotics offers the foundation for sustained growth in an environment where traditional staffing models look increasingly untenable. Early adopters will develop competitive advantages that compound over time, while late movers may find themselves perpetually disadvantaged in both talent acquisition and operational efficiency.
The question isn’t whether AI-enabled robots will reshape supply chain operations—that transformation is already underway. The critical decision facing business leaders is whether they’ll proactively shape this evolution or reactively respond to competitive pressures once their options become more limited and expensive.
Europe’s demographic winter demands timely action. For forward-thinking supply chain executives, AI-enabled robotics represents not just a solution to current staffing challenges, but a strategic foundation for long-term competitive success in a potentially shaky marketplace.
Morne Rossouw, Chief AI Officer at Kyriba, on leveraging AI skills to enhance decision-making and compliance in financial services
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At the intersection of innovation and responsibility, the finance sector faces a pivotal challenge… The ‘trust gap’ in AI adoption. CFOs and treasury leaders are aiming to safeguard their organisations’ financial health. The promise of AI’s transformative power is often tempered by concerns around security, transparency and regulatory compliance. Yet, as the latest IDC InfoBrief and Kyriba CFO survey reveal, there is a clear path forward. It is one that requires essential AI foundation skills and a thoughtful approach to AI solutions.
Understanding the Trust Gap
The potential for AI in treasury and finance is compelling. Over 84% of treasury professionals agree Generative AI will significantly impact treasury processes within the next 24 months. However, the journey to widespread adoption is hindered by what many see as a ‘trust gap’. There is a divide between transformative promise and concerns about security and privacy risks.
These real concerns cover several aspects, first and foremost: risk aversion. Many finance professionals by training are inherently compelled to act with a risk mitigation mindset. By extension, many are cautious about the ‘black box’ nature of artificial intelligence and its role in decision-making. They prefer systems where they can better understand and interpret outcomes. Another layer is the pressure to adhere to the industry’s strict and evolving compliance requirements. These are now expanding to cover legal and industry standards around adoption, such as the EU AI Act.
Data quality and security further complicate the picture. Financial data is highly sensitive, and organisations must address issues of accuracy, bias, and privacy when integrating AI solutions. In addition, there is a skills gap to overcome. Many finance professionals may lack the newly emerging need for expertise to leverage these tools effectively and securely in a financial context, making the development of new competencies essential for successful adoption.
Building a Culture of Trust for AI
Despite concerns, the interest in and potential value of artificial intelligence to streamline and optimise treasury operations are clear. In fact, the latest studies show:
44% of treasury professionals see immediate value in AI-enhanced cash management
50% prioritise AI for financial fraud detection
46% focus on risk management applications¹
Achieving success with artificial intelligence requires more than simply adopting new technologies. It demands a broader cultural transformation. Structured training programs are critical for helping finance teams develop confidence and competence in using AI. And gaining hands-on experience with AI tools in real-world scenarios allows professionals to apply their knowledge and adapt to evolving capabilities.
As one CFO noted: “AI is redefining the CFO’s mandate as we speak. With the right foundation and skills, I don’t believe AI widens the trust gap; it closes it.”
Essential Foundational Skills to Bridge the Trust Gap
Narrowing the trust gap between the immense opportunities of AI with the real potential risk requires organisations to develop three critical foundation capabilities. The first is communication and interaction. Finance professionals should learn how to engage in clear dialogue with AI systems by asking effective questions, refining requests, and understanding how to guide AI tools to support financial reporting and analysis.
The second foundational skill is data storytelling. Transforming complex AI outputs into clear, actionable insights helps make financial data more accessible and meaningful to stakeholders. This means not only interpreting results but also presenting them through compelling narratives and visualisations.
As a final safeguard, teams should develop a systematic approach to validating AI-generated insights to ensure that outputs align with regulatory requirements and business logic. This process is crucial for maintaining compliance standards and fostering confidence in AI-driven decisions.
Trusted AI requires a Trusted Platform
Organisations can build trust in AI adoption by prioritising security and transparency in their technology choices. Selecting tools and platforms that provide enterprise-grade security and offer explainable insights is vital. Equally important is ensuring that customer data remains private and is not used to train external models, as is the use of built-in validation tools to support compliance.
Trust is further built by user-led design. Intuitive interfaces make it easier for finance teams to interact effectively with new technologies. Leveraging visual analytics and dashboards enhances the ability to tell stories with data, while comprehensive validation frameworks help support regulatory and business frameworks.
Establishing a trusted platform foundation is the final piece. Building on robust data infrastructure allows organisations to define key AI foundation skills. Investment in training and certification programs helps finance professionals stay up to date with best practices, while real-time validation and oversight of AI-driven decisions further reinforces organisational trust.
The Path Forward
The potential impact of increased AI skills, in tandem with secure solutions, is immense. Enhanced decision-making becomes possible through improved cash visibility and forecasting, while compliance is strengthened through systematic validation and fraud detection. Efficiency gains are realised via optimised AI/Human collaboration, and more accurate and insightful financial reporting is achieved through advanced data storytelling. Organisations also benefit from reduced processing time thanks to intelligent automation.
In an era where trust underpins financial and broader business leadership, success depends on developing strong foundational capabilities alongside robust solutions. Responsible AI – such as Kyriba’s Trusted AI portfolio – emerges as a strategic partner for CFOs and treasury teams, providing not just the technology but also the framework for skill development essential to closing the gap.
Through this comprehensive approach – combining foundation skills and trusted solutions-organisations can confidently embrace AI’s transformative potential while maintaining the security, compliance, and transparency essential to modern financial operations. The result is a future where skilled professionals leverage AI to drive data-driven business decision making that can unlock unprecedented levels of financial performance and agility.
Nigel Pekenc, Partner at Kearney, gives us insights provide insights on current key trends in supply chain, as well as his thoughts on nearshoring and reshoring.
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How are global supply chains evolving to become more resilient in the face of ongoing disruption, such as geopolitical shifts, raw material shortages, and logistics volatility?
“Supply chains are undergoing a fundamental shift from static, efficiency-led structures to adaptive, digitally managed ecosystems. Companies have moved beyond simply adding redundancy or diversifying suppliers. Instead, they are building globally distributed and closely connected networks, using real-time visibility and predictive analytics to spot vulnerabilities early and respond flexibly. Strong supplier partnerships in key locations and centralised digital control towers that compile multi-tier insights are now essential to manage disruptions ranging from geopolitical unrest to material shortages and transport breakdowns. The aim is no longer just resilience but adaptive responsiveness, enabling businesses to adjust their supply chains dynamically and in real time.”
Nearshoring continues to gain attention but rarely replaces full-scale global operations. How do you see companies striking the right balance between proximity, efficiency, and cost?
“Nearshoring has gained prominence, especially amid recent trade disruptions, but companies increasingly see it as part of a strategic mix rather than a full replacement. They strike the right balance by regionalising the most critical parts of the supply chain, particularly those sensitive to lead times, geopolitical risks, or local market demands, while continuing to source globally to maintain flexibility, secure essential inputs, and benefit from specialised production. This hybrid approach often takes the form of multi-node regional hubs connected by digitally coordinated networks. The key is segmenting the supply chain by disruption sensitivity, customer proximity and value-added stages, ensuring nearshoring delivers strategic value without adding unnecessary cost. This balance enhances responsiveness, optimises costs and mitigates risks.”
What role are technologies such as AI, automation, and digital twins playing in enabling smarter, more adaptive supply chain networks?
“AI, automation and digital twins have moved from buzzwords to essential pillars of responsive supply chains. AI-driven analytics process vast, complex data to provide predictive insights, enabling proactive action amid market shifts. Digital twins offer virtual replicas of supply networks for scenario testing and stress simulation before disruptions occur. Automation enables the rapid execution of these strategies through intelligent robotics, dynamic inventory control and agile manufacturing. Together, these technologies let supply chains anticipate and adapt to disruptions, turning agility from aspiration into reality.”
With supply chains becoming increasingly multi-tiered and complex, what strategies are proving most effective in maintaining control, visibility, and risk mitigation across networks?
“Complex, multi-tier supply chains demand more than standard digitisation; they require fully orchestrated digital ecosystems. Effective companies are establishing integrated digital control towers that deliver real-time transparency and decision-making clarity across all supply chain tiers, from raw materials to end-consumer distribution. Advanced data governance protocols ensure quality information flows seamlessly through well-defined channels. Moreover, clearly established risk categories aligned to decision-making tiers within organisations empower rapid, informed decision-making. In short, the combination of robust digital infrastructure, clear governance and aligned organisational structures is proving indispensable to maintain visibility, manage risk and achieve operational responsiveness at scale.”
Looking ahead, what long-term trends do you believe will define the future of supply chain strategy, and how can companies prepare to capitalize on them?
“The future of supply chain strategy will be defined by the interplay of continuous geopolitical fragmentation, accelerated regionalisation and persistent economic volatility. Companies must architect globally distributed, digitally empowered supply ecosystems that embed flexibility and optionality by design. AI-driven predictive tools and digitally enabled scenario planning will move to the centre of strategic supply chain management, allowing businesses to anticipate disruptions and shift resources dynamically and swiftly. Preparing for this future requires immediate investment in digital capabilities, organisational readiness for decentralised decision-making and development of flexible supplier ecosystems. Companies that proactively build these capabilities today will emerge with significant competitive advantages, able to thrive and seize market share in volatile global conditions while competitors falter.”
Mark Wilkinson, Senior Vice President for OpenText’s Global Business Network, discusses AI-driven success in supply chains.
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AI in industry
AI might be transforming industries, but its ability to drive accurate workflows relies on a foundation of reliable data. For those working with supply chains, this data can generate assessments of global circumstances and highlight upcoming disruption to operations before it’s felt by the consumer.
In the past year, extreme weather, trade disputes, and geopolitics have tested the limits of business preparedness. For example, in October 2024, it was estimated that the storms that hit Valencia caused damage to its farming industry worth almost £1bn. That includes the produce lost and the rendering of underlying infrastructure as unusable. As the impact of the climate crisis drives an increase in natural disasters, supply chains must prepare for widespread disruption.
Looking to 2026 and beyond, this trend is unlikely to change for the better. To best future-proof business processes, AI will be fundamental. But where should organisations start?
Which data is good enough?
High-quality, accurate data is important for driving AI success in supply chains and providing users with accurate predictions. This enthusiasm is reflected in the expectation that the big data market will be worth over £300 billion by 2028. Despite this significant investment, most organisations, surveyed across industries, still face data-quality issues.
At present, only 12% of data and analytics professionals believe that their company’s data is ready for AI adoption despite 76% recognising data-driven decision-making as a priority. To drive success in supply chains, this lack of readiness needs to change.
Data preparation
Though action must be taken to remedy these concerns, companies shouldn’t view the quality of their own data as a blocker to innovation. Instead, they can ‘test’ the data before using it to drive insights.
As a first step, it’s essential to identify the format and quality of existing data assets. With complete knowledge of all the information available, corporations can integrate AI tools that work with their data, instead of trying to fit it into incompatible solutions.
Next, team leaders must be certain that their employees are trained on noticing hallucinations and changing processes to ensure accurate AI forecasting. Creation of the right procedures will feed into a successful long-term data governance strategy, ensuring full value is extracted by AI tools.
For ongoing insights, directly reflecting global circumstances, data must be continually fed into AI systems. By setting up the extraction of data from a reliable platform, companies can ensure that the insights they receive directly correspond with the most pressing logistical concerns.
Incompatible sources
Strategic partnerships can bring essential expertise for agile transformation, helping companies to scale at speed and improve their assessment of risks. For instance, by integrating data from a partner organisation, visibility across the global logistics landscape will be increased. Concerns arise, however, when data is formatted differently at each company. To mitigate the chance of hallucinations, data-trained workers should be proactively advised to scan insights for duplicates, misspellings, and inaccurate information.
Visibility
For operational success amid an ever-changing global landscape, the importance of preparing and ‘cleaning’, data should not be understated. To ensure accurate insights are produced by AI tools, integrated solutions should be compatible with current data-formatting, proactively mitigating the chance of hallucinations. To derive full value, the same ‘cleaning’ procedure should be used for partner data. By taking the right steps at the beginning of the adoption journey, business leaders can drive effective insights, consistently being updated, to support future growth.
We caught some precious time at Kinexions with Jennifer Dorsch, who outlines the transformation programme underway there.
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If ever there was a company that embodied the transformational spirit of Kinexions, it’s Syensqo, the Belgian multinational materials company. Established in December 2023, through the spin-off from Solvay, Syensqo is both emerging from its legacy company, whilst simultaneously transforming its operations during an era of unprecedented disruption. A challenging situation to say the least.
Jennifer Dorsch is the Global Head of Supply Chain Center of Excellence at Syensqo; a woman who by her own admission is “transformation driven” and skilled in operational leadership, process optimisation and leveraging technology to achieve best-in-class performance. She is seeking to spearhead global transformation initiatives, enhancing efficiency and growth through streamlined processes, systems and strategic simplification.
An inspirational leader
A results-oriented senior executive, and a former Supply Chain Excellence Director at Solvay, Dorsch has a proven record of leading high-performing teams, driving impactful change and delivering measurable results spanning the industrial, supply chain, and finance functions. “As Head of the Global Supply Chain Center of Excellence at Syensqo, I spearhead transformation of the E2E supply chain,” she explains, backstage at the Fairmont Hotel, Austin.
The core values of the CoE are based on creating an efficient and resilient supply chain through simplification, standardisation and harmonisation with efforts prioritised in support of company objectives. “We measure the benefits of transformation through supply chain improvements and cost savings and deploy effective change management strategies to ensure adoption of new systems and processes aimed at improving KPIs in support of company objectives,” she reveals. “We also created accountability in support of change management.”
Jennifer Dorsch, Global Head of Supply Chain Center of Excellence at Syensqo
Emerging from a legacy
Syensqo recently split from Solvay representing specialty chemicals while the commodity side remains Solvay. “The split of the company put us right into a transformation and the first challenge to be tackled was planning. And so we’re now using Kinaxis Maestro as a foundation for that. We’re taking it as an opportunity to bring all of our business units into a harmonised way of working through one platform. These are five business units that did things entirely differently. They didn’t even know who each other were and yet now they’re working together. This is quite transformational,” she enthuses.
Of course, there are challenges to implementing any kind of transformative program and change management nearly always tops the poll as the most demanding. “The hardest part is the change management. There were folks that couldn’t understand, couldn’t envision what it was going to be like. Everyone naturally feels that their way is unique and often don’t understand the other parts of the business. But change takes time. We had to create platforms for the teams to get together across the businesses to view the details because supply chain is very detail oriented. Supply chain professionals like to see the facts and to see how each other works in order to understand how valuable it would be for each of them to change the way they work to come together.”
According to Dorsch it’s vital to bring the people along with you on the journey. “It can’t be top down. They need to understand why and they need to feel it. However now there are more and more asking for it. Now they’re asking for Maestro and Kinaxis, which is great.”
Agility is key
So, how has Maestro enhanced agility and resilience and efficiency at Syensqo? “Well, it’s going to help us with the transparency, primarily. We will now have the information at our fingertips to make decisions in real time. We’ll be able to pull more of our planning upstream. Constraints realised further upstream in the planning relieves the pressure of the plant floor where it’s quite busy. The plant floor will be much, much calmer I would say.”
Maestro is also able to enhance the customer side too. “Our customers will certainly see a difference,” she reveals. “Our service levels will see a real improvement too. We’ll be making the right inventory and have it in the right place at the right time, ultimately improving business outcomes. Working capital and customer service will also improve.”
The people
A lot of what’s been happening at Kinexions is technologically rooted, but the power of people is also being stressed as vital in these major transformation projects. “Oh they are,” she affirms. “People are stressed. They need to feel protected. And the Kinaxis teams have done a very nice job of helping the teams feel supported by giving them examples of other companies that they’ve done this for. This lets them know it’s normal to feel stressed and to not be sure until you go live. However, you need to let them know that you’re there for them. The more examples they go through, the more comfortable the users feel. But it does take time.”
Disruptive and volatile as these times are, at least a platform such as Maestro gives users the ability to meet some of these daily challenges. “Yeah, it certainly does. I mean, the way we’re able to handle resiliency currently is that people have to work a lot harder. But the way we’re going to be able to handle resiliency going forward, when we have challenges, is going to be completely different because we’ll have such better transparency in our ability to react and respond. We will definitely adjust our focus onto using AI to make the decisions. All the routine decisions will be automated through AI and AI agents.”
So, what would Dorsch say to those supply chain leaders who have yet to make the leap into harnessing emerging technologies? “I would say think about the people that are working in the supply chain and improve their quality of life. The more you give them to make their jobs easier, the less stress there is on them. Let the system take the stress, not the people. It’s a way to retain your top talent. I would turn it more in that direction. Not to mention the fact that you get to improve outcomes for customers, financial statements, all of that, but crucially for your employees too.”
Lysan Drabon, Managing Director at the Project Management Institute (PMI), on the critical role of project management in successfully integrating Artificial Intelligence (AI) as a tool for driving sustainability initiatives within FinTech and financial services
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The financial services sector, traditionally associated with spreadsheets and skyscrapers, is undergoing a green transformation. FinTech, at the forefront of this evolution, is increasingly leveraging Artificial Intelligence (AI) to drive sustainability initiatives. However, the path to a greener financial future isn’t paved with algorithms alone. Effective project management is the crucial compass, guiding these AI-powered initiatives towards tangible and lasting impact.
The potential for genuine progress hinges on a structured, project-based approach. Without it, AI risks becoming a costly distraction. Failing to deliver on its promise of a more sustainable financial ecosystem.
The challenge is significant. Financial institutions face growing pressure from investors, regulators, and customers to demonstrate their commitment to ESG principles. AI offers powerful tools for achieving these goals. From optimising energy consumption in data centres to identifying and mitigating climate-related financial risks. Yet, as Project Management Institute’s (PMI) recent research reveals, success is far from guaranteed.
The findings highlight a clear disparity between organisations that strategically integrate AI into their sustainability efforts and those that treat them as separate endeavours. Those with a robust project management framework, capable of balancing these complex initiatives, are far more likely to achieve meaningful results.
So, how can FinTech companies and financial institutions effectively harness the power of AI to drive sustainability? The answer lies in prioritising three key elements within a project management framework: data readiness, leadership preparedness, and strategic alignment.
Data Readiness: The Foundation for Sustainability in Finance Using AI
AI algorithms are only as good as the data they consume. In the context of FinTech and financial services, this means establishing robust data collection, management, and utilisation processes. These must capture a wide range of sustainability-related metrics.
This includes data on energy consumption, carbon emissions, investment portfolios, and supply chain practices. Project managers must champion data readiness as a fundamental project requirement, ensuring that data is accurate, consistent, and readily accessible.
Imagine trying to assess the ESG performance of an investment portfolio when data on the environmental impact of underlying assets is incomplete or unreliable. A “single source of truth” for sustainability data is essential. It provides a reliable foundation for AI models to accurately assess risks, identify opportunities, and track progress towards sustainability goals.
This also means addressing the ethical considerations around data. Financial data is highly sensitive, and project managers must ensure that AI systems are used responsibly and ethically, protecting data privacy and preventing bias.
Leadership Preparedness: Building Sustainability-Savvy AI Teams
The successful integration of AI for sustainability in fintech demands a new breed of leader. Project managers must not only possess the traditional skills of planning and execution but also cultivate a deep understanding of both AI technologies and the nuances of sustainable finance. This requires a proactive approach to talent development, fostering a culture of continuous learning and experimentation.
Building successful teams means bridging the gap between data scientists, financial analysts, sustainability experts, and regulatory compliance officers. Project managers must act as translators, delivering effective communication and collaboration across these diverse disciplines. They need to be adept at identifying and nurturing talent. Whether through upskilling existing employees or recruiting individuals with specialised expertise.
Moreover, leadership preparedness extends to the ability to navigate the ethical complexities of AI in finance. Project managers must be equipped to address potential biases in algorithms, ensure data privacy, and promote transparency and accountability in AI-driven decision-making. This requires a strong commitment to responsible innovation and a willingness to challenge conventional thinking.
Strategic Alignment: Embedding Sustainability into FinTech’s DNA
AI-driven sustainability initiatives must be aligned with broader organisational objectives. Project managers must ensure sustainability is embedded into the project’s core strategy. Every stage of a project must be evaluated for its environmental and social impact.
This requires buy-in from senior management and establishing clear metrics for measuring sustainability performance. Additionally, it means developing frameworks for reinvesting AI-driven sustainability gains into further initiatives. This creates a virtuous cycle of continuous improvement.
Consider a FinTech company developing an AI-powered platform for lending. Without strategic alignment, the project might focus solely on optimising loan approvals, potentially overlooking the social and environmental impact of lending decisions. Project managers must work with stakeholders to define clear sustainability goals. And also establish measurable metrics, and ensure that these are integrated into the project’s overall objectives.
Beyond Efficiency: A Holistic Vision for Sustainable Fintech
AI offers immense potential for automating tasks and optimising processes. Moreover, it’s crucial to remember that sustainability is about more than just efficiency. Fintech companies and financial institutions must adopt a holistic approach that considers the environmental, social, and economic impacts of their operations.
Project managers play a vital role in ensuring that AI is used responsibly and ethically, with a focus on transparency, accountability, and fairness. This includes addressing potential biases in AI algorithms and protecting data privacy. Furthermore, it also means ensuring AI systems are aligned with human values. They must contribute to a more equitable and sustainable financial system.
By embracing a structured, project-based approach, FinTech companies and financial institutions can unlock the full potential of AI to drive genuine and lasting sustainability improvements. Project management is not just a supporting function; it’s the linchpin for success in the age of AI-driven sustainability. It’s about building the right foundations, equipping the right teams, and aligning projects with the right strategic objectives.
Kinaxis, the supply chain orchestration platform developer, is leveraging agentic AI in both its world-renowned Maestro platform and beyond. SupplyChain Strategy sat down with Andrew Bell, Chief Product Officer at Kinaxis, to learn more…
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Kinaxis’ Maestro is billed as an AI orchestration platform that revolutionises how supply chain leaders handle and use their data. Built upon three fundamental principles – supply chain data fabric, an intelligence engine, and the user experience – it serves to ease the challenge of gleaning actionable insights from broad data sets, as well as automating processes that are reliant on understanding shifts in that data.
Through AI, it’s a system that users can speak with: ask Maestro a question about your data, and it will give you an answer in real-time. The AI-powered system can also simulate an endless array of scenarios, massively enhancing supply chain leaders’ capacity to prepare for the future against a backdrop of regular and often-decisive volatility around the world. Keen to learn more about the ways in which the firm is leveraging agentic AI in both Maestro and beyond, SupplyChain Strategy sat down with Kinaxis’ Chief Product Officer, Andrew Bell, backstage at Kinexions 2025, to learn more.
The three AI disciplines
Before we get into the finer details, it’s important to understand what agentic AI is and where it sits in the growing family of AI-powered technologies poised to reshape the world. “For supply chain, our view is that there are three AI disciplines that are highly relevant to what we do,” explains Bell, fresh from delivering a fascinating keynote speech to the assembled global supply chain leaders gathered in Austin, on agentic AI. “The first was predictive AI with machine learning, the second, more recently, was generative AI. Continuing on from there would be agentic and autonomous AI.
“It’s not about any one of those on their own,” Bell continues, “but rather how they come together to deliver. When I think about agentic AI, it comes down to what we demonstrated in conference: the ability to chat with your data, to ask questions about your data, to get it presented to you however you want, all based on simple prompts. It’s actually a fusion of generative and agentic AI. There’s the agent that we built that works autonomously based on prompts from users; prompts that are then interpreted by the generative side.”
According to Bell, when it comes to agentic AI, the real differentiator is the notion that it operates on its own, that it operates autonomously as a result of a user prompt or data change conditions. “The idea is that it’s able to make its own decisions as it progresses through a problem; that’s what I find so powerful about it,” he enthuses. “That’s how it differentiates from other forms of automation.”
The democratisation of data
While concerns abound regarding the disruption AI could bring to workforces, namely in headcounts and the nature of their work, Bell stresses that this form of AI, as with the others, is at its best as an enabler rather than replacer. “The first thing to say is that AI on its own, especially in the supply chain space, is not going to solve our problems,” he explains. “It’s not going to deliver the value. Its real value is its democratisation of data access through the combination of the data with tools that have the ability to access and use that data, with AI sitting on top. Then I can get to my data more easily and more quickly, and so can anyone else approved to use the system.
“Users don’t need to learn a system, they don’t need to know how to navigate complex worksheets, set up filters and all the things you do in a traditional context. It means anybody, whether that’s an entry-level planner or a C-level executive can ask data-based questions, run a scenario or a simulation or execute something with less friction. I see it as a democratisation of the power of data and as an accelerant.”
That sense of democratisation extends beyond Kinaxis’ internal use and development of its agentic AI systems, with customers and partners joining the fold to inspire new and iterative action. “We’ve approached it by building an agentic framework first, and that allows for the creation of agents and the running and execution of agents,” Bell elaborates. “That’s step one. Now we’re building our own out-of-the-box agents on that framework, as well as opening that framework up to our customers so they can build their own agents. Customers know their business best, and there might be use cases that they want to apply an agent to that we haven’t thought of yet. They’ll now have the ability to do that.
“From there, we’re using our customers and the challenges they share with us to figure out what we can build or iterate upon next. We’ve started with the ‘chat with data’ agent. Because that was the number one thing: get me access to my data. The next thing is the ability to evaluate two options and execute a change. Merck, who we’re working with, shared an agent that essentially detects late supply and takes corrective action.”
Bell is evangelical regarding the adaptability of its AI framework, allowing agents to be used in isolation, or strung together. “It’s purely going to be based on the natural language prompt from the customer,” he reveals. “The framework will know all the different agents I have access to and so it can either do what the user is asking with those agents or suggest a combination of those agents.”
Data is the key
Data is the crux that all AI roads lead to and stem from. Without high-quality data, AI isn’t capable of delivering on its potential. Creating robust frameworks, exercising high levels of data hygiene, and structuring data stores in an AI-ready fashion are paramount in both the development of agentic AI and the application of those tools. For both developers and users, Bell stresses the fundamental importance of getting that data piece right. He notes, too, that its applicable advice no matter where individuals and organisations are in their AI journey. “There is the ability to start from any position on that journey,” says Bell. “It doesn’t have to be a big bang or a one-size-fits-all. No matter what, though, it is about the data. The agents, the automation, whatever it might be, is only going to be as good as the data that it can access.
“Step one is to understand the problems you’re looking to solve and figure out which data that system would need. We have capabilities that simply do exception reporting where you can implement predefined automations where your team has said ‘these are some processes that we execute on a regular basis, and we have the data, so automate it’. You can then move up the journey and say, ‘No, we’re ready to implement agents and we’re going to start using some proven native ones before going all the way to making our own.’’
“The good news is that some of the foundational requirements apply no matter where you start in the journey. Getting the data and having the right tools in place are going to benefit you across the whole journey. From Covid to more recent impediments to worldwide networks via trade war escalation, significant global interruptions and bottlenecks over the past several years have put enormous pressure on supply chains to adapt at pace. As far as disruptive influences go, agentic AI represents a welcome boon for those who can effectively wield its potential.”
“At Kinexions 2025, we had a presentation from ExxonMobil that noted how people typically think about disruptions as a negative thing, but our job is to build a supply chain that excels at managing those disruptions,” says Bell. “When we do, we have a competitive advantage. Our job at Kinaxis is to provide the tools, systems and capabilities to deliver that competitive advantage to our customers. Disruptions are going to occur. That’s a given. We don’t know what they might be, but they’re going to happen. If we’ve given you the ability to manage them effectively, that’s going to give you a strong competitive advantage.”
As of 2025, artificial intelligence (AI) tools are revolutionising the financial industry by enhancing efficiency, accuracy, and decision-making across various…
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As of 2025, artificial intelligence (AI) tools are revolutionising the financial industry by enhancing efficiency, accuracy, and decision-making across various domains. Here are five leading AI platforms making significant impacts in finance:
1. JPMorgan’s Coach AI & GenAI Toolkit
JPMorgan Chase has integrated AI tools like Coach AI and a comprehensive GenAI toolkit to enhance client services and operational efficiency. Coach AI assists advisors in swiftly retrieving research and anticipating client inquiries. This has led to a 95% reduction in information retrieval time. The GenAI toolkit, utilised by over half of JPMorgan’s 200,000 employees, has contributed to nearly $1.5 billion in savings. The company has seen improvements in fraud prevention, trading, and credit decisions.
2. BlackRock’s Asimov
BlackRock has developed Asimov, an AI platform capable of autonomous actions such as analyzing documents and providing real-time portfolio insights. This tool enables portfolio managers to maintain situational awareness and make more informed decisions continuously, enhancing the firm’s investment processes.
3. Hebbia
Hebbia is an AI platform designed to perform complex, multi-step tasks autonomously, effectively functioning like a high-capability intern. It can handle tasks such as analysing financial filings, building valuation models, and drafting memos. Major financial institutions like BlackRock and KKR utilise Hebbia to streamline operations and free professionals to focus on strategic work.
4. Datarails FP&A Genius
Datarails offers an AI-powered Financial Planning and Analysis (FP&A) platform that automates data consolidation and financial reporting. It provides workflows, templates, and data visualisation tools to facilitate budgeting, forecasting, scenario modelling, and financial analysis. These enhance the speed and accuracy of financial decision-making.
5. Feedzai
Feedzai is a data science company that develops real-time machine learning tools. These identify fraudulent payment transactions and minimise risk in the financial services industry. Its AI-based applications are used for fraud detection, risk assessment, and regulatory compliance. They are helping organisations manage and mitigate financial crime risks effectively.
These AI tools exemplify the transformative impact of artificial intelligence in finance. Offering solutions that enhance operational efficiency, risk management, and strategic decision-making.
Anshul Srivastav, Senior Vice President and Head – Europe for Zensar Technologies on securing AI with blockchain
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Artificial Intelligence (AI) is rapidly transforming financial services. According to The Bank of England, 75% of financial services firms are already using AI. A further 10% are planning to use it in the next three years.
Firms are deploying AI because of the benefits it can bring. These include enhanced data and analytical insights, improved anti-money laundering (AML) and fraud detection and efficiencies in cybersecurity practices. As well as providing customers with better, more personalised services.
While the wide-scale deployment of AI brings a range of benefits for the financial services sector, it’s also creating additional risks. Especially when the AI systems used to make trusted decisions are becoming a prime target for cyber-attacks.
Attacking AI
Bad actors can manipulate AI systems to make them malfunction or operate in ways that weren’t intended. This can have potentially severe consequences.
Using what’s known as data poisoning attack, threat actors can intentionally compromise or alter datasets used by AI to influence the outcomes of the model for their own malicious ends.
For example, an attacker trying to bypass the AI-powered fraud detection systems of a bank could attempt to inject false data into the system during a data training cycle the intention would be to manipulate the system into believing certain false transactions are legitimate. Ultimately this enables the threat actor to steal money or sensitive data without being noticed.
AI systems can also result in additional threats to data privacy. Like many workers, financial service professionals can use Large Language Models (LLMs) like ChatGPT to aid with queries and tasks.
However, this brings the risk that sensitive information could get uploaded to the model if the employee inputs certain data, such as contracts or confidential reports. This data might be saved by the model, opening businesses up to data leaks. Because with the correct prompts, it’s possible for a user from outside the company to tease out this confidential information from the LLM.
These privacy concerns can be exacerbated by the black box nature of AI. Often, it isn’t publicly detailed how the algorithms and the decision-making process behind them operate. This lack of transparency can lead to mistrust among users and stakeholders. As well as potential issues with regulatory compliance. For example, the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
All of this means that the use of AI in financial services, while beneficial, is creating new security challenges which need to be addressed. The solution to this is the integration of blockchain technology to create a secure, transparent, and trustworthy AI ecosystem. And by leveraging blockchain’s inherent security features, vulnerabilities in AI systems can be countered.
Blockchain Explained
Blockchain consists of a chain of blocks, each containing a list of transactions. Each block is linked to the previous one, forming a secure chain. This structure ensures that once data is recorded, it cannot be altered without changing all subsequent blocks. These mechanisms ensure that all participants agree on the state of the blockchain. Therefore preventing fraud and enhancing security.
This is achieved through three key pillars. The first is data immutability, which ensures it can’t be altered or deleted once recorded on the blockchain. Guaranteeing that the data remains consistent and trustworthy over time, ensuring its integrity.
The second pillar is decentralisation, based on how blockchain functions through a network of independent nodes. Unlike centralised systems, where a single point of failure can compromise the entire network, decentralisation distributes control and data across many nodes. This reduces the risk of system failures, as no single target point exists, meaning decentralisation enhances security and resilience.
Cryptographic security is the third pillar. Blockchain uses a system of public and private keys to secure transactions and control access. The public key is visible to anyone, while the private key is a secret code known only to the authorised party.
These fundamentals of blockchain, combined with the transparency and security it offers, can help financial services organisations address the security challenges they’re being faced with by the rapid deployment of AI.
Combining Blockchain with AI for Improved Data Security
Integrating blockchain with AI can massively aid with securing data integrity. For example, through creating tamper-proof records. By making immutable records of AI training data and model updates, complete with timestamps and links to previous entries, this ensures a tamper-proof history of the data. Enabling stakeholders at financial services companies to verify the integrity of the data used in AI models. Therefore improving security of the whole system and protecting it against attacks.
Combining AI with blockchain can also help to counter potential data privacy implications introduced by the deployment of AI in financial services. Blockchain techniques like zero-knowledge proofs allow the data to be verified without revealing the actual data. This can help financial services firms to verify the data they’re using is correct. While also still maintaining the required data privacy and regulatory compliance.
In addition to this, implementing AI with blockchain technology can aid with building trust and transparency in how AI systems work and what they’re used for. By providing a transparent record of AI decision-making processes, the blockchain allows stakeholders to review and verify the process. All the while ensuring there’s accountability of who made changes and when. This arrangement could therefore help financial services providers prevent data poisoning and other attacks targeting their AI systems.
Building a Secure, Transparent, and Trustworthy AI Ecosystem
The rapid adoption of AI is changing the financial services industry. However, according to The Bank of England’s survey, only 34% of financial services firms said they have ‘complete understanding’ of the AI technologies they use.
Much of this can be attributed to how the technology is new, but also how the algorithms which power AI technology are often mysterious in their nature. This results in risks around malicious attacks and data privacy issues. However, by combining AI frameworks with blockchain technology, these security issues can be addressed.
By taking these steps, stakeholders can collectively contribute to building a secure, transparent, and trustworthy AI ecosystem. An ecosytem that leverages the strengths of blockchain technology to address current and future challenges.
Hermann Tischendorf is the Chief Information & Technology Officer at MTN MoMo (the telco’s mobile money division). He reveals a bold roadmap for leveraging FinTech to drive financial inclusion across the African continent.
“MoMo is comparable in monthly active users to some of the top ten FinTechs globally. We’re playing in the same league as Revolut or Nubank – but in much more complex markets,” notes Hermann. “Access to financial services is fundamental. Without it, people are excluded from the global economy. Our services are the equaliser. They allow individuals in frontier markets to participate in trade, store value, and ultimately improve their quality of life.”
Pima Community College: Digital Transformation on a Public Sector budget
Higher education is typically seen through this lens. Slow to adopt new technologies, traditionally inflexible, and held back by a lack of funding. At Pima Community College in Tucson, Arizona, a quiet revolution is underway that subverts these expectations. The college is a publicly funded, two-year higher education institution. Serving Pima County and beyond, it has an annual student body of 38,000 served by almost 2,500 faculty and staff.
Led by Isaac Abbs, Assistant Vice Chancellor for IT and CIO, the college is undergoing an extensive IT transformation. This has unlocked immense value through bold, visionary leadership. Crucially, it is being achieved without a major increase in budget explains Abbs.
“If, as an IT leader, you become a truly innovative partner and move the organisation forward, the dollars are there.”
State of Missouri: Security as a Foundation for Innovation
Megan Stokes, Director of Cloud Security & Strategy at State of Missouri, digs into the many ways in which the agency is leveraging technology – and how it’s keeping the citizens of Missouri at the forefront.
“I have the opportunity to guide agencies through best practices, helping them access the right resources, the right expertise, and make sure that the solutions they’re building on are really secure and well architected going forward,” she explains. “That includes a focus on risk management, access control, optimisation, governance and compliance, and long-term strategy. There’s always something new to think through, and that keeps the role really exciting and engaging. There’s always lots of work to be done.”
RAKBANK: A Banking Transformation in the UAE
Our cover story explores the digital transformation journey of RAKBANK in the UAE. Head of Digital Transformation, Antony Burrows, reveals the agile practices, enterprise-wide enablement and people-first culture delivering digital banking with a human touch.
“Culture is the cornerstone,” Antony stresses. RAKBANK codifies this into its Four Cs Framework – Connect, Communicate, Collaborate and Celebrate. “Here in the UAE, banks are pivoting from a model of ‘we know everything’ to recognising that one of the best ways to deliver continuous change and value to customers is through partnerships with startups and FinTechs. It’s no longer banks versus startups – it’s banks and startups, working together for the customer. This shift is especially meaningful as banks expand beyond traditional services to focus on customers’ broader financial lives.”
Russell Gammon, Chief Solutions Officer at Tax Systems, on the benefits of AI in automating routine processes to make time for higher level strategic tasks
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In the past two and a half years since the launch of ChatGPT – and the likes of Copilot – the world has been gripped with generative AI fever. However, after the initial rush of enthusiasm, many businesses today are taking a more cautious approach. Trying to identify tangible benefits and use cases that can prove its worth before making costly investments.
One industry where the use cases are becoming more evident day by day is Financial Services. Repetitive and time-consuming tasks, traditionally completed manually with all the risk of human error that entails, can now be automated. Capabilities such as machine learning, generative AI, and advanced data analytics algorithms are being used to help ensure organisations remain compliant through delivering accurate, timely calculations, tax filings and reports. And creating clearer visibility.
AI Revolution
By automating routine processes, such as data analysis and reconciliation, finance executives can spend more time on higher level strategic tasks. AI can also provide insights beyond the capacity of humans thanks to its ability to crunch vast volumes of data, It can uncover trends that might otherwise go unnoticed. This enables real-time reporting and analysis with AI insight forming the basis of smarter decision-making.
For finance, this is just the beginning of the AI revolution. Look deeper into any finance sector and a huge variety of more specialised applications are revealed. Take the tax industry, for example, where a sizeable cohort of professionals still spend a considerable amount of time checking long lists of numbers on invoices or using spreadsheets to track spending. Not only is this work frustratingly boring, it is also prone to human error. AI has the potential, at a single stroke, to handle such tasks.
Navigating Choppy Regulatory Waters
Staying in the tax-related field, AI can also play a pivotal role in handling incoming regulations, such as Pillar Two. Multinational corporations are grappling with the complexities of this legislation. AI is emerging as a game changing tool in compliance management, transforming tax reporting, risk mitigation, and regulatory adaptation.
AI is being used to automate compliance and reporting processes. It can streamline data aggregation, ensure accurate reporting, and adapt to evolving regulations. AI-powered compliance tools optimise the evaluation, monitoring, and reporting of Pillar Two obligations. This can reduce complexity and improve precision. They can also integrate and standardise financial data across jurisdictions, improving consistency in tax computations.
These solutions seamlessly connect disparate systems, extracting and harmonising data from multiple sources regardless of format. By normalising and processing this information in line with BEPS regulations, AI can swiftly identify potential compliance risks. Advanced algorithms can flag irregular transactions between related entities and pinpoint inconsistencies in transfer pricing. This helps to detect possible profit-shifting activities before they become regulatory concerns. AI thus has the potential to change compliance management from a costly obligation to a strategic advantage.
Be Wary of AI’s Limitations
So, there is clearly a lot of potential for AI to transform financial services in terms of daily operations and compliance. However, it is important to remain wary of its limitations. Chief amongst them, is AI’s propensity to ‘hallucinate’ or make information up if it can’t find the right answer. That casts a shadow over the accuracy of all of its output. And underlines the importance of professional gatekeepers who can verify AI content and ensure it is correct.
AI also currently lacks the ability to interpret subtle context, which humans can more easily respond to. This can feed into spurious responses and misinterpreted data. However, with the right training, monitoring and oversight, AI tools can overcome such weaknesses.
Supporting, Not Replacing, the Human Touch
Understandably, given AI’s potential, many are concerned about the impact on jobs. If AI can digest thousands of lines of data and spit out a report in seconds, what do we need interns for? But it’s important to see AI as an augmentation of existing human talent, not a replacement for it.
As noted above, the possibility of hallucination means that qualified professionals will always have a role to play in quality checking output. So, what we are seeing is the development of a symbiotic relationship wherein professionals are freed from the drudgery of repetitive grunt work. They can focus on more strategic objectives, while AI handles it under their careful eye.
For the tech-savvy Gen-Z entering the workplace today, this is a hugely positive change. The finance and tax industries have become a less attractive career option for this generation, due to the traditional processes and lack of technological innovation. What graduate wants to spend their days entering data after years of studying their chosen subject? With AI ready as a helping hand, they can enter the workplace and use their skills and knowledge to assess the technology’s output, rather than spending hours manually doing it themselves. The finance industry is now in a position to embrace this opportunity that AI has presented. And encourage new talent into the industry.
Given the financial services sector is plagued with skills shortages, and ever-growing workloads, employers can now offer more attractive career opportunities. Furthermore, striking the right balance to drive improved efficiency, productivity and performance and reap the rewards of an AI-enabled future.
Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.
Day three featured more impactful sessions across all four pillars, offering attendees more valuable insights and strategies for innovation.
Highlights from Key Sessions at Money20/20 Europe:
How to Create and Leverage FinBank Partnerships
The discussion focused on the evolution and success of FinTech partnerships with banks. Key points included the shift from transactional partnerships to more collaborative, value-driven relationships, emphasizing joint KPIs and product creation.
Alex Johnson, Chief Payments Officer, Nium
“You really have to differentiate. You really have to stand out for a bank to say, ‘Yeah, I like what you offer enough to go through, six months of onboarding.’ Dare I say, maybe more.”
John Power, SVP, Head of JVs & AQaaS, Fiserv
“The legacy system, it’s a fact of life. They’re there. They’re pervasive. They’re going to be here for a long time, and banks historically have made huge investments in those platforms and systems. So I think both the challenge for the for the bank and the opportunity for the FinTech is, how do you at the front end of those legacy systems develop new products that can scale and that you can bring cross border easily and readily.”
“It really is cutting the line to be able to deliver opportunity for customers and to be able to expand propositions for new customers.”
“The economic development supply chains shifting to low to middle income countries are incredibly important right now, and cross border payment rails have not been good in low middle income countries.”
Where Fintech goes Next: Tapping into Platforms and Verticals
The discussion centred on the democratisation of financial services through embedded finance. The panel emphasised the importance of data quality, personalisation, and strategic partnerships in delivering seamless financial experiences – ultimately enhancing customer satisfaction and improving business efficiency.
“Embedded finance is going to be defined by region and use cases.”
Amy Loh, Chief Marketing Officer – Pipe
“Small businesses don’t want to manage their business through a bunch of different tools that are stitched together. They’re looking to platforms to do everything for them and keep high end services.”
“Most platforms or merchants out there trying to diversify revenue, and they will get auxiliary revenue, or maybe get primary revenue through FinTech activity.”
The Neobanks Strike Back
In a dynamic exploration of neobanking’s evolution, Ali Niknam revealed bunq’s remarkable journey from a tech-driven startup to a sustainably profitable digital bank. By leveraging AI across every aspect of their operations, bunq has transformed traditional banking, reducing support times to mere seconds and creating a hyper-personalised user experience. Niknam emphasised the power of user-centricity, showing how innovative features like simple stock trading and multi-language support can democratise financial services.
The bank’s strategic approach – focusing on user needs rather than investor expectations – has enabled them to expand thoughtfully, with plans to enter the UK and US markets. By embracing technological change and maintaining a relentless commitment to solving real customer problems, bunq exemplifies the next generation of banking.
Ali Niknam, Founder & CEO, bunq
“Somewhere in the 70s, we let go of the gold standard, and now currencies are basically floating. The only reason why a dollar or a euro is worth what it’s worth is because of trust and perception. Philosophically, it’s very logical that we have found another abstraction layer by introducing stablecoin, which is not much else than a byte number that has a denomination currency as a backing asset that itself doesn’t have anything as a backing asset. A lot of people might ask, ‘Why would you need a stablecoin? We have euros. I go get a coffee, pay with Apple Pay or cash.’ But there are many countries on this planet where the local currency is not stable. If your country has an inflation rate of 30,000% like Zimbabwe, you would really love to use a different currency. The US dollar has been the currency of choice, but as a normal person, you cannot access the US dollar. A US dollar stablecoin that you can access by simply having a mobile phone – that’s going to be transformational for large groups of people.”
Innovating When Regulation Can’t Keep Up: Lessons from NASA
Lisa Valencia covered an array of topics, from her 35 year career at NASA and Guinness World Record to the rise of private entities like SpaceX, which has launched 180 missions this year, and the increasing role of public-private partnerships in space exploration. The speaker also touched on international collaborations, particularly with the European Space Agency and the Italian Space Agency, and the potential for space tourism and colonization of the moon.
Lisa Valencia, Programme Manager/Electrical Engineer – Pioneering Space, LC (ex NASA)
“Back in the day, NASA got 4% of the national budget. Now it’s down to just 0.1%, so we’ve had to get creative with private partnerships. SpaceX is the perfect success story. They came to us in 2007 needing money after some rocket mishaps, and look at them now! From my balcony, I see their launches every other day. They’re planning 180 launches this year alone.Talk about a return on investment!”
“We’re planning to colonise the South Pole on the moon. The idea is to extract water and hydrogen from the regolith—both for living there and for fuel.”
Scaling Internationally in 2025: Funding, Innovating, and Breaking into New Markets
The conversation focused on the growth and strategy of fintech companies, particularly those with a strong presence in Europe and the US. The panel featured Ingo Uytdehaage, CEO and co-founder of Adyen, and Alexandre Prot, CEO of Qonto. Both leaders expressed a preference for organic growth over acquisitions, emphasizing the importance of scaling efficiently before pursuing an IPO.
Ingo Uytdehaage, CEO and co-founder of Adyen
“I think an important part of scaling a company is not just thinking about your product, but also considering the markets you want to address, and how you ensure you become local in each country.”
“We realised over time that if we really want to bring the customers, we need to have the best licenses to operate. A banking license gives you a lot of flexibility.”
“Being independent from other companies, other financial institutions, that gives you flexibility to build what your customers really want.”
“I think it’s very important, also in Europe, that we continue to be competitive. If you think about regulations and AI, we shouldn’t try to do things completely differently compared to the US.”
Alexandre Prot, CEO of Qonto
“We need to be very strict about tech integration and avoiding legacy which slows us down.”
“We still need to scale a lot before we have a successful IPO. A few team members are working on it and getting the company ready for it. But, the most important thing is just scaling efficiently in the business, and maybe an IPO would be welcome in a couple of years.”
Putting The F in Fintech
The panel discussion focused on the role of women in FinTech based on personal experiences.
Iana Dimitrova, CEO, OpenPayd
“At times, being underestimated is helpful, because if you’re seen as the competition, driving an agenda is becoming more difficult. So what I found, actually, over a period, is that bringing your emotional intelligence, leaving the ego outside of the outside of the room, and just focusing on execution is is incredibly helpful.”
Megan Cooper, CEO & Founder, Caywood
“The moment we start defining ourselves as like a female leader or a female entrepreneur, you almost kind of put yourself in a bit of a box. And so I think just seeing yourself on an equal playing field and then operating it on an equal playing field and interacting in that way is quite advantageous.”
“We can’t just want diversity and hope it happens. We actually have to be intentional about creating it.”
Valerie Kontor, Founder, Black in Fintech
“Black women make up 1.6% over the FinTech workforce, but when we look at the financial reality of black women by the age of 60, only 53% of black women have enough money in their bank account to retire. We need to start marrying people in FinTech and the people that we need to serve.”
Money20/20 Europe 2025 closed its doors but the next edition of the conference will return to Amsterdam from June 2–4, 2026, promising to continue the tradition of shaping the future of financial services…
Day two of Money20/20 Europe 2025 at RAI Amsterdam continued the momentum with a focus on digital assets, stablecoins, and…
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Day two of Money20/20 Europe 2025 at RAI Amsterdam continued the momentum with a focus on digital assets, stablecoins, and the evolving regulatory landscape. The event attracts over 8,000 attendees, including FinTech leaders, investors, and policymakers, all eager to explore the future of finance.
Money20/20 Conference Themes & Tracks
Money20/20 Europe 2025 is structured around four thematic content tracks:
Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.
Day two featured more impactful sessions across all four pillars, offering attendees further valuable insights and strategies for innovation.
Highlights from Key Sessions at Money20/20 Europe:
Digital Wallets and Co-opetition
A standout session featured industry leaders from Fluency, Curve, PayPal, and BLIK discussing the competitive yet collaborative nature of Europe’s digital wallet ecosystem. The panel delved into how traditional financial institutions and FinTech startups are navigating partnerships and competition to enhance user experiences and expand market reach.
Africa’s Fintech Innovation
Another significant discussion spotlighted Africa’s role in global fintech innovation. Representatives from 500 Global, Tech Safari, and Moniepoint highlighted how African startups are leveraging technology to drive financial inclusion and create scalable solutions that could influence global markets.
Digital Assets
A standout session featured Waqar Chaudry, Head of Digital Assets for Financing & Securities Services at Standard Chartered. In a fireside chat titled “The Digital Assets Opportunity: How Banks Can Win at Web3,” Chaudry, alongside Sygnum Bank’s Aliya Das Gupta, delved into the evolving landscape of digital assets.
Chaudry highlighted Standard Chartered’s initiatives in digital asset custody, tokenisation, and the launch of tokenised money market funds. Furthermore, he discussed the development of stablecoin solutions aimed at improving liquidity and settlement times. Chaudry underscored the importance of banks adopting robust digital asset strategies to meet growing client demands and navigate the complex regulatory environment. Drawing from his regulatory background at the Abu Dhabi Global Market, Chaudry provided a unique perspective on balancing innovation with compliance.
WealthTech Evolution
Leaders from Raisin, Upvest, and PensionBee explored the transformation of wealth management through AI and APIs. The panel emphasised the importance of personalised financial services and the integration of technology to meet the evolving needs of consumers.
Central Bank Digital Currencies (CBDCs)
A fireside chat with officials from the European Central Bank and the Bank of England provided insights into the development of the digital euro and pound. The discussion covered technical challenges, regulatory considerations, and the potential impact of CBDCs on the financial ecosystem.
Navigating the Evolving Cyber Threat Landscape
The financial services sector faces an unprecedented convergence of threats with sophisticated cyber attacks and the rise of new technologies… Recorded FutureCEO Christopher Ahlberg assessed the evolving threat landscape and strategies for building secure digital ecosytems. He was joined by In Security CEO Jane Frankland and Mastercard EVP Johan Gerber
Networking, Partnerships, and Brand Activations at Money20/20
Notable Announcements:
Money20/20 and FXC Intelligence Report: A collaborative report titled “How Will Europe’s Money Move in the Future?” was released, offering insights into the future of European cross-border payments and the impact of emerging technologies.
Policy Exchange Roundtables: Money20/20 introduced focused roundtable discussions involving central banks, regulators, and industry leaders to address critical regulatory challenges in the digital financial landscape
Day two of Money20/20 Europe 2025 underscored the dynamic interplay between traditional financial institutions and emerging FinTech innovations. Discussions on digital assets, stablecoins, and regulatory frameworks highlighted the industry’s commitment to embracing change while ensuring stability and compliance. The second day underscored the event’s role as a catalyst for innovation, collaboration, and growth within the fintech industry. As the conference progresses, stakeholders remain focused on shaping a resilient and inclusive financial future.
Money20/20 Europe 2025 opened its doors to a full-capacity audience at the RAI Convention Centre in Amsterdam. Bringing together the…
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Money20/20 Europe 2025 opened its doors to a full-capacity audience at the RAI Convention Centre in Amsterdam. Bringing together the world’s leading innovators, institutions, investors, and influencers from across the fintech and financial services spectrum. With more than 8,000 delegates from over 2,300 companies in attendance, the opening day set a high-energy, insight-rich tone for the rest of the week.
“Money Morning Live”
The day kicked off with “Money Morning Live”. A signature fast-paced keynote session hosted by Tracey Davies (President of Money20/20), Scarlett Sieber, and Zachary Anderson Pettet. The morning show served as a pulse check for the industry. Combining thought leadership with entertainment to engage both newcomers and veterans.
Rahul Patil, CTO of Stripe, delivered a keynote on AI’s role in payments infrastructure. Highlighting how machine learning is now essential for fraud detection, customer service, and onboarding. He emphasised AI should not merely be viewed as an efficiency tool, but as a strategic pillar to create personalised user experiences. And deliver scalable innovation across markets.
David Sandstrom, CMO at Klarna, reflected on the Swedish FinTech giant’s evolution, particularly its use of generative AI for customer engagement and internal operations. Sandstrom noted Klarna’s AI assistant, which now handles two-thirds of its customer queries globally, has dramatically improved both customer satisfaction and cost efficiency.
Money20/20 Conference Themes & Tracks
Money20/20 Europe 2025 is structured around four thematic content tracks:
Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.
Day one featured impactful sessions across all four pillars, offering attendees valuable insights and strategic foresight.
Highlights from Key Sessions at Money20/20 Europe:
Open Banking & Payment Rails
“Putting the Bank Back in Open Banking Payments”, saw speakers from Token.io, Santander, and BNP Paribas examine how banks are reclaiming relevance in the open banking conversation. While FinTechs initially led the charge, the panel noted banks now play a crucial role in building trusted, interoperability, and high-volume “pay by bank” solutions. The debate touched on customer adoption hurdles, PSD3’s role in shaping future APIs, and the monetisation challenges still plaguing the open banking model.
Card Issuance at Scale
In a fireside chat led by Thredd’s President Jim McCarthy, representatives from Railsr, Worldpay, Flagship Advisory, and Caxton discussed the complexities of issuing card programs globally. The group addressed fragmentation across regulatory environments. Especially in regions like LATAM and Asia-Pacific. They urged the need for programmatic flexibility, local compliance, and better BIN management. The panel agreed that the future of card issuing lies in seamless orchestration between platforms, banks, and third-party fintechs.
Agentic AI: Ready for Prime Time?
A standout session focused on the concept of Agentic AI — autonomous agents capable of completing financial tasks without manual prompts. Industry leaders from NVIDIA, bunq, and Visa debated how ready the financial services sector truly is for deploying such systems. While the technology is progressing rapidly, concerns around regulatory clarity, model interpretability, and risk frameworks remain.
NVIDIA’s Head of Financia Technology, Jochen Papenbrock, stressed the need to democratise access to compute infrastructure. And bunq’s AI evangelist, Ali El Hassouni, showcased how the challenger bank is testing semi-autonomous agents in customer support workflows. Meanwhile, Visa SVP for Products & Solutions, Mathieu Altwegg,emphasised the importance of embedding guardrails in agentic systems to ensure ethical AI practices. Especially in credit scoring and wealth advisory roles.
Scaling AI Across the Enterprise
A collaborative session featuring leaders from Stripe, Starling Bank, AWS, and Swift delved into the challenges of scaling AI initiatives beyond prototypes. The discussion spotlighted the importance of clean, real-time data pipelines, strong governance structures, and cross-functional collaboration between engineering, data science, and compliance teams.
Networking, Partnerships, and Brand Activations at Money20/20
Notable announcements:
Beyond the conference rooms, the exhibition floors buzzed with product demos, startup pitches, and impromptu huddles among VC firms, banks, and emerging FinTechs. Exhibitors such as Plaid, Adyen, Marqeta, and Fireblocks showcased new tools for embedded finance, real-time treasury management, and blockchain settlement.
Wise teased a new enterprise FX tool tailored for SMEs.
Checkout.com introduced an AI-enhanced fraud prevention dashboard.
Avalanche Foundation launched an initiative to bring blockchain-based micro-insurance products to underserved markets in Eastern Europe.
A particularly significant development emerged around stablecoins, with clear signals that regulated, bank-issued digital currencies are entering a new phase of maturity:
U.S. Megabanks Signal Joint Stablecoin Initiative Executives from JPMorgan Chase, Wells Fargo, Bank of America, and Citigroup confirmed that initial groundwork has begun on a joint U.S. dollar-denominated stablecoin, subject to the passage of the pending GENIUS Act (Guiding and Establishing National Innovation for U.S. Stablecoins). The stablecoin aims to offer faster, cheaper cross-border settlement and programmable liquidity for enterprise clients. Bank leaders emphasized that this would complement, not replace, traditional banking rails.
Ripple Expands in the UAE In a regional announcement, Zand Bank and fintech firm Mamo revealed a partnership with Ripple, using its blockchain infrastructure to enable real-time, low-cost cross-border remittances. This move, anchored in the UAE’s pro-digital asset stance, aligns with broader ambitions to make the country a hub for regulated digital currencies.
Institutional Stablecoin Custody Panels featuring speakers from Fireblocks, Anchorage Digital, and Circle addressed the evolving role of stablecoins in treasury operations and FX management. There was widespread agreement that tokenised cash equivalents, including USDC and EURC, are increasingly being used for short-term settlement and yield farming, particularly in Asia and Europe.
These discussions signalled a broader institutional acceptance of stablecoins, with an emphasis on compliance, transparency, and integration into traditional finance rather than bypassing it.
Day one of Money20/20 Europe 2025 delivered on its promise of convening the brightest minds to create the future of finance. From headline-grabbing keynotes and deep-dive panels to global product launches and off-stage networking, the conference created a rich mix of thought leadership, practical innovation, and human connection.
Whether it was the evolution of AI in banking, the future of programmable money, or the balance between innovation and regulation, the discussions revealed a clear consensus: collaboration will define the next chapter of FinTech. Day two at Money20/20 promises even more, with upcoming sessions on decentralised finance, digital identity, and CBDCs.
Dave Murphy, Head of Financial Services EMEA & APAC at Publicis Sapient, on unlocking data to unleash the intelligence with AI
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In today’s financial services landscape, the promise of artificial intelligence is everywhere… Hyper personalisation, intelligent automation, real-time insights, and AI-assisted customer experiences. But here’s the truth: AI doesn’t run on ambition. It runs on data.
If your customer and transactional data remains locked inside monolithic core systems, even the most sophisticated AI will underdeliver. The most effective path to AI-powered transformation isn’t a complete rebuild of your core – it’s strategic decomposition. By making high-quality data available in near real-time to your channels and platforms, banks can unlock AI’s full potential without overhauling their entire architecture.
At Publicis Sapient, we believe unlocking your data is the critical enabler for harnessing the full value of AI across the financial enterprise. It is no longer necessary to completely rebuild your core infrastructure. Instead, what’s required is strategic decomposition of monolithic systems to ensure near real-time data availability to your channels and AI applications.
The Data Access Conundrum
Banks are acutely aware that their legacy systems create data silos. Research reveals that 70% of banks’ IT budgets are still spent on maintaining legacy systems. Moreover, more than half cite the limitations of their core as the primary barrier to transformation.
Despite a shared recognition of the need to change, many institutions remain hesitant, concerned by the perceived complexity, cost and risk of restructuring their data architecture and overhauling foundational platforms. But this hesitation comes at a cost. As customers demand more personalised and seamless experiences, and digital challengers launch AI-enabled services at speed, traditional institutions risk falling behind.
Why Data Accessibility Unlocks AI’s Potential
The simple truth is: AI cannot thrive in isolation. It needs high-quality, accessible, and timely data. It needs customer and transactional information that’s available near real-time. And it needs a composable, event-driven architecture where data can flow freely across customer journeys and operational workflows.
Decomposing monolithic core banking systems enables all of this. By creating strategic APIs and data layers, banks can liberate critical information from legacy platforms and make it available to AI-powered services without the need for complete core replacement. In our work with leading banks globally, we’ve seen accessible data unlock:
1:1 personalisation at scale
Real-time fraud detection and risk modelling
AI-assisted customer onboarding and service
Automation across lending, compliance and operations
This is not theoretical. It’s already happening. In one engagement, we helped a regional bank transform its operating model via a phased core modernisation programme – delivering a one-to-one return on investment over five years by shifting from reactive IT spend to proactive value creation through accessible data.
Progressive, Not Paralysing
One of the biggest myths around core modernisation is that it requires a disruptive, ‘big bang’ transformation. That’s no longer the case. Advances in architecture, engineering tools, and AI-powered development platforms – such as our own Sapient Slingshot – now make it possible to modernise progressively and liberate critical data, rather than rebuilding everything from scratch.
Techniques like multi-core routing, event-driven orchestration and domain-driven design allow banks to gradually make customer and transactional data available near real-time to channels and AI applications – all without jeopardising day-to-day operations or requiring full core replacement.
Reorienting Around Data and People
Technology alone is not enough. Successful transformation requires a cultural shift – one that reorients the organisation around data, agility, and human outcomes. The future-ready bank is not only AI-enabled but data-led and human-centric.
By unlocking and democratising data through modern architecture, banks can power everything from predictive decision-making to better colleague collaboration. We are already seeing leading firms embed AI into their customer and employee journeys. Not as add-ons, but as integral parts of reimagined experiences built on liberated data.
The Future Belongs to the AI-Enabled
As AI capabilities continue to evolve, the divide between data-rich and data-poor, and AI-enabled and AI-limited institutions will widen. The leaders will be those that treat transformation not just as a technical challenge, but as a strategic imperative – reshaping how they operate, compete and serve.
Now is the time to act. Unlocking your data through strategic core modernisation is no longer a question of ‘if’, but ‘how’. Because in the age of AI, the intelligence of your bank will only ever be as strong as the data it can access and learn from, and ultimately the systems that underpin it.
Find out more from Publicis Sapient about core modernisation here
US Department of Homeland Security: Integrating with the Intelligence Community
Zeke Maldonado, CIO at the US Department of Homeland Security (DHS) is tasked with integrating the Department with the intelligence community. During times of change, governments need innovative, strategic leadership more than ever. And that’s where inspirational figure like Maldonado come into play.
“I remain committed to the DHS mission and want to take it to the next level. Many of the services we provide require substantial improvements, and I am eager to see how our modernisation efforts can help achieve the desired objectives. We play a crucial role in automating and enhancing the vetting process for non-US citizens, making it significantly more efficient.”
Cotality: The AI-powered Property Platform
Cotality,the AI-powered property and location intelligence platform, is making the real estate industry more efficient, smarter, and more resilient against climate change by leveraging the Google Cloud Platform.
Chief Data and Analytics Officer, John Rogers, explains how… “Buying a home is the biggest purchase in most people’s lives, so we’re passionate about making sure the system works for them.”
Nemko Digital: Pioneering Trustworthy AI
Nemko boasts more than 90 years of building trust in physical products, Today, Nemko’s digital division is leading the way in defining that trust in an increasingly complex and connected world with its pioneering approach to trustworthy AI reveals Managing Director, Dr Shahram Maralani.
“We want to be one of the top five players in this space. Our goal is to make the world a safer place.”
DPW is set to hit New York for the second year in a row, bigger and better than in 2024, and with an extensive list of experts set to speak.
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After the success of last year’s DPW NYC Summit, Digital Procurement World is making the event bigger and even better for 2025. DPW New York 2025 will take place at the extremely stylish ZeroSpace Brooklyn on the 11th and 12th of June. The theme this year is ‘Put AI to work’, focusing on the practical applications of artificial intelligence, and the opportunities for innovation across procurement.
The speakers have not yet been finalised and more may be added, but the event will include:
Brian Solis, Head of Global Innovation, ServiceNow
Jennifer Moceri, CPO, Google
Al Williams, CPO, Invesco
Eva Choe, CPO, The Chlorox Company
Kat Devlin, Head of Procure-to-Pay Operations and Travel & Expense, OpenAI
Oliver Gall, CPO, Prudential Financial
Maria Jesús Saénz, Director Digital Supply Chain Transformation Lab, MIT
Victor Miller, Chief Compliance Officer, Honeywell
Adam Brown, Global Director Procurement Technology Platform, Maersk
Mitchell Toomey, VP Sustainability & Responsible Care, American Chemistry Council
Stefanie Fink, Head of Global Digital Procurement, Kraft Heinz
Carlos Hernandez, Head of Procurement Excellence & Framework, Sanofi
Rosalia Snyder, Director Source-to-Pay, Microsoft
DPW New York is set to be a hub of inspiration and insight, with a broad range of figures sharing their knowledge and experiences with guests. After developing the concept of DPW in 2019, Founder Matthias Gutzmann’s event has grown into something that entices procurement professionals from all over the world. 2024 saw the DPW team putting on an intimate, invite-only New York event. This year, DPW is scaling up – and we at CPOstrategy to be there on the ground floor.
Dave Murphy, Head of Financial Services EMEA & APAC at Publicis Sapient, on why retail banking is at an important crossroads and must react
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Retail banking stands at a pivotal juncture. As digital-first generations reshape customer expectations and competitive pressure from FinTechs and neobanks intensifies, traditional banks face a critical choice: modernise now or risk obsolescence. Publicis Sapient’s latest Global Banking Benchmark Retail Banking Report underscores that “digital by default” is no longer an aspiration. It’s an immediate necessity.
Drawing on insights from 600 retail banking executives across 13 countries, the report highlights a convergence of transformative forces… The accelerated adoption of Gen AI, the decline of legacy IT infrastructure, and an urgent need to reimagine customer engagement for a younger, mobile-first demographic.
Digital or Die: A Defining Moment
Retail banking has been evolving for over two decades, but the stakes have never been higher. In Q1 2025, JPMorgan Chase reported a net income of $14.6 billion, up 9% year-over-year. This was driven by robust trading revenues and investment banking fees. Meanwhile, UK neobanks are making significant strides. Revolut achieved a net profit of $1.0 billion in 2024, marking its first billion-dollar annual profit, with revenues soaring 72% to $4.0 billion. Monzo also reported its first full year of profitability, posting a pre-tax profit of £15.4 million and doubling its revenue to £880 million.
Despite these advancements, 62% of retail banking executives admit their pace of transformation lags behind competitors. This isn’t a minor delay – it’s a strategic disadvantage in a market where 44% of new currents accounts are already being opened with digital banks and FinTechs.
Gen AI: Catalyst and Compulsion
Among all the changes underway, generative AI has emerged as the most powerful and potentially disruptive force. According to the benchmark study, data and AI are the top investment areas for digital transformation over the next three years. Executives are betting big on AI not only to improve customer engagement but also to modernise operations and accelerate core transformation. The impact of Gen AI in banking is tangible. It can:
Personalise customer journeys at scale
Accelerate software development lifecycles
Write code and automate data management
Deliver hyper-relevant product recommendations
Power AI agents with human-like customer service abilities
In short, Gen AI makes what was once prohibitively expensive and time-consuming not only possible but scalable.
The banking customer has changed
The report makes it clear: retail banks must stop building for yesterday’s customer. Gen Z, who will make up one-third of the workforce by 2030, already prefer mobile-first, always-on banking. They value immediacy, customisation, and authenticity. A staggering 83% of Gen Z consumers say they are frustrated with current bank processes.
Compounding this generational shift is the growing irrelevance of traditional customer segmentation. Today’s consumers defy linear categorisation. The same individual can be a small business owner, a parent, and a new homeowner. Yet banks often treat them as three separate customers because of product-centric data silos.
The core problem with legacy thinking
Legacy systems continue to be the biggest barrier to meaningful transformation. 70% of banking executives say their legacy infrastructure is hindering their ability to deliver the digital experiences customers expect. Many core systems are COBOL-based and nearing end-of-life. Yet banks are reluctant to modernise due to perceived risk and complexity.
The irony is clear: the risk of maintaining outdated systems now outweighs the risk of change. With Gen AI, banks finally have the tools to confront the 800-pound gorilla in the room – core modernisation.
Why Core Modernisation is the linchpin
Modernising the core is about more than infrastructure. It’s the key to unlocking the full value of AI, data, and digital transformation. A modern, cloud-native core enables:
Real-time access to first-party and third-party data
Agile delivery through microservices
Better governance and regulatory transparency
Faster go-to-market with new apps and services
Retail banks that modernise their core can stop building costly middleware just to access data. Instead, they gain a unified view of the customer and the agility to respond to banking market shifts in real time.
The virtuous cycle of AI and Core
What’s truly powerful is the feedback loop between Gen AI and a modernised core. Gen AI helps accelerate the core transformation by generating code, automating testing, and streamlining documentation. Once modernised, that core then enhances Gen AI’s capabilities with clean, structured data. This virtuous cycle creates exponential value, making digital transformation faster, cheaper, and more sustainable.
Retail banks are already allocating 35% of their customer experience digital transformation budgets to Gen AI. Furthermore, many are embedding AI across the entire software development lifecycle using tools like Sapient Slingshot to reduce human error, increase test coverage, and ship better code faster.
From Product-Centric to People-Centric banking
Ultimately, the report urges retail banks to shift from a product-centric to a people-centric mindset. That means designing experiences around life moments, not product categories. It means knowing that the mortgage customer is also a small business owner and a parent, and offering solutions that reflect that reality.
With modern core systems and Gen AI, banks can personalise outreach, tailor financial advice, and meet customers where they are. This holistic view is essential not only for growth but also for loyalty.
The era of deferral is over. Banks can no longer afford to delay core transformation. Gen AI has lowered the cost, reduced the complexity, and increased the speed of change. The only question left is whether banks are ready to lead or risk falling behind.
Publicis Sapient is working at the intersection of Gen AI and core modernisation every day… Helping banks link strategy to execution and deliver on the full promise of digital transformation. The future of retail banking isn’t coming – it’s already here. The time to act is now.
SupplyChain Strategy descended upon Austin, Texas, to join the supply chain leaders keeping the world moving at Kinexions 2025 –…
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SupplyChain Strategy descended upon Austin, Texas, to join the supply chain leaders keeping the world moving at Kinexions 2025 – Kinaxis’ flagship event spotlighting the next leap in autonomous, AI-powered orchestration.
From agentic AI to a unified data foundation accelerated through its collaboration with Databricks, Kinaxis showed how it’s turning orchestration from aspiration to execution – with the speed and certainty today’s businesses demand.
Early morning and the sun was blazing outside the palatial Fairmont Hotel, in downtown Austin. Inside, there was a palpable excitement as a thousand attendees of Kinexions congregated for breakfast. We certainly felt honoured to be representing SupplyChain Strategy courtesy of Kinaxis. Kinaxis are the software gurus who have both transformed supply chain through their Maestro platform. They have also attracted the leading lights of the function from many of the world’s biggest companies. ExxonMobil, Eaton, Volvo Cars, Colgate-Palmolive, Merck & Co., General Motors, National Instruments, and Schneider Electric have all come to Texas.
Kinexions started as it meant to go on. The headline ‘A Revolution’ dominating the screens behind the huge, purple-tinted stage as the keynote speakers walked on to huge applause. Bob Courteau, Interim CEO, Kinaxis, Mark Morgan, President, Commercial Operations, Kinaxis and Andrew Bell, Chief Product Officer, Kinaxis kicked proceedings off with a blistering and inspirational set of presentations. The message was clear: true orchestration, meaning a fully connected, always aware, and-able-to act-instantly supply chain – is finally within reach. This places supply chains firmly at the table as strategic value creators and, crucially, as protectors of business.
It was a morning session that truly set the tone of this three-day event. Concerns raised by Kinaxis’ 45,000 global users – including tariffs, labour shortages, cyber-attacks and the effect of disruption on investment – were front and centre of this event with myriad symposiums, workshops and presentations that showcased how Kinaxis’ Maestro can orchestrate and empower fully-connected supply chains globally. Indeed, the tariffs on imported goods into the US dropped during Kinexions and so the timing of this conference, entirely devoted to the bolstering of supply chain operations during highly uncertain times, seemed somewhat inspired. In short, those who are transforming are surviving and outperforming.
Unified data
Kinaxis is transforming too, we were informed, as the new partnership with Databricks was unveiled. Kinaxis Maestro and Databricks’ Data Intelligence Platform have combined to power faster insights, unified data and scalable AI across global supply chains, enabling organisations to unify their data, accelerate AI adoption, and respond to change with speed and confidence. This collaboration meets growing demand for more agile, data-driven supply chains and strengthens Maestro’s supply chain data fabric. In short, this move is helping companies coalesce data from core systems like inventory and procurement, alongside external inputs such as meteorological patterns and market movement, all within one single source of governed truth, ripe for innovation. As supply chains continue to evolve, this collaboration positions both companies to lead the next era of AI-powered transformation, where decisions are faster, disruptions are less disruptive, and performance is driven by unified data.
Linked to the foundational collaboration between Kinaxis and Databricks was the second huge unveiling at Kinexions: agentic AI. Guests were shown just how easily they could create and deploy intelligent agents using an intuitive GenAI interface to enhance decision-making, respond to disruptions faster and optimise workflows, through a powerful, in-development feature of Maestro. These are agents that go beyond surfacing data to deliver real-time insights and perform actions like addressing exceptions, managing supply allocation, or adjusting safety stock. There were numerous workshops taking place over the three days where clients could get their hands on the new tools and see just how easily they could transform their supply chain operations through AI. As was stressed throughout Kinexions, this is something that is happening right now.
A community of innovation
Kinaxis places real value on keeping the dialogue open with its clients and that’s the core motivation behind Kinexions, North America and its APAC and EMEA sister events set to take place in Tokyo and Amsterdam later this year. Indeed, during our time in Austin, we were lucky enough to sit down with supply chain leaders from Sanofi, IBM, Qualcomm and Syensqo as well as leading lights from Kinaxis. You can read the interviews from those discussions, and more from Kinexions, in next month’s SCS.
The quality of the guest speakers during the three days was incredible. Staale Gjervik, President, Supply Chain, ExxonMobil discussed how the giant is bringing orchestration to its multinational supply chain, solidifying ExxonMobil’s position as a global leader by establishing an enterprise-wide global supply chain organisation. Elsewhere, Global Director of Strategy and Planning for GM, Vijay Bharadwaj and Director of Supply Chain, Alexander Heavin shared how they are now able to run a global S&OP process to better serve customers and “stay on the road to success”.
Diego Pantoja-Navajas, Managing Director, Enterprise AI Value Strategy at Accenture and Chris Reynolds, Senior Director, Digital Supply Chain Planning & Intelligence at Pfizer provided a thought-provoking discussion on how multi-agentic AI is transforming the pharmaceutical supply chain. Abhijit Pattewar, Senior Manager, Global Modelling & Network Design at Schneider Electric – the leader in sustainable energy management and digital automation – delivered an engaging talk on emerging techniques for reducing CO2 emissions without sacrificing efficiency or growth.
Paying it forward
One of the standout discussions at this year’s Kinexions was an inspiring lunch session hosted by Lizet Tymon, VP Supply Chain, Rehlko and Rozena Dendy, Global Sales & Operations Planning Leader, ExxonMobil designed to celebrate, empower, and connect women who are making a difference in their workplaces and communities. Candid stories of the moments when mentorship, support, and solidarity helped them break barriers and build bridges to success will resonate with the audience for years. Each participant wrote down one action they committed to taking to support another woman, as part of the Pay-It-Forward Commitment. “Let’s build a legacy of women helping women, together!”
One woman who has long been an inspiration is real estate mogul and business expert Barbara Corcoran who presented ‘How to build your business through troubled times and prosper’. Corcoran, currently a Shark on ABC’s hit reality show, Shark Tank, knows that bad times are the best times to move ahead. Indeed, she survived and prospered amid 18% interest rates, the bankruptcy of New York, the subprime mortgage crisis, and the tragedy of 9/11. In this session, Barbara shared “lessons from the trenches” to demonstrate her leadership methodology on how to adapt quickly, pivot, and turn every obstacle into the new opportunity it really wants to be. It’s an ethos she has certainly embodied through her career, evident in the establishment and success of The Corcoran Group, started with a mere $1,000 loan.
And the winner is…
The winners of the 2025 Kinaxis Customer Awards were also announced in Austin, further cementing links between Kinaxis and its community. “These awards honour companies and individuals pushing the boundaries of supply chain innovation, efficiency and sustainability.”
ExxonMobil, Sanofi, Schneider Electric, and British American Tobacco (BAT) were recognised for their excellence in supply chain transformation. Additionally, Hanu Gadila (Merck & Co.) received the Champion Award, and Jeffrey Jones (Qualcomm) was honoured with the Lifetime Achievement Award for their industry contributions.
2025 Kinaxis Customer Award Winners
Pioneer Award: ExxonMobil Recognising companies that have implemented Kinaxis within the past three years. ExxonMobil is changing how the industry applies sales and operations planning. They’re leading the way in fuels, setting a new standard for Advance Planning Solution capabilities for the industry.
Champion Award: Hanu Gadila, Merck & Co. Honoring individuals demonstrating leadership, vision, and perseverance in supply chain transformation. Hanu Gadila has enhanced Merck’s use of Kinaxis Maestro™, optimising planning capabilities and efficiency through collaboration and advocacy.
Lifetime Achievement Award: Jeffrey Jones, Qualcomm Recognising long-term contributions to the supply chain industry. A steadfast Kinaxis advocate for nearly 20 years, Jeffrey Jones has championed Maestro, supporting industry-wide transformation. Jones stated, “It has been a privilege to work alongside such talented professionals and to contribute to the evolution of our industry. I look forward to continuing our journey of innovation.”
Excellence Award: Sanofi Awarded for measurable business impact through supply chain strategy. Sanofi is modernising its supply chain to reach best-in-class performance for unleashing its ambition to become the world’s leading immunology company. By leveraging digitalisation and tailored Kinaxis Maestro implementations, Sanofi has enhanced agility, resilience, and efficiency, enabling faster decisions, better risk mitigation, and seamless end-to-end operations.
Impact Award: Schneider Electric Recognising positive environmental and social contributions. Schneider Electric, the leader in sustainable energy management and digital automation, successfully conceptualised incorporating emerging CO2tools & techniques of Maestro for achieving growth and profitability with planet-friendly practices.
Innovation Award: British American Tobacco (BAT) Highlighting innovative applications of Kinaxis technology. BAT co-developed the first-ever production wheel and interchangeability functionalities, enhancing constraint management, SKU transitions, and automation.
Parting thoughts
As a veteran to many events such as Kinexions, it was refreshing to feel a jolt of genuine excitement at an event that was showing how things can actually change today, rather than in the future. This wasn’t an exercise in hypothesis, it was a call to action. If you want to harness what AI can do in orchestrating your supply chains in these unpredictable times, then act. Now.
As the four floors of symposiums, workshops and speeches were wrapping up, there was no time for rest for the guests, as it was left to none-other than the three-time Grammy-award-winning and Austin-born, Nelly to finish things off to a rapturous reception from the crowd. Hot In Herre boomed around the room, Nelly spraying the crowd with water, as another highly successful Kinexions drew to a close. It was an event that will live long in the memory. And as we departed the hospitable Austin and the incredible team behind Kinexions, it was clear that we would have to return.
Kinexions 2025 is made possible by its platinum sponsors Accenture, Capgemini and Scott Sheldon; and gold sponsors 4flow, Genpact, Microsoft, Google Cloud and Spinnaker SCA. For more information about Kinexions, including Kinexions EMEA 2025 and Kinexions APAC 2025, please visit www.kinexions.com.
Vikas Krishan, Chief Digital Business Officer & Head of EMEA at Altimetrik, on the disruptive power of AI in FinTech
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AI is already disrupting every area of the Financial Services Industry, and is being included in almost every strategic conversation around technology-enabled transformation. This transformation is exemplified by industry leaders like JP Morgan Chase. CEO Jamie Dimon has championed a £12 billion annual investment in data and technology, overseeing over 400 AI use cases. These include fraud detection, customer service improvements and operational efficiencies across the bank. The core platforms underpinning the industry risk buckling under the weight of modernisation. AI is gradually loosening the components of legacy institutions and presenting fresh opportunities. These are scalable, resilient and adaptable to the agile needs of Financial Services. Through this reimagining of core platforms, those who choose to act now can expect to leapfrog their competition. Meanwhile, those who fail to act now risk obscurity, lack of productivity and being disregarded by their consumer base.
The transition to new architectures
For decades, banks have relied on legacy systems to power their core operations. These often ageing platforms are becoming increasingly difficult and expensive to maintain. They have been built both in languages not commonly used and architected with a different business reality in mind. Many frequently lack the flexibility required to meet the demands of today’s digital-first customers. They also struggle to integrate with modern financial technologies. A significant challenge facing organisations is the accumulation of technical debt. There is a cost to additional work or rework caused by choosing quick or limited solutions over more robust, maintainable approaches. Over time, this can lead to significant issues that compound the challenges of legacy systems.
This lack of nimbleness is often the byproduct of a Frankenstein approach to architectural systems. Many financial institutions have traditionally built new features or attempted to fuse together two platforms. This is a delicate balancing act, requiring extensive planning and careful execution. If done with limited oversight, challenges can arise. These include operational disruptions, increased security risks and obvious incompatibility issues. The high risks and cost burdens associated with maintaining legacy platforms has led many banks to reconsider traditional merger approaches. Increasingly opting for modern, cloud-based microservices driven solutions that offer enhanced scalability, security and integration potential.
Meeting the challenge
As the industry establishes governance around this necessary transition, core platforms are being replaced by newer, more adaptable microservice-based architectures. Navigating this evolution requires leveraging an industry partner with a deep understanding of the complexities and risks involved. There are challenges moving from monolithic core systems to flexible, modern frameworks.
If we think back five years or so, many players in the market were already aware of this critical shift. Companies like Misys and Avaloq were acquired by private equity firms and given substantial investment to advance digital initiatives, developing solution suites. The reason for this was clear, everyone understood the market was changing. However, the challenge still remains in managing the migration of large, complex platforms. The key question has always been how to de-risk these migrations when moving to newer architectures. This is an issue across organisations, and it is something that we at Altimetrik actively work with clients in financial services to address.
Data first with AI
If we consider platforms such as core banking or payments systems, the data generated from these transactions should, in theory, hold value. However, gaining insights from legacy platforms is significantly more challenging and the cost of extracting and utilising that data is often prohibitive. It is here that a data-driven approach to AI must be agreed upon.
High-quality, accurate data lies at the core of every successful AI implementation. AI thrives on data; the more precise the data, the better the AI can learn and provide reliable insights. This fundamental truth highlights the importance of data integrity within the AI ecosystem. However, many financial institutions are struggling in this area, both in effectively using internal data and leveraging accurate, timely external data. As companies grow, their data environments become increasingly complex, adding to these challenges.
As financial services organisations expand, they often face the challenge of data silos, declining data quality and scattered, disconnected data repositories. This leads to a fragmented data ecosystem. It can limit AI’s potential to deliver meaningful insights and drive improvements. This transformation requires active leadership from the top. Successful digital transformation depends on executive-level commitment and understanding. Leaders like Charles Scharf of Wells Fargo demonstrates how CEO ownership of data and AI initiatives drives organisation-wide adoption and success. Their hands-on approach ensures these technologies aren’t just IT projects, but core business strategy enablers.
A Single Source of Truth with AI
To overcome this, financial institutions should establish a Single Source of Truth (SSOT) and in doing so move away from older, somewhat clumsy core platforms. An SSOT will provide a unified, consistent view of data across the organisation. This accelerates decision-making with greater confidence. As demonstrated by successful implementations across the industry. For exmple, Bank of America’s AI-powered virtual assistant Erica providing personalised financial advice to Wells Fargo’s modernised data infrastructure. This enables enhanced risk assessment and management. By centralising core data, an SSOT enables the identification of operational inefficiencies, better monitoring of customer behaviours and effective execution of strategies to foster growth.
The key question is how to successfully de-risk this transition from a fixed cost base to a more flexible, agile one. This transition is essential for becoming an outcomes-focused business with greater adaptability. So, how can technology help achieve this?
One approach involves what is often (unfortunately) referred to as a Strangler Pattern. Instead of a wholesale shift from one platform to another, this modulated approach guides clients on a journey that focuses on gradually moving specific functionalities. By decomposing the legacy system function by function, we rebuild each component within the new platform. This allows the old system to run in parallel until fully replaced. Thus shrinking the monolithic structure in a manageable, low-risk way. It is a method preferred by many large financial services players when they move to become digital businesses.
By working within a digital business methodology that prioritises outcomes over technology, we gain significant advantages. The beauty of this function is its flexibility. When implementing a new function, the management of a FS firm may discover it isn’t meeting expectations or fulfilling business needs. And yet these clients still have the security of the old platform to fall back on and can easily revert back to the original system and refine the new function before trying again. This way of working ensures a safety net. It can reduce risk and enable iterative improvements without causing major disruptions to business operations.
The full picture
The transformation of core platforms through AI presents both immense opportunity and significant challenges. Those institutions willing to embrace this change, adopting data-first approaches and modern architectures, are poised to redefine the industry landscape. The transition, whilst complex, can be managed through measured strategies allowing for gradual, low-risk modernisation. As we move forward, the success of financial institutions will increasingly hinge on their ability to harness AI’s potential. They will need to create unified data ecosystems and adapt to the evolving needs of the digital age. Financial services businesses must embrace AI and modernise their core platforms or risk becoming as obsolete as a floppy disk.
Arsalan Minhas, AVP Sales Engineering, EMEA & APAC, at Hyland, on how AI revolutionising financial services
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Artificial intelligence (AI) is revolutionising financial services, reshaping how institutions detect fraud, personalise customer experiences, and optimise investment strategies. From AI-powered chatbots assisting customers to machine learning models predicting market trends, the technology is driving unprecedented efficiency and insight.
Yet, alongside these advancements come new challenges. AI-driven scams are evolving in sophistication, algorithmic biases raise ethical concerns, and regulatory scrutiny is increasing. As financial institutions accelerate AI adoption, they’re walking the fine line between harnessing its benefits and mitigating its risks.
AI in fraud detection and prevention – strengthening security measures
One of the most critical areas where AI has transformed financial services is fraud detection and prevention.
Traditional fraud prevention methods relied on static rule-based systems, which were often ineffective at identifying evolving threats. Such systems aren’t necessarily equipped to keep up with the sheer pace of financial service operations today, which has led to a surge of interest in automated alternatives.
AI, particularly machine learning algorithms, offers a dynamic solution by analysing vast datasets in real time to identify anomalies and potential fraud. AI also enhances biometric authentication methods, such as voice and facial recognition. This can ensure secure access to accounts, reducing the reliance on passwords, which are vulnerable to breaches.
According to a recent McKinsey report, AI-driven fraud detection systems can reduce financial fraud losses by up to 50%. Making them a crucial asset for financial institutions. These unprecedented levels of speed and versatility has made AI a priority for even the biggest players.
Of course, fraud detection is not without its challenges. Criminals are also leveraging AI to create sophisticated scams, such as deepfake-based identity fraud. And the introduction of new technologies can challenge cybersecurity initiatives.
With that in mind, financial institutions must constantly update their AI models to stay ahead of emerging threats. Regulatory compliance adds another layer of complexity, as AI’s decision-making much align with consumer protection laws and data privacy regulations like GDPR and CCPA.
The future of Customer Experience
On the customer-facing side of things, Artificial Intelligence is transforming the customer experience through hyper-personalised financial services. Gone are the days of generic banking interactions. AI now enables financial institutions to tailor services based on individual customer behaviours, preferences and financial goals.
Leading UK banks like NatWest and Lloyds Bank have invested heavily in AI-powered virtual assistants. NatWest’s digital assistant, Cora, has handled millions of customer interactions, providing real-time financial insights, bill reminders, and even fraud detection alerts. Similarly, HSBC uses AI-driven tools to analyse spending patterns and offer personalised financial advice. The ability to assess transaction data allows banks to recommend budgeting strategies, suggest tailored loan offers, and predict future financial needs, making banking more intuitive and customer centric.
AI-driven robo-advisors, such as those offered by Nutmeg and Moneyfarm, have revolutionised investment management by providing algorithm-based financial planning. These platforms leverage AI to assess risk tolerance, market trends, and historical data to offer personalised investment strategies with lower fees than traditional financial advisors.
While such tools can be incredibly effective, they do raise concerns about data privacy and algorithmic bias. The more AI knows about an individual’s financial habits, the greater the risk of data misuse or bias in lending and investment recommendations.
Financial institutions must therefore ensure transparency and fairness in AI decision-making to build customer trust and meet regulatory regulations. The basis upon which customers share their personal data, and the protections that it is afforded, are a non-negotiable for any serious financial organisation.
Redefining market strategies in trading and investment
According to Deloitte, Artificial Intelligence is poised to be one of the most disruptive forces in investment management. High-frequency trading (HFT) firms now rely on AI algorithms to process vast amounts of market data within milliseconds. It also enables hedge funds and investment firms to predict market movements by analysing patterns from historical data, social media sentiment, and global economic indicators.
Leading firms like Man Group and XTX Markets have harnessed AI to enhance their trading strategies and portfolio management. Man Group, managing $175 billion in assets, utilises machine learning tools to develop its platform, ManGPT, to analyse trades and optimise investment decisions.
Similarly, XTX Markets, a London-based trading firm, employs advanced AI models to execute millions of trades daily, emphasising AI-driven strategies over sheer speed. Predictive analytics have become an indispensable tool in portfolio management, helping firms adjust their strategies based on real-time market fluctuations.
Naturally, these automated tools require to-the-second oversight from the business itself. The 2010 Flash Crash, in which the stock market plunged nearly 1,000 points within minutes, was exacerbated by algorithmic trading. AI-driven trading models can react unpredictably in volatile markets, amplifying risks if not properly regulated. Humanised AI – the combination of human and AI working in concert, rather than automated systems working in isolation – is crucial.
The future of AI in financial services
As Artificial Intelligence continues to evolve, its integration within financial services will only deepen. Institutions that successfully integrate AI into their operations will gain a significant competitive advantage. Benefiting from enhanced fraud detection, superior customer experiences, and data-driven investment strategies.
These businesses must also navigate the complexities of regulatory compliance, data privacy, and ethical AI deployment. The EU’s AI Act is one of many policies aiming to create the most robust governance structures for AI applications, and finance is no exception.
Striking the right balance between innovation and regulation will be crucial to ensuring AI remains a force for positive transformation rather than disruption. Financial institutions must prioritise transparency, human oversight, and ethical considerations in deployment to fully realise its potential while maintaining consumer trust.
The financial industry is on the brink of an AI-driven revolution. With careful implementation and responsible oversight, the technology has the power to make financial services more secure, efficient, and customer-friendly than ever before. Institutions that embrace this technology while addressing its challenges will shape the future of finance, redefining the way money is managed, invested, and protected in the years to come.
Scott Zoldi, Chief Analytics Officer at FICO, explains why there should be no AI alone in decision making processes
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Many AI models are black boxes and developed without proper consideration for interpretability, ethics, or safety of outputs. To establish trust, organisations should leverage Responsible AI. This defines standards of robust AI, explainable AI, ethical AI, and auditable AI. Under Responsible AI, developers define the conditions that lead to some transactions having less human oversight and others having more. But can we take people out of the decision-making loop entirely? To answer that question, let’s look at some developments in Responsible AI.
Trust in Developing AI Models
One best practice that organisations can adopt is maintaining a corporate AI model development standard. This dictates appropriate AI algorithms and processes to enable roles that keep people in the loop. This will often include the use of interpretable AI, allowing humans to review and understand what AI has learned for palatability, bias, ethical use and safety. Auditable AI will then codify the human-in-the-loop decisions and monitoring guidelines for operational use of the AI.
Responsible AI codifies all the essential human decisions that guide how AI will be built, used and progressed. This includes approving or declining the use of data, removing unethical relationships in data (i.e., illegal or unethical data proxies), and ensuring governance and regulation standards are met. Responsible AI leverages an immutable blockchain that dictates how to monitor the AI in operation. And the decision authority of human operators, which can include conditions where AI decisions are overruled, and operations move to a ‘humble AI model.’ AI Practitioners are keenly aware that even the highest performing AI models generate large number of false positives. So, every output needs to be treated with care and strategies defined to validate, counter, and support the AI.
A Responsible AI framework
There should be a well-defined process to overrule or reverse AI-driven decisions. If built in a Responsible AI framework, these decisions are codified into a crystal-clear set of operating AI blockchain frameworks well before the AI is in production. When there is a crisis you need clear preset guidance, not panicked decision making. This blockchain will define when humans can overrule the AI through alternate models, supporting data, or investigative processes. This AI operating framework is defined in coordination with the model developers, who understand the strengths and weaknesses of the AI. And when it may be operating in ways it wasn’t designed, ensuring there is no gap between development and operation. When auditable AI is employed, there are no nail-biting decisions in times of crisis. You can rely on a framework that pre-defines steps to make these human-driven decisions.
Companies that utilise Responsible AI frameworks enforce usage adherence by auditable AI, which is the operating manual and monitoring system. Embracing Responsible AI standards can help business units attain huge value. At the same time they can appropriately define the criteria where the businesses balance business risks and regulation. Domain experts/analysts will be given a defined span of control on how to use their domain knowledge and the auditable AI will monitor the system to alert and circumvent AI as appropriate.
Drawback prevention begins with transparency
To prevent major pull-back in AI today, we must go beyond aspirational and boastful claims to honest discussions of the risks of this technology. We must define how involved humans need to be. Companies need to empower their data science leadership to define what is high-risk AI, and how they are prepared or not to meet responsible/trustworthy AI. This comes back to governance and AI regulation. Companies must focus on developing a Responsible AI programme, and boost practices that may have atrophied during the GenAI hype cycle.
They should start with a review of how AI regulation is developing, and whether they have the tools to appropriately address and pressure-test their AI applications. If they’re not prepared, they need to understand the business impacts of potentially having AI pulled from their repository of tools. And get prepared by defining AI development/operational corporate standards.
Companies should then determine and classify business problems best suited for traditional AI vs. generative AI. Traditional AI can be constructed and constrained to meet regulation using the right algorithms to meet business objectives. Finally, companies will want to adopt a humble AI approach to have hot backups for their AI deployments. And to tier down to safer tech when auditable AI indicates AI decisioning is not trustworthy.
The vital role of the Data Scientist
Too many organisations are driving AI strategy through business owners or software engineers who often have limited to no knowledge of the specifics of AI algorithms’ mathematics and risks. Stringing together AI is easy. Building AI that is responsible and safe and properly operationalised with controls is a much harder exercise requiring standards, maturity and commitment to responsible AI. Data scientists can help businesses find the right paths to adopt the right types of AI for different business applications, regulatory compliances, and optimal consumer outcomes. In a nutshell: AI + human is the strongest solution. There should be no AI alone in decision-making.
Fouzi Husaini, Chief Technology & AI Officer at Marqeta, answers our questions about Agentic AI and its applications for businesses
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Agentic AI is emerging as the leading AI trend of 2025. Industry figures are hailing Agentic AI as the broadly transformative next step in GenAI development. The year so far has seen multiple businesses release new tools for a wide array of applications.
The technology combines the next generation of AI tech like large language models (LLMs) with more traditional capabilities like machine learning, automation, and enterprise orchestration. The end result could lead to a more autonomous version of AI: Agents. These agents can set their own goals, analyse data sets, and act with less human oversight than previous tools.
We spoke to Fouzi Husaini, Chief Technology & AI Officer at Marqeta about what sets Agentic AI apart whether the technology really is a leap forward in terms of solving AI’s shortcomings, and how Agentic AI could solve business problems.
1. What makes AI “agentic”? How is the technology different from something like Chat-GPT?
“Agentic refers to the type of Artificial Intelligence that can act as agents and on its own. Agentic AI leverages enhanced reasoning capabilities to solve problems without prompts or constant human supervision. It can carry out complex, multi-step tasks autonomously.
“GenAI and by extension Large Language Models, the most famous example being ChatGPT, require human input to solve tasks. For instance, ChatGPT needs user prompts before it can generate content. Then, sers need to input subsequent commands to edit and refine this. Agentic AI has the capability to react and learn without human intervention as it processes data and solves problems. This enables it to adapt and learn much faster than GenAI.”
2. Chat-GPT and other LLMs frequently produce results filled with factual errors, misrepresentations, and “hallucinations”, making them pretty unsuited to working without human supervision – let alone orchestrating important financial deals. What makes Agentic AI any better or more trustworthy?
“All types of AI have the possibility to ‘hallucinate’ and produce factually incorrect information. That being said, Agentic AI is usually less likely to suffer from significant hallucinations in comparison to GenAI.
“Agentic AI’s focus is specifically engineered to operate within clearly defined parameters and follow explicit workflows, making it particularly well-suited for having guardrails in place to keep it on task and from making errors. Its learning capabilities also allow it to recognise and adapt to its mistakes, ensuring it is unlikely to hallucinate multiple times.”
“On the other hand, GenAI occasionally generates factually incorrect content due to the quality of data provided, and sometimes because of mistakes in pattern recognition.”
“In fintech, Agentic AI technology can make it possible to analyse consumer spending data and learn from it, allowing for highly tailored financial offers and services that are more accurate and help to create a personalised finance experience for consumers.”
3. How could agentic AI deployments affect the relationship between financial services companies and their customers? What about their employees?
“The integration of Agentic AI into financial services benefits multiple parties. First,
integrating Agentic AI into their offerings allows financial service companies to provide their customers with bespoke tools and features. For instance, AI can be used to develop ‘predictive cards’. These cards can anticipate a consumer’s spending requirements based on their past behaviour. This means AI can adjust credit limits and offer tailored rewards automatically, creating a personalised experience for each individual.
“The status quo’s days are numbered as consumers crave tailor-made financial experiences. Agentic AI can allow fintechs to provide personalised financial services that help consumers and businesses make their money work better for them. With Agentic AI technology, fintechs can analyse consumer spending data and learn from it. This allows for more tailored financial offers and services.
“As for employees, Agentic AI gives them the ability to focus on more creative and interesting tasks. Agentic AI can handle more routine roles such as data entry and monitoring for fraud, automating repetitive tasks and autonomous decision making based on data. This helps to reduce human error and enables employees to focus more time and energy on the creative and strategic aspects of their roles while allowing AI to focus on more administrative tasks.”
4. How would agentic AI make financial services safer?
“Agentic AI has the capability to make financial services more secure for financial institutions and consumers alike, by bringing consistency and tireless vigilance to critical financial processes. With its ability to analyse vast strings of information, it can rapidly identify anomalies in spending data that indicate potential instances of fraud and can use its enhanced reasoning and ability to act without human prompts to quickly react to suspicious activity.
“While a human operator will be susceptible to decision fatigue, an AI agent could always be vigilant and maintain the same high level of precision and alertness 24/7. This is vital for fields like fraud detection, where a single missed signal could lead to significant consequences.
“Furthermore, its capability to learn without human interaction means that it can improve its ability to detect fraud over time. This gives it the ability to learn how to identify new types of fraud, helping it to adapt as schemes become more sophisticated over time.”
5. What kind of trajectory do you see the technology having over the next year to eighteen months?
“In fintech, Agentic AI integration will likely begin in the operations space. These areas manage complex, but well-defined, processes and are perfect for intelligent automation. For instance, customer call centres where human agents usually follow set standard operating procedures (SOPs) that can be fed into an AI system, which makes automation easier and faster than before.
“In the more distant future, I believe we will see Agentic AI integrated into automated workflows that span entire value chains, including tasks such as risk assessment, customer onboarding and account management.”
Tech Show London is coming to Excel March 12-13. Register for your free ticket now!
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Stuart Cheetham, CEO at MPowered Mortgages, on how AI-powered technology allows mortgage lenders to fully underwrite loan applications in minutes
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AI technologies are about to have a huge impact on the mortgage market… In November last year the founders of Revolut announced plans to launch a “fully digital, instant” mortgage in Lithuania and Ireland in 2025. Details were sketchy but the company said that mortgages will be part of a “comprehensive credit offering” it intends to build.
Neobanking progress with AI
Digital only banks, like Revolut and Monzo, are renowned for using the power of technology and data science to create efficiencies and improve customer experience. The reason neobanks have been so successful is because they provide a modern, convenient and cost-effective alternative to traditional banking. This is done a transparent way, through fast onboarding, 24/7 app access and instant notifications. All with a user-friendly interface.
While many financial services sectors have embraced financial technology in the way Revolut and Monzo have for the retail banking sector, the mortgage sector has struggled to make a real breakthrough here. Why hasn’t the mortgage industry caught up one might ask? Mortgages are complex financial products, existing at the intersection of justifiably stringent regulation. They represent the single biggest financial commitment people make in their lifetimes. Financial advisors who source mortgages on behalf of borrowers are hindered at every stage by outdated systems and inadequate or commoditised product offerings.
Disrupting the Mortgage Market
The mortgage industry is one financial services sector that has been yearning to be shaken up by the FinTech industry for some time. While it’s encouraging to see a successful brand like Revolut enter this market, what is less known is that huge progress is being made already by smaller and less well known FinTech disruptors.
For example, the mortgage technology company MQube has developed a “new fast way” of delivering mortgage offers using the cutting edge of AI technology and data science. Today, it still typically takes several weeks to get a confirmed mortgage offer. This is one of the major reasons the homebuying process can be so time consuming and stressful for brokers and borrowers. The mortgage process is characterised by bureaucracy, paperwork, delays and often frustratingly opaque decision-making by lenders. This leads to stress and uncertainty for consumers, and their advisors. And at a time when they have plenty of other property-purchase related challenges to contend with.
Our proprietary research shows us, and this will come as no surprise, that the biggest pain point for borrowers and brokers about the mortgage process is that it is time consuming, paperwork heavy and stressful. Imagine a world where getting a mortgage is as quick and as easy as getting car insurance. This is MQube’s vision.
MQube – AI-powered Mortgages
MQube‘s AI-powered mortgage origination platform allows mortgage lenders to fully underwrite loan applications in minutes. MPowered Mortgages is MQube’s lending arm and competes for residential business alongside the big banks. It uses MQube’s AI-driven mortgage origination platform and is now able to offer a lending decision within one working day to 96% of completed applications.
The platform leverages state-of-the-art artificial intelligence and machine learning to assess around 20,000 data points in real-time. This enables lenders to process mortgage applications in minutes, transforming the industry standard of days or weeks. It automates the entire underwriting journey, from application to completion. This helps to provide a faster service, reduce costs, mitigate risks, and to make strategic adjustments quickly and effectively. By assessing documents and data in real-time during the application, it is able to build a clearer and deeper understanding of a consumers’ circumstances and specific needs. Applicants are never asked questions when MQube can independently source and verify that data, leading to a streamlined and paperless experience. Furthermore, this whole process reduces dependency on human intervention.
The benefits of AI
More and more lenders are seeing the benefits AI and financial technology can bring to their business. They are beginning to adopt such AI-driven financial systems which are scalable and serve to address systemic problems in this industry. The mortgage industry is still some way behind the neobanks, but what’s hugely exciting to see is the progress that has been made so far. Moreover, if FinTechs continue to innovate this sector and if lenders continue to embrace financial technology and use at scale, then getting a mortgage could genuinely become a quick, easy and stress free process. At this point, the mortgage industry could begin to see a shift in consumer perception and change in consumer behaviour. A new frontier for the mortgage industry is upon us.
FICO’s use of Blockchain for AI model governance wins Tech of the Future: Blockchain and Tokenisation award
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Global analytics software leader FICO has won the Tech of the Future – Blockchain and Tokenisation award. The Banking Tech Awards in London recognised FICO for its innovative work using Blockchain technology for AI model governance. FICO’s use of blockchain to advance responsible AI is the first time blockchain has been used to track end-to-end provenance of a machine learning model. This approach can help meet responsible AI and regulatory requirements.
FICO’s AI Innovation and Development team has developed and patented an immutable blockchain ledger. It tracks end-to-end provenance of the development, operationalisation and monitoring of machine learning models. The technology enforces the use of a corporate-wide responsible AI model development standard by organisations. It demonstrates adherence to the standard with specific requirements, people, results, testing, approvals and revisions. In addition to the Banking Tech award, Global Finance recognised FICO’s blockchain for AI technology with The Innovators award last year.
Responsible AI
“The rapid growth of AI use has made Responsible AI an imperative,” commented Dr. Scott Zoldi, chief analytics officer at FICO. “FICO is focused on technologies that ensure AI is used in an ethical way, and governance is absolutely critical. We are proud to receive another award for our groundbreaking work in this area.”
FICO is well-known as a leader in AI for financial services. Its FICO® Falcon® Fraud Manager solution, launched in 1992, was the first fraud solution to use neural networks. Today it manages some four billion payment cards worldwide. FICO has built advanced analytics capabilities into FICO® Platform, an applied intelligence platform for building decision management solutions.
We chat with the CIO of Urenco, Sarah Leteney, about the ways this unique business leverages technology, and the big difference a small team can make.
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Urenco does things a little differently. It has to. It supplies uranium enrichment services and fuel cycle products for the nuclear industry – a niche that requires a lot of specialist care and attention. Urenco has a clear vision for the net zero world. A world in which carbon-free energy is the norm. And for its CIO, Sarah Leteney, this means approaching the world of technology in different and interesting ways.
Leteney speaks exclusively to Interface Magazine about what it means to operate IT in a high-risk environment that requires an enormous amount of consistency. She also discusses the types of systems that are vital to Urenco, how the business leverages suppliers, bringing in the most talented possible people, and how Urenco balances a small team with a high pressure environment.
How does the role of CIO within the nuclear industry differ from one for a consumer goods company?
Most CIOs spend their time thinking about how to talk to customers through the rapid exchanges that are needed to maintain the flow of high volumes of traffic. They need to know how to keep up with their competitors in terms of customer experience and how to quickly bring new products to market.
At Urenco, we are quite literally the polar opposite of this. We are concerned with the consistency and timeliness of highly individualised communications with our customers, how internal control software can enable the accurate flow of information to our regulators, and how to support our teams to keep track of every gram of raw material, and product in our organisation. Our systems are vital to keep our operations safe and reliable. It is not fast-paced – rather a very careful and considered environment where accuracy is everything.
What is it like to enable and provision services in such an environment? Can you keep in touch with market trends? Is there much recognition of what you do?
I work in a high threat environment and there are many special considerations to understand. There is a certain cadence and rhythm to what we do and we have to work at a pace which suits the organisation, rather than keep up with the latest trends in the IT industry. Although, we do keep abreast of developments through networks such as Gartner and Aurora and introduce them where appropriate and relevant.
In relation to the recognition of this role, like every other CIO out there, you are noticed more when something is not working properly. That said, Urenco is very good at making you feel as if you are part of something that matters. People readily ask you questions and understand when something is a minor glitch compared to something more significant. And we actively encourage people to report issues because that is how you get continuous improvement. Overall, the organisation takes care of my team, we’re not under siege when things go wrong and what we do is widely appreciated.
What sorts of systems are you looking after and what are the challenges around these?
We have all the same systems that you see in many other large organisations, plus a few really niche products used only in our industry.
Like lots of businesses, we are on a SAP journey, moving existing systems into S4. This programme impacts all parts of the organisation and we have to drive the changes forward from a business point of view. We consider the IT team an enabler for this work as it’s ultimately the transformation of our business processes which we are trying to facilitate.
We also look after the information assets of the organisation – both the structured and unstructured data. Like many organisations, it’s an on-going process to work out how to extract genuine business insights from vast amounts of historical data which has been stored in multiple places and not always in the most logical manner. We have a significant amount of historical information which still remains important (think plant designs and maintenance records, etc.) so effective archiving and retention policies are very much at the forefront of our minds. It’s so easy to over store or over classify information in an effort to be ‘safe rather than sorry’, but in reality, as well as increasing on-going costs, this sort of behaviour tends to make it harder to find what you need. We are investigating new technologies to help us search through our data faster and more effectively than ever before.
We’re also currently extending into the Operational Technology sphere, sharing our experience and tools with our OT colleagues and directly addressing operational security challenges, investing significantly in our cyber defences to further strengthen our plant security services.
What is it like to work in a company with a large turnover but a relatively small number of employees? How does that affect the service you provide?
We try to think through what every employee needs from IT and provide them with the level of service their role requires, regardless of their position in the business. We are in the fortunate position where having fewer employees means individual changes to software, hardware, or SAAS costs tend to have a less significant impact on our profitability than in many organisations with higher staff complements. Many organisations have tiers of users which determine the level of service received. However, in our organisation, every minute of everyone’s time is important, as we don’t have many employees driving our engine forward. We are investing in our employee experience as one of the key organisational imperatives working alongside our colleagues in the People and Culture team, and this is going to be an on-going focus for us for the next few years.
Whilst the company turnover is important, it is less of a driving factor for us in IT. We benchmark ourselves against what proportion of operational expenditure we are investing in IT and IS to ensure we invest an appropriate amount in IT for an organisation of this size.
How do you work with your team to ensure they can provide the most effective service to the business?
We are organised primarily around our production sites, with a centralised team to provide shared services like architecture and finance. The organisation is only two layers deep in most teams, so information flow is mainly managed by direct cascade. The senior team is made up of heads of shared functions and site IT managers, and opinions flow freely between them.
Our IT Leadership team has a monthly two-day meeting where we come together in person. We sit together without our PCs and the constant pinging of information. This helps us to realign, to reprioritise matters, and include coaching and learning techniques. We all have daily pressures in our lives, and these meetings are about supporting each other and working effectively together.
Once a quarter we also visit one of our sites as a group, hosted by our IT site managers. This is critical to us because we cannot do our jobs without thoroughly understanding the experience of IT services on the ground. These visits also allow us to meet up with our business colleagues as part of their site leadership teams so we can exchange experiences and strategic thinking quite freely in person.
We also run monthly townhall meetings for all members of the IT team, and invite our colleagues from Information Security to join us. We have found this to be a really valuable information exchange point. IS can hear exactly what we are saying to the wider team on the ground, so they can gain real insight into our issues first hand. Our key suppliers are also invited to these sessions on a quarterly basis, again to foster free exchange of information.
How about diversity and inclusion – what are you doing within that area and what have you achieved?
This is one of the biggest areas I would like to tackle further. Within our company, like the whole of the nuclear sector, the age of our employees is increasing year on year as we have a very low employee turnover. So we have a small number of vacancies on an annual basis and we are working hard to get a better talent pool for when these opportunities arise, reaching out to people with a wider range of backgrounds.
Our strategy includes blind sifting, engaging with people who have had periods of time out of the workplace and may need to work certain hours, and being open to job-sharing. It is possible for us to be very flexible and we are trying to ensure this is known out in the world of recruitment.
One area we are doing really well in right now is neurodiversity. We have a significant proportion of our team who identify as neurodivergent and a new staff network focussing on the specific issues of importance to this community was actually started by a member of our team.
I’d love to see an ethnicity and gender mix in the future which is closer to the population norms in each of our operating countries and I’m pleased to say that our talent acquisition partners are working hard to promote our roles in new talent pools with a much more diverse population.
How do you work with your suppliers to maintain a good relationship with them?
We’re currently in the process of diversifying our IT supply base. We have had a couple of really strong suppliers for a long period of time who work very closely with us, but what we are aiming to do now is widen our group of key suppliers to create a supplier ecosystem consisting of four different types of partner – Advisory, Development, Configuration, and Support. A key part of this initiative will be about embedding the behaviours we would like suppliers to demonstrate when working with us to create an inclusive and transparent relationship, which we are progressing through setting up a Urenco Academy to provide initial onboarding and on-going behavioural reinforcement of Urenco’s core values across our partnerships.
You recently won a CIO 100 award. How did that come about and what reaction did you get from people who know you?
The CIO 100 award came about through my external mentor asking me why I wasn’t looking at it! He encouraged me to put myself forward for consideration. Sometimes you need a bit of a push from a critical friend to remind you that whilst you see how much remains to be done, it’s good to acknowledge the great results you have already achieved.
The most gratifying thing about the whole experience for me was that you are judged by really experienced CIOs, so they fully understand the complexity of what you do. I’m incredibly grateful and humbled to be included in such an inspiring group of people, who are all wrestling with organisational struggles and trying to keep up in a fast-paced world, solving problems all day, every day.
My colleagues were delighted for me and sent lots of congratulatory messages. I think my team were slightly surprised because they also don’t always see what a good job they are all doing. One of them was even inspired to send an AI-created poem in celebration!
Urenco gave me the opportunity to take on a challenging and exciting role initially as an interim CIO. They chose to promote from within despite having strong external candidates, and not only that, but they asked if I would like to have a mentor in my first year to help me to cement the skills I wanted to strengthen for my own peace of mind. I’m not sure what else I could have asked for from this organisation. When I look at the award all I really think, looking back over the last three years, is ‘how amazing is that’!
Paul O’Sullivan, Global Head of Banking and Lending at Aryza, on the rise of AI in banking
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The banking sector stands at the crossroads of technological innovation and operational transformation. AI is taking centre stage in reshaping how financial institutions operate. The banking sector is beginning to recognise AI’s potential. It can address challenges, enhance operational efficiency, and deliver more personalised customer experiences.
The Current State of AI in Banking
Research reveals that while a number of banking organisations have yet to fully integrate AI into their operations, key areas such as debt recovery are leading the charge. The slower pace of adoption can be attributed to the highly regulated environment of banking. Because transparency, compliance, and customer trust are non-negotiable. However, despite this cautious approach, banks that have implemented artificial intelligence are already seeing significant benefits, particularly in risk management.
AI’s Role in Risk Management
Effective risk management is a cornerstone of the banking sector. AI is proving to be a powerful tool in this area. By analysing vast amounts of data and providing predictive insights, AI enables banks to mitigate risks early. They can strengthen customer portfolio stability, and make data-driven lending decisions. These capabilities are essential in a landscape where financial risks can escalate rapidly.
Beyond the expected benefits, banks have also reported enhanced customer insights as an unexpected advantage. By leveraging AI to analyse customer behaviours and preferences, banks can tailor their products and services more effectively. Furthermore, they can improve customer satisfaction and experience, whilst fostering long-term loyalty.
Challenges to Adoption
Although organisations are experiencing a multitude of advantages, the integration of AI in banking is not without its hurdles. Legacy IT systems, stringent regulatory requirements, and concerns around data privacy pose significant challenges to widespread adoption. Banks must ensure AI-driven decision-making processes are effective. Moreover, they must also be fully transparent and compliant with industry regulations. Further highlighting the importance of a gradual, strategic approach to AI implementation.
Opportunities Ahead
The potential for AI in banking extends far beyond risk management. From streamlining operational workflows to enhancing customer personalisation and improving decision-making. AI is set to drive innovation across the sector. For example, AI-powered chatbots and virtual assistants transform customer service by providing instant, 24/7 support. They can handle complex interactions, enhancing customer satisfaction. At the same time, advanced analytics enable banks to analyse behaviour patterns, predict trends, and personalise product offerings. Furthermore. enhancing cross-selling opportunities and driving deeper customer engagement. These tools are becoming strategic enablers for innovation in the financial landscape.
A Call to Action
For banks to fully realise the benefits of AI, they must address the digital transformation gap, modernising outdated infrastructures and fostering a culture of innovation. This includes investing in technologies that align with their strategic goals, ensuring robust data security measures alongside maintaining compliance with evolving regulations.
As the banking sector continues its journey towards digital maturity, AI will play a pivotal role in defining its future. By overcoming current barriers and embracing AI-driven solutions, banks can not only enhance operational efficiency but also deliver the seamless, personalised experiences that customers now expect in an increasingly digital world.
About Aryza
At Aryza know that in today’s highly regulated world, there is huge value in quickly guiding your customers through the product that best fit their immediate needs, through a seamless journey that is tailored to their specific circumstances.
We created smart platforms, responsible and compliant products, and a unique system of companies and capabilities so that businesses can optimise their customers’ journey through the right product at the right time.
For our teams across the globe, the growth of Aryza is a good news story and a testament to our clear vision and goals as an international business.
And also front of mind as we build a global footprint is our impact on the environment. Aryza is committed to reducing its carbon impact through the choices it makes and we are pleased to say that we follow an active roadmap.
Xerox has been a household name for decades. For many, it’s associated with photocopiers and printers. After all, it’s the…
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Xerox has been a household name for decades. For many, it’s associated with photocopiers and printers. After all, it’s the largest print company in the world. But it’s also a technology powerhouse that’s been at the forefront of a great deal of innovation. It has undergone a journey of evolution and reinvention into an IT and digital services provider. That’s what led to the business acquiring a large managed service provider, Altodigital, in 2020.
Derek Gunton has spent nearly 20 years in the technology sphere. He came to Xerox as part of the Altodigital acquisition. Altodigital also started out as a management print organisation and evolved into the IT services side, so its journey mirrors Xerox’s in many ways. “Now, as we move into the next technological age powered by AI and automation, we’ve put ourselves in a good position,” says Gunton.
“Xerox continues to evolve as a company. It recently announced the acquisition of another large managed services IT business called Savvy, which will double the size of the IT services business. That gives us a lot of speciality, a lot of scale, and prepares us for that leap into the technologies of the future.”
Supporting Lanes Group’s technology
Xerox has been supporting Lanes Group in its own growth journey for a few years now. It doesn’t provide print services, but the IT and digital services Xerox is gradually becoming known for. The relationship began during the COVID-19 pandemic, when the working environment was very different. Businesses were trying to figure out how to continue to operate as normally as possible and provide certainty for staff.
“There were just two of us from Xerox working with them, and we were talking about room planning software,” says Gunton. “How do you manage how many people are in the building? How do they book spaces, or manage people in line with the COVID legislation that was in place? The conversation started there. Then, we were asked what we could do around providing some managed service desk support just to assist the internal team at the time – and it’s grown from there. Four years later, we have over 30 members of staff dedicated to the Lanes account, supporting more than 4,000 users across over 50 states.
“We’re very much an operation that compliments Lanes Group. The thing that has always worked well is that we have the ability to respond and scale. Lanes have been on their own journey over the last few years to the point that they’re truly industry-leading, and we’ve managed to keep up whilst always looking to innovate, make suggestions, and bring new solutions to the table.”
An integrated technology partnership
Lanes Group supports key utilities including water and gas. What it does is absolutely critical. If there are problems in those areas, millions of people can be affected. So while Lanes has a huge responsibility to always be ready to support those utilities at all times, Xerox has just as much of a responsibility to be in a position to support Lanes.
“It’s massively important, and everybody in our business is briefed on what Lanes does to ensure we understand that responsibility,” says Gunton. “In my career, I’ve seen lots of different structures in terms of how we work with clients. Sometimes it can be very much a supplier-client relationship where it’s very siloed and formal. What sets our relationship with Lanes Group apart is that it’s a very integrated partnership. There are several meetings every week. There are dedicated program managers, and every product area has its owner. We have very strict SLAs to adhere to and the only way to deliver what Lanes needs is through communication and mutual support.”
Streamlining inconsistencies
A perfect example of the collaborative relationship between Xerox and Lanes Group is the secure network solution Xerox put in place. Effectively, Xerox mapped out and replaced the network infrastructure of all Lanes Group sites, giving better visibility, better control, and a better user experience.
“When we first reviewed the sites, there were over 50 of them running independently. That was difficult for the IT team to manage,” says Gunton. “It led to a lot of inconsistencies. We had mixed feedback from end users. Our aim was to introduce a technology system that would give the users the ability to have a consistent experience across all sites. We worked with our partners at HPE to identify the latest Ariba access solutions available, and deployment across all sites has been very successful. It’s also improved security, giving users the ability to skip length authentication processes. The user experience is really smooth now, which is what we were after.”
Creating agility
Working as partners, not in a supplier-client capacity, has made all the difference for the two businesses. From robot process automation to take manual tasks away from humans, to the increased use of AI-driven tools, Xerox is providing Lanes with what it needs to be agile. It’s a relationship based on trust and a shared goal.
“I do appreciate the help from the stakeholders at Lanes, because they embrace the same kind of culture,” Gunton says. “Often we’ll do joint meetings where we all address the same problem or desire to innovate together. We trust each others’ skill sets and openness to really come up with a solution. Ultimately, it’s all people-driven. It’s based on having really clever people in the right places, and we’ve built up a really solid team over the years.”
The evolution Lanes Group is going through isn’t going to slow down any time soon. That means Xerox’s work won’t either. Gunton states: “Our broad priorities with Lanes also reflect the current UK landscape. Data integration and automation are the areas we’re continuing to focus on. We have to think about how we deliver that. In terms of data, there needs to be one true source. You have to be really confident in the information you have, being as accurate as possible.”
What’s key for Xerox is ensuring that Lanes Group is able to shift from being reactive to more proactive. That is its focus. “We’re already delivering technology solutions to better equip Lanes to respond in that manner. I think the next year is going to be really exciting as we continue to develop that. We believe that we will continue to put Lanes at the forefront of their industry with the solutions that we supply.”
There were many inspiring themes on peoples’ lips at DPW Amsterdam 2024, including collaboration. One of the major reasons procurement…
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There were many inspiring themes on peoples’ lips at DPW Amsterdam 2024, including collaboration. One of the major reasons procurement professionals flock to DPW is the opportunity to learn from their peers, strategise with them, and make connections in order to partner up and grow. We sat down with Dr Matthias Dohrn and Sudhir Bhojwani, business collaborators of several years who prove the benefits of coming together for growth.
Dohrn is the CPO of BASF, a global chemical company, making him responsible for direct, indirect, and traded goods. Prior to this role he headed up a business unit – and things weren’t going well. It got to the point where the question of how to drive performance became a priority. The business needed to consistently drive value, not just be, in Dohrn’s words, a “one-hit wonder”.
“I’ve been in a lot of meetings where people come together and say, ‘we should do something’ – but the next month, you have the same meeting and nothing has changed,” Dohrn explains. “Structuring an organisation in a manner that really drives and extracts value, that’s key.”
This eventually led to meeting with ORO Labs and asking how it could help BASF build a solution that enabled the growth it needed. Sudhir Bhojwani, CEO and Co-Founder of ORO Labs, knew Dohrn already from his SAP Ariba days He even credits him with explaining what ‘supplier management’ means. When he co-founded ORO Labs, his team wanted to focus on being a procurement orchestration platform and build smart workflows.
“When Matthias was running his business unit, as he mentioned, he had this Excel-based process where he was running thousands of measures,” Bhojwani explains. “It was an interesting process. We let him know that our workflow could solve his problems way more efficiently. So we worked with this business unit at that time and saw some positive results. Roughly a year later, Matthias took over as CPO and wanted to bring in the same structure that we’d implemented at the business unit, but on a bigger scale.”
Kicking off the project
Getting this project off the ground meant having a business case, first and foremost. This required actually sitting down with the people who do the ordering, because procurement needed to understand the options it had. “So, with every plant in BASF – all approximately 150 of them – we had to talk to them, and look at the individual spend of each plant,” Dohrn explains. “This included direct procurement of raw materials, energy, logistics, indirect spend for services, and so on. Then we had brainstorming workshops, generating between 30 and 50 improvement measures per workshop.
“Then, because it’s bottom-up, you bring in the performance management tool to prioritise the measures. Then you go through the business case and confirm the value. As these measures go through the implementation levels, it’s very satisfying because you can see how you’re making progress in driving value every day. The people who own the measures set the timeline themselves, and there are incentive schemes behind the best ideas.”
Driving value to motivate people was a priority from the start, and something BASF discussed with ORO Labs early on. People are able to see the status of their measures thanks to ORO Labs, which means they’re able to see the results and also see other peoples’ great ideas. “You create a wave of people who are driving value, much faster,” Dohrn adds.
Addressing the challenges
From Bhojwani’s perspective, there were multiple challenges when approaching BASF’s requirements. Fundamentally, ORO Labs was building a brand new workflow, as BASF required a very different take on what that means. ORO understanding how that translated to what BASF needed was the first challenge.
“We needed to understand the structure Matthias has, and what the work streams should look like,” Bhojwani explains. “We had to figure out how to model these work streams within our tool in a way that made sense. An indirect work stream is not the same as something in direct material; those things are very different. So here’s where our workflow tool worked quite well. We could customise how direct material work streams should behave, compared to indirect work streams, how country A should behave compared to country B, and so on.
“It was important that we could bring flexibility, and that we could solve workflow problems in innovative ways. Another challenge was the user experience part. We had to make sure that the system worked for everybody, otherwise nobody would participate in the system. We had to keep working on it, keep fixing it, and that took a good 18 months of tweaking. The biggest thing has been understanding how BASF actually generates value, and how a workflow can help. It’s been very interesting.”
Identifying the value
Collaborating with ORO Labs has unlocked an enormous amount of value for BASF. Dohrn has seen the business come together thanks to the work that was put into communicating and collaborating with every site across businesses and functions, and BASF is continuing to conduct workshops for further improvement. There’s also, of course, the EBIT being gained from the business cases, putting BASF on track to generate sustainable savings.
“There’s been a real mindset change,” Dohrn states. “We’re now really focused on value, and we’re using this ORO Labs tool to hold each other accountable. You can see the progress every day. We call it the iceberg because you can see below the implementation levels. Everything starts off below the water line – no value created yet, just potential. Then you see it moving beyond the zero line into the positives, and every day I can see the difference between now and yesterday with just a click. It’s so fulfilling to see what we have created.
“We’re able to see the interaction with the plants, the interaction between people, and interaction with the requisitioners, and we can create something positive together. I think that’s huge. It’s only going to bring more and more value over the next few years. People are used to the tool now, they find it easy. It has created value and everyone’s happy because the cost pressure on the plants has gone down.”
Tonkean is built differently. Tonkean is a first-of-its-kind intake and orchestration platform. Powered by AI, Tonkean helps enterprise internal service…
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Tonkean is built differently.
Tonkean is a first-of-its-kind intake and orchestration platform. Powered by AI, Tonkean helps enterprise internal service teams like procurement and legal create process experiences that transform how businesses operate. The transformation hinges on four key functionalities, intake, AI-powered orchestration, visibility, and business-led configuration (no-code), which internal teams leverage to use existing tools better together, automate complex processes across teams and tools, and empower employees to do better, higher-value work.
Jennifer O’Gara is the Senior Director of Marketing, Director People and Talent at Tonkean. O’Gara’s route into procurement came when Tonkean became active within the space. “While we initially focused on solving complex process challenges across entire enterprises, we quickly realised how much procurement could benefit from this approach,” she explains. “Procurement processes are inherently complex and collaborative and cross-functional, making them a perfect fit for Tonkean’s orchestration capabilities. We were right. Since we entered the market, we’ve been blown away by how enthusiastically process orchestration has been received. That’s keeping us excited about procurement.”
This year, DPW Amsterdam 2024’s theme was 10X, with a focus on the importance of companies aiming for a moonshot mindset instead of an incremental approach. As far as O’Gara is concerned, achieving 10X improvements in performance is within reach for procurement, but it requires a shift in how the function thinks about growth. “It’s not just about doing more of the same faster—it’s about fundamentally rethinking the processes that drive your business,” reveals O’Gara. “Your processes are like your company’s infrastructure. When you optimise at the process level, you don’t just create incremental gains; you can fundamentally transform the way you operate at scale. You can remove bottlenecks permanently, facilitate easier collaboration org-wide, and drive true, reliable automation across all your teams and systems. The result is exponential performance improvements that can be sustained over time. Aiming for 10X isn’t just a lofty goal—it’s achievable. The key is focusing your improvement efforts at the process level.”
However, the journey to 10X isn’t straightforward. Some organisations believe they can just layer new technology on top of old processes. According to O’Gara, this won’t unlock 10X growth and will still leave your company lagging behind. “Getting to 10X starts, instead, with building better processes—and moving away from the idea that any one technology will do the trick,” she says. “For example, AI. AI is powerful, but it’s just a tool, and it’s only valuable if used strategically. To truly unlock 10X improvements in performance, you need to integrate technologies like AI into your core processes in a way that’s structured, strategic, and scalable. You will only ever be as innovative or adaptive or as effective as your processes are dynamic, dexterous and dependable. How do you build better processes? That’s where process orchestration comes in.”
Process orchestration refers to the strategy — enabled by process orchestration platforms — of coordinating automated business processes across teams and existing, integrated systems. These processes can facilitate all procurement-related activities. Importantly, they can also accommodate employees’ many different working preferences and styles.
Instead of simply adding to an organisation’s existing tech stack, process orchestration allows companies to use their existing mix of people, data, and tech better together. One promise of process orchestration is to finally put internal shared service teams like procurement in charge of the tools they deploy.
This goes a long way towards solving one of the enterprise’s most vexing operational challenges: the inefficiency of over-complexity born of too much new technology. It also allows procurement teams to truly make their technology work for them and the employees they serve. As opposed to making people work for technology. Process orchestration breaks down the silos that typically separate working environments. No longer do stakeholders have to log in to an ERP or P2P platform to submit or approve intake requests, just for example. The technology will meet them wherever they are.
“It helps you create and scale processes that can seamlessly connect with all of your existing systems, databases, and teams, while accommodating the individual needs of your employees and meeting them in the tools they already use,” adds O’Gara. “Orchestration allows you to automate processes across existing systems—like ERP, P2P, and messaging apps—so data flows automatically between them. It allows you to surface technologies like AI when and where they’re most impactful for stakeholders.”
Speaking of AI, it remains one of the biggest buzzwords in procurement. Indeed, anything that offers Chief Procurement Officers cost savings and efficiency will prick their ears, but the question remains: can the industry fully trust it? O’Gara believes it is ‘overhyped.’ “When it first emerged, it wasn’t just seen as a new tool—it was almost treated like magic,” she explains. “The hype still hasn’t died down, and that’s been a problem. It’s created unrealistic expectations and skewed perceptions of what innovation with this sort of technology actually entails; I can’t tell you how many procurement leaders have admitted to us that they’re getting pressure from the C-suite to invest in AI-powered tools just because they have ‘AI’ in the name.”
While clear with her scepticism regarding generative AI’s current place in the market, O’Gara recognises its potential. “Generative AI’s potential is huge—especially if it’s deployed strategically at the process level,” she reveals. “It could truly transform procurement, shifting teams from transactional roles to strategic partners who are involved early in the buying process and appreciated for their unique expertise—and for the unique business value procurement alone can deliver. But AI on its own isn’t going to save procurement. The reality is, many organisations jumped into the AI hype without a real strategy, and that’s why they haven’t seen its full value yet. The key is integrating AI thoughtfully into core processes—that’s when we’ll start seeing its real potential.”
With an eye on the future, O’Gara expects the next year to continue to revolve around AI adoption, but in ways that deliver real value. “I think we’ll see procurement truly stepping into a more strategic role, with businesses recognising procurement as a key partner, not just a back-office function,” she says. “This shift will be driven in part by new technology, especially process orchestration and AI, helping procurement bridge gaps in communication and collaboration across teams. Another big trend will be the rise of personalised, consumer-like experiences in procurement—making buying and approval processes smoother, more intuitive, and better tailored to the needs of individual users. It’s an exciting time, and we’re just scratching the surface of what’s possible.”
It’s impossible not to be inspired by the energy at a DPW event. DPW Amsterdam 2024 was buzzing with that…
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It’s impossible not to be inspired by the energy at a DPW event. DPW Amsterdam 2024 was buzzing with that same energy, its attendees soaking in information and inspiration from speakers, peers, other experts. We caught up with Rujul Zaparde, Co-Founder and CEO of Zip, at the event to dive into the procurement landscape and chat about the specific qualities DPW brings to the sector.
Zaparde is the Co-Founder and CEO of Zip. At the beginning of Zip’s journey, Zaparde and his fellow founder, Lu Cheng, based the company around their own experiences as end-users of the procurement process. They took their lived confusion around having multiple intakes for a contract, for the purchase request, and all the different complicated components of the process, and created a solution.
“And so, we started Zip and created the category of intake and procurement orchestration. We’re very grateful to have been named the leader in the category,” says Zaparde, in reference to having just been named a category leader in IDC’s first ever Marketscape for Spend Orchestration.
So, as is often the case, procurement is something Zaparde fell into. In this case, he got involved with procurement specifically to solve pain points. Prior to Zip, he was a Product Manager and Cheng was an Engineering Leader, both at Airbnb; they knew very little about procurement. “We were just end-users,” he explains. The upside of this was that they were able to come into the industry fresh, without the baggage and legacy issues that can come with being in a sector for a long time.
UX first
“At Zip, we really try to take a user experience first approach,” Zaparde continues. “What we found is the highest leverage change you can make in any procurement organisation is to make it easier for your employees to actually adopt and follow whatever the right process is. If you do that, then all of finance, procurement, accounting, and even IT find that they’re suddenly swimming with the current, not against it. And you can’t do any of that unless you solve for user experience.”
Taking away problems, the way Zip does, also takes away a barrier to ambition. The theme of DPW Amsterdam 2024 was 10X, a term on the lips of many across all sectors. Once immediate issues and pain points are addressed, 10X is something businesses can aspire to, with many talks and workshops during DPW Amsterdam focusing on how to approach this.
Getting the mindset right
For Zaparde, 10X thinking is a necessity for growth. “You have to aim for 10X to even end up at something X,” he explains. “That requires ambition. I also think that when you think in terms of 10X, and your mindset is angled towards incremental change, you’re much more open to thinking of solutions that are perhaps a little more risky. It changes your perspective.”
A mindset shift needs to happen before anything else. This involves considering the needs of procurement and the wider company, having a north star in mind, and then breaking changes down to an incremental level.
“Then you can start to think about the steps you need to take to get there,” Zaparde explains. “A big component of this is bringing along your peers and stakeholders across every function that’s tangential and critical to the core procurement workflow and path.”
Innovating for good
The work Zip does is indicative of the shift towards continuous improvement and advanced technology that procurement has been going through in recent years. There are things that are possible now that weren’t possible even a year ago, thanks to the vast innovations being made. One of the hot topics right now is generative AI, something that’s opening up a world of possibilities.
“It’s the elephant in the room right now,” says Zaparde. “With the capabilities that gen AI unlocks, you can automate a lot more. That allows you to cut down a lot of the transactional and operational work that procurement and sourcing organisations are doing. Procurement is tired of the status quo. It’s been an underserved function for over 20 years, and I’m glad that’s finally changing. I feel privileged for myself and Zip to be part of the conversation, and that we’re seeing all these amazing changes happening.”
Zaparde believes we’re already seeing the benefits of the major changes that have occurred over the last couple of years in procurement. In fact, he knows this, because Zip has helped its customers save around $4.5bn of spend over the last two years, which is an astonishing statistic.
“One customer of ours, Snowflake, achieved over $300m in savings alone,” Zaparde continues. “We’ve seen tangible benefits already. The way procurement is evolving isn’t a hypothetical thing – it’s really happening.”
Fragmentation on fragmentation
The key, again, is overcoming base level issues for the sake of evolution. This is precisely what Zip provides, after all. But sometimes, the issue is at a data level. Unclean data is something that technology leaders are talking about a great deal right now, with some feeling that it holds them back from implementing new technology. Zaparde believes that businesses should be questioning why their data isn’t clean from the start, rather than worrying about trying to cleanse existing data.
“You don’t just clean your data – the real question is why is your data not clean in the first place?” he muses. “You have to have a clean entry point for it. I don’t think I’ve ever spoken to a Fortune 500 CPO that said they had clean data. I think it’s because of the upstream processes in intake and orchestration. If all the cross-functional teams – the IT review, the legal review, the finance – are being manually shepherded by the procurement operations organisation, then how can you possibly end up with clean data?
“People are keying the same information into multiple systems, which might mean they answer in similar – but different – ways. So you end up with fragmentation on fragmentation. But if you have one single door to that data, you’ll be able to drive only clean data, because it’s a funnel. If you let everyone have different swim lanes that never intersect, you won’t have clean data.”
As 2025 approaches, Zip has multiple product capabilities and features coming up that Zaparde and his team are very excited about. This includes leveraging gen AI, something we’re seeing incredible utilisation of across the sector.
For Zaparde, attending events like DPW Amsterdam to talk about what Zip does and interact with peers and clients alike is a joyous part of his job. “DPW is really accelerating the rate of change in the procurement industry. That’s very much needed, and it’s energising to see so many incredible people from the procurement world in one place. I love spending time with these forward-thinking procurement leaders at this event.”
Catching up with Mitha-Ai’s Co-Founder, Arash Saberi, we dive into the vital importance of a solid data foundation.
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Whether we’re talking about gen AI, 10X, or any other kind of advanced tech solution, data is at the core of the discussion. And when data isn’t clean or ready for the implementation of something being built on top of it, businesses can end up significantly held back. Mithra-Ai is an organisation that helps its customers to build trust in their data, which is a core issue for many.
“That sets us apart,” says Arash Saberi, Co-Founder of Mithra-AI. “We help procurement leaders and category managers create, execute, and realise their strategies. This is backed by reliable, comprehensive data, both internal and external, tailored specifically for their categories.
“Maintaining high-quality data is crucial as it influences the accuracy and reliability of AI-driven insights and recommendations. That’s where Mitha-AI comes in. Our cleansing, enrichment, and auto-classification engines ensure that procurement stakeholders, including data scientists, begin with a reliable data foundation.”
Cleaning and classifying data
Mithra-Ai is an AI-native SaaS solution, which starts off by proposing a meaningful spend hierarchy for every category. What’s key is that this is paired with an automated cleansing and classification engine. This is so important because the only way to achieve truly clean data is to make sure it enters the system clean in the first place.
“Clear visibility into categorised spending eliminates uncategorised expenses and wrong assumptions,” says Saberi. “When supplemented by relevant external data intelligence, category managers are empowered to negotiate with confidence, achieve greater savings, and monitor initiatives effectively.”
A world beyond cost savings
When launching Mithra-Ai in 2021, the company’s founders rightly foresaw that the role of procurement would evolve beyond focusing merely on cost savings, and become the central hub of every organisation. Because of that, they knew that accurate, reliable information was needed – hence the necessity for Mithra-Ai.
As procurement has shifted, the status quo is no longer good enough. It’s an exciting time for the sector, but also one of high demand in the race to adopt increasingly advanced technology. But it’s necessary for efficiency and growth.
“Tesla and Nvidia exemplify the power of embracing change over maintaining that status quo,” says Saberi. “Procurement is facing intense pressure to evolve with organisational needs. Those organisations can opt for incremental changes, which will likely slow them down, or pursue a 10X leap to maintain competitive advantage. The latter requires bold and decisive leadership from heads of procurement.”
The road to 10X thinking
The way to drive 10X thinking, Saberi believes, is through having a clear vision of your goals. Sometimes businesses, especially ones which are going through major change or those navigating outdated legacy systems, are at risk of losing sight of their goals. But having that vision is a foundational necessity, regardless of what stage you’re at.
“Set aspirations high, and question existing norms,” says Saberi. “Procurement leaders can draw inspiration from startups by fostering a culture of innovation through small-scale initiatives that can rapidly expand. Reevaluate the skills and team structure necessary for future success.”
Another important aspect to bear in mind when considering these things is the level of risk you’re willing to undertake when setting goals and aspirations. “That’s often overlooked,” Saberi continues. “Determining the acceptable level of risk is crucial. It significantly influences partner selection and the outcome of RFPs.”
Thinking big, starting small
While ambition is vital to 10X thinking and beyond, businesses must also make sure they don’t bite off more than they can chew. Launching into adopting huge volumes of advanced technology can lead to overwhelm and can make a business stall rather than evolving. A more careful approach is required.
“Think big, start small,” says Saberi. “Prioritise high-impact, low-effort initiatives over those requiring significant effort. Many transformation projects fail to deliver the expected benefits and incur high costs during the program.” This is another reason to decide on the appropriate risk level early on, in order to guide prioritisation decisions and transformation pace.
It’s an incredibly exciting time for procurement, and that includes Mithra-Ai. In a very short time, it’s developed several foundational modules for its data-driven category management solution. This includes the Collaborative Initiative Tracker that was launched during DPW Amsterdam 2024 – just one of Mithra-Ai’s inspiring undertakings as we approach 2025.
“The tracker means that procurement teams can now involve multiple stakeholders in collaboratively tracking and enhancing the impact of key initiatives, such as cost-saving measures,” says Saberi. “Exciting times lie ahead.”
DPW Amsterdam is the perfect stage for launching a solution like this. It’s an event that inspires a culture of innovation, bringing procurement professionals together to teach, learn, and shout about their latest additions to the procurement landscape.
“DPW stands out as the premier procurement tech event of the year,” says Saberi. “Practitioners can explore and engage with procuretech suppliers, showcasing valuable use cases and personal stories across multiple stages. DPW is a catalyst for ideation, creating trust and confidence in the benefits of applying cutting-edge technologies to improve business outcomes. This year’s event felt even more international than previous years. I look forward to seeing it continue to grow.”
Saberi’s main takeaway from DPW Amsterdam this year is that a solid data foundation is essential – something he was well aware of as part of Mithra-Ai. “Without it, transformation projects and new technologies will struggle to succeed,” he concludes. “In the past two years, there has been increased focus on sustainability and risk intelligence, driven by numerous new solution providers. However, during the DPW Amsterdam 2024 conference, we observed new trends coming up and, again, more focus on data quality, which works to our advantage.”
When we’re talking about technology in procurement, the importance of partnership is a major component for success. No business is…
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When we’re talking about technology in procurement, the importance of partnership is a major component for success. No business is an island, and joining forces with experts is, increasingly, the direction many move in for the sake of growth.
At DPW Amsterdam 2024, we met many businesses who were looking around at the procurement sector in search of either what direction to move in next, or who they can help. The event is one that brings people together to learn, to teach, to discover the cutting edge of procurement, and be inspired by it. So when we sat down with the CEO of Fairmarkit, Kevin Frechette, it wasn’t surprising that he brought Nick Wright, who leads bp’s Procurement Digital Garage, into the conversation.
For Frechette, one of the best things about working in the advanced procurement technology sphere is joining forces with other businesses to help them keep improving, and vice versa. “Having the chance to work with people like Nick, who are pushing the envelope when it comes to autonomous sourcing, is amazing,” he explains. “We’re fired up to be at DPW, absorbing this atmosphere.”
While it’s something of a running joke in the procurement world that most professionals in the sector don’t deliberately choose it, Wright actually did. “I went to university and thought ‘wow, I fancy a career in procurement or vendor management’. I know a lot of people don’t have that story, but I’ve been doing something I’m passionate about from the beginning. I love making deals, whether I’m buying a car, a house, or something for BP.” The Procurement Digital Garage he leads exists to look at problems being faced across procurement, and figuring out possible solutions.
For Frechette, the intention wasn’t to start a company in the procurement space, but his team quickly saw the opportunities within it. “We had this ‘aha’ moment,” he says. “It was a tough pivot. There was a lot of debate, a lot of late nights. I’m super glad we made it because we got to be in a space where people can be forgotten about, and we’re able to give them centre stage.”
The realistic approach to 10X
DPW itself exists to put procurement under the limelight. Each event is themed in a way that gets conversations flowing around the next big thing in procurement. For Amsterdam 2024, this theme was 10X – something Frechette believes isn’t achievable right off the bat.
“It’s something to strive towards,” he says. “It’s something where you work on getting a little better every single month, every quarter. You keep getting those small wins, and you build credibility. There’s no silver bullet. You just have to start the journey and learn as you go.”
For Wright, it’s about not getting caught up in the hype, but figuring out what’s realistic. “There’s a lot of hype out there, and the beauty of something like my team at the Procurement Digital Garage is to weed out that hype, because what’s right for us might not be right for someone else. Having a team that’s out there in the market, testing and figuring out what’s real, will put you in good stead.”
“There’s a leap of faith element that can be challenging to achieve, before you can really strive for 10X,” Frechette adds. “It’s like Amara’s Law: humans typically overestimate the value of technology in the short term, but underestimate it in the long term. So the hype is needed. We have to help people on that journey and sometimes, a leap of faith is needed. For the people that risk it, it’s exciting, and they’re then well positioned for the future.”
However, again, managing expectations is important. “People might be on the sidelines expecting a 10X solution,” says Wright. “But the reality is, you’re going to get 5% here, 10% – smaller pockets of improvement.”
The benefits of advanced technology are absolutely being seen at this stage, but being realistic about the future outcomes is important. “The benefits are there – not at the scale of 10X – but if you just make a start, you’ll achieve wins,” says Frechette. “You broadcast those wins across the organisation. That generates excitement, and then you can work on the next thing because you have ground swell.”
How ‘the future’ has changed
What’s interesting is that this 10X focus, this drive towards incremental wins, has reframed the way businesses plan for the road ahead. ‘The future’ used to mean having a three or five-year plan. Now, the future is only 12 months away.
“The thought process right now is ‘what can we do that’s super optimistic in just 12 months’?” says Frechette. “Then you can put in realistic time frames and set off on a sprint to get there. You have to be able to move fast. We have launches every two weeks now, and we have to be flexible with our roadmap along the way. But we always know where we’re going – we have a north star.”
“To me, that’s the only way to do it,” Wright adds. “I don’t have a crystal ball. Nobody knows what’s going to happen in two or three years. So what’s the point of creating a plan that’s going to get you to a certain point in those two or three years? You have to work on small iterations, make adjustments, change direction as necessary.”
It’s part of what makes Fairmarkit and BP an active partnership – the ability to be flexible and open up discussions at every point. It’s all about real-time feedback and trust-building, to the extent that both parties feel like they’re on the same team.
The right people in the right places
Because ultimately, it’s the human element that makes transformation happen. Having the right people in place is one of the elements that’s key to making sure implementing advanced tech for the sake of business strategy works at all. “It’s about access to talent and making sure you’ve got a capable user group that can make the most of that technology,” says Wright. “You don’t need to be a data scientist, but you do need to have the right mindset to take advantage of the tools you’ve got.”
“I agree – you have to get the right people on the bus,” adds Frechette. “You all have to be committed to going on the journey together. Prioritise where you start and where you’re going to have the most value with the lowest risk, and have people on your side who can give suggestions and ideas.”
While the much-discussed talent shortage can create challenges there, DPW as an entity proves that not only does procurement keep becoming more appealing and exciting, but where there are gaps, there are digital tools. “I’ve noticed a lot of folks under 30 who are here at DPW Amsterdam, and they’re genuinely interested in procurement,” says Wright. “We’re at a tipping point that makes me really excited about the profession I’m in.”
‘Digitalisation is just the beginning’ according to Crowdfox, a business which aims to improve procurement by bettering the ordering process…
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‘Digitalisation is just the beginning’ according to Crowdfox, a business which aims to improve procurement by bettering the ordering process while lowering costs. That tagline speaks to Crowdfox’s dedication to advancing procurement using the exciting tools the sector now has at its disposal, and this push to innovate is being driven, in part, by Martin Rademacher, Crowdfox’s CSO. We sat down with Rademacher at DPW Amsterdam 2024, the exciting vibe of the event spreading far and wide around us.
Rademacher is responsible for everything to do with Crowdfox’s customers. From sales, to marketing, to customer onboarding and success, and everything in between – that’s Rademacher’s wheelhouse. His background is in management consulting, with a focus on procurement and supply chain. So, while he started out in sales, he soon decided that procurement was the direction to move in.
“During my time as a consultant, I found procurement very interesting because it’s so versatile,” explains Rademacher. “Of course, it’s about the transactional phase with suppliers – but also you’re so connected with R&D, production, logistics, and so on. You have so many fields of application.”
10X thinking
At DPW Amsterdam, the overall theme of the two-day event was 10X. The concept of the 10X rule is around taking a goal you’ve set for yourself and multiplying it by 10. It’s an aspirational tool, coaxing all of us to aim higher. In procurement, that means innovating.
“In the last two years we’ve seen tools like ChatGPT trigger some big adaptations in the procurement world,” says Rademacher. “I think there is the opportunity now to achieve 10X in terms of efficiency gains. Especially when it comes to making better decisions, more quickly, in order to analyse data. We’re now finding out what AI can really do, and focusing on how that can help with strategy.”
For Rademacher, he believes people have the right tools to achieve 10X – it’s now about implementing those tools properly, and having the right culture.
“In the last couple of years, implementing tools has become much easier than it was a decade ago,” Rademacher continues. “They’re so well designed that they fit into large procurement systems, and can connect with other best-of-breed tools. I’d say implementation should be the focus, but it’s not that complicated anymore. AI tools especially are really intuitive. As a result, you don’t need much in the way of change management. People just intuitively cooperate with AI.”
The question of security
The big challenge, Rademacher believes, is data protection. When it comes to barriers preventing a 10X approach, concerns around data privacy are among the biggest issues. As a result, organisations have to take the necessary precautions before plunging into making major technological changes, or risk falling at the first hurdle.
“In the EU, it’s all about data protection,” says Rademacher. These concerns led to the Artificial Intelligence Act (AI Act) coming into force in the EU in August 2024. It was created in response to the rise in generative AI systems, and ensures that there’s a common regulatory framework for AI within the European Union. “Companies are very concerned about their data, but I wouldn’t call this an obstacle – more like a challenge.
“The key is making sure you have a protected environment. Start with a pilot in a limited space, for instance, and then make sure you can find a solution you can control in a safe environment that suits your operations.”
Shooting for the stars
With these measures in mind, it’s never been easier to implement new technologies and aim for that ambitious 10X goal. Certainly, advanced tools have never been more accessible, or more straightforward for businesses to educate themselves about. Even as recently as two years ago, integrating multiple elements of advanced tech – like genAI – wasn’t really possible.
“It definitely wasn’t easy to combine sources the way we can now,” says Rademacher. “Now, you can provide a much better user experience experience not only for procurement professionals, but for anyone who takes advantage of what procurement introduces to the company. Finding the supply to fulfil your demand is so much easier now. You no longer have to have difficult conversations starting with an email to your procurement professional to identify whether you’re allowed to purchase from a certain vendor, and whether they’re vetted or not. Streamlining processes like that makes that information quick and easy to identify.”
Additionally, we’re at a point with advanced technology where the tools we have access to are capable of handling more and more volumes of data at an extremely fast pace. “In consulting, for example, every project started with an analysis of the status quo of a firm,” says Rademacher. “We’d figure out who the vendors are, the categories, and the spend. Depending on the workforce, this could take one or two weeks. Now, with the tools we have access to, you can gather this information in 24 hours.”
The evolution continues
While we’re seeing many of the benefits that come with genAI and other advanced technologies already, it’s only the beginning of what we can achieve using these tools. GenAI is at a peak right now, but according to Rademacher, it might take another five years to achieve its full productivity level. “There’s also this ambitious idea going around of fully autonomous procurement, and it’ll likely take a good 10 years to reach that level of productivity,” he adds. “On the other hand, nobody is talking about robotic process automation anymore because we’re almost there with that already.”
Another challenge is data quality. The cleanliness of an organisation’s data can make or break its use of advanced technology, which is where making the right connections with service providers comes in. “It’s a good example of when to find the right partner,” says Rademacher. “Find someone from the innovative tech space who you think you can rely on. Don’t try to do it all on your own – that’ll just hold you back more and more. Be bold; find the right partner to make the most of your data and that helps you constantly improve. There’s a lot of talent out there, a lot of solutions that are really helpful for organisations of all sizes. You’ll improve step by step.”
There’s no doubt that it’s an exciting time for procurement. The atmosphere at DPW Amsterdam 2024 was electric for that exact reason. The event, in Rademacher’s words, has “a really strong influence on the sector and enables attendees to learn about how the landscape is developing in real time”.
“The AI-driven future is already a reality for us,” he states. “We’re beyond the pilot phase with our AI tool, ChatCFX, and now we really want to drive market share. 2024 going into 2025 sees us in a good position with high user visibility, and now we’re adding ChatCFX to the game, pushing it into the European market. We’re at DPW Amsterdam to meet the players who are looking for a solution exactly like ours, making it an invaluable place to be.”
Certain procurement pain points can prove debilitating for a business, freezing it in its tracks when it’s trying to grow…
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Certain procurement pain points can prove debilitating for a business, freezing it in its tracks when it’s trying to grow and improve. This is where companies like Candex are able to step in and turn a headache into something so simple, it requires no further thought.
Danielle McQuiston is the Chief Customer Officer at Candex. She’s been with the fintech startup for five years, spending two decades prior to that working in procurement at Sanofi. Candex is a technology-based master vendor that allows customers to engage with and pay one-off or small suppliers without setting them up in their system. This means that the system doesn’t get clogged up with suppliers that are rarely or never going to be used again.
“We’re primarily used for what companies consider tail spend, and we typically deliver it as a punchout catalogue for a really simple user experience,” McQuiston explains. That ability to support lots of customers was what drew her to the role. “Coming to Candex, I was very excited about what they were doing and wanted to help as many companies as possible.”
Addressing tail spend
That ability to address tail spend in a unique way is the main thing that differentiates Candex. It’s an enormous problem for procurement professionals. The way Candex delivers it is through a digital plug-and-play solution, removing the need to be dependent on human intervention. “It’s a horizontal solution for any good or service, and it’s available in over 45 countries now,” says McQuiston. “It becomes part of the customer’s ecosystems and leverages the P2P process. It’s super compliant, and allows a lot of control.”
With this tool in place, Candex’s customers are able to gain much better control over their smaller purchases, defining what is allowed to be purchased. For many, this tool allows them to put tighter restrictions on purchases than their e-procurement systems are able to do. Additionally, Candex runs suppliers through screenings every day, which generally doesn’t happen for small, rarely-used suppliers.
“We run really detailed compliance and sanction screening against all those vendors, taking away a really daunting task from customers,” McQuiston states. “Customers probably check those suppliers once when they’re being set up, but then they never look at them again. Every day, we’re checking them, and keeping an eye on them when our customers can’t.”
Candex’s reporting is extremely detailed, and provides customers with the kind of real-time visibility they wouldn’t normally get – even in their own systems. Reports are generated weekly or monthly, including the diversity status of suppliers. This is data that a lot of clients then feed directly into their Power BI tools and data lakes, meaning they’re able to integrate it seamlessly into their other data.
Cleaning up the data
The whole purpose and aim of Candex’s tool is to make life easier for its customers, streamline its processes, and improve efficiencies. To that end, standardisation is key when it comes to business improvements, and that includes preparing data prior to implementing new technologies and processes. When it comes to ensuring a business’s data is healthy – before launching into major tech changes – accepting the necessity of making foundational change is key.
“Data cleansing processes are ugly, cumbersome, and long – and everyone has to do them,” McQuiston comments. “But you have to accept that you’re going to have to do something, if you want to get a handle on your spend. First and foremost, you need to standardise the way you name things, the way you put data in the system, and you need a really strict discipline around that. All of those things will make backend processes a lot easier.”
It’s just one of many considerations CPOs need to bear in mind when seeking out technology solutions and implementation. Modern procurement departments have a seat at the wider business table now, and what they do impacts the entire business. So when it comes to utilising solutions for the sake of the business at large, there are many factors to think about.
“As with any data or technology, it’s all about garbage in and garbage out,” says McQuiston. “Any advanced technology should be used with caution and viewed with a critical eye. You have to start with knowing what you want out of it.
“A lot of times, people put technology in place because it looks interesting, but you need to start with the problem and work backwards. If the issue is user experience, you need to make sure that whatever you’re implementing focuses on a positive UX. If the problem is unclean data, you need to make sure you’re putting in place all the foundational elements you need to make that better. Always start from the perspective of implementing a technology based on a problem, rather than the other way around.”
Improving UX in 2025
It’s a seriously dynamic time to be involved in procurement right now, as evidenced by the intense buzz around us at DPW Amsterdam as we sit with McQuiston. As we look ahead, she envisions that procurement will have an increasingly powerful impact on user experience. This is particularly important at a time when tasks are becoming increasingly automated, with less and less direct human interaction.
“We’re also seeing a pretty big leap forward in terms of best practice sharing amongst our clients,” says McQuiston, something that events like DPW also encourage. “For Candex, a big theme of 2024 has been getting our clients together to share best practices and information, helping them to develop further expertise in the field. 2025 will have more of the same, but there’s now a higher level of maturity out there in the way customers are considering tail spend. As people continue to onboard solutions, it will be interesting to see how that impacts the UX in relation to Candex. We’re always looking for ways to make our tool more user-friendly and add better functionality.”
All of this is why Candex’s customers love the company. On a base level, Candex takes a complex pain point and makes it simple. In a broader sense, the reason Candex is becoming so popular is the way it works with people. “The most common feedback we get from customers and suppliers is that we’re great to work with because we’re so flexible,” says McQuiston. “We hired a team of procurement experts, so our team is made up of people who really understand the pain of our clients, and can anticipate their fears, their needs, and cater to those.”
The buzz of DPW Amsterdam draws in the most innovative minds across the industry. They’re there to have riveting conversations…
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The buzz of DPW Amsterdam draws in the most innovative minds across the industry. They’re there to have riveting conversations with their peers, to inspire, to teach and learn in kind. And they’re there to keep an eye on an industry that doesn’t stop changing for the better.
This is a big part of the appeal for Fraser Woodhouse. Woodhouse leads the digital procurement team within Deloitte in the UK. His team historically focused on large-scale transformations, providing a backbone for suite implementation. Increasingly, however, it’s turning its attention to helping clients navigate a plethora of technology solutions. The goal is to help them build and scale, and take advantage of some of the more niche functionalities available. These are things that can be highly daunting for many customers, which is why Deloitte is there for support.
“We’re helping clients ask the big questions,” Woodhouse explains as he sits down with us at DPW Amsterdam 2024. “How do you connect the technology in a way that allows data to flow from one system to another? How do you deal with processes that are connected to solutions which all have their own release cycles? How do you approach change management? That underpins so much of where the value is going to be achieved, and a lot of the providers will be focusing on it. They just might not have the same capability that Deloitte can provide.”
For Woodhouse, getting involved with procurement was a total accident. He even left the sector at one point, but his strong foundational knowledge – and the exciting landscape procurement is enjoying right now – lured him back in. “It changes faster than I can get bored with it, that’s for sure,” he explains. “Procurement is fascinating.”
Aspiring to greatness
Especially now, with constant conversations around genAI, 10X, and beyond. Procurement is only becoming more interesting, more enticing, drawing young professionals in to fill gaps in the talent pool. 10X was actually the theme of DPW Amsterdam this year, a notion that’s on everyone’s lips. And for Woodhouse, it’s absolutely something to aspire to.
“Aiming for 10X is sensible. You just have to consider your timescale. I’d caution against running before you can walk, but a culture of experimentation is important. Running small-scale pilots can help you hone in on where you really want to see value, or where value is likely to be generated. Starting with requirements is a fundamental thing at the moment, but you shouldn’t underestimate how long that will take. And it’s a continuous consideration, because requirements change. Just keep trying to refine your solution in order to take advantage of everything that’s out there right now.”
Fotograaf: MichielTon.com
Having the wrong mindset is one of the major barriers to adopting 10X thinking. It all starts with the company’s culture, and whether that’s one of growth or not. “I imagine most of the people here at DPW Amsterdam have already made that mental shift,” says Woodhouse. “Last year, people were still trying to understand how they, as big companies, could utilise startups. That’s changed now, and it’s amazing to see companies that were startups three years ago working with all these big enterprise customers.
“They have scaled and grown in partnership with those customers. Mindset is so important, and having the wrong one will only create barriers and missed opportunities.”
Always improving, never slowing down
When it comes to the advantages that technology has brought to procurement in the last few years, the list is endless. Procurement has gone from an overlooked segment of any given organisation, to having a seat at the table and helping make major business decisions. 10X thinking – whether it goes by that name or not – has been spreading across the segment and fuelling businesses to aim higher.
“The layers of automation have really improved,” says Woodhouse. “A year or so back, there were a handful of use cases that you could truly automate, but now you can do it at a much larger scale. Another big change is around security concerns. There are more tried and tested case studies to draw upon now, and solutions are more readily available. You don’t necessarily have to be a pioneer, because someone else has already taken that first step.”
The question of data
Something else that holds businesses back, despite the innovation at their disposal, is an element that can be harder to change: poor quality data. When trying to implement advanced technology solutions, bad data can make or break their success.
“It’s always useful to focus on that and have a dedicated work stream,” Woodhouse advises. “You need someone who really understands data. I think there’s a tendency to try to boil the ocean before you even get going in your transformation, which isn’t necessarily a bad thing. Cleaning up your data before you start, and having a fresh foundation will help you make decisions on what to implement on top of that good data.
“Doing all of that is obviously hugely beneficial, but it’s going to slow you down, in many cases. There are ways around that, like embedding the cleanup of data within the new processes. Data is important – we shouldn’t underestimate that – but there are different approaches to solving the issue of poor quality data, like buying it or using genAI to restructure your data into something more powerful. Either way, you need a strategy.”
Novel thinking 101
Some businesses fall into the trap of thinking that they can’t achieve specific things because their data isn’t in the right position, but novel thinking around data can allow them to still drive forward. “You’ve just got to focus on it. You can’t assume the data’s going to fix itself,” Woodhouse adds.
Novel thinking is certainly something that can be seen at DPW events, and DPW Amsterdam 2024 was no exception. People congregated there to learn, to share stories, to inspire. For Woodhouse, the magic of the digital procurement sector right now is that everybody recognises that their journey has no end. While that may be daunting, it’s a positive thing and keeps procurement professionals striving for more.
“It’s a continuous improvement journey, and I think the best-performing organisations will recognise that, and invest in the business capability to continue that journey,” Woodhouse concludes. “That’s how you get proper value. I love hearing about how people frame problems differently, and how they approach the solutions.”
Making procurement slicker, more streamlined, is the name of the game right now – and this is precisely why Globality…
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Making procurement slicker, more streamlined, is the name of the game right now – and this is precisely why Globality exists. It’s an organisation which leverages advanced, native-built AI to make sourcing more autonomous for Fortune 500 and Global 2000 companies, meaning it has a finger on a pulse of the technology tools procurement now has access to as the industry shifts and evolves.
Keith Hausmann is the Chief Customer Officer at Globality. He has been working in procurement since the early 90s, both in industry as a service provider, and now, at a technology company. He came to Globality from Accenture, where he ran the operations business. During his first real job after college, Hausmann was also part of a training program at a major Fortune 500 company, working closely with a COO. At some point they got into a conversation about salespeople seemingly having an advantage over procurement people due to their access to information, knowledge, and training. The COO suggested that they launch a company to help support procurement. For Hausmann, it was a serendipitous entry to the industry.
“I came to Globality because I saw the business was struggling with how to scale, automate, and deliver a differentiated user experience. Ultimately, I found it really compelling, and joined about five years ago.”
Achieving 10X thinking
Hausmann admits that the concept of what procurement is has only been defined relatively recently, and he’s been in the industry long enough to have seen the shift happen and suddenly accelerate over the last few years. Now, procurement professionals are in a position where they’re able to think big, and they have the tools to support that way of thinking. One of the most-discussed topics right now is 10X, whereby businesses are setting targets for themselves that are 10 times greater than what they can realistically achieve.
“There continues to be, and always has been, so many mind-numbing manual activities that go on in procurement spaces,” says Hausmann. “We’ve built small armies of teams to handle those things. I think 10X has prompted us to take a step back and ask if there’s now technology that can uplift the role of people in the function and take on some of those automatable tasks. Whether that’s writing RFPs, discovering suppliers, or analysing proposals – these are all things that can be automated in today’s technological world. With 10X thinking, you can imagine the many, many, many things that can be automated and just go after them.
“There are barriers, of course. The biggest one is not being able to convey a compelling vision of what we want people to do in the new world. It’s not necessarily about making them go away – it’s about making their daily jobs, lives, and work more valuable. There are so many things around category thinking and strategy that don’t get done because people are spending so much time on tasks that could be automated. So I think the barrier is creating that vision and that plan to shift the operating models, roles, and the skill sets to something new and different.”
People power
Hausmann believes that if roles are reshaped and honed in response to automation, it’s less likely that there will be resistance to change because employees will know exactly what they’re doing, rather than being concerned about their future. “They have to know what they’re doing before they jump on board. It just requires a mindset change and good change management.”
Hausmann believes it’s down to the CPO to drive that change management by conveying the activities, impacts, roles, and operating model they envision. If they can paint a picture of how humans can impact things in a new way, alongside the new technology rather than against it, suddenly it’s an exciting prospect and people are keen to make a bigger impact.
CFOs and CPOs joining forces
While CPOs now have a long-deserved seat at the table to help push change business-wide, CFOs’ roles are also expanding and having an increased impact on procurement. “I think they’ve always influenced what’s going on in procurement,” says Hausmann. “CFOs are the champions of many things, but certainly improving the bottom line of the company. They’re also champions of using technology to make the organisation more resilient, more scalable, and more efficient. There was a time when people thought that the CTO or CIO would be doing that, but more often than not, the CFO is the ultimate owner of improving business impacts. More and more, we’re seeing our customers leaning on the CFO to help them make decisions about investments that have a big impact through technology and AI.
“These days, the relationship between the CFO and CPO is wildly different to what it once was, and CFOs are showing more interest in procurement as a function than ever, making a difference to the bottom line. It makes sense because, in theory, procurement controls one of the biggest cost line items in a company, besides raw headcount.”
Matching the pace of technology
The fact that we still need to focus on change management and relationships confirms that the way procurement is changing isn’t just about the technology. Far from it. However, technology is moving at an incredible pace and needs to be taken seriously. There are things that are possible now which couldn’t be done even one or two years ago.
“A few years ago, technology couldn’t write an RFX document for you,” Hausmann says. “Technology could not instantaneously bring to light the most relevant suppliers from within a customer’s supply base, or in the broader market. It couldn’t write a contract, or an SOW, or a work order. It can now. Those are things that are near and dear to my heart that were impossible 3-5 years ago.”
With these tools in mind, procurement professionals are able to think about the future in short-term stints. Five-year plans are no longer good enough when it comes to the way procurement is shifting – a year is now the maximum for putting plans in place.
“I’ve always thought that procurement, from the perspective of technological advancement and investment perspective, should sit under a broader business umbrella,” says Hausmann. “I’d guess that probably 50% of companies in the world right now have some kind of program in place to save money or improve agility by investing in technology. And speed to market is more important than ever, so sourcing can’t be a bottleneck.”
Looking ahead, Hausmann expects to see many of the unique, differentiated technology providers becoming interoperable together, because big enterprises want services that operate and scale well in combination with others.
“We’re seeing that a lot, and working with our customers on how we improve interoperability and integration,” he says. “Tools will become more seamless, more easy-to-use, more scalable. Another big thing is, and will continue to be, analytics. It’s a hot topic in procurement, and I think there are profound opportunities to be deployed. For Globality, we’ll continue to endlessly innovate on user experience, ease of use, and beyond.”
“I’m overwhelmed,” are Matthias Gutzmann’s first words when asked about DPW Amsterdam 2024. At the end of the bustling two-day…
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“I’m overwhelmed,” are Matthias Gutzmann’s first words when asked about DPW Amsterdam 2024. At the end of the bustling two-day event, we sat down with Gutzmann, the company’s founder, and Herman Knevel, DPW’s CEO, for a debrief. Gutzmann also quite rightly pointed out that the final word on summarising those 48 hours is in the hands of the sponsors and attendees, but if the countless conversations we had with said sponsors and attendees are anything to go by, it was the best DPW event yet. And Gutzmann and Knevel agree.
“I really think that’s the case,” says Gutzmann. “We almost doubled the number of exhibiting startups, we had over 120 sponsors, more startup pitches than ever, and all the feedback I’ve heard so far has been amazing. There are always things you can do better, but I’m absolutely happy.”
Across the 9th and 10th of October, DPW Amsterdam welcomed over 1,300 attendees through its doors at Beurs van Berlage, Amsterdam. Those attendees arrived from 44 countries across 32 industries, and the event itself featured 72 sessions with 140 speakers across five stages. It’s abundantly clear that people are deeply passionate about DPW.
“On day one, it was already packed at 8:30 in the morning,” Knevel states. “The energy in the room was contagious, and the numbers speak for themselves. The startups, the innovators, the corporates, the mid-market – everybody who’s here has a genuine interest in what these guys are bringing to the procurement space.”
Reconnecting with the vision
Gutzmann describes that intangible energy as “bringing a little bit of joy back to procurement”. For many years, procurement was a very ill-defined concept – almost as ill-defined as the role of CPO. The shift has been a quick one, accelerated further by the COVID-19 pandemic, and events like DPW Amsterdam are part of the reason why. CPOs having somewhere to go, to meet, to learn about the procurement landscape is vital, hence that inspiring energy that permeates every DPW event.
“A lot of people are missing that vibe,” Gutzmann continues. “It’s why I founded DPW. I was inspired by Mark Perera [Chairman of DPW], who I worked with at Vizibl, and had great technology while also being so inspiring. I realised we needed to connect founders with CPOs. I think every CPO should talk to one startup founder per week, at least. It’s important that we listen to their vision.”
Striving for 10X
The core of those visions for the 2024 event revolves around the concept of 10X, the idea being that you set targets for your business that are 10 times greater than what you think you can realistically achieve. It keeps people ambitious, always striving for greatness, and it’s especially prevalent in startup culture – hence Gutzmann’s belief that CPOs should be connecting with them more.
“Deciding on 10X for this year’s theme was serendipity,” says Knevel. “The term came along and Matthias said, ‘this is it – this is what we need in procurement’. This is what the industry needs, and we’re exploring it, diving deeper.”
“Last year’s theme was ‘Make Tech Work’, which was all about getting the basics right in order to scale,” Gutzmann continues. “This year we said, ‘how can we take it further?’ We are entering the biggest wave of AI yet. That technology is giving us the opportunity and the possibility to scale outcomes. The world around us is changing so fast, so we need to be more agile, scalable, and faster in procurement. It’s a very ambitious, maybe lofty theme, but it’s a mindset more than anything else.”
“It’s the mindset that drives innovation and speed,” Knevel adds. “That’s really important in this age of procuretech and supply chain tech.”
When it comes to honing that 10X mindset, it’s all about having a purpose in mind. A lot of the procurement professionals we spoke to at DPW Amsterdam called this a ‘north star’, which is the phase Gutzmann uses too. “That’s where it starts. There’s so much procurement can do. There are so many problems in the world, and I believe procurement can be the solution to many of those. So I think it starts with the CPO and their leadership, their vision. You also have to embrace startup innovation, be more experimental in the way you work, instigate new ways of working, and be bold in your thinking. You also have to remember it’s okay to fail.”
Growing DPW
Something that’s particularly impressive about DPW Amsterdam 2024 is that it’s actually the second of the year. Back in June, DPW ventured into the North American market with an intimate summit held in New York City, which CPOstrategy was fortunate enough to be invited to. Planning one wildly popular event a year is one thing, but venturing into a whole new part of the world with an additional one is incredibly dedicated.
“I’m a bit more conservative when planning ahead, so there probably wouldn’t be a New York event without Herman encouraging me,” says Gutzmann. “I’m glad he said ‘let’s go for it’. It was a short-term plan, but it was ultimately very successful and the right decision.”
Knevel adds: “The feedback we got from sponsors and delegates was quite impressive. They were asking for more. And it’s not just Matthias and myself – we have a great team here. This is a massive production, but we made the jump and it’s paid off.”
Inspiration for 2025
When it comes to the lessons Gutzmann and Knevel have learned in response to this event, it’s more about narrowing down the influx of ideas DPW gives them. By the time we spoke with them at the end of the Amsterdam 2024 event, their heads were spinning with inspiration.
“I have so many ideas,” says Gutzmann. “Every year we reinvent the show, so we never rest. We’re always asking what we can do better. How can we improve? I think this year we maxed out the number of sponsor stands that are possible to have. We doubled the number of under-30 attendees. There’s the potential to go a little deeper on the talent side, connecting students with the corporates and building a proper program around that.”
There was also the Tech Safari this year. The idea was to make the expo hall easier to navigate, since it was more crowded than ever this year. Members of the DPW team acted as ‘super connectors’ to help attendees find the right solutions and help startups find new customers. The aim was to simply make it easier for everyone involved to find what they’re looking for in small groups,enabling them to find who they wanted, talk to them, and ask questions. It turned out to be an amazing interactive experience for people, making sure they felt thoroughly looked after and valued.
“Plus there’s an opportunity to cater more to the corporates coming in,” Gutzmann continues. “Perhaps we will build a custom program for them around the event. Some of them are already coming in with teams and doing annual leadership meetings outside of the venue, but I think there’s scope to show them solutions and do some workshops within the event. We can also do more with day zero, where we have site events. There’s much more we can do.”
Giving CPOs what they want
As for the broader future of the event, DPW’s heart lies in Amsterdam and will continue to do so. The organisation is building its team even further and putting strategies in place for future events, allowing it to move forward. “We follow the demand of what our customers want,” Knevel says. That’s what really drives DPW and how the event is themed and set up. The organisation listens to CPOs so it can give them exactly what they need, and what will help the industry level up further and further.
“There are things we’re still developing,” says Gutzmann. “For example, the podcast studio [something introduced in its current form for 2024] is something Herman is very passionate about, so it was great to test it out here. There’s more we can do with that. We have so many ideas and it’s important to engage our amazing team on these ideas and see what they think along the way.”
“We’re ideating a lot,” Knevel adds. “And we’re asking our ecosystem what we should do more of.”
“Ultimately, we’re bringing in the voice of the customer to make sure we’re giving them what they want and need,” Gutzmann concludes. “That’s the whole purpose of DPW.”
Scott Zoldi, Chief Analytics Officer at FICO considers whether the current AI bubble is set to burst, the potential repercussions of such an event, and how businesses can prepare
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Since artificial intelligence emerged more than fifty years ago, it has experienced cycles of peaks and troughs. Periods of hype, quickly followed by unmet expectations that lead to bleak periods of AI-winter as users and investment pull back. We are currently in the biggest period of hype yet. Does that mean we are setting ourselves up for the biggest, most catastrophic fall to date?
AI drawback
There is a significant chance of such a drawback occurring in the near future. So, the growing number of businesses relying on AI must take steps to prepare and mitigate the impact a drawback or complete collapse could have. Research from Lloyds recently found adoption has doubled in the last year, with 63% of firms now investing in AI, compared to 32% in 2023. In addition, the same study found 81% of financial institutions now view it as a business opportunity, up from 56% in 2023.
This hype has led organisations to explore AI use for the first time. Often with little understanding of the algorithms’ core limitations. According to Gartner, in 2023 less than 10% of organisations were capable of operationalising AI to enable meaningful execution. This could be leading to the ‘unmet expectations’ stage of the damaging hype/drawback cycle. The all-encompassing FOMO of repeating the narrative of the incredible value of AI does not align with organisations’ ability to scale, manage huge risks, or derive real sustained business value.
Regulatory pressures for AI
There has been a lack of trust in AI by consumers and businsses alike. It has resulted in new AI regulations specifying strong responsibility and transparency requirements for applications. The vast majority of organisations are unable to meet these in traditional AI, let alone newer GenAI applications. Large language models (LLMs) were prematurely released to the public. The resulting succession of fails fuelled substantial pressure on companies to pull back from using such solutions other than for internal applications. It has been reported that 60% of banking businesses are actively limiting AI usage. This shows that the drawback has already begun. Organisations that have gone all-in on GenAI – especially those early adopters – will be the ones to pull back the most, and the fastest.
In financial services, where AI use has matured over decades, analytic technologies exist today that can withstand regulatory scrutiny. Forward-looking companies are ensuring they are prepared. They are moving to interpretable AI and backup traditional analytics on hand while they explore newer technologies with appropriate caution. This is in line with proper business accountability, vs the ‘build fast, break it’, mentality of the hype spinners.
Customer trust with AI
Customer trust has been violated by repeated failures in AI, and a lack of businesses taking customer safety seriously. A pull-back will assuage inherent mistrust in companies’ use of artificial intelligence in customer facing applications and repeated harmful outcomes.
Businesses who want their AI usage to survive the impending winter need to establish corporate standards for building safe, transparent, trustworthy Responsible AI models that focus on the tenets of robust, interpretable, ethical and auditable AI. Concurrently, these practices will demonstrate that regulations are being adhered to – and that their customers can trust AI. Organisations will move from the constant broadcast of a dizzying array of possible applications, to a few well-structured, accountable and meaningful applications that provide value to consumers, built responsibly. Regulation will be the catalyst.
Preparing for the worst
Too many organisations are driving AI strategy through business owners or software engineers who often have limited to no knowledge of the specifics of algorithm mathematics and the very signifiicant risk in using the technology.
Stringing together AI is easy. Building AI that is responsible and safe is a much harder and exhausting exercise requiring model development and deployment corporate standards. Businesses need to start now to define standards for adopting the right types of AI for appropriate business applications, meet regulatory compliances, and achieve optimal consumer outcomes.
Companies need to show true data science leadership by developing a Responsible AI programme or boosting practices that have atrophied during the GenAI hype cycle which for many threw standards to the wind. They should start with a review of how regulation is developing, and whether they have the standards, data science staff and algorithm experience to appropriately address and pressure-test their applications and to establish trust in AI usage. If they’re not prepared, they need to understand the business impacts of potentially having artificial intelligence pulled from their repository of tools.
Next, these companies must determine where to use traditional AI and where they use GenAI, and ensure this is not driven by marketing narrative but meeting both regulation and real business objectives safely. Finally, companies will want to adopt a humble approach to back up their deployments, to tier down to safer tech when the model indicates its decisioning is not trustworthy.
Now is the time to go beyond aspirational and boastful claims, to have honest discussions around the risks of this technology, and to define what mature and immature AI look like. This will help prevent a major drawback.
Fred Fuller, Global Head of Banking at Endava, on how banks can effectively communicate AI advancements and demonstrate ROI to investors
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There is no single solution, AI or otherwise, that can prepare financial institutions for the modern world. To build a bank capable of successfully navigating the challenges of the future, a long-term digital transformation strategy is required. Especially relevant in the wake of recent IT outages,
At present, according to Endava’s Retail Banking Report 2024, 67% of banks are still heavily reliant on legacy systems. This leads to wasted budget and decreased efficiency. With limited resources available to modernise their tech stack, company leaders are often forced to choose which technology-type to prioritise. When doing this, 50% have chosen artificial intelligence (AI).
Is AI alone enough?
Can AI overhaul archaic processes or are there too many hurdles in the way? The first hurdle to successful digital transformation in financial services is overcoming the employees’ perception of the process. Time and time again, corporations have failed in the goal to integrate solutions that successfully feed into a long-term tech strategy. Often, this is due to wide-spread change fatigue. When exhausted by continuous efforts to change their day-to-day, workers become resistant to transformation. The best way to overcome change fatigue, and drive digital transformation in financial institutions, is through overhauling legacy systems. And adopting solutions that will stand the test of time.
Legacy Systems
Across the world, outdated legacy systems are holding financial institutions back and costing them billions. From 2022 to 2028, this expense is expected to grow at a rate of 7.8%. Not only do these archaic processes cost money, but they force banks to contend with a multitude of siloes. From departments to data. We live in a world where neobanks are growing in popularity. They are able to provide a frictionless customer experience using their modern tech stack. Traditional organisations must rid themselves of siloes to enable all areas of the business to leverage AI. In turn, this will provide them with strong data collection and support from departments who are agreed on next steps.
At present, three quarters of financial institutions feel they need to modernise their core. Without this change, they lack the secure, data-driven foundation necessary to utilise AI and see return on their technical investments.
The benefits of AI integration
Once a strong foundation has been laid, it becomes easier to see the practical benefits of integrating AI. For example, when data is no longer siloed by legacy systems, using chat bots to support customers with simple queries creates an efficient consumer experience. There are internal benefits too. AI can spot potentially suspicious activity, flagging it before it is too late. Or analysing data to ensure risk management and process automation. Despite its wide-reaching capabilities, AI alone is not the only option for financial institutions…
Routes to the future
Endava’s Retail Banking Report also showcased the variety of solutions that banks are using to improve their tech stack. 45% of respondents recognised data analytics, in and of themselves, as a top area for investment. Meanwhile 30% flagged IoT, and 14% the Metaverse.
There’s a reason for the emphasis on strong data. It not only supports the integration and use of AI-fuelled capabilities, but it is the driving force behind numerous functions of the bank itself. Of those surveyed, 37% aimed to use data to improve customer service. 34% to strengthen security, and 33% to personalise products and improve the customer experience.
As well as attracting and retaining consumers, business leaders can benefit from their access to strong data by attracting and retaining talent. With 39% of failed digital transformations viewing lack of employee buy-in as a factor, financial institutions are encouraged to educate workers on their technology integration plans, and ensure solutions are user-friendly. Fortunately, looking ahead, 20% of banks surveyed seek to use data to improve the workplace.
A bank’s priority – looking ahead
More than ever, banks are reliant on data to keep operations running smoothly. From providing customers with a personalised experience to improving the workplace in the competition for talent, there are a multitude of reasons to ensure the foundations of your tech stack are strong.
Doing so makes integration of new technology a smoother experience for all. To this end, it’s no shock that 50% of banks are keen to embrace AI, using it to benefit customers and speed up processes. However, with many hampered by the legacy technology and the ever-looming threat of change fatigue, integration of any technology should be carefully planned, customer focused and data led.
Combining advanced technology with a people-led focus is the name of the game for Bravo Consulting Group. Bravo was founded…
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Combining advanced technology with a people-led focus is the name of the game for Bravo Consulting Group. Bravo was founded in 2007 by President and CEO Gino Degregori. He had his sights squarely set on leveraging Microsoft technologies to deliver cloud services, application modernization, and cybersecurity compliance. Bravo’s aim is to simplify how organisations create, share, and secure their intelligent information. In nearly 17 years of its existence, the business has grown into a premier Microsoft solutions provider serving the federal government, the Department of Defense, the Intelligence Community, and multiple Fortune 500 organisations.
Human-centric leadership and core values
Degregori began his career in software engineering and entrepreneurship. However, he quickly realised that his true calling was beyond just developing software and implementing Microsoft technologies. “I saw an opportunity to build an amazing organisation that provides real value to our customers through our people and innovative solutions,” Degregori explains. “While the cloud didn’t exist in 2007, development, automation, and security were already crucial.”
Degregori founded Bravo on core values that remain the cornerstone of the company today. “Our vision is to attract and create kind leaders who make an impact on our customers, partners, and communities,” he explains. “We lead with empathy, embracing kind leadership. This means prioritising the growth and wellbeing of our team members and clients. We view every interaction from a win-win perspective with a strong sense of accountability.
“It’s not just about implementing technology in your organisation; it’s about truly advancing the mission. Collaborating with great people enables us to deliver outstanding results,” he emphasises. Degregori also hosts The Kind Leader Podcast where he discusses empathetic leadership with industry leaders, embodying the values Bravo champions.
By fostering a culture of empathy and innovation, Bravohas established itself as a leader in cloud services, application modernization, and cybersecurity. Degregori’s commitment to building a people-centric organisation ensures that Bravo not only meets but exceeds the expectations of its clients, driving meaningful and impactful results.
Strategic partnership with AvePoint
Bravo’s commitment to collaborating with exceptional partners has been the cornerstone of its longstanding relationship with AvePoint. For 15 out of its nearly 17 years of existence, Bravo has partnered with AvePoint—a testament to the enduring strength and value of this collaboration. When Bravo first started, the Microsoft ecosystem was rapidly evolving, with many businesses transitioning away from legacy systems. AvePoint’s advanced SharePoint migration and administration tools played a pivotal role in this transition, enabling Bravo to assist over 100,000 users across various verticals in successfully migrating and managing their content and data.
“Our partnership with AvePoint allowed us not only to migrate vast amounts of content and data efficiently but also to reduce costs, which we passed on to our customers,” says Degregori. “It was a phenomenal opportunity to leverage AvePoint’s tools for seamless content and data migration. We recognized early on that AvePoint was poised for significant success, and from then on, our collaboration deepened, enabling us to develop even better solutions.”
This partnership is a key reason customers choose Bravo. By integrating Bravo’s expertise in the Microsoft ecosystem with AvePoint’s suite of tools, Bravo delivers a unique value proposition centred on data management, compliance, and AI-driven solutions. Customers benefit from a holistic approach that not only prepares them for new technologies but also ensures regulatory compliance, cost efficiency, and superior results.
Together, Bravo and AvePoint empower organisations to confidently navigate their digital transformation. Leveraging Microsoft’s advancements in AI and AvePoint’s robust data management tools, they offer cutting-edge solutions that address the evolving needs of modern businesses. This collaboration enables organisations to optimise their data, maintain stringent compliance standards, and harness the power of AI to drive innovation and efficiency.
Expanding horizons through collaboration
For the first decade, Bravo focused exclusively on the federal sector. Recently, Degregori made the strategic decision to expand Bravo’s services into the commercial sphere. “Our strong partnership with AvePoint was instrumental in this successful expansion,” he says. “AvePoint is a global organisation, and through our collaboration, we developed a strategy to penetrate the commercial market. We leveraged our combined services, expertise, and certified professionals at Bravo to build trust and confidence with the AvePoint commercial folks.”
The unique relationship between Bravo and AvePoint has facilitated this long-standing and successful collaboration. Degregori attributes their success to three key factors: communication, clarity, and trust.
“First, strong communication ensures continuous understanding. Second, clarity about our collective goals – focusing not just on our objectives but also on AvePoint’s – allows us to align our efforts effectively. Lastly, trust is paramount. We need to rely on each other through both successful projects and challenging ones. This mutual trust ensures we can support each other through thick and thin,” Degregori explains.
“We are always learning. When things don’t go as planned, we sit down, discuss the lessons learned, and find ways to improve. This continuous learning and mutual support strengthen our partnership and drive our shared success.”
Future growth
The future of Bravo and AvePoint is exceptionally promising as technology evolves at an unprecedented pace. Both organisations are at the forefront, leveraging the Microsoft ecosystem. With Microsoft’s substantial investments in generative AI, their reach is set to expand even further into the Fortune 500 globally.
“This momentum allows us to continuously leverage advanced tools, integrating them to deliver unparalleled value to our customers,” says Degregori. This focus on the human element—the customer—ensures that Bravo remains true to its core values.
“I am immensely grateful for the opportunity to lead an incredible organisation like Bravo and to maintain a long-term partnership with AvePoint. Ultimately, while we discuss technology and solutions, it’s all about people. We’re constantly seeking ways to connect better as partners and employers. This human-centric approach is what drives us to deliver superior solutions.”
This vision and commitment to both technological excellence and human connection make Bravo and AvePoint’s partnership not only resilient but also highly impactful for their clients. Together, they are poised to lead the way in digital transformation, ensuring that organisations are not only equipped with the latest innovations but also supported by a team that values their success.
Sejal Mehta and Andrew Rodgers from Odgers Berndtson’s Global FinTech Centre of Excellence and Randy Bean, a Senior Advisor to Odgers Berndtson and industry author, explore the dynamics shaping leadership in the UK fintech sector
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The UK FinTech sector is undergoing a significant transformation, marked by maturation, consolidation, and a more selective investment landscape. Funding is increasingly funnelled towards profit-generating scale-ups, and away from newer entrants.
At the same time, the sector is shaped by a multi-generational workforce with varied perspectives. Meanwhile rapid advancements in AI foster apprehension and excitement. These converging factors make FinTech one of the most dynamic and competitive spaces to work in today. This presents both challenges and opportunities for its leaders.
From our perspective as global FinTech executive search and leadership advisors at Odgers Berndtson these shifts are reshaping the demands placed on leadership. They are also influencing what it takes to lead effectively in this fast-changing sector. Here, we explore the leadership trends that are emerging as a result.
Ethical FinTech leadership
Venture capital funding is now more selective and private equity investors are increasingly targeting fintechs with solid exposure. This is creating a difficult environment for new start-ups. Those attracting funding are typically cash-positive scale-ups.
Amidst these challenges, more FinTech firms are opting to list on the NASDAQ rather than the London Stock Exchange, as the UK navigates more stringent regulation. The need for payments licences, extensive reporting, and compliance demands weigh heavily on FinTech leaders.
In this landscape, we’re seeing leaders with experience in regulated financial services bring a valuable skillset. The ability to operate within defined regulatory frameworks while generating growth. FinTech boards are looking for leaders with high authenticity and who can make ethical decisions. And while balancing ambition and growth with the realities of working in a highly regulated space.
Founder replacements
We are in the midst of the FinTech sector’s maturation. Start-ups are transitioning into scale-ups, requiring different leadership competencies. For many, this requires the founder to step down or step into a board role and appoint a CEO who can take the business through its next stage of growth.
This requires leaders who are commercially driven, capable of shaping market strategies, and adept at understanding customer needs and product-market fit. Navigating risk and regulation becomes crucial, while the founder’s creative, opportunity-led approach typically no longer dominates the new operational and strategic demands.
Boards and investors are looking for CEOs with a broader skillset and deep regulatory expertise. These leaders must also be able to attract and retain the type of talent that can sustain growth and innovation, while maintaining the ‘DNA’ that made the business so attractive in the first place.
A multi-generational workforce
Intergenerational divides are becoming more pronounced for all businesses and noticeably in sectors like FinTech. Here, younger generations with fresh perspectives are working alongside older, more experienced professionals – often from traditional financial services backgrounds.
This diversity in age, experience, and approach can be a powerful asset, but only if integrated effectively. Typically, Gen Z and Millennials prioritise flexibility, technological integration and experimentation. Meanwhile, Boomers bring valuable expertise in regulatory environments and operational effectiveness, but may be more accustomed to traditional structures and leadership styles.
Increasingly, we see FinTech leaders attempt to bridge these divides by emphasising open communication, promoting mentorship opportunities, and encouraging cross-generational collaboration. With less funding and more regulation, FinTech leaders recognise the need to identify and capitalise on the strengths of a multigenerational workforce if they are to succeed.
Leadership team dynamics
As FinTech companies scale, leadership is no longer just about the capabilities of individual leaders but about the dynamics of the entire executive team. Successful scale-ups understand the importance of assembling a leadership team that brings a diverse mix of skills, and generational perspectives to the table.
We are starting to see FinTech companies think about leadership team dynamics as they scale up. Boards are looking for a blend of strategic, operational and ethical considerations. As well as how well team members work together. Do they solve problems cohesively? Are there any unresolved tensions or conflict? Are they aligned and equipped to collectively deliver on the leadership mandate?
Many leadership teams are not optimising their potential due to misalignment of strengths. For example, we recently worked with a FinTech creating an executive team profile to identify the leadership competencies needed to deliver their mandate. This exercise enabled the team to reallocate executive responsibilities for strategic initiatives based on the required strengths, regardless of traditional job roles.
Polarising views on Gen AI
Leading organisations are experiencing a transformational moment due to accelerated interest in AI and Generative AI. 89.6% are increasing their investments in AI, while 64.2% of companies have indicated that AI will be the most transformational technology in a generation. In response, organisations are hiring for the data and AI leadership roles required to prepare their companies for an AI future.
However, this integration of Gen AI has sparked both excitement and nervousness, particularly around issues of data protection and privacy. Generational differences are especially noticeable. Younger professionals are often less concerned about data privacy, while older generations remain cautious about the security implications.
This divergence in attitudes can create tension within the organisation, as leaders grapple with how best to leverage Gen AI while ensuring compliance with stringent data protection regulations. For some FinTechs, AI is seen as a specialised area requiring dedicated focus. Meanwhile, others believe AI represents a fundamental shift in how business can be conducted and AI strategy should be woven into the fabric of every leader’s responsibilities.
This divide in attitudes reflects the broader challenges we see FinTech companies face in incorporating AI. Leaders must now navigate the balance between embracing innovation and safeguarding sensitive information. They must also ensure AI is not seen as a siloed function. It must be an integral part of their commercial and strategic vision. Given the fundamental changes in the sector, the emphasis on leadership capabilities is changing for both the individual and executive team.
Hugo Farinha, Co-founder and CTO at Virtuoso QA on why AI is driving organisational change across financial services
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We’ve seen an enormous amount of discussion concerning all aspects of AI since the emergence of Chat GPT made it headline news. However, most articles and conversations focusing on its business impact seem to wilfully ignore the ‘elephant in the room’. Namely, the inevitable organisational change AI will usher in, especially for employees.
AI technology driving change
To ignore change is folly, and likely to have the exact opposite effect that businesses and AI technology vendors want. We can’t pretend workforces won’t be disrupted by such a seismic technological advance. Certain job roles will become obsolete. Business leaders can’t run the risk of creating a culture of fear and uncertainty among employees who are unlikely to be fooled.
It’s true AI could lead to leaner operations, particularly in insurance and finance companies, with fewer employees needed for routine tasks, but only half the story. Smart businesses will almost certainly reinvest cost savings into new growth areas that require specific human talent. Companies that maintain a strong human element in customer service and personalised offerings will differentiate themselves in a crowded market. The rise in AI-driven, agile companies will create faster market shifts and greater competition.
While AI has the potential for productivity and efficiency gains, and even to do the same with less if needed, I actually don’t predict major job culls in the next few years. AI is particularly good at data processing and data analytics, in insurance for example. So, when more data can be processed and analysed, human intervention can make better informed decisions as a result. In the short to medium term, data analysis and decision making will remain firmly in the human realm. But powered by AI.
The Future for Artificial Intelligence
Meanwhile, the technology is still evolving, and organisations need to build a model that layers over the top of AI – powered by it, rather than replaced by it. Despite the hype, we are still a long way from AI becoming an entity that can lead, implement and operate itself to a purposeful end. But it will increasingly power applications overlaid by strategic, human-led frameworks.
To achieve this, leaders must bring their teams with them on the journey. In the field of testing for example, developers have traditionally written code as part of their role. This is a very time consuming and laborious task. Historically skills gaps have led to delays in progress. But the ability to ‘outsource’ to AI has freed up the time of those developers to focus on the purpose of that code in relation to the product. And, ultimately, the customer. Similarly, leaders in all fields need a broader understanding of AI use cases such as these to make effective strategic decisions. For example, on hiring. Understanding when to bring in more people and when to bring in new technology to complement the skills of your existing team means understanding AI’s strategic implications, technical capabilities and limitations.
An Evolving Job Market
From the perspective of the employee, the job market will continuously evolve alongside AI advancements. It will require ongoing adaptation and learning to stay relevant. Skills such as empathy, communication, and negotiation will remain vital. These are differentiators and difficult for AI to replicate. Understanding AI tools and data analysis will be increasingly important, even for non-technical roles. The ability to adapt to new technologies and continuously learn will be essential. Moreover, as AI becomes more integrated, the need for professionals who understand the ethical implications and regulatory requirements will grow exponentially.
Driving growth and job creation in this new world will require a different mindset to the current received wisdom. From both employees and leaders. In addition to the advances and changes already discussed, AI also has the potential to level the playing field, enabling smaller or newer companies to compete more effectively with, and even seriously threaten, established players. With many traditional barriers to entry such as burdensome start-up costs removed, new business models are likely to emerge. In much the same way as they did in the early days of the internet. Investors will be on the lookout for the next ‘giant killer’.
This will create opportunities for those with the foresight to upskill, as well as for those looking to start their careers. Although those opportunities and the jobs of tomorrow may not yet be completely clear. What is clear, however, is that established businesses cannot afford to be complacent. Change is inevitable and empires can be toppled overnight by technology as disruptive as AI. By embracing it early, leaders in those businesses will have the opportunity to spot and fix the gaps and redundancies in their business models that the technology and its capabilities exposes before the market does so more painfully and publicly.
Our mission is to enable and lead the world’s quality-first revolution. QA tools haven’t kept up with the demands of the testing world. Virtuoso is here to deliver with AI-powered, low-code/no-code test automation to support the modern business.
“Virtuoso technology represents the foundation for software quality in the digital world, and we are proud to be a critical, guiding force in the era of AI.”
Cullen Zandstra, CTO at FloQast on mitigating the risks of AI to deliver benefits to financial services
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There’s a lot of buzz around Generative AI (GenAI). What’s not always heard beneath the noise are the very real and serious risks of this fast-developing AI tech. Let alone ways to mitigate these emerging threats.
Currently, one quarter (26%) of accounting and bookkeeping practices in the UK have now adopted GenAI in some capacity. That figure is predicted to grow for many years to come.
With this in mind, and as we hit the crest of the GenAI hype cycle, it’s critically important that leaders focus closely on the potential risks of AI deployment. They need to proactively prepare to mitigate them, rather than picking up the pieces after an incident.
Navigating the risky transition to AI
The benefits of AI are well-proven. For finance teams, AI is a powerup that unlocks major performance and efficiency boosts. It significantly enhances their ability to generate actionable insights swiftly and accurately, facilitating faster decision-making. AI isn’t here to take over but to augment the employees’ capabilities. Ultimately improving leaders’ trust in the reliability of financial reporting.
One of the most exciting aspects of AI is its potential to enable organisations to do more with less. Which, in the context of an ongoing talent shortage in accounting, is what all finance leaders are seeking to do right now. By automating routine tasks, AI empowers accountants to focus on higher-level analysis and strategic initiative, whilst drawing on fewer resources. GenAI models can help to perform routine, but important tasks. These include producing reports for key stakeholders and ensuring critical information is effectively and quickly communicated. It enables timely and precise access to business information, helping leaders to make better decisions.
However, GenAI also represents a new source of risk that is not always well understood. We know that threat actors are using GenAI to produce exploits and malware. Simultaneously levelling up their capabilities and lowering the barrier of entry for lower-skilled hackers. The GenAI models that power chatbots are vulnerable to a growing range of threats. These include prompt injection attacks, which trick AI into handing over sensitive data or generating malicious outputs.
Unfortunately, it’s not just the bad guys who can do damage to (and with) AI models. With great productivity comes great responsibility. Even an ambitious, forward-thinking, and well-meaning finance team could innocently deploy the technology. They could inadvertently make mistakes that cause major damage to their organisation. Poorly managed AI tools can expose sensitive company and customer financial data, increasing the risk of data breaches.
De-risking AI implementation
There is no technical solution you can buy to eliminate doubt and achieve 100% trust in sources of data with one press of a button. Neither is there a prompt you can enter into a large language model (LLM).
The integrity, accuracy, and availability of financial data are of paramount importance during the close and other core accountancy processes. Hallucinations (another word for “mistakes”) cannot be tolerated. Tech can solve some of the challenges around data needed to eliminate hallucinations – but we’ll always need humans in the loop.
True human oversight is required to make sure AI systems are making the right decisions. We must balance effectiveness with an ethical approach. As a result, the judgment of skilled employees is irreplaceable and is likely to remain so for the foreseeable future. Unless there is a sudden, unpredicted quantum leap in the power of AI models. It’s crucial that AI complements our work, enhancing rather than compromising the trust in financial reporting.
A new era of collaboration
As finance teams enhance their operations with AI, they will need to reach across their organisations to forge new connections and collaborate closely with security teams. Traditionally viewed as number-crunchers, accountants are now poised to drive strategic value by integrating advanced technologies securely. The accelerating adoption of GenAI is an opportunity to forge links between departments which may not always have worked closely together in the past.
By fostering a collaborative environment between finance and security teams, businesses can develop robust AI solutions. They can boost efficiency and deliver strategic benefits while safeguarding against potential threats. This partnership is essential for creating a secure foundation for growth.
AI in accountancy: The road forward
The accounting profession stands on the threshold of an era of AI-driven growth. Professionals who embrace and understand this technology will find themselves indispensable.
However, as we incorporate AI into our workflows, it is crucial to ensure GenAI is implemented safely and does not introduce security risks. By establishing robust safeguards and adhering to best practices in AI deployment, we can protect sensitive financial information and uphold the integrity of our profession. Embracing AI responsibly ensures we harness its full potential while guarding against vulnerabilities, leading our organisations confidently into the future.
Founded in 2013, FloQast is the leading cloud-based accounting transformation platform created by accountants, for accountants. FloQast brings AI and automation innovation into everyday accounting workflows, empowering accountants to work better together and perform their tasks with greater efficiency and accuracy. Now controllers and accountants can spend more time delivering greater strategic value while enjoying a better work-life balance.
Russ Rawlings, RVP, Enterprise, UK&I at Databricks, on the future of AI in FinTech
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Strict regulation, along with time and cost restraints, means financial services must take a measured approach to technological advancements. However, with the emergence of GenAI, particularly large language models (LLMs), organisations have an opportunity to maximise the value of their data to streamline internal operations and enhance efficiencies.
Embracing GenAI has never been more important for organisations looking to stay ahead of the curve. 40-60% of the global workforce will be impacted by the growth of AI. Moreover, global adoption of GenAI could add the equivalent of $2.6tn to $4.4tn in value annually to global industries. The banking sector stands to gain between $200-340 billion.
But whilst the financial services industry can gain incredible benefits from GenAI, adoption is not without its challenges. Financial organisations must prioritise responsible data management. They must also navigate strict privacy regulations and carefully curate the information they use to train their models. But, for companies that persevere through these obstacles, the benefits will be substantial.
Building customised LLMs for financial services
Consumer chatbots have brought GenAI to the mainstream. Meanwhile, the true potential of this transformative technology lies in its ability to be tailored to the unique needs of any organisation, in any industry. Including the financial sector.
Risk assessment, fraud prevention, and delivering personalised customer experiences are some of the use cases of custom open source models. Created using a company’s proprietary data, these models ensure relevant and accurate results. And are more cost-effective due to their smaller datasets. For instance, banks can use a customised model to seamlessly analyse customer behaviour and flag up any suspicious or fraudulent activities. Or, a model can leverage sophisticated algorithms to assess an individual’s eligibility for a loan.
Another huge benefit of these tailored systems is trust and security. Deploying a custom open-source model eliminates the need to share sensitive information with third parties. This is crucial for organisations operating within such a highly regulated industry. This approach also democratises the training of custom models. Furthermore, it allows organisations to harness the power of GenAI whilst retaining control and compliance.
Using data intelligence to boost AI’s impact
To truly harness the power of GenAI, organisations must cultivate a deep understanding of data across the entire workforce. Every employee, regardless of how technical they are, must grasp the importance of proper data storage. Also how data can be used to improve decision-making.
Organisations can use a data intelligence platform to help implement this. Built on a lakehouse architecture, a data intelligence platform provides an open, unified foundation for all data and governance. It operates as a secure end-to-end solution tailored to the specific needs of the financial services industry. By adopting such a platform, businesses can eliminate their reliance on third party solutions for data analysis. They can create a streamlined approach to data governance and accelerate data-driven outcomes. Users across all levels of the business can navigate their organisation’s data, using GenAI to uncover important insights.
The future of AI in the financial sector
The path to success lies in embracing GenAI as a canvas for crafting bespoke solutions. Whilst no two financial institutions are exactly the same, the industry’s tools must strike a delicate balance between supporting specific use cases and addressing broader requirements, Customised, open source LLMs and data intelligence platforms hold the key, sparking transformative change across the sector. These tailored solutions will empower financial businesses to integrate cutting-edge innovations and ensure security, governance and customer satisfaction. Organisations that embrace this change will not only gain a competitive edge, but also pave the way for larger transformations, re-shaping the financial landscape and setting new standards for the industry.
Databricks is the data and AI company with origins in academia and the open source community. Databricks was founded in 2013 by the original creators of Apache Spark™, Delta Lake and MLflow. As the world’s first and only lakehouse platform in the cloud, Databricks combines the best of data warehouses and data lakes to offer an open and unified platform for data and AI.
Pat Bermingham, CEO of B2B digital payment specialist Adflex, asks what impact will Artificial Intelligence really have on B2B payments?
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Visit any social media newsfeed and countless posts will tell you AI means “nothing will ever be the same again”. Or even that “you’re doing AI wrong”. The volume of hyperbolic opinions being pushed makes it almost impossible for businesses to decipher between hype and reality.
This is an issue the European Union’s ‘AI Act’ (the Act), which came into force on 1 August 2024, aims to address. The Act is the world’s first regulation on artificial intelligence. It sets out how to govern the deployment and use of AI systems. The Act recognises the transformative potential AI can have for financial services, while also acknowledging its limitations and risks.
Within the debate about AI in financial services, B2B payments are an area where AI has huge potential to accelerate digital innovation. Let’s go beyond the hype to provide a true perspective on what AI really means for B2B payments specifically.
Understanding what AI is, and what it isn’t
AI is a system or systems that can perform tasks that normally require human intelligence. It incorporates machine learning (ML). ML has been used by developers for years to give computers the ability to learn without being explicitly programmed. In other words, the system can look at data and analyse it to refine functions and outcomes.
A newer part of this is ‘deep learning’, which leverages multi-layered neural networks. This simulates the complex decision-making power of our brains. The deep learning benefits outlined later in this article are based on Large Language Models (LLMs). LLMs are pre-trained on representative data (such as payment/transaction/tender data). Deep learning AI does not just look at and learn patterns of behaviour from the data. It is becoming capable of making informed decisions based on this data.
Before we explore what this means for B2B payments, let’s make one caveat clear: human supervision is still needed to ensure the smooth running of operations. AI is a supporting tool, not a single answer to every question. The technology is still maturing. You cannot hand over the keys to your B2B payments process quite yet. Manual processes will retain their place in B2B payments. AI tools will help you learn, adapt and improve more quickly and at scale.
The AI Act – what you need to know
The Act attempts to categorise different AI systems based on potential impact and risk. The two key risk categories include:
Unacceptable risk – AI systems deemed a threat to people, which will be banned. This includes systems involved in cognitive behavioural manipulation, social scoring, and real-time biometric identification.
High risk – AI systems that negatively affect safety or fundamental rights. High-risk AI systems will undergo rigorous assessment and must adhere to stringent regulatory standards before being put on the market. These high risk systems will be divided into two further categories:
AI systems that are used in products falling under the EU’s product safety legislation, including toys, aviation, cars, medical devices and lifts.
AI systems falling into specific areas that will have to be registered in an EU database.
The most widely used form of AI currently, ‘generative AI’ (think ChatGPT, Copilot and Gemini), won’t be classified as high-risk. However, it will have to comply with transparency requirements and EU copyright law.
High-impact general-purpose AI models that might pose systemic risk, such as GPT-4o, will have to undergo thorough evaluations. Any serious incidents would have to be reported to the European Commission.
The Act aims to become fully applicable by May 2026. Following consultations, amendments and the creation of ‘oversight agencies’ in each EU member state. Though, as early as November 2024, the EU will start banning ‘unacceptable risk’ AI systems. And by February 2025 the ‘codes of practice’ will be applied.
So, with the Act in mind, how can AI be used in a risk-free manner to optimise B2B payments?
Today’s B2B payment platforms are not one-size-fits-all solutions; instead, they provide a toolkit for businesses to customise their payment interactions.
AI-based LLMs and ML can be used by payment providers to rapidly understand and interpret the extensive data they have access to (such as invoices or receipts). By doing this, we gain insights into trends, buyer behaviour, risk analysis and anomaly detection. Without AI, this is a manual, time consuming task.
One tangible benefit of this data analysis for businesses comes from combining payment data with knowledge of a wide range of vendors’ skills, products and/or services. AI could then, for example, identify when an existing supplier is able to supply something currently being sourced elsewhere. By using one supplier for both products/services, the business saves through economies of scale.
Another benefit of data analysis comes from payment technology experts. Ours have been training one service to extract data from a purchase order or invoice, to flow level 3 data, which is tax evident in some territories. This automatically provides the buyer with more details of the transaction, including relevant tax information, invoice number, cost centre, and a breakdown of the products or service supplied. This makes it easy and straightforward to manage tax reporting and remittance, purchase control and reconciliation.
AI-driven data analysis isn’t just a time and money-saver, however. It also adds new value by enabling providers to use the data to create hyper-personalised payment experiences for each buyer or supplier. For example, AI and ML tools could look out for buying and selling opportunities, and perform a ‘matchmaking supplier enablement service’ that recommends the best payment methods – and the best rates – for different accounts or transactions. The more personalised a payment experience is, the happier the buyer and more likely they are to (re)purchase.
Efficient data flows mean stronger cash flows
Another practical application of AI is to help optimise cash management for buyers. This is done by using the data to determine who is strategically important and when to pay them. It could even recommend grouping certain invoices together for the same supplier, consolidating them into one payment per supplier, reducing interchange fees and driving down the cost of card acceptance.
AI can also perform predictive analysis for cash flow management, rapidly analysing historical payment data to predict cash flow trends, allowing businesses to anticipate and address potential challenges proactively. This is particularly valuable in the current economic climate where cashflow is utterly vital.
By extracting value-added, tax evident data from a purchase order or invoice, AI can rapidly analyse invoices and receipts to enable efficient, accurate automation of the VAT reclaims process. Imagine: the time comes for your finance team to reclaim VAT on recent invoices and receipts, but they don’t have to manually go through every receipt or invoices and categorise them into a reclaim pile or not reclaimable. It sounds like a dream but it will be the reality for business everywhere: AI does the heavy lifting and humans verify it, saving significant time and resources.
The third significant benefit of AI is automated invoice reconciliation. By identifying key information from an invoice and recognising regular payees, AI can streamline and automate the review process. This has the potential to significantly speed up transactions and enable more efficient payment orchestration.
Binding together all supporting paperwork, such as shipping, customs, routes, and JIT (just-in-time) requirements can also be done by AI, and it’s likely to be less prone to human error.
This provides an amazing opportunity to make B2B payments faster, reduce costs and increase efficiency. Businesses know this: 44% of mid-sized firms anticipate cost savings and enhanced cash flow as a direct result of implementing further automation within the next three years. According to American Express, 48% of mid-sized firms expect to see payment processes accelerate, with more reliable payments and a broader range of payment options emerging.
When. Not if.
There are significant opportunities to leverage AI in B2B payment processes, making it do the heavy lifting. It is, however, essential to view these opportunities with a balanced understanding of the limitations of AI.
While all the opportunities for AI in B2B payments outlined here are based on relatively low-risk AI systems, human oversight of these systems is still essential. However, with all the freed-up time and resource achieved through the implementation of AI, this issue can be avoided.
AI in B2B payments is not an if, but a when. The question is, when will you make the jump, hand in hand with technology, rather than fearing it or passing full control over to it.
In order to grow, it is essential for users to see the tangible benefits. For example, by enhancing efficiencies in account payable (AP), businesses can reallocate time and resource previously spent in AP to other areas. Early adopters are starting to test the water but only time will tell how much of an impact AI will make.
Most businesses will likely wait for the early adopters to fail, learn and progress. If something goes wrong in B2B payments, it can have a huge impact on individuals, businesses and economises. Only when the risk is clearly defined and manageable will AI truly become the gamechanger in B2B payments that all the hype claims.
“Adflexhas been at the heart of the B2B fintech revolution from the beginning. We are known for fostering innovation and helping companies harness the power of digital payments. Our technology and expertise bring together buyers and suppliers to make transactions fast, cost-effective and straightforward to manage. We take the pain out of the supply chain by delivering seamless and secure payment integration that adds value to both buyers and merchants.”
Michael Donnelly, Head of Client Success at BlueFlame AI, on how to prepare your firm to attract and retain the next generation of AI talent
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In the fast-paced world of financial services, a new generation is stepping in with high expectations for generative artificial intelligence (AI) in the workplace. Recently, BlueFlame AI conducted a specialised training session for one of our private equity clients, aimed at their newly hired summer intern class. The experience was eye-opening for us. Furthermore, it also provided a great lesson in the growing importance of AI in the industry and the expectations today’s young professionals have as they enter the workforce
AI & LLMs
The comprehensive training session covered vital areas such as AI and Large Language Models (LLMs), a review of the most popular use cases the industry has adopted, and hands-on practical training in prompt engineering. Moreover, our goal was to show this next generation the skills they’ll need to leverage these tools effectively. New roles could revolutionise alternative investment management processes like due diligence, market analysis, and portfolio management.
We also used this as an opportunity to survey the group about their experience of and expectations for AI use in the workplace – and it yielded some striking insights. A significant 50% of the interns reported using ChatGPT daily, with 83% utilising it at least weekly. Furthermore, these numbers suggest young professionals expect these tools to be available to them in their professional lives. In the same way they are available in their personal lives and set to become as commonplace as traditional software in the workplace. The interns’ expectations regarding AI’s impact on their work efficiency are even more telling. An overwhelming 94% believe these tools will enhance their productivity, indicating strong faith in the technology’s potential to streamline tasks and boost performance.
These high expectations have key implications for employers. A significant 89% of interns expect their employers to provide enterprise-grade AI/LLM access. This statistic is a wake-up call for companies that have yet to invest in AI technologies, highlighting the need to stay competitive not just in terms of products and services but also in workplace technology provision.
Talent Acquisition & Retention
Perhaps most important is AI’s potential impact on talent acquisition and retention. One-third (33%) of interns surveyed indicated they would reconsider their choice of employer if they didn’t offer access to enterprise-grade AI/LLM tools. A response that could throw a serious wrench into any Financial Services firm’s hiring plans.
The message is clear for businesses looking to stay ahead of the curve when it comes to supporting their employees. Investing in AI technologies and training is no longer optional. Firms must be ready to meet the expectations of the incoming workforce. They need to provide them with the best technology to maintain a competitive edge in an increasingly AI-driven business landscape. Companies that embrace AI and provide their employees with the tools and training to harness its power will likely see significant productivity, innovation, and talent retention advantages.
AI Revolution
Private and public investment firms stand to benefit greatly from this AI revolution. As this new generation brings its enthusiasm and expectations for technology tools into the workplace, firms that are prepared to meet these expectations will be better positioned to tap into fresh perspectives, drive innovation and reap significant efficiency and productivity gains. And if firms can take a proactive approach to training and commit to developing a forward-thinking, AI-enabled workforce, they will be able to enhance their teams’ capabilities and shape the future of work in the financial sector.
Generative AI and the workplace expectations it has created mark a new paradigm in the market. The next generation of professionals is not just ready for AI – they’re demanding it. Firms that recognize and act on this trend will be well-positioned to lead the pack when it comes to innovation, efficiency and talent acquisition.
Founded in 2023 BlueFlame AI is the only AI-native, purpose built, LLM-agnostic solution for Alternative Investment Managers.
Our cover star, EY’s Global Chief Data Officer Marco Vernocchi, tells Interface why data is a “team sport” and reveals…
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Our cover star, EY’s Global Chief Data Officer Marco Vernocchi, tells Interface why data is a “team sport” and reveals the transformation journey towards realising its potential for one of the world’s largest professional services organisations.
Welcome to the latest issue of Interface magazine!
Global Chief Data Officer, Marco Vernocchi, reflects on the data transformation journey at one of the world’s largest professional services networks.
“Data is pervasive, it’s everywhere and nowhere at the same time. It’s not a physical asset, but it’s a part of every business activity every day. I joined EY in 2019 as the first Global Chief Data Officer. Our vision was to recognise data as a strategic competitive asset for the organisation. Through the efforts of leadership and the Data Office team, we’ve elevated data from a commodity utility to an asset. Our formal data strategy defined with clarity the purpose, scope, goals and timeline of how we manage data across EY. Bringing data to the centre of what we do has created a competitive asset that is transforming the way we work.”
PivotalEdge Capital
Sid Ghatak, Founder & CEO of asset management firm PivotalEdge Capital, spoked to us about the pioneering use of “data-centric AI” for trading models capable of solving the problems of trust and cost.
“I’ve always advocated data-driven decision-making throughout my career,” says Ghatak. “I knew when I started an asset management firm that it needed to be data-centric AI from the very beginning. A few early missteps in my career taught me the importance of having a stable and reliable flow of data in production systems and that became a criterion.”
LSC Communications
Piotr Topor, Director of Information Security & Governance at LSC Communications, discusses tackling the cyber skills shortage, AI, and bringing together the business and IT to create a cyber-conscious culture at a global leader in print and digital media solutions.
Topor tells Interface: “The main challenge we’re dealing with is overcoming the disconnect between cybersecurity and business goals.”
América Televisión
Interface meets again with Jose Hernandez, Chief Digital Officer at América Televisión, who reveals how the company is embracing new business models, and maintaining market leadership in Peru.
“Launching our FAST channel represents a pivotal step in diversifying our content delivery and monetisation strategies. Furthermore, aligning us with global trends while catering to the changing viewing habits of our audience,” says Hernandez.
Also in this issue of Interface, we hear from eflow about new approaches to Regtech; get the lowdown on bridging the AI skills gap from CI&T; and GCX on the best ways to navigate changing cybersecurity regulations.
Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer…
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Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer support, and automate processes, making banks more efficient and customer-focused.
Research by McKinsey shows that over 20 percent of an organisation’s digital budget goes towards AI. The study links significant investments in AI to a 10-20 percent increase in sales. AI will play a central role in boosting efficiency, customer service, and overall banking productivity.
Introduction to AI in Personalised Banking
Delivering personalised experiences is crucial for customer satisfaction and retention. AI helps banks achieve this by collecting and analysing customer data. This data is then used to create recommendations, product offerings, and even financial advice tailored to each customer’s needs.
AI tools can optimise workflows through a technique called prescriptive personalisation, using past data to predict future behaviour. Real-time personalisation takes this further, incorporating current information alongside historical data.
This allows banks to deliver highly customised virtual assistants and real-time recommendations powered by natural language processing (NLP) models. These AI-powered assistants not only build trust and user engagement but also simplify interactions with the bank.
Tool 1: Predictive Analytics
Predictive analytics, powered by AI tools, unlock a new level of customer personalisation in banking. These tools analyse data to uncover hidden patterns and trends that traditional methods might miss. This knowledge reveals sales opportunities, possibilities for cross-selling, and ways to improve efficiency.
Predictive analytics use past data to forecast customer behaviour and market trends. This foresight allows banks to tailor marketing strategies and sales approaches to meet changing customer needs and capitalise on emerging opportunities.
Tool 2: Chatbots and Virtual Assistants
One key advantage of chatbots is their constant availability. This is especially helpful for customers who need assistance outside of regular operating hours.
AI chatbots learn from every interaction, improving their ability to understand and meet individual customer needs. By integrating chatbots into banking apps, banks can provide personalised banking experiences and recommend financial products and services that fit a customer’s specific situation.
Erica, a virtual assistant developed by Bank of America, handles tasks like managing credit card debt and updating security information. With over 50 million requests handled in 2019 alone, Erica demonstrates the potential of chatbots as efficient assistants for customers.
Tool 3: Recommendation Engines
Banks use AI tools to analyse vast amounts of customer data, including purchases, browsing habits, and background information. This deep understanding helps banks recommend products that truly fit each customer’s needs.
These personalised recommendations extend beyond credit card suggestions. AI can identify potential investments or loans that align with a customer’s financial goals. By providing customers with relevant information, banks allow them to make informed financial decisions.
Tool 4: Sentiment Analysis with AI
AI sentiment analysis translates written text into valuable insights. AI uses NLP to understand emotions and opinions in written communication. By examining things like customer feedback, emails, and social media conversations, banks gain a much clearer picture of customer sentiment.
Tool 5: Voice Recognition
AI-powered voice assistants offer a convenient way to handle everyday banking tasks. From checking balances to paying bills, all a customer needs are simple voice commands.
These assistants use NLP to understand customer requests and respond accurately. Voice authentication adds another layer of security by verifying customer identity during transactions.
Tool 6: Process Automation
Robotic Process Automation (RPA) automates repetitive tasks, boosting operational efficiency. It tackles up to 80 percent of routine work and frees up workers for more valuable tasks requiring human judgement.
RPA bots can handle tasks like issuing and scheduling invoices, reviewing payments, securing billing, and streamlining collections – all at once. NLP empowers these bots to extract information from documents, simplifying application processing and decision-making.
Tool 7: Facial Recognition with AI
Facial recognition helps banks verify customer identities during tasks like opening accounts, accessing information, and making transactions. Compared to traditional passwords, facial recognition offers stronger security and greater convenience. It eliminates the need for remembering complex passwords or worrying about stolen credentials, making banking interactions smoother and less error-prone. This technology also helps prevent fraud by identifying attempts to impersonate real customers.
Capital One AI Case Study
Capital One demonstrates how AI can personalise banking. Their AI assistant uses NLP to understand customer questions and provide immediate answers. Capital One also incorporates AI into fraud detection. Machine learning and predictive analytics help pinpoint suspicious credit card activity to strengthen security measures.
Conclusion
AI tools offer a significant opportunity for banks to improve customer experiences and achieve long-term success. By personalising banking services with AI, banks can better meet individual customer needs. This leads to higher satisfaction and loyalty, which enhances the bank/customer relationship.
AI has the potential for an even greater impact. As banks integrate more advanced AI capabilities, they can create even more engaging and personalised interactions. This focus on ‘hyper-personalisation’ could be the next big step for financial institutions to set them apart in a competitive market.
Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects…
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Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects that 75 percent of financial institutions will invest $31 billion in integrating AI into their existing systems by 2025. The trend is driven by customer demand for faster and more convenient banking options.
AI excels at analysing enormous amounts of data. This lets banks find patterns and trends to personalise customer service and boost efficiency. For example, AI-powered chatbots offer 24/7 help with basic questions, freeing up customer service staff for trickier issues. AI can also analyse customer behaviour to predict their needs and suggest relevant services or support, from personalised investment options to flagging suspicious account activity.
Benefit 1: Increased Efficiency
Long wait times and impersonal interactions often leave customers frustrated with traditional bank customer service. Fortunately, AI streamlines the experience by providing quick and accurate answers. It eliminates the need to navigate complex phone menus.
AI personalises interactions and saves customers from endless button-pressing and long hold times. AI in customer service can also analyse vast amounts of customer data. The data helps banks anticipate customer needs and recommend tailored solutions, preventing problems before they arise. This results in higher customer satisfaction and a smoother banking experience.
Benefit 2: Personalisation
AI can analyse vast amounts of customer data, including purchases and browsing habits, to create detailed customer profiles. These profiles help banks recommend relevant products and services that fit individual needs.
For instance, a customer who often pays bills online might be recommended a new budgeting tool. Similarly, someone who regularly saves for travel could receive information about travel insurance or currency exchange. These personalised suggestions can come through various channels, like the bank’s website, email alerts, or chatbots.
Benefit 3: Cost Savings
Cost savings are a major advantage of AI-powered customer service in banking. One key way AI achieves this is through automation. Chatbots powered by AI can handle many routine customer inquiries, freeing up human agents for complex issues. This reduces labour costs while also improving response times.
AI also helps with better staffing management. It can analyse past data to predict how many calls are coming in. Banks can then ensure they have the right number of agents available, avoiding overstaffing or understaffing that can significantly impact costs.
Benefit 4: 24/7 Support
Traditionally, reaching a support agent often meant waiting on hold during peak hours. However, AI in customer service is transforming the industry by offering immediate assistance through chatbots. These virtual assistants provide instant support the moment a customer reaches out.
Unlike human agents with limited working hours, chatbots are available 24/7. This ensures customers get help whenever they need it, regardless of location or time zone. This is especially valuable in the globalised world, where customers might need support outside of regular business hours.
A great example of this success is Photobucket, a media hosting service. After implementing a chatbot, they offered 24/7 support to international customers. This results in a three percent increase in customer satisfaction scores along with a 17 percent improvement in resolving issues on the first try.
Benefit 5: Multilingual Support
AI-powered chatbots offer multilingual support, breaking down language barriers and creating a positive banking experience. These chatbots can figure out a customer’s preferred language at the start of a conversation. This ensures clear communication, no matter what language the customer speaks.
Conclusion
A study by Global Market Insights predicts the conversational AI market will reach $57.2 billion by 2032. This technology is making big strides in banking, particularly by automating routine tasks and inquiries. By taking care of these repetitive tasks, AI frees up human agents to focus on more complex customer issues. This improves efficiency and helps banks manage their operating costs. A streamlined customer service experience builds trust and loyalty, which can lead to business growth for financial institutions.
McKinsey & Co. is seeing an increase in the number of clients seeking artificial intelligence-linked projects, reports Bloomberg. Faster adoption…
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McKinsey & Co. is seeing an increase in the number of clients seeking artificial intelligence-linked projects, reports Bloomberg. Faster adoption of the technology is helping the consulting titan and its peers boost revenue, across industries like Insurtech, following a period of tumult.
About 40 per cent of the New York-based firm’s client projects involve the technology. The number of AI-related customers in the past 12 months is approaching 500, Rodney Zemmel, senior partner and head of the firm’s digital business, said in an interview.
“We believe the long- or the medium-term economic implications are very real,” Zemmel said. He was a final candidate in the recent global managing partner leadership elections at the firm. According to people familiar with the matter, who asked not to be identified discussing confidential information.
Though there’s some degree of hype around AI, “we’re seeing the organisations that are doing that are getting value from it,” Zemmel said. “It’ll be a little longer, and maybe, a little harder than people think, but we’ve got no doubt that the value is there,” he added.
AI adoption across Insurtech
Among those deploying automation rapidly are the traditional and regulated industries such as banking and insurance, Zemmel said. In a June report, Citigroup Inc. said AI is poised to upend consumer finance and make workers more productive. Additionally, with a high potential for 54 per cent of jobs across banking to be automated. Citi also said that the technology could add $170 billion to the industry’s coffers by 2028.
JPMorgan Chase & Co. Chief Executive Officer Jamie Dimon has called AI “critical” to his company’s future success. He also noted the technology can be used to help the firm develop new products, drive customer engagement, improve productivity and enhance risk management.
The surge in automation has come as a relief for the broader consulting industry. It has been battling a slowdown in demand for its traditional services. McKinsey, Ernst & Young and PricewaterhouseCoopers have been cutting jobs to weather the slump. Furthermore, Accenture Plc shares tumbled in March after the company warned it’s seen financial-services customers, including Insurtech, rein in spending on its software.
AI’s rise is also diverting some budgets toward specialist consultancies. Although AI-focused units like McKinsey’s QuantumBlack are growing rapidly, according to Zemmel.
McKinsey – QuantumBlack
McKinsey, which has advised everyone from the U.S.’ Pentagon to China’s Ping An Insurance Group Co., currently has about 2,000 people working across QuantumBlack. It has 7,000 staff in total in tech-related fields, according to Zemmel’s estimates. McKinsey’s headcount stood at about 45,000 globally as of 2023 and revenues were at a record $16 billion.
Zemmel said that the firm is still evaluating how the use of AI will impact its own headcount over the longer run. McKinsey had earlier warned about 3,000 of its consultants that their performance was unsatisfactory and will need to improve.
“We’re certainly planning on being agile about it,” Zemmel said. “One thing that’s clear is everybody in our organization’s going to need to know how to use AI and incorporate in their day-to-day work if they’re going to remain relevant to their clients.”
AI-powered threat detection, automation, and data analysis are empowering fintech cybersecurity teams to more effectively meet the challenges of an evolving world.
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Artificial intelligence (AI) is driving a new generation of modern cybersecurity solutions. The technology is transforming how organisations protect against evolving digital threats, as predictive and big data analytics bring new benefits to the sector.
How is AI transforming cybersecurity for fintech teams?
AI’s importance in cybersecurity lies in its ability to provide advanced threat detection, automate responses, and adapt to evolving threats. It can also handle large amounts of data, making monitoring networks and detecting issues easier without increasing risks.
AI learns from past experiences, recognising patterns and improving over time. This makes it good at spotting weak passwords and alerting the right people. AI can also block harmful bots that try to overload websites. AI automates large amounts of tasks, allowing for 24/7 monitoring and quicker responses to security threats.
Its machine learning algorithms analyse vast datasets in real-time, identifying patterns and anomalies to detect emerging threats. As AI excels in behavioural analytics, it establishes a baseline of normal behaviour to spot deviations that indicate security threats.
Unlike traditional methods that rely on predefined signatures, AI can identify zero-day threats—new and previously unknown vulnerabilities—promptly. This proactive approach allows organisations to respond swiftly, preventing potential breaches before they occur.
AI also enhances threat intelligence by automating the analysis of code and network traffic, freeing up human analysts for more complex tasks. It, in turn, facilitates automated incident responses, rapidly mitigating attacks and minimising damage.
Predictive AI in Fraud Detection
AI is revolutionising fraud prevention by using predictive and behavioural analysis to detect and prevent fraudulent activities. By analysing historical data, AI identifies patterns that often precede fraud. This approach not only enhances detection accuracy but also reduces false alarms, distinguishing between normal and suspicious behaviours with greater precision.
In real-time, AI monitors multiple transactions simultaneously, flagging suspicious activities as they happen to mitigate risks promptly. It learns individual customer behaviours to detect anomalies, such as large transactions or unusual patterns. These triggers prompt alerts for investigation or automated protective measures, such as account freezing.
Despite challenges such as data privacy and the need for extensive datasets, AI’s advancements in machine learning promise increasingly effective solutions for protecting financial systems.
Industry case studies: Vectra and Kasisto
Fintech companies like Vectra use AI-powered technologies such as Cognito to automate threat detection and response. These systems analyse vast datasets to detect and pursue cyber threats swiftly, ensuring comprehensive security measures against malicious activities.
Tools like Kasisto’s KAI enhance customer experiences by providing personalised financial advice through AI-driven chatbots. This demonstrates AI’s versatile applications in improving both security and service delivery within the fintech sector.
AI’s use cases in cybersecurity are expected to increase. AI will revolutionise how users are authenticated. It will use advanced biometric analysis and behaviour tracking to make it harder for unauthorised users to gain access while ensuring a smooth experience for legitimate users.
This approach strengthens security by verifying identities with methods like fingerprints or facial recognition and detects unusual behaviours for added protection. AI’s ability to learn continuously from new data means cybersecurity systems will become smarter and more effective over time, adapting quickly to new threats.
The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment,…
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The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment, many are embracing artificial intelligence (AI) for assistance with investment decisions. AI acts as a powerful tool, improving efficiency and effectiveness across various aspects of asset management.
From analysing market trends to building diversified portfolios, AI’s strength lies in processing massive amounts of data. Furthermore, it uncovers hidden patterns empowering managers to make data-driven investment choices across financial services.
Introduction to AI in Asset Management
Asset management involves managing investment portfolios for individuals, institutions, and businesses. This includes stocks, bonds, real estate, and other financial assets. The main goal is to grow value over time while minimising risk and meeting client goals.
AI is transforming asset management with its data processing and analytics capabilities. Additionally, AI algorithms can quickly analyse massive amounts of financial data, market trends, and economic indicators. This helps uncover hidden patterns and connections that human analysts might miss. A data-driven approach empowers asset managers to make better investment decisions and develop more accurate market forecasts.
Portfolio Management
AI is transforming asset management by offering powerful tools for better decision-making. Moreover, machine learning (ML), AI analyses vast amounts of historical market data to identify patterns and predict future trends, providing valuable insights for building portfolios.
Natural language processing (NLP) lets computers understand human language. NLP can unlock information from unstructured sources like news articles, social media, and analyst reports. The algorithms then analyse sentiment and extract key information that feeds into portfolio decisions.
AI optimisation algorithms help construct optimal portfolios. These algorithms consider risk tolerance, return goals, and investment limitations. By using these tools, portfolio managers can create portfolios designed to maximise returns while minimising risk.
Risk Management
AI is changing how investment decisions are made. The AI algorithms can analyse massive amounts of historical market data and complex risk models.
The analysis provides a deeper understanding of individual asset risk and the overall portfolio’s exposure. With this knowledge, investment managers can proactively identify potential risks and develop strategies to lessen them.
AI offers real-time risk monitoring. An AI-powered system continuously tracks portfolio performance, alerting managers to any significant changes in risk. This allows for swift adjustments as market conditions evolve.
Automated Trading
Traditional automated trading tools execute trades based on pre-programmed instructions from human traders. These tools function within the parameters set by the user and can’t analyse markets on their own.
AI offers truly independent systems with tools that can analyse markets using technical and fundamental analysis with minimal human input.
AI uses sentiment analysis, ML, and complex algorithms to process vast amounts of information and identify trends. This data-driven approach removes the emotional bias that can affect human traders.
Case Studies
The asset management industry is seeing a rise in firms using AI to improve performance. A recent example isDeutsche Bank’s collaboration with NVIDIA. This multi-year project aims to integrate AI across their financial services. This includes virtual assistants for easier communication and AI-powered fraud detection. The bank expects faster risk assessments and improved portfolio optimisation.
Morgan Stanley is also making strides in AI adoption. Partnering with OpenAI, their financial advisors now have access to a massive research library at high speed. Advisors can explore client portfolio strategies and find relevant information in seconds, leading to better-informed advice.
Future Prospects
APwC report predicts artificial intelligence will significantly boost global GDP, contributing up to $15.7 trillion in 2030. This advancement could reshape asset management in the coming years, leading to entirely new business models and investment strategies.
One future possibility involves fully automated investment platforms powered by AI. These platforms would manage investment portfolios with minimal human involvement and use real-time data analysis to create personalised investment plans.
Moreover, AI could pave the way for more dynamic investment strategies that respond to market changes. By constantly analysing market conditions, AI can automatically adjust investment portfolios to optimise returns and minimise risks. This could lead to more resilient and adaptable investment systems that are better equipped to navigate various market environments.
We chatted to Gabe Perez from RiseNow about prioritising humans during technological transformation.
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RiseNow, as a procurement and supply chain strategy and design firm, is firmly plugged into the needs of the sector’s functions as they evolve. Its growth has been organic thanks to customers demanding exactly what they want. They can’t simply implement tech with the goal of ‘go live’ anymore. They need expert help to define the real outcomes.
RiseNow provides end-to-end guidance for customers. This ensures that when they implement new systems, they explore the whole picture from the beginning. It was a topic discussed in detail at DPW NYC in June, where we met up with Gabe Perez, Chief Strategy Officer.
“What we’re seeing in the market is that people are asking for guidance around operating models,” says Perez. “Our focus right now is trying to keep up with demand. There are a lot of different service providers out there.
“We’re showing RiseNow’s clients how to design, execute, and operate. So we’re really focused on helping customers end-to-end, whether they’re optimising what they currently have, or starting from a new platform.”
Humans first, then technology
As procurement continues to digitise, roadblocks that hinder technology’s effectiveness and promise of value become more apparent. One of these is implementing technology for technology’s sake. Or, simply using tech to digitise already-existing processes versus examining the why behind those processes.
“As David Rogers from Columbia Business School said, the best technology is not the most important part of digital transformation,” says Perez. “People are at the core of it. Procurement has to start focusing more on outcomes and let that drive technology. People are running to technology for answers, but they don’t have the right operating model set up by the right people. Plus, there’s a huge talent shortage.”
Addressing the talent shortage
Outside of technology, the talent shortage across procurement was a repeated topic of conversation during DPW NYC. Just as it is during CPOstrategy’s general conversations with leaders. Procurement has been too vague a concept for too long, and overlooked in the grand scheme of many businesses for decades.
“One of the issues is making roles attractive,” Perez states. “I recommend proposing the problems you’re trying to solve and asking whoever you’re interviewing: ‘how would you solve this?’ Because with all the cool tech we now have at our fingertips, they’re going to come up fresh ideas. The talent exists – they’re just not being engaged and attracted. That’s where tech comes into play.”
And technology moulded by a people-centric focus was another major theme of the day at DPW NYC. “While AI in procurement is a huge topic right now, creativity is still going to come from humans – not artificial intelligence,” Perez points out.
“You need human minds to see the value of things. This is to figure out how money can be driven out of the bottom line and into the top line. Humans are still needed for proving that procurement needs to take risks to be better. AI is a great tool, but it still needs us.”
Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new…
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Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new heights, including in the insurance industry.
Financial technology development has offered a better customer experience with enhanced accessibility and convenience. Mobile banks and digital wallets make it possible to contact the customer service team through online platforms. With the help of AI, FinTech companies escalate their services by offering more personalised, prompt, and efficient service.
AI Chatbots and Virtual Assistants
Conversational AI, which focuses on creating human-like interactions like chatbots and virtual assistants, improves customer service efficiency.
Chatbots are automated programmes that promptly address customer service queries. They can assist customers with inquiries and provide support for product information, account balances, or transaction details. AI-powered chatbots can give an immediate response and handle multiple customers at the same time.
Meanwhile, virtual assistants are voice-activated apps that can comprehend and carry out tasks based on users’ commands. These assistants offer personalised support by understanding the customers’ needs. For instance, they can deliver investment guidance tailored to customers’ risk tolerance and financial objectives.
These AI solutions can also assist human assistants by handling routine tasks, allowing them to focus on more complex work. Thus, the employment of AI assistants can reduce operational costs and effectively allocate resources to more important tasks.
Personalised interactions with AI
This approach can provide more personalised interactions by using algorithms and predictive tools to understand and respond to each customer’s preferences. AI algorithms can analyse large datasets of customers’ past interactions, browsing behaviour, and demographic information.
Meanwhile, predictive analytics tools can be used to anticipate customer needs and offer relevant financial products or services. These recommendations are constantly updated based on real-time client interactions and feedback.
24/7 Support
AI-powered customer service has the benefit of around-the-clock availability. It can operate continuously without being bound by office working hours like human-based customer service. Faster response times and enhanced availability help FinTech companies improve overall customer satisfaction.
Case Studies
Paypal, a digital wallet company, is one of the FinTech companies that has successfully used AI to improve its customer service. After implementing chatbots, PayPal experienced a 20 percent decrease in customer support costs and a 25 percent increase in user engagement. These chatbots can handle routine inquiries, resolve issues, and make personalised product recommendations.
Another example is Citi, a US retail bank that developed an AI-powered Customer Analytic Record (CAR). This programme can consolidate customer data, including financial records, used products, and interactions across online banking. The data is linked to automated decision-making AI software for analysis. The system can then recommend personalised offers to customers via text and mobile banking.
Future prospects
According to David Griffiths, Citigroup’s chief technology officer, AI has the potential to revolutionise the banking industry and improve profitability. With the continuous development of AI technology, the fintech industry can further improve its customer service.
Ronit Ghose, another executive at Citigroup, predicts that in the future, every client will have an AI-powered device in their pocket. This innovation will improve their financial lives with enhanced AI in customer service.
However, there are still concerns about AI’s scalability limitations in handling vast amounts of tasks. In addition, AI’s access to customers’ data makes security an important area to ensure its credibility. FinTech companies should ensure digital compliance to earn customers’ trust.
FinTech Strategy and Interface joined Publicis Sapient at Money20/20 in Amsterdam for the launch of its third annual Global Banking Benchmark Survey and spoke with Head of Financial Services Dave Murphy about its findings
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The third annual Global Banking Benchmark Study from Publicis Sapient draws on insights from 1000+ senior executives in financial services across global markets. The study focuses on the goals, obstacles, and drivers of digital transformation in banking.
Global Banking Benchmark Study
The study was launched during Money20/20 Europe in Amsterdam last month. Eoghan Sheehy, Associate MD, and Grace Ge, Senior Principal, highlighted the banking industry is focused on improving existing processes rather than introducing new ones. Data Analytics and AI are identified as key priorities for digital transformation. Additionally, there is a focus on internal use cases and efficiency.
Eoghan and Grace also discussed the challenges faced by the banking industry. These include regulation, competition from companies like Amazon, and the need to attract talent. They emphasised the importance for financial institutions of modernising core infrastructure. Also, building cloud infrastructure to support ongoing digital transformation. Moreover, the study notes the prevalence of the development of custom-made tools and internal use cases for AI implementation. Furthermore, Eoghan and Grace provided examples of repeatable use cases and discussed the success factors for Data Analytics and AI.
Four key takeaways from Publicis Sapient
Four key tracks came out of the study…
Modernising the core will always be important. But modernising the core for its own sake and also building the cloud infrastructure that supports it or allows for it to be modern. A decent chunk of the survey responders are still very focused on this. Executives are stating they want to make sure their people can make the best use of the beautiful core they’ve now built.
GenAI is an area of thoughtful experimentation for the Neobanks. We’re talking about scaled microservices here. Instances where, across Neobanks, you’ll have the same machine learning model and the same GenAI text generator facilitating retail and SMEs. That’s pretty sophisticated and something everyone has to contend with.
Data Analytics transformation is a key priority using GenAI to do so along with bringing new talent into the game.
Payments has been a big theme at Money20/20… We’re seeing lots of activity around ancillary individual product areas.
“The study focuses on how to think about solving problems end-to-end. Banks are dealing with legacy issues and taking a customer first view into solving the challenges. The practical application of AI across the banks is a significant theme as they look to automate decision-making and deliver better credit risk models. AI is finally delivering a set of use cases that truly can impact the way banks operate and build their own technology.” Dave Murphy, Head of Financial Services, EMEA & APAC
Be among the first to receive the study by signing up here
This month’s cover story sees our sister brand Fintech Strategy reporting from Money20/20 Europe in Amsterdam – a pivotal event…
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This month’s cover story sees our sister brand Fintech Strategy reporting from Money20/20 Europe in Amsterdam – a pivotal event in the fintech calendar, drawing over 8,000 participants from 2,300 companies worldwide.
Welcome to the latest issue of Interface magazine!
The RAI Amsterdam Convention Centre was the location for the world’s leading fintech conference. Money20/20 Europe offered a unique blend of insightful keynotes, panel discussions, and networking opportunities that underscored the transformative power of emerging technologies in financial services. We met with SC Ventures, Lloyds Banking Group, OSB Group, AirWallex, Plaid, Paymentology, Episode Six, Mettle (Nat West Group) and more to take the pulse of the latest trends across the fintech landscape.
Under the theme of ‘Human X Machine’, Money20/20 Europe explored the relationship between humans and intelligent machines, focusing on how the partnership between artificial and human intelligence will forge a new era in finance…
Publicis Sapient: Global Banking Benchmark Study
Interface was also proud to partner with Publicis Sapient at Money20/20 Europe for the launch of its third annual Global Banking Benchmark Survey. The survey draws on the insight of over 1000 senior executives in financial services across various global markets and focuses on the goals, obstacles, and drivers of digital transformation.
We spoke with Head of Financial Services Dave Murphy about its findings. “The survey focuses on how to think about solving problems end-to-end. Banks are dealing with legacy issues and taking a customer first view into solving the challenges. The practical application of AI across the banks is a significant theme as they look to automate decision-making and deliver better credit risk models.”
At the launch event for the study, Eoghan Sheehy, Associate MD, and Grace Ge, Senior Principal, highlighted that banks are primarily focused on improving existing processes rather than introducing new ones. Data Analytics and AI are identified as key priorities for digital transformation, with a focus on internal use cases and efficiency.
Eoghan and Grace also discussed the challenges faced by banks, including regulation, competition from companies like Amazon, and the need to attract talent. They emphasised the importance for financial institutions of modernising core infrastructure and building cloud infrastructure to support ongoing digital transformation. The study also notes the prevalence of the development of custom-made tools and the prioritising of internal use cases for AI implementation. Eoghan and Grace also provided examples of repeatable use cases and discussed the success factors for Data Analytics and AI.
STO Building Group: Enabling and Empowering People
Claudia Healey, Chief Human Resources Officer at STO Building Group, spoke to Interface about the HR platform empowering its people in pursuit of a strategic vision… “Culture is the number one priority in a people business like STO Building Group (STOBG). If you’re not nurturing and inspiring your folks, well, they can just vote with their feet. They don’t have to stay. Or they could do worse, they could quit and stay. And that’s something we would never want. Meeting your people where they’re at, understanding their goals and aspirations, and how you can help them reach their potential is vital. Realising how you can really see your people and truly understand what matters to them, is an incredible priority.”
Also in this issue, AI hype has previously been followed by an AI winter, we hear from Scott Zoldi, Chief Analytics Officer at FICO who asks, ‘Is the AI bubble set to burst?’ Elsewhere, we round up the top events in tech and learn how businesses can ensure their cloud storage is more sustainable in an age of rising demand for data and AI. Cloud storage without the climate cost is possible explains Fasthosts CEO Simon Yeoman.
Our cover story this month focuses on the work of Arianne Gallagher-Welcher. As the Executive Director for the USDA Digital…
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Our cover story this month focuses on the work of Arianne Gallagher-Welcher. As the Executive Director for the USDA Digital Service, in the Office of the OCIO, her team’s mission is to drive a tech transformation at the USDA. The goal is to better serve the American people across all of its 50 states.
Welcome to the latest issue of Interface magazine!
Welcome to a new year of possibility where technology meets business at the interface of change…
“We knew that in order for us to deliver what we needed for our stakeholders, we needed to be flexible – and that has trickled down from our senior leaders.” Arianne Gallagher-Welcher, Executive Director for the USDA Digital Service reveals the strategic plan’s first goal. Above all, the aim is to deliver customer-centric IT so farmers, producers, and families can find dealing with USDA as easy as using an ATM.
BCX: Delivering insights & intelligence across the Data & AI value chain
We also sat down with Stefan Steffen,Executive Leader for Data Insights & Intelligence at BCX. He revealed how BCX is leveraging AI to strategically transform businesses and drive their growth. “Our commitment to leveraging data and AI to drive innovation harnesses the power of technology to unlock new opportunities, drive efficiency, and enhance competitiveness for our clients.”
Momentum Multiply: A culture-driven digital transformation for wellness
Multiply Inspire & Engage is a new offering from leading South African insurance provider Momentum Health Solutions. Furthermore, it is the first digital wellness rewards program in South Africa to balance mental health and physical health in pursuing holistic wellness. CIO, Ndibulele Mqoboli, discusses re-platforming, cloud migrations, and building a culture of ownership, responsibility, and continuous improvement.
Clark County: Creating collaboration for the benefit of residents
Navigating the world of local government can be a minefield of red tape, both for citizens and those working within it. Al Pitts, Deputy CIO of Clark County, talks to us about the organisation’s IT transformation. He explains why collaboration is key to support residents. “We have found our new Clark County – ‘Together for Better’ – is a great way to collaborate on new solutions.”
Also in this issue, we hear from Alibaba’s European GM Jijay Shen on why digitalisation can be a driving force for SMEs. We learn how businesses can get cybersecurity right with KnowBe4 and analyse the rise of ‘The Mobility Society’.
Doug Laney is Innovation Fellow at West Monroe and a leading Data & Analytics strategist. We caught up with the author of Infonomics and Data Juice to talk tech and how companies can measure, manage and monetise to realise the potential of their data
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Our cover story explores the rise of data and information as an asset.
Welcome to the latest issueof Interface magazine!
Interface showcases leadersaiming to take advantage of data, particularly in a new world of AI technologies where it is the fuel…
How to monetise, manage and measure data as an asset
Our cover star is pretty big in the world of analytics… We meet the guy who defined Big Data. Doug Laney is Innovation Fellow at West Monroe and a leading Data & Analytics strategist. We caught up with the author of Infonomics and Data Juice to talk tech and learn how companies can measure, manage and monetise to realise the potential of their information. In his first book Laney advised companies to stop being fixated on hindsight-oriented analytics. “It doesn’t actually move the needle on the business. In the stories I’ve compiled over the last decade, 98% have more to do with organisations using data to diagnose, predict, prescribe or automate something. It’s not about asking questions about what happened in the past.”
Canvas Worldwide: A data-driven media business
Continuing this month’s data theme, we also spoke with Alisa Ben, SVP, Head of Analytics at full-service media agency Canvas Worldwide. Data has transformed the organisation, and what its clients do. “We look holistically at the client’s business and sometimes the tools we have might be right for them, sometimes not. It’s more about helping our clients achieve their business outcomes.”
TUI Musement: from digital transformation to digital pioneer
At travel giant TUI, handling data effectively is paramount when communicating consistently and meaningfully with up to 25 million customers annually. David Garcia, CIO for TUI Musement, talks about the tech evolution driving the travel giant’s provision of experiences, transfers and tours. It’s a big part of its operational shift from local to global. “As a CIO, I’ve always been interested in how the tech innovations we drive can support the business and add value.”
Hiscox: making cybersecurity more accessible
Liz Banbury, CISO at Hiscox and president of (ISC)² London Chapter, talks to us about how cybersecurity can become a more accessible, realistic career path for almost anybody. “When I was at school, topics like computer science didn’t even exist,” Banbury explains. “In one of my first jobs, over in Hong Kong, we were still using a typewriter! A lot has changed. My key point here is that there’s a lot of cybersecurity professionals who are really good at their job. They are inspiring, and have come from all walks of life. Crucially, they don’t have a maths, computer science, or technological background at all. But they still make great cybersecurity professionals.
Portland Community College: Risk vs Speed in Cybersecurity
Reet Kaur, former Chief Information Security Officer at Portland Community College, discusses the organisation’s transition to the cloud amid a digital transformation journey. “I don’t want to work with people who just say yes all the time. I want my ideas challenged to help forge the excellence in the security programmes I help build.”
DBHDS: Cybersecurity in healthcare
The Virginia Department of Behavioral Health and Developmental Services (DBHDS) exists to create ‘a life of possibilities for all Virginians’ and transform behavioural health. Its focus is on supporting people across the entire commonwealth. It helps them get the support they need in order to take wellness and recovery into their own hands. In an area like healthcare, sensitive information is all over the place, meaning cybersecurity is a priority – and this is where Glendon Schmitz, CISO at DBHDS, comes in. “The security team exists to help the wider organisation achieve its objectives with data. We’re there to protect the business, not the other way around.”
Also in this issue, we schedule the can’t miss tech events and get the lowdown on IoT security from the Mobile Ecosystem Forum.
This month’s cover story sees us speak with Brad Veech, Head of Technology Procurement at Discover Financial Services.
Having been a leader in procurement for more than 25 years, he has been responsible for over $2 billion in spend every year, negotiating software deals ranging from $75 to over $1.5 billion on a single deal. Don’t miss his exclusive insights where he tells us all about the vital importance of expertly procuring software and highlights the hidden pitfalls associated.
“A lot of companies don’t have the resources to have technology procurement experts on staff,” Brad tells us. “I think as time goes on people and companies will realise that the technology portfolio and the spend in that portfolio is increasing so rapidly they have to find a way to manage it. Find a project that doesn’t have software in it. Everything has software embedded within it, so you’re going to have to have procurement experts that understand the unique contracts and negotiation tactics of technology.”
There are also features which include insights from the likes of Jake Kiernan, Manager at KPMG, Ashifa Jumani, Director of Procurement at TELUS and Shaz Khan, CEO and Co-Founder at Vroozi.
This month’s exclusive cover story features a fascinating insight into the procurement function at lighting giant, Signify.
A forward-thinking enterprise constantly reevaluating and adapting its operations against an ever-changing landscape, Signify has recently transformed its procurement function. And so we join Luc Broussaud, Global Head of Procurement/CPO and Arnold Chatelain, Transformation Program Director for Signify’s Procurement Organization to see why, and how, they have evolved procurement at the company.
Signify is a global organisation spread over all continents and Luc heads up the procurement function. According to Luc, he and his team no longer engage in traditional transactional procurement, but instead leverage digitalisation to deliver competitive prices as well as what they call ‘concept saving’, “Which is how we redesign or improve our product; leveraging the knowledge of our suppliers to make it cheaper, more efficient, easier to manufacture and install, and more sustainable for the planet.”
Luc joined Signify in 2018, after being the CPO of Nokia (based in Shanghai) and has always been working within procurement. He joined Signify with a broad skillset and a wealth of experience. “I joined because the people I talked to, from the COO to the CEO and CFO were all incredibly knowledgeable and passionate about procurement,” he reveals. Read the full story here!
Not only that, but we also have some incredible insights from procurement leaders at Heijmans, Datadog, HICX, DPW, ProcureCon Asia and SourcingHaus Research! Plus, the very best procurement events of 2023.
Amit Thawani, CIO for Consumer Data & Engagement Platforms at Wells Fargo, on the journey towards becoming a customer-centric company
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This month’s cover story reveals how a customer-centric approach to technology is helping Wells Fargo deliver stable, secure, scalable, and innovative services.
Welcome to the latest issueof Interface magazine!
It’s our biggest issue yet! The common theme this month is the focus on the creation of customer-centric technologies that offer reliable, secure and helpful user journeys from travel and banking to health and business.
Interface dives deep for insights on understanding, planning, implementing and communicating change across industries.
Amit Thawani, Chief Information Officer (CIO) for Consumer Data & Engagement Platforms (CDEP) on the technology journey at Wells Fargo: “All tech employees at Wells Fargo are tasked with working towards delivering stable, secure, scalable, and innovative services at speed that delight and satisfy our customers while unleashing the skills potential of our employees.”
TUI: Developing a technology ecosystem
Kristof Caekebeke, CIO for Product & Engagement, is a member of the leadership team that is driving the transformation of the TUI technical ecosystem which has seen Master Domain Owners taking different blocks of the ecosystem under their control to roll out across the organisation.
TUI Group
Responsible for product and engagement, Caekebeke’s focus is on building products out of the thousands of hotels, flights, experiences and cruises TUI is offering. “I’m responsible for every contact point between the customer and TUI. The websites, the mobile apps, the retail systems – any contact point we have between the customer and TUI. It’s a large team of 1,100 tech people.
A digital bank transformation journey with Banco PAN
“Until 2018 Banco PAN was very much an analogue company reliant on legacy paper processes,” recalls Leandro Marçal. Joining the bank in December 2020, to become Technology & Operations Director (CIO/COO), Marçal was tasked with accelerating a digital transformation journey.
“Banco PAN invested in innovation before I arrived,” says Marçal. “It is my team’s job to formalise the path towards becoming a digital bank. Our legacy operation was digitalising. It was an opportunity to improve the customer experience with our checking account and credit card systems.”
Pohlad Companies: The power of people
A pillar of the community in Minneapolis, Pohlad Companies is well known to Minnesotans for its influence, its charity work, and the opportunities it has created for people since the 1950s.
Alongside significant commercial real estate investments, Pohlad Companies owns a custom engineering and robotics company, a group of automotive dealerships specialising in luxury vehicles, a film production studio, and many more businesses. Famously, the Pohlad family also owns the Minnesota Twins, a Major League Baseball team.
This variety is part of what makes Rachel Lockett’s job so exciting. She’s Pohlad Companies’ CIO and has spent a decade in her current role. Lockett began her career as a programmer over 25 years ago and quickly moved into IT leadership management.
Coalfire: Embracing change in cybersecurity
If you wait for something to happen, then it’s often too late. The art of having a finger on the pulse is an essential ingredient to success. Failure to manage change and implement cybersecurity protocols could mean leaving an organisation vulnerable to hackers.
Sreeveni Kancharla, Coalfire’s first Chief Information Officer, is leading the company’s digital transformation with unwavering determination. As a cybersecurity advisor, Coalfire assists private and public sector organisations in managing threats, closing gaps, and mitigating risks. Kancharla ensures that her team stays up-to-date with the latest technologies to guard against zero-day attacks.
Uni of Kansas Health: Cybersecurity at the heart
Speed versus safety. The two topics are intrinsically linked and vital in their own individual way. But can you have both in healthcare when the risks are so great? Ultimately, there is no higher stake than saving people’s lives – it goes above everything and is why cybersecurity is so vital.
Protecting the healthcare system
“There’s nothing more important to me than patient care,” affirms Michael Meis, Associate Chief Information Security Officer at The University of Kansas Health System. “It is one of the highest callings you can imagine, to be able to help people. While the cybersecurity team and me, individually, do not directly care for patients, we enable a lot of that patient care to continue and to be able to achieve some of the goals that the health system has set to provide that healing, research, and innovation within the healthcare space.”
Also in this issue, we ask ChatGPT what the future holds for AI and learn from Zoom how businesses can leverage analytics for insights from their hybrid events.
Mike Randall, CEO at Simply Asset Finance, discusses how to build a people-first strategy that enables growth.
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As the UK economy continues to balance on the edge of a recession, employee retention is quickly being pushed to the top of CEOs’ lists. Over the past couple of years, the job market has shifted dramatically with previously unheard terms such as ‘the great resignation’, ‘quiet quitting’ and ‘hybrid working’ becoming commonplace. People are rightly prioritising their working situation and job satisfaction levels, questioning whether they believe in the organisations they are committing so much time to.
Consequently, there has been a power dynamic shift in favour of the workforce. Reportedly in the third quarter of 2022 businesses witnessed over 365,000 job-to-job resignations across the UK. In similar fashion, the phenomenon of ‘quiet quitting’ – doing the bare minimum required of a job – has become a growing concern but its rise is prompted by a growing number of employees feeling disengaged in their roles.
Against this backdrop of a highly turbulent job market, and increasingly difficult macro-economic pressures, it’s vital for CEOs to prioritise a people-first strategy to ensure healthy growth for their business in 2023. Data from Deloitte has even revealed that experts believe how engaged a workforce feels can directly correlate to overall business output, with 93% of HR and business leaders in agreement that building a sense of belonging is crucial for organisational performance.
Mike Randall, CEO at Simply Asset Finance
However, creating the right environment and recruiting, maintaining and nurturing the right talent to ensure a people first approach can be daunting. With this in mind, here are four learnings CEOs might want to consider when approaching this challenge:
1. Define your beliefs
Before CEOs and founders can hope to attract the right talent, it is critical to first distil and translate the business vision into something that can be understood by employees. Put simply, this means defining the business’ beliefs.
Some business leaders may already refer to this as an ‘employer brand’, and it can be key to not only securing better talent, but also saving a business money in the long-term. Data from LinkedIn for example, recently found that a strong employer brand can help to reduce employee turnover by as much as 28% and cost-per-hire by 50%. Defining these beliefs – or the tenets a business does and doesn’t stand for – is therefore the perfect exercise to put a vision onto paper, and clearly communicate it to its prospective talent.
2. Build a solid culture
Once these beliefs have been defined, they must be reflected, and built into a strong culture. A business’ beliefs should permeate through the whole organisation – from customer communications, to how staff are treated, to how leaders run the business. Culture should essentially be a representation of a business’ beliefs being put into practice.
Building a strong culture in a business, however, is not solely about these beliefs but also extends into how employees are equipped with the tools they need to succeed. Companies that invest in learning and development for example, have been found to benefit from a 24% higher profit margin than those that don’t, according to the Association of Talent Development. Training and development should therefore be seen as a worthwhile and necessary investment that can solidify your culture and ensure profitability, not just an unavoidable cost.
3. Invest in retention
With research from Oxford Economics estimating the average turnover per employee earning £25,000 a year to be £30,000 plus, there is an evident cost to businesses that fail to invest in retention. Tackling this will mean regularly taking the time to truly understand what makes employees tick – and more specifically, understanding their motivations, attitudes, behaviours, strengths and weaknesses.
As the past few years have evidenced, individuals are no longer deciding where they work solely based on salary, but are also thinking about employer values, flexibility, and benefits. To avoid employee churn, businesses should regularly take time to understand what drives their employees and implement retention strategies to address these drivers. Gathering and analysing employee data will play an important role here over the coming years, and should be built into a long-term strategy to optimise employee satisfaction.
4. Build for the future
A common challenge encountered by modern businesses and startups wanting to take a people first approach, can be their ability to stay committed to it. As a business grows in size and becomes successful, it can be all too easy to let external factors dictate its purpose and for it to lose sight of what it initially stood for. The reality is that when this happens, a business is in its most vulnerable state – as its beliefs become increasingly distant, and worse, employees no longer understand what it stands for.
When creating a people-first strategy its therefore important to think long-term. If there are external factors that will potentially put this strategy at risk in future, it’s crucial to identify them, and put in practical steps to mitigate them where possible. The pandemic, for example, is a prime example of an external factor that interrupted the status quo of many businesses – disrupting employees, customers and operations in general. While they can be unpredictable in nature, having a plan to get through these times can help to get you back on track and reassure talent that a solution is in place.
In this economic climate, defining beliefs, building a solid culture, and retention plan should be at the core of every business’ strategy. It’s only when these things are in place that a business can hope to attract and retain talented people that exude the same passion and values built into the heart of a business. As while a business’ growth may be defined by its leaders, it is delivered by its people who are putting that vision into practice.
Procurement is in a state of flux. Against a backdrop of economic uncertainty, the procurement landscape is volatile and requires…
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Procurement is in a state of flux.
Against a backdrop of economic uncertainty, the procurement landscape is volatile and requires agility to navigate turbulent waters. But, despite significant disruption could there still be opportunity?
Simon Whatson, Vice President of Efficio Consulting, is optimistic about the future of digital procurement and despite a challenging few years he is confident of a successful bounce back. He gives us the lowdown on the direction of travel for digital procurement in 2023.
As an executive with considerable experience in the space, we’d love to learn more about your background and how you ended up in procurement. Why was this the specialism for you and how did you get involved to begin with?
Simon Whatson (SW): “I think the one-word answer of how I came into procurement was accidental. I studied maths at university, with a year in France, before I began looking for different roles to apply for.
“Eventually, I was offered a position with a big plumbing and heating merchant with global operations. I worked in that supply chain team for two and a half years. Although it was called supply chain, a lot of the work was procurement, which involved negotiating with suppliers. It was after that stint there, that I discovered consulting and joined a boutique procurement consultancy. Now I am onto my third consultancy and I’m very happy here!
“In terms of why I’ve stayed, one of the success factors in procurement is being able to work cross-functionally. Procurement doesn’t own any of the spending that it is responsible for helping to optimise. It must work with other functions and the spend owners. I quite like the people side of that, building relationships, almost selling internally to bring teams together. That really appeals to me and is a key reason why I’ve been very happy in procurement.”
As we move into exploring procurement today in 2023. The space is filled with challenges and complexities. You only need to look at the last few years. Covid, war in Ukraine, inflation – how would you describe the world’s recent challenges and their effect on the industry and what do you feel CPOs and leaders can do to combat these issues?
SW: “I would flip it around and say that these are not so much challenges but rather opportunities for procurement. When I started my career 18 years ago, procurement was often fighting to get a voice and there were complaints that procurement was not represented at the top table, but the war in Ukraine, inflation, COVID and ESG, these are things which are now on the C-suite agenda and procurement is ideally positioned to help companies face those challenges. If you think about COVID and the war in Ukraine, procurement is in a privileged position to help with this.
“I see some procurement functions that prefer to do what they know, which focuses on the process and transactional side. However, there are also many forward-thinking CPOs and procurement professionals out there, that have really seized this opportunity of being on the C-suite agenda and drive the thinking and the solutions to some of these big challenges we’re seeing.”
Although new technology in procurement has been around for well over a decade, digitalisation has become so much more of an important topic. How would you sum up where procurement and supply chain are in terms of digital transformation today?
SW: “It’s a bit laggard, but digital transformation is difficult, and we have to recognise there are some real trailblazers. There are some firms doing some fantastic things in digital to produce better outcomes. If you contrast your experience when you’re buying something in your private life, it’s much easier than 20 years ago. You can get access to a wealth of pre-sourced things, whether it’s food, a holiday, a car, or a book. You can see reviews of what other people think of these things.
“But when you go into your workplace as a business user and you want to buy something, it doesn’t quite work like that yet. You often have to fill in a form, send it off and wait for them to come back to you. They might come back a little bit later than you were hoping and might tell you that they don’t have that part on the supply frameworks. I think people sometimes get confused about how it can be so easy to buy something as large as a car or a holiday on their sofa at home, but when they want to buy something at work, it seems to be quite cumbersome. Digital can help a lot with that, but it is incumbent on organisations and procurement functions to figure out how to recreate that customer experience that we’ve become accustomed to in our private lives.”
With a new generation of leaders growing up with technology, some might say that it could be a key driver in helping to speed the adoption in procurement along. Is this something you would agree with or what would you point to as a key driver?
SW: “I do think that it will act as one of the catalysts for further digital transformation in organisations, because if procurement doesn’t manage to recreate that customer experience that the new generation expects, then they won’t use procurement going forward and will look to bypass it.
“The analogy that I’ve used previously in this case is one of travel agents. I remember as a child, my parents were able to take us on holiday and I remember the whole process. We would walk into town to the travel agent, and look at some of the brochures of options. They often then had to phone the various airlines or resorts on our behalf. They might not be able to get through, so we’d have to come back the next day. I remember as a child being quite excited by the whole process but actually, thinking back, it was quite cumbersome. You compare that to now, with being able to review online, and you can get instant answers to your questions. It’s not a coincidence that travel agents don’t really exist anymore.”
How much of a challenge is it to not get caught leveraging technology for technologies sake? How important is it to stay true to your approach and be strategic?
SW: “We conducted a study of many procurement leaders and CPOs a few years ago, and one of the things that we found was that about 50% of procurement leaders admitted to having bought technology just on the basis of a fear of missing out, without any real understanding of the benefits that technology was going to bring. That was a real shock and a revealing find because technology is not cheap, and its implementation is quite disruptive. If you’re purchasing a system because everybody else is using it, then there could be some pretty costly mistakes. It is really important to make sure that when buying technology, it is because the benefits are fully understood.
“My advice to companies when looking to digitalise is own your data, visualise that data, and manage your knowledge. If you can focus on getting those things right in that order, and make your technology decisions to support that goal, then that’s a much better way of thinking about it rather than just jumping in and buying a piece of technology.”
It’s clear that the procurement space is an exciting, but challenging, place to be. What do you think will play a key role in the next 12 months to push the digital conversation further to take procurement to the next level?
SW: “Looking forward, one thing that procurement needs to do and continue to do is attract the best people. Ultimately, people are what makes an organisation, and it is what makes a function successful. I think procurement has often not looked for the right skills in the people that it employs. Traditionally, it’s looked for people with procurement experience and while they are valuable and required, we also need leadership potential. People who think a bit more outside the box and aren’t so process driven. A lot of what procurement has done in previous years has been process driven, so if you’re just limiting your search of people to those that have had procurement experience, you’re inevitably going to end up with a lot of people who are process driven.
“I think being bolder and recruiting people from different backgrounds with different skill sets is the way to go. If procurement can ‘own’ the ESG space, that will help with the younger generation see procurement make a difference. I think that’s one thing that will be key to success going forward.”
Check out the latest issue of CPOstrategy Magazine here.
Paul Farrow, Vice President of Hilton Hotels’ Supply Management, sits down with us to discuss how his organisation’s procurement function has evolved amid disruption on a global scale
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The hospitality industry has endured a rough ride over the past few years.
Following the COVID-19 pandemic which stopped the world in its tracks and now with millions facing a cost-of-living crisis, it’s been a period of unprecedented disruption for those involved in the space and beyond.
But it’s a challenge met head-on by Paul Farrow, Vice President of Supply Management at Hilton Hotels, and his team who have been forced to respond as the world continues to shift before their eyes.
Farrow gives us a closer look into the inner workings of his firm’s procurement function and how he has led the charge during his time with Hilton Hotels.
Could we start with you introducing yourself and talking a little about your role at Hilton Hotels?
Paul Farrow (PF): “I’m the Vice President of Hilton’s Supply Management, or HSM as we call it. I’ve been with Hilton Hotels for 12 and a half years, and my role is to head the supply chain function for our hotels across Europe, the Middle East and Africa.
“Over the past few years, Hilton has grown rapidly and has now got 7,000 hotels in over 125 countries globally. What is really exciting is Hilton Supply Management doesn’t just supply Hilton Hotels and the Hilton Engine because we also now supply our franchisees and competitive flags. While we have 7,000 hotels globally, Hilton Supply Management actually supplies close to 13,000 hotels. That’s an interesting business development for us, and a profit earner too.”
You’re greatly experienced, I bet you’ve seen supply chain management and procurement change a lot in recent years?
PF: “The past two to three years have been tremendously challenging on so many industries but I’d argue that hospitality got hit more than most as a result of the Covid pandemic. Here at Hilton, supply management was really important just to keep the business operational throughout that tough time, but I’m delighted to say we’re fully recovered now.
“Looking back, it was undoubtedly difficult, and you only have to look at the media to see that we’re now going through a period of truly unprecedented inflation. On top of the normal day job, it’s certainly been a very busy time.”
Hospitality must have been under an awful lot of pressure during the pandemic…
PF: “Most of our teams as a business and all functions have worked together far more collaboratively than ever before through the use of technology and things like Microsoft Teams and Zoom. Trying to work remotely as effectively as possible changed the way we all had to think and the way we had to do. Now we’re back in the workplace and in our offices, we’re actually looking to take advantage of that new approach.”
Inflation, rising costs, energy shortages, as well as drives towards a circular economy means it’s quite a challenging time for CSCOs and CPOs right now, isn’t it?
PF: “Those headwinds have caused and created challenges of the like that we’ve not seen before. The war in Ukraine and Russia has meant significant supply chain disruption and supply shortages of some key ingredients and raw materials. China is a significant source of materials and they’re still having real challenges to get their production to keep up with demand.
“All the local and short-term challenges are around energy and fuel pricing, so throughout the supply chain that’s been a major factor to what we’ve had to deal with. On top of that is the labour shortages. We rely heavily throughout the supply chain and within our business to utilise labour from around the world. In my region, particularly from say Eastern Europe as well as other businesses all fighting for a smaller labour pool than we had before. We are fighting with the likes of the supermarkets, Amazon’s, not just other hotel companies to capture the labour pool we need both in our properties but also within our supply chain supplies themselves.
Hilton operates a rather unique procurement function, doesn’t it?
PF: “We trade off the Hilton name because our brand strength is something that we are able to utilise and we’re very proud of, but we’ve also got additional leverage by having that group procurement model.
“We’ve got essentially two clients. We’ve got our managed estate which is when an owner chooses to partner with Hilton, they’re signing a management agreement because they want the benefit and value of the Hilton engine. That could be revenue management, how we manage onboarding clients and customers through advertising, as well as the other support we give in terms of finance, HR, marketing and sales as well as procurement.”
HSM is a profit centre and revenue driver through its group procurement model but how does this work?
PF: “Our secret sauce is our culture. It’s our people and that filters across all of our team members and indeed all of our functions. The key strategic pillars are the same for health and supply management around culture, maximising performance and so on as they are across the overall global business.
“Across our 7,000 plus hotels, the majority are actually franchised hotels because that’s the legacy of what still is the model in the US. When I joined Hilton 12 and a half years ago, the reverse is true where nearly all of our hotels in Europe, Middle East and Africa, and indeed in Asia Pacific, were and are managed. In the Europe, Middle East and Africa regions right now we’re building up close to a 50/50 split between managed, leased and franchised.”
What has pleased you most about the roll-out of the HSM?
PF: “It’s certainly not been easy because we’ve got 70 countries that sit within our region here in EMEA and Hilton’s penetration in those individual countries is very different. We may have 100 hotels in one of those markets and only one or two in specific countries. Our scale and our ability to get logistics solutions is different by market.
“Getting everyone on board to what we want to achieve to our guests and to our owners means we have to pull different levers. We have very effective brand standards. If you’re signing up to Hilton, you’re signing up to delivering against those brand standards that we believe are right for our organisation.”
What kind of feedback have you had from your clients?
PF: “Integrity is in our DNA, and we work very closely with our suppliers who we value as partners. These are long-term relationships, and we work hand in hand because we have to see that they’re successful so that we can be successful – it’s really important to what we do and we constantly look for feedback.
“With our internal and our external customers, we’ll have quarterly business reviews and so we’ll get that feedback through surveys where we are asking them to tell us what we do well and what we could do better. Our partners are now asking what additional value can you do to bring support to our organisation through ESG? So that’s what’s on the table now when it wasn’t before. But it’s not just that – it’s about the security of supply competitiveness, competitiveness of pricing, and a whole bunch of other very important things as well.”
Looking to the future, what’s on the agenda for the next few years?
PF: “We’re out there meeting and greeting people in person and there’s always new opportunities that make things exciting in what we do and how we work. Innovation’s very high on our agenda and we’re very proud of what we do in food and beverage. In non-food categories, it’s about how we support our owners and our hotel general managers to find that competitive edge and do the next big thing ahead of our competitors.”
Anything else important to know?
PF: “One thing we’ve been able to take full advantage of is how we’ve been able to grow our business by bolting on new customers. I think it’s fantastic that our competitors choose to use Hilton Supply Management because they benchmarked what our capabilities are and how competitive we are.
“Another key part of the agenda is environmental, social and governance (ESG) sustainability. Responsible sourcing and everything that sits within that is front and centre of what we do. Within that you’ve got human rights, animal welfare, single use plastics as well as general responsible sourcing like managing food waste. The list is very long, but they’re all very important.”
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Here are 10 of the most important leadership skills that CEOs need to demonstrate in 2023.
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In today’s world, a CEO needs to be lots of things to different people. The importance of having the leadership skill to being able to lead through unprecedented disruption was highlighted by the COVID-19 pandemic and helped to define what makes a good CEO.
Here are 10 of the most important leadership skills that CEOs need to demonstrate in 2023.
1. Clear communication
Communicating effectively with employees is one of the most vital skills any leader can have. By adopting a transparent mindset, it leaves little room for miscommunication or misunderstandings. But rather than just being eloquent, CEOs should deliver meaningful content too. A CEO needs to be able to communicate the essence of the business strategy and the methodology for achieving it.
2. Strong talent management strategy
People are the most important component of all businesses. CEOs who are able to recruit and retain key employees have a greater chance of increasing productivity and efficiency. After recruiting good people, the key to retaining them is by harnessing a positive work environment that empowers employees to succeed.
3. Decision-making
As a leader, thinking strategically to make effective decisions is vital to the success of an organisation. Making decisions is a key part of leadership as well as having the conviction to stand by decisions or agility to adapt when those decisions don’t have the required outcome. While all decisions might not be favourable, making unpopular but necessary calls are important characteristics of a good leader.
4. Negotiation
Negotiation is a fundamental part of being a CEO. In a top leadership position, almost every business conversation will be a negotiation. Good negotiations are important to an organisation because they will ultimately result in better relationships, both with staff inside the company and externally. An effective leader will also help find the best long-term solution by finding the right balance and offering value where both parties feel like they ‘win’.
5. Creativity and innovation
Being quick-thinking and ready to explore new options are great skills of a CEO. Creative leadership can lead to finding innovative solutions in the face of challenging and changing situations. It means in the midst of disruption, of which it has been increasingly prevalent, leaders can still find answers for their teams. Creative CEOs are those who take risks and empower employees to drop outdated and overused practices to innovate and try new things that could lead to greater efficiency.
6. Agility
Without agility over the past few years, businesses would have failed. CEOs were forced to embrace remote working following the advent of the COVID-19 pandemic whether they liked it or not. Now, faced against a potential recession, these macroeconomic events are unavoidable and have to be managed carefully. Effective leaders will have their fingers on the pulse and ready to respond to changes.
7. Strategic forecasting
Creating a clear path forward is essential to achieving uninterrupted success. The ability to look into the future and identify trends and issues to then react to is vital. Good CEOs are able to plan strategically and make informed decisions to set goals and plan for the future easily.
8. Delegation
CEOs can’t do everything. A leader tends to be pulled in a number of different ways every day and it is impossible to be on top of everything. This means the importance of bringing in a team of people who are trusted and skilled in their respective areas of expertise. Successful CEOs are expert delegators because they recognise the value of teamwork and elevating those around them.
9. Approachability
An approachable CEO who welcomes conversation and is an active listener will help employees feel at ease raising issues or concerns. This approach will help build strong relationships with staff and customers and encourage a healthy culture which is beneficial to employee retention. Leaders with strong, trusting and authentic relationships with their teams know that investing time in building these bonds which makes them more effective as a leader and creates a foundation for success.
10. Growth mindset
If a CEO arms themselves with a growth mindset it allows them to meet challenges head-on and evolve. This shines a light on improving through effort, learning and persistence. As others may back down in the face of adversity and upheaval, successful CEOs will strive to move forward with confidence. Those with a growth mindset are unlikely to be swayed as they have the tools needed to reframe challenges as opportunities to grow.
In McKinsey’s latest report ‘Actions the best CEOs are taking in 2023’, we examine three of the biggest trends on the c-level agenda
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Anyone can sail a ship when things are going well. But it takes a strong, robust and characterful CEO to steer a business through choppy waters and out the other side.
In McKinsey’s latest report ‘Actions the best CEOs are taking in 2023’, the research and advisory firm uncovered which trends are set to have the biggest impact on how CEOs lead their business throughout the year.
McKinsey’s CEO Excellence Survey surveyed 200 of the best corporate CEOs of the past 15 years. This was completed by whittling down a list of all the current and former CEOs of the 1,000 largest public companies during that timeframe. The list was subsequently filtered based on tenure, including only those who had completed at least six years in the role. From there, the CEOs were continuously shortlisted until the best 200 were determined.
Each CEO was asked to identify the top three trends that are set to determine how leaders tackle the future. Here is an insight into those findings.
1. Actions to deal with digital disruption
CEOs are targeting digital trends in three key ways: developing advanced analytics, enhancing cybersecurity and automating work. OpenAI’s launch of ChatGPT has accelerated the demand of companies looking to embrace advanced analytics for a competitive advantage. Improving cybersecurity is another key action for CEOs with the importance of guarding against external threats paramount amid strengthening and more mature cyberattacks. Lastly, automating work is another key priority to scale efficiency and eliminate boring and manual tasks which free up people’s time.
2. Actions to deal with the risk of high inflation and economic downturn
One CEO who is worried about economic uncertainty told McKinsey: “Act early to lower costs and protect the balance sheet so that you are stronger and leaner when the economy begins to turn more favourably.” McKinsey found that companies that outperformed the 2008 financial crisis cut operating costs by 1% before the downturn while the others expanded costs by the same percentage. The best performers reduced their debt by $1 for every $1 of book capital before the downturn. This can be done by reducing operating expenses, redesigning products and services as well as reassessing strategic and economic assumptions.
3. Actions to deal with the escalation of geopolitical risk
According to McKinsey, there are three actions to help manage the escalation of global and national crises. CEOs are targeting building robust compliance capabilities, creating resilience in supplier networks and investing in monitoring and response capabilities. These actions come following the challenges presented by COVID-19, the war in Ukraine and now inflation concerns. Many firms are choosing to build their trade compliance organisations and improve how they screen different customers and companies. While a defensive approach is the way forward for many, some companies see the turbulent times as an opportunity.
What does today’s CEO need to do to accelerate an organisation’s digital transformation journey?
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Digital transformation journeys are no one-size-suits-all. There is no singular way to welcome a new wave of technology into operations.
Since the turn of the century, digitalisation has had an increasingly influential impact on the way CEOs make decisions. Today’s world is full of disruption and potential risk. And with technology growing in complexity it can be challenging to lead such a revolution against a backdrop of economic uncertainty.
Embracing digital
According to KPMG 2022 CEO Outlook, which draws on the perspectives of 1,325 global CEOs across 11 markets, 72% of CEOs agree they have an aggressive digital investment strategy intended to secure first-mover or fast-follower status.
Advancing digitalisation and connectivity across the business is tied (along with attracting and retaining talent) as the top operational priority to achieve growth over the next three years. This digital transformation focus could be driven as a result of increasingly flexible working conditions and greater focus on cybersecurity threats.
However, the prospect of recession is threatening to halt digital transformation in the short-term. KPMG research found that four out of five CEOs note their businesses are pausing or reducing their digital transformation strategies to prepare for the anticipated recession.
This is reinforced further when 70% say they need to be quicker to shift investment to digital opportunities and divest in those areas where they face digital obsolescence.
When a company’s digital transformation ambition is mismatched to its readiness, it is the CEO’s responsibility to close the gap. According to Deloitte, in order to do this successfully, the CEO must assess the current level of organisational readiness for change.
This covers four key pillars that are mixed together to work out an organisation’s overall readiness: leadership, culture, structure and capabilities.
How CEOs can close the gap
Leadership: CEOs need to ensure their c-suite and other key executives are motivated and equipped to execute the vision. CEOs interviewed by Deloitte in a recent study emphasised the importance of the leadership team supporting the transformation vision and having a positive attitude and willingness to transform.
Culture: A large potential barrier to readiness in the organisation is down to culture. Low cultural readiness takes the form of bureaucratic, reactive and risk-averse ways of working that are at against the collaborative, proactive learning mindset needed for ambitious transformation.
Structure: If a company hopes to operate differently, it could mean the need for organising in an alternative way. CEOs will often need to lead the reorganisation of teams, assignment of new roles, revision of incentives, strategies to collapse organisational hierarchies or layers to increase agility.
Capabilities: CEOs need to equip their organisation with four key capabilities to harness digital for a superior capacity for change. These are nimbleness, scalability, stability and optionality which are often enabled or supercharged by digital technologies which are critical factors for competing in an increasingly disrupted world.
For now, one of the CEOs most important roles when steering the ship through disruption is to be ahead of the latest trends and tackle change head-on. By embracing a new digital future that will provide the company with long-lasting benefits, it will help create a brighter and future-proofed firm for years to come even after the CEO is gone.
Expert analysis of the tech trends set to make waves this year
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Digital transformation is a continuing journey of change with no set final destination. This makes predicting tomorrow a challenge when no one has a crystal ball to hand.
After a difficult few years for most businesses following a disruptive pandemic and now battling a cost-of-living crisis, many enterprises are increasingly leveraging new types of technology to gain an edge in a disruptive world.
With this in mind, here are what experts predict for the next 12 months…
1. Process Mining
Sam Attias, Director of Product Marketing at Celonis, expects to see a rise in the adoption of process mining as it evolves to incorporate automation capabilities. He says process mining has traditionally been “a data science done in isolation” which helps companies identify hidden inefficiencies by extracting data and visually representing it.
“It is now evolving to become more prescriptive than descriptive and will empower businesses to simulate new methods and processes in order to estimate success and error rates, as well as recommend actions before issues actually occur,” says Attias. “It will fix inefficiencies in real-time through automation and execution management.”
2. The evolution of social robots
Gabriel Aguiar Noury, Robotics Product Manager at Canonical, anticipates social robots to return this year. After companies such as Sony introduced robots like Poiq, Aguiar Noury believes it “sets the stage” for a new wave of social robots.
“Powered by natural language generation models like GPT-3, robots can create new dialogue systems,” he says. “This will improve the robot’s interactivity with humans, allowing robots to answer any question.
“Social robots will also build narratives and rich personalities, making interaction with users more meaningful. GPT-3 also powers Dall-E, an image generator. Combined, these types of technologies will enable robots not only to tell but show dynamic stories.”
3. The rebirth of new data-powered business applications
Christian Kleinerman, Senior Vice President of Product at Snowflake, says there is the beginning of a “renaissance” in software development. He believes developers will bring their applications to central combined sources of data instead of the “traditional approach” of copying data into applications.
“Every single application category, whether it’s horizontal or specific to an industry vertical, will be reinvented by the emergence of new data-powered applications,” affirms Kleinerman. “This rise of data-powered applications will represent massive opportunities for all different types of developers, whether they’re working on a brand-new idea for an application and a business based on that app, or they’re looking for how to expand their existing software operations.”
4. Application development will become a two-way conversation
Adrien Treuille, Head of Streamlit at Snowflake, believes application development will become a two-way conversation between producers and consumers. It is his belief that the advent of easy-to-use low-code or no-code platforms are already “simplifying the building” and sharing of interactive applications for tech-savvy and business users.
“Based on that foundation, the next emerging shift will be a blurring of the lines between two previously distinct roles — the application producer and the consumer of that software.”
He adds that application development will become a collaborative workflow where consumers can weigh in on the work producers are doing in real-time. “Taking this one step further, we’re heading towards a future where app development platforms have mechanisms to gather app requirements from consumers before the producer has even started creating that software.”
5. The Metaverse
Paul Hardy, EMEA Innovation Officer at ServiceNow, says he expects business leaders to adopt technologies such as the metaverse in 2023. The aim of this is to help cultivate and maintain employee engagement as businesses continue working in hybrid environments, in an increasingly challenging macro environment.
“Given the current economic climate, adoption of the metaverse may be slow, but in the future, a network of 3D virtual worlds will be used to foster meaningful social connections, creating new experiences for employees and reinforcing positive culture within organisations,” he says. “Hybrid work has made employee engagement more challenging, as it can be difficult to communicate when employees are not together in the same room.
“Leaders have begun to see the benefit of hosting traditional training and development sessions using VR and AI-enhanced coaching. In the next few years, we will see more workplaces go a step beyond this, for example, offering employees the chance to earn recognition in the form of tokens they can spend in the real or virtual world, gamifying the experience.”
6. The year of ESG?
Cathy Mauzaize, Vice President, EMEA South, at ServiceNow, believes 2023 could be the year that environmental, social and corporate governance (ESG) is vital to every company’s strategy.
“Failure to engage appropriate investment in ESG strategies could plunge any organisation into a crisis,” she says. “Legislation must be respected and so must the expectations of employees, investors and your ecosystem of partners and customers.
“ESG is not just a tick box, one and done, it’s a new way of business that will see us through 2023 and beyond.”
7. Macro Trends and Redeploying Budgets for Efficiency
Ulrik Nehammer, President, EMEA at ServiceNow, says organisations are facing an incredibly complex and volatile macro environment. Nehammer explains as the world is gripped by soaring inflation, intelligent digital investments can be a huge deflationary force.
“Business leaders are already shifting investment focus to technologies that will deliver outcomes faster,” he says. “Going into 2023, technology will become increasingly central to business success – in fact, 95% of CEOs are already pursuing a digital-first strategy according to IDC’s CEO survey, as digital companies deliver revenue growth far faster than non-digital ones.”
8. Organisations will have adopted a NaaS strategy
David Hughes, Aruba’s Chief Product and Technology Officer, believes that by the end of 2023, 20% of organisations will have adopted a network-as-a-service (NaaS) strategy.
“With tightening economic conditions, IT requires flexibility in how network infrastructure is acquired, deployed, and operated to enable network teams to deliver business outcomes rather than just managing devices,” he says. “Migration to a NaaS framework enables IT to accelerate network modernisation yet stay within budget, IT resource, and schedule constraints.
“In addition, adopting a NaaS strategy will help organisations meet sustainability objectives since leading NaaS suppliers have adopted carbon-neutral and recycling manufacturing strategies.”
9. Think like a seasonal business
According to Patrick Bossman, Product Manager at MariaDB corporation, he anticipates 2023 to be the year that the ability to “scale out on command” is going to be at the fore of companies’ thoughts.
“Organisations will need the infrastructure in place to grow on command and scale back once demand lowers,” he says. “The winners in 2023 will be those who understand that all business is seasonal, and all companies need to be ready for fluctuating demand.”
10. Digital platforms need to adapt to avoid falling victim to subscription fatigue
Demed L’Her, Chief Technology Officer at DigitalRoute, suggests what the subscription market is going to look like in 2023 and how businesses can avoid falling victim to ‘subscription fatigue’. L’Her says there has been a significant drop in demand since the pandemic.
“Insider’s latest research shows that as of August, nearly a third (30%) of people reported cancelling an online subscription service in the past six months,” he reveals. “This is largely due to the rising cost of living experienced globally that is leaving households with reduced budgets for luxuries like digital subscriptions. Despite this, the subscription market is far from dead, with most people retaining some despite tightened budgets.
“However, considering the ongoing economic challenges, businesses need to consider adapting if they are to be retained by customers in the long term. The key to this is ensuring that the product adds value to the life of the customer.”
11. Waking up to browser security
Jonathan Lee, Senior Product Manager at Menlo Security, points to the web browser being the biggest attack surface and suggests the industry is “waking up” to the fact of where people spend the most time.
“Vendors are now looking at ways to add security controls directly inside the browser,” explains Lee. “Traditionally, this was done either as a separate endpoint agent or at the network edge, using a firewall or secure web gateway. The big players, Google and Microsoft, are also in on the act, providing built-in controls inside Chrome and Edge to secure at a browser level rather than the network edge.
“But browser attacks are increasing, with attackers exploiting new and old vulnerabilities, and developing new attack methods like HTML Smuggling. Remote browser isolation is becoming one of the key principles of Zero Trust security where no device or user – not even the browser – can be trusted.”
12. The year of quantum-readiness
Tim Callan, Chief Experience Officer at Sectigo, predicts that 2023 will be the year of quantum-readiness. He believes that as a result of the standardisation of new quantum-safe algorithms expected to be in place by 2024, this year will be a year of action for government bodies, technology vendors, and enterprise IT leaders to prepare for the deployment.
“In 2022, the US National Institute of Standards and Technologies (NIST) selected a set of post-quantum algorithms for the industry to standardise on as we move toward our quantum-safe future,” says Callan.
“In 2023, standards bodies like the IETF and many others must work to incorporate these algorithms into their own guidelines to enable secure functional interoperability across broad sets of software, hardware, and digital services. Providers of these hardware, software, and service products must follow the relevant guidelines as they are developed and begin preparing their technology, manufacturing, delivery, and service models to accommodate updated standards and the new algorithms.”
13. AI: fewer keywords, greater understanding
AI expert Dr Pieter Buteneers, Director of AI and Machine Learning at Sinch, expects artificial intelligence to continue to transition away from keywords and move towards an increased level of understanding.
“Language-agnostic AI, already existent within certain AI and chatbot platforms, will understand hundreds of languages — and even interchange them within a single search or conversation — because it’s not learning language like you or I would,” he says. “This advanced AI instead focuses on meaning, and attaches code to words accordingly, so language is more of a finishing touch than the crux of a conversation or search query.
“Language-agnostic AI will power stronger search results — both from external (the internet) and internal (a company database) sources — and less robotic chatbot conversations, enabling companies to lean on automation to reduce resources and strain on staff and truly trust their AI.”
14. Rise in digital twin technology in the enterprise
John Hill, CEO and Founder of Silico, recognises the growing influence digital twin technology is having in the market. Hill predicts that in the next 20 years, there will be a digital twin of every complex enterprise in the world and anticipates the next generation of decision-makers will routinely use forward-looking simulations and scenario analytics to plan and optimise their business outcomes.
“Digital twin technology is one of the fastest-growing facets of industry 4.0 and while we’re still at the dawn of digital twin technology,” he explains. “Digital twins will have huge implications for unlocking our ability to plan and manage the complex organisations so crucial for our continued economic progress and underpin the next generation of Intelligent Enterprise Automation.”
15. Broader tech security
With an exponential amount of data at companies’ fingertips, Tricentis CEO, Kevin Thompson says the need for investment in secure solutions is paramount.
“The general public has become more aware of the access companies have to their personal data, leading to the impending end of third-party cookies, and other similar restrictions on data sharing,” he explains. “However, security issues still persist. The persisting influx of new data across channels and servers introduces greater risk of infiltration by bad actors, especially for enterprise software organisations that have applications in need of consistent testing and updates. The potential for damage increases as iterations are being made with the expanding attack surface.
“Now, the reality is a matter of when, not if, your organisation will be the target of an attack. To combat this rising security concern, organisations will need to integrate security within the development process from the very beginning. Integrating security and compliance testing at the upfront will greatly reduce risk and prevent disruptions.”
16. Increased cyber resilience
Michael Adams, CISO at Zoom, expects an increased focus on cyber resilience over the next 12 months. “While protecting organisations against cyber threats will always be a core focus area for security programs, we can expect an increased focus on cyber resilience, which expands beyond protection to include recovery and continuity in the event of a cyber incident,” explains Adams.
“It’s not only investing resources in protecting against cyber threats; it’s investing in the people, processes, and technology to mitigate impact and continue operations in the event of a cyber incident.”
17. Ransomware threats
As data leaks become increasingly common place in the industry, companies face a very real threat of ransomware. Michal Salat, Threat Intelligence Director at Avast, believes the time is now for businesses to protect themselves or face recovery fees costing millions of dollars.
“Ransomware attacks themselves are already an individual’s and businesses’ nightmare. This year, we saw cybergangs threatening to publicly publish their targets’ data if a ransom isn’t paid, and we expect this trend to only grow in 2023,” says Salat. “This puts people’s personal memories at risk and poses a double risk for businesses. Both the loss of sensitive files, plus a data breach, can have severe consequences for their business and reputation.”
18. Intensified supply chain attacks
Dirk Schrader, VP of security research at Netwrix, believes supply chain attacks are set to increase in the coming year. “Modern organisations rely on complex supply chains, including small and medium businesses (SMBs) and managed service providers (MSPs),” he says.
“Adversaries will increasingly target these suppliers rather than the larger enterprises knowing that they provide a path into multiple partners and customers. To address this threat, organisations of all sizes, while conducting a risk assessment, need to take into account the vulnerabilities of all third-party software or firmware.”
19. A greater need to manage volatility
Paul Milloy, Business Consultant at Intradiem, stresses the importance of managing volatility in an ever-moving market. Milloy believes bosses can utilise data through automation to foresee potential problems before they become issues.
“No one likes surprises. Whilst Ben Franklin suggested nothing can be said to be certain, except death and taxes, businesses will want to automate as many of their processes as possible to help manage volatility in 2023,” he explains. “Data breeds intelligence, and intelligence breeds insight. Managers can use the data available from workforce automation tools to help them manage peaks and troughs better to avoid unexpected resource bottlenecks.”
20. A human AI co-pilot will still be needed
Artem Kroupenev, VP of Strategy at Augury, predicts that within the next few years, every profession will be enhanced with hybrid intelligence, and have an AI co-pilot which will operate alongside human workers to deliver more accurate and nuanced work at a much faster pace.
“These co-pilots are already being deployed with clear use cases in mind to support specific roles and operational needs, like AI-driven solutions that enable reliability engineers to ensure production uptime, safety and sustainability through predictive maintenance,” he says. “However, in 2023, we will see these co-pilots become more accurate, more trusted and more ingrained across the enterprise.
“Executives will better understand the value of AI co-pilots to make critical business decisions, and as a key competitive differentiator, and will drive faster implementation across their operations. The AI co-pilot technology will be more widespread next year, and trust and acceptance will increase as people see the benefits unfold.”
21. Building the right workplace culture
Harnessing a positive workplace culture is no easy task but in 2023 with remote and hybrid working now the norm, it brings with it new challenges. Tony McCandless, Chief Technology Officer at SS&C Blue Prism, is well aware of the role organisational culture can play in any digital transformation journey.
“Workers are the heart of an organisation, so without their buy in, no digital transformation initiative stands a chance of success,” explains McCandless. “Workers drive home business objectives, and when it comes to digital transformation, they are the ones using, implementing, and sometimes building automations. Curiosity, innovation, and the willingness to take risks are essential ingredients to transformative digitalisation.
“Businesses are increasingly recognising that their workers play an instrumental role in determining whether digitalisation initiatives are successful. Fostering the right work environment will be a key focus point for the year ahead – not only to cultivate buy-in but also to improve talent retention and acquisition, as labor supply issues are predicted to continue into 2023 and beyond.”
22. Cloud cover to soften recession concerns
Amid a cost-of-living crisis and concerns over any potential recession as a result, Daniel Thomasson, VP of Engineering and R&D at Keysight Technologies, says more companies will shift data intensive tasks to the cloud to reduce infrastructure and operational costs.
“Moving applications to the cloud will also help organisations deliver greater data-driven customer experiences,” he affirms. “For example, advanced simulation and test data management capabilities such as real-time feature extraction and encryption will enable use of a secure cloud-based data mesh that will accelerate and deepen customer insights through new algorithms operating on a richer data set. In the year ahead, expect the cloud to be a surprising boom for companies as they navigate economic uncertainty.”
23. IoT devices to scale globally
Dr Raullen Chai, CEO and Co-Founder of IoTeX, recognises a growing trend in the usage of IoT devices worldwide and believes connectivity will increase significantly.
“For decades, Big Tech has monopolised user data, but with the advent of Web3, we will see more and more businesses and smart device makers beginning to integrate blockchain for device connectivity as it enables people to also monetise their data in many different ways, including in marketing data pools, medical research pools and more,” he explains. “We will see a growth in decentralised applications that allow users to earn a modest additional revenue from everyday activities, such as walking, sleeping, riding a bike or taking the bus instead of driving, or driving safely in exchange for rewards.
“Living healthy lifestyles will also become more popular via decentralised applications for smart devices, especially smart watches and other health wearables.”
The digital landscape is changing day by day. Ideas like the metaverse that once seemed a futuristic fantasy are now…
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The digital landscape is changing day by day. Ideas like the metaverse that once seemed a futuristic fantasy are now coming to fruition and embedding themselves into our daily lives. The thinking might be there, but is our technology really ready to go meta? Domains and hosting provider, Fasthosts, spoke to the experts to find out…
How the metaverse works
The metaverse is best defined as a virtual 3D universe which combines many virtual places. It allows users to meet, collaborate, play games and interact in virtual environments. It’s usually viewed and accessed from the outside as a mixture of virtual reality (VR), (think of someone in their front room wearing a headset and frantically waving nunchucks around) and augmented reality (AR), but it’s so much more than this…
These technologies are just the external entry points to the metaverse and provide the visuals which allow users to explore and interact with the environment within the metaverse.
This is the ‘front-end’ if you like, which is also reinforced by artificial intelligence and 3D reconstruction. These additional technologies help to provide realistic objects in environments, computer-controlled actions and also avatars for games and other metaverse projects.
So, what stands in the way of this fantastical 3D universe? Here are the six key challenges:
Technology
The most important piece of technology, on which the metaverse is based, is the blockchain. The blockchain is essentially a chain of blocks that contain specific information. They’re a combination of computers linked to each other instead of a central server which means that the whole network is decentralised. This provides the infrastructure for the development of metaverse projects, storage of data and also allows them the capability to be compatible with Web3. Web3 is an upgraded version of the internet which will allow integration of virtual and augmented reality into people’s everyday lives.
Sounds like a lot, right? And it involves a great deal of tech that is alien to the vast majority of us. So, is technology a barrier to widespread metaverse adoption?
Jonothan Hunt, Senior Creative Technologist at Wunderman Thompson, says the tech just isn’t there. Yet.
“Technology’s readiness for the mass adoption of the metaverse depends on how you define the metaverse, but if we’re talking about the future vision that the big tech players are sharing, then not yet. The infrastructure that powers the internet and our devices isn’t ready for such experiences. The best we have right now in terms of shared/simulated spaces are generally very expensive and powered entirely in the cloud, such as big computers like the Nvidia Omniverse, cloud streaming, or games. These rely heavily on instancing and localised grouping. Consumer hardware, especially XR, is still not ready for casual daily use and still not really democratised.
“The technology for this will look like an evolution of the systems above, meaning more distributed infrastructure, better access and updated hardware. Web3 also presents a challenge in and of itself, and questions remain over to what extent big tech will adopt it going forward.”
Storage
Blockchain is the ‘back-end’, where the magic happens, if you will. It’s this that will be the key to the development and growth of the metaverse. There are a lot of elements that make up the blockchain and reinforce its benefits and uses such as storage capabilities, data security and smart contracts.
Due to its decentralised nature, the blockchain has far more storage capacity than the centralised storage systems we have in place today. With data on the metaverse being stored in exabytes, the blockchain works by making use of unutilised hard disk space across the network, which avoids users within the metaverse running out of storage space worldwide.
In terms that might be a bit more relatable, an exabyte is a billion gigabytes. That’s a huge amount of storage, and that doesn’t just exist in the cloud – it’s got to go somewhere – and physical storage servers mean land is taken up, and energy is used. Hunt says: “How long’s a piece of string? The whole of the metaverse will one day be housed in servers and data centres, but the amount or size needed to house all of this storage will beentirely dependent on just how mass adopted the metaverse becomes. Big corporations in the space are starting to build huge data centres – such as Meta purchasing a $1.1 billion campus in Toledo, Spain to house their new Meta lab and data centre – but the storage space is not the only concern. These energy-guzzlers need to stay cool! And what about people and brands who need reliable web hosting for events, gaming or even just meeting up with pals across the world, all that information – albeit virtual – still needs a place to go.
“The current rising cost of electricity worldwide could cause problems for the growth of data centres, and the housing of the metaverse as a whole. However, without knowing the true size of its adoption, it is extremely difficult to truly determine the needed usage. Could we one day see an entire island devoted to data centre storage? Purely for the purposes of holding the metaverse? It seems a little ‘1984’, but who knows?”
Identity
Although the blockchain provides instantaneous verification of transactions with identity through digital wallets, our physical form will be represented by avatars that visually reflect who we are, and how we want to be seen.
The founder of Saxo Bank and the chairman of the Concordium Foundation, Lars Seier Christensen, argues, “I think that if you use an underlying blockchain-based solution where ID is required at the entry point, it is actually very simple and automatically available for relevant purposes. It is also very secure and transparent, in that it would link any transactions or interactions where ID is required to a trackable record on the blockchain.”
Once identity is established, it is true that it could potentially become easier to assess creditworthiness of parties for purchasing and borrowing in the metaverse due to the digital identity and storage of each individual’s data and transactions on the blockchain. However, although it sounds exciting, there must be considerations into how it could impact privacy, and how this amount of data will be recorded on the blockchain.
Security
There are also huge security benefits to this set up. The decentralised blockchain helps to eradicate third-party involvement and data breaches, such as theft and file manipulation, thanks to its powerful data processing and use of validation nodes. Both of these are responsible for verifying and recording transactions on the blockchain. This will be reassuring to many, given the widespread concerns around data privacy and user protection in the metaverse.
To access the blockchain all we will need is an internet connection and a device, such as a laptop or smartphone, this is what makes it so great as it will be so readily available. However, to support the blockchain, we’re relying on a whole different set of technologies. Akash Kayar, CEO of web3-focused software development company Leeway Hertz, had this to say on the readiness of the current technology available: “The metaverse is not yet completely mature in terms of development. Tech experts are researching strategies and
testing the various technologies to develop ideas that provide the world with more feasible and intriguing metaverse projects.
“Projects like Decentraland, Axie Infinity, and Sandbox are popular contemporary live metaverse projects. People behind these projects made perfect use of notable metaverse technologies, from blockchain and cryptos to NFTs.
“As envisioned by top tech futurists, many new technologies will empower the metaverse in the future, which will support the development of a range of prolific use cases that will improve the ability of the metaverse towards offering real-life functionalities. In a nutshell, the metaverse is expected to bring extreme opportunities for enterprises and common users. Hence, it will shape the digital future.”
Currency & Payments
Whilst it’s only considered legal tender in two countries, cryptocurrency is currently a reality and there is a strong likelihood that it will eventually be mass adopted. However, the metaverse is arguably not yet at the same maturity level, meaning cryptocurrency may have to wait before it can finally fully take off.
Golden Bitcoin symbol and finance graph screen. Horizontal composition with copy space. Focused image.
There is no doubt that cryptocurrency and the metaverse will go hand-in-hand as the former will become the tender of the latter with many of the current metaverse platforms each wielding its native currency. For example Decentraland uses $MANA for payments and purchases. However, with the volatility of crypto currencies and the recent collapse of trading platform FTX indicating security lapses, we may not yet be ready for the switch to decentralised payments.
Energy
Some of the world’s largest data centres can each contain many tens of thousands of IT devices which require more than 100 megawatts of power capacity – this is enough to power around 80,000 U.S. households (U.S. DOE 2020) and is equivalent to $1.35bn running cost per data centre with the cost of a megawatt hour averaging $150.
According to Nitin Parekh of Hitachi Energy, the amount of power which takes to process Bitcoin is higher than you might expect: “Bitcoin consumes around 110 Terawatt Hours per year. This is around 0.5% of global electricity generation. This estimate considers combined computational power used to mine bitcoin and process transactions.” With this estimate, we can calculate that the annual energy cost of Bitcoin is around $16.5bn.
However, some bigger corporations are slowly moving towards renewable energy to power their projects in this space, with Google signing close to $2bn worth of wind and solar investments in order to power its data centres in the future and become greener. Amazon has also followed in their footsteps and have become the world’s largest corporate purchaser of renewable energy.
They may have plenty of time yet to get their green processes in place, with Mark Zuckerberg recently predicting it will take nearly a decade for the metaverse to be created: “I don’t think it’s really going to be huge until the second half of this decade at the earliest.”
About Fasthosts
Fasthosts has been a leading technology provider since 1999, offering secure UK data centres, 24/7 support and a highly successful reseller channel. Fasthosts provides everything web professionals need to power and manage their online space, including domains, web hosting, business-class email, dedicated servers, and a next-generation cloud platform. For more information, head to www.fasthosts.co.uk
John MClure, CISO at Sinclair Group – a diversified media company and America’s leading provider of local sports and news – talks about the evolution of cybersecurity and the cultural shift placing it at the forefront of business change
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This month’s cover story explores how Sinclair Broadcast Group is embracing the evolution of cybersecurity and placing the role of the CISO at the forefront of business transformation.
Welcome to the latest issueof Interface magazine!
Communication, secure and at speed, is a vital component of the transformation journey for both the modern enterprise and its relationship with stakeholders, be they customers or partners. Putting the right building blocks in place to deliver successful change management is at the heart of the inspiring stories in the latest issue of Interface.
Our cover star John McClure progressed from a career in the military and work as a consultant in the intelligence industry to fight a new kind of foe… As CISO for Sinclair Broadcast Group, a diversified media company and America’s leading provider of local sports and news, he talks about the evolution of cybersecurity, the battle to meet the rising velocity and sophistication of cyber-attacks and the cultural shift of the role of CISO placing it at the forefront of business change.
“Sinclair is unique in terms of its different business units and how it operates. It’s my job as CISO leading our cyber team not to be an obstacle for the business; we’re here to help it move faster to keep up with market forces, and to move safely. We’re here to engineer solutions that work for the enterprise but also help us maintain a positive security posture.”
State of Florida: digital government services
We also hear from CIO Jamie Grant who is leading the State of Florida’s Digital Service (FL[DS]) on its charge to transform and modernise the way government is accessed and consumed. He is building a team of talented, goal-oriented and customer-obsessed individuals to drive a digital transformation with innovation at its heart. “Leadership is really about developing the team and investing in the people. And it turns out that when you get their backs, they appreciate it and then you can achieve anything.”
ResultsCX: putting people first
Jamie Vernon, SVP for IT & Infrastructure at AI-powered customer experience solution specialist ResultsCX, discusses what drives customer care in the 21st century, and the part technology has to play.
“We are the custodians of our customers’ customers,” says Vernon. “In this increasingly tenuous relationship with their customers, they trust us. My leadership takes that responsibility very seriously, and charges each of us with doing everything we can to provide a perfect call, or email, or chat, every time, thousands of times a minute, around the clock and around the calendar.”
Jamie Vernon, SVP for IT & Infrastructure at AI-powered customer experience solution specialist ResultsCX, discusses what drives customer care in the 21st century, and the part technology has to play.
“We are the custodians of our customers’ customers,” says Vernon. “In this increasingly tenuous relationship with their customers, they trust us. My leadership takes that responsibility very seriously, and charges each of us with doing everything we can to provide a perfect call, or email, or chat, every time, thousands of times a minute, around the clock and around the calendar.”
Also this month, Sarita Singh, Regional Head & Managing Director for Stripe in Southeast Asia, talks about how the fast-growing payments platform is driving financial inclusion across Asia and supporting SMEs with end-to-end services putting users first, and we get expert advice for the modern CEO from the University of Oxford’s Saïd Business School.
Our cover story this month investigates how Fleur Twohig, Executive Vice President, leading Personalisation & Experimentation across Consumer Data & Engagement Platforms, and her team are executing Wells Fargo’s strategy to promote personalised customer engagement across all consumer banking channels
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This month’s cover story follows Wells Fargo’s journey to deliver personalised customer engagement across all its consumer banking channels.
Welcome to the latest issue of Interface magazine!
Partnerships of all kinds are a key ingredient for organisations intent on achieving their goals… Whether that’s with customers, internal stakeholders or strategic allies across a crowded marketplace, Interface explores the route to success these relationships can help navigate.
Our cover story this month investigates the strategy behind Wells Fargo’s ongoing drive to promote personalised customer engagement across all consumer banking channels.
Fleur Twohig, Executive Vice President, leading Personalisation & Experimentation across the bank’s Consumer Data & Engagement Platforms, explains her commitment to creating a holistic approach to engaging customers in personalised one-to-one conversations that support them on their financial journeys.
“We need to be there for everyone across the spectrum – for both the good and the challenging times. Reaching that goal is a key opportunity for Wells Fargo and I have the pleasure of partnering with our cross-functional teams to help determine the strategic path forward…”
IBM: consolidating growth to drive value
We hear from Kate Woolley, General Manager of IBM Ecosystem, who reveals how the tech leader is making it easier for partners and clients to do business with IBM and succeed. “Honing our corporate strategy around open hybrid cloud and artificial intelligence (AI) and connecting partners to the technical training resources they need to co-create and drive more wins, we are transforming the IBM Ecosystem to be a growth engine for the company and its partners.”
Kate Woolley, IBM
America Televisión: bringing audiences together across platforms
Jose Hernandez, Chief Digital Officer at America Televisión, explains how Peru’s leading TV network is aggregating services to bring audiences together for omni-channel opportunities across its platforms. “Time is the currency with which our audiences pay us, so we need to be constantly improving our offering both through content and user experiences.”
Portland Public Schools: levelling the playing field through technology
Derrick Brown and Don Wolf, tech leaders at Portland Public Schools, talk about modernising the classroom, dismantling systemic racism and the power of teamwork.
Also in this issue, we hear from Lenovo on how high-performance computing (HPC) is driving AI research and report again from London Tech Week where an expert panel examined how tech, fuelled by data, is playing a critical role in solving some of the world’s hardest hitting issues, ranging from supply chain disruptions through to cybersecurity fears.
Conventional robots, like giant industrial robots used in the car industry, are set to reach $14.9bn value this year, up from $12bn in 2018.
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Robotics play a huge role in the manufacturing landscape today. A growing number of businesses use manufacturing robots to automate repetitive tasks, reduce errors, and enable their employees to focus on innovation and efficiency, causing the entire sector’s impressive growth.
According to data presented by AksjeBloggen.com, the global market value of conventional and advanced robotics in the manufacturing industry is expected to continue rising and hit $18.6bn in 2021, a 40% increase in three years.
Market Value Jumped by $5.4B in Three Years
Robots have numerous roles in manufacturing. They are mainly used for high-volume, repetitive processes where their speed and accuracy offer tremendous advantages. Other manufacturing automation solutions include robots used to help people with more complex tasks, like lifting, holding, and moving heavy pieces.
Companies turn to robotics process automation to cut manufacturing costs, solve the shortage of skilled labor and keep their cost advantage in the market.
In 2018, the global market value of conventional and advanced robotics in the manufacturing industry amounted to $13.2bn, revealed the BCG survey. In 2019, this figure rose to $14.8bn and continued growing. Statistics show the market value of manufacturing robots hit $16.6bn in 2020. This figure is expected to jump by $2bn and hit $18.6bn in 2021.
Conventional robots, like giant industrial robots used in the car industry, are set to reach $14.9bn value this year, up from $12bn in 2018.
The market value of advanced manufacturing robots, which have a superior perception, adaptability, and mobility, tripled in the last three years and is expected to hit $3.7bn in 2021. Combined with big data analytics, advanced manufacturing robots allow companies to make intelligent decisions based on real-time data, which leads to lower costs and faster turnaround times.
The BCG survey also showed most manufacturers believe advanced robotic systems will have a massive role in the factory of the future and plan to increase their use. More than 70% of respondents defined robotics as a significant productivity driver in production and logistics.
European and Asian Companies Lead in the Use of Advanced Manufacturing Robots
Analyzed by regions, European and Asian companies lead in the use of advanced robots, while manufacturers from North America lag behind. However, the survey showed 80% of respondents from the US plan to implement advanced robotics in the next few years.
The survey also revealed that manufacturers in emerging markets, especially China and India, are more enthusiastic about using advanced robots than those in industrialized countries. These companies may be looking to automation as a way to overcome a skilled labor shortage and improve their ability to compete in international markets.
Germany had the largest robot density in the manufacturing industry among European countries, with 346 installations per 10,000 employees in 2019. Sweden, Denmark, and Italy followed with 277, 243, and 212 installations per 10,000 employees, respectively.
Statistics also show that companies in the transportation and logistics and technology sector lead in implementing advanced robotics, with 54% and 53% of manufacturers who already use such solutions. The automotive industry and consumer goods sector follow with 49% and 44% share, respectively.
Manufacturers in the engineered products, process, and health care industries lag behind, with 42%, 41%, and 30% of companies that use advanced manufacturing robots. However, around 85% of manufacturers in these sectors plan to start using advanced robotic systems by 2022.
Gurpreet Purewal, Associate Vice President, Business Development, iResearch Services, explores how organisations can overcome the challenges presented by AI in 2021.
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2020 has been a year of tumultuous change and 2021 isn’t set to slow down. Technology has been the saving grace of the waves of turbulence this year, and next year as the use of technology continues to boom, we will see new systems and processes emerge and others join forces to make a bigger impact. From assistive technology to biometrics, ‘agritech’ and the rise in self-driving vehicles, tech acceleration will be here to stay, with COVID-19 seemingly just the catalyst for what’s to come. Of course, the increased use of technology will also bring its challenges, from cybersecurity and white-collar crime to the need to instil trust in not just those investing in the technology, but those using it, and artificial intelligence (AI) will be at the heart of this.
1. Instilling a longer-term vision
New AI and automation innovations have led to additional challenges such as big data requirements for the value of these new technologies to be effectively shown. For future technology to learn from the challenges already faced, a comprehensive technology backbone needs to be built and businesses need to take stock and begin rolling out priority technologies that can be continuously deployed and developed.
Furthermore, organisations must have a longer-term vision of implementation rather than the need for immediacy and short-term gains. Ultimately, these technologies aim to create more intelligence in the business to better serve their customers. As a result, new groups of business stakeholders will be created to implement change, including technologists, business strategists, product specialists and others to cohesively work through these challenges, but these groups will need to be carefully managed to ensure a consistent and coherent approach and long-term vision is achieved.
2. Overcoming the data challenge
AI and automation continue to be at the forefront of business strategy. The biggest challenge, however, is that automation is still in its infancy, in the form of bots, which have limited capabilities without being layered with AI and machine learning. For these to work cohesively, businesses need huge pools of data. AI can only begin to understand trends and nuances by having this data to begin with, which is a real challenge. Only some of the largest organisations with huge data sets have been able to reap the rewards, so other smaller businesses will need to watch closely and learn from the bigger players in order to overcome the data challenge.
3. Controlling compliance and governance
One of the critical challenges of increased AI adoption is technology governance. Businesses are acutely aware that these issues must be addressed but orchestrating such change can lead to huge costs, which can spiral out of control. For example, cloud governance should be high on the agenda; the cloud offers new architecture and platforms for business agility and innovation, but who has ownership once cloud infrastructures are implemented? What is added and what isn’t?
AI and automation can make a huge difference to compliance, data quality and security. The rules of the compliance game are always changing, and technology should enable companies not just to comply with ever-evolving regulatory requirements, but to leverage their data and analytics across the business to show breadth and depth of insight and knowledge of the workings of their business, inside and out.
In the past, companies struggled to get access and oversight over the right data across their business to comply with the vast quantities of MI needed for regulatory reporting. Now they are expected to not only collate the correct data but to be able to analyse it efficiently and effectively for regulatory reporting purposes and strategic business planning. There are no longer the time-honoured excuses of not having enough information, or data gaps from reliance on third parties, for example, so organisations need to ensure they are adhering to regulatory requirements in 2021.
4. Eliminating bias
AI governance is business-critical, not just for regulatory compliance and cybersecurity, but also in diversity and equity. There are fears that AI programming will lead to natural bias based on the type of programmer and the current datasets available and used. For example, most computer scientists are predominantly male and Caucasian, which can lead to conscious/unconscious bias, and datasets can be unrepresentative leading to discriminatory feedback loops.
Gender bias in AI programming has been a hot topic for some years and has come to the fore in 2020 again within wider conversations on diversity. By only having narrow representation within AI programmers, it will lead to their own bias being programmed into systems, which will have huge implications on how AI interprets data, not just now but far into the future. As a result, new roles will emerge to try and prevent these biases and build a more equitable future, alongside new regulations being driven by companies and specialist technology firms.
5. Balancing humans with AI
As AI and automation come into play, workforces fear employee levels will diminish, as roles become redundant. There is also inherent suspicion of AI among consumers and certain business sectors. But this fear is over-estimated, and, according to leading academics and business leaders, unfounded. While technology can take away specific jobs, it also creates them. In responding to change and uncertainty, technology can be a force for good and source of considerable opportunity, leading to, in the longer-term, more jobs for humans with specialist skillsets.
Automation is an example of helping people to do their jobs better, speeding up business processes and taking care of the time-intensive, repetitive tasks that could be completed far quicker by using technology. There remain just as many tasks within the workforce and the wider economy that cannot be automated, where a human being is required.
Businesses need to review and put initiatives in place to upskill and augment workforces. Reflecting this, a survey on the future of work found that 67% of businesses plan to invest in robotic process automation, 68% in machine learning, and 80% investing in perhaps more mainstream business process management software. There is clearly an appetite to invest strongly in this technology, so organisations must work hard to achieve harmony between humans and technology to make the investment successful.
6. Putting customers first
There is growing recognition of the difference AI can make in providing better service and creating more meaningful interactions with customers. Another recent report examining empathy in AI saw 68% of survey respondents declare they trust a human more than AI to approve bank loans. Furthermore, 69% felt they were more likely to tell the truth to a human than AI, yet 48% of those surveyed see the potential for improved customer service and interactions with the use of AI technologies.
2020 has taught us about uncertainty and risk as a catalyst for digital disruption, technological innovation and more human interactions with colleagues and clients, despite face-to-face interaction no longer being an option. 2021 will see continued development across businesses to address the changing world of work and the evolving needs of customers and stakeholders in fast-moving, transitional markets. The firms that look forward, think fast and embrace agility of both technology and strategy, anticipating further challenges and opportunities through better take-up of technology, will reap the benefits.
With virtually all companies looking at AI, what are some of the key risks they need to consider before implementation?
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Today virtually all companies are forced to innovate and many are excited about AI. Yet since implementation cuts across organisational boundaries, shifting to an AI-driven strategy requires new thinking about managing risks, both internally and externally. This blog will cover “the seven sins of enterprise AI strategies”, which are governance issues at the board and executive levels that block companies from moving ahead with AI. by By Jeremy Barnes, Element AI
1- Disowning the AI strategy
This is probably the most important sin. In this case, a CEO and board will say that AI is a priority, but delegate it to a different department or an innovation lab. However, success is not based on whether or not a company uses an innovation lab—it’s whether they are truly invested in it. The bottom line is that the CEO and board need to actively lead an AI strategy.
2- Ignoring the unknowns
This happens when companies say they believe in AI, but don’t reach a level of proficiency where it’s possible to identify, characterise and model the threats that emerge with new advances. Even if it is decided not to go all-in on AI innovation, it’s still important that there is a hypothesis for how to address AI within a company and an early warning system so the decision can be re-evaluated early enough to act. Being a fast follower requires as much organizational preparation and lead time as leadership.
3- Not enabling the culture
The ability to implement AI is about an experimentation mindset. That and an openness to failure need to be adopted across the company. Organisations need to keep in mind that AI doesn’t respect organisational boundaries. Most companies want high-impact, low-risk solutions that could simply lead to optimising, rather than advancing new value streams. It is hard to accept increased risk in exchange for impact but it will come as part of the continuous cultural enablement of an experimental mindset.
4- Starting with the solution
This is the most common sin. It’s important to be able to understand the specific problems you’re trying to solve, because AI is unlikely to be a solution for all of them, and especially not blindly implementing a horizontal AI platform. Have the conversation at board level to ensure that an overarching AI strategy, and not simply quick-fix solutions, is the priority.
5- Lose risk, keep reward
As mentioned in the third sin, it is natural for companies to want to implement AI without any risk. But there is no reward without risk. A vendor motivated to decrease risk will also decrease innovation and ultimately impact by making successes small and failures non-existent. AI creates differentiation only for companies that are willing to learn from both their successes and their failures. A company that doesn’t effectively balance risk in AI will ultimately increase its risk of disruption.
6- Vintage accounting
Attempting to fit AI into traditional financial governance structures causes problems. It doesn’t fit nicely into budget categories and it’s hard to value the output. The link between what you put in and what you get out can be less tangible or predictable, which often makes it harder to square with existing plans or structures. Model the rate of return on AI activities and all data-related activities. This demands that these activities affect profit (not just loss) and assets (not just liabilities).
7- Treating data as a commodity
The final sin concerns data and its treatment as a commodity. Data is fundamental to AI. If data is poorly handled, it can lead to negative impacts on decision-making. Data should be treated as an asset. The stronger, deeper and more accurate the dataset, the better models that you can train and more intelligent insights you can generate. But, at the same time, when personally identifiable information is stored about customers, it can be stolen, risking heavy penalties in some jurisdictions. You need to build towards data from a use case rather than invest blindly in data centralisation projects. So, now you know what not to do. Here are some of the simple things that you can do to move ahead. First, talk to your board about how long it will take to become an AI innovator, modelling it out, rather than simply discussing it conceptually.
Second, prepare for change and put in place monitoring. AI shifts all the time, so you’ll want to regularly check in to adjust and pivot your strategy. It’s important to develop a basic skill set so you can redo planning exercises with your board. Third, model out risks in both action and inaction. But don’t model them in a traditional approach, which is to push risk down to different business units and then compensate those units for reducing risk rather than managing trade-offs. Instead, view those trade-offs in terms of risks and rewards, and start to think about how you are accounting for the assets and liabilities of AI. Ultimately, you want to start to model what is the actual rate of return for all these activities that you are doing. Then benchmark it against what you see in other companies from across the industry, and that will give you a good picture of the current situation and where to go.
Understanding what it isn’t is just as important as understanding what it is, says Jim Logan who has nearly three decades of experience in financial services and technology…
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I’ve been working in the financial services space for close to thirty years now. I’ve seen many trends and technologies emerge. Some take hold, several are just a flash in the pan. Regardless of how long a concept sticks around, one thing remains: Terminology plays a material role in shaping perceptions. In a world where messaging tends to over complicate things, too many acronyms and too many buzzwords all work against what should be the primary objective: clearly illustrating value. I’ve found this to be equally true when it comes to artificial intelligence or ‘AI’.
Generally speaking, the word artificial doesn’t readily call to mind a positive image, does it? By definition, the word “artificial” has listed meanings of, “insincere or affected” and “made by humans as opposed to happening naturally.” It is the second part of this definition I’d like to explore a bit further.
Artificial Intelligence is, in fact, created by humans. And it isn’t a new fad or concept. Many don’t realize that the term was first coined by John McCarthy, Ph.D. and Stanford computer and cognitive scientist, back in 1955. AI has continued to evolve as a material concept, with practical applications across many industries, ever since.
For financial service professionals, particularly those of us involved with fighting financial crime and preventing money laundering, AI can have tremendous impact and practical application. Before we dive a bit deeper, I feel it’s important to first understand what AI isn’t.
AI is not intended to simply be a digital worker, certainly not within financial services and fighting financial crime. Yes, AI can automate various functions. We’re all familiar with the concept of ‘bots’ and virtual assistants. However, those are rudimentary examples of robotic process automation. True AI is human led and a continuous, instantaneous learning process that drives tangible value. AI is not merely a play to cut costs or replace human capital. Rather, AI enhances the bottom line by keeping compliance staff costs flat in the immediate term and enables our human experts to more appropriately manage their time, by focusing talent on investigations that matter the most.
One of the most valuable aspects of AI, in the context of anti money laundering and compliance, is the speed by which it can be deployed. We’re talking about time to market and time to value in a matter of weeks. Not months, not multiple quarters – simply weeks. But I don’t mean a generic, black box concept. I’m specifically referring to a highly precise, tailored AI solution that has extensive proof points and, more importantly, far-reaching global regulatory approval.
AI shouldn’t simply be an extension of legacy rules-based routines, nor a way to further automate the process of scoring or risk weighted alert suppression. That simply dilutes the true value of AI, and does not maximize the cost and efficiency benefits.
The cost of compliance continues to grow at a staggering pace, particularly for financial institutions and insurance companies. Equally of concern, the impact of fines for non-compliance has also skyrocketed in the last decade. Specifically to the tune of $8.4 billion last year across North America alone.
What if you could literally solve every single name screen, sanction, and transaction alert? What if you could achieve this without sacrificing any aspect of control and security? What if you could increase the throughput, efficiency and accuracy of your compliance operations without adding a single dollar of staff expense to your budget?
Let’s stop talking in terms of what if and have a meaningful conversation regarding how. I’m helping clients achieve all of these measures today and that is from a perspective proven in production. Here at Silent Eight we’re a team founded by engineers and data scientists, solving real world challenges in the anti money laundering and financial compliance market.
Artificial Intelligence isn’t scary…it isn’t a black box…and it isn’t the futuristic world of tomorrow – it is the here and now, and it’s battle tried and tested.
Temenos, the banking software company, partners with Microsoft to offer AI-driven Financial Crime Mitigation solution to help banks combat surge cybercrime during Covid-19 outbreak.
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Temenos, the banking software company, announced today a joint effort with Microsoft to enable access to its AI-powered, Financial Crime Mitigation (FCM) SaaS solution to allow banks to protect both their customers and their organization from financial crime increase during the pandemic, particularly as banks have moved to remote working to protect their staff. Temenos AI-powered, Financial Crime Mitigation SaaS solution based on Microsoft’s fast, scalable and secure Azure cloud platform can be deployed within weeks.
Temenos and Microsoft are opening up access to banks for a 14-day trial, available until 30 of June. As part of the collaboration with Microsoft, Temenos is offering system access and online tutorials for users to familiarize themselves with navigation of the system and learn how it can support them in a revised operating landscape. Temenos unveiled the open access initiative of its FCM software at its virtual event Temenos Community Forum Online, 29-30 April.
Temenos FCM provides enterprise-wide financial crime protection for a highly regulated and fast-changing environment. It allows banks’ operators to respond to alerts and collaborate with team members while working remotely. Throughout the Covid-19 crisis, Temenos customers from Tier 1 banks to regional banks and neobanks have continued to benefit from Temenos FCM’s comprehensive coverage regardless of the fact that their teams are working remotely.
Financial regulators worldwide and organizations such as the European Central Bank are warning that the Covid-19 pandemic may result in an increase in financial crime and other misconduct due to market disruptions, reduced staff, and other factors, as has been the case during past global crises. Opportunistic fraudsters and criminals are adapting their methods of targeting people and countries in distress as new threat vectors open up.
The Financial Actions Task Force (FATF), the global standard setter for combating money laundering and terrorism financing, warns businesses to remain vigilant for emerging money laundering and terrorist financing risks as criminals may seek to exploit gaps and weaknesses in Anti-Money Laundering/Combating the Financing of Terrorism (AML/CFT) systems under the assumption that resources are focused elsewhere. Fraudsters have already been very quick to adapt well-known fraud schemes to target individual citizens, businesses and public organizations. These include various types of adapted versions of telephone fraud schemes, supply scams and decontamination scams.
Jean-Michel Hilsenkopf, Chief Operating Officer, Temenos, said:“We are proud to be able to offer our cloud-native and AI technology to support banks in the fight against financial crime, which has increased as a result of the pandemic. As a strategic global banking software partner of Microsoft, we are pleased to join efforts to deliver Temenos Financial Crime Mitigation as SaaS on Microsoft Azure’s resilient, secure and proven cloud platform. We are committed to providing robust and up-to-date sanction screening, AML, KYC and fraud management protection combined with powerful AI-driven transaction monitoring and sanction screening to help banks worldwide.”
Marianne Janik, Country General Manager, Microsoft Switzerland, said: “We have been pioneering with Temenos in the cloud for a decade. We are proud to join forces to help banks use the power of Temenos’ market-leading Financial Crime Mitigation solution based on our secure, scalable and resilient global Azure cloud platform to combat financial crime surge due to Covid-19.”
More than 200 banks use Temenos FCM SaaS solution, which covers watch-list screening, anti-money laundering, fraud prevention – suspicious activity prevention – and KYC, delivering industry-leading levels of detection and false positives of under 2% vs industry average of 7% and above. Temenos FCM can be deployed as a standalone, or integrated into any banking or payments platform including cloud-native, cloud-agnostic Temenos Transact and Temenos Infinity. It provides unrivalled levels of detection and resilience against financial crime and Total Cost of Ownership (TCO) savings of more than 50%. Temenos FCM provides banks with the next generation of AI-driven FCM capabilities that can run on any public cloud, as a service or on premise.
The global developer of artificial intelligence solutions is releasing a free search platform to help clinical and scientific researchers find answers and patterns in research papers
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Information on COVID-19 is evolving fast and this AI-powered platform leverages a semantic search model that will allow users to quickly connect disparate information. The platform can execute searches based on specific inquiries, along with critical paragraphs copied from a relevant paper. Unlike keyword searches, the queries do not need to be specifically structured, and actually perform better in longer form. This initial version is configured to work with the COVID-19 Open Research Dataset (CORD-19) corpus. Element AI is looking for users and organizations from various groups to test the platform and suggest other data sets and features that could best fit their needs.
The group’s Element AI is looking to work with include:
Clinical researchers who need to incorporate many phenomena to make a rich model of the pandemic and its impacts.
Government, Public Safety and Public Health authorities looking to find best practices across different countries.
Pharmaceutical companies working on new therapies or vaccine trials, as well as identifying existing therapies that could provide immediate help.
-Scientific researchers and data scientists who are working on novel ways to connect research across the body of knowledge already available for COVID-19.
“Research data and reports are being published at an unprecedented pace as organizations scale up their efforts to respond to COVID-19. We want to contribute, and this free platform is our way to help the community locate and gather knowledge to find answers and patterns,” said Jean-François (JF) Gagné, CEO and Co-founder of Element AI. “We encourage the scientific and healthcare community to use this free platform and engage with our team to quickly ramp up and collaboratively meet the needs of the people working to slow down and contain COVID-19. We hope that their feedback and collaboration will help us quickly add features and datasets on top of what we already have made available” added Gagné.
The COVID-19 platform leverages technology from the Element AI Knowledge Scout product, which uses natural language techniques to tap into structured and unstructured sources of information. The first version will be progressively updated in coming weeks as additional datasets emerge. The site can be accessed at: https://www.elementai.com/covid-research.
Mauro Guillén Zandman, Professor of International Management, The Wharton School, University of Pennsylvania, USA Srikar Reddy, Managing Director and Chief…
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Mauro Guillén Zandman, Professor of International Management, The Wharton School, University of Pennsylvania, USA
Srikar Reddy, Managing Director and Chief Executive Officer, Sonata Software Limited and Sonata Information Technology Limited
Artificial intelligence (AI) relies on big data and machine learning for myriad applications, from autonomous vehicles to algorithmic trading, and from clinical decision support systems to data mining. The availability of large amounts of data is essential to the development of AI. But the scandal over the use of personal and social data by Facebook and Cambridge Analytica has brought ethical considerations to the fore. And it’s just the beginning. As AI applications require ever greater amounts of data to help machines learn and perform tasks hitherto reserved for humans, companies are facing increasing public scrutiny, at least in some parts of the world. Tesla and Uber have scaled down their efforts to develop autonomous vehicles in the wake of widely reported accidents. How do we ensure the ethical and responsible use of AI? How do we bring more awareness about such responsibility, in the absence of a global standard on AI?
The ethical standards for assessing AI and its associated technologies are still in their infancy. Companies need to initiate internal discussion as well as external debate with their key stakeholders about how to avoid being caught up in difficult situations.
Consider the difference between deontological and teleological ethical standards. The former focuses on the intention and the means, while the latter on the ends and outcomes. For instance, in the case of autonomous vehicles, the end of an error-free transportation system that is also efficient and friendly towards the environment might be enough to justify large-scale data collection about driving under different conditions and also, experimentation based on AI applications.
By contrast, clinical interventions and especially medical trials are hard to justify on teleological grounds. Given the horrific history of medical experimentation on unsuspecting human subjects, companies and AI researchers alike would be wise to employ a deontological approach that judges the ethics of their activities on the basis of the intention and the means rather than the ends.
Another useful yardstick is the so-called golden rule of ethics, which invites you to treat others in the way you would like to be treated. The difficulty in applying this principle to the burgeoning field of AI lies in the gulf separating the billions of people whose data are being accumulated and analyzed from the billions of potential beneficiaries. The data simply aggregates in ways that make the direct application of the golden rule largely irrelevant.
Consider one last set of ethical standards: cultural relativism versus universalism. The former invites us to evaluate practices through the lens of the values and norms of a given culture, while the latter urges everyone to live up to a mutually agreed standard. This comparison helps explain, for example, the current clash between the European conception of data privacy and the American one, which is shaping the global competitive landscape for companies such as Google and Facebook, among many others. Emerging markets such as China and India have for years proposed to let cultural relativism be the guiding principle, as they feel it gives them an edge, especially by avoiding unnecessary regulations that might slow their development as technological powerhouses.
Ethical standards are likely to become as important at shaping global competition as technological standards have been since the 1980s. Given the stakes and the thirst for data that AI involves, it will likely require companies to ask very tough questions as to every detail of what they do to get ahead. In the course of the work we are doing with our global clients, we are looking at the role of ethics in implementing AI. The way industry and society addresses these issues will be crucial to the adoption of AI in the digital world.
However, for AI to deliver on its promise, it will require predictability and trust. These two are interrelated. Predictable treatment of the complex issues that AI throws up, such as accountability and permitted uses of data, will encourage investment in and use of AI. Similarly, progress with AI requires consumers to trust the technology, its impact on them, and how it uses their data. Predictable and transparent treatment facilitates this trust.
Intelligent machines are enabling high-level cognitive processes such as thinking, perceiving, learning, problem-solving and decision-making. AI presents opportunities to complement and supplement human intelligence and enrich the way industry and governments operate.
However, the possibility of creating cognitive machines with AI raises multiple ethical issues that need careful consideration. What are the implications of a cognitive machine making independent decisions? Should it even be allowed? How do we hold them accountable for outcomes? Do we need to control, regulate and monitor their learning?
A robust legal framework will be needed to deal with those issues too complex or fast-changing to be addressed adequately by legislation. But the political and legal process alone will not be enough. For trust to flourish, an ethical code will be equally important.
The government should encourage discussion around the ethics of AI, and ensure all relevant parties are involved. Bringing together the private sector, consumer groups and academia would allow the development of an ethical code that keeps up with technological, social and political developments.
Government efforts should be collaborative with existing efforts to research and discuss ethics in AI. There are many such initiatives which could be encouraged, including at the Alan Turing Institute, the Leverhulme Centre for the Future of Intelligence, the World Economic Forum Centre for the Fourth Industrial Revolution, the Royal Society, and the Partnership on Artificial Intelligence to Benefit People and Society.
But these opportunities come with associated ethical challenges:
Decision-making and liability: As AI use increases, it will become more difficult to apportion responsibility for decisions. If mistakes are made which cause harm, who should bear the risk?
Transparency: When complex machine learning systems are used to make significant decisions, it may be difficult to unpick the causes behind a specific course of action. Clear explanations for machine reasoning are necessary to determine accountability.
Bias: Machine learning systems can entrench existing bias in decision-making systems. Care must be taken to ensure that AI evolves to be non-discriminatory.
Human values: Without programming, AI systems have no default values or “common sense”. The British Standards Institute BS 8611 standard on the “ethical design and application of robots and robotic systems” provides some useful guidance: “Robots should not be designed solely or primarily to kill or harm humans. Humans, not robots, are the responsible agents; it should be possible to find out who is responsible for any robot and its behaviour.”
Data protection and IP: The potential of AI is rooted in access to large data sets. What happens when an AI system is trained on one data set, then applies learnings to a new data set?
Responsible AI ensures attention to moral principles and values, to ensure that fundamental human ethics are not compromised. There have been several recent allegations of businesses exploiting AI unethically. However, Amazon, Google, Facebook, IBM and Microsoft have established a non-profit partnership to formulate best practices on artificial intelligence technologies, advance the public’s understanding, and to serve as a platform about artificial intelligence.
Peltarion, leading AI innovator and creator of an operational deep learning platform, today announced the findings of a survey of…
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Peltarion, leading AI innovator and creator of an operational deep learning platform, today announced the findings of a survey of AI decision-makers examining what they see as the impact of the skills shortage, and suggestions on how to overcome it. The research, ‘AI Decision-Makers Report: The human factor behind deep learning’, presents the findings of a survey of 350 IT leaders in the UK and Nordics with direct responsibility for shepherding AI at companies with more than 1,000 employees.
The
report finds that many AI decision-makers are concerned about the business
impact of the deep learning skills shortage. 84% of respondents said their
company leaders worry about the business risks of not investing in deep
learning, with 83% saying that a lack of deep learning skills is already
impacting their ability to compete in the market. These companies are exclusively
focusing on recruiting data scientists (71% of AI decision-makers are actively
recruiting to plug the deep learning skills gap), and this is already impacting
their ability to progress with AI projects:
Almost half (49%) say the skills shortage is causing delays to projects
44% believe the need for specialist skills is a major barrier to further investment in deep learning
However, almost half (45%) say they are struggling to hire because they don’t have a mature AI program already in place
“This
report shows that companies can’t afford to wait for data science talent to
come to them to progress their AI projects. The fact is, many organisations are
already starting to lose their competitive edge by waiting for specialised data
scientists. The current approach, which relies on hiring an isolated team of
data scientists to work on deep learning projects, is delaying projects and
putting strain on the talent companies do have,” explains Luka Crnkovic-Friis,
Co-Founder and CEO at Peltarion. “In order to solve the deep learning skills
gap, we need to make use of transferrable talent that can be found right under
companies’ noses. Deep learning will only reach its true potential if we get
more people from different areas of the business using it, taking pressure off
data scientists and allowing projects to progress.”
Less
than half (48%) of respondents said they currently employ data scientists who
can create deep learning models, compared to 94% that have data scientists who
can create other machine learning models. This shortage is having a direct
impact on teams: 93% of AI decision-makers say their data scientists are
over-worked to some extent because they believe there is no one else who can
share the workload. However, with the right tools, others can make a serious
impact on AI projects.
“Organisations
need to move projects forward by bringing on existing domain experts and
investing in tools that will help them input into AI projects. This will reduce
the strain on data scientists and lower deep learning’s barrier to
entry,” concludes Crnkovic-Friis. “We need to make deep learning more
affordable and accessible to all by reducing its complexity. By
operationalising deep learning to make it more scalable, affordable and
understandable, organisations can put themselves on the fast track and use deep
learning to optimise processes, create new products and add direct value to the
business.”
AI is no longer science-fiction writers dream, it’s being implemented in industries all over the world. We look at 5…
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AI is no longer science-fiction writers dream, it’s being implemented in industries all over the world. We look at 5 examples of how AI is revolutionising the retail experience Written by: Dale Benton
Marks and
Spencer
In early 2019, M&S announced a new
Technology Transformation Program, one that will allow M&S to become a
digital-first business and deliver key improvements in customer experience. As
part of this transformation, M&S has partnered with Microsoft to investigate
and test the capabilities of technology and artificial intelligence in a retail
environment. M&S will look to integrate machine learning, computer vision
and AI across every endpoint – both in its stores and behind the scenes. Every
surface, screen and scanner in its stores will create data – and enable
employees to act upon it. Every M&S store worldwide will be able to track,
manage and replenish stock levels in real time – and deal with unexpected
events.
The John Lewis Partnership is currently
partaking in a three-year trial, deploying robots to one of its farms, which
grows produce for its Waitrose & Partners brand. The robots, named Tom, Dick and Harry, are delivered
in partnership with the Small Robot Company. Each will be equipped with a
camera and AI technology to gather topographical data, while autonomously
obtaining accurate, plant-by-plant data in order to enable higher farming
efficiency. The data will also be used
to develop further machine learning capabilities. The trial will also provide
the John Lewis Partnership’s Room Y innovation team with valuable insight to
support innovation and inform how robotics and Artificial Intelligence (AI)
could be used further in other areas of the business.
One of the biggest retail companies in the world has been piloting and implementing artificial intelligence solutions across its stores for a number of years. As part of a technology program, called Missed Scan Detection, Walmart has deployed AI-equipped cameras in more than 1,000 of its stores. These cameras, developed in part with Everseen, tracks and analyses activities at both self-checkout registers and those manned by Walmart employees. If an item isn’t scanned at checkout, the cameras will detect the and notify a checkout attendant of the problem. The AI technology allows Walmart to monitor its inventory product quantities, but also significantly reduce theft across its stores.
Amazon Go represents a whole n era of shipping.
The concept is simple, walk into an Amazon Go store, pick up whatever you want
and walk back out. The idea is to create
a “Just Walk Out” experience. Described as the “most advanced shopping
technology”, customers simply download the Amazon Go app. Powerful machine
learning and AI technology automatically detects when products are taken from
or returned to the shelves, keeping track of them all in a virtual cart. Once
customers leave, Amazon will collate all of the data and produce a receipt and
charge the customer’s Amazon account.
One of the UK’s largest food retailers with more
than 120,000 colleagues in 494 stores serving over 11 million customers every
week, Morrisons turned its attention to AI with JDA Software. Looking to vastly
improve the customer experience, Morrisons looked at reducing queues at
checkouts, and improving on-shelf availability. Morrisons
invested in Blue Yonder – a Demand Forecast & Replenishment solution from JDA,
which uses Artificial Intelligence (AI) technology to improve demand planning
and reinvigorate replenishment based on customer behaviour in every store. Over
a 12-month period, Morrisons was able to generate up to 30% reduction in shelf
gaps and a 2-3 day reduction in stockholding in-store. AI technology has also
enabled Morrisons to close the execution gap, optimizing availability while
reducing wastage, enhancing shelf presentation and meeting stockholding
targets.
By Craig Summers, Managing Director, Manhattan Associates Customer experience can be make or break for retailers. In fact, recent research…
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By Craig Summers, Managing Director, Manhattan Associates
Customer experience can be make or break for retailers. In fact, recent research shows that flawed customer experiences could be costing British retailers up to £102 billion in lost sales each year. This shouldn’t be news to retailers; the modern consumer demands a connected, consistent experience that is personalised to them, whether it’s online or instore. The same research found that running out of stock in-store was the biggest contributor to lost revenue, with 79 per cent of consumers saying they would not return to make a purchase if they found their desired item was out of stock. This frustration is only amplified if an out of stock product is marketed to the consumer.
Personalisation isn’t anything new but if the basics aren’t right, retailers risk not delivering on customer experience. Many retailers still aren’t getting it right – and, explains Craig Summers, Managing Director, Manhattan Associates, inept personalisation is affecting the bottom line.
Misplaced Personalisation
The way in which retailers can engage with customers has changed radically over the past decade, from social media onwards. Add in the compelling appealing of Artificial Intelligence (AI) and the promise of incredibly accurate and timely promotional offers, and personalisation has become a foundation of any retail strategy. Yet while the marketing activity is becoming ever more sophisticated, personalisation cannot be delivered by marketing alone.
Without integrating marketing activity to the core operation, retailers risk repelling rather than engaging customers. Product offers that are out of stock in the customer’s size. Promotions not on offer at the local store. Incentives to buy an item the customer has already purchased – not a problem for a standard food or household item, incredibly annoying if it’s an expensive mountain bike or cashmere jumper. Customers are becoming increasingly familiar with ostensibly personalised offers that fail to deliver a great experience.
What is the thinking behind a promotion that cannot be purchased by the customer? Why set such high expectations when they cannot be met? Enticing a customer to click through an emailed offer may be the measure of marketing success – but when that customer is unable to make a purchase because the desired item is not available in his or her size, that is at least one lost sale and a bottom line retail failure.
Complete Experience
Are retailers listening to what their customers want from personalisation? Great personalised offers will not deliver any value if they are not linked to the rest of the business. Smart technologies, such as AI, without any doubt have a role to play in delivering personalisation – but they are not the foundation. The foundation is getting the basics right. It is ensuring that when a customer wants to buy a product – online or instore – it is available. It is about providing Store Associates with the ability to track stock anywhere in the supply chain, reserve it for a customer to try on instore or have it sent direct to their destination of choice. It is about combining stock availability information with customer insight to make intelligent suggestions, both instore and via marketing promotions.
Bottom line success is, essentially, about the quality of the interaction. And that means considering not just the accuracy of the promotional offer but the complete customer experience. What is achievable today? What can be done well? If a product is being promoted to an individual, is it available in the right size? Is it available locally, or only in flagship outlets? It is these disconnected experiences that are fundamentally undermining customer experience and brand value.
Conclusion
The future of customer personalisation is incredibly exciting. AI promises the ability to predict a customer’s desires before the customer. Fabulous. But only fabulous if that product is available to buy, at a time and place to suit that individual. Right now personalisation is about the retailer; it is about being clever with promotions. It needs to be about the customer; it needs to be about delivering the quality of experience that drives sales.
Retailers need to go back to basics: use technology to recreate the ‘corner shop model’ of the past, at scale. By creating a truly immersive experience for their customers, retailers can find a way to make personalisation profitable again.
The uptake of artificial intelligence by industry will drastically change the UK job market in the coming years – with…
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The uptake of artificial intelligence by industry will drastically change the UK job market in the coming years – with 133 million new jobs expected to be created globally.
In the UK alone, up to a third of jobs will be automated or likely to change as a result of the emergence of AI – impacting 10.5 million workers.
Ollie Sexton, Principal at Robert Walters comments:
“As businesses become ever more reliant on AI, there is an increasing amount of pressure on the processes of data capture and integration. As a result, we have seen an unprecedented number of roles being created with data skill-set at their core.
“Our job force cannot afford to not get to grips with data and digitalisation. Since 2015 the volume of data created worldwide has more than doubled – increasing (on average) by 28% year-on-year.
“Now is the perfect time to start honing UK talent for the next generation of AI-influenced jobs. If you look at the statistics in this report we can see that demand is already rife, what we are at risk of is a shortage of talent and skills.”
Demand for Data Professionals
IT professionals dedicated to data management appear to be the fastest growing area within large or global entities, with volumes increasing ten-fold in three years – an increase in vacancies of 160% since 2015.
More generally speaking, data roles across the board have increased by 80% since 2015 – with key areas of growth including data scientists and engineers.
What has been the most interesting to see is the emergence of data scientist as a mainstream profession – with job vacancies increasing by a staggering 110% year-on-year. The same trend can be seen with data engineers, averaging 86% year-on-year job growth.
Professional Services Hiring Rapidly
The rise of cybercrime has resulted in professional services – particularly within banking and financial services – hiring aggressively for information security professionals since 2016, however since then volumes have held steady.
Within professional services, vacancies for data analysts (+19.5%), data manager (+64.2%), data scientist (+28.8), and data engineer (+62%) have all increased year-on-year.
Top Industries Investing in AI
Agriculture
Business Support
Customer Experience
Energy
Healthcare
Intellectual Property
IT Service Management
Manufacturing
Technical Support
Retail
Software Development
Tom Chambers, Manager – Advanced Analytics and Engineering at Robert Walters comments:
“The uptake of AI across multiple industries is bringing about rapid change, but with that opportunity.
“Particularly, we are seeing retail, professional services and technology industries’ strive to develop digital products and services that are digitally engaging, secure and instantaneous for the customer – leading to huge waves of recruitment of professionals who are skilled in implementing, monitoring and gaining the desired output from facial recognition, check-out free retail and computer vision, among other automation technologies.
“Similarly, experimental AI is making huge breakthroughs in the healthcare industry, with the power to replace the need for human, expert diagnoses.
“What we are seeing is from those businesses that are prepared to invest heavily in AI and data analytics, is they are already outperforming their competitors – and so demand for talent in this area shows no signs of wavering.”
In a world awash with a seemingly never-ending list of technology buzzwords such as automation, machine learning and Artificial Intelligence…
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In a world awash with a seemingly never-ending list of technology buzzwords such as automation, machine learning and Artificial Intelligence (AI) to name a few, AI is one such technology that is moving away from simple hype and stepping closer to reality in procurement.
Here, CPOstrategy looks at 5 ways in which AI is being utilised in procurement…
Procurement, by its very nature, is tasked with handling huge quantities of spend and with spend comes spend data. Often described by leading CPOs as a repetitive task, understanding and sorting that spend data is now being achieved through the implementation of AI.
Through the use of AI, procurement teams can remove human error, increase efficiency and realise greater value from spend data.
Chatbots
One of the biggest ways in which AI is being implemented around the world is in the customer interaction space. In telcos, for example, customer support can now be handled via a highly developed AI chatbot that uses legacy data and context to provide real-time, and unique, solutions for customers.
In procurement, chatbots follow a similar path for both internal and external customers. With tailored and context-aware interactions, chatbots create an omni-channel user experience for all stakeholders in the procurement ecosystem.
Supplier risk identification
Procurement and risk go hand in hand and one of the biggest risks is identifying and working with the right partner. Working in partnerships, which ultimately proves to be a failure, can be extremely costly and so AI is now being used to reduce the risk of failure.
Machine Learning technology, powered by AI, captures and analyses large quantities of supplier data, including their spend patterns and any contract issues that have emerged in previous partnerships, and creates a clearer picture of a supplier in order for the procurement teams to be able to identify whether this particular partner is right for them – without spending a penny.
Benchmarking efficiency
Benchmarking is key to any organisation’s ambition to measure and continuously improve its processes, procedures and policies. In procurement, organisations such as CIPS are used as examples of best practice in which procurement functions all over the world can benchmark against and identify any gaps.
Similar to supplier risk identification, AI can be implemented within ERP systems to analyse the entirety of data that passes through procurement and present this key data in easy to digest formats.
Examples include data classification, cluster analysis and semantic data management to help identify untapped potential or outliers in which procurement teams can improve their processes.
Purchase order processing/Approving purchasing
Procurement has evolved from its traditional role as simply managing spend into a strategic driver for a number of organisations all around the world.
As the role of the CPO has changed, technology such as AI has been implemented to free up their time from the menial tasks (such as PO processing and approving purchases), allowing them to spend more time in areas of growth.
AI software can be used to automatically review POs and match them to Goods Receipt Notes as well as combining with Robotics Process Automation (RPA) to capture, match and approve purchases through the use of contextual data. This contextual data allows AI to identify and make decisions based on past behaviour.
By Robert Douglas, Europe Planning Director at Adaptive Insights, a Workday company Now, more than ever, agility is the currency…
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By Robert Douglas, Europe Planning Director at Adaptive Insights, a Workday company
Now,
more than ever, agility is the currency of success. And while agility may be
about responding intelligently to the changing nature of the marketplace, those
responses must be rooted in a plan. Today, many organizations leverage newer
technologies in the cloud for planning, having moved away from manual
spreadsheets. And while the cloud offers greater collaboration and the ability
to easily combine both historical and real-time data, it’s just the beginning. Digital
transformation is changing and will continue to change the definition of best
practice planning in organisations. As such, the next step for business planning
revolves around two key areas—advancements in AI and machine learning, and
increased automation.
The power of ‘what if’
What-if scenarios are already incredibly
powerful for strategic decision-makers. Organisations can model different
versions of the future based on historical information and predictive analytics
before choosing the best path forward. Consolidating executional data within
organisations is the first step in capitalising on future AI opportunities. However,
there is a lot more to come. In fact, compared with what AI is going to make
possible, scenario planning is still in its infancy.
Today’s scenario planning is a good proof of
concept, but as long as humans are driving the creative process—it relies on
people to ask the right questions of the right data—what-if planning is going
to be constrained by available resources. The most advanced decision-making
today is typically supported by a few best-estimate scenarios—maybe four or
five at most. However, in truth, there are many more possible futures to
potentially prepare for, and what looks like best practice now is going to seem
vastly limited in scope before too long.
As the volume and variety of available data
grows, and access to that data gets easier, AI and machine learning algorithms
will make it possible to drill down, consolidate, and leverage incredibly
granular information at the highest levels.
AI and machine
learning use cases
To consider how these AI and machine learning
algorithms will work, let’s look at a use case of a CEO aiming to achieve a 40
percent growth target over a two-year period and wants to model what that looks
like to present at the annual executive offsite. AI and machine
learning-enabled planning could help to quickly and automatically find the
optimal growth path, while accommodating any conditions and assumptions on the fly.
Essentially, the planning system could measure
historical performance and recommend a market segment mix strategy, along with
the associated budget increases in the specific marketing and sales activities
needed to support it. If they then decide they need to cap growth in sales to
smaller businesses in order to also expand into enterprises and international
markets—while also maintaining expenses at a certain increase—an alternative,
optimised model could be quickly created without any manual lifting.
A future with machine
learning
The future of business planning is not just
about thinking bigger—it is about making better decisions and operationalising
them faster. That’s where machine learning comes in. Increased automation,
driven by algorithms, is going to blur the boundaries between planning,
execution, and analysis until planning cycle times have all but evaporated.
Planners will be able to ask deep, complex strategy questions and see the results modelled in real time. As the data becomes more trusted, they will be able to make significant, informed, “just-in-time” decisions, confident in the patterns surfaced in the data. And as the line between planning and transactions systems begins to blur and disappear, plans will automatically cascade down to operational departments—even down to individual workflows—in real time.
‘Strategy’ will become the province of human-driven
innovation while planning becomes an organic, ongoing exercise of continuous
improvement inextricably linked to the transactional systems that execute
plans.
Leading the change
Today finance acts as the central junction within business planning and is, therefore, a natural steward for change, helping normalise new habits and behaviours for the rest of the organisation. As such, there is a strong case to be made for finance teams to double down on their new position as stewards of change by acting as transformation leaders—both for existing processes, and for future, unknown developments.
Finance’s role will change significantly in
order to leverage technology developments in the data-driven, AI future.
Driving collaboration with business partners, breaking down data silos, and
embracing new technologies and processes to keep pace with today’s rapidly
changing business environment will be key. The result will be an augmented,
intelligent planning process that delivers true business agility.
Everyone wants to implement Artificial Intelligence (AI) and Business Intelligence (BI) solutions. AI alone is anticipated to generate $15.7 trillion…
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Everyone wants to implement Artificial Intelligence (AI) and
Business Intelligence (BI) solutions. AI alone is anticipated to generate $15.7
trillion in GDP by globally 2030, and as this market grows, AI and BI will
shift from industry buzzwords, to key market differentiators, before eventually
becoming the new normal in the corporate landscape.
Yet
bringing AI and BI on board is a big leap if it’s your first major data
project. Stibo Systems’ Claus Jensen, Head of Emerging Technology, comments
on the role of MDM as a vital foundation to implement emerging data technology.
Most CEOs
don’t trust their own data.*
Let that
sink in for a moment.
Almost
every business is looking to data solutions to fuel the next phase of growth
and innovation. AI and BI are firmly on the agenda, yet a report by Forbes
Insights and KPMG found 84% of CEOs are concerned with the quality of the data
they’re basing their decisions on.
That’s a
significant disconnect. Businesses at board level want to implement ‘next
generation’ data projects, but don’t trust
the data that will be fed into them. For CDOs and other data leads, this
presents a difficult situation. They need to meet demand for cutting-edge data
projects, knowing that there is a certain level of mistrust in the data at
their disposal.
For many
CDOs, that mistrust isn’t limited to the CEO. Think about the data you are
currently processing: how confident are you that it’s being accurately sourced,
entered, saved, stored, copied and presented? How well do you know that data
journey once it leaves your sphere of control? Are you certain that a
single source of truth is being maintained?
The
data gold rush
It may only
be major data breaches that make the headlines, but in the global gold rush for
data, too many businesses fail to accurately extract, store and interpret data.
Mistakes
are made at every stage in the process – in fact, so bad are we at processing
data, a report by Royal Mail Data Services claims that around 6% of annual
revenue is lost through poor quality data.
It’s
equally bleak in the US, where Gartner’s Data Quality Market Survey puts the
average cost to US business at $15 million per year.
Despite
this, we’re rapidly moving the conversation from data capture to artificial
intelligence (AI), business intelligence (BI) and connected devices (IoT) – and
for good reason.
Putting
aside the issue of bad data (we’ll come back to that), businesses now have
access to more data than they can handle – according to SAS’ Business
Intelligence and Analytics Capabilities Report, 60% of business leaders
struggle to convert data into actionable insights, and 91% of companies feel
that they are incapable to doing it quickly enough to make useful
changes.
Business
Intelligence and Analytics Capabilities Report
In large
businesses, where data streams are blended from many sources, machine learning
can help data scientists monitor figures to flag outliers, irregularities and
noteworthy patterns.
Once
flagged, business leaders can use BI to bring those patterns to life, helping
pave the way for the most appropriate, and profitable, action.
Stibo Systems’ Head of
Emerging Technology, Claus Jensen, believes it’s only a matter of time before
we see AI regularly used within business product features – with machine
learning automating tasks thanks to effective data interpretation.
Jensen and
his team are working at the forefront of data: building master data management
solutions in conjunction with AI and BI. “We’re entering into a new era of data
analytics,” says Jensen. “Data scientists aren’t going away, but they can do
more and more high-level work as certain use cases are solved by AI.”
One of
these use cases is machine learning-based auto classification. “For retailers
onboarding thousands and thousands of new products every month, it’s really
time consuming for them to have the vendor categorise the product into the
vendor taxonomy.
“Machine
learning can automate this based on product description and image.”
Running
before we can walk
As exciting
as this sounds, businesses eager to install new uses for data often face
significant challenges: their data isn’t watertight, or it’s siloed, often
both.
In a piece
penned for the Financial Times, Professor of Economics at Stanford Graduate School
of Business, Paul Oyer, wrote: “Smart managers now know that algorithms are as
good as the data you train them on.” In other words, AI (and analytics for that
matter) can only ever be as good as the date you feed it.
Which
brings us back to the question of trust. What needs to happen for CEOs to trust
their own data?
While
there’s no single answer to this question, a master data management (MDM)
solution is a good place to start.
“You can
think of MDM as the foundation, a layer, that provides a single source of the
truth for data,” explains Jensen. “Analytics and machine learning is only
useful if the data you’re working on is accurate. That’s where MDM comes in; it
ensures information presented, and actions taken, are based on fact and
reality.
“Otherwise,
business analytics is just a nice and colourful way to look at bad data, and
what’s the point in that?”
In today’s market expectations are growing and the stakes are high, with one mistake potentially costing a retailer their reputation….
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In today’s market expectations are growing and the stakes are
high, with one mistake potentially costing a retailer their reputation. Due to
this level of risk, brands find reducing their hands on approach to processes
difficult, but what they don’t realise is that technology such as Artificial
Intelligence and Machine Learning could prove to be their hero, not their
villain. Entrusting their data and brand values to such technologies may seem
like a scary step, but as David Griffiths, Senior Product Marketing &
Strategy Manager, Adjuno, discusses, it’s one that will free up
retail teams to add value and cut costs.
In AI
should we trust?
There is a great deal of obstacles to overcome when it comes to the stigma attached to AI. A key challenge facing the progression of this technology is that individuals simply do not trust it. The fear of the unknown is one concern that pops up most commonly, with people battling a perceived perception that those who use this technology will lack control.
But a new age
of retail is approaching and there is now an even greater need for brands to
define their processes in order to keep up. Consumers want to receive products
that are of a high-quality and they want to receive them now. These
expectations are taking us beyond the traditional methods of retailing and
leading us into a world immersed in technology, a world that benefits from the
helping hand of AI.
Informing
key decisions
With AI,
retailers will be able to gain valuable insights in warehouse management,
logistics and supply chain management, and make more informed and proactive
decisions. This technology makes it easier to analyse huge volumes of data in
an efficient fashion, helping to detect patterns and providing an endless loop
of forecasting. Using this knowledge to identify factors and issues impacting
the performance of the supply chain, such as weather events, retailers will be
able to take a forward-thinking approach to decision-making. An approach that
will lead to reduced costs and delays.
By extending
human efficiency in terms of reach, quality and speed, this technology can also
help to eliminate the more mundane and routine work that’s faced by employees
across the retail spectrum. From tackling flow management by assessing key
products to ensuring there is enough stock available to improving production
planning, a more informed use of time will help equip brands to face every
consumer request and demand.
This is
particularly important for those brands whose product line extends further than
apparel wear, and steps into the realm of hardware. With diversity comes a need
for more proof points and in turn, an extended volume of data. Retailers will
be battling to work across an even greater number of suppliers and distribution
centres, and accommodating the expectations of a larger customer base.
Considering this, it is fundamental that every last bit of data is refined and
utilised to streamline processes. AI is providing retailers with a platform to
do this, offering the potential for significant changes across the entire
product journey.
A data
conundrum
The benefits of
using AI to consolidate data are endless. Traditionally, teams have relied on
spreadsheets to collate information, hindering their ability to forward plan.
With AI this is no longer the case, a much more accurate picture of the hero
products, sizes and colours likely to sell, can be achieved by looking at
multiple scenarios in real time and pulling them together.
This doesn’t
mean that AI will replace creative buying teams. AI doesn’t forecast trends, it
can’t predict what consumers will be buying in 2020, it can only report on the
product lines. It can however help buying teams assess partners, analyse stock
patterns, track costs, enable capacity planning and help optimise shipments.
This data is invaluable to teams, especially for any new buyers who may need
extra guidance.
Conclusion
AI is set to
transform the retail scene as we know it. But in order to make implementation a
success, there shouldn’t just be a focus on the evolution of data management,
there must be an evolution of mindsets too. After all, if a retailer fails to
jump on board with AI and embrace a new era of change, then their customers
will be the ones who suffer.
Companies that use voice plus touch interactions with their products and services are actually seen as less trustworthy and less…
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Companies that use voice plus touch interactions
with their products and services are actually seen as less trustworthy and less
engaging by their users, according to new research from emlyon business school.
The research, conducted by Margherita Pagani,
Director of the AIM Research Center on AI in Value Creation and Professor of
Digital Marketing at emlyon business school, and colleagues from ESSCA School
of Management and Florida State University, College of Business, analysed the
impact and differences between ‘touch’ interaction and ‘touch and voice’
interaction on personal consumer engagement and brand trust.
The researchers created two separate experiments,
focused on a utilitarian product and then a hedonic product, both of which had
over 90 participants belonging to generation Y, which is commonly equipped with
the latest smartphones and frequently use them for business interactions. For
both experiments, participants had to interact with the brand using their
smartphone including a phone call to the company to ask a specific set of
questions.
One group was required to interact with the brand
through the smartphone using a touch-only interaction, and the other used both
touch and voice interaction – either Apple’s Siri or OK Google. After
interacting with the company, participants were asked to rate their customer
experience. The participant’s answers were then measured to evaluate personal
engagement with the tasks, their level of trust with the brand and their
privacy concerns.
The researchers found that participants who used
the touch-only interaction experienced a much higher level of personal
engagement with the brand compared to those who used the touch plus voice
interaction.
Prof. Pagani says,
“Many companies have introduced new AI products
that use voice-activation such as Amazon’s Alexa, Google’s Home Assistant or
Apple’s Siri. These have been introduced in order to increase customer
experiential engagement, stimulate the interaction and collect more data that
allow to better personalise the experience through machine learning.
However, our study shows that in the initial phase of adoption, adding
voice recognition actually has the opposite desired effect. Even if voice may
be considered as a way to develop a much more natural interaction, the level of
cognitive efforts required to the brain using two sensory modes (voice and touch)
are higher. Therefore, consumers find it harder to completely engage with the
product and can easily be distracted”.
The researchers also found that participants who
used the touch-only interaction felt as though they had more control over the
information they shared and therefore had greater confidence in the brand.
Users stated that they found it much simpler to input information using only
one sensory method; touch.
“The lack of familiarity with how these digital
voice interactions actually work is likely to be the reason as to why consumers
are less trusting of brands that use both touch and voice. Whilst the use of
touch also garners much more control for a consumer, as opposed to voice”.
The
study, published in the ‘Journal of Interactive Marketing’ is the first of its
kind to explore the effects of new voice-based interface modes on marketing
relationships. Whilst technology multiplies the way that consumers can interact
with brands, this study shows that too much interaction can actually harm a company,
and offers managers guidance on how to increase personal engagement and brand
trust.
This month’s cover features Gary Steen, TalkTalk’s
Managing Director of Technology, Change, and Security, Gary Steen regarding the
telco’s commitment to thinking, and acting, differently in a highly competitive
marketplace…
TalkTalk is an established telecommunications company that fosters a youthful, pioneering spirit. “I like to think of TalkTalk as a mature start-up,” says Managing Director of Technology, Change and Security, Gary Steen. “We are mature in terms of being in the FTSE 250, with over four million customers, relying on our services every day through our essential, critical national infrastructure. But that said, I definitely think we start our day thinking as a start-up would. What can we do differently? How do we beat the competition? How do we attract great talent? We’ve got to come at this in a different way if we are going to succeed in the marketplace. We are mature, but we think like a start-up.”
Elsewhere we speak to Natalia
Graves, VP Head of Procurement at Veeam Software who reveals the secrets to a
successful procurement transformation. Graves
was tasked with looking at the automating, simplifying, and accelerating of
Veeam’s procurement and travel processes and systems around them, including
evaluating and rolling out a company-wide source-to-pay platform. “It has been
an incredible journey,” she tells us from her office in Boston, Massachusetts.
We also feature exclusive interviews with PTI Consulting and cloud specialists
CSI.
Plus,
we reveal 5 of the biggest AI companies in fintech and list the best events and
conferences around.
IPsoft has introduced 1Bank, the first conversational banking solution featuring virtual agent Amelia. It has been rated the top virtual…
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IPsoft has introduced 1Bank, the first conversational banking solution featuring virtual agent Amelia. It has been rated the top virtual agent in conversational AI by Everest Group.
Chetan Dube, CEO at IPsoft, commented: “With 1Bank we provide the most humanlike digital experience in the marketplace, built from the knowledge we’ve gained serving six of the world’s leading banks with conversational AI. We are giving banks the possibility of providing customers with their own personal banker around the clock.”
1Bank answers FAQs, but also resolves complex customer needs, by understanding customer intent. It can also switch context, mid-conversation. Its machine learning Learning (ML) abilities also mean that 1Bank can improve over time.
Some of the tasks 1Bank can carry out are:
advising on unpaid bills, proactively informing customers of an incoming bill and communicating any insufficient funds, making a money transfer and asking if the customer wants to set up payment for the bills when they are due.
recommending and setting up recurring payments, making payments from different accounts, opening and closing accounts.
helping customers locate transactions.
assisting with individual and potentially fraudulent charges on credit cards and disputing them, getting a new pin, getting a balance transfer or applying for a new credit card.
creating travel alerts after a customer made an airline purchase and proactively recommending the next step, such as, when traveling to exchange and withdrawing cash.
1Bank can integrate with existing tools and interfaces, and it can be added to existing applications to help customers quickly access the information and service they need. This includes mobile apps, desktop or kiosk apps, website modules, or within consumer chat applications, such as Facebook Messenger and Amazon Echo.
It is a measure of how much we take sophisticated technology for granted that the appearance of a pop-up chatbot…
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It is a measure of how much we take sophisticated technology
for granted that the appearance of a pop-up chatbot screen, asking questions
and providing sensible responses, is no longer considered remarkable.
Chatbots today inhabit websites, intranets, apps, and social
media platforms, and have become so ubiquitous as to become almost invisible.
Interacting with a text screen is a natural activity, and most users don’t seem
to care much about whether the other side of the conversation is a human or a
bundle of code.
From a corporate perspective, chatbots can be a win/win.
Increasingly reliable in their responses and cheap to operate, they are
available night and day and are instantly scalable. Whether your site or app
has one visitor a day or thousands, the bot is always eager to help.
We’ve come a long way from the disastrous early attempts at
providing AI assistance – remember Microsoft’s paperclip? – but what a good
chatbot does today is much the same as that much-loathed animated character:
identify what a user is trying to do, and offer appropriate help.
Artifical
intelligence
You will have heard of the Turing Test, which held that if a
computer could provide responses that were indistinguishable from those of a
human, the machine had to be considered intelligent. Are we there yet?
Despite some well-publicised claims, the answer is still,
probably, no. In 2014 a program called Eugene Goostman successfully tricked
Turing Test judges into believing it was a 13-year-old Ukrainian boy. This
controversial victory is still a failure, though, because nobody expects or
wants to find an adolescent behind a real-world help screen.
More relevant is the Loebner Prize for the most convincing
chatbot. This awards bronze medals each year to the best contenders, but has
never made a silver (text) or gold (audio visual) award – the equivalent of a
Turing Test pass.
A glance at the best entries from the 2018 competition shows
why. Eleven bots were asked 20 questions, winning two points for a human-like
answer and one for a plausible response. Out of a maximum of 40 points, the
winner scored 27 and the lowest just 12.
Even simple questions can make the tech fall over. The
winner, a chatbot called Tutor by Ron C Lee, answered “Do you know how to
make toast?” with “No, we haven’t”.
Chatbot limitations
While there remain limits on what a chatbot can convincingly
do, this need not be a problem if it is deployed in the right way. Recent
research from Penn State University found that while many appreciate an
apparently empathetic response from a bot, those who believe machines are
actually capable of consciousness do not.
“The majority of people do not believe in machine
emotion, so took expressions of empathy and sympathy as courtesies,” said researcher
Bingjie Liu. “However, people who think it’s possible that machines could
have emotions had negative reactions from the chatbots.”
The answer is only to use them for things they are good at,
says James Williams, who leads the development of advanced chatbots with
Nottingham-based software company MHR. While chatbots are now common in
consumer interfaces, he notes, there is much potential in the enterprise space.
Business bots
When applied within the company’s flagship human resources (HR) software, Williams says the conversational interface is an excellent way to simplify common transactions. “You’ll hear us talk a lot about reducing friction,” he says, which means anything that slows down a routine interaction.
An example is an employee submitting an expenses claim,
which MHR’s Talksuite does through an AI-driven chatbot. “Taking a picture
of a receipt is a natural thing to do, and the AI will recognise the image,
understanding the content as well as the context. Bots are really good for
processes with lots of rules or lots of steps, and here it just asks a few
questions and saves the employee a lot of hassle. Less friction.”
Knowing when not to deploy a bot can be just as valuable. Williams recounts one client which had deployed a complex chatbot for its newly joining employees, known in HR circles as the onboarding process. “The chatbot went through everything plus the kitchen sink, so the employee was there for 20 minutes or more being interrogated by a machine. It was just awful. A web-based form is a much better interface in this situation.”
His final advice is to consider the image the bot projects.
“Any personality in a chatbot tends to come accidentally, unlike a website
or an app. If you let software developers write the conversation, you might end
up with a bot that’s actually a bit of a dick. People make judgements on things
like language and punctuation. It’s fine to be personable and friendly, but it
should be clear when the user is talking to a bot and when any transition to a
human interaction takes place.”
Quest Solution Inc, provides supply chain and artificial intelligence (AI) based machine vision solutions. It has been awarded a project by…
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Quest Solution Inc, provides supply chain and artificial intelligence (AI) based machine vision solutions. It has been awarded a project by a leading supply chain and logistics provider in the US. The release doesn’t detail who the leading supply chain provider is, but it does reveal that the project is valued at around $US7 million.
A patent
that will allow for a robot to live at your home and handle your deliveries has
been filed by Amazon. The patent outlines plans for a robot that will
completely transform last mile delivery capabilities, even potentially
delivering packages in the early hours between 2am and 6am.
Back to AI, NFI Industries and Transplace are paying attention to
this technology through partnerships with firms that add AI capabilities to
transportation and distribution. Both companies have announced a partnership
with Noodle.ai
with the goal of enhancing logistics services and technology capabilities.
In a video interview with CNBC, Lance
Fritz, the CEO of Union Pacific, is concerned that supply chain
disruption won’t return to normal. He believes the biggest concern lies in
trade and that the challenges with China should be resolved as soon as
possible.
In an interview with Sky News, Peter
Schwarzenbauer, BMW board member responsible for Mini and Rolls
Royce, has said that the firm will need to think about moving production from
the UK in the event of a no-deal Brexit. Remaining would be too costly for the
organisation and some production would move to countries like Austria. Toyota
shares similar concerns with Johan van Zyl, head of Toyota’s European
operations, telling the BBC that Brexit hurdles would ‘undermine Toyota’s
competitiveness’.
Blockchain remains an interesting solution for many in the supply chain and
Blockchain Labs for Open Collaboration (BLOC) has recently started working with
NYK, a Japanese shopping company, and BHP, a mining company, to establish a
sustainable biofuel supply chain using BLOC’s blockchain fuel assurance
platform.
Also in the news: HighJump,
a global supply chain solutions provider, awarded five women in its Top Women
Leaders in Supply Chain awards; Cryptobriefings
Kiana Danial examines whether VeChain can deliver a supply chain solution; Apple
releases a supply chain document that reveals how iPhone, airpods and other
products are all zero waste; and SIGTTO GM, Andrew Clifton, looks to the LNG
supply chain.
