Author

Berkley Egenes

Published

9 June 2026

Estimated Read time

6Mins

Berkley Egenes, Chief Marketing & Growth Officer at Xsolla, on why the real AI debate is about creativity and ensuring that machines support human expression, rather than replace it

The conversation around artificial intelligence (AI) in video games has quickly zeroed in on jobs. Headlines warn of artists, writers, and designers being displaced by tools that can generate concept art at the click of a button, produce dialogue on demand, or compose background music in seconds. That fear is understandable; we’ve seen similar concerns in journalism, marketing, and content  production, but it’s also limiting. Because when we focus primarily on economic displacement, we miss the far deeper challenge: what happens to creativity when generative AI begins to produce the art, dialogue, music, and even mechanics that shape a game’s identity? 

The real debate isn’t jobs. It’s creativity.

From Efficiency Tools to Artistic Authors

In 2024, a survey by the Game Developer Conference (GDC) found that nearly half of game developers were already experimenting with generative AI tools for writing, art, or code. Industry analysts estimate that by 2030, up to 70% of mid-sized and large studios will integrate AI into their asset pipelines. These numbers signal rapid adoption, but they also risk setting expectations that AI is first and foremost an efficiency multiplier. 

And it is, at least on the surface. Game development is famously deadline-driven and resource-constrained. Developers often spend years polishing art assets, scripting dialogue trees, and composing music suites. Generative AI promises dramatic productivity gains: conceptual environments in minutes, ambient soundscapes generated automatically, even procedurally expanded quests.

But under the hood of that promise is something more complex. Creativity in games isn’t just about completing tasks faster. It’s about voice, intention, and authorship. 

When Enhancement Changes the Creative Equation

This is where the idea of job enhancement becomes more complicated than it first appears. 

Generative AI unquestionably enhances creative roles. It can allow a single environment artist to produce in days what previously took weeks. It can enable a narrative designer to draft and iterate on branching dialogue trees at unprecedented speed. In pure productivity terms, enhancement looks like empowerment: the same people, equipped with better tools, producing more ambitious worlds. 

But enhancement is not neutral. When AI dramatically increases output capacity, it changes the nature of the job itself. 

Imagine a studio with ten environment artists. Before generative AI, they produced a carefully curated set of handcrafted scenes over two years. With generative AI tools, the same team might produce twice as many environments in half the time. That is job enhancement in its clearest form: no layoffs, no replacements, just expanded capacity. 

But creative identity does not scale linearly with asset count. More backgrounds do not automatically mean a stronger artistic vision. In fact, abundance can dilute intentionality. 

As AI enhances production, the human role often shifts from originating every element to curating, refining, and selecting from machine-generated options. The artist becomes part creator, part editor. The writer becomes part storyteller, part prompt engineer. The designer becomes part world-builder, part systems orchestrator. That shift is subtle, but profound. 

When creators move from author to editor, the center of gravity in the creative process changes. Enhancement increases capability, but it can also redistribute authorship. The critical question is now whether AI makes creatives more productive. It does. The question is whether enhanced productivity deepens creative vision, or gradually distances humans from the expressive core of the work. 

Why Creativity Matters More Than Efficiency

People care deeply about the emotional resonance of games. A compelling narrative arc, a haunting melody, a dialogue choice that feels meaningful – these aren’t just outputs, they’re expressive decisions. They reflect human imagination and carry cultural and artistic weight. 

Creativity in games operates at multiple levels, like visual storytelling, narrative voice, mechanics design, and audio identity. Generative AI tools can do all of these. Large-language models can produce thousands of lines of dialogue in seconds; image synthesis tools can generate environments, characters, and textures from prompts; music AI can compose adaptive scores that fit different gameplay contexts.

The Limits of Machine Creativity

When AI generates a piece of music, art, or a story beat, it fundamentally recombines patterns learned from human-created data. There is no subjective inspiration, no lived experience, no intentional aesthetic choice behind the work, only statistical inference. That distinction is more than philosophical. It affects the game’s identity.

Consider two hypothetical scenarios:

  1. A studio uses AI to generate placeholder environment concept art that artists later refine
  2. A studio uses AI to generate final environment assets with minimal human input

In the first scenario, AI accelerates workflow, but human creativity still sets the artistic direction. In the second, the machine’s biases, training data limitations, and opaque generative patterns increasingly define visual identity, potentially diluting the studio’s artistic voice.

This pattern repeats across other domains. AI-generated dialogue might be serviceable, but will it capture the nuance of lived human experience in a way that players recognize as authentic? These are not questions with easy answers, but they are central to the cultural value of games.

The False Comfort of Economic Framing

Much public discourse has focused on job displacement. Partly because economic arguments are easier to quantify. People can point to employment numbers, wages, and productivity curves.

Creative impact, on the other hand, is harder to measure. When we frame the AI debate primarily in terms of jobs, we implicitly assume that creative work is fungible, that one person’s artistic contribution is interchangeable with another’s, including a machine’s. But in the arts, identity matters. Players choose games not just because they work well, but because they express something unique – a vision, a style, a tone. AI might replicate style, but it can’t originate meaning.

Redefining Roles, Not Replacing Them

That’s not to say generative AI has no place in game development. If used thoughtfully, it can be a powerful collaborator, a brainstorming partner, a rapid prototyping tool, and a way to push creative boundaries. 

For example, writers using AI to generate dozens of narrative alternatives in seconds may help them explore more possibilities before committing. Whereas level designers using AI to test countless iterations of mechanics can see what feels most engaging.

In these cases, AI augments human creativity, not replaces it. It expands the sandbox in which creators play. But this requires deliberate choices. Studios need creative leadership that understands when AI is a tool and when it’s overreaching. There must be clear boundaries around authorship and safeguards to ensure that AI outputs serve the human vision, not obscure or subsume it. 

Creativity as a Competitive Advantage

There’s another economic point that circles back to creativity: in a crowded market, artistic distinction is a competitive advantage. According to industry analysis, the global video game market is projected to exceed $300 billion by 2027, but growth is slowing in core segments, leading studios to compete fiercely on creative differentiation rather than sheer production volume. 

In a world where everyone has access to the same AI tools, technical efficiency alone won’t distinguish one game from another. Games that lean too heavily on generic, AI-generated material risk becoming indistinguishable. 

So the paradox emerges: AI might make game creation easier, but that very ease could erode the uniqueness that makes games worth playing.

The Future of AI in Games Should Be Artistic, Not Automatic

We should absolutely have conversations about job training, economic transition, and the ethics of data usage. But those debates should not eclipse the most important question: how do humans and machines collaborate to create meaning?

Creativity is not an economic output. It’s a human act of expression. In video games, where storytelling, mechanics, visuals, and sound converge, that act of expression is the beating heart of the medium. 

If we allow AI to take over the mechanics of creation without guarding the human spark that animates it, we risk more than job shifts; we risk a homogenized cultural future where games lose their soul. 

The real AI debate is about creativity and ensuring that machines support human expression, rather than replace it. 

Learn more at xsolla.com

  • Data & AI
  • Digital Strategy

Author

Chris Tredwell & Charis Thomas

Published

28 May 2026

Estimated Read time

5Mins

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

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.

Learn more at aqilla.com

  • Artificial Intelligence in FinTech
  • Data & AI

Author

Dan Brightmore

Published

Estimated Read time

4Mins

Read in brochure format

Welcome to the latest issue of Interface magazine! Click here to read the latest edition! Precisely: Reframing Cybersecurity for the…

Welcome to the latest issue of Interface magazine!

Click here to read the latest edition!

Precisely: Reframing Cybersecurity for the AI Era

This month’s cover star, Marcus Johnston, deploys a leadership philosophy that blends empathy, operational discipline and pragmatic innovation as Precisely scales cyber solutions to meet the demands of regulation and accelerating digital risk. Learn how this CISO is reframing Cybersecurity for the AI era. “I view the role of InfoSec as a trusted partner advisor in the goals of the company to innovate, to introduce new technology, new platforms, to constantly be on the watch for changes in the threat environment.”

Ireland Department of Agriculture: Growing a Culture of IT Transformation

Louise McKeever, former Chief Information Officer and Head of Operations at Ireland Department of Agriculture, discusses the changes she drove at the agency and how a culture of IT transformation has gone on to thrive. ““My vision is that the Department’s data has consistency, is securely accessible, understood, and trusted… We have modernised our technology landscape, strengthened our capabilities, and most importantly, made a real difference in how we serve the Department and the sector.”

Washington State Dept of Natural Resources: Building Trust & Enabling Transformation

Washington State’s Department of Natural Resources (DNR) has been on a journey of building trust to enable transformation. We go inside that technology journey with CIO Liz Lewis-Lee on a mission to deliver reliable, effective technology. “At the DNR, all of our technology has to work to support all of the people all of the time, both in the office and out in the field.”

Virginia SCC: Building a New Cybersecurity Landscape

Glendon Schmitz, Chief Information Security and Privacy Officer at Virginia State Corporate Commission (SCC), discusses the exciting challenges of building a successful cybersecurity function. ““If everything’s a priority, nothing is. So understanding one’s lack of resources from a funding perspective and also the human side is important.”

Redefining Technology Leadership Through People, Culture and Business Outcomes

From the Music Industry to the Boardroom, IT leader Angie Fyke has blazed a trail for women in tech – she tells Interface her story of redefining technology leadership through people, culture and business outcomes from telcos to retail and today at Ontario Medical Supply. “The feedback I value most is when teams say they’re clearer, more confident, and able to move faster – that’s when you know change is working.”

Screenshot

Also in this issue, we learn from Claroty why critical infrastructure can’t afford to bolt on resilience; hear from NetApp on why defending against AI-powered cyberattacks requires AI-powered defence; and OVHcloud explain why Blockchain is crucial for the future of trust in AI.

Click here to read the latest edition!

  • Cybersecurity
  • Data & AI
  • Digital Strategy
  • Infrastructure & Cloud
  • People & Culture

Author

Adnan Patka

Published

21 May 2026

Estimated Read time

5Mins

Adnan Patka, Enterprise Manager – Blockchain, AI and Web3 at OVHcloud, on why the convergence of blockchain and AI heralds a brighter future for both

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.

Learn more at ovhcloud.com

  • Artificial Intelligence in FinTech
  • Blockchain & Crypto
  • Data & AI
  • Digital Strategy

Author

Adam Gale

Published

Estimated Read time

4Mins

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

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.

Learn more at netapp.com

  • Cybersecurity
  • Data & AI

Author

Andrea Babayan

Published

12 May 2026

Estimated Read time

3Mins

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

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.

Learn more at ipsotek.com

  • Data & AI
  • Digital Strategy

Author

Dan Brightmore

Published

7 May 2026

Estimated Read time

3Mins

Read in brochure format

Welcome to the latest issue of Interface magazine! Click here to read the latest edition! Appian: Why AI is Putting…

Welcome to the latest issue of Interface magazine!

Click here to read the latest edition!

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.

Click here to read the latest edition!

  • Cybersecurity
  • Data & AI
  • Digital Strategy
  • People & Culture

Author

Dan Brightmore

Published

Estimated Read time

7Mins

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

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.

Read the full report here

  • Data & AI
  • Digital Strategy

Author

Dharmesh Acharya

Published

28 April 2026

Estimated Read time

6Mins

ZeroThreat co-founder Dharmesh Acharya on why the only way to know if your defences actually hold is to challenge them with continuous penetration testing and exploit validation

Your security dashboard is green. No alerts. No critical flags. Everything looks fine. That feeling of calm is exactly what you should be worried about. A clean dashboard does not mean your application is secure. It often means you are measuring the wrong things.

The reality is, threats are growing faster than most security programs can keep up with. Over 2,200 cyberattacks happen every day globally, which is roughly one attack every 39 seconds. At the same time, attackers are no longer looking for obvious vulnerabilities. They focus on weak access points, exposed data, and chained exploits that traditional dashboards fail to capture.

If a threat operates outside those parameters, it stays invisible. Your logs look normal, your vulnerability scanner reads low risk and your compliance status says passing. And somewhere in your environment, an attacker could be moving quietly through systems your dashboard never touches.

Let’s take a look at why green dashboards can be misleading, what they are not showing you, and what real security validation actually looks like.

The False Comfort of a Green Dashboard

There is something deeply reassuring about a green dashboard. No alerts. No red flags. And no critical vulnerabilities screaming for attention. For most security teams, that view signals control. It signals safety. But here is the uncomfortable truth: a clean security dashboard does not mean your environment is secure. It often just means your tools are not seeing the full picture.

Most monitoring systems only report what they are configured to detect. If a threat operates outside those parameters, it stays invisible. Your SIEM logs look normal. Your vulnerability scanner shows low risk and your compliance status reads “passing.” Meanwhile, an attacker could be sitting inside your network, moving quietly, and your dashboard would never know.

According to IBM’s Cost of a Data Breach Report, the average breach takes 168 days to identify and 51 days to contain it in the finance industry. That is over six months of green dashboards while real damage is being done. False confidence in security metrics is not a minor issue. It is one of the most exploited gaps in enterprise security posture today.

5 Problems with Traditional Security Metrics

Traditional security metrics were built for a different era. They measure what is easy to measure, not what actually matters. And when security decisions are based on incomplete or misleading data, the entire security program becomes vulnerable, even when everything looks fine on paper.

1. Visibility Without Context

Knowing that 10,000 events were logged means nothing without understanding what those events represent. Traditional metrics track volume, not relevance. Security teams end up drowning in data while the actual threats, the ones that matter, go unnoticed. Coverage without context is just noise.

2. Compliance Masking Risk

Passing a compliance audit does not mean you are secure. It means you met a checklist. Many organizations confuse regulatory compliance with actual cyber resilience. Attackers do not care about your audit results. They look for gaps, and compliance-focused metrics rarely surface those gaps in time.

3. Perimeter-Focused Thinking

Most traditional security metrics are built around the perimeter. But the perimeter does not exist the way it once did. Remote work, cloud environments, and third-party integrations have dissolved those boundaries. Metrics that still prioritize perimeter health give a dangerously narrow view of your actual attack surface.

4. Lagging Indicator Dependency

Traditional metrics tend to be reactive. They tell you what already happened, not what is happening right now. Mean time to detect, incident counts, patch rates, these are all lagging indicators. By the time they show a problem, the damage is often already in motion. Real security needs leading indicators too.

5. Ignoring Unknown Assets

You cannot protect what you cannot see. Shadow IT, unmanaged endpoints, forgotten cloud instances, these assets rarely show up in traditional security dashboards. Yet they are among the most targeted entry points for attackers. Metrics that only account for known assets create a false sense of complete coverage.

Hidden Risks Your Dashboard Doesn’t Show

Your dashboard reflects what your tools are configured to monitor. Nothing more. Unmanaged devices, misconfigured cloud storage, dormant user accounts with excessive privileges, these risks exist outside the monitoring boundary. They do not trigger alerts. They do not show up in reports. But they are real, and attackers know exactly how to find them.

Lateral movement is one of the most dangerous and least detected attack behaviors. Once an attacker gains initial access, they move quietly across your environment using legitimate credentials and trusted pathways. Traditional security monitoring tools rarely flag this activity because it does not look like an attack. It looks like normal user behavior. That is precisely what makes it so effective.

Third-party risk is another blind spot most dashboards completely ignore. According to Verizon’s Data Breach Investigations Report, 15% of breaches involve a third party. Vendor access, supply chain integrations, and API connections create exposure points that sit entirely outside your visibility. If your dashboard is not showing you that, it is not showing you everything.

What a Genuinely Healthy Security Posture Looks Like

A healthy security posture is not about having zero alerts. It is about having full visibility, fast response capability, and continuous validation. Organisations with mature security programs do not chase green dashboards. They build systems that surface the right information at the right time.

According to IBM, organizations with a fully deployed security AI and automation program contained breaches 108 days faster than those without. Speed of detection and response is one of the clearest indicators of a strong security posture. That cannot be measured by looking at how calm your dashboard appears.

Real security health includes knowing your complete asset inventory, including cloud workloads, third-party connections, and unmanaged endpoints. It means having continuous monitoring that goes beyond compliance checkboxes. It means your team runs regular adversarial testing to find gaps before attackers do.

And it also means your security metrics are tied to business risk, not just technical thresholds. When a CISO can clearly explain what is protected, what is exposed, and why, that is what a genuinely healthy security posture actually looks like.

How to Ensure Real Security: Exploit Validation

Knowing you have vulnerabilities is not enough. You need to know which ones can actually be exploited, and how far an attacker could get if they tried. That is what continuous exploit validation delivers. It moves security testing from a scheduled event to an ongoing process that reflects your real-world risk exposure.

AI-driven automated penetration testing makes this possible at scale. Instead of waiting for an annual pentest, these tools continuously simulate real attacker behavior across your environment. They test your controls, validate your detections, and surface exploitable paths before a real threat actor finds them. Your security team gets evidence, not assumptions.

The result is a security program that is grounded in reality. You stop relying on what your dashboard says and start relying on what has actually been tested and verified. Continuous exploit validation closes the gap between perceived security and actual security, and that gap is exactly where breaches happen.

Conclusion: Stop Trusting Your Dashboards and Start Validating

A green dashboard does not mean you are secure. It means nothing alarming has been detected within the boundaries your tools are configured to monitor. That is a very different thing. Real security is not about how calm your dashboard looks. It is about how thoroughly your environment has been tested and validated.

The only way to know if your defences actually hold is to challenge them. Continuous penetration testing and exploit validation give you evidence, not assumptions. They show you what an attacker would find before an attacker actually finds it. That shift, from monitoring to validating, is what separates a false sense of security from a real one.

Learn more at zerothreat.ai

  • Cybersecurity
  • Data & AI

Author

Dvir Hoffman

Published

27 April 2026

Estimated Read time

6Mins

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

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.

Learn more at commbox.io

  • Data & AI
  • Digital Strategy

Author

Lee Nolan

Published

23 April 2026

Estimated Read time

5Mins

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

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.

Learn more at hitachivantara.com

  • Data & AI
  • Digital Strategy

Author

Mark Talbot

Published

22 April 2026

Estimated Read time

5Mins

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

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.

Learn more at appian.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech
  • People & Culture

Author

Daniel Ehnhage

Published

20 April 2026

Estimated Read time

5Mins

Daniel Ehnhage, Head of AI Transformation at Unit4, on why those that put people and capability at the centre of their AI strategy will unlock far greater and more sustainable value than those led by technology alone.

As the head of AI transformation, it might sound counterintuitive to suggest that artificial intelligence is not the most important part of my work. It makes a significant contribution to the radical change we are looking to achieve, but the technology itself is only about 10% of the solution. A significant part of the planning and investment must be based around addressing issues like the integration of siloed information systems, the building of the organisational capability required to adopt AI safely and finding the right business case. The key is to understand that adopting AI is not only about improving existing processes – it’s about gradually reshaping how we work in a sustainable way. The goal should be phased, practical improvements that build maturity over time.

This can be daunting for any organisation that has well-established operating practices. It requires a deliberate shift from problem‑solving to rethinking how value is created across the organisation. AI is far more capable and can empower your teams to find new solutions such as gathering more intelligence about market opportunities to improve productivity and decision making. The focus should be on enabling internal teams to work smarter through safe, responsible AI adoption. If your organisation is prepared to embark on such change, you must recognise AI becomes most powerful when you bring the right data together. Today, many organisations, including ours, are still maturing in this area. Successful adopters of AI prioritise building data readiness step by step so AI can create real value without overpromising.

Obviously, the role of AI transformation then becomes much broader with the added challenge of having to implement change without disrupting existing business performance. Consequently, there are some key areas where organisations must focus their attention, beyond ensuring they pick the right AI tool…

Structural Change – Put the AI Board in Place

    AI transformation evolves how organisations work. It does not replace everything we humans do today. The goal is to focus on practical, high‑value use cases that improve productivity, quality, and employee experience without creating disruption. A widely debated expression of this change is the concern that AI will replace human employees. Personally, I think this lacks imagination around the positive impact that AI can have on a workplace. Yes, it may reduce the number of repetitive, mundane tasks, but more importantly it will create new ways of working and collaborating.

    However, given how rapidly the technology is moving it is critical your organisation puts the right safeguards in place and agrees policies about ethical usage. That requires adoption of a cross-functional AI Board to provide a framework for embracing AI which will manage the impact of the structural change. This provides focus for your organisation’s approach to AI. The goal should be to agree which tools offer the most benefit for your teams and concentrate on exploiting use cases that will deliver the most benefit.

    The AI Board should be responsible for establishing the governance structure to help the IT and cybersecurity teams to ensure the use of AI is not creating new vulnerabilities. It should provide clarity and safeguards so employees can use AI confidently and responsibly. Our goal is to enable safe experimentation – not restrict innovation.

    People Change – Enabling Collaboration and Experimentation

    The ambition should be to get employees excited about the potential of AI to open up new ways of working that can lead to rewarding opportunities and exciting new challenges. Indeed, it is widely accepted that helping your people to accommodate the change is the biggest challenge you will face, taking up about 70% of the time required to implement the technology. This is because successful implementations depend on collaboration between distinct teams, which in turn depends on breaking down barriers, both for individuals and teams.

    For example, imagine being able to use AI to analyse data from diverse systems such as customer service, product development and marketing to identify new opportunities to support customers.

    Integrating these data sources could be seen as interfering with distinct job functions, so for all employees it is critical to educate them on what will be expected of them and a good start point is explaining how they will be measured. It could include simple measures such as demonstrating usage of AI tools, but if an organisation wants employees to adopt the technology it is also important to empower them through training.

    With the right support, employees will want to experiment, which should also enable them to understand use cases for AI in their work and the competencies they need to develop. This can be achieved through opportunities for cross-functional teams to explore new ways of working and innovating and should be encouraged by senior leaders. It is crucial they set the right tone, support initiatives, celebrate successes and listen to employee feedback.

    Business Change – Building the Right Business Case

    The business case is not just about saving money to solve a specific problem. It is too easy to look at the saved hours and productivity gains from adopting AI as the sum total of the investment costs you must deal with. There are a number of internally focused requirements that you must build into your thinking about AI transformation. There will be costs around the integration work to enable AI to access data from disparate systems. Competence development must be a top priority. Time must also be allocated to the process of change management and how it may disrupt existing business processes. Security must be a top consideration. These are all internally focused tasks, but you must look at them if you are to capitalise effectively on your AI investment.

    It is tempting to become overly excited by the potential of AI as a technology, and certainly it will bring dramatic change to organisations in the years to come, but having experienced the factors necessary for successful transformations, it is absolutely critical senior leadership teams approach AI-enabled change with cool heads and clarity on what they want to achieve. Above all, they must remember success is not dependent on the implementation of the technology, but predominantly on bringing employees with them on the journey. Many commentators talk about the rise of AI-first organisations. Those that put people and capability at the centre of their AI strategy will unlock far greater and more sustainable value than those led by technology alone.

    Learn more at unit4.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Andrew McLernon

    Published

    15 April 2026

    Estimated Read time

    6Mins

    Andrew McLernon, CEO and co-founder at Interlink, on why culture is the real disruptor

    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.

    Technology should create space for humans to think more deeply, collaborate more creatively and make better decisions. If AI adoption results only in faster outputs without improved thinking, we’ve missed the opportunity.

    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.

    Learn more at weareinterlink.com

    • Data & AI
    • Digital Strategy
    • People & Culture

    Author

    Dan Brightmore

    Published

    Estimated Read time

    4Mins

    Read in brochure format

    FinTech Strategy is back with more key insights from the industry experts and thought leaders shaping the future of financial…

    FinTech Strategy is back with more key insights from the industry experts and thought leaders shaping the future of financial services.

    Read the latest issue here

    Vibrant Capital: Scaling AI on Main Street

    Our cover star 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.”

    FNB: Redefining Data Science in Commercial Banking

    We also hear from 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.”

    Virginia Farm Bureau: An Enterprise CIO’s Journey

    Shifting focus to the world of insurance at the Virginia Farm Bureau, we spoke withan Enterprise CIO at a complex mission-driven organisation. As he approaches retirement, Patrick (Pat) Caine reflects on his career as a CIO and the centennial of an organisation renowned for resiliency, collaboration, commitment to a greater cause, diversity and service to its members. “In my role as CIO, I’ve always been that person who connects the dots between business needs and technology execution. Virginia Farm Bureau is digitally relevant, collaborative, and well‑positioned for the future.”

    Mastercard: Protecting Trust in the Digital Economy

    Michele Centemero, EVP Services at Mastercard Europe explains why promoting awareness, stronger collaboration and data-sharing, and continued innovation of payments ecosystems, will be critical in reducing the impact of scams and protecting trust in the digital economy. “The combination of AI, robust identity controls and open banking can help protect consumers from scams, whether across card and account‑to‑account payments or in fraudulent account openings.”

    Thales on AI Security: How FinServ’s Budget Priorities Signal a Boardroom Shift

    Todd Moore, Global VP – Data Security Products at Thales, reveals 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. “Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring.”

    Paymentology: The First Live AI-Agent Payment Is a Test for Credit Infrastructure

    Thomas Benjaminsen Normann, Product Director at Paymentology, dissects the future for agentic payments and the progress still to be made. “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.”

    Also in this issue, we hear from Publicis Sapient, on why asset managers must redesign their enterprise for AI-driven decision intelligence; learn from Bitpace why the most resilient payments infrastructure will be the one with the most adaptability; rank the AI maturity of 12 of the largest payments networks in the latest Evident AI Index; and round up the key FinTech events and conferences across the globe.

    Enjoy the issue!

    Read the latest issue here

    • Artificial Intelligence in FinTech
    • Blockchain & Crypto
    • Cybersecurity in FinTech
    • Data & AI
    • Digital Payments
    • Embedded Finance
    • Fintech & Insurtech
    • InsurTech
    • Neobanking

    Author

    Todd Moore

    Published

    14 April 2026

    Estimated Read time

    4Mins

    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

    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 Report examines 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. 

    Read the Report

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech

    Author

    Lee Fredricks

    Published

    10 April 2026

    Estimated Read time

    6Mins

    Lee Fredricks, Director – Solutions Consulting, EMEA at PagerDuty, on why technology leaders should see 2026 as a time for operational resilience to shift from ambition to accountability

    Technology leaders should see 2026 as a time for operational resilience to shift from ambition to accountability. In 2025, too many cloud services outages and disruptions took place across the public and private sectors, and now regulatory, technological and cultural pressures are converging to say that enough is enough.

    Outages often translate into broader repercussions for the organisation, including revenue impact, customer churn, share price pressure and potentially regulatory reporting obligations. Operational metrics must now be discussed alongside financial KPIs at the board level. C-suite leaders understand accountability, especially within the very regulated financial sector.

    DORA’s First Birthday

    It’s now been one year since the implementation of the Digital Operational Resilience Act, or DORA, introduced by the EU to strengthen the digital resilience of financial institutions. By now, organisations have had time to consider moving from mere compliance to creating a competitive edge from their investments.

    Enterprise tech leaders are in the middle of a balancing act. They’re managing ongoing modernisation and transformation initiatives while navigating multi-jurisdictional regulatory scrutiny. At the same time, they face constant pressure from the board and must meet evolving customer needs—all competing for immediate attention. The stakes have never been higher. Operations teams are no longer viewed as a back-office IT function. Their success in keeping the organisation running and driving revenue is now a board-level concern.

    For organisations today, IT is business delivery.

    A year of DORA has seen organisations make the shift from focusing solely on mere compliance to setting meaningful demonstrable testing, third-party risk visibility and strictly mandated incident reporting timelines. Financial firms have lessened their exposure to risky situations. Payments providers aren’t only reliant on a single cloud region or SaaS supplier, or unable to provide evidence of real time incident response efforts and auditable logs after a disruption.

    One benefit of these overall systemic improvements is enhanced supply chain accountability. Financial institutions and their technology partners are both liable for potential penalties and reputational risk, which makes it highly critical that they can prove their resilience capabilities.

    Nevertheless, operational resilience is a continuous discipline. A fragmented incident response can expose firms to regulatory and reputational risk again and again if not addressed systemically. As such, many organisations are looking toward AI agents as part of a move towards ‘no-touch’ operations.

    From Autonomy to Self-Healing

    Under set policies, autonomous agents can handle incident response and operational tasks, such as detection, triage and remediation. AI agents deployed in operations may become the backbone of L1 (first contact) and L2 (more skilled) support. Contrast this with the traditional, reactive, ticket-driven model of IT. The industry can move much faster and with a higher successful close rate. Leveraging intelligent automation reduces mean time to detection/resolution and KPIs around lower incident volumes reaching L3. Additionally, it can lead to improved service availability percentages. Well integrated agents that actually support existing operations teams also help manage the issues around talent shortages faced by many organisations.

    A typical incident lifecycle with agentic processes includes several stages depending on the model, but can be summarised as: Anomaly detected, correlated with recent deployment, a remediation script triggered and a human notified if set thresholds were breached. Such no-touch operations are golden in any sector, but particularly with industries such as digital banking and retail, where peak traffic periods demand near-instant response and poor customer experience is a powerful motivator for users to instantly change providers.

    IT Standardisation

    In addition, consider standardisation as part of strategic infrastructure best practices. There is a role for central operations clouds and operational ‘golden paths’ as solid foundations for reliable operational scale and dependability. Standardisation enables consistent, scalable operational excellence especially across large, distributed enterprises. ‘There is one way and it is the right way’ can be a great time and stress saver for operational teams – particularly if a regulatory notification and clear evidence is required.

    For example, a global bank might define a single golden path for deploying customer-facing applications with pre-approved monitoring, incident response workflows, and regulatory reporting templates built in. In an outage, teams follow the same process and automatically capture the evidence required for regulators, avoiding confusion, delays, and compliance risk.

    All of these possibilities take us to an exciting new place for an evolved set of developer and operational roles. When organisations enable AI to reshape daily engineering work away from manual firefighting and low-value work it frees headspace and time for developers and engineers to move into more architectural thinking and intelligent oversight of automated systems. These augmented teams will be empowered to manage simple situations instantly and devote more time and attention to the more difficult issues – the edge cases and the strategic necessities.

    Enabling Agentic AI

    Using another lens, businesses with agentic IT operations capabilities support their current talent, extending their reach and the speed of their response. The winning organisations will be those who deploy agents strategically, freeing up humans for that higher-value work – i.e. L3 expert support – and setting new standards for operational excellence that customers can rely on. Ideally this means making commensurate investment in existing people, training and organisational change management. A culture of continual upskilling and forecasting that points humans to where they make the best impact will be just as important as the autonomous tech tools working alongside them.

    Autonomous agents allow many new services, and one of those can be described as self-healing operations. This evolution of the operations world is where predictive detection, automated remediation and embedded resilience all coalesce. With an autonomous process of testing, maintenance and remediation, organisations can focus on finely measuring improved customer trust. They can also enjoy the productivity and revenue benefits of high business continuity and availability.

    AI is still a new technology, and many are legitimately concerned with the concept of autonomous agents. There is a need for clear guardrails, audit trails and explainability in automated remediation, and many technology partners have invested in their ability to support across these areas. Moreover, firms must maintain direction with policy-driven automation rather than uncontrolled autonomy, particularly in regulated industries.

    Mandate Operational Excellence

    This year is very likely to reward organisations that treat operational resilience as core to their business strategy. Those investing in automation, standardisation and governance will set the pace for their industries in an AI-enabled and increasingly autonomous world.

    Regulators are already expanding their scrutiny and reliability expectations beyond financial services firms. Across the world, jurisdictions are increasingly looking to strengthen their economies and digital services in particular through resilience and cybersecurity measures. At the same time, agentic operations, and the organisational performance benefits they support, will rapidly become table stakes technology in all sectors. Inevitably, customers will judge brands on digital reliability as much as price or product features when evidence of outages are a click or a headline search away.

    Start now. Audit internal incident response maturity, review the potentially complex web of third-party IT dependencies and identify where automation makes clear business sense. While resilience is an investment in compliance, it is also critical to ensure customer trust and future stability.

    Learn more at pagerduty.com

    • Artificial Intelligence in FinTech
    • Cybersecurity in FinTech
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech
    • Infrastructure & Cloud

    Author

    Rolf Bienert

    Published

    9 April 2026

    Estimated Read time

    4Mins

    With growth in data centre power demand, driven by AI and other power-hungry applications, could microgrids hold the key? Rolf Bienert, Technical & Managing Director of global industry body, the OpenADR Alliance discusses the potential for microgrids in providing flexibility and clean energy


    Generating enough power for the demands of artificial intelligence (AI), cryptocurrency and other power-hungry applications, is one of the biggest challenges facing data centres right now. With a power grid already under pressure and in the process of trying to modernise and flex to cope with the huge demands placed on it, the industry needs to rethink the way it adapts to these challenges.

    Data Centres

    According to figures from the International Energy Agency (IEA), data centres today account for around 1% of global electricity consumption. But this is changing with the growth in large hyperscale data centres with power demands of 100 MW or more. And an annual electricity consumption equivalent to the electricity demand from around 350,000 to 400,000 electric vehicles.

    With the rise of AI and expectation of what it can deliver, the next few years are likely to see a significant rise in the number and size of data centres. This has serious consequences for the energy sector. While, technology firms are under growing pressure to make data centres more sustainable.

    Microgrids – The Opportunities

    Microgrids could be the answer in providing a more sustainable and efficient energy supply for data centres. While the concept of a microgrid can vary depending on how they are used, they can be defined as small-scale, localised electrical grids that can operate independently or in conjunction with the main power grid. They can range in size from a university to a single home.As a global ecosystem, we’re seeing them used in different scenarios, from residential to large campuses.One interesting use case is MCE, a California Community Choice Aggregator, which has established a standardised setup for residential virtual powers plants (VPPs) with OpenADR used as the utility connection to manage the prices and consumption.

    The feasibility and suitability of microgrids depends on factors like the specific requirements of the data centre, regulatory environment and the long-term goals for sustainability, resilience and cost-efficiency.

    The real value is in helping overcome grid constraints and improving reliability by managing consumption and maintaining power during grid issues. For data centres that require uninterrupted operation, this ability to deliver resilience is critical.

    Sustainability is another important advantage. By integrating renewable energy sources, such as solar or wind power, and energy storage, microgrids can significantly reduce carbon footprint. While in terms of cost savings, they can reduce operational costs by utilising local power generation and demand-response strategies.

    Microgrids are modular, which means they can grow as the data centre’s needs evolve. Plus, when it comes to regulation, they face fewer regulatory hurdles compared to other options, like nuclear power, because they can operate mostly ‘net zero’ on the grid connection.

    Microgrids – The Challenges

    For data centre operators and investors trying to address power supply and stability issues, the use of microgrids can also mean challenges.The first of these is the start-up costs. While we talk about a reduction in operational costs once up and running, set-up costs for microgrids can be high, requiring significant capital investment especially for larger data centres, so important to bear in mind.

    Sustainability may be a big plus point, but the use of renewables like solar and wind depend on the weather – and the weather can be fickle. This necessitates robust storage solutions, backup power or large grid connections to ensure reliability and stability at all times. It’s also important to stress that the effective integration of these various distributed energy sources and systems can be technically challenging, so working with good integrators and partners is paramount.

    When it comes to powering data centres, microgrids are not the only option being considered. Alternatives like small modular nuclear reactors (SMRs) are also be touted as potential power sources. In my mind, SMRs are not in competition with microgrids but could become an important baseline component of them.

    In their favour, SMRs provide a constant, high-capacity output, ideal for 24/7 operation, and a zero-emissions power source. Once operational, they offer stable costs over decades. But they also face challenges like stringent regulation and public opposition to development, while a nuclear plant, even a small-scale one, involves substantial upfront investment. This is aside from the risks around nuclear waste and safety.

    Bottom line is that the data centres are going to need a very high continuous supply of power and microgrids offer options for a more resilient and responsive energy infrastructure. Decentralised power through a network of microgrids could help dynamically manage power loads and optimise renewable energy sources – especially as demands on the grid grow as we march onwards towards an AI-powered future.

    Learn more at openadr.org

    • Data & AI
    • Digital Strategy
    • Infrastructure & Cloud

    Author

    Jamil Jiva

    Published

    8 April 2026

    Estimated Read time

    5Mins

    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

    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.

    Learn more at linedata.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech
    • Neobanking

    Author

    Richard Doherty

    Published

    Estimated Read time

    6Mins

    Richard Doherty, Head of Wealth & Asset Management, Publicis Sapient, on how asset managers must redesign their enterprise for AI-driven decision intelligence

    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.

    Learn more at publicissapient.com

    • Artificial Intelligence in FinTech
    • Blockchain & Crypto
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech

    Author

    Dan Brightmore

    Published

    31 March 2026

    Estimated Read time

    3Mins

    Read in brochure format

    Welcome to the latest issue of Interface magazine! Click here to read the latest edition! Sanofi: Supporting the World’s Health…

    Welcome to the latest issue of Interface magazine!

    Click here to read the latest edition!

    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.”

    Langham Hospitality Group: Cybersecurity Underpinning Guest Excellence

    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.”

    Click here to read the latest edition!

    • Cybersecurity in FinTech
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech
    • Infrastructure & Cloud

    Author

    Ian Franklyn

    Published

    26 March 2026

    Estimated Read time

    5Mins

    Ian Franklyn, Chief Revenue Officer at Mainstreaming, on why delivering exceptional streaming experiences won’t require just technology, but also collaboration and synergy

    Streaming video has firmly established itself as the dominant force shaping global internet traffic. From premium live sports and breaking news to on-demand entertainment libraries, audiences now expect seamless, high-quality viewing experiences on any device, at any time. For leaders across media, telecoms, and technology, the challenge is no longer about enabling streaming. It is about sustaining it at scale preserving reliability, efficiency and profitability.

    Yet, despite the central role video plays in today’s digital economy, the underlying delivery model remains fundamentally fragmented.

    Many broadcasters and OTT platforms still rely heavily on centralised, third-party content delivery networks (CDNs). These operate largely outside internet service provider (ISP) infrastructures. This model has supported the growth of streaming over the past decade. However, it is increasingly misaligned with current demand patterns, especially during large-scale live events.

    The result is a structural inefficiency that affects every stakeholder in the ecosystem. And the industry can no longer ignore it.

    The Growing Cost of Disconnection

    When millions of viewers tune in simultaneously, vast volumes of video data must travel across multiple interconnected networks before reaching end users. This often means duplicating the same streams across long-haul routes, placing unnecessary strain on transit links and core infrastructure.

    For ISPs, this translates into rising traffic volumes without proportional financial return. Networks become congested, costs increase, and visibility into traffic flows remains limited.

    Broadcasters and OTT platforms face a different but equally critical challenge. With limited control over last-mile delivery, performance becomes unpredictable at precisely the moments that matter most. Buffering, latency, and degraded video quality directly impact user experience, driving churn and damaging brand reputation.

    Ultimately, the end user bears all the consequences. Even minor disruptions during peak events can cause frustration and dissatisfaction. This consequently erodes trust, impacting both service providers and content owners in an increasingly competitive market.

    Rethinking Delivery: Moving Closer to the Edge

    Addressing these challenges requires a fundamental rethink of where and how video is delivered.

    Rather than relying solely on centralised infrastructure, delivery capacity can be deployed directly within ISP networks, closer to the end user. This edge-based approach localises traffic, reducing the distance data must travel and fundamentally improving efficiency.

    The benefits are immediate. By placing content within ISP networks, duplicated traffic across transit routes is minimised, congestion in core networks decreases, and latency is reduced. At the same time, both ISPs and content providers gain greater visibility and control over performance.

    This model is particularly valuable for live streaming, where demand is highly concentrated and unpredictable. Traditional CDN architectures, designed for distributed but relatively predictable traffic patterns, are simply not built to handle sudden spikes in concurrent viewership.

    Edge delivery networks purpose-built for video, by contrast, enable capacity to be positioned dynamically where it is needed most. This ensures that even the largest live events can be delivered with consistency, reliability, and low latency.

    From Delivery Burden to Shared Value Creation

    The evolution toward edge-based video delivery represents a fundamental shift for both ISPs, and broadcasters and OTT platforms.

    For ISPs, streaming has long been treated as a cost centre. A growing source of bandwidth consumption that drives infrastructure investment without directly contributing to revenue. As traffic volumes continue to rise, this model becomes increasingly unsustainable both economically and operationally.

    At the same time, broadcasters face a different challenge. How can they efficiently manage highly variable demand? Particularly during large-scale live events where audience peaks are both massive and unpredictable. And where failure is not an option.

    Embedding video delivery capabilities within ISP networks changes this dynamic for both sides.

    For ISPs, localising traffic reduces reliance on upstream transit. This alleviates pressure on core infrastructure, enabling more efficient use of existing capacity. It also opens new monetisation opportunities, allowing them to move beyond being passive carriers and play an active role in delivering premium streaming experiences.

    For broadcasters and OTT platforms, the benefits are equally strategic. Edge-based delivery enables them to scale live events more efficiently. Activating capacity where and when it is needed rather than overprovisioning for peak demand. This results in more predictable performance, consistent quality of experience, and improved cost efficiency.

    In this shared model, video delivery is no longer a burden for one side or a risk for the other. It becomes a coordinated effort, aligning incentives and generating value for all the stakeholders involved.

    An Ecosystem that Works in Synergy

    Realising this opportunity requires more than technology. It demands a shift toward a more collaborative operating model: a true ‘Better Together’ approach.

    This means deeper alignment across the ecosystem, bringing together ISPs, broadcasters, OTT platforms, and technology providers around shared objectives. Instead of operating in silos, each stakeholder contributes to a unified delivery framework designed to meet the demands of modern streaming.

    In practical terms, this approach increases transparency, improves performance, and aligns both technical and commercial incentives. Integrating delivery capacity within ISP networks creates a stronger foundation for long-term growth, enabling more efficient scaling as demand continues to rise.

    The result is a more resilient and adaptable ecosystem. One capable of supporting increasingly complex and large-scale streaming experiences, and responding dynamically to future demand.

    Building the Next Generation of Streaming Infrastructure

    The misalignment between how video is consumed and how it is delivered is no longer sustainable, and delaying a change will only amplify the problem

    As streaming evolves, new formats such as ultra-high-definition video and low-latency interactive services will place even greater demands on network infrastructure. At the same time, audience expectations will continue to rise, leaving little tolerance for disruption.

    Meeting these challenges requires a shift toward integrated, edge-driven architectures supported by strong ecosystem partnerships.

    By bringing video delivery closer to the viewer, the industry has an opportunity to redefine both the economics and performance of streaming. More importantly, it can move beyond the limitations of fragmented models toward a more efficient and scalable future. Ultimately, delivering exceptional streaming experiences won’t require just technology, but also collaboration and synergy, aligning the entire ecosystem to operate as one.

    Learn more at mainstreaming.com

    • Data & AI
    • Digital Strategy
    • Infrastructure & Cloud

    Author

    Martijn Gribnauis

    Published

    23 March 2026

    Estimated Read time

    5Mins

    Martijn Gribnauis, Chief Customer Success Officer at Quant, on why Agentic AI will redefine financial services

    A recent Google Cloud survey showed that only 13% of finance organisations are currently using agentic artificial intelligence. This number needs to, and will rise when you consider that 88% of financial leaders are seeing ROI from generative AI already. Agentic is the next and most advanced evolution of artificial intelligence the world has ever seen. 

    Agentic AI is not on the way. It is here and already reshaping how forward-leaning financial institutions operate. In 2026, for IT and finance leaders to build an insurmountable competitive lead they must deploy agentic AI in every area where it can safely and effectively create value. The institutions that hesitate will find their business models under threat from familiar competitors and newcomers alike.

    Reinvention of Core Processes

    Agentic AI is poised to reinvent core financial processes. Bookkeeping, record maintenance, and period-end close are nearing complete automation. Month-end processes that once required late-night, stress-filled marathons will evolve into continuous, largely automated cycles. IT teams will no longer spend evenings on high alert waiting for failures. 

    This shift also frees IT leaders, finance teams, and operations functions from monotonous repetitive tasks. Instead of focusing on system uptime and manual reconciliation, they will collaborate with the C-suite on strategic initiatives that drive growth and revenue. 

    Understanding Why Adoption Is So Low

    Despite the promise of Agentic AI, there is understandable caution. Some 80% of organisations have reported ‘risky behaviour’ from AI agents, and in the world of finance that is an alarming number. Finance is one of the most regulated, risk-averse sectors in the world. The fear of losing control remains the primary reason so few in the industry have embraced Agentic AI.

    Loss of control and fear of catastrophic error

    Financial leaders fear that an autonomous system could go ‘off script’, mis-route payments, misinterpret rules, or inadvertently cause compliance breaches. In finance, even small errors can trigger major financial or regulatory consequences.

    Security and data privacy concerns

    Large AI models require huge quantities of sensitive data. Organisations worry about breaches, cyber-attacks, or manipulation. An AI agent with improperly configured permissions could, in theory, execute fraudulent transactions or expose confidential customer information.

    Bias and fairness risks

    If AI agents make decisions using incomplete or fragmented data, they risk perpetuating or amplifying bias. At scale, biased decision-making can undermine customer trust and expose firms to legal and regulatory challenges.

    Regulatory ambiguity and audit difficulty

    Regulators are still determining how to govern agentic AI. Some organisations fear that early adoption could unintentionally violate rules or create future audit vulnerabilities.

    These fears are legitimate, but not insurmountable.

    Tackling the Adoption Barriers: A Practical Blueprint for Finance Leaders

    To capitalise on Agentic AI’s immense potential, leaders must take a structured approach grounded in business value, security, and trust.

    1. Start With Clear, Measurable ROI and Efficiency Gains

    In finance, adoption accelerates when decision-makers see proof of value.

    Start by automating repetitive processes. Agentic AI can handle tasks like data entry, reconciliation, invoice matching, and initial fraud checks faster and more accurately than humans. This leads to reduced operational overhead as automation lowers labour costs, shortens processing times, and reduces error rates. Demonstrating these savings through case studies or internal pilots is critical to changing minds. 

    AI agents can enable revenue growth by analysing huge data sets to identify new investment opportunities, optimise trading strategies, and generate personalised product recommendations. Each of these capabilities directly impacts top-line growth.

    2. Strengthen Risk Management and Compliance Through AI

    Agentic AI will improve risk management when deployed responsibly. This starts with real-time fraud detection. AI agents can monitor transactions continuously, identifying patterns that suggest fraud long before traditional systems would detect an anomaly.

    Continuous monitoring is also incredibly helpful when it comes to compliance. AI agents excel at ensuring adherence to KYC and AML regulations. They can automatically maintain audit trails, identify missing documentation, flag anomalies, and escalate issues instantly.

    Enhanced stress testing and scenario modelling can both be completed via Agentic AI. It can simulate complex market environments more dynamically than legacy tools, providing deeper insights into vulnerabilities and improving resilience. When showcased and presented in this context, agentic AI becomes a risk-reduction tool in the eyes of decision makers. 

    3. Directly Address Security and Trust Concerns

    Trust is the cornerstone of adoption. Implement enterprise-grade security architecture that includes encryption, secure APIs, strict access controls, and continuous monitoring of agent behaviour. And, use explainable and transparent AI systems (XAI) so your finance teams understand the reasoning behind decisions. XAI helps provide interpretable outputs that support auditability and regulatory compliance.

    Start small with a controlled, low-risk pilot. A proof-of-concept in a non-critical workflow helps teams understand the technology, gather evidence, and build internal support before scaling. Produce numbers based reporting that speaks the language of the people who make the decisions. Show, don’t just tell them how agentic will move the business forward.

    4. Highlight the Competitive Advantage

    Agentic AI adoption is not just an efficiency upgrade. It is a competitive imperative. AI agents create faster innovation cycles by accelerating product development, service delivery, and operational improvements.

    They also provide superior customer experience. From instant account servicing to personalised financial recommendations, Agentic AI delivers the speed, personalisation, and convenience customers expect. Plus, it scales exponentially. No matter how many people call in at the same time, an agentic agent will answer immediately. Agentic AI reduces up to 86% of time spent in complex workflows that were traditionally handled only by people. This will be huge in getting ahead of your competition. 

    5. Build Momentum Through Internal Champions

    Adoption increases when respected leaders advocate from within. Mid-level managers, AI-literate staff, or members of the C-suite who understand the technology can serve as champions. Use them and their beliefs to drive alignment, communicate benefits, and counter misconceptions. The more people from different departments and levels of the organisation that talk up the technology, the more likely you are to get buy-in. 

    Your Time is Now

    Agentic AI will redefine financial services. The organisations that act today will build capabilities, insights, and competitive advantages that late adopters will not be able to replicate. Finance leaders must begin asking where agentic AI can support their business, where it can remove friction, where it can unlock growth, and where it can transform operations. The firms that act now will lead the industry. Those that hesitate will not get the chance to catch up.

    The only remaining question for finance organisations is not whether agentic AI will change the industry, but how quickly they choose to deploy it.

    Learn more at quant.ai

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Payments
    • Digital Strategy

    Author

    Dr. Yvonne Bernard

    Published

    18 March 2026

    Estimated Read time

    5Mins

    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

    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.

    Learn more at hornetsecurity.com

    • Cybersecurity
    • Cybersecurity in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Dr Megha Kumar

    Published

    17 March 2026

    Estimated Read time

    6Mins

    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

    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.

    Learn more at cyxcel.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Fayola-Maria Jack

    Published

    16 March 2026

    Estimated Read time

    6Mins

    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

    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’. 

    Learn more at resolutiion.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Chris Larsen

    Published

    13 March 2026

    Estimated Read time

    4Mins

    Chris Larsen, Chief Technical Officer – atNorth, on shaping ecosystems that support both digital progress and the preservation of our natural environment for future generations

    The AI industry continues to grow seemingly exponentially. With 92% of companies planning to increase their AI investments in the next three years, demand for the high density digital infrastructure required to support these types of workloads is unsurprisingly at an all time high.

    Data centres have always needed a significant amount of electricity to power and cool their computer equipment. Yet the sheer quantity of data to be processed for AI and other high performance computing – such as financial trading calculations and simulation technologies – necessitates a colossal amount of energy. For example, a report from the International Energy Agency states that data centres will use 945 terawatt-hours (TWh) in 2030, roughly equivalent to the current annual electricity consumption of Japan.

    At the same time, there is growing pressure for all organisations to comply with ESG frameworks. The introduction of regulations such as the EU’s Corporate Sustainability Reporting Directive (CSRD), mandates the publication of carbon footprint disclosures. This leaves many businesses with a difficult conundrum to solve – how to balance digital advancement whilst mitigating environmental impact?

    Once a consideration for local IT teams, the choice of a data centre partner is now at the forefront of balancing these two critical trends and is beginning to garner boardroom attention.

    Data centres that are designed with environmental responsibility and community integration in mind can act as the central hub of a thriving society, an ‘ecosystem’ that supports long-term sustainability and regional economic development.

    Location and Design

    Where a data centre is built, and how, is fundamental to its efficiency and sustainability. AI-ready facilities often require rapid scaling in line with customer demand. Access to ample suitable land is essential. Modular designs allow for faster builds and easier adaptation to new innovations in cooling and hardware technologies,

    Power and connectivity are also critical. Many regions struggle to offer the necessary renewable energy and high-speed network capacity. In contrast, the Nordics provide an ideal environment. An abundance of renewable energy, a cool natural climate that enables more energy efficient cooling techniques and excellent connectivity.

    As a result, the presence of data centres can promote local investment in power, connectivity and electrical infrastructure that benefits the whole community. For example, atNorth’s ICE03 data centre in Akureyri, Iceland, facilitated the development of a new point of presence (PoP) for Farice, which operates submarine cables linking Iceland to mainland Europe. This enhances telecom reliability and strengthens digital infrastructure across the region.

    Data centres can also support the stability of local power through grid balancing services. Something that is integral to the future design of atNorth’s data centres.

    Decarbonisation and Circular Partnerships

    Data centres are incredibly energy-intensive, and so many operators are investing in ways to reduce their carbon footprint. These include utilising the most efficient infrastructure and cooling technologies.

    atNorth goes one step further and has committed to sourcing heat reuse partnerships for all of its new data centre campuses. This means that waste heat generated during the infrastructure cooling processes can be captured and redirected to support nearby businesses and homes. In Finland, for example, a partnership has been formed with Kesko Corporation that will utilise waste heat from atNorth’s new FIN02 campus to heat a neighbouring branch of one of its stores.

    These types of initiatives essentially enable data centres to act as a decarbonisation platform for their clients’ IT workloads, helping them meet environmental targets and reducing running costs too. Something that is a key differentiator for businesses such as atNorth client and partner, Nokia, that has complex technical requirements and stringent sustainability goals.

    Responsible Operations

    Beyond environmental responsibility, data centres can be a positive force in the communities in which they operate. They create skilled jobs, drive improvements in local infrastructure, and often spark growth in hospitality, retail, and leisure services. At atNorth, we prioritise hiring locally and actively support education, charitable, and community initiatives in the regions we operate.

    Similarly, a care for the natural surroundings is pivotal to promoting a successful, data centre ecosystem integration. For example, atNorth has set aside part of its DEN02 site in Denmark for biodiversity efforts, installing insect monitors to track changes in insect abundance and diversity throughout the site’s development.

    As digital demand continues to grow, so does the need for responsible and sustainable development. High-performance computing can, and should, advance without compromising environmental integrity. By partnering with data centres that prioritise environmental stewardship and social responsibility, we can help shape ecosystems that support both digital progress and the preservation of our natural environment for future generations.

    Learn more at atnorth.com

    • Data & AI
    • Digital Strategy
    • Infrastructure & Cloud
    • Sustainability Technology

    Author

    Leonardo Boscaro

    Published

    9 March 2026

    Estimated Read time

    6Mins

    Leonardo Boscaro, EMEA Sales Leader at Nutanix Database, on why sovereignty requires repeatable, compliant database operations and recovery across hybrid multicloud environments

    In conversations with customers, infrastructure leaders are being asked to deliver more control with the same people. Stronger compliance with less tolerance for error. And higher resilience in environments that are objectively more heterogeneous than they were even a few years ago. Expectations continue to rise, but the operating models used to run critical systems haven’t kept up.

    This pressure shows up first at the database layer because they sit at the centre of mission-critical services. While still being managed through manual processes, fragmented tooling, and a heavy reliance on specialist knowledge. In many organisations, when availability, security and compliance are under scrutiny, this combination creates exposure very quickly.

    Database-Dedicated Platforms

    The shift we now see in regulated organisations is toward database-dedicated platforms. Where the operating model is standardised through approved templates, guardrails, automated workflows, and built-in auditability. In practice, this means treating database workloads as a dedicated domain, with infrastructure and lifecycle operations designed together rather than as an add-on to a general-purpose environment. This approach depends on having a standardised operational layer for database lifecycle management and recovery that works consistently across hybrid and multicloud environments.

    And in regulated environments, what matters is not only being compliant, but also being able to demonstrate it repeatedly. When provisioning, patching, and recovery depend on tickets, tribal knowledge, and one-off scripts, controls become hard to test. Furthermore, audit trails are incomplete, and resilience turns into a matter of confidence rather than capability.

    How Complexity Crept In

    Most enterprise database estates grew through sensible decisions made at different points in time. A platform was added to meet a new requirement, a legacy system could not be moved, or a new tool solved a specific operational gap. Each step made sense in isolation. Over time, however, teams found themselves managing dozens or hundreds of databases across multiple engines and environments. Each with its own processes for provisioning, patching, recovery and monitoring.

    What they face now is inefficiency and operational fragility. Databases are where control, auditability and resilience intersect. So, when processes are manual or inconsistent, the risk surface expands quickly. In regulated industries, this shows up in audit pressure, long recovery times and an uncomfortable dependency on a small number of specialists.

    Why Databases Expose the Cracks First

    Many infrastructure leaders we speak to ask why databases should be their concern at all. Traditionally, databases belonged to DBA teams, while infrastructure focused on platforms and capacity. Unfortunately, it’s not that simple anymore.

    Today, infrastructure and security leaders are under constant pressure to improve compliance, reduce risk exposure and maintain availability with fewer people and less tolerance for error. Databases sit directly in that line of responsibility. Patching windows, backup failures or untested recovery plans are operational risks with business consequences.

    What becomes clear very quickly is that automation alone does not solve this. Many organisations have invested heavily in scripts and bespoke workflows to manage database lifecycles. While these efforts reduce pressure in specific areas, they often create new complexity elsewhere. Particularly when people change roles or environments scale.

    Standardisation, Not Scripting, is the Real Shift

    The real breakthrough comes when organisations move from automating tasks to standardising the operating model itself. This means treating database operations as a productised capability, with approved templates, guardrails and repeatable workflows built in from the start.

    When provisioning, patching, cloning, and recovery follow a consistent model, compliance becomes part of the process rather than something validated afterwards. Human error is reduced because the system guides operations rather than relying on memory or documentation. And audit readiness improves because actions are traceable and predictable.

    This is why many organisations are moving away from bespoke automation and toward standardised operating models, where infrastructure, lifecycle, and governance are designed together. 

    Recoverability Turns Theory Into Reality

    Recoverability is the stage at which operating models are tested under pressure. Many organisations technically have disaster recovery in place, but testing it is complex, disruptive and often avoided altogether.

    For mission-critical services, particularly in financial services or the public sector, this is not acceptable. Recovery needs to be a standard operational capability, not a specialist exercise dependent on a few experts and fragile runbooks.

    By embedding recovery workflows into the same platform used for everyday database operations, testing becomes simpler and more frequent. Switchovers, failovers and restores can be executed through guided processes, with far less room for error. This is not about faster failover, but about confidence, credibility, and the ability to demonstrate control.

    Sovereignty is Becoming Operational Autonomy

    We all know how important sovereignty is, yet it’s often discussed in terms of data location instead of dependency and control, beyond just geography. Real sovereignty must factor in where the data resides, who ultimately controls the operating model and under which jurisdiction that control sits.

    In this context, hybrid strategies work but only if they preserve consistency. Running databases across on-premise and cloud environments without a common operating model simply moves complexity from one place to another. True autonomy comes from having one set of standards, workflows and controls that travel with the workload, regardless of where it runs.

    Our customers want the freedom to adapt to regulatory, geopolitical or commercial change. And without rebuilding governance and operational processes each time. This has made portability and consistency critical.

    A Database-Dedicated Platform, Not Just Infrastructure

    What emerges from all of this is a shift in how database platforms are defined. Beyond running databases on infrastructure, databases must now be delivered through a dedicated platform experience. One where lifecycle automation, governance and recoverability are baked in, not added later.

    When you take a platform approach, you can support multiple database engines, span hybrid environments and provide a single operational plane for teams. This allows infrastructure leaders to move beyond firefighting and towards standardised, compliant operations that scale.

    Independent economic analysis from Forrester’s Total Economic Impact study supports what many organisations are already seeing in practice. When database operations are standardised, the benefits show up quickly. Faster delivery, less manual effort, and more consistent controls reduce day-to-day operational friction and lower risk. Often generating measurable returns earlier than traditional infrastructure-only programmes.

    The modern mandate for infrastructure leaders

    For today’s CIOs, CTOs and CISOs, the challenge is no longer where databases should run, but whether they are governed, recoverable and consistent by design. As digital services expand, AI initiatives place new demands on data, and regulatory scrutiny increases. Operational discipline becomes a leadership responsibility. In regulated environments, credibility is earned through evidence, with regulators and customers, and in the public sector it is earned with citizens.

    Learn more at nutanixstore.co.uk

    • Data & AI
    • Digital Strategy
    • Infrastructure & Cloud

    Author

    Adam Spearing

    Published

    5 March 2026

    Estimated Read time

    5Mins

    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

    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.

    Learn more at servicenow.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Adonis Celestine

    Published

    3 March 2026

    Estimated Read time

    5Mins

    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

    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.

    Learn more at applause.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Tom Lanaway

    Published

    2 March 2026

    Estimated Read time

    4Mins

    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.

    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. 

    Learn more at connective3.com

    • AI in Procurement
    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy
    • People & Culture

    Author

    Dan Brightmore

    Published

    Estimated Read time

    2Mins

    Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams…

    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.

    Learn more at netcall.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Payments
    • Digital Strategy
    • Fintech & Insurtech
    • InsurTech

    Author

    Patrick Cooney

    Published

    27 February 2026

    Estimated Read time

    6Mins

    Patrick Cooney, CFO at Version 1, on why, in an AI-driven operating environment, financial discipline is more important than ever

    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.

    This raises a critical question for financial services leaders. If AI is now a balance sheet issue — shaping cost structures, risk exposure and long-term value — what should the CFO’s role look like in the years ahead?

    AI is Changing How Finance Operates

    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.

    Learn more at version1.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech

    Author

    Dan Brightmore

    Published

    26 February 2026

    Estimated Read time

    4Mins

    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,…

    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+. 

    The white paper can be downloaded here.  

    * 75% x 55 million UK population aged 18+ = 41 million (Source: Statbase, Population Ages 18+ UK)

    • Data & AI
    • Digital Strategy

    Author

    Gregory Mostyn

    Published

    24 February 2026

    Estimated Read time

    5Mins

    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

    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. 

    Learn more at wexler.ai

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech

    Author

    Jack Bingham

    Published

    20 February 2026

    Estimated Read time

    5Mins

    Jack Bingham, Regional Director of Digital Native UK, Ireland & South Africa, Confluent on how data, treated properly, compounds in value to drive digital disruption

    When I talk to founders and tech leaders, one question seems to consistently come up: what separates today’s disruptors from the last decade’s? In 2010, being cloud-first was what made investors sit up and take note. In 2026, it will be streaming-first.

    I’ve spent the last year or so working closely with companies that are, quite literally, building their businesses in real time. For them, real-time capability isn’t a department or a layer that supports the business. It is the business. The acid test is simple: how quickly can you capture a critical event – a payment, a login, a failed delivery – and respond with the next best action? That focus shapes how they build products, structure teams, and think about innovation.

    Here’s what I’ve learned from them:

    Lesson 1: Data is a Product, Not a By-Product

    Many traditional companies still treat data as something to collect, store, and analyse later. The new generation of businesses, on the other hand, treats it as a reusable, governed product that everyone can access. When it’s built and shared this way, teams stop rebuilding the same foundations for every new use case. They move faster because they’re working from a single, trusted view of the truth, shortening product cycles, speeding up iteration, and spending more time solving problems that matter.

    That mindset, rather than the size of the tech stack or the number of engineers, is what sets disruptive businesses apart. In these organisations, technology, data, and business strategy move in lockstep. Decisions aren’t passed up and down hierarchies, they’re made by teams who understand both the data and the customer problem in front of them.

    When you can trust your data and respond in real time, innovation stops being a department. It becomes a reflex.

    Lesson 2: Real-Time isn’t a Feature, it’s a Foundation

    A few years ago, one of the world’s largest supermarket chains realised it didn’t have a single real-time view of its inventory. Without that visibility, omnichannel experiences were impossible. Once it shifted to a streaming architecture, every transaction became a live event that updated stock, triggered supply chains, and even made it possible to get your groceries delivered straight to your kitchen fridge – coordinated through live inventory data, smart home devices, and real-time security feeds.

    That’s the practical power of streaming: it connects what happens in your business to what should happen next so you can provide products and services that take customer satisfaction to a whole other level. Real-time data stops being a reporting tool and becomes the foundation of every decision, interaction, and innovation.

    I often ask businesses what they would do differently, if they knew the state of every event in their organisation. The most forward-thinking companies already have the answer. They’re using streaming to turn business events into reusable building blocks, creating new experiences by connecting the data they already have in smarter ways.

    Lesson 3: Culture is the Multiplier

    Being streaming-first is only half about architecture. The other half is attitude. The best digital enterprises don’t wait for permission to experiment. They map their most important business events, align teams around them, and empower people at every level to react fast and learn faster.

    And the difference is visible. Feedback loops are shorter. Structures are flatter. Failure is treated as information. This culture of continuous experimentation is why these companies can move at the pace they do.

    We often run ‘Event Storming’ workshops with teams to map their critical business events. The idea is to create alignment – getting people from engineering, product, and operations to agree on what really matters and how those moments connect. That process reveals a lot. 

    Digital disruptors go beyond simply deploying streaming architectures. They build streaming mindsets. Leadership plays a crucial role here: data must be treated as a strategic asset. If it isn’t up top, it won’t be anywhere else in the organisation either.

    Lesson 4: Streaming and AI will Converge

    AI is only as good as the data you feed it. Unfortunately, most enterprises are still feeding it yesterday’s data. Streaming-first companies already know this. They’re building intelligent data pipelines that give AI the context it needs to make decisions in real time.

    That’s how the next generation of innovators will pull ahead: not by having bigger models, but by having cleaner, faster, more connected data. Streaming is what will let AI move from reactive to predictive… and from predictive to autonomous.

    Too many organisations are cutting investment in data while pouring money into AI projects. But AI without quality data is just expensive guesswork. The companies doing this well understand that data has to be a product in its own right. And when business and technology teams design around that shared understanding, innovation follows naturally.

    Lesson 5: The Mindset of the Next Disruptors

    If I were starting a company tomorrow, I’d look closely at the critical events that run my business. I’d then make sure I had a way to capture those in the stream, make them reusable, and build every product and process around them. 

    When your business can see and act on what’s happening in the moment, you gain something no traditional architecture can give you: time. And in the next wave of disruption, that’s the only advantage that really matters.

    If we look to who we can learn from in the coming months, it’s financial services and healthcare that are moving the fastest. Real-time fraud detection, patient monitoring, and risk management are becoming operational necessities – and these industries will set the benchmark for real-time data excellence. 

    Looking Ahead to 2026

    By 2026, I don’t think we’ll talk about ‘real-time’ as a differentiator. It will simply be how modern businesses operate. Batch systems won’t disappear, but they’ll coexist within a single, streaming-first platform that delivers data whenever it’s needed.

    Once every process can react instantly, the question then becomes: can it anticipate? Can it learn? That’s where AI and streaming meet and where we move from reactive to autonomous enterprises that not only respond to the present but adapt to what’s coming next.

    Data, treated properly, compounds in value. The decisions you make with it become faster, sharper, and more confident. The companies that understand this will be the ones still leading when today’s titans look like yesterday’s news.

    Learn more at confluent.io

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Payments
    • Digital Strategy
    • Embedded Finance

    Author

    Adrian Wood

    Published

    19 February 2026

    Estimated Read time

    5Mins

    Adrian Wood, Strategic Business Development & Offer Marketing Director at DELMIA

    The era of trial-and-error manufacturing is over. By integrating NVIDIA’s Physical AI into DELMIA’s Virtual Twin technology, Dassault Systèmes is moving the industry from static automation to autonomous software-defined systems that “learn” the laws of physics before the first part is made.

    Revolutionising Manufacturing with Agile AI-Driven Production

    Manufacturing is reaching a breaking point. Rigid production and logistics systems slow setup, ramp-up and scaling. Meanwhile deterministic automation struggles with real-world change, from new variants to unplanned constraints. The future is agile, software-defined production built on modular autonomous equipment, proven virtually and deployed with confidence.

    Dassault Systèmes and NVIDIA are building the industrial AI foundation to make that future real. DELMIA contributes the virtual twin of production systems. A semantically rich model of production that connects design intent to real-world execution across engineering, manufacturing and supply chain. NVIDIA contributes physical AI and accelerated computing to simulate robotics-grade physics and perception at scale. Together, we can virtualise and orchestrate autonomous production systems. Then manufacturers can prove changes virtually and make them real faster, with less risk and rework.

    This collaboration establishes a shared industrial AI architecture. This grounds artificial intelligence in the laws of physics and validated scientific knowledge. The integration of NVIDIA Omniverse physical AI libraries into the DELMIA Virtual Twin of global production systems represents a major step forward. It allows manufacturers to design, simulate and operate complex systems with a new level of confidence and precision. Not just incremental improvements; this partnership establishes a mission-critical system of record for industrial AI that powers a new way of working.

    Virtual Twins: The Cornerstone of Modern Manufacturing

    For years, manufacturers have optimised production lines in the physical world. While effective, this approach is often slow, resource-intensive and constrained by the cost of experimentation in live operations. Virtual twin technology changes this dynamic. A virtual twin is a science-based model of a system that goes beyond visualisation, enabling realistic validation of how operations should run before changes are made in the real world.

    DELMIA empowers companies to create comprehensive virtual twins of their entire operational ecosystem. This includes everything from individual machines and robotic workcells to full factory floor layouts and global supply chains. Within this virtual environment, manufacturers can:

    • Simulate and validate production processes before a single piece of equipment is installed.
    • Optimise workflows for maximum throughput and efficiency.
    • Identify potential bottlenecks and safety hazards without disrupting ongoing operations.
    • Train operators and maintenance crews in a risk-free setting.

    The virtual twin orchestrates design, engineering, production and supply chain in one environment so decisions can be tested, trusted and reused. This capability alone delivers significant value, but its impact grows when combined with physical AI.

    Integrating AI for Autonomous Production

    The partnership with NVIDIA brings physical AI into DELMIA virtual twins. NVIDIA Omniverse provides a platform for developing and operating 3D simulations and industrial digitalisation applications using OpenUSD-based interoperability. Combined with DELMIA’s production semantics, manufacturers can test autonomous behaviour in realistic conditions before deployment.

    This is the shift from ‘mirroring reality’ to ‘proving change’. AI models accelerated by NVIDIA computing can evaluate scenarios across production constraints, resources and variability. They can help teams reduce commissioning surprises, improve flow and validate how production should respond to change, from new variants to disruptions.

    The result is the emergence of software-defined production systems. These are factories and operations where decisions remain human-led, but are continuously supported by AI that recommends, tests and validates options in the virtual twin before changes are deployed. This creates a feedback loop where the virtual world is used to validate better outcomes for the real world.

    A Practical Application: The OMRON Collaboration with DELMIA & NVIDIA Drive Real-World Success

    To understand the real-world impact of this technology, consider the collaboration with OMRON, a global leader in industrial automation. OMRON recognizes that addressing the growing complexity of modern manufacturing requires a move toward fully autonomous and digitally validated production systems.

    By combining DELMIA’s Virtual Twin of Production Systems, NVIDIA physical AI, and OMRON automation technologies, manufacturers can move from design to deployment with greater confidence. When a manufacturer introduces a new product variant or packaging change, automation often fails in small but costly ways, such as automation-grasping reliability, orientation on conveyors or downstream flow stability. Instead of trial-and-error changes on the line, teams can validate process logic, layout constraints and operating rules in the DELMIA virtual twin, then simulate realistic robot and material behaviour using NVIDIA’s AI before deployment. The result is faster adaptation and less physical rework.

    The Top 3 Broader Impacts on Manufacturing

    This fusion of virtual twin technology and industrial AI has far-reaching implications for the entire manufacturing sector including:

    1. Unlocking New Efficiencies: Software-defined production systems can continuously identify operational improvements that are difficult to see through manual oversight alone, improving throughput, uptime and overall performance while reducing avoidable losses.
    2. Advancing Sustainability Goals: By simulating processes in the virtual world, companies can minimize physical prototyping and reduce waste. AI-driven optimization within the DELMIA virtual twin helps manufacturers fine-tune their operations to consume less energy and use fewer raw materials, directly contributing to their sustainability commitments.
    3. Fostering Continuous Innovation: When the risk and cost associated with testing new ideas are lowered, innovation flourishes. Manufacturers can experiment with novel factory layouts, new automation strategies and different production workflows within the safety of the virtual twin. This agility allows them to adapt quickly to changing market demands and stay ahead of the competition.

    The partnership between Dassault Systèmes and NVIDIA is about more than just combining two powerful technologies. It’s about establishing a new, scientifically validated foundation for industrial AI. By integrating NVIDIA’s physical AI libraries into DELMIA, we are empowering manufacturers to build the autonomous, efficient and sustainable factories of tomorrow, today.

    • Data & AI
    • Digital Strategy
    • Digital Supply Chain

    Author

    Kevin Janzen

    Published

    12 February 2026

    Estimated Read time

    5Mins

    Kevin Janzen, CEO of Gaming & EdTech AI Studio at Globant, on how AI will change the way games are made and expand the market

    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.

    Learn more at globant.com/studio/games

    • Data & AI
    • Digital Strategy
    • People & Culture

    Author

    JP Cavanna

    Published

    11 February 2026

    Estimated Read time

    5Mins

    JP Cavanna, Director of Cybersecurity at Six Degrees, on balancing the risks and benefits of AI in cyber defence strategies

    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.

    Learn more at Six Degrees

    • Artificial Intelligence in FinTech
    • Cybersecurity
    • Cybersecurity in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Dan Brightmore

    Published

    9 February 2026

    Estimated Read time

    2Mins

    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…

    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 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 hiring filters 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 across 50 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.

    • Data & AI
    • Digital Strategy

    Author

    Dan Brightmore

    Published

    2 February 2026

    Estimated Read time

    3Mins

    Maxio analysis of $40B+ in billings data shows vertical focus and AI innovation driving success, while growth inflection points emerge earlier than expected

    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.

    To read the full B2B Growth Report, click here. 

    About Maxio

    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.

    Learn more at maxio.com

    • Data & AI
    • Digital Strategy

    Author

    Dan Brightmore

    Published

    Estimated Read time

    3Mins

    Read in brochure format

    Interface issue 69 is live featuring Haleon, State of Montana, Techcombank, Publicis Sapient, Oakland County, Snowflake and much more

    Welcome to the latest issue of Interface magazine!

    Click here to read the latest edition!

    Haleon: A Bold Business Evolution

    Digital & Tech Head Soumya Mishra reveals how the group behind power brands like Sensodyne, Panadol and Centrum, broke away from GSK and transformed so successfully. Haleon is itself a large organisation so separating from a huge parent company was a big challenge… “It was the biggest deal of its kind and the first to happen in this industry,” Mishra adds. “We were separating to create simplification, but we had to work hard to achieve that. There were a lot of processes and policies that didn’t make sense and needed an overhaul. This had to be backed by a culture shift that was properly communicated.”

    State of Montana: Cybersecurity Through A New Lens

    State of Montana CISO, Chris Santucci, explains the organisation’s drastic shift towards security, and how his team has become a shining example within the wider IT centralisation sphere… “Fixing security vulnerabilities came down to having built enough social capital and trust to correct. I like to stay slightly uncomfortable as a CISO and as a human, to keep challenging myself to deliver better services and greater value. The mission is to ensure Montana citizens get the support they need while keeping services secure and protecting data.”

    Publicis Sapient: Driving Banking Transformations with AI

    Financial Services Director Arunkumar Gopalakrishnan reveals how Publicis Sapient is developing the playbook for delivering successful AI-led digital transformations across the financial services landscape. “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.”

    Techcombank:

    Chief Strategy & Transformation Officer, PC Chakravarti explores the operating model, Data & AI foundations, culture and talent playbook, and the partnerships turning ambition into market leading outcomes at Techcombank in Asia. “Tech is not the limiting factor – it’s about supporting people and talent to leverage capabilities to enhance business models.”

    Oakland County:

    Sunil Asija, Director of Human Resources at Oakland County, talks building trust with collaboration and becoming employer of choice. “To build trust the culture needs to change from top to bottom, and it needs everyone to join in that good fight.”

    Click here to read the latest edition!

    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech
    • Infrastructure & Cloud
    • People & Culture

    Author

    Dan Brightmore

    Published

    27 January 2026

    Estimated Read time

    6Mins

    Some Europe & Middle East CIOs anticipate up to 178% ROI on AI investments, with further efficiencies expected as Agentic AI scales

    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 our StoryHub.

    • Data & AI
    • Digital Strategy

    Author

    Christina Mertens

    Published

    26 January 2026

    Estimated Read time

    5Mins

    Christina Mertens, vice president of business development, EMEA, at VIRTUS Data Centres on designing next gen digital infrastructure

    Europe’s digital infrastructure is entering a new phase of development. For more than a decade, growth was concentrated in a small number of metropolitan hubs. This was where connectivity, enterprise demand and financial services created natural centres of gravity for data centres. Cities such as London, Frankfurt, Amsterdam and Paris (FLAP markets) became the backbone of Europe’s cloud and colocation landscape.

    That model is now under pressure. Computing power is surging in ways that surpass forecasts made even two years ago. AI training and inference, high performance computing (HPC), analytics and modernised public services all require significant and sustained energy and cooling capacity. McKinsey suggests that global demand for data centre capacity could more than triple by 2030. It’s clear Europe needs more digital infrastructure. However, it needs that infrastructure in places with the headroom and regulatory clarity to support long term expansion. And this is why what are referred to as second-tier locations are becoming critical to expanding Europe’s digital architecture.

    In practical terms, second-tier locations are not secondary in importance. They are cities and regional areas outside the most constrained metropolitan centres, where there is greater headroom for power, land and long-term infrastructure planning. Across Europe, this includes parts of regional Germany and Italy, Iberia, the Nordics and areas of the UK outside of London. These locations are now playing a central role in how Europe expands its digital capacity.

    Why the Digital Infrastructure Shift is Happening

    The primary driver is power. Data centres require sustained, predictable electrical capacity over long periods, particularly as AI workloads increase baseline demand. In dense urban centres, electricity networks are often operating close to their limits, and upgrading them is complex, costly and slow. New substations are difficult to site, transmission upgrades can take many years, and competition for capacity from other sectors is intensifying.

    Land availability compounds this challenge. Modern data centres are no longer single buildings inserted into existing industrial estates. They are increasingly campus-based developments, designed to accommodate multiple facilities, on-site substations and future expansion. Securing sites of that scale within major cities is difficult and expensive. And often incompatible with planning frameworks that prioritise mixed-use or residential development.

    By contrast, regional and edge-of-city locations offer more physical space and greater flexibility. They make it possible to plan electrical infrastructure coherently from the outset, rather than retrofitting systems around urban constraints. For building services professionals, this changes the nature of both design and delivery.

    Delivery Challenges in Regional Locations

    While second-tier locations offer more space and flexibility, they are not without challenges. Securing grid capacity remains a critical path issue. It requires close collaboration with transmission and distribution network operators, regardless of geography. In some regions, new infrastructure or upgrades are required to support data centre demand. This can introduce complexity into delivery programmes.

    Phased development is another defining characteristic. Many campuses are designed to be built out over several years, sometimes over a decade or more. Electrical and mechanical systems need to be designed and installed in a way that supports this staged approach, maintaining operational efficiency while allowing for expansion.

    This places a premium on coordination between designers, contractors, operators and utilities. Clear documentation, consistent standards and long-term programme management become essential, particularly where different phases may be delivered by different teams over time.

    Skills and Workforce Considerations

    As data centre development spreads across a wider range of locations, skills availability becomes an important consideration. High-voltage electrical expertise, experience with resilient power systems and familiarity with data centre standards are already in demand, and that demand is unlikely to ease.

    In regional locations where specialist labour pools may be smaller, there is increased focus on training, apprenticeships and long-term workforce development. From an operator and developer perspective, the ability of contractors and consultants to provide consistent quality across multiple phases is particularly valued on campus-scale projects.

    This creates opportunities for building services firms that invest in people and develop repeatable delivery capability. Long-term relationships can be built where teams understand an operator’s standards and are involved across successive phases of development.

    The Influence of AI and Higher-Density Workloads

    AI is accelerating many of these trends. Training and inference workloads place sustained loads on electrical and cooling systems, increasing the importance of reliability and predictable performance. This reinforces the need for robust primary infrastructure and careful long-term planning.

    Second-tier locations make it easier to accommodate these requirements because they allow for comprehensive system design at scale. Space for substations, cooling plant and future expansion can be planned into the site from the beginning, rather than being constrained by surrounding development.

    From a building services perspective, this does not necessarily mean radically new technologies, but it does increase the importance of integration, resilience and accurate demand forecasting.

    Why this Matters for the Built Environment Sector

    The shift toward second-tier locations represents more than a geographical redistribution of data centres. It reflects a broader change in how digital infrastructure is planned, designed and delivered. Larger sites, longer programmes and greater emphasis on early-stage coordination place building services and electrical design at the centre of successful delivery.

    For the built environment sector, this creates sustained opportunities across design, construction and operation. Campus developments require ongoing engagement rather than one-off interventions, and they rely on teams that can think beyond individual buildings to system-level performance over time.

    Looking Ahead…

    So, it’s clear that Europe’s digital infrastructure is becoming more distributed, and that trend is unlikely to reverse. Power constraints, planning pressures and rising digital demand all point toward continued development beyond traditional metropolitan hubs.

    Second-tier locations are not a temporary solution. They are becoming a permanent and essential part of Europe’s digital landscape. For building services professionals, understanding how to design and deliver infrastructure at this scale, and over these time horizons, will be increasingly important.

    As the next phase of development unfolds, success will depend on careful planning, strong collaboration and a clear understanding of how electrical and mechanical systems underpin the resilience and performance of Europe’s digital future.

    Learn more at virtusdatacentres.com

    • Data & AI
    • Digital Strategy

    Author

    Dan Nichols

    Published

    23 January 2026

    Estimated Read time

    8Mins

    Dan Nichols, Chief Technology Officer at virtualDCS, on why cloud resilience in the financial services sector hinges on shared accountability and an assume-breach philosophy

    A powerful catalyst for transformation, the cloud is reshaping how organisations compete in the financial services sector. Beyond significant cost savings and flexibility, leaders are eager to unlock the potential of AI-driven insights, intelligent automation, and real-time business modelling. And, in a space governed so strictly by data sovereignty and privacy policies, the cloud’s ability to localise, encrypt, and control data has made it a key enabler of compliance and customer confidence.

    But as threats become more frequent and sophisticated – with attackers now targeting shared platforms and partner supply chains – organisations can no longer rely on their own defences alone. For true digital resilience, shared accountability, collective readiness, and clear governance across every cloud touchpoint are equally non-negotiable.

    All Eyes on the Money

    The industry sits at a valuable intersection of data, technology, and finance. A combination that makes it uniquely attractive to attackers. It holds some of the world’s most sensitive data, directly underpins the flow of global capital, and operates through deeply complex and interconnected systems. With every integration increasing the risk of exposure. Ultimately, the attack motivation is as simple and relentless as it is in most sectors: monetary gain. Cybercriminals target institutions precisely because of the value at stake and the speed at which disruption translates to loss.

    How the Threat Landscape is Evolving

    Ransomware groups may see insurers and payment providers as high-yield targets. They understand even seconds of downtime can induce multi-million pound losses. Under pressure to protect customer trust and avoid regulatory penalties, some firms may choose to pay in order to restore their service quickly. This dangerous perception only encourages repeat targeting and paves the way for damage to spread even further. Yet it remains a common response tactic among many.

    At the same time, the rise of supply chain and third-party attacks has made it possible for criminals to bypass even the most well-defended cloud environments. By exploiting shared platforms, managed service providers, and cloud-hosted applications, perpetrators can move laterally across multiple organisations at once, amplifying both the reach and impact of their attacks. In other words, infiltrating one vendor’s weakness can cripple an entire network in one carefully coordinated strike. And, since some firms may overlook the cloud’s shared responsibility model – presuming end-to-end security sits solely with their cloud provider – multiple blind spots can inevitably emerge, creating easy openings to exploit.

    In an environment where boundaries blur and dependencies multiply, traditional perimeter-based defences are no longer enough. Hybrid and multi-cloud infrastructures demand continuous visibility, faster detection, and coordinated response across every partner and provider. The goal is not simply to prevent breaches, but to withstand and recover from them collectively. It’s about recognising that in today’s ecosystem, no financial institution is secure in isolation.

    Inside the Ransomware Economy

    Evolving beyond the scattergun attacks of the past, ransomware now operates as a professionalised, profit-driven ecosystem, where malicious actors collaborate, trade intelligence, and lease attack tools much like legitimate software vendors. The rise of ransomware-as-a-service (RaaS) has even lowered the barrier to entry, giving less skilled affiliates access to ready-made payloads and automated encryption kits in exchange for a percentage of the ransom.

    What makes it especially destructive is the precision and psychology behind the attacks. Rather than randomly striking, attackers conduct weeks of reconnaissance – learning behaviours, studying employee hierarchies, and identifying systems most critical to operations. They often infiltrate through phishing emails or compromised credentials, quietly moving laterally through the network to gain elevated access. Once embedded, they disable defences, exfiltrate sensitive data, and target backup repositories before finally encrypting production systems.

    At that point, the goal shifts from technical control to financial coercion. Victims are locked out of their systems and presented with a ransom note demanding payment, sometimes in cryptocurrency, in exchange for a decryption key. Increasingly, the threat includes public exposure of stolen data – a tactic designed to pressure leadership into paying to protect their reputation and customer trust. Even when ransoms are paid, recovery is rarely clean: data may be incomplete, corrupted, or resold on the dark web, and repeat targeting is common once an organisation is identified as a payer.

    It’s this blend of stealth, strategy, and human manipulation that makes ransomware so difficult to defend against. By the time the encryption begins, attackers have already spent weeks ensuring recovery options are limited. This background isn’t designed to scaremonger, but to highlight why resilience must start long before an attack ever reaches the endpoint.

    The Foundations of Ransomware Resilience

    Ransomware resilience isn’t achieved through a single product or policy – it’s the outcome of strategic, technical, and cultural alignment. Financial institutions, in particular, must approach it as a continuous process of readiness: Anticipating compromise, containing impact, and restoring normality quickly and transparently:

    Assume-Breach Philosophy

    The first step is shifting from a defensive mindset to an assume-breach philosophy. In practice, this means recognising that even the most sophisticated systems can and will be breached – and building architectures and response strategies designed to limit damage when this happens. It’s a pragmatic approach, grounded in the reality that attackers are increasingly sector agnostic. No organisation is too small or too secure to be targeted, but the financial sector remains a favourite because it offers both high disruption value and potentially significant monetary reward.

    Building meaningful resilience, therefore, demands layered defence and disciplined execution. The goal is to slow attackers down at every stage – detecting them early, limiting lateral movement, and ensuring business continuity when systems are disrupted. Behavioural analytics and continuous monitoring can surface and neutralise subtle anomalies that would otherwise go unnoticed – such as phishing, spear phishing, and malware, with email still the number one entry point for ransomware.

    Zero Trust & MFA

    Meanwhile, zero trust policies and multi-factor authentication methods add a second layer of protection, blocking unauthorised access even if credentials are compromised.

    When incidents do occur, a well-practised response framework ensures action is fast and coordinated, minimising disruption across critical systems, with the ability to switch to secure replica environments to keep operations running while remediation takes place. Secure, immutable, air-gapped backups underpin it all, providing a safety net that guarantees recovery can begin from a clean and uncompromised state.

    Human readiness is equally critical. Technology can contain an attack, but only people can recover from one effectively. Regular simulation exercises, incident rehearsals, and cybersecurity awareness training help teams respond calmly and cohesively, transforming response from reactive to instinctive. This operational maturity is reinforced by strong governance. Frameworks such as DORA, NIST, and ISO 27001 provide the structure to align technical teams, compliance leads, and executive decision-makers around shared resilience goals. When combined with skilled practitioners and clear accountability, they embed security into ‘business as usual’ – moving resilience from a strategy to a sustained organisational capability.

    Why Multi-Layered Backup is Critical

    When ransomware strikes, the speed and integrity of data recovery determine whether disruption lasts minutes or days – and whether the impact cascades through wider global markets. As the last and most decisive line of defence when every other control fails, it’s also fundamental to customer trust and compliance. Yet too often, backup is treated as a static safeguard rather than a dynamic resilience layer.

    Since modern ransomware often seeks out and encrypts traditional backups first, a single backup copy or centralised repository is no longer sufficient. True resilience today depends on a multi-layered approach – combining offsite or cloud-diverse storage, immutable data copies that cannot be altered or deleted, and isolated environments to protect against lateral movement.

    How frequently these backups are tested is equally important. Too often, financial institutions only discover weaknesses when recovery is already underway, at which point strategies can’t be magically strengthened, and it becomes a race against the clock to minimise downtime and reputational fallout. Regular, automated recovery testing changes that dynamic. It not only confirms that files can be restored, but provides verifiable assurance that systems come back online in the correct order, data dependencies remain intact, and teams have the muscle memory to act quickly and confidently when the worst happens.

    The Power of Shared Accountability

    In a digital economy so deeply interconnected, no organisation operates in isolation. This is especially true in financial services, where supply chains and service providers form the backbone of day-to-day operations. While this interdependence is a strength in many ways, it also means resilience is no longer defined by how well a single institution can defend itself, but by how effectively every partner in its ecosystem upholds their part of the security chain.

    This is where shared accountability becomes critical. It recognises that cloud providers, managed service partners, and financial institutions each have distinct but complementary roles to play in securing data, systems, and infrastructure. When accountability is clearly defined – and when partners collaborate rather than operate in silos – visibility improves, incident response accelerates, and the risk of systemic failure decreases.

    Shared accountability also extends beyond contractual obligation. It’s about building a culture of collective readiness: sharing intelligence, rehearsing joint incident scenarios, and supporting smaller or less-resourced partners to raise their security baseline. The result is a unified entity capable of anticipating, absorbing, and recovering from disruption together.

    Looking Ahead

    To view cyberattacks as inevitable might seem pessimistic to some, but it’s an unfortunate truth that no amount of investment can eliminate risk entirely. In an era where threats are growing in both scale and sophistication, readiness becomes the true differentiator – particularly in such a high-stakes sector. For financial institutions, that means embedding security into culture, strengthening connections across supply chains, and continually testing their ability to withstand and recover as a united ecosystem. Only then can resilience become a strategic advantage rather than a defensive necessity, and unlock the cloud’s transformative potential with absolute confidence.

    Learn more at virtualcds.co.uk

    • Artificial Intelligence in FinTech
    • Cybersecurity
    • Cybersecurity in FinTech
    • Data & AI
    • InsurTech

    Author

    Ash Gawthorp

    Published

    22 January 2026

    Estimated Read time

    6Mins

    Ash Gawthorp, CTO and Co-founder of Ten10, on building the right foundations to shape the AI era in the UK

    A recent study shows that UK businesses expect to increase their AI investment by an average of 40 percent over the next two years, following an average spend of £15.94 million this year. With investment surging, the UK is clearly in the fast lane, but the question is whether that momentum will convert into real, durable strength.

    This rapid acceleration places the UK at a pivotal moment in its ambition to lead in artificial intelligence. Investment is rising, government focus is strengthening, and organisations across every sector are exploring AI at pace, creating a sense of real momentum. However, anyone who has experienced previous technology cycles will recognise the familiar tension that emerges during periods of rapid progress and optimism. Breakthroughs often attract significant attention and capital before entering a more grounded, sustainable phase.

    The pressure today is not on AI as a whole. Instead, it is focused on a specific path, where belief in ever-larger transformer models delivering general intelligence continues to grow. This progress has been remarkable, but it represents only one path within a much broader AI landscape. As excitement reaches its peak, the market will inevitably stabilise. The long-term value will come through robust engineering, strong talent pipelines, and successful deployment in real-world environments.

    The task now is to use this moment wisely. Long-term success depends on building deep capability at home, rather than relying on hype or outsourcing key foundations to external providers that sit outside our oversight and control.

    The Limits of Scale as Strategy

    A significant share of today’s investment is based on the assumption that increasing compute and model size will inevitably lead to artificial general intelligence (AGI). Transformer architectures have delivered extraordinary capability and accelerated progress in ways few predicted. They remain powerful systems for prediction and pattern recognition across language, images and other data.

    However, scale is not a guarantee of general reasoning or broad intelligence. Many researchers believe that transformative progress may require developments beyond today’s dominant architecture. If that proves correct, the markets surrounding large closed models will experience a natural cooling. This would be an adjustment based on speculative expectation, not a failure of AI as a discipline. The industry would then shift toward approaches that prize clarity, modularity and measurable outcomes. Engineering discipline and architectural flexibility will matter far more than sheer size.

    One Architecture Cannot Become a National Dependency

    AI will continue to advance. The question for the UK is whether it builds capability that can evolve alongside that progress, or whether it locks itself to a narrow set of global platforms. A handful of model providers currently influence pricing, model behaviour and development cycles. When enterprises rely entirely on opaque APIs, they inherit changes without knowing why outputs shift, how models adapt or when pricing dynamics move. That introduces fragility that grows over time.

    Some experimental use cases can tolerate opacity, but critical public services and regulated industries cannot. Lending, diagnostics, fraud detection and other high-stakes applications demand clarity over how decisions are formed and how logic stands up to scrutiny. In those environments, transparency and auditability shift from abstract ideals to essential operational requirements.

    If the UK intends to embed AI deeply into essential systems, it must champion architectures that allow observability, explainability, control and replacement. Dependence on decisions made offshore is not a foundation for long-term strength.

    Specialised Agents Reflect How Sustainable Systems Evolve

    A practical and resilient approach to AI is already taking shape. Rather than depending on a single model to handle every task, organisations are assembling systems made up of specialised components. This mirrors the way effective teams work, where roles are defined, responsibilities are clear, and handovers are structured. One model transcribes speech, another classifies information, and a third retrieves or summarises content. Each performs a focused function that can be observed, validated and improved.

    This modular design makes systems easier to maintain and evolve. New components can be adopted without rewriting entire frameworks. If performance changes or drift appears, individual parts can be evaluated or replaced without widespread disruption. This reflects long-standing engineering principles that value clarity, observability and the ability to substitute components when better options emerge.

    Financial efficiency supports this approach as well. Running powerful frontier models for every interaction introduces cost and latency that scale quickly. Task-specific agents can often deliver the same outcome faster and more economically. Across thousands of interactions, the savings and performance gains become significant.

    Engineering as the Anchor of Trustworthy AI

    As AI becomes embedded in real systems, success relies on foundational engineering practices. Observability, continuous testing, performance monitoring and controlled deployment are essential. These are not new concepts created for AI, but long-established techniques that have been adapted to a new class of technology.

    In early exploratory phases, it can be tempting to treat large models as something separate from traditional software systems. However, the moment AI begins to influence real decisions, the fundamentals return. Enterprises must be able to trace behaviour, explain recommendations and ensure consistent reliability, while regulators expect clarity and boards seek evidence-based decisions around technology choices, cost structures and risk.

    Organisations that approach AI as engineered infrastructure, rather than a mysterious capability, will be far better equipped to scale safely and confidently.

    Building Skills that Make Capability Real

    The UK is fortunate to have strong research institutions, a sophisticated regulatory mindset and a robust software talent base. To convert these strengths into durable national advantage, investment in skills must expand beyond narrow data expertise. Data scientists remain crucial, but sustainable AI delivery depends equally on software engineers, cloud specialists, machine learning specialists, testers, governance experts and operational teams who run systems at scale.

    Leading organisations recognise that AI delivery is a multidisciplinary effort. As architectures become more modular, value will flow from those who can integrate, monitor and guide AI systems responsibly. The UK must ensure that thousands of professionals have access to this training and experience. Real leadership emerges when capability is widely shared, not concentrated in a small group.

    Governance that Accelerates Innovation

    Strong governance does not slow innovation. It accelerates meaningful adoption by building confidence. When organisations can demonstrate transparency, control and reliability, AI can extend into more critical functions.

    For national strategy, this becomes a competitive advantage. Industries that manage financial and clinical outcomes are not resistant to technology. They simply require evidence that systems behave consistently and transparently. If the UK excels in building AI that is observable, testable and replaceable, trust will grow and adoption will move faster.

    Shaping a Resilient AI Future

    Every technology cycle begins with excitement and eventually settles into maturity. Those who succeed through this transition are the ones who invest in capability while enthusiasm is high. When the current market resets, leadership will belong to those with engineering depth, system agility, responsible governance and the skills to integrate specialised intelligence across complex environments.

    The UK has an opportunity to define this standard. Strength will come from transparency, interoperability and the ability to adapt to model and architecture changes without disruption. It is a quieter strategy than making declarations about imminent artificial general intelligence, yet it builds the resilience required to lead over the long term.

    The future will reward systems that can evolve, remain auditable and operate securely at scale. With the right foundation, the UK can shape this era of AI not through scale alone, but through excellence in engineering, governance and talent. That foundation is the true measure of AI power, and now is the moment to build it.

    Learn more at ten10.com

    • Data & AI
    • Digital Strategy

    Author

    Katja Hakoneva

    Published

    20 January 2026

    Estimated Read time

    5Mins

    Katja Hakoneva, Product Manager at Tuxera, on delivering tomorrow’s data storage security today

    Smart meters are no longer just data endpoints. They’re intelligent, connected nodes embedded into the national infrastructure. As energy networks undergo rapid digital transformation, the focus has largely been on secure communications and real-time data transmission. But beneath the surface lies the local data storage, which often becomes a critical blind spot.

    Smart meters store large volumes of sensitive data from energy usage profiles to firmware logs and grid event histories on embedded memory. If this information is accessed, altered, or deleted, it can trigger billing inaccuracies, regulatory breaches, and customer mistrust. With meters expected to operate in the field for up to 20 years, data-at-rest security is a critical requirement.

    Storage Vulnerabilities: The Silent Cyber Threat

    These embedded systems face multifaceted risks. Attackers may gain access to stored data by physically tampering with a meter or exploiting software vulnerabilities that bypass weak authentication. Malicious actors could manipulate logs to alter billing records, mislead consumption analytics, or mask larger cyberattacks on grid infrastructure.

    In many cases, such intrusions go undetected until tangible damage, such as lost revenue or reputational fallout. With increasing dependence on smart infrastructure, utilities can no longer afford to treat embedded storage as a passive component.

    Counting the Real Costs of Cybersecurity

    Securing smart meters comes with technical requirements, as well as, operational and resourcing demands. For many UK manufacturers and utilities, managing cybersecurity internally means building and retaining specialist teams, often requiring three to five full-time professionals to handle vulnerability monitoring, patch management, and threat response throughout the year.

    Aligning with regulatory frameworks frequently demands hardware upgrades to handle stronger encryption and secure configurations, impacting Bill of Materials (BOM) costs and development timelines. Many existing software stacks require optimisation to support modern security protocols within resource-constrained devices. These efforts are necessary, with a single undetected cyberattack costing companies an average of $8,851 (≈£6,900) per minute, and the consequences extending beyond financial loss to potential regulatory fines and service disruptions.

    The CRA and the new Era of Cyber Regulation

    The Cyber Resilience Act (CRA), set to come into force across the EU by 2027, will reshape how connected devices are designed, developed, and supported. For UK-based vendors serving the European market, or collaborating with EU counterparts, compliance with CRA is becoming a strategic imperative.

    Key CRA requirements include:

    • Security by design: Devices must be secure from the outset, not retrofitted post-deployment.
    • No known vulnerabilities at market launch: Products must undergo security validation prior to release.
    • Default secure configurations: Devices should avoid insecure settings out of the box.
    • Lifecycle management: Vendors must support patching and vulnerability resolution throughout the device’s operational lifespan.

    For smart meters, which often run in the field for two decades or more, the CRA introduces accountability that extends well beyond product launch. Compliance with the CRA will become part of the CE marking process, meaning global manufacturers must align if they wish to sell into the EU energy market.

    Engineering Security: Confidentiality, Integrity, and Authenticity

    Designing resilient smart meters starts with three pillars:

    • Confidentiality protects sensitive user data from unauthorised access. This includes encrypting both data and encryption keys, restricting user access levels, and securing communication channels.
    • Integrity ensures stored data remains unaltered and trustworthy. Power failures, for instance, can corrupt memory. Using flash-optimised file systems and secure boot processes can prevent such vulnerabilities.
    • Authenticity confirms that firmware and data updates come from trusted sources. Techniques like digital signatures and update validation prevent attackers from injecting malicious code into meters.

    Together, these pillars enable smart meters to meet regulatory expectations while protecting both users and grid operations.

    Future-proofing Data Storage

    Cybersecurity for smart meters is not just a feature; it requires organisational readiness. Frameworks like the CRA, NIST, and IEC 62443 emphasise secure processes, documentation, and people alongside secure products.

    For companies looking to prepare, it is smart to start with common pillars such as maintaining up-to-date Software Bills of Materials (SBOMs), conducting regular supply chain and risk assessments, keeping detailed test reports, and establishing clear incident response plans. Internally, training staff on cybersecurity best practices, setting clear data retention policies, and defining access controls and responsibilities are critical steps to ensure cybersecurity is embedded within the culture of the organisation. This approach ensures security is not a one-off compliance task but a sustainable practice that protects smart infrastructure long-term.

    Smart meters deployed today could still be operating in the 2040s. This timeline intersects with the anticipated emergence of quantum computing, which may break today’s encryption standards. Though post-quantum cryptography is still evolving, vendors must prepare now to ensure systems remain secure in a post-quantum world. Smart meter software should be designed with cryptographic agility to allow it to adapt and upgrade algorithms as threats evolve.

    Lessons from Long-Term Deployment

    Smart meters are designed for longevity, but memory wear remains a primary failure point. Meters that lack flash-aware storage systems face early data loss, increasing the cost of maintenance, replacements, and warranty claims.

    Utilities and OEMs that embed file systems capable of wear levelling, garbage collection, and secure boot processes have extended meter lifespans by more than 50%, even in challenging conditions. One example showed meters surviving over 15,000 power interruptions without any data loss.

    Integrating secure storage delivers operational and commercial benefits. It ensures compliance with CRA and other evolving global frameworks, reduces maintenance and warranty costs, minimises carbon impact through fewer replacements, enhances brand credibility and trust with procurement teams, strengthens the business case for longer-term contracts and partnerships. As the smart energy market matures, these benefits are becoming differentiators, especially as digital infrastructure grows in complexity.

    Delivering Tomorrow’s Data Storage Security Today

    The next generation of smart infrastructure will be fast and connected, as well as, secure, resilient, and regulation-ready. For vendors and utilities alike, embedding data protection deep into the meter architecture is a business-critical move.

    By preparing for the CRA today, smart meter manufacturers will position themselves as forward-thinking, trustworthy partners in tomorrow’s energy ecosystem, delivering technology that’s not only built to last but built to protect today and tomorrow.

    Learn more at tuxera.com

    • Cybersecurity
    • Data & AI
    • Digital Strategy

    Author

    Dan Brightmore

    Published

    19 January 2026

    Estimated Read time

    5Mins

    Michael Ault, Country Manager at integrated payments specialists myPOS, offers strategic advice for SMEs looking to scale through digital transformation and diversification

    Scaling a small business is one of the most rewarding, yet complex journeys for any entrepreneur. While growth brings opportunities for greater reach, higher revenue, and stronger market presence, it also demands foresight, discipline, and the ability to manage risk strategically. Securely integrating new technology is the main obstacle for 47% of SME’s, even though 76% of these businesses intend to expand their IT investment. This underscores a key point of tension, as many businesses want to grow through digital transformation but struggle to do so securely and sustainably.

    The business landscape continues to evolve with changing customer expectations, technology, and economic conditions. For UK SMEs, the key to long-term success lies in achieving growth but also in building resilience. Sustainable scaling comes down to three principles: embracing technology pragmatically, diversifying intelligently, and investing in people and partnerships that strengthen resilience.

    Leveraging Digital Transformation

    Digital transformation is the foundation of business growth, especially for small business. Cloud-based solutions, automation, and data analytics help to streamline operations, reduce inefficiencies, and create better customer experiences. However, transformation must be purposeful, not performative.

    The smartest approach is to scale technology investment incrementally, integrating flexible, modular systems that evolve with business needs. This approach not only lowers risk but also helps ensure digital maturity evolve over time. When SMEs use modular, cloud-based technology, operations run more smoothly and changes can be effectively analysed. Ultimately, resilience is not built through one-time upgrades but through a culture of continuous digital evolution.

    Diversifying Revenue Streams

    Depending on a single product, service, or market leaves a business vulnerable to sudden changes in demand. Diversification, when guided by customer insight and data can turn volatility into opportunity. Expanding into online sales, introducing subscription models, or targeting fresh customer segments can make income streams much more stable and sustainable.

    At myPOS, we know that even simple changes based on data, such as adding additional payment options or tapping into cross-border e-commerce, can help cash flow and protect against market shocks. The goal of technology is to mitigate specific challenges without adding layers of complexity.

    Investing in Employee Development

    Your people are pivotal to your ability to grow as a business; empowered teams are the engine of sustainable scale. A team that feels supported and motivated will bring fresh ideas, adapt to challenges, and push the business forward. Investing in training, mentoring, and development opportunities builds skills that pay back in the form of innovation and improved performance.

    In fast-changing industries, having employees who are confident in learning and adapting can make the difference between struggling through disruption and taking advantage of it. Equally, strong partnerships extend this resilience beyond the organisation. Building resilience at the team level creates resilience for the whole business, so fostering a culture of continuous learning and celebrating employee contributions is key to maintaining motivation.

    Focusing on Financial Health and Flexibility

    Financial resilience underpins sustainable growth. Scaling often requires upfront investment, and without healthy cash flow or reserves, opportunities can be lost. Monitoring income and expenses closely, cutting unnecessary costs, and preparing for seasonal fluctuations gives businesses more control.

    Having flexible financing options, like credit lines, small business loans, or even crowdfunding, provides a level of agility. Instead of being caught off guard by unexpected challenges, businesses with financial flexibility are positioned to respond quickly and strategically.

    Financial management software can make it easier to track performance, spot issues early, and forecast future needs. When you can see your finances in real time, you can make proactive, data-driven decisions instead of waiting for problems to happen. In markets that change quickly, this kind of financial management helps small firms plan with confidence, stay flexible, and establish a stronger base for long-term growth.

    Prioritising Customer Relationships and Feedback

    Your customers are not just buyers; they are advocates, sources of insight, and the foundation of repeat business and brand loyalty. Businesses that scale successfully often place customer relationships at the heart of their strategy by actively gathering feedback, responding quickly to issues, and personalising interactions, which shows customers they are valued.

    This loyalty becomes a form of resilience. In periods of uncertainty, a base of satisfied, returning customers provides more stability than constantly chasing new ones. Successful businesses use CRM tools to track customer preferences and automate follow-ups so no opportunity to strengthen a relationship is missed.

    Building Strategic Partnerships

    Partnerships can accelerate growth while also spreading risk. Working with other businesses, organisations, or influencers can provide access to new audiences, shared expertise, or additional resources. Collaboration can also create opportunities for joint marketing, co-branded initiatives, or innovative product and service offerings.

    In times of uncertainty, strong partnerships act as a support network. By aligning with others who share your values and vision, you create opportunities that are mutually beneficial and more resilient than going it alone. It is important to find partners whose goals and audiences complement your own for the best long-term impact.

    The next stage of small business success will be defined by resilience rather than speed, the ability to adapt, recover, and continue to create value in the fact of uncertainty. For SMEs, this means developing adaptable growth plans that include flexible technology, diverse models and empowered employees.

    Learn more at mypos.com

    • Data & AI
    • Digital Payments
    • Digital Strategy
    • Fintech & Insurtech

    Author

    Fawad Qureshi

    Published

    15 January 2026

    Estimated Read time

    6Mins

    Fawad Qureshi, Global Field CTO, Snowflake, on realising possibilities for innovation in this new AI era

    Without cloud migration, businesses face the end of innovation. In this new AI era, businesses operating within the closed architectures of legacy systems do not have the flexible, data-driven foundation to engage with these new technologies and ensure a strong pipeline of necessary innovation. And as AI continues to evolve, those not able to keep pace with innovation risk being left behind. 

    Cloud migrations are the foundation to modernise and drive business growth over the long term. When organisations migrate to a cloud-based environment, it’s crucial to focus on the tangible business value a migration will deliver, rather than simply shifting from one system to another. Moving a company’s customer-facing applications and all of their data to a cloud-based environment has the benefits that are increasingly real and measurable.

    Migration isn’t just a Plug and Play approach – Which migration fits your needs?

    There are two approaches to cloud migration, broadly speaking: horizontal and vertical, each with their own benefits and potential challenges. A vertical approach sees organisations migrating applications one by one: this approach is a good choice if certain systems have to be prioritised, or if the applications being migrated do not have many interdependencies. Vertical migration allows for focused efforts and risk management on individual systems, and requires fewer resources. Horizontal migration moves entire system layers at the same time. This is the best solution when businesses have tight deadlines to retire legacy systems, or if their systems are tightly integrated. Horizontal migrations tend to be faster by allowing for parallel work streams, but they require more technical expertise. 

    Organisations often adopt a mixture of the two approaches, for example, horizontally migrating important systems such as data platforms, while taking a vertical approach to customer-facing applications. Whatever approach an organisation takes, it’s vital that the migration also includes a culture shift, preparing employees to adapt to new, consumption-based models and the possibilities of the new technology. Migration is also just the start of the journey, unlocking the potential of AI-driven use cases and seamless data collaboration, including new ways to achieve business value. 

    Before diving straight in, ensure it’s with a Data-First Mindset

    When migrating to the cloud, a data-first approach is essential. For those acting as the catalyst for change, whether that be IT managers or even CIOs, data must be front of mind before planning any successful migration.  Understanding how data is used within the organisations, including its structure, governance needs, and how it delivers value and business outcomes, is imperative. This applies doubly when it comes to large, complex systems with many interconnected applications. 

    Before migrating, businesses must comprehensively assess their current ecosystem. It’s imperative that the end-to-end business product survives the migration, intact. Organisations should maintain internal control over core competencies around data, such as business process knowledge, data governance and change management. These areas include institutional knowledge that external parties may not grasp. Businesses should also maintain direct oversight over compliance requirements and risk management. 

    Technical activities such as cloud infrastructure optimisation, performance testing, and specialised migration tooling are something, by contrast, that can be handled by external expertise. Code conversion can also benefit from purpose-built tools that use technologies including AI. Technical parts of the immigration tend to evolve rapidly and require specialist knowledge, so are ripe for outsourcing. While doing so, those steering the migration need to ensure clear governance around outsourced activities, including regular knowledge transfer sessions. 

    Different parts of the business all have a role to play: IT and engineering lead on technical implementation, handling the technical side of business requirements, while finance will identify ROI opportunities and manage cloud costs. It helps to create a cross-functional steering committee with representation from every department to ensure that different areas of the business are aligned and ready to address challenges. 

    Adaptability and Flexibility is the key to business longevity 

    Migration is never one-size-fits-all, and business leaders should be prepared to be flexible and adapt. There are multiple kinds of horizontal migration, from a simple ‘lift and shift’ focused on moving systems as they are to a ‘move and improve’ where migration is followed by optimisation to reduce technical debt. They should be ready to adapt at their own pace, choosing data platforms which offer agnostic architecture and the freedom to choose between data models and tools to ensure minimal disruption.

    Flexibility is also important in choosing the tools used for migrations. Flexible data platforms will offer the support businesses need to deal with collaboration and governance frameworks. For businesses operating in EMEA, where different countries can have varying policies, pay close attention to issues around data quality, security and compliance, particularly when it comes to data sovereignty and issues around European data residency. 

    A Shared Destiny

    The shift to the cloud fundamentally changes security. The traditional cloud ‘shared responsibility’ model clearly demarcated duties between the provider and the customer. However, a more advanced approach is emerging: the ‘shared destiny’ model. This model recognises that in the event of a breach, reputational damage affects both parties. This shared risk incentivises the cloud provider to be a more proactive partner, actively helping customers strengthen their security posture rather than simply managing their own side of the demarcation line.

    As ‘destinies’ intertwine, you help eliminate the vulnerability created due to password simplicity. Put simply, in a ‘shared responsibility’ model, the cloud provider is only responsible for securing infrastructure, while the customer remains responsible for securing data and apps in the cloud, as well as for configuration. In a ‘shared destiny’ model, the cloud provider plays a more proactive role to ensure that their customers have the best possible security posture. 

    Taking a ‘shared destiny’ approach allows businesses to be more proactive in securing data, using approaches such as multi-factor authentication, secure programmatic access and more comprehensive cloud monitoring services. Choosing a modern, AI-driven data platform offers the best security foundations here, offering security controls across cloud service providers and the entire data ecosystem. 

    A Pathway to Growth

    In today’s world, the bigger risk is standing still. Nothing changes if nothing changes.

    If organisations are holding back on innovation due to technological limitation, then the time to migrate is clear. There is no need to face an end to possibilities when the path towards success lies in reach, offering an opportunity to bring businesses up to date with modern requirements, and pave the way for the adoption of technologies such as AI. 

    However, as we’ve seen, it’s not just a case of plug and play. Organisations must ensure a flexible, data-driven approach to migration, while keeping security front of mind via a ‘shared destiny’ approach. To deliver this, the right choice of a modern, flexible data platform will ensure the whole organisation can work together effectively and deliver a path to future innovation and growth. 

    Learn more at snowflake.com

    • Data & AI
    • Digital Strategy
    • Infrastructure & Cloud

    Author

    Robert Cottrill

    Published

    14 January 2026

    Estimated Read time

    4Mins

    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

    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

    So, it’s no surprise that ANS research found that data privacy is the top concern for businesses when adopting AI. As these threats evolve, businesses must treat AI not just as an enabler, but also as a potential vector for attack.

    The Governance Gap

    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.

    Learn more at ans.co.uk

    • Cybersecurity
    • Data & AI
    • Digital Strategy

    Author

    Joe Logan

    Published

    12 January 2026

    Estimated Read time

    4Mins

    Joe Logan, CIO at iManage, on the need to avoid the hype, manage cybersecurity, focus on ROI and balance change management to get the best results with AI

    Across the enterprise, AI promises transformational power – however, it’s not as simple as just plugging it into the organisation and instantly reaping the benefits. What are some of the top things CIOs need to focus on to avoid any pitfalls, unlock its value, and best position themselves for success with AI? 

    1) Separate the Hype from Reality

    Here’s what hype looks like: using AI to “radically transform the way you do business” or to “accelerate comprehensive digital transformation” or – heaven forbid – to “completely change our industry.” These are big statements – and absolutely dripping with hype.

    Getting real with AI requires identifying specific use cases within the organisation where a particular type of AI can be deployed to achieve a specific goal. For example, maybe you want to reduce customer churn by 20% and have identified an opportunity to use chatbots powered by large language models to provide more effective customer service. That’s what reality looks like.

    In separating the hype from reality, organisations gain the added benefit of clearing up any misconceptions – at any level of the organisation – about what AI can and can’t do, thus performing an important “level set” around expectations.

    2) Understand the Implications for Cybersecurity

    On one side, any AI tool you’re using has access to data, and that means that access needs to be controlled like any other system within your tech stack. The data needs to be secured and governed, and issues around privacy, sovereignty, and any other regulatory requirements need to be thoroughly addressed.

    As part of this effort, organisations also need to be aware of the security measures required to protect the AI model itself from bad actors trying to manipulate that model. For example: prompt injection – inputs that prompt the model to perform unintended actions – can affect the model and its responses if not carefully guarded against.

    Securing your AI system is one side of the coin; the other side is understanding how to apply AI to cybersecurity. There are a growing number of use cases here where AI can help identify risks or vulnerabilities by analysing large amounts of data, helping organisations to prioritise the areas they need to focus on for risk mitigation. 

    In summary? While any usage of AI will require you to “play defence” on the security front, it will also enable you to “play offence” more effectively. In that sense, AI has multiple implications for cybersecurity.

    3) Focus on the Right Kind of ROI

    When it comes to ROI for any AI investments, don’t narrowly focus on absolute numbers when it comes to metrics like time savings or cost savings. While well-suited to industrial workplaces that are churning out widgets every day, absolute numbers can be an awkward fit when applied to a knowledge work setting.

    The advice here for any knowledge-centric enterprise is: Don’t get hung up on the idea of actual dollars and cents or a specific number – instead, look for a relative improvement from a baseline. So, rather than saying “We’re going to reduce our customer acquisition costs by $100,000 this year”, it’d be more appropriate to focus on reducing existing customer acquisition costs by 10%. Likewise, don’t focus on each junior associate in the organisation completing five more due diligence projects per calendar year; look to complete due diligence projects in 30% less time.

    4) Give Change Management its due

    Change management has always mattered when it comes to introducing new technology into the enterprise. AI is no different: Successful adoption requires a focus on people, process, and technology – with a particular emphasis on those first two items.

    A major challenge is educating the workforce with an eye towards improving their AI literacy – essentially, enabling them to understand what’s possible and how they can apply AI to their daily workflows. 

    Know that a centralised model of control that dictates “this is how you can experiment with AI” is probably going to be ineffective. It will be too stifling for innovative individuals in the organisation. Far better to provide centres of excellence or educational resources to those people who are most inclined to take the initiative and move forward with AI experiments in their team or department. 

    One caveat here: It’s essential to have guardrails in place as teams and individuals experiment with AI, to prevent misuse of the technology. That’s the tightrope that CIOs need to walk when introducing AI into the organisation. Striking the right balance between “total control” and “freedom to explore, but with appropriate oversight and guardrails”. 

    The Future of AI Depends on what CIOs do next

    The promise of AI is massive, but only if CIOs adopting the technology focus on the right areas. And that means filtering out the hype, keeping security implications top of mind, redefining ROI, and guiding change with a steady hand. By paying attention to these areas, CIOs can safely navigate a path forward with AI. And ensure that it isn’t just a technology with promise and potential, but one that delivers actual enterprise-wide impact.

    Learn more at iManage

    • Cybersecurity
    • Data & AI
    • Digital Strategy

    Author

    Dan Brightmore

    Published

    8 January 2026

    Estimated Read time

    4Mins

    Vertiv expects powering up for AI, Digital Twins and Adaptive Liquid Cooling to shape future Data Centre Design and Operations

    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.”

    The Vertiv Frontiers report builds on and expands Vertiv’s previous annual Data Centre Trends predictions. The report identifies macro forces driving data centre innovation:

    • 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 autonomy accelerates

    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.

    Find out more at Vertiv.com.

    • Data & AI
    • Digital Strategy
    • Infrastructure & Cloud

    Author

    Jon Abbott

    Published

    7 January 2026

    Estimated Read time

    5Mins

    Jon Abbott, Technologies Director of Global Strategic Clients at Vertiv, asks how we can build a generation of data centres for the AI age

    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.

    Discover more at vertiv.com

    • Data & AI
    • Digital Strategy
    • Infrastructure & Cloud

    Author

    Dan Brightmore

    Published

    6 January 2026

    Estimated Read time

    3Mins

    CoreX, a high-growth Elite Consulting and Implementation Partner of ServiceNow and NewSpring Holdings platform company, has announced the successful completion…

    CoreX, a high-growth Elite Consulting and Implementation Partner of ServiceNow and NewSpring Holdings platform company, has announced the successful completion of its acquisition of InSource’s ServiceNow business unit. InSource is a fellow Elite Partner recognised for deep delivery expertise and an unwavering commitment to client success. The transaction officially closed in late December 2025.

    This agreement unites two high-performing ServiceNow partners in the ecosystem. Together, CoreX and InSource now operate as a single, purpose-built organisation designed to scale with intent, elevate enterprise transformation outcomes, and meet the accelerating demand for AI-enabled, end-to-end ServiceNow solutions worldwide.

    InSource integration into CoreX delivering value for ServiceNoe customers

    With InSource’s 1,500+ successful implementations and a 4.76 CSAT rating, the combined organisation, more than doubling its US-based employee headcount, now operates at a level of scale and technical depth that firmly positions CoreX among the top-tier Consulting and Implementation Partners in the global ServiceNow ecosystem. The acquisition doubles the firm’s ServiceNow certifications and brings together advanced platform specialisation and a people-first culture grounded in long-term client success.

    “This is not growth for growth’s sake, but rather a strategic, deliberate move of scale,” said Rick Wright, Head of CoreX. “By fully integrating InSource into CoreX, we have created a focused consultancy built for scale, execution, and long-term value for ServiceNow customers.”

    Reflecting on the integration, Mark Lafond, former President & CEO of InSource, added, “InSource was built on delivery strength, trust, and long-term client relationships. Joining forces with CoreX allows us to take everything we do best and amplify it on a much larger stage. This is the right home for our people, the right platform for our customers, and the right partner to accelerate the next chapter of growth.”

    By unifying CoreX’s innovation roadmap and AI readiness with InSource’s long-standing operational delivery excellence, the combined organisation now offers a truly integrated model for enterprise transformation across industries. This integration enables clients to move faster from strategy to execution while maintaining the governance, resilience, and scalability required for modern enterprises.

    Just as importantly, the acquisition strengthens CoreX’s geographic footprint and delivery capacity across key global delivery hubs, including North America and Latin America, enabling the firm to serve enterprise clients with greater speed, continuity, and depth.

    “Our acquisition of InSource fundamentally changes the scale of impact we can deliver for customers,” Wright added. “CoreX is now purpose-built to lead the next era of ServiceNow-powered transformation.”

    A Unified Approach to Enterprise Transformation

    The acquisition significantly enhances CoreX’s capabilities across Strategic Portfolio Management (SPM), IT Asset Management (ITAM), IT Operations Management (ITOM), Integrated Risk Management, Operational Technology integration, and AI-ready enterprise architecture. The combined strengths allow CoreX to solve more complex, mission-critical challenges across industries, including manufacturing, healthcare, financial services, and the public sector.

    With this transaction, CoreX is now among the top global ServiceNow Elite Partners, distinguished not just by certifications or scale, but by consistent delivery of measurable, enterprise-level outcomes on the ServiceNow AI Platform.

    About CoreX

    Founded in 2023, CoreX is a global ServiceNow consultancy specialising in business-focused transformation that unlocks hidden value from the Now Platform. Backed by unmatched industry leadership, extensive functional experience, and the most seasoned ServiceNow team in the ecosystem, CoreX delivers strategic guidance and AI-enabled innovation to power sustained success. Learn more at corexcorp.com

    About NewSpring Holdings

    NewSpring Holdings, NewSpring’s majority investment strategy, focused on control buyouts and sector-specific platform builds, brings a wealth of knowledge, experience, and resources to take profitable, growing companies to the next level through acquisitions and proven organic methodologies. Founded in 1999, NewSpring partners with the innovators, makers, and operators of high-performing companies in dynamic industries to catalyze new growth and seize compelling opportunities. Having completed over 250 investments, the Firm manages approximately $3.5 billion across five distinct strategies covering the spectrum from growth equity and control buyouts to mezzanine debt. Partnering with management teams to help develop their businesses into market leaders, NewSpring identifies opportunities and builds relationships using its network of industry leaders and influencers across a wide array of operational areas and industries.

    • Data & AI
    • Digital Strategy

    Author

    Jan Van Hoecke

    Published

    5 January 2026

    Estimated Read time

    5Mins

    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

    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.

    Learn more at imanage.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Dan Brightmore

    Published

    16 December 2025

    Estimated Read time

    3Mins

    Interface issue 68 is live featuring Microsoft, Virgin Media O2, CIBC Caribbean, Telkom, Zoom, ServiceNow, Snowflake and more

    Welcome to the latest issue of Interface magazine!

    Click here to read the latest edition!

    Driving Business Transformation Through Cloud & AI

    Microsoft’s Shruti Harish, Head of Solution Engineering for Cloud and AI Platforms across the tech giant’s Manufacturing and Mobility vertical, talks to Interface about how to achieve successful AI implementations augmented by Cloud. Our future focused fireside chat covered everything from driving value through cloud modernisation to responsible AI.

    “Leaders should align AI initiatives with clear business outcomes and foster a culture that embraces change. The focus is shifting toward AI-operated, human-led models where intelligent agents handle tasks and humans guide strategy.”

    Virgin Media O2: Democratising Data as a Cultural Movement

    Mauro Flores, EVP for Data Democratisation at Virgin Media O2, talks to Interface about the leading telco’s data journey and how it is supporting colleagues to innovate faster, make smarter decisions and deliver brilliant customer experiences.

    “Data-driven insights are essential. They’re helping power our decisions like optimising our network performance, anticipating outages before they happen, identifying and preventing fraud, personalising offers and pricing to build customer loyalty, and forecasting demand so we invest in the right things.”

    CIBC Caribbean: Shaping the future of Banking in the Caribbean

    Deputy CIO Trevor Wood explains how CIBC Caribbean is blending technology, culture, and customer-centricity to deliver seamless digital experiences across the region with a ‘Future Faster’ strategy.

    “We want to lead in every market we operate, build maturity across our practices and be architects of a smarter financial future for all.”

    And read on for deep AI insights from ANS’s CIO on why AI isn’t just for big business, Emergn’s CTO on how your business can get AI-ready and Kore.ai’s Chief Strategy Officer on taming AI-sprawl with governance-first platforms.

    We also hear from Celonis, Snowflake, ServiceNow, Make and Zoom with their tech predictions for 2026 and chart the key dates for your diary with global networking opportunities at the latest tech events and conferences across the globe.

    Click here to read the latest edition!

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Payments
    • Digital Strategy
    • People & Culture

    Author

    Santo Orlando

    Published

    8 December 2025

    Estimated Read time

    5Mins

    Santo Orlando, Practice Director – App, Data and AI Services at Insight, on how your organisation can level up with Agentic AI

    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.

    Learn more at insight.com

    • Data & AI
    • Digital Strategy

    Author

    Kyle Hill

    Published

    28 November 2025

    Estimated Read time

    6Mins

    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

    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. 

          Find out more at ans.co.uk

          • Artificial Intelligence in FinTech
          • Data & AI
          • Digital Strategy

          Author

          Jalal Charaf

          Published

          20 November 2025

          Estimated Read time

          5Mins

          Jalal Charaf, Chief Digital & AI Officer of the University Mohammed VI Polytechnic (UM6P) and Managing Director of Ecole Centrale Casablanca on how Africa can seize its moment to lead on data

          In today’s world, data is not just about numbers and technology; it shapes how people live, how governments plan, and how businesses grow. It influences who gets a loan, who receives medical care, and who has access to education. That’s why control over data, called data sovereignty, is becoming one of the most important sources of power in the 21st century.

          Unfortunately, Africa is still on the margins of this new reality. Although the continent is home to over 1.4 billion people, 18% of the world’s population, it provides less than 4% of the data used to train today’s most powerful AI systems. Most African data is stored in foreign data centres, beyond the reach of African laws and courts. This is no longer just a ‘digital divide’, it’s a dependence on outside systems that don’t fully understand or represent African realities.

          What’s Holding Africa Back?

          There are several key reasons why Africa remains largely underrepresented in the global digital economy.

          First, representation. Most AI systems are built on data from outside Africa. As a result, they often misjudge or misrepresent African realities, whether it’s credit scoring, medical diagnostics, or speech recognition. The absence of African data creates blind spots that affect real lives.

          Second, infrastructure. Africa captures less than 1% of global cloud revenue and has limited data storage and processing capacity. This forces governments and businesses to rely on distant cloud providers. Outages, costs, or policy shifts in other countries can suddenly disrupt services at home.

          Third, governance. With 29 different national data protection laws, Africa lacks a unified approach to managing data. In contrast, the European Union negotiates data rules as a single bloc. Africa’s fragmented regulatory landscape makes it harder to attract investment or protect citizens’ rights.

          Momentum is Building

          Despite these challenges, there are reasons to be hopeful. Africa’s data centre market is expected to grow by 17.5% in 2025, thanks to rising digital demand and support from investors focused on environmental and social goals.

          Several major projects are already underway. Microsoft and G42 (a technology group from the UAE) are investing $1 billion in a geothermal-powered data centre in Kenya. Equinix, one of the world’s largest data infrastructure companies, plans to spend $390 million expanding into West, South, and East Africa. By the end of this year, Rwanda and Zimbabwe will join the list of countries with carrier-neutral data centres, bringing the total to 26.

          A Blueprint in Morocco

          Morocco offers a model of what digital sovereignty can look like. In June 2025, a consortium led by Nexus Core Systems announced a 500-megawatt, renewables-powered AI infrastructure project on the Atlantic coast. Phase one, with 40 MW of NVIDIA’s Blackwell AI chips, will go live in early 2026, exporting compute power across Europe, the Middle East, and Africa.

          Critically, this infrastructure is under Moroccan jurisdiction, not subject to U.S. laws like the CLOUD Act. The project proves that African countries can host cutting-edge data systems while protecting their own legal and strategic interests.

          How Africa Can Lead

          To turn early momentum into lasting sovereignty, African governments, institutions, and partners must work together across four pillars:

          • Data creation and curation. Countries should invest at least 1% of GDP in digital public infrastructure, such as national ID systems, crop mapping satellites, and open data portals. These systems ensure that African data reflects African lives.
          • Compute and storage. Regions with access to renewable energy can build local ‘green AI corridors’ linked by neutral internet exchanges. This keeps data close to where it’s generated and cuts dependence on foreign servers.
          • Policy and regulation. The African Union should lead a continent-wide Data Sovereignty Compact, a framework to harmonise data protection, localisation, and AI ethics. A unified legal environment will attract investment and support responsible innovation.
          • Talent and research. African universities and public agencies should develop homegrown AI talent. Governments can require that models trained on African data are hosted locally. Research must be rooted in African languages, priorities, and realities, not just imported standards.

          A Role for Everyone: From Governments to Global Partners

          Governments should commit at least 10% of their ICT budgets to data sovereignty and adopt AU-wide standards. Local cloud facilities and fibre infrastructure deserve long-term funding, not just short-term pilots.

          Private industry must shift from short-lived cloud credits to permanent, on-the-ground investment. Companies should publish annual data localisation reports and follow the example set by Nexus Core Systems.

          Development finance institutions (DFIs) should support 20-year infrastructure partnerships, not just one-off tech grants. According to the Global Partnership for Sustainable Development Data, every $1 invested in data systems brings $32 in economic return. That’s a smart investment.

          Universities, civil society groups, and non-profits also have a responsibility. Open data repositories, civic tech labs, and ethical data governance initiatives must be scaled up to support innovation that’s inclusive and local.

          A Strategic Opportunity: OpenAI for Countries

          OpenAI has recently launched an initiative called OpenAI for Countries, designed to help governments build local data centres, train AI systems in national languages, and support start-ups in their own ecosystems. The program is looking for ten partner countries in its first phase. This initiative aligns well with Africa’s goals for sovereign data and democratic AI development.

          Africa’s Moment to Lead on Data

          Africa has everything it needs to become a global leader in digital intelligence. Its young population, growing tech talent, and renewable energy potential are powerful advantages. But sovereignty will not be handed over, it must be built.

          We must act now, before the rules of the digital world are written without us. Morocco’s Nexus Core project shows what’s possible when ambition meets action. It’s time for the rest of the continent to follow suit, and shape a future where Africa owns its data, tells its stories, and sets its own course.

          • Data & AI
          • Digital Strategy

          Author

          Cathal McCarthy

          Published

          10 November 2025

          Estimated Read time

          4Mins

          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

          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.

          • Data & AI
          • Digital Strategy

          Author

          Dan Brightmore

          Published

          5 November 2025

          Estimated Read time

          3Mins

          Read in brochure format

          Welcome to the latest issue of Interface magazine! Click here to read the latest edition! USDA: A Fresh Perspective on…

          Welcome to the latest issue of Interface magazine!

          Click here to read the latest edition!

          USDA: A Fresh Perspective on Digital Service

          This month’s cover story focuses on the digital transformation journey continuing at the United States Department of Agriculture (USDA). In conversation with Fátima Terry, USDA’s former Digital Service Deputy Director, we revisit the sterling work being carried out and find out how technology is being humanised to deliver value to the American people this organisation serves.

          “One of the things we did was partner with multiple USDA teams that focused on customer experience and digital service delivery for their programs,” she explains. “We also partnered with other federal-wide agencies and departments to move forward and evaluate the progress of digital transformation by cross-pollinating success models to everyone connected.”

          Ayoba: A Super-App for Africa

          Ayoba, part of the MTN telco group, is a super-app platform built in Africa, for Africa. Esat Belhan, Chief Technology & Product Officer, reveals how it is bringing more people to digital so they can be tech-savvy and educated on digital capabilities…

          “In order to do that, one thing you could do is give away free data, but that data could be easily wasted on another data-heavy app, like TikTok, in just a couple of hours. So, the real solution is that the valuable and insightful content Ayoba provides should be provided for free, and that we provide instant messaging and short video content, to keep people using our platform for their communication and entertainment needs.”

          Kraft Kennedy: Supporting MSPs with People and Processes

          Nett Lynch, CISO at Kraft Kennedy, explains how the company’s new division, Legion, solves cyber pain-points for MSPs with a collaborative, business-centred approach.

          “A lot of MSPs struggle with client strategy, they’re talking tech instead of business. We’re nerds – we love the tech, we love the features. But we need to admit clients aren’t focused on those things. They don’t necessarily care how or why it works. They just want it to work and align to their business goals.”

          And read on to hear from FICO’s CIO on using AI to transform technical operations; learn from KnowBe4 how AI Agents will be a game changer for tackling cybercrime; and discover how data centres are meeting the demands of the AI boom with Vertiv.

          Click here to read the latest edition!

          • Data & AI
          • Digital Strategy
          • Infrastructure & Cloud
          • People & Culture

          Author

          Dan Brightmore

          Published

          Estimated Read time

          5Mins

          Interface hears from Emergn CTO Fredrik Hagstroem on approaches to AI best practice that can drive positive business transformations

          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.”

          Find out more from Emergn

          • Data & AI
          • People & Culture

          Author

          Ribert Cottrill

          Published

          25 September 2025

          Estimated Read time

          4Mins

          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

          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

          So, it’s no surprise that our research found that data privacy is the top concern for businesses when adopting AI. As these threats evolve, businesses must treat AI not just as an enabler, but also as a potential vector for attack.

          The Governance Gap

          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.

          • Cybersecurity
          • Data & AI

          Author

          Anna Collard

          Published

          18 September 2025

          Estimated Read time

          8Mins

          Anna Collard, SVP Content Strategy & Evangelist KnowBe4 – Africa, on leveraging AI-driven cybersecurity systems to fight cybercrime

          Artificial Intelligence is no longer just a tool. It is a game-changer in our lives, our work as well as in both cybersecurity and cybercrime. While businesses leverage AI to enhance defences, cybercriminals are weaponising AI to make these attacks more scalable and convincing​.  

          In 2025, research shows AI agents, or autonomous AI-driven systems capable of performing complex tasks with minimal human input, are revolutionising both cyberattacks and cybersecurity defences. While AI-powered chatbots have been around for a while, AI agents go beyond simple assistants. They function as self-learning digital operatives that plan, execute, and adapt in real time. These advancements don’t just enhance cybercriminal tactics, they may fundamentally change the cybersecurity battlefield. 

          How Cybercriminals Are Weaponising AI: The New Threat Landscape 

          AI is transforming cybercrime, making attacks more scalable, efficient, and accessible. The WEF Artificial Intelligence and Cybersecurity Report (2025) highlights how AI has democratised cyber threats. Thus enabling attackers to automate social engineering, expand phishing campaigns, and develop AI-driven malware​. Similarly, the Orange Cyberdefense Security Navigator 2025 warns of AI-powered cyber extortion, deepfake fraud, and adversarial AI techniques. And the 2025 State of Malware Report by Malwarebytes notes, while GenAI has enhanced cybercrime efficiency, it hasn’t yet introduced entirely new attack methods. Attackers still rely on phishing, social engineering, and cyber extortion, now amplified by AI. However, this is set to change with the rise of AI agents. Autonomous AI systems are capable of planning, acting, and executing complex tasks—posing major implications for the future of cybercrime. 

          Here is a list of common (ab)use cases of AI by cybercriminals:  

          AI-Generated Phishing & Social Engineering 

          Generative AI and large language models (LLMs) enable cybercriminals to craft more believable and sophisticated phishing emails in multiple languages. Without the usual red flags like poor grammar or spelling mistakes. AI-driven spear phishing now allows criminals to personalise scams at scale, automatically adjusting messages based on a target’s online activity. AI-powered Business Email Compromise (BEC) scams are increasing. Attackers use AI-generated phishing emails sent from compromised internal accounts to enhance credibility​. AI also automates the creation of fake phishing websites, watering hole attacks and chatbot scams. These are sold as AI-powered ‘crimeware as a service’ offerings, further lowering the barrier to entry for cybercrime​. 

          Deepfake-Enhanced Fraud & Impersonation 

          Deepfake audio and video scams are being used to impersonate business executives, co-workers or family members to manipulate victims into transferring money or revealing sensitive data. The most famous 2024 incident was UK based engineering firm Arup that lost $25 million after one of their Hong Kong based employees was tricked by deepfake executives in a video call. Attackers are also using deepfake voice technology to impersonate distressed relatives or executives, demanding urgent financial transactions.  

          Cognitive Attacks  

          Online manipulation—as defined by Susser et al. (2018)—is “at its core, hidden influence, the covert subversion of another person’s decision-making power”. AI-driven cognitive attacks are rapidly expanding the scope of online manipulation. By everaging digital platforms, state-sponsored actors increasingly use generative AI to craft hyper-realistic fake content. They are subtly shaping public perception while evading detection. These tactics are deployed to influence elections, spread disinformation and erode trust in democratic institutions. Unlike conventional cyberattacks, cognitive attacks don’t just compromise systems—they manipulate minds, subtly steering behaviours and beliefs over time without the target’s awareness. The integration of AI into disinformation campaigns dramatically increases the scale and precision of these threats, making them harder to detect and counter.  

          The Security Risks of LLM Adoption 

          Beyond misuse by threat actors, business adoption of AI-chatbots and LLMs introduces significant security risks. Especially when untested AI interfaces connect the open internet to critical backend systems or sensitive data. Poorly integrated AI systems can be exploited by adversaries. This enables new attack vectors, including prompt injection, content evasion, and denial-of-service attacks. Multimodal AI expands these risks further, allowing hidden malicious commands in images or audio to manipulate outputs.  

          Moreover, many modern LLMs now function as Retrieval-Augmented Generation (RAG) systems. Dynamically pulling in real-time data from external sources to enhance their responses. While this improves accuracy and relevance, it also introduces additional risks, such as data poisoning, misinformation propagation, and increased exposure to external attack surfaces. A compromised or manipulated source can directly influence AI-generated outputs. Potentially leading to incorrect, biased, or even harmful recommendations in business-critical applications. 

          Additionally, bias within LLMs poses another challenge. These models learn from vast datasets that may contain skewed, outdated, or harmful biases. This can lead to misleading outputs, discriminatory decision-making, or security misjudgements, potentially exacerbating vulnerabilities rather than mitigating them. As LLM adoption grows, rigorous security testing, bias auditing, and risk assessment, especially in RAG-powered models, are essential to prevent exploitation and ensure trustworthy, unbiased AI-driven decision-making. 

          When AI Goes Rogue: The Dangers of Autonomous Agents 

          With AI systems now capable of self-replication, as demonstrated in a recent study, the risk of uncontrolled AI propagation or rogue AI – AI systems that act against the interests of their creators, users, or humanity at large – is growing. Security and AI researchers have raised concerns that these rogue systems can arise either accidentally or maliciously. Particularly when autonomous AI agents are granted access to data, APIs, and external integrations. The broader an AI’s reach through integrations and automation, the greater the potential threat of it going rogue. This means robust oversight, security measures, and ethical AI governance essential in mitigating these risks. 

          The Future of AI Agents for Automation in Cybercrime 

          A more disruptive shift in cybercrime can and will come from AI Agents. These transform AI from a passive assistant into an autonomous actor capable of planning and executing complex attacks. Google, Amazon, Meta, Microsoft, and Salesforce are already developing Agentic AI for business use. However, in the hands of cybercriminals, its implications are alarming. These AI agents can be used to autonomously scan for vulnerabilities, exploit security weaknesses, and execute cyberattacks at scale. They can also allow attackers to scrape massive amounts of personal data from social media platforms. They can automatically compose and send fake executive requests to employees. And, for example, analyse divorce records across multiple countries to identify individuals for AI-driven romance scams, orchestrated by an AI agent. These AI-driven fraud tactics don’t just scale attacks, they make them more personalised and harder to detect. Unlike current GenAI threats, Agentic AI has the potential to automate entire cybercrime operations, significantly amplifying the risk​. 

          How Defenders Can Use AI & AI Agents 

          Organisations cannot afford to remain passive in the face of AI-driven threats. Security professionals need to remain abreast of the latest developments. Here are some of the  opportunities in using AI to defend against AI:  

          AI-Powered Threat Detection and Response

          Security teams can deploy AI and AI-agents to monitor networks in real time, identify anomalies, and respond to threats faster than human analysts can. AI-driven security platforms can automatically correlate vast amounts of data to detect subtle attack patterns. These might otherwise go unnoticed. AI can create dynamic threat modelling, real-time network behaviour analysis, and deep anomaly detection​. For example, as outlined by researchers of Orange Cyber Defense, AI-assisted threat detection is crucial as attackers increasingly use “Living off the Land” (LOL) techniques that mimic normal user behaviour. Making it harder for detection teams to separate real threats from benign activity. By analysing repetitive requests and unusual traffic patterns, AI-driven systems can quickly identify anomalies and trigger real-time alerts, allowing for faster defensive responses. 

          However, despite the potential of AI-agents, human analysts still remain critical. Their intuition and adaptability are essential for recognising nuanced attack patterns. They can leverage real incident and organisational insights to prioritise resources effectively. 

          Automated Phishing and Fraud Prevention

          AI-powered email security solutions can analyse linguistic patterns, and metadata to identify AI-generated phishing attempts before they reach employees, by analysing writing patterns and behavioural anomalies. AI can also flag unusual sender behaviour and improve detection of BEC attacks​. Similarly, detection algorithms can help verify the authenticity of communications and prevent impersonation scams. AI-powered biometric and audio analysis tools detect deepfake media by identifying voice and video inconsistencies. However, real-time deepfake detection remains a challenge, as technology continues to evolve. 

          User Education & AI-Powered Security Awareness Training

          AI-powered platforms deliver personalised security awareness training. They can simulate AI-generated attacks to educate users on evolving threats, helping train employees to recognise deceptive AI-generated content​. And strengthen their individual susceptibility factors and vulnerabilities.  

          Adversarial AI Countermeasures

          Just as cybercriminals use AI to bypass security, defenders can employ adversarial AI techniques. For example, deploying deception technologies – such as AI-generated honeypots – to mislead and track attackers. As well as continuously training defensive AI models to recognise and counteract evolving attack patterns. 

          Using AI to Fight AI-Driven Misinformation and Scams

          AI-powered tools can detect synthetic text and deepfake misinformation, assisting fact-checking and source validation. Fraud detection models can analyse news sources, financial transactions, and AI-generated media to flag manipulation attempts​. Counter-attacks, like those shown by research project Countercloud or O2 Telecoms AI agent “Daisy” show how AI based bots and deepfake real-time voice chatbots can be used to counter disinformation campaigns as well as scammers by engaging them in endless conversations to waste their time and reducing their ability to target real victims​. 

          In a future where both attackers and defenders use AI, defenders need to be aware of how adversarial AI operates. And how AI can be used to defend against their attacks. In this fast-paced environment, organisations need to guard against their greatest enemy: their own complacency. While at the same time considering AI-driven security solutions thoughtfully and deliberately. Rather than rushing to adopt the next shiny AI security tool, decision makers should carefully evaluate AI-powered defences to ensure they match the sophistication of emerging AI threats. Hastily deploying AI without strategic risk assessment could introduce new vulnerabilities, making a mindful, measured approach essential in securing the future of cybersecurity.  

          To stay ahead in this AI-powered digital arms race, organisations should:  

          • Monitor both the threat and AI landscape to stay abreast of latest developments on both sides. 
          • Train employees frequently on latest AI-driven threats, including deepfakes and AI-generated phishing. 
          • Deploy AI for proactive cyber defense, including threat intelligence and incident response. 
          • Continuously test your own AI models against adversarial attacks to ensure resilience. 
          • Cybersecurity
          • Data & AI

          Author

          Dan Brightmore

          Published

          15 September 2025

          Estimated Read time

          6Mins

          Enterprise-wide AI platform security protects sensitive data and governs integrations to help organisations scale Agentic AI with confidence

          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 Mining insights 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. 

          All features announced as part of the ServiceNow AI Platform Zurich release are generally available and can be found in the ServiceNow Store

          • Data & AI
          • Digital Strategy

          Author

          Dan Brightmore

          Published

          4 September 2025

          Estimated Read time

          3Mins

          TechEX Europe – Powering the Future of
          Enterprise Technology at Amsterdam’s RAI Arena September 24-25

          TechEx Europe unites five leading enterprise technology events — AI & Big Data, Cyber Security, Data Centres, Digital Transformation and IoT — into one powerful experience designed for organisations driving change. Five events, two days, one ticket – register for your pass here.

          From scaling infrastructure to unlocking new efficiencies, this is where decision-makers and their teams come to connect, explore real-world use cases, and discover the technologies that will shape their next phase of growth.

          AI & Big Data Expo

          The AI & Big Data Expo is the premier event showcasing Generative AI, Enterprise AI, Machine Learning, Security, Ethical AI, Deep Learning, Data Ecosystems, and NLP

          Speakers include:

          Cybersecurity & Cloud Expo

          The Cyber Security & Cloud Expo, is the premier event showcasing the latest in Application and Cloud Security, Hybrid Cloud, Data Protection, Identity and Access Management, Network and Infrastructure Defence, Risk and Compliance, Threat Intelligence,  DevSecOps Integration, and more. Join industry leaders to explore strategies, tools, and innovations shaping the future of secure, connected enterprises.

          Speakers include:

          IOT Tech Expo

          IoT Tech Expo is the leading event for IoT, Digital Twins & Enterprise Transformation, IoT Security, IoT Connectivity & Connected Devices, Smart Infrastructures & Automation, Data & Analytics and Edge Platforms.

          Speakers include:

          Digital Transformation

          The Digital Transformation Expo is the leading event for Transformation Infrastructure, Hybrid Cloud, The Future of Work, Employee Experience, Automation, and Sustainability.

          Speakers include:

          Data Center Expo

          The Data Centre Expo and conference is the premier event tackling key challenges in data centre innovation. It highlights AI’s Impact, Energy Efficiency, Future-Proofing, Infrastructure & Operations, and Security & Resilience, showcasing advancements shaping the future of data centre. 

          Speakers include:

          Book your place at TechEx Europe 2025 now!

          • Cybersecurity
          • Data & AI
          • Digital Strategy
          • Event Newsroom
          • Events
          • Infrastructure & Cloud

          Author

          Dan Brightmore

          Published

          1 September 2025

          Estimated Read time

          2Mins

          Join thousands of data centre industry leaders and innovators at London’s Business Design Centre for three co-located events – DCD>Connect, DCD>Compute and DCD>Investment September 16-17

          Data Center Dynamics (DCD) is connecting the data center ecosystem. Secure your pass for three-colocated events covering the entire digital infrastructure ecosystem across two days at London’s Business Design Centre – DCD>Connect, DCD>Compute and DCD>Investment.

          DCD Connect

          Connecting the data center ecosystem to design, build & operate sustainable data centers for the AI age…

          Bringing together more than 4,000 senior leaders working on Europe’s largest data center projects. DCD>Connect | London will drive industry collaboration, help you forge new partnerships and identify innovative solutions to your core challenges.

          “First class event that presented a wide variety of perspectives and technologies in an engaging and informative forum” – Data Center Project Architect, AWS

          DCD Compute

          Uniting enterprise and hyperscale leaders driving scalable AI Infrastructure from silicon to software…

          New workloads are fundamentally reshaping IT infrastructure, as accelerated hardware innovation is enabling more new workloads. How can you keep up in this rapid cycle of new AI models, new hardware, new software, and the race to be first to market?

          The Compute event series, run in partnership with SDxCentral, empowers leaders to make sharp decisions on IT infrastructure and AI deployment. Join 400+ peers from enterprise, hyperscale, and top IT infrastructure and architecture innovators to shape the future of compute—on-prem or in the cloud.

          • 400+ Decision-Makers for IT Infrastructure, Architecture, AI, HPC and Quantum Computing
          • 60+ industry-leading speakers at the forefront of innovation across cloud and on-prem compute
          • Hosted in partnership with SDxCentral

          DCD Investment

          Connecting senior dealmakers driving the economic evolution of digital infrastructure…

          The world depends on digital infrastructure, and there’s never been more pressure on the industry to scale at speed. The Data Center Dynamics Investment series helps the leading dealmakers behind this growth to make informed decisions faster, through top-tier content, tailored networking, and best-practice sharing.

          • Dynamic Programme: A brand new format including leadership roundtable discussions allows for 2025 attendees craft their own agenda at the Forum.
          • 50 Speakers: The C-suite operators, leading investors, and advisors in data centers are converging to strategize on the industry’s evolving landscape.
          • Exclusive Networking Opportunities: The Investment Forum is separated from the main DCD Connect programme and show floor, offering private networking and dealmaking opportunities to take place in an optimal setting.

          Secure your pass for three-colocated events September 16-17 – DCD>Connect, DCD>Compute and DCD>Investment.

          • Cybersecurity
          • Data & AI
          • Digital Strategy
          • Event Newsroom
          • Events
          • Fintech & Insurtech

          Author

          Dan Brightmore

          Published

          28 August 2025

          Estimated Read time

          5Mins

          Read in brochure format

          This month’s cover star, Dr. Noxolo Kubheka-Dlamini – Chief Digital and Information Officer at Telkom Consumer & Small Business, speaks to the process of leading an ongoing digital transformation

          Welcome to the latest issue of Interface magazine!

          Click here to read the latest edition!

          Telkom: More Than a Telco

          Our cover star talks us through the process of leading an ongoing digital transformation that is pragmatic, strategic and embedded in business goals at South Africa’s largest telecommunications platform provider. “By the time we entered the mobile space in 2010, the market was already saturated,” explains Dr. Noxolo Kubheka-Dlamini, Chief Digital & Information Officer at Telkom Consumer & Small Business. “Our ambitions were constrained by limited capital, inherited legacy systems, regulatory shackles, and the sheer inertia of being a former state-run monopoly.” However, Telkom’s “willpower and commitment never faded” resulting in “notable and consistent performance against all odds”. Today, Telkom is playing a pivotal role in ensuring access to meaningful connectivity, driven by the company’s vision to become South Africa’s digital backbone: bridging the digital divide and enabling inclusive participation in its digital economy.

          Kynegos: Shining a Spotlight on Transformation, Innovation and Sustainability

          Kynegos, a spin-off from Capital Energy, is a business built on strategy. It exists to develop technological solutions for strategic industries. Capital Energy needed an independent platform that could scale digital solutions beyond the energy sector, and foster collaboration with startups and technology centres. Kynegos has filled this gap, and is being leveraged to create co-innovation ecosystems. This allows Capital Energy to develop digital tools that address current and future industrial challenges, keeping the company’s finger on the pulse. We spoke to CEO Victor Gimeno Granda, about its backstory, its values, and the road ahead. “Not only do we develop digital assets for the renewable sector, but for green data centres as well. My perspective is that sustainability is going to be more relevant than ever in the next 18 months.”

          York County: The Human Side of AI

          York County’s IT team has spent the past decade redefining what local government tech can and should be. From pioneering community cybersecurity workshops to forging statewide collaboration through ValGITE, the county has systematically brought innovation into its operations. This broad portfolio of initiatives has strengthened infrastructure and elevated service delivery. And also earned York County the number one spot in the Digital Counties Survey for jurisdictions under 150,000 population.

          “Since I became deputy director eight years ago, this has been one of my goals,” reflects Tim Wyatt, director of information technology at York County. “And over the last eight years, we’ve been in the top 10, but we finally landed that number one place. I think it’s a great reflection for my team, the county, and all the dedication to try to do what’s right by the citizens. It’s just something I’m incredibly proud of. I think it accurately reflects the hard work of my team.”

          Wade Trim: Bridging the Cybersecurity Skills Gap

          Wade Trim provides consulting engineering, planning, surveying, landscape architecture and environmental science services to meet the infrastructure needs of government and private corporations. With a cybersecurity skills gap leaving vacancies unfilled, Wade Trim’s Senior Manager of Information Security, Eric Miller, spoke with Interface about how stepping away from education-focused rigidity could unlock swathes of latent talent. “Our industry puts emphasis on certifications. However, being passed over for jobs because you don’t have a particular certification or degree in favour of someone fresh out of college has shown me that the best candidates are those that can tell me their story. What brings them to this point in their career? Tell me what qualifies you for this role. That’s how I interview.”

          York Catholic District School Board: York Catholic District School Board: Community and Communication at the Heart of IT Strategy

          The challenges facing an IT leader in 2025 call for a new kind of approach. One that favours partnerships over transactions, collaboration over competition, and centres people rather than technology for technology’s sake. These perspectives ring especially true in an organisation like the York Catholic District School Board (YCDSB). It emphasises values like “service, community, collaboration, and fait rather than academic excellence alone,” explains Scott Morrow, YCDSB’s Chief Information Officer (CIO). “It’s not actually about the technology; it’s about enablement.”

          We spoke with Morrow to learn more about his approach to IT leadership. From building and maintaining a team amid the IT talent crisis, to driving digital transformation initiatives across the organisation. And broader strategic objectives across a changing technology landscape increasingly defined by cybersecurity and the rise of AI.   

          Click here to read the latest edition!

          • Cybersecurity
          • Data & AI
          • Digital Strategy
          • People & Culture

          Author

          Dan Brightmore

          Published

          30 April 2025

          Estimated Read time

          2Mins

          Read in brochure format

          Deepak Parameswaran, Sector Head – Energy, Manufacturing & Resources at Wipro, talks innovation with National Grid’s Global Head of Data Strategy Andrew Burns

          Partners for over 25 years, Wipro and National Grid have been laying the foundation for progress… By taking data to the cloud, creating value and leveraging their common work to deliver advanced, data-driven innovations across the National Grid enterprise.

          Meeting the transformation challenge

          As a utility, National Grid seeks to provide safe, affordable, and reliable electric and natural gas service for its customers. As such, the company is hyper-focused on natural gas, electricity grid modernisation, customer satisfaction and the integration of business and technology processes across the entire business as gas and electricity demand increases across the markets. Wipro offers actionable solutions, providing the innovative technology and domain expertise necessary for organisations like National Grid to transform and become leaders in sustainability within their respective industries.

          Delivering bespoke solutions for Innovation

          Traditional utility technologies can pose challenges in terms of complexity and capital investment. With Cloud and AI technologies emerging as game changers, Wipro delivers a proven ecosystem, incorporating analytics, IoT, Generative AI, and Augmented Reality, tailored to meet the needs of customers, assets, and grid management. This makes for easier, scalable, and faster to market solutions that allow National Grid to quickly realise the benefits.
          Wipro’s Utility Enterprise solutions have delivered on key elements of the digital transformation journey at National Grid. This allows for a constant data presence across the globe, creating a common, secure cloud environment.

          Wipro’s partnership with National Grid

          Wipro’s collaboration with National Grid continues to be built on a foundation of continuous innovation, with a commitment to:

          • Staying ahead of utility business trends
          • Supporting National Grid’s clean energy transition
          • Developing sophisticated data and AI solutions for enhanced customer service
          • Maintaining agility to address emerging challenges

          “Wipro has been our biggest partner in executing use cases through the Innovation Lab, enabling us to be agile and deliver multiple projects with direct, tangible business benefits. Their support has been vital in ensuring a clear, efficient process and rapid execution, making them key to our success.”

          Andrew Burns, Global Head of Data Strategy, National Grid

          Click here to read more about National Grid’s Innovation story

          • Data & AI
          • Digital Strategy
          • People & Culture

          Author

          Dan Brightmore

          Published

          26 February 2025

          Estimated Read time

          2Mins

          Tech Show London is coming to Excel March 12-13. Register for your free ticket now!

          Unlock unparalleled value with a single ticket that gets you free access to five industry-leading technology shows. Welcome to Cloud & AI Infrastructure, DevOps Live, Cloud & Cyber Security Expo, Big Data & AI World, and Data Centre World.

          Tech Show London has it all. Don’t miss this immersive journey into the latest trends and innovations.

          Discover tomorrow’s tech today

          Unleash Potential, Embrace the Future. Hear from the greatest tech minds, all in one place.

          Dive into a world where cutting-edge ideas shape your tomorrow. Tech Show London is the epicentre of technology innovation in London and beyond, hosting the brightest minds in technology, AI, cyber security, DevOps, and cloud all under one roof.

          The Mainstage Theatre is not just a stage; it’s a launchpad for innovative ideas. Witness a stellar lineup featuring world-renowned experts from across the tech stack, influential C-level executives, key government figures, and the vanguards of AI and cybersecurity. All ready to share ideas set to rock the industry.

          GLOBAL INSPIRATION, LOCAL IMPACT

          Seize the opportunity to be inspired by global visionaries. Furthermore, with speakers from the UK, USA, and beyond, prepare to be inspired by transformative concepts and actionable strategies from technology insiders, ensuring your business stays ahead in an ever-evolving technology landscape.

          Where the future of technology takes the stage

          Secure your competitive edge at Tech Show London, the UK’s award-winning convergence of the industry’s brightest tech minds.

          On 12-13 March 2025, gain vital foresight into the disruptive technologies reshaping your market, and position your organisation at the forefront of technology’s next frontier.

          If you’re defining your business’s tech roadmap, register for your free ticket to join us at Excel London.

          Register for FREE

          Register for your Ticket

          • Cybersecurity
          • Data & AI
          • Digital Strategy
          • Event Newsroom
          • Infrastructure & Cloud

          Author

          Dan Brightmore

          Published

          21 February 2025

          Estimated Read time

          4Mins

          Read in brochure format

          February’s cover story spotlights a customer-centric vision and a culture of innovation putting NatWest at the heart of the Open…

          February’s cover story spotlights a customer-centric vision and a culture of innovation putting NatWest at the heart of the Open Banking revolution

          Welcome to the latest issue of Interface magazine!

          Read the latest issue here!

          NatWest: Banking open for all

          Head of Group Payment Strategy, Lee McNabb, explains how a customer-centric vision, allied with a culture of innovation, is positioning NatWest at the heart of UK plc’s Open Banking revolution: “The market we live in is largely digital, but we have to be where customers are and meet their needs where they want them to be met. That could be in physical locations, through our app, or that could be leveraging the data we have to give them better bespoke insights. The important thing is balance… At NatWest, we’ll keep pushing the envelope on payments for a clear view of the bigger picture with banking that’s open for everyone.”

          EBRD: People, Purpose & Technology

          We speak with the European Bank for Reconstruction & Development’s Managing Director for Information Technology, Subhash Chandra Jose. With the help of Hexaware’s innovation, his team are delivering a transformation programme to support the bank’s global investment efforts: “The sweet spot for EBRD is a triangular union of purpose, people, and technology all coming together. This gives me energy to do something innovative every day to positively impact my team and our work for the organisation across our countries of operation. Ultimately, if we don’t get the technology basics right, we can’t best utilise the funds we have to make a real difference across the bank’s global efforts.”

          Begbies Traynor Group: A strategic approach to digital transformation

          We learn how Begbies Traynor Group is taking a strategic approach to digital transformation… Group CIO Andy Harper talks to Interface about building cultural consensus, innovation, addressing tech debt and scaling with AI: “My approach to IT leadership involves creating enough headroom to handle transformation while keeping the lights on.”

          University of Cinicinnati: Where innovation comes to life

          Bharath Prabhakaran, Chief Digital Officer and Vice President at the University of Cincinnati (UC), on technology, innovation and impact, and how a passion for education underpins his team’s work. “The foundation of any digital transformation in my opinion is people, process, technology – in that order,” he states. “People and culture are always the most challenging areas to evolve because you’re changing mindset and behaviour; process comes a close second as in most organisations people are wedded to legacy ways of working. In some respects, technology is the easy part, you always implement the tools but they’ll not be effective if you don’t have the right people and processes.”

          IT: A personal career retrospective

          It’s fascinating, looking back at something as complex and profoundly impactful as IT. And for Claudé Zamboni, who is preparing to retire after over 40 years in the sector, it’s been an incredible time to be deeply involved in technology. “There have been monumental changes from when I first entered IT, where it was basically a black box,” says Zamboni. “People didn’t know what the IT team was doing, and those in IT would just handle problems without telling anyone how. It only started to become more egalitarian when the internet got more pervasive. We realised that with information being available everywhere, we would lose the centralisation function of IT. But that was okay, because data is universal.”

          Read the latest issue here!

          • Cybersecurity
          • Data & AI
          • Digital Strategy
          • Fintech & Insurtech

          Author

          Dan Brightmore

          Published

          20 November 2024

          Estimated Read time

          5Mins

          Read in brochure format

          This month’s cover story throws the spotlight on the ground-up technology transformation journey at Lanes Group – a leading water…

          This month’s cover story throws the spotlight on the ground-up technology transformation journey at Lanes Group – a leading water and wastewater solutions and services provider in the UK.

          Welcome to the latest issue of Interface magazine!

          Read the latest issue here!

          Lanes Group: A Ground-Up Tech Transformation

          In a world driven by transformation, it’s rare a leader gets the opportunity to deliver organisational change in its purest form… Lanes Group – the leading water and wastewater solutions services provider – has started again from the ground up with IT Director Mo Dawood at the helm.

          “I’ve always focused on transformation,” he reflects. “Particularly around how we make things better, more efficient, or more effective for the business and its people. The end-user journey is crucial. So many times you see organisations thinking they can buy the best tech and systems, plug them in, and they’ve solved the problem. You have to understand the business, the technology side, and the people in equal measure. It’s core to any transformation.”

          Mo’s roadmap for transformation centred on four key areas: HR and payroll, management of the group’s vehicle fleet, migrating to a new ERP system, and health and safety. “People were first,” he comments. “Getting everyone on the same HR and payroll system would enable the HR department to transition, helping us have a greater understanding of where we were as a business and providing a single point of information for who we employ and how we need to grow.”

          Schneider Electric: End-to-End Supply Chain Cybersecurity

          Schneider Electric provides energy and digital automation and industrial IoT solutions for customers in homes, buildings, industries, and critical infrastructure. The company serves 16 critical sectors. It has a vast digital footprint spanning the globe, presenting a complex and ever-evolving risk landscape and attack surface. Cybersecurity, product security and data protection, and a robust and protected end-to-end supply chain for software, hardware, and firmware are fundamental to its business.

          “From a critical infrastructure perspective, one of the big challenges is that the defence posture of the base can vary,” says Cassie Crossley, VP, Supply Chain Security, Cybersecurity & Product Security Office.

          “We believe in something called ‘secure by operations’, which is similar to a cloud shared responsibility model. Nation state and malicious actors are looking for open and available devices on networks. Operational technology and systems that are not built with defence at the core and not normally intended to be internet facing. The fact these products are out there and not behind a DMZ network to add an extra layer of security presents a big risk. It essentially means companies are accidentally exposing their networks. To mitigate this we work with the Department of Energy, CISA, other global agencies, and Internet Service Providers (ISPs). Through our initiative we identify customers inadvertently doing this we inform them and provide information on the risk.”

          Persimmon Homes: Digital Innovation in Construction

          As an experienced FTSE100 Group CIO who has enabled transformation some of the UK’s largest organisations, Persimmon Homes‘ Paul Coby knows a thing or two about what it takes to be a successful CIO. Fifty things, to be precise. Like the importance of bridging the gap between technology and business priorities, and how all IT projects must be business projects. That IT is a team sport, that communication is essential to deliver meaningful change – and that people matter more than technology. And that if you’re not scared sometimes, you’re not really understanding what being the CIO is.

          “There’s no such thing as an IT strategy; instead, IT is an integral part of the business strategy”

          WCDSB: Empowering learning through technology innovation

          ‘Tech for good’, or ‘tech with purpose’. Both liberally used phrases across numerous industries and sectors today. But few purposes are greater than providing the tools, technology, and innovations essential for guiding children on their educational journey. Meanwhile, also supporting the many people who play a crucial role in helping learners along the way. Chris Demers and his IT Services Department team at the Waterloo Catholic District School Board (WCDSB) have the privilege of delivering on this kind of purpose day in, day out. A mission they neatly summarise as ‘empower, innovate, and foster success’. 

          “The Strategic Plan projects out five years across four areas,” Demers explains. “It addresses endpoint devices, connectivity and security as dictated by business and academic needs. We focus on infrastructure, bandwidth, backbone networks, wifi, security, network segmentation, firewall infrastructure, and cloud services. Process improvement includes areas like records retention, automated workflows, student data systems, parent portals, and administrative systems. We’re fully focused on staff development and support.”

          Read the latest issue here!

          • Data & AI
          • Digital Strategy
          • People & Culture