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

Chris Gunner

Published

4 March 2026

Estimated Read time

4Mins

Chris Gunner, vCSO at Thrive – a leading NextGen MSP/MSSP, delivering global AI, cybersecurity, cloud, compliance, and digital transformation managed services – on how CISOs can position their cyber strategy to to become part of how a business navigates uncertainty

Quantification of cyber risk is a growing trend. While this can be genuinely useful, in practice it is often misunderstood or over-applied by security leaders. It can range from an arbitrary figure to attempting to model every possible risk on the register in a Monte Carlo simulation. The focus can fall on the mechanics of quantification, rather than how financial decision-makers actually use the information.

Think of the CFO – they don’t walk through every penny in the budget. Instead, they usually focus on the board-level levers that can materially affect the business. These often include three key areas: strategic optionality, removing friction from capital events and avoiding shocks and smoothing operating costs. Security conversations should be anchored the same way.

The Importance of Strategic Optionality

If faced with a credible one-year growth plan, CFOs may recommend a one-year office lease despite a 20% premium. This is because it maintains the option later of moving or re-contracting once the growth trajectory becomes more visible. Like most strategic decisions, it is about preserving flexibility in the face of uncertainty, even if that flexibility comes at a short-term cost.

If we apply this to a cyber context, there are often businesses that have taken a calculated gamble with their existing business strategies. While the plan is sound, there is a chance it might not land as expected. When they require security services, the choice between a ‘standard’ and ‘premium’ SOC frames the decision as one of optionality rather than security spend. Paying more now to preserve the ability to adapt later down the line. A simple illustration is incident response. An on-call retainer with defined response times can look more expensive than ad hoc support. Until an incident occurs and procurement becomes the bottleneck. In those moments, flexibility is often far more valuable than marginal savings achieved earlier.

Removing Friction from Capital Events

For CFOs, especially those operating in the alternative investment space, the focus is on structuring capital events. As opposed to managing day-to-day operational costs. One of the most painful points in that process is due diligence. The careful exchange between acquirer and target that aims to provide enough information for each to price risk, without giving the entire game away.

CISOs can materially influence how smooth or painful that process becomes. The most effective support often comes from understanding upfront what the diligence process will look like and preparing accordingly.

For example, they might develop executive-level ‘Security at ACME’ overviews to sit alongside more detailed trust centre or technical reports. Being available to diligence teams for interviews, and for example clearly articulating which services are outsourced to an MSSP, and why, builds credibility between those executive teams.

Decision-makers often don’t look at penetration test reports at a deal level. They are assessing whether the organisation understands its own control environment. A well-prepared CISO who can clearly explain why certain controls exist acts as a trust amplifier during transactions.

It is often the difference between a diligence process that closes cleanly and one that drifts. Two organisations can have similar maturity. Yet the one that can respond within a day with clear, consistent evidence reduces follow-up questions, avoids uncertainty premiums in pricing discussions and prevents security from becoming a late-stage negotiation point.

Avoiding Shocks and Smoothing Operating Costs

For any individual who has worked with a finance partner to define a departmental budget will know that predictability often takes precedence over absolute cost. Contract value can be secondary to payment terms, renewal timing or the ability to forecast spend with confidence.

CISOs can align with this by looking to reduce unplanned operating expenditure. In addition to understanding the cost structure of their controls by communicating with the technical pre-sales engineer, procurement and account teams.

A good example is cyber insurance. While often purchased directly by finance teams, many policies are relatively off-the-shelf and provide access to services the security team already operates or has under contract. Other policies include notable exclusions for the events most likely to occur. Such as a ransomware incident without business interruption cover. In many cases, these gaps can be addressed in-policy with a flat fee or a more predictable cost model.

The value here extends beyond risk transfer and into more predictable costs: replacing reactive spend with planned expenditure.

Aligning Cyber Conversations to Board Priorities

Across all of the above examples, the common thread is that the board is rarely asking security to prove its value in isolation, and is surprisingly comfortable with uncertainty. But they are asking whether the cyber papers support better decisions, fewer constraints and more predictable outcomes for the business as a whole.

CISOs who frame their priorities in those terms will find their conversations move away from justifying individual controls and towards understanding how security choices shape the organisation’s ability to respond to change. In that context, cyber becomes part of how the business navigates uncertainty, rather than a specialist function defending its budget. Speaking the board’s language, ultimately, is less about converting cyber risk into pounds and pence. It is more about understanding which levers matter at that level and showing how security choices influence them.

Learn more at thrivenextgen.com

  • Cybersecurity
  • Cybersecurity in FinTech
  • 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

Dr. George Papamargaritis & Dr. Konstantia Barmpatsalou

Published

Estimated Read time

4Mins

Obrela’s Dr. George Papamargaritis (EVP MSS) and Dr. Konstantia Barmpatsalou,  (Blue Team Support Manager) on why embracing a risk-led cybersecurity model will leave financial organisations better positioned not just to meet regulatory requirements but to strengthen resilience, protect customers and uphold the trust that is so essential to the future of financial systems

Cybersecurity in the financial sector was once viewed as a compliance-driven discipline. But as attackers have increasingly targeted institutions with sophisticated, persistent and often internally driven campaigns, it has become a strategic priority.

According to the Digital Universe Report H1 2025, financial services were the second most targeted industry globally, accounting for 19% of all observed cyberattacks. This reflects both the sector’s value to adversaries and the complexity of the digital ecosystems it now operates within.

Regulatory frameworks such as the FCA and PRA’s operational resilience rules, the EU’s Digital Operational Resilience Act (DORA) and NIS2 have strengthened baseline protections. However, the report’s findings demonstrate that regulation alone cannot deliver true cyber resilience. Institutions must adopt a strategic, risk-led approach that looks beyond compliance to understand real threats, behaviours and operational dependencies.

Tailored, Internal and Stealthier Threats

One of the most striking insights from the report is how targeted financial sector attacks have become. Industry-specific security risks now represent 32% of all incidents in the sector. This is an indication that adversaries are designing attacks using detailed knowledge of financial operations, from trading workflows to payment systems.

Internal activity is also a major concern. Suspicious internal activity accounts for 26% of detections across financial services, reflecting the frequency of compromised accounts, misused privileges and lateral movement. For a sector historically focused on defending the perimeter, this shift highlights the need for deeper visibility into user behaviour and identity-driven risks.

The wider threat landscape reveals adversaries are moving away from overt, signature-based attacks. In H1 2025, brute force activity made up 27% of global alerts, while vulnerability scanning accounted for 22% and known malicious indicators for 20%. Notably, direct malware payloads dropped to 0% of trending alerts, replaced by fileless techniques and living-off-the-land methods that bypass traditional defences.

For financial institutions, this is a challenge. Many compliance requirements still centre on endpoint protection, patching and malware controls. These will of course, remain important, but they cannot address threats that are increasingly behavioural, stealth-driven and identity-focused.

Operational Complexity

The financial sector’s cyber risk is intensified by its expanding operational footprint. Cloud adoption, open banking, digital identity models and extensive third-party ecosystems have all created new points of exposure. Financial services operate within a global digital infrastructure that is both vast and increasingly interconnected. This level of complexity cannot be effectively protected through compliance checklists alone.

Regulators are recognising these realities. DORA’s emphasis on ICT third-party risk, operational resilience testing and continuous oversight reflects the need for more proactive, intelligence-driven approaches. But DORA still only sets a minimum standard. True resilience requires institutions to move beyond regulatory expectations and embed cybersecurity into broader business strategy.

Strategic, Risk-Led Cybersecurity

A risk-led approach begins with understanding the threats that pose the greatest risk to operations and customers. Financial institutions remain priority targets for groups such as FIN7, TA505, Cobalt Group and various state-backed actors. Their tactics, such as credential harvesting, remote access tools, web-injection frameworks and lateral movement, are specifically designed to exploit the digital fabric of financial services.

This evolving threat profile puts identity and behaviour at the heart of cyber defence. With credential-driven and internal threats so prevalent, institutions must prioritise behavioural analytics, continuous authentication and zero-trust models that verify users and devices contextually rather than relying on static controls.

Strategic cyber resilience also needs to have continuous assurance. Traditional audits, annual testing and scheduled penetration exercises cannot keep pace with rapidly evolving threats. Leading institutions are shifting toward continuous control monitoring, automated attack simulation and persistent adversarial testing. These practices align with the Bank of England’s CBEST framework and demonstrate a sector-wide move toward ongoing, intelligence-led assurance.

Crucially, cyber risk must be treated as an operational issue, not just a technical one. Embedding cybersecurity into enterprise risk management, financial planning, product development and board oversight is essential. This integrated approach also mirrors the direction of FCA and PRA regulation, which increasingly emphasises governance, accountability, and resilience across the entire organisation.

Beyond Compliance

Financial services underpin national economies and public confidence. As digital ecosystems grow and adversaries become more sophisticated, the sector faces a dual challenge: meeting rising regulatory expectations while defending against complex, targeted attacks. It is clear that cybersecurity must evolve from compliance-driven activity to a strategic capability built on intelligence, continuous assurance and behavioural insight.

Institutions that embrace this risk-led model will be better positioned not just to meet regulatory requirements but to strengthen resilience, protect customers and uphold the trust that is so essential to the future of financial systems.

Learn more at obrela.com

  • Cybersecurity
  • Cybersecurity in FinTech
  • Digital Strategy
  • Fintech & Insurtech
  • InsurTech

Author

Dan Brightmore

Published

25 February 2026

Estimated Read time

2Mins

Children’s Mental Health Week 2026 spotlights the theme ‘This is My Place’. Tech charity founder James Tweed is calling on…

Children’s Mental Health Week 2026 spotlights the theme ‘This is My Place’. Tech charity founder James Tweed is calling on the UK’s IT departments to donate surplus laptops and devices to help some of the country’s most overlooked vulnerable children.

Rebooted

Tweed founded Rebooted to support the children of prisoners and provides laptops so they can learn at home.

“Having a parent in prison can be traumatic and often leads to a child struggling at school,” says Tweed. “If that child then falls behind digitally or is excluded from education, their long-term prospects narrow dramatically. It’s a vicious circle and we need to break it early.

“For many of these children, school is already unstable. If they also lack access to reliable technology at home, they’re starting from behind. In 2026, digital access isn’t a luxury, it’s foundational.”

A Practical Solution

With businesses refreshing hardware on regular cycles, Tweed believes IT leaders are sitting on a practical solution.

“Across the UK, thousands of perfectly usable laptops are sitting in storage cupboards or heading for recycling. Those devices could transform a child’s ability to learn, revise and stay connected to school.”

Crucially for IT heads, data security is central to the model. All donated devices are securely wiped and processed by Rebooted’s technology partner, GeTech, using certified data erasure procedures.

“Security is non-negotiable,” assures Tweed. “Every device is professionally wiped to recognised standards before it’s redeployed. IT teams can donate with complete confidence.”

Children’s Mental Health Week

Children’s Mental Health Week, launched in 2015, focuses this year on belonging and ensuring young people feel they have a place in their communities. Tweed argues that digital access plays a direct role in that sense of inclusion.

“We talk a lot about wellbeing and belonging,” he says. “But if a child can’t access homework platforms, revision tools or basic digital resources, they quickly feel excluded. Technology can either widen the gap — or help close it.”

Rebooted is now urging CIOs, IT directors and managed service providers to review surplus stock and consider structured donation programmes as part of their ESG and sustainability strategies.

“This is practical, measurable impact,” Tweed adds. “Instead of gathering dust, those devices can help ensure a vulnerable child can genuinely say, ‘This is my place.’”

IT leaders interested in donating surplus equipment can find more information at: rebooted.me

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

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

Jonny Combe

Published

19 February 2026

Estimated Read time

4Mins

Jonny Combe, President and Chief Executive Officer, PayByPhone on how urban mobility is evolving from car-centric to multimodal and the opportunity the parking industry has to play a central role by integrating payment infrastructures that support a more connected, flexible mobility ecosystem

The journey has changed. Over the past few years, the mobility industry has undergone seismic shifts toward more digital experiences. Cash payments continue to disappear and in the US made up only about 14% of all payments in 2024. Over half of the US adult population make use of mobile wallets and many companies provide payment opportunities via apps for their services. While this has made some processes more efficient and streamlined, it has also resulted in very fragmented data streams.

Consider this scenario: a commuter drives an Electric Vehicle (EV) to a rural or suburban transit hub where they park and charge, then boards a train into the city. The final mile is completed on an e-scooter, shared bike or another mode of public transport to reach their destination. One journey, four separate payment interactions across four different apps.

This is the daily reality for millions of commuters, and it exposes a fundamental challenge that not only the parking industry, but also the mobility industry as a whole must confront. Continuing to build payment infrastructure for journeys that end at the curb, is no longer enough; we should be facilitating one system for these evolved modern journeys.

City Centres Reimagined

A substantial amount of land in city centers has traditionally been dedicated to parking, but there is a growing trend where we see city centers worldwide redesigning their urban space. On-street parking is giving way to pedestrian zones and cycle lanes. Traditional car parks are transforming into multimodal hubs that are integrating EV charging, micro-mobility stations, and last-mile logistics. Technologies like automatic number plate recognition are helping to eliminate friction at entry and exit points. However, backend complexity of the redesign of urban mobility has grown exponentially.

Local authorities now juggle relationships with cashless payment providers, meter operators, EV charging networks, micro-mobility vendors, and logistics partners. Each bring their own payment rails, reconciliation requirements, and data formats. For many municipalities, simply reconciling payments between a meter provider and a digital parking platform already strains finance teams. Adding multiple mobility partners brings a significant extra load to existing operational capacity and the operational burden is only part of the equation.

The Hidden Cost of Fragmentation

The more critical issue is strategic: fragmented payment systems can create fragmented data, and fragmented data can undermine intelligent policy.

When payment information sits in siloed systems across multiple vendors, authorities lack the consolidated view needed to answer essential questions:

  • How does parking behavior correlate with public transit usage?
  • What pricing strategies would optimize utilization across the entire mobility network?
  • Where should we invest in EV infrastructure based on actual demand patterns?
  • How do we measure progress toward carbon reduction targets?

Without integrated payment and usage data, cities are making significant capital infrastructure decisions with an incomplete picture.

The Payment Layer as Strategic Infrastructure

Forward-thinking cities are, however, beginning to recognize payment infrastructure not as back-office plumbing, but as strategic architecture for the mobility ecosystem.

The solution lies in centralized payment platforms that serve as a unifying layer – ‘super apps’ as they are called in other industries. The backend of these apps should be able to consolidate transactions across multiple mobility services, automate complex multi-party reconciliations, and create unified data lakes that enable AI-driven insights.

This approach can deliver immediate operational relief: finance teams spend less time manually reconciling disparate systems, and the strategic value compounds over time. With consolidated data, authorities can model the true economics of mobility transitions, identify underutilized assets, dynamically price services to manage demand, and measure environmental impact with precision.

Building for What Comes Next

The parking industry has always been about managing physical space, yet the future is about orchestrating mobility experiences. The question for industry leaders isn’t whether parking will integrate with broader mobility systems but whether parking operators will architect that integration intentionally.

Doing so requires a fundamental rethink of the role parking payment providers play in the payment value chain, while investing and building the technology and the payment infrastructure that makes seamless, sustainable urban mobility possible.

The infrastructure we build today will determine whether cities can deliver on their mobility and sustainability commitments tomorrow. For parking industry leaders, this is both a challenge and an opportunity: to evolve from transaction processors into the essential connective layer of urban mobility. Those with the vision, and the technological ability to rise to that challenge, have a real opportunity to lead the next generation of multimodal mobility payments.

About PayByPhone                                                     

PayByPhone is a global leader in mobile parking payments. We simplify journeys for millions of UK drivers with smart, intuitive technology and user-focused features. In addition to fast, secure parking payments, drivers can also locate nearby fuel stations and EV chargers – and pay for EV charging – all in the PayByPhone app. We work with over 1,300 cities and operators across the UK, North America, France, Germany, and Switzerland. More than 110 million drivers worldwide have downloaded the PayByPhone app to simplify their parking and vehicle payments to date. To discover how our products and services can elevate your driving experience.

Learn more at paybyphone.co.uk

  • Digital Payments
  • Digital Strategy

Author

Adrian Wood

Published

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

Estimated Read time

3Mins

Join thousands of attendees at FIBE Berlin for two days of groundbreaking inquiry into the future of financial services and the tech that will drive it

Berlin’s importance in the finance and tech services industry is characterised by a combination of a vibrant startup scene, abundant capital, a supportive innovation environment and a broad network of events and collaborations. All these factors make Berlin a leading centre for financial technology in Europe. And this is where FIBE Berlin comes in – Germany’s first international international finance & tech festival.

FIBE Berlin showcases the latest trends and developments acros the industry. FIBE Berlin bridges traditional banking with the disruptive FinTech sector and offers curated networking formats to ensure you get the most out of your attendance.

Why Attend?

Your benefits in a nutshell: Engage with the European finance and tech scene over two days. Listen to international expert speakers, participate in curated networking sessions, and attend the FIBE Recovery Breakfast.

Whether you are a finance expert, a FinTech visionary or a tech investor, don’t miss out on this exciting opportunity to be a part of the FIBE Berlin community and create a new form of convention and exhibition. From Berlin for the world.

In just two days, FIBE Berlin will delve into the most groundbreaking topics that are reshaping the financial landscape. With Handelsblatt, Germany’s business and financial daily, the conference has an expert partner responsible for curating the stage programme.

In addition to dealing with the newest content, FIBE Berlin creates interactive formats and an exchange at eye level between the audience and speakers delivering expert insights. No matter whether it’s a debate, a live feed to correspondents around the world or a meet and greet with international speakers. You can count on FIBE Berlin’s conference program to be bang up-to-date, controversial and interactive.

Check out the full program at FIBE to plan ahead…

Three Unique Stages

To help you navigate FIBE Berlin’s vast array of offerings, the festival program is divided across three different stages:

  • The Festival Stage, delves into the big questions and connections within the industry, if not the world, through panel discussions and one-on-one interviews. There’s plenty of space around this big stage to listen, ask questions, and join the discussion.
  • As the name Club Stage suggests, things are a bit more intimate here. Speakers and moderators take a behind-the-scenes look at companies (be it a unicorn or an early-stage startup), collaborations, investments, and more. Again, your questions and experiences are more than welcome here.
  • Experience Fintech Berlin style at the ever so popular Späti Stage. For many, a Späti (Berlin’s corner shops), replaces the living room or kitchen table and so does the Späti Stage. Completely informal and unconventional it features quick presentations, out of the box impulses, and meet and greet sessions. Grab an ice cream, a beer, or some crisps and join the Späti crowd!

Book your tickets now

  • Artificial Intelligence in FinTech
  • Blockchain & Crypto
  • Digital Strategy
  • Event Newsroom
  • Events

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

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

Dan Brightmore

Published

16 January 2026

Estimated Read time

5Mins

Ben Goldin, Founder and CEO of Plumery, explores the key banking trends for 2026 – from fraud and digital assets to stablecoins and AI applications

As we head into the second half of the decade, several emerging trends will come to the fore in 2026. The interconnectedness among these trends is also noteworthy. Artificial intelligence (AI) and progressive modernisation act as common threads.

A strong current throughout 2026 is the shift from customer-first banking to human-first banking. This relates to the concept of ethical banking. It focuses on creating financial services that have a positive social and environmental impact. 

Human-first banking aims to get even closer to the customer by understanding their actual human needs, rather than just consumer needs. For example, a bank should be acting as a coach to improve a customer’s financial health, not solely as an advisor on which products they should buy. Banks can build trust in a digital world through tailored and empathetic interactions, effectively simulating the experience customers formerly had with their personal banker.

To attain that level of hyper-personalisation, banks will need to be capable of processing vast amounts of transactional data, which can only be accomplished by deploying AI and big data tools. This requirement, in turn, will turbocharge progressive modernisation, another trend that has been bubbling under the surface for the past few years.

Traditional banks are using progressive modernisation to deal with legacy infrastructure that is not fit for purpose in a digital-first, AI-driven world. Instead of a big bang replacement of core banking systems, which is risky and can take years, banks are creating change from within existing architecture. Banking is leveraging technologies that support a multi-core strategy. With this approach, banks can add new cores for specific products that require greater agility and innovation. Modern cores are necessary for deploying the latest AI and big data tools because they provide a unified, real-time data foundation to deliver hyper-personalisation.

Fraud Threats

Fraud will remain a top concern throughout 2026. Adversaries use AI to expand the range of techniques, such as impersonation scams and identity theft, as well as accelerate and scale fraudulent activity.

According to the UK Finance Half Year Fraud Report 2025, £629.3 million was stolen by criminals in the first six months of this year, and there were 2.09 million confirmed cases across both authorised and unauthorised fraud. Card not present cases rose 22% to 1.65 million and accounted for 58% of all unauthorised fraud losses.

However, the good news is that there was a 21% increase in prevented card fraud in the first half of 2025. The £682 million which was stopped from being stolen is the highest-ever figure reported.

To combat fraud, new and improved tools to help banks identify, verify and onboard customers will come to market in 2026. The move away from paper-based identity (ID) and widespread adoption of digital ID will play a key role in the fight against fraud. Hence the UK government’s recently announced plans to roll out a new digital ID scheme.

In addition, I expect to see a fundamental shift in fraud detection using real-time behavioural analytics, data analytics for proactive risk identification, and other applications of AI and machine learning in this space.

Digital Assets and Stablecoins

Digital ID verification is also essential for fighting fraud in the digital assets and stablecoins space. Another hot topic at several banking and payments industry conferences last year.   

In 2026, digital assets and stablecoins will become much more mainstream. Banks have left the sidelines and are now actively engaged with running pilots. For example, in September a consortium of nine European banks, including CaixaBank, ING and UniCredit, announced an initiative to launch a euro-denominated stablecoin.

Central banks and regulators are developing a comprehensive agenda for digital assets. Banks will need to blend traditional fiat currencies and assets with their digital counterparts. This trend is also driving a progressive modernisation approach, as legacy core banking systems weren’t designed to manage digital assets, nor do they support moving money via blockchain-based rails. I expect to see more banks looking to deploy a multi-core strategy where digital assets are managed and stored elsewhere, but they can still provide a seamless and unified experience to customers.

AI

Last year, I predicted that the industry would adopt a ‘meet-in-the-middle’ approach to AI, with banks beginning to uncover the real value that the technology can deliver. I also predicted consolidation, recalibration and stabilisation in the market.

GenAI Banking Applications

My predictions held true, by and large. In 2025, institutions explored what is possible, relevant and achievable within the banking context, then specifically for each individual institution within its legacy architectures and technological environments.

This trend will evolve into more practical actions and initiatives over the next 12 months to provide greater clarity around where GenAI shines versus where it’s not applicable.

To gain clarity, it’s important to understand the difference between AI and GenAI. The latter is built on stochastic principles, which uses probability to model systems that appear to vary in a random manner. This means that the same input could potentially generate different outputs – this isn’t acceptable for automated financial operations, which requires much more determinism. Hence, I believe that GenAI will be used chiefly in scenarios where there’s human intervention.

One area where GenAI is applicable is in conversational applications. For example, banks will begin launching more interactive user interfaces. Customers will be able to interact with the bank as they would a human. Moving beyond simple, frequently asked questions to actual actions.

GenAI in the Back Office

Similarly in the back office, banks can leverage GenAI to provide guidance to their employees and accelerate certain tasks. Using the technology to improve efficiency and help staff do more will have a positive impact on customer experience. Processes will take much less time.

It will also help to bring unbanked segments or non-standard customers, which are difficult and costly to onboard because they require a bespoke assessment, into regulated financial services. Applying GenAI can make the bespoke process much more efficient by providing data-driven insights to support faster and smarter decision-making. This will make it much cheaper to serve these segments. Including smaller and medium-sized enterprises, which will drive financial inclusion and improve customers’ financial health.

Learn more at plumery.com

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

Ben Francis

Published

9 January 2026

Estimated Read time

4Mins

Ben Francis, Insurance Lead at Risk Ledger, on navigating cyber threats by reinforcing security from the inside out

Cyber insurance has evolved from a straightforward risk transfer mechanism into an integral component of enterprise risk strategy. As a result, the conversation has shifted beyond simply securing coverage to embracing three foundational elements: transparency in risk exposure, accountability for security measures, and active collaboration throughout the digital ecosystem.

Rather than asking ‘are you covered?’, the more pertinent question has become ‘can you demonstrate measurable risk reduction?’. Insurers and insureds alike are recognising that what matters now is how well an organisation understands and manages its digital exposure, especially across its extended supply chain. Recent data reveals that 46% of organisations experienced at least two separate supply chain-related cyber incidents in the past year, a clear sign that exposure often lies beyond direct control. 

From Risk Transfer to Risk Visibility 

In recent years, the cyber insurance market has matured significantly. Once viewed as a reactive safety net to cushion the financial impact of attacks, it is now becoming a proactive tool for managing and mitigating risk. This shift is partly driven by insurers, who increasingly expect and work with organisations to demonstrate strong security practices and a nuanced understanding of their threat landscape, including risks deep within their digital supply chains; an area where many businesses still fall short.

At the same time, the industry faces a growing challenge from systemic cyber risk within their portfolios, as many businesses rely on the same cloud providers, payment systems and digital platforms, increasing the chance of a single point of failure. Insurers must gain visibility into how policyholders are connected, not only to suppliers but to each other. Tools and frameworks that map and monitor these interconnections will be essential to avoid underestimating the wider impact of seemingly isolated cyber events.

Mapping Beyond Third Parties

It is no secret that cyber attackers often target the weakest link in a supply chain. These are not always direct suppliers, but fourth, fifth or even sixth-tier vendors that have indirect but critical access to systems and data. Unfortunately, many organisations lack visibility beyond their first tier, creating blind spots that attackers can easily exploit. From an insurance perspective, this presents a clear challenge. If an organisation cannot account for who it is connected to, it cannot adequately quantify its risk and neither can its insurer. Mapping these extended connections is more than just a technical exercise; it means actively practiced risk governance and responsibility. Insurers increasingly want to know how their policyholders are identifying and managing indirect dependencies, particularly in sectors like financial services and retail where disruption can ripple across entire markets.

Collaboration as a Risk Strategy 

One of the more underappreciated aspects of cyber resilience is the role of peer collaboration. Unlike physical incidents, cyber threats rarely exist in isolation. A single compromised vendor can impact multiple organisations simultaneously, a fact that has been highlighted by high-profile supply chain attacks such as SolarWinds and MOVEit. 

As a result, businesses need to think beyond their own perimeters and adopt a more collective mindset. This includes building relationships with industry peers, sharing threat intelligence and participating in sector-wide initiatives aimed at improving visibility and preparedness. 

In highly regulated sectors, such as insurance, this collaboration is increasingly being encouraged by oversight bodies. Frameworks like the Digital Operational Resilience Act (DORA) in the EU and initiatives from the Prudential Regulation Authority (PRA) and the Financial Conduct Authority (FCA) in the UK are pushing for more transparency around third-party risk. In this context, openness is no longer optional; it will be a regulatory expectation. 

For insurance providers, greater collaboration between policyholders also means better data on emerging threats and more accurate portfolio management. For businesses, it offers a chance to anticipate vulnerabilities that may not yet have hit their own networks but are affecting others in their industry. 

Proactive Transparency Builds Trust 

Organisations that take a proactive, transparent approach to cyber risk management are more likely to secure cover and potentially favourable terms, not just in terms of premiums, but also in access to additional services such as forensic support, incident response sources and legal counsel. 

Demonstrating a mature cyber posture is not about claiming perfection. No organisation is immune to breaches. What insurers are looking for is evidence of a structured approach: the existence of incident response plans, robust governance, effective supply chain risk management, and above all, an honest view of risk. 

A Shift in Mindset 

Ultimately, our understanding of cyber insurance must keep evolving. It should not be treated as a simple checkbox exercise, but as a collaborative relationship between insurers and the organisations they support – one built on shared insight, clear communication, and a drive for continuous improvement.

The organisations best equipped to navigate today’s threats will be those that prioritise transparency. Not only does it lead to stronger protection, but it also builds a culture of accountability that reinforces security from the inside out.

Learn more at riskledger.com

  • Cybersecurity
  • Cybersecurity in FinTech
  • Digital Strategy
  • Fintech & Insurtech
  • InsurTech

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

        7 October 2025

        Estimated Read time

        3Mins

        Samsung and OpenAI Announce Strategic Partnership to Accelerate Advancements in Global AI Infrastructure

        Samsung will bring together technologies and innovations across advanced semiconductors, data centres, shipbuilding, cloud services and maritime technologies

        OpenAI, Samsung Electronics, Samsung SDS, Samsung C&T and Samsung Heavy Industries have announced a letter of intent (LOI) for their strategic partnership to accelerate advancements in global AI data centre infrastructure and develop future technologies together in relevant fields. This expansive collaboration will bring together the collective strengths and leadership of Samsung companies across semiconductors, data centres, shipbuilding, cloud services and maritime technologies.

        The signing ceremony was held at Samsung’s corporate headquarters in Seoul, Korea, attended by Young Hyun Jun, Vice Chairman & CEO of Samsung Electronics; Sung-an Choi, Vice Chairman & CEO of Samsung Heavy Industries; Sechul Oh, President & CEO of Samsung C&T; and Junehee Lee, President & CEO of Samsung SDS.

        Samsung Electronics

        Samsung Electronics will work with OpenAI as a strategic memory partner to supply advanced semiconductor solutions for OpenAI’s global Stargate initiative. With OpenAI’s memory demand projected to reach up to 900,000 DRAM wafers per month, Samsung will contribute toward meeting this need with its extensive lineup of high-performance DRAM solutions.

        As a comprehensive semiconductor solutions provider, Samsung’s leading technologies span across memory, logic and foundry with a diverse product portfolio that supports the full AI workflow from training to inference.

        The company also brings differentiated capabilities in advanced chip packaging and heterogeneous integration between memory and system semiconductors, enabling it to provide unique solutions for OpenAI.

        Samsung SDS

        Samsung SDS has entered into a potential partnership with OpenAI to jointly develop AI data centre and provide enterprise AI services.

        Leveraging its expertise in advanced data center technologies, Samsung SDS will collaborate with OpenAI in the design, development and operation of the Stargate AI data centers. Under the LOI, Samsung SDS can now provide consulting, deployment and management services for businesses seeking to integrate OpenAI’s AI models into their internal systems.

        In addition, Samsung SDS has signed a reseller partnership for OpenAI’s services in Korea and plans to support local companies in adopting OpenAI’s ChatGPT Enterprise offerings.

        Samsung C&T and Samsung Heavy Industries

        Samsung C&T and Samsung Heavy Industries will collaborate with OpenAI to advance global AI data centers, with a particular focus on the joint development of floating data centers.

        Floating data centers are considered to have advantages over data centers because they can address land scarcity and lower cooling costs. Still, their technical complexity has so far limited wider deployment.

        Building on their proprietary technologies, Samsung C&T and Samsung Heavy Industries will also explore opportunities to pursue projects in floating power plants and control centers, in addition to floating data center infrastructure.

        Starting with the landmark partnership with OpenAI, Samsung plans to fully support Korea’s goals to become one of the world’s top three nations in AI and create new opportunities in the field.

        Samsung is also exploring broader adoption of ChatGPT within the companies to facilitate AI transformation in the workplace.

        About OpenAI

        OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity.

        About Samsung Electronics Co., Ltd.

        Samsung inspires the world and shapes the future with transformative ideas and technologies. The company is redefining the worlds of TVs, digital signage, smartphones, wearables, tablets, home appliances and network systems, as well as memory, system LSI and foundry. Samsung is also advancing medical imaging technologies, HVAC solutions and robotics, while creating innovative automotive and audio products through Harman. With its SmartThings ecosystem, open collaboration with partners, and integration of AI across its portfolio, Samsung delivers a seamless and intelligent connected experience.

        • Digital Strategy

        Author

        Dan Brightmore

        Published

        16 September 2025

        Estimated Read time

        2Mins

        The deadline for entries for the National DevOps Awards is September 19th. Finalist will be announced September 26th. Don’t miss out – book your place before the October 14th deadline.

        For nearly a decade, the DevOps Awards have celebrated innovation and excellence in DevOps, recognising the hard work and achievements driving the community forward. As an independent awards program, it highlights leaders who are shaping the future of DevOps.  

        Being shortlisted is a significant achievement, marking you as a key player in the industry. The awards are open to businesses of all sizes, as well as teams and individuals worldwide. With 16 diverse categories, entries are judged against a clear set of criteria, ensuring fairness and prestige. 

        The awards offer a unique platform to showcase your expertise, gain visibility, and connect with top professionals in DevOps and quality engineering.  

        Join us in London this year and share your insights with some of the brightest minds in the field.  

        To enter and book your place at the awards visit the National DevOps Awards website.

        A Truly Independent DevOps Judging Process​

        The DevOps Awards ensures fair and unbiased judging through an anonymous evaluation process. All judges -led by Dávid Jámbor
        Senior Director – Technology and Secure Infrastructure BCG – are seasoned senior professionals and they assess award entries purely on merit, with all identifying information removed. This guarantees that every winner is recognised solely for their exceptional achievements, regardless of company size, budget, or market influence.​

        • Digital Strategy
        • Event Newsroom
        • Events

        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

        10 September 2025

        Estimated Read time

        6Mins

        Mike Puglia, General Manager, Kaseya Cybersecurity Labs, on how the need for regulatory support to better support industries when tackling cybercrime

        Cyberattacks keep coming hard and fast, but things are beginning to change. In the past few months, law enforcement has announced arrests of three people in the Marks & Spencer breach, seven members of the hacking group NoName057, five affiliates of Scattered Spider and also disrupted the infrastructure of gangs such as Flax Typhoon, Star Blizzard and others.  

        Earlier this year, the UK retail industry felt the pressure. Brands, including Marks & Spencer, Harrods and Co-op – and by proxy, their customers – became victims of the hacking group, Scatter Spider. Other businesses are now on high alert as this wave of security breaches is expected to continue. For as long as bad actors can reap rewards and the risk of consequences remains small, they will keep attacking. Ransomware-as-a-service lowers the bar to entry further, allowing even those without specialised skills to launch successful ransomware campaigns.

        Along with the threats, regulatory pressure on businesses is growing. Organisations must be able to prove they have strong security defences in place or risk paying hefty fines for non-compliance. However, this means we are essentially punishing the victim, not the perpetrator. By putting the onus on the victims to protect themselves, we are missing an important truth… Because there is no bullet-proof defence, even the best security strategies will not end cybercrime for good.

        It’s Time to Treat Cybercrime as Crime

        What the industry needs instead is a change in how we approach cybercrime. Rather than blaming the victims, we must start treating it as the serious criminal activity it is. It is high time we addressed cybercrime’s fundamental drivers. Opportunity, motive and the widespread perception that criminals can still get away without punishment. As is the case with physical crime, it takes a two-pronged approach to curb cybercrime: Prevention – and an effective response.

        Those who attempt physical theft, for example, face trials and potentially prison. While we have seen a growing number of cybercriminals arrested in recent months, the truth we are only scratching the surface. In the digital world, everything is accessible from everywhere, all the time. This creates an inherent vulnerability that makes perfect protection impossible. In many cases, it also makes it much harder to track down the offenders and hold them accountable.

        The Problem with Cryptocurrency and Jurisdiction

        The cybercrime landscape has also undergone a significant transformation. While in the past, hackers were mostly focused on stealing financial data, there has been a dramatic shift towards ransomware. It’s far easier to encrypt an organisation’s data and demand a ransom than finding buyers for stolen credit card info.

        This transformation has further accelerated because cryptocurrency allows cyber attackers to be paid in anonymous currency. Anywhere in the world, at any time. Previously, criminals had to physically collect payments or transfer money to traceable bank accounts. Now, they can operate with anonymity whilst easily converting their loot into real euros, pounds and dollars. This means ‘following the money’ is no longer a useful way for law enforcement to track nefarious activity. If we made it impossible for criminals to anonymously convert cryptocurrency into real currency, we could change the risk-reward calculation.

        The second key issue with fighting cybercrime is the question of jurisdiction. Many cybercriminals are based in countries where western governments have no recourse. When hackers operate from non-cooperative jurisdictions, it may be impossible to extradite them. And they may find their activities tolerated by their local government or even supported.  As we have seen with the recent arrests – the threat actors were outside of Russia and China – where many attacks come from.

        These two factors – anonymous payment systems and safe havens – create an environment where cybercrime can and will continue to flourish. While organisations can do their best to make it harder for criminals to attack, it is foolish to believe individual businesses will be able to solve the cybercrime problem on their own.

        Stop Blaming the Victim

        So, what needs to happen? First, the victim-blaming approach must change. We simply cannot regulate every business to become an impenetrable fortress. When a person is physically robbed, police respond to investigate the crime and help recover stolen property. With cybercrime, victims face reputational damage, fines and higher insurance premiums. Incidents often raise questions about where the business’ cybersecurity strategy failed, rather than a recognition that a crime has been committed against them.

        A first step forward towards solving the cybercrime problem would require governmental and societal recognition that cyberattacks represent crimes against businesses and individuals, not merely failures of those organisations to adequately defend themselves. While many countries have ramped up policing efforts against cybercrime, these are generally underfunded considering the scale of the problem.

        Secondly, we need to urgently address the anonymous payment systems that keep fuelling cybercrime. This is not an easy problem to solve, but governments must find better ways to trace and regulate how cryptocurrency is converted into real money.

        It is also time we introduced real and severe consequences for cybercriminals. The number one deterrent to any type of crime is fear of being caught and punished. The internet has essentially eliminated this, enabling hackers to operate from nations that turn a blind eye. To address this will require more political pressure on ‘safe harbour’ countries to charge, punish and extradite cybercriminals. Where nations refuse to cooperate, potential sanctions such as restrictions on internet connectivity might force governments to reconsider their tolerance for criminal activities.

        Finally, we need to acknowledge that regulations such as GDPR, PCI and NIS have their limits. Despite increasingly complex compliance requirements, cybercrime has continued to grow. While regulations can provide critical and much-needed guidance to businesses, they must be combined with properly funded law enforcement – empowered with tools to bring criminals to justice across jurisdictions.

        To truly disrupt the criminal ecosystem, systemic changes are needed. We are starting to see governments give law enforcement the tools they need, but it is very early in that process. Because ultimately, we will not solve the cybercrime problem with defence measures alone.

        About Kaseya

        At Kaseya, our mission is to empower you to simplify and transform IT and cybersecurity management with innovative platform solutions.

        Our Mission:

        Since 2000, Kaseya has delivered the technology that IT departments and managed service providers need to reach new heights of success. More than 500,000 IT professionals globally use Kaseya products to manage and secure 300 million devices.

        Kaseya’s commitment to our customers goes beyond listening to your needs and puts words into action to deliver innovative solutions that empower your business. But we don’t stop there. Kaseya’s first-of-its-kind Partner First Pledge program shares the risk our partners experience because we know a true partner is with you through the ups and downs of life.

        • Cybersecurity
        • 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

        Published

        24 July 2025

        Estimated Read time

        5Mins

        Read in brochure format

        This month’s cover story features SSEN Transmission’s journey to build a digitally-enabled, AI-ready energy business to meet the country’s clean power, energy security and net zero goals.

        Welcome to the latest issue of Interface magazine!

        Click here to read the latest edition!

        SSEN Transmission: Digitally Enabling the Grid of the Future

        James McLean is the Chief Information Officer (CIO) of SSEN Transmission, a growing Business Unit of SSE Plc. In our lead feature this month, he charts the company’s journey to build a leadership team for IT capable of meeting Transmission’s goals, while facing the daily challenges of operations and programme delivery, allied with focusing on the drive for cyber-readiness, architecture expansion and the growing need for data and analytics.

        “The business case was to stand up core systems to deliver foundational technologies capable of driving efficiencies across an expanding enterprise,” he explains. “During my first few months I dialled into how SSEN Transmission operates and considered staffing plans. What does my organisation look like? At this point there were just seven people on the IT team and as T1 was ending we had some deliverables to do in preparation to ramp up for T2.”

        “It’s been a unique and interesting challenge leading a constantly growing organisation,” reflects James. “The majority of our people have never worked for SSEN Transmission before, and they’ve come from other industries. We’ve been fortunate in the fact that our business sector is attracting strong talent keen to be part of our energy security and net zero ambition as we work towards that goal.”

        Craig Thomas, CIO at the Merit Systems Protection Board.
        Craig Thomas, CIO at the Merit Systems Protection Board.

        The Merit Systems Protection Board: Championing Public Sector Change

        Digital transformation on a public sector budget is no mean feat, and the operational requirements of a government agency compounds the challenge.

        Craig Thomas, CIO at the Merit Systems Protection Board, met with Interface to explain how he and his team overhauled each of MSPB’s legacy systems one-by-one.

        “The digital transformation has been critical to MSPB operations because the agency can absorb much more organisational change without having to spend time and money retrofitting IT systems. The environment that we’re in now requires the ability to move very quickly and to change direction with minimal effort.”

        Carnival Corporation: Maturing Cybersecurity Across Global Operations

        Carnival Corporation’s CISO, Margarita Rivera. With two decades’ experience in the cybersecurity space, she has witnessed immense change both in the fabric of the industry and in its growing importance in increasingly complex and risk-prone digital environments.

        With a wealth of multi-industry experience, deeply transferable qualifications, and a front-row seat to the profound changes seen in cybersecurity over the past 20 years, Rivera is ideally placed to lead the ongoing process of securing the company’s digital and data environments.

        “People saw cyber as just an IT or tech problem, and I think today folks realise that cybersecurity is much more than that,” says Rivera. “We’re much more involved with many other stakeholders, ingrained in other parts of the business, helping to drive change in a positive fashion and providing guardrails for faster innovation that’s accelerating the way the business can operate.”

        “When I first started, there weren’t a lot of women in the tech and cybersecurity space,” she says. “I was one of the first. I remember going to conferences and being the only woman in the room. Now, thankfully there’s been a lot of change. 

        “I recently met with a partner that’s helping us with a project here, and I looked around the room to see it’s probably sixty-forty, with the sixty in favour of having more women-representative engineers and founders. That’s quite exciting. I think there’s a special skillset that women possess that they bring to the table in terms of creativity and collaboration.”

        Appian: Redefining Enterprise Transformation With AI

        Gregg Aldana, VP, Head of Global Solutions Consulting, shares what CIOs are really asking for in 2025 and beyond, how Appian is answering that call like no other platform, and why he believes the most progressive and impactful approach to AI is by embedding it inside the most critical processes.

        Gregg Aldana, VP, Head of Global Solutions Consulting, shares what CIOs are really asking for in 2025 and beyond, how Appian is answering that call like no other platform, and why he believes the most progressive and impactful approach to AI is by embedding it inside the most critical processes.

        “When I first came to Appian a little under a year ago, one of the first things that came up was the need to spend time with customers,” says Aldana. “If you really want to learn what’s driving and going on in the industry, you’re not going to find out from just reading analyst reports or looking online. You’ve got to go out and physically meet with and talk to people that are leading these changes. Meeting with 200+ CIOs and CTOs a year gives you a front seat to reality.”

        Click here to read the latest issue!

        • Digital Strategy
        • Events

        Author

        Dan Brightmore

        Published

        Estimated Read time

        2Mins

        Read in brochure format

        Accenture is helping SSEN Transmission manage hundreds of infrastructure projects vital to achieving the UK’s Net Zero ambition. Effective delivery…

        Accenture is helping SSEN Transmission manage hundreds of infrastructure projects vital to achieving the UK’s Net Zero ambition. Effective delivery required addressing fragmented data and disconnected tools that can slow the flow of information between systems. SSEN Transmission sought a partner to help reshape its approach for data-driven execution on capital projects.

        Meeting the Digital Challenge with Accenture

        SSEN Transmission partnered with Accenture to embrace automation and digitisation in response to increasing project demands, a challenge reflected across the wider Capital Projects sector. Through the adoption of BIM (Building Information Modelling) and the implementation of Integrated Project Management (IPM), which was developed with Oracle and Microsoft, this collaboration laid the groundwork for more connected ways of working and continues to promote transformation across the organisation.

        Key Benefits Delivered

        Accenture supported with IPM (Integrated Project Management) and Building Information Modelling (BIM) customised to meet specific needs and achieve key goals: 

        • Digitise processes for a single unified environment
        • Unify data for a standardised and trusted source of truth
        • Create a scalable platform for delivering capital projects

        “With a unified real-time view of project data, SSEN Transmission has improved efficiency and strengthened collaboration across internal teams and with external partners. This allows for more time focused on higher value insight-led work, supporting better outcomes, faster decisions and much more agile delivery”

        Huda As’ad, Managing Director, Capital Projects & Infrastructure, UKI

        Building for the Future

        More than a solutions provider, Accenture helps with strategy and issupporting SSEN Transmission’s continued focus on refining best practice for smooth project delivery. The partnership is helping to evolve ways of working and strengthening the digital foundation for future readiness.

        “Our collaboration is built on a strong digital foundation that can scale with SSEN Transmission’s growing needs. By unifying systems, data, and process, we are enabling the faster adoption of new capabilities and supporting the shift towards a fully data-driven capital project delivery”

        Nithin Vijay, Managing Director, Industry X – Capital Projects & Infrastructure

        Accenture: A Partner for the Journey

        Transformation is a journey that begins with the right foundation across people, data and process. It also requires a digital partner that brings together the best of industry experience, process excellence and technology to:

        • Develop a clear, actionable strategy for digital and data transformation
        • Embed industry best practices to optimise processes and drive continuous improvement
        • Enable smarter, more consistent delivery aligned to a long-term vision, from strategy through to execution

        And that’s where Accenture makes its mark, helping clients navigate the journey with confidence.

        Learn more about how Accenture is supporting SSEN Transmission on its digitisation journey with Huda As’ad, Managing Director, Capital Projects & Infrastructure, UKI and Nithin Vijay, Managing Director, Industry X – Capital Projects & Infrastructure

        • Digital Strategy
        • Infrastructure & Cloud
        • Sustainability Technology

        Author

        Dan Brightmore

        Published

        30 April 2025

        Estimated Read time

        2Mins

        Read in brochure format

        This month’s cover story explores the innovation programme bringing everyone at the National Grid on its transformation journey Welcome to…

        This month’s cover story explores the innovation programme bringing everyone at the National Grid on its transformation journey

        Welcome to the latest issue of Interface magazine!

        Read the latest issue here!

        National Grid: A data story driven by innovation

        Transformational success with technology is about more than just ‘keeping the lights on’. Our cover story this month spotlights National Grid with the story of an innovation programme empowering everyone across the organisation on a shared transformation journey. Global Head of Data Strategy, Andrew Burns, tells Interface how connections like these are driven by data.

        “We have new energy sources, greater demand and an opportunity to gather more data than ever before. Technologies like artificial intelligence (AI) and augmented reality (AR) are revolutionising how we use that data. Today, data and these technologies are combining to increase our ability to deliver value to our customers, and society.”

        Asian Hospital and Medical Center: Leading the technology revolution in healthcare

        Asian Hospital and Medical Center, one of the largest and fastest growing premiere hospitals among the close to 30 hospitals in the Metro Pacific Health Group, is the pioneer of an integrated healthcare network in the Philippines. Frank Vibar, CITO at Asian Hospital and the former Group CIO of the MPH Group, reveals the IT strategic roadmap that will deliver a true regional hospital.

        “AHMC’s vision is to become the centre of global expertise in caring for the unique needs of our patients and the communities we serve.”

        Also in this issue of Interface…

        We hear from Tecnotree on the year ahead for the Telco industry; get the lowdown on meeting the challenges of integrating Agentic AI from Confluent; learn about the importance of Cybersecurity investment in OT (Operational Technology) from Claroty; and discover how IoT-enabled digital customers are reshaping customer experiences with Content Guru.

        Read the latest issue here!

        • Digital Strategy
        • People & Culture

        Author

        Dan Brightmore

        Published

        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!

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

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        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.

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        • 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

        31 January 2025

        Estimated Read time

        3Mins

        Read in brochure format

        We welcome the new year with a heavyweight cover story focusing on the transformation efforts of market leading multinational software…

        We welcome the new year with a heavyweight cover story focusing on the transformation efforts of market leading multinational software giant SAP

        Welcome to the latest issue of Interface magazine!

        Read the latest issue here!

        SAP: Transformation Made Simple

        “Turning transformation into a non-event is our North Star,” explains Thorsten Spihlmann, Head of Business Development for Transformation in the Cloud Lifecycle Management department at SAP. The evolution of SAP’s Business Transformation Centre (BTC) is future proofing customer experience. “The BTC is a comprehensive solution that helps users streamline the process of migration to S/4HANA,” says Spihlmann. “In the end, it’s one central platform – one central orchestration layer – which guides you through all phases of the project. The BTC enables users to access source systems, profile data for insights, enhance and transform data, provision it to target systems, and validate data integrity… Our customers’ interests are always top of mind.”

        Nestlé: A CIO Leading by Example

        Nestlé‘s Oceania’s CIO, Rosalie Adriano, dives deep into how her breadth of experience in transformational change led to her becoming one of 2024’s top 50 CIOs in Australia. “I want ideas to be freely shared. Innovation is encouraged. This approach breaks down silos and creates a sense of unity and purpose.”

        Poundland & Dealz: The Value of Digital

        Dean Underwood, IT Director at Poundland & Dealz, talks challenges, cultural shift and the company’s digitally transformation… “We must prove that spending on technology is as impactful as investing in product pricing,” he says. “For example, my request to fund a new data warehouse competes with the Commercial Director’s goal to maintain affordable prices. The customer always comes first, but investing in supply chain efficiencies lowers operating costs, helping us keep prices down. It’s our responsibility to demonstrate the value of every investment.”

        Schenectady County Government: Delivering Critical and Secure Infrastructure

        Schenectady County’s CIO Gabriel A. Benitez discusses the role of IT as a steward for citizens, leadership and the power of teams, and why security is crucial to the organisation… “We support and serve to keep Schenectady County running. That covers a broad remit, but some of the key departments we work with include Finance, Law Enforcement, Emergency Management, Public Health, Glendale Nursing Home, County Clerk, District Attorneys, Public Defender, Conflict Defender, Probation, Social Services, Veteran’s Affairs, Engineering & Public Works, and Department of Motor Vehicles.”

        Read the latest issue here!

        • Digital Strategy

        Author

        Dan Brightmore

        Published

        19 December 2024

        Estimated Read time

        7Mins

        Interface looks back on another year of ground-breaking tech transformations and the leaders driving them. We spoke with tech leaders…

        Interface looks back on another year of ground-breaking tech transformations and the leaders driving them. We spoke with tech leaders across a broad spectrum of sectors – from banking, health and telcos to insurance, consulting and government agencies. Read on for a round up of some of the biggest stories in Interface in 2024…

        EY: A data-driven company

        Global Chief Data Officer, Marco Vernocchi, reflects on the transformation journey at one of the world’s largest professional services organisations.

        “Data is pervasive, it’s everywhere and nowhere at the same time. It’s not a physical asset, but it’s a part of every business activity every day. I joined EY in 2019 as the first Global Chief Data Officer. Our vision was to recognise data as a strategic competitive asset for the organisation. Through the efforts of leadership and the Data Office team, we’ve elevated it from a commodity utility to an asset. Furthermore, our formal strategy defined with clarity the purpose, scope, goals and timeline of how we manage data across EY.  Bringing it to the centre of what we do has created a competitive asset that is transforming the way we work.”

        Read the full story here

        Lloyds Banking Group: A technology and business strategy

        Martyn Atkinson, CIO – Consumer Relationships and Mass Affluent, on Lloyds Banking Group‘s organisational missive around helping Britain prosper, which means building trusted relationships over customer lifetimes by re-imagining what a bank provides.

        “We’ve made significant strides in transforming our business for the future,” he reveals. “I’m really proud of what the team have achieved with technology but there’s loads more to go after. It’s a really exciting time as we become a modern, progressive, tech-enabled business. We’ve aimed to maintain pace and an agile mindset. We want to get products and services out to our customers and colleagues and then test and learn to see if what we’re doing is actually making a meaningful difference.”

        Read the full story here

        USDA: The people’s agency

        Arianne Gallagher-Welcher, Executive Director for the USDA Digital Service, in the Office of the OCIO, on the USDA’s tech transformation and how it serves the American people across all 50 states.

        “If you’d told me after I graduated law school that I was going to be working at the intersection of talent, HR, law, regulations, and technology and bringing in technologists, AI, and driving innovation and digital delivery, I’d say you were nuts,” she says. “However, it’s been a very interesting and fulfilling journey. I’ve really enjoyed working across a lot of different cross-government agencies. USDA is the first part of my career where I’m really looking at a very specific mission-driven organisation versus cross-agency and cross-government. But I don’t think I’d be able to do that successfully without the really great cross-government experiences I’ve had.”

        Read the full story here

        Virgin Media O2 Business: A telco integration supporting customers

        David Cornwell, Director – SMEs, on the unfolding telco integration journey at Virgin Media O2 Business delivering for Business customers

        “If you’ve got the wrong culture, you can’t develop your people or navigate change…” David Cornwell is Director of Technical Services for SMEs at Virgin Media O2 Business. He reflects on the technology journey embarked upon in 2021 when two giants of the telco space merged. A new opportunity was seized to support businesses with the secure, reliable and efficient integration of new technology.

        Read the full story here

        The AA: Driving growth with technology

        Nick Edwards, Group CDO at The AA, on the organisation’s incredible technology transformation and how these changes directly benefit customers.

        “2024 has been a milestone year for the business,” explains Edwards. “It marks the completion of the first phase of the future growth strategy we’ve been focused on since the appointment of our new CEO, Jakob Pfaudler.” Revenues have grown by over 20%, allowing The AA to drive customer growth with technology. “All of this has been delivered by our refreshed management team,” he continues. “It reflects the strength of our people across the business and the broader cultural transformation of The AA in the last three years.”

        Read the full story here

        Publicis Sapient: Global Banking Benchmark Study

        Dave Murphy, Financial Services Lead, Global at Publicis Sapient, gave us the lowdown on its third annual Global Banking Benchmark Study.

        The report reveals that artificial intelligence (AI) dominates banks’ digital transformation plans, signalling that their adoption of AI is on the brink of change. “AI, machine learning and GenAI are both the focus and the fuel of banks’ digital transformation efforts,” he says. “The biggest question for executives isn’t about the potential of these technologies. It’s how best to move from experimenting with use cases in pockets of the business to implementing at scale across the enterprise. The right data is key. It’s what powers the models.”

        Read the full story here

        Bupa: Connected Care

        Chief Information Officer Simon Birch and Chief Customer & Transformation Officer Danielle Handley discuss Bupa’s transformation journey across APAC and the positive impact of its Connected Care strategy.

        “Connected Care is our primary mission. We’ve been focusing our time, investment and energy to reimagine and connect customer experiences,” says Simon. “It’s an incredibly energising place to be. Delivering our Connected Care proposition to our customers is made possible by the complete focus of the organisation and the alignment leaders and teams have to the Bupa purpose. Curiosity is encouraged with a focus on agility, collaboration and innovation. Ultimately, we are reimagining digital and physical healthcare provision to customers across the region. Furthermore, we are providing our colleagues with amazing new tools to better serve our customers throughout all of our businesses.”

        Read the full story here

        ServiceNow: Tech disruption delivering change

        Gregg Aldana, Global Area Vice President, Creator Workflows Specialist Solution Consulting at ServiceNow, on how a disruptive approach to technology can drive innovation.

        While the whole world works towards automating as many processes as possible for efficiency’s sake, businesses like ServiceNow are supporting that change evolution. ServiceNow’s platform serves over 7,700 customers across the world in their quest to eliminate manual tasks and become more streamlined. We spoke to Aldana about how it does this and the ways in which technology is evolving.

        Read the full story here

        Innovation Group: Enabling the future of insurance

        James Coggin, Group Chief Technology Officer on digital transformation and using InsurTech to disrupt an industry.

        “What we’ve achieved at Innovation Group is truly disruptive,” reflects Group Chief Technology Officer James Coggin. “Our acquisition by one of the world’s largest insurance companies validated the strategy we pursued with our Gateway platform. We put the platform at the heart of an ecosystem of insurers, service providers and their customers. It has proved to be a powerful approach.”

        Read the full story here

        San Francisco PD: A technology transformation

        Chief Information Officer William Sanson-Mosier on the development of advanced technologies to empower emergency responders and enhance public safety

        “Ultimately, my motivation stems from the relationship between individual growth and organisational success. When we invest in our people, and we empower them to innovate with technology and problem-solve, they can deliver exceptional results. In turn, the organisation thrives, solidifying its position as a leader in its field. This virtuous cycle of growth and innovation is what drives me.” CIO William Sanson-Mosier is reflecting on a journey of change for the San Francisco Police Department (SFPD). Ignited by the transformative power of technology to enhance public safety and improve lives.

        Read the full story here

        • Digital Strategy

        Author

        Dan Brightmore

        Published

        12 December 2024

        Estimated Read time

        4Mins

        Read in brochure format

        We say goodbye to 2024 focused on the technology innovation the new year will bring. Our cover story highlights a…

        We say goodbye to 2024 focused on the technology innovation the new year will bring. Our cover story highlights a technology transformation journey change for the San Francisco Police Department (SFPD)

        Welcome to the latest issue of Interface magazine!

        Read the latest issue here!

        San Francisco Police Department: A Technology Transformation

        San Francisco Police Department (SFPD) CIO William ‘Will’ Sanson Mosier is ignited by the transformative power of technology to enhance public safety and improve lives. “Ultimately, my motivation stems from the relationship between individual growth and organisational success. When we invest in our people, we empower them to innovate, problem-solve, and deliver exceptional results. In turn, the organisation thrives, solidifying its position as a leader in its field. This virtuous cycle of growth and innovation is what drives me.”

        OSB Group- Building the Bank of the Future

        Group Chief Transformation Officer Matt Baillie talks to Interface about maintaining the soul of a FinTech with the gravitas of a FTSE business during a full stack tech transformation at OSB Group. “We’ve found the balance between making sure we maintain regulatory compliance and keeping up with customer expectations while making the required propositional changes to keep pace with markets on our existing savings and lending platforms.”

        Urenco: Accuracy is Everything

        We speak with the CIO of Urenco – an international supplier of enrichment services and fuel cycle products for the civil nuclear industry. Sarah Leteney talks about the ways this unique business leverages technology, and the big difference a small team can make. “We work in a high threat environment and there are many special considerations to understand. There is a rhythm to what we do to work at a pace which suits the organisation, rather than keep up with the latest trends in IT.”

        Langham Hospitality Group: Technology, Strategy, Innovation

        Langham Hospitality Group SVP, Sean Seah, talks hospitality informed by innovation, and falling in love with the problem, not the solution. “You’ve got to pilot something small – ideate it, then you can incubate it, and if it works you figure out how to industrialise it.”

        Midcounties Co-operative: A Digital Transfomation

        The Midcounties Co-operative is home to over 645,000 members and employs more than 6,200 people across multiple brands and locations, including over 230 food retail stores across the UK. We spoke with CIO Jacob Isherwood to learn about its approach to data management. “Whether you’re running a nursery, managing a natural gas pipeline, or selling tins of beans, data helps manage complexity and meet challenges from a place of understanding.”

        Read the latest issue here!

        • Digital Strategy

        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

        Author

        Dan Brightmore

        Published

        15 October 2024

        Estimated Read time

        6Mins

        Read in brochure format

        Our cover story reveals the digital transformation journey at global insurance services company Innovation Group using InsurTech advances to disrupt…

        Our cover story reveals the digital transformation journey at global insurance services company Innovation Group using InsurTech advances to disrupt the industry.

        Welcome to the latest issue of Interface magazine!

        Read the latest issue here!

        We’re excited to be publishing the biggest ever issue of Interface this month. It’s packed with insights from the cutting edge of digital technologies across a diverse range of sectors; from InsurTech to Travel via eCommerce, Banking, Manufacturing and Public Services.

        Innovation Group: Enabling the Future of Insurance

        “What we’ve achieved at Innovation Group is truly disruptive,” reflects Group Chief Technology Officer James Coggin.

        “Our acquisition by one of the world’s largest insurance companies validated the strategy we pursued with our Gateway platform. We put the platform at the heart of an ecosystem of insurers, service providers and their customers. It has proved to be a powerful approach.”

        Leeds Building Society: Tech Transformation Driven by Data

        Carole Roberts, Director of Data at Leeds Building Society, on a digital transformation program driven by the mutual power of people and culture.

        “We’ve made the decision to move to a composable architecture. It’s going to give us much more flexibility in the future to be able to swap in and out components rather than one big monolithic environment.”

        AvePoint: Securing the Digital Future

        Kevin Briggs, Vice President of Public Sector at AvePoint, discusses pioneering data security and management transformation in the global public sector.

        “We ensure the security, accessibility and integrity of data for customers with missions from everything from finance and health services, through to national security, innovation, and science.”

        Saudia: Taking off on a Digital Journey

        Abdulgader Attiah, Chief Data & Technology Officer at Saudia, on the digital transformation program towards becoming an ‘offer and order’ airline.

        “By the end of this year we will have established the maturity level for data technology, and our digital and back-office transformations. In 2025 we will begin implementing our retailing concept and the AI features that will drive it. The building blocks will be in place for next year’s initiatives where hyper personalisation for retailing is a must.”

        Publicis Sapient: Global Banking Benchmark Study

        Dave Murphy, Financial Services Lead, International – gives Interface the lowdown on the third annual Global Banking Benchmark Study and the key findings Publicis Sapient revealed around core modernisation, GenAI, data analytics transformation and payments.

        “AI, machine learning and GenAI are both the focus and the fuel of banks’ digital transformation efforts. The biggest question for executives isn’t about the potential of these technologies. It’s how best to move from experimenting with use cases in pockets of the business to implementing at scale across the enterprise. The right data is key. It’s what powers the models.”

        Habi: Unleashing liquidity in the LATAM market

        Employees at Habi discuss its mission to help customers buy and sell their homes more effectively.

        “At Habi, you can talk with the AI agent and you can provide information that streamlines the whole process.”

        USDA FPAC: Achieving customer experience balance

        Abena Apau and Kimberly Iczkowski, from USDA FPAC on the incredible work the organisation is doing to support farmers across America.

        “We’ve created a new structure for ourselves, based on the fact that the digital experience is not the be all and end all, and we have to balance it with the human touch.”

        Adecco Group: Digital Transformation driven by business outcomes

        Geert Halsberghe, Head of IT, Benelux, at Adecco Group, talks transformation management, cultural consensus, and ensuring digital transformation starts (and stays) focused on solving business problems.

        “It’s very crucial to make sure that we aren’t spending money on IT transformation for the sake of IT transformation.”

        La Vie en Rose: Outcome-focused Digital Transformation

        Éric Champagne, CIO of La Vie en Rose, on ensuring digital transformations are defined by communication, vision, and cultural buy-in. 

        “I don’t chase after the latest technology just because it seems cool… My focus is on aligning technology with the business strategy and real needs.”

        Breitling: Digital Transformation and the omnichannel experience

        Rajesh Shanmugasundaram, CTO at Breitling, talks changing customer expectations, data, AI, and digitally transforming to deliver the omnichannel experience.

         “The CRM, the marketing, our e-commerce channels — they’ve all matured so much… we’re meeting our customers wherever they are or want to be.” 

        Read the latest issue here!

        • Digital Strategy

        Author

        Dan Brightmore

        Published

        5 September 2024

        Estimated Read time

        3Mins

        Read in brochure format

        Our cover star, EY’s Global Chief Data Officer Marco Vernocchi, tells Interface why data is a “team sport” and reveals…

        Our cover star, EY’s Global Chief Data Officer Marco Vernocchi, tells Interface why data is a “team sport” and reveals the transformation journey towards realising its potential for one of the world’s largest professional services organisations.

        Welcome to the latest issue of Interface magazine!

        Read the latest issue here!

        EY: A data-driven company

        Global Chief Data Officer, Marco Vernocchi, reflects on the data transformation journey at one of the world’s largest professional services networks.

        “Data is pervasive, it’s everywhere and nowhere at the same time. It’s not a physical asset, but it’s a part of every business activity every day. I joined EY in 2019 as the first Global Chief Data Officer. Our vision was to recognise data as a strategic competitive asset for the organisation. Through the efforts of leadership and the Data Office team, we’ve elevated data from a commodity utility to an asset. Our formal data strategy defined with clarity the purpose, scope, goals and timeline of how we manage data across EY.  Bringing data to the centre of what we do has created a competitive asset that is transforming the way we work.”

        PivotalEdge Capital

        Sid Ghatak, Founder & CEO of asset management firm PivotalEdge Capital, spoked to us about the pioneering use of “data-centric AI” for trading models capable of solving the problems of trust and cost.

        “I’ve always advocated data-driven decision-making throughout my career,” says Ghatak. “I knew when I started an asset management firm that it needed to be data-centric AI from the very beginning. A few early missteps in my career taught me the importance of having a stable and reliable flow of data in production systems and that became a criterion.”

        LSC Communications

        Piotr Topor, Director of Information Security & Governance at LSC Communications, discusses tackling the cyber skills shortage, AI, and bringing together the business and IT to create a cyber-conscious culture at a global leader in print and digital media solutions.

        Topor tells Interface: “The main challenge we’re dealing with is overcoming the disconnect between cybersecurity and business goals.”

        América Televisión

        Interface meets again with Jose Hernandez, Chief Digital Officer at América Televisión, who reveals how the company is embracing new business models, and maintaining market leadership in Peru.

        “Launching our FAST channel represents a pivotal step in diversifying our content delivery and monetisation strategies. Furthermore, aligning us with global trends while catering to the changing viewing habits of our audience,” says Hernandez.

        Also in this issue of Interface, we hear from eflow about new approaches to Regtech; get the lowdown on bridging the AI skills gap from CI&T; and GCX on the best ways to navigate changing cybersecurity regulations.

        Enjoy the issue!

        Dan Brightmore, Editor

        • Digital Strategy

        Author

        Dan Brightmore

        Published

        8 August 2024

        Estimated Read time

        3Mins

        Read in brochure format

        Our cover story this month focuses on the work of Chief Information Officer Simon Birch and Chief Customer & Transformation…

        Our cover story this month focuses on the work of Chief Information Officer Simon Birch and Chief Customer & Transformation Officer Danielle Handley leading Bupa’s digital transformation journey across APAC and delivering a positive impact with its Connected Care strategy.

        Welcome to the latest issue of Interface magazine!

        Read the latest issue here!

        Bupa: Connected Care

        “ConnectedCare is our primary mission and we’ve been spearheading time, investment and creativity to reinvent and reinvigorate customer experiences,” says APAC CIO Simon Birch. “Delivering that ConnectedCare proposition to our customers is made possible by the collegiate focus of the organisation. Ultimately, what we’re able to achieve is supporting our most important colleagues, our healthcare practitioners working across our facilities.”

        Reflecting on that transformation goal, Chief Customer & Transformation Officer Danielle Handley believes that stakeholder engagement and alignment, while building relationships across the enterprise, have been key to their early success. “We’ve found the champions within the enterprise who are going to form part of the coalition of the willing to start to lead transformation here at Bupa.”

        Vodafone: Personalising Embedded Insurance

        Halil Teksal, Global Head of Fintech at Vodafone, discusses disruption in insurance, personalisation, and giving customers exactly what they need at the right time. “The main thing we’re aiming for is simplicity. How can we have really easy-to-use personalised solutions? At the end of the day, that’s what customers want. When they buy a smart device, they want to buy the insurance quickly from a reliable provider. It’s important that we satisfy all of those needs.”

        Young businessman writing on adhesive notes on glass partition in modern office, ideas, innovation, planning, strategy

        Walden Group: Advanced technology for a healthier tomorrow

        Denis Connolly, CIO of Walden Group and CEO of Walden Digital, talks about the incredible work the organisation is doing to leverage data and technology for the overall improvement of the world’s health. “We’ve created all these new initiatives just in the last year or so, moving from technology being a cost centre to being an R&D and development-focused organisation.”

        Also in this issue, Samer Fouani, Head of Cyber Transformation & Identity Access Management at TAL discusses the cyber journey for colleagues and customers at one of Australia’s leading insurers; Mark Turner, Chief Commercial Officer at Pulsant, explores how medium-sized businesses can best leverage new developments in AI; Martin Hartley, Group Chief Commercial Officer of emagine, examined the role of artificial intelligence in personalising the customer experience for financial services and Marius Stäcker, CEO of ToolTime, shares his four top tips for successfully implementing new software and driving digital transformation.

        Enjoy the issue!

        Dan Brightmore, Editor

        • Digital Strategy

        Author

        Dan Brightmore

        Published

        3 July 2024

        Estimated Read time

        4Mins

        Read in brochure format

        This month’s cover story sees our sister brand Fintech Strategy reporting from Money20/20 Europe in Amsterdam – a pivotal event…

        This month’s cover story sees our sister brand Fintech Strategy reporting from Money20/20 Europe in Amsterdam – a pivotal event in the fintech calendar, drawing over 8,000 participants from 2,300 companies worldwide.

        Welcome to the latest issue of Interface magazine!

        Read the latest issue here!

        In this month’s issue…

        Money20/20 Europe Review

        The RAI Amsterdam Convention Centre was the location for the world’s leading fintech conference. Money20/20 Europe offered a unique blend of insightful keynotes, panel discussions, and networking opportunities that underscored the transformative power of emerging technologies in financial services. We met with SC Ventures, Lloyds Banking Group, OSB Group, AirWallex, Plaid, Paymentology, Episode Six, Mettle (Nat West Group) and more to take the pulse of the latest trends across the fintech landscape.

        Under the theme of ‘Human X Machine’, Money20/20 Europe explored the relationship between humans and intelligent machines, focusing on how the partnership between artificial and human intelligence will forge a new era in finance…

        Publicis Sapient: Global Banking Benchmark Study

        Interface was also proud to partner with Publicis Sapient at Money20/20 Europe for the launch of its third annual Global Banking Benchmark Survey. The survey draws on the insight of over 1000 senior executives in financial services across various global markets and focuses on the goals, obstacles, and drivers of digital transformation.

        We spoke with Head of Financial Services Dave Murphy about its findings. “The survey focuses on how to think about solving problems end-to-end. Banks are dealing with legacy issues and taking a customer first view into solving the challenges. The practical application of AI across the banks is a significant theme as they look to automate decision-making and deliver better credit risk models.”

        At the launch event for the study, Eoghan Sheehy, Associate MD, and Grace Ge, Senior Principal, highlighted that banks are primarily focused on improving existing processes rather than introducing new ones. Data Analytics and AI are identified as key priorities for digital transformation, with a focus on internal use cases and efficiency.

        Eoghan and Grace also discussed the challenges faced by banks, including regulation, competition from companies like Amazon, and the need to attract talent. They emphasised the importance for financial institutions of modernising core infrastructure and building cloud infrastructure to support ongoing digital transformation. The study also notes the prevalence of the development of custom-made tools and the prioritising of internal use cases for AI implementation. Eoghan and Grace also provided examples of repeatable use cases and discussed the success factors for Data Analytics and AI.

        STO Building Group: Enabling and Empowering People

        Claudia Healey, Chief Human Resources Officer at STO Building Group, spoke to Interface about the HR platform empowering its people in pursuit of a strategic vision… “Culture is the number one priority in a people business like STO Building Group (STOBG). If you’re not nurturing and inspiring your folks, well, they can just vote with their feet. They don’t have to stay. Or they could do worse, they could quit and stay. And that’s something we would never want. Meeting your people where they’re at, understanding their goals and aspirations, and how you can help them reach their potential is vital. Realising how you can really see your people and truly understand what matters to them, is an incredible priority.”

        Also in this issue, AI hype has previously been followed by an AI winter, we hear from Scott Zoldi, Chief Analytics Officer at FICO who asks, ‘Is the AI bubble set to burst?’ Elsewhere, we round up the top events in tech and learn how businesses can ensure their cloud storage is more sustainable in an age of rising demand for data and AI. Cloud storage without the climate cost is possible explains Fasthosts CEO Simon Yeoman.

        Enjoy the issue!

        Dan Brightmore, Editor

        • Digital Strategy