Andrew Power, Head of UK&I at Tricentis, on why the right approach to AI can deliver the foundation for more resilient, predictable systems

Artificial intelligence is reshaping software delivery in financial services. Code that once took teams weeks to develop can now be generated and deployed in a matter of hours. This isn’t just about faster delivery; it changes the fundamentals of how software is built and how it behaves in production.

Financial institutions have moved quickly to integrate AI across core systems, from customer operations to anti-money laundering (AML) and software development to capture efficiency and innovation gains. UK parliamentary evidence shows adoption is already widespread, with the majority of firms using AI, and more planning to follow.

But as adoption spreads and becomes more embedded within key systems, so does exposure. Risk is no longer confined to individual defects, but shaped by how quickly those defects can spread across interconnected environments.

AI has removed the limits on how quickly software can be created, but not on how confidently it can be trusted, and financial institutions can now generate and deploy code faster than they can safely validate it.

This creates a new paradox: AI is both accelerating the pace of software change and increasing the speed and scale at which failures can materialise.

Machine-Speed Failure

AI-driven development shortens the distance between change and consequence. Software updates can move through the pipeline from creation to production with significantly less friction. However, this also reduces the time available to identify, flag and contain any issues before they have an impact.

AI-driven software changes don’t just move fast, they scale fast. Unlike traditional failures, these are systemic risks. A single misstep in an AI-generated update can propagate unpredictably.

For financial services, this is especially significant when key systems are deeply interconnected, spanning complex layers of infrastructure, integrations, and third-party services. Even a minor defect can propagate quickly across systems, amplifying its impact.

What would once have been contained can now escalate, cascading across systems and causing wider disruption that affects customers, operations and, in some cases, market activity. In financial services, this is not just a technical issue but a business risk with direct implications for customer trust, regulatory compliance and financial stability. The challenge is no longer simply identifying defects but maintaining confidence in what is being deployed.

This risk is already being felt across the sector. Institutions are accelerating delivery to meet customer expectations and competitive pressures, but often without corresponding advances in validation. Tricentis’ research shows 68% of financial services organisations anticipate outages or serious incidents due to poor software quality.

Regulatory Pressure for AI is Increasing

The issue is also drawing attention from regulators. Earlier this year, the UK Treasury Committee warned that current approaches to AI in financial services are inadequate and could expose customers and the wider system to “serious harm”, highlighting the need for stronger guardrails, clearer accountability and more robust oversight to deploy it safely.

Traditional resilience frameworks were never designed for systems evolving in real time, and AI can no longer be treated as a marginal technology risk. It must become central to how organisations manage and assess resilience.

This marks a shift from software quality being an engineering concern to a board-level issue of operational resilience. If machine-speed change is the new operational hazard, then failure to address it becomes a strategic issue rather than a technical one. With that in mind, financial leaders must acknowledge AI’s dual role as both a driver of risk and a mechanism for preventing it.

AI as Both a Safeguard & Source of Risk

AI also offers the most effective and scalable way to manage the risks it introduces. Advanced AI-driven validation, continuous monitoring and risk-prioritised testing can identify issues earlier than any manual process, helping reduce the likelihood they reach production.

In effect, the same AI that accelerates software creation must now be applied to validation and governance – operating at the same speed and scale.

The same capabilities that facilitate rapid software production can be applied to validation and governance, continuously evaluating system behaviour, detecting anomalies and prioritising testing based on potential business impact, rather than volume. This allows organisations to move beyond rigid approaches and towards more adaptive, responsive quality models that more accurately reflect the way AI behaves.

Instead of relying on standard periodic testing cycles, systems can be validated on an ongoing basis. This enables earlier intervention before issues escalate.

AI can also help organisations better understand the complexity of their own systems. By analysing dependencies across applications and infrastructure, it becomes possible to identify which processes are most critical and where failures would have the greatest impact.

From Acceleration to Control

There is a clear mismatch in how financial organisations approach AI. While many are leveraging AI to accelerate development, far fewer are evolving their validation and governance to keep pace, and it’s in this gap that risk emerges.

This is the “confidence gap”, where organisations can create software faster than they can safely deploy it.

To address this imbalance, firms must treat software quality as a core component of their AI strategy. Development and validation must move forward together. Governance must adapt to continuous, AI-driven change. This requires a move from static testing and coverage metrics to continuous, risk-based validation, where software is assessed in real time based on potential business impact.

If AI is the engine driving software creation, validation must act as the braking system – built in, not bolted on at the end. At machine speed, gaps in control become points of failure. The aim is not to slow innovation, but to ensure it progresses in a way that is sustainable and safe. When validation keeps pace with development, firms can move quickly and competitively, whilst maintaining control over how risk is introduced and managed.

This is a change we are seeing across large enterprises adopting AI-driven quality approaches, where validation, monitoring and governance are increasingly orchestrated together rather than treated as separate processes.

Preventing the Next Outage

The financial sector has already seen how quickly failures can escalate in complex, interconnected environments. In March, an IT error at Lloyds Banking Group exposed the private financial information of nearly half a million customers, prompting the bank to issue £139,000 in compensation.

Such incidents aren’t isolated: over the last two years, more than 33 days of unplanned banking outages have been reported to Parliament, underlining the scale of the issue.

As AI increases the velocity of change, it also raises the stakes for getting it wrong. But the irony is that it also provides the tools needed to prevent these failures from happening in the first place. AI is both contributing to the risk of outages and becoming the most effective way to prevent them.

By applying AI to continuous validation, monitoring and risk detection, organisations can spot issues earlier, understand their potential impact and intervene before disruption occurs. This shifts the focus from reacting to outages to preventing them, and it’s where the paradox becomes constructive. AI doesn’t have to be a source of instability.

With the right approach, it can become the foundation for more resilient, predictable systems. Those that fail risk trading innovation for instability. In the AI era, speed without confidence is simply another form of risk.

Learn more at tricentis.com

  • Artificial Intelligence in FinTech
  • Cybersecurity
  • Cybersecurity in FinTech
  • Fintech & Insurtech

ServiceNow, Celonis, Snowflake, Zoom and Make deliver their 2026 tech predictions for emerging technologies, including agentic AI, the role of the CIO, data governance, autonomous operations and more…

Louise Newbury-Smith, Head of UK&I at Zoom

AI elevates both manager effectiveness and employee autonomy

Moving forward, AI will simultaneously strengthen managerial capabilities and empower employees to work more autonomously. Managers will gain real-time insights into workload distribution and collaboration patterns, allowing them to support wellbeing, performance and development, without relying on manual check-ins. At the same time, intelligent workflows will give employees greater control over how they work enabling them to personalise tasks, streamline processes and focus on higher-value activities. This dual uplift will reduce friction, improve team culture, and create a more balanced workplace environment.”

AI fluency becomes the new foundational skillset

“The next phase of upskilling will blend technical and human capabilities. Employees will be expected to understand how to collaborate with AI, interpret its recommendations, and challenge outputs when necessary. Training and change management will be essential to realising the full value of these emerging tools. For IT teams, this means not only deploying the technology but also leading adoption across the workforce.

Darin Patterson, Vice President of Market Strategy at Make

2026 will be the year businesses of all sizes finally turn AI’s promise into measurable value

“Companies will shift from experimentation to dependable automation that powers productivity, decision-making, and customer experience behind the scenes. AI will be judged less by novelty and more by real outcomes, whether orchestrating marketing campaigns, managing workflows in professional services, or enabling personalised, frictionless customer interactions. With maturing standards like Model Content Protocol and Agent2Agent moving into widespread use, organisations will gain the stability and coordination needed for scalable multi-agent systems that quietly keep operations running.

As these technologies advance, AI’s complexity will fade into the background. Concepts like embeddings and prompt engineering will be built into everyday tools, allowing smaller businesses and non-technical teams to deploy automation quickly and confidently. In 2026, the winners will be the companies using AI for practical, connected automation that drives results, while standalone chatbots and overly complex approaches fall away. The future belongs to businesses that stop chasing hype and start running on AI.”

Cathy Mauzaize, President, Europe, Middle East and Africa (EMEA) at ServiceNow

The governance vs. speed tension will define leadership in 2026 

“As AI becomes core to how organisations operate, leaders will face a growing challenge: how to maintain trust without slowing down innovation. Across EMEA, this balance between governance and speed is becoming the defining measure of AI maturity. The EU AI Act marks a turning point that moves regulation from theory to practice. But rules alone won’t create responsible AI. The real test will be how organisations translate compliance into everyday practice, embedding accountability and transparency into workflows, data, and decisions.  

The University of Oxford’s Annual AI Governance Report 2025 found that leading organisations are embedding governance directly into workflows, not treating it as a compliance exercise. In doing so, they’re maintaining innovation speed while reducing AI-related risk. 

The leaders who succeed will treat governance not as a brake, but as an engine of trust and resilience. They’ll build cultures where transparency, explainability, and ethical use are built in, not bolted on. They’ll use clarity to move faster, not slower. Doing this will require a central, single-platform lens of LLMs, AI agents and workflows.  

This is what will separate compliance from competitiveness. AI must remain fast enough to drive innovation yet be governed tightly enough to earn trust. The leaders who get this balance right will define the next phase of growth, proving that responsible AI and rapid progress can coexist.”

CIOs must lead the enablement of agentic AI with a view to future risk 

“2026 will mark the rise of Agentic Platforms – networks of intelligence that blend human and machine work to drive speed, accuracy, and innovation. These agents will increasingly operate alongside people, managing workflows and simplifying complexity – not to replace human judgment, but to strengthen it.  

Yet, as this new layer of work evolves, so does a new layer of risk. The challenge will no longer be shadow IT, but ‘shadow AI’ – models and agents developed outside governance frameworks. This creates vulnerabilities for compliance, privacy, and security. Although regulations are evolving across regions, innovation is already moving faster than policy. CIOs and boards will need to anticipate, not react, staying one step ahead of regulatory change to avoid future disruptions. Agility will be the differentiator. 

The leaders who succeed will do so by adopting flexible, adaptive platform architectures, able to connect data, governance, and decision logic by design. These platforms will allow organisations to monitor, verify, and coordinate AI activity across every functions, ensuring that trust, compliance, and performance advance together.”  

Peter Budweiser, General Manager Supply Chain at Celonis

The race to autonomous operations will be won by orchestration

“Enterprises have spent a decade automating tasks. But in the agentic future, the differentiator won’t be how many tasks you automate, it will be how well you orchestrate outcomes. In 2026, leaders will shift from fragmented automation to coordinating AI, people and systems across the entire workflow. This is the only way to transform business processes into truly autonomous operations.

Supply chains will become the proving ground for orchestration. AI will dynamically reroute shipments, rebalance inventory, surface capacity constraints, and coordinate suppliers and planners in the same loop – turning fragile networks into intelligent, adaptive ecosystems that are able to respond instantly to tariffs, disruptions and volatility.

The strategic driver behind supply chain transformation is no longer just cost – its competitiveness. Orchestration lets companies coordinate AI agents, humans, and systems in real time, so their supply chains become more agile, more efficient, and better able to support new business opportunities.”

Dan Brown, Chief Product Officer at Celonis

The AI revolution will run on context

“After years of experimentation, companies will realise that AI can’t improve what it doesn’t understand. In 2026, competitive advantage will shift to organisations that give AI the operational context it needs – a living digital twin that shows how the business actually runs. This is how AI learns to sense, reason, act, and improve responsibly.

Context-aware AI will reshape supply chain decision-making. Instead of optimising isolated steps, AI will understand the full flow – predicting bottlenecks before they occur, identifying exceptions that matter, and orchestrating recovery plans grounded in financial and service-level impact. This closes the gap between planning and execution.

AI can’t drive business value without understanding how your business flows. When you give it that context – the real-time visibility into how work gets done – the trust comes naturally. You see why it made a decision and how to make it better. That’s when AI becomes enterprise-ready.”

Baris Gultekin, Vice President of AI, Snowflake

Data becomes a more powerful moat for Enterprise AI

“The pace of innovation in frontier AI models has provided the enterprise with an incredibly powerful and mature foundation. Give or take a few benchmarks, model capabilities are reaching a high floor, offering similar, state-of-the-art performance. Similarly, as building AI-powered apps becomes faster and easier to build for people of all technical backgrounds, the features that distinguish one product from another will also begin to fade. 

By 2026, we’ll see this commoditisation accelerate across the entire AI stack. In this new landscape, an organisations’ sustainable competitive advantage won’t be the model or application itself, but the unique, proprietary data an organisation holds and its ability to reason over it. The companies that master the ‘data flywheel’ – using their unique data to create better AI, which in turn generates more unique data – will establish meaningful differentiation for years to come, and continue to benefit from improvements to the AI tools themselves.”

Agent Interoperability will unlock the next wave of AI productivity

“Today, most AI agents operate in walled gardens, unable to communicate or collaborate with agents from other platforms. This is about to change. By 2026, the next major frontier in enterprise AI will be interoperability – the development of open standards and protocols that allow disparate AI agents to speak to one another. Just as the API economy connected different software services, an ‘agent economy’ will quickly emerge, where agents from different platforms can autonomously discover, negotiate, and exchange services with one another. Solving this challenge will unlock compound efficiencies and automate complex, multi-platform workflows that are impossible today to usher in the next massive wave of AI-driven productivity.”

Dwarak Rajagopal, Vice President of AI Engineering and Research, Snowflake

The future of AI agents is in self-verification, not human intervention

“In 2026, the biggest obstacle to scaling AI agents – the build-up of errors in multi-step workflows – will be solved by self-verification. Instead of relying on human oversight for every step, AI will be equipped with internal feedback loops, allowing them to autonomously verify the accuracy of their own work and correct mistakes. This shift to self-aware, ‘auto-judging’ agents will enable the development of complex, multi-hop workflows that are both reliable and scalable, moving them from a promising concept to a viable enterprise solution.” 

Mike Blandina, Chief Information Officer, Snowflake

AI will redefine the role of the CIO from IT Operations to Enterprise Innovation

“In the next year, the role of the CIO will shift from ‘IT’ to ‘ET’ – from information technology to enterprise technology leadership. Traditional metrics like ticket counts will still matter, but forward-looking CIOs will adopt a solution mindset. The modern CIO must leverage AI not just to source tools, but to engineer outcomes. Instead of recommending SaaS vendors, CIOs will assemble multiple LLMs to build solutions to solve today’s problems while anticipating what’s next. The IT function will no longer be just about infrastructure – it will be about delivering corporate intelligence with AI-driven solutions and providing leverage across every critical business platform. AI will redefine the CIO as a business innovator, not just a technology operator.”

CIOs will become an organisation’s number one sustainability steward

“In 2026, CIOs will be expected to own the responsibility for tech-driven sustainability. As enterprises face mounting pressure from regulators, investors, and customers to meet climate goals, CIOs will be expected to deliver the data, platforms, and AI-driven insights that make sustainability measurable and actionable. From optimising cloud workloads for lower energy use to applying advanced analytics that cut supply chain emissions, CIOs will increasingly be at the centre of corporate sustainability strategies. This isn’t just about compliance reporting, it’s about leveraging technology to transform sustainability into a source of efficiency, growth, and differentiation for the enterprise.”

  • Data & AI
  • Digital Strategy

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

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

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

Discover tomorrow’s tech today

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

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

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

GLOBAL INSPIRATION, LOCAL IMPACT

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

Where the future of technology takes the stage

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

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

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

Register for FREE

Register for your Ticket

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