AI & cost of legacy systems in UK banking

Date:
Thu, 02 Apr 2026 14:04:23 +0000

Description:
UK banks spend ~3.3bn/year just maintaining legacy core systems, and AI is exposing the cracks.

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now subscribed Your newsletter sign-up was successful Join the club Get full access to premium articles, exclusive features and a growing list of member rewards. Explore An account already exists for this email address, please log in. Subscribe to our newsletter UK banks are spending approximately 3.3 billion every year simply to keep legacy core systems running roughly a quarter of their total IT budgets. Nearly half acknowledge that these platforms struggle to keep up with everyday demand and strategic priorities.

For decades, this cost has been accepted as the price of stability. Core systems process millions of transactions daily, underpin regulatory compliance, safeguard customer trust, and keep money moving. Replacing them has long been seen as too risky, too costly, or too disruptive. Artificial intelligence is changing those assumptions. Article continues below You may like Why enterprise AI ambitions are outpacing legacy modernization A practical blueprint for scaling AI in financial services Airlines dont have
an AI problem. They have a foundational technology problem Abhijit Shetti Social Links Navigation

SVP, Solutions and Data Tribe Head at Mphasis. AI is accelerating the pace of software change. Tools that speed up coding, automate testing, and support documentation are compressing development cycles. Business stakeholders increasingly expect faster releases, more personalized services, and quicker decision making.

Yet while AI capabilities are advancing rapidly, many banks application delivery models have not kept pace. Teams remain constrained by manual, fragmented practices built for a slower era. This gap between what AI makes possible and what legacy environments can realistically support is becoming impossible to ignore. The hidden fragility of delivery models Legacy systems are only part of the challenge. The deeper issue lies in how change is delivered around them.

In many banks, requirements are not unified or consistently defined. Business logic is embedded in disparate applications, duplicated across silos, or interpreted differently by multiple teams. Institutional knowledge often resides with a small group of long tenured experts rather than being captured in shared, structured assets. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.

Over time, this fragmentation creates layers of technical and operational
debt that are difficult to see and even harder to manage. Introducing AI
tools into this environment does not automatically resolve these issues. In fact, without clear requirements and harmonized rules, AI can amplify them.

AI generated outputs are only as reliable as the inputs they are given. When business logic is ambiguous or inconsistently defined, AI simply reproduces that inconsistency at scale. Teams may appear to move faster, but releases remain brittle, unpredictable, and high risk because there is no stable foundation beneath the surface.

The result is a familiar paradox: banks invest in AI to accelerate delivery, yet change still feels slow, expensive, and fraught with compliance risk. AI has broken delivery; it has revealed just how fragile traditional ones
already were. What to read next Cracking the AI code: realizing AI's true value in finance Finding stability in an age of relentless AI innovation How CIOs Can Implement AI with Real Financial Intelligence Why legacy costs keep rising The 3.3bn annual maintenance bill is not simply a function of outdated technology. It is a symptom of accumulated complexity.

Over decades, banks have built dense webs of integrations, custom
workarounds, and point solutions around their core platforms. Each regulatory update, product launch, or merger adds new layers.

Documentation quickly becomes outdated as logic is re-written to meet immediate needs, while deep system knowledge increasingly sits with a shrinking pool of experts nearing retirement.

In this environment, even small changes can trigger cascading impacts.
Testing cycles stretch because teams fear breaking systems they no longer fully understand. Release windows are tightly controlled and surrounded by risk mitigation processes. Innovation becomes constrained by the need to preserve stability.

AI shines a light on this complexity. When leadership teams see what modern, AI assisted development can achieve in less constrained environments, the contrast with their own systems becomes stark. The opportunity cost of maintaining the status quo becomes harder to justify. From automation to intelligence led delivery If AI is exposing the limits of legacy systems and outdated delivery models, the answer is not simply more automation. What is required is intelligence led delivery.

Intelligence led delivery embeds business logic and decision context directly into the software development lifecycle. Instead of allowing rules drift across multiple codebases or relying on individual interpretation, organizations centralize and codify them in structured, reusable formats.

Requirements become standardized and traceable from business intent through
to implementation and testing. Decision logic becomes explicit, version controlled, and auditable.

With this foundation in place, AI operates on much firmer ground. Code generation aligns with approved business rules. Testing automatically maps to defined decision paths. Impact analysis becomes more predictable because dependencies are visible and understood.

This reduces rework and risk while shortening time to value through clarity and control rather than speed alone. Scaling AI responsibly For financial institutions, speed cannot come at the expense of resilience, security, or regulatory compliance. Intelligence-led delivery places decision logic at the heart of the lifecycle making change more controlled, observable, and explainable.

As AI adoption increases, regulators are paying closer attention to how automated decisions are designed, implemented, and governed. Organizations that cannot clearly explain how business rules are applied across systems
face heightened scrutiny.

Making logic explicit and traceable helps address these concerns while still enabling innovation.

This approach also supports talent evolution. By reducing reliance on tribal knowledge and manual interpretation, teams can focus on higher value work: refining business logic, improving customer experiences and exploring AI enabled services. A practical path forward Transformation does not require a wholesale replacement of core systems. Few banks can afford that level of disruption. Progress can begin with delivery models themselves.

By identifying high change areas, codifying business rules, and aligning AI tooling with structured decision management, banks can gradually reduce dependency on fragile processes. Over time, maintenance overhead decreases, release confidence improves, and a realistic path to incremental
modernization emerges.

The billions spent maintaining legacy cores represents both a burden and an opportunity. Redirecting even a portion of that investment toward
intelligence led practices enables a shift from defensive maintenance to proactive innovation.

AI is neither the enemy of legacy systems nor a silver bullet. It is a catalyst, revealing weaknesses built up through years of incremental change. Banks that rethink how they deliver software will be best positioned to scale AI safely and sustainably.

Those that do not may find that the greater risk lies not in adopting AI too quickly, but in holding on to delivery models that can no longer support the pace of modern banking. We've featured the best AI chatbot for business. This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro



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