AI is an accelerator, not a shortcut, when tackling legacy systems

This post is part of our Delivering Public Safety Outcomes at Pace series.

Modernising legacy technology has never been about simply replacing what exists. As we saw in the previous article, the real challenge is understanding complex systems and improving them without disrupting the services that depend on them.

AI is now accelerating that process. It is changing how teams analyse, rebuild and improve legacy systems, but it is not removing the need for careful engineering or clear thinking.

Ben Pirt, Principal Technologist at Made Tech, describes the impact of AI. “It has been phenomenally helpful in understanding a legacy codebase,” he says. “With the right inputs, AI can analyse structures, surface relationships and explain how systems behave in a way that would previously have taken weeks or months to piece together.”

That kind of visibility matters because discovery is often the slowest part of modernisation. Teams need to understand not just what a system does, but why it behaves the way it does, and how that connects to real-world processes. AI does not remove the need for that work, but it can accelerate it significantly.

Speeding up redevelopment without losing control

It is also starting to change how redevelopment happens. In one example, teams used AI to extract behaviours from a legacy codebase and treat those behaviours as a set of specifications. They then used AI to reimplement those behaviours in a new language, bringing tests across at the same time.

The results were immediate. “A week-long test got through a huge amount,” Ben says. “For certain types of work, particularly where patterns are well understood, AI can speed up delivery in a way that would have been difficult to achieve even a year ago.”

That is especially true for more repetitive tasks. Building API endpoints, following established patterns and generating boilerplate code are all areas where AI is already performing well. As Ben puts it, it is “insanely good” at following structured instructions.

It would be easy to see this as a shortcut to solving legacy problems, but the reality is more complex. AI can move things forward quickly, but it can also replicate existing issues just as fast if it is not used carefully.

“If you just say to AI, ‘port this code’, it’ll do it,” Ben explains. “But it might not do it very well.” In that scenario, the risk is that you carry forward the same poor design into a new environment, rather than improving it.

That is why engineering discipline still matters. If anything, it matters more. Strong testing, clear specifications and careful validation are what make AI useful rather than risky.

Just as importantly, organisations need permission to approach AI incrementally. That often means starting in low-risk areas, testing where it adds value, and creating space for teams to learn without feeling they have to bet the service on a single decision. In practice, responsible innovation often depends as much on creating that permission as it does on the technology itself.

Ben describes using AI within a controlled, test-driven approach. Behaviour is extracted, verified against the existing system and then used to guide the new implementation. The AI is not left to decide what “good” looks like on its own; it is constrained by clear rules and expectations.

“If you force it down a rigorous path, you can get extremely good quality,” he says. “Without that structure, the outputs are far less reliable.”

That structure also has to include transparency and governance. If AI is helping analyse, generate or recommend changes to legacy systems, teams need to understand how those outputs are reached, how decisions are validated, and where accountability sits.

Avoiding a new generation of technical debt

There is also a broader question about how AI is used across organisations. As tools become more accessible, it becomes easier for teams to build solutions quickly. That can be a positive shift, but it also introduces a familiar risk. Geraldine Mathews, Client Partner at Made Tech, highlights the concern that organisations may start solving problems in isolation. One team builds something for one part of the service, another team builds something elsewhere, and the overall journey becomes more fragmented rather than less.

“It’s got to be about the full user journey,” she says. “Without that focus, there is a real chance of creating a new layer of technical debt on top of the old one.”

Geraldine continues: “This is where the conversation becomes particularly interesting. AI is often positioned as a way to reduce technical debt, but it may also change what technical debt looks like. Instead of slow, ageing systems, the risk becomes fast-moving, disconnected ones.

“The technology itself is not the issue. The challenge is how it is applied. Without a clear delivery approach, strong architecture and a shared understanding of user needs, speed can work against you.”

AI is an accelerator, not a shortcut

At the same time, expectations are rising quickly. Organisations are seeing demonstrations of AI rewriting legacy systems and naturally begin to expect similar results. As Geraldine notes, “the expectation is going to be very high now”.

“That creates pressure to move faster, but it also increases the importance of getting things right. Delivering quickly is only useful if what you deliver is coherent, maintainable and aligned with how people actually work.”

This is also where risk assessment becomes practical. Rather than asking whether AI should or should not be used, the better question is where it is appropriate, where human oversight should remain, and how risks can be reduced through staged delivery. That is often where Made Tech works closely with clients, assessing the service, identifying suitable use cases, and proving approaches safely before scaling them into live environments.

Geraldine continues: “There is also a question about how far AI can go in redefining legacy modernisation. Some suggest that older systems can simply be translated into modern stacks with minimal effort. While that is technically possible, it risks missing a key opportunity.

“Porting a system does not improve it. It changes the environment it runs in, but it does not address the underlying design issues or the mismatch with user needs. Without that deeper work, the same problems are likely to resurface.”

That is why the fundamentals remain the same. Understanding users, designing around real workflows and building systems that can evolve over time are still central to successful modernisation. AI can support that process, but it cannot replace it.

In practice, the most effective use of AI is as an accelerator rather than a solution in its own right. It helps teams understand systems more quickly, test ideas more thoroughly and deliver certain types of work more efficiently.

What it does not do is remove the need for judgement. As Ben puts it, the best engineering practices still apply, and in many cases, they become even more important when AI is involved.

Looking ahead, the role of AI in legacy modernisation is likely to evolve quickly. The organisations that benefit most will not be the ones that adopt it fastest, but the ones that use it most thoughtfully.

In that sense, AI does not change the goal of modernisation. It changes how effectively that goal can be achieved, provided the focus remains on building systems that genuinely work for the people who rely on them.

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