Can AI help fix legacy code?
AI tooling is everywhere right now, and its promise is compelling: faster fixes, cleaner code, less time trawling through legacy spaghetti. For product leaders sitting on fragile apps with creaking codebases, it sounds like a golden ticket. But, where does the hype end and the risk begin?
Whether you’ve been handed a messy app that no one wants to touch, or you’re worried that your dev team is about to use AI to ‘clean things up’, here’s what you need to know before diving in…
Old code has a habit of fighting back. Without documentation, context or test coverage, even minor changes can create knock-on issues that are hard to spot, until it’s too late. If your developers are spending more time decoding than delivering, it’s likely that your legacy system is costing you:
- Missed opportunities - as new features take too long to ship
- More bugs and hotfixes - due to fragile foundations
- Over-reliance on a few key individuals to keep things working
Throwing AI at the problem can feel like a nice shortcut. In some special cases, it is. But in the majority of others, it’s a one-way ticket to even more technical debt.
Used well, AI can save time on the boring stuff - spotting duplicate logic, scaffolding tests, generating documentation, even suggesting small refactors. These wins give developers back their time and reduce friction in your workflow.
But, here’s the catch: AI tools don’t understand your business logic. They don’t know which edge cases matter, why something was implemented a certain way, or what’s truly safe to change. When AI rewrites sensitive logic or refactors performance-critical areas, the cost of being wrong is high and the regressions are hard to trace.
The result? You’re left with a messier codebase, more things to unpick, and no clear way to tell what broke and why. This is why we say ‘use well’. AI has its uses, but they are specific. Here’s where we feel AI to help you best, rather than harm you:
- Speed up repetitive work: Boilerplate code, documentation suggestions, test scaffolding
- Highlight common inefficiencies: Duplicate logic, unused dependencies, basic refactor suggestions
- Support knowledge transfer: Generate rough module explanations that help new devs onboard faster
This is where AI shines: saving time in low-risk, high-volume areas.
No you know what AI can do, you need an idea of what to avoid. When the stakes are higher, AI tools often fall short. While an expert can use AI (almost) anywhere, if you’re a team just looking at using AI for the first time, here’s where you should typically avoid relying on AI:
- Complex refactors or architecture changes: AI can’t assess why something was built the way it was
- Security or compliance-sensitive code: You risk breaching regulations if AI suggests non-compliant changes
- Performance-critical logic: It might automatically attempt to ‘improve’ code that’s already been carefully optimised, creating slowdowns
In these cases, AI cannot replace your developers. It’s just not possible, and that’s okay!
AI’s suggestions and capabilities can be impressive, but they’re also detached from your app’s reality. That’s not a safe mix when the margin for error is small. It’s not about banning AI from your workflow - it’s about applying it where it can help, and avoiding it where it can harm.
It’s all well and good knowing where to and to not use AI - but do you know where to start with your app at all? Before making any changes to a brownfield app, you must always lead with clarity.
Our audit is built for exactly this. We’ll go through your app, take a look under the hood, and point you in the right direction - before you get tangled up in the mess.