I remember the first time I plugged an AI code review tool into our repository. I expected magic. It flagged three thousand issues in the first scan. Three thousand. Most were trivial, like naming conventions and line lengths and a missing docstring here and there. My team looked at that dashboard and just stopped looking. That was my mistake. I thought more data meant more insight, but what I actually did was bury the signal under a mountain of noise.
The thing about technical debt is that it's not just messy code. It's the decisions we deferred because we had a deadline. It's the module everyone is afraid to touch. An AI can point at a function and say complexity score 34, consider refactoring, but it can't tell you that function is also handling credit card processing for three separate legacy systems and nobody has touched it since 2017. That context lives in people's heads, not in the code.
Over time I learned to filter. I set the AI to only flag issues that actually mattered, changed files, security vulnerabilities, performance hotspots. Not style. We already have a linter for that. The moment I narrowed the scope to real risk, the same tool became useful. It caught a null pointer dereference in a payment flow that our human reviewers had missed for weeks. That was a turning point. I started to see AI review less as a replacement for human judgment and more as a second pair of eyes that never gets tired but also never understands the business.
Technical debt detection is where AI has surprised me. It can trace dependencies across modules and show which parts are most coupled, which have the highest churn, which tests are flaky. It can spot duplication that isn't obvious because the code is spread across different teams. We used that to prioritize a refactoring effort on our order processing service. But here's the catch: the AI didn't tell us what to fix. It just showed us the mess. We still had to sit in a room and argue about which debt was worth paying down and which we could live with forever. That conversation is purely human.
I made another mistake later. I let the AI auto-approve trivial changes, formatting, import sorting, simple renames. It felt efficient until a developer used that trust to slip in a small logic change inside a formatting commit. The AI approved it because the diff looked clean. A human would have noticed the conditional flipped. That shook me. Now I have a rule: no automated approval for any pull request that touches business logic, even if it's just one line. Tools are tools; accountability is ours.
AI code review and debt detection are helpful, but they don't replace the uncomfortable conversations about why the debt accumulated in the first place. If you don't address that, you'll be scanning forever.