Use case assessment
Is AI code review worth building?
Genuinely useful, and the failure mode is volume rather than accuracy.
Real value on a narrow band of review work. Point it at everything and developers stop reading it, which costs you more than not having it.
What it usually means
Automated comments on pull requests: bugs, style, security issues, missing tests.
Is it genuinely AI?
Partly, and the split matters. Style and many classes of defect are caught by linters and static analysis, which are deterministic, fast and free. If those are not fully deployed, that is the first move.
What models add is the band above: logic that is valid but wrong, missing edge cases, code that does not match the intent stated in the pull request description. That is genuine value and static analysis cannot reach it.
The failure mode
Volume. A reviewer that comments on everything trains developers to dismiss it, and once dismissal is habitual the useful comments go with the rest. This happens within weeks and is very hard to reverse.
The discipline is to be aggressive about what it stays quiet on. A tool that comments on one in five pull requests and is right when it does is worth more than one that comments on all of them.
What the simpler version looks like
Linters and static analysis, fully configured, failing the build. Then add model review on top of that floor rather than instead of it.
Measure what you have first: what proportion of human review comments are style or convention? If it is most of them, tooling you already have solves this.
What it costs to run
Inference scales with pull request volume and code size. Prompt and configuration maintenance as the codebase evolves. And the attention cost of every comment, which is the real one — a false positive costs a developer's concentration, and that is the budget being spent.
Where it sits under the EU AI Act
Internal development tooling is not an Annex III high-risk use.
The thing to check is not the AI Act but your code leaving your environment. Where the model runs, what it retains, and whether that is compatible with your customer commitments is the question that stops these procurements.
When it is worth building
Buy, do not build — this is a mature category. Worth adopting when static analysis is already in place, pull request volume is high enough that human review is a bottleneck, and you will tune it down aggressively in the first month.
Not as a substitute for human review on anything that matters. It is a filter that catches things tired people miss, which is genuinely valuable and is not the same as a reviewer.
Common questions
Does AI code review replace human review?
No. It catches a band of issues that static analysis cannot reach and tired reviewers miss — logic that is valid but wrong, missing edge cases, code that does not match stated intent. It is a filter, not a reviewer.
What makes AI code review fail?
Comment volume. A tool that comments on everything trains developers to dismiss it, and once dismissal is habitual the useful comments are lost with the rest. Tuning it to stay quiet is the work.
What should you do before adopting AI code review?
Deploy linters and static analysis fully. Then measure what proportion of human review comments are style or convention. If it is most of them, tooling you already have solves the problem.
Cutting enterprise change requests 15 to 7 minutes — narrowing scope until the thing was trusted, which is the same discipline.