An AI review tool is only useful if it helps the reviewer make better decisions faster. That sounds obvious, but many tools optimize for the wrong thing: lots of comments, flashy demos, or generic advice that is easy to generate and hard to trust.
The features that matter
- Repository context, not just diff context
- Low-noise findings that point to real risk
- Integration with the team's existing workflow
- Clear explanations for why something matters
- Consistency across many PRs, not just a few examples
Signs of a weak tool
- It comments on everything
- It gives vague, non-actionable feedback
- It cannot explain its reasoning clearly
- It is hard to fit into your normal review process
How teams should evaluate one
Test it against your real code. Look for whether it helps reviewers notice important issues faster and whether it reduces the time spent on repetitive comments. A useful AI reviewer feels like leverage, not overhead.