Internal note to the team, we need to improve test coverage and keep shipping, which means we should treat AI as a helper in the workflow, not as a replacement for testing discipline.
AI-assisted development changes the shape of our risk. It can produce more code faster, but it also increases the chance that small logic mistakes, brittle selectors, and shallow test cases slip through review. The answer is not to add more manual checking everywhere. The answer is to be more deliberate about what we review, what we automate, and where we let AI help.
What changes when AI writes part of the code
The first thing that changes is review. When a developer uses AI to draft a feature, a test, or a refactor, the reviewer is no longer only checking intent and style. The reviewer also needs to check whether the generated code matches the product rule, whether it introduced a hidden dependency, and whether it quietly weakened coverage.
That does not mean every AI-assisted change deserves extra ceremony. It means our review checklist should shift from "does this look correct" to "what did the model assume, and did we verify those assumptions?" That is especially important for test code, because generated tests often look plausible even when they do not prove much.
