In practice, we often see two extremes. If there is a lot of documentation, AI can generate a  large number of cases, many of which add little value. They may look technically reasonable, but they do not necessarily improve coverage in a meaningful way. The QA engineer still has to review them, understand which cases matter and remove the rest.

The opposite happens when documentation is incomplete. In that case, the generated tests are often too superficial. They cover what is explicitly written, but they miss relationships, dependencies and risks that are obvious only if you understand the product and the surrounding system. The output can still look convincing, but that does not mean it gives enough confidence in the quality of the feature.

This is where AI exposes a familiar problem rather than eliminating it. A model can work only with the context available to it. If important product knowledge lives in people’s heads, in previous incidents or in an understanding of architecture that was never documented, the generated test cases will not magically contain that knowledge.