“AI wrote 200 test cases for this feature in ten minutes.”
AI test generation is real progress. But the bug that matters still slips through.
Why?
Because AI generates from what it’s given: the spec, the code, the ticket. So it covers what’s written down. And one wrong assumption at the start travels through every step.
The gaps teams run into most often
- A case the requirement never listed.
- A journey across several systems, with each system tested only against its own spec.
- A change in workflow A that workflow B reads, with no ticket mentioning it.
- Real data that’s messier than the data the AI generated.
Four questions worth answering before the next sprint
- Which end-to-end journeys matter most, written down before any test is generated?
- What does each test check that the code can’t simply agree with?
- Who reviews AI-generated tests before they count as coverage?
- If you deleted a third of your AI-generated tests, would anything fail?
Teams that answer these get something new: AI-speed test generation pointed at the journeys that carry the risk. Coverage that grows where the risk is, not where the documents are.
