What happens when you give an AI your acceptance criteria and ask it to write test cases?

After years of building frontend applications across e-health and e-learning products, I've sat in enough sprint reviews to notice a pattern: QA test cases are written the same way every time. Happy path first, a handful of negative cases if the deadline allows, edge cases if the tester has seen that bug before. The process is repetitive, experience-dependent, and the first thing to get cut when a release is running late. So I started experimenting — feeding acceptance criteria directly to an AI and asking for a complete test suite. Here's an honest account of what works, what doesn't, and what it actually changes about the process. What the AI gets right immediately The output quality on structured coverage is genuinely impressive. Given clear acceptance criteria, the AI will produce happy path cases, negative scenarios, boundary conditions, and precondition states faster than any manual process — and it won't skip the boring ones. It also structures the output consistently: steps, ex