AI writes the obvious test cases. That is the point.
The value is not the scenarios it generates. It is the hours it gives back for the ones it cannot.
Handed a requirement, a language model will produce a competent list of test scenarios in seconds: the happy path, the empty field, the boundary, the duplicate submission.
Those are exactly the cases a tired human writes badly at five in the afternoon, and exactly the cases that are never the interesting ones.
What it is good at
Volume and consistency on the mechanical half. Enumerating combinations. Spotting that a requirement says "the user may" twice with two different meanings. Turning a failure report into a reproducible scenario that someone else can run.
What it is not good at
Knowing that this particular flow touches a settlement window, and that a failure there costs a reconciliation, not a retry. Knowing which customer is on an exception contract. Knowing that the last three incidents in this area all came from the same integration behaving differently on Mondays.
That knowledge is not in the requirement. It is in the heads of people who have been there, and a model has no access to it unless someone puts it there.
Where the time goes instead
The point of automating the obvious half is not shipping faster. It is that the quality analyst's afternoon moves from writing cases nobody will argue with to sitting in a refinement and asking the question that changes the design.
The honest caveat
A generated suite looks complete, and looking complete is a risk of its own. Coverage you did not reason about is coverage you cannot defend when the regulator asks.
