Most enterprise AI programmes begin with a capability and go looking for a use. The order is backwards, and it produces demonstrations rather than deployments, because nobody defined what a correct answer looks like before the work started.
Start from a decision, not a dataset
A useful starting point is a decision somebody currently makes by intuition, several times a week, with consequences when it is wrong. That framing gives you an evaluation set almost for free, because the people making the decision already know which past calls were good ones.
Confidence is part of the output
A prediction a workshop manager cannot interrogate does not get acted on. It gets ignored politely, and six months later the programme is described as having failed. Exposing the model's confidence and the evidence behind it is not a nice-to-have; it is the difference between a system that changes behaviour and one that produces a dashboard.
The smallest model that clears the bar is usually the right one to ship. It is cheaper to run, easier to explain, and its failure modes can actually be enumerated — which is what maturity means.