There is a pattern to AI projects that fail. They begin with a broad exploration, produce a long list of possibilities, and stall at the point where someone has to own a workflow change. Everyone learned something. Nothing changed.
The projects that work start narrower. Find the tasks where a competent person is doing something repetitive with text, documents or data. Rank them by the value released, the effort required and the consequence of being wrong. Then build the top one or two properly, inside the systems people already use.
Guardrails matter more than model choice. Where does a human review the output? What happens when confidence is low? What data leaves your estate, and under what agreement? These questions decide whether the thing survives its first month in production.
Cost deserves the same discipline as any other infrastructure line. Usage patterns for AI features are lumpy and easy to underestimate. Model it before launch, instrument it after.
The unglamorous truth is that most AI value in mid-sized businesses comes from removing internal friction, not from a customer-facing feature. That is fine. Margin is margin.