Organizations rarely lack AI ideas. They lack a defensible way to choose between them. Without one, the loudest sponsor wins, the pilot succeeds in a demo environment, and nothing reaches production.
Score on value, feasibility and exposure
Value is the annual dollar impact if the use case works at target accuracy. Feasibility covers data availability, integration surface and whether an acceptable evaluation set can be assembled. Exposure captures regulatory, reputational and safety risk if the model is wrong.
Score each axis one to five. Anything below three on feasibility is not a first-cycle candidate regardless of value, and anything above four on exposure needs a human-in-the-loop design before it is scheduled.
Insist on an evaluation set before the build
If nobody can produce two hundred labeled examples of the correct outcome, the use case is not ready. That constraint alone removes most of the ideas that would have failed later and much more expensively.
Design the fallback first
Every production model needs a defined behavior for low-confidence outputs: route to a person, apply a conservative default, or decline. Deciding this before the build determines the interface, the staffing model and the audit story all at once.