The best AI pilot is not necessarily the most impressive demo. It is a use case with a valuable decision, usable data, a feasible route into daily work and a risk level the organization can manage.
Two pilots compete for funding: one offers dramatic predicted savings but has no usable outcome history; the other offers modest savings with clean data, a committed owner and a clear review path. The second may teach the organization more and reach production sooner. A feasibility framework makes that trade-off explicit.
Score the opportunity in four parts
Estimate business value from a concrete decision or task. Check whether representative data and credible outcomes exist. Examine deployment friction: integration, user training and response time. Review privacy, safety, fairness and other relevant risks with the right owners.
Make pilot exit criteria explicit. A team should know what performance, adoption and risk evidence would justify scaling, further testing or stopping. Include a rollback plan and a responsible owner. This protects the organization from pilots that linger indefinitely because no one wants to declare them unsuccessful.
Compare against a simple baseline
Could a clearer rule, better workflow or standard dashboard solve most of the problem? If so, start there. A pilot should show what additional value an AI approach contributes.
Design a stopping rule
Agree on success measures, time frame and unacceptable outcomes before development. Make room to stop, narrow or redesign if the evidence is weak. A modest pilot that teaches the team how to make a better decision is more valuable than an expensive prototype no one can responsibly operate.
