Organizations are good at starting pilots. They are often less explicit about ending them. An AI experiment can accumulate users, integrations and recurring costs while its original question remains unresolved. The NIST AI RMF Playbook offers practical background for managing AI risk; an exit plan is one way to turn that discipline into an operational decision.
Write the decision before the demo
A pilot should answer a concrete question. Can the system reduce a defined task’s completion time without increasing error? Can staff identify and correct its mistakes? Is the total cost acceptable under realistic usage? Each question should have an owner and an agreed method of measurement.
The team can then define what evidence would justify expansion, what would require revision and what would end the experiment. These conditions prevent a successful presentation from substituting for a successful operational test. They also make it easier to discuss disappointing results without treating them as a personal failure.
Preserve a route back
An experiment should not become the only place essential information lives. Keep records exportable and document the existing process well enough to restore it. If users depend on the tool during the trial, explain how work will continue if the service is paused or removed.
Supplier changes deserve attention as well. Pricing, model behavior and availability can evolve. A pilot that works under one configuration may need another review when those conditions change. Record the version and assumptions that supported the original decision.
Treat closure as a result
Ending a pilot can be a useful outcome when it prevents a larger commitment to a weak idea. The organization should retain the lessons, evaluation cases and unresolved questions. A disciplined experiment produces knowledge even when the product does not proceed.
