Start with an operating problem
Choose a decision or workflow where better information, speed or consistency matters. Define what will change for users before selecting a model. An operational question makes the evaluation testable: who makes the decision today, what information is missing, and which mistakes would matter? A small set of representative cases should include difficult exceptions, not only polished demonstrations. Compare the proposed tool with the current process on quality, time and human effort before widening access.
Redesign work around value
A prototype rarely scales unchanged. Revisit hand-offs, decision rights, data access and exception handling so people and systems can work together in real conditions. Workflows need clear points at which a person can inspect, correct or reject an output. Data permissions, records of changes and escalation routes should be designed with the process rather than added after launch. A useful pilot identifies the hand-offs that must change and the capabilities staff need to use the result responsibly.
Build governance into delivery
Assign ownership for quality, privacy, security and review. Monitor outcomes after launch, let staff challenge results and retire applications that no longer serve their purpose. The NIST AI Risk Management Framework offers a way to organise questions about governance, measurement and ongoing management. Leaders can assign a named owner for each use case, define what constitutes an unacceptable result, and review evidence at regular intervals. Where the benefit cannot be shown or the risk cannot be managed, stopping is a valid outcome.
Decide what deserves scale
Before funding wider deployment, ask whether the use case changes an important decision, whether staff can challenge its output, and whether the organisation can monitor quality after release. Approve an expansion only with a defined owner, a comparison against the existing process and a plan for exceptions. This turns a technology experiment into a governed operating choice.
Reference: NIST AI Risk Management Framework ↗