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Choose a bounded decision
Start with a specific workflow, such as categorising incoming requests for a reviewer. Identify the people affected, the data involved and what a wrong output would mean. Avoid allowing a broad demonstration to become an undefined operational system. Decide what the pilot must never do as clearly as what it should attempt.
Establish a baseline
Compare the proposed AI approach with the current process or a simpler rule-based method. Define the measures before testing: useful accuracy, review effort, response time and cost may all matter. A model that improves one measure but creates substantial rework may not improve the overall workflow.
Use representative evaluation examples
Create permitted test examples that reflect routine cases, difficult cases and known failure conditions. Keep evaluation examples separate from model development where applicable. Record false positives, false negatives and situations where the system should defer to a person. For generated answers, assess support for claims and the effect of missing or misleading context.
Design the review and stop conditions
Decide who checks outputs, what evidence they see and how they override a result. Restrict access to data and tools to the task’s needs. Define conditions that pause the pilot, such as repeated unsafe output or an unexpected increase in errors. NIST’s AI Risk Management Framework is a useful reference for structuring risk discussions; using it is not a certification.
Make the go-or-stop decision explicit
Document the baseline, test results, limitations, operating cost and proposed responsibilities. Expand only when the evidence supports the next step and the controls are workable. It is reasonable to revise the use case, keep a human-led process or stop the experiment. A useful pilot produces a decision, not merely an impressive demo.
