How to Evaluate an AI Pilot Before Scaling

Agree the decision criteria

Before reviewing results, restate the business objective and acceptance criteria. Identify the task scope, representative cases and baseline used for comparison.

If success criteria were missing, gather a fair baseline before making expansion claims. Do not select only the strongest examples from a demonstration.

Review quality and exceptions

Check accepted outputs, substantial corrections, missed errors and cases the system could not handle. Separate routine tasks from difficult or higher-consequence situations.

An illustrative proposal pilot should test incomplete briefs and conflicting requirements as well as straightforward examples. Keep human review effective when workload increases.

Calculate the complete operating effort

Include input preparation, verification, corrections, training, support, subscriptions and integration maintenance. Distinguish time made available from actual cash savings.

Compare the whole process with the original approach. Document assumptions and avoid projecting a small pilot’s best result across the entire business.

Check readiness for a larger scope

Confirm support ownership, permissions, data maintenance and fallback procedures. Test whether more users or new information create different risks.

NIST’s AI RMF 1.0 treats AI risk managementThe ongoing process of identifying, assessing and addressing as ongoing. Reassess when the operating context changes.

Choose expand, revise or stop

Expand in a defined next stage when evidence supports it. Revise when the task remains valuable but the workflow needs changes. Stop when benefits do not justify costs or unresolved risks.

Record the decision and next review point. Link the next stage with your business transformation roadmap and AI adoption plan.

Sources and further reading

• NIST AI Risk Management Framework 1.0: Core