Choose one measurable unit of work
Define the task precisely: a resolved internal question, a reviewed document or a correctly prepared service request. Record its inputs, accepted output and responsible reviewer. Avoid a pilot scope that combines several unrelated jobs, because the results become hard to interpret. Agree what the system may do, what requires approval and which failures would make the use case unacceptable.
Measure the current process
Collect a representative baseline for volume, time, error rates and exception handling. Include work performed by reviewers and the delays caused by missing information. Separate active handling time from waiting, and note where estimates replace measurements. A fair comparison needs the same task population and an explicit account of differences, such as a pilot receiving cleaner documents than the production team normally sees.
Build the evaluation before tuning the system
Use realistic cases that include routine requests, difficult inputs and examples the system should escalate. Keep a portion separate from development so repeated tuning does not simply teach the system the test. Define scoring with subject-matter experts. For a knowledge assistant, inspect whether sources support the answer; for an agent, inspect whether the right action occurred with the correct permission.
Calculate cost per accepted outcome
Count model usage, retrieval or storage, infrastructure, integration maintenance, monitoring and human review. An inexpensive generated answer can become costly if it needs extensive correction. Compare cost per accepted task and total process time with the baseline. State assumptions about volume, provider prices and support rather than presenting one pilot result as a guaranteed saving at enterprise scale.
Test boundaries as well as usefulness
Check access restrictions, sensitive information handling, unexpected tool results and behaviour when source content contains conflicting instructions. OWASP’s GenAI risk material is a useful reference for application-level risks. NIST’s AI Risk Management Framework provides a broader voluntary risk-management reference. These references inform a proportionate test plan; they are not a certificate or proof that a particular implementation is compliant.
Make the rollout decision explicit
Record the achieved results, unresolved issues and requirements for operation. A rollout should name the service owner, support route, usage controls and changes that trigger re-evaluation. The next step may be a wider controlled trial, a narrower use case or a redesign. Continuing only where the evidence supports it keeps investment connected to operational value rather than enthusiasm for the demonstration.
Further reading
Put the guidance to work
AI opportunity assessment, use-case prioritisation, pilot economics, supplier evaluation, operating models and practical team training.
AI strategy & adoption