How to Evaluate an AI Pilot Before Expanding It

An impressive demonstration is a starting point for an AI project. A pilot should show how the system behaves on the work your organization actually needs to complete, including the awkward cases. Define the evaluation before the pilot so success does not become whichever result looks most favorable at the end.

Choose a bounded task and a named business owner. Examples include drafting a support response, extracting selected fields from approved documents or summarizing an internal report. Specify the information available to the system and the action it is allowed to take.

Define an acceptable result

Write criteria a reviewer can apply consistently. For a document-extraction task, that might include correct values, useful source references and explicit treatment of missing fields. For a response draft, criteria might include factual accuracy, relevance, tone and whether it makes an unauthorized promise.

Keep severe errors separate from minor presentation issues. A well-written answer that exposes restricted information or triggers the wrong business action should not receive a passing assessment because its wording is polished.

Build a representative test set

Use examples covering normal work, common variations and known exceptions. Include incomplete inputs, conflicting information and cases outside the system’s intended scope. Remove unnecessary sensitive information and use approved test data where possible.

Reserve some examples for evaluation after changes so the team is not only measuring tasks it has repeatedly tuned against. Have a knowledgeable reviewer record the expected outcome or assessment criteria. When reviewers disagree, resolve the business rule instead of treating the disagreement as a model defect.

Evaluate the full workflow

Measure what happens before and after the AI step. How much preparation is required? How long does review take? How often must staff correct the output or complete the task manually? Which failures are easy to detect, and which could pass unnoticed?

The NIST Generative AI Profile is a companion to its voluntary AI Risk Management Framework and addresses trustworthiness across design, use and evaluation. It is a reference for structuring questions about a system; using it does not amount to a certification of your pilot.

Compare with a meaningful baseline

Document how the task is completed today. Use a comparable set of work and include the time needed to check the result. A pilot that produces a draft quickly may still create more effort if staff must investigate every claim.

Record operating costs at the workflow level: model usage, retrieval, platform fees, review time and support. Keep assumptions visible. If volume or task complexity changes, revisit the estimate rather than extrapolating a small demonstration into a promised return.

Choose expansion criteria in advance

• Which tasks must meet the agreed acceptance criteria?
• Which error types prevent a wider rollout?
• What review effort can the team support?
• Who can approve new data sources or actions?
• What monitoring and manual fallback must be ready?

Decide whether the next step is a larger supervised pilot, a narrower use case or a production release with defined limits. A pilot that reveals a weak source collection or an unclear process has still produced useful evidence.

Keep evaluation after launch

Assign ownership for collecting failed examples and reviewing changes to prompts, models, sources and integrations. Maintain the test set as the business learns. Check that changes improve the intended task without weakening access boundaries or creating new failure modes.

Give staff an easy way to report a questionable result with enough context to reproduce it. Keep the response process clear so reported problems lead to a decision rather than a growing folder of screenshots.

LeapHover can help define the pilot, build a practical evaluation set and connect the results to a rollout decision. Bring the task you want to improve and the standard of work your team needs to trust.