How to evaluate an AI pilot before approving production
A practical evaluation framework covering task quality, workflow completion, exceptions, operating cost, and production controls.
SHORT ANSWER
Evaluate an AI pilot against a representative task set, the complete workflow outcome, documented failure categories, human correction effort, latency, cost, permissions, and safe fallback. A strong model score alone is not a production decision.
Define the production decision first
A pilot should answer a specific decision: whether a bounded workflow is valuable and controllable enough to operate, what must change before release, or why the idea should stop. Without that decision, a team can demonstrate impressive output while learning little about production risk.
Write the current baseline, target users, accountable owner, allowed inputs, required output, and unacceptable failure before selecting a model. These constraints decide what the evaluation needs to measure.
- Which workflow step is changing?
- What remains under human approval?
- What is the current time, error, or queue baseline?
- Which failure would make rollout unacceptable?
Build a representative evaluation set
A convenient set of clean examples usually overstates readiness. Sample normal, difficult, ambiguous, incomplete, adversarial, and permission-sensitive cases from the real input distribution. Keep the expected outcome and review notes outside the model under test.
Version the set. When prompts, models, parsers, retrieval, or business rules change, rerun the same cases and record regressions instead of relying on memory or a few screenshots.
Measure the workflow, not only the response
Task correctness matters, but the buyer experiences the complete path. Include the rate of work completed without intervention, correction time, exception queue size, downstream import success, latency, cost, and the operator's ability to understand what happened.
- Task-level correctness under an agreed rubric
- End-to-end completion and downstream acceptance
- Human review and correction effort
- Failure detection rather than silent failure
- Latency and cost across the full workflow
Classify failures before improving the average
Separate input quality, missing context, retrieval, model reasoning, schema, business-rule, integration, permission, and operator-interface failures. Each class has a different fix. Increasing model size does not repair a missing source permission or a broken downstream contract.
The pilot is ready to expand when important failure classes are detectable, routed, and economically manageable, not when every case is forced through automation.
Require production controls in the decision
Before approval, document access, data retention, logs, model and prompt versioning, rollback, provider failure, rate limits, manual fallback, and who owns operational review. If the pilot cannot explain these controls, it has not evaluated production readiness.