Short answer: Define the baseline and acceptance threshold before testing, then measure quality, time, cost, adoption, exceptions, and the amount of human correction required.
Write the decision before running the test
State what decision the pilot supports: proceed to a limited production phase, revise and retest, pause for missing data or process work, choose a simpler solution, or stop. Without that decision, teams can celebrate an impressive demonstration while avoiding the evidence that matters.
Establish the current baseline
Measure the workflow before AI is introduced. Useful baseline measures include:
- volume and completion time;
- waiting time and backlog;
- correction, rework, and escalation rate;
- quality or accuracy assessed by qualified reviewers;
- cost per completed case;
- customer or staff impact;
- the number and severity of exceptions.
Use the same definitions for the pilot. A faster result is not an improvement if reviewers spend more time correcting it.
Define acceptance thresholds
Set minimum quality, maximum harmful-error rate, maximum review time, acceptable cost, and permitted latency before testing. Separate critical failures from ordinary imperfections. One severe privacy, safety, or commitment error can matter more than a high average score.
Evaluate representative cases
Create a fixed test set with normal examples, difficult examples, incomplete inputs, multilingual cases where relevant, and situations where the correct action is to abstain or escalate. Keep the evaluation set separate from examples used to tune the solution.
Record the model and version, configuration, prompt or instructions, sources, output, reviewer decision, correction, latency, and usage cost for each run.
Measure the complete workflow
| Dimension | Practical measure |
|---|---|
| Quality | Accepted outputs, critical errors, unsupported claims, and correction severity |
| Time | End-to-end time including human review and exception handling |
| Cost | Provider usage, infrastructure, review, support, and maintenance |
| Adoption | Appropriate use by intended staff, not raw login counts |
| Risk | Privacy, security, bias, harmful action, and contractual exceptions |
| Reliability | Variance across repeated tests, languages, and difficult cases |
Keep human review visible
Track how often reviewers accept, edit, reject, or escalate an output and how long each action takes. If the process depends on one expert silently repairing weak results, the pilot has moved work rather than removed it.
Use explicit shutdown criteria
Stop or isolate the pilot when it uses prohibited data, produces a critical harmful error, bypasses review, exceeds the approved budget, cannot be reproduced, or loses the accountable owner. Pausing is a valid result when the workflow or source data needs improvement first.
Make the final recommendation auditable
The closing report should contain the baseline, test set, results, failures, corrections, full cost, unresolved risks, and a recommendation with conditions. Preserve evidence for the decision. “Users liked it” and “the demo worked” are observations, not production acceptance criteria.