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What to measure in an AI pilot: quality, time, cost and actual use

A pilot succeeds when the workflow improves under realistic conditions. A scorecard keeps quality, reviewer effort and actual use visible alongside speed and cost. Agree on definitions and acceptance thresholds before seeing results so an attractive demonstration does not determine the verdict.

By Guillermo Ayala López · 3 min read

Manager comparing baseline and pilot task scorecards with the same measuring frame. — AI-generated editorial illustration.

In this article

Write definitions before collecting results
Use a quality rubric that people can apply
Interpret the results
Set a review point and a decision rule

A pilot can generate hundreds of answers while contributing little useful work. Count completed, acceptable tasks and measure the whole workflow. The scorecard should let the business owner judge whether quality, effort and day-to-day operation support another step.

Write definitions before collecting results

Choose a representative case mix and record the existing method. Define an eligible task, acceptable completion, material error and escalation. Identify who judges quality and how disagreements are resolved. Keep these definitions consistent across baseline and pilot.

A reusable pilot scorecard
Measure	Operational definition	Review responsibility
Quality	Tasks meeting the required facts and completeness criteria.	Subject expert checks a documented rubric.
End-to-end time	From eligible input to accepted result, including waiting and corrections.	Process lead compares similar case types.
Review effort	Active minutes spent reading, checking and editing.	Reviewers record ordinary and difficult cases.
Unit cost	Operating expenditure and allocated review effort per accepted task.	Finance and technical lead reconcile inputs.
Actual use	Eligible tasks attempted with the tool and completed through it.	Supervisor checks reasons for avoidance.
Failure and escalation	Unavailable service, unsupported output or manual referral by cause.	Service owner investigates patterns.

Use a quality rubric that people can apply

For hypothetical policy-answer drafts, score whether the answer addresses the question, reflects the current policy, cites the right passage and avoids unsupported commitments. Treat a materially wrong coverage statement separately from a minor wording edit. An average can conceal the error that matters most. Reviewers should first calibrate using several shared examples. If they disagree, clarify the rubric and revisit affected results. Where practical, hide whether an answer came from the baseline or pilot so formatting preferences do not determine the judgment.

Interpret the results

Suppose an invented forty-case comparison finds faster preparation overall, but eight cases require substantial correction and most users avoid the assistant for difficult requests. The pilot may be useful for a narrow category and unsuitable for broader coverage. Break the result down by case type rather than presenting one headline. Check the denominator. Thirty accepted tasks out of forty attempted is different from thirty out of one hundred eligible tasks. Record technical outages and user avoidance instead of excluding them. Sample size and case selection limit what the result can establish.

Set a review point and a decision rule

Agree which quality conditions must be met, what review workload is affordable and what failures require stopping the affected workflow. Numerical thresholds belong to the specific task; none is universally sufficient. The sponsor approves them with the process expert before the pilot. OpenAI's development process includes testing and feedback; the NIST profile provides a broader basis for evaluation. Your scorecard turns those principles into local evidence, separate from a financial return model. Bring a draft rubric and baseline sample to an AI pilot assessment to agree on what would count as useful progress.

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