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Pilot evaluation

Count the review, the corrections, and the useful result.

Evaluate an AI workflow with comparable examples, quality checks, and the full cost of human review.

A practical playbook with an illustrative example.

# your-team
JaredILLUSTRATIVE WORKFLOW
Review these pilot records against our original acceptance criteria. Compare similar cases, include failed attempts, and separate setup time from recurring effort. Identify the most consequential errors and where review time went. Recommend a next step, with the limits of the evidence clearly stated.
A specific outcome + a clear boundary
Interactive example. No real customer data or external actions.
Start with the outcome

Make the work easier to move forward.

A pilot should answer a practical question: is this workflow worth continuing? Measure the whole handoff, from gathering context to approving a usable result. A fast draft can create extra work if someone must reconstruct its evidence. Define a good outcome before looking at results.

Teams can mistake output volume for progress. More summaries, longer documents, or quicker drafts do not show that a decision improved or that work became easier. Small trials also produce misleading comparisons when one attempt uses clean inputs and another inherits a difficult, incomplete project.

What to bring to the task

  • A clearly bounded workflow and its accountable owner
  • Recent comparable examples of the existing process
  • A shared definition of acceptable quality
  • A simple log for time, corrections, exceptions, and outcomes
A practical working method

From context to a useful result.

  1. Write the decision rule first

    Agree what would justify continuing, narrowing, or stopping. For example, require every material claim to be traceable and total human handling time to improve without more serious errors. Choose thresholds based on the team's needs. Record the rule before the pilot so a polished example cannot quietly change the standard.

  2. Establish an honest baseline

    Time the existing process on representative cases, including source gathering and final review. Note project complexity, missing information, and unusual interruptions. Use a few recent completed examples when necessary, but label retrospective estimates. Compare like with like rather than treating a rough memory as a precise benchmark.

  3. Log the full human workload

    For each pilot run, record preparation, checking, correction, approval, and recovery time. Keep one-time setup separate from recurring effort, then report both. Record elapsed turnaround separately from hands-on minutes: a result may require little work yet arrive too late to support the intended decision.

  4. Classify corrections by consequence

    Separate cosmetic edits from factual errors, missing context, wrong recipients, and unsupported commitments. Count abandoned outputs as attempts. Ask the reviewer whether the result was usable and what still required independent investigation. One serious error may matter more than several minutes saved across otherwise straightforward runs.

  5. Review variation before expanding

    Look at ordinary and difficult cases separately. Check whether improvements persist when sources are incomplete or the regular reviewer is absent. Summarize the typical result and range, with concrete failure examples. If evidence is thin, extend the bounded trial rather than claiming a general productivity gain.

The human review checkpoint

Treat pilot measurements as local evidence about a particular workflow, source set, and team. Do not turn a small sample into a company-wide savings claim or assume that acceptable drafting quality authorizes independent external action.

Before you make it a routine

  • The acceptance criteria existed before evaluation
  • Baseline and pilot cases are reasonably comparable
  • Failed attempts and review time are included
  • The next decision follows the evidence and its limits

A few useful questions

How many attempts are enough?

Enough to see the ordinary variation and relevant failure cases for this workflow. A rare, consequential action needs a different evaluation from a routine internal summary. State what your sample has not tested.

What if quality improves but time does not?

That may still be worthwhile if better evidence or fewer omissions serves the original goal. Make the trade-off explicit and check that the reviewer values the improvement.

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