An AI Pilot Scorecard That Can Actually Stop a Rollout

A team can finish an AI pilot with a folder of impressive drafts and still have no answer to the funding question. Before the first live case, the workflow owner and finance partner need to agree on what counts as a completed job, how many failures they will tolerate, and what the whole process costs. The scorecard below turns that agreement into a scale, revise, or stop decision.

Choose one job and one comparison

Name the job in ordinary terms: prepare a renewal brief, classify a support request, or answer a policy question using approved documents. Record who starts it, who checks it, and where the accepted result is saved. Compare the pilot with the current process on the same kind of cases. A different mix of easy work can make either method look better than it is.

Use a recent baseline window long enough to include ordinary and difficult cases. Keep the case selection rule before the team sees AI outputs. If only simple cases are eligible, say so; the result cannot justify a broader rollout. NIST’s AI Risk Management Framework calls for testing under conditions similar to deployment and continued monitoring afterward. That supports the comparison method, not a universal sample size or a guaranteed return.

Put five measures on one page

• Accepted output: completed cases that meet the old quality standard, divided by all eligible cases. Count repaired and rejected cases separately.
• Time: active human minutes and elapsed time from request to acceptance. Faster drafting does not shorten a waiting approval queue.
• Quality: material corrections and their severity, using the same review rubric for the pilot and baseline.
• Full cost: software, setup, integration, review, retries, support, and incident work per accepted case.
• Use: the share of eligible staff and cases that actually follow the new process, with reasons for opting out.

Add one workflow-specific safeguard, such as an unsupported customer claim, a wrong record update, or disclosure of restricted information. A material failure can stop a pilot even if its average time is excellent. NIST’s measurement guidance calls for tracking performance and errors in use, rather than assuming a pre-deployment test settles the matter.

Write the decision rule before the result arrives

Here is an illustrative rule for a low-risk internal brief, not a standard for every organization: pilot cases must meet the existing acceptance standard at least as often as baseline cases; material errors may not increase; median human touch time must fall; and total cost per accepted brief must stay inside the agreed budget. A single prohibited data disclosure pauses live use immediately. Your owner should set thresholds from the current process, the consequences of an error, and the business target.

Specify who can change the rule. The operations owner labels cases and owns the work definition. A reviewer outside the build team checks a sample of successes and all material failures. Finance checks the cost ledger. The sponsor makes the scale decision. If the team revises a threshold after seeing the pilot data, publish both the original and revised result; otherwise the scorecard becomes a post-hoc story.

Keep the denominator visible

Suppose 200 eligible requests enter an illustrative pilot. The assistant attempts 150, and reviewers accept 120, repair 20, and reject 10. The accepted-output rate is 120 of 200 eligible requests, or 60%; it is 120 of 150 attempted requests, or 80%, only if the question is how attempted cases performed. Both can be useful, but they answer different questions. The untouched 50 still need a route and a cost.

Do not turn released minutes into payroll savings on the scorecard. Record the hours, then name the operating action that uses them: a smaller backlog, faster customer response, more cases at the same staffing, reduced overtime, or an approved budget reduction. Our AI business-case guide separates those outcomes because they have different evidence requirements.

Decide what happens after four weeks

At the review meeting, ask for the case ledger, not a slide of favorite outputs. Scale one step if the rule passes and the owner can support monitoring at higher volume. Revise and retest when the failure is fixable but the agreed threshold was missed. Stop when a safeguard fails, review work overwhelms the gain, or the team cannot identify accepted work. A small pilot that returns a clear no has saved a larger budget.

Useful next steps

• Build the AI business case — Define the workflow, baseline, and financial consequence before a pilot.
• Evaluate an AI workflow — Turn real cases into a repeatable test.
• Estimate AI return without counting time as cash — Stress the assumptions after the pilot.