AI pilot evaluation: count the work after the draft
Contents
Should this pilot grow?
Evidence before expansion.
Your AI pilot produces a draft in six minutes instead of twenty. That looks like a win until you count twelve minutes of review and the corrections. In the fictional example below, ten finished briefings take longer with AI. Here is how an executive can turn that disappointing result into a clear decision about the next experiment.
Measure a finished briefing
The unit of work is one internal account briefing accepted by the account owner. A finished briefing has the current status, confirmed commitments, open questions and supporting references. A draft that still needs checking has not reached the finish line.
Before the pilot, choose comparable cases and a quality standard. Include correction and abandoned-attempt time. Record setup separately from recurring effort so a large one-time cost does not disappear, but also does not get charged to every future briefing.
This example uses an invented operations team and invented timings. No customer result, product test or savings forecast is being reported. Its contribution is the calculation and the completed management decision, which you can replace with your own observations.
The faster drafts produce a slower batch
The team’s existing process takes 20 minutes per briefing, including preparation, writing and review. Ten comparable briefings therefore take 200 minutes. For the pilot, preparation is already included in the recorded drafting or review time; it is not an uncounted task.
Each AI-assisted briefing takes 6 minutes to draft and 12 minutes to review. Eight pass review without further work. Two require another 15 minutes of correction each because the draft treated an open commitment as confirmed. Both are corrected before the briefings are accepted. Those extra correction minutes are not already included in the 12-minute review figure.
| Work | Calculation | Minutes |
|---|---|---|
| Existing process, complete task | 10 × 20 | 200 |
| Pilot drafting | 10 × 6 | 60 |
| Pilot review | 10 × 12 | 120 |
| Pilot additional corrections | 2 × 15 | 30 |
| Pilot recurring total | 60 + 120 + 30 | 210 |
| Additional recurring effort | 210 − 200 | 10 |
What the result supports, and what it does not
The pilot used ten more recurring staff minutes for the same number of accepted briefings. It has not demonstrated a time saving. Setup, maintenance and tool charges would add to that comparison; a faster draft does not erase them.
The team had chosen a 10% recurring-effort reduction as its decision threshold before the pilot. For this batch, that means no more than 180 minutes while meeting the same acceptance standard. The observed 210 minutes misses that threshold by 30 minutes. The threshold is this fictional team’s choice, not a general benchmark.
The correction notes identify a specific weakness: two drafts converted open commitments into confirmed ones. The review also took twice as long as drafting. Those findings suggest a testable next step, not a mandate to buy more seats.
The sample is small. NIST’s Generative AI Profile, MS-2.5-001 cautions against extrapolating capability from narrow or anecdotal assessments. Do not multiply this batch into an annual saving, or infer performance across other departments.
A completed executive decision
The sponsor chooses to narrow the experiment for one more batch, without expanding the rollout. The open-commitment errors are a concrete, potentially addressable problem. The team will supply a simple confirmed/open field in each source packet and keep human review. It will not lower the quality standard to make the numbers look better.
The next batch includes the same mix of routine and exception cases as the first. The team measures source-packet preparation too. Otherwise the fix could appear successful merely because someone did extra unpaid-in-the-calculation work before starting the timer.
| Field | Recorded decision |
|---|---|
| Outcome so far | Ten accepted briefings; eight passed first review, two needed correction. Recurring effort: 210 minutes versus 200 baseline. |
| Decision | Continue one bounded experiment; do not add users, departments or autonomous actions. |
| Change being tested | Mark each commitment confirmed or open in the source packet. Preserve the same case mix and acceptance standard. |
| Owner and capacity | Operations manager Mara owns the next batch. Existing reviewers check every briefing; no additional reviewer is assumed. |
| Learning budget | At most 60 one-time staff minutes to adjust the briefing format, plus the next ten-case batch. Record that setup cost separately. |
| Continuation test | No more than 180 recurring minutes for ten accepted briefings, including packet preparation, drafting, review and corrections; no unacceptable failure may reach the recipient. |
| Review | On 3 November 2026, after the ten cases: if the threshold is missed, stop this drafting experiment and retain the manual process. If met, propose one further comparable validation batch. |
| Purchase or expansion | No broader purchase or rollout approved. Direct tool cost and maintenance must also be accounted for before an expansion decision. |
Why continue at all?
A pilot can fail its first target and still earn a limited second experiment. Here the owner has identified a specific error, one change to test and a finite learning budget. That is different from extending the trial indefinitely because the demo was impressive.
There is a useful arithmetic check on the hypothesis. If drafting stayed at 6 minutes, review fell from 12 to 8, and correction effort remained 30 minutes, the recurring total would be 10 × (6 + 8) + 30 = 170 minutes. That is a what-if calculation, not a prediction. Any added recurring preparation or maintenance belongs on top.
If the source labels save four review minutes but take five minutes to prepare per case, the apparent improvement disappears. The owner must measure the whole workflow again. Moving work out of the visible draft step is not the same as removing it.
A team with no available reviewer, an uncontainable failure or no credible change to test could reasonably stop now. Another team might value a separately measured improvement in completeness even without time savings. That would require an explicit quality objective and evidence, not a new success story invented after the numbers disappoint.
Bring one decision to the leadership meeting
Ask for the accepted output count, complete effort, correction causes and proposed next action on one page. Keep setup and cash charges visible beside recurring staff time. Released time is capacity; it is not automatically a reduction in payroll cost.
Use “expand,” “narrow” or “stop” with an exact scope. In this example, the answer is narrow: one changed briefing format, ten more comparable cases and a pre-agreed stop condition. It is not “AI worked” or “AI failed” across the company.
If the team needs help building a complete input packet, the context-engineering briefing example shows that separate task. If the obstacle is procurement evidence, use the AI knowledge security meeting checklist. This decision sheet is about whether the work is worth continuing.
Prepared by HeyBrain Editorial Team with AI assistance. All organizations, people, timings, observations and decisions in the worked example are fictional. The arithmetic is illustrative; no customer savings, product experiment or independently measured productivity result is claimed. The original publication date is retained. Primary guidance was checked on 13 October 2026.
