AI evaluation
How to evaluate an AI automation pilot
A pilot should answer whether a particular workflow is worth continuing. Count the effort needed to produce an acceptable finished result, including review and corrections. Compare that with the process you use today, then decide whether to expand, revise or stop.
Measure the existing process
Choose a unit of work, such as one inquiry organized or one draft reviewed. Record how long representative tasks take and what an acceptable outcome includes. Keep ordinary and difficult cases visible rather than selecting only simple examples. Note whether waiting time is included; elapsed time and active staff effort answer different questions.
Checklist
- Define one completed task consistently.
- Use examples representative of normal work.
- Record quality and time before changing the process.
Count review and correction time
Measure the pilot from input to accepted output. Include time spent checking the result, correcting it and recovering from failures. If a task falls back to the original process, include that work rather than dropping it from the sample. The calculator below is a planning aid using your assumptions; it is not evidence that a deployed system will achieve those values.
Checklist
- Record preparation, review and correction separately.
- Include failed and escalated tasks.
- Compare the same definition of a finished result.
Set a quality gate before calculating savings
Agree on which mistakes are tolerable, which need correction and which require stopping the pilot. A workflow that saves time but produces unacceptable outputs has not passed. Review examples against the source information rather than judging how polished they sound. A small pilot can identify useful issues, but it cannot prove reliability for every future situation.
Checklist
- Define unacceptable errors with the process owner.
- Keep a correction log without unnecessary private data.
- Review difficult and missing-information cases.
Make a continue, revise or stop decision
Review the measured results alongside subscription costs, implementation effort and maintenance needs. Assign responsibility for source changes and failures before expanding. Keep the baseline and evaluation notes so later changes can be compared fairly. The right outcome may be a smaller scope or an ordinary software rule rather than a larger AI deployment.
Checklist
- Compare operating effort and costs separately.
- Document limitations and acceptance results.
- Choose an explicit next step and review owner.
Worked example: time is only one acceptance check
Invented inputs for arithmetic demonstration, not client results. Software and implementation costs are excluded from these time figures.
| Input | Illustrative value |
|---|---|
| Current active effort | 12 minutes per task |
| Pilot handling | 4 minutes per task |
| Review and correction | 3 minutes per task |
| At 100 tasks per month | (12 − 4 − 3) × 100 ÷ 60 = 8.3 hours of estimated effort reduction |
Estimate the change in staff effort
The starting values are illustrative. Replace them with your own measurements. This estimates time only, excluding implementation, software costs and any claim about output quality. Nothing you enter is stored or sent.
8.3 hours less staff effort per month, based on these inputs.
Calculation: (current minutes − pilot handling minutes − review minutes) × tasks ÷ 60. A positive time estimate does not establish that the workflow meets your quality or business requirements.
Turn your notes into a project brief
Share your goal, existing tools or website, relevant examples and the questions you still need to resolve. Do not email passwords or private customer records.
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