How to evaluate an AI automation pilot before expanding it

Define the unit of work and its baseline

Use a task with an observable finish, such as preparing a reviewed service-request summary from an approved inbox and customer record. Record what the current process produces, who reviews it and how much hands-on time it takes. Separate time spent actively working from elapsed time waiting for another person. An automated draft may be fast while the overall queue remains unchanged.

Compare similar work on both sides. Include ordinary requests, incomplete records and ambiguous cases from a sample the business is permitted to use. Define how sensitive details are handled before preparing the sample. Keep examples used to tune the workflow separate from a final evaluation set, so the result also reflects cases the system has not been adjusted to answer.

Measure the result after human review

For each case, record completeness, factual errors, required corrections, review minutes and final disposition. Count an accepted result only after the agreed reviewer confirms it. Keep “accepted without edits,” “accepted after correction,” “escalated” and “failed” distinct. A workflow that generates many drafts but makes the reviewer reconstruct the answer may not reduce work.

An illustrative calculation: if 100 comparable requests take 8 minutes each today, the baseline is 800 minutes. If the pilot requires 3 minutes of review and 1 minute of correction per request, plus 60 minutes of weekly oversight for that same batch, it uses 460 minutes. The difference is 340 minutes of staff capacity before implementation effort and other costs. These are example numbers, not a MAS customer result or a promised saving.

Check costs and difficult cases together

Track provider usage, retries, connected service charges and support time for the same measurement window. Divide recurring workflow cost by accepted outcomes, including the cost of unsuccessful attempts. Report setup effort separately. A low price per generated answer can hide expensive review or repeated failures; a time saving also does not automatically become a cash saving unless the business can use the released capacity.

Test an unavailable source, missing required field, duplicate request and timeout after an attempted update. The expected response should be explicit: ask for information, wait, escalate or verify whether the update already happened. A retry must not create a second customer record or send the same message twice. Test these behaviors before allowing broader access to live systems.

Agree on the expansion decision before the pilot starts

Choose acceptance thresholds with the process owner: acceptable error types, review effort, cost per completed task and required exception handling. There is no universal success percentage for every workflow. A request-classification aid and an action that changes a customer order have different consequences. Require evidence for each critical behavior, name the person who can pause the workflow and preserve the manual fallback.

MAS designs AI business workflows around existing tools, approvals and measurable outcomes. Bring a process description, a permitted example input, the expected output and the baseline you can currently measure. We can turn those into a pilot scope with explicit responsibilities and acceptance checks. If you have not chosen the process yet, use the workflow opportunity map first, then return to this evaluation plan.

Before your next step

• Comparable baseline and evaluation cases, including exceptions.
• Quality and time measured after review and correction.
• Recurring cost per accepted outcome and separate setup effort.
• Named approval owner, stop condition and manual fallback.