Define the task and the baseline
Choose a specific task with a clear start, finish and acceptable outcome. “Use AI in operations” is too broad to evaluate. “Draft a response to a routine customer enquiry from an approved knowledge base” gives you a defined workflow and an outcome to inspect.
Document the current process before changing it: task time, checking time, rework, direct costs and relevant errors. If the baseline is inconsistent, record that variation rather than selecting a convenient comparison.
Decide what good looks like
Set acceptance criteria before running the evaluation. Depending on the task, those criteria may cover factual correctness, completeness, tone, data handling, escalation and the consequences of a wrong answer. Give reviewers the same rubric for both approaches.
A faster draft is not an improvement if the final output needs substantial correction. Include cases that are routine, ambiguous and likely to fail, while keeping the evaluation proportional to the consequences of the task.
Measure the complete workflow
Measure AI generation, human checking, corrections and any follow-on rework. Compare end-to-end time at an agreed quality threshold. Record model or tool version, settings, input type and collection date so the result can be interpreted later.
A simple working calculation is net minutes saved per task = baseline time − AI-assisted time, including review and rework. Any monetary conversion also requires a defensible hourly cost, task volume and treatment of setup and subscription costs.
Assess risk alongside performance
Map who could be affected, what information the system handles and what happens when it fails. Define who can override the output and when the task must be escalated. A low-risk internal drafting task and a consequential customer decision need different controls.
NIST’s AI Risk Management Framework provides a voluntary structure for considering trustworthiness through design, use and evaluation. It is a reference point, not a certification that a workflow is safe or compliant.
Set the next decision in advance
Choose a continuation rule before seeing the results. For example, a team could require an improvement in total task time with no deterioration in defined quality criteria, alongside acceptable data handling and a workable escalation process. The thresholds should fit that team’s task and risk.
If a pilot succeeds, expand gradually and monitor whether the result survives different users, inputs and tool updates. A small pilot is evidence about its tested conditions, not a universal productivity claim.
Sources and editorial note
This is an editorial guide, not a report of original participant research. It was prepared with AI assistance and draws on the following public references. Examples and suggested approaches are explanatory, not empirical findings.
Published 15 October 2026. Version 1.0.