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PEAKMINDED INSIGHTS · PRODUCTIVITY · AI TOOLS
Measure what changes in the workflow: time, quality, rework, and the ability to use AI responsibly.
A faster first draft can be useful. But if employees spend the saved time correcting errors, the organization may gain little. Corporate training buyers need a measurement plan that connects learning to completed work, rather than tool usage alone.
The 2023 revised NBER working paper Generative AI at Work studied the introduction of an AI assistant among 5,179 customer support agents. It reported a 14% average increase in issues resolved per hour, with larger gains for novice and lower-skilled workers and minimal impact for experienced, highly skilled workers.
That finding comes from a specific setting, tool, and outcome measure. It does not establish a universal productivity gain, predict your organization's ROI, or prove that every AI training program produces the same result. Use the research as a reason to test thoughtfully in your own environment.
Select a task that happens often enough to observe several examples and has a clear quality standard. Consider internal status updates, initial research organization, or drafting a process document from approved material. Choose an approved AI tool and keep the task boundaries consistent.
Write down what counts as completed work. If a project update must be checked by a manager, the task ends after that review, not when AI produces its first response. Include drafting, checking, revision, and approval in the time measurement.
Define the quality rubric before comparing results. Where practical, have a reviewer evaluate outputs without knowing which method produced them. Record task difficulty and employee experience so an easy AI task is not compared with a difficult baseline task.
Collect a baseline using the existing workflow. Then train the pilot group and repeat comparable tasks with AI. Keep a log of the task, completion time, review result, revisions, and exceptions. A before-and-after comparison is informative, but other changes can affect the result. A comparable group using the existing workflow can strengthen the evaluation when feasible.
Calculate time reduction as (baseline time minus pilot time) divided by baseline time. For an illustrative example, a task that drops from 40 to 30 minutes shows a 25% time reduction. That is a made-up calculation example, not a PeakMinded customer result. Report the number of tasks observed and the range of results alongside the average.
Saving employee time may create capacity for higher-value work. It does not automatically reduce payroll or produce cash savings. A business case should identify how freed capacity will be used and account for tool costs, training, review, administration, and ongoing support.
Agree on expansion criteria before the pilot: acceptable quality, a useful time improvement, manageable review effort, and no unresolved data-policy issues. If speed improves while accuracy falls, revise the workflow and training before scaling.
Employees need to understand the inputs, the task, the tool's limits, and the final acceptance standard. Compare AI tools with the same approved task and rubric rather than choosing a winner from a single impressive answer. Reassess when the workflow or tool changes.
Source: Brynjolfsson, Li, and Raymond, Generative AI at Work, NBER Working Paper 31161, November 2023 revision. The scorecard and pilot design are PeakMinded editorial recommendations. Prepared October 7, 2026.