Measure one clearly defined workflow against a stable baseline, then compare teams or rollout groups where possible. Track time alongside accepted output, quality, rework and coordination—not just how quickly people complete one activity. Finally, find out whether the time released became useful capacity. Without that last step, fewer minutes in a task do not automatically mean more productive work.
Start with a workflow, not a company-wide AI score
Choose a repeatable task where you can identify the work, its completion point and its result. Define who is included, what counts as one unit of work, and the start and stop points for measuring time. Examples include accepted support cases per paid hour or review-ready drafts completed per week.
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Keep the definition consistent throughout the measurement period. If the team changes what counts as a completed case, or starts including a different type of draft, a before-and-after comparison will no longer describe the same work.
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Before rollout, measure a representative period. Along with task time, record workload volume, task mix, staffing, quality and relevant constraints. These details help distinguish an AI effect from a change in the work itself.
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A simple before-and-after comparison is easy to run, but changes in workload, seasonality, staffing or processes can also change the result. A stronger design compares workers offered AI access with a contemporaneous group not offered access. Randomly assigning access, when practical, makes that comparison more informative; a staged rollout can also create comparison periods or groups.
If neither is feasible, compare matched periods in the same workflow and document what else changed. Treat the result as an association, not proof that AI caused the difference.
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Measure time, output and quality together
Use a small set of measures that captures both speed and whether the work was useful. For the same workflow and period, consider:
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- Useful output per paid hour: completed work that is usable, rather than raw drafts, clicks or cases touched.
- Quality or customer outcome: errors, acceptance rates, customer satisfaction or another outcome suited to the work.
- Rework: corrections, escalations or additional effort needed to make the output usable.
- Adoption: who used the tool, how often and for which tasks.
- Coordination: handoffs, review time or delays when work depends on other people.
Do not assume that faster completion alone is a gain. A faster first draft can be offset by more review; more cases handled can be offset by more errors. The useful question is whether the team produced more accepted work, maintained quality, or freed capacity without shifting hidden effort elsewhere.
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Separate access from actual use
Report the result for people offered access separately from the result among active users. The first measures the effect of the rollout offer in the group studied. The second describes people who chose to use the tool, but users may differ from non-users in experience, workload or motivation, so that comparison is not automatically causal.
This distinction matters in published evidence too. Microsoft Research’s April 2025 summary of a six-month randomized experiment reports estimates both for access and for users. Its figures should not be merged with the later NBER working paper’s figures: the publications describe related research with different sample descriptions and reported estimates.
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- Durable Office Design：Spiral binding allows the book to lay flat while thick paper reduces ink bleed making it a reliable attendance book for daily business operations
- Professional Employee Records：Designed as an employee sign in and out book this log book helps maintain clear and organized attendance records for employees contractors and teams
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Interpret published time-saving figures in context
There is no universal time-saving benchmark that predicts what an arbitrary team will achieve. Published numbers describe particular populations, tools, tasks and observation windows. Keep the method and scope attached to every figure:
| Published result | What it describes | How to read it |
|---|---|---|
| 1.4% of total work hours reportedly saved; 1% to 5% of work hours assisted by generative AI | Alexander Bick, Adam Blandin and David J. Deming, National Bureau of Economic Research (NBER), 2025 revision, based on nationally representative U.S. surveys. Source | These are survey-based estimates. The 1.4% figure is reported time saved; the 1% to 5% range is work hours assisted, not hours saved. Neither is an experimental estimate for a particular company. |
| Two fewer hours of email per week among frequent users in the second half of the experiment | Eleanor W. Dillon, Sonia Jaffe, Nicole Immorlica and Christopher T. Stanton, NBER working paper revised November 2025; six months, 66 firms and 7,137 knowledge workers. Source | The authors did not detect a change in task quantity or composition from individual-level AI access. This is an estimate in a specific workplace and tool context, not a general team benchmark. |
| Three fewer hours, or 25% less email time weekly among users; 1.4 hours for the intent-to-treat estimate | Microsoft Research’s April 2025 summary of a six-month randomized field experiment, describing roughly 6,000 workers. Source | These are figures from that summary’s version and population description. Do not combine them with the later NBER paper’s reported estimates. |
| 14% average increase in issues resolved per hour; 34% for novice and lower-skilled workers, with minimal impact for experienced and highly skilled workers | Erik Brynjolfsson, Danielle Li and Lindsey R. Raymond, NBER working paper from 2023, later published in 2025; 5,179 customer-support agents. Source | The result varied substantially by worker experience and skill. An overall average can conceal who benefits most. |
| 11% fewer individual emails read, 4% less time interacting with email and 10% more documents edited | Microsoft and LinkedIn’s 2024 Work Trend Index, describing a six-month randomized study of 60 Copilot customers and 3,000 individuals. Source | This is a vendor-published report. Its methodology also describes a separate survey of 31,000 employed or self-employed knowledge workers across 31 markets, fielded February 15 to March 28, 2024; do not confuse those survey results with the workplace experiment. |
Different studies measure different outcomes. For example, an email-time change does not by itself establish that a worker completed more valuable work. The NBER field experiment did not detect changes in task quantity or composition from individual-level AI access, and the Microsoft Research summary reported no significant meeting-time effect. As the authors put it in their April 2025 summary, access primarily affected “behaviors that could be changed independently and not behaviors that required coordination to change.”
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Check where released time went
Time saved in the measured task is only potential capacity. Ask whether people used it for more valuable work, reduced overtime, handled additional demand, or had it absorbed by other requirements. If the saved time cannot be connected to a useful outcome or a deliberate reduction in effort, report it as time released from that task—not as realized business value.
Report variation and uncertainty
State the comparison period, number of people or work items, effect estimate and uncertainty where available. Break results out by task and experience level when group sizes support a meaningful comparison; small groups can produce unstable estimates. Repeat the measurement after usage and workflows have had time to settle, keeping definitions consistent so that later results remain comparable.
For two AI interventions or rollout groups, use the same workflow and compare time per accepted unit, useful output per paid hour, quality and rework, worker experience or task mix, and coordination or handoff effects. Distinguish people offered access from active users, and survey-based self-reports from experimental estimates.
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