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To find out whether AI is improving your team’s work, measure a specific work outcome before and after access—and compare the change with a similar group that did not get access at the same time. Track quality and downstream results alongside speed or volume. Log AI availability and use separately: a tool being used is not proof that work got better.
Choose an outcome that reflects the work
Start with a recurring task or workflow and the result AI is expected to affect. Define the population you are evaluating—such as support agents handling a particular queue—and the unit you will measure, such as cases resolved per working hour.
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Choose a measure that fits the work rather than relying on a broad label like “productivity.” Depending on the task, useful outcomes could include throughput, elapsed time, error rates, rework, or customer results. A count of emails or documents is activity data, not a direct measure of performance or business value. Microsoft Research makes this distinction in its July 2024 report, Generative AI in Real-World Workplaces.
Build a comparison, not just a before-and-after story
A simple comparison of this month with last month cannot tell you whether AI caused a change. Workload, staffing, policy, seasonality, or a process redesign may also have shifted. Record those changes and compare the AI group with a credible alternative.
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- Set a baseline. Measure the chosen outcomes for the people or workflow before the rollout, using a consistent definition and collection method.
- Choose a comparison. Where practical, randomly assign access. If that is not feasible, introduce AI in phases and compare an early-access group with a similar group or workflow that has not yet received it.
- Specify the evaluation in advance. Decide which outcomes matter, how long you will observe them, and what improvement would count as meaningful before examining results.
- Note concurrent changes. Track changes to staffing, workload, training, processes, or targets that could affect the same outcomes.
Randomized and staggered workplace studies illustrate why a structured comparison is more informative than impressions alone. For example, the NBER’s 2025 study across 66 firms used a six-month field experiment; its results describe that population and intervention, not a guaranteed effect for another team. NBER Working Paper 33795.
Pair speed and volume with quality and downstream results
Faster completion or more output may be valuable, but neither tells the whole story if the work needs more correction or produces worse outcomes later. Select measures from three dimensions where they fit the workflow:
- Throughput or time: for example, issues resolved per hour or elapsed time to complete a task.
- Quality: for example, error or rework rates, or a consistent review of a sample of completed work.
- Downstream value: for example, a customer outcome relevant to support work.
Define quality checks before the evaluation starts and apply them consistently to both groups. There is no universal quality rubric in the cited studies; the right measure depends on the task. If privacy safeguards prevent reviewers from seeing work content, telemetry alone may not establish whether the output is accurate or aligned with the goal.
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Separate access, adoption, and outcomes
Keep a record of who was eligible, who received access, who used AI, how often they used it, and for which tasks. These are different facts from whether the work improved. Report the effect of offering access to the eligible group; if you also analyze actual users, explain how you handled the fact that people who choose to use AI may differ from those who do not. A comparison of adopters with non-adopters alone does not establish a causal effect.
Adoption can also be uneven. An NBER working paper published in August 2026 reports that generative AI use spans many occupations and tasks, while fewer than half of workers adopt it within most of them. A team-wide average can therefore blend regular users with people who rarely or never use the tool. NBER Working Paper 35677.
Look for differences across tasks and people
Break results out by role, task, and experience when the sample is large enough to support a useful comparison. An overall average can hide a gain for newer staff alongside little change for experienced staff, or improvements on one task alongside setbacks on another. Avoid treating a result from one workflow as evidence about the entire organization.
In a study of 5,179 customer-support agents, researchers reported an average increase of 14% in issues resolved per hour, with a larger 34% gain for novice and lower-skilled agents and minimal impact for experienced and highly skilled agents. The study appeared as an NBER working paper in 2023, was revised in November 2023, and was published in the Quarterly Journal of Economics in 2025. Those figures belong to that support setting, not to teams generally. NBER Working Paper 31161.
Check whether faster work changes anything else
Individual task speed is not necessarily the same as more valuable team output. Time saved might go to other work, reduce after-hours work, or be absorbed by coordination and review. Measure the effects that matter to the workflow rather than assuming saved minutes translate into extra output.
In a six-month experiment across 66 firms and 7,137 knowledge workers, the second half of the study saw the 80% of treated workers who used the integrated AI tool spend two fewer hours on email each week and reduce work outside regular hours. Researchers did not detect shifts in task quantity or composition from individual-level access in that setting. The result is evidence about those workers and that intervention, not a promised productivity gain for another team. NBER Working Paper 33795.
Read study results within their limits
AI effects depend on the work, the people, the intervention, and the outcome being measured. A field experiment with 776 professionals found that individuals using AI matched the performance of teams without AI on product-innovation challenges. That finding concerns a particular task and experimental setting; it does not establish that AI can generally replace teams. NBER Working Paper 33641.
An NBER survey of nearly 750 corporate executives reports variation in perceived productivity effects across sectors, firms, and industries. Executive reports and expectations provide context, but they are not controlled, team-level causal estimates. NBER Working Paper 34984.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11For your own evaluation, report the population, tasks, time window, comparison, measures, adoption, and uncertainty. Do not adopt a percentage threshold from a different job as your team’s success target; define a threshold that matters for your own work before seeing the results. The Microsoft report’s accompanying PDF discusses limits in using workplace activity measures to assess quality and goal alignment.
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