To find out whether AI is making your team more productive, measure changes in a defined work outcome—not just logins, prompts, or time spent in an app. Set a baseline, compare AI users with a credible control group where possible, and track speed alongside quality and downstream results. Then break the findings out by task and role: an overall average can hide who benefits and who does not.
Decide what “improving work” means for your team
Start with a recurring task or workflow and name the outcome AI is expected to change. “Productivity” is too broad to measure on its own. For a support team, it might mean issues resolved per hour; for another team, it could mean shorter completion time, fewer errors, less rework, or better customer outcomes.
Keep four different kinds of evidence distinct:
- Access: who is eligible to use the tool and who has it available.
- Usage: whether people use it, how often, and for which tasks.
- Task performance: how quickly and well the defined work gets done.
- Business value: whether the result improves an outcome the organization cares about, such as customer experience or reduced rework.
Access and usage help explain exposure to AI; neither proves that work improved. Likewise, more emails or documents produced is not necessarily better performance. Microsoft Research cautions that activity counts do not directly map to productivity, performance, or business outcomes, and that privacy measures hiding content can limit quality assessment. Use telemetry as context, then pair it with direct outcome and quality measures. Microsoft Research’s July 2024 report
Build a comparison that can reveal an AI effect
A simple before-and-after comparison is easy to run but difficult to interpret. Workload, staffing, seasonality, policies, or other process changes may shift at the same time as AI access. A stronger design compares an AI-enabled group with a similar group that has not yet received access, while recording relevant changes to the workflow.
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Preferred option: randomize or phase access
Where practical, randomly assign eligible workers or teams to receive access now or later. If everyone will eventually receive the tool, a phased rollout can create a temporary comparison group. Decide the comparison and outcomes before examining results, rather than choosing the most favorable metric afterward.
When randomization is not practical
Compare the participating team with a similar team, task, or workflow that has not yet adopted AI. Document differences between the groups and any concurrent changes. The more those groups differ, the less confidently you can attribute an observed change to AI.
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Measure speed, quality, and downstream effects together
Choose a small set of measures that fit the workflow and define them before the evaluation starts. A practical set includes one measure of throughput or elapsed time, one quality check, and one downstream outcome where relevant. The studies do not establish a universal quality rubric; teams need to choose checks appropriate to their work.
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- Throughput or time: for example, issues resolved per hour or elapsed time to complete a task.
- Quality: error rate, rework, a consistent human review, or another task-specific standard.
- Downstream outcome: a relevant result such as customer sentiment for a support workflow.
Speed alone can reward rushed or incomplete work. More volume alone can reward low-value activity. Check whether faster completion produces acceptable quality and whether it changes the outcome the team is responsible for.
Track access and adoption separately from results
Record who was eligible for access, who received it, who used it, how frequently, and for which relevant tasks. Report these alongside outcomes, not as substitutes for them. An “AI is available” result answers a different question from “AI improved performance.”
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Distinguish the effect of offering access from results among people who actually used the tool. The first can show what happened under the rollout policy; the second can be useful context, but frequent users may differ from nonusers in experience, motivation, or task mix. An adopter-only comparison should not be presented as causal unless the analysis accounts for selection into use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Look for differences across roles, tasks, and experience
Report results by role and task, and by experience where the sample allows. A team-wide average may conceal a gain for one group and no gain—or a different effect—for another. Avoid making fine-grained claims from groups too small to support them.
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Published results show why context matters:
- In a study of 5,179 customer-support agents, researchers reported an average 14% increase in issues resolved per hour. Gains were larger for novice and lower-skilled workers (34%) and minimal for experienced and highly skilled workers. That result describes this support setting, not a general benchmark for other teams. NBER, “Generative AI at Work”
- In a field experiment involving 776 professionals working on product-innovation challenges, individuals using AI matched the performance of teams without AI. This finding concerns that experimental task; it does not establish that AI can generally replace teams. NBER, “The Cybernetic Teammate”
These different findings are a reason to test the team’s own tasks, not to select a percentage from another occupation as a target.
Check for displaced work and spillovers
An individual task may get faster without reducing total workload: time could move to other tasks, or faster individual work could change coordination demands. Measure relevant outcomes beyond the task itself when the question is whether the team’s work improved overall.
In a six-month experiment across 66 firms and 7,137 knowledge workers, the 80% of treated workers who used an integrated AI tool spent two fewer hours on email each week during the experiment’s second half and reduced work outside regular hours. Researchers did not detect changes in task quantity or composition from individual-level access in that setting. This is a study-specific result, not a guaranteed team gain. NBER, “Shifting Work Patterns with Generative AI”
Set the success bar before reviewing results
Choose in advance what improvement would matter to the team and what trade-offs are unacceptable—for example, faster completion only if quality does not fall. There is no universal minimum improvement threshold established by the cited studies. Define a meaningful bar for the particular workflow, then report the observed result with its time window, population, comparison, adoption, and uncertainty.
Keep the conclusion within the evidence: an improvement in one task or group does not prove that AI raised productivity across the organization. Survey results can provide broader context, but they are not a substitute for a controlled team-level estimate. A 2026 NBER survey of nearly 750 corporate executives reported productivity effects varying by sector; it reflects executive reports and expectations, not a controlled causal estimate for a particular team. NBER, “Artificial Intelligence, Productivity, and the Workforce”
Quick Recap
A practical evaluation checklist
- Define the scope: specify the recurring task or workflow, eligible population, and outcome AI is meant to affect.
- Choose measures: select a time or throughput measure, a quality check, and a relevant downstream outcome; decide these before looking at results.
- Set the baseline and comparison: randomize or phase access if feasible; otherwise select a similar group or workflow and record important differences.
- Record exposure: track eligibility, access, adoption, frequency, and task use separately from performance.
- Run the evaluation: use a defined time window and record other process changes that could explain a result.
- Analyze responsibly: examine differences by role, task, and experience where sample size permits; check for changes in quality, downstream outcomes, displaced work, and coordination.
- Report the limits: state who and what were measured, the comparison used, the time period, and how uncertain the result is. Do not generalize beyond that scope.
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