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The defensible way to measure AI productivity is to evaluate a defined task or workflow, not to count logins or assume that saved minutes equal business value. Record a baseline, compare workers with AI access with a credible comparison group, and measure useful output per unit of time alongside quality, rework, worker experience and costs. Report the study period, who had access, who actually used the tool, and which roles and tasks changed. A short pilot can show an effect under its conditions; it cannot by itself prove a lasting, organization-wide productivity gain.
What “AI productivity” should mean
Productivity is useful output divided by the resources used to produce it. For an AI-enabled workflow, the most useful denominator is usually paid labor time, although a project may also track compute fees, review time, training time or other operating costs. “Useful” matters: a faster draft that requires extensive correction is not a productivity gain.
Define the unit of work before collecting data. Examples include accepted customer cases resolved per paid hour, claims processed per hour after quality review, or a completed deliverable accepted by its downstream owner. Time spent in an application, number of prompts and number of logins describe activity or adoption; they are not productivity outcomes.
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Use a primary outcome and a small set of guardrails. A primary outcome might be accepted outputs per paid hour. Guardrails can include factual accuracy, policy compliance, rework, customer escalation, employee effort and data-handling incidents. This prevents a speed improvement from being reported while hidden quality or people costs rise.
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Set the evaluation question and scope
Write a decision question
State the decision the pilot must inform, such as: “For tier-one support agents handling billing cases, does access to the assistant change accepted cases per paid hour over eight weeks without increasing escalations?” A decision question identifies the intervention, population, task, period and success conditions.
Specify the intervention
- Name the AI system and the features enabled, such as drafting, retrieval or suggested replies.
- Document model or product-version changes during the period.
- State whether use is optional, encouraged or required, and what human review is mandatory.
- Record training, prompt libraries, workflow redesign and other changes introduced with the tool.
Define the eligible population
List the roles, teams, locations, shifts and experience bands eligible for access. A result for experienced engineers performing code review should not be generalized to new support agents or a different workflow.
Choose a study period
Include enough pre-intervention observations to characterize normal variation and a clearly dated follow-up. Record seasonal demand, staffing changes, policy changes, product launches and other events that could affect the outcome. A six-week change during a holiday period may not represent a normal quarter.
Choose denominators and outcomes that fit the work
Use one primary measure that answers the decision question, then pair it with quality and people measures. The table below shows common choices; adapt the definitions to your workflow.
| Work type | Possible primary outcome | Quality or downstream guardrail | Useful denominator |
|---|---|---|---|
| Customer support | Accepted issues resolved | Reopen rate, escalation rate, customer satisfaction or audited accuracy | Paid agent hours or staffed handling hours |
| Email and administrative work | Completed, accepted actions or cycle time for a defined process | Error rate, missed commitments, manager acceptance | Paid hours spent on the process |
| Document or content production | Deliverables accepted by the named reviewer | Revision count, factual errors, compliance defects | Paid production and review hours |
| Software development | Changes that pass the team’s acceptance criteria | Defects, rollback, review findings and security issues | Engineer hours, with task complexity recorded |
| Back-office processing | Cases completed and approved | Sampling error rate, exception rate and downstream corrections | Paid processing hours |
Measure output, not just elapsed time
Time to complete a task is informative when the task definition and quality threshold stay constant. It is not a complete business-productivity measure by itself. For email work, for example, fewer hours in email could mean less time spent on low-value messages, but it could also mean delayed responses or work moved elsewhere. Pair time with completed, accepted outcomes.
Make quality observable
Use an existing audit rubric where possible. Define what counts as an error, a rework event, an unacceptable answer or a downstream rejection before looking at treatment results. If only a sample is reviewed, report the sample size and sampling rule. Keep the same rubric for the baseline and comparison groups.
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Track costs and possible harm
- Subscription or usage fees and implementation labor.
- Training, prompt design, integration and support time.
- Human review, correction and rework time.
- Overtime or changes in work intensity.
- Privacy, security and data-governance incidents or investigation time.
- Effects on learning opportunities, autonomy, workload and job satisfaction.
Establish a credible baseline
Measure the same task before access begins or measure a comparable group that does not receive access. Capture the existing workflow, average task mix, staffing, hours, quality and backlog. Preserve the raw task counts and denominators, not only a dashboard percentage.
Record the factors that can change the result: case difficulty, customer mix, queue volume, shift, tenure, manager, policy, software outages and holidays. If the AI is introduced while another process is redesigned, note that co-intervention and treat the resulting estimate as the combined change unless you can separate the effects.
A baseline should cover normal variation rather than a single “typical” day. If the process is highly seasonal, include the same season in the comparison period or use a comparison group exposed to the same demand pattern.
Use a comparison instead of a before-and-after anecdote
Randomized access
When operationally and ethically feasible, randomly assign eligible workers or teams to receive access now or later. Randomization makes the groups comparable on average and estimates the effect of being offered access under the study conditions. Keep assignment records and analyze workers according to their assigned access, even if some never use the tool.
Staggered or phased rollout
A rollout by randomly selected waves, teams or dates can provide a comparison between earlier and later groups. Document the rollout schedule and check that outcomes did not diverge before access. Do not let managers choose the first wave based on expected benefit without accounting for that selection.
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If random allocation is impossible, use a comparison group with similar tasks and baseline performance and explain why it is credible. Compare changes over the same calendar period and adjust or stratify for observable differences such as workload and tenure. The result remains more vulnerable to unmeasured changes than a randomized design.
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Why a simple before-and-after can mislead
Output can change because of seasonality, learning, staffing, queue composition, a new policy or regression to the mean. A before-and-after chart is useful for monitoring, but it cannot identify the AI’s contribution without a credible counterfactual.
Separate access from actual use
Measure three stages separately:
- Access: the worker was eligible and technically able to use the AI.
- Activation: the worker opened or configured the feature.
- Sustained use: the worker used it under a prespecified rule, such as in more than half of observed weeks.
The effect of offering access answers a deployment question. A comparison of frequent users with non-users answers a different, descriptive question because frequent users may be more motivated, more skilled or assigned easier work. Label usage analyses clearly and avoid presenting them as unbiased causal effects unless the design supports that claim.
Use product telemetry only for adoption measures. Combine it with task-system records, time records and quality audits for outcome measures. Prompt counts and tokens can indicate intensity or cost, but not whether the work was valuable.
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Write the analysis plan before inspecting treatment results. Define the primary outcome, exclusion rules, handling of missing data, quality sample, subgroup cuts and the minimum period required for inclusion. If the workflow changes during the pilot, record the change and decide whether to analyze the periods separately.
A simple productivity rate is:
accepted outputs ÷ paid hours
For a randomized or phased comparison, report the difference in average rates (or the percentage change) between the AI-access group and the comparison group over the same period, with a confidence interval or other uncertainty range when your analytic method supports it. State the number of workers, tasks and weeks behind every estimate. Avoid false precision from very small samples.
Report missing tasks, unreviewed outputs and workers who leave the sample. If quality is measured on a sample, include the sampling fraction and uncertainty. A result that is large but based on a handful of reviewed cases should be treated as preliminary.
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Segment results where the sample allows
Show effects by role, task type, experience, organization or team, and utilization level when there are enough observations for a stable estimate. Predefine the most important segments to reduce the temptation to highlight a chance result.
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Published evidence illustrates why segmentation matters. In a staggered rollout involving 5,179 customer-support agents, access to a conversational assistant increased issues resolved per hour by 14% on average. Novice and lower-skilled agents saw larger gains, while experienced and highly skilled agents saw minimal impact. This is an estimate for that customer-support setting, not a general expected gain for every job.
Do not infer that a pattern in one subgroup is universal. A segment result may reflect task mix, coaching, manager practice or a small sample. Show the number of observations and treat exploratory subgroup findings as such.
Include worker experience and safeguards
Ask workers structured questions about usefulness, effort, confidence, time pressure, autonomy, learning, monitoring and data practices. Use the same questions at baseline and follow-up where possible, and report response rates. Pair survey responses with observed outcomes; self-reported improvement is valuable context but is not an experimental output estimate.
Explain what telemetry and work samples are collected, who can see them, how long they are retained and whether they are used for performance decisions. Minimize collection to what the evaluation needs. Give workers a channel to report harmful outputs, excessive workload or sensitive-data concerns without retaliation.
Track work intensity explicitly: overtime, work outside scheduled hours, queue pressure, break interruptions and time spent correcting AI output. A tool that raises throughput by shifting unpaid effort or review burden to employees has a different business and ethical result from one that genuinely reduces effort.
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What published studies currently show
The figures below are context-specific and use different designs and outcomes. They should not be averaged into a single “AI productivity” number.
| Study and population | Design and measure | Reported result | How to interpret it |
|---|---|---|---|
| Erik Brynjolfsson, Danielle Li and Lindsey R. Raymond, NBER working paper, 2023, revised November 2023; 5,179 customer-support agents | Staggered rollout; issues resolved per hour | 14% average increase | Larger gains for novice and lower-skilled agents; minimal impact for experienced and highly skilled agents. Specific to the studied support setting. |
| Eleanor W. Dillon, Sonia Jaffe, Nicole Immorlica and Christopher T. Stanton, NBER working paper, 2025, revised November 2025; 66 firms and 7,137 knowledge workers | Six-month field experiment; access to an integrated generative-AI tool | Treated workers who used the tool in more than half of sample weeks spent two fewer hours on email per week in the second half of the experiment and reduced work outside regular hours | A workflow and time finding for a frequent-use group. Researchers did not detect shifts in quantity or composition of tasks from individual-level provision; it is not a universal productivity percentage. |
| OECD, 2025 review | Review of evidence on productivity, innovation and entrepreneurship | Effectiveness depends on task and user experience; human-AI collaboration is central | Long-term business effects and workers’ understanding of AI limits remain open questions. |
| OECD, 2024 workplace paper | Worker survey | Four in five respondents said AI improved performance; three in five said it increased enjoyment of work | Self-reports, not experimental causal estimates. The paper also identifies concerns about work intensity, data collection and inequality. |
| Microsoft Research synthesis, 2024 | Synthesis of workplace studies | Measured time and accuracy effects vary by role, function, organization, adoption and utilization | The report discloses Microsoft funding and notes that many included studies had not been peer reviewed at publication. |
| Fabrizio Dell’Acqua and colleagues, NBER working paper, 2025; 776 professionals at Procter & Gamble | Preregistered field experiment on product-innovation challenges | Individuals using AI matched the performance of teams without AI on the studied work | This experimental task does not establish that AI generally replaces teams. |
| Alexander Bick, Adam Blandin and David J. Deming, NBER working paper, 2024, revised February 2025 | U.S. survey of generative-AI adoption | Respondents estimated time savings equal to 1.4% of total work hours in late 2024 | Respondent-reported time savings, not an experimentally measured productivity increase. |
Turn a time saving into a business-value estimate carefully
Convert time into value only after verifying that the released time is actually redeployed. A worker spending two fewer hours on email has not automatically created two hours of additional productive capacity; the time may become higher-value work, training, idle capacity or more meetings.
A transparent estimate can show:
- Observed change in accepted output per paid hour.
- Incremental labor, software, integration and review costs.
- Any change in quality, rework, customer outcomes or overtime.
- The proportion of released time that was demonstrably redeployed to a valued task.
- A range for uncertainty rather than a single forecast.
Keep realized results separate from a scenario model. Label assumptions about adoption, wage rates, utilization, duration and redeployment. Do not multiply a short-term task effect across every employee or year without evidence that the task, quality and adoption remain stable.
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- Write the scope sheet. Record the tool, enabled features, workflow, roles, eligibility, dates, primary outcome, quality guardrails and decision threshold.
- Map the current process. Identify inputs, human decisions, AI touchpoints, review steps, handoffs and the system of record for output and time.
- Extract baseline data. Capture task volume, paid hours, task mix, quality and rework for a representative pre-period or comparison group.
- Choose the allocation design. Prefer randomized access; otherwise use a documented phased rollout or matched comparison and state the limitations.
- Instrument adoption. Log eligibility, activation and sustained use separately from outcomes.
- Run the pilot without changing definitions. Keep the quality rubric and task unit stable; log policy, staffing and model-version changes.
- Audit quality and people measures. Review a prespecified sample, survey workers and monitor intensity, overtime and data concerns.
- Analyze by assignment and by use. Present the access effect first, then clearly labeled descriptive or adjusted usage analyses.
- Stress-test the result. Check alternative task mixes, missing data, outliers, seasonality and whether gains persist after initial learning.
- Make a bounded decision. Continue, redesign, expand to a specified workflow or stop, with the conditions and follow-up period written down.
Example: evaluating an AI support assistant
Suppose a support organization wants to test suggested replies for billing cases. It defines accepted cases resolved per staffed hour as the primary outcome, with reopen rate, audited accuracy, escalation rate and overtime as guardrails. Agents are randomly assigned to access now or after the eight-week evaluation. The team records case difficulty, shift and tenure, measures outcomes during four baseline weeks and eight follow-up weeks, and logs activation and weekly use.
The report would show the access-group and comparison-group rates, the difference with uncertainty, quality results, adoption, and subgroup estimates for novice versus experienced agents. If frequent users resolve more cases but also receive easier queues, that usage comparison is described as selected and not treated as the causal effect of the assistant. A decision to expand would specify the support tier and quality limits for which the evidence applies.
Common measurement failures and fixes
- Counting logins as productivity: use logins for adoption and accepted task output for productivity.
- Reporting speed without quality: add audited accuracy, rework and downstream acceptance.
- Using only a before-and-after chart: add a randomized, phased or otherwise credible comparison.
- Comparing users with non-users as if random: report access effects and label usage analyses with selection caveats.
- Pooling every role: segment by task, role and experience when sample size permits.
- Ignoring work intensity: track overtime, after-hours work, correction effort and worker feedback.
- Changing the denominator mid-pilot: freeze the task and time definitions, or analyze periods separately.
- Overstating a short pilot: state the dates and follow outcomes over a longer period before claiming durable organization-wide effects.
- Hiding uncertainty: publish sample sizes, missing data, quality-review coverage and uncertainty ranges.
- Treating survey optimism as causal output: present self-reported benefits as worker experience, alongside observed task measures.
Plan for longer-term evidence
Track the same primary and quality outcomes after the novelty period, model changes and staffing adjustments. Separate the initial learning curve from steady-state performance. Recheck whether workers continue to use the tool, whether task mix changes, and whether review or compliance costs accumulate.
Long-term business effects remain an identified evidence gap. Maintain a dated record of tool versions, workflow changes, staffing and policy so that later results can be interpreted. Reassess whether released time became valued output rather than assuming that it did.
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Quick Recap
What a credible report should contain
- Tool, version or feature set and human-review rules.
- Population, roles, task definition and eligibility.
- Baseline period, follow-up period and calendar context.
- Allocation or comparison design and its limitations.
- Primary outcome formula, quality measures and cost measures.
- Access, activation and sustained-use rates.
- Results by relevant task, role and experience groups.
- Sample sizes, missing data, quality-audit coverage and uncertainty.
- Worker feedback, intensity measures and data-governance safeguards.
- A conclusion limited to the studied conditions, with a plan for continued tracking.
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