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Evaluate AI productivity in your business by testing one defined workflow against a measured baseline, tracking quality and downstream outcomes as well as speed or volume, and separating results by role, experience, and actual tool use. Published studies offer useful context—not a forecast for your company: they measure different tasks and outcomes, and their findings vary across workers and settings. A credible pilot should establish what success means before rollout, compare like with like, and count deployment costs and unintended effects before you decide whether to expand.
What does “AI productivity” mean for your business?
Productivity is not simply the number of tasks completed or the minutes saved. For a business decision, it means producing more valuable, acceptable work—or producing the same value with fewer resources—without shifting hidden costs to customers, coworkers, or later stages of the process.
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That definition matters because AI can affect several different points in a workflow. It may reduce the time an employee spends drafting an email, increase the number of customer issues resolved in an hour, help a developer complete more tasks, or change whether customers make a purchase. Those are distinct effects. A time saving is not automatically a higher output; higher output is not automatically better quality; and neither alone proves higher revenue or profit.
Start with the business decision you need to make. Are you deciding whether to continue a small pilot, expand access to a team, redesign a workflow, or stop using the application? The right evidence depends on that decision. A local test should answer whether a specific AI-enabled process improves work under your operating conditions—not whether “AI” in general works.
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How do you choose the workflow and define success?
Choose a narrow, repeatable task with a clear owner and a result you can observe. “Use AI to improve productivity across the company” is too broad to evaluate: different teams have different work, risks, and measures.
Describe the work before selecting a metric
Write down who performs the task, what information they start with, what steps they take, where the AI enters, and what counts as completion. Include the handoffs after the AI-assisted step. For example, an AI response suggested to a support agent is not necessarily a resolved customer issue; it must still be reviewed, sent, and accepted or acted on by the customer.
- Task: Name the specific work, such as resolving incoming support issues or completing a defined type of software-development task.
- Inputs: Record the information workers receive and any relevant constraints, such as task complexity or customer segment.
- Intervention: Specify the AI capability and how workers are expected to use it. Access to a tool is not the same as using it on suitable work.
- Completion: Define the accepted end state, including any required human review, customer confirmation, or quality check.
Choose one primary outcome and supporting guardrails
Pick one primary measure that represents the decision you are trying to make. Depending on the task, that could be accepted work completed, issues resolved per hour, cycle time, conversion, or cost per successful outcome. Set secondary measures to catch trade-offs rather than treating every available metric as a separate definition of success.
- Quality: Acceptance, error rates, accuracy, rework, or a task-specific review score.
- Downstream effects: Customer sentiment, retention, conversion, or later work caused by mistakes or incomplete output.
- Worker effects: Time spent on the task, time outside regular hours, workload, or changes in the composition of work.
- Use: Whether people used the AI, how often, and for which eligible tasks.
Set the metric definitions and decision thresholds before looking at pilot results. Otherwise, it is easy to select whichever number improved after the fact and overlook quality loss or a shift in the work being counted. A measure should reflect what the business values: lines of code, for example, are a count of code, not a direct measure of correctness or maintainability.
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How should you establish a baseline and comparison?
Measure the existing workflow before the AI change, then compare it with the AI-assisted workflow over the same period. When operationally and ethically practical, randomly assign access or rollout timing; where that is not practical, document how the comparison group differs from the people using AI.
- Record the pre-pilot workflow. Use the same outcome definitions you plan to use during the pilot. Capture relevant differences in task mix, workload, staffing, experience, and process.
- Choose a comparison design. If feasible, randomly assign eligible workers, teams, or rollout timing to AI access versus the existing workflow. If you use a non-random comparison, identify who is included in each group and record meaningful differences between them.
- Measure both groups over the same period. A before-and-after comparison can be distorted by seasonality or changing workload. Track major events such as staffing changes, new procedures, unusual demand, or changes in the type of work handled.
- Keep the task and outcome definition stable. If the AI changes what counts as a completed task or which tasks workers take on, document that change rather than treating the before and after numbers as directly equivalent.
- Record adoption and learning. Track who actually uses the tool, on which tasks, and how use changes over time. Separate the initial adjustment period from later performance instead of assuming access produces immediate or uniform effects.
There is no universal pilot duration or sample size established by the studies summarized here. The practical requirement is enough comparable observations to distinguish a real change from normal variation in the work. If results differ substantially across workers or task types, a single overall average may conceal what is happening.
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Field experiments provide evidence about particular applications in particular settings. Their measures are not interchangeable, and none supplies a guaranteed productivity return for another business.
| Study and setting | Reported result | What the measure means |
|---|---|---|
| NBER Working Paper 33795, issued May 2025 and revised November 2025; 66 firms and 7,137 knowledge workers | In the second half of a six-month experiment, the 80% of treated workers who used the integrated generative AI tool spent two fewer hours on email each week and reduced time working outside regular hours. | A time-use change among users in that setting. The authors did not detect shifts in the quantity or composition of workers’ tasks from individual-level access; the result does not establish a broad output or revenue gain. |
| NBER Working Paper 31161, issued April 2023; published journal version in 2025; customer-support agents at a particular firm | Issues resolved per hour increased by 14% on average, and by 34% for novice and lower-skilled agents; the most experienced and higher-skilled agents saw little or no effect. | A task-specific productivity measure with different effects by experience. The estimate is not a general forecast for other support teams. |
| Three randomized software-development field experiments at Microsoft, Accenture, and an anonymous Fortune 100 company; Microsoft Research summary June 2025, with journal publication online in 2026 | A combined 26.08% increase in completed tasks across 4,867 developers; adoption and gains were greater among less experienced developers. | Completed tasks in the study settings. Individual experiments were noisy and varied; this is evidence about coding assistants in those settings, not every kind of knowledge work. |
| Bank for International Settlements Working Paper 1208, September 4, 2024; CodeFuse coding field experiment | A 55% increase in lines of code for programmers using the LLM, statistically significant primarily among junior staff. | Lines of code were the measured output. The result does not establish that code quality improved or that lines of code are a universal productivity measure. |
| Columbia Business School online-retail experiments, described as spanning six months in 2023–2024; seven consumer-facing workflows | Sales treatment effects ranged from 0% to 16.3%, depending on the application. The summary estimated approximately $5 in annual incremental value per consumer across the four applications with positive effects. | Results from one retailer’s scale and workflows. Neither the sales effects nor the per-consumer estimate should be carried over as a value estimate for another business. |
| Management Science study of a GPT-4-powered business assistant for Kenyan entrepreneurs, published online in 2026 | The study could not reject the null hypothesis of no average effect on revenues and profits. It reported that lower-performing entrepreneurs did worse, while higher-performing entrepreneurs may have benefited. | An average result that did not show a statistically distinguishable effect, alongside variation by baseline performance. Access to advice alone did not guarantee better business outcomes. |
The studies illustrate why a pilot needs a locally meaningful outcome. Email time, issues resolved per hour, completed tasks, lines of code, retail sales, and entrepreneurs’ revenue and profit answer different questions. A percentage from one measure cannot be applied to another as if it were a common AI productivity rate.
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The NBER knowledge-work paper also notes that some authors were employed by Microsoft, the tool maker, and that Microsoft reviewed the paper for privacy concerns; the authors state they had full discretion over results and estimates. That context is worth knowing when interpreting the study, while the reported result should still be read according to its measured population and outcome.
How do you interpret differences between workers and tasks?
Report results by role, experience, baseline performance, and task type when the pilot has enough observations to support those comparisons. An overall average can hide a benefit for one group, no measurable change for another, or harm for a third.
The customer-support study found a larger increase for novice and lower-skilled agents than for the most experienced and higher-skilled agents. The software-development experiments also reported greater adoption and gains among less experienced developers, while the CodeFuse study’s statistically significant gains were primarily among junior programmers. In the Kenya entrepreneur study, the average revenue and profit effect was not distinguishable from no effect, but results varied with entrepreneurs’ baseline performance. These are not evidence that the same pattern will appear in every business; they are reasons to check rather than assume uniform impact.
- Compare people doing comparable work, not simply everyone with access against everyone without it.
- Distinguish whether a difference reflects role, task difficulty, prior performance, experience, or actual usage.
- Show subgroup results only when the number of observations makes them reasonably interpretable; avoid firm claims about groups your pilot could not measure reliably.
- Keep the overall result visible alongside subgroup results so a favorable result for one segment does not conceal the effect on the full workflow.
Usage is informative but not a substitute for an outcome. People may use a tool more because it fits their task, because they have learned how to use it, or because their work differs from that of nonusers. Treat adoption as a measure to explain the result, not proof that the intervention caused a productivity gain.
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How do you test quality, application behavior, and risk?
Evaluate whether the AI application behaves as intended as well as whether the workflow’s business measure improves. A faster process that produces unacceptable errors, creates extra review work, or harms customer outcomes is not a productivity improvement.
NIST’s ARIA pilot evaluation report, published November 13, 2025, describes model testing, red teaming, and field testing. It also discusses dialogue annotation, tester questionnaires, and measurement trees. These are useful evaluation ideas: combine structured tests of system behavior with observation of real use, and map a top-level business goal to the evidence needed to assess it.
Build a practical quality check
- Define unacceptable outputs or failure modes for the workflow before rollout.
- Review a consistent sample of AI-assisted work, including cases workers choose not to use AI where relevant.
- Track corrections, escalations, rework, customer responses, and other downstream signals that the primary speed or volume measure misses.
- Record cases where the application fails, behaves unexpectedly, or encourages users to rely on output that should be checked.
Use a measurement tree to connect the business outcome to intermediate signals. For example, a proposed gain in resolved customer issues might depend on response time, whether the response is accurate, and whether the issue remains resolved. This makes it easier to see where a gain or failure originates instead of treating one headline number as the whole story.
How do you turn pilot results into a business decision?
Translate measured change into value only after accounting for quality, adoption, costs, and any operational changes required to sustain the workflow. The cited studies do not provide a universal ROI figure or a general cost benchmark for another business to use.
- Estimate the incremental outcome. Compare the primary measure for the AI and comparison groups, with the task definition and relevant differences kept visible.
- Check that the gain is acceptable. Review quality, rework, customer or worker effects, and risk measures alongside the primary result. A faster task that creates costly corrections may not improve the overall process.
- Account for implementation and operating changes. Include the resources and process changes required to introduce and maintain the workflow. Do not count an apparent time saving as cash savings unless the business can actually convert it into lower cost or useful capacity.
- Decide by use case and population. Continue, adapt, expand, or stop based on the evidence for the workflow and groups you measured. A result for one team does not establish a company-wide effect.
- State the limits of the decision. Record the time period, tasks, participants, usage, comparison design, and remaining uncertainty so leaders know what the result does—and does not—support.
Do not calculate your expected return by multiplying a published percentage by your payroll, sales, or task volume. The retail estimate of approximately $5 annual incremental value per consumer, for instance, belongs to the four positive-effect applications at the retailer described in the Columbia summary; it is not a general dollar value per customer.
What should a credible pilot report include?
A concise decision report can make the result auditable and prevent a promising metric from being mistaken for a complete business case. Include the following information:
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- The primary outcome and its exact definition, plus quality, downstream, worker, and usage measures.
- The baseline period, comparison design, participants, and differences that may affect interpretation.
- How adoption and learning changed during the pilot, including task fit where observed.
- Results overall and by relevant role, experience, baseline performance, or task type where sample sizes permit.
- Unexpected effects, errors, rework, customer outcomes, operational changes, and costs considered in the decision.
- The conclusion the evidence supports and what further evidence would be needed before a wider rollout.
A strong report can conclude that the evidence is mixed or too limited to justify expansion. That is more useful than claiming a universal productivity lift from a result that applies only to one task, group, or measure.
Frequently Asked Questions
Can I use time saved as my AI productivity metric?
Yes, if reducing time is itself the outcome you need and you also check whether the work remains acceptable. Time saved alone does not tell you whether the employee completed more valuable work or whether the time was absorbed elsewhere in the process.
How can a small business test AI without a large experiment?
Use a narrow, recurring workflow and record the same task and outcome for AI-assisted and existing-process work during the same period. A small test can inform a local decision, but it may not support reliable conclusions about subgroups or rare outcomes.
What if employees use AI only on some tasks?
Record which eligible tasks actually involved AI and compare outcomes with that usage in view. Do not treat licensed access as exposure to the intervention, or assume that the workers who use it most are directly comparable to those who do not.
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Do published AI productivity percentages predict my company’s results?
No. The reported percentages come from distinct settings and measures, including support issues per hour, completed developer tasks, lines of code, and sales. They are context for choosing what to test, not transferable estimates of your expected return.
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