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Evaluate AI as a specific workflow change, not as a company-wide percentage. Define the task and the business outcome first, measure performance before and after deployment against a credible comparison, and track quality, adoption, worker effects and downstream results alongside time or volume. Then segment the findings by role, experience and actual use before converting any improvement into financial value. Published figures—from two fewer email hours per week to a 26.08% increase in completed developer tasks—describe particular tools, populations and settings; they are context, not a forecast for your business.
What does “AI’s impact on productivity” actually mean?
Productivity is the amount of valuable, acceptable work produced for the resources used. An AI assistant can change several parts of that equation independently: time per task, tasks completed, quality, rework, customer results, employee workload or operating cost. A shorter task is not automatically more productive if errors, escalations or support costs rise.
Start with one workflow, such as resolving support issues, completing a code change or processing an email queue. Specify the people doing it, the inputs they receive, the AI capability being added and the condition that makes a task complete. “Use AI to improve productivity” is too broad to test; “increase accepted support resolutions per paid hour without lowering customer sentiment” is testable.
How do I choose a workflow and a meaningful outcome?
Choose work that is frequent enough to measure, has a stable definition of completion and matters to customers or the business. Write the primary outcome before anyone receives access to the tool.
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Define the unit of work
- Actor: the role or team performing the work, including experience level.
- Inputs: tickets, documents, code repositories, customer messages or other information available at the start.
- AI intervention: drafting, retrieval, classification, code suggestion, summarization or another precisely described capability.
- Completion rule: the event that makes the item count, such as an accepted code change, a resolved issue that remains closed, or a sent email meeting the normal standard.
- Business connection: the customer, revenue, risk, cost or capacity measure the workflow is expected to influence.
Choose one primary measure
Use one pre-specified measure for the main decision and a small set of guardrails. Examples include accepted work items per paid hour, issues resolved per hour, cycle time to an approved deliverable, conversion rate, or cost per successful outcome. Do not switch to an easier metric after seeing the results.
Separate outputs from outcomes
Output is what the workflow produces; outcome is what happens because of it. Lines of code, drafts or tickets closed are outputs. Defect-free releases, retained customers, sales or reduced cost are outcomes. Measure both when the business decision depends on the latter.
How should I establish a baseline?
Record the same definitions and measurements for a period before deployment. The baseline should represent normal workload, staffing, seasonality and quality—not a manually selected “good week.”
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- Capture volume, handling time or cycle time, completion and acceptance status.
- Record errors, rework, escalations, refunds, defects or other quality signals.
- Include customer outcomes such as sentiment, retention or conversion when they are part of the workflow’s purpose.
- Log staffing, queue size, case mix, policy changes, outages and other events that could move the metric.
- Keep the measurement definition and data extraction method unchanged during the pilot.
For a new process with no historical data, run a short observation period using the intended production procedure without the AI feature. Mark that period as a baseline rather than comparing against a target chosen after deployment.
What comparison makes an AI pilot credible?
A comparison group or randomized rollout helps distinguish an AI effect from workload, staffing and seasonal changes. When randomization is practical and ethical, assign eligible people or work units to receive access at different times or by lottery.
Randomized access or rollout
Randomize at the level where spillovers are manageable. If workers share answers or queues, randomizing individual people may contaminate the control group; team or queue-level assignment can be more credible. Keep the assignment rule, dates and exclusions documented before results are viewed.
Matched or staggered comparison
If randomization is not feasible, define a comparison group with similar roles, workload and experience. Measure both groups over the same calendar period and document every important difference. A staggered rollout can provide an untreated period for later adopters, but record any anticipation, training or process change that occurs before access.
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Control the calendar and operating context
Compare like with like: holiday peaks, product launches, policy changes, staffing shifts and unusual queues can overwhelm a modest AI effect. Report these events rather than silently attributing their impact to the tool.
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Which productivity, quality and worker measures should I track?
Use a metric tree: one primary outcome, supporting process measures and guardrails that would make an apparent gain unacceptable.
| Layer | Examples | Why it matters |
|---|---|---|
| Primary outcome | Accepted work per paid hour; issues resolved per hour; completed and approved tasks; conversion; cost per successful outcome | Answers the business question you defined |
| Speed and capacity | Cycle time, queue age, after-hours work, throughput | Shows where time or capacity changed |
| Quality | Defect, error, rework, escalation, rejection or rollback rate | Prevents faster low-quality work from looking productive |
| Customer or user result | Sentiment, retention, complaint rate, conversion or satisfaction | Tests whether output creates value downstream |
| Adoption and behavior | Eligible users, active users, tasks assisted, suggestion acceptance, override rate | Separates access from actual use and task fit |
| Worker effect | Hours outside normal schedules, workload, confidence, learning time, reported friction | Captures costs or benefits that system logs miss |
Predefine acceptable guardrail limits. For example, an increase in resolved tickets is not a win if repeat contacts or customer complaints exceed the limit you set before the pilot.
How do I measure adoption and the learning curve?
License or login records show eligibility, not use. Record whether the AI was invoked, for which task, whether its suggestion was accepted or edited, and how long the worker has used it.
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- Define an active-use event, such as an AI-assisted item that reaches the normal completion state.
- Report the share of eligible workers who use the feature and the share of eligible tasks that receive assistance.
- Mark training, prompt changes, model updates and policy changes on the timeline.
- Separate an initial learning period from the steady-state estimate; publish results for each when the sample permits.
- Record non-use reasons, including poor task fit, latency, trust, access problems or policy restrictions.
The distinction matters. In the NBER Working Paper Shifting Work Patterns with Generative AI (2025), 80% of treated knowledge workers used the integrated tool in the second half of a six-month experiment. The reported time change applies to users in that setting, not automatically to every person given access.
How should results be segmented?
Report an overall estimate only after checking whether it hides materially different experiences. Segment by role, experience, baseline performance, task type and actual adoption when the sample is large enough to support a stable estimate.
Role and task
An assistant that drafts routine replies may help a support queue but have no effect on complex escalations. Keep task definitions separate rather than averaging unrelated work into one “AI productivity” number.
Experience and baseline performance
Less-experienced workers may gain more from guidance, while experts may face little benefit or additional review work. The customer-support and developer field studies below both report stronger gains among less-experienced workers. A Kenyan entrepreneur experiment found the opposite direction by baseline performance: lower-performing entrepreneurs did worse, while higher-performing entrepreneurs may have benefited. An average can conceal these opposing effects.
Adopters and non-adopters
Show results for eligible users and for the full eligible population, labeling each denominator. User-only results describe the effect among adopters; the business decision may depend on the cost and feasibility of reaching non-adopters.
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What do published AI productivity effects actually show?
The figures below are useful benchmarks for questions to ask, not promises for a different company. Each uses a different task, outcome and design.
| Setting and source | Reported result | Important qualification |
|---|---|---|
| Knowledge work and email; NBER Working Paper 33795 (2025) | Two fewer hours of email per week among the 80% of treated workers who used the tool in the second half of a six-month experiment | 66 firms and 7,137 knowledge workers; no detected individual-level shift in task quantity or composition; some authors were Microsoft employees, and Microsoft reviewed the paper for privacy concerns while authors retained discretion over results and estimates |
| Customer support; NBER Working Paper 31161 (2023 working-paper issue; journal version 2025) | 14% average increase in issues resolved per hour; 34% for novice and lower-skilled agents | Little or no effect for the most experienced and higher-skilled workers; conducted with one firm and assistant |
| Software development; Microsoft Research summary (June 2025; journal publication online 2026) | 26.08% increase in completed tasks across three randomized field experiments with 4,867 developers | Experiments at Microsoft, Accenture and an anonymous Fortune 100 company were noisy and varied; adoption and gains were greater among less-experienced developers |
| Coding; Bank for International Settlements Working Paper 1208 (September 4, 2024) | 55% increase in lines of code | Statistically significant primarily among junior staff; lines of code were the measured output and do not establish code quality or general software productivity |
| Online retail; Columbia Business School summary of experiments in 2023–2024 | Sales treatment effects from 0% to 16.3% across seven consumer-facing workflows | Effects depended on the application and were consistent with higher conversion in positive cases; one retailer’s scale and workflows |
| Online retail value estimate; Columbia Business School summary | Approximately $5 annual incremental value per consumer across four applications with positive effects | An estimate for the studied retailer and applications, not a general dollar value for another business |
| Entrepreneurial performance; Management Science, published online 2026 | No statistically distinguishable average effect on revenue and profit | Effects varied by baseline performance: lower-performing entrepreneurs did worse, while higher-performing entrepreneurs may have benefited |
Different denominators and outcomes explain why these figures cannot be averaged into a single “AI productivity uplift.” Time saved, issues resolved, completed code tasks, lines of code and sales are not interchangeable.
How do I analyze the pilot without overstating causality?
Calculate the change for the AI group and the change for the comparison group over the same period. The difference between those changes is the estimate you discuss, with uncertainty and the sample definition attached.
Estimated effect = (AI group after − AI group before) − (comparison after − comparison before)Use the same unit of analysis throughout—for example, worker-week, ticket, release or customer—and state whether the estimate is per eligible participant or per actual adopter. Report the number of units, the dates, the mean and distribution, and a confidence interval or other uncertainty measure when your analyst can calculate one. A large percentage from a small or highly variable sample may be less decision-useful than a smaller, stable effect.
Check whether groups had similar pre-pilot trends. Investigate missing data, workers who changed teams, tasks that were excluded and any model or process update during the measurement window. Treat a result as suggestive rather than causal when the comparison is weak or the outcome definition changed.
How do I test the AI application itself, not just the business metric?
A favorable throughput number can coexist with unsafe, biased or unreliable behavior. The NIST Assessing Risks and Impacts of AI (ARIA): Pilot Evaluation Report (November 13, 2025) describes model testing, red teaming and field testing, including dialogue annotation, tester questionnaires and measurement trees. Adapt those ideas to the workflow.
- Create representative test cases, including edge cases and prohibited requests.
- Have qualified reviewers score factuality, completeness, policy compliance, tone and required escalation behavior.
- Red-team prompts that could expose confidential data, produce unsafe advice or bypass approval rules.
- Compare model behavior across languages, customer types, accessibility needs and other relevant groups.
- Log incidents, overrides and near misses, and define who can pause or roll back the feature.
- Repeat tests after model, prompt, retrieval-data or workflow changes.
Connect these application tests to business guardrails. For example, a support assistant’s response-quality score and escalation rate belong beside issues resolved per hour, not in a separate report that decision-makers never see.
How do I convert measured changes into business value?
Translate only the incremental, verified change—not a published percentage—into money. Start with the primary outcome and its economic value, then subtract all recurring and one-time costs.
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Value components
- Additional accepted work or sales multiplied by the contribution margin per unit.
- Verified labor capacity released for other valuable work, using the portion managers can actually redeploy.
- Reduced rework, defects, escalations or after-hours costs, if the baseline and pilot measure them reliably.
Cost components
- Licenses, usage, integration, data preparation, security review and monitoring.
- Training, prompt or workflow design, evaluation and ongoing human review.
- Migration, change management, support and the opportunity cost of employees learning the system.
- Residual costs from errors, privacy incidents, customer compensation or rollback.
A simple decision expression is net annual value = incremental annual benefit − incremental annual operating and implementation cost. Use a range when the effect or redeployment assumption is uncertain. The cited field studies do not provide a universal ROI formula or general cost benchmark, so a company-wide return cannot be inferred from any one headline percentage.
What should the final go, change or stop decision contain?
Write a one-page decision record that lets another manager reproduce the conclusion.
- Scope: workflow, roles, task definition, AI version and pilot dates.
- Design: assignment method, comparison group, baseline period and exclusions.
- Primary result: effect size in the original unit, denominator and uncertainty.
- Guardrails: quality, customer, worker and risk measures, including breaches.
- Variation: role, experience, baseline-performance and adoption segments that were measured reliably.
- Economics: incremental value, all material costs and the assumptions behind redeployment.
- Action: expand, revise the workflow, run a longer test or stop, with a named owner and review date.
Expand when the primary outcome improves meaningfully, guardrails remain within limits, adoption is sustainable and the value range exceeds the full cost range. Revise when gains are confined to a task or group that can be better supported. Stop when quality, safety or economics fail the pre-set threshold, even if a volume metric rises.
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Calling access adoption
Fix it by reporting active use, assisted-task share and reasons for non-use.
Using time saved as the only benefit
Fix it by measuring accepted output, quality and what workers do with released time.
Counting low-value output
Fix it by requiring acceptance, defect checks or a downstream customer result; lines of code alone do not establish software value.
Comparing unlike workflows
Fix it by holding task, baseline, experience mix, duration and outcome definition constant before comparing pilots.
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Ignoring heterogeneous effects
Fix it by segmenting where sample size allows and avoiding claims about groups the pilot could not measure reliably.
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Changing the metric after seeing results
Fix it by preregistering the primary outcome and guardrails internally, then labeling exploratory analyses as exploratory.
Overlooking operating changes
Fix it by logging staffing, workload, seasonality, policy, model and process changes alongside the outcome data.
How can a small business run a practical first pilot?
A small team can use the same logic without a large analytics program.
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- Collect several weeks of baseline records using the normal process.
- Choose a comparison team, queue or later rollout group; if none is feasible, document why and treat conclusions as weaker.
- Train users, record actual assistance and allow a clearly labeled learning period.
- Review a fixed sample of outputs for errors, rework, policy compliance and customer impact.
- Analyze the primary outcome, guardrails and segments before calculating financial value.
- Make a bounded decision—expand to the same workflow, redesign it, extend measurement or stop—rather than declaring that AI works everywhere.
This approach keeps the question local: did this capability improve this defined work under these operating conditions, at an acceptable total cost and risk?
Frequently Asked Questions
How long should an AI productivity pilot run?
Run long enough to cover normal workload cycles and the initial learning period, then report learning and steady-state windows separately. A fixed number of days is not transferable across businesses; queue volatility, task frequency and adoption determine when the estimate becomes stable.
What if I cannot create a control group?
Use a documented before-and-after baseline, a staggered rollout, or a matched team, and label the result as observational. Record concurrent staffing, policy, seasonality and workload changes so readers can judge how much confidence to place in the estimate.
How many tasks or employees do I need for a useful result?
There is no universal threshold because variability and the size of the expected effect determine statistical precision. Estimate the sample needed for your primary outcome with an analyst, and avoid slicing into role or experience groups that are too small to support reliable conclusions.
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Yes, as a worker-effect measure rather than a substitute for operational outcomes. Ask about workload, confidence, friction and unplanned after-hours work, and compare responses with usage logs and quality results.
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