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Use a documented before-and-after study with a counterfactual, measure named tasks in minutes, and pair time with quality, rework, output and work-hour spillovers. Randomly assigning access or rollout timing gives the clearest estimate. If randomization is not practical, build a matched comparison group and adjust for role, grade, demographics, prior AI experience and interest in AI. A credible result says not only how many minutes changed, but for whom, on which tasks, with what uncertainty, and where the time went.
Employee opinions are useful for finding tasks to study, but they cannot by themselves prove a productivity gain. Telemetry, task samples, quality reviews and workload records provide the evidence needed to distinguish genuine capacity from faster work that creates corrections or simply moves work to another part of the day.
What does “saving time” mean in a workplace AI evaluation?
Define the outcome before collecting data. “Time saved” can mean fewer minutes on one task, fewer total working hours, more completed work in the same hours, or capacity redeployed to other priorities. These are different estimands and should not be combined into one vague productivity number.
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- Task-time effect: the change in minutes for a named activity such as drafting an email, searching for information, summarizing a meeting, coding or editing a document.
- Weekly-hours effect: the change in total hours worked, including work outside scheduled hours.
- Throughput effect: the change in completed, accepted units such as cases, tickets, documents or reviewed lines of code during a fixed period.
- Redeployed-capacity effect: the additional core work, planning, mentoring or recovery time made possible by the change.
A tool can reduce email time without reducing weekly hours, because employees may use the difference for backlog, planning or training. Report each outcome separately rather than presenting “19 minutes saved” as a headcount reduction.
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How do I create a credible counterfactual?
The counterfactual is what would have happened to comparable employees without access during the same period. Without it, a before-and-after change could be caused by seasonality, a new process, staffing changes, training or a temporary workload dip.
Preferred design: randomized access or rollout
Randomly assign eligible people to receive the tool now or later, where labor relations, safety and governance permit. Keep the assignment record, eligibility rules and rollout dates. Analyze everyone according to the group they were assigned to (intention-to-treat), even if some assigned employees never open the tool. This estimates the effect of offering access, which is normally the decision an employer controls.
A delayed-access group is often easier to approve than a permanent no-access group: everyone receives the tool after the measurement window. Stratify randomization by business unit, role or grade when those characteristics strongly affect task mix.
Fallback design: matched comparison group
If randomization is infeasible, match users with non-users who had similar baseline task times, role, grade, business unit, demographics, prior AI experience and stated AI interest. Measure those variables before rollout, not after people know their result. Report any remaining imbalance and adjust for it in the model. A matched design is weaker than randomization because unmeasured motivation or manager support can still differ.
Rollout timing as a natural comparison
When departments receive access in waves, compare early and later waves during the period when only the early wave has access. Record the exact activation date and account for calendar effects such as quarter-end work. Do not call a simple company-wide pre/post comparison an experiment unless a credible comparison exists.
How should task time be measured?
Start with a baseline period before access and repeat the same measurement at fixed intervals after rollout. Define the start and stop events in operational language so two people would classify the same work the same way.
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Build a task dictionary
For each task, specify the input, completion event, exclusions and unit of analysis. For example, an information-search task might start when an employee opens the case record and end when a source-backed answer is saved; time spent waiting for a customer response is excluded. Keep the definition unchanged after launch.
Use several measurement methods
- System telemetry: event logs can show document edits, email activity, prompt use or task timestamps. Document exactly what an event means; an open window is not the same as focused work.
- Task sampling: employees can start and stop a short timer for a representative sample of tasks. Sampling reduces diary burden while retaining task-level detail.
- Structured time diaries: ask workers to classify activities in short intervals. Use reminders and a defined coding scheme to reduce recall error.
- Work-system records: ticket, case, code-review or document systems can provide completion counts and timestamps, but they may omit invisible coordination and quality work.
- Observation or screen studies: use only with clear consent, limited scope and strong privacy controls; observation itself can change behavior.
Use telemetry to validate exposure and timing, not to infer every minute of concentration. Combine it with sampled task measures and workload records.
Calculate the task effect
For each task, compare the average or median baseline minutes with post-rollout minutes in the treatment and comparison groups. A simple difference-in-differences estimate is:
(post − baseline in access group) − (post − baseline in comparison group)If a drafting task falls from 30 to 22 minutes for the access group while the comparison group falls from 30 to 28 minutes, the estimated tool effect is 6 minutes, not 8, because 2 minutes of improvement occurred without the tool. Label hypothetical calculations as examples and retain the full distribution, not only the mean; a few heavy users can distort an average.
What quality and rework measures belong beside minutes?
Faster completion is not a net saving if it causes errors, extra review or downstream complaints. Pair every primary time measure with at least one quality measure appropriate to the task.
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Useful quality signals
- Blinded ratings against a written rubric, with raters unaware of access status.
- Error, omission, escalation or compliance-failure rates.
- Number and severity of revision cycles before acceptance.
- Customer, peer or manager outcomes that already have a stable measurement process.
- Output volume completed and accepted during the same hours.
Pre-register the scoring rubric and sampling plan. If AI-generated text is reviewed more heavily than human-only work, include that review time in the total task cost. For coding, for example, count defect fixes and review effort rather than only lines produced.
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How do I measure adoption and actual exposure?
Separate being offered a license from using the tool. Record assignment, activation, frequency, task type, training received and whether the tool was available but bypassed.
Report two effects
- Intention-to-treat: the average effect of being offered access, including non-users. This supports rollout decisions.
- Usage-on-the-treated: the effect among people who actually used the tool, estimated with a documented exposure rule. This is useful for understanding potential but is more vulnerable to self-selection.
Do not define “user” only as someone who reports liking the tool. Use an auditable threshold, such as at least one qualifying event in a specified week, and show how results change under reasonable thresholds.
Where does saved time go?
Ask employees and managers how released minutes were redeployed, then check those answers against work-hour and output records. Options should include more core tasks, planning, mentoring, training, breaks, fewer after-hours hours, administrative work and additional assigned workload.
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The UK Department for Work and Pensions (DWP) evaluation found that 89% of respondents said they redirected saved time to other work tasks. That means fewer minutes on a task should not automatically be described as a reduction in workload or staffing need. Track scheduled and unscheduled hours separately so a tool that reduces evening work is visible even when total weekly hours remain stable.
What do major evaluations show about time savings?
Results vary by task, population, tool and measurement method. The studies below illustrate how to report the design and its limits rather than treating one percentage as universal.
| Study and date | Design and sample | Observed result | Interpretation limit |
|---|---|---|---|
| NBER field experiment, revised 2025 | Randomized access to an integrated generative-AI tool across 66 firms and 7,137 knowledge workers for six months | Among treated workers who used the tool, 80% spent two fewer hours on email each week and reduced work outside regular hours | No shift in the quantity or composition of tasks; the result concerns email time and after-hours work, not a universal productivity gain |
| DWP Copilot evaluation, trial October 2024–March 2025; report 2025 | 3,549 staff; surveys, econometric analysis and interviews | Estimated average saving of 19 minutes per day across eight routine tasks after controls; information search 26 minutes, email writing 25 minutes, summarizing information or research 24 minutes | Task estimates are averages across the trial population and should not be transferred to every role or tool |
| HMRC Phase III, report 2026 | 3,000 licenses randomly allocated; user survey, Microsoft Viva Insights monitoring, focus groups and a task-based exercise | Established a mixed-method template combining behavioral, task and qualitative evidence | The design demonstrates triangulation; a license allocation is not itself a minutes-saved result |
| Google engineering RCT, 2025 | 96 full-time software engineers performing a complex task | Estimated about 21% less time on that task | Confidence interval was large; internal tooling and one task may not generalize to other engineering work |
| ILO evidence review, 2026 | Review of available studies | Productivity gains appear real but uneven and often unverified; worker-reported savings of a few percent of hours have not consistently become higher measured output, earnings or employment | Aggregate evidence supports caution about converting reported time into economic outcomes |
| Microsoft/LinkedIn Work Trend Index, 2024 | Vendor-reported six-month randomized trial across 60 Copilot customers | Users read 11% fewer emails, spent 4% less time interacting with email and edited 10% more documents | Vendor evidence is useful context but should be reported separately from independent evaluations |
How long should an evaluation run?
Use a baseline long enough to cover normal variation in the task, then measure immediately after training and again after the novelty period. A six-month window, as used in the NBER field experiment, can reveal whether early use persists across changing workloads. The exact duration depends on task frequency: a daily email task needs fewer calendar weeks than a monthly compliance review.
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Schedule a persistence check
- Collect baseline task and quality data before access.
- Record training completion and first use.
- Measure an early post-launch period to detect onboarding effects.
- Repeat at a pre-specified later date after workflows stabilize.
- Compare effect size, adoption and quality across periods.
Report training or interface changes between waves. A new model version can make a later estimate incomparable unless the version is recorded.
How should uncertainty and subgroup differences be reported?
Publish the sample size, effect in minutes and percentage, confidence interval, dates, geography, tool version, task definition and missing-data rules. Show medians or distributions when a small group of heavy users drives the mean.
Analyze roles and baseline task frequency separately. A search-heavy analyst, a caseworker and a software engineer may have very different opportunities to save time. Pre-specify key subgroups to avoid presenting whichever result looks largest after the fact.
What pitfalls can invalidate a time-saving claim?
- Self-selection: enthusiastic users may already be faster. Use random access or adjust for measured baseline differences.
- Recall-only measurement: people may misestimate small changes. Pair surveys with task samples or system records.
- Telemetry overreach: clicks and open windows do not equal focused work. Validate what each event represents.
- Quality blindness: a shorter draft that needs extensive correction is not a saving. Count rework and review.
- Novelty effects: early enthusiasm can fade. Repeat measurement after adoption stabilizes.
- Contamination: comparison employees may access the tool through shared accounts or informal help. Monitor and report cross-group exposure.
- Changing workload: a demand surge or staffing change can hide or mimic an effect. Include workload and staffing measures.
- Blended averages: one company-wide figure can conceal no effect or harm in a critical subgroup. Report task and role distributions.
- Confusing capacity with savings: redeployed time may produce more output, better service or less overtime rather than fewer paid hours.
How can I compare two workplace AI tools fairly?
Run the same task definitions, baseline period, quality rubric and follow-up schedule for both tools. If simultaneous randomization is possible, assign comparable employees to tool A, tool B or a control condition; otherwise use matched groups and disclose the design limitation.
| Comparison axis | What to measure |
|---|---|
| Task coverage | Which named tasks each tool supports and how often those tasks occur |
| Time distribution | Median, spread and tail changes in minutes, not only an average |
| Quality and errors | Acceptance, revision, defect and escalation rates |
| Adoption burden | Training hours, activation, repeat use and support requests |
| Telemetry validity | Whether logged events map to real task starts, stops and completions |
| Persistence | Effects after onboarding and after version or workflow changes |
| Privacy constraints | What data can be collected, retained and inspected, and by whom |
| Destination of time | Additional accepted output, reduced overtime, service improvements or other redeployment |
What is a practical measurement plan for a small team?
A small pilot can still be rigorous if it limits scope and documents decisions.
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- Write the estimand and task dictionary. State whether the primary outcome is minutes per task, weekly hours or accepted throughput.
- Collect a baseline. Capture task minutes, quality, volume, after-hours work and current AI experience before access.
- Assign access or create a match. Randomize when possible; otherwise pair comparable employees and record remaining differences.
- Instrument exposure. Log license assignment, training, actual use, task type and tool version without collecting unnecessary content.
- Run the first follow-up. Repeat the same task and quality measures at a fixed interval.
- Check rework and spillovers. Add revision cycles, downstream errors, output and where employees say the time went.
- Repeat after stabilization. Test whether the result persists after novelty and training effects decline.
- Publish uncertainty. Include sample size, confidence intervals, subgroup results, missing data and design limitations.
How should privacy and governance be handled?
Collect the least data needed to answer the estimand. Prefer task category, timestamps and outcome codes over storing message content or prompts. Tell employees what is monitored, who can see individual records, how long data are retained and whether results are used for performance management.
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Aggregate reporting protects individuals but can hide small-group harms. Set a minimum cell size, separate evaluation from disciplinary decisions and involve worker representatives where required. Document access controls and deletion dates before the pilot begins.
How do I turn results into a defensible business conclusion?
State the causal design first, then the measured effect, quality result, uncertainty and destination of time. A strong conclusion might say that offering a tool reduced a defined search task by a measured number of minutes relative to a comparison group, with no detected quality decline, while most released time was redeployed to other work. It should also name the roles, dates and tool version covered.
A weak conclusion converts a self-reported average into a promise of universal savings. The ILO’s 2026 review is a reminder that reported time reductions do not consistently translate into higher output, earnings or employment. Treat capacity, service quality and reduced after-hours work as distinct outcomes and decide which one matters for the operational question.
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Can timesheets alone prove that an AI tool saved time?
No. Timesheets usually aggregate many activities and are vulnerable to rounding and recall error. They can support a weekly-hours analysis, but task logs, sampled timers or workflow records are needed to identify which activity changed and whether quality held.
What should I do if employees use the tool outside the assigned pilot group?
Measure contamination rather than silently excluding those people. Record actual exposure, keep the original assignment for the intention-to-treat analysis, and run a secondary analysis that describes results by verified use.
How can I measure savings for work that happens only a few times a month?
Extend the baseline and follow-up calendar so enough events occur, or use a standardized task exercise in addition to live work. Keep the exercise separate from production-time estimates because practice conditions can be faster than normal work.
Should time saved count as a benefit if the manager immediately assigns more work?
It can still be a capacity benefit, but it is not a reduction in workload or paid hours. Report the added accepted output, service level or backlog change as the destination of the time rather than labeling it employee free time.
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Report a quality gain, not a time saving. A tool may justify adoption through fewer defects, better consistency or lower risk even when measured minutes per task are unchanged.
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