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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Measure what changes in the work—not just whether employees use the AI tool. Define the intended benefit first, establish a baseline, compare outcomes over time or against a credible comparison group, and track quality, effort, employee experience, and risks alongside speed or output volume.
Start with the work outcome, not the tool
Before selecting metrics, state what the initiative is supposed to improve and for whom. “Increase AI adoption” describes tool use, not a better work outcome. A useful objective names the work and the intended change: for example, completing a defined task faster without reducing correctness, improving customer-relevant quality, reducing avoidable effort, or enabling work the team could not previously do.
Choose the unit you will evaluate—such as a task, employee, team, or workflow—and specify the baseline, comparison condition, evaluation period, and what would count as a meaningful improvement. Align the measures to the objective: if the goal is service quality, usage and time saved are intermediate signals, not proof that service improved.
NIST explains that the utility of trustworthy AI products and services depends heavily on reliable measurement and evaluation of both the technology and its use. Its AI measurement and evaluation materials include a customizable approach to assessing systems against organizational goals in the TEVV-Athlon framework. As of October 4, 2026, the TEVV-Athlon material is an initial public draft, with public comment scheduled through October 6, 2026—not final guidance. NIST’s TEVV-Athlon page describes the framework and its status.
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Choose a comparison that can support your conclusion
The central question is not simply whether performance changed after rollout, but whether the AI initiative plausibly contributed to the change. A randomized or staged rollout can help separate its effect from changes in staffing, demand, training, or process. When randomization is impractical, use a comparison group if feasible; otherwise, document a pre/post design and note important changes that occurred at the same time.
For an example of workplace evaluation, a 2024 Microsoft Research review describes a randomized field experiment of Copilot for Microsoft 365 involving more than 60 organizations and over 6,000 employees. Those are the scope figures for that study, not a forecast of the benefit another employer should expect. Microsoft Research’s review of generative AI in real-world workplaces discusses the field experiment, while its AI and Productivity Research Initiative describes its broader research program.
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If there is no credible comparison, be precise about what the evidence can show: a before-and-after change is an observed change, not by itself a causal estimate. Record the rollout dates, changes to the workflow, training, staffing, and demand so decision-makers can judge alternative explanations.
Build a balanced scorecard for the work
Use a small number of primary measures tied directly to the objective, then add supporting measures that reveal trade-offs or unintended effects. Pair speed or volume with quality and effort: a faster first draft may not save time if review, correction, and rework increase.
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| Dimension | What to measure | Why it matters |
|---|---|---|
| Speed and throughput | Task completion time or completed work, including time spent on rework. | Shows whether work moves faster or more work is completed, while accounting for correction. |
| Quality | Correctness, completeness, reliability, or quality that matters to the customer or recipient. | Tests whether quicker or higher-volume output is still useful. |
| Effort and workload | Employee effort and time spent checking, editing, or correcting AI output. | Shows whether effort is reduced or shifted to a less visible part of the workflow. |
| Reliability and risk | Error severity, exception or escalation rates, and reports of failures or harms. | Surfaces problems that an average completion-time measure can conceal. |
| Employee experience | Reported usefulness, confidence, collaboration, control, and ability to do higher-value work. | Adds employees’ perspective on how the change affects their work. |
| New or expanded work | Tasks employees can now perform, or work they can take on more broadly. | Captures value that conventional throughput measures may miss when AI changes the work itself. |
| Distribution of effects | Differences by role, experience level, or other relevant employee groups. | Shows whether an overall result hides uneven benefits or burdens. |
Employee surveys are useful for measuring perceived usefulness, control, collaboration, and other experiences. Label them as self-reported outcomes: by themselves they do not establish that AI caused a productivity gain. Microsoft’s 2026 Work Trend Index, for example, combines self-reported dimensions including creativity, doing more kinds of work, higher-quality first drafts, work ability, collaboration, control, career prospects, high-value work, and likelihood of staying. Microsoft reports a survey of 20,000 AI-using workers across 10 markets, fielded February 18 through April 7, 2026. These are dimensions and sample details for that vendor-published survey, not a causal estimate of what an employer’s initiative will achieve. Microsoft’s 2026 Work Trend Index report describes the measure and survey.
Account for work that changes, not just work that speeds up
Some initiatives aim to accelerate an existing task; others let employees do different or additional work. In the second case, a historical measure such as output per hour may not capture the benefit because the new work had no comparable baseline. Define the new task or capability as an intended outcome, then assess its quality, usefulness, effort, and consequences rather than forcing it into an old throughput metric.
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Microsoft Research’s first AI and Productivity Report cautions that evaluating new tasks calls for a different strategy from evaluating whether existing work is done faster or better. Decide which case applies before selecting the primary measure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate the deployed system in real work conditions
A model’s score on a test set does not establish that an AI initiative is safe or effective in employees’ daily work. NIST’s AI RMF Measure guidance says performance estimates alone are insufficient; it emphasizes understanding deployment context and documenting test sets, metrics, and the tools used for testing, evaluation, verification, and validation. NIST’s Measure guidance provides the related AI RMF Playbook material.
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Use representative tasks and actual operating conditions for evaluation, and give employees a route to report failures and harms. NIST’s ARIA pilot illustrates testing that included model testing, red teaming, and field testing, and discusses assessing validity with measurement trees. The 2025 pilot report involved five participating organizations and seven AI applications; those figures describe the pilot, not a required sample size or a prediction for a workplace program. NIST’s ARIA Pilot Evaluation Report describes the evaluation.
Compare measurement plans before choosing one
Use these questions to check whether a proposed evaluation is strong enough for the decision at hand. They are a practical synthesis of NIST’s emphasis on contextual evaluation and the task- and survey-based measures in Microsoft’s reporting, not an official published scorecard.
| Axis | Questions to ask |
|---|---|
| Causal strength | Can the design distinguish the initiative’s contribution from other changes? |
| Work relevance | Do the tasks and outcomes represent actual work rather than convenient benchmark items? |
| Outcome balance | Are quality, effort, rework, and risk measured alongside speed or volume? |
| Employee coverage | Are relevant roles and experience levels represented, and can subgroup differences be seen? |
| Burden and privacy | Can you collect useful evidence without unreasonable employee burden or intrusive monitoring? |
| Interpretability | Can decision-makers tell what changed, for whom, and under what conditions? |
There is no universal metric set or single threshold that defines “better work” for every organization. The right measures depend on the task and intended benefit; report the result with its comparison, conditions, and limits so readers can distinguish a measured change from a supported claim about cause.
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