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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsMeasure whether the work is getting better—not just whether employees are using AI. Define the intended benefit before rollout, record a baseline, and compare results over time or with a credible comparison group. Track speed or output alongside quality, effort, rework, employee experience, newly enabled work, and harms. More usage, faster drafts, or a higher model score alone does not show that employees are doing better work.
First, define what “better work” means for this initiative
Start with the work problem, not the tool’s available dashboard. The right outcome depends on the job and the stated goal: faster task completion, fewer errors, more complete work, less effort, more reliable service, or work employees could not previously do. A time-saving measure is only an intermediate signal if the goal is better service quality.
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Before rollout, write down the unit you will evaluate—task, employee, team, or workflow—the intended outcome, the baseline period, the comparison condition, and the evaluation period. Also state what size or kind of change would count as meaningful to the people who do or receive the work. There is no universal metric set or threshold for “better.” NIST notes that trustworthy AI depends on reliable measurement of both the technology and its use; its customizable TEVV-Athlon framework is an initial public draft, not a universal scorecard. See NIST’s AI measurement and evaluation overview and the TEVV-Athlon framework announcement.
How can you tell whether AI caused the change?
A before-and-after comparison can show that an outcome changed after deployment, but it cannot by itself establish that AI caused the change. Staffing, workload, seasonality, training, policy, and process changes may also affect results. The stronger the claim you want to make, the more important a credible comparison becomes.
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- Set the baseline: Measure the chosen work outcomes before employees start using the AI initiative, using the same definitions you will use afterward.
- Choose a comparison: Where practical, use a randomized or staged rollout so comparable groups receive the initiative at different times. If that is not feasible, select a comparison group or document a pre/post design and the changes happening alongside the rollout.
- Keep the evaluation period meaningful: Measure long enough to distinguish an early learning period from routine use, and record when training or workflow changes occur.
- Report what the comparison supports: Describe the measured change, the group and period it applies to, and important limitations. Do not turn an association or a usage increase into a causal productivity claim.
Workplace evaluations are feasible: Microsoft Research describes a 2024 randomized field experiment of Copilot for Microsoft 365 involving more than 60 organizations and over 6,000 employees. Those figures describe that study, not an expected effect for another organization. Microsoft’s AI and Productivity Research Initiative outlines its research program, and its review, Generative AI in Real-World Workplaces, describes the field experiment.
Which outcomes should you measure?
Choose a small number of primary outcomes tied to the initiative’s goal, then add supporting measures that can explain why they moved. The precise measures depend on the work, but measuring speed or volume without quality and effort can reward output that creates more downstream work.
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| Outcome dimension | What to measure | What it can reveal |
|---|---|---|
| Speed and throughput | Task completion time or completed work, including work sent back for revision | Whether employees complete work faster or handle more of it |
| Quality | Correctness, completeness, customer-relevant quality, or another job-specific quality measure | Whether faster or higher-volume work remains useful |
| Effort and rework | Employee workload and time spent checking, correcting, or rewriting AI output | Whether apparent time savings are offset by review and repair |
| Reliability and risk | Error severity, escalations, exceptions, and failures that affect employees or customers | Whether the workflow is dependable and where intervention is needed |
| Employee experience | Reported usefulness, confidence, control, collaboration, and ability to do higher-value work | How the initiative feels and changes employees’ work, not a causal productivity effect by itself |
| Newly enabled work | New tasks or expanded work that employees can now perform | Value that conventional throughput measures may miss when the work itself changes |
| Distribution of effects | Results by relevant role, experience level, or employee group | Whether gains or burdens are unevenly distributed |
Employee surveys are valuable when you want to understand perceived usefulness, control, collaboration, or confidence. Label those findings as self-reported; they do not alone establish that the AI initiative caused productivity gains. For example, Microsoft’s 2026 Work Trend Index describes an AI impact measure built from self-reported outcomes including creativity, doing more kinds of work, higher-quality first drafts, work ability, collaboration, control, career prospects, high-value work, and likelihood of staying. Its survey covered 20,000 AI-using workers across 10 markets, with fieldwork from February 18 to April 7, 2026. Those dimensions can inform survey design, but the survey’s reported outcomes are not a controlled causal estimate. See Microsoft’s 2026 Work Trend Index report.
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How should measurement change when AI enables new work?
If employees are using AI to do a different kind of task, comparing only the speed of old tasks can miss the initiative’s main value. Microsoft Research’s first AI and Productivity Report cautions that new-task use calls for different evaluation strategies than using AI to perform existing work faster or better.
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For newly enabled work, define what counts as useful work before measuring it: for example, whether the task was completed, whether its quality met the relevant standard, who benefited, and what employee time or expertise it required. Record the output and the effort involved rather than forcing the result into a historical throughput measure that may not fit.
Does the evaluation reflect actual working conditions?
Test results from a model evaluation or benchmark do not establish that the deployed system will work safely or effectively in employees’ daily tasks. NIST’s AI Risk Management Framework Measure guidance says performance estimates alone are insufficient and calls for understanding deployment context, while recommending documentation of test sets, metrics, and testing, evaluation, verification, and validation tools. Apply that principle by evaluating representative tasks under actual operating conditions and giving employees a way to report failures or harms. See the NIST AI RMF Playbook’s Measure guidance.
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NIST’s ARIA pilot report describes model testing, red teaming, and field testing, and discusses using measurement trees to assess validity. It reports that five organizations participated and submitted seven AI applications for evaluation; those are the pilot’s participants and applications, not a workplace-benefit estimate. See the NIST ARIA Pilot Evaluation Report.
How do you judge whether a measurement plan is credible?
Use these questions to review the plan before relying on its results. They are practical evaluation criteria, not an official NIST scorecard.
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| Check | Question 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 only convenient benchmark items? |
| Outcome balance | Are quality, effort, rework, and risk considered alongside speed or volume? |
| Employee coverage | Are different roles and experience levels represented, with subgroup differences visible where relevant? |
| Burden and privacy | Can the evidence be gathered without unreasonable employee burden or intrusive monitoring? |
| Interpretability | Can decision-makers tell what changed, for whom, and under what conditions? |
These criteria synthesize NIST’s emphasis on contextual evaluation with Microsoft’s examples of task-based and employee-reported outcomes. They help expose a common weakness: a metric may be easy to collect without answering whether the work improved. Relevant sources include the NIST TEVV-Athlon framework announcement, the NIST Measure guidance, and the 2026 Work Trend Index.
What should the final decision report say?
Present the result in terms of the intended work outcome, not an undifferentiated “AI impact” score. State the comparison, time period, affected roles, quality and effort results, and any important concurrent changes. Separate measured changes from employee perceptions, and describe adverse effects alongside benefits. If the evidence is too weak to support a causal conclusion, say what changed and what remains uncertain rather than claiming proof.
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