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Research Note

AI Productivity Measurement Evidence Record

Workplace AI effects vary by task, worker, system, and measure.

Aug 4, 20262 min readBy Dalton Anderson

AI Productivity Measurement Evidence Record

Evidence pattern

Workplace AI effects vary by task, worker, system, and measure.

Brynjolfsson, Li, and Raymond's customer-support field study examined 5,179 agents and reported an average productivity increase in issues resolved per hour, with larger effects among less-experienced workers and limited effects among the most experienced workers. That result belongs to the studied support workflow.

Dillon and colleagues' 66-firm field experiment reported reduced email time and less work outside regular hours among users of an integrated generative AI tool, while detecting no shift in the quantity or composition of tasks from individual access alone. The authors disclose relevant Microsoft employment and review.

Dell'Acqua and colleagues' published consulting experiment found that effects differed across tasks and could worsen performance outside the tested system's capability boundary.

These studies support measurement in context. They do not supply a universal ROI percentage.

Measurement unit

The unit should be accepted work, not generated text. Start time includes setup, source collection, prompting, waiting, review, correction, formatting, filing, support, and incident handling. Completion occurs when the result meets the same acceptance standard as the baseline.

Quality must cover consequential correctness, completeness, traceability, fit, and harmful failure. Average time cannot erase a rare severe error.

Comparison design

Use comparable tasks, stable definitions, representative cases, a preserved baseline, and the same outcome standard. Record task difficulty, worker experience, tool version, configuration, data boundary, reviewer, and source conditions.

Report sample size, missing cases, uncertainty, outliers, adoption rate, and changes during the test. A self-reported estimate of time saved is a perception measure, not a complete ROI calculation.

Financial boundary

Net value should include licenses, integration, administration, training, review, correction, support, compliance, incidents, vendor management, and opportunity cost. Avoid converting every minute into realized cash savings unless labor, capacity, demand, and accounting treatment support that claim.

The decision can be approve narrowly, modify and retest, pause, or stop.

Sources

Follow the evidence.

  1. NIST AI RMF Measure guidanceairc.nist.gov
  2. ftc.gov: ai companies uphold your privacy confidentiality commitmentsftc.gov
  3. youtu.be: 0cC1Ez33ryIyoutu.be
  4. daltonanderson.ghost.io: ai in the workplace a practical guide to get starteddaltonanderson.ghost.io
  5. NIST AI Risk Management Frameworknist.gov
  6. NIST AI Resource Centerairc.nist.gov
  7. eeoc.gov: prohibited employment policiespracticeseeoc.gov
  8. eeoc.gov: us eeoc and us department justice warn against disability discriminationeeoc.gov
  9. nber.org: w31161nber.org
  10. open.spotify.com: 7LIXDoSM2gG97vFGftskQsopen.spotify.com
  11. NIST Privacy Frameworknist.gov
  12. nber.org: w33795nber.org
  13. eeoc.gov: strategic enforcement plan fiscal years 2024 2028eeoc.gov
  14. NIST Generative AI Profilenvlpubs.nist.gov
  15. ftc.gov: start security guide businessftc.gov
  16. dol.gov: ten 07 25dol.gov
  17. hbs.edu: dell acqua et al 2026 navigating the jagged technological frontier 5c589c8c fbb5 458f b285 c944746cd717hbs.edu
  18. cisa.gov: cisa and uk ncsc unveil joint guidelines secure ai system developmentcisa.gov
AI Productivity Measurement Evidence Record