Describe what AI did on a specific task, what you checked or changed, and whether any time saving is an estimate. Using AI does not by itself prove that you produced more, improved the work, or saved time overall. A clear account keeps those outcomes separate—and avoids turning a personal impression into a productivity claim.
What workplace AI-use figures do—and do not—show
U.S. surveys show that generative AI has become a meaningful part of some workers’ routines, but adoption figures are not productivity measurements. A Federal Reserve Bank of St. Louis analysis of pooled August and November 2024 survey responses found that 21.8% of U.S. workers had used generative AI at work in the previous week. In that survey, 9% reported using it every workday and 14% on at least one, but not every, workday. Those are self-reported usage figures, not proof that workers produced more. St. Louis Fed, February 27, 2025.
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A separate paper by Bick, Blandin, and Deming, published online in Management Science on January 20, 2026, reports that 27% of employed respondents in nationally representative U.S. surveys used generative AI for work at least once in the previous week as of late 2024; 10% said they used it every workday and 17% on some, but not all, workdays. The figures differ from the St. Louis Fed estimates because the sources and survey measures differ. Neither figure tells you what happened to a particular worker’s output. Bick, Blandin, and Deming, *Management Science*.
Keep four outcomes distinct when describing your own work:
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- Use: whether and how often you used an AI tool.
- Time: whether a task took less time, and how you know.
- Output and quality: whether you completed more work or produced a better result, judged against relevant requirements.
- Experience: whether the work felt easier or more enjoyable.
One outcome does not establish another. A faster draft is not automatically a better one, and a better-feeling workflow does not demonstrate increased output.
How to describe your own AI-assisted work
- Name the bounded task. Say what you were trying to do, such as preparing a first outline, extracting themes from material you are permitted to use, or drafting a routine message. These are examples, not a guarantee that AI is appropriate for every task.
- Check the rules before using work material. Follow your employer’s approved-tool and data-handling requirements. Do not enter sensitive, protected, or non-public information into a public AI tool unless the applicable rules and safeguards allow it. Requirements vary by workplace; federal-agency findings show privacy-policy compliance can be a practical management challenge, not that one rule applies to every employer. U.S. Government Accountability Office, July 29, 2025.
- State what the AI did. Be precise: did it draft, summarize, reorganize, or suggest wording? Avoid implying that it completed the whole assignment if it contributed only one part.
- Explain your review. Identify what you checked against the source material, requirements, or relevant expertise, and what you corrected. For consequential employment decisions, the U.S. Department of Labor recommends meaningful human oversight. U.S. Department of Labor, October 16, 2024.
- Separate a time estimate from a verified result. If you say a task took less time, explain how you arrived at the estimate. Do not describe estimated time saved as measured productivity or as extra output unless you can substantiate that output.
A concise, candid explanation might be: “I used [tool] to draft an outline for [task]. I checked it against [materials or requirements], corrected [specific issue], and revised the final version. I estimate it saved about [amount] on this task, based on [how you made the comparison].” If you cannot explain the comparison, leave out the number.
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Why “time saved” is not the same as “more productive”
In the November 2024 survey, the St. Louis Fed analysis estimated that reported time savings amounted to 1.4% of total work hours across all workers, including non-users. This was a survey-based estimate, not a direct measurement of extra output. The authors also calculated a model-based estimate of a 1.1% aggregate productivity gain from reported time savings. That figure is a potential modeled gain, not a measured, realized increase in productivity. St. Louis Fed, February 27, 2025.
The distinction matters even when a person genuinely finishes a task sooner. The St. Louis Fed authors note that if employees complete the same work faster without employers knowing, they may use the time as on-the-job leisure. That could improve workers’ welfare without appearing as increased measured productivity. As they put it: “If workers are now able to complete the same tasks in less time without their employers’ knowledge, they may take their time savings as on-the-job leisure, which would increase welfare but not productivity.”
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Population averages also cannot tell you what happened in your role, on your task, or with your level of AI use. Treat estimates about survey respondents as context, not as a personal benchmark or a promise of future gains.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to disclose, and when
Be transparent when your employer’s rules, the task, or the audience calls for it. A useful disclosure names the affected work, the AI tool, what it contributed, the purpose, and the human review. Do not imply that this is a universal disclosure rule: employer policies and external publication requirements may differ.
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For scientific work specifically, the CDC’s May 28, 2026 guidance recommends describing relevant AI use and human oversight; its recommendations reflect best practices as of that date and are not exhaustive. They are scoped to scientific products, not a general policy for all workplaces. CDC, “Considerations for Disclosing Generative AI Use in Scientific Work”.
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If your manager asks whether AI improved your performance, answer with evidence at the level you actually have: for example, the task you completed, whether it met the requirements, what you reviewed, and whether the time comparison is an estimate. A broad claim about productivity needs more than the fact that you used a tool.
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What worker surveys say about benefits and concerns
An OECD workplace survey found that four in five workers said AI improved their performance at work, while three in five said it increased their enjoyment of work. Those are reported experiences, not causal estimates that apply to every job. The OECD also documented concerns about work intensity, data collection and use, inequality, and exposure to automation. Benefits and risks can coexist. OECD, March 15, 2024.
Workplace adoption is also moving beyond isolated individual experiments. In 11 selected U.S. federal agencies with AI inventories, GAO found reported generative-AI use cases increased from 32 in 2023 to 282 in 2024. These counts describe the selected agencies, not all federal agencies or workplaces generally. GAO, July 29, 2025.
What remains unknown
The cited evidence does not establish AI’s long-run causal effect on an individual worker’s output, wages, workload, or job security. The St. Louis Fed authors identify workplace adjustment and whether gains eventually appear in aggregate productivity measures as open questions. That is a reason to describe observed outcomes carefully—not to assume either that AI has no value or that its use automatically raises productivity.
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