Report · AI & Tech

Many Americans in the labor force say AI could do some of their work tasks

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Among Americans in the labor force, a plurality (37%) say AI could handle some of their work tasks, according to a Verasight survey conducted July 30 to Aug. 4, 2025.

Roughly a quarter (27%) say AI could handle not much of their work, and another 23% say none of it. At the higher end, 10% think AI could do most of their work and 3% think it could do all of it.

Topline

single choice

Topline distribution

Many Americans in the labor force say AI could do some of their work tasks.

Regardless of how much of your work is currently done with AI, how much of what you do in your job do you think can be done with AI?

  • Some 36.8%
  • Not much 27.0%
  • None 22.9%
  • Most 10.1%
  • All 3.1%

2025 · base n 971 · +/- 3.9%

AI Adoption Survey July 2025

Source report pending

Methodology

Source report pending
Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting
Field dates
2025-07-30 → 2025-08-04
Base (unweighted)
971
Margin of error
+/- 3.9%
Module
AI Adoption Survey July 2025

Source

  • 01
    Source report pending

Citation

AI Adoption Survey July 2025, fielded July 30-August 4, 2025, N=971 US adults age 18+, +/- 3.9%.

Source report pending

Verasight survey methodology

How Verasight conducts surveys.

This page describes the Verasight general survey contract, separate from how the Data Library packages it. When a wave report is published, its field dates, sample sizes, and module breakdown are listed in that report.

Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting.
Population
US adults age 18+.
Sample design
Surveys are run as omnibus or single-topic waves. Omnibus waves are split into modules with their own respondent set, typically around one thousand respondents per module.
Field window
Each wave specifies its own field dates. Most omnibus waves field across roughly two weeks.
Weighting
Per-module weighting to CPS targets including age, race and ethnicity, sex, income, education, region, and metropolitan status.
Partisanship benchmark
Pew Research Center's NPORS benchmarking surveys, three-year running average.
Vote benchmark
2024 presidential vote population benchmarks.
Margin of error
Typically about plus or minus 3.4 to 3.6 percent per module at standard module sizes. Question-level MoE is recomputed when a base shrinks materially below the module baseline.
Reporting
The canonical report hub is reports.verasight.io. Verified legacy reports remain on their original hosts, and unpublished reports are marked pending.
Transparency
Verasight is a member of the American Association for Public Opinion Research Transparency Initiative.

The canonical report hub is reports.verasight.io; older reports may remain on verified legacy hosts.