strategyQ5to verify
2024 nationally representative survey — 23% of employed respondents used GenAI at work in the previous week; 1–5% of total work hours are AI-assisted
A late-2024 nationally representative survey found that 23% of employed respondents had used generative AI at work at least once in the previous week, with AI-assisted hours estimated at 1–5% of total work hours — establishing that workplace adoption is broad but per-worker intensity is still low.
Past-week GenAI use among employed respondents + share of total work hours that are AI-assisted23% used GenAI at work at least once in the past week; 1–5% of total work hours are AI-assisted
- Sample
- Nationally representative survey; exact N not extracted to verification.
- Methodology
- Cross-sectional nationally representative survey with self-report on past-week GenAI usage at work.
What this means
- Establishes the workplace-adoption baseline for late 2024 — broad but shallow. The discourse around 'AI transformation' is operating ahead of the per-worker intensity numbers.
- Combined with the Stanford 51-deployments + McKinsey State of AI 2025 findings, suggests the adoption-vs-impact gap is rooted in low per-worker intensity, not just organizational friction.
- Useful baseline for tracking the trajectory — if per-worker intensity remains in the 1–5% range while organizational coordination work scales, the 'access ≠ transformation' story is strengthened.
Source
(Title to verify — 2024 nationally representative GenAI workplace adoption survey)
Nationally representative survey (publisher to verify — cited in AHI institutional-economics review) · (authors to verify) · 2024 · peer-reviewed
Context
- What came before
- Pre-2024 GenAI workplace-adoption estimates were largely vendor surveys with poor sampling discipline. The cited nationally-representative survey is among the first methodologically rigorous baseline.
- What comes next
- Verify exact publication, authors, N, and survey instrument. Track quarterly to monitor the per-worker intensity trajectory. Pair with MIT NANDA GenAI Divide (95% pilot failure) for the adoption-vs-impact gap.
- Where this lands
- Encyclopedia Part I (foundations — adoption baseline), Part II (workforce — current state of AI in work).