peopleanalyst

Research substrate

Insight Cards

Atomic quantitative findings from the research underlying the magazine and the AI Human Interaction Guide. Each card carries a single headline finding, full source attribution, methodology, and framing claims. Cards cite into longer editorial work by ID.

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).
strategyQ5to verify

2025 large field experiment across 66 firms — individual-level AI access produces narrower effects than expected

A 2025 field experiment across 66 firms found that individual-level access to an integrated AI tool produced narrow effects — mainly less time on email and less after-hours work — rather than a broad shift in task composition. The interpretation is that individual-level AI provision, without coordinated workflow + governance changes, does not produce firm-level transformation.

Change in task composition + work hours under individual-level access to an integrated AI tool, across 66 firmsNarrow effects: less email time + less after-hours work. No broad shift in task composition from individual-level provision alone. Exact magnitudes not extracted to verification.
Sample
Across 66 firms; exact employee N and firm-size distribution not extracted to verification.
Methodology
Field experiment with individual-level access to an integrated AI tool, measuring task-composition and work-hours outcomes.

What this means

  • Direct empirical evidence that AI 'access' alone is not the binding constraint — workflow + governance + coordination must shift in parallel for firm-level effects to materialize.
  • Pairs with the Stanford 51-deployments finding (95% of enterprise AI failures are organizational not technical) and the McKinsey State of AI 2025 finding (88% adoption but only 6% high-performers see >5% EBIT impact) — three independent results converging on the same 'access ≠ transformation' point.
  • Supports the encyclopedia's core network-mediated-adoption thesis: AI tools encountering an unchanged organizational topology produce narrow individual-level effects rather than systemic ones.

Source

(Title to verify — 66-firm 2025 field experiment on AI provision)

Field-experiment / academic paper (specific venue + URL to verify; cited in AHI institutional-economics review) · (authors to verify) · 2025 · peer-reviewed

Context

What came before
Optimistic case for AI productivity gains rested on individual-level controlled-task experiments + early field results (Brynjolfsson customer-support 14% gain). The 66-firm result narrows that picture for the individual-access intervention.
What comes next
Verify exact paper, authors, N (employees + firms), and effect-size estimates. Pair explicitly with Stanford 51-deployments + MIT NANDA GenAI Divide + McKinsey State of AI 2025 as the converging-evidence cluster on the access-vs-transformation gap.
Where this lands
Encyclopedia Part I §1.3 (methodology gap), Part II (workforce — what individual-level AI provision actually does), Part VII (network-mediated adoption — the explicit topology argument the encyclopedia builds toward).
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