Firm Data on AI
Source: NBER (Yotzov, Barrero, Bloom, Davis et al.) · 2026-02
We survey nearly 6,000 senior business executives at US, UK, German, and Australian firms to develop new evidence on AI adoption and its effects on jobs, productivity, and output. Specifically, we ask executives about AI usage, its effects at their own firms over the past three years and, looking ahead, what they anticipate over the next three years. We find four main results. First, 69% of firms actively use AI, with higher usage rates at younger and more productive firms. Second, more than two thirds of executives regularly use AI, but their usage rate averages only 1.5 hours a week. Third, executives report little own-firm impact of AI over the last 3 years, with nine-in-ten reporting no impact on employment or productivity. Fourth, these same executives predict sizable effects over the next 3 years, predicting that AI will boost productivity at their firms by an average of 1.4%, raise output 0.8%, and cut employment 0.7%. In contrast, employees anticipate that AI will raise employment 0.5% at their firms in the next 3 years, highlighting an expectations gap between employers and employees.
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Research framings built by reading the notes related to this paper — the questions it feeds into.
How do AI-exposed occupations change in employment, wages, and skills?- Why do early-career workers fear AI job loss more than senior workers?
- How much do self-reported executive expectations align with actual payroll outcomes?
- Do companies consider redeployment before cutting staff for AI?
- Do employers reorganize work tasks around AI before cutting jobs?
- How do payroll data and employer announcements differ in measuring AI job displacement?
- Why might companies choose to label layoffs as AI versus restructuring?
- Are AI layoffs concentrated in specific job categories or widespread across industries?
- Does task-level AI exposure predict which jobs will be rehired versus eliminated?
- Does AI job-loss fear match actual hiring or employment declines?
- Why do aggregate employment statistics miss losses in specific occupations?
- Do survey expectations of job cuts eventually match observed employment data?
- Has AI actually displaced workers in payroll data so far?
- Why do executives report no AI impact on jobs today?
- Why do most organizations lack reliable data on AI's actual impact on productivity?
- How can we isolate AI's contribution from other sources of output growth?
- Do companies time productivity claims to coincide with public offerings or fundraising?
- How much of employee time with AI goes to understanding its outputs rather than original work?
- Does AI assistance typically reduce support staff headcount or increase productivity?