AI Exposure and Adoption Among U.S. Firms

Matthew Dey, Emin Dinlersoz, Lucia Foster, Cheryl Grim, John Haltiwanger, Peter B. Meyer, Sabrina Pabilonia, Aditya Pande, Martha Stinson, Sean Wang and Zoltán Wolf

Working Papers from U.S. Census Bureau, Center for Economic Studies

Abstract: Measures of exposure to artificial intelligence (AI) are widely used to study where AI is likely to affect firm and worker outcomes, yet limited evidence exists on how closely exposure relates to realized AI adoption at the firm level. We examine this relationship by linking 14 firm-level exposure measures, constructed from occupational exposure estimates in the literature and occupational employment shares from the Bureau of Labor Statistics Occupational Employment and Wage Statistics program, to direct measures of firm AI adoption from the Census Bureau’s Business Trends and Outlook Survey. Exposure is positively and statistically significantly associated with adoption, but explains only a modest share of its variation. The strength of the relationship varies considerably across standardized exposure measures: a one-standard-deviation increase in firm-level exposure is associated with a 4–11 percentage point higher firm adoption probability, falling to 1–8 percentage points after controlling for year and sub-sector fixed effects. The exposure–adoption relationship is also heterogeneous across firm-size classes and sectors. Exposure is thus an informative but incomplete signal of adoption. More broadly, the results have implications for the interpretation of exposure-based measures in studies of the economic effects of AI.

Keywords: Artificial Intelligence; AI adoption; AI exposure; firm-level data; occupational mix (search for similar items in EconPapers)
JEL-codes: D22 J23 O33 (search for similar items in EconPapers)
Date: 2026-09
New Economics Papers: this item is included in nep-ain, nep-bec and nep-sbm
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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:26-61

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