Artificial Intelligence: what drives adoption in EU countries?

Gilbert Cette, Giuseppe Nicoletti and Océane Vernerey ()
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Giuseppe Nicoletti: LUISS - Libera Università Internazionale degli Studi Sociali Guido Carli [Roma]
Océane Vernerey: LEDi - Laboratoire d'Economie de Dijon [Dijon] - UBE - Université Bourgogne Europe, CRESE - Centre de REcherches sur les Stratégies Economiques (UR 3190) - UMLP - Université Marie et Louis Pasteur - UBFC - Université Bourgogne Franche-Comté [COMUE], LISA - Laboratoire « Lieux, Identités, eSpaces, Activités » (UMR CNRS 6240 LISA) - CNRS - Centre National de la Recherche Scientifique - Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli]

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Abstract: This paper uses results from successive waves of the Eurostat survey on ‘ICT usage and e-commerce in enterprises' to study the determinants of AI adoption in a country/industry/time panel covering 17 countries and 24 industries of the European Union over 2021–2025. We relate adoption to several potential drivers: the perceived obstacles to adoption, the business environment (such as investment in organizational capital), the policy context (such as policies affecting the capabilities and incentives to adopt and use AI technologies) and demography (shares of different age groups within employees). We summarize some of the potential drivers using Principal Components Analyses and take a diff-in-diff approach to highlight how the influence of nation-wide drivers of adoption differs depending on industry exposure to them. We find robust statistical associations of both adoption and the extent of AI use (at least three AI technologies) with mismatch obstacles (data availability, expertise, technological incompatibility, or AI's perceived lack of usefulness for the firm) whose significance has increased over time, as well as with costs and personal concerns (legal uncertainty, ethical and privacy concerns) whose significance has decreased over time. Human capabilities (such as ICT skills and organizational capital) and innovation policies are positively related to AI adoption, while policies curbing either workforce flexibility or competition in industries that are key providers of intermediate inputs are inversely related to adoption. Finally, we find evidence of an inverted U-curve between workforce age and AI adoption, with adoption being weaker where the age distribution is tilted towards young or elderly workers; however, higher AI exposure in an industry moderates these demographic effects, perhaps due to skill-leveling effect of the new technology.

Keywords: AI - Artificial Intelligence; Adoption of new technology; AI adoption; Firm-level determinants (search for similar items in EconPapers)
Date: 2026-02-10
Note: View the original document on HAL open archive server: https://hal.science/hal-05560286v2
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