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Original title:
Who adopts AI? Evidence on firms, technologies and workers
Authors: Pulito, G. ; Pytliková, Mariola ; Schroeder, S. ; Lodefalk, M.
Document type: Research reports
Year: 2026
Language: eng
Series: CERGE-EI Working Paper Series, volume: 818
Abstract: Using two waves of nationally representative Danish firm surveys linked to employer– employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technology-specific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it.
Keywords: artificial intelligence; digitalisation; technology adoption
Project no.: EH23_025/0008693
Funding provider: GA MŠk
Institution: Economics Institute AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://www.cerge-ei.cz/pdf/wp/Wp818.pdf
Original record: https://hdl.handle.net/11104/0378609
Permalink: http://www.nusl.cz/ntk/nusl-697450
The record appears in these collections:
Research > Institutes ASCR > Economics Institute
Reports > Research reports
Authors: Pulito, G. ; Pytliková, Mariola ; Schroeder, S. ; Lodefalk, M.
Document type: Research reports
Year: 2026
Language: eng
Series: CERGE-EI Working Paper Series, volume: 818
Abstract: Using two waves of nationally representative Danish firm surveys linked to employer– employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technology-specific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it.
Keywords: artificial intelligence; digitalisation; technology adoption
Project no.: EH23_025/0008693
Funding provider: GA MŠk
Institution: Economics Institute AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://www.cerge-ei.cz/pdf/wp/Wp818.pdf
Original record: https://hdl.handle.net/11104/0378609
Permalink: http://www.nusl.cz/ntk/nusl-697450
The record appears in these collections:
Research > Institutes ASCR > Economics Institute
Reports > Research reports
Record created 2026-05-09, last modified 2026-05-09