Scandinavian Working Papers in Economics
Working Papers,
Örebro University, School of Business
No 2026:3: Who Adopts AI? Evidence on Firms, Technologies and Worker
Giuseppe Pulito (), Mariola Pytlikova (), Sarah Schroede () and Magnus Lodefalk ()
Additional contact information
Giuseppe Pulito: ROCKWOOL Foundation Berlin, Postal: ROCKWOOL Foundation Berlin, Gormannstraße 22, 10119 Berlin, Germany
Mariola Pytlikova: CERGE-EI, Charles University and the Economics Institute of the Czech Academy of Sciences, and AIAS, Aarhus University, Postal: CERGE-EI, Charles University and the Economics Institute of the Czech Academy of Sciences, Politických vězňů 7, 110 00 Prague 1, Czech Republic
Sarah Schroede: Aarhus University and Ratio Institute, Postal: Aarhus University, Nordre Ringgade 1, 8000 Aarhus
Magnus Lodefalk: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
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 technologyspecific. 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; Technology Adoption; Digitalisation; Human capital; AI Exposure Measures.
Language: English
67 pages, March 27, 2026
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