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Who Adopts AI? Evidence on Firms, Technologies and Workers

Pulito, G., Pytlikova, M., Schroeder, S. & Lodefalk, M.

Publiceringsår

2026

Publicerat i

GLO Discussion Paper

Sammanfattning

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 large-language-model-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.

Who is afraid of AI? Who should be?

Engberg, E., Görg, H., Hellsten, M., Javed, F., Lodefalk, M., Längkvist, M., & ..

Publiceringsår

2026

Publicerat i

Kiel Policy Brief, 2026.

Sammanfattning

Occupations that are highly cognitive, non-physical, and low in social interaction — typically higher-skill white-collar roles such as data analysts, software developers, and translators — turn out to be highly AI-exposed

Occupations requiring manual dexterity or intensive interpersonal contact — such as construction labourers or nursing aides — remain among the least exposed to current AI technologies

Aggregate occupational exposure to AI has risen markedly since 2010, with especially rapid gains in the late 2010s and early 2020s

Our baseline estimates show no detectable effect of AI exposure on total firm employment, while it is associated with clear skill upgrading

Engberg, E., Görg, H., Hellsten, M., Javed, F., Lodefalk, M., Längkvist, M., & .. (2026). Who is afraid of AI? Who should be?. Kiel Policy Brief, 2026.

Ratio Working Paper No. 388: Same Storm, Different Boats: Generative AI and the Age Gradient in Hiring

Lodefalk, M., Löthman, L., Koch, M., & Engberg, E.

Publiceringsår

2026

Publicerat i

Ratio Working Paper Series.

Sammanfattning

We show that the age composition of employment within Swedish employers shifts after the arrival of generative AI, with no corresponding reduction in aggregate labour demand. Using 4.6 million job advertisements from Sweden’s largest recruitment platform, we find that the broad decline in postings since 2022 aligns with monetary tightening rather than AI, exploiting Sweden’s seven-month gap between the Riksbank’s first rate hike and the launch of ChatGPT as a timing test. We then use full-population employer–employee register data and an employer-level difference-in-differences design to estimate how AI exposure affects employment composition across six age groups. An event study documents an accelerating decline in employment of 22–25-year-olds in high-AI-exposure occupations, reaching 5.5 per cent by early 2025 relative to less exposed occupations within the same employers, while employment of workers over 50 rose by 1.3 per cent. The widening age gradient suggests that generative AI reshapes hiring composition rather than aggregate demand, with the adjustment burden falling disproportionately on entry-level workers.