Artificial Intelligence for Public Use | Ratio

Artificial Intelligence for Public Use | Ratio

Liknande innehåll

Artikel (utan peer review)

DAIOE: Dynamic AI Occupational Exposure (dataset and construction pipeline)

E. Engberg, H. Görg, M. Hellsten, F. Javed, M. Lodefalk, M. Längkvist, N. Monteiro, H. Kyvik Nordås, G. Pulito, S. Schroeder & A. Tang

Publiceringsår

2026

Publicerat i

Zenodo

Sammanfattning

DAIOE is a dataset of occupation-year measures of exposure to artificial intelligence, accompanied by its construction pipeline and documentation. The index combines progress across public AI benchmarks with occupational ability profiles to estimate how exposure changes over time. The dataset covers multiple occupational classifications, including Swedish SSYK. It is a research data resource, not a peer-reviewed article.

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.