Who Adopts AI? Evidence on Firms, Technologies and Worker.

Autor/inn/en	Pulito, Giuseppe; Pytlikova, Mariola; Schroede, Sarah; Lodefalk, Magnus
Institution	Örebro universitet / School of Business
Titel	Who Adopts AI? Evidence on Firms, Technologies and Worker. Gefälligkeitsübersetzung: Wer setzt KI ein? Erkenntnisse zu Unternehmen, Technologien und Arbeitnehmern.
Quelle	Örebro (2026), 67 S. PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Reihe	Working Papers / Örebro University, School of Business. 2026,3
Sprache	englisch
Dokumenttyp	online; Monografie
Schlagwörter	Künstliche Intelligenz; Maschinelles Lernen; Fachkenntnisse; Informationstechnik; Roboter; Diffusion; Unternehmen; Unternehmensgröße; Beschäftigungsstruktur; Qualifikation; Qualifikationsstruktur; Alter; Anwendung; Arbeitspapier; Auswirkung; Infrastruktur; Sektorale Verteilung; Abhängig Beschäftigter; Dänemark
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." The study refers to the period 2023-2024 (Author's abstract, IAB-Doku).
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2026/4