IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp18515.html

Who Adopts AI? Evidence on Firms, Technologies and Workers

Author

Listed:
  • Pulito, Giuseppe

    (ROCKWOOL Foundation Berlin)

  • Pytlikova, Mariola

    (CERGE-EI Prague)

  • Schroeder, Sarah

    (Aarhus University and Ratio Institute)

  • Lodefalk, Magnus

    (Orebro University, Ratio Institute, GLO)

Abstract

Using surveys of Danish firms and individuals linked to employer-employee administrative data, we analyze AI adoption across technologies, business functions, and workers. We show that AI adoption is driven primarily by firm capacities rather than performance. Adoption is strongly associated with firm size, digital infrastructure, and workforce composition, particularly education and STEM intensity, while productivity and capital intensity explain little of the variation. Conditional on AI adoption, larger and more digitally mature firms deploy advanced technologies more broadly. Moreover, AI technologies diffuse across multiple business functions while other advanced technologies remain function-specific. Individual-level evidence mirrors these patterns and points towards workforce readiness as a key determinant of AI adoption. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict actual 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.

Suggested Citation

  • Pulito, Giuseppe & Pytlikova, Mariola & Schroeder, Sarah & Lodefalk, Magnus, 2026. "Who Adopts AI? Evidence on Firms, Technologies and Workers," IZA Discussion Papers 18515, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp18515
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp18515.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Rammer, Christian & Fernández, Gastón P. & Czarnitzki, Dirk, 2022. "Artificial intelligence and industrial innovation: Evidence from German firm-level data," Research Policy, Elsevier, vol. 51(7).
    2. Navon, Guy, 2009. "Human Capital Spillovers in the Workplace: Labor Diversity and Productivity," MPRA Paper 17741, University Library of Munich, Germany.
    3. Erik Brynjolfsson & Lorin M. Hitt, 2000. "Beyond Computation: Information Technology, Organizational Transformation and Business Performance," Journal of Economic Perspectives, American Economic Association, vol. 14(4), pages 23-48, Fall.
    4. Nicholas Bloom & John Van Reenen, 2007. "Measuring and Explaining Management Practices Across Firms and Countries," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(4), pages 1351-1408.
    5. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    6. Østergaard, Christian R. & Timmermans, Bram & Kristinsson, Kari, 2011. "Does a different view create something new? The effect of employee diversity on innovation," Research Policy, Elsevier, vol. 40(3), pages 500-509, April.
    7. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    8. Kostøl, Fredrik B. & Svarstad, Elin, 2023. "Trade Unions and the Process of Technological Change," Labour Economics, Elsevier, vol. 84(C).
    9. Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2024. "AI adoption in America: Who, what, and where," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 375-415, March.
    10. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    11. Guy Navon, 2009. "Human Capital Spillovers in the Workplace: Labor Diversity and Productivity," Bank of Israel Working Papers 2009.05, Bank of Israel.
    12. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    13. Humlum, Anders & Vestergaard, Emilie, 2024. "The Adoption of ChatGPT," IZA Discussion Papers 16992, IZA Network @ LISER.
    14. Timothy F. Bresnahan & Erik Brynjolfsson & Lorin M. Hitt, 2002. "Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(1), pages 339-376.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xueyuan Gao & Hua Feng, 2023. "AI-Driven Productivity Gains: Artificial Intelligence and Firm Productivity," Sustainability, MDPI, vol. 15(11), pages 1-21, June.
    2. Timothy DeStefano & Richard Kneller & Jonathan Timmis, 2026. "Strategic Asset Importance and the Adoption of Data-Driven Management Practices," CESifo Working Paper Series 12930, CESifo.
    3. Arntz, Melanie & Genz, Sabrina & Gregory, Terry & Lehmer, Florian & Zierahn-Weilage, Ulrich, 2024. "De-routinization in the fourth industrial revolution: Firm-level evidence," ZEW Discussion Papers 24-005, ZEW - Leibniz Centre for European Economic Research.
    4. Prashant Garg & Tommaso Crosta & Jasmin Baier, 2026. "Global Automation Atlas," Papers 2605.17086, arXiv.org, revised Jul 2026.
    5. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    6. Fibla Gasparín, Ma. Teresa, 2010. "Productivity in southern European small firms: When and how work organization complements process innovation," Working Papers 2072/179600, Universitat Rovira i Virgili, Department of Economics.
    7. Pastor-Merino, Ana & Martínez-Barbero, Xavier & Vicente, María R. & Domenech, Josep, 2026. "Does AI boost firm productivity? A web scraping and LLMs approach," Telecommunications Policy, Elsevier, vol. 50(2).
    8. Richard Dion & Robert Fay, 2008. "Understanding Productivity: A Review of Recent Technical Research," Discussion Papers 08-3, Bank of Canada.
    9. Erik Brynjolfsson & Wang Jin & Kristina McElheran, 2021. "The power of prediction: predictive analytics, workplace complements, and business performance," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 217-239, October.
    10. Massimo Giannini & Barbara Martini & Cristiana Fiorelli, 2023. "How does firms' broadband adoption affect regional TFP in Italy?," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 40(3), pages 1025-1050, October.
    11. Johannes Lehmann & Michael Beckmann, 2024. "Digital technologies and performance incentives: Evidence from businesses in the Swiss economy," Papers 2412.12780, arXiv.org.
    12. Benmelech, Efraim & Zator, Michał, 2025. "Robots and firm investment," Journal of Financial Economics, Elsevier, vol. 174(C).
    13. Tan, Joanne, 2024. "Multidimensional heterogeneity and matching in a frictional labor market — An application to polarization," Labour Economics, Elsevier, vol. 90(C).
    14. Andres, Raphaela & Niebel, Thomas & Sack, Robin, 2025. "Big data and firm-level productivity – A cross-country comparison," Information Economics and Policy, Elsevier, vol. 71(C).
    15. Dawei (David) Zhang & Gang Peng & Yuliang Yao & Tyson R. Browning, 2024. "Is a College Education Still Enough? The IT-Labor Relationship with Education Level, Task Routineness, and Artificial Intelligence," Information Systems Research, INFORMS, vol. 35(3), pages 992-1010, September.
    16. Viete, Steffen & Erdsiek, Daniel, 2018. "Trust-based work time and the productivity effects of mobile information technologies in the workplace," ZEW Discussion Papers 18-013, ZEW - Leibniz Centre for European Economic Research.
    17. Chuan, Amanda & Zhang, Weilong, 2023. "Non-college Occupations, Workplace Routinization, and the Gender Gap in College Enrollment," IZA Discussion Papers 16089, IZA Network @ LISER.
    18. Anabela Santos & Francesco Molica, 2026. "Institutional Context and the Employment Effects of Artificial Intelligence in European SMEs," Working Papers TIMES² 2026-082, ULB -- Universite Libre de Bruxelles.
    19. Maarten Goos & Melanie Arntz & Ulrich Zierahn & Terry Gregory & Stephanie Carretero Gomez & Ignacio Gonzalez Vazquez & Koen Jonkers, 2019. "The Impact of Technological Innovation on the Future of Work," JRC Working Papers on Labour, Education and Technology 2019-03, Joint Research Centre.
    20. Viete, Steffen & Erdsiek, Daniel, 2015. "Mobile information and communication technologies, flexible work organization and labor productivity: Firm-level evidence," ZEW Discussion Papers 15-087, ZEW - Leibniz Centre for European Economic Research.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp18515. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mark Fallak (email available below). General contact details of provider: https://edirc.repec.org/data/izaaalu.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.