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Identifying and characterising AI adopters: A novel approach based on big data

Author

Listed:
  • Flavio Calvino
  • Lea Samek
  • Mariagrazia Squicciarini
  • Cody Morris

Abstract

This work employs a novel approach to identify and characterise firms adopting Artificial Intelligence (AI), using different sources of large microdata. Focusing on the United Kingdom, the analysis combines data on Intellectual Property Rights, website information, online job postings, and firm-level financials for the first time. It shows that a significant share of AI adopters is active in Information and Communication Technologies and professional services, and is located in the South of the United Kingdom, particularly around London. Adopters tend to be highly productive and larger than other firms, while young adopters tend to hire AI workers more intensively. Human capital appears to play an important role, not only for AI adoption but also for firms’ productivity returns. Significant differences in the characteristics of AI adopters emerge when distinguishing between firms carrying out AI innovation, those with an AI core business, and those searching for AI talent.

Suggested Citation

  • Flavio Calvino & Lea Samek & Mariagrazia Squicciarini & Cody Morris, 2022. "Identifying and characterising AI adopters: A novel approach based on big data," OECD Science, Technology and Industry Working Papers 2022/06, OECD Publishing.
    • Handle: RePEc:oec:stiaaa:2022/06-en
    DOI: 10.1787/154981d7-en
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    Cited by:

    1. Andres, Raphaela & Niebel, Thomas & Viete, Steffen, 2024. "Do capital incentive policies support today’s digitization needs?," Telecommunications Policy, Elsevier, vol. 48(1).
    2. Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2024. "The impact of ChatGPT on human skills: A quantitative study on twitter data," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    3. Rathi, Sawan & Majumdar, Adrija & Chatterjee, Chirantan, 2024. "Did the COVID-19 pandemic propel usage of AI in pharmaceutical innovation? New evidence from patenting data," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    4. Nicolas Ameye & Jacques Bughin & Nicolas van Zeebroeck, 2024. "From experimentation to scaling: what shapes the funnel of AI adoption?," ULB Institutional Repository 2013/378623, ULB -- Universite Libre de Bruxelles.

    More about this item

    Keywords

    artificial intelligence; productivity; technology adoption;
    All these keywords.

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