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How uncertainty shapes herding in the corporate use of artificial intelligence technology

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  • Ameye, Nicolas
  • Bughin, Jacques
  • van Zeebroeck, Nicolas

Abstract

In its recent form, Artificial intelligence (AI) is an ensemble of technologies, which can be defined as machine-based systems for effective enterprise automation and influential decisions”. If businesses that use AI can potentially reap a competitive advantage, the optimal exploitation of such a complex ensemble of technologies remains uncertain as well as requires to have competitive access to complements such as data or new skills. Existing models of organizational use of technologies often ignore either the dynamics of competitive interactions (which can lead to pre-emption or epidemic diffusion) or the role of uncertainty, or both. In the case of AI, one type of uncertainty is particularly important: uncertainty about the technology's use cases (i.e., what to do with it). This paper proposes to apply a real options perspective to the Technology-Organization-Environment (TOE) adoption framework in order to uncover important patterns in the use of AI among firms. The results are threefold: (1) we find evidence of significant epidemic effects in AI use, (2) uncertainty moderates epidemic effects, and (3) the impact of uncertainty depends on its source: an uncertain AI use case reduces herd behaviours while uncertainty about use case returns still favours them. These results highlight the importance of exploration and collective learning in the diffusion of emerging and complex technologies, especially when companies struggle to identify the most profitable use cases for the technology.

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  • Ameye, Nicolas & Bughin, Jacques & van Zeebroeck, Nicolas, 2023. "How uncertainty shapes herding in the corporate use of artificial intelligence technology," Technovation, Elsevier, vol. 127(C).
    • Handle: RePEc:eee:techno:v:127:y:2023:i:c:s0166497223001578
    DOI: 10.1016/j.technovation.2023.102846
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    References listed on IDEAS

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    1. repec:ulb:ulbeco:2013/283916 is not listed on IDEAS
    2. Jacques Bughin & Nicolas van Zeebroeck, 2018. "Artificial intelligence: Why a digital base is critical," ULB Institutional Repository 2013/367869, ULB -- Universite Libre de Bruxelles.
    3. Wonseok Oh & Sangyong Jeon, 2007. "Membership Herding and Network Stability in the Open Source Community: The Ising Perspective," Management Science, INFORMS, vol. 53(7), pages 1086-1101, July.
    4. Jacques Bughin & Nicolas van Zeebroeck, 2018. "Artificial intelligence: Why a digital base is critical," ULB Institutional Repository 2013/283916, ULB -- Universite Libre de Bruxelles.
    5. Kevin Zhu & Kenneth L. Kraemer & Sean Xu, 2006. "The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business," Management Science, INFORMS, vol. 52(10), pages 1557-1576, October.
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    Cited by:

    1. Jacques Bughin, 2024. "The Role of Firm AI Capabilities in Generative AI-pair Coding," Working Papers TIMES² 2024-076, ULB -- Universite Libre de Bruxelles.
    2. Charles Hoffreumon & Chris Forman & Nicolas van Zeebroeck, 2024. "Make or buy your artificial intelligence? Complementarities in technology sourcing," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 452-479, March.
    3. Rana, Nripendra P. & Pillai, Rajasshrie & Sivathanu, Brijesh & Malik, Nishtha, 2024. "Assessing the nexus of Generative AI adoption, ethical considerations and organizational performance," Technovation, Elsevier, vol. 135(C).
    4. Nektarios Gavrilakis & Christos Floros, 2024. "Volatility and Herding Bias on ESG Leaders’ Portfolios Performance," JRFM, MDPI, vol. 17(2), pages 1-22, February.
    5. 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.
    6. Chiarello, Filippo & Giordano, Vito & Spada, Irene & Barandoni, Simone & Fantoni, Gualtiero, 2024. "Future applications of generative large language models: A data-driven case study on ChatGPT," Technovation, Elsevier, vol. 133(C).

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