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Organizational learning for exploring Generative AI: CORE-sandbox experiments

Author

Listed:
  • Dov Te’eni

    (TAU - Tel Aviv University)

  • Myriam Raymond

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université, GRANEM - Groupe de Recherche Angevin en Economie et Management - UA - Université d'Angers - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement)

  • Frantz Rowe

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université, IUF - Institut universitaire de France - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche)

  • Etienne Thénoz

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université)

  • Philippe Trimborn

Abstract

Generative AI (GenAI) holds potential for organizations, offering transformative opportunities while simultaneously raising concerns about its associated risks. Like many emerging technologies, GenAI presents organizations with a significant challenge: navigating uncertainty before making large-scale decisions about which systems to adopt and how to implement and leverage them. Managers cannot rely solely on general knowledge of GenAI; they require insights tailored to their specific organizational context. Drawing on an 18-month study of sandbox experiments conducted within a large international service organization, this paper presents CORE-sandbox experiments as a structured framework for systematically learning about the critical dimensions of uncertainty surrounding GenAI. The framework organizes learning into four key domains: Capabilities, Opportunities, Risks, and Ecosystem. The paper also advances the discourse on organizational learning and dynamic capabilities by demonstrating how in-situ and ex-situ learning cycles reinforce one another and how second and third-order organizational learning emerge under conditions of high uncertainty before GenAI rollout decisions are made.

Suggested Citation

  • Dov Te’eni & Myriam Raymond & Frantz Rowe & Etienne Thénoz & Philippe Trimborn, 2026. "Organizational learning for exploring Generative AI: CORE-sandbox experiments," Post-Print hal-05461433, HAL.
    • Handle: RePEc:hal:journl:hal-05461433
    DOI: 10.1016/j.ijinfomgt.2026.103029
    Note: View the original document on HAL open archive server: https://nantes-universite.hal.science/hal-05461433v1
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    References listed on IDEAS

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