IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03068380.html
   My bibliography  Save this paper

Stratégie & Intelligence artificielle

Author

Listed:
  • Henri Isaac

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

Given the rapid development over the past decade of methods qualified as "artificial intelligence" (AI), questions arise about how these methods might fit into a firm's strategies, or even replace them. This view overlooks the aspects of strategy-making that are marked with a high degree of uncertainty and many an ambiguity. The limitation inherent in building tools for decision-making that massively rely on sets of data restricts somewhat the possibility of this happening. Although it is unlikely that AI will some day steer a firm's strategic decisions, its use in corporate strategies is already a reality that is modifying the architecture of resources and qualifications within firms. This new architecture of the creation of value requires an internal reorganization for it to be deployed in business process strategies. Given the nature of the decisions automated by AI, it is imperative for firms to set up a body of governance that will define the doctrine for using such a technology.

Suggested Citation

  • Henri Isaac, 2020. "Stratégie & Intelligence artificielle," Post-Print hal-03068380, HAL.
  • Handle: RePEc:hal:journl:hal-03068380
    Note: View the original document on HAL open archive server: https://hal.science/hal-03068380
    as

    Download full text from publisher

    File URL: https://hal.science/hal-03068380/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    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. Bram Klievink & Bart-Jan Romijn & Scott Cunningham & Hans Bruijn, 2017. "Big data in the public sector: Uncertainties and readiness," Information Systems Frontiers, Springer, vol. 19(2), pages 267-283, April.
    2. Claudio Vitari & Elisabetta Raguseo, 2016. "Big data value and financial performance: an empirical investigation [Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data]," Post-Print halshs-01923271, HAL.
    3. Bertschek, Irene & Kesler, Reinhold, 2022. "Let the user speak: Is feedback on Facebook a source of firms’ innovation?," Information Economics and Policy, Elsevier, vol. 60(C).
    4. Yingjie Zhang & Beibei Li & Ramayya Krishnan, 2020. "Learning Individual Behavior Using Sensor Data: The Case of Global Positioning System Traces and Taxi Drivers," Information Systems Research, INFORMS, vol. 31(4), pages 1301-1321, December.
    5. Jörg Claussen & Christian Essling & Christian Peukert, 2018. "Demand variation, strategic flexibility and market entry: Evidence from the U.S. airline industry," Strategic Management Journal, Wiley Blackwell, vol. 39(11), pages 2877-2898, November.
    6. 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.
    7. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    8. 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.
    9. Liu, Weihua & Wang, Siyu & Lin, Yong & Xie, Dong & Zhang, Jiahui, 2020. "Effect of intelligent logistics policy on shareholder value: Evidence from Chinese logistics companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    10. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    11. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    12. Xu, Shengxiang & Chen, Hsinghung & Dong, Shuli & Guo, Zizheng, 2023. "Can upgrading information infrastructure improve the innovation ability of companies? Empirical evidence from China," Telecommunications Policy, Elsevier, vol. 47(6).
    13. Taha Havakhor & Rajiv Sabherwal & Zachary R. Steelman & Sanjiv Sabherwal, 2019. "Relationships Between Information Technology and Other Investments: A Contingent Interaction Model," Service Science, INFORMS, vol. 30(1), pages 291-305, March.
    14. Urbinati, Andrea & Bogers, Marcel & Chiesa, Vittorio & Frattini, Federico, 2019. "Creating and capturing value from Big Data: A multiple-case study analysis of provider companies," Technovation, Elsevier, vol. 84, pages 21-36.
    15. Chae, Bongsug (Kevin), 2019. "A General framework for studying the evolution of the digital innovation ecosystem: The case of big data," International Journal of Information Management, Elsevier, vol. 45(C), pages 83-94.
    16. Siobhan O'Mahony & Rebecca Karp, 2022. "From proprietary to collective governance: How do platform participation strategies evolve?," Strategic Management Journal, Wiley Blackwell, vol. 43(3), pages 530-562, March.
    17. Ekene Okwechime & Peter Duncan & David Edgar, 2018. "Big data and smart cities: a public sector organizational learning perspective," Information Systems and e-Business Management, Springer, vol. 16(3), pages 601-625, August.
    18. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    19. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    20. Yanfei Lan & Zhibing Liu & Baozhuang Niu, 2017. "Pricing and Design of After-Sales Service Contract: The Value of Mining Asymmetric Sales Cost Information," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-25, February.

    More about this item

    Keywords

    prise de décision stratégique; gestion de l'innovation; machine learning; changement organisationnel;
    All these keywords.

    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:hal:journl:hal-03068380. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.