IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03276433.html
   My bibliography  Save this paper

Onboarding AI

Author

Listed:
  • Boris Babic
  • Daniel L. Chen

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique)

  • Theodoros Evgeniou
  • Anne-Laure Fayard

Abstract

In a 2018 Workforce Institute survey of 3,000 managers across eight industrialized nations, the majority of respondents described artificial intelligence as a valuable productivity tool. But respondents to that survey also expressed fears that AI would take their jobs. They are not alone. The Guardian recently reported that in the UK "more than 6 million workers fear being replaced by machines AI's advantages can be cast in a dark light: Why would humans be needed when machines can do a better job? To allay such fears, employers must set AI up to succeed rather than to fail. The authors draw on their own and others' research and consulting on AI and information systems implementation, along with organizational studies of innovation and work practices, to present a four-phase approach to implementing AI. It allows organizations to cultivate people's trust—a key condition for adoption—and to work toward a distributed cognitive system in which humans and artificial intelligence both continually improve

Suggested Citation

  • Boris Babic & Daniel L. Chen & Theodoros Evgeniou & Anne-Laure Fayard, 2021. "Onboarding AI," Post-Print hal-03276433, HAL.
    • Handle: RePEc:hal:journl:hal-03276433
    Note: View the original document on HAL open archive server: https://hal.science/hal-03276433
    as

    Download full text from publisher

    File URL: https://hal.science/hal-03276433/document
    Download Restriction: no
    ---><---

    More about this item

    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-03276433. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.