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A Better Way to Onboard AI

Author

Listed:
  • Boris Babic

    (INSEAD - Institut Européen d'administration des Affaires)

  • 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

    (INSEAD - Institut Européen d'administration des Affaires)

  • Anne-Laure Fayard

    (NYU - New York University [New York] - NYU - NYU System)

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. "A Better Way to Onboard AI," Post-Print hal-03624592, HAL.
    • Handle: RePEc:hal:journl:hal-03624592
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