IDEAS home Printed from https://ideas.repec.org/a/eee/bushor/v67y2024i4p357-368.html
   My bibliography  Save this article

Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders

Author

Listed:
  • Tursunbayeva, Aizhan
  • Chalutz-Ben Gal, Hila

Abstract

In the evolving digital landscape, organizations and leaders face increasing pressure to adopt and effectively utilize artificial intelligence (AI), which is steadily entering the management, work, and organizational ecosystems and enabling digital transformations. We observe AI-based applications assisting employees in daily tasks, project management, decision-making, and collaboration. But the successful adoption of AI is a complex and multifaceted process that requires careful consideration of various factors. What are the specific factors affecting the full adoption of AI from a multilevel viewpoint? This article presents a framework-based checklist concerning technology, organizations, and people (TOP) designed to assist digital leaders in navigating the challenges associated with AI adoption. Drawing upon extensive research and industry insights, this checklist provides digital leaders with a comprehensive tool to assess and address critical considerations during the adoption of AI. By systematically evaluating the technology, organization, and people dimensions, organizations and digital leaders can enhance their chances of a successful digital transformation and gain a competitive advantage in the digital age.

Suggested Citation

  • Tursunbayeva, Aizhan & Chalutz-Ben Gal, Hila, 2024. "Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders," Business Horizons, Elsevier, vol. 67(4), pages 357-368.
  • Handle: RePEc:eee:bushor:v:67:y:2024:i:4:p:357-368
    DOI: 10.1016/j.bushor.2024.04.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S000768132400051X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.bushor.2024.04.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:bushor:v:67:y:2024:i:4:p:357-368. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/bushor .

    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.