IDEAS home Printed from https://ideas.repec.org/a/eee/techno/v131y2024ics0166497223002596.html
   My bibliography  Save this article

Decoding AI readiness: An in-depth analysis of key dimensions in multinational corporations

Author

Listed:
  • Tehrani, Ali N.
  • Ray, Subhasis
  • Roy, Sanjit K.
  • Gruner, Richard L.
  • Appio, Francesco P.

Abstract

Artificial Intelligence (AI) stands ready to impact all aspects of business, from optimizing operations to personalizing services and enhancing customer value. However, many organizations grapple with implementing AI solutions due to a lack of necessary infrastructure and mechanisms. In short, many companies are not adequately prepared to adopt AI. To make matters worse, the literature does not offer sufficient insights into this issue. To help address this issue, in this article, the authors explore what it means to become ‘AI-ready.’ Specifically, this study identifies the various dimensions of AI readiness through in-depth semi-structured interviews with top- and middle-level managers from 52 multinational corporations in Southeast Asia, primarily in India. This study employed a qualitative data analysis approach to construct a grounded theory model focusing on AI readiness. The methodology involved systematic examination and coding of data to identify key themes and patterns, enabling the development of a comprehensive theoretical framework. The findings suggest that AI readiness can be categorized into eight dimensions: informational, environmental, infrastructural, participants, process, customers, data, and technological readiness. This study makes a significant contribution to marketing, management, and information systems by conceptualizing the AI readiness construct and identifying its key dimensions.

Suggested Citation

  • Tehrani, Ali N. & Ray, Subhasis & Roy, Sanjit K. & Gruner, Richard L. & Appio, Francesco P., 2024. "Decoding AI readiness: An in-depth analysis of key dimensions in multinational corporations," Technovation, Elsevier, vol. 131(C).
  • Handle: RePEc:eee:techno:v:131:y:2024:i:c:s0166497223002596
    DOI: 10.1016/j.technovation.2023.102948
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0166497223002596
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.technovation.2023.102948?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:techno:v:131:y:2024:i:c:s0166497223002596. 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.sciencedirect.com/science/journal/01664972 .

    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.