IDEAS home Printed from https://ideas.repec.org/a/ids/ijassi/v13y2026i1p17-43.html

Navigating AI integration in HRM: qualitative insights from TISM methodology

Author

Listed:
  • Vijay Kumar Jain
  • Amit Deval
  • Anjali Dimri
  • Preeti Sharma
  • Bipin Chandra Kandpal

Abstract

Artificial intelligence has emerged as a critical component of human resource management in today's commercial environment. As businesses struggle to remain competitive and efficient, they are turning to AI-powered solutions to streamline operations and maximise resources, therefore, the objective of the paper is to identify the important drivers of AI adoption in HRM and explore interlinkages among them. Sixteen drivers were identified based on extensive literature review and expert's opinions. Total interpretive structural modelling (TISM) is employed to classify the select divers in order to develop six levels hierarchical structure based on their driving power and dependence. The analysis reveals that HR readiness; technology advancement and lack of trust and transparency are the most important drivers for AI implementation in HR in organisations. The study facilitates decision makers to take required action to encourage the AI adoption in the organisations. This will further help firms to develop more efficient HR process by embracing new technologies such as machine learning and natural language processing, helping them remain ahead of the competition in today's quickly changing business climate.

Suggested Citation

  • Vijay Kumar Jain & Amit Deval & Anjali Dimri & Preeti Sharma & Bipin Chandra Kandpal, 2026. "Navigating AI integration in HRM: qualitative insights from TISM methodology," International Journal of Applied Systemic Studies, Inderscience Enterprises Ltd, vol. 13(1), pages 17-43.
    • Handle: RePEc:ids:ijassi:v:13:y:2026:i:1:p:17-43
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=152686
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:ids:ijassi:v:13:y:2026:i:1:p:17-43. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=205 .

    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.