Navigating AI integration in HRM: qualitative insights from TISM methodology

Vijay Kumar Jain, Amit Deval, Anjali Dimri, Preeti Sharma and Bipin Chandra Kandpal

International Journal of Applied Systemic Studies, 2026, vol. 13, issue 1, 17-43

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.

Keywords: automation; performance expectancy; employee well-being; robotic process automation; HT chatbot and virtual assistants; total interpretive structural modelling; TISM. (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=152686 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijassi:v:13:y:2026:i:1:p:17-43

Access Statistics for this article

More articles in International Journal of Applied Systemic Studies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().