Chapter 4: Navigating Inhibitors to Artificial Intelligence Adoption in Human Resource Management Practices: A Multi-criteria Decision-making Approach

Fourth Industrial Revolution led to massive rise in the usage of advance technologies like AI, machine learning, mobile technology, geo-tagging, Internet of Things, speech recognition, virtual reality, big data and biometrics (Budhwar et al., 2023; Siradhana & Arora, 2024). These leading technologies are transforming the way businesses operate globally and locally, thereby affecting the workplace processes, job design and employee engagement (Malik et al., 2022; Mohapatra et al., 2023). AI is ‘a broad class of technologies which permits a computer to do tasks that need human cognition like adaptive decision-making’ (Agarwal et al., 2024; Tambe et al., 2019). In HRM, as in various other management domains, emerging technologies such as AI have become more than merely tools – they are now leading players in shaping landscape (Arslan et al., 2022; Hossin et al., 2021; Vrontis et al., 2023). It consists of different technologies that allow computers to conduct tasks which require human cognition such as adaptive decision-making (Arslan et al., 2022; Mer & Virdi, 2023; Zenjari et al., 2024). The International Data Corporation has forecasted that ‘global spending on artificial intelligence will rise from $85.3 billion in 2021 to over $204 billion by 2025, with a compound annual growth rate (CAGR) of 24.5% from 2021 to 2025. Artificial intelligence was spotlighted in the Gartner’s top ten technology trends for the year 2019 and 2020’. Moreover, economic analyses performed by PwC, IBM, Deloitte and Gartner Research estimated that adoption of AI will enhance global GDP by 15% by 2030 contributing an additional $15.7 trillion to economy (Chowdhury et al., 2023). Extant literature underlined several benefits of AI adoption like improved business productivity through resource management and optimised operations (Faulds & Raju, 2021), enhanced decision-making (Paschen et al., 2020) and business model transformation (Duan et al., 2019). It also decreases employee costs while boosting overall experience of the employee and job satisfaction (Mohapatra et al., 2023). It led to growing adoption of AI in HRM sub-functions such as talent acquisition, video interviews, talent prediction and performance evaluation (Upadhyay & Khandelwal, 2018; Vishwakarma & Singh, 2023), employee training and development (Maity, 2019) and employee engagement (Bankins & Formosa, 2020). Current studies in literature have further underlined AI’s role in making HR analytics easier (Margherita, 2022) and its effect on HRM processes (Vrontis et al., 2023). However, in spite of interest in AI’s applications and potential benefits in HRM, many organisations have struggled to achieve the anticipated positive outcomes (Halid et al., 2024; Sharma et al., 2023). Acceptance of AI in HRM practices is still not at full scale that is necessary for usage and adoption of AI in organisations (Nawaz et al., 2024; Panda et al., 2023; Singh & Pandey, 2024). This makes it crucial to thoroughly understand the inhibitors to AI adoption in HRM practices. While there are studies in extant literature on AI adoption in HRM practices, studies focussing on the factors inhibiting AI adoption in HRM practices are limited. This chapter fills this gap by identifying key barriers preventing AI adoption in HRM practices. Additionally, few studies have applied MCDM methods such as the fuzzy AHP to evaluate these barriers. Thus, this chapter aims to attain following objectives: