AI and analytics conundrum: unpacking the barriers in modern HR with ISM and MICMAC analysis

Purpose This study aims to identify and model deterrents to adopt and institutionalize analytics and artificial intelligence in modern human resource (HR) using interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) approach. Design/methodology/approach A comprehensive investigation of the literature and feedback from experts led to the identification of 16 deterrents in this study. After that, the ISM tool is used to find connections between the identified deterrents in the HR ecosystem and MICMAC which helps in categorising deterrents on the basis of driving and dependence power and provides deeper insights into their roles and significance. Findings Employee resistance and HR transformation are highly influenced by other factors but exert minimal driving power. Data availability, leadership support, communication and collaboration, legal, ethical and regulatory compliance, and infrastructure and resources exhibit strong influence and dependence, making them highly sensitive and crucial. Training and development, learning culture and change management, and data privacy and security have strong driving power with minimal dependence, indicating their foundational role in shaping HR transformation. Research limitations/implications This study will assist policymakers and owners/managers in the HR ecosystem in recognising and comprehending the importance and applicability of analytics and AI obstacles while developing HR strategies. Originality/value This study explicitly focuses on data analytics and AI technology in the current scenario. It also explores the relationship between deterrents and their driving and dependence powers.