AI-Driven Human Resource Management and Its Role in Sustainable Human Capital Development

Abstract

:  The rapid integration of artificial intelligence (AI) into human resource management (HRM) functions has generated both significant enthusiasm and substantial scholarly concern. Systems that screen candidates, detect disengagement, and model attrition risk have moved beyond pilot programmes. and are being widely deployed to the point that both practitioners and scholars have struggled to keep pace. It is not entirely technical, but conceptual. What do we in fact have in the way of structures to query the idea of whether these instruments are good on people not merely on quarterly hiring measurements? It is that supra-nationality that the current paper dwells on, as the focal point of the overlap between AI-driven HRM and sustainable human capital development (HCD) and the issue of how intelligent systems implemented in recruitment, performance management, learning and development, and workforce planning influence the end results in terms of equity, capability accumulation, employee wellbeing, and organisational resilience. It will be analysed based on a systematic review of peer-reviewed works published in 2015-2023 and a synthesised conceptual framework based on the human capital theory (Becker, 1964), the Technology Acceptance Model (Davis, 1989), and the principles of responsible innovation (Owen et al., 2012). While these theoretical foundations have individually informed research on human capital formation, technology adoption, and innovation governance, they have rarely been integrated into a unified framework capable of evaluating AI-HRM through a sustainability lens Based on this, the paper will come up with five functional groups of AI-HRM tools and weigh the combined impact of the tools on the requirements of sustainable development as proposed in the United Nations 2030 Agenda. What is emerging is, literally, a good-and-bad mix: AI-HRM tools are demonstrably quicker in matching skills, cheaper to run in transactional HR, and can give rise to more specific and precise workforce analytics, yet are also associated with major and poorly recognised risks - algorithmic bias, digital exclusion, or a silence of employee choice amongst themselves. It turns out that whether these costs pay off more than the gains depends greatly on context organisational culture, the regulatory environment, and workforce digital literacy are all found in the empirical literature to be important moderating variables. The paper develops this evidence to develop an integrative conceptual framework that follows the pathways of adoption of AI-HRM tools to generate (or avoid) sustainable HCD outcomes based on four mediating paths that take into account the widely divergent situations of practitioners in both high-income and lower-income country settings. By linking AI-HRM tool adoption to sustainable human capital development outcomes, the study offers both a theoretical bridge between HRM and sustainability scholarship and a practical evaluative framework for policymakers and organisational leaders.” The implication of the argument goes beyond the human capital theory, and to the governance discourse of responsible AI in employment, which remains in its early formation.