Artificial intelligence and the reconfiguration of competency management systems in organizations

Abstract

Artificial intelligence (AI) is reshaping work and human resource management, yet existing reviews largely treat competencies as secondary outcomes of AI adoption and offer limited theory-driven integration of how competency management itself is transforming. This study addresses that gap by systematically examining how competency management has evolved in AI-enabled contexts, how dominant theories explain AI-driven competency change, and where those theories require extension. Using a PRISMA 2020-guided systematic review of 187 Scopus-indexed journal articles, this study combines bibliometric mapping (keyword co-occurrence, temporal overlay, and bibliographic coupling) with directed qualitative content analysis to link research fronts with underlying theoretical mechanisms. The findings show that AI-related competency change extends beyond technical skills toward hybrid and portfolio-based configurations that integrate technical understanding, managerial judgment, learning agility, governance capabilities, and psychological readiness. The analysis demonstrates that no single framework sufficiently explains these shifts. Human Capital Theory, the Resource-Based View, and Dynamic Capabilities each illuminate partial mechanisms, while complementary perspectives from HRD, socio-technical systems, organizational economics, and psychology are needed to account for task contingency, human-AI complementarity, structural redesign, and employee readiness. The study contributes a theory synthesis that re-conceptualizes competency management as a dynamic, multi-level, and socio-technical system. It offers implications for designing adaptive competency architectures, aligning HRD interventions with AI-enabled work systems, and embedding governance capabilities within workforce development strategies.