Mapping the research landscape of artificial intelligence in talent management: a topic modeling approach

Abstract

This study aims to map and interpret the intellectual landscape of research on artificial intelligence (AI) in talent management. It identifies the dominant themes and research trajectories shaping this domain. Using a latent Dirichlet allocation topic modeling approach, a machine learning-based text-mining technique, a corpus of 243 journal articles published between 2010 and 2025 were analysed. Eight major research themes emerged: AI Adoption and Cultural Change, Digital Transformation and Smart Technologies, Analytical and Bibliometric Approaches, AI-based Learning and Capability Development, Sustainable HR, AI-enabled Recruitment and Talent Acquisition, AI-driven talent management (TM) practices and Employee Experience and Behavioural Change. Research has evolved from descriptive and technical explorations to human-centric and ethical considerations of AI in HRM. Methodologically, this paper demonstrates the value of machine learning-based text analytics as a robust and scalable approach for synthesising literature. Theoretically, this study maps the thematic architecture of AI-enabled TM research through a socio-technical perspective and develops three conceptual propositions for future testing. From a managerial perspective, the findings highlight that the real value of AI in TM extends beyond operational automation. It encompasses strategic talent enablement, continuous learning and sustainable performance management. This study provides data-driven syntheses of AI and talent management research. It integrates bibliometric mapping with topic modeling. Furthermore, it advances a socio-technical perspective of AI-enabled HRM, conceptualising AI as both a technological and strategic capability.