Talent management and employee performance in the age of artificial intelligence: A systematic literature review - Lukman, Jimoh, Adams (Edelweiss Applied Science and Technology, 2026)
The integration of artificial intelligence (AI) into talent management has transformed human resource practices and reshaped employee performance dynamics. This study presents a systematic literature review examining the impact of AI-driven talent management on employee performance, key technologies, methodological trends, and emerging research gaps. Using the PRISMA framework, 65 peer-reviewed articles published between 2010 and 2023 were identified from Scopus, Web of Science, ProQuest, and Google Scholar. Methodological rigor and transparency guided the selection process, while VOSviewer bibliometric mapping visualized thematic clusters, co-citation networks, and the field’s intellectual structure. Findings indicate that AI applications enhance efficiency, accuracy, and scalability in recruitment, learning and development, and performance management. The strongest performance gains occur when AI adoption is supported by transparent governance, managerial oversight, and alignment with organizational strategy. Four major thematic clusters emerged: AI technologies, HRM and talent management systems, employee performance outcomes, and explanatory frameworks linking technological capability, human capital, and organizational systems. Although studies employ diverse quantitative, qualitative, and conceptual methods, longitudinal, cross-cultural, and ethics-focused research remains limited. Key gaps include long-term AI effects, ethical governance, and emerging tools such as generative AI. The review outlines future research directions and practical guidance for responsible AI adoption to enhance human capital performance.