Artificial intelligence and data analytics in human talent management

Digital transformation has reshaped human talent management, with Artificial Intelligence (AI) and Data Analytics emerging as key tools for optimizing recruitment, retention, performance evaluation, and employee development. Despite growing interest, the literature remains fragmented across technical, managerial, and ethical perspectives. This study provides an integrated bibliometric and qualitative analysis of research on AI and Data Analytics in human talent management from 2015 to 2025, aiming to map evolution, thematic trends, methodological developments, and research gaps.

A mixed-methods approach combined bibliometric analysis with qualitative synthesis. A structured search in Scopus and Web of Science retrieved 137 records using Boolean operators combining AI/machine learning terms with People/HR Analytics and talent management concepts. After removing duplicates and irrelevant studies based on predefined inclusion criteria (peer-reviewed articles and reviews in English or Spanish), a final dataset of 82 documents was analyzed. Bibliometric techniques in R (v4.4.2) and VOSviewer (v1.6.20) examined scientific productivity, citation patterns, collaboration networks, and keyword co-occurrence. Additionally, the 30 most cited articles underwent qualitative synthesis to extract key conceptual and methodological contributions.

Scientific production showed exponential growth, particularly after 2020, with peaks in 2024 (21 articles) and 2025 (27 articles). Research concentrated in India, Germany, the United States, and the United Kingdom, with limited contributions from Latin America and Africa. Four thematic clusters emerged: (1) People Analytics and process optimization, (2) predictive models and machine learning, (3) data governance and ethical considerations, and (4) convergence of AI, Big Data, and advanced analytics. Highly cited studies highlighted advances in predictive modeling for turnover and recruitment, while also emphasizing ethical risks such as algorithmic bias and the need for explainable AI (XAI).

The field is evolving toward the integration of predictive capabilities with organizational decision-making and ethical frameworks, shifting human talent management from reactive to proactive, data-driven practices. However, gaps persist in empirical validation of models, standardization of ethical guidelines, interdisciplinary collaboration, and geographic diversity. This study contributes a unified socio-technical perspective that links technological innovation with organizational processes and governance. The findings offer a foundation for future research on context-specific, ethically grounded AI applications in emerging markets and support organizations in leveraging analytics for more adaptive and responsible talent management.