🤖 AI Summary
Despite growing AI adoption in HR digital transformation, a coherent theoretical framework and empirical synthesis for systematically applying AI to talent decision-making remain lacking.
Method: We propose the first AI technology taxonomy tailored to HR contexts—the “Talent Management–Organizational Management–Labor Market” tri-dimensional framework—clarifying data-task-model mappings. Integrating machine learning, natural language processing, graph neural networks, and causal inference, we align methods with core HR tasks including resume parsing, performance prediction, and attrition forecasting. We further identify interpretability, fairness, and real-time processing as critical technical challenges.
Contribution/Results: Based on a systematic review of 300+ scholarly articles, we construct the most comprehensive AI-for-talent-analytics research map to date. This work establishes a methodological foundation for academia and delivers an actionable, implementation-oriented roadmap for intelligent HR systems in industry.
📝 Abstract
In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.
