AI-powered talent chain management with multi-agent systems for industry and innovation growth

作者：Rong‐Fu Wang, Xiufen Zeng, Fuchao Li, Bin Wang, Juan Zhang · 发表于：Scientific Reports · 年份：2026 · DOI：10.1038/s41598-026-60531-9 · 研究领域：AI and HR Technologies、Human Resource and Talent Management、Expert finding and Q&A systems

This study presents an AI-powered talent chain management framework for supervised talent mobility prediction, with a particular focus on next-occupation classification from career histories. Given an individual’s chronologically ordered ESCO occupation-code sequence and job-title text sequence, the model predicts the ESCO occupation label corresponding to the next career transition. Existing transition-based and neural sequence models often rely on local occupational statistics or generic sequential representations, making it difficult to jointly capture structured occupational taxonomy information, job-title semantics, temporal career dynamics, and uncertainty in heterogeneous career records. To address these limitations, we propose an adaptive talent dynamics planner built upon a multi-agent modeling perspective. The framework integrates a constraint-driven workforce optimizer, an agent-based collaboration forecaster, and an uncertainty-aware mobility evaluator to represent talent states, occupational compatibility, collaboration-related dependencies, and risk-aware decision refinement. In the prediction model, ESCO-code embeddings and job-title text representations are fused into transition-state representations, temporal order encoding captures direction-sensitive career dynamics, and uncertainty-aware refinement regularizes noisy or ambiguous occupational transitions through perturbation consistency. Experiments are conducted on KARRIEREWEGE and the DECORTE career histo...