AI Summary & Key Takeaways
AI InsightTechnical evaluation results indicate improvements in decision accuracy and response time, while economic analysis demonstrates increased workforce productivity and reduced operational costs compared with traditional workforce planning methods.
📌 3 Key Takeaways
Abstract
Modern organizations face increasing challenges in workforce planning due to dynamic labor demands and complex resource management requirements. Traditional planning approaches often rely on manual analysis, which limits decision accuracy and responsiveness. To address these challenges, this study proposes an intelligent workforce planning system based on Agentic Artificial Intelligence (Agentic AI), knowledge graph technology, and explainable decision support mechanisms. The proposed system integrates structured workforce data representation, automated decision analysis, and multi-agent coordination to support efficient workforce planning processes. Technical evaluation results indicate improvements in decision accuracy and response time, while economic analysis demonstrates increased workforce productivity and reduced operational costs compared with traditional workforce planning methods. The findings confirm that intelligent workforce planning systems can enhance organizational efficiency and support data-driven decision-making. The proposed framework provides a practical and scalable approach for improving workforce planning performance and achieving sustainable economic outcomes.