Application of Artificial Intelligence in Workforce Planning in the Field of Human Resources Management


DOI: https://doi.org/10.15308/Sinteza-2026-379-386
Authors:
Aleksandar Ćosić

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Keywords:
Artificial Intelligence, Workforce Planning, Human Resources, Predictive Analytics, Machine Learning


Abstract:
Workforce planning is one of the most critical functions of human resource management (HRM), requiring accurate forecasts of staffing needs, labor market analysis, and strategic alignment of organizational capacities with business objectives. In the last decade, artificial intelligence (AI) has dramatically transformed this process through the introduction of sophisticated tools for predictive analytics, natural language processing, and machine learning. The emergence of agentic AI, autonomous systems capable of reasoning and acting without continuous human oversight represents a further qualitative leap that is beginning to reshape how organizations approach workforce planning. This paper analyzes the ways in which AI technologies, including both generative and agentic AI systems, are changing traditional workforce planning practices, identifies key advantages and limitations, and discusses the ethical implications of applying algorithmic systems in the HRM domain. The research findings suggest that AI significantly increases the accuracy of staffing needs forecasting and reduces talent acquisition costs, while simultaneously generating new challenges regarding algorithmic bias, privacy protection, and the governance of autonomous decision-making.

CITATION:

IEEE format

A. Ćosić, “Application of Artificial Intelligence in Workforce Planning in the Field of Human Resources Management,” in Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2026, pp. 379-386. doi:10.15308/Sinteza-2026-379-386

APA format

Ćosić, A. (2026). Application of Artificial Intelligence in Workforce Planning in the Field of Human Resources Management. Paper presented at Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2026-379-386

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