USING NEURAL NETWORKS FOR HR: POSSIBILITIES, PROSPECTS, LIMITATIONS

Abstract

The article systematizes contemporary approaches to integrating neural networks and artificial-intelligence (AI) tools across all core human-resource management (HR) processes. Drawing on BCG industry reports and specialized studies, it demonstrates that neural networks accelerate recruitment through automatic résumé screening, candidate ranking and round-the-clock chatbot support, thereby reducing recruiter workload by up to ten percent and shortening average vacancy-closure time by roughly forty percent. The paper outlines analytical models that predict turnover risk and help design personalized career trajectories. Adaptation functions include automatic generation of check-lists, early detection of burnout signs and development of individual learning programmes. Confidentiality challenges are analysed, notably potential data leaks and model “hallucinations” that necessitate multi-level fact-checking. The practical value of hybrid architectures that combine on-premise and cloud neural networks to balance security and cost efficiency is underlined. Separate attention is given to the growth of prompt-engineering skills among HR professionals, enabling precise task formulation and mitigating legal risks. Examples are provided of chatbots conducting brief preliminary interviews and clarifying candidate expectations. The article notes that analytical modules can aggregate employee feedback and generate dynamic mood reports at the team level. The authors emphasize that successful AI integration requires continuous data-quality monitoring and regular algorithm updates. The findings confirm the high efficiency and future relevance of neural-network integration into HR processes, provided that information-security requirements and ethical standards are strictly observed.