Artificial Intelligence–Based Predictive Models in Human Resource Management and Their Importance in Developing Human Capital

The domain of human resource management is witnessing rapid transformation driven by advanced developments in artificial intelligence technologies, especially predictive models, which have become a pivotal tool for supporting strategic decision-making and developing human cap ital. This study aims to offer a systematic review of the scientific literature discussing the application of artificial intelligence–based predictive models in human resource management and to analyze their contributions to improving managerial decision-making effectiveness and human capital development within organization. The study adopts a systematic literature review methodology in accordance with PRISMA guidelines, drawing on the Science Direct and Taylor & Francis databases. The processes of searching, screening, and filtering resulted in the selection of 51 scientific studies, which were subjected to qualitative thematic analysis within various the oretical frameworks, most notably human capital theory and the knowledge-based view. The results demonstrate that predictive models contribute to enhancing the accuracy of recruitment decisions, predicting employee turnover, supporting strategic workforce planning, and determining skill gaps, thereby promoting human capital development. In contrast, the study emphasizes persistent challenges associated with data quality, algorithmic bias, and ethical and regulatory considerations. The study concludes by highlighting the need to adopt integrated governance frame works to ensure the effective and responsible use of predictive models in human resource management.