Adaptation of Artificial Intelligence Methods for Human Resource Management in Ground Transportation Organizations

This paper examines the adaptation of artificial intelligence (AI) methods for human resource management in ground transportation organizations. Digitalization in the transport sector has increased the complexity of HR tasks, including recruitment, training, motivation, and retention of employees. Traditional HR approaches are often insufficient to address challenges such as high staff turnover, rising training costs, and strict safety regulations. The paper clarifies the classification of AI methods applied to HR systems, including machine learning, neural networks, expert systems, frame-based technologies, and fuzzy logic. An author’s model of HR management using AI is proposed, integrating recruitment, training, motivation, and monitoring processes. Furthermore, an economic forecasting model based on ROI evaluation is developed to assess the feasibility of AI implementation under baseline, optimistic, and pessimistic scenarios. The findings demonstrate that AI contributes to cost reduction, productivity growth, and improved safety, while also enhancing the resilience of transport systems. The results confirm the practical significance of AI-driven HR digitalization for the strategic development of ground transportation organizations.