Research objectives and hypothesis/research questions:
The article discusses the use of artificial intelligence in human resource management in logistics companies, with particular emphasis on recruitment, training and performance management processes. The considerations presented in the paper allow us to formulate the following research questions, which constitute the basis for the argument in this study: 1. Does artificial intelligence increase the efficiency of recruitment processes in logistics companies? 2. Is logistics employees’ level of engagement and productivity affected by the personalization of AI-based training? 3. Does the implementation of AI in the HR of logistics companies have only positive effects?

Research methods:
The paper uses case study analysis and literature data as the main research methods. The author used examples of artificial intelligence implementations in logistics companies to illustrate practical applications of the technology in HR processes. These methods made it possible to combine theoretical discussion with a practical approach to the topic.

Main results:
The main findings presented in the paper indicate that there are significant benefits to applying AI to HR processes in logistics companies. AI significantly improves recruitment efficiency, enabling faster and more precise matching of candidates to job requirements through the automation of CV analysis and the use of chatbots. Personalization of AI-based training improves employee engagement and productivity, while optimization of work schedules and accurate forecasting of staffing needs allow for better human resource management. The implementation of AI also contributes to lower operational costs and more efficient operations in the logistics environment. At the same time, the paper points out that the effective use of AI requires a responsible approach, taking into account potential risks such as algorithmic biases and employee resistance.

Implications for theory and practice:
The work results directly apply to logistics companies, demonstrating the practical benefits of implementing AI. Automating recruitment, personalizing training, and forecasting staffing needs can significantly improve companies’ operational efficiency. At the same time, the author highlights the need to implement AI responsibly, taking into account risks such as algorithmic biases and employee concerns, which can help organizations minimize barriers to adopting new technologies. These findings can serve as guidelines for HR managers implementing AI in their organizations.