Background: Artificial intelligence (AI) is poised to profoundly reshape logistics and supply chain management in the 21st century—much as the steam engine, electricity, and computers transformed industries in earlier eras. AI is recognised as a key driver in developing next-generation supply chains, enhancing organisational agility, and improving delivery efficiency. It supports a wide range of operational and managerial functions, including preventive, reactive, and corrective actions within logistics systems. Most importantly, AI reduces the risk of human error, thereby mitigating economic and business losses and lowering operational costs. This article aims to identify the most critical areas of AI application in logistics and supply chain management, explore its broader potential uses, and discuss solutions to implementation challenges, with particular emphasis on the human factor. As a holistic review, the study contributes to the literature by mapping current AI applications, highlighting emerging trends, and underscoring human-centric challenges, thereby providing a foundation for further research. Methods: A systematic literature review was conducted following the SALSA (Search, Appraisal, Synthesis, and Analysis) approach to address the research gap in understanding the practical applications of AI tools in logistics and supply chain management. Results: The analysis revealed five key directions for implementing AI technologies in logistics and supply chains. Human factors—and the related phenomenon of “dehumanisation”—emerged as critical challenges for managers overseeing transformations that extend beyond IT integration towards the systemic adoption of AI in ​​operations and management. Conclusions: The potential applications of AI in logistics are diverse, encompassing demand forecasting, inventory management, delivery route optimisation, resources planning, and process monitoring, among others. Each of these applications influences the role of humans within organisations and operational processes, requiring strategic, tactical, and operational adaptation. The study identifies its limitations and proposes directions for future research on AI applications in logistics and supply chain management. Human-machine interaction should remain a central focus of future investigations into AI-driven transformations.