INNOVATIVE MANAGEMENT MODELS IN INTERNATIONAL LOGISTICS: FROM ADAPTIVE MANAGEMENT TO ARTIFICIAL INTELLIGENCE

The article examines the transformation of management systems in international logistics under the influence of the Fourth Industrial Revolution. The evolution of management approaches is analyzed: from traditional models to adaptive management and the integration of artificial intelligence (AI). The role of digital technologies (Big Data, Blockchain, IoT, autonomous systems) in ensuring supply chain resilience is defined. The benefits and risks of implementing intelligent systems are discussed, particularly in the context of demand forecasting, route optimization, and warehouse automation. Based on the analysis of practical cases of global leaders (Amazon, Tesla), the necessity of transitioning to hybrid management models combining human intelligence with algorithmic solutions to enhance global competitiveness is substantiated. The primary objective of this research is to analyze the evolution of management approaches, tracing the trajectory from traditional models to adaptive management, and ultimately to the full-scale integration of Artificial Intelligence (AI). The study aims to define the specific role of digital technologies in enhancing supply chain resilience and to evaluate the necessity of new management frameworks. he authors conclude that adopting such hybrid approaches is essential for companies aiming to enhance their global competitiveness and adapt to the volatility of modern markets.Furthermore, the article provides a critical assessment of the operational shifts required for modern logistics managers. It argues that the integration of artificial intelligence should not be viewed merely as a technical upgrade, but as a strategic imperative that redefines organizational hierarchies. By analyzing the «human-in-the-loop» methodologies employed by market leaders like Amazon and Tesla, the study highlights that the most effective supply chain models are those that leverage AI for high-speed data processing and routine automation, while reserving complex, high-level decision-making for human experts. This distinction is presented as a crucial factor in mitigating the risks associated with total automation and ensuring adaptive capacity in a volatile global market.