This study analyzes the integration and application of artificial intelligence (AI) technology in supply chain management. The research process, in which experts were deliberately selected, aimed to identify how AI is utilized to support decision-making, automate processes, and optimize activities such as demand forecasting, inventory management, and transportation. The results indicate that large enterprises, equipped with advanced technological infrastructure and high-quality data, are better prepared to implement AI solutions. In contrast, small and medium-sized enterprises (SMEs) encounter significant barriers, such as high implementation costs, lack of technical knowledge, and challenges related to the integration of AI with existing systems. The conducted study aligns with the theoretical approach, reflecting theoretical perspectives. The adoption of AI in supply chains is often constrained by the complexity of data management and integration issues, emphasizing AI’s ability to transform operational efficiency through predictive analytics and real-time decision-making. The results highlight the need for further scientific exploration regarding the long-term impact of AI on supply chain performance, particularly in terms of its effects on supply chain agility, sustainability, and risk management.