The Role of AI in Enhancing Supply Chain Resilience: Insights from Industry Experts

This study investigates the role of artificial intelligence in enhancing supply chain resilience, drawing insights from industry experts to understand the transformative potential and challenges of AI adoption in supply chain management. In recent years, supply chains have faced unprecedented disruptions due to global crises, technological shifts, and evolving consumer demands, making resilience a critical focus. AI technologies, including machine learning, predictive analytics, and real-time data processing, have emerged as vital tools for maintaining continuity and improving decision-making under uncertainty. This research employs a qualitative methodology, engaging with 15 industry experts to gather in-depth perspectives on how AI is being integrated into supply chain practices and the tangible impacts observed. The thematic analysis of expert insights reveals that AI significantly contributes to risk mitigation, supply chain visibility, and adaptive planning. By harnessing real-time data, AI systems enable proactive responses to disruptions, enhancing operational agility. Moreover, AI-driven optimization techniques improve logistics efficiency, while predictive analytics support accurate demand forecasting and inventory management. Despite these advantages, the research identifies several barriers to successful AI implementation, including high initial costs, data integration challenges, and concerns regarding data privacy and workforce adaptation. Addressing these issues requires a strategic, phased approach to technology integration, fostering collaboration between AI developers, supply chain professionals, and policymakers to create sustainable and resilient systems. The study concludes that while AI offers substantial benefits for supply chain resilience, its successful implementation demands a balanced approach that considers technological, organizational, and ethical dimensions. Future research should focus on developing practical frameworks for AI adoption and assessing long-term impacts on supply chain sustainability and workforce dynamics.