This study examines the transformative impact of artificial intelligence (AI) on supply chain operations through a comparative analysis of traditional versus AI-enabled processes. The research evaluates five four operational areas: efficiency and cost reduction, decision-making, supply chain visibility, and customer experience. Findings demonstrate that AI-driven systems achieve superior performance, delivering 20-30% improvements in demand forecasting accuracy, 25-40% fewer disruptions compared to conventional methods. The analysis highlights AI's ability to enable real-time, data-driven decision-making and end-to-end supply chain transparency through technologies like IoT, machine learning, and blockchain. However, the study identifies significant adoption barriers including high implementation costs, data integration challenges, workforce skill gaps, and evolving regulatory requirements. Strategic recommendations are proposed to overcome these hurdles, including phased implementation approaches, data infrastructure modernization, workforce upskilling programs, and ethical AI governance frameworks. The paper also discusses critical policy considerations and future research directions, particularly in generative AI applications, autonomous supply networks, and sustainability optimization. As global supply chains face increasing complexity, this research suggests AI adoption is transitioning from competitive advantage to operational necessity. The study concludes that organizations which successfully implement AI while addressing adoption challenges will gain significant resilience, responsiveness, and cost advantages in an increasingly digital and volatile global marketplace. The findings provide valuable insights for practitioners seeking to harness AI's potential while navigating implementation complexities in supply chain transformation.
Yusuf et al. (2025) studied this question.