Artificial Intelligence in Supply Chain Analytics: Business Value Creation, Adoption Barriers, and Managerial Implications

Artificial intelligence (AI) is fundamentally reshaping supply chain management by offering unprecedented capabilities for optimization, prediction, and automation. This paper synthesizes current research and industry analysis to examine the multifaceted impact of AI on supply chain analytics. The primary objective is to quantify the business value and return on investment (ROI) generated by AI applications in core functions such as demand forecasting, inventory management, and logistics. The analysis reveals substantial, measurable benefits, including significant cost reductions, enhanced service levels, and improved operational efficiency. However, the realization of this value is contingent upon overcoming significant adoption barriers, which are categorized as financial, organizational, and technical. The paper explores solutions to these hurdles, ranging from alternative funding models to strategic change management. Furthermore, it provides a forward-looking analysis of emerging technologies, including generative and causal AI, distinguishing their current proven applications from their long-term potential. The study concludes by proposing a set of managerial implications and strategic frameworks designed to guide organizations in developing a robust AI strategy, fostering an AI-ready culture, and navigating the complexities of implementation to unlock the full transformative potential of AI in their supply chain operations.