The Convergence of Supply Chain Management and Artificial Intelligence: Challenges and Opportunities

Artificial Intelligence (AI) is rapidly transforming the architecture and performance of global supply chains. The convergence of data-rich operations with machine learning (ML), reinforcement learning (RL), and generative large language models (LLMs) enables unprecedented levels of automation, foresight, and adaptability in supply chain management (SCM). This paper synthesizes recent literature (2023–2025) to examine how AI technologies reshape core SCM functions, forecasting, inventory optimization, logistics routing, procurement, and risk management, while identifying the governance and organizational challenges that shape adoption outcomes. Findings indicate that AI integration delivers measurable efficiency and resilience gains but also introduces new risks related to data interoperability, explainability, cybersecurity, and ethical governance. A governance-first operating model is proposed, emphasizing transparency, human oversight, and regulatory compliance as key enablers of sustainable AI deployment. The study concludes with a phased implementation roadmap and a future research agenda focused on responsible, interdisciplinary innovation at the intersection of AI and SCM.