AI-Driven Risk Mitigation in Global Supply Chains to Build Resilience for U.S. Competitiveness: A Conceptual Review and Framework-Based Study

Global supply chains face unprecedented disruptions, from pandemics and geopolitical shocks to extreme climate events. These shocks expose limitations of traditional, reactive risk management approaches such as buffer inventory and backup suppliers. This study adopts a conceptual review and framework-based research design to develop an AI-driven risk mitigation cycle across four phases: risk identification, risk assessment, risk response, and continuous learning. Drawing on illustrative industry cases (Amazon, UPS, and Walmart), the paper explains how predictive analytics, machine learning, and sensor-enabled monitoring can improve early warning, real-time decision support, and adaptive resilience. The study’s contribution is a structured framework that links AI-enabled risk mitigation to both firm-level resilience and broader U.S. competitiveness. Practical implications are provided for managers (governance, capability building, and implementation priorities) and for policy stakeholders concerned with supply chain security and performance in volatile global markets.