The rapid adoption of artificial intelligence (AI) in operations and supply chain management has transformed decision-making related to demand forecasting, inventory optimization, supplier selection, and logistics planning. While AI-driven systems offer substantial efficiency and cost advantages, they also introduce emerging risks related to system reliability, bias, over-automation, and unintended sustainability trade-offs. This study develops an interdisciplinary framework that examines how AI-enabled operational decisions influence performance, risk exposure, and sustainability outcomes in operations and supply chains. Drawing on the responsible AI, risk management, and sustainability literature, the paper conceptualizes AI as both an operational capability and a source of systemic risk. Using secondary evidence and illustrative operational scenarios, the study identifies key AI-related risks, such as model opacity, data bias, and reduced human oversight, and analyzes how these risks affect operational resilience, environmental efficiency, and social responsibility across supply chains. The framework highlights the moderating role of human-centered AI governance mechanisms, including transparency, accountability, and decision oversight, in mitigating risk while preserving performance gains. The study contributes to operations and supply chain management research by integrating AI risk management with sustainability objectives. Managerially, it provides guidance on designing responsible and safe AI systems that balance efficiency, resilience, and long-term sustainable value creation.