Purpose This conceptual paper examines how artificial intelligence (AI) can be systematically integrated into supply chain management (SCM) training to support sustainable management objectives. It focuses on how AI-enabled training can enhance strategic adaptability and risk-aware decision-making to increase operational efficiency in increasingly volatile and sustainability-oriented supply chains. Design/methodology/approach This study adopts an integrative literature-based approach, drawing on publications from 2010–2024 across the SCM, AI, and training and development domains. Guided by the resource-based view, transaction cost economics and the dynamic capabilities framework, the paper synthesizes this literature into thematic clusters and develops a conceptual framework for AI-driven supply chain training. The review process follows a PRISMA-inspired structure to transparently report the identification, screening, eligibility and inclusion of relevant studies. Findings The analysis reveals that AI-enabled training, through simulations, real-time decision-making exercises, and predictive analytics, can enhance dynamic capabilities, reduce coordination and information costs, and foster data-driven competencies. These mechanisms collectively enhance supply chain resilience, risk management, and sustainability performance. The paper identifies five strategic insights that connect AI capabilities to training design, demonstrating how AI can move SCM training from static, process-oriented models toward adaptive, learning-oriented architectures. Originality/value This paper contributes by bridging classical SCM theories and AI-enabled training, a relatively underexplored area in supply chain education. By bridging the gap between traditional SCM theories and the practical needs of modern digital operations, the paper offers novel insights into AI’s transformational potential in enhancing supply chain adaptability and managerial decision-making. Additionally, it sets a foundation for future empirical research to validate and expand upon the proposed theoretical constructs.
Stephanie Bilderback (2026) studied this question.