The proliferation of Artificial Intelligence (AI) promises transformative potentials for supply chain management (SCM), yet empirical evidence on realized supply chain performance gains remains fragmented and context-dependent. This article presents a comprehensive conceptual investigation into how AI-driven innovations interact with traditional supply chain management practices to influence supply chain performance, resilience, and long-term value creation. Drawing exclusively on a curated selection of literature — spanning empirical studies on SCM practices and performance, machine‑learning applications in demand forecasting, and critical analyses of AI adoption barriers — this research identifies recurring patterns, tensions, and open questions. The analysis reveals that while AI-enabled capabilities (e.g., demand forecasting, supplier scouting, logistics optimization) can significantly augment supply chain responsiveness and resilience under dynamism (Belhadi et al., 2021; Bottani et al., 2019; Gao & Feng, 2023), their effectiveness is highly mediated by data quality, organizational readiness, integration scope, and governance (SupplyChainBrain, 2019). Traditional supply chain practices remain foundational: empirical studies continue to show that SCM practices contribute significantly to performance, whereas strategy alone often proves a weak predictor (Sukati et al., 2012). The paper concludes by proposing a conceptual integrative framework that maps prerequisites for effective AI‑SCM synergy, outlines potential trade‑offs, and suggests directions for future empirical research to validate and refine the framework.