The COVID-19 pandemic underscored the importance of resilient supply chains (SC), leading to research on Artificial Intelligence (AI) in disruption detection and contingency planning. AI enhances data analysis, demand forecasting, and resource optimization, improving SC efficiency, resilience, and sustainability. This study presents a literature review aimed at identifying the current trends in the use of AI data-driven methods, knowledge-based methods, and hybrid methods that combine both, to support strategic decision-making within the supply chain management, such as, supplier selection, network design, capacity definition, or risk management. The objective is to define a conceptual framework that supports determining which methods are most used to support each of the strategic decisions, so that can be applied to enable robust and informed decision-making in supply chain management.