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Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations. Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived from theory and a systematic literature review and iteratively refined through a multi-round Delphi study involving experts in AI, data science, and SCM. Formative evaluation focused on requirement-alignment checks and expert ratings of statement relevance and clarity, followed by internal-consistency reliability analysis of the instrument scales. A summative organizational case study demonstration applied the finalized instrument to generate a maturity profile across six AI-SCM capability dimensions and to surface capability gaps and improvement priorities. The demonstration illustrates how organizations can use the CMM to benchmark AI-enabled supply chain capabilities and to prioritize transformation initiatives across analytics, automation, interoperability, resilience, and learning-oriented practices. This research conceptualizes AI as a higher-order organizational capability that enhances information-processing capacity and strategic alignment in supply chain operations. It advances academic understanding of AI-enabled organizational capabilities while providing a practitioner-ready maturity model and assessment approach for building intelligent, resilient, and innovation-driven supply chains.