Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment instrument were iteratively refined and validated through expert review using a Delphi based process to strengthen relevance, clarity, and practical usability. The resulting model enables organizations to assess current capability, identify priority gaps, and plan improvement actions aligned with operational goals across planning, design, and execution activities. A case-based application demonstrates how the instrument produces an organizational maturity profile and supports decision making on capability development priorities. This research contributes a practitioner ready assessment and planning tool and advances scholarship by offering a validated framework for staged and performance aligned AI integration in supply chain management.