Abstract

Artificial Intelligence (AI) is emerging as a cornerstone technology in reshaping how supply chains operate, compete, and evolve in the digital era. As supply chains grow more interconnected and data-driven, the strategic integration of AI offers significant potential to optimize logistics, enhance forecasting accuracy, drive sustainable practices, and elevate resilience against global disruptions. However, a critical challenge persists: despite widespread enthusiasm for AI, organizations often lack a robust, standardized Capability Maturity Model (CMM) that can diagnose their readiness, benchmark progress, and guide incremental adoption within the unique and multifaceted contexts of Supply Chain Management (SCM). This TREO Talk addresses this pressing issue by introducing the results of an exhaustive Systematic Literature Review (SLR), designed to identify, compare, and synthesize existing AI maturity models with explicit focus on their applicability to SCM. Our research systematically surveyed 955 articles across nine academic databases, including IEEE, Scopus, and Web of Science - applying PRISMA 2021 protocols and rigorous inclusion/exclusion criteria. Sixty-six qualified studies were synthesized to reveal not only the recurring maturity dimensions but also the critical gaps in model coverage, validation, and scalability. Key maturity dimensions distilled include Data Processing, Analytics & Insight Generation, Automation & Decision Support, System Integration, Innovation & Learning, and Operational Resilience. These dimensions form the basis for a proposed SCM-centric AI CMM that accommodates dynamic business needs, real-time data flows, and cross-functional stakeholder interactions. What sets this contribution apart is its comprehensive treatment of organizational readiness as a multi-level construct spanning strategy, ethics, infrastructure, talent, and governance. Our proposed model introduces 12 strategic enablers of maturity, including ethical AI compliance, collaboration ecosystems, talent development pathways, and advanced performance measurement capabilities. Moreover, it offers practical mechanisms for mapping current capabilities, identifying gaps, and prioritizing AI investment through well-defined Key Performance Indicators (KPIs) and maturity benchmarks. This TREO session seeks to create a forum for interdisciplinary engagement, drawing on perspectives from information systems, operations, and digital innovation scholars. Attendees will engage in critical reflection on the model’s structure, its adaptability across sectors, and its potential integration with digital twins, blockchain, and IoT frameworks. Emphasis will be placed on the path toward empirical validation, cross-industry benchmarking, and AI governance frameworks that ensure accountability and trust. Ultimately, this TREO Talk aims to stimulate scholarly discourse and practical momentum around a transformative maturity model that empowers organizations to unlock AI's strategic value and align it with the demands of next-generation supply chain ecosystems.

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