Drivers and constraints of agentic AI in supply chain management: an exploratory study

Recent advancements in Artificial Intelligence (AI) have led to the emergence of AI Agents, autonomous systems performing complex tasks and pursuing defined objectives with minimal human interaction. Early research in AI Agents in supply chain (SC) management is pointing to their potential for enhancing visibility, resilience, and operational efficiency. However, as AI Agents are an emerging technology, several aspects of their application remain unclear. The literature lacks understanding of the SC processes they can support, the main drivers and constraints influencing their adoption, and practitioners’ perspectives on these aspects. Moreover, as their implementation inevitably reshapes human actors’ roles, scholars have called for further investigation into the nature of agent interaction with humans. This exploratory qualitative study addresses these gaps through semi-structured interviews with experienced practitioners in AI and SC. To the best of the authors’ knowledge, this is among the first studies to jointly address, through an integrated framework, the mapping of SC processes that AI Agents can support, the associated drivers and constraints to adoption ranked according to SC practitioners, and the human-AI Agent collaboration roles for each category of processes. The study reveals novel AI Agent-specific elements and introduces a conceptual framework alongside four testable propositions.