From Generative AI to Agentic Supply Chains: A Bibliometric and Thematic Analysis of Emerging Artificial Intelligence in Operations and Supply Chain Management

This study provides a bibliometric and thematic examination of the emerging literature on Generative Artificial Intelligence, Large Language Models and Agentic AI in Operations and Supply Chain Management. Based on a final corpus of 203 articles published between 2022 and 14 August 2026, the analysis demonstrates that this research domain has expanded rapidly, particularly during 2025–2026, while simultaneously becoming more specialised in its technological and operational focus. The findings show that the field is developing around three major thematic streams: Generative AI Adoption and Integration in Supply Chain Management, LLM-Enabled Decision Support and Operational Intelligence, and Agentic AI for Adaptive and Disruption-Responsive Supply Chains. Bibliographic coupling further identified five contemporary research fronts relating to intelligent AI architectures, sustainability and human-centric supply chains, resilience and performance, organisational adoption, and AI-enabled risk sensing and visibility. The combined thematic and overlay analyses indicate an emerging progression from the use of Generative AI for information generation and organisational integration, toward Large Language Models for operational intelligence and decision support, and increasingly toward Agentic AI systems capable of participating in goal-directed operational activities. This progression should not be interpreted as evidence that fully autonomous supply chains have already emerged. Rather, it indicates a developing research frontier in which AI systems are being assigned progressively greater roles in sensing, reasoning, planning, coordination and selected forms of execution. A central contribution of this study is therefore the positioning of the Agentic Supply Chain as an emerging socio-technical system in which intelligent agents may increasingly interact with supply-chain data, digital twins, enterprise systems and human decision makers to support adaptive operational responses. Human oversight, accountability and organisational governance remain essential components of such systems. The study also reveals that the field remains theoretically and empirically immature in several areas. Questions concerning multi-agent coordination, human–agent collaboration, autonomous decision boundaries, governance, cybersecurity, explainability and real-world operational performance require substantially greater investigation. Future research should therefore move beyond conceptual demonstrations toward empirical studies capable of evaluating when, where and under what governance conditions agentic technologies create sustainable operational value. Overall, the evidence suggests that the next stage of AI-enabled Operations and Supply Chain Management will not be defined merely by the ability of artificial intelligence to generate information, but increasingly by its capacity to interpret operational environments, coordinate decisions and act toward defined supply-chain objectives. Understanding how this transition can occur responsibly, securely and effectively represents an important research agenda for the development of future Agentic Supply Chains.

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