Generative artificial intelligence (GenAI) in procurement and supply chain management: applications, opportunities and challenges

Purpose This article aims to explore the transformative potential of generative artificial intelligence (GenAI) in procurement and supply chain management (SCM), with a focus on its practical applications, strategic implications and integration challenges. Design/methodology/approach Adopting a conceptual approach, this article presents the authors’ views and arguments, supported by a review of existing literature and informed by insights from discussions with industry experts. The discussion in this conceptual article is anchored in the supply chain operations reference model. Findings This research addresses critical questions regarding the practical and strategic impacts of GenAI, emphasizing its ability to simulate scenarios, deliver real-time insights and enable data-driven decision-making. At the same time, it acknowledges the barriers to adoption, including system integration challenges, data privacy and security concerns and the skills gap in effectively deploying GenAI tools. Research limitations/implications This work focuses on selected industries and regions, and it needs to be extended further to increase the generalizability of the findings. Ethical, technical and social dimensions, such as bias, data privacy and workforce implications, are briefly addressed but require deeper exploration. Additionally, the dynamic nature of GenAI may render some recommendations obsolete over time, making continuous evaluation necessary as the technology evolves. Originality/value This article provides recommendations and identifies future research trajectories to guide researchers and practitioners in harnessing the potential of GenAI for impactful and sustainable transformation in supply chain and procurement.