Generative AI in supply chains: a qualitative study on decision-making and human-AI collaboration

Abstract

Generative Artificial Intelligence (GenAI) has recently gained attention as a technology that supports supply chain decision-making due to its unique natural language capabilities. In this context, it is seen as a top-layer augmentation to traditional analytical and optimisation-based technologies, accelerating decision support needed to keep businesses competitive. While extant literature largely discusses the conceptual potentials of GenAI, empirical evidence on its implications for human-AI collaboration and its boundaries with other AI technologies remains limited. Therefore, the present study adopts an abductive qualitative approach to explore GenAI-based decision-making through semi-structured interviews with experts. The empirical data were analysed using thematic analysis and subsequently interpreted through the lens of a recent AI decision-making framework. By theorising the empirical insights, this study clarifies GenAI’s current role as a cognitive decision-support layer rather than an autonomous decision-maker, along with its limitations. The findings contribute to the literature by providing empirical validation of GenAI as an augmentative technology and by offering evidence-based guidance on how organisations can responsibly leverage GenAI to enhance decision quality, operational efficiency, and operational resilience.