Generative Artificial Intelligence in Logistics-Intensive Organizations: Use Cases, Organizational Impact, and the Challenge of Measuring Operational Value

Abstract

Recent generative artificial intelligence (GenAI) solutions attract high expectations, while their operational contribution in real-world logistics settings remains unclear. This study investigates how GenAI supports work in companies with logistics-intensive operations and how these organizations assess the added value of GenAI use cases. Between January and March 2026, we conducted 11 semi-structured expert interviews with decision-makers and artificial intelligence experts from Austrian organizations with established logistics operations. The transcripts were manually coded using ATLAS.ti 25. Interviewees frequently describe GenAI as a cognitive support tool for drafting, summarizing, translating, or information retrieval. Interviewees reported time savings in these activities, whereas end-to-end process automation remained limited. The findings indicate that GenAI adoption in logistics is still at an early stage. Formal assessments of operational or financial benefits using key performance indicators (KPIs) were largely absent in the organizations interviewed. Although many firms show strong interest in exploring new use cases, there is a lack of mature, out-of-the-box solutions tailored to logisticsspecific processes. Some respondents from large organizations reported the development of customized in-house AI solutions, while generalpurpose GenAI tools dominated the reported applications overall. It can be concluded that, in current practice, GenAI’s near-term contribution in logistics lies in augmenting human work in manual processes rather than completely automating processes and replacing workers.