A research framework for the evaluation of operational impacts of AI solutions in logistics use cases

Abstract

The adoption of artificial intelligence (AI) in organizations is often fuelled by promises of improved efficiency and innovation, while its practical value and operational impact remain unclear. This paper presents a research framework for analysing the added value of generative AI (GenAI) in logistics organizations, with a focus on “difficult to automate” tasks and processes. It combines literature-based model development, semi-structured expert interviews, field studies, and user-cantered experiments to examine how GenAI affects task performance, user behaviour, and organizational structures. It integrates qualitative insights with experimental and modelling approaches to support a systematic assessment of value creation, efficiency gains, usability, user acceptance, and organizational impact. Preliminary results from expert interviews indicate that GenAI is primarily used for cognitive support enabling time savings in manual tasks, and less for process automatization. Eventually, the presented framework will provide a structured basis for the development of evidence-based roadmaps for user-centred AI adoption in logistics.