Thu, 07. May 2026

AI Transformation in Logistics: Insights from the Future Logistics Event

This year’s “Future Logistics” event highlighted the critical challenges surrounding AI transformation and data-driven logistics - themes that sit at the very heart of our chair’s research in digital innovation and information systems. Following an opening by Transport Minister Oliver Krischer, the forum provided a platform to discuss the strategic integration of artificial intelligence into complex operational environments.

Prof. Frederik Ahlemann, Professor of Information Systems, Digital Innovation and Performance Management at the University of Duisburg-Essen, moderated the panel discussion “Beyond Technology: Lessons, Challenges and Outcomes of AI Transformation,” contributing an impulse on the expected landscape of 2031. The discussion centered on the transition from traditional dispatching toward data-centric systems and service-oriented business models focused on reliability. A key takeaway from the session was that AI transformation should not be viewed as a purely technical challenge; rather, it represents a systemic shift requiring interoperable information systems, integrated data infrastructures, and the active inclusion of the workforce.

The event also highlighted the long-term impact of collaborative research, exemplified by the startup red cable robots. Having emerged from a former ZLV research project within the BMBF Leading-Edge Cluster (EffizienzCluster LogistikRuhr), the spin-off demonstrated how research-driven innovation can evolve into scalable industrial solutions.

For our chair, the event offered an excellent opportunity to connect ongoing research in AI-enabled information systems, digital transformation, and smart infrastructures with current developments in the logistics sector. At the same time, the discussions reinforced that future logistics ecosystems will depend not only on technological capabilities, but also on governance structures, cross-organizational data integration, and human-centered transformation.