Programme time: 13-Oct | 15:30 | 75 min
Room: Infante Room (Catalyst Marketplace)

Moderator: TBC
Speakers
•     Svein Ivar Sagatun — Head of Robotics and Drones (Equinor)
•    Tjibbe Bouma — Strategic Advisor (SPRINT Robotics)
•    Øystein Skotheim — CSO (ScoutDI)
•    Thordur Arnason (Capgemini)

Full description and objectives

This workshop addresses the transition of AI-based freight transportation solutions from applied research and pilot projects to operational logistics decision support. Freight transport is a high-impact domain for European competitiveness and sustainability, but deployment is often constrained by fragmented data, limited validation in real operating conditions, interoperability barriers, user trust, and the difficulty of integrating AI outputs into existing logistics workflows.

The session will use applied cases as practical anchors: predictive models and transfer learning for estimating transport cost and CO₂ emissions in modular construction freight, and ongoing work on a digital platform for last-mile logistics in a port-area context. These cases will be used to extract transferable lessons for other freight contexts, including multimodal transport, port logistics, last-mile planning and industrial cargo movement.

The objectives are to identify the main barriers to freight AI deployment; discuss how end-users should be involved during development and validation; examine data, interoperability and workflow-integration requirements; and define practical conditions for technology transfer. Participants will contribute to a draft checklist for moving freight AI solutions from pilot-level maturity toward trusted operational use.

Why you want to join?

This workshop aligns with the ADRForum theme “From Lab to Deployment: Transfer and Validation”, addressing how AI-based methods for freight transportation can move from applied research and pilots into trusted operational decision-support tools.

Freight transportation provides a relevant environment for AI deployment due to operational uncertainty, fragmented data, multimodal coordination, emissions pressures, and the need for solutions trusted by logistics operators, ports, shippers, and public authorities.

Two applied cases will anchor the discussion. The first explores predictive models and transfer learning for estimating transport costs and CO₂ emissions in modular construction freight, developed through a PRR-funded project with industrial collaboration. The second addresses a digital platform for last-mile logistics in a port context, using the Port of Leixões as a use case. Both demonstrate the transition from research towards operationally relevant solutions.

The workshop will examine key barriers to technology transfer, including data availability and quality, interoperability, model validation, user involvement, and integration into existing logistics workflows. Participants will discuss what evidence is required for operational adoption, how end-users can contribute earlier to development and validation, and which technical and organisational conditions enable solutions to scale across companies, ports, and transport contexts.

The expected outcome is a practical checklist for progressing freight AI solutions from pilot maturity towards trusted deployment. By strengthening academia-industry collaboration and European capabilities in freight digitalisation, the session contributes to competitiveness, resilience, sustainability, and Europe’s capacity to deploy AI in strategically important real-world sectors.

Running agenda