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.

Our workshop relates primarily to the ADRForum theme “From Lab to Deployment: Transfer and Validation”. It addresses how AI-based methods for freight transportation can move from applied research, prototypes and pilot projects into operational logistics decision-support tools.

The proposed session is centered on AI for freight transport, with a strong emphasis on the practical transfer and validation challenges that arise when predictive models are developed for real logistics environments. Freight transportation is a relevant domain for the ADR community because it involves operational uncertainty, fragmented information across stakeholders, multimodal coordination, emissions pressure, and the need for decision-support tools that can be trusted by logistics operators, ports, shippers and public-sector actors.

The workshop will use two applied cases as anchors for a broader discussion. The first concerns predictive models and transfer learning for estimating transport cost and CO₂ emissions in modular construction freight, developed in the context of a PRR-funded project with industrial collaboration. The second concerns ongoing work on a digital platform for last-mile logistics in a port-area context, with a use case in the Port of Leixões. These cases will be presented as practical examples of the transition from applied research to operationally relevant tools.

The main contribution to ADRForum is a focused discussion on AI deployment and technology transfer in a critical logistics domain. The session will examine how data availability, data quality, interoperability, model validation, user involvement and workflow integration affect the transfer of AI solutions from lab environments to logistics practice.

The expected outcome is a practical checklist for moving freight AI solutions from pilot-level maturity toward trusted operational deployment. The session will address three guiding questions: What validation evidence is needed before freight AI can be adopted operationally? How can end-users be involved early enough to improve technology transfer? Which data, interoperability and organisational conditions are required to scale AI-based freight decision support across companies, ports and transport contexts?

This framing is aligned with ADRForum’s emphasis on accelerating technology transfer, strengthening academia-industry collaboration, and supporting validation pathways for AI and data-driven systems in real-world European sectors. It also contributes to Europe’s strategic capability by strengthening local knowledge, skills and deployment capacity in freight digitalisation, a domain that is central to industrial competitiveness, resilience and sustainability.