Last week, our team had the privilege of participating a high-level roundtable at the CIDAI (Centre of Innovation for Data Tech and AI) in Barcelona, encouraging industry dialogue around sharing data across the logistics, mobility, and AI ecosystems. Stefano Persi, CEO, discussed how AI can practically support more efficient, sustainable, and resilient logistics, focusing on multimodal transport and the role of rail freight in building smarter logistics networks.
The discussion formed part of the broader work around the CIDAI white paper, created through its think tank with contributions from public and private stakeholders. As participants in this effort, we were pleased to contribute to the roundtable, where Stefano Persi shared practical examples and case studies highlighting the role of AI and trusted data sharing in real logistics environments.
Industry insight
Rail freight volumes remain below the European average and a €5M fee exacerbates the challenge. Transporting goods by rail continues to face operation and structural challenges, even as demand is expected to grow significantly.
In line with the Climate Neutrality 2050 objectives, rail freight volumes are projected to double compared to recent historical averages. Achieving this growth will require not only infrastructure investment, but also better coordination, optimisation, and collaboration across the logistics ecosystem.
From CIMALSA’s perspective, multimodality is a central lever for improving logistics efficiency and sustainability. The optimal model combines rail for medium- and long-distance transport and road transport primarily for first and last-mile operations. AI enables this shift by supporting the reallocation of transport flows from truck to rail, optimising routes, schedules, and capacity usage. This approach can significantly reduce emissions compared to road-only transport, while maintaining operational flexibility.
Data sharing and the role of Data Spaces
A recurring challenge identified is the reluctance of operators and logistics agents to share information. While concerns around privacy are legitimate, they often limit system-wide optimisation. Data spaces were highlighted as a key enabler, providing:
- Secure data exchange and controlled access
- Clear rules on how data is shared and used
- Technical foundation for AI tools to suggest routes, estimate costs, and simulate operational scenarios.
By ensuring data trust, data spaces allow AI to support better decision-making without compromising sensitive business information.
Challenges facing the sector
Five major challenges shape the future of rail freight and logistics:
- Pressure for sustainability and decarbonisation
- Resilience in the face of global and geopolitical crises
- Urban congestion and last-mile regulation
- Digitalisation interoperability, and cybersecurity vulnerabilities
- Organisational readiness and technological transformation
These challenges are closely interconnected and require coordinated responses.
Where AI can deliver tangible value
AI is already demonstrating measurable benefits in transportation and distribution operations, where it can deliver improvements in efficiency, cost, and sustainability. For Mosaic Factor, this includes automation projects for container loading and unloading at ports.
Despite its potential, barriers hinder AI adoption. These challenges include:
- Lack of an AI strategy within organisations,
- governance issues and data fragmentation,
- lack of quality historical data, difficulty in evaluating the ROI of AI initiatives,
- and a complex and inconsistent regulatory environment. Addressing these barriers requires clearer strategic alignment between technology, operations, and regulation.
Participants aligned on a three-level model for the sector:
- Digitalisation: basic digitisation and automation, where progress is already visible.
- Data sharing: secure exchange of data, enabling network visibility.
- Visibility and success stories: more networks than logistics and gaining visibility of success stories.
Advancing through these levels is essential to unlocking the full potential of AI in logistics.
Key takeaways
Logistics efficiency depends on the effective combination of innovation, sustainability, and regulation. The key question for operators is what value is created by sharing data. AI, when combined with multimodality and trusted data-sharing frameworks, can significantly enhance the efficiency, sustainability, and resilience of rail freight and logistics systems.
During the roundtable, Stefano highlighted three projects Mosaic Factor contributed to:
- Pioneers: Container Transport Forecast (EU Green Ports Initiative)
- Port of Antwerp-Bruges: Cargo Flow Predictor
- Disruptive: Detection & Classification of Logistics Network Disruptions
Together, these projects show the practical impact of AI and data collaboration in real logistics operations, grounding the CIDAI roundtable discussion in tangible solutions.
Click here to read the full white paper from CIDAI.