As intermodal logistics continues to grow, AI is creating new opportunities to automate processes, accelerate development and enhance operational responsiveness.
The technology is advancing rapidly. The challenge is turning greater capability into better decisions.
The intermodal sector does not face a growth problem. It faces a complexity challenge.
Rail freight volumes are forecast to grow significantly over the coming decades, while operators are managing increasingly interconnected networks spanning transport providers, terminals, depots, rail services and customers. Every movement, delay, schedule change or equipment issue can have consequences across the wider operation.
Most operators already have access to large volumes of data. The challenge is understanding what that information means across the wider network and turning it into intelligence that supports better decisions as conditions change.
AI can help organisations build integrations, automate workflows and surface insights faster than ever. However, technology alone does not remove complexity. In many cases, it simply moves information more quickly across the network.
As Jim Slade, UK Commercial Director of Fargo, explains:
"AI doesn't remove complexity. The real opportunity is to help operators understand the wider impact of decisions across the network and respond with greater confidence."
As intermodal operations continue to evolve, the conversation is shifting from visibility and connectivity towards intelligence, decision-making and operational certainty.
The organisations that succeed will not necessarily be those with the most technology. They will be those who can turn information into intelligence, intelligence into decisions, and decisions into operational certainty.
That conversation - and the role Network Intelligence can play in supporting it - is one Fargo is continuing at Multimodal 2026, as part of its vision to become the Operating System for Intermodal Logistics.
Fargo Group is exhibiting at Multimodal on stand 4000
