“AI” is an umbrella term that covers all techniques enabling machines to mimic human capabilities such as learning, detecting, reasoning, and planning. It’s broad, yes — but essential to grasp what comes next.
Machine learning analyses data, identifies patterns, and helps anticipate what is likely to happen.
In transport, it’s used to improve ETA accuracy, spot inconsistencies in documents, or track how prices evolve over time.
Deep learning goes further by learning directly from raw data such as images or scanned documents.
It is used, for example, to automatically read PODs or detect damages on goods.
The difference compared to machine learning is simple: ML learns but often requires supervision; deep learning learns “on its own” and handles much more complex data.
LLMs (Large Language Models) can answer questions, summarise information, analyse content, or synthesise data scattered across multiple systems. They have become a powerful support tool for transport teams, who can access reliable insights within seconds.
This is where things really get interesting. A LLM answers — an agent acts.
By connecting to TMS, WMS, ERP systems or booking portals, agents can book a slot, validate a POD, monitor a claim, or update a schedule automatically.
They work behind the scenes and take over tasks that used to consume a lot of time and attention.
The sector combines the perfect ingredients for AI. Data volumes are exploding. Documents are numerous, sometimes redundant. Exchanges involve many actors, each with their own systems. Customers are less and less tolerant of uncertainty. And environmental pressure pushes every player to optimize their flows.
In such an environment, AI helps automate, anticipate, smooth out operations and, ultimately, make everything more reliable.
Many concrete use cases are already live: automated POD validation, advanced ETA prediction, multimodal optimization, virtual freight agents and AI-assisted dispute management. The next frontier? Augmented Control Towers where humans and AI agents work side by side, each contributing in their own area of value.
To explore these topics in more detail and discover real-world applications that improve operational performance, download the full white paper: