Artificial intelligence is steadily finding its place at the heart of transport and supply chain operations. And it makes perfect sense: processes are becoming more complex, customer expectations are rising, and the amount of data generated every day is… enormous. In this environment, AI is no longer a gimmick. It’s becoming a real lever to work faster, smarter, and with far more visibility.
Before diving deeper, it’s worth resetting the fundamentals and understanding how AI can genuinely fit into daily transport operations.
1. The basics: AI, Machine Learning, Deep Learning, LLMs and autonomous agents
Artificial Intelligence: the starting point
“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: learning from data
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: taking it a step further
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: understanding language
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.
AI Agents: when AI takes action
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.
2. Why does AI fit in so naturally into the transport industry?
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.
3. Making AI Operational: a Seven-Step Method
- Clarify objectives
Define exactly what AI should improve: cost reduction, OTIF performance, carbon optimization, task automation...
- Organize data and make it reliable
Identify existing sources, check data quality and assign clear responsibilities.
- Set up an appropriate architecture
An API-first approach makes it easier to connect existing systems.
Using interoperability standards such as MCP helps systems and AI agents communicate seamlessly.
- Building and orchestrating agents
Each agent must have a clear mission: predicting an ETA, validating a document, managing a claim…
An orchestration layer ensures that agents work together, in the right sequence.
- Secure and reinforce compliance
Control access, isolate client data, enforce GDPR requirements, and monitor the quality of AI-generated outputs.
- Test, measure, adjust
A controlled pilot reduces risks and validates the relevance of each use case.
The key KPIs include ETA accuracy, CO₂ impact, dispute rates, productivity and customer satisfaction.
- Supporting teams
Train, communicate, reassure and highlight early wins. Adoption is critical for long-term success.
4. Transformation already underway
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.
Download the White Paper: “AI & Transport — How Artificial Intelligence Is Transforming the Supply Chain”
To explore these topics in more detail and discover real-world applications that improve operational performance, download the full white paper:
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