What’s possible in logistics with AI agents

An AI agent in supply chain is closer to a junior colleague who works the overnight shift. It reads emails, checks systems, contacts carriers, updates records, and follows the procedures your team has agreed on. It does this continuously, across every shipment, without getting tired or forgetting a step. The difference between that and a chatbot is the difference between a tool that waits on you to give commands to fix problems and one that handles them.

Three tiers of automation, and why the differences matter

Supply chain technology has gone through a few waves of automation, and it helps to be specific when assessing your needs.

The first tier is rules-based automation. If a shipment status changes to “delivered,” update the TMS. If a carrier hasn’t provided a location update in six hours, send an alert. If-then rules work well for structured, predictable scenarios where data flows through a single system.

The second tier is copilots and chatbots. Ask a question, get an answer. Useful when someone needs to pull up information quickly, but reactive by design. They wait to be asked.

AI agents are a third tier. They monitor conditions across multiple systems, decide what to do based on context, take action, and record the outcome. They can read an email from a carrier in German, extract an updated ETA, cross-reference it against the delivery appointment, and trigger a reschedule if needed. No one prompted them. They identified the problem and worked through it.

Road freight is where this becomes especially relevant because the coordination work happens across carrier portals, email threads, scheduling systems, ERPs, and phone calls. Rules-based automation breaks down when there’s no single system of record. Agents work across the seams between systems, the same way a human coordinator does.

A delayed shipment, start to finish

A full truckload shipment from a manufacturing plant is running late to a distribution centre. In most operations today, someone notices the delay in a tracking system, or worse, gets a call from an unhappy customer. This requires checking the carrier portal, emailing the dispatcher, waiting for a reply, calling when the reply doesn’t come, updating the TMS manually, notifying the customer service team, and adjusting the receiving appointment. That process can take hours. Sometimes a full day if the carrier is slow to respond.

An AI agent handling track and trace works through this differently. It detects the delay from real-time tracking data and cross-references it against weather, traffic, and historical patterns for that lane. It contacts the carrier automatically via email, in the carrier’s language if needed, and requests an updated ETA. When the response comes back, the agent extracts the relevant information, updates internal systems, notifies the customer service team with a revised timeline, and triggers an appointment change at the receiving facility. The whole sequence takes minutes.

No human touched it until there was a genuine decision to make, like rebooking a high-priority load on a different carrier. The agent handled the coordination. The human handled the judgment call.

This is running in production at scale. One European-headquartered multinational consumer goods company reported that what used to take 90 minutes per inquiry now takes seconds, freeing hundreds of hours annually. Those hours went to customer relationships and process improvement, not email chains and data entry.

The scheduling problem no one has solved with software

Anyone managing delivery appointments across multiple receiving facilities knows the pain. Each facility has its own process. One uses a portal. Another requires an email to a specific address with a specific format. A third wants a phone call. Multiply that across dozens or hundreds of facilities, and you have a team of people spending their mornings on the phone, in email, and toggling between browser tabs.

Traditional scheduling software helps if everyone is on the same system. They rarely are. The fragmentation is the reason scheduling has resisted automation for so long.

AI agents manage the full appointment lifecycle across every channel. They read incoming emails from carriers and receivers, log into portals, book slots based on real-time ETAs, adjust when things change, and send confirmations back. They can do this because they read and respond in context, the same way a human coordinator would, but without the bottleneck of a single person handling 50 facilities.

The agent isn’t replacing the scheduling team. It’s absorbing the routine bookings, the confirmations, the reschedules that follow a standard process. The team still handles the exceptions that require a conversation, a negotiation, or a judgment call about priorities.

Why European road freight is well suited for this

Several characteristics of European road freight make it a strong fit for agent-based automation. Cross-border operations add layers of documentation and coordination that domestic supply chains don’t face. Carriers communicate in multiple languages. CMR documentation requirements create administrative work that scales with shipment volume. And receiving facilities across different countries often have incompatible scheduling systems and communication preferences.

An agent that can read an email from a Spanish carrier, extract shipment data from a French bill of lading, and book an appointment through a German retailer’s portal is doing the kind of work that currently requires multilingual coordinators or multiple regional teams.

There’s also a customisation angle worth mentioning. The first generation of supply chain AI agents were pre-built to handle common workflows like track and trace or appointment booking. The next step is the ability to take a company’s specific operating procedures and translate them into agent-readable instructions that can be deployed in days, not months. For European operators with country-specific compliance requirements or carrier communication norms that vary by region, the technology adapts to how they work rather than requiring them to change their processes.

What this changes, and what it doesn’t

AI agents are not replacing logistics teams. The people who manage road freight operations bring judgment, relationships, and institutional knowledge that no technology replicates. What agents do is absorb the repetitive coordination work that keeps those people from using their expertise: the carrier follow-ups, the status checks, the appointment booking, the data entry between systems.

The companies deploying these agents today started with a single workflow, usually delay management or appointment scheduling, proved the value, and expanded from there. Agents earn trust through results on a bounded problem before they’re given more responsibility.

For European road freight operators, the most productive starting point is to look at which daily tasks are high-volume, rules-driven, and spread across multiple systems and communication channels. Those are the workflows where agents deliver immediate value.