AI in Transport Management Systems (TMS): What's Really New?

Transport Management Systems have been optimising routes, consolidating shipments and monitoring deliveries for years. So what does Artificial Intelligence actually add?

It's a fair question. Many capabilities now associated with AI have existed in TMS solutions for decades, powered by optimisation algorithms, business rules and automated workflows.

The real opportunity is not necessarily doing something entirely new. It's making existing processes more predictive, adaptive and intelligent.

Here are four practical examples.

1. From traditional planning to learning from experience

Traditional TMS solutions optimise transport plans using parameters such as costs, capacity, routes and delivery windows. Many also use historical data to improve planning assumptions.

AI can take this further by recognising more complex patterns in transport performance.

Imagine a carrier that consistently takes longer than expected on certain routes or days. Machine learning can analyse historical shipment data to predict more realistic transit times, rather than relying primarily on standard times or simple averages.

The added value: More accurate planning assumptions, fewer unexpected deviations and potentially better delivery reliability.

2. From detecting delays to recommending solutions

Your TMS can already alert you when a shipment is running late.

But what happens next?

AI can help assess the impact of a delay, identify which deliveries need immediate attention and recommend corrective actions.

For example, an AI agent might suggest rescheduling a delivery appointment or evaluating alternative transport arrangements. Some solutions can even initiate certain follow-up actions automatically.

The added value: Less manual investigation, faster problem resolution and more effective exception management.

3. From automated carrier selection to predictive procurement

Traditional TMS solutions can already select carriers based on rates, contracts, capacity and predefined business rules.

AI can go further by learning from historical carrier behaviour and changing market conditions. It can predict suitable spot rates, identify carriers most likely to accept a shipment and even adjust offers based on their responses.

The added value: Faster spot procurement, fewer rejected tenders, less manual work and potentially lower transport costs.

4. From navigating screens to having a conversation with your TMS

Traditionally, finding information means navigating through screens, filters and reports.

With generative AI, users can increasingly ask questions in natural language.

"Why couldn't these shipments be planned, and what can we do about it?"

An AI assistant can help analyse the situation, explain possible causes and suggest solutions. Some solutions can even perform certain TMS actions through conversational commands.

The added value: Faster access to information, less administrative effort and better support for transport planners.

So, should AI be a key consideration when selecting a TMS?

Absolutely, but the value depends on how AI is applied.

Traditional optimisation and automation remain essential, while AI creates new opportunities to improve predictions, support decision-making and automate increasingly complex tasks.

The key is to understand which AI capabilities are relevant to your transport operations and what measurable benefits they can deliver.

And not every TMS offers these capabilities today, at least not with the same level of maturity.

When selecting a TMS, the important question is not simply "Does it have AI?" but "What does its AI actually do, and what measurable value will it deliver to our business?"

At TMSselection, we help companies evaluate TMS solutions independently, based on their operational needs, functional requirements and business objectives.