As artificial intelligence transforms logistics, its impact depends on the application. Machine learning and generative AI have driven meaningful progress in dynamic predictions and automation. Agentic AI unlocked possibilities that were once out of reach. These systems don’t just analyze data or generate content — they act autonomously to achieve goals much like a human would.
Agentic AI is remarkably capable, provided it’s trained by logistics experts and operates with a deep, logistics-specific dataset. It can intelligently perform tasks across the lifecycle of a shipment, from price quotes to tracking updates to invoicing. It can handle tasks in different formats, whether that’s email, voice or documents. It can interpret different kinds of data, connect data from different places, determine what’s missing and go find it.
It’s also exceptionally fast and reliable. An AI agent for processing orders, for example, can process 20 orders simultaneously in 90 seconds — a person has to do it sequentially.
When it comes to applying agentic AI to different transportation modes, you’re solving for varying levels of complexity. The trucking industry is highly fragmented, meaning you’re potentially working with hundreds of thousands of carriers, but a truckload consists of just one customer’s freight. In the less-than-truckload market, there are only about 120 carriers — but trucks have to pick up multiple companies’ freight, which gets redistributed to other trucks once it gets to the terminal. For ocean shipping, a container’s journey has multiple legs from door to port to door. Even something seemingly as simple as a price quote requires an AI agent to consider more variables and apply more reasoning.
Looking ahead, you’ll see AI acting more predictively and proactively. As more shippers leverage an agentic supply chain, it will continuously think, learn and adapt. Supply chains will essentially be self-optimizing.