How AI could streamline your trucking operations in 2025

Companies have used artificial intelligence for years, Burak Cendek, partner at Autotech Ventures, told FleetOwner. A great example is Microsoft’s Cortana (now Co-Pilot) and Apple’s Siri. Then, according to Cendek, more “generalist investors” began investing in AI for specialized industries: “One for legal applications, one for accounting applications,” Cendek said. “Then they discovered that AI is applicable to a lot of processes and workflows in logistics.”

AI has become a dominant force in logistics because it can simplify and streamline workflows. Fleets are also willing to pay more for the technology when they see value in its use. That value stretches right to their workforce budget.

When a fleet uses AI to simplify and streamline tedious tasks, such as manual data entry, the fleet’s employees have more time to focus on other jobs within the business. More so, when a fleet using AI no longer needs to outsource tasks to a call center, for example, that savings goes directly to their bottom line.

“People are not going to pay millions of dollars for software,” he said, “but when you go into AI, you are starting to tap into this workforce budget ... it's easier for people to understand that ‘Oh, instead of me paying a call center a million dollars, I'll pay AI to do the same job, but less.’”

Because fleets are willing to pay more for their technology and AI services, “that ceiling of how much [a tech company] can charge for software is not applicable anymore,” Cendek said, which now also enables technology companies to invest more into their offerings.

See also: AI for trucking: Ready for prime time?

AI integration vs. translation

Truck tech companies often tout integration among different systems, advertising as one system that can integrate all fleet operations into a single pane of glass. In many ways, AI can help do the same.

While AI could be seen as the ultimate integrator—even Cendek said that with AI, integration problems disappear—it actually acts more as the ultimate translator, according to Matt Salefski, co-founder and CEO of Rectangle, a company that builds AI agents for fleets.

To unpack AI as the ultimate translator, Salefski offers this example: A fleet driver might input a freight delivery time into a TMS, which is recorded in the TMS as “‘Delivered At Date’ or something similar,” he explained. “If we’re integrating with a shipper, in their system, it might be ‘Freight Delivered Time,’ or something like that.

“What AI can do is help reconcile that and say, ‘OK, well, this is probably the same field,’ which is basically what humans do when they’re building out legacy integrations.”

In this way, AI translates a term with a single meaning into a different term with the same meaning. This ability to translate now increases the capabilities of AI in logistics. 

But AI capabilities go even further, simplifying carrier and shipper customer portals.

“We now live in this world where every carrier has a customer portal, every broker has a customer portal, every shipper has a carrier portal,” Salefski said. With AI, “one of the things you can do is essentially make these systems, or these platforms, talk to each other—just like how a human is taking information from their TMS and putting it somewhere else, we can do that same thing ... by leveraging AI.”

What else makes AI a translator as opposed to an integrator is that instead of simply moving data from one system to the next, AI can “build in business logic to automate” the next task. For example, if a fleet prefers that intrastate tenders always be routed to a specific worker and interstate tenders always be routed to someone else, AI can recognize these patterns and eventually automate the task so that human intervention isn’t necessary.  

AI can also develop “business logic.” This happens when a fleet manager or administrator determines an initial list of rules and parameters for the AI and then lets the AI’s machine learning take over from there.

One example is if a customer prefers only to note timestamps in 15-minute increments. As fleet personnel manually input times—such as 5:03 into 5:00 and 5:09 into 5:15—the AI will learn the patterns and begin to assign the timestamp based on those initial inputs automatically.

But again—AI can do much more.

At the end of 2024, FleetOwner covered ways fleets are using artificial intelligence in their operations today, and if 2025 is the year that AI becomes the most-used technology in the trucking industry, we can be sure there will be more coverage of AI in trucking to come.