How AI Is Transforming Truck Maintenance

2025 will be remembered for many things, but there's no doubt that the widespread adoption of artificial intelligence by non-tech people will be one of them — including in the truck maintenance shop.

AI was suddenly everywhere all at once. It popped up in sketchy YouTube videos. It cut a hit song. An “AI actress” debuted. Every single product seemed to have something “AI” going for it. Businesses all over the world began planning AI strategies and drawing up official guidelines.

As artificial intelligence moves from hype to practical deployment, trucking fleets are beginning to use AI-driven maintenance systems to improve shop efficiency, reduce downtime, and enable predictive maintenance.

One of them is Pitt Ohio, which started evaluating technology and AI in the shop late last year.

“We're looking at mobility for the technicians to get away from paper and go to tablets," says Jason Dolence, manager of parts and warranty.

"I think the AI and the mobility are two big initiatives that we're working on in 2026.”

Why AI Adoption Accelerated Across Trucking in 2025

One of the sharpest technology specialists in trucking today is Brent Ellis, vice president of operations at Decker Truck Line and a 2025 HDT Truck Fleet Innovator.

But he takes some of the AI hype with a grain of salt. It seems every time you turn around, someone is announcing a product for trucking fleets that has new AI features.

“Adding AI to something isn’t earth-shattering news, in my opinion," he says. "In a lot of cases, what they’re promoting isn’t really AI. It’s just updated computer programming based on identifiable patterns.”

Nevertheless, artificial intelligence shows great promise in truck maintenance.

AI will soon be able to give fleet managers powerful insights into operations ranging from single trucks to large fleets. This information will allow maintenance professionals to fine-tune vehicle specs and deliver true predictive maintenance in a way that no human ever could.

What AI Really Means for Fleet Maintenance Operations

In fact, those things are already happening, according to David Begin, marketing director at DataDis, a fleet maintenance software developer and supplier.

Begin says AI-powered maintenance systems are absolutely making a significant impact on fleet operations in 2025.

“The industry has moved past AI being a buzzword at industry trade shows,” he says. “Many key players in the maintenance technology space have released tangible AI tools that fleets are using to improve.”

These tools can provide fleets with predictive maintenance applications, AI-driven invoicing, parts ordering and inventory optimization, and deeper, AI-driven analytics.

“All of these tools are helping fleets of all sizes improve planning and drive efficiency in their shop floor operations,” Begin adds. “And they help fleets save hard dollars by reducing downtime and eliminating repetitive manual tasks for their staff.”

Ellis says he’s been having lots of conversations with industry insiders and suppliers about both the latest generative AI as well as machine learning, an earlier form of artificial intelligence.

“As yet, no one has answered the basic questions I have about this technology,” he says.

“How do you ‘re-teach’ a machine when certain requirements change? And how do you know for certain that the new information being uploaded is valid and useful?”

Gregg Mangione has an answer. The executive vice president of maintenance for Penske Truck Leasing, one of the largest commercial vehicle leasing and maintenance providers in North America, explains that "across a given maintenance landscape, AI draws on a wide range of inputs, including operational and repair records, vehicle [telematics] data, and training and knowledge content, rather than relying on telematics alone.

“Bringing those inputs together allows intelligence to be delivered directly into maintenance workflows,” he says.

“At Penske Truck Leasing, AI is applied deliberately across maintenance operations to improve shop efficiency, decision-making, and customer experience, supported by strong data, analytics, and operational expertise.”

How AI Builds on Existing Fleet Maintenance Data

The first thrust of AI in the shop is through machine-learning-driven capabilities that support proactive diagnostics and guided repair, according to Mangione.

By connecting multiple maintenance and operational data sources, including vehicle IoT data, AI maintenance systems can identify potential issues earlier, reduce road calls and unplanned downtime, and improve shop efficiency by supporting technicians during diagnosis and repair.

AI is an extension of how maintenance operations already work — not a sudden technological shift, he says.

“Maintenance has long been data-driven. What has evolved is the ability to connect and apply that data at scale.

"By bringing together operational and repair history, warranty and inspection records, training and knowledge content, and vehicle IoT data, AI turns information that already exists into practical guidance inside the shop.”

From a shop efficiency standpoint, Mangione says, this means less time spent searching across systems and more consistent execution of repairs.

When intelligence reflects the full operating context rather than a single data source, it supports better decisions, repeatable processes, and more predictable outcomes across fleets of any size.

For fleet maintenance managers, this translates into fewer road calls, more predictable repair planning, and better technician productivity.

Integrating AI into Existing Maintenance Systems

These capabilities are designed to be integrated into existing maintenance systems rather than deployed as separate tools, adds Rohit Talwar, senior vice president of software engineering at Penske Leasing. He says that approach supports smoother adoption and operational consistency by fleets.

“The learning curve is intentionally minimal because these capabilities are embedded into existing workflows,” Talwar says.

“Technicians and supervisors are not asked to learn how to interact with AI or build prompts. The intelligence is generated behind the scenes and presented within the systems people already use.”

From an adoption standpoint for fleets, Talwar says the bigger focus is on change management, rather than training.

“Experience across other AI-assisted initiatives inside Penske has shown that trust and clarity matter more than instruction,” he explains.

“People need to understand how the technology supports their work and where human judgment remains in control. When that foundation is in place, adoption follows naturally.”

How Pilot is Using AI for Better Specs, Diagnostics, and Predictive Maintenance

One of the longstanding frustrations with all the electronics and computer brains on today's trucks is that in many cases, you have to deal with a multitude of different manufacturers and suppliers to decipher all the data.

“Every OE has its own maintenance platform,” says Brent Hickman, director of equipment, maintenance, and fleet sales at Pilot Flying J, which operates one of North America's largest fuel tanker fleets. "And they’re only going to give you limited information when you’re diagnosing a problem. You often get a specialized numeric code – but that doesn’t really tell you anything useful.”

He's been working with a number of companies as Pilot's private fleet works to integrate AI into its maintenance operations. One of them is the cloud-based telematics and fleet management platform Samsara. And, Hickman says, Samsara’s AI maintenance program does recognize those long and complex codes. 

“Samsara’s AI will ‘touch’ the OEM database, or it'll build recognition of that code on its own,” he explains.

With one click on a fault code, Hickman says, Samsara's system will give the technician a general overview of the part or issue in question.

Then the technician can ask AI in real language what the impact of that code is in terms of a time frame for a potential failure.

With those instructions, AI then pulls any relevant information from its own internal database. It also searches online for additional information. It can then return to the technician with detailed information on the severity of the issue, potential problems it might cause in the future, and a troubleshooting tree to diagnose and trace it to the root cause of the failure.

How AI Supports Technicians Instead of Replacing Them

The AI revolution will forever change the way most of the world works, Begin says, including truck maintenance and repair.

“Imagine a world where you know what will break before it happens so you can react before you have an expensive roadside incident,” he says.

“You can have just the right parts, at just the right time, in just the right place. Your technicians can have all the information they need to do their jobs at their fingertips, or better yet, just by asking an AI agent in real time.  Processing invoices is fully automated. You can make deeply informed decisions about the type of equipment to purchase or when to sell an asset before doing any more maintenance without doing any manual research.”

Why Diagnostics, Telematics, and Maintenance Are Converging

As with any new technology, there will be growing pains as AI moves to the forefront of fleet operations.

That’s a reality driven home by a just-released white paper from Noregon Systems, Unpacking the Commercial Vehicle Diagnostics Industry 2026.

One of the most important structural shifts highlighted in the report is the continued erosion of traditional boundaries between diagnostics, service information, telematics, and shop operations.

The “diagnostics-first” service model Noregon outlined in its 2025 white paper is accelerating toward a more integrated ecosystem. This is a shop environment where data flows seamlessly across vehicles, tools, shops, and fleets.

This new dynamic is already changing how maintenance gets done, according to Noregon. 

Technicians now have much better visibility into vehicle health than ever before. As a result, shops are moving from reactive repairs to predictive and prescriptive maintenance. And fleets are increasingly using data to schedule service proactively, rather than responding to roadside failures.

At the same time, rising “sensorization” and distributed electronics on trucks are pushing vehicle complexity to new levels. 

Advanced emissions systems, telematics architectures, and electronic control units are just a few examples of vehicle systems that now require deeper technical expertise and more sophisticated tools to diagnose and repair.

In 2016, Noregon said, shops averaged roughly six technicians per diagnostic tool. By 2025, that figure had dropped to 2.5 technicians per tool. This reduction in technicians is placing unprecedented strain on service networks and making productivity gains essential.

How AI Is Reshaping the Fleet Maintenance Service Model

Faced with labor shortages and more complex vehicles, fleets and service providers are turning to automation, remote diagnostics, and AI to close the gap, the white paper found.

Noregon sees accelerating adoption of these technologies as one of the defining trends of 2026. Remote diagnostics reduce unnecessary shop visits. Automated workflows cut administrative time. And AI-driven insights help technicians identify root causes faster and avoid misdiagnosis.

Just as important is the emergence of “platformized," “single-pane-of-glass” service ecosystems. Instead of juggling disconnected tools, shops and fleets increasingly want unified platforms that deliver accuracy, reliability, and operational coherence.

In a margin-constrained environment, operational efficiency isn’t a nice-to-have — it’s the difference between surviving and falling behind,

But, interestingly, for the first time ever, the technology itself will help humans define how it is used and what its boundaries are.

How AI Improves Fleet Operations Beyond the Maintenance Shop

It’s important to understand that AI will affect every single aspect of running a fleet operation, experts say. And those benefits will extend beyond the shop and into the front office, as well

Manginone says back-office support teams will also benefit from AI technology. This includes 24/7 coordinators and other roles supporting customers when a unit is down on the road.

AI will deliver faster access to accurate information, which helps reduce cycle time and improve response consistency. 

There is also a downstream value for customers, according to Mangione. That’s because maintenance data is a major component of what customers use in fleet performance tools, and AI will improve repair data quality at the point of entry. This, in turn, strengthens the value of maintenance reporting and analytics. It also supports customer experience through improved billing accuracy and documentation quality.

AI will also transform how drivers work, says Begin.

He says drivers will see improvements from operating safer, more reliable vehicles. And should there be a roadside incident, new AI-driven tools like Rescue from DataDis are dramatically improving the process for drivers, fleets, and vendors. 

Dispatchers will help drivers by optimizing routing decisions with additional context and insights from the maintenance and predictive analytics platforms. 

And fleet owners will quickly achieve ROI thanks to lower fuel costs, lower operational and shop costs, and extended asset life. 

And finally, Begin says, environmental teams see reduced emissions, supporting sustainability goals. 

In short, he says, AI will enhance overall productivity, coordination and profitability for every operation in a fleet – from top to bottom. 

And that’s what AI ultimately is: A new partnership between humans and intelligent machines. And it’s a partnership that promises to take commercial vehicle maintenance to new levels of efficiency and productivity in the very near future.

Editor's Note: HDT Editor and Associate Publisher Deborah Lockridge contributed to this story.