While some future sentient super-AI might come to the conclusion that there are too many people, most fleets, particularly in their maintenance departments, don’t have enough. Because of the more advanced vehicle systems, the job is only getting harder and more expensive to perform, despite every fleet striving to do and spend only what is necessary.

The American Trucking Research Institute (ATRI) found that maintenance and repair costs per mile rose 8.6% from 2024 to 2025 and 45% from 2019 to 2025. If that pace continues through 2030, maintenance spend per mile would nearly double over the decade, pricing out the countless trucking companies that opted for longer ownership cycles to mitigate new truck price increases. To survive in this landscape, fleets have to find a way to do more with less.

And this is where AI (not the human-killing kind) comes in. In its many forms—agentic, generative, and LLM, to name a few—AI has shown it can help maintenance departments do everything from speeding up repairs with more accurate diagnostic information to avoiding them through predictive insights.

According to Noregon’s 2026 Unpacking the Commercial Vehicle Diagnostic Market report, the majority of shops and fleets count accurate/effective troubleshooting and technician hiring as their top two issues.

No longer ‘emerging’

Increased adoption of digitization, paired with the convergence of telematics, remote diagnostics, and data analytics, has provided the perfect ecosystem for AI to thrive. And because AI acts as a force multiplier for innovation, those areas will continue to operate more efficiently as more AI is added. It’s not a stretch to imagine that AI tools will soon be the norm rather than the exception.

On the indie shop side, AI is already taking hold in a few key areas.

According to Fullbay’s latest State of Heavy-Duty Repair report, 19% of survey respondents currently use AI for diagnostic support, with another 61% saying they plan to in the future. On the customer communication side, 18% of shops use AI receptionists to retain otherwise missed calls, with 39% planning to in the future. While very few employ AI for parts/inventory management and predictive maintenance, more than 40% of shops said they would at some point.

Fleet leaders are coming around, too. According to research firm Escalent’s 2026 Q1 survey of over 1,000 fleet decision makers, AI was the only technology to hold its ground on the firm’s Fleet Technology Index. The FTI measures certain core and emerging technologies’ market readiness for adoption. Due to fuel prices, the economy, and regulatory changes, the overall index dropped 16% year over year. EVs’ score went down 8% year over year, and telematics 7%. AI, though, stayed nearly flat and had the highest overall score at 52. Last year it hit 53 after posting a 34 on the FTI in 2024.

“We tend to think of an index score of about 50 as sort of where it’s entered the mainstream consciousness,” said Lucas Lowden, an insights consultant in Escalent’s Automotive & Mobility group. “You could argue really that AI is no longer emerging—it’s here, and it’s in practice.”

AI on the move

Lowden pointed out that while other technologies require a substantial amount of human capital, such as a dedicated professional to monitor and navigate a telematics dashboard, AI can enhance human workers, even at smaller fleets, in a scalable way.

“AI is approachable,” he added. “You can start small and work up and scale it. And it can be used to solve a lot of different problems and challenges.” Lowden said this is particularly advantageous for “fleet decision makers who historically aren’t necessarily tech-oriented.”

The convergence of telematics, data analytics, and AI has also created a “three-headed monster,” Lowden noted. And with fleets feeding this monster more data, it becomes more effective at taking a bite out of downtime.

Sean Cornwell, a lead mobile diesel technician at Cox Fleet, sees this every time he goes out on a job. Aside from the usual repair tools and his Autel scan tool, the most important thing he brings to jobs is his smartphone. From here, he and his colleagues can access Cox Fleet’s AI-embedded app loaded with features to cut down time onsite.

The repair vendor’s agentic solution provides prompts to quickly build repair estimates while another AI solution analyzes diagnostic trouble codes, suggests root causes, and provides a step-by-step repair plan.

And he said he can start work faster because the customer approval process is streamlined: “As quick as like five minutes after I submit [the work order], they’re getting emails with PM forms and recommended repairs…it saves a lot of time and cuts a lot of guesswork out of it.”

The ability to access maintenance history and have a list of probable causes also aids Cornwell’s productivity. One important factor is being able to see the operating conditions when the DTC appeared.

“I can go in on every unit and see previous repairs, which helps eliminate some of that additional troubleshooting time, especially if it’s an on-and-off problem, or if the problem only happens going 50 mph down the road,” he offered.

Cornwell noted that when chasing electrical issues, the ability to see previous readings and specific fault codes saves time by helping narrow his troubleshooting window and more quickly pinpoint the real issue.

The drawback is that Cox’s AI only accesses internal work orders. If, for instance, a trailer light was swapped by another repair vendor and that led to an electrical issue, Cornwell would still have to go through the conventional motions to find it.

He does strongly believe that the industry will someday advance to a point where a customer will have the option to share all of their repair info with whatever vendor they want to get better results.

Even so, while Cornwell sees the value in really tough jobs, he doesn’t envision highly complex AI-assisted diagnostics helping on every job.

“At the end of the day, are we probably going to sift through that information on every job? Probably not,” he surmised.