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Arguably the top buzzword for 2026, artificial intelligence is no longer a hypothetical concept in maintenance operations. Its promise of sharper diagnostics, tighter planning and smarter spending is now showing up in the diagnostic bay, the back office and the strategic toolkit of maintenance leaders.

As the trucking industry gains familiarity with AI, decision-makers increasingly see it not as a mysterious black box but as a set of practical tools — ones that help shops diagnose faster, plan smarter and manage spend more tightly, provided they are deployed with clear goals and good data.

“To accurately fix an issue, you need that correct tool,” said Chuck Ralston, director of Truck Care Academy and mechanical services at Love’s Travel Stops. “AI is no different. ... AI is not a black box. It’s a toolbox.”

Ralston and other fleet maintenance and technology leaders discussed AI’s emerging role during a technical session at the Technology & Maintenance Council Annual Meeting and Transportation Technology Exhibition in Nashville, Tenn., in March.

Smarter Diagnostics

Panelists agreed that AI’s near-term power lies in shifting shops from reactive fault handling to proactive maintenance.
Sid Singh, chief revenue officer at Intangles, detailed how digital twins built from J1939 data can spot problems before dashboards light up. The idea, he noted, traces back to aerospace.

A telematics device, Singh explained, gathers about 400 data points per vehicle to create a live virtual engine in the cloud. Algorithms then monitor subsystems — such as aftertreatment, battery, alternator and air intake — for early signs of suboptimal behavior.

“Digital twin is a concept that is used by NASA,” he said. “They’ve been using it for the last 35 years, simply because they cannot go to a distant planet. … They had to create a data-driven model of that ecosystem to simulate their space travels.
“There is no check engine light on the dashboard of the driver. But there is a magic wand which is telling me that the vehicle is not performing optimally because something is not right in that particular ecosystem.”

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Ken Sills, director of engineering at Diesel Laptops, described a complementary “human in the loop” machine learning approach. His system flags anomalies from continuous telematics data and routes them to ASE-trained experts before any recommendation reaches a fleet technician.

The technician then makes the final decision, he said, which trains the model and improves accuracy over time. For maintenance leaders, the payoff is fewer false alarms and more actionable alerts.

“We don’t send [all the data] to the end-user because it’s too low-precision,” Sills said. “[The ASE experts are] looking at the anomalous data and flagging it as, ‘This is a real problem,’ and ‘This one was just an anomaly.’ ”��

Technician Gap

Throughout the session, panelists connected AI directly to the technician skills challenge.

Maryam Khan, founder and CEO of Axle Mobility, framed AI as a knowledge transfer engine for maintenance organizations.
“We all have the techs at the shop. They are the best, and that’s where all the knowledge is, but that is hard to document,” she said. With AI-supported documentation tools, she added, shops have a unique opportunity to capture that knowledge before veteran techs retire.

Khan emphasized that well-designed AI workflows can reduce cognitive load on newer technicians and improve documentation quality without removing the need to understand systems.

Chris Davies, founder and fleet strategy consultant for intelFleet Solutions, said AI can help narrow the experience gap by giving less seasoned technicians better guidance. “It empowers technicians and gives them the tools they need to actually learn faster instead of flipping through manuals.”

Ralston cautioned, however, that shops must ensure AI remains an aid, not a crutch.

“We have to be careful that we’re not dumbing it down to where they don’t truly understand what they’re doing,” he warned. “We still need that understanding of the system.”

Clean Data

On the administrative side, Khan argued that one of AI’s most immediate wins lies in automating repair orders and invoices.
“Think about all the paperwork in your fleet, the purchase orders, parts requisitions,” she said. “As fleets and service providers, we deal with a lot of paperwork.”

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Her company focuses on making the repair order the “atomic unit,” cleaning data at the point of entry with features such as shop-tuned voice to text, automatic VMRS coding and a live repair order coach that flags missing details or warranty indicators. The goal, she stressed, is trustworthy data, not dashboards for their own sake.

“We don’t want dashboards. We want clean data first,” she said.

Sills reinforced that the machine learning itself is the easy part.

“That is at most 5% of the work that my data scientists do,” he said. “Most of what they’re working on is asking the right question and cleaning the data.” For most managers, that translates directly into better visibility on cost per mile, component life and vendor performance.

Cultural Shift

Ultimately, panelists framed AI not as a stand-alone strategy but as a set of tools that must be embedded into existing processes and culture.

“I like to think AI is a tool. It’s not a strategy,” Davies said. “It’s a tool that you can embed in your process and in your procedures, not actually replace it.” He encouraged maintenance leaders to identify technicians who embrace AI and can help demonstrate its value to more skeptical veteran technicians.

Several panelists agreed that this stage of AI in the maintenance shop is best described as democratization — AI is not a single person’s responsibility but rather a broader change management challenge across the shop. Ralston summarized the moment by saying the competitive edge will go to maintenance organizations that can pair expertise with the right AI tools, on the right problems, at the right time.

“AI is not replacing industry expertise,” he said. “It’s changing how that expertise is applied."