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AI tools speed diagnostics, but data quality still critical
Systems that run repair, parts data in parallel can save time on the shop floor, panelists say
TMC Chairman Mark Kennedy also is vice president of equipment for Knight-Swift Transportation. (American Trucking Associations)
Key Takeaways:
- Panelists said AI can help technicians access diagnostic information, parts availability and repair guidance more quickly.
- Speakers emphasized that accurate, well-managed data is essential for AI systems to deliver reliable results.
- Industry leaders said collaboration among fleets, OEMs and dealers is necessary to maximize AI’s benefits.
PITTSBURGH — As artificial intelligence moves into heavy-duty vehicle repair, industry leaders say the technology will not replace technicians. Instead, it could streamline repairs by getting them the information they need faster.
The discussion, titled “AI on the Shop Floor,” was part of The AI Summit at American Trucking Associations’ Technology & Maintenance Council Fall Meeting on Sept. 23.
Panelists framed the conversation around TMC Recommended Practice 1604, the industry benchmark for rapid repair assessment, which sets a two-hour target from vehicle arrival to an approved estimate. They illustrated the process through a fictional technician named Alex.
Under traditional processes, Alex must log a vehicle, diagnose the complaint, check parts availability and obtain authorization before any repair work begins. Matt Johnston, division head of commercial solutions at Design Interactive, said that model does not align with how people process information.
“We have to do things serially, and a computer will do that in parallel,” he said, noting that technicians must piece together information from fault codes, service manuals, bulletins and vehicle histories while “the clock is running.”
Sound data is crucial
Mark Kennedy, vice president of equipment for Knight-Swift Transportation and TMC general chairman, likewise believes diagnostic delays should not be attributed to technicians such as Alex.
“I think the problem is Alex getting the information he or she needs,” he said. “If your data isn’t good, Alex is going to be going down a bunch of different rabbit holes.”
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Kennedy described a scenario in which a vehicle’s diagnostic system reports an issue from the road, an AI system reserves a bay and sources parts, and a technician receives a likely repair plan before the truck arrives.
“When the truck actually shows up, Alex is being told, ‘This is what you need to do,’ ” Kennedy said. “I think that’ll take those two hours and shrink it down to actual repair time.”
Knight-Swift Transportation ranks No. 8 on the Transport Topics Top 100 list of the largest for-hire carriers in North America.
Rocco Marrari, vice president of sales at Pedigree Technologies, said embedded AI and telematics systems already can detect anomalies in real time.
“These systems pick up that blip,” he said. “Based on that truck, that model, that location, throughout the history of that life cycle ... it has an idea of what may be occurring. We have enough data to get you about 70% there.”
Finding technicians
On the dealer side, Tommy Slaymaker, director of fleet services for The Pete Store, said AI can also help match jobs with the right technicians and tools.
“We’re able to take the information and [see] the trouble codes, [figure out] what to do with it from here,” he said. “[We also know] what technicians are available next, the [best] success rate for these trouble codes ... and the right tools to do the job.”
Arpan Podduturi of Samsara examines how AI, telematics and onboard cameras are converging to protect drivers and reduce risk. Tune in above or by going to RoadSigns.ttnews.com.
Panelists also said AI could serve as an on-the-job coach, helping less-experienced technicians become more effective diagnosticians.
“With today’s AI,” Marrari said, “it can take a marginal technician and make them one that is a go-to technician ... they’re not just building it from scratch.”
Kennedy cautioned fleet professionals against viewing AI as a cure-all, noting that failures on brand-new equipment can still require deep troubleshooting and disciplined data management.
As the session concluded, speakers challenged fleets, OEMs and dealers to share data and align on standards so AI systems can reach their potential. Without that collaboration, they said, the prospect of faster, smarter repairs will remain only partly realized.
