Special Coverage
`;
bodyParent.insertBefore(welmod, bodyParent.firstChild);
window.parent.document.querySelector(“#myModal”).style.display=”block”;
const scriptElement = document.createElement(‘script’);
scriptElement.textContent = ‘document.getElementById(“destination”).setAttribute(“value”, location.pathname);’;
document.body.appendChild(scriptElement);
window.parent.document.querySelector(“.modalclose”).addEventListener(“click”, function(){
window.parent.document.querySelector(“#myModal”).style.display=”none”
});
window.parent.document.querySelector(“.modalclose2”).addEventListener(“click”, function(){
window.parent.document.querySelector(“#myModal”).style.display=”none”
});
window.parent.document.querySelector(“.modalclose3”).addEventListener(“click”, function(){
window.parent.document.querySelector(“#myModal”).style.display=”none”
});
} else {
// Check if the ad was shown recently
var lastShownTime = localStorage.getItem(‘lastShownTime’);
var currentTime = Date.now();
var timeDifference = currentTime – lastShownTime;
var bodyParent=window.parent.document.querySelector(“body”);
// if (!lastShownTime || timeDifference >= (180 * 60 * 1000)) { // 240 mins aka 4 hours 180 for 3
// Show the div
g=document.createElement(‘div’);
g.setAttribute(“id”, “div-gpt-ad-1689715929001-0”);
g.setAttribute(“style”, “height:0;”);
g.setAttribute(“class”, “text-center”);
bodyParent.insertBefore(g, bodyParent.firstChild);
// Store the current time in localStorage
localStorage.setItem(‘lastShownTime’, currentTime);
// }
}
Business
Technology
Equipment
Safety
How AI tools target fleet maintenance inefficiencies
Systems can prioritize repairs, automate paperwork and preserve technicians’ wrench time
September 24, 2026 1:47 PM, EDT
Brevik and Dieterich on the panel. (Karen Foote/American Trucking Associations)
Key Takeaways:
- Experts at TMC’s AI Summit on Sept. 22 said AI could prioritize fleet repairs, automate administrative tasks and increase technicians’ productive time.
- AI tools can analyze fault codes, histories, invoices and parts data, while one invoice system achieved about 92% accuracy, a panelist said.
- Fleets should target key business problems, require measurable returns and test vendors’ claims before investing in AI systems, panelists said.
PITTSBURGH — Artificial intelligence could help fleet maintenance teams prioritize repairs, reduce administrative work and make better use of technicians’ time.
Technology experts offered that assessment during American Trucking Associations’ Technology & Maintenance Council AI Summit on Sept. 22.
During the “AI 101: What Fleets Need to Know” panel, speakers described how AI-enabled maintenance systems can turn fault codes, repair histories, invoices and parts data into daily decisions for shop managers, technicians and parts personnel. They also emphasized that the technology is intended to support, rather than replace, maintenance professionals.
Greg Dieterich, president of new products and product specialists at Samsara, illustrated the opportunity through three fictional maintenance employees: Amber, a shop manager; Marcus, a lead technician; and Pete, a parts manager.
In a traditional workflow, Amber arrives early to review handover notes, emails, driver vehicle inspection reports and deferred repairs before assigning work. The process can elevate the loudest request rather than the highest fleet risk. She also spends time entering and validating invoices from outside repair providers.
Dieterich said AI could analyze live fault codes, inspection reports and deferred work overnight, then present a prioritized view of fleet risk and an optimized schedule for preventive and reactive repairs. The system also could match jobs with technicians based on their skills, certifications and availability, then create work orders automatically.
Document-recognition tools could ingest outside repair invoices, check for overcharges or missed warranty coverage and flag exceptions for review.
“The art of walking in an hour and a half early to figure all this stuff out from all these different data sources, that goes away with AI,” Dieterich said.
For Marcus, the lead technician, Dieterich focused on reducing the steps that pull an experienced employee away from repairs. A mobile app could organize assigned work by bay and ensure required parts and special tools are staged in advance.
Voice tools could allow a technician to update a work order without leaving the bay or typing at a terminal. AI could convert spoken observations into structured repair records. Digital parts requests could further reduce trips to the parts counter.
“So there is no real downtime, no real inefficiency there,” Dieterich said. “It is all wrench time, and it makes his life way easier, too.”
On the parts side, predictive tools could replace sticky notes and manual counts by setting minimum and maximum inventory levels based on consumption, current fault trends and expected demand. AI also could prepare draft purchase orders for the parts manager, Pete, to review and scan packing lists for quantity or item mismatches.
Together, those tools could help a shop manager prevent a roadside failure and recover warranty dollars, enable a technician to complete repairs without touching a keyboard and allow a parts manager to clear a reorder queue in a fraction of the time.
From alerts to maintenance decisions
Justin Brevik, senior strategic account adviser at Fleetrock, said labor shortages, rising costs and reactive maintenance make those efficiency gains increasingly important.
He described AI as a way to turn maintenance from a cost center into a competitive advantage through several foundational applications. An AI invoice-import tool can scan outside repair invoices and capture parts and labor line items in about 15 seconds. Brevik said such a system can achieve about 92% accuracy, leaving employees to review and correct the remaining exceptions.
Automated purchase order and invoice matching also can compare current prices with past purchases, flag increases and give fleets information to challenge or renegotiate vendor pricing.
Repair-order assistants can answer technicians’ natural-language questions and provide step-by-step guidance, including required parts, for a specific engine or repair. Full fault-code analysis can move beyond a simple dashboard alert by estimating time to failure, repair cost and expected downtime.
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.
Predictive maintenance models can combine active fault and check-engine data with repair histories and broader year, make and model trends to forecast component-level failures. Analytics assistants can then allow managers to ask plain-language questions rather than build complex reports.
“With today’s systems, you can simply go in and ask, ‘What trucks are costing me the most in tires this year, and what tire is performing the best in my application?’ ” Brevik said.
Dieterich advised fleets to identify their three most important business problems, such as insurance costs, preventable crashes or unplanned downtime, before selecting an AI project. The investment should be tied to a clear return within a defined period, he said.
“You have to anchor everything in value,” Dieterich said. “If you are going to invest in AI, you have to see a return.”
Brevik offered a similar warning about vendor claims. Fleets should test products marketed as AI and require suppliers to demonstrate measurable gains in efficiency or operating performance.
The maintenance case for AI, the panelists said, rests on practical gains: better repair prioritization, less clerical work, more complete records, stronger parts control and more time for skilled employees to perform the work fleets still need people to do.
“We are not replacing maintenance professionals,” Brevik said. “We are still going to need them all.”
Cummins Expects X10 to Lift Heavy-Duty Truck Engine Share
