When your truck breaks down on the highway, the repair bill is only part of the problem. Deliveries are delayed, drivers are stranded, towing costs are higher than repairs, and customers are calling.
The National Transportation Research Institute estimates that lost productivity costs the industry more than $25 billion annually. One roadside breakdown can cost between $450 and $760 in direct repair costs, followed by towing, rental replacement, and lost revenue. Fleet operators have long treated these losses as a fixed cost of doing business.
Predictive maintenance using artificial intelligence is beginning to rewrite that calculation.
Turn sensor data into early warnings
Commercial trucks already generate vast amounts of data. A typical heavy-duty vehicle generates more than 25,000 data points each day from onboard sensors that track engine temperature, oil pressure, brake wear, and fuel consumption. Until now, much of that data sat unused in disconnected maintenance systems. For the first time, fleet managers learn that problems occur when trucks become stuck.
AI changes the model. Machine learning systems ingest real-time sensor readings along with past repair records to identify combinations of signals that tend to precede a particular failure, often weeks before the failure occurs. The output is not raw data, but rather specific recommendations, such as service windows that fit a specific vehicle, specific component, or route schedule. Repairs move from roadside emergencies to planned store visits.
McKinsey estimates that AI-powered predictive maintenance can reduce maintenance costs by 10% to 40% and reduce downtime by up to 50%.
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OEMs and operators move in
Volvo Trucks North America announced AI-powered adaptive maintenance as part of its Blue Service Agreement in October 2024. This replaces fixed service schedules with intervals that are dynamically adjusted based on each truck’s actual usage (fuel consumption, idle time, oil condition).
Magnus Gustafsson, vice president of connected services for Volvo Trucks North America, said many fleets are over-maintaining their trucks, creating unnecessary costs. “By applying AI to optimize maintenance intervals based on truck specifications, operating conditions, and actual usage, our customers can maximize uptime,” Gustafsson said.
Volvo’s Uptime Center in Greensboro, North Carolina, monitors approximately 85,000 connected trucks across Europe, with experts reviewing AI-generated alerts and coordinating service visits before breakdowns occur. Volvo and Mack Trucks have developed a connected system that reduces the time required to diagnose faults by 70% and reduces repair time by 25%.
Due to this change, fleet operators are facing difficult times. According to ATRI’s 2025 Operating Costs Report, non-fuel operating expenses rose 3.6% in 2024 to the highest level ever, with average operating margins below 2% for most trucking sectors. Stefano Danelli, fleet ecosystem strategist at Fleetio, told PYMNTS that parts and labor costs are up more than 10% year over year. Fleets that retain vehicles longer to avoid replacement costs are absorbing an increasing hidden cost in the process: increased downtime.
Capital and Adoption Follow
Aircraft and mobility companies are directing working capital toward technology. According to a PYMNTS Intelligence study, 89% of fleet companies will be using at least one external working capital solution in 2024, with strategic deployment increasingly directed toward digital fleet management platforms and AI-based tools. Top performers realized an average profit of $15.6 million.
The main constraint for widespread deployment is data infrastructure. Many carriers are still running disconnected legacy systems, which means AI models don’t have access to the full maintenance history they need to make accurate predictions.
ATRI reports that the average number of miles driven between failures increased from 37,700 to 38,249 in 2024, and attributes preventive maintenance practices to being a key factor. Whether AI systems can push this number even further depends on how quickly fleets close data gaps.
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