Fleets' reliance on AI continues to grows, but has long way to go

Artificial intelligence (AI) is gaining a foothold in the commercial vehicle maintenance segment, from AI-powered dash cams that monitor driver fatigue and distraction to generative AI solutions that can help fleets generate ads to hire technicians. But according to Fleet Advantage’s latest benchmark on heavy-duty truck fleets’ use of AI, while interest in this technology is sustained, particularly for predictive analytics and maintenance scheduling, confidence and adoption levels still have some growing to do.

Fleet Advantage conducted its survey in March 2025, specifically examining organizations with heavy-duty Class 8 trucks and how they have (or have not) implemented AI in all aspects of their operations.

AI adoption

For general adoption, the company found that no fleets had fully integrated AI in their operations, though 62% had partially done so, and 38% had experimented with the technology. Predictive analytics was the most prevalent application, with almost two-thirds of fleet respondents using AI and historical data to improve their maintenance and repair scheduling. Far fewer fleets said they were also using AI for machine learning (28.6%), and even less were using natural language processing (9.52%).

This suggests that fleets are more confident when using AI to crunch their data and evaluate outcomes based on those numbers. For example, the highest AI use case for fleet respondents was for route optimization (42.9%) and maintenance scheduling (33.3%). In comparison, 57% of respondents were only moderately confident in relying on AI-generated insights for larger decision making, such as procurement. And 10% or less of heavy-duty fleets were using AI for fleet planning, lifecycle management, and leasing and purchasing decisions.

But this doesn’t mean this will always be the case, especially as AI continues to develop. After all, Fleet Advantage found that 28.6% of respondents said they were occasionally using AI for resale value forecasting, and almost half said they were planning on doing so.

“According to these results, while fleets are increasingly growing interested in the use of AI for various aspects of managing their operations, they remain unsure about the confidence of the data being produced,” Fleet Advantage stated in a release.

And where are fleets most interested in pursuing AI? Fleet Modernization planning (42.9%) and predictive maintenance (28.6%) were top contenders, with advanced fuel management trailing just behind (14.3%). But there’s more to come in AI development as well. Fleet Advantage polled their respondents on their use of agentic AI, or AI that can accomplish specific goals with greater autonomy, according to IBM. In this case, Fleet Advantage found less than 20% of respondents used this type of AI, but far more (57%) were using AI for data processing, if not decision making—and that most were using both open-source and proprietary data to do so.

Challenges and obstacles

According to Fleet Advantage, the biggest challenge to implementing AI for heavy-duty fleets was data integration issues (38.1%), and the biggest obstacle was limited technology infrastructure (28.6%). After that, the reliability and accuracy of data ranked as the second-most-common obstacle and challenge in implementing AI (24% in both categories). But 1 out of five fleet respondents said that lack of expertise and skilled personnel presented problems, too.

With all of this data taken into account, “AI is no longer a concept these organizations are curious about, it is now a strategic data technology resource that can drastically drive measurable outcomes when responsibly used with accurate, proven data,” said Hadley Benton, CTP, EVP of Business Development for Fleet Advantage. “This survey illustrates how and where organizations with transportation fleets are finding benefits with AI, but it also shows where these businesses still need proper guidance from their asset management partners.”