How Is AI Changing Maintenance for Fleet Owners?

Physical AI solutions are, understandably, moving at a staggered pace when you compare them to the breakneck speeds of AI development in general.

A part of this, experts argue, is a trust problem, especially since new physical innovations can lead to more dangerous consequences if underdeveloped or misunderstood. However, there is also the issue of data management and information silos.

I spoke to Sri Teja Kolluri, product lead at Motive, about how fleet owners can upgrade their maintenance processes and ensure success when using AI.

A Trust Gap Remains for Physical AI

When I’ve spoken to logistics experts about the trajectory of physical AI, none of them have been surprised that it’s development has been significantly slower. It’s easy to sit at a laptop and understand the capabilities of Claude, but integrating AI into driver safety, for example, has real world consequences.

Sri Teja Kolluri, product lead at Motive, sees this as a problem that still needs to be solved. “There’s a general excitement about things, but beyond drafting emails, how can we actually use AI in real life?”

Motive’s latest fleet maintenance report suggests fleet owners are trusting AI more than previously. 80% of those surveyed said they would be comfortable with AI summarizing or creating reports, 67% would allow it to schedule or initiate work, and 60% would trust it to approve routine or low-risk tasks.

However, less than half (47%) said they would be comfortable with fully automated workflows without human review, suggesting human oversight remains a critical part of AI workflows.

How AI Can Help Transform Maintenance

Maintenance is one area where Motive and Kolluri feel AI can make a huge difference. Significantly, 80% of survey respondents cited rising maintenance and repair costs as their top operational challenge of the second half of 2026.

Despite the opportunity for AI in maintenance, 60% of owners surveyed aren’t using AI-based predictive or condition-based maintenance tools. Comparatively, 88% of owners are using AI-powered driver safety technology, like AI dash cams and safety programs.

I asked Kolluri about why maintenance may be lagging behind compared to other fleet processes. “With a lot of maintenance workflows, what’s happening is that they’re using different systems that don’t talk to each other. So, you have data scattered across your stack or they’re using spreadsheets, which again, it’s very hard to actually use as input for AI.”

However, Kolluri says maintenance teams are already seeing benefits by opting for a more integrated system. Watco, a transportation and logistics company, reports that problems now appear sooner, more assets stay in service, and it can identify the real cost of operating its fleet.

Hesitant maintenance teams, he continues, should prioritize the “key use cases” that will break down any informational silos in your operation. “I think that’s where you can identify the right solutions to adapt and solve that problem,” he says.

Small Steps and Integration are Key to AI’s Success

Using AI in isolated, random incidents is rarely as productive as using it as part of a connected system. Accountability and ownership are lost, and privacy and compliance decisions are made in reactive bubbles.

Integration and tackling information silos, Kolluri says, are incredibly important to get right when AI becomes involved.

“I think it’s very important that you work towards an AI operation that is well integrated and solves a lot of the data silos.” – Sri Teja Kolluri, Product Lead at Motive

Motive’s report findings confirm the problem. 80% of respondents said their fleet technology environment has some systems integrated, but with significant gaps. Overall, only 13% described their systems as well-integrated that share data automatically across platforms.

As well as integrated systems, Kolluri urges businesses to take small steps forward when introducing AI. “In general, AI might feel scary because, hey, there’s this powerful tool that we can use to automate a lot of things.”

“I think the best opportunity is just start small. Identify these opportunities and then slowly you’ll find that you have built reasonable AI operations,” he says.

Furthermore, don’t start from nothing. AI should be used to accelerate and improve your existing processes. A big part of this is understanding where AI can realistically fit, rather than forcing it.

“Audit your processes, identify where you are spending the most manual effort and where the most repetitive tasks are, and identify those opportunities to see where you can deploy AI. I think that’s the one thing, once you do that you can start taking baby steps towards adopting AI in that specific workflow,” Kolluri says.