AI Agents for Fleet Managers: 35% Are Researching AI. Only 5.6% Use It Broadly.

Fleet managers are not short on data. Telematics, fuel cards, work orders, inspection apps, and lease paperwork pile up every day. What they're short on is time to act on it. And the latest industry numbers show AI hasn't closed that gap yet.

Fleetio's 2026 State of Fleet Management report surveyed more than 600 fleet professionals across North America, from government and utilities to construction, trucking, and telecom, between December 2025 and January 2026. It paired the survey with platform data from over 1 million vehicles and $7 billion in service spend. The headline on AI: 35.1% of fleets are researching it, but only 5.6% use it broadly.

The numbers behind a busy fleet office

The AI number makes more sense next to the rest of the report:

• 54.4% of fleet managers say rising costs are their top concern, followed by regulations and emissions mandates (46.1%).
• 30.8% still rely on spreadsheets for tracking.
• Only 6.7% describe their maintenance as "fully scheduled." 48% live in a half-scheduled, half-reactive world.
• Platform data shows 53.7% of maintenance is scheduled, 40.1% unscheduled, and 6.2% emergency.

When fleets were asked what was holding them back on AI, the top hesitation was accuracy and reliability (50.8%). And when asked what they want AI to fix first, the answer was practical: automated preventive-maintenance scheduling and predictive maintenance.

The real time sink: exceptions

One stat in the Fleetio report says a lot about where the hours go. The median time to start a work order is 31 minutes. The average is 6.7 days.

Most jobs start quickly. The ones that don't are what eat the week. Fleetio names the causes: communication gaps (31.5%), technician availability (27.4%), and unscheduled service volume (25.2%). These aren't engineering problems. They come from waiting on a parts quote, a driver who didn't confirm drop-off, or an approval stuck in someone's inbox.

That's exactly the kind of follow-up work an AI agent can carry. It doesn't fix the truck. It chases the people and paperwork around the truck so the repair starts sooner.

Older vehicles, bigger bills

Fleets are also running older assets. The average vehicle age in Fleetio's data is 6.4 years. Vehicles 10+ years old account for about 12% of miles but about 34% of service spend. Cost per mile climbs from $0.06 for vehicles 0-5 years old, to $0.15 at 6-10 years, to $1.10 past 10 years.

That makes replace-or-repair decisions some of the most expensive calls a fleet manager makes. They often get made off a spreadsheet, or after a breakdown forces the issue.

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Why the agentic hype cooled off

A second survey, from truck lessor Fleet Advantage, shows the same pattern from the private-fleet side. In its 2026 Use of AI in Fleets survey, presented in April and released at the NPTC conference in May, 87.1% of respondents said they use generative AI for back-office tasks, driver feedback, and pulling answers out of maintenance manuals and SOPs. Every operational use case it measured grew year over year. Route optimization went from 42.9% to 71.0%, and maintenance scheduling from 33.3% to 64.5%.

But expectations for fully agentic AI came back down to earth. The share not using agentic AI at all nearly doubled, from 19.0% to 38.7%. Interest in agentic AI for procurement fell from 57.1% to 9.7%. Every barrier got worse: data integration issues jumped from 38.1% to 71.0%, and inaccurate data from 23.8% to 64.5%. Only 9.7% have a formal way to track AI ROI.

One caution: Fleet Advantage sent the survey to more than 2,500 executives but doesn't publish how many answered, and the percentages suggest a small sample. Treat it as a direction, not a precise measurement. The direction matches Fleetio's, though. Fleets that tried the big promises ran into messy data.

Where AI agents fit first

The lesson from both reports is to start with the dull, repeatable work around maintenance and paperwork, not with "autonomous fleet." Good first jobs for an agent:

• Work-order chasing. Confirm vehicle drop-off with drivers, request and compare vendor quotes, and nudge approvers when a job sits too long. This goes straight at that 6.7-day average.
• Preventive-maintenance scheduling. Read mileage and engine-hour data, book PM slots before they're overdue, and reschedule when a vehicle is out on a route. This is the use case fleets said they want most.
• Invoice and warranty checks. Match vendor invoices to work orders and flag repairs that should have been covered under warranty.
• Answering "what does the manual say?" Pull answers from SOPs, maintenance manuals, and compliance guides for drivers and techs. This is already the most common generative AI use in Fleet Advantage's survey.
• Replace-or-repair prep. Gather cost-per-mile, repair history, and lease terms for aging units into one summary, so the decision starts from data instead of a breakdown.

Each of these keeps a person on the final call. The agent does the gathering, chasing, and drafting, and the fleet manager decides.

Fix the data you already have

The 50.8% of fleets worried about accuracy are right to be. An agent working from a spreadsheet with stale odometer readings will schedule the wrong service, just faster. Before handing work to an agent, pick one source of truth for vehicle records, connect it to telematics where you can, and decide who owns cleaning it up. Fleet Advantage found 51.6% of respondents already collect telematics and ELD data but haven't connected it to any AI tool. That's the cheapest win on the table.

What this means for fleet teams going forward

Fleets aren't behind on AI because they're skeptical of technology. They run some of the most data-heavy operations in any company. They're behind because the work that eats their week sits between systems, in phone calls, emails, and approvals. That's where AI agents are most useful right now. The fleets that move first won't be the ones with the flashiest pilot. They'll be the ones that shave days off the exceptions, one work order at a time.