By David Prusinski, CEO of VMS.AI
July 15, 2026
Fleet management has never been simple, but the operational burden on fleet managers today has grown well beyond what any small team can absorb. Vehicles multiply, data accumulates, and cost pressure intensifies. Meanwhile, the tools meant to help too often add complexity instead of relief.
A Role Under Siege
A typical fleet manager juggles vehicle health monitoring, maintenance scheduling, vendor coordination, repair order creation, compliance documentation, and cost tracking, often simultaneously and with limited staff. Research from GoCodes finds fleet managers spend an average of five hours per day on repetitive administrative tasks. That is more than half a workday consumed by work that demands constant attention but little strategic judgment.
The downstream costs are significant. The 2026 Fleet Benchmark Report identifies communication gaps as the top obstacle to completing maintenance on time, followed by technician availability and unscheduled service volume. These are coordination failures, not technology failures. And according to a 2026 fleet maintenance scheduling report, unplanned downtime can cost up to $2,000 per vehicle per day, with emergency repairs running four to five times the cost of planned maintenance.
Data Without Action
Fleet operators are not starved of data. Industry research shows that 80% of telematics users access less than 25% of their platform’s features, and 75% say their data is not meaningfully actionable. The 2025 State of Fleet Management Report found that 72% of fleets use dedicated maintenance software, yet more than half still run spreadsheets and paper forms alongside it. The problem is structural: most fleet software was built to surface information, not act on it. Alerts fire and dashboards populate, but the work of turning signals into repair orders and booked appointments still falls on the fleet manager.
Fragmented systems make this worse. The average fleet operation runs on a patchwork of telematics platforms, maintenance tools, and vendor portals that do not communicate with each other. The Cisco AI Readiness Index 2025 found that only 34% of enterprises feel prepared to actually scale AI capabilities, and fleet operations are no exception.
Dealers and Fleet Operators: Different Pressures, Same Root Cause
For auto dealerships managing loaner fleets or internal vehicle inventories, the strain centers on service lane throughput and repair order backlogs. A single missed maintenance window on a loaner vehicle can trigger customer complaints, liability exposure, and lost revenue. Service advisors who should be focused on customer experience spend their time on hold with vendors or manually reconciling vehicle status across disconnected systems.
Commercial fleet operators face a related but distinct challenge. A fleet manager outlook survey reports that 62% of fleet professionals say their job is more challenging now than in prior years. Smaller operators without dedicated support staff handle fleet administration themselves, meaning every hour spent on scheduling or paperwork is an hour not spent running the business. Larger fleets face a different failure mode: too much fragmented data across too many systems for any human team to monitor continuously. Critical issues get missed not from inattention, but from operational overload.
The Difference Between AI That Recommends and AI That Acts
The fleet industry is beginning to adopt AI tools, and the early results are encouraging. Systems that help managers approve repairs faster, surface diagnostic context, and flag priority vehicles are a genuine step forward. But a critical distinction is beginning to emerge: the difference between AI that advises and AI that acts.
Advisory, or generative, AI reduces the cognitive load of sorting through data. It surfaces recommendations and helps humans decide faster. That is real value. But it still requires a human to execute every next step: make the call, write the repair order, book the appointment, follow up with the driver.
Agentic AI does not stop at the recommendation. It takes the action: identifying the issue, assessing severity, creating the repair order, booking the service appointment, communicating with vendors and drivers, and reporting back on what was done. The fleet manager is informed rather than burdened. The work gets done whether or not anyone is logged in.
For an industry where execution overhead consumes the majority of a fleet professional’s day, advisory AI reduces decision time. Agentic AI eliminates execution time entirely. That is not a marginal improvement. It is a structural change in how fleet operations function.
The fleet industry has spent years building tools that make data more visible. The next investment needs to be in systems that close the loop: from signal to action to filed result, without requiring a human at every step. That is the standard fleet technology should be held to, and increasingly, it is the standard operators are beginning to demand.
About The Author
David Prusinski is the CEO of VMS.AI, a leader in AI-based uptime management. He is a 30-year technology veteran with 15 years dedicated to pioneering connected vehicle solutions.
