Fleetio’s latest benchmark reveals that older trucks are draining budgets while AI‑driven maintenance tools remain experimental. Analyzing 1.2 million commercial vehicles, the report shows vehicles over ten years consume a third of service spend, yet only half of fleets are even piloting AI. If you manage a fleet, the data forces a hard look at asset age and technology strategy.
Aging Trucks Are Cost Monsters
Older assets are the silent budget killers. Vehicles older than ten years represent just 12 % of total miles, but they soak up roughly 33 % of all service dollars. That disparity means every mile you drive on an aging truck costs you significantly more than on a newer one.
Per‑Mile Service Costs Jump After Ten Years
The per‑mile expense climbs from $0.20 on brand‑new rigs to $1.10 once a vehicle passes the decade mark. In plain terms, a ten‑year‑old truck costs five times more to keep running per mile. If you’ve noticed rising repair invoices, the age profile is likely the root cause.
- Identify vehicles approaching the ten‑year threshold.
- Prioritize preventive maintenance for those units.
- Consider phased retirement or replacement to curb escalating costs.
AI Adoption Remains Early
More than half of surveyed fleets are still researching or piloting AI, but fewer than six percent have deployed it at scale. The biggest roadblocks are doubts about accuracy and reliability, which keep many managers from fully trusting the technology.
Barriers to Scaling AI
Accuracy concerns dominate: half of respondents say they can’t guarantee AI predictions will be correct enough to act on. Additionally, limited data quality and a shortage of skilled technicians make integration tricky. Until these issues are addressed, AI will stay a pilot‑only tool for most fleets.
Practical Steps for Fleet Leaders
You can start small and build confidence. Begin with low‑risk use cases where the payoff is clear and the error margin is acceptable.
- Inventory Forecasting – Use AI to predict parts demand and reduce stockouts.
- Brake‑Wear Prediction – Pilot a focused model to catch wear early without overhauling the entire maintenance program.
- Data Hygiene – Clean and standardize vehicle data to improve AI output.
- Gradual Rollout – Expand AI applications only after you’ve validated accuracy in the initial pilots.
By tightening maintenance loops, reassessing vehicle age, and treating AI as an experimental ally, you’ll shave waste, boost reliability, and protect your bottom line—one mile at a time.
