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Predictive maintenance with AI: 18 months of real fleet data
Maria Castillo2026-02-147 min read
What happened when we fused telematics, oil analysis, and DTC trends into a predictive model across 220 fleet vehicles.
Eighteen months ago we started fusing telematics, oil analysis, DTC trends, and inspection history into an AI model that flagged failures 14 to 60 days in advance. Across 220 fleet vehicles, the results have been clear. Mean time between unplanned failures went from 18,000 miles to 31,000 miles. Roadside breakdowns dropped 47 percent. Average cost per unplanned event dropped 38 percent. The math is simple: a planned PM bay slot costs a fraction of a roadside tow plus parts plus driver downtime plus customer impact. The catch is data quality. The model is only as good as the telematics health and the discipline around oil sampling. For fleets without strong baseline data, we run a 90-day data hygiene phase before the model starts producing reliable alerts.