By Tom Coker, chief operating officer at Intangles UK & Europe Operational inefficiency in fleet management rarely looks like a problem at first. It tends to show up quietly, through small patterns that repeat often enough to become normal. A driver leaves the engine running for 10 minutes while waiting at a collection point. A vehicle on a well-known route is consistently using more fuel than comparable assets doing the same run. Brake wear on one shift pattern is climbing faster than the rest of the fleet, but nobody has connected those dots yet. A vehicle sits in the depot for an extra day because the fault that grounded it had been showing warning signs for weeks, and no one caught it in time to plan around it. None of these is catastrophic individually. But across a fleet of fifty or a hundred vehicles, the financial effect is real and largely avoidable. Unplanned vehicle downtime costs UK commercial fleet operators an estimated £2 billion annually, according to analysis drawing on DfT, RAC, and ABI data. That figure covers direct repair costs but excludes staff wages, missed deliveries, and contract penalties that stack up on top. The data problem most fleets already have This is not a new challenge, and it is worth being honest about why it has persisted. Commercial vehicles have been generating vehicle health and operational data through ECUs, CAN bus systems, and onboard telematics for years. The issue was never a shortage of raw information; it was the absence of any practical way to interpret that information across an entire fleet. Manual review of telemetry data across individual vehicles rarely identifies failure patterns early enough for maintenance teams to intervene before it leads to downtime. By the time the anomaly is spotted, it may be too late