Key takeaways
- AT&T reported saving $7M a year from fleet uptime across 7,000 trucks with a model that predicted when vehicle batteries would fail.
- AT&T found that drivers who worked their brakes harder also had batteries that failed sooner, and changed its practice to replace batteries when it replaced brakes.
- H2O Driverless AI returns reason codes with each prediction, so a fleet manager sees which readings put a truck at risk.
- The model's scoring pipeline scores every truck on a regular cycle, inside your own environment.
A broken-down truck is a missed appointment
When a service truck breaks down, you pay for the tow and the repair. The bigger cost is the work that does not get done: the technician is stuck, the day's jobs slip, and customers wait.
Fixed maintenance intervals do not solve it. They still miss the part that fails between services, and they replace parts that had plenty of life left. Your fleet already produces the signal: telematics, fault codes, repair orders and duty cycles show which trucks are wearing faster, but nobody has the time to read them truck by truck.