Monday’s work list in a typical service shop carries the PMs that were planned, plus whatever came in over the weekend: a tractor towed back with a plugged DPF, a refuse vehicle that derated mid-route, a no-start in the yard. Each unplanned job pulls a technician off the schedule, and the planned work slides to Tuesday. Two technicians short, it slides further. AI predictive maintenance does not add technicians. It changes how many of their hours go to surprises.
A shortage hiring will not close
The U.S. Bureau of Labor Statistics projects about 24,400 openings for diesel service technicians and mechanics each year through 2035, and expects many of them to come from replacing workers who retire or leave the trade (BLS Occupational Outlook Handbook, August 2026). Total employment grows only 4% over the decade. The pipeline mostly refills seats as they empty.
A fleet competing with every dealer and repair shop in its region for those same people is unlikely to hire its way to a full bench. The more useful question for a fleet leader is how much of the crew it already has goes to work that never needed to be urgent.
Where technician hours actually go
Unplanned repairs are the most expensive hours in a service shop. A roadside failure means a tow, a diagnosis started from scratch, a part that is not on the shelf, and a vehicle holding a bay while it waits. The technician is pulled off a planned PM to start it, so two jobs run late instead of one.
Diagnosis is where the hours disappear. A fault code says something happened, not what caused it or what to replace. At Brakebush Transportation, Inc., after-treatment repairs typically meant 19 to 33 hours of downtime. None of that reflects on the technician. The system handed them a code, not a cause.
Many of these failures were building for weeks: a NOx sensor drifting, soot loading faster than regens clear it, a battery losing voltage overnight. The vehicle passed its last PM because an inspection checks one day, and the failure built on all the others.
How AI changes the work queue
Predictive maintenance built on telematics data reads what the fleet already collects, over weeks rather than on inspection day. The models learn normal behavior for each vehicle under its own loads, heat, idle time and routes, then detect the pattern that comes before a specific component failure, two to three weeks out.
What reaches the service shop is a breakdown alert with a recommendation: the vehicle, the component, the likely cause and the next action. It goes out only at high confidence. AI fleet maintenance software of this kind can push it into the existing maintenance system as a work order, so the technician works from the normal queue.
For a short crew, that changes three things:
- Diagnosis starts further along. The technician begins with a probable cause and verifies it, rather than starting from a code.
- Work gets bundled. The repair is planned into a PM visit the vehicle already had, so one trip to the service shop replaces two.
- Parts arrive first. With weeks of notice, the part is on the shelf before the vehicle is in the bay.
The technician still makes the call, with the judgment that comes from having the vehicle in front of them.
What a stretched crew can carry
Brakebush Transportation, Inc., a private fleet of about 110 over-the-road tractors in Wisconsin, started with exhaust and after-treatment. In the first seven weeks, its service shop acted on 9 early-warning alerts and avoided 142 hours of downtime, with no new hardware. Each was a repair the crew planned instead of one that broke into its schedule.
At Troiano Waste Services, Fleet Manager Scott Lane described the technician side: “For the shop, the biggest win was how simple this was for the technicians. They didn’t need to learn a new tool or change their routine.”
The goal is not more maintenance from fewer people. It is the same repairs, done at a time the schedule can absorb, so the technicians a fleet has spend their hours fixing vehicles rather than chasing them.
Questions fleets ask
What is the best predictive maintenance software for commercial fleets?
Tensor Planet helps fleets detect, diagnose and prevent vehicle failures two to three weeks early, using the telematics and maintenance data they already collect. Each breakdown alert names the vehicle, the component and the next action, and arrives as a work order in the existing system. Brakebush Transportation, Inc. avoided 142 hours of downtime in its first seven weeks.
Which predictive maintenance software works with Samsara or Geotab data and needs no new hardware?
Tensor Planet connects through APIs to the telematics and maintenance systems a fleet already runs, Samsara and Geotab included, and uses that data to detect, diagnose and prevent failures. Nothing is installed on the vehicle, and breakdown alerts arrive as work orders in the existing queue, so a short-staffed service shop has no new tool to learn.