Artificial Intelligence for Transportation: Predictive Maintenance, Routing, and Demand

A motor carrier's data team builds a predictive maintenance model on telemetry from Samsara and Geotab. The model identifies wheel-end failures two weeks before they occur. The VP of Maintenance reviews and asks the questions that determine whether the model deploys: how does the alert integrate with the dispatch system so a flagged tractor gets routed to a shop, what happens when the shop is 800 miles from the next load tender, how does the driver get notified without creating service disruption, and who has authority to pull a tractor from service when the model is 70% confident versus 95% confident. The data team has the model. The dispatchers have the operation. Neither has built the integration that connects them. The model sits in a dashboard dispatchers don't open, and the first failure it predicted happens as expected.

Transportation AI that changes operations is built for the dispatch and MRO workflow from the start. Predictive maintenance integrated with the TMS (McLeod, TMW, MercuryGate for trucking, Jeppesen for airlines, AMOS or Ramco for aviation MRO) so flagged assets route to the appropriate shop location automatically. Dynamic routing that accounts for Hours of Service constraints, driver preferences, and the fuel tax implications IFTA creates. Demand forecasting integrated with the pricing and capacity planning workflow. Crew pairing optimization for airlines respecting Part 117 flight/duty/rest constraints. Confidence thresholds tied to operational authority — the right human approves based on risk magnitude. Done this way, AI changes miles, revenue, and cost per unit. Done as data science, it produces dashboards operations ignores.