The asset told them it was failing. Nobody was reading it.

Reactive maintenance is a decision to pay full price

Asset failures were detected after the breakdown. That is the defining constraint, and everything downstream inherited it: unplanned downtime, a service call raised under pressure, and a cost that is always higher than the same intervention scheduled a week earlier.

The response was slow as well as late. Work orders were created manually and technicians assigned manually, which put service response at 48 hours from a failure that had already happened.

• No real-time view of asset health, so there was no way to identify a developing failure early even when the signals existed.
• No prioritisation across incidents. With nothing ranking severity, the most urgent job competed with the rest of the queue on equal terms.
• Scheduling could not be proactive, because there was nothing to schedule against until something broke.

The alert is worth nothing if dispatch is still manual

Prediction is the headline, and on its own it would have moved very little. An accurate 48-hour warning that lands in a process which then takes 48 hours to dispatch a technician has spent its entire margin on paperwork.

So prediction and dispatch were built as one path. An insight raises a work order automatically, the work order finds the right technician by skill and availability, and assignment stays visible and re-routable while the day is still in motion. The prediction buys time; the automation is what stops it being given back.

Einstein for the signal, Field Service for the response

Service Cloud carries service management and incident workflows, with Field Service handling technician scheduling, assignment and field operations. Einstein AI provides the predictive asset failure insight that triggers the whole sequence, turning asset health into a maintenance action rather than a dashboard nobody watches.

MuleSoft connects asset and service data across systems (the integration without which "asset health" stays trapped in the equipment layer) and a ServiceNow integration ties this into the wider service operations and incident workflows already running.

Machine failures fell 75%, service response went from 48 hours to 12, and incident resolution is 20% faster with 95% live assignment visibility. The last number is the one field managers notice: they can see where every technician is and re-route instantly, rather than finding out at the end of the day what actually happened.