“Plenty of anomalies look scary in a graph but never cause a failure,” Diaz explains. “The skill is figuring out which ones connect directly to business impact. That’s where predictive crosses the line from an engineering tool to a service strategy.”

A European service director I spoke with recently was blunt about this: “Our guys don’t trust every ping. They want to see it proven on the shop floor before they believe it. Predictive works best when the technician can say, ‘I’ve seen this before, and yes, it matches what the system predicted.’ Otherwise, it just feels like another false alarm.”

The payoff can be significant. Fewer emergency breakdowns. More technicians freed from crisis runs. First-time fix rates climbing because the visit was planned instead of rushed.

“If you can cut even 20 percent of your reactive calls, the difference in your operation is huge,” Diaz says. “Your teams suddenly have breathing room, and customers see you as proactive. That’s when you stop being a cost center and start being part of the value conversation.”

The cultural shift is just as important as the numbers. Customers no longer judge aftermarket service only on how quickly problems get resolved. They begin to notice that problems don’t occur as often in the first place. That builds confidence, and with confidence comes loyalty.

From Metrics to Mindset

If the benefits are so clear, why isn’t predictive maintenance everywhere? Diaz doesn’t hesitate: scale.

“Pilots are easy,” he says. “Rolling it out across ten different asset types, across multiple regions, with different field teams and spare-parts networks—that’s where it gets tough. Integration is the hurdle, not adoption.”

One global OEM executive, speaking on background, echoed the same concern: “The problem isn’t the sensor data—it’s making all the handoffs invisible to the customer. If the FSM system doesn’t talk to logistics, or if parts aren’t pre-positioned, the whole predictive promise unravels.”

That’s the sticking point: tying together diagnostics, FSM platforms, alert thresholds, and inventory management in a way that feels seamless. Miss one link and the predictive advantage weakens. Companies that solve this will redefine what aftermarket service looks like.

Looking ahead, Diaz expects the conversation around predictive maintenance to shift away from technology and toward outcomes. “The tech will keep evolving, no doubt,” he says. “But customers don’t care about your algorithm. They care that they didn’t have a line go down at two in the morning. They care that the hospital MRI was available when they needed it. That peace of mind is what matters.”

That perspective forces leaders to reconsider how they measure service success. Traditional metrics like response times still count, but Diaz argues that a better question is how many failures were prevented altogether. How much production time was never lost because the issue was caught early?

“Those are the numbers that prove your value,” he says. “And they’re the ones customers remember, even if you never put them in a slide deck.”

Predictive maintenance isn’t a distant idea anymore – it’s a capability already reshaping field operations and customer expectations. The hard part is making it scale without losing sight of why it exists in the first place.

For Diaz, the bottom line is simple: “The real mark of success is when the customer never has to pick up the phone. If you’ve done that, you’ve moved service from fixing failure to protecting performance. That’s where the aftermarket needs to be.”