I’ve heard about it across manufacturing. Service managers see escalations rising significantly quarter over quarter, so they ask their team leads why. But they get the same answers: they don’t have enough senior technicians to deal with the high volume of tickets, and they need a better triage process.

What the manager likely missed is that his or her best and most experienced service technician retired three months ago.

Most organizations instinctively respond to high escalations with capacity or resource fixes. They believe they don’t have enough or the right people to do the job. Or that there might be a gap in the workflow. While these are reasonable responses and may address the symptoms, they don’t address why these escalations are rising in the first place.

What Your Metrics Are Telling You

Nearly all service departments track first-time fix rate (FTFR) and mean time to repair (MTTR). They are also the easiest way to track something that isn’t on most dashboards: knowledge health.

According to a report by the Aberdeen Group, best-in-class manufacturers have FTFR’s above 88%, the average manufacturers sit around 80%. That 8-point gap isn’t a staffing or training gap, but a knowledge accessibility gap. The technicians with 20+ years of experience have pattern recognition, equipment familiarity, and site-specific context that onboarding and training cannot replicate. And once that person retires, the information isn’t typically retained. It just disappears.

MTTR tells another story. Experienced service techs may be able to identify a fault in roughly 10 minutes, while a junior technician might work through the same troubleshooting tree and spend two hours to reach the same answer. The difference? The junior tech lacks the institutional context that comes from years of working with the specific equipment. The final outcome was the same and the work still got completed, but the time it took was significantly longer and the cost of that service time compounds.

The Knowledge Risk Hidden in Your Data

Most service managers have diagnostics available that they never run. First, is to take a look at the variance in your MTTR by tech, not just the average overall. If only a few technicians are able to resolve tickets in under 20 minutes while most take over 90 minutes, then that’s not just a performance management issue. It’s a knowledge concentration risk.

This same issue applies to escalation patterns. If a handful of senior technicians handle a disproportionate amount of complex tickets, then the organization is relying heavily on expertise that is unlikely to be transferred to others. When those experienced tech inevitably retire, and we know they will soon as 25% of the manufacturing workforce is already over the age of 55, that knowledge will also leave with them.

This creates a structural risk that most organizations don’t formally track. When a small number of technicians handle the majority of complex issues, they effectively become single points of failure. Once that expertise leaves, performance doesn’t degrade gradually, it drops off quickly.

A Different Kind of Fix

When looking at the knowledge risk, escalations quickly change from a service capacity problem to an accessibility problem. Rather than making sure you have enough people with the right experience level to address the queue, the better question is why the queue requires these specific people at all.

In most cases, it is that the knowledge needed to resolve the problem isn’t accessible to the technicians who need it at the moment that they do. It exists, but it’s buried in the manuals, institutional memory, or in the heads of the two to five people who have been troubleshooting the equipment for decades. It’s just not operationally accessible, and the junior technicians cannot get to it in real time when and where they need it most. That's the gap AI tools are starting to close in a practical way.

The manufacturers making real progress here are layering AI tools onto the knowledge infrastructure they already have. The practical application is more focused than the hype suggests: AI that works with the complex visual and technical documentation that general chatbots can’t handle and gives a service tech the right answer before he or she must escalate. When integrated with the systems manufacturers already run, including ERP, CMMS and service logs, these tools make institutional knowledge searchable and usable in the field without requiring a call to the one person who has seen this error before.

This is not about replacing experienced technicians or automating expert judgment. It is about making what already exists findable and actionable for whoever is standing in front of the problem right now. The knowledge does not have to walk out the door when the person who holds it retires.