By Tim Zinkl

Over the past six weeks, I visited and spoke with maintenance teams across the United States. From large HVAC manufacturing facilities to medical distribution networks, road safety equipment makers, specialty chemicals plants, Tier 1 automotives, and several others in between. Different industries, different sizes, different levels of sophistication.

Your best technician is a single point of failure

At almost every facility I visited, there was one person, sometimes two, who held the real knowledge. Not in a system. In their head.

At a specialty manufacturer in Illinois, the maintenance team told me some shifts run with a single technician. No pairing possible. If that person doesn’t know how to fix something, the asset will stay down longer. At another facility, a reliability engineer told me straight out: there’s no middle generation. The experienced workers are aging out, the newer ones are still learning, and there’s no one in between to bridge the gap.

One maintenance manager I spoke with in Southern California put it best. He said his goal was to turn every Tech 1 into a Tech 3. Not by training them for years, but by making sure every technician has access to what your best technician knows, in the moment they need it.

For me that clearly shows that knowledge retention isn’t an HR problem. It’s an operational risk that starts accumulating long before anyone actually retires.

Your work orders are telling you almost nothing

I heard this everywhere, and the data backs it up.

One facility rated their overall data quality at around 60%. The timestamps, asset IDs, and part numbers were reasonably reliable. The troubleshooting descriptions? Consistently useless. “Reset machine.” “Machine broken, fixed it.” “Called supplier.”

The reason isn’t that technicians are lazy. It’s that the documentation process is designed for people who sit at desks. Typing detailed notes on a tablet at the end of a hot, loud shift is not something most field technicians will do consistently, and expecting otherwise is wishful thinking.

You probably have data you’re not using

Most facilities I visited had years of maintenance history sitting in their CMMS. One had five-plus years in their system, plus an internal AI chatbot already built on top of it. But when I asked what they could actually do with that data, the answer was usually: not much yet.

Another facility had HMI data flowing into Tableau dashboards, but only retaining a few months of history. A third had 250 to 300 gigabytes of breakdown photos and videos that had never been processed or made searchable.

The data exists. What’s missing is the ability to make it useful at the moment a technician is standing in front of a broken machine.

Speed to resolution is the metric that matters most

At a highly automated distribution company, the VP of Maintenance told me they operate on same-day delivery. When a conveyor goes down, every minute counts in a way that’s directly visible to the customer. They run 60% planned work, 40% reactive, and that 40% is where the pain lives.

A maintenance manager at a Tier 1 Automotive company told me his technicians arrive on-site without enough context to even bring the right tools. The work order says there’s a problem with the injection molding machine. That’s it. They show up, assess, go back for parts, come back again. Every one of those trips is avoidable downtime.

The pattern I saw everywhere is that the latency between “something broke” and “it’s fixed” isn’t usually about technical skill. It’s about information access. Technicians can’t find the relevant manual section. They don’t know if this specific error has shown up before and what fixed it. They’re troubleshooting from scratch every time.

Reducing that latency, getting the right information to the right person in the first minutes, is where maintenance organizations have the most room to improve quickly.

Multilingual teams are an underappreciated challenge

This one surprised me with how consistently it came up.

One facility runs three shifts with a workforce that speaks English, Spanish, and Vietnamese. Another manufacturer has operations in Mexico and described struggling to communicate standard procedures across the language barrier. A maintenance manager I spoke with was manually translating work orders for his Spanish-speaking team - himself, every day.

This isn’t a niche problem. It’s widespread, it adds friction to every part of the maintenance workflow, and it’s rarely talked about.

What the better-positioned organizations are doing differently

The teams that seemed ahead of the curve shared a few traits.

They were honest about their data quality problems and were actively trying to fix the input, not just the output. They thought about documentation as a workflow design problem, not a discipline problem. And they were looking for tools that fit how technicians actually work, on mobile, in loud environments, often under time pressure, rather than tools designed for engineers sitting at a desk.

One reliability engineer I spoke with made a point that stuck with me. She said she’d watched a technician try to use ChatGPT out of desperation to solve a machine-specific problem. It didn’t work, obviously, no context, no machine history, no relevant docs. But the fact that he tried tells you something about how real the need is.

The gap between what maintenance teams need and what they currently have is significant. But it’s also very solvable. The knowledge exists inside most organizations. The data is there. The challenge is making it accessible in a way that actually fits how people work on the floor.

That’s the problem worth solving.