Bill BrownFounder of ServiceTitan Hacks community (9k+ pros) | INC 5000 Contractor | Helping contractors win with AI & Automation

What it actually takes to predict equipment failure before it happens #ServiceTitan

27 Aug 2026 · 2 min

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Equipment failure prediction fails because the industry lacks unit-level visibility linking data to serial numbers, make-and-model context for probabilistic diagnoses, and integrated connectivity between sensors and building systems. Technicians need all three to arrive prepared with a 60% chance diagnosis instead of guessing on site.

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What are the biggest gaps standing between where we are today and true predictive analytics? I almost process this in layers, Amanda. The first layer being what can we know and understand about a singular piece of equipment? So basically tying back whatever data we can to a serial number. In other words, trying to understand what the service history looks like for this particular unit so that it can inform the next service call, bringing in the context, right? The next layer, that I think about that supports this predictability future is the make and model level context. What data can we give the technician ahead of time with that particular context to say, look, based on the data, on what we know here, it could be 60% this or 70% that, to help them diagnose even faster and more accurately. The third thing when we talk about gaps is figuring about this third layer of physical connectivity, whether that's through sensors. We talked earlier about, you know, BMS and control systems, how those exist today, and they do give levels of predictability to a building owner. But we've got to tie all of that together. This is all still disjointed and disparate. That's probably where I would say the industry is thinking long and hard about that, that third layer and how do we remotely monitor in a more affordable and streamlined way to achieve even more predictive results.