About this episode
AI-powered predictive maintenance has been on the radar for years, but for most facilities, it still hasn’t fully landed.Chris sits down remotely with Colin Morris, Senior Director of Solution Consulting at MaintainX, the AI-powered maintenance and asset management platform built for the industrial frontline. Colin has spent eight years working in this space, long enough to have watched maintenance shift from an afterthought to a strategic asset across North American manufacturing.They cover the real barriers to AI adoption in maintenance: unstructured data sitting across disconnected systems, outdated assumptions about what predictive tools should deliver, and the foundational steps most facilities skip before they’re ready.Colin walks through what parts data to collect and why, how maintenance has evolved from cost center to cost saver, and where agentic AI is taking the industry next, including what scheduling looks like when an agent does the first pass and a human approves the plan.In this episode, find out:Whether today’s manufacturers have the data infrastructure AI actually needs, and why having data and having usable data are two very different thingsThe gap between what AI-driven predictive maintenance promises and what tends to happen when facilities try to put it into practiceWhy a predictive system that shows no faults can mean things are working exactly as they should, and how confirmation bias leads teams to misread that signalThe foundations most facilities skip when digitizing, and why jumping ahead without them creates problems that are hard to undoWhat parts information every facility should have on record, why it matters more than most teams realize, and what happens when a critical component is not cataloguedHow maintenance’s status has changed over eight years, from a cost center most facilities avoided spending on, to a core part of a facility’s digital strategyWhat AI looks like across maintenance operations today and where it genuinely adds value versus where human judgment still needs to leadEnjoying the show? Please leave us a review here. Even one sentence helps. It’s feedback from Manufacturing All-Stars like you that keeps us going!Tweetable Quotes:“A lot of customers do have the data. The biggest challenge is it’s super unstructured and in different systems, so getting it into a format AI can actually use is still a huge challenge.”“People expect predictive maintenance to surface issues, but if an asset is running well, nothing’s going to happen. No insights are sometimes good insights. That means things are operating the way they should.”“Historically, about 60% of a technician’s time is admin work. If you can give even 10–20% of that time back, tha
