A plant-side engineering service that turns existing control, maintenance, and process data into practical equipment-reliability improvements.
Added Aug 11, 2026
Electronics manufacturers collect extensive equipment, control-system, maintenance, and laboratory data, but facilities teams often lack the time and applied analytics expertise to use it for reliability improvements. Engineers are being asked to adopt AI while maintaining safety, documentation quality, legal compliance, and sound operational judgment. This creates a gap between management's adoption goals and the plant team's ability to identify and deploy a defensible first use case.
Offer a fixed-scope, on-site reliability pilot covering one equipment set or facility system. The service connects available operational data, identifies failure or performance patterns, and delivers a tested alert, diagnostic aid, or maintenance recommendation workflow alongside technician training and operating documentation. Early delivery should combine industrial controls expertise, data analysis, and hands-on implementation rather than selling a standalone software platform.
Recent hiring requirements show manufacturers placing AI adoption directly inside facilities, controls, and maintenance roles rather than treating it as a separate research function. Plants already possess relevant monitoring and maintenance data, making narrowly scoped pilots possible without replacing their control infrastructure.
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* Partner with manufacturing, automation, and data science teams to evaluate and deploy AI-driven solutions that improve equipment reliability, throughput, cost, and manufacturing competitiveness. * Drive adoption of AI-enabled ways of working to support continuous improvement, knowledge management, and productivity enhancement across the engineering organization.
No, the gray square is out now, so now if I look left, I'm looking. Thank you, Sam, otherwise I'm trying to look at the audience as we go. Yep, perfect. Okay, so, I mean, why don't we start it off? I'll kind of open up with a little bit of a discussion and talk about, like, why AI in manufacturing now? Like, why is this a good time for it, and why does it make sense? And so, you know, you're going to see a little bit of both buzzwords, but also, like, real, I want to have the real discussion here, not just the buzzwords that you see in front of you on it. But some of these movements are very real. The first one, I think, is that is really interesting is that, you know, 70 to 80% of people are exploring, in manufacturing, exploring AI, but less than 30% have actually scaled any deployment.
We'll do the best we can. So, Sam, what I wanted to talk about today, and I hope that, and I think you're going to have plenty of questions for us as well, right, is why AI in manufacturing now? Why not two years ago? Why not two years from now? These questions are perfectly legitimate questions, and what the driving forces of AI adoption really are in the industry. And again, based on what I see, I'm one human being on this big rotating planet, but by all means, we touch over 1,000 different customers, prospects, projects, so on and so forth around the globe every year. And so I'm happy to share some of the wisdom of what we're seeing. I also want to talk about sort of understanding the types of AI.
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