This sponsored episode of Great Question: A Manufacturing Podcast focuses on building AI that works for manufacturing teams and features two thought leaders from MaintainX, a maintenance and asset management platform purpose-built to help frontline industrial teams run more efficient, resilient, and safer operations.
Nick Hasse is the co-founder of MaintainX—and he’s a repeat contributor to Smart Industry and has spent thousands of hours on the factory floor helping manufacturing leaders transform their operations with frontline-friendly software and AI.
Roshan Satish is the lead product manager for applied AI at MaintainX, who is leading the development of AI-powered maintenance tools including MaintainX Copilot.
During this episode, they spoke with Thomas Wilk, editor-in-chief of Plant Services, which is a sister brand of Smart Industry’s at Endeavor Business Media.
If you don't think about how are these tools actually going to be used by your frontline folks, how does that signals and information being generated make it into the hands of someone who has to act on it, and turn a wrench or push a button or reset a circuit board, you’re missing the whole plot all over again.
This is what we’ve seen with the first digital transformation wave—there’s been a lot of progress with new technology across the board and in the manufacturing space specifically, but there’s no other industry where you think about the complete juxtaposition of analog workflows versus digital workflows, especially in manufacturing. Even on the floor, you’ve got this incredible automation, this incredible and machinery, and yet people are still using whiteboards, Post-It notes, clipboards, radios, and paper-heavy processes.
See also: Agentic leaps past Gen-AI in its ability to solve production plant problems
So you have this opportunity to reset wit AI and rethink the strategy from first principles. Hopefully, folks can learn from the mistakes that were made with the first wave of digital transformation and focus on the end user as a core stakeholder in these conversations.
TW: Do people on the front line, as you're talking about, do they know the areas of their workflow or daily process that could be most benefitted from AI? Or is this something that people on the carpeted side might have more exposure to?
NH: I think today the learning curve is pretty similar in terms of the maturity level of understanding. That creates a great opportunity for folks like us and like yourself to try to get ahead of that. Folks have been doing things the same way for the last 25, 30, or 40 years in these environments, and they’ve had glimmers of hope in these opportunities, that maybe something will work but have been burned before, so they might be a little more resistant to some of these changes than say if this was the first time that technology was being introduced to them.
See also: Leveraging real-time production data: Q&A with Adam Mullen
I think the starting point is trying to understand, where are your bottlenecks? what are the things that are creating challenges today? One of the lines I use when talking to frontline workers is, "You didn’t get excited about working in this field because you love doing paperwork and submitting POs. What really got you excited about it?" They like to solve problems, they like to work with their hands, knowing what’s happening." So, I ask "what are the things that don’t bring you joy in your day-to-day workflow?" and start highlighting those examples.
I don’t need them to go full circle and tell me how AIs going to change that for them. But that helps bring up challenges and issues, and elevates those to people who can connect those dots and surface the right problems so that we can have a good starting point. Those are questions that any leader today can slip on a pair of steel toes, walk out on the floor and ask their team right now, and they’ll probably learn more about their business and bottlenecks, or a perspective that they hadn’t considered. They can get that information pretty quickly.
That would be my advice on how to get those workflow opportunities out. I’m not asking the frontline teams to come up with AI solutions, but I’m asking them to surface the problems so we can work with them on that front.
TW: Part of building an effective AI strategy always includes identifying and developing champions on the teams—those who from a cultural perspective not only embrace the technology but lead others into it. What are some things that you both have seen in terms of how organizations sort out those champions from the skeptics?
NH: There are two stakeholders here, and this is a multi-stakeholder conversation. When thinking about an ERP transformation, you’ll work very closely with your CFO and CIO, right, and one of those will really take the mantle and run with it. But when you’re talking about AI initiatives, you need someone who is excited about surfacing opportunities from the floor, from the frontline.
I’ve seen that be just an AI-enthusiastic technician who’s geeking out with ChatGPT on the weekend, and is constantly thinking, “it would be so cool if we could try this and that.” Maybe they don’t understand how the technology fully works, but they’ve seen things that made then say “wow” in their personal exploration, that they’re willing to be adventurous enough to help provide some insights and be a champion once those experiments start to take place.
See also: Hardware-agnostic AI: Creating an open market for industrial automation
On the other side, you need someone who’s IT-facing, OT-facing. The folks I’m seeing who get really excited about it today are people on the controls engineering side, because they have access to this treasure trove of data, and they’re pretty confident that it’s meaningful, which is why they spend so much time aggregating it. They know they’re just scratching the surface of what’s possible. And now that they're starting to see what the potential of AIs, the promise of what AI can do, people, a lot of them are doing that Judge Judy wrist-tapping motion: “When can I just throw this on in my data set and start to do something that I know it can do.”
