Smart Plant: Aligning AI, Automation & People - What Manufacturing Leaders Need to Know

Welcome to the Industry 4.0 Club's fireside chat. The club's purpose is to engage the power of diverse worldwide talent to accelerate the global evolution to Industry 4.0. We accomplish this by hosting and supporting respectful, thoughtful, and interdisciplinary open conversations and creating quality content on wide-ranging Industry 4.0 topics to expand the reach of our community. My name is Mike Ungar, and I am the CEO of the Industry 4.0 Club. I have over 40 years of experience in both manufacturing and leadership, most of that at Michelin. I'm joined today by my co-host, Mike Yost. Mike is chairman of the Industry 4.0 Club as well. Mike, would you mind introducing yourself further, please?

Not at all, Mike. Thank you and welcome, everybody. Thank you, Lisa, for joining us. As Mike said, my name is Mike Yost. I'm the volunteer chairman of the club, one of its co-founders, and Mike's co-host here on the fireside chats. I've been in industry almost 40 years myself, a good chunk on the solution provider side. in the last 16 years on the industrial nonprofit side. So my goal is always to meet interesting people and see what we can learn from them. So we're really excited that this afternoon we have the pleasure to speak with Lisa Ryan, who is the founder of Gradigy. We're going to be talking about her most recent book, which is Smart Plant: Aligning AI, Automation, and People. So Lisa is a manufacturing leadership expert. She's obviously an author and a certified speaking professional who helps manufacturers strengthen human judgment, workforce engagement, and knowledge transfer in this age that we're in of AI. Through more than 200 in-depth interviews with manufacturing leaders on the Manufacturers Network podcast, she identified a growing challenge. And that is that as automation advances, critical human judgment and institutional knowledge can quietly erode if organizations are not intentional about developing their people alongside the technology. So drawing on her Smart Plant Framework and the 6 Gears of Strategy Retention Model, Lisa works with manufacturers to build resilient cultures, preserve expertise, and ensure AI and automation enhance and not replace the workforce capabilities. So just one final note that if you're with us live listening on LinkedIn, if you have questions or you'd like to comment on any parts of the discussion, please go ahead and drop them into the comments box. We will do our best to integrate them into the conversation. So with that, welcome, Lisa.

All right, it's great to be here.

Yeah, we're glad, you know, we're glad you could take some time out. I know On a personal note, I get to congratulate you on 30 years of marriage, and I understand you just celebrated, and what a wonderful milestone. So congratulations on that.

Thank you. Yeah, I'm keeping this one.

There we go.

So, you know, let's jump into your expertise and just talk a bit about AI and manufacturers. You know, we know that many manufacturers are accelerating their use of AI and automation. To improve efficiency and competitiveness. It's something that's— I mean, you can't turn on LinkedIn and not, you know, or in other avenues and hear something about that really every day. So what are some of the biggest mistakes that you're seeing that leaders are making when they focus too heavily on technology without also investing in people?

Well, it's interesting because I think that AI does a great job. I mean, I'm friends with the robots and they help me run my business quite well for doing everything. But I also think that there's this feeling that robots are magical, AI is magical, and it can make all the decisions that humans used to be able to make. So we're just going to turn it over to the machines and just ignore that person who's been working on that machine for the last 40 years and has built up this sensory library where basically, I mean, they can see, they can smell, they can feel when something's about to go wrong. But if the dashboards are saying, hey, everything's cool, then a lot of times leadership is more likely to say, hey, the machines say that the dashboard is green, you know, move along. And you only can do that to your people so many times before they start saying, what's the point of me speaking up? And you start to lose not only that tribal knowledge that we are not doing a good job of capturing from our employees before that silver tsunami walks out the door and takes all of that information and knowledge and sensory library experience with them.

Yeah, appreciate that, Lisa, and your perspective. And in the book, you talk about aligning AI, automation, and people. Can you talk about what true alignment actually looks like on the plant floor and how can manufacturing leaders figure out if that alignment is missing?

Well, every time you are making a decision as far as something that you're going to automate or use AI for, to really sit down and say, what judgment, you know, is this something that we're removing human judgment from? this process, this process. I mean, there's going to be some processes where it's just okay, humans don't need to get involved as much, but where there's critical decisions that can be made. And it's, it's interesting because there's, there's examples in the book, for example, where they had— where Oak Ridge Industries had 6 managers on the floor. And they're like, you know what, We don't really need that because it's a little bit redundant. So why don't we just go down to 4? I mean, makes perfect business sense, right? I mean, you're saving the payroll. You're becoming more efficient. But what they didn't consider was by having the 6 managers, they were able to spend more time with the people. They were actually able to walk through the plant to see what's going on, to connect with their employees. Versus when they went down to only 4. Now it was a lot more that I don't have time to talk to you, and they lost some of that connection. So there's things that look really good on paper, but if we actually sat down and asked the question, okay, what would we lose by taking this step and being okay to As one of my friends likes to call it, murder board it. Basically put the thought of an idea, this is what we're going to do, and have the other people in the room just try to murder it and tell you all the reasons why it won't work or why you're going to use it, what you're going to lose.

I think that's an interesting approach you shared there about trying to break something apart. Can we break it? Can we break it? What does it look like? How does it— if not, let's address the places we're going to break before we get it out there so we make it more robust before it hits the floor. I think that's a really cool idea. I think about this issue of the alignment side and I struggle. It's interesting. Now I'm going to go back 10 years, let's say. Working on the shop floor, we were trying to create an environment where our employees would use data to make decisions, right? So I didn't want them to think, to use their intuition. I wanted them to— I was asking the question, what does the data tell us? Because quite often they would tweak based on their intuition, right?

Hmm.

And so we would drum that into them day after day after day. Now comes along AI, we're going to say to them, wait, no, what do you think it should be? How do you— because it's almost like it's a— we used to use the term hat burn, where it's like one day I'm talking this way, the next day I'm talking this way. I know it's not normally that dramatic, but talk about this evolution and as a leader, how do I make that evolution?

Well, it's interesting because when we first started automating things, It was really this garbage in, garbage out. So the humans worked alongside the robots and learned to program them and how to get the robot to do what you wanted it to do. And it was really a lot of the human expertise that went into that process. But as AI continues to actually learn and continues to learn and gets better making decisions, the problem with AI is that it makes decisions based on past performance. It doesn't have the ability to make those judgments as far as, okay, well, what happens, uh, you know, what is that one-off that can potentially happen that again somebody on the line who's been working with that vendor or working with that part or doing that particular process is going to know? Oh, okay, well, when we are— we get this part in and it's cold outside, we have to let it sit a little bit bit more, whatever, the robots, because we don't take into consideration the one-offs. And I think that that's the difference in that automation from 10 years ago, you had more of them working side by side, I guess, instead of the AI making more of the decisions today. And we're just like, hey, You're doing a great job. Go ahead.

I'm curious, you know, over my career, historically, we used to, you know, try to lock the process down as much as possible. And then the data coming out, as Mike was talking about, is really around the exceptions, right? So I have a trigger, I have an alarm, I have a warning, I'm slowing down, whatever it is. So do you see— it sounds from the conversations that you're talking about here that we're talking about what's getting automated is different types of decisions, right? Like, it's not just let me know when there's a problem, but, you know, the AI potentially could be looking at any number of different variables or inputs or what have you. So I guess, do you see that as being a difference that the tools today are opening up what's possible for people in their The day-to-day operations?

Well, they're definitely opening a lot of things up in the operations. The caution is, and when I was working with one association, they were very focused. I went to one of their safety meetings. It was actually in the textile industry. And they are talking about the policy and standards versus the norm. Okay, you can have all the policies you want. You can have all the standard procedures, SOPs, you name it. But what are the employees actually doing? What is the norm? You know, everybody says from a safety standpoint, yeah, you can stop the machine at any time. You know, if you feel that something's going wrong, you just feel free to shut the line down from a safety standpoint. But what happens when people actually do that? What are the repercussions? You know, if they save the day, Then they're a hero. But if nothing happened, then they're going to say, well, that was a total waste of time. Do you know what you just cost us from shutting that line down? And people see that there's a negative consequence for taking positive action. And again, they stop doing it. So whatever you're saying, as far as these performance— these standard operating procedures, you have to make sure that you're modeling the behavior. that you want to see and make sure that everything that we're doing is a learning experience. I mean, I keep thinking about the fact of how AI has changed my life and my business in the 3 years I've been using it. I can't even imagine what it's going to look like 3 years from now. I mean, I really can't imagine. And we're all learning because every day the data is changing. If you look at the numbers, as far as how everything is changing so quickly compared to what it was doing 10 years ago. I think that when we have that curiosity and we're willing to let people make mistakes, to fail forward, to learn from their experiences, and also, and I know we're gonna talk about, but capturing the tribal knowledge that's walking out the door so that we can make sure the next generation who is very tech savvy, but they also have the day-to-day expertise that they need so that we can be successful in manufacturing.

I appreciate that. I just want to remind everybody, if you're with us live on LinkedIn, that we're talking with Lisa Ryan, the founder of Gravigy, talking about aligning AI, automation, and people. And Mike, I don't mean to jump and ask all the questions, but my mind's racing. So I guess one of the things I'd like to stick with, if you don't mind for a second, Lisa, is the point about innovation and such. And I've used a couple times in public an example of my son who's in his first job, not in manufacturing. He's seen as a bit— he worked for a small company and he's seen as a bit of a, I don't know, Wise beyond his years, right? He uses AI to do things that the organization has never been able to do before. And so he's innovating and they're loving it, but they're giving him no guardrails, right? Like, there's— it's just like, figure it out, kid. And so I'm wondering what you're seeing either directly in the conversations you're having, you know, with manufacturers or more broadly across the industry. I think there's got to be a balance there between unleashing the innovation but having some set of guide rails or guardrails to keep everybody safe. Are you seeing that?

No, I see it across the board because you have that scale, but then you also have that mentality of, who do you think you are bringing these new ideas to us? We've been doing it this way for 40 years.

You need to pay your dues.

dues. You know, we may do that. You know, one of the things that I share all the time in my programs is, you know, you're a, you're a manager, you're sitting in your office, and you have that brand new employee walk into your office, sit down uninvited, and look at you and say, you know, I know I've only been here for 3 days, but I see some things And I'd like to share them with you. Now, there's going to be some managers that they are doing everything in their mind so their eyes do not roll back in their head because they're like, who do you think you are, new kid on the block? But what if they're right? You know, what if these people are coming in like your son? They see the world differently. They're tech savvy. So I think that there is A— The guardrails, it's just hard to determine what those are going to be. Because we want to learn the lesson, we want to be able to keep innovating, and maybe taking— having checkpoints, you know, what is the actual goal? Because a lot of times before, we would look at productivity as face time. If you were working 10 hours a day, and you were at the plant, it didn't matter if you played solitaire all day. You were, you were, you were there. And now, if we focus on results, and people are getting it done in, you know, 5 hours, okay, cool. That's great. But looking at the projects that your son is given, and employees like your son are given, what is the, the result that we're looking for in that? Because otherwise, it's just a continuous experiment.

Appreciate that.

No, I think that's— I was trying to think through, you know, I meant, I know in the book you talk about going back to how quickly you use the example of looking at the future about a plant that's completely run by AI, okay, just as an extreme, and the productivity gains that that plant has gained as a result and how wonderful the results must be, right, until the catastrophe hits.

Right.

It's interesting to me because I think a wise manager, and I wonder if you see it this way, the role that you just alluded to is you want individuals to speak up and talk about things and bring things to people's attention that maybe the AI can't predict.

Right.

It almost is as if you can't capture, you almost can't book, I'm using the word book, all those productivity gains because you're going to want to shut the line down. And listen to the individual and coach and talk about this and realize that, hey, that's not really reality. Reality is you still have to be able to— if you want people to be involved, and we do, in order to pick up on those things that could happen, we're going to have to accept that we're going to take some downtime. We're going to take some time, have those conversations. Is that what you're— That's how I kind of took what that's about, is that all those benefits, you're going to have to back off a little bit and And keep some of that so you can continue to coach and develop your teams.

Well, and it was funny because when I first started putting together the book and I was leading with my mastermind and I was talking about the fact that Oak Ridge had 500 days of perfect uptime and we were talking and there was one guy, the author of the murder board theory, was saying, well, you know what? If I'm seeing all those numbers, man, I'm happy about it. You get, you know, how are you? Why are you telling me that I'm going to make less, more, less money and be, you know, with, and take a hit on productivity when we're putting human judgment in instead of just letting the machines go? So I really had to think about that and look at different aspects. If we look at the cost of if they would have just listened to Marta or listened to Devin when he first reported That, that bearing wasn't sounding right, then how much would that have saved them from shutting down the line for a couple hours to repair the bearing versus having 6 days that the plant sat idle? So when we're looking at the, you know, just what could happen, you know, the other thing is, if we think about The, the plants that have been around for a long time, and now with, you know, venture capital coming in, and they are concerned about, you know, let's make money now instead over the long term. Everything that we're talking about is for the long-term productivity and viability of the organization with the employees adding that in. But if we're looking at our our focus on years instead of quarters, I think that that may take a little bit of that kind of fear away. I don't know if that answered your question.

You're right. It reminds me of a plant I worked in. And the folks that worked around the line, they would ask the question, of the leadership, are you an owner or are you a renter? The concept being that, you know, a renter's thinking about the short term, making their impact, getting out of there. But if you're an owner, you're thinking about the long term. And for a lot of them, if they perceived you as a renter, they knew they could outlast you.

Mm-hmm.

Right? You know, hey, you may want me to do something different, but— and I may kind of soft it a little bit and answer yes and kind of maybe do it halfway. Because I knew you'd be gone in a period of time. I think what you're talking about is, do we really as leaders take and act like owners for our facilities and our folks so that we think about the decision today, but also maybe when we're not there in 10 years, what's this going to be like? What kind of environment or culture are we trying to create? I think that's a great way to look at it.

I wonder on top of that, Lisa, do you feel, do you see that, I mean, this is obviously a massive leadership challenge, opportunity issue, et cetera. Do you feel that manufacturing leaders are ready? I mean, the speed with which this is coming at us, right? I mean, do you think that by and large that they feel comfortable that they know what AI is, where it fits, et cetera, et cetera, to be able to lead?

No, I mean, I think with, as with any organization, that there's going to be people who are really good at it. But for the most part, because it's just changing so fast, we don't know. And we don't have the— there's all these misconceptions about the younger people coming into the workplace that they don't want to work, and they don't want to be loyal. And they don't, you know, they just want to come in and have all these expectations. And it's like, yeah, they think differently. Now, back in the day, it was kind of this mentality that if you want to work for my company, you have to change to work for me. The organizations that are going to be the most successful are the ones that are thinking, how can I change my organization and my culture to attract the people that I need for long-term success. We need to change the philosophy. And as many associations as I speak to, most of them get it or are at least trying to get it. They're, they're learning about it.

Yeah.

But there's always, always in the room, at least one or a couple people that think that this is nothing but a bunch of psychobabble BS. All I need to do is just pay my people more money and they'll stay. No, No, they won't. We create the workplace culture. And if you look at human judgment, which is what the book focuses on, human judgment being part of the infrastructure, putting it every bit as important as marketing, as finance and accounting, all the other things that we keep track of when we focus on our workplace culture and the integration of technology with humans and look at everything and know that whatever we think today, just like the weather in Cleveland, it's going to be different tomorrow.

I mean, that really honestly brings me back to the point of your book, right? If people are not yet ready to lead because they don't necessarily feel confident in understanding what AI is, If you take a technology-only approach to it, you're going to have serious problems, right?

Yeah. Well, and employees have to be in that because a lot of times I speak at a lot of conferences and there's vendors there and they have all these bright, shiny objects and this new technology that's super cool. But unless they're getting the buy-in from their people to say, hey, this is how we can integrate our system that's been around since the 1960s You know, and people who are coming in that don't even know how to turn a wrench, for goodness sake. So how do we make sure that not only are we integrating the technology that we need, but we're getting the buy-in from our employees who are willing to do it? You know what, we're automating this area to make your life easier because we know that this is a crap part of your job and it's physically straining on you. So we want to be able to automate this so that you can be more involved and more strategic part of the operation, whatever it is. But we're having those conversations with our employees, being as transparent as we can as far as the why we're doing this, and also being open to their feedback to say, hey, that sounds like a great idea, or did you ever think about this? Hey, I just got this new app, whatever it is. But it's really about communication.

It sounds like you're really— I mean, just to kind of summarize, you're talking about a lot of the capabilities that a manufacturer is going to need to be around for the long haul. Are there other things you'd like to highlight about that? So what's going to separate the winners from the losers, let's say, in 10 years, besides what you've already talked about? Anything that comes to mind?

Well, part of it is really, again, going back to that tribal knowledge, that legacy. If we look at the, the knowledge that's walking out the door, I was working with a group recently and asked them the question, what do you know about your job that no one's ever asked you about? And it was really an interesting discussion because It's one of those things that we don't even know what we know because we've been doing it for so long. And maybe it's something that, that person retires, and 3 months later, we're like, well, Frank's been taking care of that the whole time. None of us have any idea what's going on. So how are we capturing that knowledge so that we have it and putting it in a way? Because we also know— that baby boomers have been really close to the vest as far as, I'm holding this information because this is my power. This is what's going to keep me here because I know stuff. But if you instead approach it of, you know what, Bob, you've been here for 30 years, you've done an amazing job. We want to be able to capture that. So it's all about your legacy, Bob. Now physically, we know you are not going to be here for 30 more years. Okay, it's just not probably going to happen. And we want the knowledge, skills, the expertise that you've been giving us to carry on in this legacy. And that's why we're, you know, giving you this intern or wanting you to mentor this person or doing consulting work or whatever it is, so that people can get inside Frank's head or Bob's head or Jane's head, whoever it is, and get that knowledge that we don't even know how valuable it is until we don't have that person to count on anymore.

Yeah, that's great.

Yeah, just one comment on that. A lot of the work that we do here in the region centers on having conversations about tensions that we see In the marketplace and what you're talking about as far as knowledge capture and what's my value to the company versus what is the, you know, who owns the information, the knowledge, right? What's the value of it? That sort of stuff. It is a tension and we're starting to poke at it here. And I think another thing that's really interesting is a lot of the manufacturers that I speak with know that it's a problem, but they don't even know how to define what the problem is. They don't have a plan for who owns the problem and, you know, a line of attack of how they're going to go solve the problem. So I think we'll be talking about this for a good while yet to go.

Absolutely.

So, so we've— gosh, we've had the pleasure to spend 30 minutes talking about— talking with Lisa Ryan about her her perspectives in her new book, Smart Plant: Aligning AI, Automation, and People. So as we wrap up, Lisa and Mike, maybe we'll start with you. A final thought, Mike, for the audience, and then Lisa, maybe you'll leave us with a final thought as well.

Yeah, I mean, I guess my takeaway, a couple things that popped up throughout this is, you know, what a leadership opportunity, challenge, etc., that it is being in the modern age where we are. with the speed of technology and managing the alignment with our people, current, future workforce. And it just reminds me, at one point Lisa was talking about how ingrained safety is, right? That people can shut a line down. I mean, like for decades that has established itself as a baseline to— the DNA of manufacturing. And it feels to me that we're at a point where this understanding of digital capabilities and such has to similarly get ingrained into the DNA of manufacturing operations. So I'm excited about watching that happen and helping in any way that we can, but appreciate all your perspectives, Lisa.

Thank you.

Lisa, how about your final thoughts for our audience today?

It really is about making human judgment continue to be part of the infrastructure of the organization. Listening to your people, capturing that knowledge, looking at the line as far as where does human judgment still matter, what can get us in trouble, but also Going back to, we have the policies, the SOPs, the procedures, all of that. But what is the norm? What happens when somebody does come forward and share their judgment, share their knowledge? Is it something that's being celebrated and congratulated, whether it was a problem or not? Or is there a punitive reaction if there was no— issue. So these are things that's certainly not going to be solved overnight, and there's not going to be one fix for everything. Every single situation is going to be different as technology continues to grow. But people, we're always going to be here and we're always going to need them.

Thank you. That's a wonderful way to end our conversation. So, Lisa, thank you so much for joining us to share your some of the key concepts and ideas from your book, Smart Plants: Aligning AI, Automation, and People. I really appreciate your time.

You're very welcome.

Yeah, thank you.

The Industry 4.0 Club, we'd like to thank you for joining us today, and we encourage you to take some action. So that's either, you know, pick up Lisa's book and take a read, attend a workshop, encourage your plant to develop a smart manufacturing roadmap, We want to know what you're doing to help advance the deployment of Industry 4.0. Drop us a note on our website at www.industry4oclub.com or on our LinkedIn page and just let us know what's happening. We'd love to hear from you and understand what you're doing to, to advance the deployment of Industry 4.0. We also host conversations like this weekly, so if you would like to be a future guest on one of our broadcasts, again, go to our website or our LinkedIn page and drop a note and let us know. And Perhaps we can get you on a later show. And with that, again, thanks, Mike. Thanks, Lisa. We really appreciate it.

Thanks, everybody.