Strategic Frameworks for AI Adoption & Business Transformation

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 thoughtful, respectful, 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'm the CEO of the Industry 4.0 Club. I have over 40 years of management and leadership experience, most of that in manufacturing with Michelin. I'm joined today by my co-host Mike Yost, who's chairman of the club. Mike, would you introduce yourself further, please?

Oh, absolutely. Thank you, Mike. Uh, welcome, Ben. As Mike said, my name is Mike Yost. I'm the volunteer chairman of the club, uh, one of its co-founders, and Mike's co-host on our regular fireside chats. Been in industry for about 35 years, a little bit more than that, um, about half of it on the industrial solution provider side and the second half of my career on the nonprofit side. And what I enjoy doing is meeting and talking with people, as Mike mentioned, the diverse worldwide talent that can help us figure out what Industry 4.0 is all about. And so today I'm really excited that we have the pleasure of speaking with Ben Davis, who's the Director of Product Delivery and Business Development at Endeavor AI. He's going to be speaking with us today today about strategic frameworks for AI adoption and business transformation, and it's a preview of the conversations he's going to be having at the MD&M Midwest event in Minneapolis, Minnesota on October 29th. Mike, you want to tell us about the event?

Yeah, thanks, Mike. So the MD&M Midwest event is the premier event for professionals and businesses in advanced manufacturing, medtech, and critical industries. Education at the MD&M West event goes beyond presentations, from immersive workshops and expert-led conferences to live demos and shop floor sessions. MD&M West gives you more than insights, it gives you real-world experience to implement into your daily operations as soon as you leave the show. Every session, every workshop and keynote is built to help you think bigger, work smarter, and lead The future of advanced manufacturing.

So more about Ben. Um, he brings a business-first perspective to how agentic AI can reshape supply chain, smart manufacturing, and enterprise operations. Um, previously as the EVP and Chief Digital Officer at Cambria, he led a transformative use of automation, data, cloud computing, and smart manufacturing to increase productivity, improve quality, and reduce costs. Leadership that earned him the 2025 CIO of the Year Award. Didn't mean to give you a promotion, Ben, sorry.

Thank you.

So drawing on that experience delivering technology solutions across 6 different continents, Ben is a recognized voice on turning emerging technologies into scalable capabilities that lead to measurable business value. And so, Ben, we are really looking forward to the conversation. Thank you for being here.

Well, thank you, Mike, and Mike, for having me. And let me say how excited I am to have the MDM Midwest coming to my home state of Minnesota, where we can share a lot of ideas and showcase everything new about transforming manufacturing.

Super. Welcome. So, Ben, let's just jump right in. So I wanted to talk to you about AI adoption. So you've spoken about the fact that successful AI adoption is ultimately more of a leadership and change management challenge than a technology challenge. So what separates leaders who are successful in turning AI into business value from those whose organizations remain stuck in pilots and experimentation?

I think it's, it's a very good question, and I think that it comes down to leaders listening to the people that are running the processes every day and finding ways to hack those processes to do them better. I think good AI engagement, like you mentioned, Mike, is all about change management. We're finding that the technical piece, there's still challenges in it, but using the AI to build the technology is probably the best use case for AI. So having a leader who can engage the people who are going to work alongside the AI as it's implemented and empowering them to own how that's going to work in a new way. Is really what makes a difference. So I would say leaders who have the vision and listen to the input from the people that run the processes every day, who have ideas about how processes can work better, whether it's on the factory floor or in the warehouse, is the key to making sure that an AI implementation is successful. Then I'd say the second piece of that is to make sure that there's agreed KPIs that are going to be put in place that are visible to everyone that prove or disprove that the AI is truly making a difference. Every implementation I've seen of an agent has that type of business-driven KPI that's applying visibility to how well the AI is doing, and the way that it's functioning isn't displacing people, it's complementing people so that they can be more strategic.

So Ben, sticking with that leadership topic, right? You've talked about or described moving from a pull model of technology adoption where it's up to the employees to discover the solutions for their problems to more of a push model led by the executives that you're talking about. Can you talk about what that looks like in practice? Like, how important is it that leaders personally understand using AI and creating value, as you just articulated, for adoption?

Sure, as far as in practice, I think it's very important for the delivery arm, usually the IT or the OT department, to have AI DNA or, or an ability to understand and deepen their AI capabilities, and then partner with someone in another department who has a vision to change as well. And I think those 2 teams serve as a cohort or a center of excellence to drive AI into the business. One thing that I've done in my past is built a center of excellence, one for power users and 2 for leaders, with people, common people on both teams to serve as the conduit between the 2 so that adoption for AI would happen faster. And what we did is we pulled in, in Power users who really understood our processes and systems, who were excited about AI in the power user COE, and leaders who had a really good vision for how they wanted the business to scale and improve into the leadership COE.

I think it's a very good practical example. I'm wondering, you used the frame or the phrase AI DNA. And one of your— a mutual friend of ours who happens to live in Minneapolis talks not specifically about AI, more about digital tools and such in manufacturing operations and across the enterprise, and talks about the digital capabilities becoming another pillar of manufacturing the way quality and safety have over the years. So does that align with what you're saying, that AI is a part of that? We're all learning how to be digital, and it sort of permeates throughout the entire organization, top to bottom?

Yeah, I think that it certainly is. I mean, there's so many places for digital transformation, digital twins, automation, AI, machine learning to complement good manufacturing processes and actually positively influence safety and quality in ways that we haven't thought of before, that having that DNA in that setting is really, really important. I think having a cohort of leaders within an organization builds trust for those that are maybe outside of the cohort to reach out to people that are approachable, that they know and trust, that are part of the mission to spread that AI DNA throughout an organization.

Yeah, I think if I can summarize it maybe in a different way, what I heard you say, and it's something I think that's really important to capture. This leadership piece is so important, right? Leaders that are going to define the strategy, share expectations, and move things forward, willing to lead change. But also then this presence on the shop floor and understanding about it and leadership at that level as well. You mentioned the power users, for example, who are willing to to experiment and learn and then help their peers come along with the process. Do I have that right? Is it kind of both of the— those elements are both key for success, I think.

I think that's absolutely right. I mean, leaders have to be— there can't be any ivory towers when it comes to AI. Leaders have to be very hands-on, and they're going to want to be hands-on, right? Because they're invested in making sure that a business is profitable. How do you get to profitability? You make sure that you're processes scale, your people understand how processes can be transformed with AI to complement them so that things can happen more rapidly and accurately.

That's great.

Yeah, you know, we're all, you know, AI's everywhere, right? And I think certainly from a leadership perspective, there's a lot of pressure on manufacturers, manufacturer leadership to have have a plan, right? And, you know, whether that's coming up from the people who are grabbing it and using it, or it's being more thoughtful and, you know, strategic about it and implementing it out through the organization, right? Everybody probably already has an AI plan, whether they're in front of it or not. I know you talk about the sort of the crawl, walk, run approach, and you have a framework you talk about the of 5-5-5. Just wonder if you can explain that for everybody, not in depth. I know you're gonna go more into it in your, your session at the event. But how can leaders use that kind of a framework to build confidence and then momentum towards larger transformations?

Absolutely. So that framework that, that you mentioned, Maik, the Rule of 5-5-5, is what are, what's something that takes 5 times longer than it should that someone does in your organization every day? What's something that 5 people are doing that could be collapsed into one or could be done by an automated tool like AI, and what, what item is either costing 5 times more or we're missing 5 times revenue because we do not have automation and something's taking too long to process in a setting. So that framework is used to prioritize objectives to deliver AI, and in my new role at Endeavor, we work very closely with our customers to identify what those objectives are, get all the stakeholders in a room, understand how a process that exists in their enterprise can be reimagined and redone in a new way, and then apply AI to it. And in doing that, we're encouraging our customers to put that 5-5-5 rule into play to understand where the high-value targets are, because if you're doing an AI project for the first time, you want it to be successful, you want to use some sort of framework to evaluate and prioritize what you're going to go after first. And those target-rich environments with, you know, high-value, low-hanging fruit, not too much friction, and leaders and doers that are very eager to change something because they know there's value in it and they know they can measure it, are what's going to drive where people get started with this. That's been my experience.

Well, I think it's a great framework. And Mike, you know, we hear people come on here all the time and talk about you gotta understand the business value, right? But to have that kind of framing, I think even back to your point about the AI DNA, right? Like if I'm walking around and I'm looking at, you know, what is taking me 5 times longer than I think it should be taking, right? Or how could I, you know, grow by 5x or, you know, drive more revenue that way? I think that's a great framing to instill in people.

Yeah, Ben, so in your experience, let me just dive a little deeper into that contrast. You know, it's a little bit of chicken and the egg. What I mean by that is certainly you need to have a business problem to solve, but if you're also not familiar with what AI can do, you won't know how to use it or pull it in. So you've got to take time and learn the technology Maybe for technology's sake, if you don't mind me saying it that way. So how do you balance those 2? How do you bring in the technology so people understand what the potential is, while at the same time ensuring you don't become enamored with it, that you focus on the business result when you go to implement it?

It's a really good question, Mike. And I think that the tech— the focus on the technology has got to come after the focus on the process optimization, the focus on the data. And most importantly, the focus on the people that want to make a change, right? That's— this is truly a team sport between leaders and doers when it comes to analyzing a process and identifying how to transform it. And that's really got to be the core focus of the whole instance or exercise. So start with the process, work with the leaders and doers to identify how the process can be improved and reimagined in a way that gets us to 5 fewer people doing it, or 5 times less, or 5 times more revenue. Then look at the data. Everybody has a data governance challenge in this world, and I've never— I used to go to a lot of CIO events, and I always asked the question, raise your hand if you don't have a data governance problem in your enterprise. Everyone does. The best way to address a data governance problem is not to try to start a data governance project, an ETL event across all your systems to make your data clean. It's to find an objective that moves the needle in your business and clean the data to support the implementation of the AI. So that data focus is number 2, and the last thing is the technology. People certainly need to understand how the what what capabilities the AI has, and people also need to understand not to fear it so they don't get the sense that it's going to replace them because in our experience we don't see that happening. As I mentioned, we see our our our customers becoming more efficient. and productive and their employees and their customers being happier. So there definitely needs to be an understanding of what the AI can do as you scope your AI project, but technology to me comes third behind data and then certainly process and people first.

Just want to take a second here to remind everybody that we're talking with Ben Davis, Director of Product Delivery and Business Development at Endeavor AI, head of his sort of previewing his October 29th session that he'll be speaking at the MD&M Midwest event in Minneapolis, again talking about strategic frameworks for AI adoption and business transformation. So Ben, you know, a lot of the conversations today about AI focus on job displacement, jobs, even your 5-5-5 framework talks about, is there something that, you know, is taking 5 times longer or 5 people are working on it versus 1. How do you see the roles changing in manufacturing and other operations? And what are the unique, like, human-centered capabilities that you think are gonna get more and more valuable as AI takes over some of the other tasks?

Yeah, that's a really good question. I would say that I see the roles changing in manufacturing, certainly gonna definitely be more hands-on. Work and more time for hands-on things like maintenance, safety improvements, quality optimization. I think quality and safety, as I mentioned before, I've seen delivered solutions where those were improved with AI, and it complemented the resources on the floor to be freed up and do more strategic things and increase throughput and increase accuracy and minimize and optimize OEE. So I think that's where AI really comes into play. How do we take something away where there's issues with human accuracy that people honestly probably don't like to do? And how do we put our employees in a position where they feel really good about what they're doing and they're doing something meaningful and they're using their mind and their critical thinking skills more and more and they're doing less kind of mundane or repetitive tasks that just take up a lot of time, and that time doesn't equal a ton of value.

It's interesting, we recently had a guest on the show talking about capturing worker knowledge, you know, primarily maintenance and technicians and such. And the feedback there was if you actually engage the employees and show them what is possible and put them at the center of driving change, that they're Their feedback has been very positive, that the people, you know, take pride in what they do and they're willing to share and, you know, aren't as afraid of the AI displacing them as others. So I think that aligns with the approach that you're talking about.

It sure does. I mean, they've got to almost think of it— it's almost like adding an AI to the org chart. They're going to— we're going to have someone new join the team and we want to change the way that we're doing something. And ladies and gentlemen, you're the experts that do this stuff every day. So we need your input to bring this new team member up to speed as quickly as possible.

Yeah.

And it's, it's, it's going to give you an opportunity to think differently and use your critical thinking skills and feel really good about what you do. And it's going to give us some productivity gain to free you up to be more strategic. It's an opportunity for you to be more strategic in the organization.

Yeah, and the new team member isn't going to eat any donuts in the break room either.

No, so there's going to be more donuts to snack on for everyone.

I love it. Leadership is a key part of this. We've talked about the importance of leadership. There's really— of course, models are always sometimes oversimplified, but to take a simple thought here. There are those leaders who are transformative that are already going to figure out how to leverage it, leverage AI and make— and really use it to solve problems and learn what it can and can't do. And then there are those leaders who are kind of going to wait for existing technology providers to bring AI to them. And they'll sort of just— they'll use it as it becomes available. As AI moves towards outcome-based rather than seat-based economics, what risks do companies face by waiting From your perspective? And how should leaders decide when to move more aggressively versus when to be patient?

That's a good question, Mike. I think that I've never seen the pace of technology change so rapidly in my career. And I've, like I said, technology for 31 years. I note that those companies that are embracing AI early and tying it to things that are most important to their business are finding ways to make their customers happier and their employees and their investors happier because that delivery of AI is proving out theories that may have been on the, on the whiteboard for years and proving to the board, the employees, and the market that they can do things differently that they've never been able to do before. So I think companies who aren't taking advantage of that are going to run into issues with pace as they compete in the market. Now, are there times where you need to wait or confirm before you, you start? I would say from a security perspective, you need to think about things like how secure is the data? Am I putting the data in a frontier model where I don't have legal protection? Am I putting in a ChatGPT or an Anthropic or a Google and I don't have legal protection to keep my intellectual property protected when it goes out to that model? That's pretty important. And then from a financial perspective, am I putting information into a frontier model that's going to charge me tokens that I don't have oversight into? We hear a lot of that concern where people are using frontier models and they're eating up a bunch of tokens. At the end of the month, they get this surprise bill that they're not very happy about. So I think a pause around cybersecurity and around how the model is going to charge you for, for how you use it is a place where you'd pause with AI. But other than that, I think companies need to be all systems go, and if they're not, they're going to struggle to compete in the market.

Yeah. I wonder, Ben, Mike and I have talked about this a couple of different times because I agree with you, the speed of change of the technology is mind-boggling. But I can almost see it as a positive, right? Like, if you think you're going to get ahead of the technology, if you're going to have an answer, you're going to know it and know you know, where it fits and where it doesn't and how safe it is and yada yada yada. It's probably not realistic, you know, as a manufacturing leader, right, or leading a business. So it almost forces you to give up on trying to get ahead of the technology and say, well, how do I fit it into my organization, my operations, my people, right? Then it comes back to the principles that you're espousing here. So do you think that might actually be a benefit for us here that This stuff's moving so fast?

I think it will be a benefit because people just have to embrace it and they have to have a strategy for embracing it based on process optimization and business improvement and measurement.

Perfect. Well, we've been talking for the last 20, 25 minutes or so with Ben Davis. Ben, as we get ready to wrap up our discussion today, maybe I'd love to hear maybe, Mike, a final thought from what you've heard today, and then Ben, we'll give you a chance with for the last word, so to speak, before we move on today. Mike, you want to start us off?

Yeah, sure. I mean, there's lots of great stuff that Ben has experienced. I would certainly encourage everybody to make it to Minneapolis and meet him in person and hear from his experience and such. And so actually, maybe Ben, if you could also just sort of frame for people what you expect them to take away from your conversation, that would be great. But, you know, I think, you know, practical frameworks is what we came here to talk about, and Ben has given that with his rule of 5-5-5 and ways to think like that. And then also the notion of centers of excellence that have both, you know, the technical teams and the leadership teams and, you know, a common thread to learn together, I think was a really fantastic point. Thank you for sharing, Ben.

My pleasure, and thank you again for having me, and thank you again for bringing the event to my home state. I'm very excited about that. And what you hear from me in that session is more about this framework, more about how you engage an agent in whatever part of the business you're in specific to manufacturing distribution in a manner that it's a team member. in a manner that leaders and doers are working together to identify how that new team member is going to work and to help the mission of improving process across an organization, across a manufacturing setting, so that OEE is optimized and we've got a safer and higher quality environment where people are finding success using their mind and, and doing less button pushing.

That's excellent. Thank you so much. Well, I want to take a moment. Thank you, Ben, for Thank you, Ben, for taking time out of your busy schedule to share your perspective on AI adoption. And as we've said already, Ben's going to be speaking at MD&M Midwest on October 29th in Minneapolis, Minnesota. And we hope you're going to decide to join Ben and the Industry 4.0 Club at the event. Here's where you can go to register. So please do so, and we'll see you at the end of October. The Industry 4.0 Club would also like to encourage you to take action. To advance the deployment of Industry 4.0. You've heard some good advice today on, you know, how to maybe bring AI to the— to your facility. But whether that's reading an article or attending a workshop, uh, creating a smart manufacturing roadmap or a strategy around AI, we'd love to hear what you're doing. We'd love to, to understand how you're doing, how you're helping to advance the integration of technology into your business. Drop us a note at our website at www.industry40club.com or on our LinkedIn page and let us know what you're doing. In addition, we host conversations like this weekly. If you'd like to be a guest on one of our future shows, we'd love to hear from you as well at our website or our LinkedIn page. And with that, Ben, thank you so much for taking time out of your schedule. Mike, you as well. And we wish everybody a good rest of the day and rest of the week.

Thanks, gentlemen.

Thank you both. Thanks, Ben.

Thank you.

Bye.