Episode Transcript
[00:00:00] Speaker A: Manufacturers have more technology than ever. So why does digital transformation still feel so hard?
In this episode of Control Alt Manufacturing, we're breaking down the real difference between modernization and transformation. Why system silos keep killing momentum. And how connecting hardware, software and data can finally unlock value on the plant floor. No hype, no buzzwords, just what actually work.
Hello, hello, hello, everybody. Welcome back to the Ctrl Alt Manufacturing podcast, Resetting and Rethinking Manufacturing. This is a podcast that explores some of the people, technologies and strategies that are driving the digital transformation of manufacturing. I am Gary Cohen. Just got back from vacation, a little jet lagged, but otherwise fine. Joining me is the fresh and energetic Never jet lag. Stephanie. Neal. Stephanie, how you doing today?
[00:01:02] Speaker B: I'm good, Gary. I think I'm doing a little better than you today, actually.
[00:01:05] Speaker A: I am going to admit on the podcast that when I say I'm jet lagged, I literally. We did like five minutes of this podcast and I realized I never hit record.
So we're redoing it.
This is take two for us.
[00:01:20] Speaker B: That's all right. That's all right.
[00:01:23] Speaker A: Yeah, it's off to a good start.
How are you feeling?
[00:01:28] Speaker B: Um, I, you know, I feel good, but, you know, I wanted to introduce something a little special and different for this podcast, something that maybe we can carry through 2026, because I feel like we spend a lot of time on this podcast talking to our guests, getting to know them, letting. And our audience loves that.
But I recently found out that you have a big fan base out there, Gary, and so I want to do a little thing called get to know Gary. I want to give the fans what they want, which is to know a little bit more about you.
[00:01:58] Speaker A: I want to know where you're, where that's cut, where that data is coming from, that I have a huge fan base, but I'll just take your word for it.
[00:02:06] Speaker B: Yeah. So let's quickly, let's just do something real quick and like, ask you. Let's start with you just said you're back from vacation, you're jet lag. Tell us where you've been, my friend.
[00:02:15] Speaker A: I was in Barcelona, Spain, or Barcelona, Spain. It was, it was terrific. I had a great time. We went there. It's the first time I'd been to Spain, first time I'd been to Barcelona, obviously, an incredible city. It was, I mean, there's people everywhere, everybody walking, great food city, incredible art and architecture. We went to the Picasso Museum. We ate at.
I will just say we ate at more than one Michelin starred restaurant. While we were there?
Yeah, it was fantastic. Loved it.
[00:02:51] Speaker B: Did you have some paella?
[00:02:53] Speaker A: We did, actually. That was the one meal that we had that was not like, out of this world. Fantastic. But we knew it wasn't going to be. We were going to a flamenco show, which we decided to do while we were there, and we needed to grab something quickly beforehand, so we stopped at a little place and got paella.
[00:03:11] Speaker B: Oh, well, I mean, that's really.
I mean, you gotta love paella, even if it's not the best.
[00:03:19] Speaker A: How bad can it be?
[00:03:20] Speaker B: How bad can it actually be? But yeah, no, I'm very envious of your trip. I haven't been to Barcelona since I was in high school when I went for my, you know, high school trip because I made it to level five Spanish and of course I can't speak any Spanish, but it got me to Barcelona.
I'd love to go back. I hear so many great things about it. But until then, I will just live through your experience.
[00:03:43] Speaker A: Exactly. You and I still haven't debriefed about the vacation, so I'll tell you all about it.
Yeah, I sort of let my Spanish founder too, unfortunately. But I pick it up a little bit when I go on trips to Spanish speaking countries. I can understand it well, but because I haven't spoken it in so long, I lose the word. And so if I'm trying to speak it, it takes a little while. So, yeah, it was still a terrific trip. And I just want to let you know that if we're doing get to Know Gary, there is going to be a Get to know Stephanie coming. So for the huge audience of Stephanie fans out there, you're going to get to know Stephanie as well.
[00:04:19] Speaker B: Yeah, you're all going to be bored to tears.
[00:04:23] Speaker A: Enough. Enough about us. We've got two terrific guests with us today. Luis Atencio and Dan Furrow. I met both of them at Automation Fair. They're both Wesco. We had a great conversation there and it was one of those that I finished talking to them and within seconds I was like, we have a podcast. You should come on the podcast. So, yeah, really happy that they agreed to do it, I think. You know, as I said, Stephanie, you and I both attended Automation Fair and there are a few themes that really stood out to me. And we see this at a lot of shows we go to, which is there's no shortage of technology and manufacturing right now. There's no shortage of business data in manufacturing right now. But there still is a lot of friction or confusion about how it all is supposed to work together. You know, it's everybody is on some stage of their digital transformation journey. It's talked about everywhere. It was the buzzword at these shows five or 10 years ago.
But when you really dig into it and look at what manufacturers are doing, a lot of them are still struggling to take all of this information they have and turn it into real measurable outcomes on the plant. Floo.
[00:05:37] Speaker B: Yeah. And I think that right now it's such a, you know, an important time because as you said, we hear about all this technology, but what do you, how do you actually use it and use it successfully and make sure that it just, it doesn't become obsolete or you're not doing it right and the project fails. And just recently I was talking to the editors over at engineering.com, the sister brand to Control Engineering and we were talking about technology failures and the fact that we go to all these conferences and shows and we talk about this cutting edge technology, but what happens to it? So one of the editors talked about Microsoft HoloLens and how it was all the rage several years ago to talk about using it for AR VR training.
But then Microsoft pulled the plug in late 2024. And again we go out there as reporters and we're talking to all these subject matter experts and we're asking what is this great cutting edge technology going to do? And then it doesn't go anywhere. So maybe it's because of cost or there's no practical application or scalability, but that makes me think about AI and are we hyping it up too much and who is using it right so that it becomes an integral part of the enterprise architecture. And so when I asked myself that question, I started to do a little research and I guess what I found out that Wesco is doing it right because on January 21st Wesco International announced it is on Fortune magazine's AIQ50 list that recognizes the top Fortune 500 companies most effectively using AI to create business value.
And they're in really good company on that list there's like Alphabet, which is Google's parent company, and Nvidia and Coca Cola and ExxonMobil and Amazon and the list goes on. So I think we're in a real treat in for a real treat here based on the knowledge that our guests have.
[00:07:40] Speaker A: Yeah, it's great. And then, you know, today we're going to dig into some of this stuff we've been talking about in AI digital transformation. What really separates modernization from digital transformation? Why orchestration and data matter so much and what all this looks like in real world manufacturing environments. And Stephanie and I, as reporters and not experts in this stuff, are lucky enough to have two people here who are very well versed to have and ready to have this discussion in Luis Atencio and Dan Furrow of Wesca.
Dan is the senior Vice President and General manager for Wesco's US industrial global accounts and international markets. His well rounded career path across many functions in geographic theaters gives him a deep knowledge of Wesco's value proposition as well as customers, suppliers and vertical markets. Luis Atencio is a solutions architect for Wesco's smart manufacturing team. He works at the intersection of AI, automation and industrial operations, designing systems that help teams move from uncertainty to execution.
His focus is in translating complex ideas into architectures, demos and decision frameworks that are clear, reliable and built to hold up under real world plant conditions. Gentlemen, I think I've introduced you enough. Come on in. Welcome to Control Alt Manufacturing.
I can take a podcast. Oh, there we go.
[00:09:00] Speaker C: Don't forget to mention Alpha Int in Spanish.
[00:09:02] Speaker D: Yeah, you could almost the whole podcast in Spanish if you want, but I wouldn't be able.
[00:09:06] Speaker B: You could do the whole interview in Spanish.
[00:09:09] Speaker C: I know, I don't get it.
[00:09:10] Speaker B: We won't know what you're saying, but go ahead.
[00:09:13] Speaker A: I might actually know what you're saying. I just won't be able to respond in Spanish.
[00:09:17] Speaker D: Yeah, it'll be a very one sided podcast.
Thank you. Thanks for having us. I got very jealous listening about your vacation. Barcelona, one of my favorite cities on the planet, no question about it. So we'll have to catch up offline and share some tips and tricks about the city. I haven't been in a little over a year, but always looking for an excuse to get back.
[00:09:36] Speaker A: Absolutely. There's trips that I go on and I think it's great and I love the cities and it's a nice place to visit. And then there's places that I go that I think, oh, I could live here. Barcelona felt like that to me.
[00:09:48] Speaker C: Nice.
[00:09:49] Speaker D: Yeah.
[00:09:49] Speaker A: Yeah.
All right, let's stop talking about travel and start talking about what you guys are here for.
As I mentioned in the intro we hear these terms digital transformation and modernization and they're often used interchangeably, which may not be correct. So how do you differentiate between those two in a manufacturing context?
[00:10:11] Speaker D: Yeah, that's a good question, Gary. And you do hear people use them relatively interchangeably.
And a lot of times we get into conversations with partners and customers and for us it's really important that we do kind of break it out into really talking about the two separately because really one does enable the other. And so when we talk about modernization, you know, that's really about building out the, in most cases, physical infrastructure, so called that hardware layer that really acts as the foundation for the future. And so, you know, you think about investments in smarter machinery, smarter equipment, ultimately smarter processes that are going to do a lot more collection of data than legacy equipment.
And that modernization and that data collection capability is what then starts to enable the digital transformation. So you can't go through a proper digital transformation without making sure you first kind of check the box on a robust modernization effort. And so digital transformation is when you start to get to the phase where you can properly aggregate and leverage all that data that your new modernized infrastructure is capable of creating. And so that's when you get into depending on what the business drivers of the digital transformation are. And you can leverage that data to improve efficiency, improve quality, improve production rates, et cetera, et cetera. And more often than not also look at building resiliency into operations. So that's kind of how we try to talk to partners is let's go through that journey of modernization to enable the future state digital transformation.
[00:11:56] Speaker A: Can I ask why do those two efforts create so much friction for manufacturers when they're trying to move from the strategy of it to the execution?
[00:12:05] Speaker D: Yeah, so that's a really good question.
Candidly, it's a question with a boatload of answers.
And friction is a good word, right?
Probably worth saying up front. Friction in and of itself is not necessarily a bad thing, right? With any change, you're going to go through some friction and that's going to lead to better outputs. So friction, you kind of have to embrace it, I would say. But the answer to your question probably depends on what stage in that journey a manufacturer is on. And also, candidly, also is somewhat dependent on the size of that manufacturing company.
You know, if it's a relatively smaller operation. Some of the friction comes in the early stages where, you know, you've got to get a capex budget to make some investments, or you've got to look at the ROI timeframe and make sure that's acceptable for, you know, the corporate financial goals. And so there's a lot of that kind of early stage friction and just getting a project started.
Then when you get to that kind of modernization stage, you got a lot of new hardware, you got a lot of new data coming online, but all that data is in silos and you got a lot of folks who are thinking, hey, you know, we were sold on the value that this is going to provide us. But until you get to that second stage of digital transformation, you just got a lot of data sitting in silos with not a lot of folks knowing how to get value out of it. And so that is kind of a friction point as well.
Then when you get deeper into it, obviously there's a lot more technology that's coming online in your operation.
And that technology has complexities.
And without a really good partner who's working on the orchestration of that entire solution, there's a lot of friction there with how to operationalize it, how to manage it, how to troubleshoot it.
And then even when you get to. There's never really an end state with digital transformation, because you can always evolve and iterate. But even when you get to something close that looks and feels like an end state, you got friction because you've moved a lot of cheese in the operation and you got people who.
Someone's going to have to have the burden of managing all of these new components and new partners, especially if you have a more fragmented digital transformation without kind of a more cohesive single point of orchestration like we talked about. So there's a lot of points throughout all of that where there's the potential for friction.
But like I said before, if you embrace that friction and you kind of work through it in a proactive and constructive way, friction is not a bad thing. It's probably a sign that you're changing enough that you're on the right track.
[00:14:41] Speaker B: So, Dan, you just. You talked about fragmentation, you talked about data silos, and system silos come up constantly in these conversations. What if these silos actually look like in real manufacturing environments and why are they so hard to break down?
[00:14:56] Speaker D: Yeah, so that's a good question. And it really is dependent on the type of operation that you're talking about. You know, I think sometimes the silos can be, you know, from one part of an operation to another.
Oftentimes it can be between different functions of the organization. Right. You've got a lot of manufacturing output that also needs to feed into, you know, product management and to category and to finance. And so you've got different disparate systems, even outside of the manufacturing operation, that aren't properly accessing data. And it's really. What if you think about kind of the further stages of that digital transformation and you get deeper into thinking about the software layer that sits across all of it, having that single thread that works through all the systems to access that data from the different points becomes so critical. And that's why very rarely do you look at, I shouldn't say very rarely. There are certainly cases where you've got a smaller integration that just maybe sits on one specific part of a manufacturing process. But when you talk about proper digital transformation, you're talking about a layer that is enterprise wide and you have a lot of different best in class types of software that need to access that information. And so plugging that all into a common backbone, that's where it becomes a little more art than science. And I would say where it becomes really important to have a partner that has done a lot of these integrations and has come across all of these problems and challenges before and knows how to get through them.
[00:16:31] Speaker B: So yeah, interesting having that partner. I'm just wondering what helps ease these transitions. Whether manufacturers modernizing or pursuing digital transformation or trying to do both at the same time, is it having that partner that can ease the transition?
[00:16:48] Speaker D: I mean, that's definitely a part of it. From our experience, if, you know, if you have an integrator, a consultant who maybe doesn't have as much experience doing complex transformation work in that specific vertical, we see that leading to challenges that very candidly we have to come in and solve in a lot of cases.
But you know, the ease of transition, you know, when you think about transition, you're kind of balancing future state needs with current state reality.
And in just about all cases, our manufacturing companies we work with are looking to do this digital transformation in a way that reduces and in some cases eliminates downtime. Right. No manufacturing company can afford a significant lapse in productivity. And so that's kind of the biggest challenge is how do you implement without downtime.
And that's where orchestration and having that single point of contact to manage the hardware and the software and the integration of it become so critical.
These integrations, we say this often, like any integration, is make or break, but breaking is not an option. So you have to make sure you make it. And that requires a lot of planning, a lot of contingencies, and a lot of experience.
[00:18:14] Speaker A: Dan, perfect segue you just gave me. I think, Luis, I think you and I talked about this when we were at Automation Fair, that you guys kind of emphasized orchestration over point solutions.
Why does orchestration matter so much when you're integrating hardware, software and systems?
[00:18:30] Speaker C: Sure. Well, I would say that one key difference between modernization and digital transformation is the, the drivers that are behind them.
Modernization is more like, I Would say building the capacity because it is driven by probably obsolescence or cybersecurity risks.
But digital transformation is the one that addresses the actual business outcomes, the behavioral change.
And both fronts represent a huge burden for operations teams. So one thing that we, let's say consider our philosophy is the outcome before the algorithm. Think of that performance, that quality issue, that business operation that needs to be addressed other than just having data for the sake of data.
And we have a huge internal network of specialists. We all have many, many experience. We had decades of experience either from the technology or from verticals.
I worked for a brewing company back in Venezuela many years ago.
That's why I consider myself a beer advocate. But that's a separate thing. The thing is that I understand user needs and our team has this capability of understanding the needs that are behind the actual wishes of our customers and addressing them from the business outcome and not from the technology perspective.
[00:20:15] Speaker A: I love that. If you saw me look down, I wrote down outcome before algorithm. I love that you talked about data there.
It's often described as kind of the bridge in digital transformation. I talked about it in the intro. We have more data than we've probably ever had before.
Not even probably remove that. We have more data than we've ever had before. Why is access to this data so critical and where do manufacturers struggle to get it?
[00:20:44] Speaker C: Well, data is already there. You know this very well. We are in that age.
There is this digital transformation maturity level that we call goes from 1 to 5, from paper based operations to autopilot, you know, and most customers sit in between probably level two or three where you have like some islands of automation and semi, semi automatic or semi integrated records or analytics. But you still struggle to bring all the data together. Right.
So I would say that getting access to data would start by assessing the integration efforts.
Of course it depends on the context of every customer and every industry, of course.
But that together with establishing a correct DataOps approach, that's probably a new word or term for some. But the thing is that DataOps is about establishing and honoring data governance and accountability and identify who in the organization is, let's say, I would say not the most ideal, but that person that ultimately serve their internal stakeholders with data for building other use cases.
For example, if I work in a quality organization, I might benefit from a prediction or a quality prediction to act in advance, but if I work in maintenance department, I would probably benefit from the same prediction for triggering an automatic work coding in my maintenance system.
So same data sources, probably same assets, but different use cases.
So what's important here is that who owns the data, is it maintenance, is it quality, is it production? There's no accountability today or many organization lack of it or this is kind of new.
So that's part of our role as trusted advisors, like to guide our customers in that organizational change. Because we need to evolve and understand that data as the most important asset today.
Well, one of most important assets must be treated accordingly, of course.
[00:23:14] Speaker B: Yeah. So I mean, I had a question and Luis, you're probably already answering this, but how can manufacturers realistically unlock and use the data they already have, which you said, we already have this data in legacy systems and newer systems. Does that pose a problem?
[00:23:31] Speaker C: Yeah, I mean, I probably mentioned this, but like with modernizing the plant, you will build that foundation.
Modernization can be seen as the cornerstone for the evolution of your organization.
But it is not just modernizing because we want to be, I don't know, up to date with the latest controller or with the latest AI thing for a successful implementation and for this to be a sustainable effort.
We would walk the plant, interview the operators, understand the different needs around all the stakeholders, and find that common ground where we can start building. So we are benefit from the.
We all benefit from the same data, probably in many different manners, but we all are, let's say, rowing in the same direction when it comes to our business outcomes. So a proper discovery, a proper assessment of the current scenario, the current context and understanding what's behind lines and prioritized use cases would be an interesting approach. And that's essentially, or that's more or less our blueprint, like getting into the veins of the operation and understanding what's behind it and build from there and guide our customers in this transformational journey.
[00:25:07] Speaker B: And you had given an example of using the same data sources but different use cases. Can you share any other examples where a thoughtful combination of hardware and software made a measurable impact on plant operations?
[00:25:21] Speaker C: Sure.
Let's say that instead of relying on manual checks or dashboards, the equipment or systems can automatically alert teams when something drops below a performance threshold. For example. Right. We had this at Automation Fair.
We had this palletizing robot. It was moving boxes from one side to the other for the sake of the demonstration. But you can see it as the production environment, as your shop floor. Right there we had an area scanner where when you step into it or get close to the area scanner, it will trigger a maintenance work order directly. In the maintenance management system, we used an area scanner, but it can be a temperature sensor. A vibration sensor, just anything that can give me a hint of the condition of the equipment. So that's one type of integration we had. Another cool example, we had this AI troubleshooting tool in the hmi. So let's say I'm the operator and I'm having an issue. Let's say I just had a joint collision.
If I input that, that issue to the chatbot, I would get a step by step procedure of how to clear that situation and how to fix the problem. But if I'm not, if I don't feel comfortable enough with doing that, I can again use the same hmi and with a single click I could trigger an automatic work order in the maintenance management system. So that's just an example of the compute power that we can deploy at the machine level and also how we can close the gap with the enterprise by, let's say, triggering events and letting other teams know that we're having issues, hence reducing unplanned downtime and of course increasing OE and you name it. But that would be a couple of good examples that we already discussed last November.
[00:27:37] Speaker A: And I do want to point out. Oh, go ahead, Dan, please.
[00:27:41] Speaker D: Something that Louis said there because it triggered a thought for me. But you know, you had asked earlier about kind of common friction points and one thing Luis just said kind of hits on one that I didn't really mention. But one of the challenges we see relatively often is around tribal knowledge and the role that that plays on a very tactical level and real level in a manufacturing operation.
And as you know, most companies are dealing with similar labor related issues. Aging workforce, renewing workforce, ensuring that that tribal knowledge is continuous is critically important.
There's never a full replacement for tribal knowledge. It is tribal knowledge for a reason. However, when you have the level of data coming in that Luis has mentioned and you can have a very strong AI layer of inference on top of that, that's the closest you can come to creating a real buffer and some serious risk mitigation around the loss of any tribal knowledge. And that's becoming a real game changer for us. And you know, Luis mentioned the AI model that we were, you know, I think we walked you both through at Automation Fair. That's one of many examples of how we're using AI to bring real value to customers and solve real problems that they're facing as they're dealing with that aging workforce dilemma.
[00:29:04] Speaker A: And I do want to point out Dan mentioned that they walked us through that at Automation Fair.
Luis was the kind enough to do a video showing us that AI model. So if you go on my LinkedIn page or the Control Engineering LinkedIn page, you'll see that video and you can see what that looks like in action.
[00:29:21] Speaker B: Yeah, I saw that video.
I was going to mention that, too. So thanks for bringing that up, Gary. Thank you.
Dan And Luis, as you work through customers executing these initiatives, is there a common theme you see in projects that are successful and in those that stall?
[00:29:39] Speaker D: Yeah, I'll, I'll give you my take on it, Louise, Feel free to, to come in over, over the top. But, you know, as I mentioned before, there's really no two integrations that are the same. Right. Even we work with a large, a lot of very large complex national manufacturing companies and, you know, they'll have a vision of, hey, we've got 20 sites we want to do a cookie cutter integration around.
And even though that starts as the vision, it ends up as 20 incredibly different integrations.
And so there's really not a lot of commonality around any integration. However, what I would say is a very common theme of failure is when there are partners involved that don't have the right set of experience.
I mentioned before, it's very different by verticals. You get a lot of specialist consultants who play in special single verticals. Some are much more broad. And so making sure you select the right partner with the right level experience is probably the single most critical factor in a successful integration.
And outside of that, the other kind of common theme is when you get through it, you will have good, actionable data. And if you've got the right software layer across it, you're going to unlock the value of that data. So those are kind of the consistent themes of when things go well. And sometimes we've seen things maybe not go so well and we've got to come in and do some problem solving. But there's definitely not a lot of consistency in what we face in any of these types of integration challenges. So, Luis, I don't know if you want to maybe expand beyond that.
[00:31:14] Speaker C: Yeah, I would say that. And adding on your point, many of these, let's say, Industrial Digital Transformation Initiative fail because they start with the wrong question. Like sometimes we hear we need AI. What can we use it for?
But this leads to proof of concept without impact, orphan models, fragile demos that never scale. We've seen that. I was part of one of these continuous improvement teams with great ideas, but never scaled because we never managed to, let's say, interact across the organization and really asking the better question, which is what is the business outcome that we're addressing. So it all start with that with oee, mttr, scrap energy, you name it. There are so many.
Knowledge management, of course, is one of the most important these days. But asking the right questions is key for guaranteeing sustainable success with digital transformation initiatives.
[00:32:31] Speaker A: Makes perfect sense. You guys have been great so far, but I have one last question for you. You've given us plenty of time here, but I like to give our audience a little practical knowledge on the way out.
You started touching on this before both of you, but what best practices would you share with manufacturers who are just starting or maybe restarting their modernization or digital transformation journey?
[00:32:53] Speaker C: Right, I'd like to take that, Dan.
So well, first, asking the right questions always help, of course. And from the, I would say technical side or technical standpoint, like there is, it would be very beneficial to stick to it. OT convergence frameworks.
We've seen this like, I don't know, we acquire new equipment or we want to double the size of our operation, but we tend to fail with the proper integration or the proper, I would say, security assessments and topology designs and things like that. So there is this very famous framework that Rockwell Automation and Cisco co developed that is called Converged Plant Wide Ethernet, or cpwe.
So it essentially gives you the reference architectures and topologies that you can mimic or use for developing your own and guarantee that you are scaling in a robust and resilient manner. So being aware of how you are integrating your plant, that would be one best practice. And the other one is adopting a DataOps approach early is beneficial. Understanding how are we accountable for data and how are we going to govern data in our organization is also very beneficial for this digital transformation initiatives.
[00:34:39] Speaker A: Guys, fantastic information. Loved having you guys on. Like I said, I had the conversation with you both at Rockwell and thought you'd be perfect for the podcast. And as it turns out, you were. Luis, Dan, thank you so much for being on with us today.
[00:34:52] Speaker D: Yeah, thanks so much for the time. I appreciate the invite.
[00:34:55] Speaker C: Mucha gracias.
[00:34:58] Speaker A: Thank you for starting and ending with that. It makes me feel like I'm back in Barcelona.
All right, gentlemen, thanks so much.
[00:35:05] Speaker B: Thank you.
[00:35:05] Speaker C: Thank you. Bye bye.
[00:35:08] Speaker A: All right, Stephanie, good, good stuff from those gentlemen. Luis Atencio. Danforo. Every once in a while we have one of these and I'll write something down. Cause I'm like, that's a great one. And the outcomes before algorithm I love because we keep coming back to this. You know, Ryan Crownover of Vertec talked about this when he was on the podcast several weeks ago.
You can't come up with the solution unless you understand the problem. I think Alicia Lomas talked about that, too, is figuring out what the actual thing you're trying to solve is and solving it instead of doing what Luis said, which is, we want to use AI. How can we use AI?
[00:35:44] Speaker B: Yeah, no, I was going to bring that up as well. And I like when Ian talked about asking the right questions, which falls into line with that. I think that sometimes we're just not asking the right questions. We're just adopting technology for the sake of technology, and that's not what is going to give us the outcomes that we need.
So, yeah, it was great conversation and, you know, thanks for meeting with them at Automation Fair and bringing them on the podcast.
[00:36:12] Speaker A: Yep. This is what I do now. Whenever I meet somebody, I'm like, I shouldn't say this, because then if I meet somebody and don't invite them, they're going to be like, am I dull?
I know maybe I wasn't that interesting. But, yeah, when I meet somebody like them, I'm like, come on. So it's, It's. It's good for everybody.
Guys, thank you so much. Guys, audience, everybody, thank you so much for coming on and spending a little bit more time with us on the Control Op Manufacturing podcast. Love having you. Love having these conversations.
[00:36:43] Speaker B: Yeah. And I'm going to go with my. My signature outro.
[00:36:47] Speaker A: Bring out the Boston.
[00:36:48] Speaker B: Yeah, Have a wicked good day.
[00:36:51] Speaker C: Bye, everybody.
