[00:00:00] Speaker A: As 2025 winds down, it's time to take stock of what really mattered in manufacturing this year. On this Ctrl Alt Manufacturing Year End Special, engineering.com's Michael Wallette joins us to unpack the biggest manufacturing trends of the year. From AI and automation to cybersecurity, connected workers, and the ever elusive roi. It's a candid look back and maybe a little bit of a glimpse ahead at where the industry is really.
Hello, hello, hello, everybody, and welcome back to the Control Alt Manufacturing podcast, Resetting and rethinking manufacturing. As always, we are very happy to have you back with us. Today we're going to be going exploring some of the people, technologies and strategies that are driving the digital transformation of manufacturing. I, as always, am one of your hosts, Gary Cohen, the other host.
[00:00:56] Speaker B: I am Stephanie Neal. And the. Hello, hello, hello again. The Matthew McConaughey is in the house. All right, all right, all right.
[00:01:04] Speaker C: I know.
[00:01:04] Speaker A: I feel like I've got to get rid of it now because I don't want to be cribbing off Matthew McConnell.
[00:01:08] Speaker B: I like it, I like it.
[00:01:09] Speaker A: Although, you know, you could do worse.
Today should be a fun one because we're talking to one of our colleagues. So, you know, Mike is with engineering.com which is one of our sister brands to control Engineering. And it's our year end episodes. We're going to take a look back at what really moved the needle this year, what felt like hype, what's coming next.
And Michael Wallet is going to be great at that. He's been covering North American manufacturing for 20 years. So he's seen these launches and pivots and the promises and the pitfalls. So I think we're in pretty good hands today.
[00:01:42] Speaker B: Yeah, no, he's great. And again, he's really focused on digital transformation. That's what he's covering for engineering.com and when we look back at 2025 and everything that happened with supply chain and tariff concerns and skill shortages, um, and it's just this need to be agile, the ability to be agile. So throw a little AI in there and the manufacturing floor is ripe for a digital transformation. But what does that really mean? And I think Mike can help us walk through that.
[00:02:10] Speaker A: Yeah. One of the things that I think is always interesting about digital transformation, Stephanie, you and I go to a lot of shows. I'm sure Mike goes to a lot of shows as well. And there's always kind of a buzzword at every show like the buzzword. Now it's generally AI, but for a while it was digital transformation. That's what everyone was talking about. And so I think it for a while got this. There was this idea that it was this flash in the pan marketing phrase, but it's really something every manufacturer is doing at this point. It's just a matter of how far down the path they've gotten. But I mean, if you're in manufacturing and you're not using some of these AI tools, whether it's more advanced tools like Digital Twin, whether it's AI, whether it's cybersecurity, you are very much behind the times, but you don't hear as much anymore about it. It's not the buzzword at the shows anymore because I think everybody just realized we're doing this, we are on this path already, but it doesn't make it any less important.
[00:03:09] Speaker B: I think you're right. And I recently read something where an executive said, it's a matter of survival. You have to be on this path or you're not gonna survive in the future. So I think that's probably why we're not hearing it so much as a buzzword. It's there, but, but it's ingrained in everything that we see in the technologies and the processes.
So yeah, so I think that the.
[00:03:31] Speaker A: Kind of stuff that works, it's pretty consistent, you know, machine data for predictive maintenance. Like I said, digital twins, cloud, erp, integrated AI, connected supply chain. Like every factory plant, manufacturer, company has its own version of what that transformation looks like, what that roadmap looks like.
But it's those same technologies that are being used, the ones that, that make this, your digital transformation journey successful. So it's not really what tools exist, it's how you can deliver these tools to deliver value in your environment and not create disruption.
[00:04:12] Speaker B: And we've heard a lot of that from some of the system integrators that we've been interviewing over the course of this podcast. And you know, just how do you like, what's the low hanging fruit? Where do you start?
How do you do this without creating chaos on the planet? Plant.
And so it's a definite skill. And that's the other thing that we need to talk about, and that always comes up is the lack of, or the skill shortage and the need for talent to be able to implement some of this stuff.
So, yeah, there's a lot going on.
[00:04:47] Speaker A: Yeah, that skill shortage is always interesting. One of the obviously facets of digital transformation is AI.
And I've heard so many people at so many different shows and talks saying everybody's worried that AI is going to take our jobs. But if you are versed in AI, you are going to be more valuable than ever. You just need to kind of immerse yourself in these new technologies in order to make yourself valuable. But there still is a skill shortage because, you know, a lot of people who are in engineering and control, engineering and automation are getting older and getting toward retirement age and it's bringing in that new generation of people to pick up. Um, yeah, with that, I mean, look, Mike knows this stuff as well as I do. So let's introduce our guest for today, Michael Wallet.
[00:05:35] Speaker B: Yes.
[00:05:35] Speaker A: He's from Canada, Senior Editor of engineering.com he's an award winning journalist and media professional who's covered the North American manufacturing sector for the last 20 years. Prior to joining Engineering.com, he spent 10 years as an editor of CanadianManufacturing.com and two years at a bustling B2B marketing agency developing content campaigns for very large manufacturing clients. Mike, thanks for joining us.
[00:05:59] Speaker C: Thanks for having me. I think I turned my camera on too early in the introduction. I didn't expect it to go on so long.
[00:06:06] Speaker A: People want to look at you more than they want to look at us anyway, so it's probably better that your camera was on.
[00:06:11] Speaker B: Absolutely. Yeah.
[00:06:11] Speaker A: They should be fun. I mean, we're kind of doing a little bit of a year end wrap up of what we've seen and what digital transformation is and isn't. But it's kind of interesting to be talking to somebody else who is covering the industry. You know, Stephanie and you and I are all in this industry and covering it doesn't mean that we are engineers, but it means that we've got a pretty dangerous knowledge of what's going on out there. So should be fun.
[00:06:36] Speaker C: Yeah, I agree. I look forward to it.
[00:06:39] Speaker A: Well, I want to start with you said before that digital transformation isn't really a new thing. We covered that in the intro. It's really new. It's just this latest phase of advanced manufacturing. So wondering if you can kind of unpack that idea. Why do you think that term gets misused or you know, thought of as something new and novel.
[00:07:01] Speaker C: Right. Well, you know, I don't think it's, it's mis misused and it's not new or novel either.
You know, humans have this need to put ideas and information in buckets and then name those buckets things. And digital transformation is one of those things that we have kind of aggregated all of the latest digitized advanced manufacturing functionality into for ease, really, I think. But it's no different than what manufacturers have been doing for the last 30 or more years.
You know, if you think back to the advent of PLCs, for instance, you know, that changed the game in the 80s and brought along, you know, factory automation, which again changed the game. And I'm not talking about like Ford Model T automation, I'm talking about like McDonnell Detwiller and like the heavy aerospace automation.
And then, you know, we got into Industry 4.0, which, you know, was increased connectivity and data flow, but not what we are currently calling digital transformation. But each one of those sort of paradigm shifts were transformative in a sort of digital realm.
So I think companies that have been around for a long time are very comfortable with this sort of thing.
And if they've been around this long, they've probably got the capital to invest in it and experiment with it.
So we're starting to see the uptick now.
I think back to the intro, Stephanie. You mentioned how, you know, companies have been doing this thing in different forms. And the way you described like the need to implement this without disruption and you know, bring workers on it all reminds me of the conversations we were having around lean manufacturing in the early 2000s, 2001.
You know, I would talk to, you know, chief economist of the CME, which in Canada is like the APMA.
And he was warning manufacturers 25 years ago, if you don't get on the lean train, you won't be around for very long.
And you know, you have to do it. You have to find a way to do it without disruption. You have to find a way to get buy in from your employees. You have to find a way to fund it and get buy in from the C suite. These are all the same conversations we're having with digital transformation. So it's funny how the same things kind of come around again and again and again.
[00:09:36] Speaker A: Yeah, obviously this has been going on. People have been digitizing for decades. Whether it was like you said, industry 3.0, 4.0, obviously we're kind of moving into 5.0 now. Is there something that makes this current wave different?
[00:09:49] Speaker C: Well, yeah.
The newest technology is just so far beyond anything we've experienced before.
And it's not just the newest technology, like being able to buy like a new edge sensor, vision sensor or temperature gauge or something put in your plant and have it talk to some. It's the computational power that we have available now at scale.
Everybody can have access to it through the cloud. And it's made the it's given manufacturers the ability to go down this digital transformation journey in a way that they were never able to before. Like before, you would have to invest in an on site, the server farm to generate the kind of computational power that we all have access to now over the cloud, AWS or Oracle and any of the different cloud services that are on offer. So I think that's the big thing. The democratization of compute is really what's made it possible for everybody.
[00:10:52] Speaker B: But it's, it's possible for everybody to implement, but everybody does it a little differently. Right, Mike? So I'm just curious if we can expl, like why this is such an, like an individual type of project and how manufacturing leaders can figure out what's worth pursuing versus what's just the shiny technology.
[00:11:17] Speaker C: Right. Well, and that's, that's the thing is that the, the reason it's kind of different for everybody is because everybody's situation on the floor is different. Like on the shop floor, the technology that you have access to, you have access to the same technology everybody else does. You can call up Siemens or your local, you know, value added reseller for software, everybody can get the same stuff.
But the problems you're trying to solve, even though they're all based generally around productivity, speed, agility, waste reduction, when you get to the shop floor, the problems are all different and custom essentially. So you have to be able to take a look at what's available, take a look at what the problem is you're trying to solve, take a look at the staff and the expertise that you have on hand that can help you solve it and find a way to mesh all of those together in a way that solves your problem. And that's why it's individual and that's why people like us keep talking about this. Because you never know when you're going to tell the story that somebody else is like, oh, that's really similar to a problem I'm having. Let me look at that idea.
I think that's why we do this kind of stuff. Right. Is, is to kind of help out in that way, share those stories. And, and that's why it's so individual because everybody's a little different in how they're doing things, even if they're making the same products.
[00:12:41] Speaker B: Yeah. And things change so fast. Right. Like into your point. That's why we're like, we're out there talking about the technologies that are evolving and you know, it overwhelming. I mean if you just think about it in terms of AI and all the new tools that come out in AI and like, how do we use everything? It's probably the same with what's happening on the manufacturing floor and all these new tools, they must be completely overwhelmed. But let's not talk about the negative for right now. Let's talk about the positives of digital transformation. Where do you see the value being created today? Are there specific technologies or strategies that you can point to that are maybe actually delivering results?
[00:13:21] Speaker C: Right. Well, I'm not going to point to specific technologies because any technology can be the answer for any particular situation.
Maybe that's not the right way to say it.
There's a space for each of these technologies in manufacturing. It might not be for you, it could be for somebody else. But the technologies themselves are there for a reason, because they do something. But what everybody's looking for, I think is these boosts in efficiency, productivity, agility.
And I think what we've got going on with digital transformation, now that we're a good five to ten years into pretty standardized adoption, is that we're seeing a lot of these sort of boosts in productivity and agility coming from the technology. Like we talk about, like real time machine data, using it for predictive maintenance. That's a real piece of value.
We've been talking about preventative maintenance, then prescriptive maintenance, now predictive maintenance again for 20, 30 years.
Each time the promise is reduced cost, more uptime, better utilization of equipment and staff. And each time we found a way to increase the gains on the gains of the previous model that we've used.
And this comes from the increase in not just the way we crunch and analyze our data, but in the technology that we have out there on the shop floor.
Digital twins for me is something that's huge. Digital twins again, these have been used by very large corporations for 30 years. They've been used for infrastructure, they've been used in banking, they've been used in a lot of different areas. But now the economies of scale have made it that it's more affordable for companies to get into. I mean, now you buy a Siemens motor or drive and it has its own digital twin embedded directly in its software.
So you can just upload it, it'll talk to your system and add itself to the digital twin of your plant. And I think it's such a powerful technology to use the simulations you can run. When trying to say, bid on a new project.
If you have a new client, you're like, how can we work this into our current production scenario? Well, let's simulate it and now you can do it without just guessing. You can actually run the simulations or hey, our client has sent us new specs for a product.
Do we need to buy new machinery for this? Well, you simulate. The digital twin is a very, very powerful tool.
Robotics, that's the other thing. Now robotics again have been around for 30 years.
I remember going to IMTS and seeing massive transformer sized robotic arms moving car bodies around.
Now you go to a trade show and you see a cobot the size of a human hand manipulating a screw into like a very small, small space. I mean the things that we can do with robotics now can provide so much value for the small and medium sized manufacturer that have never had access to this kind of stuff. You know, you think about $100,000 machine or $6,000 cobot. A cobot you can rent and wheel out on the shop floor and put it in front of a machine over here. And then tomorrow you have a different run, you wheel that cobot over to the end of the other line and put it there.
Amazing amount of value can be derived from that sort of functionality.
And there's so much more. I mean I've got a list, but I feel like I've talked too much mentioned cloud based ERP plm, the combinations now of ERP plm manufacturing execution systems, all on common data layers that you can apply. You don't have to write your own APIs anymore. Again, this just another fantastic way to get at your data in a way that makes sense, that you can make actionable decisions on.
It's amazing to me at this time in my career watching all of this.
[00:17:31] Speaker A: Come out and we talked a little bit about the skills shortage out there in the intro.
Obviously all of this new technology is coming in, all the things that you just mentioned that people are using.
So obviously the way engineers and operators are working is very different today compared to what it was even five years ago.
What new skills out there are becoming critical. So an engineer, plant, floor operator is not false falling behind at this point.
[00:18:00] Speaker C: You know, you kind of got me on that one because they're all changing, right?
If you're not in a strategic management role, if you're in an execution role, then your job has been changing for the last five years.
And I think that it's more of a mindset change these days. Like yes, you have to understand the technical side, but I think that's a mindset of being able to pick up and learn the technical side of this new technology or that new technology that you're Putting in being able to be adaptable and being sort of, we used to say digital native, where you would understand how to use the Internet really well, but now it's way beyond that. You have to be a technical native. You have to be able to understand the, the purpose of the new technology, the way it was built, the way it is, the way it functions, the way it does. And if you can do that, then I think you've got a spot in manufacturing for a long time.
But what we've seen a lot, and like, I hate to say it with AI, but AI has changed the way engineers do their work.
And if you think about, I know, you know, you mentioned design and cad, you think about an engineer would run CAD simulations, right? And that would be. Their job is to sit there and run the simulations and iterate on a design. But now with AI, you can have your system spit out a thousand iterations in a day. And that designer's job is now to validate those options, see which is the best fit, not just for the client, but for the shop floor, for the manufacturing side, the ability to make it in the least expensive way possible.
So those skills are changing. You no longer need to run simulations. Be a simulation technician. You need to be somebody that can evaluate the designs as they come across your desk.
It's the same off the shop floor test and validation. Engineers used to go to the machine and plug in and then interpret the results that the machine spits out. But now there's API layers that collect all of that data and put them on like a data sheet or a dashboard.
And then you sit there and all this information is now in front of you without having to go to the machine. So that job has changed. The core competencies there have changed as well. So I think that when you're looking at that, it's the execution jobs that are going to change the most, not the sort of strategic jobs at the top of the food chain.
[00:20:46] Speaker B: I just want to jump in. Gary, I know you have more questions, but I was going to ask you, Mike, about AI and the workforce. And since you're talking about it now, I don't want to lose your train of thought, but do you get a sense or when you're talking to people who are engineers who are using these AI tools, was there resistance in the beginning?
The question being, how do you introduce AI and everything that goes with it without alienating the teams or triggering that fear of replacement response?
[00:21:23] Speaker C: Yeah. And again, this is the same story that we had when automation and robotics started to become Very, very common, you know, replacing jobs. People are worried about it, but everybody I've spoken to, right now, at least AI is there to augment the human, not replace the human.
I had a very recent conversation with Jeff Holland, who is. I think he's the chief operating officer at Snowflake here in Toronto. Snowflake is an AI developer. They develop apps, they help companies develop apps. And everybody that's reaching out to him is looking for ways to improve productivity using AI, but not by replacing people, by augmenting them, by taking away repetitive tasks or tasks we're not really good at. Like, if you think about the things that AI in a computer is good at, it's, you know, reasoning, it's very fast calculations, it's vast data sets. These are things humans are terrible at. We, I mean, if you think about how many bits of information are in the sentences that I'm speaking right now, I mean, it would take a calculator from the 1980s to compute that. Right. And then we're talking about the amount of data in terabytes. I mean, you know, humans can't conceive of that level of data. So with AI in particular, and over the next five or so years, I think that's the big key. And you have to be able to communicate that with your team. Be like, this is here to make you better at what you do and make you happier while you're doing it. It's not here to replace you because it can't. It's got no arms, it's got no legs, it can't move around a plant, it can't take stuff.
[00:23:07] Speaker B: Personality.
[00:23:08] Speaker A: Yeah, that was a big thing in cybersecurity too, when people were afraid of it when it first came out. It's like you can have a human going through terabytes of code and code all day looking for an anomaly. We're really bad at that because humans naturally get bored and it's a lot of information to sort through, whereas an AI system can pick up an anomaly really easily. They're excellent at that. So, yeah, I think you're right. This idea that it's not trying to replace humans, it is trying to make us more efficient and kind of stop us from doing some of the stuff that we weren't that good at to begin with.
[00:23:41] Speaker C: Yep. And things that weren't really great for us to do, like automation working in, like, hazardous environments or, like, dirty environments, things like that, you know, people don't need to be put in those situations anymore.
We have technology to do that. And Those people can be deployed in other ways that are way more rewarding for them. So I think that, you know, it's, it's going to follow along that same path.
[00:24:07] Speaker A: Yeah. Because I'm at heart a negative person. And ask a negative question here.
What are some of the, the biggest integration issues or maybe ROI challenges you're seeing out there for companies? So a lot of these digital initiatives are still stalling, you know, are still failing or still not making it past proof of concept stage. So what are some of the sticking points that you're seeing?
[00:24:29] Speaker C: Yeah, you're talking about pilot purgatory for the most part.
And it's a real thing.
I think a lot of it there. Like, so, like, for us, it's hard to follow this technology story because it's changing so often. And it's our jobs, it's what we do. We, we follow the story and it's, it's challenging to follow for somebody running a company or for a plant manager that's trying to improve manufacturing situation, they have a whole other job to do and then follow this. So it's really hard to know what's out there. And I think that's the big thing.
You look at what's kind of finds its way in front of you.
You don't know what you don't know kind of in these situations. So you look at what's in front of you, you use what you're exposed to and see how it goes. And then that combined with that, let's face it, a lot of these manufacturers are using legacy systems. Whether it's legacy machines that they've been using for 20 years that essentially they, they depend on, it's part of their brand. It's the only thing that can produce the product that they're producing.
Or they invested in a lot of, say, PLCs and sensors that are not the smart technology. They did it 10, 15 years ago. These things are still working, everything's working fine. But they don't have the smart technology. So there's a lot of legacy issues and integrating into that that I think is causing some problems.
Not so much in the willingness to adopt, but in how long it takes to roll out that adoption. Right.
The second is data.
Chasing data is a huge problem right now, especially for companies that have never really had to do it.
Nobody was hiring data scientists in a manufacturing environment five years ago. Right. But now they are.
And now it's sort of like you're expected. So you have to be able to understand that. And I think that that's a heavy lift. Trying to learn how to manipulate and manage and absorb all of the data that's produced in a manufacturing environment is a challenge. And that's expensive.
Like, you know, to be honest, it in a period now where we've just had disruption after disruption and increasing costs after increasing costs, manufacturers, in order to continue to compete, are now expected to invest in digital transformation initiatives. And, you know, they're probably kind of scraping the bottom of the barrel a little bit in terms of capital allocation.
So I think that that's something that we're seeing companies do, or I think that's a reason why companies are doing smaller scale transformations to begin with.
But that's not actually a bad thing. I think that's probably a good thing. Even though the rollout is slow, it helps you learn a lot of lessons about how to roll it out, and it helps you learn a lot of lessons about how not to do it.
And then you can take the ROI from that small project and use it to fund the next project and the next project and the next project. And that's how we're seeing a lot of success. And that's how you can kind of do it without like, you know, breaking the bank sort of.
Not long ago, I was at an ARC event and I talked with Doug Warren. He's a senior vice president at Aviva. It's a software company owned by Schneider, a big software company owned by Schneider. And he was candid with me saying that you'd be surprised how few people are actually taking this out of the pilot stage.
I think his quote was, specifically, there's a lot of people dipping their toe or their foot in the water, but not a lot of people taking a full dive. So, pardon me, I think that the uncertainty of the ROI, or will it ever end, is this a constant investment that I'm putting myself into? I think that that is having some impact now, along with the current sort of economic situation, which is followed by, you know, five years of ridiculous disruption. I mean, you know, manufacturers have had a lot to face, so I think the unknowns are probably forcing their hand a little bit.
And then lastly, if you don't mind me continuing to talk, which I do a lot, is the baseline of digitization. So if you haven't got the investment already made earlier in basic digitization of your factory, you can't do any of this stuff. So you have to start at the ground floor. You have to be able to digitize your manufacturing facility before you can get rolling.
[00:29:14] Speaker B: So.
So when we're talking about the baseline. There's also talk about capturing tribal knowledge as the first step to real digitalization.
So in your opinion, Mike, how important is that foundation and what's the risk if companies skip it?
[00:29:29] Speaker C: Yeah, well, tribal knowledge is the secret sauce for any manufacturing company. If you look at any super successful plant, somewhere deep in the recesses of that plant is a guy that's been there a long time that knows how everything works. Right. And that's the knowledge you have to try to sort of spread out across the plant and you have to capture it before that person retires or moves on or gets promoted.
So it's super challenging to do it always has been. I mean, retaining that tribal knowledge has been a problem for 20 years, if not longer.
Especially because, you know, we've seen now like, like the reduction of, like the mass workforce where companies are employing thousand people to do stuff because they've now put in automation to do all of, you know, like the little work. So the number of employees with that tribal knowledge is smaller, and then it's more important.
So I think you have to be able to try to capture that through digitizing the results of their work and the outcome.
And then by doing that, you can sort of lay out the groundwork for digital transformation because you kind of have that now digitized. It's in the digital realm, so to speak.
[00:30:52] Speaker A: Absolutely. And I think another big problem for smart factories, connected factories right now with cybersecurity.
You mentioned legacy systems. A lot of these weren't built with cybersecurity in mind. With smart factories, connected factories, there are more things connected to the Internet than ever, which creates more risk than ever from a cybersecurity standpoint.
What is one of the big kind of underrated risks manufacturers are overlooking out there? And how can they try to get ahead of it?
[00:31:18] Speaker B: It.
[00:31:19] Speaker C: Yeah, it's the that it ot convergence, really. Over the last five years, we've heard a lot of operational technology being undermined and then hacked. And that's how manufacturers, like we've seen manufacturers being shut down because of their operational technology being the, the loophole or the zero day, if that is. Cybersecurity is not really my thing. But, you know, they have these old machines and they're encouraged to put edge devices on them. So they, they do that and they connect everything and then it's a big gaping hole for somebody to walk in and say, I'm going to shut you down and threaten you with ransom. So I think that's the big thing.
And then passwords. I mean, honestly, Passwords.
We're still talking about passwords these days, but that's the way it is. And the problem is there's so many passwords that you have to have that if you have like these complicated, you know, 30 character passwords now, you just can never remember them. There's solutions for that. Master Pass or, you know, apps that have all of your passwords. But I don't, I'm not great at this and I have actually lost my password for that in that situation. So I lost like 30 passwords. I lost access to everything. And it's a terrible event. So I, I think that, you know, companies are still trying to wrap their head around the fact that their CNC machine can be the hole in their cybersecurity rather than their information technology.
[00:32:52] Speaker A: Yeah, I was going to say something before we go on the, the, you know, you talk about passwords and why are we still worrying? Everybody knows you've got to change your passwords. They've got to be, you know, it. But some of the biggest hacks that have happened that have impacted nations Colonial pipeline, Oldsmar were because somebody didn't change a password or left the default passwords in or something that, you know, you hear it and you're like, how does a piece of our critical infrastructure not change a password? But it still happens all the time.
[00:33:22] Speaker B: No, that's what I was going to say too. See, great minds think alike. Gary, remember the days when past the password was password.
[00:33:30] Speaker C: Yeah. Password 1, 2, password 1, 2, 3.
[00:33:34] Speaker A: Or your pet's name or your kid's name, something really easy to guess. And then I was also going to say I did a webcast earlier this week.
One of my favorite people out there, Jim Cook, he's a director at BW Design Group and he's got a cybersecurity background. And you talked about that Itot divide. He always calls it marriage counseling. It's like you've just got to get these people in a room and have them talk to each other because it doesn't really understand what OT does and OT doesn't really understand or maybe care about what it does. And, you know, a lot of the things that it tries to layer on top of OT don't work as in, you know, yeah, we just need to shut everything down so we can run all our updates. Well, I'm making beer or whatever, staplers. I'm not going to shut my plant down and shut my production line down for three hours so you can run updates. So it's just like we have to just get them in a room and have them talk to each other so we can start solving some of these problems that are still out there.
[00:34:30] Speaker C: Yeah, you just. I think there's like a $2 million update, right. It shut down my plant floor for three hours and that's the end of that.
[00:34:37] Speaker A: Exactly right. All right, I'm going to ask you to be a prognosticator here. I'm going to have you look a few years out, three to five years out.
There's so many new technologies that are coming online.
Which technologies do you think are actually going to move the needle for manufacturers in these next few years? Whether that's AI co pilots or digital twins, like we mentioned earlier, edge computing, like what's hype and what's real?
[00:35:03] Speaker C: Oh, wow. How much time do we have? We got all kinds of time, tons of time. So I mean, we'll get the obvious one out of the way. AI, I mean, it's kind of the answer to everything right now. It's something that I've been focusing on a great deal covering digital transformation because that's been the big story for the last two or so years, is AI. And I think, you know, we talk, you hear a lot of talk right now about the AI bubble. And people in manufacturing are hearing this and they're thinking, oh, is AI done? Is AI over? And I mean, like, the important thing to remember here is that when you talk about an AI bubble, you're talking about financials, you're talking about people getting, you know, cleaned out financially. You're not talking about functionality. You know, you think about the dot com bubble, right? The dot com bubble was massive. It, you know, destroyed retirements, empty bank accounts all over the place.
But the Internet's the most important thing that's ever happened to us and it's the most important thing we use every day. And AI is going to be the same thing, bubble or not.
And personally, I think manufacturing is one of the very best positioned industries to take advantage of what AI is really, really good at, especially over the next few years.
It's got, you know, with AI agents in particular, agentic AI, you have your AI agent, you train it on your own internal data. So you're not going to get the hallucinations that you get out of these commercial level AIs like ChatGPT that have been trained on the history of the human, you know, the civilization. They've been trained on everything so they can pull all sorts of stuff out. But if you train it specifically on your data, you train it offline.
Then you put it online, you use it and then, you know, in a month you reevaluate, you take it offline, you do the training again. You are getting rid of most of the hallucinations and, you know, incorrect results you're going to get. And the value that a manufacturer, especially a small and medium sized manufacturer can get from the force multiplier of an AI agent, helping them make decisions and helping them run their machinery, run their energy usage, find new materials. I mean, the possibilities are kind of endless in manufacturing. So I think AI, especially agentic AI, is the big trend to watch over the next three to five years. The other one ties into its digital twins and simulation.
So powerful and now so widely available with the compute power that we have at our fingertips.
Not taking advantage of, of a digital twin to make decisions in your company is, I think, a mistake even for a small company.
Unless you can look out and count your staff on one hand, you should probably be looking at doing simulations to make decisions. I think that's a big thing. And then you have the edge. You guys at Control Engineer do such a great job of covering the edge and all the different devices that are out there and how they interact with how you use them, what's coming around the corner, I think that there's so much untapped potential with what people can do now with legacy machinery using edge devices to bring them into the simulation game and into the AI era. That's the connective tissue that's going to make it happen. For most of these small and medium sized companies that do not have the, you know, the capital weight to invest in all of these different things, I think edge computing is really, really there. And then to see humans, you know, using your simulations and your AI to make the humans work better, be it, you know, with handheld systems or training, breaking down language barriers to send work instructions to sites in different jurisdictions, all of it I think is probably something we're going to see a lot of. I've got more, but I mean I could write stories and stories on this.
[00:39:12] Speaker A: Well, you probably are. Go to engineering.com and look the big.
[00:39:16] Speaker C: Thing to look out for. The big trend in the software side, for me anyway, that I've noticed is the trend towards outcome pricing.
So you think about everything that we are doing now is run by software.
Usually you would buy a seat.
That's your license, you buy a software seat and then your super user uses that software to create value for your company.
If you have a user that's really, really good, they create much more value than a sort of run of the mill user. But everybody's still paying that same price for the same seat, right? Essentially.
But there's been a lot of talk now with AI delivering outcomes over and over and over again. How does a company charge for that? You know, do you sell tokens, do you sell seats? Do you do outcome based pricing? And I think that's where a lot of companies are leaning towards it.
Autodesk just had their investor day. October 7th, I think it was. I'll check my notes quickly. Yes, October 7, 2025. And big part of that was talking about what they call their consumption model. And it's essentially outcome based pricing, trying to find a way to do it.
And one of the big things that actually is happening is that it's affecting not just their customers, but their resellers. So their whole value added channel of, you know, resellers for their software, they're going to have to change the way they do things. They have to become strategic partners more now in how you apply this software and how you apply AI, how you apply their technology, not just selling you a seat and bna, you're good to go. Call me next year.
So big changes on how you're going to use software, will it be in the next five years? Maybe not. I might be looking a little bit too far ahead there, but I think it's something that you should consider for sure.
[00:41:10] Speaker B: Good to know.
I hadn't considered it.
That's new stuff for me. That's good.
So let's wrap up this conversation. Mike, after covering the Good, the Bad and the Overhyped, is digital transformation really living up to its promise?
[00:41:27] Speaker C: Good question.
[00:41:29] Speaker B: And if it's not, what needs to change?
[00:41:31] Speaker C: I think it is in some places and in other places it's not, you know, what needs to change.
Okay, first of all, yes, I think digital transformation does do what it's being sold to do. I think that when applied correctly, with knowledgeable engineers running the show and a network of help, I guess a network of peers that are doing the same thing, you're going to gain a lot of value over time and make yourself a lot more agile and a lot more resilient to disruption.
If you digitally transform your company now, is every manufacturer experiencing that utopia? No, they're not. But that's never been the case with any sort of new technology. There are people that, you know, implement it right. There's people that have some difficulties implementing it and everything in between.
So I think the important thing is, and we discussed it earlier, if you're not doing it at scale. Start small and then do another small thing and another small thing and next thing you know you've done a big thing. And I think that's probably the way that you go about it. And the big change is that, is that, you know, get started doing it but you know, don't take off too big of a bite.
[00:42:54] Speaker A: Yeah, we did a podcast that'll come out a little bit after this one with Ryan Crownover and he said the same thing of like, don't try to take it all at once. Don't try to have a huge technology initiative. All dump little wins, little incremental wins. Building on each other is the, is the way to go with that. So, Michael, wallet. Great stuff. I'm going to let you plug your site here because you know, we're all part of one big family. So if they want to learn more from you, where are they going?
[00:43:19] Speaker C: Yeah, go to engineering.com. it's as easy as that. We have a ton of different sort of pillars of news and technology based features that we cover from, you know, digital transformation. With me, a big part of it is simulation and design, how to use cad. We have incredible editors covering that. Michael Alba is the editor of that section. He's been doing it a long time and is an engineer who learned how to do that stuff. So he's kind of a good place to go.
We cover a lot of Aerospace, Automotive.
3D printing with Ian Wright is a phenomenal source of information.
So if you're in manufacturing, you should probably be checking out not just control engineering, but when you're done there, come on over to
[email protected] and we've got your back.
[00:44:10] Speaker A: If you like control engineering, you're probably going to like engineering.
[00:44:13] Speaker B: If you like control engineering, you're going to love engineering dot com.
[00:44:17] Speaker A: No, no, you're going to love both of them.
Don't make him look better than me.
No, they're both great size. Michael, thanks so much for being with us today. We really appreciate it talking to you for our little year end wrap up here. Yep, we're at the end of the year. Crazy, right?
[00:44:31] Speaker C: Thanks for having me. Appreciate it and have me over anytime. I mean it's not like I'm busy or anything.
[00:44:36] Speaker B: So get back to work. By the.
[00:44:40] Speaker C: On that note, thanks a lot guys.
[00:44:41] Speaker B: Thanks Mike.
[00:44:41] Speaker C: Thanks Mike.
[00:44:42] Speaker A: Appreciate it.
Always nice to talk to a colleague there and yeah, I mean especially somebody who's as knowledgeable as Mike is and has been doing this for as long as he's been doing. But yeah, yeah, good stuff. I mean, I also think it's kind of good every year because the technology moves so fast to kind of take stock of like, what happened this year, what changed, what's different? And man, it is moving fast.
[00:45:07] Speaker B: Yeah. And you know, as we found out today, Mike is so plugged in to all the different areas of digital transformation and you know what he's been reporting on over the course of the year and always sort of looking ahead.
It's just great to get his perspective and again, encourage anybody to, after you are on Control Engineering, to mosey on over to engineering.com and see what they've got over there because there's so many good in depth stories and interest, interesting stories and fun stories too.
So.
[00:45:41] Speaker A: Yeah, yeah. One last thing I was thinking of is while we were having that conversation, and this is when people who don't work in engineering talk about technology and we say it's moving fast, and they're like, yeah, whatever, technology always moves fast. I always think about that day. It was probably four years ago now when ChatGPT became open to the public. So Micah talked about the democratization of technology.
And I mean that if it was dropped on a Monday, the world was different on a Tuesday.
It's just suddenly like, oh, everything's changed. I can use this new technology to do xyz. And you know, we've been figuring it out like the general public has been figuring it out for the last several years. I know you're not super high on AI or you don't love AI, but you know, that change happens up.
Yeah, but I mean, that change happened for just regular people. And that stuff is happening obviously in manufacturing as well. And the shifts are happening just as fast and they're just as impactful.
All right, I think we're good for today. I think we're good for the year. Happy holidays. Are we supposed to say Happy holidays? Yeah.
As always, thanks so much for joining us. Really. Check out engineering.com and check out Michael Wallet's stuff. Very good stuff over there. Please come check out the, the other sites that we have. Whether that's control engineering, that's controlengine.com, consulting, specifying engineer, plant, engineering, all great information.
If you want to know what's going on in the world of engineering and manufacturing right now, those are your places to go. Or you could just, you know, listen to this podcast and listen to Stephanie and I, because we're fairly smart occasionally.
[00:47:20] Speaker B: A little bit.
[00:47:21] Speaker C: A little bit.
[00:47:22] Speaker B: We have smart people.
[00:47:23] Speaker A: We do. We look smart because we bring smart people on and talk to them and then we go. Yeah. No, I thought that same thing.
[00:47:29] Speaker B: Yeah. I do a lot of head nodding. Yes. Oh, yes. Yes.
[00:47:32] Speaker A: Yeah, I would have said that, too.
Yeah. All right, everybody, thanks so much for being with us.
We will talk to you on the next one. And I want to say next year, but I don't know if this will be our last one of the year. It very well might be. So if so, we'll see in 2026. Thanks for joining us.
