Chris Luecke: If you're looking to get buy-in on AI and you're looking for places to apply it. Think about all the things you hate collectively as an organization and apply AI to that. 

Narrator: You are listening to Augmented ops where Manufacturing meets innovation. We highlight the transformative ideas and technologies shaping the front lines of operations, helping you stay ahead of the curve in the rapidly evolving world of industrial tech.

Natan: Welcome everybody. We are back with Chris Luki at Augmented ops. For those of you who don't know Chris, I think about Chris as Manufacturing nomad. He's roaming the great plains of operations, I think mostly in the United States, but maybe a little bit in other places. But he is also pretty well known to the community as the host of the Manufacturing Happy Hour, which is a awesome podcast and venue you should all check out.

But he's mostly out there talking to people, walking shop floors. Welcome, it's great to have you. 

Chris Luecke: It's great to be on the show, Natan and you're right, a lot of my focus is around the United States and North America, but you know that's where my audience is based. That means I'm jumping overseas to learn, Hey, what are people doing well in Germany?

What are manufacturers doing? Whether it's in California, whether it's in New York, whether it's in the Midwest, my focus is really trying to find the tips and tricks that are working in different parts of the country, in different parts of the world, and equip Manufacturing leaders with that information so they can.

Optimize their own processes wherever they 

Natan: may be. I think it's an important topic because we hear about this all the time through different lens, from the proclamations of let's re industrialize the United States, or let's upscale the workforce. But if we don't focus on the process engineers or the, which I think is the collective term for the folks who actually do it on the front lines, none of that happens.

And you were a production engineer, right? 

Chris Luecke: I was, yeah. This episode is gonna be a bit of a time machine. 

Natan: Yeah. So why are production engineers and process engineers so critical? 

Chris Luecke: Yeah. So I'll give a little bit of my background first and then dive in, dive into that. So yeah, for context, I've been in.

Sales and marketing for the majority of my Manufacturing career, largely working with Rockwell Automation. But before that, I got my start. I'm a degreed mechanical engineer from Marquette University, and I was working as a process engineer at Anheuser-Busch. So the beer company maybe, folks can see the connection as to why I started a.

Beer driven happy Hour podcast, like Manufacturing happy hour. The funny thing is though, I was actually working on their aluminum can making part of the business, so they own a company called Metal Container Corporation, which makes a portion of the aluminum cans that they use to can their beers, and I got to see firsthand.

What it was like being on the frontline of a company for context, when I was there, to an extent, I was a process engineer. I was a project manager. I think the easiest way to define what that looked like was I was wearing a few hats, which I think is the case of many frontline engineers in Manufacturing.

So we were working on a project to install. Hard guarding and a new exhaust system on the part of the aluminum can making process that applied the paint that applied the design to the aluminum can. So when I had joined, another engineer had already identified that this was an issue because first of all the hard guarding part of the project safety issue, right?

We wanna make sure the equipment is protected, but also that folks can do their job. In a very efficient, disciplined manner. The second part was not having the proper exhaust system on the coder was creating quality issues, right? So as a process engineer, our job was to first identify that problem and realize, hey, if we can solve this.

We can optimize the process, we can make the lives for the folks working easier, safer, and we can improve our throughput, improve quality, et cetera. So at the end of the day, process engineering is always going to involve problem solving, but it very quickly turns into managing expectations, change management, working with the other folks that are on the front lines, and then the project management that goes along with making that all possible.

Natan: So Chris when was that? What year is that? 

Chris Luecke: This was 2007. 2008 timeframe. So we're talking almost 20 years ago. 

Natan: The cloud is getting very cloudy. So can you think for a sec, just as we warm up here, what is your tech stack you need to do all this stuff? Sounds like understanding machinery and how to interface to it on a mechanical level, obviously the process, likely there were some data involved somewhere here.

Chris Luecke: Yeah. 

Natan: When you think about not just the text, the tools, like what do you have at your disposal? In 2007 to do your job. 

Chris Luecke: Yeah, and actually Natan, I'd love it if you filled in the gaps with me here a little bit 'cause you were probably closer to some of the tech stack and the automation and the data behind that process.

Keep in mind, I was a 20-year-old process engineer that. Most of my focus, to be honest and maybe this is a sign of what process engineering is like now, I was really focused on the people that were out there managing expectations with the different contractors, the folks on the front line that leveraged that equipment every day.

So to an extent, the tech stack. At that point in time in my career was a secondary thought in my mind. But you mentioned that, hey, the cloud was getting cloudy at that point. I can say for a fact that term had not crossed my radar in any fashion at that point. Especially, I would say I was more a pure mechanical engineer at that point.

I hadn't dove into the world of automation as much yet, so you know, to an extent I hadn't gained that full appreciation for the power. That having automation and the tools and the data analytics solutions we have now could unlock for a process engineer. How 

Natan: did you do simple stuff like a project plan and maybe a value stream map or something like that?

It was all like on the whiteboard or. Emails or what? What did you do? 

Chris Luecke: Yeah, this is interesting 'cause I know we're gonna touch on AI at some point in the conversation. So as an entry level process engineer, you're totally right. There was, the whiteboard was a primary tool. I was learning what a Gantt chart was for the first time to leverage project management.

And this is gonna seem archaic in our terms now, but part of my job was to. Be the core communicator of the group and make sure all the different parties, the multiple contractors involved, were making sure this project got executed on time, on schedule, et cetera. Like a core responsibility was making sure the meeting minutes from our conversations were dialed in, action items were assigned accordingly and those were sent out.

So 

Natan: a lot of project management, in fact, a lot of 

Chris Luecke: project management without the tools we have today. Get rid of a lot of those, I wouldn't call 'em menial tasks, but the manual work that goes into making the project possible. 

Natan: Yeah, on that front the world is generally improved and there's just a lot more tools and experience in organizations. 

Chris Luecke: Yeah. And that's what I was reflecting on as I was putting my process engineering hat, project manager hat back on yes, there were a lot of manual processes back then. Things that, you know. Fathom or whatever note taking tool you're gonna use to take care of for you.

Now, the project management tools are largely AI driven in a lot of ways, but what's similar between then and now is that the interpersonal aspect was the key piece that I saw. That went into being a successful process engineer, successful project manager, and that's still the case today. Now we're just trying to manage how we leverage those tools and how we get buy-in from other people to leverage those tools.

Yeah. Or at least that's one piece of it. 

Natan: Yeah. When we were preparing for this, I found this, pretty interesting phenomenon that I think it's well known and it just gives good meaning to this sort of need for upskill, reskill, all those big words. That people talk about the workforce at large.

I think it's stating the obvious that, people are digital natives and they're born with internet and mobile and now. With AI and picking it all up and like the two phenomenas that I'm juxtaposing is on one hand last decade seems like there's in general 20% growth for traditional engineering roles.

And if you look at CS, it's likely more around hundred percent growth. That said Cs, the past couple years, have been declining a little bit because, people are scared because macro conditions change. Now people for some reason are. Scared that AI is gonna take their job and I think we're still gonna need tons of engineers and we'll get there.

Talk about how people become orchestrators, or engineering is about orchestration. So that's one phenomena. So again, engineering. Enrollment grows. But on the other hand, the gap in how many engineering and technical roles, if you look at what Manufacturing needs, it's not getting any better despite more people going engineering, which suggests people are not coming into the field.

So with that setup, you've been to how many locations? I think you gave me the number. It was like 200 and. Oh hundred something. 50, 60. Yeah. Hundreds I would 

Chris Luecke: say. Yeah. I've recorded, if you include bonus episodes, over 300 podcasts, I've probably been to maybe double that number in factories if I had to take a guess.

Natan: So the process engineer of the now, who are those people you're meeting? What are they like? What do they talk to you about when you get them one-on-one, maybe off camera? 

Chris Luecke: Yeah. So what I would say, one thing that hasn't changed from my time of being a process engineer to now is. A big part of what you do is you're observing how work gets done.

There's that human element. You're seeing how the folks on the front line are interacting with the equipment, interacting with the process, and you're leveraging that. As you solve the problems of what, let's say the data and analytics show you about the process, right? When you look at the throughput, the quality associated with this equipment, you have more tools to show you how the equipment is performing, how it relates.

To other parts of the process, but it's still so important to figure out, hey, how does that person that's interacting with the machine the hard guarding around the machine as to my example, how does that go into how we actually work with this equipment? So the way I would describe it is, yes, there's more information, there's more visibility into how the process is performing, but.

That's useless if you can't combine it with how someone actually interacts with that equipment on a regular basis and how you work with the people that are the front lines to get their buy-in, understand what they're doing. I think. As time goes on, I think what we need to do to get more people excited about process engineering and work in Manufacturing in general is highlight how much of a human activity this is, how much of a communicative activity this is, rather than just focusing on the tech and the data, et cetera.

Natan: Is there something you experienced like in your travels that. You are like, wow, this company, the people I met are just phenomenal and that's why they're so successful and it really starts with the, those folks on the shop floor. 

Chris Luecke: Yeah, I would say it's a two-way street in a lot of ways. When, I always think back to when I started Manufacturing Happy hour.

I shouldn't say start, so I started Manufacturing Happy hours, like this campy video series in like 2016. I was filming. Videos on, on my iPhone, right in the, in an automation lab. As it evolved into, as I described today, it's a leadership podcast described as a Manufacturing podcast.

Natan: And I 

Chris Luecke: knew the first episode that I was gonna do is like a long form, 40 minute, hour long interview was going to be how to create. A great culture in Manufacturing, and I would say culture starts at the top. I think it requires that two-way street, but with all the facilities I've walked through, if there's mediocre leadership or absentee leadership, you're not gonna have enthusiasm on the front lines.

You can

Natan: feel it right away. 

Chris Luecke: You, oh, 100%. That is one thing. I think anyone listening to this that's walked through the door of a facility, you get a vibe. Immediately as to whether or not the work on the front line is gonna be top quality, proactive, because you can tell just like what the energy in that facility is.

So to give you, go back to my example. Dan Voight was my first full-length podcast interview on Manufacturing happy hour, and we talked about how to create a great culture in Manufacturing and for context, they are a food process equipment manufacturer based in Northern California, Sonoma County.

That is not the first spot people usually think of when they're like, what would be the most profitable, easiest spot for me to build an equipment manufacturer that's not, 

Natan: that's not gonna be the spot. So the California regulation, we could have a whole op episode just on that 

Chris Luecke: one. 100% Name your list of reasons why Northern California not to do it wouldn't be the ideal spot.

Yeah. So I'm like what a great spot to figure out how culture's allowing this company to be successful. And what I've seen from being inside of these facilities, it takes guys like Dan that are out there on the front lines, solving problems with their team members on a regular basis. Dan obviously has his day-to-day responsibilities as well as the leader of the company, but if you're not out there getting on the shop floor, showing that you are just as willing to get involved in those team huddles to solve those type of situations, it's little things like that and I think that's one of the things that, that people find on Manufacturing happy hour.

A lot of the lessons I share aren't necessarily earth shattering, right? Yeah. But it is a lot of habits that folks need to build into what they're doing. So when someone like Dan is setting that culture from the top, you see it go down to. At the other levels within the organization, when Dan is open to new technologies and changes that can translate to the rest of the organization when you are getting hands on with them as well.

And we can get into a whole conversation around change management to this, but that's, when I see what companies are doing this well. It's the companies where you have that vibe when you walk in the door. There's a proactive mentality to how this company does its business versus reactive to whatever order or situation comes up that day.

And it takes a leader that is hands on just like Dan. 

Natan: This is we're talking about the leadership that inspires the individuals within their organization to do that. And when it all comes together, that culture is so critical. I was reading, and this is back to the re industrialized, the United States perspective to this, because if we don't have enough engineers who can run the machines and to optimize the process and like to process the material to make things, then the innovation cycles slow down.

Because if you don't have production, you don't have innovation. Or we outsource it or rely on supply chain. And I was reading this term that I think is starting to pick up, I dunno if you heard it, it's called Shenzen Speed, so it's I haven't 

Chris Luecke: heard 

Natan: it, 

Chris Luecke: but I can just based shenzen speed.

I can visualize what that might be like, describe it. 

Natan: So it's like how quickly. You can get from idea concept to a full blown production line and execute on your innovation end to end like to get something in production. And that has to do with, your process and things like that.

And it's measured by how quickly can you go set up. Production and ship products to market. And I think the numbers I've seen mostly is like from how long does it take to get something like ev battery development cycles and the construction and go live of the giga plants that would make that battery.

So Europe takes like from concept engineering test eval, 36 to 48 month. You wanna guess what it takes for Chinese or Koreans? It's, BYD does that at 21 months. 21 months. So basically, te Tesla is a good place in the middle, like 36 months or 29 months for the factories. Yeah. So what did we learn from that?

If we take it to production engineering? Process engineering of the future. It's actually a continuation of the innovation cycle because you can't really, oh. People over there design the product and people over here are just making it, and so it's all connected. 

Chris Luecke: I like that you bring up the reindustrialization concept because I think there's a tremend.

Opportunity for us to rebrand what the true role of an engineer is and what the true role of a process engineer is when we put it in the context of that story of national competitiveness, national security, et cetera, and what yeah, Manufacturing means to a nation like I Natan. I don't know about you, but I always.

I shouldn't say always, but when I'm thinking about the history of Manufacturing, I think back to World War II when 

Natan: yeah. The 

Chris Luecke: United States was very able to quickly repurpose factories that were making cars into making tanks. I'm, 

Natan: I'm sitting in one, yeah, we are sitting in Assembly Square in an old Ford factory that used to make the ED sell, but before that was making armored vehicles that were shipped from the East coast to Europe.

And it's always inspiring to think that it actually happened here. Of course, after that it became a Kmart. That sounds 

Chris Luecke: like the timeline of where we've gone 

Natan: lately. Yes. Yeah. And then there was no longer need for a Kmart, and now we're here. Exactly. So it's our own little private renaissance, but Knudsen and all those leaders like that, that got the call, they were ready to repurpose the industrial base because it was here.

Now it's not entirely here anymore, or like elements of it are not here, and companies are less vertically integrated as people wanna say. And also they grew and became more, multinational companies. But anyway, how do you change that reality, like from the elements in your workforce who are right there on the.

Floor, day in, day out. And those process engineers, can they actually do more? Like we touch on it, one is extending your innovation, but once you're running for you to be cost competitive then it goes back to the productivity game. Which opens up an interesting topic now as you well forecasted will hit because AI is definitely changing the game.

I think across many of the aspects of our work life and potentially personal life and what we consider productivity. And the numbers show that 80% of the manufacturers plan to spend 20% or more of their improvement budget on AI and smart Manufacturing. God help us. You know what I mean by that is I don't know.

That's great that they're gonna invest, but like I'm not sure they know in what to invest and like how to give the tools to the process engineers that would actually do something with it. What AI actually does in operational environment, I. Personally think the gap between the hype and reality is pretty high.

Agreed. And and pretty soon I think we'll hear about, we'll just have the preface ai, so like AI, pilot, purgatory, ai, that. What are you seeing? I'm sure the past year. In your travels, you met a lot of people doing or asking, and so what's the, what's happening on the ground in, in, in those type of efforts?

Chris Luecke: Yeah. And I'm gonna do my best to bring this back to some very fundamental ways to think about AI and how to get buy-in on the things that it's good at today. In a Manufacturing environment. I was just at Waukesha County Technical College which is right outside of Milwaukee. Cool.

And we were having a conversation about, what does applied AI. Look like in Manufacturing and we had folks from the local business community, we had educators, we had students involved in this conversation in my mind, the perfect environment for having a discussion around that and looking at what's changing in Manufacturing.

And one of the best comments. To be brought up was if you're looking to get buy-in on AI and you're looking for places to apply it, think about all the things you hate collectively as an organization and apply AI to that, which I think is one of the most fundamental best ways to think about artificial intelligence because I think there's a lot of concern around AI for a number of reasons right now.

But if we can focus on applying it to things that no one likes, that's a great application. And one of the examples that I continue to see coming up and I can name one company that's, providing solutions in this regard to, to help with it, is scheduling and task allocation. I'm gonna put my process engineering hat back on.

We were talking about how my job 20 years ago, close to 20 years ago, in 2007, 2008, was. Looking for the problems to solve, but also seeing how the people interact with that and getting buy-in from the stakeholders that work on that equipment every day. If I have the tools to focus on, let's say the minutiae of my job, we're talking about Manufacturing, scheduling, task allocation, looking at resource constraints.

Artificial intelligence is a great tool. To aid in balancing that out. And when you look at those constraints at that point, then you free up your time to speak with the people. To hang out with the people that are actually going to be doing the work. There's a company called iter Idea that is one of these, just one example of an organization that is focused on.

Artificial intelligence for Manufacturing, scheduling and task allocation. But the reason I use this example is because you're able to take one of those things that in the past would've been you looking at a whiteboard or in your war room at the factory trying to figure out how are we gonna get all this done?

How are we gonna get all this made? With artificial intelligence, you can get most of the way there to figure out, okay, this is the optimal way to schedule. This task, given all the other things we need to build, make, et cetera. Now as a process engineer, I can focus on working with the people that are gonna do the work, that might have to be involved in some of the change management.

So I'm able to put my focus towards the more human tasks, which ultimately is gonna be so key as we bring more artificial intelligence into our facility. So I hope that kind of paints a good picture of what that looks like. 

Natan: In your conversations, do you hear people talk about this kind of amorphic term?

It seems like everyone is using it, but it's not very clear what it is, like this idea of agent. What's the lingo? Are they saying I'm using ai? Are they saying, Hey, I made an agent? What are you hearing? Agent continues to 

Chris Luecke: enter the vernacular a little more this year. I would say here's where I see things right now.

I hear AI mentioned from folk that's the go-to term right now. Ai, artificial intelligence for folks on the factory floor for folks running a Manufacturing facility where the term agent. It comes up all the time, and not surprisingly so it's the tech providers, right? When I get into a conversation with Siemens and Nvidia.

As to, how their solutions are, providing pattern recognition without hours and hours of having to teach it on your own with physical products, right? That's where I hear them talk about agents. Things like that. But I think the term agent, it's not quite there as the go-to term in Manufacturing because I think folks are still going through their, let's say, entry level artificial intelligence applications right now.

What are, I'd be curious, what are you seeing and hearing in that regard? 

Natan: Yeah, I call it the quantum state of AI in operations. Two realities coexist at the same time. So it's everybody knows they gotta start getting smart and fast and ready to use all sorts of AI techniques, whether it's to wrangle data or, use it to augment people, empower people to do better data analysis.

Of course, the tool, we think a lot about how to help people build stuff better, faster. And then what you build in Tulip ultimately is production systems. That are connected, manage, orchestrate, all that kind of good stuff. We introduced this idea of agents, so that becomes an extension of a human.

For lack of a better term. I will not go in so far as a digital worker because I think that's really presumptuous in the context of operations and quasi disrespectful. So like that's one reality. They all know they need to use it, but on the other hand, they can't. And the reason they can't is because of trust.

And when you unpack what trust means, first of all, for AI to work, just think about yourself, right? You use AI on a daily basis. You probably been doing that the past two or three years with chat GPTs. Then maybe you went deep research, then maybe you vibe code here and there, what, whatever it is you're doing, you surely might be doing it in your line of business for marketing.

Maybe you have some agents running your campaigns and all that kind of stuff. And you're in control. You have trust with those tools that you developed yourself. The people in operations don't have trust with the tools because they're not built for them. Cursor is an IDE, it's great, but it's built for engineers who live in software development pipelines that is where the trust is. So that trust is actually dual. It's between the person and the tool. And that's the place where you can get very comfortable in iterating, but it's also between the organization. And the people who use the tools, so in other words, if you think about us, as Tulip, we trust our engineers to use whatever it is, cloud code, GitHub, copilot, you name it.

To do what? To develop software, because we know there's a software development pipeline that takes care of whatever comes after that. Where is that for operations? Like you can't take we're gonna try and optimize our process and we're gonna put it in this. What IDE for operations. So I don't think that exists.

I think some platforms like Tulip is the closest approximation, and this is where a lot of the inventive steps that we're taking to give the tools to the meet, meet the people who need to use ai, where they actually need to use it right at the value stream level, like from the description level, all the way to the setting up the process and then doing.

Continuous improvement on lines. So that means they need to compose and recompose all sorts of application. It means that they need to have MCP tools that are connecting and moving around context between various systems. So when the engineer sits there and says, oh go, let's do shift, end of shift reports, and bring it home like on a really well grounded example, if you want that engineer to change the end of shift reports from.

An hour that this person needs to sit at the end of the shift. What do they need to do? They need to look at all the data coming through the shift, write up the deviation. They need to make sure the, the Kanban flows are where they need to be. They need to potentially log some safety issues that happen in the shift.

All the usual stuff that happens. So they know what to do. And perhaps they're in different systems in combination of. Spreadsheets and paper, whatever. But imagine that they have a good system they can orchestrate and have agents capable of assisting them and with the context, say look, I summarize this a summarize that, Hey human, can you make sure, or hey, process engineer, can you make sure that this is what it is?

And now instead of doing it for one value stream, they can do it for five. 

Chris Luecke: Yeah. 

Natan: And that means that you can have more productivity. We see all sorts of stories like that all the time, and still it's early days and mostly it's because the, yeah, the trust is not there, but it'll come.

It's an imperative. It's like there's no doubt that this is coming. 

Chris Luecke: I have some thoughts on what are some of the steps to take, getting that trust there, because I think that was a huge part of what you were just sharing. And at the end of the day, I'm often thinking about timeless things that folks are gonna need in their career, whether it's artificial intelligence.

AI agents or whatever the new technology is, 20 to 30 years from now, process engineers are going to be able to need to get buy-in. And that comes from getting trust from their team members. And I think right now, we were just talking about, why am I hearing more about, let's say, artificial intelligence in Manufacturing than say, a lot of people talking about, oh, we're using AI agents for this today.

I think it's easier to feel like you have some sense of control over. Let's say a tool that's been created by artificial intelligence versus saying an agent. I think an agent sometimes comes with the baggage of that. Emotional idea that, oh, this agent is doing my job now, and things like that. And I think that's maybe why I haven't come across that term as much in the field.

Like I said, I hear plenty from the technology providers, but when I'm on the floor of a mom and pop manufacturer, they're talking about artificial intelligence. I'm not hearing the term agent popup quite a bit, but one other line. That I came across recently. In addition to that comment of figure out the things your company collectively hates to do and apply AI to that's one way to get buy-in, right?

Being like, here's this new technology tool. 

Natan: That's a corollary to eliminating waste, right? Yeah. Yeah. Study lean principles. Deeply, those have not changed. 

Chris Luecke: Yeah, absolutely. There's another line that I think is equally as powerful, and this came out of an individual named Caleb.

Caleb is actually going back to school right now to study data and artificial intelligence after, decades working in lending and business. He sees where the world is going, so he's going back for. Retraining reeducation and he had a very great way to describe what artificial intelligence is doing and a way to get people over some of their concerns around it.

Artificial intelligence is not going to steal your job, but it will. Steal some of the tasks that you do in the future. And when I think of that, I think of, me as a business owner now, right? Expense reporting, invoicing, all these things that aren't core to me, growing my business, right? If artificial intelligence can steal all those tasks from me.

I'll take that all day long, but when I have conversations, I talk about what the AI empowered worker of the future looks like. Because I think the more that we can get folks thinking about artificial intelligence as something that we. As humans are harnessing versus something that's replacing us.

The more we can get that mentality into people's minds, the more it can be like any other technology that's come up over the past centuries where it's yes, it's new, it's scary, right? Anything new is scary, right? But it's nothing that we can't handle. We've done it before and we've used it to bring ourselves forward as a species, as an industry, et cetera.

So I'm really focused now on having conversations around. What it's gonna take to create the AI empowered worker of the future, which is where I see things going. 

Natan: So let's close on one last area. 'cause we were talking a lot like from the perspective of, the process engineers themselves, but they don't work in a void.

So if you have to give advice for leadership, what can leaders do to. Make sure that their pro engineers are indeed empowered and can transform or evolve to be this new kind of hybrid type of professional. 

Chris Luecke: I think back to when I worked at Rockwell Automation. Rockwell Automation obviously went through their own digital transformation, et cetera, when they were building new facilities across the world and things like that.

But the most powerful lesson that came out of, let's say like an early digital transformation example. Was getting the right people in the room. If I'm a leader, I am getting the right roles in the room. Like you know when the concept of digital transformation started, if you could get your IT people and your OT people in the same room leading the project, speaking with one another on a regular basis, you eliminate one of the most common sources of finger pointing.

In a digital transformation style project, and the same goes for any sort of transformation. We're talking about ai, I'm making sure operations, I'm making sure leadership, and I'm making sure my best on the front line are sitting in that room making it happen, right? Because how are you gonna find the things that.

Everyone hates that you can apply AI to, make sure you have every voice represented in that room. That would be my advice to leaders is be thinking about the different personas that you need in the room, and I just named what I feel are some of the key ones. I'll just do it again. Frontline workers, process engineers, operations, get it in there and get leadership in there.

Those are five that come right to mind that I would make sure I have in the same room talking about these projects. 

Natan: That's great. The one thing I would add for leaders that they should be in the trench encouraging their people to build and try and build themselves even if they're less technical or afraid of failing or whatever it is, because the stuff is actually pretty approachable.

Like even if you're not that technical, that you could fairly easily. As they say, lead from the front, that would help your people open up their mind and go huh, I can actually build it. And I'm guessing that's what happened. Like when people starting to massively use this thing that we take for granted called Excel, they were like some kids you knew how to run with this new tech and, do cool stuff. That kind of, oh wow, this is like a digital ledger. It's yeah, that's what it is. Amazing. And I think it was so interesting for people across the ladder. That it just got adopted because of that.

And I can't prove what I'm saying right now, but if I have to guess, the organizations that adopted it faster had leaders that were like getting their hands dirty and trying around how to figure out Excel or whatever it was to do that. So get in there to help your people. Chris, it's been great.

We're coming out of time and this was a, it was a deeper episode and I really appreciate you coming around. Look forward to hosting you here when you're on the tour next year, and we'll see you soon. Thanks so much. 

Chris Luecke: Looking forward to being back in Boston and hanging out.

Natan, always good catching up. 

Narrator: Thank you for listening to the Augmented Ops podcast from Tulip Interfaces. We hope you found this week's episode informative and inspiring. You can find the show on LinkedIn and YouTube. Or at Tulip dot co slash podcast. If you enjoyed this episode, please leave us a rating or review on iTunes or wherever you listen to your podcasts.

Until next time.