Episode Transcript
[00:00:03] Speaker A: Welcome to AINA Insights, where prominent leaders and influencers shaping the industrial and industrial technology sector discuss topics that are critical for executives, boards and investors. INA Insights is brought to you by INA AI, a firm focused on working with industrial companies to make them unrivaled segment of ONE leaders. To learn more about INA AI, please visit our website at www.ina.AI.
[00:00:40] Speaker B: Welcome to another episode of the Titanium Economy podcast hosted by ina. Today we are joined by Mark Schwartz, group president of AECO software at Trimble. Trimble is a $3.7 billion technology company connecting the physical and the digital worlds. It's the operating system for construction, geospatial and transportation.
ARR has grown from 1.3 billion in 2020 to 2.3 billion today as it completes its shift to subscription software. Mark joined Trimble in 2010 via acquisition, spent a decade in finance and ops, and became Chief digital officer in 2020 before taking command of AECO segment, home to Construction One, TechLA, SketchUp, Viewpoint and Agent Studio. Most recently, he unveiled Trimble's AI strategy at Dimensions 2025 and announced the acquisition of Document Crunch, adding AI powered contract risk analysis to the ecosystem. His path to this moment began not in technology, but in financial accounting, a detail that turns out to matter quite a bit when your job is to make digital transformation real, not just strategic. Mark, great to have you with us.
[00:01:51] Speaker C: Thank you. It's great to be here.
[00:01:53] Speaker B: So, Mark, to start the interview, I want to focus on Trimble overall. Right. Trimble started as a positioning hardware company and has spent the last decade becoming a cloud software business. As we mentioned, ARR has grown from 1.3 to 2.5 billion. And you have been in the middle of that transformation the whole time.
Where does it stand today and what was the hardest part about getting there?
[00:02:16] Speaker C: You know, I think we're nearing the conclusion of the conversion. I think we still have work to do where our hardware as a service offerings are becoming more and more prevalent as we grow the business and there's still more of that business we can convert. But both our transportation and our AECO businesses are almost all recurring and subscription based with some consumption models on top. So I would think we're well on our way through the conversion.
The hardest part is with transformations. I think too often we lean on the technology. It's a technology implementation.
My approach, which comes from my accounting background, comes from thinking it in terms of three things, which is it's the people, the process and the technology.
And I know that when you take technology and bend it to the Process, you lack scale, you lack productivity, you gain complexity, and systems break down over time. So my approach has always been it's 80% people. Get the people right, get the change management right, get them in the right mindset for how we're going to do things in the future.
Build the process around those people and then wrap the technology around those, that process to enable your teams to operate at the highest levels. And I think we've taken that from our own internal transformation to now our product strategy, external, which is to take this world, this complex physical world that we build in, and make it a system of systems that operates like a complex aircraft and control tower and radar systems and radios and pilots and airline operations systems and ground crews. Those all have to work together, data from multiple systems, talking to each other in real time, interacting in the physical world to get people from one place to another safely. And that's the mentality we're bringing to this transformation that we're helping drive in construction.
[00:04:30] Speaker B: Awesome. I think it's a very interesting perspective, Mark, right. Because if I look out in the world today, with the pace of advanced technology, it has almost become a commodity, right? It's almost become a hammer looking for a nail that I have the technology and let me just implement in whatever fashion it suits best. And to your point, right, make the processes bend to it. You bring up an interesting perspective that 80% of it is actually people. And when I look at Trimble, right, you guys have grown through M and A and have had to include so many teams and peoples and processes as you have traversed this trajectory. So how has this focus on people materialized? Right. Any examples that you can give me where that focus has actually created value or created impact, which you were proud of? Yeah.
[00:05:14] Speaker C: I mean, for the first 10 years I worked here, we acquired companies, we grew through acquisition, and we didn't integrate any of them. In fact, our philosophy was these companies we acquired had this inherent value, and we wanted to foster and grow that value, not encumber it by different integrations or mixing of teams and potentially losing key people. I think what I learned over time is that if you do those things right, and you create an organization for scale, you can overcome that and in fact, accelerate out of it by getting teams to work together to solve common problems. When you work in different teams, you get different answers, you get different decisions, you get different choices, and ultimately you wind up with the scenario you just mentioned. And we call that the appification of construction, where teams are all over the place, they're not talking to each Other, and they're implementing different technology and they wake up one day, they don't know where their data is. They don't have the same data from job to job. They're not using the same workflow.
This person's doing it this way, that person's doing it that way. All of that knowledge is retained out in these node points of operation.
And CIOs would come to us and go, what do we do?
And really, I would tell them all the same thing. It comes back to, we spent two years defining those workflows that we were going to use internally and those processes and documenting them. And then we built the organization around them. We, we worked with the people to get them bought into that vision and train them on the jobs that now needed to be done. And then the technology was easy. To your point, it's easy to put in technology.
My kids install five apps a day.
They're up and running in 30 seconds.
And that's the interesting concept of AI, where everybody's talking about code and software being commoditized. It's like creating products was not a barrier six months ago or a year ago because they were popping up all over the place. So it being faster now isn't really a fundamental shift in the world we've known and we've lived in.
So I think that's what it comes down to is to do it right, you have to really work with the people into that, and you have to do it in a consistent way. Otherwise the technology's fragmented, the data's fragmented, the workflow's fragmented, and you don't get integrated workflows, you don't get connected workflows. You don't get common data, data environments that sit through these systems of record that everybody's using.
And guess what? You can't put AI on top of anything if it's that fragmented and you can't find the data.
So at the end of the day, these things have become fundamental. And AI is just a forcing function, at least for our industry, to kind of get its act together and start putting those things together to drive the next inflection of productivity.
[00:08:27] Speaker B: Yeah, it's an interesting approach that you mentioned. Right. Because when you think about M and A. Right. Typically think people think about, I'm going to acquire this company on the day one is integration. Like, I just need to make them work the way we are. And then we become this new. They have to adopt the process and the workflow.
When I talk to industrial clients, right. And they typically are looking for Ease of doing business.
Right. The customers want one vendor, one contractor. Can you give me the entire solution? I don't want to go to five people to solve five different problems. Can you solve a bigger piece of the pie for me? Right. So how does that approach of yours of having these acquisitions that run independently, which fosters innovation and marry that, probably you want to go with a one Trimble kind of approach to your customers, that here's everything that we can provide and here's a suite of solutions that would work for you. How do you kind of marry that together?
[00:09:18] Speaker C: So that was the journey we went on, is we couldn't sustain that vision. We couldn't be the ecosystem that solved a suite of problems that our customers were looking at doing it the way we were doing it. So today, one, we've transformed our organization to be fully integrated when we do an acquisition, and we've done several over the last couple of years. We call it day zero integration. Now, is it really day zero? No, but it's like day zero is the vision segment. And what we really mean by day zero is like on day one, we want to be in position that the critical functions are as integrated as they can be.
[00:09:58] Speaker B: Got it.
[00:09:59] Speaker C: So we can focus on what we believe is the value driver of bringing that talent of people and the capability set into the ecosystem.
And for me personally, like I look to, the first simple questions is, can we contract and can we sell and drive pipeline as one team? Because if you can do that, you have a lot of time to deal with everything else. And so we focus very heavily on getting to market and using the full capability of Trimble's, go to market, engine, brand and reach as quickly as possible. And so that's when we say day zero, that's what we try to drive. Got it. And then in the case of even the recent acquisition of DocumentCrunch, it'll take us 90 days or 120 days to get the rest of the team and systems and process fully integrated into the full motion.
That is now a model that works for us. And we've repeated it four or five times now, and we've gotten better each time because each time we're able to look at what worked, what didn't work, and modify the process to achieve.
But most importantly, we bring these capabilities in with a belief that Trimble plus this capability, one plus one equals three or four. And that starts with being able to deliver it through the full engine and machine of Trimble. And so that's the immediate focus.
[00:11:25] Speaker B: Yeah. So I Want to focus in on that. And I think at Dimensions it was last November you unveiled the Trimble Agent Studio, an open platform for building and deploying AI agents across engineering and construction workflows. Right. Which I think was to your point, like presenting one front where a lot of problems and a suite of solutions can be offered. Right. So what's the vision there? Right. And why does construction specifically need that agentic approach?
[00:11:53] Speaker C: You know, it's a very interesting question and I will tell you the concept of Agent Studio started with an internal look at how we could empower are 1500 developers within AECO to build agentic workflows faster. How are we going to do that? And that started with okay, so we don't wind up with again, the age old problem is this one's on that, this one's on that, this one's on that. So we built a platform for our internal developers to build those workflows on top of Trimble technology and deploy them in a common way at scale, using a common framework as a platform that is available to the whole of Trimble.
And what that turned into as we got going was, well, we should open this up to the customers.
And the problem statement or the thesis behind that was we can't anticipate everywhere this technology goes. Even pre opus 4.7 and cloud code, we were still learning every day new applications, new potential, new possibilities. And I think we can all agree that the technology is moving so fast that no one's going to be able to fully keep up with every possible outcome. And then we have a world that's very fragmented. So why not empower them to solve those, I would say, final mile workflows that are unique to their special circumstance or their proprietary ways of doing things.
Well, we could focus on core workflows that we believed would be unique at scale to everyone. And so we kind of took an approach of let's divide and conquer. And by opening that platform up, we give them access to what we have access to. We know from every CEO and CFO and CIO we talk to, we know what the core and common problems are and we can start to stock that studio with those workflows and, and with those agents.
And then we can also empower them to do maybe what's unique to them. Where, you know, the debate is always like, you know, what's secret sauce for our company and what's not. Yeah. And you know, we want, we wanted to be able to empower both sides of that equation. And that was the thesis behind the
[00:14:17] Speaker B: strategy yeah, that's very interesting. And actually when you think about the discourse for AI, right, especially among investors, everyone's talking about agentic approach. Right. The other side of that equation is those agents need to work on some underlying data. Garbage in, garbage out. And you guys being focused on construction, there is plethora of data that you had access to or could position yourself into doing. Right. So these construction sites generate enormous amounts of data. Right? There's scans, there's voice memos, there's field service reports, various things like that. Right.
And traditionally, at least I understand most of that data never made it back to a central office or a planning organization or a, an organization that could take some action with that information.
So for you or according to you, how is AI closing that gap? This agentic workflow that you mentioned, how is it taking that data and putting in a place where your customers or construction companies would actually use that data to make decisions?
[00:15:13] Speaker C: Yeah. Well, I'll start with within Trimble Connect, we have over 39 million projects. And those 39 million projects have tens of millions of files and documents that we've been storing for our customers over years. And if you take, you know, even like a standard kind of run of the mill, you know, 50 story, you know, skyscraper, you know, the amount of documents, if you, if you piled the documents up that it takes to build that 50 stories, it would be exponentially higher than the building itself. The sheer amount of documents that, that and files that, that, that that project would generate. And so one, let's start with the basics. What's AI really good at? Finding things. It's good at reading things. And so we believe when you provide an agentic layer, even for the simplest of task of if I literally are generating that much data, how do I find it? But more importantly, how do I find what's relevant in it?
[00:16:14] Speaker B: Exactly.
[00:16:14] Speaker C: So we all know like, okay, first it's search for a document, but then it's am I searching for a specific phrase in a document? Or in the case of document crunch, am I looking for a certain risk profile or a certain risk clause or an exposure?
Putting that level of intelligence on top of it helps the customer in ways that are unimaginable. And that's just the crawl part of the equation is how do you sift through this much data? How do you know you're dealing with the most up to date design? How do you know you have the most up to date change order? How do you know you have the most up to date budget or estimate or takeoff in a process. And, you know, that's just the easy stuff. And then we get into the QC and the QA impacts of the construction that's happening in real time. And you know, what's interesting to me is not this data that's being stored. I mean, that's super cool and we can do great things with it.
What's really interesting to me is, is how that data interacts with each other and how our users are decisioning. And that's what we're studying now. Because if I can understand the decision process, the 500 emails that go back and forth to determine an answer and an outcome, then we can become predictive with those agents. We can become an operational intelligence layer to the physical world in real time and based on the digital world that we understand very well and the decisions that are taking place. And that's where it gets extremely exciting. Because today, that site, super, that's been around for 40 years, he hangs up his shingle and he leaves one day and retires. And guess what? The 40 years goes with him. And so we have to find ways to understand, develop and retain that 40 years of decision making and bring that to the next generation of workers that are coming into this field and then empower them to be better, faster, and operate cheaper, greener with less rework and get better outcomes and have owners get more for every dollar they're spending for an outcome.
[00:18:23] Speaker B: That's great.
I think the hallmark of a successful business, right, is not just how you're doing things right, but also how you're sharpening and focus and deciding what not to do. Right. And Trimble has been active in shedding businesses such as agriculture and mobility. Right. So explain that thought process for me. And how for you, for example, at Eco, how has that unlocked, helped your team focus and sharpen their focus?
[00:18:45] Speaker C: Yeah, I mean, as a publicly traded company, our number one job is we are the stewards of shareholder capital. And we have a responsibility to deploy that capital in the best, most effective way to generate a return for our owners. We take that responsibility very seriously. For us, what those decisions led to is easier, simple, crisp capital allocation decisions.
We're a simpler company in the past to be able to understand and for us to manage the choices of how to deploy capital are cleaner and easier to decide because we lack some of that complexity today. And I think, you know, that helps us. It helps us be faster, it helps us be more focused. I think with focus comes speed. Speed in delivery, speed in development, speed in decision making. You know, complexity from the outside in is Also complexity for the inside out. So everything people were seeing, you know, we also had to deal with in terms of running the company. So I think, you know, we take that responsibility seriously. And I think those hard decisions, and they are hard, incredibly hard, they have yielded a company that is more focused, that is able to deliver faster, that is able to make capital allocation decisions with more intention and more focus and less competing objectives. And I think that's been a net positive to the overall return we've been able to deliver to our shareholders. I would say that, you know, Trimble hasn't fully divested some of that. You know, our agriculture business is in a joint venture with agco. That mobility business isn't a joint venture as well and we retain ownership and part of that. So we're not fully out of that business, but I guess we're fully out of the conflicting priorities of that business as being a minority shareholder.
[00:20:42] Speaker B: Yeah, I think you mentioned something about hard decisions. Right. Another hard decision, or at least it could be. Right. We were talking about AI writing much of the production code at Trimble. Right. And what does that signal for how industrial software companies will be run and for the people in them or how are they going to operate going forward?
[00:21:03] Speaker C: Yeah, that's an interesting derivative of the SaaS apocalypse narrative, which, by the way, I don't subscribe to, not surprisingly. I work in a software company and I would say that is it changing? Yes, but it's changing for everyone simultaneously. And the first thing I say, if you had a four year backlog of stuff that you needed to do and you improved productivity by 50% now you still have a two year backlog, you still have a list that is longer than what you're actually capable of doing. And so have we seen immense positive results from our work with this technology and what's possible?
Absolutely. We have not crossed the threshold of running out of things to do. What we have been able to do is certain things much faster than before. We've been able to do it with smaller teams and we've been able to deliver it, I think with better quality. But it is not by any stretch of the imagination, 100% written.
We still are very much a human in the loop. In fact, we spend more time taking code out of things than adding code to it.
The code that's written by AI isn't actually the most efficient code. It works. But I think the other thing people don't understand is, yeah, you can vibe code your way to a prototype in a day, in an afternoon. I've Done it and I'm an accountant, which is shocking. It's really easy. But that is a far cry from deploying enterprise grade software on a highly secure database that has SOC 3 compliance and FedRAMP. Like that doesn't happen in an afternoon or a week or a month or by two people working out of their garage. And so I think what people still have to understand is yes, the great thing is you can get technology in the hands of people faster. It means we can get feedback from customers in the industry faster on what works and what doesn't. That means stuff we do will have a higher batting average of success in the market and they'll get it faster.
What it doesn't mean is, you know, it's super easy. It's like in Colorado we have fourteeners, 14,000 foot peaks and some of us that are crazy like to climb them.
What we talk about is like someone will say, oh that's an easy 14er. Yeah, easy relative to the other 14ers, not easy. And so yes, it's easier to build software today, easier than it's ever been before.
It is still complex and hard to deliver enterprise grade software to the market that is highly secure in a guess what environment where not so nice people have access to the same tools and capabilities.
And so what I tell the market is you have to work with people you trust, people that take cyber seriously, people that invest in it. It's not about having it in the afternoon that you came up with it. It's about having it in a ways that it's safe and secure and your data is protected and in a world that's becoming increasingly more threat oriented. So I would say it's still a hard problem. It's not as Easy as a LinkedIn post would make it sound, at least not to do it right. And not to do it with quality and the care and the trust that, that our customers expect of us. And I would say that's probably true for most big software companies.
[00:24:28] Speaker B: That's excellent. I'll use that as a segue. Right into your own journey as well, Mark, because I'm very interested. Right, because you came up through audit, moved into early investor relations tech, lended at an RFID startup that got acquired by Trimble and then spent a decade in finance, then ops before becoming cdo.
So how's that background shaped the way you lead a digital business?
And how would you describe that it gives you that more conventional tech career wouldn't have.
[00:24:57] Speaker C: Yeah, you know, it's oddly, it's impacted me, obviously it's also impacted the type of people that I hire on the team, which is. I wouldn't have predicted years ago.
It comes back to what I was saying about, you know, air travel, working in the number of fields and operations and finance and accounting and audit. And I actually spent some time in tax.
What it leads you to understand is there's no such thing as a system of record. For instance, hard concept for people to get.
Systems of record are dependent on what user you're talking about. If I'm talking to an accountant, he'll tell me his system of record is the erp.
The HR person would tell me it's the HRIS system.
The salesperson will tell me it's the CRM. So that's the world we live in. And we live in this world of systems. Of systems. And understanding the complexity of the system of systems and how they work together is what I gained from that experience.
And now I seek that out. I seek out people who understand that the world is not as simple as the screen in front of them.
[00:26:09] Speaker B: Mm.
[00:26:10] Speaker C: It's not as simple as the work that they're doing or the workflow they're in.
That workflow connects to another workflow at some point.
None of us operate in jobs that are closed loop processes where we see every part of it start to finish and it just goes in a circle. You know, the world is more like, you know, a manufacturing line and you do a part of it and it goes to another part. So people that understand how those parts intersect and that your part done right, done well, done a certain way impacts the next person, how that data interacts with the next system, how we don't lose productivity in those handoffs, which is a huge problem, and really having a deep basis for that. I formulated that by implementing Oracle and other ERP systems and then implementing HRIS systems and then implementing CRM systems, whether it be Siebel or Salesforce, and then understanding by working in these different teams where that data was going, who was using it next, how it was interacting with each other. That's the experience I gained and it's now the experience I look for in the team as a result. Because I think that view of the world, that it's not simple, that it's complex, and how things work together, matter and understanding how they work together matters to achieving what we're really after as a purpose, which is transforming the way the world works. I think you really have to understand that nuance.
[00:27:39] Speaker B: Yeah, yeah. I think another interesting aspect that you have brought to trimble which I actually was very interested in personally as well. We have the car here. Right. You've chosen to put Trimble's logo on a nascar. I'm a Manchester United fan myself. But you're going into a partnership with Liverpool.
So explain to me how the genesis of that idea, right, and what those sort of partnerships mean for Trimble.
[00:28:00] Speaker C: Well, I mean, I can tell you in Liverpool, you know, there's 150 million people that love this idea, and then there's like 600 million people like you that don't. You know, here's what mattered to us. Like, go back to our earlier conversation with the acquisitions, the multiple brands, the different products.
You know, people really didn't know who Trimble was. What we did, the breadth of capabilities. I would say we have more capabilities in construction engineering than any other company on the planet. We serve architects, we serve engineers, we serve contractors, subcontractors, specialty contractors, owners, the public sector, municipalities, state governments, healthcare. No one else on the planet serves that breadth of capability. And guess what? On the other side, we have design, we have the build or the construct phase, and we have the operate. And we operate in every single one of those. And think of the intersection points of all the data. So nobody does it, but we were doing it as 50 companies. Yeah. And so it was really important to me a few years ago to say, like, no more. Like the world needed to know who we are. We were so proud of what we were doing, how we were doing it, but nobody knew. And what we say now is we will not be the best kept secret in construction technology anymore. And you know what? We build the stadiums that those soccer teams play in and those football teams play in. We build the tracks that these race cars run around in. That's the why we were already doing it and no one knew. And now you, you know, we're starting to take our position on the center stage of the world we're helping build. Yeah. And that's important to show everybody. And I think it's important for the world to understand that we are Trimble. We are not all these different things. We are going to deliver as one. We will be one ecosystem. We will be one company.
We have that desire internally. We have that desire to do that for our customers. And it starts with presenting ourselves that way. And that's, that's what we're doing there. And so, yeah, we're running race cars around tracks that we built and we're going to play a whole bunch of football matches and stadiums that we help design and Construct.
And guess what? It's not going to stop there. That's just the beginning.
[00:30:25] Speaker B: That's great. That's great. I think moving towards how you visualize the future and where the industry in Trimble is growing, construction productivity has been notoriously flat for decades.
Yeah, right.
And there's a lot of optimism that AI is finally going to change that.
Do you share that and what would need to happen for that to be true?
[00:30:49] Speaker C: Well, first I challenge the thesis. Like, we look at that statistic. We've seen the McKinsey study behind it. Like, I'm not so sure. I think the way they look at that, it would tell you that that's true. I think what's really happened is, look, we're at. Construction's at a maximum capacity.
So, you know, every customer we talk to has backlog. They have more work than they can execute in some case, years, more work. In the United States alone, there are hundreds of thousands of employees short of being able to deliver what they've already contracted to deliver next year.
So, you know, I look at that in the meantime, we've delivered, as you said, like, billions of dollars over the years of technology that we know delivers productivity. So you look at that and you go, okay, there's definitely more productivity, but things have got more expensive and more importantly, they've gotten much more complex. A building built today versus a building built in 1984 is very different. Also, the tolerance for someone getting hurt, injured or killed is much lower than it was. I mean, we go all the way back to, you know, the.
The famous picture of the guys in New York, the steel workers, and I'm like, that would never happen today. And so we have these higher requirements, and some of those drive productivity down. And we haven't improved permitting and other government processes that are in the way of driving full productivity. But I think of AI is as it applies, is like the natural next evolution of a tool that we can use to help drive the next inflection point of productivity. And we have example after example where we drive 20%, 30% productivity improvement by using these tools.
What if with AI, it can be 40, 50, 60?
But some things have to change. It's no different than the transformation you go through in a development team. I can give AI tools to our developers and they get 10% productivity, maybe 50.
But if I completely change how those teams work, if I change the structure of the team, the roles in the team, if I change the workflow of the team and then empower it with AI, we see 30, 40 and 50% productivity improvements from those teams.
So if you think about that, if we can deliver this capability and simultaneously change how these companies go about their work, we can unlock that type of productivity just like the world has done for coders out there now. And then maybe we can finally bend the curve of productivity that's been stuck flat for decades. So I do think it is for us, it was, you know, taking what used to be a surveyor's job, that was a two person job. And with the invention, the robotic total station that we sell around the world, you know, it's now a one person job. That's a massive productivity improvement. That's, you know, that's the type of productivity we can see from this technology.
You know, is it going to do everybody's job? No. Is it going to take everybody's job? No. I don't subscribe to that, but I do subscribe to it. Allowing every one of these people that interact with the technology to do more, to do it faster, to do it better, but most importantly to increase their batting average of success, to make less mistakes because they can do more analysis and have better informed decisions when they make them.
[00:34:26] Speaker B: Yeah, that's amazing. I think, to end with, right. Just want to understand kind of learnings that you've had along the journey. So you've now moved from being the CDO to heading one of the most important businesses for Trimble.
What are some things that you wish you had known at the start of this journey which you now realize are major impact on how you're making decisions or how you're making these companies successful?
[00:34:50] Speaker C: I wish I knew how little I was gonna sleep. That's probably the first thing I've gone through this journey now through a pandemic and the invention of AI and, you know, everything that's coming, coming with it. I feel like I've worked a lifetime in four years. But I'll tell you, you know, for all of the things I wish I knew is like the excitement behind what I'm still learning.
And so it's not even what I wish I knew. It's like how much I now know, I don't know as we work through it and how much, you know, every day I get to come here with, I think, the greatest team on the planet, the most amazing people that work with such a purpose and such a mission to do what we do every day and how much they teach me and how much I continue to learn from them, I think has been the greatest gift that I've received from doing this. Job. And as hard as it is, as stressful it is, like, I don't regret one minute of it, one day of it. I'm grateful I get to do this every day. I love it. I love working in this industry, in this field, and, and with this team, and, and I love winning. And we play to win every day. And that, for me, is is fun. And so the old adage is true. If you love what you do, you'll never work a day in your life. And, boy, I'm having a good time right now.
[00:36:10] Speaker B: All right, Mark, thanks for being so generous with your time, and it was great having you on our podcast.
[00:36:14] Speaker C: Thank you. It was great to be here.
[00:36:21] Speaker A: Thanks for listening to Aina Insights. Please visit Aina AI for more podcasts, publications, and events on developments shaping the industrial and industrial technology sector.
