Mike Chung: 0:08

Welcome to Auto Care ON AIR, a candid podcast for a curious industry. I'm Mike Chung, senior Director of Market Intelligence at the Auto Care Association, and this is Indicators, where we identify and explore data that will help you monitor and forecast industry performance. This includes global economic data, industry indicators and new data that will help you monitor and forecast industry performance. This includes global economic data, industry indicators and new data sources. Hello and welcome to another episode of Auto Care ON AIR Indicators . I'm Mike Chung, today's host, and I'm really happy to have Clyde Calhoun, founder and CEO of Root Idea, joining us. Clyde, if you'd like to take a moment to introduce yourself to our crowd.

Clyde Calhoun: 0:54

Sure thing, and Mike, thanks for having me on the program and hello to all of your listeners as well.

Clyde Calhoun: 0:57

Again, Clyde Calhoun, I'm the CEO and founder of Root Idea. Our company helps our clients really accelerate business outcomes leveraging AI, and you can think about that as really applying AI to the biggest problems that matter, and so we support that from a technology deployment perspective. So everything from workflow automation, ai agent build out, tech stack modernization all the techie stuff but then we also do the work outside the algorithm. Modernization all the techie stuff, but then we also do the work outside the algorithm. So everything from AI strategy policy and governance, change management, risk management and, of course, custom training and basically applied AI in terms of team implementation. So all the things that you need to be successful, and we've had the opportunity to work with both small companies and Fortune 500 alike, public and private. You know, the cool thing about this space right now is that everybody's trying to understand how they can best take advantage of the technology, and so this is an exciting time for us and exciting to be here today.

Mike Chung: 1:55

Clyde, thanks for that introduction. It sounds like you've worked with a range of companies, you said, from Fortune 500 to smaller companies and, I'm guessing, across industries too. Is that right?

Clyde Calhoun: 2:06

Very true. I mean we've covered traditional manufacturing companies in building and construction to financial companies in the service sector, a range of companies. We have not played specifically in the auto care segment, but I'm excited about really understanding the opportunities there and really connecting with folks in that space as well.

Mike Chung: 2:28

Oh, thank you and terrific to hear that. So when I hear a phrase like workflow automation, I'm just kind of curious. Can you give me a couple of examples what that might look like across, whether manufacturing or services?

Clyde Calhoun: 2:42

Yes, I think a great example of that is you think of most companies that they have their month-end financial closing process and a lot of times they have quite a few people engaged with doing all the accounting reconciliation, all the cross transfers and all of that stuff, and for years and years that's been the most arduous task for finance teams to really wrangle, and so now we're seeing implementation of AI in that space to make decisions about what goes where. How do you code certain items to alleviate the workload for people on the finance team? So that's something that I think is kind of cuts across a wide variety of industries as you look at closing the books at month end.

Mike Chung: 3:22

Thanks for that example, and can you tell me what some of those challenges and hurdles could be?

Clyde Calhoun: 3:29

Yeah, I think the biggest thing is getting people comfortable number one with the technology.

Clyde Calhoun: 3:33

That because there's this sense that, hey, I don't know if I fully trust AI, I don't know if I can turn over this thing that I've been wrapped into year after year to this technology that I don't really know much about.

Clyde Calhoun: 3:45

So there's overcoming that hurdle, you know, first and foremost. But then the second challenge is, you know, taking that time to actually train these models to be very good at those responsibilities and getting them comfortable with the information. And that part takes time because oftentimes you end up with a dip in performance before you see it improve, because you know this is a learning technology and you know oftentimes it's worse before it gets better. And then the third thing that I think is crucial as well is, you know, working with the data, because most organizations have real challenges when it comes to the quality of their information, real challenges when it comes to the quality of their information, and there's some work on the front end as well with getting that cleaned up to a state where you can start using it for training and developing these models that can take over and really automate processes.

Mike Chung: 4:35

I appreciate hearing that and, like you said, it sounds like companies need to have the long-term sort of perspective in terms of it might be a little painful and we might. We have to invest some time and resources to this and we may not see fruit until a few months from now, if not a few quarters from now.

Clyde Calhoun: 4:55

Yeah, absolutely, and you know, and there are a number of companies that will come in and promise you that, hey, we can get this up and running in 30 days or three months and, like I said, technically you can get the software installed in that timeframe. But you really have to think about this. As you know, you mentioned a long-term investment. This is like a year, 18-month type of endeavor to get these types of systems embedded into the way that organizations work. And, at the end of the day, I think that's an important point, because we're so accustomed to technology deployment or software deployment, and this is something different than that. When you're talking about AI, you're really talking about a change in the way that people work, and it's more than just the technology side. It's really all the people workflows that surround that and there's a big change management effort engaged and you've got to be prepared upfront to invest in the change management as well as the technology.

Mike Chung: 5:52

That makes a lot of sense. A few years ago, when I was in market research consulting, I had a client in the fintech space, and so they're doing financial services backend types of processing, and I remember my contact at that company was telling me about when Mike Chung to my broker to say please transfer X dollars from account X to account Y. And I'm thinking about a couple of things One, nonstandard forms. Two, something like optical character recognition, handwriting detection, optical character recognition, handwriting detection. Three, a very regulated industry. And then four, the need for kind of human QAQC, if you will.

Mike Chung: 6:53

So I know that's a lot, but you mentioned in your opening remarks what I heard is basically management consulting services to help implement and guide these processes along. So could you comment on this in terms of like you opened with an accounting closing the books example, but I'm thinking about that example that I just gave, but also from the standpoint of I'm in an accounting finance department. We're implementing AI. Should I be scared about my job going away? So can you talk to that a little bit as well?

Clyde Calhoun: 7:27

Yeah. So a lot to unpack there, and I'll start with that last part on is my job okay? Because that's ultimately what people want to know about, I would say, at a macro level, there are widely varying opinions about what will happen for the future, and you can look at Goldman Sachs, for example, and they've estimated that 300 million jobs globally could be displaced by AI. On the flip side, you look at the World Economic Forum and they're estimating that about 78 million jobs will be net, will be created by AI. So I think there's a wide disparity in the predictions there. I think there's a wide disparity in the predictions there. I'm more in the camp of there probably will be more jobs displaced by AI than created by AI, given the nature of not only AI but automation and robotics and the development of that piece too.

Clyde Calhoun: 8:16

But when it comes to the individual level, which is what people really care about, what about my job? I see that as a double-sided question, because there are companies right now that are making some reductions in force or not replacing employees, whether that's on the programming side or customer service, or even to some extent, on the sales side. So that is actually happening right now. But the thing that I always say is that you know, this is a technology that can make you infinitely more valuable in your job if you embrace it and use it, and so, rather than asking that question of is it going to replace my particular role, I think the question everyone should be asking is how can I use this to advance my work and my career and be more valuable to the company, because it has great potential to do that.

Clyde Calhoun: 9:06

Interesting thing related to that piece is that employees are finding it to be very productive as well.

Clyde Calhoun: 9:12

I think almost 70% of employees in the US are actually using generative AI to some extent at work right now, with about a third of employees actually paying out of pocket their own money to use these solutions which they're engaging at work.

Clyde Calhoun: 9:28

Now, the flip side about that is that some of that work is underground, so they're using it, but they're not necessarily telling their leader that they're using it, but nevertheless, they're seeing great value in it, and so you know. Circling back to your point on the data and what happens there, these systems are getting better. They're getting better at interpreting and inferring data, and so, where you've got these systems with different types of information in different formats, they're getting better and better at being able to bridge that gap. Program interfaces are continuing to develop and you start having more agents talking to agents as well. We're going to be able to bust through it where there's a lot of manual labor right now and reconciling some of that data. All that's really going to be bridging the gap as the technology continues to advance, and pretty soon you're going to have AI really cleaning up data on your behalf.

Mike Chung: 10:22

That's really helpful to know. I appreciate those insights. Clyde, thinking about the kind of implementation piece, when you're working with your clients, can you tell me a little bit about how workers in their current position, how they're being kind of coached in terms of here's, how this technology will enhance your job but also be prepared, as you kind of alluded to, to use this to your advantage, to be more productive, things like that.

Clyde Calhoun: 10:51

Yeah, very much so, and I'll actually just start by again. You're seeing quite a dichotomy in terms of the employer response around AI. So in most organizations, about a third of employees are really embracing the technology and are enthusiastic about it, and about a third of employees are actively resisting the technology, either out of fear or just concerns around what it can mean in terms of the way that their role will change. And what we're seeing is that not enough companies right now are really being clear from a leadership perspective on telling their story about how AI is going to transform the organization. So, as we work with our clients, a lot of our focus up front is really on the strategy piece, because if leaders aren't clear in communicating their intent around AI, what are they trying to achieve? How are they going to shape the organization going forward?

Clyde Calhoun: 11:43

Employees are left to make up stories in their own mind about what it means for them, and almost all those stories are negative just from the way that people are generally wired. So we work with them on that. And then, of course, the other thing, too, in terms of implementing AI, is really focusing on employee training, and we do a lot of work as well with function-specific training around AI, because it's not enough just to kind of roll out just the general training across the organization. If you know, for example, on the operations side, you know you don't necessarily know how to apply AI to solve overtime or inventory issues. Or you know production, you know efficiency issues, that's a different way of using these AI tools versus someone in sales that needs to know how to do customer segmentation and generate insights and look at lead qualification and those types of skills and so very much so we engage with our clients on building out exactly what's needed in each area so that AI becomes part of the way that teams are working, naturally.

Mike Chung: 13:13

This is a little bit of a related question, I think, and so I asked about, like service companies. We gave some examples of kind of financial professional services and accounting, for example, and and banking transaction. I'm thinking about manufacturing or automotive aftermarket. Can you tell us what types of applications you've seen AI in more manufacturing-oriented companies? I would imagine you could apply AI across an entire company in terms of some of the things we've talked about, but also from a production standpoint, kind of supply chain management and how you're, I guess, producing widgets, whether continuous batch or what have you. But can you maybe give us some examples of where you're seeing AI used across manufacturing facilities?

Clyde Calhoun: 14:06

Sure, sure. I think one of the you know, the easiest and probably most common applications for AI in the manufacturing space is demand forecasting. And you know, we all know that the better that you're able to predict, you know what's needed for any given operation, you know you're able to manage your inventories much better and meet the expected demand. And so we're seeing some great gains in demand forecasting accuracy and improvement, which leads to reduction in stockouts or inventory issues or, you know, overstocked inventory. So that's one of the immediate applications. But we're also seeing some great applications for just efficiency and improvement for production operations.

Clyde Calhoun: 14:46

So there is one company that we worked with recently that they actually map all of their different process variables to their AI program and actually turned over complete control to it. I mean, most of the time, traditionally in manufacturing we've set the upper and lower parameters for how a machine may operate. They actually took the guardrails off and said, hey, ai figure out exactly what these settings need to be. And so not only did it optimize the settings within the things that were known, but, because they had other variables plugged in, it actually identified some other levers to move into the mix and actually generated a 10% improvement in manufacturing efficiency. So those are the types of things that really make me excited about what the technology can do, and I think the expectation as well is that for most manufacturing organizations over the next five years, as the technology evolves, there's an expectation of 30% productivity expected generally from a manufacturing point of view. As you think about the power of this technology engaged, 30%.

Mike Chung: 15:53

that's significant, that's for sharing that example, clyde, and I'm thinking about the demand forecasting data that might be brought in that's perhaps internal to the company from external sources. Where do you see companies in terms of drawing data, but also challenges that they might have, either selecting the right data source, making sure it's high quality? Can you speak to that for us a little bit Clay.

Clyde Calhoun: 16:19

Yeah, so I think you touched on exactly that. This is one of the advantages of leveraging AI, as opposed to traditionally looking at data inside of an organization, because historically, companies will rely on their historical ERP data, whether that's SAP, oracle, oracle or whatever the case may be and so they've got kind of a very narrow set of information. But with AI you can fold in other things into the mix. You can look at weather, you can look at things that are happening with strikes warehouse strikes or logistics strikes or things that are happening on the shipping side into the mix as well, and layer all of that information on top of your inside data, and so it creates a really powerful tool. And then, in the case of AutoCare, just to kind of build on that a little bit more, you've got customer information downstream as well that you can pull back and fold into the mix, provided you can integrate the data across the value chain, and so the possibilities are really significant there.

Clyde Calhoun: 17:24

But again, there is a learning curve with all of that as well, because it doesn't happen overnight where you implement this AI system and then all of a sudden, everything is better. You've got to give it time to learn and adjust and adapt is better. You've got to give it time to learn and adjust and adapt, and so one of the things I also talk with companies about as well is that even if you don't have a lot of quality data yet, it doesn't mean you can't start collecting. It doesn't mean that you're out of it because you don't have 10 years of history. You can continue to build. As long as you start feeding the information into these AI models, they can learn on the fly.

Mike Chung: 17:59

That makes a lot of sense and I appreciate hearing that because I think it could be like the scene from the Hurt Locker where the fellow at the end of the movie he's in front of all the cereal boxes and it's like paralysis and analysis paralysis, right, where, if you haven't started yet, it could be daunting because you might think, oh well, where do I start?

Mike Chung: 18:20

But it's heartening to hear your recommendation that let's just go ahead and get started and allow the models to adapt and grow and get better and, of course, like we were talking about before, give it that time to mature. And you touched on some interesting things there because, as you highlighted, things like kind of the numerical ERP data, and then you highlighted strikes, and in my head I'm thinking tariffs or, and you said, weather, and so I think that goes to the types of data that you can have in terms of is it numerical, is it binary, is unstructured, is it? What types of scenario planning there might be. I think about Monte Carlo simulations, things like that, not to get totally into the weeds of all of the statistical modeling and forecasting, but that's, I think, what we look to these AI solutions to do to just fold in many more variables more quickly and then do a lot more of that analytical modeling, forecasting a lot faster than we could have, say, 10 years ago, 20 years ago or even today with an Excel spreadsheet.

Clyde Calhoun: 19:28

Absolutely. And again, as you think about the pace of the technology advancing, I mean, that is the promise. I mean, even with what's available today, you know there's some really cool things that you can do, but when you think about just where we are right now versus even a year ago, you know you flash forward into the future and think, hey, five years, what will that capability really be? And it's really inspiring from my point of view, and scary to some extent. You know too, when you think about it. I think artificial general intelligence is really right around the corner, sooner rather than later, and at that point where AI is truly thinking as opposed to just predicting, we may be in a whole different territory at that point.

Mike Chung: 20:11

So two thoughts come to mind. One of them is thinking about data when you're advising your clients and thinking about our listeners kind of things that anybody should keep in mind with regard to data quality sources, things like that.

Clyde Calhoun: 20:27

I mean, I'd say the biggest thing that I just kind of mentioned a little bit earlier is that it's OK if you don't have it yet, but go ahead and start. It's OK if you don't have it yet, but go ahead and start. And then the second thing that I would say as well is that as you think about building out an AI program, just recognize that there's some work involved up front and you're probably under-resourcing everything that you're thinking about doing. I mean, that is the biggest thing that we see with almost every client. They had in mind that it was going to take X amount of people and money to get it done and in reality it was, like you know, 2x or 3X to make it a go. And I think the reason for that is again kind of going back to what I was saying earlier.

Clyde Calhoun: 21:15

This is a major change for the way that companies are working and in fact, most companies just aren't very good at managing large scale change, and it almost seems that the bigger you are as an organization, the harder it is to change.

Clyde Calhoun: 21:23

And I think too often, particularly with large companies, there's a belief well, we've got all these people in our organization, surely we can just do it all inside, we've got all the capacity that we need.

Clyde Calhoun: 21:35

But when you're talking about large scale change in an organization, almost everybody underestimates what it really takes. And just to put that into perspective, you know, if you think about, you know just the cost of deploying some of these systems. You know the vast majority of companies I think the data is pretty clear Over 75% of companies spend more than a million dollars on these AI solutions. As a rule of thumb, you can think about 5% of revenue as a way to gauge it, and that's just for the technology piece. And when you think about implementation, you're probably tacking on about 20% more to the cost of the technology solution and that translates into a lot of people across any size organization to really get it up and running. And so for us, we end up being contacted by many of our clients because they've hit some constraints from a headcount or capacity internally and they just need some extra hands to really help them figure it all out and drive the change forward and make it work.

Mike Chung: 22:37

Those numbers are very helpful. You said maybe 5% of revenues. What was it again for implementation?

Clyde Calhoun: 22:44

20%, yeah 20% of your technology costs. You need to think about the change management portion on actually making the system work.

Mike Chung: 22:53

So, in terms of seats at the table, the decision makers like who within an organization are you conferring with? I'm thinking of technology leaders, C-suite operations managers and so forth, but who are? I think you would certainly need to get buy in across the entire company in order to have a successful go at this.

Clyde Calhoun: 23:16

Yeah, and it's interesting that you bring that point up because we actually engage with all of the above. And it's very interesting that there's a lot of conflict within organizations now when it comes to AI and who owns it. I mean because in many organizations the IT team is kind of driving the show, but when you think about it, where else does IT really drive business strategy, you know? So it's almost kind of disconnected it to some extent. There was actually a writer survey from back in March that indicated that two-thirds of CEOs expressed the view that friction between departments around AI is, you know, is creating a major headache for them, with some of them even stating that it's kind of tearing the company apart because you do have these competing factions.

Clyde Calhoun: 24:06

But the data is also pretty clear that the best performing early adopters are those that actually work collaboratively across the breadth of C-suite functions to really get clear on their AI perspective and drive the organization forward. And we see that very much in the clients that we've worked with as well. One of the things that we've done recently we do a lot of strategy days with executive teams and the intent's not only just to kind of map out exactly where the organization needs to go, but also to bring that alignment across the different functions and clarity around who's making decisions about this, who's going to own it, as we've got these use cases that come up, who's going to make the call on where we prioritize? Because without that clarity and alignment it's hard for AI initiatives to be successful.

Mike Chung: 25:00

Definitely, I think, having the collaboration across the company, because it can, depending on the application it can just be applied in so many different ways across the company and being able to have that trust, have that we're in this together and this is how it can improve our company is and ultimately, our customers right. I think that's really going to be an important cornerstone for a successful initiative.

Clyde Calhoun: 25:27

Yeah, and if I can just tag on one more thought too, I mean, I think one of the questions that leaders can ask themselves is that can your employees articulate what your AI strategy is? That is a telltale sign of whether you're going to be successful with AI or not, Because if they can't, it either means that the leadership team isn't clear on what the strategy is and the vision for AI, or they haven't communicated effectively to employees, or their AI effort isn't connected to the things that matter most in the organization. So I think that's one of those watchouts, that it's a good litmus test for whether you're going to be successful with AI.

Mike Chung: 26:03

I'm glad you brought that up because when I was rattling off the leadership I wasn't necessarily including the rank and file. But without that communication and transparency and trust building, if you will, across the organization, then it's just going to be a lot harder for any organization. I can imagine and just like another question about that, because I can imagine where listeners might think okay, well, what does it take to get started? You touched on it a little bit, but I'm thinking about the types of services that your company provides, and it sounds as much management, change management, organizational kind of consulting, as it does the technology provenance. But can you highlight that?

Clyde Calhoun: 27:05

Can you speak to that Clyde management issues? It's not having that alignment across the leadership team or not communicating and engaging employees. It's not really educating and empowering employees to use some of these systems. Those are the types of things that typically break down, and so when we engage with clients, we do a lot on the front end of just getting feedback from employees. We do a lot of focus group and survey work to get the pulse for how people are thinking about AI in the organization, what are the cultural norms or how do people feel about change and innovation. You know what's the perspective on leadership, and all of that feedback is very helpful in terms of crafting a change management strategy that you can execute AI effectively, and so we do do a lot of work on what we call the work outside the algorithms, but it's the way that you really generate results and we found that to be very successful.

Clyde Calhoun: 28:03

The average return on investment for these types of or AI done well is around 40%, which is huge when you think about it. I'll also put this in perspective. The majority of companies that successfully implement AI also see revenue bumps. So there's data from McKinsey from late 2024, as well as from Stanford from 2025, that about 70% of companies are seeing revenue increases when they adopt AI successfully, with about 10% of those seeing double-digit gains in revenue. So that work on the stuff outside the algorithm is essential in adopting these.

Clyde Calhoun: 28:42

But it also starts with implementing AI on the problems that matter most, and sometimes it takes us working with clients to get them to see the most important things. Just to give you an example of that, we actually worked with a Fortune 500 company that they'd actually had a couple dozen proof of concept pilots ongoing in the 18 months prior to engaging with us and we started working with them. We did a strategy workshop and we did some follow-on work with their leadership teams, but we ended up identifying two specific areas that were going to improve their financial results with AI. That turned out to be pricing optimization and demand forecasting, and for all the two dozen plus pilots that they were running, none of them touched on pricing optimization and demand forecasting. So they had a lot of activity, but they were working on the wrong things, and so that was really kind of the aha for them that, hey, we really need to be clear and intentional about applying AI to the things that matter most to the organization.

Mike Chung: 29:47

Fascinating. Thank you for that overview. A little bit ago you mentioned kind of the state of the art now and how the technology will improve by leaps and bounds in the next year and then the next five years. Doing better querying, is it more data? Is it more horsepower in the infrastructure on the computing side, what kind of things will contribute to increased technology performance?

Clyde Calhoun: 30:21

Yeah. So I think it's going to be a combination of all of those things. When you think about the size of the data sets that these models are being trained on, those continue to get bigger and bigger. The compute capability continues to grow, so just the technology itself. And then there's also development of new types of models On the generative side.

Clyde Calhoun: 30:41

We're probably most familiar with large language models as being kind of the default over the last several years, over the last several years. But there are new variations of that in terms of how these systems are working that offer the ability to go beyond the level of prediction today and really start to make that transition into general intelligence. And then, of course, the hardware systems like quantum computing kind of layered in there as another lever that may take us to a different place and capability. All of that is rolled into it. So I'm very excited about where this is headed and how quickly it's headed. And it's kind of to the earlier comment that you made If you're sitting on the sidelines at some point, it's going to be almost impossible to catch up, given how fast everything is advancing.

Mike Chung: 31:33

And it seems like the standard is the bar is rising for companies to employ and implement these technologies in order to achieve the revenue growth, to achieve the efficiencies that you highlighted in, whether it was a Stanford or the McKinsey studies there. So I feel like the bar just keeps rising and the longer you sit out, the harder it's going to be to catch up.

Clyde Calhoun: 31:56

Yeah, for sure. And you know, one thing I wanted to touch on too. I mean because you know we work with some large companies, but we also work with smaller organizations as well, and I think sometimes, as a smaller enterprise, you feel like, hey, maybe this is just too much for me. You know, this is good for large companies, but not necessarily for my smaller business, and that's simply not the case either, because there are solutions for smaller organizations.

Clyde Calhoun: 32:22

Interestingly, I actually called a small business here in the Charlotte area and they actually had an AI agent that they engaged to actually field all their customer service queries and it was really really good. I mean, it announced itself as an AI agent up front, but the conversation was so natural that at the end of the call I ended up saying have a nice day. I mean, it was just kind of one of those things that it just felt so natural and, honestly, I've called that company on several occasions and this is by far the best conversation that I've had with one of their customer service people, so to speak. So I say all that to make the point that, yeah, I mean this is so much better than it used to be from a technology perspective. It's only going to get even more advanced and whether you're a smaller company or a large enterprise, you really need to think about getting on board with this and jumping in the pool.

Mike Chung: 33:20

And it sounds like some of the for the smaller companies are those who may not have, who are wading into the pool as we speak. Identifying those quote unquote low hanging fruit to kind of build successes and kind of make it more palatable, if you will, across the organization should be perhaps one way to approach it. Great. So, thinking about you know switching gears here a little bit Clyde, I know that when we were talking we talked a little bit about your background and you've been in materials engineering, materials science, ceramics engineering, and you mentioned I think it was fiberglass and other, I guess, industries that you've been. Can you just tell me what kind of a little bit about your past professional background and how it brought you to where you are now? Yeah, so I'll just kind of start from bit about your past professional background and how it brought you to where you are now.

Clyde Calhoun: 34:13

Yeah. So I'll just kind of start from an organization perspective. I've had the chance to work for some great companies, started my career with Lockheed Martin in the defense sector doing a nuclear weapons R&D. That was kind of where I cut my teeth and spent a number of years as well with GE and their energy business and working still on the technology side but also on the quality side as well. So Six Sigma, lean manufacturing, supply chain sourcing, working in those functional areas.

Clyde Calhoun: 34:47

And then with Owens Corning, the fiberglass manufacturer and composites manufacturer, roofing manufacturer. That was a great experience as well, and through my career, in addition to R&D production quality operations, I also spent some time in sales and marketing as well. So I covered the full range. But for me that's been very helpful because, as I think about working with our clients now, I understand how all the different business functions essentially operate and how each functions, thinking about particular challenges and the importance of bringing it all together and then, particularly from a continuous improvement perspective, my time at GE and Owens Corning just really helped me to shape how you go about large-scale enterprise change and what that looks like, and so that's been a lot of fun, you know, working in that space and helping companies just figure it all out and do something very different. I actually enjoy new things and new technology and things that are on the cutting edge, and so this has been a blast for me, just kind of working in my wheelhouse.

Mike Chung: 35:54

Well, congratulations for being able to achieve so much. It sounds like a fascinating career journey and I just wonder if a teenage Clyde Calhoun could look into the future and see what you're doing now. Would you have ever dreamt or guessed that you'd be doing artificial intelligence consulting?

Clyde Calhoun: 36:12

You know, I would guess that there was a chance that teenage me would have guessed there. Actually, I got my first computer when I was what? Ninth grade. It was a Commodore VIC-20.

Mike Chung: 36:26

So if you remember those days where two and a half K of memory was all you had.

Clyde Calhoun: 36:28

You remember those days where two and a half K of memory was all you had. But the cool thing about that was, you know, when you, when you wanted to play a game, you actually had to program the game and then play it. Well, program it, debug it and then play it. So I've always enjoyed that, that you know the computer side and technology and so for me, you know, I could see myself being in this role. As I flashback to my teenage years, oh, thanks for sharing that.

Mike Chung: 36:54

How exciting, and I'm sure a lot of us are waxing nostalgic now about our first computer If it had a cassette player to load a program and you know, the funny thing is that when I went to college I got my first you know, I guess you know semi-professional computer.

Clyde Calhoun: 37:10

at that point in time it had 64K of memory.

Mike Chung: 37:15

That's a lot for those days. Yeah, that was a lot for those days.

Clyde Calhoun: 37:17

And I remember the salesman. I got it from Radio Shack and I remember the salesman saying that's all the computing power you'll ever need.

Mike Chung: 37:30

But we also think about the Apollo missions landing on the moon. And you know, even a cell phone from 20 years ago is much more powerful than what NASA had decades ago, Right? So it's just quite amazing what technology has done. And when we started our, before we got on air, you talked about Toledo, Ohio, and you were lived in Toledo for a bit of your career and life, it sounds like. Am I remembering that correctly?

Clyde Calhoun: 37:56

That's correct. So Toledo, ohio, the glass city. So I spent about a dozen years there and I'm originally from South Carolina but you know, spent some time there in the Midwest and I can honestly say that was as cold as I've ever been. I remember we've had a couple of weeks where temperature didn't get above zero and you've got like minus 20 degrees at some point in time. But again, I miss the people there, great folks in that part of the country.

Mike Chung: 38:27

but I'm glad to be back in the Carolinas Well it's kind of scary then to think if it was only zero in Ohio, what would it be like for Michiganders and Canadians? Right? I actually grew up an hour south of Toledo in Lima, so right off of I-75,. I played a lot of tennis growing up and got to play against some good schools in the Toledo area like Sylvania Northview, sylvania Southview, toledo, st John's, toledo, st Francis. So I don't know if any of those ring a bell to you, but I'm kind of from that same part of the country.

Clyde Calhoun: 38:59

Yeah, my son went to Southview. So yeah, I know them very well, yeah.

Mike Chung: 39:03

Yeah, great, that's wonderful. So you know, Clyde, it's been such a pleasure having you on our broadcast. You know, Clyde, it's been such a pleasure having you on our broadcast and thank you so much. Is there anything else that our listeners should be keeping in mind when they're thinking about AI?

Clyde Calhoun: 39:16

solutions, yeah. So, again, thanks for having me on first of all, but you know, the thing I would leave with is that, hey, this is a journey. You know this is something that you know. You're not expected that you're going to adopt AI overnight and it's going to cure everything that you've got with your business, but it is important that, as a business leader, that you start, that you get it into the pool and test it out, and so, again, we've had a lot of great fun helping our clients do that, and so always here if support is needed.

Mike Chung: 39:46

Terrific, and we just passed Independence Day. I hope you and your family had a wonderful Fourth of July and thinking about next major holidays on the US calendar, let's just say Labor Day. You're inviting friends over for, I don't know, a neighborhood kind of block party cookout. What kind of things would you and your family be serving at such a cookout cookout?

Clyde Calhoun: 40:07

Ah, for the cookout. And first of all I've got to say Labor Day weekend, college football, the start of week one, so super excited about that, but we're definitely going to be having burgers and brats and ribs.

Mike Chung: 40:20

That is top of the agenda. Yeah, are you doing some slow cook like pulled pork or anything like that? Beef briskets I mean you mentioned Carolina. I kind of have to ask.

Clyde Calhoun: 40:29

Yeah, so I'm a big brisket fan, but I'm not a smoker in terms of having like a smoker, like a green egg or anything like that. So, I work off the gas grill and I do a pretty good job on the ribs with that. It's the marinade that really does it, and as long as you soak them long enough, you're still gonna get those tender ribs.

Mike Chung: 40:52

So it sounds to me like you're an overnight marinade, if not longer.

Clyde Calhoun: 40:55

Yeah, you got it. That's the key.

Mike Chung: 40:57

Good to hear All right. Well, again, mike Chung with Auto Care Association. This is the Indicators episode of the Auto Care ON AIR series. Again, it's been a great pleasure to have Clyde Calhoun, of Root Idea, with us. Please make sure you like and subscribe to the series, and, Clyde, thanks again for joining us and I wish all of you a great rest of your day. My pleasure, thanks very much. Thanks for tuning in to another episode of Auto Care ON AIR. Make sure to subscribe to our podcast so that you never miss an episode. Don't forget to leave us a rating and review. It helps others discover our show. Auto care ON AIR is proud to be a production of the AutoCare Association, dedicated to advancing the auto care industry and supporting professionals like you. To learn more about the association and its initiatives, visit autocareorg.