Leaders in ERP: Kevin Miller, CTO of IFS

Shawn Windle: This is Shawn Windle, Founder and Managing Principal of ERP Advisors Group. Thank you for joining our podcast, Leaders in ERP. We have been just extremely fortunate to talk to some of the top leaders across the entire ERP industry. And even today, we're kind of going, I think, to the next level, especially when we talk about international firms, organizations are working with people across really lots of different geologies, geographies, pardon me, and lots of geologies probably with different kinds of asset intensive companies and field services and aerospace and design and defense. So, I'm really happy to have Kevin Miller, CTO of IFS.

Kevin Miller: Oh, thank you for having me, Shawn. It's a real pleasure to be here today. Happy to talk about IFS and all topics, ERP and AI.

Shawn Windle: I know, here we go. Yeah, it's like how many acronyms can we use? I think we get a bonus from that.

Kevin Miller: That's what our industry is infamous for.

Shawn Windle: Yeah, it's really true. Now, I think a good place to start, I'm ready to jump in, but I really want to know a little bit more about you, Kevin, and kind of talk about your background and how you got your position.

Kevin Miller: So, I started way, way back implementing large ERPs for multinational corporations. And that's really where I cut my teeth on those kinds of projects to really understand the benefits of a back-end system, and what it could provide for multinationals or one to two plants, just organizing all that data from supply chain to manufacturing to finance to everything in between. So, I've been at IFS now in this role about 7 1/2 years, and what an exciting ride it's been in terms of where the product's going, where the industry is going in terms of AI and how we apply that in a real-world sense. So exciting times.

Shawn Windle: Yeah. I'm thinking about, it's kind of interesting. We were chatting a little bit earlier and something that I kind of look for in people, I've probably never said this on a podcast, but I'd like people who've actually been in the trenches, who've actually lived through the situations of looking the client in the eye and saying, well, Yeah, we still need to do this, and we're supposed to go live tomorrow, but I'm gonna do everything I can for you. So, you come from that consultant background?

Kevin Miller: Yeah, and I think there's no better substitute for experience than actually being in the trenches. I mean, experiencing, oh my gosh, we have a crisis, how do we solve this? Or we really think if we take this initiative, it can help the organization. How do we do it? And it's okay to try things and fail because that's where the experience comes from, right? But unless you've been through that on the front lines, it's hard to relate to companies that are going through those same struggles. So, I think that not just me, but everyone on our team brings that industrial experience to say, I've run a plant before, I've been in field service before, I've been in aerospace and defense to have to meet certain deadlines and stringent tolerances and capabilities. So, it's that level of industry experience that I think brings the real-world context to what we're trying to achieve.

Shawn Windle: Yeah, that's fantastic. And especially for your role as CTO, again, we were chatting earlier, you were talking about some of the conversations that you're having with clients and they're going through like real business problems and you're looking at how you can use the platform, the technology, make sure that they have what they need. Even before they need it, right? Just, if you would, tell us a little bit about kind of that CTO role. We've interviewed several CTOs, but I've actually never asked a question, like, what do you actually do? Because I think our listeners are, they're missing out on this opportunity to talk to guys like this.

Kevin Miller: To me, I have the most exciting job on the planet. I mean, it's really this combination in the culmination of technology, business challenges, product, interactions with prospective customers and customers. I sit on the executive team for IFS in the Americas region as well. So, I'm before, during, and after, part of that relationship with customers as they go through the journey of what can technology do for us? How can it solve real-world problems? And I think the reason I've found my home here is that I get this big sandbox of all these neat toys to play with, but it has to be applied in real-world outcomes. And AI right now has a considerable, still amount of hype around it. I did not bring my magic box today to open on the table and solve all the problems, but I think we have that background and context to say, you know, here's an example. And this will seem very obvious, but for our customers in field service, what we realized, and you get this through ride-alongs, you get this through tangible feedback from the field, is it's hard to get the work that was completed back into a system. I may be wearing gloves. I might have PPE on. I might have, you know, dirty hands, and I'm trying to get stuff into this tiny little device. It's like, how obvious is it that doesn't work? And so, one of our real world scenarios that we built was the ability to overlay AI on top of voice to text. So, I can use any device to actually record what I did, but the AI is interpreting, oh, you were on site because of geofencing. You were there for 2 1/2 hours and recording that time. You used and consumed these spare parts to fix it. You ran these tests, got these results, and all of that is auto-populated back into that service request, that work order, where I don't have to type that in. And so now it becomes more of a situation where I tell the system what I did, and there's a quick check to make sure it interpreted everything right, and then I'm accepting. So, I don't have to type all this stuff into a small, tiny device, and I messed up. Used the wrong things. It's so much simpler and it's so obvious, but it took us years to get to that point to say, how do we not think of this sooner, right? And so those types of experiences with folks on the front line, whether that's a production floor, whether that's someone trying to solve distribution challenges of logistics and saying, gosh, disruption is the new norm today, right? And it could be anything from supply chain struggles to geopolitical to everything in between, how can I use a system to proactively help me look for those potholes before all of a sudden I realized today that shipment I was expecting is not coming for another month. And now that's bad experiences for me, for my customers, for my suppliers, and anyone in the chain. So that's the most exciting piece of the role, is I get to see those front world real challenges and how we can apply technology to it.

Shawn Windle: Yeah, I think that's awesome. The truth of the matter is, like you said, we've been talking about these concepts for literally decades, but maybe tell me kind of from your perspective on sort of how back office systems and kind of ERP has evolved from some of the kind of blocking and tackling of just having all the data in one place to now what you're talking about, which seems like sort of next generation.

Kevin Miller: Yeah, I mean, and there's still as you would know, some of this to some extent, but we used to call these islands of excellence, right? Because everyone ran the business on Excel and there were all these islands, right, of the business. And I think back office was kind of a necessity to say, how can we get all of this in one system? And it really became more of a repository or a data container to help facilitate reporting where I could tie those different business units together. And candidly, I think a lot of organizations, if I was in one department, I just cared about the data that I worked in, and then it was someone else's issue after I was done with it. And I may have inherited good transactions, I may have inherited bad ones. Poor finance folks, they got the snowball that came to the bottom of the mountain. And now I think we're at this really interesting inflection point because I can use models within AI to not only say, is this data good, but what are the insights I'm missing? I may be programmed just from a historical perspective to look at certain KPIs and metrics, right? What indicators are helping me run the business? But what am I not seeing? What is there in that data that AI could help me see and more importantly, interpret and make suggestions for? So, to your point, I don't think this is necessarily a giant leap. It's kind of the evolution of where we're going now, because I want that data organized to give me the best output and outcomes to better run the business, to become more efficient, to react faster, to do things better. So, I think it's part of that natural evolution of where the technology has come.

Shawn Windle: Yeah, it's so interesting to think about, going through from maybe not, I wasn't, I was, I did, I started my career doing COBOL programming. So, mainframe environment to the mid-range when I was in.

Kevin Miller: You know, we former COBOL programmers are in high demand still.

Shawn Windle: I can probably be making a ton more money programming right now, but I love my team. Don't worry, Rebekah, I'm not going anywhere. I'm going to stick around for a bit here. But I know so many of those systems are still running too. They're running a lot of infrastructure for the world. But if you just look at all these different evolutions that we've gone through, I think you're probably our first guest to actually say what you just did, which AI is a natural evolution. And I really appreciate that because if our listeners and other people that find these podcasts, like if they understand that view of it, two things happen. One is they don't come in with this, oh my gosh, I've got to do this.

Kevin Miller: Tear it all down and rebuild it all.

Shawn Windle: Exactly, and which is like the riskiest thing in the world to do in any enterprise software, but also secondly, to have more realistic expectations about the value that you should be getting. There should be additional value, just like we saw with the Cloud and everything else, so I think that's a great point, but speaking of... what do you think are the biggest technology issues that manufacturers and distributors, asset intensive companies are dealing with today? Like when your conversations with these people, what are they saying?

Kevin Miller: Yeah, I think it's such an interesting time because we know these organizations have a ton of pressure right now, whether it's a board, whether it's a PE owner, whether it's management that is saying, I'm hearing all these amazing stories about AI. What are we doing, right? Should we be coding our own things? Should we be building things? Should we be developing our own models and all of those types of things? And I think there's just a lot of muddiness, right? That's saying, what's the best path? Because I want to select a system that gives me a really good foundation from a data perspective. Those systems that will serve you best are the ones that are, a, open to collaborating with other systems. We know manufacturers and distributors, they might use more advanced analytics, data warehouse and data lake, lake house tools. How does your ERP interact with that? Does it share it in a secure way to say, we're not going to be monolithic, right? We're going to help you get those insights from all aspects of the business, whichever tool you're using. And it has to be easily adoptable so that it's scalable. And what we're seeing right now is a lot of companies want to develop a pilot or a POC, and then sometimes they get stuck in that phase. And it's like, wait a minute, we have 1000 users we want to scale this to. How do we launch out of that pilot phase to that? And so, we have a lot of questions, a lot of concerns around data and the quality of that data and the consistency of that data, which by the way, tools from AI can help with. It can spot patterns that we might not be able to see. It can spot anomalies that we might not be able to see on the surface. So it will help with that piece. But how can I do an incremental project that gets me huge efficiencies rather than to your point, I don't want to burn everything down and start from the ground up. There's so many easier ways to say, I want to apply an AI layer, I want to apply an AI tool to that existing data schema. It's already there. You might have, in some cases, decades of historical information that's valuable. And we see projects around even looking at the lifespan or the remaining health of an asset. I have the ability now not only to look at things like work order history or preventive maintenance history, but what about actual field experience of other customers for that piece of equipment in the field? What insights can I get? Can I overlay spare parts models? Can I overlay CAD models? All of this at the touch of a button for a technician is hugely invaluable because another thing that dovetails to this is sort of, you know, I can say this affectionately, the graying of America, right? Like the workforce. And it takes a tremendous investment to get new hires up to speed. But if I can help supplement that institutional knowledge with the AI providing that tangible feedback loop to the new technician or an apprentice, I'm able to leapfrog and cut down on some of that onboarding time that might have taken us years to gain that proprietary knowledge. But now with all of this insight, all of this data front and center, I'm getting it in context. So it's easier for me to scale new hires or a workforce in terms of I'm AI enabled. And so, I don't want to make AI the hero. I want to make that work or the hero using AI. That is kind of the approach there.

Shawn Windle: There's so many things in what you just said, but I think one of the most important things that I would reiterate for, again, the people that are listening to the podcast and they're like, I got to do something.

Kevin Miller: Where do I get started?

Shawn Windle: Where do I get started? You get started by listening to Leaders in ERP podcast. That's a great place to get started. Or the website, Rebekah, the team have incredible stuff out there that's so helpful. But you go by going to helpful people. But one of the most important things of AI, that I think most specialists, people would agree, is the data set. And the thing about the ERP that has, I mean, I do have more gray hairs now since the last 16 years of having my own company as an advisor and all the hundreds and hundreds and hundreds of implementations that I'm not necessarily doing like my team's doing, which makes it harder for me because I'm like, oh gosh, what's going on? You know, and they're doing great. But we have for years just rigorous, just strict discipline around data in an ERP. Now, we know that sometimes you get in open purchase orders that nobody closes for whatever. There is definitely data cleansing and hygiene and blah, blah, blah that has to happen. But the structure of the relational database management systems that ERP is built on enforces that if there is a purchase order, there's a maybe a purchase request before that, it got approved, it became the purchase or there's an approval, there's a receipt against it, there's an invoice. You know, three-way matching is like, we just think about that just naturally. I think most of us do. But ERP forced that level of data. And I'm telling you, other, we work with a lot of different kinds of applications, CRM systems or patient care reporting for fire departments, like you name it, we've worked with HR systems. And a lot of the cleansing and structuring is depending on the person. Whereas ERP, just the way it's built as a transaction processing system really enforces it. Now, I think for IFS, especially when I think about the breadth of solutions that you all have, like we tend to think of you guys as a, you know, focused and very good in specific industries, and that you have multiple modules that all interact together. Don't you think that gives you a leg up on AI initiatives for your clients because they have that structured data already in your systems.

Kevin Miller: I do. And here's the reason. I think you hit on a really important point. ERP systems, you know, there's been some debate recently around SASpocalypse and other things, right? But I think the most effective solutions are the ones that are layering components of both traditional ERP and also AI tools. Because you made a really good point in that ERP built the rigorousness in the discipline of regulatory compliance of gap accounting analysis, and a lot of companies right now are saying, Look, I could go to Cloud Code, I could build something like this. Exactly, you could, but it does a bit remind me of the IT group. 30 years ago to build a proprietary application, held the business by hostage, right, for 30 years until they wanted to retire, and then all of a sudden it was like, I don't want this anymore. And so for us, we have worked really hard to design AI solutions around the frontline worker. And we sometimes call it AI with a hard hat. And the reason for that is customers are going to be at different parts of that journey. And if you want to agentic, which I want that context, I want the ability to fetch and get with real background and real knowledge coupled in that. If I want agentic, which means I want to automate this stuff, I want to create loops that do things based on rules that are just automatic, and then they're giving me the insights. Or if I want a platform like IFS that allows you to build these AI tools within the confines of the application and the platform, I'm not having to take that high risk of saying, we're going to go try this. No one wants surprise token bills. No one wants surprise consumption bills around, man, this works great, but I didn't realize the cost. Like maybe I could have hired two people versus my token consumption. So all of that needs to take into balance. But we've kind of built these tools with the guardrails to say, you can do this within the platform. Like our customers can use that agentic platform to build any agent or any digital worker they want. We even include 70 connectors to other systems like CRM or data warehousing and things like that because it is now to the point where I want to interact very quickly, securely, and accurately with other systems of record. To get the insights that we need.

Shawn Windle: I love it. It's a very exciting time. And I think, like you said, we've been working on the promise for what we're able to deliver now. But now you guys have a very strong focus on manufacturing, distribution, asset intensive, I think utilities, I think mining, definitely aerospace and defense. I have to look down on my list to make sure I cover everything. It's A lot. Why does that industry depth and experience that you guys have really matter when it comes to these enterprise software, even AI projects?

Kevin Miller: Yeah, and this is interesting because I think the AI teams, and I affectionately refer to them, by the way, as the AI propeller heads, they're super good on all of these current tools. I mean, they understand how they work, they understand how to deploy. But I think my average tenure on my team is just shy of 12 years, which is pretty amazing from a tech perspective. But what that means is that industrial experience, not just on the folks that implement, but the folks you're interacting with on the pre-sales side that are showing you these solutions, they have either been in industry or they have implemented the product. And that's all the way through the IFS ecosystem. So not just employees, but our partners also come from industry. And so we're trying to say, we know both the background of what the customer is trying to achieve within their business, and we've stayed really true to those six industries, by the way. We've not really veered from that in the decades that we've been around, but we've now applied the AI tools on top of that industrial experience. And so, for us, we feel that we can deliver value a little sooner because we have the background, like you don't have to educate us on what your industry does. You don't have to teach us how we make things on a production floor, right? That institutional knowledge is there. And that helps us kind of cut through what's hype and what's real, what's going to impact. Like the field service example, for instance, it's like, just went off like a light bulb. Like, let's make it easier to get the work that was completed back into the system. Those types of examples allow us to accelerate that.

Shawn Windle: Okay. Yeah, I'm thinking through this concept of AI maybe 3, 4 years ago, right? So say, gosh, just 2021 or even 2020, mostly 2021. I think we're all busy with other things in 2020. We're actually, that's where we saw many, many clients finally moving to cloud-based solutions. But when I think about the initial AI discussions, the painted picture, the picture that was painted by the industry leaders was, gloom and doom, right? It's going to take over the world and nobody's going to have a job, and and and, I have a theory on why that happened that way, but it's not appropriate for my podcast. Maybe there's another way to do that. Just to get into that just for a second, though, it's that the power of AI is so obvious that then as a race, we kind of want to control that, right? And so, then building out this kind of fear, now there's regulations and there's certain people that have the key to the castles and others, which I think is hellaciously wrong. The way things have turned out has been awesome. Like if you're Open AI, your competition isn't just, co-pilot and Grok and Llama, it's now Nvidia. We're talking about that a little bit and other organizations that are looking for the full stack solutions. So, there's all this stuff with AI. I could just go on and on as I'm really like taking more responsibility, I think, as an advisor, frankly, for like do we tell a client to go with an agentic platform, best of breed, and then what happens if that gets bought and then it gets eliminated, and again, the large language models are building out all these applications are giving out tokens for free so they can learn the scenarios, and is... is Claude going to take over for an ERP company? I don't see that happening because of the industry experience. And you mentioned, of course, regulatory and then aerospace and defense and utilities and not only that, but you also have real safety issues that you guys have been dealing with. So give us like the unadulterated view of IFS and the development and how you guys have sort of been here at some of these industries from, I mean, I think some of your biggest clients, I don't think it's confidential, but there's people that are in aerospace and defense that have very large ships that represent the largest countries in the world as some of your customers. What does that really mean in terms of what you know about the clients?

Kevin Miller: No, it's a great question. And I think, you know, we, I can't underscore how seriously we take that responsibility because, you know, there are certainly the AI firms that are building amazing things with frontier models, right? But do I, as a business, especially A&D or anything else, mining, anything that's hard goods like that in an asset perspective, do I want to hand all that data off to a model like that? Do I have the right safeguards in place? Do I have assurances? Do I have, those pieces that protect my data. And so, where I think a firm like IFS comes in is we have those relationships with the Anthropics, with the OpenAIs, with the Microsofts, et cetera. And we're trying to say, what does this mean for the industries we serve? So, I don't want to say we're a filter, but in some cases we're saying, that's great, but not super applicable, nor would we probably want that data into a model like that. But if we did it this way, it's safer. It meets regulatory and accounting compliance. It helps to build in those safeguards to it. And I think that's why even advisory is so important to say, let's look at this holistically. What do we want to achieve? We obviously, as a business, we want to produce and service things more efficiently. We want to do it more safely. We want to do it faster than our competition. With a better workforce than our competition. And those, I think, are the hallmarks of focus to say, this we know, we have customer use cases, we have real-world examples of where we've applied this, and to your point, in an incremental fashion to say, this has been a challenge for us, how do we apply AI to solve the problem? Using that historical context of the data schema and all the transactions that we have, and that has been bearing the most fruitful outcomes, what I see across the customer landscape.

Shawn Windle: I love that. Yeah. I think that there's so many thoughts that I have going through my mind as we're talking here. It's such a renaissance for ERP.

Kevin Miller: It's amazing.

Shawn Windle: It's incredible. And I would even offer this. In years before, and Rebekah will remember some of these conversations. Thank you, Rebekah, for everything, as always. She’s amazing. We would talk about the hyperscalers play with ERP, right? Like, wow, you know, you could buy an ERP vendor and drive more usage into the data centers, et cetera, et cetera. Well, that's like ERP is a small piece of the hyperscalers business. Even those that own that are a hyperscaler and have ERP, the vast bulk of their revenue is not from, you know, the dynamic platform, not using a name. So, you would, anyway, there's a whole thing there. But I can see where we thought that there was going to be an acquisition, especially with some play that was occurring with executives. You could see as other people in the room maybe even experience this, where there was sort of this cross-pollination between some of the hyperscalers and the ERP vendors. And then that didn't happen. Actually, it didn't. You know, you can look at sort of the NetSuite acquisition by Oracle, which is different. That was a play into the SMB market, the small medium-sized business for Oracle. I don't think that the frontier models, the open AIs, and these guys are really going to want to have the responsibility, frankly, that you guys have had for decades. I mean, even if in some of our other interviews and in selections, we're looking at the AI native ERP vendors, and they're building out a GL and financial reporting and consolidation and multi-company, everything and currency translation and intercompanies and eliminations and da da da da da. It's like you guys have had those things for decades. So it is sort of interesting where if I think if I'm listening to this podcast and I'm running on my treadmill and thinking about, okay, I know I got to switch ERP, sometimes people come to us and say, I'm not just going to do anything because I need to see where this shakes out. You will never buy an ERP if you wait for it to shake out, right? It's evolving in real time. I guess what I'm trying to get to is as you're talking through and listening to the scenarios from your actual customers on what they're looking for, as well as you guys looking proactively into the market. It does seem like the best bet for AI is really to go with what your ERP vendor is doing.

Kevin Miller: Yeah, I mean, I think it's a balance because, you know, if I look at the GL, right, I don't want to reinvent the wheel. Some of that stuff has been table stakes for years. But let's take this scenario. Traditionally, companies assign payment terms to a customer. Hope and pray that customer pays by the terms, but the reality is everyone in this room probably pays different in some regard. Some early, some late, some right on time, might be different historically. If I can use AI in the context of applying some algorithms to the historical way that customer has paid, now I have a more realistic cash projection. I'm not reinventing GL. I'm using what's there, but I'm applying newer AI technology to it to say, give me a more realistic projection of my cash, rather than just saying, based on the due dates, this is what you should receive. Well, I probably am not, but I want to know based on how those customers in that OpenAR have paid in the past, what is a better prediction of my cash? And by the way, AI, maybe go connect to Dun & Bradstreet or some outside service to say, is there a material event happening that could affect my ability to pay? So I pose the question, where do I get the higher return on investment and value? Is it building a new GL from scratch? Or is it saying, gosh, you have years of historical payment data here. Let's apply some algorithms to it to say, guess what? I've improved cash flow by 10%, 15%. That's probably more valuable to the business than going off on a project to rebuild and reinvent GL. So those are the kind of use cases we're looking at to say, can we reimagine, not reinvent, but reimagine using these newer techniques and technologies? You know, there's still, there's a mine of data in that.

Shawn Windle: Tons.

Kevin Miller: In that container, in that ERP system, right? Let's use it to our advantage rather than say, I think I can build a better mousetrap.

Shawn Windle: Right, when the mousetrap's been written for decades. Yeah, and then my mind starts to go into, and I think there have been some vendors who have explored what I'm about to say and then have quickly backed off from it, which is, well, now with cloud-based solutions, with the tenant-based data, and the accessibility that the ERP software vendor has to many, many, many customers' data, when you start looking at AR, isn't it interesting to consider? Okay, well, my customer is also the customer of many other vendors out there. Can we pull that data together? And that's where usually the conversation is like, just stop talking about it. Don't even go through it.

Kevin Miller: You found the next battleground, my friend. And that's where I say, like, I think that's where advisory comes in so important because a lot of vendors will say, we have these policies, we have these procedures, but really what's reality? Is your vendor hosting single tenant, multi-tenant? Do they have access to your data? Do they explicitly say, we will not use your data to train our models for other customers or industries? Those are the type of safeguards, I think, that are important when investigating a new system to say, not just for right now, but what's three years look like, what's five years look like? No one, I don't think, likes to replace ERP because tentacles are everywhere. It is like, I always tell people, it's a mini MBA on the organization to do an ERP project because you learn things that you never knew, that you never expected. And part of my favorite times in sitting in those project meetings was You'd be at the table with executives, and they would say, here's our process for doing that, and I'd always look around and I'd see an operations person shaking their head. That's how we say we do it, but that's not really how we do it, because it's human nature to find the path of least resistance. And so, a lot of shortcuts, and sometimes you refer to that as paving the cow paths, right? Like, do we really want to do those things? You now have the ability to reimagine, again, not reinvent, but reimagine what can AI tools do to help us in those areas. What is leading to that behavior in the first case, right? Is it material shortages? Why material shortages? We don't have the insight on the supply chain to see what's coming or where the disruption is. But if we did, that challenge would go away. We'd move to something else. So it is an amazing time to revisit all of those challenges with a new lens on what we can do.

Shawn Windle: But also with a kind of a historical viewpoint of ethics, right? I mean, that's something that we look at personally as a firm, like which models are we going to feed client data into, right? The answer has been none. Like we have not even gone there. We're building out some proof of concepts as we work with specific clients and they understand that because our team sees this vision for what is true. But I can't reiterate this enough that if you have basically these startups that are worth almost a trillion dollars, and they're giving away usage of their applications to basically learn what applications, then they can rebuild and then resell to others. It's like working, you know, it's like being in bed with the enemy. Like you don't know what's going to happen. Whereas when you look at a rich legacy enterprise software vendor, probably like you said, the SASpocalypse site, like it was almost like this opportunity to make these companies devalued, not just from a market perspective, like a valuation, but also like the importance to a society perspective. And you can't lose sight of that. You all for decades have been responsible for the most proprietary information of these organizations.

Kevin Miller: Yeah, and that is trust that's earned. It can be lost in one bad decision. Right. And so, I think for us, we take that stewardship very seriously. And that's one of the hallmarks of our interactions with even prospective customers is to say, this is important for us, for you to know how we treat these things, how we treat your data, how it's going to be governed, how it's going to be orchestrated. Does just any agent in the world have access to that? How do we facilitate that? And so I think to your point, a lot of questions around not just governance, but security and permissions, the sharing of that data, right? Some organizations will say, we're going to anonymize it. We don't know it's yours. They know it's yours. These models are very sophisticated, right? And so, I think you have to, your point, find the right partners in that pursuit that are going to, have your best interest in mind, know your industry, know what's acceptable and what's not in terms of, from an ethical standpoint. And that's a lot to take in, right? That's for someone looking at, I have to modernize, I have to do something. This is overwhelming, right? Where do I start with all of that? So that's the important role.

Shawn Windle: Right, I think that's great. Maybe to wrap it up here, what do you think, Kevin, from a five years from now, right? This is going a little off script, but I want to get into your mind because you're having phenomenal conversations with your customers today and they're coming at you with use cases. You're talking to your tech teams. They're showing you amazing prototypes. And like, what do you really see if you're a customer of ERP, say IFS in the future? How are you interacting with the system? What's different? What's exciting about it?

Kevin Miller: Yeah, I think we're finally at the point, you know, you've started to hear this term recently about headless ERP, or headless systems. And at the end of the day, it just means as a user, or even as an agent, I want to interact with that data, and it might not be through the traditional user interface. Companies like IFS are trying to be at the forefront of that to experiment with new licensing models. So that old traditional approach as per user, per license, it's not really applicable anymore because I might have different systems interacting, digital workers, agents. That's not the licensing model that companies want to be stuck to, so we're trying to experiment with things like price by assets, price by volume, price by transactions, or different things like that to make it more adoptable for those businesses, but if I look at the five years, I'll say automation's probably the big one. The ability for me to say, I want to ask the ERP system questions from Teams or Slack or some other collaboration platform where I don't even have to be logged into that system, right? And I want to get those suggestions through the medium that I'm most comfortable working in. The ability to be open to that, to allow users to interact with that data in whatever form they want, the ability to adopt things like cobots and robots, where if I have safety concerns in certain warehouses or issues like that, I've seen some amazingly automated warehouses just in the last three months with fully autonomous picking, packing, shipping, and it's helped with safety. It's helped with, I can work odd hours if I have certain deadlines and crunches, right? I can just have them at it. And it's meant to augment and help that human workforce, by the way. But I think that's going to be the real key is how do we help to automate to squeeze out the efficiencies. Because your competitors are doing it. Get with a partner that can help you.

Shawn Windle: That's fantastic. Yeah. Kevin Miller, Chief Technology Officer of IFS. This is great. Thank you for sharing your insights and your honest viewpoint of where we're at, where we're going, and excited to see what IFS has.

Kevin Miller: Yeah, Shawn, thanks again for having me. It's my pleasure.

Shawn Windle: Yep, you bet. And thanks everybody for joining us for another version of Leaders in ERP.