Private Equity AI ROI in the Office of the CFO

[Music] Get ready to peer behind the curtain of the Private Equity Universe with each episode of Best but Never Final. Hi, I'm Lloyd Metz, joined by Doug McCormick and Sean Mooney. Together, we'll navigate the corridors of Private Equity revealing the uncommon knowledge, challenges, successes, and lessons that drive the world of Private Equity and business forward. Let's go. [Music] It is great to be back with Lloyd and Doug. Doug and Lloyd, how are you guys? Great. Good to be here. Hey, Sean. Hey, Doug. Good to see you guys. It's been a minute. It has. It has. I've missed you guys. I did too, actually, low-key. I'm not going to go there. [Laughter] What was really interesting, I think last one, we got a lot of compliments on the episode because it was like setting the stage for the things that you can do on AI. And to be candid, guys, I think there's the AI exhaustion, but there's also AI resistance and kind of failure to act going on. I think it would still be good to drill down on this topic a little more, but really, how do you practically do it and get ROI versus everyone having fancy Google? Absolutely. The concept of AI exhaustion doesn't resonate with me. I think this is going to be front and center here for a long time. So if you're exhausted, get ready. It's going to get worse. Ding, ding. Guess what's going to win change. You're a hundred percent spot on. If we all run towards it, the world will be our oyster. If we resist it and ostrich it. Change will win. I don't know about you guys, but I don't want to get left behind. Fomo. [Laughter] All right. So one of the things that I thought would be helpful, to talk about this beforehand, is we happen to have a wizard of AI enablement within the office of the CFO in our midst here at Blue Wave. And so one of the biggest areas that we see just through equipping the PE industry with AI enablers and tools is one of the areas of high star wide for sure, in terms of things you can do right now and get payback is in the office of the CFO. So I invited our CFO, Jeff Berry, to join us here today, to pull back the curtain and just kind of actually say here's what we're doing and you can too. So Jeff, thanks for joining. Thanks for having me. So Jeff, you want to share just a little bit on your background and then we'll jump in. Sure. I'm a former public equity investor, turned operator, been finance leadership seeds in middle market companies for the last eight years yourself. And they've gone from having perfect public market or CC approved data to very messy data inside growth state companies. So very excited for all the unlocks that AI is now bringing. And what I like about Jeff, he went to a football college, it's called Vanderbilt University. Many people didn't know it was a football college, but it is now. And then Doug and Lloyd, like closest to their hearts, because I always see them with their rings on and their flags behind them. He went to this little school right outside of Boston that you guys went to as well. I try not to hold that against him. I know that that's going to mean a lot to Doug and Lloyd. Try to keep that part of the background secret. What do you ever do with that Lloyd? I mean, where do you go? I do nothing with it. I just let it go. I'm not taking the bait. I'm poking him on. I'm poking on. Sometimes they don't take the bait, but it's okay. They resist it. They're learning to resist my pokes. So I play the everyday guy, man of the people here, you all make those conversation here. And that's where I feel best. There's nothing about you that speaks me out of the people. Jeff, it's really good to finally meet you. I've heard Sean rant and rave about you for a better part of the year. So I'm really excited for this conversation. So Doug, why don't you kick us off? How do you want to take this? I think this is a super interesting conversation for a couple of reasons. In my little ecosystem, you know, 10 portfolio companies, I often feel like the CFO is in this interesting place of being the traffic cop between AI initiatives that the private equity sponsor wants to initiate and trying to figure out how to access and prioritize in the company. And I think that's probably because people in your seat are generally pretty tech savvy. You sit in the very data-intensive part of the business. You know, as you think about your own ecosystem, it feels to me like there's there's a foundational element for everything that you accomplish that can then be translated across the organization. In that crazy world of you're at that critical juncture in something that's happening so quickly, I'm just interested in hearing your thoughts on what's changing where you're finding opportunities. And you know, if you could start with maybe a little bit of a conversation around, do you have a framework that you're using as you think about prioritizing the things you want to accomplish in this area? Yeah, it's a great question. And I think what doesn't change with AI is the sort of same North Star as before AI, which is you have to have good data. You have to have good processes and you've got to be able to understand working backwards what you want to know about your business daily weekly monthly. And once you figure out those things, that's what AI can really supercharge. And so, you know, when I think about enablers of AI and finance and accounting functions, I think about five things. First, I think about data and the data structure. I think about understanding your processes. If you add bad processes, AI can't fix them. And so it's really important to get your arms around them before AI can accelerate and improve your processes. Three, you've got to understand prompting and increasingly more so how to use the coding tools that have come out. Understanding the policies around what you can put into LLMs and what's secure. And then five, understanding the AI enabled tools that you can bolt on top of your core systems and data. And, you know, happy to go into it, indeed, one of those. But I think the two that I would continue to circle are making sure that you have that foundational data layer that's very structured. Really understanding across multi dimensions, how different data points are connected and what you want to do with that data. It relates to processes. They're sort of free low cost $20 a month tools. That will follow along clicks on your computer that will actually map out what your teams are doing. What buttons they're pushing. And then that will help you say, hey, I've got a hundred step process to do bank reconciliation that shouldn't be a hundred steps. And now that I've actually mapped it out and know where the bodies are buried, I can start to layer on AI to automate some of those manual tasks. After a while, I'll pause there happy to unpack any one of those five things. Let's stick a pin in it. I'm curious. When you started at Blue Wave, did you come in already with that five part framework? Or did that framework evolve over time as you spent more time figuring out what you needed at Blue Wave? It's definitely evolved. And I'm very fortunate thanks to it, Sean's foresight around the structure of our data, which has really been a very strong sort of base camp from which we could build off of most companies, I would say. R&S sort of clear of white in terms of investing in their data early and getting it as structured as we have here. And so I think the framework has definitely been evolving as we've seen what AI can do. And as it evolved so to does the framework. For example, the models two years ago couldn't do reliable variance commentary. Now they can do that. I think you have to evolve your framework as the current changes. Can we just unpack that data part a little bit more? It's mentioned a lot. I'm still trying to get my brain around what exactly does that mean? Can you give an example of what you mean by having the data at Blue Wave structured? What does data encompass? Are you talking about just financial and accounting numbers? Are you talking about other elements of the business? I think it really starts with connecting your CRM and your ERP and joining that data together. And what you want in your data lake or your data cube, you don't want just sales by customer. You want sales by rep, by customer, by geography, by product. As many dimensions as you can possibly capture, you want that. Think of it in columns and rows running across as if it were in a giant Excel file. You want as many columns and rows of data joined together as possible so that you could pivot the data or look at the data in many different dimensions as possible. And when you have that with AI building a narrative layer on top of it, it can be really powerful in terms of how you attack the questions that you want to ask of your data. When you get feeds out of your ERP system and directly out of your CRM, is it structured or is it just like a big stream of consciousness or the things that we have to do to quote and quote, like organize it or transform the data to make it useful? You really do have to transform it and use unique identifiers to be able to join data from your CRM and your ERP. So if you wanted to be able to say, let's use that, sales by rep, by product, by geography example. In a standard ERP or in QuickBooks, you wouldn't be able to see that level of data. And so what you would want is that unique identifier that can join it from your CRM and bridge the data with as many dimensions as possible. And that way an analyst or someone at the CFOs of work doesn't have to do Excel gymnastics to be able to get to that question by downloading data from the CRM that's probably not clean. And then joining it with an Excel with ERP system data that probably has its flaws as well. You want that in sort of one single source of truth, ideally in a data lake. So we have a full time data architect at Blue Wave, who's been on our team for a number of years. A large part of his job is to take the data from these data. different systems. At this point, it's our ERP, it's our CRM, it's our sales automation and management system, it's our marketing automation system. He will pull all that data and it comes in in like streams. He will put it say, "Okay, this is what sales equals. This is what equals costs a good sequel." He'll put them literally in tables. He organizes it because what comes out of your systems is really just like a bunch of gobbling goop. Then what he'll do is what's called semantic layering. For that, we have a software system. He'll type in sales. This is the revenue that we earn. Rep is like there's two types of rep. We have business development reps and account management reps. He's literally describing and type form what these fields mean so that the AI, when it reads it, goes, "Oh, this makes sense." Then what will happen is for the things that we're always asking pretty regularly, and this girl is literally every week we're doing new common calculations. She'll say, "Hey, in order to make this calculation if someone asks it on our internal system, we casually call Jarvis, that everyone has access to." It's like take this field plus that field divided by this field to get this answer or something similar. There's been a huge investment just saying, "Hey, keep our data clean. Be, put the data in structure, and then see like literally typing notes saying this is what this data means so that AI can interpret it." But anyone can do this in like three months. The way you described it there, I'm going to say two things and I'm looking for a little feedback. The first is, I assume the lift to go back and recreate a lot of this data is pretty data intensive or time intensive. You guys kind of picked a spot and said, "Hey, this is the point we're going to start collecting the appropriate architecture that we can analyze on a GoFore basis." Early on, you don't have much to compare it to historically because you just don't have the data. You kind of pick a spot and move forward. That's question one. Then the way you describe it though, it does feel like this is perpetually evolving. As you deliver new insights, you have new questions and so you continue to like, I guess increase the different fields that are going to be relevant for you in a decision-making basis. I'll defer to Jeppo where I will say those. If you have common processes and common weight you're capturing things and that's been consistent over time, you can then look back in time and go back in time. But if you're doing things differently all the time and you're constantly changing things, then your year-over-year comparisons get harder. But that also in my mind raises the urgency of like, "You better get going now to get a common standard in because you want that year-over-year." That problem just gets bigger and bigger over time. Jeff, what's your perspective on it? I think yesterday is when companies should have done this. I'll never not have the data that I have now at Blue Ways. That just will be a very foundational investment. I'll always want to make because we're just so dynamic we're able to do with it. This episode is brought to you today by Aridian Capital Partners, a lower middle-market private equity firm focused on partnering with family and founder-owned manufacturing, service, and distribution companies. ICV Partners, an innovative private equity firm supporting management teams of leading companies at the lower end of the middle market. In Blue Ways, connecting the most proactive business builders in the world with the best of the best service providers, interim executives, AI advisors, for critical, variable, on-point, and on-time due diligence and value creation needs. Sean, a question for you. What did you see or what gave you the conviction to make the investment in data? Because that's the starting point. It sounds like a lot of work. It sounds like it's expensive. It sounds like you got to hire some people that you've never had on the team before. But you must have seen something that gave you the conviction to plow ahead. What did you see and what was it? And I'm asking on behalf of our listeners who may be wrestling with, "Hey, I'm not there. We're not there. Our data is not clean. I barely understand data. How do I get started?" That's part of the theme of the series of conversations. How do you get started and then how do you get in the game? What was it that got you going and committed to making that investment in data? This all started 15, 20 years ago. It really started at my prior PE firm where I was co-ling the area that invested in information data and politically enabled businesses. Those were the types of companies where we could see every day what you could do with this data, how you should structure it, understanding that data in itself are just ingredients, like flour, sugar, salt. But if you put them together, you can create cakes proverbially. This goes back to 2008 where we would immediately get in, start structuring the data, making sure the things we can do with it use it. The capabilities weren't that great. When we started in 2017 effectively, my aspirations for what Blue Wave could do right out of the gates was much greater than the robotic capabilities that existed. We just couldn't do the algorithm of things that we can do very easily now. But I knew someday the robots were going to get there. There was a commitment like every day we're going to work on keeping this data as structures we can because eventually the robots are going to get there and be smart enough. Almost every year it used to be called machine learning before AI. I would call the groups in our network. Is this stuff ready? In most years it was no, you have to have a Google or a then Facebook budget to use these tools. But then what happened is Facebook started rolling out these tools. About a year and a half before chat GPT came out, the groups that we're talking was like, "Hey, it's ready." But a lot of these products have been commercialized, like PyTorch and things like that. I was like, "All right, let's go." Then we'd actually start building our own AI engines and things like that. But it really came from a deep understanding, frankly just by the virtue of the mentors I had at the last firm and the types of companies that are investing realize how important this data is. It was built in from day one. But I'll tell you, literally every week we're getting projects going with lower-middle market porcos to start getting their data structured. You can do it relatively reasonably because now the AI can help you structure the data. It's not that expensive in the grand scheme of things. You need the audacity to get moving now because as Jeff will tell you, it limits what you can do in the future if you don't do it. Paul Parkshawn, give us a sense for when you say it's more reasonable now, bigger than a bread box, smaller than. Put like huge brackets on this because it's going to depend on the complexity of your organization, the complexity of your data stack and this data subit. But in general, you can do quite a lot to get your data structured. Call it 25 to $75,000 for a project. And there's going to be pluses and minuses probably more on the plus. And then the one thing that you're going to have to do is you're also going to have to pay someone to kind of keep it evolving. We actually have a full-time data architect that does that. But we also use an outsource partner because it's getting so popular. And you can do that depending on your needs and use cases, you're going to spend probably another 50 to 100 grand a year on an outsource partner to keep it evolving and going. And all the cool stuff we're doing now. But you can get to a really pretty high level of capability in three months if you have the conviction to do. Is that fair, Jeff? Yeah, totally agree. We talked about data structure for a second. Just hit on process as a little bit. You mentioned these tools that sounds very or well-in. Just walk me through how the tools work and how easy that is. I've been in situations where you join a company. Billing is in the person's head another person decides to leave a week after you join and you've got to figure out how to go and build 150 customers. With or without AI, you want documentation. So if someone you someone gets sick, your next person up has documentation that they have to complete the accounting and finance workflows. That's just good hygiene in general. Now there are AI enabled tools that are either free or 20 bucks a month that follow your screen. Every time you click, it will generate a screenshot with a description of what you're doing to basically form SOP so that someone doesn't have to manually and painfully write out each step that they're taking. And then as an added benefit, your otter just will like it because you've got documentation. Once you have the documentation, you can put it into an LLM and ask it for free consulting to diagnose your process. And you can say, "Hey, I'm doing this 100 step accounting workflow through manual Excel manipulation. How can I make it better?" And so AI can both help create the documentation, help improve what you're doing, but I think it's sort of that initial step to just get it down on paper that whether or without AI is just good practice. I'll share the Jeff did a great job for us as we had a lot of the things structured, but our processes weren't really mapped when he joined. And so that was the tool that he used. Put huge brackets on this because this world changes very rapidly. But what's the name of the tool that you use for that when you first did? And I don't know the state of it today or not. But the tool that we used is called Scribe. And again, there's a free version, then there's a $20 a month team version. The founder was a former McKinsey consultant who used to go and she said that she would sit with the best person at the company where she was doing an engagement and would record all the steps that they were taking to suggest process improvements. And that's where this, you know, one and a half billion dollar company that I get came out of those McKinsey engagements. And so really it becomes sort of your brain for processes. And so I highly recommend whether it's Scribe or other tools like it to just get everything down in one central place so that you don't have single points of failure. And literally what it's doing is it's kind of just like recording what the person is doing step by step and it turns it into a value stream map that's documented, notated. And I believe even visualized. That's right. Screenshots are exactly what they're doing. And I'll tell you what, for me, the way that I learned, if I were to sit down with an accountant and say, "Hey, tell me what you're doing," I would get lost by step three because I'm just not that kind of learner. I'm more of an active learner and visual learner. But being able to actually review these documents and say, "Okay, now I understand this process." That gives me the tools I need to figure out how to actually improve the process. And once again, this is kind of an AI-enabled tool that's relatively inexpensive that anyone can use day one. And you can map your processes, which in itself is really valuable, because very few companies end up doing that. It's the unfun, unsexy part of anyone's job. But now you just have them do their job and it maps it. That becomes a huge benefit if you have unexpected turnover because someone can now jump into that role and say, "Oh, here's how you do it." And I can visually see each step. So the ramp time is a lot slower. And then the third benefit that Jeff does is then he feeds it into one of our large language models and say, "Act as a lean six sigma expert, improve my process for me." And it'll look through all this and find the abilities to simplify. And you've actually done this, Jeff? Actually done it. It's led to meaningful time savings across several of our workstreams. Can you give us an example? Let's talk about AP. We had a really old system, cards and expense system that didn't have approval workflows. And so what we would be doing is an accountant would be emailing different people in the organization saying, "We got this invoice over email. Forward the email to them and say, do you approve?" And then, okay, that person approves. And my head, I know that it's over $25,000. So I've got to escalate it to Sean because that's our threshold when it goes to the CEO for approval. And it was taking 40 to 50 hours a month. So that person to just basically be sending emails saying, "Do you approve?" And then, how many people to get them to approve? What we did instead was put in an AI enabled AP and expense system that basically had those approval workflows so that it could basically ping the people directly to say, "Do you approve of this invoice?" Read the invoice through AI and then code it to the right general ledger account. So if you took a new, or I would say, this is a travel and entertainment expense, not a software expense. So there's no human error related to coding. And then, it would push the vendor directly into our ERP system so that you wouldn't have to download the data out of the AP system and then manually re-upload it into the ERP system to capture the transaction level data. And so through all of that, that's saving us 40, 50 hours a month. And when you first used the tool to kind of just codify what you're doing, it kind of was able to look at that system that you just articulated and come to that conclusion, and then suggest the system or was the new system kind of your thought creation. I think fortunate to be in several CFO groups where people are trading best practices. And so this one came out of that one. And Elant would probably also recommend sort of the top systems as well. I'm sure that you could do it that way, but always I recommend coming to Blue Wave for more vetted recommendations on your unique set of circumstances. I think one of the interesting takeaways for me as you describe that is we talked about the importance of data upfront, which I think is really the holy grail in terms of better business decisions. But this is an area that's really less about having good data and more about having good documented processes that allow you to drive efficiencies in a different way. So for those that are still struggling with the data problem, this is an interesting entry point in my mind of continuing to make progress while you solve the bigger problems. You don't even need clean data. You just need to know what are the workflows that I want and how do I get everyone to adopt it as really sort of a challenge. And the thing that I think really helps with mapping these processes is that everyone kind of has an intuitive sense of like how big of an opportunity or challenge it is. Until you map it, you don't really know how big the opportunity is or the message, right? And then when you do that, suddenly you can start force ranking what you're going to work against because I think we're a lot of companies getting trouble with is they're working on a hundred projects. Some are a little small and have no impact. Some are really big. And this really helps Jeff kind of focus on ease of impact and size of opportunity in terms of what we kind of tick off next. And that was almost impossible to do until he mapped the processes out. So Jeff, I'll be a little bit maybe controversial here. So when you describe your five areas data, I was like, wow, I can't wait to hear about that. Processes as like, wow, I can't wait to hear about that. Enabled tools, super exciting to me. And I kind of like I'm prompting policies interesting, but like less so. And so like make the counter argument. What about those two or super interesting in your mind or critical to the whole ecosystem? I think policies is an easy one. I think you just need to do two things. One, you need to set a culture of AI adoption. And that's going to kind of top down where everyone should be using the tools. And they shouldn't make it seem like it's an afterthought or they don't have time for it. It's got to come into their workflows day to day. One of the things I give Sean a lot of credit for is mandating that everyone go through a specific cloud training and the anthropic specific training that's free that they put out there. Everyone gets a certificate of completion. Just that culture around hate. These things are coming. If you want to work here, you got to use these tools. That's a policy that I think is worth enforcing. Two, it's setting clear guard rails around what you can and can't put into the system. We said, hey, we're not going to put social security numbers and banking information into the system. We can put in our data such that we can chat with our data inside of cloud. So I think setting those guard rails and just getting everyone to opt in is an important thing. Prompting is really evolving. You used to have to do sort of very specific prompts and very structured prompts. I think the models are getting better where you can give a little bit less context, but where it's getting really interesting is inside of coding tools. I've been using cloud code for sort of ad-coded advanced FB&A related work streams. You're sort of doing more software engineering type work. And you've got to be very specific around what you wanted to do if you're building advanced models. Super healthy. People here, cloud code and people here work streams that are used frequently in the context of AI discussions or commentary. Can you give a more specific tangible example of how do you use cloud code and what were you trying to figure out build or solve as it relates to your FB&A function? Obviously, two examples that I think are really interesting recently. So like most companies, you close the deal and you book the deal. You don't build it until later on. And so that naturally creates a bookings versus Billings gap. And you want to understand when you're going to bill and when your cash is going to come in. And so what I was able to do through one of our AI and able tools was pull into Excel all of our bookings data and then pull in into another tab all of our Billings data. What I worked on with cloud code is I said, hey, in column A, I have the data, the booking, in column B, I have the amount of the booking. Then in the other tab, I've got, you know, this is my data structure. What I want you to do is I want you to join the booking and the building by the unique identifier. And I want you to tell me when we think everything is going to bill. And then I want to be able to create a waterfall chart that shows this is how much I booked. This is how much we're going to bill in the future. This is how much we build against prior year cohorts of bookings. And this is our ending Billings amount. So I can understand exactly where the bookings versus Billings gap is coming from. And you know, it billed a what I would give to a FPNA analyst to be able to do or something that probably would have taken me a couple hours in Excel, it probably did in 20 minutes. That's one example. I think more for a private equity audience, this is a really interesting one. We recently renewed credit facility and but had some amortization features related to the credit facility that I haven't modeled since I was a, you know, second year analyst at Morton Stanley. And so what I said was, hey, here's a PDF of the term sheet. I have a 700 row free statement financial model. What I want you to do is build in a debt schedule so that if we choose to draw in it, create a switch where we draw in it and then create the amortization based on the term sheet, then I want you to wire in everything into the three financial statements. So the balance sheet balances so that it reflects the draw in the cash flow statement. So the interest expense comes into the income statement and so on. And it just did. And again, it probably would have taken me probably several hours to figure that out, where the models are just doing it in 20 minutes. Gosh, so to that point, now that you got me thinking, how many people out of curiosity are in your office of the CFO and your FPNA team at Blue Wave today? I'm the FPNA team slash the FO. By initially not coming in, I was going to need to hire someone at FPNA and then because our data is so structured and because of some of the tools that we've been able to bolt on to our systems, I don't think I'm going to need someone. So Jeff has that in his budget and has had it, but he keeps on saying, I don't think we need it. Get out of here. What did your team look like when you started at Blue Wave? Yes, so we had four people in accounting. We've got fairly complex billing. So we have two people focused on billing, what on the general ledger and then we had a controller, or if he was really systems minded. And I thought he was sort of too good for sort of daily APAR, you know, general ledger stuff. And I said, hey, I'm going to path you. You're going to help me agentify all of our systems. And so I moved him into a financial systems director role to sort of help build out even more automation. And now we have an accounting manager who's sort of overseeing the kind of day-to-day workflows. So when you started, there were four people. We still have four, we sort of reshuffle. Got it. Same four people broadly defined, maybe one moved on. but still works closely with you, added another, but the company is what, three, four, five times bigger? - Yeah, probably three times bigger. - It's great operating leverage. If you were to map forward five years, how much more do you think you would need? - I don't think we're gonna need more people. - Multiple, multiple times bigger. - I think that's an amazing setup for kind of the enabled tools discussion. Maybe Jeff, you just frame it out as here are the activities at the Office of the CFO manages, and then give us a kind of play-by-play of use cases in each of those major mandates where you're deploying AI and what the tools can do for us. - Yeah, let's say APAR General Ledger, FPNA, which is sort of core office of the CFO activity, is then we consider to go broader from there. AP, we talked about, they're very good tech first, and there's some companies that are just raising billions of dollars every year to solve accounting workflows. And they've really sort of mastered a lot of these workflows so that you don't have to manually coding voices. You have approval workflows. You can basically build agents for policies if someone can ask, "Hey, is this office chair in budget? What I come to headquarters is getting a dog sitter for my dog in budget, and then the policies can read your policy. It can literally take your handbook and then give person guidance on the policies." So AP is really good. Those tools tend to be very low cost because you get cash back on the credit cards that come with the tools. And so it's basically virtually free to get 50 hours of a month of time savings. JR is harder. Every company I've been at billing is difficult. We put in an AI enabled tool that can read contracts, create revenue schedules from those contracts, and then push the data from the revenue system or record directly into the ERP system. That's probably saving us another 50 hours a month. And the people in charge of billing are no longer air traffic controlling and spending time reading contracts. They're being able to do higher-level work, and that's been a big unlock. General ledger, there are different tools that are sort of automating reconcilations that I think are really interesting like your monthly bank racks or your balance sheet reconcilations. Automating tasks of a month in closed checklists. We've actually deployed our FPNA tool for accounting purposes because of our connected data and we're able to bring multiple data sources together to do those reconcilations. So we've sort of recurved me to an FPNA tool versus getting a general ledger tool. The FPNA space has gotten really interesting. I'd say in the last five years for all these tools, there's just been an explosion of capital coming to solve the Office of the CFO challenges. In the FPNA tool, what people have to decide is do they want something that's cloud-based or do they want something that's excel-based? I personally like to work in Excel, and I think our company's at a maturity stage where Excel is going to take us through a very large revenue number where we don't have to move to a cloud-based type of planning tool. And so what's great about it is in the budget process, I can build out an Excel template with an Excel add-in, push that data to the cloud, and then download all of the data into a centralized model versus what I've done this previously at deconsolidating, deconsolidating, 25 different department level budgets, and then just the hours of manual work that goes into that can go away with these FPNA tools. So it makes reporting and forecasting very, very straightforward. What's really interesting on this, just from Color Commentary from my lens, is Jeff can basically pull in all of the data anywhere in the company at a snap of a finger, and then he has plugin tools that can help them analyze. We'll be in leadership team meetings where our one-on-ones and someone will have a question that three years ago would have taken like a week. And Jeff will be able to say, here, give me one moment, he'll pull the data in real time, he'll do some quick analysis, both just kind of intuitively, but then also cranking and having some run some ad hoc modeling. He'll give us a key insight to a real-time question in real time in ways before that would take weeks. And you think about, you know, the art of business, so much of it is just speed and reps and iteration and A/B testing, ongoing micro-left and micro-right, and removing blocks of time. And what Jeff is able to do in ways built for us, that just enables us to move so much faster without sacrificing quality or excellence. - Say more about that, Sean. People talk about feedback loops and uta loops. And it sounds like that's what you're describing. How have you seen that shape decision-making, launching initiatives at Blue Wave? What does that look like and feel like today versus a year ago or three years ago? - Two things. I just think the speed of insight, you have a key business question, it would take weeks. And we were still probably then, three years ago, better than probably the vast majority of companies, just even with what we had. Because normally what you do is, okay, we have a question. And Jeff would say, okay, I'm gonna go with my team. We're gonna go pull the data, we're gonna join it. We're gonna do some analysis. Then we're gonna bring it back. And then we're gonna say, oh, well, actually do it this way, not that way. And then another week would go, because everyone's got 100 balls in the air. And so a multi-week process goes down to multi-minute in terms of just being able to get key answers because we have the data, it's structured. We know it'll give us at least a pretty confident, directionally correct answer. I think there's usually another iteration that you wanna finalize on. And we do that across our entire organization, like, hey, how are the reps doing? And then Jeff can pull it all down, like, okay, we're pretty meaningfully expanding the go-to-market part of our business right now. And he can pull down in real time every single rep and how they're doing versus others and blah, blah, blah. And you can do this like a whole performance analysis in seconds is fascinating. That speed of business is something that we could never accomplish. The other thing that it gives us is, I don't know about your companies or you all. We were pretty early adopters of dashboarding and we would make invisible and gamifier of business across key metrics where people could see what they were doing. But that was a function of having a business intelligence analyst who knew Sigma or knew Tableau and could essentially engineer these dashboards. And then anytime you wanted to change, it was a huge exercise. Now, anytime any of us want information, we go into our system that we call Jarvis, where all that's there and they just ask a question. And so today, like, if I want a dashboard, I just go into Clawed and Co-Work and I have it build a dashboard that is exactly the way I want it. There's some iteration 'cause like, oh no, I want it this way, that way. It's not a mind reader within minutes. It's probably a 30 minute exercise. I'll get something that I would have waited on for months. And then it's the exact way I want it. But Jeff wants to look at things slightly different than I do. And so he can have his dashboards exactly, he wants them. I can look at mine the way I want them. And you don't have to have an overworked B.I. person who's doing an engineering task. And so it's just like the customization of insights and the speed of insights are exponentially better than they were even one year ago. I completely agree. We're sort of running into a problem where we have so much data. It's what do you do with it? And that's where I think it comes down to sort of processes. And one of the right ways to sort of operationalize the data, one of the right keyings is you want to get into so that with all this data, sort of don't turn left and turn right. And so like another example, like our head of technology who we had on the last group, James, who's also really sharp. If people seem like particularly in technology, there's a tendency to say I want to have a feature farm. And I'm going to do 1,000 things. There all could be little things that no one wants. Or they could all be a couple big things, but they're going to take five years. And so what he's built in our driver's system is essentially an ROI engine. And so anything that he's considering building and proposing to the leadership team and me, he'll basically come in, which is the other shanger law that every team aspires for is like, what's the ROI in this investment? I was compared to other things. And he will go through his whole roadmap and he's got a forced ranked list of like here's where I think the ROI is relative to these others. And it includes an assessment of outcome and ease of impact. These are all the things that I've always wanted. But we never really had the chance, because it was just too hard and there are only 25 hours in a day. And to Jeff Point, now it's the idea of like, these things are happening faster. And now it's the idea of like, there's probably the bigger thing is like editing and what matters most, because you know, just like anything, it now probably turns into like, you're doing 1,000 analysis. And what are the fewer that matters? That's a good segue to my question for both you and Jeff with this increased automation, increased intelligence built in to your processes, time savings. Instead of months, you're getting answers in days, hours, or minutes. What are you doing with that freed up time? How is your day, Jeff, changed? How does that look today versus last year versus three, four years ago? And I'm going to come to you, Sean, with the same question. It certainly changes how you work. You freeze up time for higher value work. But it's less time fine-tuning the model, updating our financial model. So it's literally 700 rows. I can actualize the model and have a rolling forecast with two key strokes or 700 rows. And so instead of spending a few hours updating the model every month once we close the books, I can actually spend more time engaging with the data and understanding the drivers of the business and then figuring out what actually happened. On another example, we have a 50-slide reporting package. It's a similar process to update that every month. A couple of clicks in Excel and PowerPoint. And then I can give it to Claude to actually write the commentary. That would have taken a few days in my prior life to do that. So we can get insights faster. I can be more of a customer of the data versus a producer of the data. But it needs more time for experimentation. It's sort of what are problems in the business for problems in my function that I want to solve? And how can I use these tools to help me solve them? - I'm gonna extrapolate a little bit and just give me a reality check. You talked about AP and ARs, kind of 50 hours of savings a week. You got a four person team. I'm extrapolating and basically saying 10 hours a week for a person that was their function. It feels to me like you could realistically say to yourself, I think the AI tools across your entire suite of services or your mandate can generate efficiencies of about 25%. And importantly, it's the least value at 25% of your day. And so in terms of like capacity to actually drive the business versus deliver the business, it's gone up more. Is that a reasonable way to think about like the size of the prize here? - For sure. And it's, you know, I don't want a six-figure employee being a professional sender of emails to follow up with people. I want them figuring out sort of where we have gaps in controls, where we are today, when we're 10 times bigger, what are we to be doing today to get in front of those potential gaps? - And I think that's an excellent point. So there are hard productivity savings. And for us, we don't view it as an deal that like all this is like a cost savings, right? For us, this is enabling the people we have to spend more time productively working on the business versus in the business, in doing the higher value added things. And we as an organization are still hiring lots of people, probably more so than we otherwise would have if we didn't have these tools. Because the field and the frame of usage of things we can do is just so much bigger and faster. And so, yeah, in a zero-sum game, if it was just us, and we didn't have larger objectives, and we just wanted to optimize around steady, as she goes, there would probably be great cost savings opportunities. But for any business that aspires to grow, in works in a matrix we have competition, you're playing against the rest of the world. And so we're redeploying any productivity into our team and into our growth, so that we can do more with less. For us, I don't view this, and I think there's a lot of fear. And all jobs are going away. I don't view the jobs going away at all. Through our lens, we're hiring by leaps and bounds, just because we're able to do more faster. I agree with everything you said. And the whole premise in my mind is that you have a growth mentality where you're redeploying those assets to growth. And if you think big picture, though, if a market's not growing, you've got to be taken share. And so you think about from a societal perspective, I still think there's huge productivity benefits that are going to allow us collectively as a society to grow faster. Isn't there going to be a big share gain and loss between those that have adopted? You're using speed as a competitive tool now where in your marketplace, you're going to be a more dominant provider. It's like the implication, if you believe what your opportunity is. The tools don't really help you grow the market. It helps you be more competitive in the market, don't you think? I think it's both. I think you're absolutely right. You can be more competitive in market. You can take share. But as these tools increase productivity, inevitably, what people worry is like, OK, well, the price points are going to come down. But inevitably, if you look at these periods of time where you increase productivity and price points, maybe you're going down, usage goes way up. What happened with computer chips? Oh, the price is going down, down, down, down. But what was a lot of that got more and more competitive? The other great example is like radiologists. Seven years ago, they stopped training radiologists because one of the first use cases on machine learning, which now became AI, was like Cancer Cell recognition. But they were able to figure out. It was like, oh my gosh, robots are going to do all this stuff. But seven years ago, what you used to have to do is you'd have to wait a month for someone to read your scan. And it was really expensive. So no doctors ever would order X-rays or MRIs. Today, the cost of that has gone way down. But the radiologists are sold out across the country right now. Because now, the cost of a scan is so much lower. And you get it in two hours versus four weeks. And so I do think there's an expansion of the pie that it causes as well. I think it's more of an N not or. But it's a good point. But I do think it also will create more abundance within categories. I like your take on it. It's the Goldilocks situation, right? It's growth of markets, anti-inflation, and anti-employment. And to be fair, the sad reality of times like this is there are going to be periods and time where jobs go away. And that's really hard when that impacts you. And it's super hard to justify. But when, if you look at the broader benefit, if you were ascribed when the printing press came out, you were not too psyched. But what did it do? It created this explosion of information across the world that accelerated the advancement of our societies. And I think this is kind of maybe somewhere to that. - I suck Sean and he and I into a conversation pontificating on where the society's headed. Jeff, if you could just give us a little more, like get back to the tactics of, okay, we talked about ARAP is really good tools, F-P-N-A. One of the other most compelling tools you're contemplating. And let's assume you have a some-per-view into HR and some of the other functions like sales, a little bit more broadly. Anything really interesting or compelling? - Yeah, I think one of the HR use cases that I've been using personally is turning AI into my own executive coach. There are a lot of really good executive coaches out there that have open source their process. And you can basically download their documents, create a project and clot or chat GPT and say, hey, I want you to be my executive coach. Here's a situation I'm dealing with. Use this person's open source documentation on how they coach executives and guide me through this situation. Sometimes when I have to give difficult feedback, we use a system called predictive index, which is basically a Myersburg sort of personality task, but a little bit more modern, sort of how I would describe it. And I'll say, hey, I need to give feedback. This is the situation. I need to give feedback to this person. This is their personality type. Help me guide the feedback so that it lands with them, so it doesn't come across critically. And it'll walk me through, okay, the way that you're gonna give the feedback to a strategist is different from how you give feedback to a captain personality type. You're a strategist and so you're thinking about it this way, but you really need to sort of have the empathy of this type of person to give that kind of feedback. So that's a really good HR use case that doesn't cost anything. Outside of the cost of the personality task. And sales, I'm a little bit less dangerous here, but there are a lot of AI-forward CRMs that are coming out. There are sort of revenue systems of record related to call recordings and coaching and feedback, even sort of role-playing avatars that are very interesting. There's certainly gonna be a lot of good use cases for our teams with those tools. Anything you can offer us in terms of, so within your core mandate, where do you think this has had a big picture, big opportunities, anything insightful there? We're gonna evolve from a month in close to a continuous close, where you have agents that are continuously reconciling transactions. And month in close can get to be a day one or day zero type of activity, depending on the type of business that you run, because the agents are just doing the work that you'd be doing a few days after the month in all the time. That certainly won the AI-enabled ERP systems that are sort of challenging me incumbents. Are really, really interesting. We'll see if they can sort of get scaled with really, really big businesses. That's certainly a space that we're gonna watch closely. There's a lot of lessons that you've learned. And we get a lot of questions with these things. What are we doing with our customers who want to see kind of how we do these things in terms of rolling it out themselves? I would say for middle market, private equity and lower-biddle market, private equity on businesses, people are still very early in their journey. We sort of talk to a lot of the things that we did today. Sort of what are foundational load and no cost, things you can do if you want to invest a little bit. This is sort of further up the curve in terms of what you'd want to prioritize. And so we have a lot of conversations all the time with private equity firms, as well as their portfolio companies around how to start deploying these things and where are just some quick wins that you can get? If you're a customer of ours, just give us a call and we'll tell you exactly what we're doing and hear the tools to use. And it's free, commercial alert, but if it's free, it's a pretty good price. (laughing) - I'd really like the points that you made Jeff and some of the points that you were making, Sean, about deploying time, mind, space, and even head count because you can think about the business and work on the business. And as you think about growing, whether your market's growing fast or not, whether your Greenfield or taking market share, thinking on the business and deploying people and creativity on opportunity is just fundamentally good for your company, good for your business. That's pretty meaningful. It has been profound, and I'll even say personally, what's interesting is maybe two years ago, and part of this is just like the stage of our business and growing. I like, I never had a moment to think. In part, because there's just certain stuff I needed to do myself to feel and understand the data. I mean, that's probably the PE background. It's like, sometimes I just had to play with it. But now, I'm actually able to time box multiple hours across the week, whereas just like free, because I freed up time that I normally would have been, even as a CEO, just doing kind of like very manual nonsense. And it's enabled me to have like multiple hours. I just keep it free, and it gives me a chance to think in ways that I've never had in my entire career. I don't know if Jeff, you've had that kind of opportunity as well, but it's like, there was always that line, like I'll work on the business versus in the business for the vast majority of my, my, I always felt like I was just in the business. But I would say, oh yeah, I'm working on it, but I never really was. So now for the first time ever, I think like I am. Totally agree. And Philipp LeFop, the founder of CO2, was on CNPC yesterday. And he talked about the way that he thinks about it is, you can go to sleep now at night and have a thousand people working for you and you wake up and you can work your day at work. And it's very different than doing the work of a thousand people, which is the world that we were in. First of the one that we're in now, that frame really resonated with me. Enjoy the conversation, Jeff. Thank you. Yeah, me too. Appreciate the time, Jeff. This is great. We're having me. Well, we're really fortunate to have Jeff on our team as you guys can tell here. And so you better not come and get him. Thanks, Ellen. (laughing) If any listeners want to follow up or ask questions, how should they go about it? Reach out to you, reach out to Jeff, post it on LinkedIn. I think you can do all of those. We're easy to find and we try to be everywhere to find. So you can go to our website. You can go on reach Jeff or me on LinkedIn. Increasingly, we're gonna have connectors that any of our customers or people who wanna be customers gonna have that are built in directly into your teams, your Slack, your LLMs. And we're gonna try to help you figure out how to actually frame your challenges and opportunities easier and better the way we do things and make us easier to help. We're pretty much findable anywhere, but thank you for asking. Great. Stay tuned, everybody. Awesome. Appreciate the time, guys. A special thanks to a RIDIAN Capital Partners, a lower-middle market private equity firm focused on driving transformational growth through consolidation strategies by partnering with family and founder-owned manufacturing, services, and distribution companies. Learn more at RIDIANCAPAL.com. ICV Partners, an innovative lower-middle market private equity firm supporting management teams of leading companies at the lower end of the middle market. Learn more about ICV at ICV Partners.com. And finally, Blue Wave, connecting the most proactive business builders in the world with the best of the best service providers for critical, variable, on-point, and on-time due diligence and value creation needs. Learn more about Blue Wave at bluewave.net. For further information on RIDIAN, ICV, and Blue Wave, and relevant topics discussed during the episode, please see the episode knows for links. The views and opinions expressed in this program are those of the individuals presenting and do not necessarily reflect the views or positions of any other person's or entities, including those reference tier-in. No representations, warranties, financial, legal tax, or other advice are made herein. Consult your advisors regarding any topics discussed during this episode. (upbeat music)