Private Equity AI ROI in the Office of the CFO

Speaker A: 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.

Speaker B: It is great to be back with Lloyd and Doug. Doug and Lloyd. How are you guys?

Speaker C: Great.

Speaker B: Good to be here.

Speaker A: Hey, Sean. Hey, Doug. Good to see you guys. It's been a minute. It has, it has.

Speaker B: I've missed you guys.

Speaker A: I did too, actually. Low key.

Speaker C: I'm not going to go there. Yeah.

Speaker B: All, uh, right. 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 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, like, how do you practically do it and get ROI versus everyone having fancy Google?

Speaker A: Absolutely.

Speaker C: 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.

Speaker B: Ding, ding. Guess what's going to win? Change. You're 100% 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.

Speaker C: Fomo.

Speaker B: All right, so one of the things that I thought would be helpful, and we talked about this beforehand, is we happen to have a wizard of AI enablement within the office of the CFO in our midst here at bluewave. And so one of the biggest areas that we see just through, uh, equipping the PE industry with AI enablers and tools is one of the areas of highest roi 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.

Speaker D: Thanks for having me.

Speaker B: So, Jeff, you want to share just a little bit on your background and then we'll jump in?

Speaker D: Sure. I'm a former public equity investor turned operator, been finance leadership Seats in middle market companies for the last eight years or so and have gone from having perfect public market RCC approved data to very messy data inside growth stage companies. So very excited for all the unlocks that uh, AI is, uh, now bringing.

Speaker B: 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, uh, know that that's going to mean a lot to Doug and Lloyd.

Speaker D: Try to keep that part of my background secret.

Speaker C: What do you ever do with that, Lloyd? I mean, where do you go?

Speaker A: I do nothing with it. I just let it go.

Speaker D: They're not taking the bait.

Speaker B: I'm poking them on. I'm poking them, uh, 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 m man of the people here, role in this conversation here. And that's where I feel best.

Speaker C: There's nothing about you that speaks man of the people.

Speaker A: Jeff, it's really good to finally meet you. I've heard Sean rant and rave about you for better part of a year. So I'm really excited for this conversation.

Speaker B: So Doug, why don't you kick us off? How do you want to take this?

Speaker C: I think this is super interesting conversation for a couple reasons. In my little ecosystem, 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. As you think about your own ecosystem, it feels to me like 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 are you finding opportunities and 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?

Speaker D: 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 uh can really supercharge. And so 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. Two, I think about understanding your processes. If you have bad processes, AI can't fix them. And so it's really important to get your arms around them before AI uh can accelerate and improve your processes. Three, you've got to understand prompting and increasingly more so how to use the uh, 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 happy to go into each 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 as it relates to processes. There are 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 reconciliations that shouldn't be 100 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. I threw a lot. I'll pause there. Happy to unpack any one of those five things.

Speaker A: Let's stick a pin in uh, it. I'm curious, when you started at Bluewave, 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 Bluewave?

Speaker D: It's definitely evolved and I'm very fortunate thanks to 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, aren't as uh, clairvoyant 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 evolves, so too does the framework. For example, the models two years ago couldn't do reliable variance commentary. Now they can do that. And so I think you sort of have to evolve your framework as sort of the current changes.

Speaker A: Can we just unpack that, uh, 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 BlueWave structured? What does data encompass? Are you talking about just financial and accounting numbers, or are you talking about other elements of the business?

Speaker D: 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'd 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 sort of 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.

Speaker B: When you get feeds out of your ERP system and directly out of your CRM, is it structured or is it just a big stream of consciousness? And are there things that we have to do to organize it or transform the data to make it useful?

Speaker D: 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

Speaker A: in

Speaker D: a standard erp or in QuickBooks, you wouldn't be able to see that level of data. And so what you want is that unique identifier that can join it from your CRM to enrich the data with as many dimensions as possible. And that way an analyst or someone in the CFO's org doesn't have to do Excel gymnastics to be able to get to that question by downloading data from a CRM that's probably not clean and then joining it, uh, with, in 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.

Speaker B: So we have a full time data architect at Bluewave who's been on our team for a number of years. A large part of his job is to take the data from these different systems. And at this point it's, it's our erp, it's our CRM M, it's our sales automation and management system, it's our marketing automation system. And he will pull all that data and it comes in and like streams and he will put it say, well okay, this is what sales equals, this is what equals cost of goods SQL. And he'll put them literally in tables. So he organizes it because what comes out of your systems is really just like a bunch of gobbledygook. And then what he'll do is what's called semantic layering. And 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. And so he's literally describing in type form what these fields mean so that the AI when it reads it goes, oh, this makes sense. And then what will happen is for the things that we're always asking pretty regularly and this grows like literally every week we're doing new common calculations. They'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. So there's been a huge investment just saying A, keep our data clean, B put the data in structure and then C, like literally typing notes saying this is what this data means so the AI can interpret it, but anyone can do this in like three months.

Speaker C: 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. And so 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 go forward basis. And early on you don't have much to compare it to historically because you just don't have the data. So you kind of pick a spot and move forward. That's question one. And then the way you described it though, it does feel like this is perpetually evolving as you deliver new insights, you have new questions and so you continue to, I guess increase the different fields that are going to be relevant for you in a decision making basis.

Speaker B: I'll, um, defer to Jeff, but what I will say though is if you have common processes and common way 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

Speaker C: in because you want that year over year, right?

Speaker B: Yeah, that problem just gets bigger and bigger over time. I mean Jeff, what's your perspective on it?

Speaker D: Yeah, I think yesterday is when companies should have done this. I'll never not have the um, data that I have now at bluewave that just will be a very foundational investment I'll always want to make because we're just so dynamic with what we're able to do with it.

Speaker B: This episode is brought to you today by Iridian Capital Partners, a lower middle market private equity firm focused on partnering with family and founder owned manufacturing, service and distribution company. ICV Partners, an innovative private equity firm supporting management teams of leading companies at the lower end of the middle market and bluewave, 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.

Speaker A: 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. 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 um, conviction, uh, 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, uh, data's not clean. I barely understand data. How do I get started?

Speaker C: Right.

Speaker A: 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?

Speaker B: This all started 15, 20 years ago. So it really started at my prior PE firm where I was colleagueing the area that invested in information, data, analytically enabled businesses. And so those were the types of companies where we could see every day, like, what you could do with this data, how you should structure it, understanding that data in itself are, uh, just like ingredients, like flour, sugar, salt, but if you put them together, you can create cakes. Proverbially, this goes back to 2008, right, where we would immediately get in and start structuring the data, making sure the things we can do with it, use it. The capabilities weren't that great. So when we started in 2017, effectively my aspirations for what Blue Wave could do right out of the gates was much grander than the robotic capabilities that existed, right? So, like, we just couldn't do the algorithmic things that we can do very easily now. But I knew someday the robots were

Speaker D: going to get there.

Speaker B: And so there was a commitment, like every day we're going to work on keeping this data as structured as we can, because eventually the robots are going to, are going to get there and be smart enough. Almost every year. It used to be called machine learning before AI and I would call the groups in our network like, is this stuff ready? In most years it was no. You have to have like a Google or uh, then Facebook budget to use these tools. But then what happened is like Facebook started like rolling out these tools. And so about a year and a half before ChatGPT came out, the groups that we were talking was like, hey, it's ready. A lot of these products have been commercialized, like Pytorch and things like that. And I was like, all right, let's go. So then we actually started building our own AI engines and things like that. But it really came from, like a deep understanding, frankly, just by the virtue of the mentors I had at the last firm and the types of companies I, uh, invested in realized how important this data is. So it was kind of built in from day one. But I'll tell you, like, literally every week we're getting projects going with lower middle market porkos to start getting their data structured. And you can do it relatively reasonably because now the AI can help you structure the data and 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.

Speaker A: And ballpark. Sean, give us a sense for when you say it's, it's more reasonable now, bigger than a breadbox, smaller than.

Speaker B: 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 the status of it. But in general you can do quite a lot to get your data structured. Call it 25 to $75,000 for a project.

Speaker A: Right.

Speaker B: Uh, and there's going to be pluses and minuses, probably more on the plus. And then, um, 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 outsourced partner because they're 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 outsourced partner to keep it evolving and going and all the cool stuff we're doing now. But you can get to, uh, a really pretty high level of capability in three months if you have the conviction to do it. Is that fair? Jeff?

Speaker D: Yeah, totally agree.

Speaker C: We talked about data structure for a second. Just hit on processes a little bit. You mentioned these tools. That sounds very Orwellian. Just walk me through how the tools work and how easy that is.

Speaker D: I've been in situations where you join a company, billing is in the person's head and then the person decides to leave a week after you join and you've got to figure out how I'm going to go and bill 150 customers with or without AI. You want documentation. So if someone leaves, someone gets sick, your next person up has documentation to be able to complete the accounting and finance workflows. That's just good hygiene. Uh, 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 SOPs 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 auditors will like it because you've got documentation. 2. 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 hundred 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 with or without AI is just good practice.

Speaker B: I'll share that 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 it? And I don't know the state of it today or not, but the tool

Speaker D: 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 one and a half billion dollar company, that idea 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.

Speaker B: 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.

Speaker D: That's right, screenshots of exactly what they're doing. And I'll tell you what, for me, the way that I learn, 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.

Speaker B: 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. Two, 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 their 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 of this and find the abilities to simplify.

Speaker A: And you've actually done this, Jeff, actually done it.

Speaker D: And it's led to meaningful time savings across several of our work streams.

Speaker A: Can you give us an example?

Speaker D: 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. In 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 for that person to just basically be sending emails saying do you approve? And then hounding people to get them to approve. What we did instead was put in an AI enabled AP and expense system that basically had those approved 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 an Uber, it would say this is a, ah, 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.

Speaker C: 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?

Speaker D: I've been fortunate to be in several CFO groups where people are trading best practices. And so this one came, this one came out of that one. An LLM 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 bluewave for more vetted recommendations on your unique set of circumstances.

Speaker C: I think one of the interesting takeaways for me as you described, 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.

Speaker D: 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 is really sort of the challenge.

Speaker B: And the thing that I think really helps with mapping these processes is 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 mess is. Right. And then when you do that, suddenly you can start force ranking what you're going to work against. Because I think where a lot of companies get in trouble with is they're working on 100 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.

Speaker C: So Jeff, I'll be a little bit maybe controversial here. So when you described your five areas data, I was like wow, I can't wait to hear about that Processes, I was like wow, I can't wait to hear about that. Enabled tools. Super exciting to me and I kind of like eh, prompting policies. Interesting but like less so and so like make the counterargument. What about those two are super interesting in your mind or critical to the whole ecosystem?

Speaker D: 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 come 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 Claude training and the anthropic specific training that's free that they put out there. Everyone gets a certificate of completion. Just that culture around, hey, 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 guardrails 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, but we can put in our data such that we can chat with our data inside of claude. So I think setting those guardrails 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 the coding tools. I've been using CLAUDE code for sort of, I'd call it advanced FB and 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 want it to do. If you're building advanced models, super helpful.

Speaker A: People hear CLAUDE code and people hear work streams. They're used frequently in the context of AI discussions or commentary. Can you give a, uh, more specific, tangible example of how do you use CLAUDE code and what were you trying to figure out, build or solve as it relates to your FP and a function?

Speaker D: I'll give two examples that I think are really interesting recently. So like most companies, you close the deal, you book the deal, you don't bill it until later on. And so that naturally creates a bookings versus billings gap. And you want to understand, you know, when you're going to bill and when your cash is going to, is going to come in. And so what I was able to do through one of our AI enabled 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 Claude Co is I said, hey, in column A I have the date of 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 billing by the unique identifier and I want you to tell me when we think everything is going to build. 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 built a, what I would give to a FP&A 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 a uh, credit facility and but had some amortization features related to the credit facility that I haven't modeled since I was a second year analyst at Morgan Stanley. And so what I said was, hey, here's a PDF of the term sheet. I have a 700 row three statement financial model. What I want you to do is build in a debt schedule so that if we choose to draw on it, create a switch where we draw on 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 that the balance sheet balances so that it reflects the draw and the cash flow statement. So uh, 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.

Speaker A: Gosh. So to that point now you got me thinking. How many people, out of curiosity are in your office of the CFO and your FP&A team at BlueWave today?

Speaker D: I'm um, the FP&A team CFO. I initially thought coming in I was going to need to hire someone in FP&A. 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.

Speaker B: So Jeff has that in his budget and has had it, but he keeps on saying, I don't think we need it.

Speaker A: Get out of here. What did your team look like when you started at Bluewave?

Speaker D: Yeah, so we had four people in accounting. We've got fairly complex billings. We have two people focused on billing, one on um, the general ledger. And then we had a controller who was really systems minded and I thought he was sort of too good for sort of daily ap, Arkansas general ledger stuff. And I said, hey, I'm going to path you, you're going to help me identify 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.

Speaker A: So when you started There were four people.

Speaker D: Still have four, but uh, we sort of reshuffled.

Speaker A: 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?

Speaker D: Yeah, probably three times bigger.

Speaker C: It's great operating leverage.

Speaker B: If you were to map forward five years, how much more do you think you would need?

Speaker D: I don't think we're going to need

Speaker B: more people at multiple, multiple times bigger.

Speaker C: 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 that 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.

Speaker D: Yeah, let's say AP, Arkansas General Ledger, FP&A, which are sort of core office of the CFO activities and then we can sort of go, go broader from there. AP we talked about, they're very good tech first systems 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 code invoices, you have approval workflows. You can basically build agents for policies so someone can ask the agent, hey, is this office chair in budget, when 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 a 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, it's virtually free to get 50 hours 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 of 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 reconciliations that I think are really interesting like your monthly bank recs or your balance sheet reconciliations, automating tasks on um, the month end close checklist. We've actually deployed our FP and a tool for accounting purposes. Because of our connected data, we're able to bring multiple data sources together to do those reconciliations. So we've sort of repurposed an FP&A tool versus getting uh, a general ledger tool. The FP&A space has gotten really interesting and 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 FP and a 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 when uh, I've done this previously, I'd be consolidating and deconsolidating 25 different department level budgets and then just the hours of manual work that goes into that can go away with these FDA uh, tools. So it makes sort of reporting and forecasting very, very straightforward.

Speaker B: 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 plug in tools that can help him analyze. We'll be in uh, leadership team meetings or 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 the art of business, so much of it is just speed and reps and iteration and a B testing and going micro left and micro right and removing blocks of time. And what Jeff is able to do and what he's built for us, it just enables us to move so much faster without sacrificing quality or excellence.

Speaker A: Say more about that, Sean. People talk about feedback loops and OODA loops and it uh, 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?

Speaker B: Two things. One, 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 would do is, okay, we have a question. And Jeff would say, okay, I'm going to go with my team, we're going to go pull the data, we're going to join it, we're going to do some analysis, then we're going to bring it back and then I'm going to say, oh, well, actually do it this way, not that way. And then another week would go because everyone's got a hundred 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 want to 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 he 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 them visible and gamify our 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 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 Claude and cowork and I have it build a dashboard that is exactly the way I want it. There's some iteration because 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 the exact way he wants them, I can look at mine the way I want them. Um, and you don't have to have an overworked bi 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.

Speaker D: 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 what are the right ways to sort of operationalize the data? What are the right cadences you want to get into so that with all this data you sort of don't turn left, then turn right.

Speaker B: 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 a thousand things. There all could be little things that no one wants, or they could all be like a couple of big things, but they're going to take five years. And so what he's built in our Jarvis 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 Shangri La that every team aspires for is like, what's the ROI on this investment? How does it compare to other things? And he will go through his whole roadmap and he's got a force 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 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's point, now it's the idea of like, these things are happening faster now. It's the idea of like, it's probably the bigger thing is like editing and what matters most because, you know, just like anything it now it probably turns into like you're doing a thousand analysis. You know, what are the fewer that matters?

Speaker A: That's a good segue to my question for both you and, 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 has 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.

Speaker D: It certainly changes how you work. It frees um, up time for higher value work. 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 keystrokes 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 figuring out what actually happened. Another example we have a 50 slide recording 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 needs more time for experimentation. It's sort of what are problems in the business or problems in my function that I want to solve and how can I use these tools to help me solve them?

Speaker C: I'm going to extrapolate a little bit and just give me a reality check. You talked about AP and AR as kind of uh, 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, your mandate can generate efficiencies of about 25% and importantly it's the least value add, uh, 25% of your day. And so in terms of capacity to actually drive the business versus deliver the business, it's gone up more. Is that a reasonable way to think about the size of the prize here?

Speaker D: For sure. And I don't want a six figure employee being a, ah, professional sender of emails to follow up with people. I want them figuring out where we have gaps and controls, where we are today when we're 10 times bigger. What do we need to be doing today to get in front of those uh, potential gaps.

Speaker B: And I think that's an excellent point. So there are hard productivity savings and for Us, we don't view it as a uh, deal that like, oh, this is like a uh, 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 and 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 she goes, there's probably great cost savings opportunities. But for any business that aspires to grow, works in a matrix, uh, where you have competition, you're playing against the rest of the world. And so we're redeploying any productivity into our team and into our uh, 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 around, oh, 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.

Speaker C: 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 taking 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 is 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?

Speaker B: 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, okay, well the price points are going to come down. But inevitably, if you look at these periods of time where you increase productivity and productivity price points maybe go down, usage goes way up. What happened with computer chips? Oh, the prices are going down, down, down, down. But with Moore's law, they 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 7 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, uh, 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 like two hours versus four weeks. And so I do think there's like an expansion of the pie that it causes as well. I think it's more of an and not or but I mean it's a good point point. But I do think it also will create more abundance within categories.

Speaker C: I like your take on it. It's the Goldilocks situation, right? It's growth of markets, anti inflation and anti unemployment.

Speaker B: And to be fair, the sad reality of times like this is there are going to be periods 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, like if you were a scribe when the printing press came out, you were not too psyched.

Speaker D: Right?

Speaker B: But what did it do? It uh, created this explosion of information across the world that accelerated the advancement of our societies. And I think this is kind of maybe similar to that.

Speaker C: 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 ar, AP is really good tools. FP and A. What are the other most compelling tools you're contemplating? And let's assume you have some purview into HR and some of the other functions like sales a little bit more broadly. Anything really interesting or compelling?

Speaker D: 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 sourced their process and you can basically download their documents, create a project in Cloud or ChatGPT 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 uh, a system called Predictive Index which is basically a Myers Briggs sort of personality test, 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 they don't, so it doesn't come across critically and it'll walk me through. Okay. The way that you're going to give the feedback to a strategist is different from how you give feedback to uh, 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 get 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 tests 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 going to be a lot of good use cases for our teams with those tools.

Speaker C: Anything you can offer us in terms of. So within your core mandate, where do you think this is headed? Big picture, big opportunities, Anything insightful there?

Speaker D: We're going to evolve from a month end close to a continuous close where you have agents that are continuously reconciling transactions. And month end 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 end all the time. That's certainly one the uh, AI enabled ERP systems that are sort of challenging the incumbents are really, really interesting. We'll see if they can sort of get scale with really, really big businesses. That's certainly a space that uh, we're going to watch closely.

Speaker B: There's a lot of lessons that you've learned and we get a lot of questions for 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?

Speaker D: I would say for middle market private equity and lower middle market private equity owned businesses, people are still very early in their journey. We talked through a lot of the things that we did today what are foundational low to no cost things you can do if you want to invest a little bit. This is further up the curve in terms of uh, what you'd want to prioritize. 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 what are just some quick wins that you can get.

Speaker B: If you're a customer of ours, just give us a call and we'll tell you exactly what we're doing. And here are the tools to use. And it's free commercial alert. But you know, if it's free, it's a, uh, pretty good price.

Speaker A: I really like the points that you made, Jeff, and some of the points uh, that you were making, Sean, about deploying time, mind space and even headcount. 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 you're 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.

Speaker B: It has been profound. And I'll even say personally, what's interesting is maybe two years ago and part of this is just the stage of our business and growing. 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. That's probably the PE background. Sometimes I just had to play with it. But now I'm actually able to timebox multiple hours across the week where it's just 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 where 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 work on the business versus in the business. For the vast majority of my, my adult life, 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 I am totally agree.

Speaker D: And Philippe Lafont, the, uh, founder of CO2, was on CNBC 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 review their work. And it's very different than, um, doing the work of a thousand people, which is the world that we were in versus the one that we're in now. That framing really resonated with me.

Speaker A: Enjoy the conversation, Jeff. Thank you.

Speaker C: Yeah, me too. Appreciate the time, Jeff.

Speaker D: This is great. Thank you for having me.

Speaker B: Well, we're really fortunate to have Jeff on our team, as you guys, uh, can tell here, and so you better not come and get him.

Speaker A: 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 out on LinkedIn.

Speaker B: 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 going to have connectors that any of our customers or people who want to be customers going to have that are built in directly into your teams, your Slack, your LLMs. And we're going to 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.

Speaker A: Great. Stay tuned everybody.

Speaker C: Awesome. Appreciate the time guys. A special thanks to Iridian 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@iridiancapital.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@icvpartners.com and finally, BlueWave. 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 BlueWave at uh. For further information on Iridian, ICV and Bluewave and relevant topics discussed during the episode, please see the episode notes for links.

Speaker B: 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 persons or entities, including those referenced herein. No representations, warranties, financial, legal, tax or other advice are made herein. Consult your advisors regarding any topics discussed during this episode.