How to Save Time in FP&A Using Structured AI Analysis with Nick and Dan

Speaker A: Welcome to the Future Finance show where we talk about treasury management on bars.

Speaker B: You have someone actually analyzing the book. So ignore the, the technical side of doing the accounting and closing the books. We do a little bit of work there, but really our focus is on the FP&A side. For that FP&A side, if the person has time, they're probably spending somewhere between 20 to 50 hours a month. Just look at peeking around the numbers, seeing, hey, I can create more value here, I can adjust pricing here. I believe our tool cuts that 20 to 50 hour exercise down to a few minutes of human time, tops. So it is 20 to 50 hours per month of time savings directly. But here's the big kicker. In a lot of companies there people are just too busy managing the day to day. So there's not somebody who's stepping back often and just thinking about the numbers and looking through the data and doing analyses.

Speaker A: Future Finance is brought to you by qflow AI, the strategic finance platform, solving the toughest part of Planning and analysis. B2B Revenue align Sales, marketing and finance seamlessly speed up decision making and lock in accountability with qflow AI. Welcome to another episode of Future Finance. I'm Paul Barmhurst, the FPA guy, and I have here with me my trusted FPA I guy, Glenn. How you doing, Glenn?

Speaker C: I'm good, I'm good. Good to see you, Paul.

Speaker A: Now, uh, you're not in your normal location. Where are you this week?

Speaker C: I am, uh, doing what all smart business people do, and that is combining business trips with checking off family obligations. So I have a client in Greenville, South Carolina, which is where my in laws happen to live. So brought my wife with me. And, um, we are, uh, we're doing client work and, uh, and hanging out with the in laws. And, um, thankfully they have good Internet. You never know when you're traveling what we're going to end up with, but, uh, it feels like we're, we're kind of cooking here.

Speaker A: Got it.

Speaker C: So we're mixing business with pleasure today, as every day. Yep.

Speaker A: All righty. Well, we have, uh, two guests with us. Glenn, why don't I give you the pleasure of introducing our guests this week.

Speaker C: You know, I hate reading intros, Paul, but I'll, I'll dive in and give this a shot. Hey, Nick and Dan. I'm. I'll go ahead and introduce you to our audience and I'm, I'm really looking forward to talking to you guys. Our guests today are Nick Jain and Dan Satel, co founders of Eagle Rock cfo, a Tech enabled fractional CFO firm that uses proprietary software to automate core FP and a work for its clients. These two have pretty different paths to the same company. Nick is a Harvard MBA out of McKinsey and Bain Capital who then went the operator route. He was CEO at IdeaScale, a B2B SaaS company where he nearly quadrupled EBITDA margins at 18 months. And he's been CFO at a logistics company and e commerce startup. Dan is a Stanford chemical engineering grad and also a Harvard MBA who spent almost seven years at Prime Camp Management, which for those who don't know, it's one of the most respected and secretive fund shops in the business. Before co founding Zenbato, a VC secondary trading platform that grew to over a billion in annual volume. He's also an active growth equity investor in AI, uh, and Frontier Tech through Greensands Equity. Together they launched Eagle Rock earlier this year to bring AI powered analytics and fractional CFO services to the mid market. Nick, Dan, welcome to Future Finance.

Speaker B: Thank you for having us, Glenn.

Speaker C: Yeah, I feel like you're gunning for our jobs. Not the podcast host jobs, our day

Speaker A: jobs everybody's gunning for.

Speaker B: I mean, AI is gunning for all of our jobs, let's be honest.

Speaker D: Right.

Speaker A: So what will you do first, Nick, when AI takes all your jobs?

Speaker B: Well, Dan and I are just trying to stay about a year or two ahead of it, to be honest, but I've got an 18 month hold, uh, at home, and I don't know what the future holds for him, to be honest.

Speaker A: Yeah, we will get to the real questions here in a minute, but it is pretty crazy to watch the speed of all this. Do you sometimes just go, where does this end? What is going on? Do you ever have those moments, like, who would have thought they would go this quick and change this much?

Speaker B: Uh, I'll let Dan take that.

Speaker A: I don't know.

Speaker B: I love this stuff.

Speaker C: I'm drinking the Kool Aid from the fire.

Speaker D: Yeah, I'm pretty m much counting on Nick and hopefully, uh, he makes some agents that help me survive the AI apocalypse ahead.

Speaker A: Yeah, Glenn's my uh, survival toolkit, so I get it, Dan. All right. I guess we should get a little serious here now that we've had a little bit of fun. So. Love, uh, the backgrounds. Why don't we, I think we'll start here on this question. We'll, we'll start with you, Nick, and then Dan, we'll let you add anything you want. So tell us, uh, just a Little bit kind of about yourself and how you started building Eagle Rock cfo, how that came about.

Speaker D: Sure.

Speaker B: So Dan and I have spent our careers both being investors and operators and AI started becoming a thing a couple of years ago. And as Dan and I finished up our last kind of professional opportunities, we connected last summer. We'd been, we'd been friends for about a decade. We had never really worked together. We started talking to AI last summer and said hey, there's something here. Where can we go build something really cool and exciting? And an obvious area seemed to be hey, AI is really good at analysis and dealing with complex kind of multi part situations. And we're pretty, we understand how businesses make money. Let's stick those two together and launch kind of a AI automation business for, for the uh, for the FPA world.

Speaker A: So yeah, they are gunning for our job, Glenn. Exactly right. Dan, did you want to add anything to that?

Speaker D: Yeah, I mean I think I've just been blown away by this whole process. Probably the part that scares uh, me the most is just when I compare the tools that Nick has done most of the engineering work on our side, what he's built. When I think about comparing that to the chatbots I interact with as a community consumer every day, oh man, the accuracy rate is just so much better. And Nick can go into the details later about why it doesn't hallucinate. But it's just a different world what we're doing for the FP and a function than I see with CHAP GPT

Speaker C: day to day, that's the world that I'm living in. And so my day job when I'm not doing podcasting, I'm um, doing AI implementations and they're all bespoke. Every client I have is a Snowflake. We haven't productized anything. But also, so I do implementations but I also do a lot of training and I was working with our head developer. We're building an LMS system to train uh, for a Fortune 500 company to train their team on how to use AI. And I was talking to the developer and he said, you know, this may be the last thing we build because the agents are getting so good, if everybody can use them, they don't need us anymore. And it's super. And uh, like you guys were saying, really I would say with the last four months because I, everybody's been talking, calling everything that they build in AI, uh, an agent. And true agents haven't really been a thing until very recently, uh, where they could go off and do long range functions and all that. You know, people are just calling their, you know, modified chatbot an agent or whatever. And so but we're just with whether it was the open claw experiment or even what especially I guess what Claude is doing right now, anthropic is doing with agents is just amazing. And I guess all that preamble talk about this, I'll, um, and I'll throw this to either one of you, but if your tech handles the heavy lifting, I'm wondering, because we were talking a little bit before we, we came on air too about deterministic versus probabilistic. What we, you know, the trial balance has to balance what we know we can automate.

Speaker B: Sure.

Speaker C: But we don't need generative AI to come in and weigh on it. So I'm wondering for you guys, how did you make those decisions and what is your stack without giving away any, you know, secret sauce proprietary information? But what's the approach you guys took to? This is hardcore just Python or math that we're doing standard here and this is where we're putting AI in, or are you just leaning more heavily into letting agents run?

Speaker B: So no, to the latter half, if you let agents run, they basically make stuff up or head off in the wrong direction or my favorite thing, sometimes they miss the forest for the trees and sometimes they miss the trees for the forest and they do that consistently. So look, uh, let me attend the tech stack and then I'll go back to kind of some of the design principles. Our tech stack is really five pieces, only two of which use AI in any way and not honestly not agentic AI, although Dan and I personally do most of our work using agents. Our client work and the software and technologies we offer to our customers tend to be, um, basically software solutions that leverage AI rather than agents. So our tech stack is basically five things, right. It requires a data ingestion engine. How do we eat data? A little bit, uh, very little AI is involved there. That's just traditional old school code. Secondly is compressing data because one of the things you run into with AI is if you drop a giant spreadsheet or a hundred thousand word book into AI, it starts hallucinating in very predictable ways. So you got to compress that hundred thousand words or that spreadsheet with 4 million lines down to something smaller. So we do a little bit of data compression and that's still old school technology. Thirdly, we do a little bit of tagging of the data. Still old school technology. Python scripts, JavaScript scripts, whatever. Okay, level four, the fourth piece is where AI comes in. So we now have this huge chunk of, or the smaller chunk of data that is well categorized and organized. And then we ask AI, hey, help us figure out what's going on here. And we asked that very, very tactically across thousands of questions like, hey, what was, uh, what are the major expenses? Hey, are there vendor consolidation opportunities? Hey, are there. Is there accounting fraud? So rather than just asking AI, hey, what's up? We'll ask it an entirely vetted, targeted list of thousands of questions that rely on domain expertise. And the fifth piece, it's not fancy. We leverage a little bit AI. We make it look pretty by creating a presentation layer. But really, like, the cool stuff is what happens in the kind of levels or phases one through four. Data ingestion, compression, tagging, and asking the AI to make kind of synthetic or synthesis decisions. The presentation layer just makes it look cool so clients pay for it.

Speaker C: You guys are solving for what every finance team, whatever, what every CFO's office is trying to figure out right now. And then, uh, it's, I guess that probabilistic versus deterministic. And your answer made perfect sense. But that's what everybody. If you, if you've never done anything with machine learning and you're not familiar with the technology, it just feels like magic. And people are just thinking, thinking, oh, take whatever process I'm doing and just sprinkle some AI on it and boom, it's fixed. And that's so I loved hearing your, your approach to it. And yes, we are in an AI, uh, era, and it's all driven, but the fact that you go back to leaning on just the fundamentals of deterministic coding, and this is, you know, this is what we're creating. This is the same for, for all businesses. Glenn.

Speaker A: It's almost like two plus two is

Speaker C: supposed to equal four. Yeah. Unless you ask generative AI. Uh, right. Yeah.

Speaker A: I, I had a roommate in college that had a shirt that said two plus two equals five for larger values of two. He also had one that said, if you can read, uh, this, you're over educated. And it was in Latin and about 10 others like that that he wore every day. So it made for fun. All right, so talk a little bit about how your kind of fractional CFO model works. Obviously there's a technology behind it. There's a human. And so how are you managing the workflows? Where does the human step in, you know, kind of take us through a little bit of the process here.

Speaker D: Nick gave you a sense for how the system works. Most of the time the technology can handle just about everything. I think there's some reluctance. It's uh, not just in FP and A. It's kind of everywhere. People, uh, that trust AI. Uh, Nick talked a lot about the data tagging and the organization of the data that we do before we ask questions. And that's really kind of the key to reducing the hallucination rate along with some verification layers. But we still do have the human element there essentially to handhold with the clients and get them comfortable with the answers that the system has figured out. Nick has told me that, uh, maybe one out of 100 questions the AI will still get wrong. And we do do a little bit of human cleanup, but from what I've seen from the AI answers, it seems to be close to 100% correct. You know, we're doing a lot of extra work with our early clients just to make sure they're really happy. So there are some cases where a client has asked us to go help find a loan or help find insurance policies and we haven't developed any AI bots that will take phone calls for us yet. So if we're, if we're talking with another party about financing or some sort of outside service that we need to get to the company, then we're still doing a little bit of that human monkey work ourselves.

Speaker A: Did you just call our job monkey work?

Speaker B: Aren't we all just hairless monkeys?

Speaker D: Some.

Speaker B: Some more hairless than others?

Speaker C: Yeah, I was going to say Paul is not a hairless monkey. One thing I wanted to hit on though is your, uh, your go to market and I think you're hitting a pretty interesting segment. And I think this is just one of the segments you're hitting. But going after the fractional CFOs. I know, I see so many of them out there, single shingle fractional CFOs who are trying to come in and do a turnkey solution. And that means they have to be an expert in a lot of areas. And it could be, you know, maybe that if it's a small enough one, maybe that fractional CFO is actually doing some basic bookkeeping and doing, you know, controller work and more strategic 13 week cash flows and all that. How, how did you nail down that market? What did you see that said, uh, hey, fractional CFOs need a tool like this.

Speaker A: This.

Speaker B: Let me take a crack at it. I think it's three things potentially. Number one, what do Dan and I actually have no expertise on, right. I don't have any expertise on, you know, let's say the creative side of marketing. So maybe Dan does. But like that's would probably not be a good area for us because we couldn't speak credibly or acquire clients or seem intelligent to them. So finance is an area where we have domain expertise. Specifically on the FPA strategy side, number two is where are people still doing a lot of manual work either on blackboards or on Excel or pieces of paper? Obviously FPA is another kind of category for that. And number three is where are there missed opportunities for value creation, how companies run themselves. So for example, in any normal company over probably 5 or 6 million bucks in revenue, no one is going through every single cost line of them every single day. Because people have lives to live, right? No matter how big your finance team is, they're not looking at every single line at them. M guess what, for an AI tool that's, you know, a penny of cost, right? So as you think about the confluence of those three factors, what are we good at? Where are people still using legacy tools that can be empowered or made better or using better tools? And where is there a lot of real hard value creation left in how companies are, you know, leaving money on the table in their operations? And that's where we came together and said, look, FPA is uh, an area that is ripe for automation and AI powered tooling. And it's an area that every company really has, right? Like almost every single company has an FP and a department or CFO or COO or somebody who's basically looking at the numbers to try and figure out how to make the company better every single day. And that's an area that and Dan and I are passionate about to kind of add icing on the cake.

Speaker C: And I guess the, the follow up on that, if you're Talking about fractional CFOs, I've lived this world and I've seen it a lot. That means they're coming into companies that I've got QuickBooks or maybe Xero and they, you know, they might have HubSpot or you know, some other pipe drive or something. They've got some disparate systems, data's a mess. If it's a founder led company, their chart of accounts could be that like the default QuickBooks chart of accounts and there's sort of a mix, a blend between cash and accrual accounting. I mean that, that's what you know, sort sort of shingles Small firm CFOs are dealing with. Was that part of your build, was how can we help them clean this up? Because it's always so painful when they, when you first come in and you realize, oh, this, it's going to take me 60 days just to understand the chart of accounts here.

Speaker D: I think that's exactly right. Um, if you look at a big company, they already have a team of analysts and data, uh, science people who can help them get the answers. But when you're talking about that single shop FP and a fractional CFO guy, he's got to figure out how to do it all himself. So giving him the tools, or the tools, uh, to do this the way that a big company could as a single individual, I think is, uh, is a big part of our mission too.

Speaker A: Makes a lot of sense. But I'm curious, what do you guys do? How does the tool manage that? If the chart of accounts is a mess, do you go work with the company to clean it up or how do you deal with that? Because we all know in this small space, it's rarely clean from day one. And Nick is laughing.

Speaker B: I'll chuckle. So, yes, 100% true. I, we actually, one of our, you know, look, our, our tool does a bunch of things. One of the features that it does is it looks through all your data and says, hey, here's where it's a mess. And sometimes it says it's a mess because it's wrong for technical reasons, like one plus one is equal three, which is, we know in accounting shouldn't be true. And sometimes it says, hey, this journal entry kind of looks fuzzy. Instead it should be over here. And sometimes it says, hey, your chart of accounts is a mess and you need to go fix it up. So generally we are not going in there unless it's a quick task. And actually, you know, changing the chart of accounts that is our clients work, or their accountants work, or their controller or their fractional CFOs work. But the good news is our technology does figure out when it is either technically or qualitatively, an area for improvement. Because as, as funny as it is for one of our clients, we said one of the biggest areas for value creation for them was actually just getting their accounting right because they couldn't figure out their P and L correctly.

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Speaker B: That sounds like, isn't that like a Ponzi scheme?

Speaker A: Basically that's something like what Enron tried to do, but in a more sophisticated way. Another question just kind of want to ask is, you know, we know noticed you guys are doing some white labeling of the platform. So obviously that's quite a bit different than running a consultancy business. You have some human stuff. So how did you come up with the approach? What's kind of driving it? What's the uptake been like? Because it sounds like you're offering the software obviously to other fractional services, but you're also offering some of your own fractional services with the software. So how do we walk us through all that?

Speaker D: Certainly from the perspective of what we've built, it can be applied both to a company that has a CFO in place or to a company that doesn't have a CFO. So the white label is our version for fractional CFOs for full time CFOs for somebody who wants to use this without our hand folding. And I think a lot of good finance people are capable of doing that. There are some people who just don't want to deal with the technology directly. Technology is not their thing. And that's where our consulting service comes in. And we can either work directly with the existing CFO of a company or we can work directly with the CEO if they're kind of at the stage where they're thinking about bringing on a CFO not ready for a full time person. So maybe it is kind of uh, a fractional CFO consulting in its own way in that service. Or it could just be kind of the early stages of starting to implement some of those things as the company is scaling over time.

Speaker C: And what's your sense of, I know it's early days still. But what's your sense of how much time can be saved, how much more? You know, the real value proposition of this for whether you're going into a firm or through, you know, white labeling through other fractional CFOs, it's going to be what's the time savings? How fast are we closing the month? What insights did we not have before? Like, what's your, your sense of like. Because uh, again spent a lot of time in this space. I just coming in where there was no CFO before or there was a glorified bookkeeper called like head of finance or something and it's just kind of a mess and, and getting to the other side. I think every when Dan, I think to your point, there's always that precipice of we kind of need a cfo, we can't yet afford a cfo. What are we going to do? And I think that's probably the m, maybe a sweet spot for you guys. But what's your sense of the company that comes, that brings you guys in, what they're saving and what they're having that they didn't before?

Speaker A: Sure.

Speaker B: I think our value proposition happens in two ways. Number one is if you have someone actually analyzing the book. So ignore the, the technical side of doing the accounting and closing the books. We do a little bit of work there, but really our focus on the FPA side for that fp and a side if the person has time, there's probably spending somewhere between 20 to 50 hours a month just looking, peeking around the numbers, seeing, hey, I can create more value here, I can adjust pricing here. I believe our tool cuts that 20 to 50 hour exercise down to a few minutes, uh, of human time tops. So it is 20 to 50 hours per month of time savings directly. But here's the big kicker. In a lot of companies there people are just too busy managing the day to day. So there's not somebody who's stepping back often and just thinking about the numbers and looking through the data and doing analyses. And for those companies it's this is just a purely value additive service where you don't have somebody trying to like go around optimize your costs or think about vendor consolidation or pricing or whether you have all the right insurance policies because people got stuff to do. And that's an area where it's not a time saving, it's just directly accretive to your bottom line.

Speaker C: Makes total sense. Uh, Paul, do we have time for one more question before we get into our uh, AI generated.

Speaker A: We always have time for one more question.

Speaker C: Glenn, you have the domain expertise. You've been through building this. We talk to finance people all the time. Who, I mean, it's. At this point, I think the wave is, you know, it's, we've jumped over the top of the Gartner, uh, hype cycle and we're, you know, I think we're. Now, if you're not doing something with AI in your business, you are officially a laggard at this point. But I'm, I know I talk to finance teams every day. They want to start automating, but they don't, they don't have the trust yet or really the understanding. And I'm sure this is an area where companies like you guys will come in and, and help them. But for a CFO or a controller, somebody that wants to just get started using AI, uh, bring automation in, what guidance do you, do you have for them if they're really like, okay, I understand, I'm behind the curve. What do I need to do? That's not just uploading all my financials directly into ChatGPT or Claude or whatever.

Speaker D: Yeah, so I, I mean, I think, um, the, the big issue with, with loading financials directly into one of these chatbots is, as Nick mentioned, when they're looking at a lot of data, the AI will either miss the forest for the trees or miss the trees for the forest. They won't, it can't summarize, get the big picture of what's going on with the data. It'll get lost and hallucinate and give the wrong piece of data for the wrong question. Uh, so I think that's where working with a system like ours that has the structured financial knowledge and content built into it is going to ensure the AI gives the right answers. Otherwise, you know, that 20, 50 hour task that Nick was talking about, I mean, maybe you could speed up half the work by going question by question, data point by data point too, as a CFO or FBA person. But, you know, just, it's still going to be a lot of time that's still probably 10, 25 hours, uh, out of a week to, you know, go through, find the right data, do the right calculation, and then feed the right data, the right time to the AI system. So it gives you the right answer.

Speaker A: Makes a lot of sense. All right, Glenn, who do you want to, who do you want to ask the personal question to? You get to pick.

Speaker C: All right, am I going first on the questions this week? Uh, we have.

Speaker A: You go First, Glenn, I don't think we've done that yet. That's right.

Speaker C: You normally go, so, all right, so here's what we do, guys. Every week we take your LinkedIn. These are really weird and technical this week. So good luck, guys, with these questions we're asking.

Speaker B: But.

Speaker C: So every.

Speaker A: Every week, blame the A.I. not us. Uh, that's what we do when it's wrong.

Speaker C: We take your LinkedIn profiles, we take whatever, you know, sort of public information is out on the web, and we move around. I've been on CLAUDE a while, just because I'm doing everything in Claude these days, and we say, come up with some quirky personal questions, um, for each of them. So my approach is always, well, AI Created the questions. Sometimes I'll tell it, pick your best. Or, you know, pick the best question to ask.

Speaker D: And.

Speaker C: And I let AI do it. Paul has a little bit like an. I don't know if you're doing this in Excel. You want to explain what you do on your side?

Speaker A: No, you. I do one of two things. I either let you keep a human in the loop and pick a number between 1 and 25, or I use the random number generator on the web, whichever one comes up first when I search it and pick a number between 1,25. Oh, I could use AI to be that random number generator, but I'm afraid of my hallucinate and give me 26 instead of 25. I just don't quite trust it yet. So I go with the deterministic site.

Speaker C: Yeah. And I. You know what, Dan, I'm going to put you in the spotlight first. And I'm just going to go ahead and have A.I. uh, select a question here. So let's see, Paul, it's number one again. This is. We're in some kind of weird. Like if we're at the roulette table, we would.

Speaker A: The last three weeks, it's all been one through PI. So, you know, if that continues and we can take those odds somewhere else, maybe we can make some money. Glenn?

Speaker C: Yeah. So, number one again. So, um. All right, Dan, the question is this one's not bad. Some of these are just weird, but this one's. This is a good question, actually. You studied chemical engineering at Stanford. How does a chem E end up in fractional CFO services?

Speaker D: Oh, man, that's a good question. You know, I ended up doing some research work while I was at Stanford on project finance. There was a professor I was working with at the time, really interesting guy who's, uh, doing work on you know, building cities from scratch in the Middle east was the first project I did with him was looking at special economic zones around the world, how these cities were being built and creating a database of all the different countries in the world and the cities that they were doing that. So that was my introduction to the world of finance. And it led to, uh, you know, one finance thing after another. And here I am not really ever having done any chemical engineering in my career 20 or so years later. But I, I do like to think that the engineering background helps me to think about companies, gives me a better understanding of uh, the science that they're working on a lot of the time. And sometimes that does have implications for the finance too. I mean, you don't want to uh, buy the wrong chemical then have your plan explode. So uh, I like to think, uh,

Speaker A: I've heard that bad.

Speaker D: I don't, I don't cut the wrong horse at least.

Speaker C: And obviously your dog is a cow fan because we said Stanford and he just went nuts in the background.

Speaker D: Oh, you can hear the dog. I was hoping that these modern microphones were uh, shutting her down in the background, but she likes to bark. There must be some sheep walking by outside my house. She's hurting all the.

Speaker A: Not a problem.

Speaker C: I was gonna say. I do think that um, engineering, it's a lot of the same mindset that goes into. It's, it's that problem solving mindset, it's that uh, sort of understanding how and why things work. And it's so the same sort of thinking that pushed you through engineering, I would say applies pretty well and in strategic finance too. So it's actually. They seem correlated.

Speaker D: Yeah.

Speaker A: Ah, I found many of the best people I worked with in FP and A had an engineering background because that analytic and that problem solving and math nature they brought with them. So I not surprised to see, uh, you ended up finding.

Speaker D: I think that's right. I think it's all an optimization problem. You want to make sure that you help the company make, uh, as much money as possible while maintaining a high quality of service and making sure the customers are as happy as possible as well.

Speaker A: Glenn, what do you think? Should we hire them to optimize our businesses?

Speaker C: I think our businesses are beyond hope, Paul. I don't know.

Speaker A: I know they probably are, but we've got to do a last ditch effort.

Speaker D: Come on.

Speaker A: But we can at least talk. You can talk with the best of, um, them.

Speaker C: That's right.

Speaker A: That's about all. That's about all, Glenn and I can Do Glenn can do AI I can talk and grow a beard. That's all I have left for me at this point. So I'm gonna lean into the beard more and more. All right, no m. Back to the questions. Nick, you got one of two options. Do you want to pick the number between 1 and 25, or you want the random number generator 17. He didn't even hesitate. He's like, I already know what number.

Speaker C: I want you to make a roulette player. You were.

Speaker B: You were committed. I've never played roulette, but I'm a very good poker player.

Speaker C: Okay. That was the question. Do you like poker? How weird.

Speaker A: If. All right, we're done. Thanks. You've been CEO, cfo, and CIO at different companies. Which hat fits best and which one did you find the hardest?

Speaker B: I think for me, the. The hat that fits the best is the CEO, and the hardest was the cfo. Because in the cfo, I think there's. I'll mention why the.

Speaker C: The.

Speaker B: The CFO one has been the hardest for me. The math and analysis side, that's the easy part of the CFO job. It's some of the kind of delicate. Call it the politics of around being CFO on two dimensions. Firstly, as, uh, cfo, you're often the bad cop in a good cop, bad cop way, right? When customers, uh, you know, if you're increasing price on customers, you have to tell your sales guys, blame the cfo. I've done this myself. Like, tell my sales guys, blame me. I'm the cfo. You know, blame it on the, you know, evil CFO or the right. And then conversely, you officially, you know, in most companies, CFOs have limited hard power. They are the keeper of the money. Uh, they have some, you know, governance rights. They can say no, but you can't just say no because it's a wrong decision. You have to build a lot of. You don't have a lot of kind of direct control over the operations of the business, although you are theoretically, you know, one of the smartest people in the room when it comes to knowing what's going on in the business. And I found that very difficult, both always being painted as the, you know, the bad cop again, sometimes by choice, but it hurts, right? And then secondly, like having limited direct power and having to use a lot of influence, I think that's really difficult, especially when I see, hey, we're doing this wrong. Like, why can't I just go fix it when that is someone else's kind of purview. Or scope. You don't want to step on toes. The CEO hat I think solves that problem quite a bit because you have the latitude to just go do everything again. You still have to be delicate about not disenfranchising people or disintermediating your senior staff, but there's a lot more latitude to like move fast and change things as the data or evidence changes over time rather than stick to an arbitrary plan. Because someone above you said to do this.

Speaker C: Nick, that's so interesting. I don't know Myers Briggs or any of these personality things. Everything that you just said, like, I would completely flip. I loved being the jerk. I loved people. Like, it's like, bring it, let's go. And I, I served. I was a, I've never had the CEO role. I've been a coo, cfo, and kind of a half assed CTO at one point too. But the CFO role, I always felt like as the cfo, you got to be the, uh, the adult in the room. Like I keep thinking of the WeWork founder showing up with his long hair and barefoot and you know, being the, the big vision guy. And then someone's got to be in the background the who knows socks and uh, not socks.

Speaker B: Who understands community adjusted EVA dog lens. Is that what you were going to say?

Speaker C: Yeah, I mean, it just, uh, it's funny. And then the CEO side, it's like the visionary part just seems exhausting and dealing with all those people seems exhausting. It's like, let me be the right hand person and just solving problems and anyway, it's very funny. As you were talking, everything you were saying, I was exactly 180 for and

Speaker A: I haven't been any of those, so.

Speaker B: A, uh, rose by any other name.

Speaker C: Paul, you're the chief of my heart officer. I don't know. Is that.

Speaker A: Wow.

Speaker B: CVO cheat beard officer.

Speaker D: Right.

Speaker A: I go, I've been told by people I need to start a beard brand.

Speaker C: Absolutely.

Speaker A: Looking at this podcast, I don't see many customers.

Speaker C: Yeah.

Speaker A: And therein lies one of the problems. Everybody who follows me is in finance. I don't think most finance people have big beards. I don't think it's a high target audience. Is beard the first thing that comes to mind when you think finance?

Speaker C: No neck tattoo is

Speaker A: all right. So I just started tattoo parlor before I start a beard. Brad, how did we get so off the rails, guys? How did you let it go like that?

Speaker C: Well, Nick and Dan, we really appreciate you coming on and very, um, I think you guys have hit a sweet spot. I wish you guys the best of luck because I know there are so many fractional CFOs who are trying to figure this out themselves and help their clients too. So I know you've got your own consulting too, but I think there could be a real sweet spot in having a tool that fractional CFOs could come in and be armed with. So super interesting to see how this shakes out for you.

Speaker B: Thanks for having us on. We really appreciate it.

Speaker D: Thanks for having us.

Speaker A: Thanks for joining. Really appreciate it Nick and Dan and we hope you guys have a great rest of your day and good luck building. I know uh scaling is always an exciting and scary time at the same time. Thanks for listening to the Future Finance show and thanks to our sponsor qflow AI if you enjoyed this episode, please leave a rating and review on your podcast platform of choice and may your robot podcast overlords be with you.