What CFOs should fix before scaling AI in finance

Speaker A: Hello and welcome to the EY Better Finance CFO Insights podcast, a series that explores the changing dynamics of the business world and what it means for finance leaders of today and tomorrow. I'm Miles Courson from EY Today. I'm delighted to be joined by Mike Whitmire, CEO and co founder of flowcast. So, Mike, welcome.

Speaker B: Thank you for having me, Miles. Excited to be here, Mike.

Speaker A: As we start, CFOs today are hearing a lot about AI and automation and the pressure to adopt new technologies quickly. But when you talk to finance teams, many are still focused on getting the fundamentals right, understanding workflows, improving processes, and making sure the data behind their reporting is reliable. You've got a really interesting vantage point on this. You started your career in public accounting. You're now the CEO and co founder of flowcast. And through your podcast Blood, Sweat and Balance Sheets, you're talking to a lot of CFOs, controllers, finance leaders, about how the profession is evolving and transforming. Let me kick off with a question. As you're advising CFOs and finance leaders today, so much of what we talk about is just compounding problems and things on top of each other. What's the one thing that you're recommending CFOs to stop doing that's actually holding them back from achieving the potential of the finance organization they're leading to stop doing?

Speaker B: That's a great question. A lot of it is you can kind of no longer operate in the status quo environment, which is a very vague terminology. So we like to try to put that into practice a little bit more. And what we found is we have conversations with a lot of CFOs and controllers, and there's pressure from the board around deploying AI and how to adopt it and implement it. Then you go into the reality of a conversation, and I remember There are many CFOs I've chatted with where they're getting that pressure. And then we go in to explain floqast, kind of show them what we do from a AI perspective. And oftentimes the conversation pulls back to, I just need visibility to what my team's doing. I don't even know what's going on with my team. I need a checklist. And it's like, okay, maybe we actually need to stop focusing on AI so much right now and get the fundamentals in place and get the basics laid out there. And I'm finding that conversation, which is really the founding story of floqast, is around helping your team collaborate Getting control around your monthly close process reconciliations, posting journal entries, that's sort of the meat and potatoes of getting your financial statements out. And you need to be good at those things before you can really dive too far into AI, uh, deployment and adoption. So oftentimes we're like, hey, maybe stop focusing too much on the AI part and let's get the foundation right. And that's getting your people working on the same page, getting your workflows documented, getting your data centralized is a really important part of all this or other data projects that need to occur before we head too far down the road with AI. But we don't want to encourage putting a band aid on a broken foundation or trying to fix a broken foundation. You have to go back and do the hard work of fixing the foundation before you build on top of that, to extend the home building analogy that we had going on earlier today. So it's really pretty basic stuff.

Speaker A: There's such a temptation to look at the new shiny objects and want to talk about how you do the exciting stuff. But that point you make about getting the foundation right is important. And are there things that you've, organizations you've worked with, you've seen do really well in that foundational build? What are the things that your uh, experience really make a difference?

Speaker B: One of the things we noticed is, and I'll go back to my experience doing this, what's interesting about accounting and the month end close is it's a large team of individuals often working on their own tasks. And you need to orchestrate all that to get one common goal achieved, which is issuing the financial statements and getting that the auditors and from EY's perspective, hopefully getting it to them on time or early to give some time to get the audit done. So it is really challenging to get control around what the team is working on. And so that's just kind of an early focus of ours. And what we found is when you go to market, oftentimes those individuals have their own little checklist that they use to get through the day. So it's not that there's no documentation, it's that that documentation is decentralized. We generally find ourselves going to a controller. Are you aware of those checklists? Do they exist? And then you kind of get the team to start sending them, sharing them, um, you centralize it and all of a sudden you pick your head up and you do have a month end close checklist, or you have a checklist for running your accounting operations and then you can get that into A solution like flowcast to have a little bit more control around it, have it centralized, and give controllers that dashboard view into everything that's going on within their team. And then once you have the workflow documented, then you can start to look through the work and understand, hey, where can we deploy automation? Where can we deploy AI? Where does it make sense? And so I really view the foundation of a checklist as your roadmap to automating some of the work within your accounting department. The general situation we see is there some documentation is just not all on the same page or it's very basic. It's things like close cash, close ar, close fixed assets. And it's like we need a little bit more detail and documentation around how we do our work for that. So that's generally what we found. We come in and we help with. On a baseline is centralizing your documentation or expanding a bit more on the documentation that you have today.

Speaker A: You And I both CPAs, child accountants, we've worked in this world for a long time. And one of the things that strikes me is it's almost a cliche, but accountants like structure and like definition, and we all grew up with checklists and being able to follow through. So in terms of how this change management works and the adoption, presumably this is something that when you work with things, actually it's welcome because people want to embrace it. They like the structure, given the training and the background they have.

Speaker B: We have that very clear perspective of what it's like to get through the job every day and every month. And it's comical when I juxtapose how our software works and what it does with sort of what expectations are from those outside of the industry, namely my investors and our and venture capitalists, because we come in with a very practical approach and something that's easy to use for accountants, but from a tech perspective, it doesn't necessarily come across as cool as they would expect. So for example, with AI right now, the expectation from those outside of the accounting world is that accountants are going to log into a page with a text box, just like AI, and they're basically going to say, do accounting for me and it's going to spit out all your financial statements. And I'm like, guys, that sounds really cool. I'm sure you have entrepreneurs coming in, pitching all of that to you. The idea of a command line gl, I'm sure is becoming really popular. But I'm telling you, as an accountant, the idea of doing that and not having context around where my work's being tracked, how it's getting done, what's my audit readiness around this? Are these numbers accurate or not? Do I really trust an agent spitting out one thing is just terrifying and is not going to ultimately be adopted by the market Accountants. We need a certain level of control. We need context into how all of our work is getting done. And within accounting, the context is really, where's the checklist? Where's my documentation? Where are the reconciliations? Where are the journal entry? And I want to see all that in one page and have access to it to give me comfort that the work is getting done and it's getting done accurately.

Speaker A: You used the word comical. And one thing I'll also put a plug in for is your PBC YouTube series.

Speaker B: Oh, thank you.

Speaker A: I think it's so funny. I mean, again, this is the ability to poke a bit of fun at the accounting profession, but also tell the story of what it's actually like to your point, to help people understand what happens in accounting is phenomenal, that you're doing the profession a great service with that. Uh, and you clearly are one of the most marketing savvy accountants.

Speaker B: Uh, as you say that. What's kind of interesting is you might be able to draw a comparison with how the show PBC comes off as so by accountants for accountants. Because we were in the weeds, right? I was at Ernst and Young, then I was at a, uh, publicly traded company that went through audits. So I saw it on both sides. And in talking with accountants from other companies and auditors from other firms, it's consistent kind of how it works behind the scenes. And the amount of inside baseball humor, all the acronyms. We have the, like, seemingly simple challenges around requesting documentation, then getting into a fight over it. It's all very real for accountants. It's very much by accountants for accountants. So, yeah, thank you for the plug. I appreciate it. If those of you who haven't seen PBC, we've shared it on YouTube. It's sort of like the office, but very, very directly for accountants and auditors with a lot of inside baseball humor.

Speaker A: Yeah, highly recommend. Just come back to this AI So, Mike, because obviously we want to talk about it, this question of AI readiness and where we are on the journey. It would be helpful, I think, sometimes to bust a few myths. And I think goes back to your previous comment. Oh, AI will solve it. Yeah, but how? One of the questions I think is really important is where are, uh, organizations that you're working with on the AI readiness journey? Because I think this expectation is going to happen overnight and it's going to be so dramatic. To your point, there's actually a lot of foundational stuff that needs to be done to achieve that. What's one thing that you would say to CFOs controllers and their finance teams that they need to be able to come together on and talk about to really get right, to be able to scale up that investment. And what do you think the starting point is in general terms at the moment? Mhm.

Speaker B: Looking across our install base, we still believe that about half of them are very early in that journey, beyond the documentation phase, but more in that either trying to optimize their processes to really then drive automation on an optimized checklist. That's about half of them. About 30% of our client base, they've deployed automation capabilities pretty aggressively. So that's something like maybe ingesting data from one system, reviewing it and posting a journal entry end to end, something to that effect, a rather basic one. And then we have that last 20% that is starting their AI journey. And generally what we like to recommend is starting small with it, improving value and ensuring that your team adopts it. Because one of my big concerns is a company goes out there and makes a really big purchase, they buy into a really big vision, then they don't get any practical value out of it. And so we want to come to the table with a practical approach for looking at one, two or three processes and, and think about how can we deploy AI across those processes to make us more efficient. And we have about 20% of our customers at that phase starting to roll it out. Then there's a small subset, call it a, uh, half a percent. But they were very aggressive in the journey and they've deployed AI across dozens of workflows. At this point, one thing that isn't considered much, at least from the outsider's perspective, is how much nuance there is to accounting. I think the phase of your company, where they are in the life cycle, what the growth plans of the company are and whether you're public or private, those are all really big factors to consider and how you should use AI and how much control you want around it. If you're a small accounting organization, let's say you have two or three people within your accounting department, you don't really plan on growing, quite frankly, you might not have a need for intense documentation. You have two people who can just talk to each other in a room. It might slow you down to have a checklist and actually try to document this Stuff presumably you don't have audit requirements so you don't really need to think too deeply around what are the controls around AI. And if it's close enough that's good if you can get it done faster. So there might be an opportunity to use some of the self serve AI tools out there, do a little DIY software building as well with some of the offerings that are there. But if you have plans on scaling, if you care about controls, if accuracy and control is really top of mind for you or you ultimately plan on going public, I think that's when you need a purpose built application to make sure that you scale and you scale accurately when AI came out. EY has deeply beaten into my brain the idea of what could go wrong. So I'm just thinking about the what could go wrongs and there were a whole lot and I mean the number one one I landed on is just accuracy and the concerns over a ah, material weakness being found within a company using AI in an irresponsible way. So that's what I think about as a scaling organization or a larger enterprise and how they're going to do it in an audit ready way. And thanks to my years at EY for that.

Speaker A: I uh, don't think any of us that have spent any time in audit remember that what could go wrong in the WCGW acronym. To go back to your point about everything having to be acronymized, I appreciate your honesty that this doesn't apply to everybody. Where would you say the tipping point is? What level of scale and complexity complexity does it start to make sense to make that investment in the documentation and really understanding the workflow?

Speaker B: We have found six people in the accounting department is that breaking point at which you need to start documenting work. That's the point at which you have a controller managing a team. That work is decentralized and you do have the risk of institutional knowledge leaving the building. So if someone on the team leaves, it's going to be hard to pick up that work if it's not written down quite frankly. Now the need for it I would say depends a bit on the company's strategy at that point. If you're going to remain at that size, you might be able to get by, you might be able to not. But if you plan on growing, which a lot of our clients start at ah, six or so people within accounting, then they expand internationally, they add more entities, they have more currencies to deal with, reconciliations get harder intercompany comes along, consolidations, maybe you do some M&A as well, you start to grow. And all those things make accounting much more complicated. And if you're able to start with a well documented foundation at six accounting team members and have that continue as you scale up, it's going to make your life much easier and much more efficient as you start to really grow into a larger enterprise and deal with those complications. And then ultimately, if you do choose to go public, that's where having already operated in a bit of a controlled environment for, for years ahead of that timeframe makes it much easier to get SOX compliant. It's less of a documentation exercise. And then quite frankly, the big one is it's less of a culture shock to the team when you have to adhere to controls, hit deadlines and things like that. You've already softly been working in that environment. So it's just not as big of a culture shift. One of the roles I was brought into at my prior job was to help with getting ready to go public. And a lot of the documentation around the risk and control matrix, SOX compliance and all that. And what I was surprised to find out was the documentation was the easy part. I mean, I worked pretty hard on it, but it was the easy part to get that written down. The culture shock that came along with it and the behavior change was the really hard part. And when auditors are out there, if, uh, behavior doesn't change quickly, they're going to find a control issue and it's going to blow up into a much bigger problem than that. So having a tool in place before you go public, you softly get people used to working in that environment. The culture shock just isn't as extreme and you're going to have better control out the gate as a public company.

Speaker A: But you also work with large scale established enterprises. Is that change management and that culture shift different? If you're working with an established public company, how do they embrace the technology and the benefits of what's being brought to the table?

Speaker B: You're right. We'll go into organizations where they have hundreds of people in the accounting department already and that's a good example where generally a company of that scale, they're going to have either that situation we were discussing earlier where everyone has a little bit of their own checklist and that's how they get through their day job, or they probably have a documented checklist, it's just a bit manual and wastes a lot of the time of the controller to keep it updated, have status update meetings, all that good stuff. So the behavior change is generally getting them to log into an application and hit a sign off button and have some collaboration review note process going through our system. So it's all documented. So it's not as extreme, I would say as moving from an uncontrolled environment to a controlled environment because it is a different way of working. Whereas we're coming in and helping them document the way they already work. And so just getting them to log into flowcast, it's a pretty easy training. They just kind of get it. You really don't need a training. You start signing off on stuff. We've had some funny stories of trainings in the past where people accidentally came off mute and were talking, why am I even here? I get this kind of thing which on the one hand stings, on the other hand is wonderful to hear that they don't think they need the training because they can use it. But it's not really all that difficult. If you're at that early documentation phase, then I would say as you move into more automation capabilities, into more AI that does require more training and some people are just going to be bought in on it and some people aren't. And that's another really difficult one is when you just have maybe some stubborn personalities who don't want to do something new. It can be really hard to change that. So we try to arrive at uh, who are the people within the department that are open to new uh, technology and want to learn it and those are generally the people who are most successful with that behavior change because from their perspective it's more about the efficiency they're going to get out of it and they're going to like their job more than it is about having to change the way they work. They don't care as much about that. They care more about getting better.

Speaker A: We talked about some of the foundational stuff, the inputs to get right. Look, I think we all recognize the opportunities AI presents in terms of better collaboration, faster reporting, breaking down organizational silos. Where do you think we are on the journey of AI proving the value? And are uh, you seeing organizations able to really demonstrate a return um, on the investment yet?

Speaker B: So just yesterday we had our company qbrs. So our quarterly business reviews and our final topic was internal AI efficiency. We're going through it and we are taking three approaches internally to how we deploy AI. The first is our IT department rolling out some of the more generic tooling that everyone uses on a bit of a day to day basis. For example, summarize your emails and things like that. Sort of the low hanging faceline stuff that's a little bit more difficult to quantify on an ROI basis. If you're talking to a cfo, that's a use case where you're hard pressed to be able to drive a hard ROI around automating this much work and delivering this much value. But I think we all know it's making your team more efficient and taking some of the rote work out of the equation, just harder to point to an roi. The second use case is we have a head of our AI team. He goes through and uses tooling to automate specific processes and workflows that our team has identified it as hyper inefficient and help automate that. So one example for us internally is we have quarterly reviews with our clients. That used to be a manual process where our account manager would spend about an hour or so digging through various systems, understanding how their customer is using it and preparing a presentation to then drive that he went in and plugged into all those systems, is able to automatically create that presentation now. And so now our account managers, rather than do the one hour exercise, they spend about five minutes reviewing it, making sure everything's good, and then deliver that. So we have 3,500 customers. We strive to have a quarterly call with all of them. So that's 14,000 calls we're having a year times an hour. That's 13,000 hours of savings that we've gotten from automating that one process. So easy to quantify on that one. But then my favorite use case is we have operational leaders within each group and what they do is they go out and pick a solution that is built for their department and they own that solution and deploy it on the behalf of their team to drive efficiency. We use a great one on our go to market engine and it's able to integrate with a lot of different solutions that we have. It's also able to go out in the market, understand what companies are up to, and helps us generate leads and generate demand and reach out to customers way more accurately. And that's where all of a sudden you can point to, hey, we used to do this job with 40 people, now we do it with 30 people because of how much efficiency we've gotten through that one area. So I look at those are, uh, the three kind of ways to deploy it. And there's an increasing ROI story as you move down the use cases there.

Speaker A: That's a really helpful articulation. I appreciate sharing that because I think there's multiple ways to measure it. Right. And you can focus very much on sort of the input side and the efficiency saving. But uh, I think the more interesting question is how are you actually taking that time that's freed up and how are you measuring the additional value that's being generated to that process? In your case, more time with customers, more time actually building products, whatever the right metric is. I think there's an opportunity for CFOs and finance leaders to shift some of that narrative. I think a lot of the conversation has been around the input and the efficiency measurement versus the value creation story.

Speaker B: Yeah, there are two ways to think about the value creation. You can either think about it in terms of expense savings or a growth multiplier. We like to use it as a growth multiplier here at floqast. So on that customer success example, from my perspective, that doesn't mean we should move on from account managers and have more clients per account manager that we're hosting and remove the ratio because I think that's going to result in a degradated, um, customer experience with us. I would rather free that time up for them and allow them to be more proactive, more critical thinking and be more hands on with the client rather than spend so much of their time on administrative work. It's really a win win.

Speaker A: You're a technology company who spend a lot of time talking about the tech side, but where is the line? Where do you see the humans taking over the finance organizations you're working with? What is the role of people in this process going forward and how can they again upskill to be ready for that?

Speaker B: This has been a bit of a squishy question even for me as well to think through. I've always said, well, it'll allow accountants to focus on more of the critical work. There's going to be a human loop to really review judgment being made by AI and ensure that it's accurate and in line with what the company has to be doing. And then I'm sure you're all aware AI COSO framework came out about how to use Genai within finance and accounting. And looking through that, it has solidified my opinion that humans are going to become the ones reviewing the judgment of it. I and in fact extended my opinion around that. If you're going to be the AI user within an accounting department, it's on you to understand the AI COSO framework and how the way you're using AI is then going to trigger audit procedures on the various capabilities that are being used by that AI technology. And so it is a big shift in work and understanding really what risk is coming along with deploying AI, and what do I have to do as a human to mitigate that risk and in line with adhering to the AI COSO framework. And on, um, that note, I'm really interested, but I think there's going to be a lot of learnings over the next several years around how we audit, how AI is being used in accounting departments and where the work shakes out. I think is actually going to land a lot on the people using AI and then the auditors at the end of the quarter and the end of the year.

Speaker A: You're absolutely right. I think there's a question of auditability in the context of AI is going to be a very important topic over, uh, the next few years, and it's going to evolve. And obviously the pace with which technology is evolving means it's going to be very dynamic. So I think as a finance leader, as you're thinking about your AI strategy, thinking about that auditability question, engaging with your auditors as part of that journey is going to be absolutely critical.

Speaker B: I'll, uh, layer onto that. I think it's also ensuring that the IT department understands those requirements. Three of our larger customers have come to us and their IT department has said, hey, we want to build Flowcast, we want to replace it, and we go, okay, that's a real shame. We've loved working with you. In the spirit of partnership, we just want to make sure everything is being considered here and namely the compliance side of it. So just ensure SOC1, SOC2 readiness is there. You know, you're maintaining the application, so you're always prepared for that. You're a publicly traded company, you're going to have a Sarbanes Oxley framework you need to adhere to as well. It's really important to understand the risk the CFO is taking on if you're not using a SOX compliant solution. And what we found is once IT understands all of that stuff, they want to go work on a different project. So three out of three times they've gone and worked on something else because they don't want to take on the risk of blowing up the audit. And the CFO very much appreciates that. So when I think about technology deployed here, it's really important that you have a partner who's thinking about compliance and auditability out the gate. And let's turn agents into humans for a second. You can think about agents as staff doing work, and you can either have that decentralized or built by an IT department that doesn't fully grasp the COSO framework requirements behind it. And you're going to end up with decentralized AI, which results in audit risk that comes along with it. And then at some point, you're going to pick your head up and want all of your agents working in a centralized location that has control around it, which is the founding story of flowcast. It's we have a bunch of people working in a decentralized way that creates risk. For me, it creates, uh, accuracy concerns. And we need an application to centralize where all the people are doing their work. That same thing is going to play out with AI. It'll be people using it independently. You'll have AI sprawl, it's going to create risk, and then you're going to want to centralize it in one place. So why not just do that from the beginning? Build out your AI capabilities within a solution like flowcast and use our tooling and then the auditability for that.

Speaker A: Yeah, I think the point you're getting at, uh, is this mental model of as a leader, you will have a team of people, a team of agents that you need to manage. Now, I think, again, you may have different views on where on the spectrum the balance of agents versus people is, but I think when it comes back to as a leader, you've still got to understand what you're asking people to do. You've got to understand the risk and be able as a leader to effectively manage the resources under your control. Sort of goes back to the point you were making, which is, does the leader in that situation have the main knowledge and understanding, particularly in areas like auditability, to be effective in that leadership position? Does that resonate? Is that the way you're thinking about it?

Speaker B: It does. And the shift of focus that's required is more so on the back to the what could go wrong and what is the agent not doing properly? A good comparison to make is. So I'm pretty deep. I use a lot of the AI tooling to build just fun websites and fun projects kind of on the side. And if you're a software developer using AI, a big part of what your job ultimately becomes is fixing the bugs that AI creates to the point where my favorite application that I use for building all this, one of their canned prompts is check my app for bugs. And that is an app that the AI has built itself. And it so frequently creates bugs which that there's a prompt to go back and create it. Which is fine for software development. It's fine if I'm building a fun little application on the side. But if that's the main thing you have to focus on as a software engineer, which is really good at writing code, that's what it's the best at right now. If your main focus is having to fix what it broke, that idea is unacceptable in accounting. I can't have a fun little prompt that says check my financials for bugs or errors that we can't even get it to the financial statement level. And so QA ultimately becomes the biggest job of the accountant is doing QA testing around the work that the agent is doing. So I think understanding how it's playing out in software engineering, where it's most advanced right now, is a good leading indicator for how it's going to play out in accounting and sort of supports the thesis that we've had from the beginning.

Speaker A: Yeah, and I think that's a conversation I've been having numerous times that particularly in the context of large language models, you're probabilistic doesn't work versus deterministic in a financial reporting situation. So as you think about where are the right places to be deploying the right tools. Anything you can share on that?

Speaker B: Yeah. Even within a given process, within one single given process, you're going to have a combination of deterministic and probabilistic use cases. One example I'll give you is one of our more advanced larger accounts. They're a private equity firm and they have what's called a fund allocation journal entry that they have to perform. So we went out and we helped them do that. It started with integrating with 27 different systems that they have internally. So now you have an ITC that comes around your integration point with all of that. We take the documentation for them, we centralize it. Then we use our product called flowcast Transform to allow them to take that data, shift it around and structure it in the way that they need to work with it. That is a very deterministic use case because we use AI to write code behind the scenes to do that work on a recurring repeatable basis. Transforming data is not the hardest problem out there. And you can use code to do it accurately and do it quite frankly, more cheaply as well. If you don't have to run a model every time you get it done at a better cost basis. So now you have that block that is deterministic. Then we create a journal entry for you. We'll populate that journal entry trail. And that is now back to an ITGC audit to make sure that the Data that was created over here by the transformation product is properly populated in the journal entry. Then we have an agent that is a first pass journal entry review agent. So you run the agent, it goes through, it does a first pass. It looks for things like do the debits not add up to the credits? Was this signed on, was it not? There's really foundational things that it does and that's in the spirit of saving the human reviewer some time before they go in. They then do their human review of the journal entry template, make sure it's accurate, it all lines up. So now we have an integration point which is an ITGC control. We have a deterministic part of code that needs to be tested in one way. Then we're populating the flowcast application which needs to be tested in another way. Then you have an agent doing probabilistic work looking at the journal entry. Now we need to understand the judgment and the model that's being used to do that work. Then you have a human in the loop review process before it's ultimately posted into your gl, that journal entry. And so just that one workflow, that extracting data, booking a fund allocation and getting it into the gl, when we added it up, it hit all eight capabilities that were in the COSO framework. That is an intense audit for one process. Now for the client, it saved them, it was about 900 hours a year that it saved them in terms of getting that work done. But now they have to do a little bit more work to ensure it's being done. So we brought the savings down, but it also created a new form of work for them to do. And now when audit comes out, audit's going to have to audit that process and it will be more work for the auditor at the end of the year. So there are trade offs to it. And that's a good example of how robust the audit documentation would need to be. And it's our responsibility of flowcast to produce that audit evidence for you so you're compliant. And that's something we focus on. But just one little workflow example presents a whole lot of risk with it and requires a nuanced way of thinking about it.

Speaker A: I appreciate you sharing that example. I think it's a great example. And just decomposing that process and looking at the different applications of AI in the various steps that process towards an ultimate outcome, I think is really helpful. And that's the level of grand narrative people need to start thinking about. AI is going to solve it. No, it's how is AI going to solve it at each step in the process and what is the right tool to be deployed? So I really appreciate you articulating that.

Speaker B: Yeah, of course.

Speaker A: As we wrap up, you're obviously CEO of Overtech company. I'm sure you've got a roadmap for where you're going from a development perspective. I'm sure that's anchored to having a point of view on where you think the future of the finance organization is going. What do you think the finance organization is going to look like and what does that mean for senior finance leaders personally to be ready? And also what do they need to do to get their teams ready?

Speaker B: One of the cool positions we found ourselves in, and I feel very grateful that we're in this position, is we do in some ways get to shape the future of how the technology is going to work. I mean this in the humblest way possible, but we are at a good amount of scale. We have customers all across the spectrum in terms of verticals and sizes that we can work with and we can help drive how AI should be used within accounting and our mission at flowcast. And I think it's great for accounting, but it's also the right way to do it is we want to make sure that accountants own the future of accounting. With AI, we want to make sure that this technology is in the hands of accountants. We think that will result in the most efficient work and the most accurate work being done and allow for the most thoughtfulness to be deployed across AI. The other option is allowing it to do everything. And if it is going out and deploying AI, you're going to miss the accounting nuances. It's going to insert risk into the equation and I don't want that to happen. So we're building software to empower accountants to own that work, which means it's incumbent upon us to make sure it's really easy to use, it's easy to learn. And we think that with all of that, it is going to create a new role in accounting. So you'll have your. I talk about there really being three roles within the future of accounting. They're still going to be humans. You're going to have your controller. Their job is going to be to manage people, manage the whole process, make sure everything's on track. And that is great for certain personalities. They love managing people. Personally, despite running a large organization, that's not my favorite thing in the world. I hire great people who do a lot of the people management around here, but some people Enjoy it. And so that will be one of the roles in the future. The second is going to be still that technical gap accounting expert who understands regulation, understands GAAP, accounting guidance, all that good stuff. And they're the ones who are there to make sure that new work is being interpreted properly and done properly. You know, ASC606 comes out, someone needs to do that work and get it in place and make sure it's deployed across the org, that role will still exist. And then the third is going to be uh, more of a technologist looking role. They're going to be the ones who are really owning all the agents, building them, um, deploying them and managing them in conjunction with the regulation expert, doing that work together to make sure that all the work is done in a compliant manner. It's going to look much more like a technologist. And the reality is the role is probably those who are financial transformation experts today. That's a bit of the morph in their job is to really, really driving more of the technology and the adoption. Not just collaborating with it, but more so owning it to drive that transformation. And we've put out a program, we call it the Flowcast Certified Accountant. And the idea is to educate accountants to be that type of new role and be successful within the accounting industry. So if I'm a cfo, what's really important right now is identifying the people and the talent that are good at this naturally and want to do more of the tech focused work. That's it. Everything starts with people. It starts with people and then process and then technology. It's an old trope, but it is for a reason. It's very accurate. And so step one is identifying the right people within your organization to take on um, those three different roles. Make sure your process is documented and then talk about how do we deploy technology more aggressively against that.

Speaker A: Fantastic. Well Mike, that's a great way to finish. As we close out we like to have some rapid fire questions just so we can learn from you as a leader and share some wisdom that you've accumulated over the years. Is there a particular all time favorite quote you have and why does that stand out for you?

Speaker B: It's very basic. Hard things are hard. We're trying to do hard things here at floqast and that means that every day is going to be hard, it's not going to be easy and so just kind of get used to it. Is one of um, them it has mixed reception but it kind of is what it is. So I talk about that one and then there's debate of who this quote is attributed to or not. I believe it was Ford. It was, hey, if I asked the customers what they wanted, they would have said a faster horse. And I agree with that. So then I talk about, okay, that quote should be applied to different life cycles within a product development. I think it is very much our job at FLOQAST to innovate and be the zero to one the person who has the idea for the car, not the faster horse. But then once you've invented the car, work very closely with your customers on how to make that car as good as possible for them. Um, I'm not necessarily the guy who's going to have the idea around putting air conditioning in a car, but if someone's driving in a car all day and they get really hot, they might be like, can I have this thing that makes it colder in here? We'll be like, yeah, we'll go do that for you. We'll build that. Oh, you want to listen to music in your car? Cool, we'll go put a radio in for you. Things like that. You need to work closely with your customers to build out the product such that it's really great for them and it creates a great user experience. But it's our job to innovate and get from that zero to one phase and really try to be forward thinking around how accountants should work in the future. The faster horse quote is one I like delivering as well.

Speaker A: I love that. And, um, you're right, it's Henry Ford. It's the Model T example. But I think the point you made around understand where you are, you driving the disruption or you drive in the, uh, continuous improvement and making sure your organization is aligned effectively where you are in that stage. That's great. I love this generative one. That's fantastic. Was there a particular piece of advice that someone's given you during the course of your career that had the most impact?

Speaker B: I have a mentor, one of our first board members at Flocast. And I remember early on talking to him about. We had a really difficult conversation. I had to close an office down and that meant we had to let go of about 25 people. It was a really tough decision to make. And I remember talking to him like, how do you deliver this message to the company? What's the right way to do it? And he just said to me, hey man, the truth shall set you free. That's it. The more open and honest you are and transparent about the decision, like at the end of the Day what you're doing is right for the business and just explain your logic, explain why, and be open and honest about it and people will respect it and appreciate it. And the truth shall set you free. As a leader, I think is an insanely great piece of advice and it's something that I try to carry through with everything I do to the company. That one means a lot for me and has driven a lot in my leadership style.

Speaker A: One of the pieces I, uh, go back to is bad news doesn't age. And I think it's a similar theme. Right. I mean, just get it out there and deal with it. You will be much more constructive rather than procrastinating on stuff. So that's, that's great. And then obviously you're an entrepreneur. You've got this rapid growth company. You've got a lot of pressures on your time. How do you maintain well being and balance?

Speaker B: I think it's important to have hobbies outside of work for sure. So I fit some of the stereotypes of accountants. I love golf and I love baseball. So I do get out in golf. If you can see behind me, I do have a golf simulator. I'm fortunate enough to have that at home. So I get to uh, do some of the golfing without spending a ton of time away from my family, which is also really important. I think having family time, focusing on your family. I have an 8 year old daughter. I don't want to miss her growing up and so being able to carve out time to do that is something you have to do intentionally. But I think it's important to create balance in your life. So yes, I spend a lot of time on Floatcast. It takes a lot of my headspace up. But I also need to have free time to do things outside of work and that's to me, hobbies and family. And I think that makes me be better at Flo Cast as well.

Speaker A: I fully agree with that. I'm lucky to be able to blend both. I got my kids playing golf early and they love it. So I get.

Speaker B: Oh, awesome. That's awesome. Yeah. My daughter just started playing softball. It's a lot of fun to go to the games and watch them learn and have fun.

Speaker A: Well, we'll get a bit of a golf swing sooner rather than later because that softball swing doesn't help the golf swing.

Speaker B: No, the earlier you start golfing, the better for sure.

Speaker A: It's been a great conversation, really enjoyed talking to you and thanks for being on the show.

Speaker B: Yeah, likewise. Miles. Thank you so much for having me this is really cool being an EY alumni to this interview has been a lot of fun.

Speaker A: Take care. Thank you. You too. If you've enjoyed this or any episode of the EY Better Finance CFO Insights Podcast, please subscribe or leave a rating review. You'll find related links@, uh ey.com betterfinance and as always, thank you for listening.