The CFO Should Own AI, Not IT | Glenn Hopper on The AI-Ready CFO

0:00Welcome to the future finance show where we talk about Don't boil the ocean at once don't treat it as a single okay, we're doing AI now That's one project everything you do whether it's variance commentary AP automation AR automation close good requisite whatever you anything in the finance and accounting process That you're going after treat that as a standalone project so they can be done with that like if you go in and say Okay, I'm gonna automate the clothes. That's our AI pilot But don't start there start with the ones that are easiest to access and go through and work those Compare them against the baseline that you have evaluate over time. I use a 90-day pilot gather evidence as you go and then at the end of the 90 days There's a decision gate do we kill this project? Future finances brought to you by Qflow dot AI the strategic finance platform

1:04Solving the toughest part of planning and analysis B2B revenue align cells marketing and finance seamlessly speed up decision-making and Lock-in accountability with Qflow dot AI I Welcome to another episode of future finance super excited to be joined once again by my co-host Glenn hopper Glenn how you doing? I'm good. I'm in my guest today. Or am I the co-host? I don't know how we structure I don't know I believe you're the man with the sultry sounds if I remember right So our audience probably what is he talking about? I told my wife I would have a little fun on the episode So hopefully I won't be too much in the dog house But she was going on and on about how much she enjoys listening to Glenn because he really knows his material is really smart And I was like wait a second my chop liver like I just sound so nice. So Glenn

2:04Just want to let you know you sound intelligent. My wife was impressed I told her I'd introduce you with sultry sounds and I would just be the mascot who's the kind of the bearded wonder or the I can be so to speak exactly you know the funny thing is I was a journalist before I went to business school And I always felt like I had a you know a face for radio, but unfortunately a voice for print Kind of that sounds like Billy Bob Forton with a mouthful of marbles I don't know maybe maybe I've gotten better at my diction Since then but it's really it's it's that it's the off-putting southern accent I think maybe lulls people into a sense of calm You think that might be it. I like the accent and I think the biggest thing is she's she was saying she can let's see all day is She just really appreciates how well you know your stuff. She commented about that She really make it fall they can help her learn a topic and you know the topic well So there you go tell your wife I said thanks and And if she ever needs help falling asleep she could just listen to one of our podcast episodes I will definitely let her know that I I've considered it myself. No, I'm alright. So today

3:10Glenn's gonna be My co-host slash our guest. So we'll go guest more than anything, but We're gonna talk about his new book book number three, right? That is it. Yep. So let's start with Tell our audience what the title of the book is and give us just a brief little summary. Let's start there The title of the book is the AI ready CFO and Paul I know you and I have our running gag about the AI CFO because CFO is kind of just a CFO But what I'm arguing in the book is the CFO in the era of AI is even more than the CFO and I'm Well, we'll get into it a little more detail, but really This book who would have guessed that I would write three books on AI and finance with Spoiler alert fourth book already in the works and coming out this spring We can talk about that later, but when I wrote my first book

4:11It was pre-Generative AI I was during COVID where we all had to come up with things to do while we were trapped in our house non-stop the first book was hey machine learning is Pretty cool. You should use it in finance But this was it was published in 2021 back then it was Great machine learning is cool, but what finance team is going to be able to hire a bunch of data scientists and Engineers and people to write Python and all that so great concept, but maybe a little bit ahead of its time but then generative AI comes along and suddenly oh wait that barrier to be able to use data science principles in finance is Knocked down because we can all vibe code our our way to Fame, and fortune I guess, but so then I wrote the second book, but the super interesting I don't know I'm maybe I'm bad at timing the market, but the second book was AI mastery for finance professionals and yes

5:12I do talk about Generative AI and that the second book was kind of the book that I wanted to write initially and it was just Think about all the applications all the ways that you could use AI and finance so we talk about Classical AI and machine learning and that and but we talk about Image recognition and Generative AI and just across all whether it's corporate finance or private equity VC High finance across the board. How can AI be used and that's great for sort of a Okay, let me just understand the universe of AI and how it applies to finance the third book the AI ready CFO was really okay We all have these tools in our hands right now and you can train all day on how to use cloud or how to use chat GPT or Pick whatever the the latest and most popular model is That doesn't really move the needle and for leaders in AI leaders in any industry or profession now You need to know how to incorporate AI so my whole reason for this book was I'm getting pressure as a CFO

6:18Whether it's from my CEO from the board Whatever I'm being asked for an AI strategy. I'm not an AI expert So how do I come up with the strategy and I also address things like And I think it's getting better now, but kind of everybody's using AI right now Whether it's approved or not and there's all kinds of risks around that with people using their own tools and all that So we talk about that and really the whole idea of this book is Handsome with a manual so that if we're starting with I thought I could just be a finance person and run a finance team and be the you know Have fiduciary responsibility for the company and now I'm being asked to do all this other work that isn't related to what I went to school for so It's really meant to just be go from not sure what to do to okay Now I've got a 90 day plan a six month plan a two-year plan on how we're gonna roll out AI effectively Well, definitely dig into that but something you said at the end I thought was really interesting you talked about how you know finances now being able to take on kind of a role

7:22Or I had didn't learn in school or I didn't expect to have to be able to do now They're being asked to do out with finance and I'd love just kind of a little bit of a I guess almost a detour for a second here When it go off the rails so to speak Non AI on an AI show but bear with me So you mentioned that and it feels like over the last decade I'd love your take having been a CFO. I haven't been in that seat I've seen a lot everything I read says the CFOs being asked to take on more and do more You have people to say hey the CFO should own data. You have many you saw a sales force create a chief operating finance officer You see a lot of CFOs have CFO and COO or small company sometimes they owned IT and HR and It feels like I have a friend who he's he's president and CFO not a combination you see very often But he's the president of the company and the CFO and so just love to get your thoughts in general It feels like you know, this is one more thing finance is kind of being asked in many cases or expected

8:26Or in your case you're gonna we're gonna talk about how you kind of argue Leading a lot of this. Yeah, fairs. That's gonna lead to burnout or or expecting too much from CFOs We just love your thought on that whole kind of yeah, and I've and I've talked about this for a long time and I had I was in under 50 million revenue businesses and I was in not a full turnaround Stage but when I was brought into companies I was brought in by the investors and it was usually a company that was Kind of long in the tooth was kind of flat wasn't really doing anything So not failing but just not growing the way of private equity Sponsor would want them to so I would have to come in and figure out how to make changes And because of that a couple times I did have the CFO slash COO role And I also had the power of the board and the investors behind me So I could get access to data. I could get things done maybe more than a normally if you're just swimming in the finance lane But when I think about what had to be done

9:27The finance was this scorecard and the forecasting that we had to do was how we were gonna if whether we were raising money from debt or we were selling the business or or whatever doing we had to present Like a bigger company than we were and a lot of times there there were layoffs and restructurings around me coming And all that is background to say I was in that seat where add finance and operations and IT sometimes and HR all rolling out to me very small teams with each But trial by fire I guess but it forces you out of that it was very easy and I remember the first CFO I worked for back A million years ago He could stay right in like we would have our our it was a startup telecom company and we could have our our monthly meetings Where the whole company got together and everybody would give their briefs and the CFO all he did was get up there and read We're EBITDA positive in this market. We're cash flow positive in this market We're trailing in this we're ahead of for and it was all just reading out that report card

10:28And I think that was pre Sarbanes oxley and and all that and I think the CFO role has changed Since then and the technology has changed and the idea We have more power to take in more data and use it in our forecasting and to be able to If we're spending less time making you know manual journal entries Then we can spend more time on that strategic value and the the tools have changed the access to data that we have It's changed the regulatory environment has changed and I In my time in finance going back 30 years. Yes, the role Has changed and to be most effective. I call it a land grab if you will But I want access to these other departments as well and it does make sense I'm not always because it's they're two very big jobs But you know in a much larger company carrying the role of CFO and C OO seems like it would be a pretty big burden Especially when you have ESG requirements on top of it and all the other things that um

11:32We're accountable for but whether I actually own all of those or whether they roll up to me I want access to all that information I want all the data in the company because I'm going to use it and my role is a CFO Yeah, and I think your second point is the important one Whether you own it or not you need to understand it and whether CFO should be a C OO size complexity matter I think most people price aid manufacturing you need that C OO that deep understanding right and you're He nodding like yeah, please don't take me to the C OO in a manufacturing Yeah, but the manufacturing especially we need to be in lockstep on the data we're using for all the Job costing and forever, you know order costing and all I mean all we need the detailed accounting And so it's interestingly the system in a manufacturing environment The ERP the in the operating system if they're not the same piece of software they need to be linked because we need the operating reporting and the financial reporting around it The resource planning is real the the the RP and the ERP is much more important in the manufacturing side

12:37Whether you call it MRP ERP whatever right totally know what you're meaning in that data But kind of stepping back to where we started all this on owning more You know you made to point in your book That finance really now needs to own AI adoption kind of own the AI thing so when you say own What does that mean for finance and how do they successfully how does the CFO Leadership do that with this continued ever growing responsibility. Yeah, and I get a lot of pushback on this, but it doesn't change my steadfast My steadfast commitment to it. Let me I'll tell you the why and then we'll talk about the how so For the best reporting not I mean not just general ledger that we're reporting on I want access to all the data, but I think about if We're supposed to be unbiased reporters of the financial data and we don't have a Dog in the fight really with trying to make sales look better or trying to make operations look better or

13:39Any other department in the company and we're already sort of we are the Arbitr of truth and what comes out of finance is uh, we have to Sign the the financials and and stand by your financial reports and I think and we have to have financial controls that are in place So I think for a long time we've already sat at the intersection of enterprise data and the internal controls And no other function really has to balance their efficiency against control the way finance does we got Process procedure we have to stand up to audit and we have the right mindset. We just didn't know it because Now it's not like if you're the cfo you're not doing a p and a r and if you're the cfo and you own data It's not like you have to do the engineering, but if you know What is in the realm of possibility and you have this process thinking and you understand that the data Then you're already in a place the same controls that we use for people are what we use for AI

14:41So if you think of the cfo is maybe the chief accountability officer That's the person who owns the tradeoff between speed and risk and we talk about that in the in the book alive So that that how to do it is not Oh, you need to go back to school and learn how to become a developer you need to understand What I go back to process thinking again and under understanding all the controls that are in place and applying those and then A fund that you do need a fundamental understanding of the data universe and what works and what doesn't and In AI what works out between when we talk I'm not going to beat this horse, but the deterministic versus probabilistic and where it makes sense to apply it and and to manage the the whole function Including the the data side and it it does take more work, but the idea is as AI and automation are deeper embedded and become a bigger part of How your team runs then your team can spend more time and it might be re-skilled or upskilled or restructure the team

15:41To guess what we do have some engineers in the finance organization now who are building around this so that it's not just You know Bob and AR vibe coding an app that not really sure what it does under the hood or whatever But that we can start formalizing some of this so it's a shift and change is always hard and I will now working with clients right now The change management is to me though a harder part than the technology itself You know it's funny. You say that isn't it almost always the case? Don't projects in your opinion I'd love this whether it's an ERP implementation AI FPNA tool CRM whatever it It might be Any big technical change isn't the change management almost always the hardest part you know what I've really noticed whether and I'm thinking about specific clients I've worked with Whether you're doing something where you're migrating a massive database into a new platform and you're trying to put in Automation around that and the semantic layer around the data so that other people can query it you start talking to

16:48GBAs and in folks that were in charge of the old Oracle database who say wait a minute My job is based on I'm the only person who can pull this because I've got a query that's You know 7,000 words long and I know how to pull this and I know where everything is and that's my job security right there So now you're saying anybody with a chatbot in an MCP account Into the system can get the data then what does that do for me? And that's the same with when I talk to people who do very Manual processes. They're like I'm not going to tell you my process because then You're I'm going to go away. So the change management. I think is wrapped up and in To your point, it's not just AI It's if we're bringing in a new ERP. Well, one well with with an ERP. It's different It's kind of like you can I have to learn a whole new system I'm this thing's a piece of junk, but at least I know how to kick it in the right place to make it work Yeah, at least it might go junk. Yeah, so it is feeding. Yeah Yeah, and change change is hard and people like to just be in that groove and when you're in an era of

17:51Rapid massive technological change like we are right now I don't think AI is going to take all of our jobs But if you're not going to learn AI Then you're not part of the program. I've got a whole story It was in my first book about the guy who was this was in the late 90s with my my first employee was keeping a paper literal Yes, I remember that story from the book Yeah, I mean in that guy didn't you know he was he was older at the time, but uh, you know He kind of aged out of where the world is he had one of those big old CRT the big square monitors on his desk Was never even I'm he was always just in his ledgers and reading facts at the set Could you imagine trying to survive a job a business job that way today? Yeah, I mean that's just it. It's yeah, it changes tough because we like to do what we do and clock out and go home and not think about work anymore But I don't know I mean all you can you can either ride with it or put your head in the sand, but if like being in You know post the dot com bubble and still never having logged into the internet or whatever

18:55So one of the key tensions that we've talked about is kind of that whole argument that really the cfo needs the lead adoption Right finances that function said it's kind of has to balance controls Against efficiency data. We already Have to if not own data We better have a seat at the table where we're making sure all the definitions the data dictionary Things are consistent across the company Otherwise, we're the ones that look foolish in the board meeting when nothing dies. Yeah, exactly. Yeah We're the ones look foolish. Yeah, you can't go out to investors if your numbers aren't aligned So there's a certain ownership you already have to have your argument is because of that because of efficiency You also kind of need to lead the adoption. So how does How does the cfo do that? How You know, I'm sure you give some guidance on how they need to think about it and leading this in fact one thing I know you talk about is your 98 pilot cycle the mgp some of those things But talk about if we got a cfo listening right now and he's like wait

19:56I need to own this and what's kind of the maybe that step-by-step? How should they think about this kind of lay out a little bit of how you talk about that in the book or how you think about it? Ever feel like you're going to market teams and finance speak different languages This misalignment is a breeding ground for failure In pairing the predictive power of forecasts and delaying decisions that drive efficient growth It's not for lack of trying But getting all the data in one place doesn't mean you've gotten everyone on the same page meet qflow.ai the strategic finance platform Purpose built to solve the toughest part of planning and analysis be to be revenue qflow quickly integrates key data from your go-to-market stack and accounting platform Then handles all the data prep and normalization under the hood It automatically assembles your go-to-market stacks make segmented scenario planning a breeze and closes the planning loop

21:01Create airtight alignment improve decision latency and ensure accountability across the team What's working out really well for me is so that this is AI time so everything is is Accelerated absurdly so I finished writing this back in January I guess and so now here we are heading into September the book's supposed to be out at the end of the month But so I started writing it last summer and putting together these frameworks What at the time I mean they were taken from other technology ERP implementations for example, we're part of it But a lot around setting up data systems and really setting up everything we had to do for data Is the same thing we do for AI with some some different wrinkles I'd written all these in the abstract but since I finished the book

22:01I've been doing these with clients so I actually am seeing these work in real time So rather than and I'm an agile guide to from from way back So everything is about make it the smallest Bite-sized chunks that we can have and let's knock them out one at a time in an orderly process So what I came up with in the book and I go into ROI and Running a portfolio of projects and all that but really if I boil it down into okay I've been mandated to go you know, I'm doing air quotes here do some AI Where do I start? So where you start is the same place you do if you're going to implement a new ERP or if you're going to Set up a data warehouse or if you're going to add and a new piece of software into the your tech stack in the finance department is How are we ready for this do we do we have a data dictionary? Do we know our source of truth for all of our data How stable is our data how accessible is our data? So the whole first step is just what is our readiness across our systems our processes our data

23:06What do we have documented where are their road blocks speed bumps whatever Metaphor you want to pick there? But where are areas that need improvement and that might Hamper AI rolling it out and you have to look at it more I think it starts with the actual audit of what the people are doing and and where the people are running into problems because Those are exactly the areas that you'd want to apply AI So if you can solve for that you're going to get some quick wins and that change management becomes easier too But I'm I'm jumping ahead a little bit. It's but it's understanding This is where we are today and I want to say in this readiness part go ahead and define put numbers on our closed process now takes 13 days We want to get it down to seven our You know reconciliation takes this many hours for this account or what you have that that baseline so that when you start putting AI in You have something to measure again. It's amazing. How many people start an AI project?

24:06But they never did that baseline and then they want to come back and say well, there's not really ROI on this It's like well, what are you comparing it against if you've taken the time to say This is where we were before you would have a much better idea than just by going off vibes to compare it So the first step is is determining that readiness and sometimes it helps to have an outside person come in and do the assessment because People get blind to their own this is the way we've always done it and that change management piece and the existential threat around AI comes in But if you can do it internally, that's great. Just understand the baseline. Where are we today? What do we need to do before we can bring in AI and then when you after you've done that you can know okay This is where an area is let's come up with some pilots What could we try to throw AI at and see if we can fix it but don't boil the ocean at once Don't treat it as a single okay, we're doing AI now. That's one project everything you do whether it's variance commentary AP automation, AR automation Close you record whatever you anything in the finance and accounting process that you're going after treat that as a standalone project

25:12So they can be done with that like if you go in and say okay, I'm going to automate the close That's our AI pilot that we've picked the wrong pilot. That's a massive process You might be able to bring it in later and have AI do parts of it But don't start there start with the ones that are easiest to access and then and go through and work those Compare them against the baseline that you have evaluate over time I use a 90-day pilot gather evidence as you go and then at the end of the 90 days There's a decision gate. Do we kill this project? Do we scale it based on evidence or do we maybe pause it and come back to it because we realize the AI models aren't there for it yet so Readiness thin slice delivery Evaluate the evidence have a decision gate and move on and if you're looking at all these AI projects as part of a portfolio Then just like a VC investor. It's uh or any investor obviously You're going to have some winners and losers, but then you don't sink the whole plan because not every project wins

26:13So it's really just about Looking at the process and having an organized way to go through and do it So I get it so the framework kind of I'm hearing is You it has to start with the readiness assessment and that is really two things You got to understand your data and the readiness of your data and you need to understand your processes Right and then from there as you decide on what to Pilot or where AI would apply you got to treat each of those as kind of a portfolio And you really want to start small and test them And then you know, so you've done your testing You're finding certain things that work you then move into an implementation phase of kind a how do we now make this the new process is that kind of the The step three or you kill it because you found that I'm like you said the model is not there so you kind of then have to have a go no go from that pilot It feels like a lot of companies kind of get stuck there. I've heard a lot of this pilot help So to speak kind of like excel or anything else where

27:16You're really struggling to get out of that. Why why do you think that is any thoughts there? Yeah, I mean It's it's really it's trust to me because it may work and if you it could work 98% of the time or you could have a five-nines, you know This is great, but we all have heard the horror stories about AI hallucinating and if you don't If it's black box AI and you don't have like an agent harness or guard rails around it and you're just relying on the black box Or maybe you're not maybe you have you do have all that in place and you still think I'm the one signing the financials here. It you know, it's my name on the line. I can't go to the auditor and say well That's what the magic bot said so it we are in trust and it's or we're in You know trust but verify I guess some people haven't even gotten that far But even I mean I'm as big an evangelist as there is out there for the use of AI but there's like I have AI will go through and

28:17Create invoices for me and can draft emails for me But I never let AI click sinned on any of that stuff and I in you know, and I'm I feel pretty I in it in as far as the invoice creation It's been months since it made a mistake creating an invoice. That's still though I want that human in the loop. So I think everybody's kind of there and people are at different levels of How much they want AI involved in what they do and how much they're gonna trust it to do but it's and I'm trying to think I'm trying to come up with an answer of earning that trust and really It's just gonna take more time and more reps to get it But that's what it comes down to even so you go through that and you've got the evaluation and it works perfectly But people are still scared to flip that switch and say okay now it's in production This is what we do so I have clients that I've that have been running parent what are Going back to March that have been running parallel process like the AI does it But they're still doing it manually every month since then checking it and it's going this particular client

29:17It's going really well But I think they're just not ready to let it go and I get that and to me it's it's more similar to hiring a new employee and maybe Maybe like you hired A the brightest employee that you've ever hired before but they have zero actual experience and that's kind of how we're like It's gonna take a while to earn your trust if they get one thing wrong Especially a big thing wrong early on they may never But we're calm that you know It's funny when you mentioned the trusting I was talking to Someone in this space who's done some training and you know runs a business and he's like I have AI doing my books And doing almost all my accounting and invoicing that he's like I don't think I trusted out a big company And it was interesting and he goes I realized it's kind of like ironic or at no fees the word have a critical that like I use it with all mine But mine small You know and so there's definitely that element that's hard to overcome with the trust And so what do you say to those that are struggling with the trust

30:22Any what what do you tell them like look you just have to turn it over at some point and continue to verify is that the answer or what do you tell them I guess this is two separate ones, but they're they're in lockstep Big themes across the book are governance and accountability the real way that you earn that trust is having an audit trail If you're if it's not just this happened in a black box and I don't understand what happened So all the agent harnesses that I'm building right now that I would advise anyone is the Building audit trail see and then build in human in the loop gates too So it'll go through and it'll say okay the system I should add one open second go more example, but the system pulled this data from this system with this timestamp It did this transformation to it applied it here the AI then Evaluated here's the prompt that it got here's the response that it got here's the timestamp Then it went to this human in the loop they either signed off on it or rejected it or edited along the way

31:27and then This is how the math was done not by AI but by this deterministic part You know by code because we know we have a data dictionary We met the threshold at the readiness point that we knew how to tell the AI and how set up the agent harness so that we're all Using the same data dictionary for our KPIs or whatever metric we're reporting and then when you can see Step by step what happened there for every transaction that goes a really long way towards building that trust So I wouldn't say I'd never advise anyone well you just have to trust it It's been going for six months because If you yeah, it's gotten the right answer But if there's a lack of clarity around it And you don't understand where that answer's coming from I mean how are you ever going to trust that? So it sounds like Even with AI it goes back to It's one of things that's made excel the spreadsheet stand the test at time You can audit it. It's transparent

32:27You can see what was done now does that have a great change log in the sense of when every change was made no AI can allow that so you need to build into any of your processes to help you overcome that hurdle You need to make it as transparent as possible You need the harness you need the the looping that we talk about the multiple reviews and So it sounds like the better you build the process the more you should be able to trust it Which would be true whether it's AI or not right a good process As to see if I'm going to be a lot more comfortable about regardless In fact a bad process in some ways I almost want I don't know if I don't want to say I want the machine to do it But at least I know it will quick and I know it's going to do it to say not with AI But generally you know it's going to kind of do it the same way right versus the human you never know so It's funny. Yes, there's always a one human, but I guess it's just it boils back to good processes that what I'm hearing Yeah, and this is the This is the tough part right now on those processes So when I go in and work with science will you will pick you know a couple of examples

33:31I'll say give me the full SOP give me the source files and we'll go And I'm not shilling for one model over the other But I think everybody knows and finance. We're kind of kind of everybody's using Anthropic these days But this company I'm working with now they pulled a couple of finance processes and we were thinking Let's see because it's a bigger project. They're going to have a data warehouse and it's going to Be a lot more automation that happens we'll call this the top down Sort AI implementation But until that's done we're trying to do bottom up AI implementation where I look at AI It's the equivalent of giving every employee their own RPA their own robotic process automation, but instead of having to go Do the setup there whatever you just talk to a chatbot and you build a skill or whatever to do this But there are limitations around that but where the process is getting I think it's okay And this is what what tell my clients too, and I talk about it in the book If you have employees who are thinking About their processes and they're working to automate their homework

34:34just using whatever Company-approved tool there is that's out there and that it may be you know with your connectors and connected to MCP servers and connected to different parts of your Text stack and maybe into your dropbox or share point or wherever your files are saved and you're And those employees are thinking they used to just do this They may not have had their process documented anywhere for them Now they're having to think about it because they're training the AI on it and You'll get some level of results from that and it may not be as consistent But because they're going through and doing this work right now Those are the roadmaps you'll use for the things that the employees can't do themselves And that's where you do the top-down implementation where okay all this we've we've mapped it out We've written SOPs for how to do each of these tasks We've been doing them in our desktop app for whatever AI tool we're using But really this needs to be a set in stone formal process that is actually connecting to our Data warehouse into whatever source of truth and all that even if we're not there today

35:37The work that we're doing to document these processes and to see what AI can do at that bottom-up level This is setting is up for success when the company is ready or when our software Our software providers may start doing a lot of this for us or The frontier labs themselves may come out with a tool that lets you do more of this without having to Code anything, but you know if there were a drag and drop way like you know N8n version 2.0 that you could build an agent harness That you'd have employees doing doing that more that's helpful You know, we have about 10 minutes left here. So I want to cover you know a couple other things in the book So we've talked about why finance should own it We've talked a little bit about how to think about the pilot cycle You know, it really comes down to documentation the whole trusting build building appropriately What's your view on companies the whole build versus buy Right, this is an age old discussion. We've had with software since the computer came out in the 60s

36:40And it's not going away, but what's your take when it comes to AI build versus buy? It's the same as with Every other build versus buy tech decision. There's I think in the book. I have it build by partner It's rare honestly. I love partner as an option And I think because I've been in smaller businesses It's been easier to work with other smaller businesses and do some of that partnering But a lot of times big businesses Partnering can be difficult. So we'll just we'll for this conversation. We'll just talk about build versus buy the whole argument for outsourcing and for using AI in general We're not to say I any kind of automation would be Our company was built to design and manufacture whatever this widget is That should be our area of expertise. Everything else is a cost center if it's not immediately dedicated to the Design and deployment and sale and of that product then why should we focus there that said companies get to

37:40a scale and There are all the advantages if you actually own something especially in an era where the data itself Is the fuel for the whole AI boom and if you build it you own the data you own the construct and all that But trying to determine Okay, do we really have the resources and if you're a manufacturing facility that has great people in that industry But you haven't needed a data team or really a data warehouse beyond whatever's in your your tech stack Then that's not going to make a lot of sense for you to Build your own AI tool on the buy side technology is moving so fast if the time to be able to Use the tool from something that someone else has already designed is pretty nice but If we are really in another and we are definitely in a bubble around AI and who's going to Survive and who's not but the companies that are out there and now there's so many startups right there out there right now and

38:42If are you going to if you're an enterprise level company? Are you going to trust to start up to Handle your data and to be around and all this it's sort of the it's everything. I'm saying here is the same debate We've had for any sort of software decision going back Decades it's just it seems accelerated right now. So the same rules apply just an accelerated cycle. Yeah So often you know, I laugh as you talk about of this right I learned everything I needed to know like in life in kindergarten It's just you got to figure out how to apply it to new technology or new situations It's like you know so many of these frameworks different things there They're the same with a twist and the twist now they I kind of sounds like so all right. We got about five minutes left here two things first What's next you've written three books now you did the one on kind of just tech and automation in general You did your AI book on here. Let me help you master AI really understand it now you've written the AI ready cfo You hit it. There's a fourth book. So what's next? Yeah, so the fourth book is gonna be a technical manual and I don't I don't know

39:47What's wrong with me Paul? I've got I'm busy But I am passionate about this and the first book was for I'll call it middle managers and up and it really it was someone who wanted to be Data first and their organization but was in some management level where they could impact change the second book was just Let's look at the whole industry anybody at any level could in finance could benefit from that this book is for leadership but there's gonna be a Requirement for re-skilling and upskilling for the people actually doing the work So my fourth book is gonna be on building agents and it's not gonna be theoretical It's going to be we're going to use the anthropic agent sdk and we're going to build agents that work in finance and more important than what we actually build in the book is going to be this mindset Is what you're going to use? Because this is this is the future and so if I could come out of the

40:50Heady strategic CFO level and get down to I think there's an opportunity for a pretty big impact because Think about if you're an fpna analyst who wants to move forward and you're graded excel and you're great you have analytical thinking and graded bi But you want to set yourself up for the future I think this being able to build agents yourself would be a big step in that in that direction got it So this is really going to be a hands-on technical book to help people versus A book where you're talking to leadership and maybe it's a weird little flex on my part because Yeah, I was my first CFO role was like in 2007 and so I've been Technically out of the weeds, but I'm also enough of a nerd. I remember being in junior high then Yeah This is like it's like a weird flex for me because I want to it's kind of like I'm sure you you have this urge sometimes Or it's like let me show you what I can do in excel well every day you I'm sure you have that that are your moments

41:53I have that urge especially if I'm walking people going Trinjie yeah, yeah, so that so this book is kind of me saying I'm not just talking this stuff I'm I'm building it and I'm going to show you how so I'm a little bit excited about it in that way I just I guess I'm just not going to sleep for the next six months while while it gets written Well, let's let's face it sleep is overrated. I mean that's the bottom line. All right, so as we wrap up here You got just a couple minutes left if somebody's interested in getting the book How do they get hold of it when does it come out where can they find it? So you can pre-order now and it's at Barnes and Noble books million amazon all your favorite I have talked to the publisher and everyone always says amazon it's that sort of you know Just monopoly power almost that they have but it is it all your favorite booksellers It is available for pre-order now September 29th is the date that I'm working from I did see that amazon had a couple day delay on that but Sometime in the last week of september first week of october at your favorite bookseller

42:58Now that we'll put a that we could put a link in the show notes make it easy for them We definitely will put a link in the show notes on this one and You know Glenn thank you for giving me the opportunity to interview you and listen to your sultry sounds today It was a pleasure to Get to ask all the questions and honest serious note. I think it's awesome that you've written another book I think I'm sure it'll be really helpful for people and excited for your Next book on agents because that's where the world's heading whether people like it or not They're becoming more and more important. I'm sure we'll be talking more about them on the show But thanks again. It was a lot of fun any parting thoughts before we wrap this one up my parting thought is gonna be a question for you Mr FP and a guy When am I gonna be interviewing you about a book that you've written? I almost said one hell freezes over but I keep toying with the idea. I just haven't been able to bring myself to find the time So I'll ask you real quick since we have one more minute

44:00What should I write next my my training partner? I've tentatively committed so I probably got to write a few chapters She's doing a storytelling book and wants me to do some chapters on data visualization So there's that one the other I thought of doing is writing a book of what I've learned from podcasts So one on fpna maybe one around technical and soft skills and what leaders have said and sharing the key insights I've gained I thought of doing one on my financial modeling What should I write you know me pretty well Glenn if you're to tell me to write one book What do you think would be my book the compilation sounds interesting and I guess it would come down to how much you want To make the world listen to your ideas versus how much you think I could really be an aggregator because I've talked to so many brilliant people And I could bring all their thoughts together. So for me. I'm always I'm the Old man yelling at the clouds So I'm always just like we're gonna listen to me and what I want but you do have I how many hundreds of podcasts? If you recorded I've done over 400 I own the content to

45:03Probably 340 there is a wealth of knowledge out there that you've got and you've taught you've had some incredible guests on the show too So that would be interesting and you could you know the great editorial that editorializing ability is you pick the pieces you want and you can craft an narrative based on What you remember from the conversations and in what you want to drive home so you can use those bigger voices to get your idea across I kind of like that one already well I appreciate that when like I said when it freezes overall get started It's already fall so winters not far behind so you never know But thank you again for letting me interview you and maybe one of these days will have you interview me on something I don't know we can you can interview me about how I have no clue on what I'm really doing with a I mean Did I admit that don't don't listen to that my folks all right Well, thanks as always we appreciate you listening and we just ask If you stayed this long reach out and let us know what you think we'd love to hear from you

46:05What you'd like us to cover in the show how you like it leave a review you could tell us I like it you suck Glenn's great Paul's boring whatever we none of it will offend us so on that note we bid you a do Thanks again Glenn thanks Paul 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 overlords be with you