Empowering Fractional CFOs with AI and Automation for Better Financial Solutions with Joe Manganelli

Welcome to the Future Finance show where we talk about robots and how they will come for our jobs.

Even really smart, talented, capable people that.

Don'T make it even the best hitter in the world maybe hits one out of three.

Before the show, you mentioned the importance of walking before running.

I think about this walk before you run and I think about it in kind of three layers. One is just like continuing to look at and adopting tech tools. Second, that kind of leads into team and culture. The third point, think of yourself as a leader.

Generative AI technology is going to filter.

Its way into all businesses, companies adopting agents to develop solutions for them. And that's ultimately what we're going for.

Generative AI is primarily most people are using chatbot. When do you see that changing?

There's levels of human judgment. So I think it's going to be an incremental step. It's not going to be all at once.

I mean the reason that it's not more proactive is because people having to go to.

Future Finance is brought to you by qflow AI, the strategic finance platform solving 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 AI. Foreign welcome to Future Finance Monologue this week. This week the monologue is by Paul Barnhurst. I've been thinking a lot about how people are using gen AI. I find it really interesting what we're seeing being the dominant use cases. As it started, everybody thought, wow, it's going to take away all the mundane. It's going to take away all the processes, the data cleaning and allow us more time to think and be creative. In reality, in many cases we're using it for creativity. Been really interesting to watch how the majority of us are using it. So I found a Harvard Business Review article. I did some Google searching and they did a study and I just want to share the top six items they listed. They put it into categories. 23% Technical Assistance and Troubleshooting 22% Content Creating and Editing 17 Personal and Professional Support 15% Learning and Education 13% Creativity and Recreation 10% Research, Analysis and Decision making. Really interesting how they're using it. We're learning. We're using it to educate ourselves to be creative for content creation, for editing. It shows its broad use when you look at those themes. Then they said what are the top areas people are using it for? And they did several. But here are some of the top which I found very interesting. At the top is generating ideas. Definitely not what we thought when this first started, everybody thought it would take away the mundane, automate the routine tasks and the technical. But the number one thing we're doing it for is what it's supposed to free us up to do. I mean, it's great, but it's really interesting, right? It's generating ideas. You know, number four, editing text. No surprise. That makes sense. Drafting emails at the top. Excel formulas I found interesting. One of the top ones was making complaints. So what I want to challenge people is to find more ways to use Gen AI to help with things like Excel formulas, which is number 14. You're challenging your assumptions in a model, using it to improve, maybe to give you different ideas for scenarios, for sensitivity. Let's see if we can use it a little more technically. Maybe help us build an algorithm for a model. That would be my challenge. We're great. Many of us are really good at using it for summarizing stuff. I use it for my podcast all the time to summarize transcripts. I use it to help with content, but I could get better in the technical area. That's my challenge this week, is start thinking about more ways beyond what you're doing and find one new way to use Gen AI. And if you're not using it, find one way to use it. I hope you enjoyed this monologue. I'm going to keep it really brief and short this week, but I just want to share a little bit of how people are using Gen. Feel free to email Glenn and I, or to comment on our LinkedIn post. How are you using Gen AI? In what ways are you using it? Until next time, thank you for joining me. The FPA guy signing off.

Hey, guys.

Traveling this week, but wanted to reach out to everyone from New York, where I'm promoting the launch of my new book.

So thank you for tuning in to future Finance Regular listeners know that in.

Each episode of this podcast, we explore the ways in which AI is transforming the financial landscape. And today I'm excited to share some insights from my new book, AI Mastery for Finance Professionals. So please forgive me the shameless self promotion, but I believe this is an important topic. Important enough that I wanted to write a book about it. So I want to talk about why I wrote the book. I truly believe that a deep understanding of AI is no longer optional for those of us in the finance world. It's essential. As finance professionals, we've always valued our ability to analyze, question and understand the tools we use, and AI should be no different. The finance industry demands accuracy and trust and we need to ensure that AI is being used responsibly and effectively. This book is designed to empower finance professionals, regardless of technical background, to confidently engage in AI projects within their organization. AI Mastery for Finance Professionals offers a comprehensive and accessible roadmap for navigating the AI driven future of finance. I start the book by demystifying the fundamentals of AI. I break down concepts like machine learning and deep learning into clear, understandable terms. And then we move on to specific AI techniques used in finance with practical examples and case studies to demonstrate how these technologies can be applied to real world financial tasks. This book isn't just about theory, it's about equipping finance professionals with the knowledge and insights they need to lead AI adoption and leverage it to drive growth and efficiency. So here's just a few of the topics we cover. First off, we talk about AI techniques in finance. We talk about machine learning, deep learning, computer vision, natural language processing and how they're being used in finance today. We also examine real world use cases across various financial domains including trading and investment management, risk management, fraud detection, customer service and corporate finance.

And we also talk about how to.

Implement AI in financial institutions. This section offers practical guidance on developing AI strategy, building AI capabilities in house or maybe partnering with vendors, and measuring the ROI of these AI initiatives and establishing effective governance and risk management frameworks.

And we also look ahead to the.

Emerging AI technologies like explainable AI, federated learning and quantum machine learning and discuss their potential impact on the industry as well. By understanding the algorithms that power AI, the data it relies on, and the assumptions it makes, we can move from passively accepting its outputs to actively shaping its applications. We can mitigate risks, drive innovation, create more precise tools, and find new ways to integrate this technology into our daily workflows. This book aims to elevate you from an AI novice to an informed user, enabling you to understand the transformative power of AI, grasp how this technology is revolutionizing financial processes, improving accuracy and driving data driven decision making. You also learn how to apply AI techniques to real world financial tasks using examples and case studies and develop strategies for implementing AI in your organization, building capabilities and measuring the return on investment of AI initiatives. What I hope this book does is help readers stay ahead of the curve. In addition to explaining the fundamentals, we also explore emerging AI technologies and understand their potential impact on the financial industry. AI Mastery for Finance Professionals is more than just a guide to AI. It's a call to action for finance professionals to step up and help shape the future of our industry by Deepening our understanding of AI, we can harness its power to drive innovation, efficiency, and success in this new era of finance.

Today on Future Finance, we are joined by Joe Manginelli, the Chief Revenue Officer of Amalgam and founder of Calculate, a finance and accounting consultancy for startups. Joe has a strong entrepreneurial background with a passion for streamlining financial processes and modernizing accounting through technology. His company, Amalgam, started as a simple Excel macro, but evolved into a comprehensive platform that integrates with major financial tools like QuickBooks, Xero, and Shopify, helping businesses of all sizes automate and simplify their financial management. We're excited to dive into Joe's journey and learn how technology continues to shape the finance function. Joe, welcome to the show.

Thanks for having me, guys.

Yeah. So, Joe, I'm excited to have you on. We actually started our business relationship through my day job, and I got a demo of your product and I thought, this is amazing, and it's right up the alley. So I appreciate you coming on to talk about Amalgam. So for those in our audience who haven't, like, I have had the benefit of a demo and seeing the product up close, tell us what Amalgam is, who the target user is, and kind of how it works.

Yeah, for sure. Well, thanks for having me on, guys. This is great. And yeah, I guess I've refined the pitch enough, Glenn, to have you be impressed in other ways to have me on. So thanks again. But, yeah, Amalgam at the highest level is really a tool that allows teams to access their financial and business data more easily. The biggest use case for us is sort of the accounting finance team aspect of things. So that's where we kind of come out of. It was born out of a previous company of mine, Calculate. Well, yeah, we were. We were writing kind of macros and things to that. To that level and sort of expanded it. So, yeah, so kind of the core is with finance and accounting teams and really drives us sort of reducing the amount of time to close books, produce financials, do analyses, and really get analytics and thoughts around the financial side of the business.

Thanks for that. So I'm curious, kind of Glenn mentioned in the intro, we know a start is a Excel macro and it built into a full platform that it is today. So kind of what are the challenges you faced? How did that evolve from a macro to a full platform? And what were kind of some of the. Maybe the key steps and milestones in that challenge?

Yeah, for sure. And I almost maybe break it out into two halves. I think building a Product is sort of just something simple that we were using on the day to day. Like I mentioned, had this company calculate we were the outsourced finance and accounting team for emerging businesses. And it really just was driven by the fact that we noticed there's too much time jockeying data around, downloading CSVs, connecting to different systems and things of that nature. So we wanted to kind of reduce that sort of non value added time and be using our brains more. And so one connection here, one report there and it sort of built itself up. So I would say from a sort of like technical product standpoint, I wouldn't even say there were a ton of challenges other than, you know, just getting the right API connections going and things of that nature because we were just driven by the work we were doing on the other side is, you know, kind of converting a product that you were using yourself into something that you could sell to others and our primary customers, accounting firms and finance organizations. And as you know, I'm, I'm, I'm taking a tool that slightly changes your process and asking process driven people to embrace it. And oh by the way, they, they're sometimes a little cheap when it's not their money. So, so those are some of the challenges on the sales side but from a technical side we just really let the work and sort of the core stuff that we do drive the need and you know, finding the lowest hanging fruit and just building from there.

Got it. And so sounds like this really came out of doing advisory services and helping startups is where this tool came from. So is the focus finance departments, fractional CFO? Do you deal a lot with fractional CFOs then with this tool?

Yeah, yeah, I would say the kind of two core are fractional CFOs, fractional accounting teams as well as just Intel CFOs and accounting teams as well and probably more in maybe the startup space and others. But a lot of those teams just have such fractured data sets that they're dealing with different Systems, different logins, MFAs you're dealing with who's getting a code texted to their phone. So I can log into this or to that and so really being able to consolidate that, allowing you to be system agnostic. So if it is an outsourced accounting team or a finance team, you have some clients are on QuickBooks, some are on NetSuite, you can really use the same processes kind of regardless of system in that case.

Yeah, I'm sure on the outsourced you run into pretty much every system imaginable.

Yeah, yeah, yeah, yeah.

So one other question, kind of shifting gears a little bit. I noticed from your profile you started at the beginning of this year your own business investing in companies. What has that experience been like so far? How, how did you get started in that?

Yeah, so, so mostly just like I was, I've been dealing with entrepreneurs as sort of their outsourced accounting and finance team. Got that bug obviously just through my work and starting a firm, but then also working with others and then, yeah, I think mostly I just like love that part of building a company, testing hypotheses. I think it's, you know, even more interesting these days with sort of, you know, all of the AI push and sort of every, every early stage company. And I can talk to a little bit this if you want around, you're seeing companies that are, you know, really driven by AI as a core function of it, or maybe they're on the opposite end is it's a total hedge against AI because there's so many unknowns. And if they're not doing AI things today, is it, is it something where they are, are thinking about, hey, let's go win part of the market and then develop the product and expand it a bit more. So. So we're seeing a lot of interesting things on that side, whether it be investing or sort of starting up things from scratch.

And I'm curious, you have a general philosophy when it comes to AI and investing. Like, are you looking. Do you think it's all going to be the big tools that win or what do you look for? Because we're seeing so much out there and it feels like. I don't know if you agree with me here, Glenn, but feels like a lot of these products have kind of been rushed to the market. Like they're not. Definitely not mature and a lot of similar things. How do you kind of cut through that and think about AI when it comes to investing? What are you looking for? What's kind of your philosophy?

Yeah, I think it falls into those kind of three buckets I was just mentioning. I think one is just like if you want to call it AI, agnostic. So I've invested in an Irish whiskey brand. Right. Like, I'm not too worried about the threat of AI in that department. I think kind of the middle there is maybe tools that are certainly clearly going to be enhanced by AI as we go forward, but you still need to establish a market. Like the market kind of becomes before the product to a certain extent or development of the product. And so working on starting up a business that helps individuals get second citizenships that they're eligible for. So not a super complex thing today, but like that's just, you know, as we get that business up and running, like an LLM can just run with that. Right. Like, there's not something that, that a human really needs to be laboring over. And then I think the third bucket is kind of like the more intense AI stuff. So working on a business that's sort of looking at instructive direction basically for sales teams and getting that on an individual basis. Right. So sort of a little bit more cutting edge, really interesting stuff. But I think it's tough to say kind of where it goes. I think, you know, are we going to go more of the, like where software was, where kind of we're building all custom solutions, or is it all going to be kind of agents and you're just, agents are out there and you can pay for an agent and you just sort of customize things yourself? Right. Like, it's tough to say. And I think that's one of the difficult things about as like kind of an investor or builder or business is like if you go all in on the customized route, are you going to get, you know, beat up by really larger companies with a lot more resources and they're going to customize better than you can with better data or on the other side, is it, you know, you're going to do a bunch of work, maybe even get some market share, but then a couple years down the road, everyone's just using agents to do all the work that you just customized. So it's a tough space and I think, you know, kind of case by case basis. But that's the kind of exciting part about entrepreneurship. Right. Like you're taking a chance and you don't know exactly where it's going to go.

Yeah, it's always a little bit of risk or shot, I wouldn't say shot in the dark, but that risk and that you just never know exactly where it's going to end up. Right. You have your hypothesis and only time will tell if your hypothesis is right or not.

Yeah. And I, and I think, you know, we, we have all these anecdotes or these stories of these really successful ones and I don't know the numbers, but just to make it up for one and every one of those, there's a hundred of people who made a bet and it failed, or even really smart, talented, capable people in sort of a more cutting edge, higher risk area. There's just a lot of don't make it.

Well, I mean like you just use the sports example. You take baseball, right? Even the best hitter in the world maybe hits one out of three.

Yeah, yeah, yeah. And you're. You. I've got a big. You've got a big baseball fan here. So. So definitely. And then you've got other guys who are, you know, if we're going to be really specific, like a guy like Kyle Schwaber who makes, you know, tens of million dollars a year and he's up there less than 20% of the time, you know, getting a hit.

So I just want to be show Ohtani make what he makes. I'm a Dodgers fan.

We all do.

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So Joe, you know, for viewers who are, this is an audio platform so you know, they're not getting the benefit that I did of looking at your product and seeing how it works. So I think that a description of, you know, if somebody's trying to picture amalgam, they're probably picturing this software interface and you know, trying to think of what it looks like but you know, seeing the demo, this is just connections and you're actually working in Excel and so I want to talk a little bit about that, but also something that I think is. Can be easily overlooked, especially when you're concerned like with fractional CFOs in accounting, is that you're connecting to all these platforms but you're not actually storing any of the data. So I guess kind of a two part question here, if you could talk a little bit about the. Just the Excel interface and how people work with the data, but also about the security and efficiency benefits of the approach of not saving that data, just passing it through, you know, especially in data sensitive environment.

Yeah, yeah, for sure. So the first One is really on the side of like kind of the interface. And, and I think a big part for us is like we wanted a, we wanted a product that people can pick up pretty easily. So building it within Excel makes a lot of sense. Second, you can integrate it with the things that you're already using. So if part of your data isn't available or if it's some sort of user generated piece of information that can be incorporated and we can still do the different connections. And then also just like accountants are great folks and finance people are great folks, but like they don't always are ready to adopt a certain interface or learn something new. So to be able to just plug into what they know, we'd be to get a little bit more market share without having to ask them a lot to learn a lot of things. So really you think about it. On one end we're pulling in data from a certain source. We're able to put it in Excel if we need to manipulate it. You can manipulate it kind of on the terms that you're familiar with and then on the back end be able to load that into another system. So pull data from Shopify, manipulate it, and then I'm able to load my inventory cogs and revenue kind of into the journal entry. So trying to keep it simple. And I think that's just a broader theme for us which leads into the data side of things. And so, you know, everyone's concerned with data for their customers, but we have our customers clients to worry about as well. Right. So, and then they probably care about that more than they care about their own data. And so for us, what we really tried to do is say, hey, we don't actually need to store this data. We can go out, you can use your own credentials. So the users within Amalgam are connecting their user logins and things to that nature, using their credentials to connect to that Shopify, for example, and then we're just instructing their machine to go and copy and save that data into that Excel file. So our code has the instructions to do that, but not actually capturing the data. We like it for a couple of reasons. One, it's a no brainer from a data security situation. So when I'm talking with a potential customer, like we're not spending much time at all there, okay, get it moved on and it gets through their IT teams really easily. And also it's an easier technical problem for us to not have to worry about storing the data. Sometimes you're looking at hundreds of thousands of rows of Information that it just is not a problem from a technical standpoint. And it enables us to focus on building more of the value to the customer base than focusing on those distractions, if you will.

Yeah, and that simplicity kind of gives it a level of elegance, too. It's just like, it's, you know, no frills. It's just. You're in Excel, you're just getting the data much quicker and easier. It's right here. Once it's set up, it's just kind of set it and forget it, Right?

Yeah, exactly. And then other things like SOC2 audits and things like that are just way easier. Right. Like, we're talking about like, like, you know, so many different things than having to worry about, you know, where we're warehousing this data and who could have access to it and that kind of thing.

So are you just storing passwords and usernames or is there other data you have about each customer? How do I think about it? Is it really just kind of almost a tool? That's a connector. I mean, I know I'm simplifying, but. Right. A connector to a lot of different things that allows you to put it in Excel as your output.

Right? Yeah, that's pretty much it. And I'm fine to call it simple. Right. Like, I think we're always overthinking things and we're always looking for a sample. And I say this to customers, I say, hey, that dashboarding is the last 5 or 10% of your work. It might look nice and be fancy, but, like, how are you getting those data sets together and using them really matters. So I'd rather work on that first 90% than the last 10%. So, yeah, using that simplicity. And then we also have things built in that like, connections expire after time. We don't want zombie accounts sitting around and things like that. So we have those layers built in for kind of additional layers of security and kind of, you know, keeping a clean house, so to speak.

You know, so the. In the AI investment space right now, if so, you know, there's the kind of the rising tide lifting all boats. There's the AI generative. AI technology is going to filter its way into all businesses and make, you know, it's going to improve efficiencies, but trying to pick the businesses outside of the, you know, the frontier models, the foundation models, the OpenAI anthropic and, you know, all those outside of those, the businesses who are building on those. You know, it's interesting because I've been looking I've been in this space and I'm on an advisory board for a couple of companies and I've been an angel investor in a couple and you know, you look at them and you think, well, I don't really know what the moat is around this investment, you know, could, it would keep somebody else could from coming and doing this or from having open AI pivot and then just take over your business. But I don't think OpenAI is very clearly focused. They want AGI, they don't care about move, they don't care about, you know, going horizontal into different industries. If they get AGI, they can go into any industry. But so I, I wonder in the kind of, the way I've looked at investments around AI is the timeline is compressed. You're not betting on the long, for the ones that are, you know, outside of the, the big models, you're not betting on kind of the long term because I think the likelihood of any of these startups doing their own IPO at some point down the road, it's pretty small even factored into the amount of, you know, successful ones. But I think that maybe, I still really believe that the way most businesses are going to start interacting with generative AI in a more meaningful way than just going to the web based interactions with it, it's going to be built into the software platforms they're already using. So maybe for these startups, if they build something that is unique enough that instead of going their own route, they're going to get bought by an Oracle or by a SAP or somebody and brought in as the AI Genie or you know, whatever in, in that product rather than going, going it alone as a standalone. And it's going to be Joe, to your point, I mean it's, agents are going to be the future and I do think some of these startups are in a place where they're, you know, they're going to be able to build their own sort of agent frameworks as well.

Yeah, yeah. Especially in the B2B space, is it maybe more on the personal space? You know, we'll all have that like you know, assistant on our phone from whatever big tech has sort of developed. But I think on the B2B side for sure. Yeah. And I think you, you've raised a good point in the sense that making an investment in sort of transformative technology are two different things. Right. Like I could have a very unsexy kind of very practical business, right. Like, and everyone else can be just running circles around me from a tech perspective. But maybe I've carved out a specific market or I've solved a particular problem where I, you know, I can get bought out by someone maybe on an outsized valuation. Like I have. I had one client years ago in the accounting firm where they had a really small market that they just focused on and they had a really great solution and they were able to have a really profitable business that they ended up selling on an outsized amount to a much larger player. So, so I think that's part of it too. Like when we're, you know, when I'm talking with entrepreneurs or talking with founders and stuff like that, you know, being able to sit with them and say, like, okay, well, well, you know, the technology and the product are really great and they can be really product focused. But where does that fit in the market? And, and even, you know, from an investment standpoint, I kind of think about it like, you know, you think of, you know, product market people, and I really, you know, think about it in the reverse. If I've got really smart people and I've got them in a really large market, like, we'll figure out what product is, is the right approach. And I think, you know, thinking about something practical and not boiling the ocean is really, really useful from a, from an investment standpoint.

Before the show, you mentioned the importance of kind of walking before running when it comes to AI. So for you and what you're seeing, what are the first finance function should take to get ready for AI and kind of how does amalgam or how could amalgam fit into that strategy?

Yeah, no, great. And I love the little, like, layer in there for me to throw a commercial, so I'll take it. No, but, but, no, you've raised on, I think, a point that I've become really passionate about because I'm talking to a lot of folks. I think amalgam's a great kind of like, first level for that. But I hear vernacular all the time that tells me they don't necessarily know what they're talking about. I think they. There's a lot of these impressions that I get of, oh, when AI comes, then this will not be a problem anymore, or anything like that, which. Okay, that's one perspective. But my feeling is if you're thinking about it and talking about it that way today, in five years, or whenever that moment comes that you're thinking about, your organization isn't going to be ready at all. And so kind of I think about this walk before you run sort of thing. And, and I think about it in kind of three layers for an organization. And this could apply, you know, to an outsourced accounting firm, but really any organization, especially like internal roles and things like that for finance. But I think one is just like continuing to look at and adopting tech tools. Not every tool needs to be a huge lip. I think that's where we've been successful with amalgam. If it's a much lighter lift, you can sign up today and you're rolling. And so being able to kind of get the muscles like working in sense of like, okay, what are we looking for in this solution? What's a problem? Where can we find it? How much lift is there? I think that's number one. I think second, that kind of leads into the team and culture. So even referring to yourself as a tech enabled service, getting that on people's minds, that you're selling it also within your team, bringing those people in for those tech tools that you're adopting, how you're bringing them in and getting them to start the language and vernacular. If you're downloading CSVs, you're doing it wrong, right? There's so many instances. And here's your plug for amalgam. Like if you're using amalgam, you don't need to be downloading CSVs and clicking around and doing all that kind of stuff, right? So I think that's second. And then the third point I'd say is just like you as a leader of your organization or team or whatever it is, like think of yourself as a leader in this space. Like carve out, put time on your calendar, just carve out, listen to podcasts like this, right? Like think about things in terms of you being a leader so that you're able to navigate this and really build it up and be walking into that running as things develop.

Makes sense to me. Appreciate that. So I'm curious, you know, right now, today I think we talked a little bit about this, right? Generative AI is primarily, most people are using chat bots, prompts. When do you see that changing? I've talked to a lot of leaders who want to see Gen AI to be more proactive, to help alert us to certain things, to show up on our screen and be like, hey, did you recognize this? Have you thought about that? I would love your thoughts on kind of when do you think we'll be there, how we're progressing, kind of how do you see this unfolding A little bit Knowing that obviously it's a little bit of a guess. None of us will probably Be right. But we all have opinions.

Yeah, yeah, for sure. And this is an interesting thing you bring up because, you know, you go into Gemini and it's like, hey, do you want a recipe for a viral Marsala? And it's like, I don't like mushrooms. Why are we doing this? You know, like, you know, and the prompts are kind of humorous at times. And I, I think, you know, I think we're in a really interesting time of exploring that. I think a lot of it is also around human behavior. Like we don't just think of things, right. Like, we need to be prompted ourselves. And I think that's an interesting element there. But, but I think from a business aspect, it's just going to be what's really practical and, and you know, just to say it directly, the stuff that's replacing humans, doing it. Right. Like I'm Talking about downloading CSVs, manipulating data to become journal entries to load that. Like those are things that a human doesn't need to do. Right now we need the level of judgment to interpret that data and manipulate it into the right way. But in five years from now, that's not going to do that. Right. And we still need, hey, people are still using checks on one end, we're paying with checks on the other end, we're having robots do work for us. But there's levels of human judgment. So I think it's going to be an incremental step. It's not going to be all at once. And that kind of leads that reference to that walk before you run. But yeah, I think there's just, it's going to come down to like the more practical, tangible things rather than just the prompts about a new recipe that are on the other end of the spectrum, I guess.

Yeah, yeah, I've definitely seen some of those. Right. You open up chat, GPT or Google and it has these lists of the type of questions you could ask and they're like, doesn't help me, not interested. Why would I ask this? Where did this come from? And then you type what you want.

Yeah, yeah, yeah.

We, we've all been there and I, and I agree. AI, starting with the tasks. Anything that's very repetitive, that's task oriented, that can be laid out step by step. Those are ideal, ideal things to automate, whether it's RPA or generative AI or machine, whatever you're using. There are different types of AI to address that. But it'll be really interesting as we see it becoming more and more proactive. Right. We've seen it in some of the AI like Netflix. Here's the movies we recommend for you or hey, based on your web search, here's the ads. It's kind of just taking it to that next level it seems like to be and really integrating it into our business software in such a way that oh, we noticed this is due today. I put the draft together for your budget variance commentary, review it and add your own comments or you know, those type of things. Things.

Yeah. And that's where now I mean if you think about the way most people, unless you are, you know, an ML engineer or you have have a full team who's putting this together. I mean the reason that it's not more proactive is because people are have having to go to Gemini or go to Chat GPT or Claude to do it's not built into their workflows and there's, you know, co pilot is getting there slowly. This I was. They kind of oversold even their latest release in Copilot. I was pretty excited when I saw it was going to come out. And then I've played around with it some. It's better but we're still, we're still not there. But I think that when you're going to start seeing it be more proactive is when it's actually integrated into your GL or into your ERP or CRM or you know, whichever system you're using.

No, exactly. Maybe that's like we've talked a little bit before about like agents and companies adopting agents to develop solutions for them and maybe that's a vote for that. Right. Of like there's a lot of these customized things. You know, they'll be strong enough to be able to do it. I just need someone internally to sort of build that out and you sort of just get a sea of that. And maybe there's kind of two applications we're talking about. One is just like total replacement of a process and then the other side is maybe like supporting the judgment side that we're talking about. Right. Like developing certain structures that provide instructions or, or, or things to that nature of like okay, here's what's going to come up or here's what you're seeing, here's what we suggest and you're prompted to make a decision faster and sort of in a more powerful way.

Yeah.

And you know, to that point, one thing that computers are great at is variance analysis. So I think about. I've done a lot of demos and work around just looking at. So you close your books and Your monthly, you know, budget to actuals, loading budget to actuals in spotting those variances and those in trends and differences where it can do that kind of first pass. I think about like every time that, you know, first thing after the controller tells me the books are closed, I'm going through and looking at that variance report. And you know, it takes time to do that, but if you could immediately, once the books are closed, you proactively get that first round of variance analysis and it tells you what to do. So the step you have to take right now is you have to load it up into the LLM and then ask for it. But if it were in that workflow, it's like you click a button, the books close, and here's your first report with some charts and graphs and all that. That's. And that's gotta be coming soon.

Yeah, I think that, I think folks, some folks are already using some things like that. I mean, I haven't really seen it as much. And then I get excited about kind of the next layer onto that is like, okay, well how can we be predictive or instructive around that and, and pulling out other data sets, right? Like pull in macroeconomic information, pull in seasonal. What's happening, what are, what are indicators there? So then you as the CFO or, or whatever your role is, is taking, okay, how did the company perform? Great, that's my rear view mirror. But like what are my budgets, projections, ways the business I think are going to perform kind of in a vacuum. Or you're, you know, the old way of doing a projection. But then also pull in these other things of like, okay, well, what does sales actually look like with the seasonality and the way the macro economy is performing and consumer sentiment and things to that nature. And maybe you pull in information from your marketing team that's always looking at different things. What is their sentiment and how maybe there's a new product. Those types of things you're able to layer in and be, just, just be a lot smarter. Right? Like that's, that's ultimately what we're going for.

Yep. Future Finance. Right, Paul, that's it.

There you go. The future. I've heard of a show that's called that. Who came up with that name again? Glenn, Was that, that was you, right?

No, I think that was you. I think that all. Any, anything creative and good about the show comes from you.

It's probably came from Generative AI. We just don't want.

That's for.

Yeah, spe. Generative AI. We have our next section, so this is our kind of fun get to know you section, where Glenn runs it through a different AI model each time and comes up with a list of 25 questions to ask a podcast guest. And he's been doing it a few different ways. He'll elaborate on that. We have two different approaches we take here. I take the. You get to pick between one method of random and another method of random. So there's 25 questions. You can pick a number between one and 25, or I can let the random number generator pick a number between 1 and 25, and I'll ask you the corresponding question.

All right, I am going to pick the number just because I need to, like, maintain a foundation of humanity here. Like, we, you know, my brain. My brain is talking about codes that I'll never understand. And so, you know, so I'll pick my own number, if that's okay.

Go for it. What's your number between one and.

Let's go with number 21.

21. All right. This is a fun one. What's one song that always gets you pumped up or makes you feel happy?

Oh, man. So I maybe have an anecdote, but when I used to work at Goldman, and I was expecting maybe a particularly difficult day, my walk from the train into the office was a couple minutes, and I would listen to welcome to the Jungle because it just felt like that was like an appropriate, like, amp up song. It's like, okay, we're coming into the jungle and sort of prepare myself there. So maybe that's a good answer for you.

I like it. So we'll have to see if our audio team on the back end when they produce this, they can put a little welcome to the Jungle music in here for this question. I'm sure they.

We're gonna have to pay those royalties, though, Paul.

We'll have to.

Good point. We'll have AI do a karaoke version of it.

Yes, perfect.

Perfect. Actually, we should do that. We should get Udo or something.

All right.

All right. Yeah, yeah. All right. So, yeah, we were trying to keep this. These questions from getting. Getting stale when asking about them. But also, I have a tendency to just think so many personal questions are cheesy. So this, for me, this is taking the human out of the loop. And I'm just like, I don't. This is going to be a ridiculous question that you're getting. I'm not responsible for it. It came from. From AI, so. And I, you know, I've gone through and I've used. We were doing, like, A sort of a bake off between them. But I. In recent weeks I've gone and I'm just going back to chat GPT. It just seems to do the best with it. But I have, I've started uploading the bio of the guest instead of just having general ones. I've now uploaded the bio and said, you know, it's for this podcast and have it kick out 25 questions. And then I've got the human completely out of the loop here in that I just go back to my initial prompt and I'm. I'll tell it. Give me one to ask the guest. So. Okay, here we go. All right, here we go. So turning it over completely to the machine. The question is this is good. This is good. I don't know. I don't know what the answer is going to be but what's the most embarrassing thing that's ever happened to you in public?

In public. In public. You know what? I was thinking of one the other day and so we talked about baseball so maybe this is relevant too. But I was like a little bit of an overweight kid and I was. But I made the all star team one year like in sixth grade I made the Little league all star team. And, and I remember that like they do like the little intro where they call everyone out and I, you know, being the bigger kid was a little shy to begin with, like being over late and I like turned the corner out of the dugout and I slipped into a giant puddle of mud. And so then I had to like play the entire All Star game with like my whole left side just being covered in mud because it was a little bit of a rainy day and so you know, super happy to be part of the six year old or sixth grade all star team. But like you know, those are one of the things where like you're getting out of the shower and you're just consumes your brain for some reason. So that's. That'll be my baseball related anecdote.

So I love that because it. I can easily picturing that being a scene from you know, sandlot or Bad News Bears or whatever. It's like the, you know, just the one kid coming out and slipping in the mud. But that's great. Good, good answer. I don't mind would be probably way like I could probably have most embarrassing thing ever like in. In public every week, probably every episode of a podcast I'm doing something we.

Can start that as a new.

Yeah.

Glenn's embarrassing moment.

Embarrassing moments brought to you by Glenn Hopper and ChatGPT Joe well, this is great.

This is a lot of fun and we really appreciate you coming on and giving us your insights. And you know, great to hear what's going on with Amalgam and your thoughts on AI and baseball.

Yeah, well, no, this is great. Thanks so much for having me, guys. I love the podcast and super happy to be on and this was fun. Thanks so much.

Yeah, thank you Joe. Really appreciate it. 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. Sam.