Empowering Fractional CFOs with AI and Automation for Better Financial Solutions with Joe Manganelli
In this episode of Future Finance, hosts Paul Barnhurst (The FP&A Guy) and Glenn Hopper welcome guest Joe Manganelli. Joe shares his journey from creating a simple Excel macro to founding Amalgam, now an integrated platform for financial automation. With rich insights on AI in finance, entrepreneurship, and investments, Joe discusses the evolution of his company, modern finance's future, and why simplifying processes is often key to innovation.
Joe Manganelli is the Chief Revenue Officer of Amalgam and founder of Calculate, a finance and accounting consultancy for startups. With a background in finance and accounting for emerging businesses, Joe has deep expertise in building practical tech solutions for finance teams. His current focus with Amalgam is making financial data easily accessible, automating workflows, and developing tools that streamline the finance function.
In this episode, you will discover:
How Amalgam helps finance teams by simplifying and centralizing financial data through Excel.
The steps finance functions can take to prepare for AI integration and data automation.
Insights into the challenges of scaling a tech product from a macro to a platform.
Joe’s perspective on investing in AI technology and the importance of aligning with market needs.
The practical future of AI in finance, from predictive analysis to process automation.
In this episode, Joe Manganelli offers a wealth of insights into the intersection of finance, automation, and AI, underscoring the importance of simplifying financial workflows for greater efficiency. Joe’s perspective as both a founder and investor provides valuable guidance for anyone navigating the evolving landscape of finance and technology.
Follow Joe:
LinkedIn: https://www.linkedin.com/in/joemanganelli/
Website: https://www.amalgam.me/
Join hosts Glenn and Paul as they unravel the complexities of AI in finance:
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
AI Mastery for Finance Professionals: https://a.co/d/h8cTGeR
Follow Paul:
LinkedIn: https://www.linkedin.com/in/thefpandaguy
Follow QFlow.AI:
Website - https://qflow.ai/future-finance
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.
Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.
In Today’s Episode:
[01:47] – Real Use Cases of Generative AI
[09:14] – Introduction to guest Joe Manganelli
[11:13] – Overcoming Challenges and Scaling the Product
[13:51] – Joe’s Investment Philosophy in AI and Startups
[19:36] – Amalgam’s Data Security and Efficiency Approach
[28:05] – The ‘Walk Before You Run’ Strategy for AI in Finance
[30:33] – The Proactive Potential of Generative AI in Finance
[35:16] – Future of Predictive Financial Analysis with AI
[38:01] – Fun Q&A segment with Joe
[42:01] – Closing Remarks and Wrap Up
Full Show Transcript
[00:01:47] Host1: Paul Barnhurst: 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 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 and the data cleaning and allow us more time to think and be creative. The reality in many cases, we're using it for creativity. And 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 it's 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.
[00:03:28] Host1: Paul Barnhurst: 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 I. 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. I 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 FP&A guy signing off.
[00:05:00] Host2: Glenn Hopper: 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 was wanting 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 I 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.
[00:06:38] Host2: Glenn Hopper: 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 an 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.
[00:08:16] Host2: Glenn Hopper: 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.
[00:09:14] Host2: Glenn Hopper: Today, on future finance, we are joined by Joee Manganelli, the Chief Revenue Officer of Amalgam and founder of calculate, a finance and accounting consultancy for startups. Joee 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.
[00:09:54] Guest: Joe Manganelli: Thanks for having me, guys.
[00:09:56] Host2: Glenn Hopper: 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 and who the target user is and kind of how it works. Yeah.
[00:10:22] Guest: Joe Manganelli: 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, you know, 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, 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. Well, yeah, we were writing kind of macros and things of that to, to that level and sort of expanded it. So, yeah, so kind of the core is with finance and accounting teams and really drives a 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.
[00:11:12] Host1: Paul Barnhurst: Thanks for that. So I'm curious kind of Glenn mentioned in the intro, we know it started as an 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 key steps and milestones in that challenge?
[00:11:31] Guest: Joe Manganelli: Yeah, for sure. And I almost maybe break it into two halves. I think building the product is sort of just simple, 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 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 customer is accounting firms and finance organizations. And as you know, I'm taking a tool that slightly changes your process and asking process driven people to embrace it. And oh, by the way, they're sometimes a little cheap when it's not their money. 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.
[00:12:52] Host1: Paul Barnhurst: Got it. And so it 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. You deal a lot with fractional CFOs then with this tool.
[00:13:06] Guest: Joe Manganelli: Yeah, I would say the kind of two core are fractional CFOs, fractional accounting teams as well as just in-house 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. You're dealing with who's getting a code texted to their phone so I can log in to 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 who are on QuickBooks, some are on NetSuite. You can really use the same processes kind of regardless of system in that case.
[00:13:44] Host1: Paul Barnhurst: Yeah, I'm sure on the outsource to run into pretty much every system imaginable.
[00:13:49] Guest: Joe Manganelli: Yeah.
[00:13:51] Host1: Paul Barnhurst: 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 did you get started in that?
[00:14:05] Guest: Joe Manganelli: Yeah. So mostly just like I was I've been dealing with entrepreneurs as sort of their outsourced accounting and finance team. And so 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 then others, if they're not doing AI things today, is it? Is it something where they are thinking about, hey, let's go winds part of the market and then develop the product and expand it a bit more. So we're seeing a lot of interesting things on that side, whether it be investing or sort of starting up things from scratch.
[00:15:00] Host1: Paul Barnhurst: 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 it 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. So 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?
[00:15:27] Guest: Joe Manganelli: 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 that's not something 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.
[00:16:28] Guest: Joe Manganelli: I think, you know, are we going to go more of the like where software was, we're kind of 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, 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.
[00:17:18] Host1: Paul Barnhurst: Yeah. It's always a little bit of risk or not. I wouldn't say shot in the dark, but that risk in 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?
[00:17:34] Guest: Joe Manganelli: Right, yeah. And I and I think, you know, 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 in every one of those, there's 100 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.
[00:17:56] Host1: Paul Barnhurst: Well, I mean, like you just use the sports example. You take a baseball, right? Even the best hitter in the world maybe hits one out of three.
[00:18:05] Guest: Joe Manganelli: Yeah. And you have 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 Schwarber 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.
[00:18:21] Host1: Paul Barnhurst: So I just wanted to show a tiny make what he makes. I'm a Dodgers fan.
[00:18:25] Guest: Joe Manganelli: We all do. We all do.
[00:18:28] Host1: Paul Barnhurst: Ever feel like your go to market teams and finance speak different languages. This misalignment is a breeding ground for failure, impairing 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 B2B 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 stats, make segmented scenario planning a breeze, and closes the planning loop. Create airtight alignment, improve decision latency, and ensure accountability Ability across the teams.
[00:19:36] Host2: Glenn Hopper: So, Joe, you know, for viewers, 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 can be easily overlooked, especially when you're concerned, like with fractional CFOs and 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 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 environments.
[00:20:38] Guest: Joe Manganelli: Yeah, for sure. So the first one is really on the side of the interface. And, and I think a big part for us is like 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 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 able 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.
[00:21:44] Guest: Joe Manganelli: 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 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 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, 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, to the customer base, then focusing on those distractions, if you will.
[00:22:55] Host2: Glenn Hopper: Yeah. And that's that simplicity kind of gives it a level of elegance to 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?
[00:23:07] Guest: Joe Manganelli: Yeah, exactly. And then other things like soc2 audits and things like that are just way easier, right? Like we're talking about like, you know, so many different things and having to worry about where we're warehousing this data and who could have access to it and that kind of thing.
[00:23:21] Host1: Paul Barnhurst: 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 write a connector to a lot of different things that allows you to put it in Excel as your output.
[00:23:39] Guest: Joe Manganelli: 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 sense. 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. The last 10%. So yeah, using that simplicity. And then we also have things built in that like connections expire after a 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.
[00:24:17] Host2: Glenn Hopper: You know, so the, in the AI investment space right now. 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 open AI, 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, that would keep somebody else from coming and doing this or from having open AI pivot and to just take over your business. But I don't think open AI is very clearly focused. They want AGI. They don't care about. 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 wonder and the kind of the way I've looked at investments around AI is the timeline is compressed.
[00:25:27] Host2: Glenn Hopper: 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 Joee, 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.
[00:26:33] Guest: Joe Manganelli: Yeah, yeah, especially in the B2B space, 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've raised a good point in the sense that making an investment in sort of transformative technology are two different things, right? Like, I can 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, 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 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 an investment standpoint.
[00:28:05] Host2: Glenn Hopper: Before the show, you mentioned the importance of walking before running when it comes to AI. So for you and what you're seeing, what are the first steps of finance function should take to get ready for AI. I and kind of how does Amalgam or how could Amalgam fit into that strategy?
[00:28:21] Guest: Joe Manganelli: 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 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 is 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 there's a lot of these impressions that I get of, when It 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 lift. I think that's where we've been successful with Amalgam. It's a much lighter lift. You can sign up today and you're rolling.
[00:29:26] Guest: Joe Manganelli: And so being able to kind of get the muscles like working in the sense of like, okay, what are we looking for in this, 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 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.
[00:30:31] Host1: Paul Barnhurst: 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 chatbots 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 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.
[00:31:11] Guest: Joe Manganelli: 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 to a recipe for a veal 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 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 I think from a business aspect, it's just going to be what's really practical. 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 you know, 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 into that reference to that walk before you run. But yeah, I think there's just 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.
[00:32:35] Host1: Paul Barnhurst: Yeah, yeah, I've definitely seen some of those. Right. You open up ChatGPT or Google and it has these lists of the type of questions you could ask and you'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.
[00:32:51] Guest: Joe Manganelli: Yeah.
[00:32:52] Host1: Paul Barnhurst: We've all been there and I and I agree that AI starting with 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 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 me, 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.
[00:33:43] Host2: Glenn Hopper: 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 a full team who's putting this together. I mean, the reason that it's not more proactive is because people are having to go to Gemini or go to ChatGPT or Claude to do. It's not built into their workflows. And there's, you know, copilot is getting there slowly this I was they kind of oversold even their latest release and 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.
[00:34:31] Guest: Joe Manganelli: No, exactly. And maybe that's like we've talked a little bit before about like agents and companies adopting agents to, to 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 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. You're prompted to make a decision faster and sort of in a more powerful way.
[00:35:14] Host2: Glenn Hopper: Yeah. And, you know, to that point, one thing that computers are great at is variance analysis. So I think I've done a lot of demos in work around just looking at. So you close your books and your, your monthly, , you know, budget to actuals, loading budget to actuals and spotting those variances in those, in trends and differences where it can do that kind of first pass. I think about like every time, 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 LM and then ask for it. But if it were in that workflow, it's like you click a button, the books book's closed, and here's your first report with some charts and graphs and all that. And that's got to be coming soon.
[00:36:09] Guest: Joe Manganelli: I Think that 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 pulling out other data sets, right. Like pull in macroeconomic information, pull in seasonal what's happening. What are indicators there. So then you as the CFO 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 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 ultimately what we're going for.
[00:37:08] Host2: Glenn Hopper: Yep. Future finance. Right Paul.
[00:37:11] Host1: Paul Barnhurst: Yeah. There you go. The future of finance I've heard of a show that's called that. Who came up with that name again. Glenn. Was that that was you, right?
[00:37:18] Host2: Glenn Hopper: No, I think that was you. I think that all any anything creative and good about the show comes from you.
[00:37:22] Host1: Paul Barnhurst: It's probably came from generative AI. We just don't want.
[00:37:25] Host2: Glenn Hopper: That's true. Yeah.
[00:37:26] Host1: Paul Barnhurst: Speaking of 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 to 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 1 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.
[00:38:01] Guest: Joe Manganelli: All right. I am going to pick the number just because I need to, like, maintain a foundation of humanity here. Like, 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.
[00:38:15] Host1: Paul Barnhurst: Go for it. What's your number? Between 1 and 10.
[00:38:17] Guest: Joe Manganelli: Let's go. Let's go with number 21.
[00:38:20] Host1: Paul Barnhurst: 21. All right. This is a fun one. What's one song that always gets you pumped up or makes you feel happy?
[00:38:30] Guest: Joe Manganelli: Oh, man. So I maybe have an anecdote, but. But when I used to work at Goldman and I was expecting maybe a particularly difficult day, on 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, and sort of prepare myself there. So maybe that's a good answer for you.
[00:38:57] Host1: Paul Barnhurst: 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.
[00:39:04] Host2: Glenn Hopper: I'm sure we're gonna have to pay those royalties though. Paul. We'll have to.
[00:39:08] Guest: Joe Manganelli: Yeah. Good point.
[00:39:09] Host1: Paul Barnhurst: Hey, have AI do a karaoke version of it? Yes. Perfect.
[00:39:13] Host2: Glenn Hopper: Perfect. Actually, we should do that. We should. All right, Yeah.
[00:39:19] Host1: Paul Barnhurst: Glenn. Yeah.
[00:39:20] Host2: Glenn Hopper: All right. So, Yeah, we were trying to keep these questions from 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 I so, and I, you know, I've gone through and I've used what 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 ChatGPT. It just seems to do the best with it, but I have, I've started uploading the bio of the guest.
[00:40:00] Host2: Glenn Hopper: Instead of just having general ones. I've now uploaded the bio and say 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'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?
[00:40:36] Guest: Joe Manganelli: In public? You know what? I was thinking of one the other day. And so we talked about baseball. So maybe this is relevant to, 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 was being covered in mud because it was a little bit of a rainy day. And so, you know, super happy to be part of this 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.
[00:41:30] Host2: Glenn Hopper: So I love that because I can easily picturing that being a scene from, you know, Sandlot or Bad News Bears or whatever. It's like, you know, just the one kid coming out and slipping in the mud, but that's great. Good. Good answer. I don't. Mine would be probably. Wait. Like I could probably have. Most embarrassing thing ever. Like in public every week, probably every episode of a podcast. I'm doing something new.
[00:41:52] Host1: Paul Barnhurst: We can start that as a new segment, got a minute? Yeah.
[00:41:53] Host2: Glenn Hopper: Glenn's embarrassing moment. Yeah.
[00:41:55] Host1: Paul Barnhurst: Embarrassing moments brought to you by Glen Hopper and ChatGPT.
[00:42:01] Host2: Glenn Hopper: Joe. Well, this was great. This was 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.
[00:42:15] Guest: Joe Manganelli: Yeah. Well, no, this is great. Thanks so much for having me, guys. I love the podcast and and super happy to be on. And, this was fun. Thanks so much.
[00:42:23] Host1: Paul Barnhurst: 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.