CEO and Principal Analyst at Amalgam Insights, Hyoun Park helps CIOs & CFOs create the ROI and strategic business cases for better AI and IT FinOps. He is also host of the weekly podcast, This Week in Enterprise Tech.
In this episode Hyoun discusses some of the quick AI wins for finance departments.
- My journey Starting as a CRM administrator to analyst
- Cloud spend getting out of control (unexpected cloud bills for $20m!)
- AI use cases: invoices, contracts, billing and spending contracts (dealing with 1000 software contracts) and reconciliations
- Agentic AI and uses in Finance
- Zero based budgeting and Forecasting in the AI age
- ROI for AI investment
- People who will lose their jobs in AI in finance vs those who will survive
- My futurist prediction
Connect with Hyoun on LinkedIn: https://www.linkedin.com/in/hyounpark/
Host of weekly podcast, This week in Enterprise Tech: https://www.buzzsprout.com/2319034
http://www.amalgaminsights.com/
Blog Post and Transcript
Glenn Hopper:
Welcome to FP&A today, I’m your host, Glenn Hopper. Today we have an incredible guest joining us. Hyoun Park. Hyoun is the CEO and principal analyst at Amalgam Insights, where he helps companies optimize it costs, build financial business cases, and navigate the evolving landscape of AI and finance. With over 20 years of experience in IT, expense management, FP&A tools and business analytics, he brings a wealth of knowledge to our discussion. Today, we’ll dive into the latest trends in finance technology, AI driven decision making, and the future of FPNA. Yun, welcome to the show.
Hyoun Park:
Oh, thanks so much, Glenn. Pleasure to be here.
Glenn Hopper:
Yeah. You know, ever since you and I had our first meeting with, uh, we, we did a, a webinar with, uh, Brian Lapus, who many of our listeners will know from, uh, association for Finance Professionals. But even when we were talking about what we were gonna cover in that webinar, I thought I gotta get you on the show. So here we are weeks and weeks later, and we finally have you on the show. So thank you again for joining <laugh>.
Hyoun Park:
Oh, yeah, I’ve been looking forward to it.
Glenn Hopper:
Well, you know, maybe you’re a little bit different than most of our guests. I’m either, I’m, I’m either talking to a a, a fp and a, uh, you know, seasoned veteran or a, a VP of finance or a CFO, and you have this kind of IT background, but I know with the finance crossover and everything, so I know you’ve, you’ve done IT expense management, ai, finance, all that for the past 20 years, um, in including your industry analyst role for FBNA tools, which I think our listeners will be very interested in hearing about that. But kind of walk me through your career and, and how you got where you are.
Hyoun Park:
Yeah, so I’ll be honest, a lot of my early career was, I guess, a, you know, stepwise mistakes to get to where I’m going. When I graduated from college, I had a degree in women’s and gender studies, as well as I had taken all the pre-med classes. So I had a bunch of science and math under my belt, even though it doesn’t look like it, uh, from my degree. So, uh, based on all this, I had to figure out what am I gonna actually do in the real world. Um, I found out that chemistry, uh, jobs at the bachelor’s level are pretty boring. And, uh, women’s and gender studies jobs were not in, uh, full, uh, de, you know, supply at, at that point. So it, it, it was the.com era, the, the late nineties, early two thousands, and there was a ton of jobs in tech.
Hyoun Park:
So, um, I got into a tech company and finally learned there’s this thing called, uh, CRM, you know, customer relationship management. And from that, I learned that there was a database behind that CRM, and then I learned that databases are used for other applications. And then I started following the money and realized that there were applications that deal with payments and deal with budgeting and deal with accounting. So, over about about six year period, I shifted from doing basically CRM administration to going into databases and then getting into payments. During that time, I realized, wow, there’s this whole bigger picture of how technology deals with the way that we do, uh, do, uh, transactions in the business world. Like, I know there’s also consumer payments, but I was really interested in the business side of dealing with invoicing and dealing with the customer relationships associated with the business, and then how that fit into planning, budgeting, and forecasting it. It’s just where I was pushed into over that initial five or six year period. And I would say that everything after that period has been trying to figure out how these markets work to a greater extent, <laugh>.
Glenn Hopper:
And so when you were in that six year period and you were understanding all this, was your role CRM administrator, or were you more working with the database or a little bit of both? What, what, what kind of roles were you
Hyoun Park:
In? Yeah, I would say great thing about that period of my life that six years of working in startups is that your job can change rapidly depending on your interests. So I started as a CRM administrator, like within my first six months of being in the workforce, because nobody else understood what this customer relationship software was supposed to be. And then I was off to the races from a, a self-learning perspective that I had to learn the database behind this. And then I started teaching myself sql, and then I started working with the actual database itself. And then I got my next job at another startup where I got to work more deeply with databases in general. And then from that point, I started working with the payment and billing systems of the telecom company I was working with. So then I started learning about OSS and BSS based systems, like all the operational and billing systems that the telecoms use.
Hyoun Park:
And from that point, I started learning more about the accounting aspects of what was happening. And, and this all happens fast, because in a startup, you don’t have time to learn stuff. You either get it quickly or you don’t get to do the job because that, that job gets filled. So each of these sprints were like three to six months to get up to speed and then to start doing the work. And it, it ended up being almost like, uh, a graduate program just to figure out tech, uh, I shouldn’t say graduate because it’s not like, uh, my initial understanding was that, that deep, maybe more like a, a new bachelor’s or like a certificate program, but they were paying me to learn <laugh>.
Glenn Hopper:
Yep. Yeah. And that’s the amazing thing about startups is you are exposed to so much and you, there’s not anyone you can turn to. So it’s, uh, you know, the, the pedagogy of the startup is, you know, it’s not going to the professor’s office hours and, and getting help. It’s, there is no professor, we need to figure this out right now. So it, I mean, you’re, you’re forced, you know, trial by fire forced to learn. Yeah. But I’ve talked to so many people and, and I, I share the same experience where, um, just that startup environment lets you wear so many hats and be exposed to so much. So it really is like a continued education.
Hyoun Park:
Yeah. I will say I had a couple of great bosses who were able to sometimes help me with subject matter expertise, sometimes just able to hand the next book into my hands to, to teach to so I could read about what’s happening and say, and just give timelines. Like, you have to learn this in two months, or else I’ve gotta hire somebody to do it. <laugh>. Yeah.
Glenn Hopper:
It’s interesting that with this experience and background, you know, it would’ve made sense for you to go on and become, uh, you know, A DBA or a, you know, even moving into, you know, data and, and analytics mm-hmm <affirmative>. And, uh, but with amalgam insights, or even like going into it, but with Amalgam Insights, it’s such a laser focused service that you provide. So it’s not just doing it, it’s helping companies cut the IT costs and optimize the, their financial strategy. So tell me about what Amalgam Insights does and kind of how you got to this as a, as a focus.
Hyoun Park:
Sure. I started Amalgam Insights in 2017 as a bespoke industry analyst firm. Uh, and we have two main areas of focus. One is it cost management, where we, where we look at telecom, network software and cloud spend, and, uh, solutions to be able to cut those costs. The second is around as the initials of amalgam insights, uh, intimate ai. Uh, in 2017, I knew that AI was going to be a big topic in it. I just didn’t know when we were already starting to see efforts with data science and with people learning more about these data science and machine learning platforms. Uh, I, I was even hearing from finance people who were already trying to get master’s or certificates in, uh, machine learning because they knew they were gonna have to improve their chops from a forecasting perspective. And from a a strategy perspective, it, it wasn’t everybody, you know, it was probably one in a hundred, but it was just enough to see that there was an emerging trend in this area.
Hyoun Park:
So I wanted to be on top of that trend and see where AI ended up going. And then of course, uh, when generative AI took off three years ago that it was off to the races on the AI front. But I think it’s important to also remember that there’s, there were many other efforts of AI over the past 60, 70, 80 years. Uh, everything from statistical forecasting to, uh, algorithms to machine learning, you know, all, all these things that came before the, the last couple of years of generative ai. So I’ve been working on providing these solutions, uh, solution guidance to end users, uh, on the end user side. And then I also help vendors with product messaging, because often, uh, they create products, but they don’t know how to align what they’re doing to actual use cases. So I help them both with the language and kind of the roadmap for, for those, some of those efforts as well.
Hyoun Park:
And I got started with doing that in, back in 2008, I became an industry analyst at a company called the Aberdeen Group, where I was introduced to this business. I initially thought I would be there a year, I would talk to a hundred cool companies, and then I would probably choose one of those to go to, to do my next startup work. And I did meet a hundred cool companies, but what I found is that I actually loved talking to all these companies and figuring out what was happening next, and seeing what was next on the roadmap, and being able to compare between all these different solutions and start mapping out what the future might be able to look like based on all of these confidential briefings that I was getting. And I just got hooked on the job
Glenn Hopper:
<laugh>. Yeah. And, you know, you’re in a unique position, uh, one because of the, you know, the focus and the research that you’re doing, but two, because you are getting two way communication with the people in these companies. So I get asked a lot of questions, and I know the little slice of the world that I do business with, and where, you know, what problems they’re having and how they’re, where they are and sort of mm-hmm <affirmative>. Their data maturity, AI use, um, you know, systems technology and all that. But it’s a very small group, whereas you’re, you know, a big part of what you do is talking to all these people. So I guess, and I, obviously I wanna lean way into ai mm-hmm <affirmative>. But before we do that, just thinking about technology in general and, um, with the kinds of, of customers that you’re working with, what are, and, and because of the focus on cutting it costs and opt optimizing their strategies. And I think, you know, it’s, it’s, maybe it’s, it’s still anecdotal, ’cause I’m asking you to tell me this now, but you’re talking to so many, your sample size is much bigger. So I’m wondering, what are the biggest inefficiencies that you see, see in enterprise IT and in finance today?
Hyoun Park:
Um, I would say that from an IT cost perspective, there’s a lot of focus on cloud right now. A lot of companies started using cloud because they thought it would be cheaper, and it is cheaper to start, uh, almost always. It’s easier to start with cloud, but those costs can quickly get out of hand. If you’re using something like Snowflake from a data warehousing perspective, or if you have created AWS Amazon web services, it’s very easy to duplicate a lot of services and then lose track of what is live and what is not live. Uh, especially if you’re an engineer and your job is just to get something working quickly, you might, uh, quickly put up 5, 6, 10 different instances of basically the same job, uh, because you’re doing comparisons and trying to figure out what works and what doesn’t and doing all these experiments. But if you don’t shut off those 10 instances afterwards, you’re paying obviously 10 times as much to do do the work.
Hyoun Park:
So stuff like that happens all the time in the cloud. And then I would say on, uh, going back to the finance side, I think the biggest challenges right now are I, I’m not gonna say this is anything new, but, um, understanding marketing and sales spend, as well as being able to, uh, adjust budgets more quickly. We obviously got tested with this really hard in the covid shutdown era, but I, I feel like a lot of that, uh, experimental, uh, faster cycles of, uh, finance have started to, uh, go away again. And people have gotten, I don’t wanna say lazy, but, uh, back, back to the traditional cycles. Um, and it’s not necessarily a time to do that when, uh, business is changing so much. And frankly, we have a pretty volatile, uh, geopolitical and business environment right now.
Glenn Hopper:
<laugh>. Yeah. Yeah. So I, and I don’t wanna go too far off on a tangent, but I’m actually personally curious about this. I worked at several companies that had pretty significant cloud spend. Me as finance guy, even someone who considers myself a fairly technical finance guy mm-hmm <affirmative>. I don’t know how many, how much S3 storage is too much, right. Or what, you know, or what, what servers we spun up that can be spun back down. And like, so trying to understand all that as a, as a CFO was, was stressful. And it, well, you know, even like on the marketing spend, yeah. I can go look at it and say, okay, we did, you know, this many pay per click ads, and the price was up because we were going, you know, I can understand all that, but it’s hard. And then even trying to extract that information from engineers, engineers don’t wanna go ex, you know, but the technical debt is a, is a real Yeah. Problem. So if you’re coming in and advising companies, you know, you could do a one-time assessment. W what’s the guidance for companies like ongoing to, uh, to monitor cloud spend and how they could understand that from the office of the CFO?
Hyoun Park:
Yeah. More
Glenn Hopper:
Than just from the engineers
Hyoun Park:
First, I’ll talk about the, the biggest challenges, or that even when you’re looking at something like marketing spend, usually that marketing spend is identified with a campaign or a product. There’s usually a stakeholder who owns that campaign or, uh, that effort that you can go to and ask questions at a pretty, uh, at a finance level that, that are answerable. Like, what, what are, is this actually cogs? Is this ongoing, uh, you know, what, what kind of investment is this? And they’ll be able to answer your question. And with cloud, uh, a lot of that categorization doesn’t necessarily exist. A lot of cloud is organized by what is called tagging, which is just the best effort of the engineer at the time to add their own keywords or tags or identifying marks associated with each bit of cloud. And these tags often have nothing to do with your GL or your customer IDs or your, any of the other marks that you would normally use as a finance person to cross charge or identify, uh, charges and costs.
Hyoun Park:
So there’s already this big mistranslation, uh, happening that, that has to come into place if you’re really going to organize cloud spend from a formal corporate perspective. Uh, so that, that, that never helps. But then I, I think the biggest challenge after that is that it, it, it is really hard to figure out, uh, both how much something costs when you’re starting to put it in place. And partially because when you code something in and you put in your S3 bucket into your program, like, there’s nothing that tells you at that point, oh yeah, that, by the way, did you know that’s gonna be $75,000 a a month if you do it that way? You know, nobody tells you that. You just see, you know, dash dot dollar sign, colon, you know, keyword, you know, code, code, you know, even for the programmer, uh, you know, that’s, that’s all they have at, at a, as a starting point.
Hyoun Park:
They don’t really know what the cost is until they see the bill for the most part. So in aligning finance to cloud costs, it can be really challenging to simply match up what finance is looking for from a cross charging and planning and budgeting perspective with what the engineer is providing. And that, that mapping has definitely been a challenge in managing cloud costs. I, I feel that it, there’s also a challenge that on the, uh, finance side in general, when financial professionals are looking at it in general, there’s, uh, there’s not quite the right amount of information, uh, for cloud, uh, costs to be aligned to the business the same way that marketing, finance, and other operational costs are often, uh, mapped to either a product or a business unit.
Glenn Hopper:
Yeah. It’s <laugh>, it’s, so I’m laughing because, um, our audience is probably thinking, I’ve lost my mind, uh, because I had just a couple of weeks ago, uh, Damon Fletcher on who, um, he was the, uh, CFO at Tableau and at DataRobot, but he now has a company called Caliper, I think that they track cloud costs. And so we spent a lot of time talking about cloud costs there. Now I’ve got you on, and we’re talking about it again, <laugh>, and it’s been, it’s been several years since this was an issue to me, but it was such a, an issue every month. I mean, just seeing how much we were spending on, on the cloud, and, and I felt powerless because I didn’t know <laugh> what was going on. And it’s, um, it’s, you know, it’s funny, you, you mentioned the sales and marketing spend, and it’s, uh, that John Wannamaker quote, um, I know half the money I spend on advertised, uh, on advertising is wasted. The problem is, I don’t know which half and <laugh>. And so, you know, that sort of just makes sense, you know, and, and it’s always hard to figure out the ROI on an on on a marketing budget, but then with it, you don’t even know the widgets. What are we even counting? What am I paying for? What are we doing? So, yeah, so apologies to the listeners for dwelling on cloud costs, but it was, uh, <laugh>, you can tell I’m having PTSD about it. <laugh>. Yeah.
Hyoun Park:
There’s a lot of people who get obsessed about this stuff. When in, when I’m looking at the cloud finops space, uh, I currently cover over 80 solutions, and a lot of these founder stories are very similar. It’s, uh, I was either an IT director or a finance manager, and I got, I got ambushed by this $20 million cloud bill, and it’s haunted me ever since to the point I had to create a software company just to get these nightmares out of my head. It seems to be a pretty common, uh, obsession out there. <laugh>.
Glenn Hopper:
Yeah. That’s funny. Alright, well, I’ll, I’ll finally, I’m gonna let that go, but I can’t say, I’m not gonna bring it up again next week. <laugh>, <laugh> fp NA today is brought to you by Data Rails. The world’s number one fp and a solution data rails is the artificial intelligence powered financial planning and analysis platform built for Excel users. That’s right, you can stay in Excel, but instead of facing hell for every budget month end close or forecast, you can enjoy a paradise of data consolidation, advanced visualization reporting and AI capabilities, plus game changing insights, giving you instant answers and your story created in seconds. Find out why more than a thousand finance teams use data rails to uncover their company’s real story. Don’t replace Excel, embrace Excel, learn more@datarails.com.
Glenn Hopper:
Now, the other thing that I dwell on is ai. And, and because this is, this is the panel you and I were on, and, and also because you are talking to so many companies, so I don’t remember if we talked about it in this web, in the webinar that we did together, but a question I get all the time is, what are practical applications of generative AI in finance right now today? And I know, I mean, I know some companies that are out there, I know some companies that are doing, you know, bespoke stuff where they’ve got some cool stuff that they’ve built out in-house. I know a lot of companies that have given their employees access to chat GPT and put guardrails in and letting them use generative ai. But I, so the question I would ask you is, are any of the companies you talk to, would you say, wow, this company, and you don’t, you don’t have to say the company name, but just an indication of what they’re doing. Wow, this company’s really ahead of the curve. They’re actually have integrated generative AI in some kind of meaningful way in their workflow.
Hyoun Park:
Yeah, so I’m not gonna say that the AI use cases I’ve seen are, uh, say, you know, world shaking use cases. But I do think there’s been some really interesting work done, uh, with generative AI to parse invoices and to better understand contracts. Because every corporate contract is a mess. You know, there’s always a hundred pages of cover your butt, uh, language in every single contract that you have. And so how do you enforce these things and how do you figure out, uh, which terms are potentially most, uh, challenging? Uh, of course you can read through it yourself and you can eventually get to them, but, uh, generative AI provides a decent shortcut for helping procurement and finance and accounting to work together to enforce contract terms. And then also simply from, uh, billing perspective, especially when you’ve got, uh, some of these more complex spend categories where you can have a lot of vendors in a specific, uh, marketing area, for instance, in SaaS, you, the average enterprise at this point has, uh, at like a large Fortune 500 enterprise has over a thousand apps that are, uh, in their market.
Hyoun Park:
And a lot of these invoices are not standardized in any particular way. They, they do not follow a set data format. So often it can be useful to use something like generative AI to automate the, some of the parsing of these invoices and find some of this information rather than have to manually check every single one. So that, that’s another area where I’m starting to see generative AI being used to, uh, find things, uh, that would not be easy to do, uh, humanly. Um, I would say that going, taking a step back, not just from generative ai, but from AI in general, there’s a lot of interesting work with transaction matching, uh, as well, which is not necessarily new, but it, it’s definitely something to, to keep in mind if you’re still have an old school mindset of manually checking everything there, there’s a point that it’s just not practical to look at these thousands and millions of transactions and always try to audit and, uh, match by hand.
Glenn Hopper:
Yeah. Yeah. Reconciliations are an area where it’s just, just begging for, um, for even more automation than what we’ve seen now. It’s funny that you mentioned the, the contracts, because that was one of my first really good practical uses. I was doing some work for a public company that was headed into audit. They had, uh, made an acquisition, uh, it was a like 170 page asset purchase agreement, and there were just so many details. And the auditors, as auditors do, had a million questions and we, nobody could get a handle on treatment of deferred revenue. And it’s, you know, and it was mentioned in, in multiple places. But I think about, you know, using the, uh, PDF viewer and trying to search for keyword or whatever, and just how bad before, uh, before generative ai, it was, and this is, might have still been in the 3.5 era of chat GPT, so you couldn’t upload, you know, maybe there was a cap on how big a document you could, but I found something called like, chat PDF, uh, that has now gone away because I assume they’re gone because, you know, you can do it all in the, in the, um, frontier models.
Glenn Hopper:
Yeah. But, um, and it was a lifesaver. I I can remember it was like eight 30 at night, and I’m, I thought if I have to read flip through this entire 170 page a PA, I’m never gonna find it and I’m gonna lose my mind. You plug it in there and you just ask in context and getting the relation, and then, you know, tying it to accounting rules, trying to find those sort of enterprise wide where we just inject generative AI in our workflow across the company. It’s hard to get to those use cases right now, but really the power of generative ai, I think is when you put it in the employee’s hands directly, and they’re able to automate or get better at their specific job. They have their own use cases. You can’t dictate the use case, but they’ll find ways to use this new superpower. Is is that kind of what you’re saying as well?
Hyoun Park:
Yeah. The phrase that I’m, I’m using most often I find myself using most often right now is to look for the work that your workers hate to do. It’s, if you just hate doing work, you know, that’s one issue. But, uh, most of us like doing certain types of work and hate doing other types of work, and it’s the type, it’s the kind of work that you hate doing that often is well suited to some sort of automation because you hate doing it, not because it is bad work, but because you feel like, you know, your time could be better spent doing something else. So, you know, actively look for those types that that use case. Uh, talk to your, talk to your coworkers and your employees about the, the, the work that feels like it is just a extreme waste of time, time or really difficult to do over and over and over and over again, but, but has to be done for, uh, compliance or, uh, you know, or doing simply doing a good job.
Glenn Hopper:
Yeah. Yeah. When I think about, ’cause you know, obviously people ask, ask me all the time and of how generative AI can be applied in, in finance. We’re talking a lot about sales and marketing in this episode, but it’s easy to find use cases there because it’s a large language model. It’s the language and it’s, you know, that’s the <laugh> purview that it, it works in and and makes the most sense. But when you’re doing, uh, whether it’s accounting entries or, or financial, uh, statement analysis or whatever it is, you have to, you know, find ways to step in there. But I think that there are applications and, and to me, the biggest application is it’s, we, we talked about democratization of data for years. In a way, generative AI feels like democratization of data science. And what I mean by that is you don’t, you don’t have the barrier of, of you mentioned, you know, learning SQL and or learning Python. And the, the way that you used to have to access data science was through these computer languages, that that’s a huge barrier to entry. But now if you can just in plain language interact with your data, that’s a potential breakthrough.
Hyoun Park:
Yes. And <laugh> part of me, uh, thinks about all this time I spent, uh, learning how to build reports, learn sql, uh, at least have an idea of what’s going on with Python and R. And now, um, I don’t actually need any of that because I can tell chat GPT, uh, to do it or to write me a quick script. And of course, um, I, I wouldn’t have chat GPT write an entire app for me, but it can write specific scripts or specific, uh, app outlets, uh, that are, have some sort of specific functionality and or, or translate, uh, the code into, uh, some or back into English for me. And that actually saves me a lot of time as well as, because then I can have this little functionality that I can use, uh, from a, from a data science perspective, but also if you’re just trying to do applied data science now you just ask the model to do a thing and it just does the thing. And it is completely, uh, impossible for the end user to tell that that actually took a lot of work, <laugh> to figure out it would’ve taken, uh, a couple weeks to, to do through a human, uh, and to open up a ticket and to get this app written and to actually get the result that you’re getting
Glenn Hopper:
<laugh>. Yeah. You know, the funny thing, if I’m an IT guy right now, I’m freaking out about citizen development because it’s making it so much easier. And if I’ve, my IT department, if we’re not shipping what the, what our, our internal clients are asking for, they’re gonna just go out and start blowing up CoLab projects and just, or, or rep it, you know, built, built having these, uh, systems, build code for ’em and doing stuff on their own, which as someone who throughout my career was very guilty of citizen development, I, I, you know, thinking of this from an IT perspective, that’s pretty scary. Uh, yeah.
Hyoun Park:
To
Glenn Hopper:
Think about what’s going on with your data and what are they uploading this in and how’s it being used? ’cause it’s just so easy now.
Hyoun Park:
Yeah. It’s potentially a gigantic compliance and security problem, because the problem isn’t that these apps won’t work at all, or that these, uh, you know, that the prompts aren’t being asked correctly, but that the people who are doing all this stuff in ai, uh, line of business people, the, you know, ca citizen, uh, developers don’t necessarily know what issues are out there <laugh> from, from a security and compliance perspective. Uh, so, and that’s the part I worry about most. And I think that there’s gonna be all these agents and scripts a year from now that are just a abandoned or, uh, and, um, you know, create a new type of technical debt for us that I, I don’t even know what it’s gonna look like yet, but I do know that when technology gets abandoned that, uh, we always have some sort of problems that come along afterwards. <laugh>. Yeah.
Glenn Hopper:
Yeah. And if you’re <laugh>, if your controller is out there, uh, posting stuff on GitHub, not being a developer <laugh>, and just, you know, and not having the control. I mean, who knows what wants
Hyoun Park:
Or, or just link out there or even know well meaning, Hey, how do I do this thing? And even in asking the question, you’ve just accidentally, uh, uncovered some backdoor for your own c corporate data
Glenn Hopper:
<laugh>. Yep. Yep. <laugh>, you know, you mentioned earlier people going back to school for, you know, data science and for machine learning engineering and all that. You know, if I weren’t the geek that I am, and I were just starting out in my, uh, fp and a career, I think that obviously domain expertise in finance is important, but also having that, um, you don’t have to learn to code a lot, just know enough to sort of recognize it and, and understand what’s going on. But having domain expertise in data science, no matter what field you’re in, I think is gonna be important. Because now if you can access, and you can be a data scientist who doesn’t have to be a great coder, if you know the right questions to ask your data-driven decision making and your ability to generate reports and find correlations and do things with the data, you know, you have a, a superpower now, but you have to know sort of the rules of, of big data and, you know, does your data even classify as big data? And if, if so, and if not, what that means and how to do the analysis.
Hyoun Park:
Yeah. And I think that there’s even a a further sense that, uh, if you know what questions to ask of the data, you can then tell all of your other financial stakeholders, including your line of business stakeholders, here’s the type of questions you can ask your data, and then bring that feedback back to me because I know what to do with that. And if you just ask your data how to, uh, how to summarize, uh, your costs this way or all of your expense reports in this format, um, then, then you don’t have to do the additional work of formatting. And I get exactly what I’m looking for because I have just shaped this prompt and, or this, uh, quote, or this, uh, query exactly how I needed to work with the rest of my, uh, budgeting, uh, challenges. That’s, I think that’s one of the big advantages to knowing how the AI works, so you can then start creating these even just citizen prompts that are better aligned to your financial department and your own governance needs.
Glenn Hopper:
Yeah. And you know, thinking about that, we are at a place now because we’re early in the, in the technology where you do have to think, you know, I, I hate the idea of, of having to have a prompt library, but truthfully like it, there are certain ways that you need to interact with the data. So every, there’s information that the GPT needs to have before it can respond to you. So you kind of do have to save, like, okay, we’re using gap accounting here, this is, we need this rule, we need the response like this site, whatever, you know what, whatever. So these prompts get long just to make sure that you’re not getting a hallucinated answer and, and getting it in, in a format that you want. And it’s, so, it’s clunky now. It can still be efficient, but we are, we’re at this bleeding edge of this. If you’re using this now, I mean, how do you see, like, and to me that’s an adoption hindrance is when you have to come out of whatever your ERP or your CRM and put this data into, you know, an external system to manipulate it and then come back and then have it, have the data come back to you in a format. What’s it gonna take to overcome that hurdle where the generative AI is just in our, our SaaS tools that we’re using today.
Hyoun Park:
So we’re starting to see something called Age Agentic AI show up, and, uh, two, two things. Age agentic, AI and computer use, uh, AI where, uh, AI is starting to figure out how to click around on your, uh, computer and go through websites and be able to get to a response. So basic consumer, uh, version of this might be you use computer use ai, and you ask it to order Cheetos on Amazon for you. Not a fantastic example, but I’m just saying that, uh, based on this, uh, if your computer use AI is set up correctly, it can go through, it, can read it, it can type in Cheetos in the search button, uh, bucket, and then click on the Cheetos, you know, click one, click order and have it come to your house that it’s still pretty rudimentary. But, uh, having an AI that can do that level of work on your, on your computer or on your website is a starting point, because then you can figure out which fields on the website or in your system, uh, need to be aligned to, uh, whatever, uh, target system or, you know, your financial system.
Hyoun Park:
It perhaps, uh, it is in place. So I, I think you’re gonna need some of these autonomous agents to do some of the backend work of mapping one piece of data to another piece of data or, uh, one set of fields to another set of fields, and do do some semantic mapping in the middle. Uh, we’re talking about some pretty, um, sophisticated stuff, honestly, uh, to, to get to that point of making that an, to make your scenario and, uh, a more realistic, uh, automated AI powered, uh, process at this point.
Glenn Hopper:
So I gotta tell you, um, operator was, that’s open AI’s computer use agent, um, for, for our listeners, uh, was the final nudge I needed to upgrade to the $200 amount <laugh>, um, open ai. And, and here’s, here’s why I, I’m not using this for any kind of client data. It’s still too early. I’m experimenting like crazy with it to see how much I can push it. But for our listeners, operator is the agent that, uh, Hyoun was talking about that can it just, if any, if you’ve ever seen RPA at work where the mouse is magically moving across the screen and, and doing things on your behalf, that’s what operator looks like. And I will say this is pretty interesting to me from an automation standpoint going forward. ’cause if you’ve ever worked with any of these, um, RPA companies, it was cool, but rule-based and the implementation was very expensive, took a very long time, very, very picky, could slide off the rails, you’d go and correct it, and it would learn and come back. But, um, it was very difficult to do RPA, now the operator is individual RPA. So think about if every employee had a tool that was, you know, that was more reliable, we’re, we’re in alpha testing phase, but if every employee had their own RPA agent or agent’s plural, I mean, it’s, the potential there is is pretty interesting.
Hyoun Park:
Yeah, I’m looking forward to that. And then the agentic part of having these ais being able to actually finish off, uh, specific tasks, uh, of course you’re gonna have to test that out before you make any a, any AI based agent, uh, autonomous. But, um, because they, that has to be done BA based on a variety of scenarios in a variety of language prompts before you really let it loose on its own. But I think there, there’s some potential there. <laugh>.
Glenn Hopper:
Yeah, it’s interesting to me, it seems counterintuitive, but when there’s APIs and connectivity that the computer use model and where the AI is moving the mouse around and interacting the way a human would, it seems counterintuitive. But when you think about it, it’s the same reason that robots are being built in a human form factor. It’s the, the world that we operate in is designed for humans. So 10 fingers, two arms, you know, six feet roughly, and, you know, whatever weight, eyes, yeah. Yeah, mm-hmm <affirmative>. So to navigate the world, then, um, that’s, you know, it makes sense that if we’re gonna build a, a robot to do that, it should have that shape and the digital world. Yes, a lot of sites have APIs, and if there are APIs, that’s great, but think of all the data that’s out there and, and what’s on the web. You’re not gonna have APIs and, and, you know, backdoor access to all these systems. So if it operates the way a human does, it’s, it’s gonna be interesting to see how the web itself evolves if there’s another, you know, uh, there’s the, the regular web, the dark web, and then the agent web where they’re just going through and maybe something is optimized there, but you know, the, the amount of traffic when the agents and, and computer use really catches on. It’s gonna be interesting to see what it does to the web in general.
Hyoun Park:
Yeah. But fundamentally, I think, uh, one of the big differences now is that for most of our work lifetimes, we’ve had to learn how to think more like computers. We have to put our data in the right spreadsheet cell, we have to access an API where we learn some schema, uh, that is based on a piece of software. So we have to think like the software to make things happen. And now, uh, we finally kind of flipped that on its head a little bit and said, okay, now computer, you gotta meet me halfway and you gotta start, uh, learning English and, you know, doing this as I, the way that I would phrase it or, or at least, uh, be able to go through the website the way that I would go through it. And if you’re failing, um, you know, the shows, uh, that you’re not ready to, to do work yet. And, and kind of holding the computer to that standard of basic, uh, human competence, uh, for, from a user experience perspective. I, I, I think that’s an interesting flip that has happened that we’re in starting to hold the computer responsible to understand a human UX rather than forcing the human to be in the computer UX
Glenn Hopper:
<laugh>. Yeah. Yeah. Very, very good point. I hadn’t thought about it that way, but that is absolutely right. So you and I, this is, every time we talk, I feel like you, we, we should just be in a think tank. Just, you know, <laugh> some academic ivory tower. We’re just, you know, brainstorming the future. But for a lot of our, our listeners, you know, they’re looking for more practical applications. And I always, I struggle with this question, so I’m gonna, I’m gonna throw the ball in your court and ask, you know, you and I have just talked about what we see for the future, and I think we’re probably right, who knows what the, the timeline is on it. But you know, right now, CFOs and, and people are, are getting pressure to, you need to implement ai, you need to use this. I mean, where should finance teams be investing in AI right now? And, and what should they expect in an AI implementation? We talked about the generative ai, kind of where it is. We haven’t really talked a lot about sort of the classical machine learning and what you can kind of the deterministic and, and these algorithms that have been, we’ve been using for 15 plus years. If I’m given a directive and a budget to implement AI for the finance team, what should I be thinking about doing today?
Hyoun Park:
Yeah, I think definitely consolidation is, is a starting point. I, I do think that we need to go back to, uh, or, or at least, uh, thinking again about, uh, zero based budgeting and planning, budgeting and forecasting. Uh, part, part of the reason that those have been challenging is that it is hard to get to this constituent, you know, starting points and really figure out how to build that all from scratch without a whole lot of manual work. Uh, I think AI can be a way to fill in some of those gaps a little bit more easily. And also to build a more of a portfolio of planning, uh, of plans, uh, that could be dependent on what if situations that are a little bit more holistic than simply changing a single cell or a single unit. You can ask broader questions with ai, uh, than you can with, uh, simply doing basic what if analysis.
Hyoun Park:
Uh, for instance, you can ask generative ai something like, if the business is more aggressive, how will that affect the budget? And it’ll give you a number of suggestions of key spend areas or key revenue areas where you might see significant changes based on what typically happens in the world or what typically happens to a business. So it’s not gonna be perfectly aligned to your business, but it will at least provide a starting point for you to say, oh, well, I hadn’t really considered that this business unit is unusually volatile compared, uh, and dependent on having, uh, strict business execution. And that might be a problem if we try to get more aggressive or if we cut off funding a little bit because we have to push money from one place to another. Okay. Or, you know, maybe have one business unit that is like groceries where, uh, you have to deliver every two weeks or your stuff goes rotten.
Hyoun Park:
Just little things like that that can be pointed out quickly through generative AI that may not pass the test of simply manually looking through each category of what you’re looking for, especially if you’re a large conglomerate or multinational, or if you have experimental areas, or if you are using interesting names for your business units, <laugh>, you know, all all sorts of reasons that you might not necessarily pick up on something that generative AI would pick up on. I think there’s some strategic value to be able to use generative AI at, uh, like that as well at a time when the CFO is being asked to be a strategic officer in more and more companies. Yeah.
Glenn Hopper:
And you know, <laugh> and this one, and this is what you and I, the webinar we did together was about, and it’s, I’m, I’m guilty of this, um, but I, I, I think with, with reason, but we still need to, to try to figure it out, but the ROI for an AI investment right now, and like, how do you account for that? It’s, I’m not saying like wholesale swaths of jobs being wiped out by a ai. So if you’re trying to use, you know, efficiency and, uh, replacing employees, I mean, yes, I think, you know, automation will, you know, there, there will be maybe if you used to have 10 people in your, uh, accounts payable department, maybe you can do the same job with three in the future. You know, I, I could, I could see that happening, but you can’t, if that’s your only ROI, it’s not today, and it’s a way out and it’s, uh, you know, speculative.
Glenn Hopper:
But there’s also, you know, the, the kind of insights you were just talking about, there’s a value to those. It’s hard to put what that value is, but the whole idea of <laugh>, we’ve been talking about digital transformation for 30 years, but the whole idea of being in a transformed company that is making data-driven decisions, there is a value to be put on that, that, uh, it’s hard to factor into just an ROI calculation, but I mean, and, and because you help companies with their, their budgets around this kind of stuff, what’s your approach to ROI for AI investments?
Hyoun Park:
Yeah, so I know a lot of the vendors try to dance around, uh, labor replacements and say that their AI is about helping employees to be more productive. I’m gonna be honest, there are a bunch of line level jobs that are gonna be taken out by ai, and the people who are gonna lose their jobs are the people who only know how to do the job itself, but don’t know why they’re doing the job. Whereas if you’re an ap, uh, or ar and you know why the payments are being processed, um, and like you have an idea of how that fits into cash flow, or you have an idea of how this fits into, uh, people being able to operationally, uh, buy what they need to do their jobs, if you have that next step of logic, um, you’re gonna be able to use the ais and do whatever the next version of your job is.
Hyoun Park:
If you think your job is simply to scan the invoice into the machine and, you know, click the button and enter the number into the software, yes, you are going to lose your job. So that is one point of value. But I, I, I think of production, uh, that productivity as kind of a, a red herring because it’s, it’s zero sum game. You know, you don’t make more revenue by being more productive. You just, uh, cut, you know, you just change your margin a a little bit, but it’s not really a growth exercise. Uh, I, I think it’s much more interesting to think about the potential growth, uh, associated with bringing AI into place, uh, what you’re able to pursue, uh, what you’re able to do differently as a business, hopefully a ways to be able to cut areas out of your production or your supply chain or, or manufacturing efforts, uh, to be able to move forward.
Hyoun Park:
And I know not all of this is directly related to the, the finance function, but I think it is something that, uh, finance people should keep in mind because finance will be asked to be a stakeholder or a gatekeeper for budget, uh, for AI projects. So you need to start identifying where the value does really exist and to be able to push back on AI projects where, uh, people can’t articulate where AI, uh, actually provides growth, uh, uh, across the business. I think that’s probably gonna be the more important part for finance. Uh, looking at AI in the near future, it’s not necessarily how AI directly affects the finance department, but knowing that finance will be one of the departments that decides whether an AI project will move forward, uh, by looking at how legitimate the ROI is. And I think it’s really important from that perspective to think about the growth and the revenue at the, the revenue chain and the value chain that is so, that is associated with the business actually making money.
Hyoun Park:
And where AI fits into that. And that is usually not cutting five seconds off of a specific job, but, uh, unless it is a job that is done a hundred times a day by every employee in the company, uh, usually the, the increase improvements will come from things like, uh, supply chain risk or, uh, manufacturing improvement or things that are more tangible or logistics, uh, thi things that will actually affect how much gas you use, how much power you use, how the skews that you have in place that those, those nuts and bolts things that actually run your company. Yeah.
Glenn Hopper:
Very well said. So as always, I think you and I could just keep going all day together, but I need to start bringing this home. I’m gonna switch gears a little bit, and then the first, uh, question I’m gonna ask you is, ’cause I get asked this all the time, and I’m always wrong. I’m a terrible futurist. I love talking about the future, but if you ask me to, to put a date or, or a guess on anything, I’m no, I’m no Ray Kurzweil <laugh> <laugh>. Um, how about a, a bold AI prediction? Oh,
Hyoun Park:
I, I, I I got the bold book right, right here, Pete. Oh
Glenn Hopper:
Yeah. I still right behind you. Yeah, <laugh>. Alright, so a bold AI prediction for this year, for 2025.
Hyoun Park:
I, I know this doesn’t change the market as a whole, but I do think there’s gonna be at least one startup that shows this year that is successful, that is basically just one person and thousands of agents and people are gonna wonder what the hell is this? Because there are no employees, it’s just one person and it, it just drives 50 million or a hundred million in revenue and it’s gonna come out of nowhere and love it. It’s gonna end up being this case study for people to look at for decades,
Glenn Hopper:
<laugh> brilliant people or agents to look at for decades, maybe, I dunno, <laugh>. Okay. Well, so now I’m gonna bring it home with our, our two questions that we ask all of our guests. So the first one is, what is something that most people don’t know about you?
Hyoun Park:
Uh, I’ll say that although I, I pointed out that I was a women’s studies major in a college. Uh, one thing I actually did as part of that was I spent my junior year at a women’s college. I was the only male student there. This has nothing to do with, um, I guess politics or transness or anything. I’m just a normal cis male dude who was at a women’s college with 2200 women. So that was, uh, a really interesting and challenging year of my life. <laugh>, I love I bet. I bet. But, um, if you’ve ever lived with, uh, like a, been a guy who worked, uh, lived with a group of women, like multiply that by like 500 <laugh>, like e everything, everything you dealt with there. And, and that’s what I dealt with
Glenn Hopper:
<laugh>. Wow. Yeah. That had to be, that had to be amazing Uhhuh. So that is very interesting. The last question that we ask all of our guests is, what is your favorite Excel function and why?
Hyoun Park:
You know, I’m always going to have, uh, a little piece of my heart dedicated to the B lookup. I feel like putting B
Glenn Hopper:
Lookup, I’m with you, brother. I’m right there with you. You
Hyoun Park:
<laugh>, like B lookup was the first time I felt like I was in Excel Pro. It’s like I have the power to find things in a big, and, you know, I’m not just just just dealing with, you know, B two plus C two equals D two anymore. It’s like I now control this entire spreadsheet. I know what’s going on in it and you know, I’ve got these V lookup and h lookups and I can figure out, you know, what is conditional, what is not. Like I, I can do all this stuff now. Like I have the power <laugh> <laugh> all started with the V lookup
Glenn Hopper:
<laugh> Yep. VLOOKUPs. And then pivot tables right after that. Oh yeah. And you’re like, whoa, we
Hyoun Park:
<laugh>. Mm-hmm <affirmative>.
Glenn Hopper:
Alright. So finally, how can our, our listeners connect with you and learn more about Amalgam Insights and, and follow you? What’s the the best way for them to
Hyoun Park:
Do that? Yes. So I both have a website, amalgam insights.com, as well as I do a weekly podcast called This Week in Enterprise Tech, along with Charlie Arajo, who is the Chief Strategy Officer at Symphony ai. And, uh, we talk about the biggest ai, uh, news topics of the week and how that is affecting the CIO, uh, and CFO offices.
Glenn Hopper:
Awesome. We’ll be sure and put links to that in the, uh, in the show note. And now I’ve got another podcast to add to my already, uh, busy podcast listening schedule. So I’ll put you up there with, uh, Scott Galloway and Kara Swisher and, uh, the Hard Fork guys and all that <laugh>. Yeah. Yeah. Y thank you so much for, for coming on the show. Really appreciate your time. Yeah,
Hyoun Park:
It’s always a pleasure to talk to you, Glen <laugh>.
