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And now on to the show.
From data rails, this is Fpna Today.
Welcome to Fpna Today.
I'm your host, Glenn Hopper.
Today, we have an incredible guest joining us, Hyun Park.
Hyun 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, Hyun 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 FP &A.
Hyun, welcome to the show.
Oh thanks so much Glenn.
Pleasure to be here.
Yeah, you know ever since you and I had our first meeting with, we did a webinar with Brian Lapidus, many of our listeners will know from Association for Finance Professionals.
But even when we were talking about what we were going to cover in that webinar, I thought I got to 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.
Oh yeah, I've been looking forward to it.
Well, let's dive in because I think maybe you're a little bit different than most of our guests.
I'm either talking to an FPNA seasoned veteran or 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 done IT expense management, AI finance and all that for the past 20 years, including your industry analyst role for FB &A tools, which I think our listeners will be very interested in hearing about that, but kind of walk me through your career and how you got where you are.
Yeah. So I'll be honest, a lot of my early career was, I guess, stepwise mistakes to get to where 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 from my degree.
So based on all this, I had to figure out what am I gonna actually do in the real world.
I found out that Chemistry jobs at the bachelor's level are pretty boring and women's and gender studies jobs were not in full supply at that point.
So it was the dot -com era.
The late 90s, early 2000s.
And there was a ton of jobs in tech. So I got into a tech company and finally learned there's this thing called CRM, customer relationship management.
And from that I learned that there database behind that CRM.
Then I learned that databases are used for other applications.
Then I started following the money and realized that there were applications that deal with payments and deal with budgeting and deal with accounting.
Over about a 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 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's just where I was pushed into over that initial five or six year period.
I would say that everything after that period has been trying to figure out how these markets work to a greater extent.
—And so when you were in that six year period and you were understanding all this, was your role as CRM administrator, or were you more working with the database or a little bit of both?
What kind of roles are you in?
Yeah, I would say the great thing about that period of my life, about six years of working in startups, is that your job can change rapidly, depending on your interests.
So I started as a CRM administrator 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 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, At 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, all the operational and billing systems that telecoms use, and from that point, I started learning more about the accounting aspects of what was happening.
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 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 ended up being almost like a graduate program just to figure out tech. I shouldn't say graduate because it's not like my initial understanding was that deep, maybe more like a new bachelor's or like a certificate program but they were paying me to learn.
Yeah. But that's the amazing thing about startups, is you are exposed to so much, and there's not anyone you can turn to.
So it's, you know, the pedagogy of the startup is, you know, it's not going to the professor's office hours and getting help.
There is no professor.
We need to figure this out right now.
So I mean, you're forced, you know, trial by fire, forced to learn.
But I've talked to so many people, and I share the same experience where that startup environment lets you wear so many hats and be exposed to so much. So it really is like a continued education.
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 teach so I could read about what's happening and just give timelines like, you have to learn this in two months or else I've got to hire somebody to do it.
Yeah, so I think it's interesting that with this experience and background, you know, it would have made sense for you to go on and become, you know, a DBA or, you know, even moving into, you know, data and analytics.
And 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 their financial strategy.
So tell me about what Amalgam Insights does and kind of how you got to this as a focus.
Sure. I started Amalgam Insights in 2017 as a bespoke industry analyst firm.
And we have two main areas of focus.
One is IT cost management where we look at telecom network software and cloud spend and solutions to be able to cut those costs.
The second is around as the initials of Amalgam Insights intimate AI.
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. forums, I was even hearing from finance people who were already trying to get masters or certificates in machine learning because they knew they were going to have to improve their chops from a forecasting perspective and from a strategy perspective.
It wasn't everybody, it was probably one in a hundred, but it was just enough to see that there was an emerging trend in this area.
So I wanted to be on top of that trend, and see where AI ended up going.
And then of course, 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 were many other efforts of AI over the past 60, 70, 80 years.
Everything from statistical forecasting, to algorithms, to machine learning.
all these things that came before the last couple of years of generative AI.
So I've been working on providing these solution guidance to end users on the end user side.
And then I also help vendors with product messaging because often they create products, but they don't know how to align what they're doing to actual use cases.
So I helped them both with the language and the roadmap for some of those efforts as well.
And I got started with doing that.
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 100 cool companies, and then I would probably choose one of those to go to do my next startup work.
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.
Yeah, and you know, you're in a unique position, one because of 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 what problems they're having and where they are in their data maturity, AI use, systems technology and all that.
but it's a very small group.
Whereas you're a big part of what you do is talking to all these people.
So I guess obviously, I want to lean way into AI.
But before we do that, just thinking about technology in general, and with the kinds of customers that you're working with and because of the focus on cutting IT costs and optimizing their strategies.
Maybe it's still anecdotal cuz 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 in enterprise IT and finance today?
I would say that from an IT cost perspective, there's a lot of focus on cloud right now.
Just trying a lot of companies started using cloud because they thought it would be cheaper and it is cheaper to start almost always.
It's easy 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.
Especially if you're an engineer and your job is just to get something working quickly, you might quickly put up 5, 6, 10 different instances of basically the same job, 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 the work.
So stuff like that happens all the time in the cloud.
And then I would say on going back to the finance side, I think the biggest challenges right now are, I'm not going to say this is anything new, but understanding marketing and sales spend, as well as being able to adjust budgets more quickly.
We obviously got tested with this really hard in the COVID shutdown era, but I feel like a lot of that experimental faster cycles of finance have started to go away again.
And people have gotten, I don't want to say lazy, but back to the traditional cycles.
And it's not necessarily a time to do that when business is changing so much. And frankly, we have a pretty volatile geopolitical and business environment right now.
Yeah. And I don't want to 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 every month.
And I, you know, me as finance guy, even someone who considers myself a fairly technical finance guy, I don't know how much S3 storage is too much or what servers we spun up that can be spun back down.
So trying to understand all that as a CFO was stressful.
Well, even like on the marketing spend.
Yeah, I can go look at it and say okay we did this many pay per click ads and the price was up because we were going.
I can understand all that but it's hard and then even trying to extract that information from engineers.
But the technical debt is a real problem.
So if you're coming in and advising companies, you could do a one -time assessment.
What's the guidance for companies ongoing to monitor a Cloudspend and how they could understand that from the Office of the CFO, more than just from the engineers.
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 mapping has definitely been a challenge in managing cloud costs.
I feel that there's also a challenge that on the finance side in general, when financial professionals are looking at IT in general, there's not quite the right amount of information for cloud costs to be aligned to the business the same way that marketing, finance, and other operational costs are often mapped to either a product or a business unit.
I'm laughing because our audience is probably thinking I've lost my mind because I had just a couple of weeks ago Damon Fletcher on who was the CFO at Tableau and at DataRobot but he now has a company called Caliper, I think, that they track cloud costs and we spent a lot of time talking about cloud costs there.
Now, I've got you on and we're talking about it again.
It's been several years since this was an issue to me but it was such an issue every months, just seeing how much we were spending on on the cloud.
And I felt powerless because I didn't know what was going on.
And it's funny you mentioned the sales and marketing spend and it's that John Wanamaker quote, I know half the money I spend on advertising is wasted.
The problem is I don't know which half.
And so that sort of just makes sense.
And it's always hard to figure out the ROI 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 apologies to the listeners for dwelling on cloud costs, but it was...You can tell them having PTSD about it.
There's a lot of people who get obsessed about this stuff.
When I'm looking at the cloud spin -off space, I currently cover over 80 solutions and a lot of these founder stories are very similar.
I was either an IT director or a finance manager and I got ambushed by this $20 million cloud bill and it's haunted me ever since.
Yep. You had to create a software company just to get these nightmares out of my head It'll be a pretty common obsession out there Yeah, that's funny.
So alright. Well, I'll finally I'm gonna let that go but I can't say I'm not gonna bring it up again next week but So now the other thing that I dwell on is AI 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 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 guard rails in and letting them use generative AI.
But… so the question I would ask you is, do you… are any of the companies you talk to, would you say, wow, this company… and you don't have to say the company name, but just an indication of what they're doing, wow, this company is really ahead of the curve, they're actually have integrated generative AI in some kind of meaningful way in their workflow.
Yeah. So I'm not going to say that the AI use cases I've seen are, say, world -shaking use cases, but I do think there has been some really interesting work done with generative AI to parse invoices and to better understand contracts.
Because every corporate contract is a mess.
You know, there's always 100 pages of cover your butt language in every single contract that you have. So how do you enforce these things and how do you figure out which terms are potentially most challenging?
Of course, you can read through it yourself and you can eventually get to them.
But 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 billing perspective, especially when you've got some of these more complex spend categories, where you can have a lot of vendors in a specific marketing area.
For instance, in SaaS, the average enterprise at this point has like a large Fortune 500 enterprise has over a thousand apps that are in their market.
And a lot of these invoices are not standardized in any particular They do not follow a set data format.
So often, it can be useful to use something like generative AI to automate some of the parsing of these invoices and find some of this information, rather than have to manually check every single one.
So that's another area where I'm starting to see generative AI being used to find things that would not be easy to do humanly.
humanly. 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 as well, which is not necessarily new, but it's definitely something to keep in mind if you still have an old school mindset of manually checking everything.
There's a point that it's just not practical to look at thousands and millions of transactions and always try to audit and match by hand.
Yeah, reconciliation is an area where it's just begging for even more automation than what we've seen now.
It's funny that you mentioned 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 made an acquisition, it was like 170 page asset purchase agreement, and there were just so many details and 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 multiple places.
But I think about using the viewer of the PDF viewer and trying to search for keywords or whatever, and just how bad 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 and maybe there was a cap on how big a document you could.
But I found something called, like, chat PDF that has now gone away because I assume that gone because, you know, you can do it all in the frontier models.
And it was a lifesaver.
I can remember, like, 8 30 at night.
And I thought if I have to read, flip through this entire 170 page APA.
I'm never going to find it.
And I'm going to 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.
And I mean, it was just. It's weird, like 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 superpowers.
Is that kind of what you're saying as well?
Yeah, the phrase that 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.
If you just hate doing work, you know, that's one issue.
But most of us like doing certain types of work and hate doing other types of work.
And it's the type, 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 because you feel like, you know, your time could be better spent doing something else.
So, you know, actively look for those types of issues, that use case.
Talk to your coworkers and your employees about the work that feels like it is just an extreme waste of time or really difficult to do over and over and over and over again.
But has to be done for compliance or doing simply doing a good job.
Yeah. No, it's when I think about because obviously people ask, ask me all the time and of how generative AI can be applied in finance like it's, 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 purview that it works in and it makes the most sense.
But when you're doing, whether it's accounting entries or, or financial 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 to me the biggest application is it's 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 have the barrier of you mentioned, you know, learning SQL and or learning Python and 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.
Yes, part of me thinks about all this time I spent learning how to build reports, learn SQL, at least have an idea of what's going on with Python and R. And now I don't actually need any of that because I can tell chatgpt to do it or to write me a quick script.
And of course, I wouldn't have chatgpt write an entire app for me, but it can write specific scripts or specific applets that have some sort of specific functionality or translate the code into or back into English for me.
And that actually saves me a lot of time as well because then I can have this little functionality that I can use 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 impossible for the end user to tell that that actually took a lot of work to figure out, it would have taken a couple of weeks to do through a human.
And to open up a ticket into get this app written and to actually get the result that you're getting.
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 my IT department, if we're not shipping what our internal clients are asking for, they're going to just go out and start blowing up colab projects and just having these systems build code for them and doing stuff on their own.
Which, as someone who throughout my career was very guilty of citizen development, thinking of this from an IT perspective, that's pretty scary to think about what's going on with your data and what are they uploading this in and how's that are being used?
Because it's just so easy, though.
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 the prompts aren't being asked correctly, but that the people who are doing all this stuff in AI, line of business people, the citizen developers don't necessarily know what issues are out there, from a security and compliance perspective.
So that's the part I worry about most. And I think that there's going to be all these agents and scripts a year from now that are just abandoned and create a new type of technical debt for us that I don't even know what it's going to look like yet.
But I do know that when technology gets abandoned, that we always have some sort of problems that come along afterwards.
Yeah, if your controller is out there posting stuff on GitHub, not being a developer, and And just, in not having the control, I mean, who knows.
Or just even though, well, meaning, hey, how do I do this thing?
And even in asking the question, you just accidentally uncovered some backdoor for your own corporate data.
Yep. Yep. You know, you mentioned earlier people going back to school for data science and for machine learning engineering and all that.
So the interesting thing in trying to figure out if I'm, if I weren't the geek that I am and I was just starting out in my F .P .N .A.
career, I think that obviously domain expertise in finance is important.
But also having that, you don't have to learn to code a lot, just know enough to sort of recognize it 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 superpower now, but you have to know sort of the rules of big data.
And you know, does your data even classify as big data, and if so, and if not what that means, and how to do the analysis, but...
Yeah, and I think that there's even a further sense that 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 stake holders, 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 summarize your costs this way or all of your expense reports in this format, 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 quote
or this query exactly how I needed to work with the rest of my budgeting challenges.
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.
Yeah, and you know, thinking about that if you we are at a place now, because we're early in the in the technology, where I hate the idea of having to have a prompt library, but truthfully, like there are certain ways that you need to interact with the data.
So everything 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, we need this This rule, we need the response like this." These prompts get long just by setting up to make sure that you're not getting a hallucinated answer and getting it in a format that you want.
It's clunky now. It can still be efficient, but we're at the bleeding edge of this if you're using this now.
To me, that's an adoption hindrance, is when you have to come out of what your ERP or your back and then have it have the data come back to you in a format?
Like how do you see what's it going to take to overcome that hurdle where the generative AI is just in our SaaS tools that we're using today?
So we're starting to see something called Agentic AI show up.
And two things, Agentic AI and computer use AI, where AI is starting to figure out how to click around on your computer, and go through websites, and be able to get to a response.
So basic consumer 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 just saying that, based on this, if your computer -use AI is set up correctly, it can go through, it can read, it can type in Cheetos in the search bucket and then click on the Cheetos, you know, click one, click order, and have it come to your house.
Still pretty rudimentary, but having an AI that can do that level of work on your computer or on your website is a starting point because then you can figure out which fields on the site or in your system need to be aligned to whatever target system or your financial system perhaps is in place.
So I think you're going to 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 one set of fields to another set of fields and to do some semantic mapping in the middle.
We're talking about some pretty sophisticated stuff honestly, to get to that point of making that and to make your scenario and a more realistic automated AI -powered process at this point.
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I got to tell you, Operator, that's OpenAI's computer use agent for our listeners, was the final nudge I needed to upgrade to the $200 a month in open AI.
Here's why. I'm not using this for any kind of client data.
It's still too early.
I'm experimenting crazy with it to see how much I can push it.
For our listeners, Operator is the agent that Young was talking about.
Now, if you've ever seen RPA at work, where the mouse is magically moving across the screen 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 because if you've ever worked with any of these RPA companies, it was cool but rule -based.
And the implementation was very expensive, took a very long time, very, very picky, could you'd slide off the rails, you'd go and correct it, and it would learn and come back, but it was very difficult to do RPA.
Now the operator is individual RPA, so think about if every employee had a tool that was more reliable, we're in alpha testing phase, but if every employee had their own RPA agent or agents, plural, the potential there is pretty interesting.
Yeah, and so I'm looking forward to that and then the agentic part of having these AIs being able to actually finish off specific tasks.
Of course we're gonna, you're going to have to test that out before you make any AI -based agent autonomous, but because that has to be done based on a variety of scenarios and a variety of language prompts before you really let it loose on its own.
But I think there's some potential It's interesting to me, it seems counter intuitive, 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 world that we operate in is designed for humans.
So 10 fingers, two arms, six feet roughly.
And whatever weight ...
Years, those, eyes, yeah.
So to navigate the world, then it makes sense that if we're going to build 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 what's on the web.
You're not going to have APIs and backdoor access to all these systems so if it operates the way a human does, it's going to be interesting to see how the web itself evolves if there's another regular web, the dark web and then the agent web where they're just going through and maybe that something is optimized there but the amount of traffic when the agents in computer use really catches on, it's going to be interesting to see what it does to the web in general.
Yeah, but fundamentally, I think 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 that is based on a piece of software.
So we have to think like the software to make things happen.
And now we finally kind of flipped that on its head a little bit and said, okay, now computer, you got to meet me halfway.
You got to start learning English and you know, doing this as a, the way that I would phrase it, or at least be able to go through the website the way that I would go through it.
And if you're failing, you know, this shows that you're not ready to do work yet and, and kind of holding the computer to that standard of basic, the human competence from a user experience perspective.
I think that's an interesting flip that has happened that we're starting to hold the computer responsible to understand human UX rather than forcing the human to be in the computer UX.
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 we should just be in a think tank just, you know, some academic ivory tower, we're just brainstorming the future.
But for a lot of our listeners, they're looking for more practical applications.
And I always struggle with this question.
So I'm going to throw the ball in your court and ask...
You and I have just talked about what we see for the future and I think we're probably right.
Who knows what the timeline is on it.
But right now, CFOs and people are getting pressure to, you need to implement AI.
You need to use this.
Where should finance teams be investing in AI right now?
And what should they expect in 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 kind of the deterministic and these algorithms that 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?
Yeah, I think, definitely, consolidation is a starting point.
I do think that we need to go back to a zero -based budgeting and planning budgeting and forecasting.
Part of the reason that those have been challenging is that it is hard to get to this constituent starting points and really figure out how to build that all from scratch without a whole lot of manual work.
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 of plans 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 than you can with simply doing basic what -if analysis.
For instance, you can ask generative AI something like, if the business is more aggressive, how will that affect the budget?
and it will 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 going to 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 and dependent on having a strict business execution and that That might be a problem if we tried to get more aggressive, or if we cut off funding a little bit because we have to push money from one place to another.
Or maybe you have one business unit that is like groceries where you have to deliver every two weeks or your stuff goes rotten.
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.
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 like that as well at a time when the CFOs being asked to be a strategic officer in more and more companies.
And this one, and this is what you and I, the webinar we did together was about, and it's, I'm guilty of this, but I think with reason, but we still need to try to figure it out.
The ROI for an AI investment right now.
And how do you account for that?
I'm not seeing wholesale swaths of jobs being wiped out by AI.
So if you're trying to use efficiency agency and replacing employees.
I mean, yes, I think, you know, automation will, you know, there will be maybe if you used to have 10 people in your accounts payable department, maybe you can do the same job with three in the future.
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, you know, speculative, but there's also, you know, the kind of insights you were just talking about there's a value to those, it's hard to put what that value is.
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.
It's hard to factor into just an ROI calculation.
And because you help companies with their budgets around this stuff, what's your approach to ROI for AI investments?
Yeah, so I know a lot of the vendors try to dance around labor replacement and say that their AI is about helping employees to be more productive.
I'm going to be honest, there are a bunch of line -level jobs that are going to be taken out by AI and the people who are going to 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, or AR, and you know why the payments are being processed, and you have an idea of how that fits in the cash flow, or you have an idea of how this fits into people being able to operationally buy what they need to do their jobs.
If you have that next step of logic, you're going to be able to use the AIs and do whatever the next version of your job is.
If you think your job is simply to scan the invoice into the machine and 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 think of production, that productivity, as a red herring because it's a zero -sum game.
You don't actually make more revenue by being more productive.
You just change your margin a little bit.
But it's not really a growth exercise.
I think it's much more interesting to think about the potential growth associated with bringing AI into place, what you're able to pursue, what you're able to do differently as a business.
Hopefully, a way to be able to cut areas out of your production, or your supply chain, or manufacturing efforts to be able to move forward. And I know not all of this is directly related to the finance function, but I think it is something that finance people should keep in mind because finance will be asked to be a stakeholder or gatekeeper for budget 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 people can't articulate where AI actually provides growth across the business.
I think that's probably going to be the more important part for finance 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 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 revenue chain and the value chain that is associated with the business actually making money and where AI fits into that.
that. And that is usually not cutting five seconds off of a specific job unless it is a job that is done a hundred times a day by every employee in the company.
Usually the improvements will become from things like supply chain risk or manufacturing improvement, or things that are more tangible or logistics, things that will actually affect how much gas you use, how much power you use, the SKUs that you have in place, those nuts and both things that actually run your company.
Very well said. And agree with all that.
So all right. 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 going to switch gears a little bit.
And the first question I'm going to ask you is because 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 put a date or a guess on anything, I'm no Ray Kurzweil.
So, I guess I'm going to ask you, how about a bold AI prediction?
So, a bold AI prediction for this year for 2025?
I think at least...
I know this doesn't change the market as a whole, but I do think there's going to 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 going to wonder what the hell is this because there are no employees.
It's just one person and it just drives 50 million or 100 million in revenue and it's going to come out of nowhere.
And it's going to end up being this case study for people to look at for decades.
People or agents to look at for decades.
Okay, well, so now I'm going to bring it home with 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?
I'll say that although I pointed out that I was a women's studies major in a college, 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, I guess, the politics or transness or anything.
I'm just a normal cis male dude who was at a women's college with 2 ,200 women.
So that was a really interesting and challenging year of my life.
I bet. If you've ever lived with a group of women, multiply that by like 500.
Like everything you dealt with there, that's what I dealt with.
Wow. Yeah, that had to be amazing.
So that is very interesting.
All right. And the last Next question that we ask all of our guests is, what is your favorite Excel function, and why?
I'm always going to have a little piece of my heart dedicated to the VLOOKUP.
I'm with you, brother.
I'm right there with you.
VLOOKUP 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 array.
And I'm not just dealing with B2 plus C2 equals D2 anymore.
It's like I now control this entire spreadsheet.
I know what's going on in it.
I've got these VLOOKUP and HLOOKUPs.
I can figure out what is conditional, what is not.
I can do all this stuff now.
I have the power. All starting with the VLOOKUP.
VLOOKUPs and then PivotTables right after that.
You're like, whoa. Love it.
All right. All right, so finally, how can our listeners connect with you and learn more about Amalgam Insights and follow you?
What's the best way for them to do that?
Yes. So, I both have a website, amalgaminsights .com, as well as I do a weekly podcast called This Week in Enterprise Tech, along with Charlie Arawho, who is the chief strategy officer at Symphony AI.
And we talked about the biggest AI news topics of the week and how that is affecting the CIO and CFO offices.
Awesome. We'll be sure and put links to that in the show note.
And now I've got another podcast to add to my already busy podcast listening schedule.
So I'll put you up there with Scott Galloway and Kara Swisher and the hard fork guys.
Hyun, thank you so much for coming on the show.
We really appreciate your time.
Yeah, it's been a pleasure.
