AI, Decision Culture, and the Future of Enterprise Finance with Matija Nakic
In this episode of Future Finance, hosts Glenn Hopper and Paul Barnhurst sit down with Matija Nakic, CEO and Co-founder of Farseer, to discuss how AI is changing financial planning, enterprise software, and decision-making. Matija shares why asking better questions matters, how finance teams can build stronger AI workflows, and where Excel still fits in modern planning.
Matija Nakic is the CEO and Co-founder of Farseer, an AI-native SaaS platform for business modeling, planning, and analysis. With a background in computer engineering, an MBA, and experience across B2B enterprise software, she has progressed from developer to product director and now leads Farseer’s mission to improve how financial professionals plan, model, and make decisions.
In this episode, you will discover:
Why asking the right questions matters with AI.
How finance can build a stronger decision culture.
Why clean data and governance still matter.
How AI can speed up planning and forecasting.
When to use Excel versus planning software.
AI can dramatically accelerate finance work, but speed alone does not create better decisions. Matija explains that effective AI adoption still depends on fundamentals such as clean data, consistent definitions, connected data sources, business logic, and human judgment
Follow Matija:
LinkedIn: https://hr.linkedin.com/in/matija-nakic
Company: https://www.farseer.com/
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
Follow Paul:
LinkedIn: https://www.linkedin.com/in/thefpandaguy
Disclosure: Portions of this episode (such as the introduction or promotional segments) use AI-generated voice narration produced under human editorial review.
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.
Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.
In Today’s Episode:
[00:00] - Trailer
[05:22] - Why Farseer Was Built
[08:35] - AI and the Future of Enterprise Software
[13:30] - Building Faster with AI
[17:24] - Why Asking the Right Questions Matters
[22:55] - The Three Levels of AI Adoption
[24:57] - Building a Decision Culture
[32:04] - When Excel Is Still the Right Tool
[35:57] - Governance, Context & Audit Trails
[36:37] - Rapid-Fire Questions
[39:21] - Turning Frustration with Planning Into Farseer
[40:59] - Closing Thoughts
Full Show Transcript:
00:02:07.820 — 00:02:21.860 · Co-Host: Glenn Hopper
Welcome to Future Finance. I am Glenn Hopper, along with my co-host Paul Barnhurst, and Paul, I'm pretty excited. Today we actually have a guest, so people aren't just going to listen to us ramble on for an hour; we'll have someone who actually knows what they're talking about.
00:02:22.420 — 00:02:24.700 · Host: Paul Barnhurst
Doesn't everybody like to listen to us ramble?
00:02:24.860 — 00:02:27.860 · Co-Host: Glenn Hopper
We like to think so. Anyway, tell us about our guests. What are we doing today?
00:02:27.860 — 00:02:33.700 · Host: Paul Barnhurst
Well, we're really excited to have a guest with us today. We have Matija Nakic. Welcome to the show.
00:02:33.700 — 00:02:35.020 · Guest: Matija Nakic
Thanks, guys. Thanks for having.
00:02:35.020 — 00:04:18.810 · Host: Paul Barnhurst
Me. Really excited to have you. So I'll give a little backstory and I'll go through a bio. We we met each other almost five years ago now. It would have been before I had even started my business. I think it was late 21 when you were just starting to get funding and you had just started. Yeah, yeah. And so one of the first tools I saw, and I followed her journey.
It's been exciting to see. So really excited to have you on the podcast. I know we've been able to chat several times through the years, and so a little bit about her background, and then we'll jump into things. So After graduating in computer engineering, Matija completed an MBA program to combine technical and business knowledge. Throughout her career, she worked at HT Eronet, Infocumulus, Span, and Five. From the very beginning of her career, B2B software for large enterprises was her passion, and she has managed incredible teams to deliver amazing enterprise solutions and products.She has gone through all phases, from a developer to a product director, and is now the CEO and co-founder of Farseer. Farseer is a fast-growing AI native SaaS for business modeling, planning, and analysis. She successfully leads the company towards the goal of disrupting the planning software field and finally providing financial professionals a true solution to their everyday business problems. Farseer's long-term vision is to empower business users to build any type of enterprise software on Farseer.
00:04:18.850 — 00:04:22.490 · Guest: Matija Nakic
Thanks, Paul. It sounds better when my marketing team writes it,
00:04:23.610 — 00:04:32.130 · Guest: Matija Nakic
but. But it's all true. I have this feeling that everything led me to farce here, you know, which is a bit of a romantic way to look at your career.
00:04:32.130 — 00:04:48.210 · Host: Paul Barnhurst
But isn't it always fun to read something when marketing writes it versus how you would write it? And you're like, yeah, I like this girl. Sounds cool. Yeah, it definitely like, wait, is that really me? That's what I usually think when I see something that marketing wrote.
00:04:48.250 — 00:05:21.900 · Co-Host: Glenn Hopper
And then there's the error. When people use cliches like the length, the stuff you see all over LinkedIn. A good friend of mine was just promoted, and he sent me the promotion announcement, and I was giving him a hard time about how that was worded. One of the things it said was delivered consistent results across a complex and dynamic operating environment.
And I was making fun of him because I thought some copywriter had done it. But he's like, I had to write that myself. So maybe I think it is better to to let marketing handle that, especially when self-promotion can be a tough thing to do.
00:05:22.180 — 00:05:43.940 · Host: Paul Barnhurst
That's funny. All right. Well, great. So I want to start with this question just kind of level set for our audience. Obviously Farseer is a planning software, primarily financial but does other stuff. And why did you start, you know, a tool in this space, very fragmented. There's lots of options out there.
So why did you think the market needed another tool?
00:05:43.940 — 00:07:13.400 · Guest: Matija Nakic
We didn't think about the market at the time. We thought about our own issues though, truly, because as Zdenko, who's one of the co-founders, and I worked together in a telco environment and we're both engineers, and we were flabbergasted by the way that functions or doesn't function. Uh, so it started as a hobby almost.
And, um, Matt and Luca joined as, as the other two co-founding members, uh, great software engineers, great people. And then we started thinking about what this could be, you know, in researching to other's company. Do other companies have the same problem? Uh, is there any existing solutions that.
That's when we got to the market, but not before writing a lot of code, you know. So that was that was that was kind of the first thing. That's always the first thing that that engineers do. And and basically when, when I joined the team, it was like, okay guys, let's see, you know, where this is all going. What what we could be whose problems can be solved.
Let's start with that. And curiously enough, we ended up immediately in the enterprise segment, which is not a typical startup path. Usually startups sell to other startups and kind of grows from there. And we ran into these very data heavy, very complex planning environments from day one, and that really shapes the company and the product.
00:07:13.440 — 00:07:17.840 · Host: Paul Barnhurst
Yeah, I agree with you. That's not the usual path. Like you mentioned. You kind of start small.
00:07:17.840 — 00:07:33.000 · Guest: Matija Nakic
Definitely not. But as we understood the market better and understood the problems better, we immediately knew that this is an engineering problem. Sure. You know, there are
00:07:34.440 — 00:08:16.170 · Guest: Matija Nakic
nice ways to build a dashboard, probably nicer than it is in Excel today, but in the end of the day, in an enterprise with so many people doing, planning, reporting, simulations, with so many data sources, with so many outside data sources that people are starting to add to, to faster lately to, to improve their forecasting.
Uh, it's just a performance problem. So we started building our own database and calculation engine, if not from the first week, then from the second week. So five plus years now into the journey, and we're actually now kind of, uh, getting the dividends from, from that early decision.
00:08:16.210 — 00:08:29.490 · Host: Paul Barnhurst
I remember seeing that the first time you had rebuilt it, I think we talked like it was a Friday or something like the engineers are celebrating. We've made this much of an improvement in the database as one of your first early rebuilds. I know you've been working on that for a long time.
00:08:29.610 — 00:08:31.610 · Guest: Matija Nakic
True. Still are. Still are.
00:08:31.650 — 00:08:33.969 · Host: Paul Barnhurst
I'm sure it's a never ending thing.
00:08:33.969 — 00:08:35.289 · Guest: Matija Nakic
Never ending story. Yeah.
00:08:35.330 — 00:10:21.680 · Co-Host: Glenn Hopper
So I am super interested in the idea of letting business users build any type of enterprise software on faster, because no one would ever pay me to sit in front of a blinking cursor and write code. But I've been for my whole career. I've been a citizen developer, and whenever it would tell me no or I would be in on the dev side, if I was stuck in a backlog, I would build it myself and run, you know, run it on my own laptop or whatever to try to get around it.
And I'm thinking now with generative AI, so many people vibe coding out there and I'm coding will, you know, you can build anything you want without knowing much about it. But man, the dangers of it, of not understanding the schema. Having heard of a semantic layer, not knowing what OAuth is or role based access or where, or even making your GitHub repository public instead of private people.
Things people would do if you're just if you don't have a programing background. But I think about all the limitations of the software that's out there and all the workarounds that people have had to have around that. And I'm wondering and and maybe this maybe there's two sides to the question. One internally maybe.
But with the rise of generative AI and vibe coding, the way that you can, you can build finance software or on the flip side of it, how the users might be trying to build software. And is there anything you're doing differently today just sort of in that vibe coding world? And and I'm thinking with what your, your vision is here, that this is probably a really good mode differentiator for you guys, that if people are building in your platform rather than just out on GitHub and hosting something on Vercel or whatever they're doing with their vibe coding.
00:10:21.680 — 00:13:29.630 · Guest: Matija Nakic
I love the the intro. Uh, for 15 plus years we've been promised that, you know, people can build their own enterprise software. In the end, all of those platforms, they have a very high barrier of entry. And there are some people like you, but I would say there are not enough of them. And, you know, for for decades now, you have this crazy situation where you have a group of developers, a group of business people, and a lot of money gets burned, you know, between these two not communicating properly and not understanding each other properly, regardless of if if it's in house people building something for the business or outside vendor that comes in and tries to replicate the processes and the models of of the business side.
So, uh, we noticed a couple years back that people started hacking faster and building things that were not a really like, like, uh, car fleet management solutions, you know, database of all the marketing projects, all the investments, ROI, things like that, and, you know, ESG planning, ESG reporting and different things.
Because as engineers, we haven't built a platform which lets you log in and immediately you have you kind of building blocks of how to build to PNL. We built a platform that has these distilled building blocks, actually, you know, so to some people that could understand it and see it, this meant, oh, wow.
So this is, you know, a robust tool that I can use to basically model everything. And that's what they did. So this really informed our vision. And we worked a lot on the UX to make this as more, uh, as easy as possible for, for our customers. But then you get the, the LM story, and this is where things start exploding.
So we built our own AI modeler internally and our we call it mindshare. And our productivity in the professional services team is way above ten x. So we're seeing crazy things happening here to the point where we're reorganizing the way that our team works completely. so how this works now, instead of, you know, having a big project team for an enterprise with 6 or 7 different roles, a project manager, you basically have 1 or 2 people that are architects that are highly experienced, that can kind of navigate the modeler and build the backbone of the model, and then just kind of observe and teach the customer to fish rather than handing them, putting the fish on their plate.
And and this has been a crazy ride. And this is just like a couple of months, uh, back that this started happening. So this is extremely exciting. And we really see before the end of the year having at least 1 or 2 customers, enterprise customers that will, with oversight, build their whole solution end to end.
And this is something that we're very, very excited about.
00:13:29.670 — 00:14:57.460 · Co-Host: Glenn Hopper
Wow. And I imagine you're also seeing your speed to deploy gone through the roof as well. Crazy wild times. And And I think about I argue with, uh, with Clyde every day about the way it writes copy. But I don't have that issue with code. And really I'm is these are large language models. So you would think that the language part would be the best, but the, uh, the advances in code and I'm, I'm always doing bake offs between Codex and Clyde code and seeing, you know, just because it's so cheap now and so quick to, to push stuff to production.
Again, these are obviously anything I push to. A client will go through a QC and uh, people with a little bit more skills than I do. So I'm not pushing, you know, unsafe AI slop out there. But it is it's amazing just for whether it's building a little app to track your workouts or, or something that is actually a full blown, you know, client dashboard that has a chatbot that you can talk to your data with the semantic layer and all that.
I mean, things that we're deploying today relative to how long it used to take us to roll products out. It's pretty amazing. And I know you have a much higher bar working with enterprise clients though, but you also have the the architects and the and the skill set in there to do it. And it's it'd be interesting to see.
It feels like, you know, if you're on an assembly line factory, everything just got ten x just flying down the down the production.
00:14:57.580 — 00:16:05.830 · Guest: Matija Nakic
And I would love to show you this. It's really wild. It's really wild because we did we invested a lot of effort in this, you know, layer of, of knowledge that that goes above the, the LM and the results are staggering. But I wanted to add a few more things. So we are we we already had JavaScript, as you know, part of the platform where you could just write code.
And this is the most widely used language out there. And you can automate stuff. And this paired with AI is then, you know, a superpower. And we're also going to add SQL. So we're working on that as well. And our plan is opening fast here by the end of the year for highly technical users so they can, you know, really get their hands on it and play with it because we believe in builders and this is something that the FNA or the APM category of tools forgot somehow.
You know, IBM had a really good community and was quite a technical product, but there was no spiritual successor, I would say, of this approach until we came along. So so this is the path moving forward.
00:16:05.870 — 00:17:09.880 · Host: Paul Barnhurst
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00:17:13.480 — 00:17:14.439 · Host: Paul Barnhurst
Exciting.
00:17:15.000 — 00:17:20.439 · Guest: Matija Nakic
It's it's amazing how AI has changed the way we work in so many ways.
00:17:20.520 — 00:17:22.520 · Host: Paul Barnhurst
But you know, I think everybody's.
00:17:22.520 — 00:17:24.160 · Guest: Matija Nakic
Trying to get the most out of AI.
00:17:24.400 — 00:17:48.570 · Host: Paul Barnhurst
Everybody is struggling with figuring it out. And one of the things you mentioned with AI recently in a post. I'd love for you just to elaborate. Share your thoughts on it. You put a post where you said one of the most important. Or it's more important than ever for people to ask the right questions. Kind of elaborate on that.
What do you mean by the right questions and why is it so important?
00:17:48.610 — 00:20:09.520 · Guest: Matija Nakic
AI is tricky. It can give you a wrong answer with high confidence, but it can also give you a great answer to the wrong question, which is worse than useless. It's just plain dangerous and it's just a multiplying force, you know, like that. I think I even use a Bible quote there, but I have the feeling that, you know, people that are smart were smart because they learned how to ask the right questions.
And, you know, be intellectually honest and, you know, not let go until they get something that clicks, that makes sense. And here it's true, but it's like ten x or 100 x because you have this multiplier in your in your hands. And it's the same like if you're not drilling down and if you don't have real world experience, it can it can be very dangerous.
So that's let's say from the perspective of a person using AI. I mean, you mentioned copy, Glenn. I'm also very annoyed by it. I don't want to get AI slop sent over to me. You have to be the the editor. And being the editor is the hardest work. All the best writers are like very, very good editors of their own work.
So this is, this is something that really matters. And it's the same with codes. It's actually, you know, the editing is what, what really counts. But if we talk from an organizational level, we're seeing the same pattern. We have customers that use faster AI and an amazingly clever way to really get true business benefits.
I said that finance should move from precision culture to decision culture. And these are exactly the teams that have decision culture where they have, you know, fast here in front of the board during the during the board meetings and asking really right questions. And that was fascinating for me to to see because we participated on one of those.
And then you have some teams that are really like basic level, kind of still learning how to use it and not getting so much value out of it. And of course, we we tried to to help them as much as possible to, to get to the right questions. So yeah, I guess right questions are foundational.
00:20:09.520 — 00:21:15.690 · Co-Host: Glenn Hopper
Now thinking about it in the in the board meeting that feels like the the Star Trek computer where you just like you have this omniscient, this all knowing computer out there and you're asking it and I, I know exactly what you mean, though, because it's like when you create a project and you give it the context and you put the files in, or if you have it connected to the right system, you know, in the context of that, it's going to be pretty good at answering questions, but also the underlying memory.
And that just whether it's anthropic or OpenAI or any of them that are holding in or building on you, that context is growing too. So I think sometimes people forget, like, you just start a new chat, they forget that they're coming in. And the AI is basically like that movie memento where it doesn't remember all that and they'll ask a question, but maybe from its memory, it's picking up some tiny bits of context so it can give you a really wrong answer.
So having an environment like faster where it has that, again, the semantic layer and the data and everything behind it, where it knows within guardrails. Okay. We're answering the question based on this is is significant.
00:21:15.690 — 00:22:02.180 · Guest: Matija Nakic
And it's and it's governed right. It's governed. It's audited. It's you can see every change, whether it's done by a human or an AI. And this really gives people a confidence, uh, because director of, of, uh, a in one large enterprise company told me recently, like the the business users are driving us crazy.
They got completely open access to ChatGPT and Claude, and she said, now they're throwing some excels in there. And they're asking me, oh, my EBITDA is like, lower than you. She she said, we have like 18 definitions of EBITDA, you know, but their Claude doesn't have that. We really strive to have the, the right, the right context there.
And then the questions are making much more sense.
00:22:02.220 — 00:22:17.420 · Co-Host: Glenn Hopper
Yeah. That reminds me of the era of when everybody wanted a self-serve data mart. And the idea is everybody can come just pull the data that they want. But if they're not, if they're not following the same data dictionary that like you said, well, sales define the pipe. Yeah, yeah, yeah.
00:22:17.460 — 00:22:19.300 · Guest: Matija Nakic
Good old data dictionary.
00:22:21.980 — 00:22:25.590 · Host: Paul Barnhurst
The Dictionaries data Foundation. Have we been hearing this for 20 years now?
00:22:25.630 — 00:22:35.670 · Co-Host: Glenn Hopper
Yeah. And somehow people still it's still not sexy. Nobody wants to do it, but it's too found. And now it's just like, if you don't have your data right and you're trying to throw AI on top of it, good luck.
00:22:36.110 — 00:22:55.430 · Guest: Matija Nakic
Same principles still apply. You know, the laws of software haven't changed. It's always going to be garbage in, garbage out. AI is just amplifying the garbage out and it's giving it to everybody as opposed to 1 or 2 data analysts. So the problem is becoming just heavier. You know.
00:22:55.470 — 00:24:56.860 · Co-Host: Glenn Hopper
Ah, you mentioned before I want to talk about the three levels of AI use. And I always I think I see this too when I do a lot of training for teams, because before we do any kind of implementation, we like to get that with a data foundation. But then the sort of enablement and training foundation and I think about.
So generative AI has already hit the peak of inflated expectations, gone through the sort of trough of disillusionment. I would argue that it's sort of coming up into the knot, sort of. I don't know, I haven't seen the latest Gartner, but I would imagine they have it positioned in the plateau of productivity.
Yeah, yeah, I would imagine that generative AI is generally sitting there right now. But there's the hype cycle, but there's also the adoption curve. And I you know, early on I thought people were the adoption. I thought it mirrored the the hype cycle. But really what I'm seeing is the people who rode the front end of that wave going back to 3.5, you know, ChatGPT 3.5.
And through that, that was really the the early adopters, the bleeding edge and all that. And now where I would think we're in laggards, I think that it's the central mass of adoption is coming through right now. So if you spend all day using these tools, you think about it as if like, that's just everybody knows that.
But when you talk to users and how they're actually using it at work, this goes with what you said about the three layers of AI and finance, and the first one being, you know, the copy paste layer where you're talking to the chatbot and having those interactions just kind of ask, answer, take that, copy and paste it somewhere else or whatever they're doing with it.
And then some are automating processes. But that third layer, and I think this goes along with sort of the faster mission to is creating that decision culture. And you referenced it in the in your last answer as well. So if we could dig in a little bit on that, maybe talking about the layers and then really what that peak layer of, of having AI as part of a decision culture, what does that look like and how does finance create one.
00:24:56.900 — 00:29:05.820 · Guest: Matija Nakic
Well, the first layer, like you said, I think majority of people in companies are still here, which is throwing, uh, excels building some models, doing some analysis, kind of ad hoc work. And this is fine. Uh, it brings some value. But, you know, on the company level. I'm not sure how much you know the CFO or the board can.
Can feel some value from from this. Sometimes it even gives you more work than you used to have, right. In certain situations. And then you have the second layer, which is a bit more advanced, where there's more orchestrated approach in a company. Uh, for, for AI, usually, you know, using somebody from the big four, some vendors, taking a specific process and just running it more, more efficiently, cash collection or whatever.
And I think this is this is very, very valuable. And this is a small fragment of the companies, but the most advanced ones are certainly already here. And they're not even bragging about it, you know. So that's one thing to know as well. They want to gain competitive advantage, of course. And then the ultimate layer in my book of AI would be really getting to decision culture, where you have a single source of truth and AI on top of it, And we see far here as the single source of truth, regardless if the UI is far seer AI or, you know, clod or ChatGPT.
Because because we're opening towards this as well or in Excel, and we think users should pick their environment and work where they feel comfortable working most. So we even have teams, you know, and slack as where where our AI AI chatbot approaches the user or gets answer from them. So this decision culture basically requires the the old school stuff, you know, clean data, data dictionary connected data sources that are continuously updated with only the changes.
Business logics, which is basically context human judgment in the end of the day, because it's impossible to just run things, you know, and have AI run things. So so it's kind of human in the loop. And there are two levels here. One level is like. Like I mentioned the mines here, the builder, the modeler that helps our customers build any kind of a model really quickly.
Different kinds of reports, different kinds of planning sheets and so on. But the other parts that we've built is called workflows. And workflows essentially enable our customers to automate standard set of steps so they can run it more efficiently and faster. And this very often includes a human in the loop.
So for example, flash report some sort of deck that needs to be handed to the board, uh, once per week or once per month. Regular forecasting cycles where where something comes in and something changes. Uh, and then it needs to go through a certain set of people to do kind of a high level forecast, but also it enables a much more frequent forecasting based on a change we all know about, you know, cocoa prices and things like this.
But but these are real world scenarios where our customers in the CPG had this drama and it took them weeks to to replan. Maybe it even took 1 or 2 weeks for somebody to even react and say, hey guys, you know, I don't think our plan is valid anymore. So so you can also do these ad hoc things much, much faster because the whole cycle or almost the whole cycle is automated and it just brings a human when it needs an answer or a confirmation.
And we are still early on in these workflows, and we're testing it with, I think, 5 or 6 different customers at this point. But we already see the the great benefits that they're, that they're getting from this. So the the decision layer would in our eyes look, look like that.
00:29:05.860 — 00:29:48.200 · Co-Host: Glenn Hopper
Yeah. It's such a fascinating time because I think if you've been working with the tools and the models for a couple of years, you can sort of see that. But if you're just now jumping on the AI train, that sounds. It still sounds like science fiction, but with the longer running authentic tasks. And also, like you said, building frameworks, whether it's an agent harness or whatever, where you have the guardrails and you have it where this piece is deterministic, AI is working here.
It's repeatable, it's auditable through the whole thing. Like that's two years ago would have sounded like sci fi, but we're really starting to see that out there now. And I know with the work that far seer is doing, that's got to be a you're you see that future and are locked in on it and moving towards it.
00:29:48.400 — 00:29:49.240 · Guest: Matija Nakic
Absolutely.
00:29:49.280 — 00:30:02.000 · Host: Paul Barnhurst
It's crazy how quick it's moving and what AI can do right now. It just keeps getting better and better. And I think, like you said, uh, Glenn, maybe we need all watch Space Odyssey again or something. Where?
00:30:03.960 — 00:30:09.690 · Co-Host: Glenn Hopper
I don't know if we want Hal, but I don't know. I might take Hal over. Uh, some of the recent cloud outputs I've got it.
00:30:11.090 — 00:30:12.690 · Co-Host: Glenn Hopper
Just don't turn off the life support.
00:30:13.690 — 00:30:16.130 · Host: Paul Barnhurst
Only a few nerds will understand that are listening.
00:30:16.170 — 00:30:26.170 · Guest: Matija Nakic
I could agree with Glenn. I would say hell, recently the copy is really bugging me because the language is not human at all. Okay, I'm not a native English speaker, but.
00:30:26.210 — 00:30:28.810 · Host: Paul Barnhurst
But come on, it's not X, it's y.
00:30:29.650 — 00:30:31.290 · Guest: Matija Nakic
Yeah yeah yeah yeah yeah.
00:30:31.290 — 00:30:42.090 · Host: Paul Barnhurst
There there are a lot of things. I just read it and like I can't put that out in the. That's not going into the world. Yeah that's getting a rewrite before anyone sees that one.
00:30:42.250 — 00:30:49.130 · Guest: Matija Nakic
So being the editor that's our job now ruthless editor of AI slop.
00:30:49.730 — 00:31:27.740 · Host: Paul Barnhurst
A lot of truth to that because it can produce so much so fast. Oh, so you guys will laugh about this and we'll get back to the question. I was preparing for a different podcast interview I had to do, and copilot had my transcript, so I just asked it to summarize and come up the questions I should ask. And, you know, normally it's those I do about, you know, 10 to 12 questions on this podcast.
It came up with 2500 words. It was like eight pages long. There was literally like 100 questions. And I just responded with, and how does this fit in? I mean, I gave some snarky response and it was like, yeah, I weigh over, get it? And I'm like, you think.
00:31:28.180 — 00:31:38.420 · Guest: Matija Nakic
The fact that we're talking with AI like it's a person that's that's also really, really fun. Yeah. Think, you know that that comment is like, you think I know.
00:31:38.420 — 00:31:49.900 · Host: Paul Barnhurst
And I love how people like some people I know they'll refer to Claude as a man, or others will call it a woman or, you know, it's we give it genders. Sometimes it's really interesting to watch.
00:31:50.020 — 00:31:51.780 · Co-Host: Glenn Hopper
Yeah, I call it Uncle Claude.
00:31:52.340 — 00:31:56.340 · Speaker 9
Here, here we we here we have this Italian variant.
00:31:56.460 — 00:32:02.260 · Guest: Matija Nakic
Claudio. I don't know why, but it's sticking in farce here. Maybe because we're across Italy here. So.
00:32:03.540 — 00:32:37.670 · Host: Paul Barnhurst
Yeah, you're pretty close there. All right, so I want to ask an Excel question because, you know, PHP and a software and PHP and a wouldn't be good without mentioning Excel. We hear from a lot of vendors, we hear a lot of this, hey, you need to get off Excel. Excel is dead. Excel's a a terrible tool to use. It's error prone.
So I love your take. When is, you know, Excel the right tool. Because I think we all know it's not going away and nor do I think it should. There's always a place for a spreadsheet. But what's your take? When should you be using Excel? When should you be looking at a purpose built tool? Maybe just share some of your thoughts there.
00:32:37.710 — 00:32:42.630 · Guest: Matija Nakic
I think this is sheer laziness. You know, to say Excel, bad software, good.
00:32:42.830 — 00:32:43.350 · Speaker 9
Is.
00:32:43.350 — 00:35:23.630 · Guest: Matija Nakic
Bad marketing and it's not telling the truth. I mean, you know this and we've talked about this a lot. We we always saw Excel as a source of inspiration because it is inspiring. And it still to this day I think it's the best tool. We, as you know, tech people have given to humanity. But of course it has its limitations.
So it really the answer is it depends. I mean, Excel is great. Excel is great. If you are doing rapid modeling, you know, new ideas, some commission structure, new pricing model. You want to test it fast. It's unbeatable for for speed and modeling and isolated analysis. One person checking things. Great tool when in general when there's low collaboration burden I would say you know it's really great.
And we know that there are problems when multiple teams are touching the same numbers versioning, chaos, hidden formulas, things that are a great audit risk. So also you know when you need real data inside and you need it refreshed quickly, that also becomes a problem. There's a lot of problems with like large amounts of data.
And then you have all these crazy techniques that people use called Excel cutting and so on. Distributed ownership. So, you know, we always say a lot of people planning a lot of spreadsheets, probably there's there's a pain point. And also for industries that are very sensitive. And in terms of planning, like we see this a lot in pharma and CPG companies that that have their own products.
If you plan poorly, you really lose cash. So you either don't sell as much as you could because your demand forecasting is off, or you have way too much stock and you're burning money, basically. Yeah, I would say Excel is great if it's working for you as, as as your, you know, one off one person excel jockey, please continue.
And in general, you know, there are just companies that don't have such a complex planning environment. And we had inbound leads coming to us where our sales team would say like, you know, please keep doing it in Google Sheets. This is great. Like, you really don't need us. This structure. Nice. Looks great.
There's like five of you. It works perfectly. But, you know, in the enterprise, it's pretty chaotic. And it's. And it's very dangerous. But I would never say something like Excel has these billion dollar errors. You can make billion dollar errors in the planning solution too, you know. So so I think that also needs to be said.
00:35:24.110 — 00:35:42.430 · Host: Paul Barnhurst
Yeah. I'm probably saying is the errors are a human error. Now could you say the software could be designed differently to limit them. Sure. Like having a database on the back end and some things, you know Mike, it has a chance of that error but it's still a human error. It's not the tools fault error.
00:35:42.630 — 00:35:42.870 · Guest: Matija Nakic
Yeah.
00:35:42.910 — 00:35:49.710 · Host: Paul Barnhurst
Still I'm with you. I, I hate when people use that as the reason you should buy a tool. Well, Excel makes a lot of mistakes.
00:35:50.030 — 00:35:54.190 · Guest: Matija Nakic
It's lazy marketing. It's lazy marketing, basically. Uh, but.
00:35:54.230 — 00:35:54.830 · Host: Paul Barnhurst
I'm with you.
00:35:55.270 — 00:35:56.070 · Guest: Matija Nakic
In the.
00:35:56.350 — 00:35:56.790 · Speaker 9
AI.
00:35:57.270 — 00:36:37.000 · Guest: Matija Nakic
Context. It's kind of playing in our in our benefit. Especially in the enterprise segment, precisely because of the things that we've mentioned. You know, the data dictionary, the context, the the stamps, who change what and where. You know, was it human? Was it AI? Was it some demand forecasting model that that did something?
You really need this audit trail to, to understand what's happening to to your numbers. And you need context as well, right. It's not just stamping things, it's also context from the people that have to explain why they did something. And then if you have all that in one place, it becomes much easier to to make decisions.
00:36:37.120 — 00:37:35.290 · Host: Paul Barnhurst
Agreed. All right. So we're going to move into our fun little section. We do at the end. We're right toward the top of our time. So we'll we'll go through this pretty quick. But how it works is we give your bio the questions. We came up with access to the internet, and we ask AI to come up with 25 kind of quirky, fun, personal questions about you.
and we use different models. This one was fable 5.1. And then you get to pick. So there's two options. I take one approach for one question, Glenn takes another. You can keep a human in the loop and pick a number between 1 and 25. And I'll ask that question. Or I can use the random number generator to pick a number seven.
All right. She didn't even hesitate. So it says you keep your hobbies as a reset with no deadlines and no KPIs. So what is the hobby and how bad are you at it? I don't know how it came up with that question, but that's today's question.
00:37:37.410 — 00:38:28.420 · Guest: Matija Nakic
Okay, I go to the gym just to stay healthy and fit. I wouldn't even call it a hobby. I think it's just kind of a support system to everything else that I have to do in a day, but I never, ever. So I have this app, but I never, ever put in the kilograms or anything like that because I think I don't want to see data ever, you know, if I'm outside of the company and I and I, and I work by feeling and it's I'm also crazy like that in running.
So you know how people have these fancy Garmin watches and this and that. And I was like, I go out without a phone, I don't have a smartwatch, and I run until my teeth or my gums start tingling. And then I know that I had like that. I'm drained, that I'm like just really tired and I can go home. So I guess, uh.
00:38:29.660 — 00:38:32.180 · Host: Paul Barnhurst
Well, I've never done that one, I love that.
00:38:32.380 — 00:38:52.380 · Co-Host: Glenn Hopper
Uh, okay. So weird, but I so I am obsessed with Strava and tracking everything but the talking about your gums tingling. This has started in recent years for me. If I do a long bike ride or it's really in the heat, more long bike ride or run, my lips go numb and I asked my doctor about it. He was like, you sound crazy.
I've never heard that in my life. No, it's like now I'm hearing it from you.
00:38:52.380 — 00:38:53.740 · Guest: Matija Nakic
So it's like, okay.
00:38:53.780 — 00:38:54.990 · Co-Host: Glenn Hopper
That actually the thing?
00:38:55.030 — 00:39:00.390 · Guest: Matija Nakic
Okay, I just use that. No, I have no smartwatch and I don't bring a phone, so.
00:39:00.790 — 00:39:07.230 · Co-Host: Glenn Hopper
All right. Well, that's. I'm, I'm I'm glad I found a kindred spirit with the same, uh, mouth going numb thing when after I work out.
00:39:08.910 — 00:39:09.990 · Co-Host: Glenn Hopper
So it makes me feel less than.
00:39:10.030 — 00:39:12.910 · Host: Paul Barnhurst
Running together till you're numb. You're. You're coming, you're now.
00:39:14.790 — 00:39:17.550 · Guest: Matija Nakic
Go running till your gums are numb. Try saying that.
00:39:17.990 — 00:39:20.310 · Host: Paul Barnhurst
I know I butchered it when I said it.
00:39:21.390 — 00:39:49.110 · Co-Host: Glenn Hopper
All right. So I take the human out of the loop in my selection. And I figure I wrote the question. So then I'll ask a different AI. And today it was Jim and I. I feed it the questions and your bio, and I say which of these is the most interesting. So on mine it came back with you. That's a pretty good question. You once wrote that back in your telecom days you had strong contempt for financial planning.
Uh, what was the moment that turned that contempt into a company?
00:39:49.110 — 00:40:45.730 · Guest: Matija Nakic
That's a good question. I think the contempt turned it into a company there. There was no box in between. It was luck. This is horrible. How is it possible that people still live in this two dimensional array? And it was crazy because we would get to March and we still didn't have a plan for that running year.
So it was complete psychosis, really. You know, as part of this managerial team, I was the captive of of this Excel hell that we were living in, and it was super inefficient. You know, nobody knew anymore after 6 or 7 cycles who said what? Where's which version? If the marketing and sales actually synchronize?
Most of the times they didn't. So it would really put a company into jeopardy because we didn't really coordinate things properly. So yeah, I guess that's the answer. There wasn't much in between companies.
00:40:47.010 — 00:40:57.940 · Guest: Matija Nakic
Yeah, yeah. The thing in between was was the, you know, the question. There must be a way that this can be better, you know, and let's try to build that.
00:40:58.700 — 00:41:18.820 · Host: Paul Barnhurst
Great. Well, Maria, thank you so much for joining us. It's always a pleasure to chat with you. Uh, been a huge fan of Faqir. I love the area you're in, being there in Croatia. Beautiful country, and keep building. Thank you for carving out 45 minutes for us. We really appreciated the chat, so thank you.
00:41:18.820 — 00:41:22.140 · Guest: Matija Nakic
Thanks, Glenn. Thanks, Paul. It's been a pleasure as always.
00:41:22.180 — 00:41:23.300 · Host: Paul Barnhurst
Thanks for listening.
00:41:23.340 — 00:41:38.420 · Host: Paul Barnhurst
To the Future Finance Show, and thanks to our sponsor, Kubeflow AI. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice, and may your robot overlords be with you.