How AI is Being Used to Make Better Decisions and Build Faster Models with Nicholas Moen

FP&A teams will need to move faster to keep up with the rest of the business. They will be able to. I think the AI will be a good equalizer between that. A great FP&A is back to what FP&A is. Today our guest is Nicholas Moen, Director of Finance at Section, a rare strategic CMA who can optimize business decision frameworks and build conversational AI prompts. After starting his career leading finance through dense operational complexity, transitioned into multifunctional executive leadership. Opus 4.6 really transformed how I interact with finances, and it's almost completely reoriented how I work. I think any company right now that's saying, oh, we eliminated so many jobs, so we're layoffs because of AI, that's false. They just overhired. Understanding data structure is probably one of the biggest technical skills, probably even before AI.

Any parting advice you want to give our audience around being a better business partner.

I would say learn some skills from—

Welcome to SP&A Unlocked. I'm your host, Paul Barnhurst, and this podcast is all about FP&A. It's where finance meets strategy. Each week we bring you conversations and practical advice from thought leaders, industry experts, and practitioners. Who are reshaping the role of FP&A in today's business world. Speaking of strategic impact, we have here with us today Nick Mullen. Nick, welcome to the show.

Thanks for having me.

Very excited to have you. So we're, we're continuing our series. For those who are wondering around AI, we got a lot of episodes where we're going to be talking about people that are leaning in heavily to AI. So before I get into that, let's go ahead and introduce Nick, share a little bit of his, his bio and his background. So Nick Moen is a CMA and Director of Finance at Section, an AI software company that helps enterprises, enterprise organizations deploy AI to their workforces. At Section, Nick runs finance, people, ops, and operations, actively deploying Agentic workflows and an AI-native tech stack across all three functions, rethinking how each operates. Before Section, Nick spent nearly six years as a finance operator at Waste Technology Company, where he built and scaled financial systems and teams across a combined hardware, software, and services model. He has a track record of seeing through operational complexity, identifying bottlenecks, and building systems to eliminate them. Love the background, and once again, welcome.

Thank you.

Alrighty, we're going to start with this question. I always love to see how people answer it. In your opinion, or kind of from your view, what does great FP&A look like? How would you define it?

I think nowadays that question, the answer to it is a bit of a moving target. And what I mean by that is I feel like with AI and how it's rapidly iterating. Because I remember when Opus on Claude, Opus 4.6 came out, I was not using Claude. I was using a different model and I wasn't really doing as much finance, but Opus 4.6 really transformed how I interact with finances and it's almost completely reoriented how I work. So I'm, I'm going a bit of a long way around this, but. Even in the past 5 years I've worked, I've gotten a little bit more removed, especially with AI, from the day-to-day weeds, maybe Excel spreadsheet building or anything like that, and moving more and more towards essentially the decision framework of FP&A. So what I think FP&A, I think this has kind of always been what FP&A has been about, but I think it's more about is How can we reach the right decisions and information in the business? And AI's really pushing us to that. So in my mind, great FP&A today is what great FP&A always been, but just less of the modeling, more of like the analysis, deciding what the decisions are, what are the key levers to the business, and just rapidly getting there more quickly and just focusing more on that. And I think it's just going to continue to be pushing teams into that direction. As AI just gets more advanced because one, the business is just going to move faster too, because finance is not the only, it's not the only part that's getting impacted by AI. And if AI is like velocity, you can do more quicker. That's going to impact the rest of the business. And I mean, if you worked alongside marketing, sales, you know, the more high velocity teams as finance, you you kind of always like, I don't know, me sometimes I feel like I'm trying to keep up. They're going to move faster too. So I think FP&A teams will need to move faster to keep up with the rest of the business, but they do, they will be able to. And I think the AI will be a good equalizer between that. So to, in my mind, a great FP&A is back to what FP&A is on a fundamental level. Level, which is business partnering, and be able to be in lockstep with the rest of the business. And I honestly think that's, AI is going to be the almost catalyst to that.

Yeah, I've heard a lot of people say something similar of it really allows us to focus on what we should have been focusing on all along and less of the data prep and the cleaning and all the fun things that we all enjoy, right?

I love it too. Yeah, I love doing that stuff. And it's kind of like, It's like a bittersweet. It's like you, you kind of giving it up, but at the same time you're kind of doing what really drives the value too.

It's exciting for sure. All right, let's get into AI and Claude. You know, you talked about kind of AI as a velocity tool. So, you know, when you and I chatted, and I wanna start with this question, you mentioned that you haven't used a spreadsheet for a new analysis in a few months. I think you said since like January or something, but basically since you began using Claude. Talk about that.

I would say it again, it was like Opus 4.6 was the turning point for me. Like I stopped making spreadsheets and I stopped, didn't stop using them, but I stopped making them. And at a certain point I just started asking Claude to make them. Now I gave it all the information, all the context it needs, but I started to realize that what Claude's like the, it's the first version it makes, it's actually pretty good. Especially if you tell it the right things, you kind of have to treat it like an intern still. Maybe not even an intern. I think it's gotten a little better than like, if you grade these competencies based off—

It might be the junior analyst or whatever.

Yeah. It's more of like a junior role now. It's kind of getting a little bit more smart. It's, it's, it's done with college. It kind of has a few reps under its belt and it's like, you can probably give it a task and can give some fairly coherent. Yeah, it's, I think it's at that point now. Now I don't think it actually nails it right outta the gate. Usually I have to take the last mile. It gets me 80% of the way there. And then I, I bring it into a spreadsheet and we use Google Sheets. So I bring it to Google Sheets and modify it, challenge the assumptions, maybe tweak the assumptions, maybe fix some kind of dumb formulas that it did, but I think for the most part, it does a lot of the bones and the framing quite well. And that's time. Like if you open up a new spreadsheet, you have to build that. And like, I'm assuming you've done a lot of modeling, but it's, uh, and I'm sure a lot of people listening have done a lot of modeling. Those framings are not like exciting work or anything like that. You're building out your assumption rows and your tabs and all that. And especially if you can give Claude kind of your way of modeling, you can especially replicate that. It can build that like version 1 for you, and then you can take it and mod it from there. And it's a meaningful amount of time. And especially when I'm building and I'm, I kind of have an idea of what I need to model, but it's not fully formed. So it helps me talk it out with Claude. That's, that's kind of how, like, I kind of like go back and forth. It gives me kind of like a mockup and I'm like, no, that's not it. That's not it. That's not it. I need to, what if we change this variable? Like, no, we need to rethink how we waterfall this out. I was actually doing that today. And it can reiterate and even rebuild an entire model a lot quicker than I could rebuild an entire model. So if I just start off a bad way of my assumptions were just all wrong. And then when we built it out, it's like, no, this is not working. We need to go. It can just take it apart and rebuild it a lot faster than I can. And then when I feel like I actually have something good, then I bring it over and then I own the spreadsheet, especially it's like a recurring model. Then I refine it. I get the formatting correctly. It's, I will say Claude is still bad on the formatting. It's really bad in formatting actually, but that's okay. That's an easy fix. I can do that part. And I'm still doing that. Like I still haven't had a reason to change that method. It's, it allows me to build things a lot more quickly.

And how much time do you spend validating? 'Cause we all know Claude or any AI makes mistakes. How do you think about that with all of it?

Yeah, I think that's a good question. I spend a good amount of time validating and, but I, I put it kind of on a sliding scale of like, who is using this information and how, what decisions is it driving? If it's pretty low stakes, yeah, I'll, I'll go look through the assumptions and make sure the formulas tie out and everything like that. But like, if this is for, like, let's do an extreme example. If this is for like reporting numbers to the board or CEO, yeah, I'm probably going to dig pretty, pretty hard and just check everything else out. But I'd be doing the same thing with any employee as well. I kind of see that the same thing. And it's really like, it's a balancing act of like, how high stakes is this information versus how thorough you need to be. Because one thing I also do is I'll take the model and have Claude, or you could use ChatGPT or anything, and I'll tell it, okay, now go check your work. Like, go look at everything, go look at the formulas. And it actually does come back. It's like, oh, I made a mistake. Let me go fix that real quick. And then you do that a few more times and say, okay, now go check your mistakes, go review your work. You can even copy and paste the prompt and it's going to keep doing it over and over again. Eventually it'll say, yeah, everything looks good. And then you can take that and then you could maybe save yourself some time. You could still check everything. You're still going to want to check everything, but it's going to be pretty clear, especially if you tell it to design it like in a more— I'm very much a fan of the KISS principle. I want to keep things as simple as possible because that makes it as easy to audit. So if you have a design, it's also a lot easier to validate everything as well, especially if you have the assumptions flow instead of having one behemoth of a formula.

Makes sense. And yeah, no, I've heard someone else say to me, like, if you're using, you know, AI, make sure you ask it to check itself at least 3 times for model building. It kind of goes in line with what you said and it will find mistakes and get better. We asked one to figure out, you know, early on none of 'em could tell me why the balance sheet didn't balance. And we'd always ask 'em to correct it. Favorite answer I got along the way was, Your balance sheet is out by $1.2 million. That's only a, you know, 30 basis points. That's an acceptable error rate. No, you don't understand how balance sheet works. That is not acceptable.

You're no longer the balance sheet.

You are fired, Claude. Yeah, actually I think it was early. They may have been using Claude. It wasn't Claude's Excel, but they were using one of their early models and this was 6 months ago now, but it even a little longer and it was like, oh boy, we got a ways to go if this is the type of answers we're getting.

Yeah. Oh, one, one thing is actually I got this from our CTO and I quite love this prompt is, uh, he just simply says, what would you do if you were me? Simple thing. And especially since these tools are kind of understanding who you are as a person, the more they use it, they kind of understand you. They know, hey, I'm Nick, director of finance, and it kind of gets my analytical profile and it'll just run through the same things, but maybe it'll take it from my perspective, kind of like that prompting method where you assign a persona. You're kind of doing that in a way of assigning your persona to the, to Claude or ChatGPT. And then it's gonna take that approach and think of it from what it thinks my perspective is. And then again, copy and paste that 3 more times and it's gonna keep running it through and running through. It's actually quite effective. It's, it's one of those classic cases, it's a very simple thing you can say that's very powerful.

I haven't tried that exact one, so I'll have to give that a try. I was already thinking in my head, I'm wondering what it would say about this. So thank you for that. It's a good, good one, the other one is just, you know, remind it to keep checking. And so I think, you know, we'll keep going here, but it sounds like in the spreadsheet, your advice to people is just start using AI to help you build.

I think that really what it comes down to is like, don't try to boil the ocean. Don't take your most complicated thing and try to have AI do it. Keep that to yourself. That's okay. Make, make that your special sauce. But like, Try to eliminate all those little things that you don't, maybe are not as value-add. It doesn't even need to be FP&A. It could be your emails. Get an agent to run through your emails. It could be having it summarize, if you have Slack and you integrate with Slack, have it summarize your Slack messages for the day. Maybe Teams. Anything that's not, so to speak, to that special sauce. Like try to get rid of that so you can focus on those things that are really valuable. Especially if going back to like what great FP&A looks like, like anything that gets you closer to business partnering. I think, I think that's really how, and a great way to start using AI is try to eliminate those. And it might be a few, 5 minutes here, it might be 10 minutes there, maybe 30 minutes there. But all those, all those minutes add up to hours over the course of the month, which gives you more time to actually put your brainpower. To something that's actually valuable and that requires brainpower.

That's good advice. I know you mentioned you've been using Claude Code and Lovable quite a bit. Had you ever written code before you started using them?

I was fine, decent at VBA. Not gonna say I'm the best, enough to kind of hack together solutions. Struggled through SQL enough to accomplish a few projects, but I'm not gonna say I could, I could probably look at SQL code and be like, that's, that looks like gibberish nowadays. Other than that, no, I don't code. I don't, I never really had strong desire to learn. Yeah. So with vibe coding, like I, I know it's making code. I don't know what it kind of means. I could probably read some variables, especially with this like string text. You could probably work your way backwards and I've modified string text, but yeah, I'm not, I'm not a coder by any means and nor would I ever claim to be one. So.

What made you decide, hey, I'm gonna try to code some stuff. Was there a need where you thought, hey, this is supposed to be good enough, I can solve a problem, or it was just like, hey, this looks interesting. So how did you decide, hey, coding, you know, using Lovable, those could potentially help me in, in my work?

Yeah, I was given a Lovable subscription, so I decided to try to use it. I mean, and the real story is it was some, it was an AI summit. And they had like a vibe coding, like, here's, here's Lovable and we're going to vibe code. And it's like, okay, well, I'm here. I'm going to do it. And I was like blown away that I could have a mockup app in 5 minutes of prompting. Like, and I'm, I'm talking like an actual like SaaS app. Now it was, it was a mockup. It's not like nothing production. It was, I believe what it was, uh, was it inventory tracker for my sister's, uh, nonprofit where she collects baby supplies for families that need it. And she has like a bunch of supplies and just random bits and ends, but she doesn't like have a way to track it. So it was in a Google Sheet and it was kind of a mess. I'm like, hey, this is a great use case. It's pretty low stakes. And let's see if I can get some inventory tracking. It did it so well. And I was like, this is crazy. Like it was so easy. And then to iterate on it, if you're using ChatGPT or Claude, you can use Lovable. It's the exact same method. It's just this exact same prompting methodology. And just because it's writing code is not really any way to be intimidated by it. It's doing that for you. You don't even have to worry about it. I'm not going to say the code's great. I've, I've heard from, you know, engineers I've talked to, it's not good code. So if you're trying to make it like an app to sell on, like to the world, you may want an engineer to take that code and make it not breakable.

Sure, you might want to treat it as an MVP.

Exactly.

Or a wireframe, or yeah, whatever you want to call it, but not production ready, so to speak.

Right, exactly. And, uh, I would— it— there may be a time when it gets there. I don't think it's that time right now, especially if people are going to pay you money for the app. But it's pretty simple and pretty quick to use. It's just I think for me it was starting because it, it felt intimidating. It's like, I can't build an app. Like that's, that's not really my skillset. And then when I did like one in 5 minutes, I'm like, oh, I can build an app. This is like, this is, this is different. This is way different.

Yeah, no, make, makes a lot of sense. Oh, I know you, so you ended up building a commission software for the company. How long'd that take you?

Less than 60 minutes, honestly.

And how often are you using it?, we use it every commission cycle now.

It is completely calculates our commissions.

And did an engineer have to do any coding with it or you did 100% of it? Is it sitting in the cloud? Talk a little bit about that.

Yeah. So what we did, and we're, we're iterating on it. Okay. But it's—

So you're not gonna claim it's perfect?

No, it's not perfect. The calculations are perfect. I'm gonna make that clear. Our reps are being paid accurately. But that being said, I would say the app is not—

Good clarification point. I'm sure reps listening right now going, what?

The apps are like, the apps are good for me to use. I would not give it to somebody else because it's a bit more of like my brain as an app and no one wants that. But that being said, how I actually went about it is we have a, you know, I don't wanna say it's a simple commission structure, but it's a very nuanced one and there's, multiple triggers. I gave, I actually did this a two-part, mostly because I, Lovable has more expensive to use, but I gave Claude our commission agreement and basically pull the calculations out in the triggers, analyze it, understand it, then give me a prompt that I can give to Lovable. I did actually say Lovable. It gave me a prompt, took that prompt, copy paste in the Lovable, and Lovable gave me basically something that was accurate. In the first prompt, but it wasn't quite what it needed to be because I couldn't finesse it as I need. Like it hardcoded the rates. I didn't want to hardcode rates. I want to be able to change those. So this would have been a very complicated workbook to make. And that's actually what kind of started it is I started making the workbook and I'm like, I don't want to do this. This is, this is so much. I would have to do this per rep and there's just so much I'd have to build out. This is at least 300 rows. So I'm like, you know what, let's try it. At worst, I'm 30 minutes out of my day if it doesn't work, but if it does work, I am sitting pretty. So I gave it to Claude and gave it to Lovable and I'm like, this is way easier.

No, I'm already thinking of, I've been meaning to, there's some, there's two things I'm wanting to vibe code and I just need to sit down and do it. But—

What are those things?

They're, they're not FP&A things. They're just some stuff for my business, some Yeah. Process things that some, something I want to get on my website. You know, basically some questions you'd fill out and it would give you different options type of things. See how kind of questionnaires to give you options.

So. Yeah, that's a great use case for it as well.

Yeah, no, I think it'll be good. I have the data now. I've done a lot of the work on all the backend stuff and I'm like, okay, I'm sure it could help write some of the logic and just work through it all and see what I can build. It's just time. You know how it is. You just gotta sit down and do it. We all pick where we spend our time and I haven't spent enough time on it.

Well, I, and I think that, that touches on a good point. Like what's great about a tool like Lovable, you can use Claude for this as well. This is what I've challenged my team to use this stuff for, is Lovable is great for making that microapp that's completely bespoke to who you are. So if you have that one process that's just uniquely you, And you wish, ah, I wish there was software for this, but no one's going to make it. That's what Lovable can do. Like it can make those like little micro SaaS apps that you've always wanted, but never could have because no one's going to build it. There's not a big enough market for it. And an engineer is not going to build it for you internally because it's just so like nuanced and bespoke. That's where I'm finding a lot of power in it. Just something that's, I need this automated, especially with agents nowadays. Agents also kind of filling that gap as well.

All right, so when you and I chatted a few weeks ago, you mentioned you finished an analysis for the CEO using Claude on your phone while putting your son to bed. Tell us the story.

Yeah, yeah, so this was like classic end-of-day request. It's like, it's okay. I was going to log off and go make dinner. Obviously, I realized that this needs to be done.

Yep. They wanted it yesterday.

Right. Yep. So what I did was it's, it wasn't like one of those things where it's like, okay, I'm gonna just get this done in 30 minutes. This was part, this was like a pretty massive deep dive in our model assumptions. So I gave Claude like all the documents it would need that's on my computer. I gave her a model. I gave it other supporting documents, anything that it would need to do need to have for this analysis, it had it as a conversation. And then I closed my laptop, made dinner, played with the kids for the evening. My youngest is 2, uh, so we still put him to bed and like any 2-year-old, he lays there awake for an hour, just not going to sleep. So I knew that, okay, I had all the context I needed in the chat. So I start, I pull out my phone and I just start prompting, like, it has the context, so now let's work through the analysis. So I'm just telling it. This is how you analyze it, this is how you should look at it. And okay, now build the spreadsheet. And I'm like reviewing the spreadsheet on my phone and that was painful, but I wasn't typing in it at least. And I was, I was understanding the assumptions, the model, like redoing it. And essentially the, the, we were supposed to, like, we wanted to view some cost assumptions in buckets. We didn't really have it modeled out and viewed. It basically just described everything it needs to think through and how it can pull the assumptions, where it can pull the assumptions. And how it can organize those assumptions better based off like the vendor or the supposed vendor, then structure that into an output that I can give back to the CEO that's a bit more to the point, cut all the fluff and just get direct to the point. And I did that over the course of just the night, just putting my son to bed. I didn't need my laptop or anything. That, I think that to me was like, this is a completely different way to work. I never thought I could do a full financial analysis on my phone.

Yeah.

Right. I'm gonna get creative when you have kids.

Oh, what can I say? I do, I do have a daughter, but a little older now. She's 13, so she puts herself to bed. What's your thoughts on the whole idea of AI is gonna take our jobs?

I think the answer is nuance and I, I feel like that's just a bad framing in general. It's gonna, I'm not gonna say it's not gonna eliminate jobs. Of course it's gonna eliminate jobs, but I think it's not as simple as it's just gonna completely decimate work. And to be clear, I think any company right now that's saying, oh, we eliminated so many jobs, so we're layoffs because of AI. That's not, that's false. They just overhired. That's not the case. They're just using, they just found it. They found a serendipitous moment where they could have a reasonable excuse to just lay off 20,000 people.

I said that was my view on when Square did that.

Mm-hmm.

And a lot of people disagreed with me. Some agreed. I, I could believe there's some that are due to AI, but when you're making huge cuts and saying it's all AI, you overhired.

That's my— Yeah.

So I, I, I'm mostly with you. I think it's a little, it's nuanced, but yes, there's definitely an overhire component.

No, I, I do think they have AI gains.

Sure.

And maybe some of those roles were actually cut because of AI.

But— Not 50%?

No, I don't think so. They, they, I, I would like, I would be very interested how they've utilized AI if they cut 50% of their workforce.

I would as well. And I agree with you. I mean, I talked to somebody who is very close in the investment banking industry and is talking to the banks all the time. And he said right now they can't eliminate a single position because of AI. Now they have people that are all being more efficient, but they're not at a point where they could actually cut people. Now, will they get there? I'd say yes. I think most areas will get where there could be cuts, but this idea that, you know, huge layoffs are all due to AI, I'm with you. I don't buy it. I think that's a messaging versus reality for the most part.

I think where the answer gets more nuanced is I think it's gonna have a reckoning on entry-level work.

Yes, I've had a lot of discussion around that.

Yeah, and I think it's gonna be a question I think collectively as people, how we figure that one out, because obviously if you don't have entry-level workers coming in, you don't have people to train up to be back into the middle career and senior career. So you basically sever the top of the funnel. So there's nothing going in the funnel. I don't think I have an answer to that. Very hard answer to find because what a lot of AI is really good at nowadays is, is the entry-level work.

Yeah, no, I, Yeah, I've said, okay, there has to be some changes the way we do education. There's a question of how much time you have to invest in somebody in fundamentals. What does that look like? You know, if you think of a lot of skill trades, right, you do an apprenticeship for several years. Is there almost gonna be some kind of apprenticeship type thing where you're working along AI so you get those good fundamentals? It'll be interesting to see, but I'm with you. Something has to change. I don't know the answer either, but it's a fascinating thing to discuss and to think about because I think education has to change. I think for many companies that entry hiring also has to change. How much of each? What does that final look like? I don't know. I don't think anyone knows yet.

I think to the point, entry-level work exists. It, you just raise the bar, raise the floor to be more complicated. Of course, then you have the problem of having people have the skill sets to do that. The apprenticeship is a very interesting concept, and I'd be willing to bet that companies would be more and more interested in that as a recruiting tactic. It's not like, oh, we're gonna just take these unskilled people and get no value from them. It's, it's very much an intentional investment approach. And I think you see that with bigger companies that have like rotational job programs for new workers and stuff like that. They exist. It's probably just gonna have to be more of it.

I mean, it also creates a little more risk in that, you know, the younger workforce churns jobs a lot more than we used to historically. And if that first year you're getting less value, do you really want to, you know, you're taking more risk in the sense of knowing they could leave early. So it'll be interesting to see how they kind of work through all that.

Yeah. And at this point we're getting out of finance talk and more into talent retention. But I think that's to your point though, it's like, it becomes a function of how do you retain talent now? You gotta be a bit more competitive if. Right.

But at the end of the day, I mean, It's an FP&A show and many of us lead people. So these are things we have to start thinking about. You know, kind of filter down to FP&A as well. But definitely we've got a little out of the typical lane, but I'm trying to bring it back.

Yeah, yeah. Well, I think even for my team, I do think very consciously in how to keep them retained and keep them engaged and like a lot of work to recruit and retain very exceptional people.

Hiring people's expensive and it's easy enough to make mistakes. You don't want it to become, you don't want it to compound. And so you got to think about these things.

Yeah. And I think that's what AI is going to push us more into as well, especially as senior leaders, is we're going to have to think more about our teams too. I mean, we all already do, but I think we're gonna have to do more of it, especially in the early days where we especially have to, if people are good at AI, we want to make sure we want to keep them because it's, it's harder to replace that. It may over time that'll normalize a little bit, but Sure.

Right now it's like anything, early computer, early spreadsheet. Oh, there's a new technology, you want to keep that person that knows it well, 'cause it's gonna be hard to replace them. 5 years from now, it could be a very different story.

Chaotic time. Gotta change the frameworks every 6 months.

It's a time of incredible change.

It's—

Yes. Part of me's like, man, I would love to be working for somebody and, you know, kind of automating and seeing all this. And there's another part of me that was like, no, I wouldn't. You know, like I was saying that the other day, 'cause I had someone explaining to me the stuff he's doing with Claude. He has to be in the 1/10 of 1% of finance people, if not even higher. I mean, the way he's thinking about the stuff he's doing is just off the charts. And it's like, that would be really cool. And then I'm like, that'd be a ton of work. But you know, I'm figuring out more and more use cases. So kind of speaking about use cases, I want to ask a question here on that, and then we're going to go back to training for a minute, but in a little different aspect of talking about the whole, you know, new employees. So what's the one workflow that you think every FP&A person should automate first? Where would you say they should start? I mean, obviously I know it's not going to apply to everybody, but what's kind of that low-hanging fruit that you say, if you're not doing this, try it.

This might be a little bit of a different answer, but for me, context is everything, especially when I'm building assumptions, trying to understand things. So getting a lot of the business partnering. So one, one thing that I utilize a lot is a software called Granola. And I mean, I think we've all been on calls, sales calls, or any type of call where like a bot comes in and just records everything.

Yeah, I join every call now and there's usually some note taker before there's a person. I'm like, I'm here with your 3 AI tools. Thanks for joining.

Yeah. And they're 5 minutes late, so you're just kind of sitting awkwardly with the robot.

I'm gonna start saying things to it. I hate to see if they notice.

Yeah. To test their recap notes. But Granola is a little bit different. It sits on your machine and it transcribes, so it doesn't actually need access to the meeting. It just, I'm assuming it takes in the audio from the meeting and just transcribes it. And it's actually pretty good. It's actually really accurate. And one thing I like about it is it's completely agnostic what you use. So if you need to do a Teams call and it's not on your calendar, it'll pick it up and start recording. Why this is like probably one of the most key linchpin pieces of software for me lately is I take all the information I get from Granola and feed it back into Claude because they have an MCP, they can connect. I have it query a lot of the context that I pull from meetings. So I don't need to really focus on note-taking. I could just be engaged with the conversation. I mean, today I was building out an assumption model and I already had a lot of conversations about this assumption model. So now that I'm actually building it, I'm querying Claude that's pulling from my Granola, get all the conversations and put everything in a neat, tidy format so I can just jog my memory. I think that's something like that is incredibly helpful for me to be more effective at picking smart assumptions, building out things that are actually impactful to business. And more importantly, just gets everything grounded back to reality. Because I can, I can think of all the assumptions and how things should be done, but you know, it's, it's a Mike Tyson quote. Everybody has a plan till you get punched in the face. It's kind of the same with my, my plan's perfect until it hits operational reality and then all the assumptions go topsy-turvy. That to me has probably been the biggest lift for finances, even if it's not a finance workflow.

So I appreciate that. Thanks for sharing that. Let's talk training. How are you training your team? Any tips? I mean, obviously you guys have been using this pretty heavily. You have several people kind of rolling into you. So how are you going about training and making sure your finance team is using AI and that you're consistent across the org?

Yeah. Well, I, I would, I want to like qualify this as I'm very fortunate that the company I work at, I know everybody has different security policies, especially depending how large they are. We're AI transformation company, so naturally we're going to use any AI tool we have because it's a strategic advantage. So I have access to a lot of AI tools. That being said, how I get my team to use it more often, I do have kind of advantage that my team is motivated to use it because it's kind of baked in our culture. But tactically, I think one big thing is hackathons, like team hackathons. It's a sit down, carve out some time. and like, let's pick Lovable as an example. It's like, you pick a use case that you need, start by coding Lovable. We'll all do this together. We'll do our own thing and do a show and tell at the end. See which one people like. And I like this approach for two reasons. Once it gets people actually using it. And I think a lot of part of AI is just people not familiar with it. It's a tool, it's a software, but it's not like traditional SaaS. It's a completely different kind of software. so the only way to get used to it is to use it. So that gets people using it. It gets past the point I had with Lovable of like the, like the starter gun, like what, like at the, at the beginning, beginning of the race, it's like, okay, now when I start running, I can finish the race. It gets people started on it so they feel less intimidated. And then it has the second thing is it gets people to show what people have made. And then you can ask questions, well, how, why did you do it that way? Or how did you even do that? And then people start knowledge sharing. Cause I think that's the other, that's, I think that's the other component of getting your teams trained is you want them to share knowledge. There is no, there is no progress in training if people are like hoarding AI knowledge themselves, whether intentionally or unintentionally. So knowledge sharing is quite important. It's like, hey, I did this, or I noticed that AI can do this now, or like, and that has almost like a compounding effect on learning because now people, I mean, this, this is also just kind of basic learning principles in general, but when people can work off of each other, they can, they can move a lot more quickly, move a lot more fluidly. So instead of, because now let's say you have a team of 5 and they're all just in isolation using AI, every discovery is just them learning more. Versus if they learn different discoveries from the 4 other teammates, now you have 5 other potential events of discovery on how to use AI. And I think that's a big thing about AI too, is I don't think everyone's quite even fully realized what it's capable of.

Yeah, I, I would agree. I mean, we're still all learning. I don't think anyone, anyone who claims they fully understand the capability is lying. That would be my opinion. But—

No, there's people who are quite good at it. I'll give them that. But yeah, I don't, I think we're seeing where it's an iceberg where we're just seeing the top of it.

Well, it's a lot like Excel. If anyone tells me they understand all the formulas, all the functions, all the languages, they're an expert in everything in Excel. I'm gonna call BS. Now multiply that times 10 for AI. I mean, the, the best in the world is maybe using 20% of Excel.

Mm-hmm.

The best in the world might be using 2 to 3% of probably what all the use cases and capabilities are for AI.

I thought I was pretty good at Excel, and then I watched the, the Excel Championships. I'm like, I don't think I'm good at Excel.

Yeah, I've, I've interviewed several of the world champions, and you wanna be humbled on your Excel skills. Watch them for about 2 minutes.

Yeah, I felt that way when I tuned into that. It was a few years ago. I was like, wow.

You want a great video?

I'll do a lot more of that stuff.

I'll share this to anybody. So last year's world champion, he's been a world champion before, Dem Early, Dermot Early. He's originally from Ireland, he lives in New York, and he's a Microsoft MVP. And he recorded a video with Nadella, right? The CEO, where he's showing him about the formula he did. He's teaching Nadella Excel and he's looking at, he has this look on his face like, wow, you could write something like that? Like you could do that? I mean, it's just a classic, like what better promotion video can you have than you are teaching the CEO of Microsoft how to use Excel? Almost, you know, like you could see him just having this look like, wow, you're doing that in Excel.

So. That'd be a great moment.

Yeah, it's a really good, go out on, you, he just put it up this week. Go out on LinkedIn. And you'll see it.

Yeah.

That's perfect. It's worth watching. It's just 2 minutes long, but it's a, it's a good little video. I know, I know the guy, I've interviewed him, great guy. But we digress there. So there's an example. All right, so we're getting close to our time, so we're, I'm gonna pick a few more questions here. First, I like to ask a couple FP&A questions, so I'm gonna ask 2 and then we're gonna get through a couple get to know you questions. What's the number 1 soft skill FP&A professionals should master?

Yeah, I'm gonna give you two answers and try to make them quick. I think the one is kind of obvious is partnerships. AI is going to really stress that. We're going to have to be very good partners because that's the one thing AI can't do. I'm still on the belief that people want to talk to people generally.

So I— I am as well.

I agree with this. So I'm banking on that. But I think the other soft skill is learn how to be a product manager because the way— and I've been fortunate enough to work with product managers. And if you're not in tech, it's just basically the person that helps design the product and gives the schematics to engineering. They take what a customer wants, understands what they need, and then has engineering to build. So a good product manager can build, suss out what a customer needs. AI is very structured to think that way, at least right now it is. It's being able to understand a question behind the question or like a need behind the need and essentially ask really smart questions, really think through things in a very nuanced, even first principles way and describe things in an articulate, clear manner. You can almost think of your, your, your LLM as the engineer. So you need to be able to translate a vision, a thought into something that Claude or ChatGPT can understand and build correctly. And I think that type of skill will extremely benefit anybody in finance as they're trying to build things, especially having a model being built into a spreadsheet. Prompting is, will live or die how good that model will be.

What about technical skill?

I still bank that understanding data structure is probably one of the biggest technical skills, probably even before AI. I went, I went pretty hard. I'm no data scientist, but I got pretty good, at least competent enough to navigate data schemas to at least understand it. And I think that's just compounded pretty massively, just being able to understand how data's structured, especially with AI and be able to structure it so it's contextable for AI. It can understand it a bit more clearly. Messy data, it's garbage in, garbage out. So you have to structure it.

When I'm interviewing people that are using AI a lot, that is becoming a more and more common axe or some kind of data thinking, systems thinking. You know, understanding data structure, what they all answered a different way, but that theme, I might've heard it once in the last year. I've now had it 3 times in the last week. And that's an area I'd always prided myself. I was a little bit more of a data person in FP&A.

Yeah.

And I'm seeing now, I've always said that's a very important skill and it's just getting raised up the, up the list.

It's going to be a non-negotiable.

So I'm comfortable hearing Excel is the number 1 technical skill. It's really interesting to watch this transition.

Yeah.

All right, we're gonna get to know you a little bit. And I might tweak some of the questions I have here. I might have a little bit of fun. Are you a music person?

I love to listen to music. I've tried and failed to learn an instrument. I do get and do enjoy music theory, even if I can't quite grasp it fully.

We'll go with the music question then. If you could only listen to one album for the rest of your life, what album are you picking?

This is like pick my favorite child.

Yes. What is your favorite child? I'm just kidding.

Go on. Let's get that on recording.

We'll send this to your wife.

Yeah. I told my kids I'm gonna be on YouTube and yeah, let's, let's get that on recording. Yeah. So, wow. What's my favorite album? I will say I'm gonna, I'm gonna do a cheap thing. I would say one of my favorite albums right now that I could listen frequently, 'cause I just started, started getting, I just got a record player and started collecting records. Um, the one, the one I'm listening to a lot, it's, it's, uh, Black Holes and Revelations by Muse. It looks phenomenal on record. I mean, I've been listening to Muse for quite some time and it's just, it's a great end-to-end album. You can listen from beginning to end and it's just the story that built itself.

Nice. All right. This is gonna be another kind of fun one, but it's much easier answer. I, even though you haven't prepped Claude, he, she, or it.

I think I say it more than anything.

And have you named any of your agents?

Oh, okay. So this is lame to admit. I have an agent that helps me fill out bank forms because I got sick of writing bank forms for vendors, or excuse me, customers. So I made an agent that could fill out the forms for me. Phyllis, as in Phyllis Form.

Nice. Oh, fun. And then last personal one, and then we'll wrap up here. If you could go anywhere in the world tomorrow, you're taking a 2-week vacation, where are you going?

Maui.

Maui. Have you been before?

I've lived there for about 5 years.

Ah, got it.

I know it on a local level, and it just, it's one of those places that will probably never be replaced as my top destination point.

Alrighty. As we wrap up, first one, any, Parting advice you wanna give our audience around being a better business partner, as we know that's becoming more and more important. Any advice you'd give?

Yeah, I would say learn some skills from your sales team. Good ones. A good, and let me—

Not the diva ones in case anyone's wondering.

Not the diva ones, no. The ones that are really good at selling and you'll, you'll know why they're good at selling is they're very good listeners. They really try to understand what your problem is. And understand like the pain that you're, they're trying to solve. And they really, they, they, let's, someone would say this to me all the time is, um, listen to understand, not to respond. So being able to really pay attention and understand what people are saying, understanding their issues or what they're thinking or their, where their mind is and not trying to sound smart or anything, just really trying to understand and listen. is gonna probably do more things than any other skill or technique you could ever do when it comes to partnering.

What you said there, the way I sum it up is, uh, on the listening, one of the best ways I've heard is, is Stephen R. Covey, Seven Habits of Highly Effective People. Seek first to understand, then to be understood. Yeah, I just— that, that's what I always think of when I hear that. It's just so critical and I forget myself doing it, but you wanna learn to get better at listening, do 400 podcast episodes.

It, it's a, it's a skill too. It's a skill that you have to develop. It's not something, I mean, some people probably come, come across naturally, but it is, it is a skill that you have to build and can develop. It's, there's techniques out there, you can look it up, but, um, on how to, how to kind of develop that. But it, it is something very, anyone can do.

100% agree. It, it is a skill that can be learned. Different people are —better at certain aspects of it than others naturally, just like some people are better athletes in certain ways than others. We all have our strengths and weaknesses, but most everything can be learned in life, I believe.

Yeah, absolutely.

Not everything, there are exceptions, but— All right, well, we'll go ahead and wrap up here. I think this is a good place to stop. So if anyone wants to get in touch with you, learn more about you, LinkedIn the best way to do that, or how should they reach out?

LinkedIn's always great. Just tell me that if you message me or connect with me, just message that you heard me from the podcast or else I'm just, you're just gonna probably go into my BDR purgatory and I just want to—

Just say you heard, you heard about Nick. It, you're excited because you saw him talk to some guy with a big beard and yeah.

Yeah. Yeah.

Well, because we'll say, it'll be like, wait, big, what? I better read this.

Yeah. Just, just an hashtag. #BigBeard. I don't know who you, where you heard me from.

Well, you do know, uh, I'm not sure if you're a basketball person at all, but you know who James Harden is?

I don't know. I do not. No.

Okay. So he's a huge basketball player and his Twitter handle is @TheBeard.

There you go.

Go look him up online. He has a huge beard.

Oh, that's great branding.

So I'm like, cuz people have said you need that and he already has it.

Yeah.

Can't take his handle. So if someone's like, you need to do a commercial with, you know, they joke James Harden, like, you know, great. And I just gotta laugh. So anyway. Well, I think we've covered, we've covered beards, we've covered education system, we've even covered some FP&A, we've covered coding. Did you ever think you'd be on an FP&A podcast and cover those topics?

I didn't even think I was gonna be on any podcast. So let's—

Well, there you go. Well, thank you for joining me. I've really enjoyed it. And good luck with things. Continue to build out AI and exciting times. So thank you again.

Thank you. And thank you for having me.

That's it for today's episode of FP&A Unlocked. If you enjoy FP&A Unlocked, please take a moment to leave a 5-star rating and review. It's the best way to support the FP&A Guy and help more FP&A professionals discover the show. Remember, you can earn CPE credit this episode by visiting earmarkcpe.com, downloading the app, and completing the quiz. If you need continuing education credits for the FPAC certification, complete the quiz and reach out to me directly. Thanks for listening. I'm Paul Barnhurst, the FP&A Guy, and I'll see you next time.