I Would Never Let AI Touch a Number and what to do about that with Alok Ajmera

Speaker A: AI as it stands today is, is a probabilistic engine, right? And I think part of it is like not everyone really understands how. Actually I take step back, no one really understands how AI works like this. Like I've actually had a chance to see all the labs, like they're like, this is really great. If you think about AI at its really simple as core, it's like grade 12 math. It's a multivariable regression analysis at like monstrous scale. It's matrix math. That's really what it is. And not many people, even the researchers that are building these capabilities, even understand why this math produces the output and outcomes that it produces. But it's good. And so we figured this out.

Speaker B: Welcome to another episode of FP&A unlocked, where finance meets strategy. I'm your host, Paul Barnhurst, AKA the FPA Guy. 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 world. Together we'll uncover the strategies and experiences that separate good FP&A professionals from great ones, helping you elevate your career and drive strategic impact. Today's guest is someone who's earned the coveted seat at the table as the CEO of Profix. I'm thrilled to welcome on the show today. Alok Amera, welcome to the show.

Speaker A: Thanks Paul. Excited to be here.

Speaker B: Yeah, really excited to have you. And so the audience knows. Kind of funny how this one came together. You had shared a post on LinkedIn about, uh, AI and the whole idea of a CFO not wanting to let AI touch any of the numbers. Right. That fear of it's going to hallucinate, it's going to mess up my numbers. And I responded to the post, you said something back and sent you a DM and said, hey, would you be interested in talking about the conversation? And you said yes. So not the typical way I get my guests, but I'm really excited to have you. So thanks for responding. We'll start there.

Speaker A: Yeah, no, I appreciate it. Uh, well, first of all, I appreciate the thoughtful comments you made on the post. I followed your work over the last couple of years and uh, yeah, excited to talk about my favorite topic, which is actually FP and A.

Speaker B: Well, I'm excited to get into it. So let me give our guests a little bit about your background and then we'll jump into the topic. So Loke is the CEO of Profix. As I mentioned, the financial performance management platform, purpose built for the office of the CFO. With more than 22 years at Profix, he has helped over 3,000 CFO teams across North America, Europe and beyond modernize how they close, consolidate and plan. Backed by Global software investor H.G. capital, Profic serves finance leaders across industries ranging from financial services to manufacturing, bringing rigor and speed to the full FPA life cycle. Alok uh is one of the few SaaS CEOs who has watched the office of the CFO evolve from spreadsheet first to system first and now to AI first. He believes the finance teams that thrive won't just adopt smarter software, uh, they'll learn to work alongside intelligent agents to transform what FP and A can do. So I like to start every episode with this question and I'm excited to hear your answer. Not only being a CEO, but a CEO of an FPA software. What does great FP and A look like to you? What does that mean? What do you expect kind of from your FP and A department?

Speaker A: Yeah, great question. Look at the end of the day, FP and A done really well I think is a force multiplier in the business. Uh, and I think there's three things that really in my mind really demonstrate or articulate a world class FP and a function. Uh, the first really is data stewards. I think uh, financial operational data that's existing in the business, uh, really bringing it to one spot, making sure everyone is looking at the same uh, information, making sure there's governance, there's controls, et cetera. So I think that's the base foundation. It really resides in like good crisp, clean data and they're making sure everyone's is looking off of that information. The uh, second really is really deep collaboration with the business, uh, stakeholders across the organization. You know oftentimes FPA is just providing reports and analysis. But I think really world class FP&A is when you're partnered with the business, you're collaborating in a bi directional way. You're supporting with data, you're supporting analysis, you're involved in decision making, I think that's key. And then the third one really is collaborating with the broader C suite and board around the forecasting and planning. It's really understanding where the business has been but most importantly where is it trending and what's actionable and not actionable. So in those kind of three things, like if you can master those capabilities, I think it puts you on the operational on of what FPA professionals are doing. And again going back to the force multiplier, like businesses that have world Class FP and A tools, uh, perform and outperform their peers. It is such an important function.

Speaker B: Yeah. The way I like to say when you talk about tools, the one I say is when you have the right people, the right process and then you bring in the right technology, that's really when magic happens. It's an enabler. You need the right people, you need the right processes and you can do great things with subpar technology. I've seen it, I've lived it unfortunately, more than once. But you could do so much more when you're not constantly fighting against the data and fighting against the technology and the tools and you know, does you're, you actually have time for the important stuff is what I found.

Speaker A: Yeah. Paul, just to add to that, uh, like you and I were talking about this previously, you know, accounting and FP and A are slightly nuanced. Right. Like one is rooted in a science. It's like there's process or structure, there's rules. FP&A is a little more art. There's creativity that's kind of enabled in terms of what you can do. And I think the challenge sometimes is we take accounting functions and accounting skill sets and accounting people and we naturally move them into FP and A and we don't unlock the open ended curiosity and creativity that actually makes FP and A I think really special and ultimately leads to better FP and A organizations.

Speaker B: I love that you said that. I had a boss that goes, he had the opinion, he's like, I'm not sure accountants, most accountants make good FP and A professionals. And that was part of his reasoning. And I always get people reaching out to me kind of I want to move into FP and A. And one of the first questions, if I ever talk to him, I'll be like, are you sure recognize here's the difference in the roles. Are those the things you like? Is that what you wanting to booing? And I still remember I had a great accounting partner and I was looking for an analyst role and I went to him, I said, are you interested? I think you'd be great in this role. It's like no, FPA is just a bunch of making up numbers. You guys just do funny math. I like it all tying out I'm good where I'm at. I said that's fine because you're great. I love having you as a partner there. And so it was that reminder of you got to know your personality. It's not one's better than the other or one they're different. And there are benefits to both roles 100%. But I loved his answer. He's like, you're just all funny, map. I'm. Hey, I take offense to that. I want us question. On your LinkedIn profile, you talked about how you're building profits into, you know, a dominant global office of the CFO platform. And something you mentioned several times on there is a bhag, right? The big hairy, audacious goal. So how do you measure something like that? How do you know when you're the dominant global office of the CFO platform? Have to ask, because I love seeing that on your platform. I can tell you dream big and, you know, have big goals.

Speaker A: I, I came across the concept of a BHAG maybe, uh, a decade or so ago, and, uh, the concept really resonated with me. And I'll get to your question in a moment, but, uh, I'll take a step back and talk about bhags for a second. One of the challenges, you know, I'll speak in the context of software companies, but I think it applies to everyone. When you're, uh, when you're early in this, in the life cycle of a company here, maybe you're in startup mode, et cetera, there's like a unifying purpose. And that oftentimes is not necessarily the mission that you're on. It's survival. Right? Uh, so at the beginning, everyone is, everyone in the organization is unified to the single goal of, like, making it. We just got to survive and prove that we have a viable business. And as you continue to be successful and as you grow and then at some point you get very large, it's like that connection for everyone. The purpose oftentimes can get dissipated. Now, if your business is mission driven, where you're like, we are saving babies, it's like that, that is unifying and that's purpose built. Right? Uh, in my world, it's like we're building software for corporate finance groups now. I love it and our customers love it. But, like, if you're the average person who's an engineer who's not connected to FP and A, it's like there's no mission in that directly. And you've lost the mission of, hey, we're trying to survive and prove that we can do it. And so I found when teams are, like, really motivated, they're. They're focused exclusively, and they really. And you get the most out of them and they enjoy the work that they're doing. Like, purpose is at its heart. And so I stumbled upon this concept of a bhag. And I started playing with it a decade or so ago and I found it was a rallying call. It's like, hey, we're trying to do something really hard. And here's exactly what that is. It's very uncomfortable when I say it out loud. You probably when I roll out these bhags on, uh, every couple of years I update them. My team usually like has a visceral like, ugh, here we go again. Like how are we possibly going to do that? And that becomes unifying. And then when you can figure out a way and you hustle and you, you iterate and then one day you cross the line and you can go back and say, hey, remember that thing that we said we were going to do? And it was really difficult and daunting, but we did it. You get the check mark and the team feels amazing. And so I, I've just found these are so good. They're aspirational goals. So now let me get back to your question. You know, for me, bhags have no timelines. It's not like I want to get to this place by this time. It's like, hey, this is what we're trying to do. We don't know how we're going to do it. It's a little bit uncomfortable. Like I said, it's a little open ended. There's no clear path to get there. But it's a directional guidepost. So we as an organization can then continuously iterate to figure out how we work closer and closer. At Profix, I've always had two beheadings. It's like a quantitative and a qualitative. The quantitative one is the easier one because it's like, it's a number and you can measure it. Half of my team needs a number that can be measured. The other half is like, hey, who cares? It's just a number. And so the other BHAG is always qualitative. And the qualitative one is the more mushier one where it's like, well, what does it exactly mean? And everyone has a slightly nuanced version. And because it's an aspirational goal, I think it, I think it's okay. And so for Profix, it's always been there's one revenue number. It's like when we were, when we had zero revenue, it's like, hey, we're going to get to 5 million in revenue or 10. And once we crossed off 10, we were like, uh, let's get to 20, let's get to 50, let's get to 100, let's get to 500. And each time we cross one off, I've been doing it for, like I said, a decade or so. And it's like you feel this visceral, like the team that's been on that journey feels like, I can't believe we did it. And then I put the next one up and it's like, oh, uh, man, we got to do it again. And the qualitative one is what you alluded to, uh, you know, the global office and CFO platform. The qualitative one for profits is we want to build software that fundamentally changes how our customers work in a positive way. So if you think about FPA or even broader cfo, a lot of the processes and the day to day work that happens is rooted in technology from like 20 or 30 years ago. And you know, I look at it and I'm a technologist more than I am a practitioner of FP&A, and it's like these processes don't make much sense. There's a, there's a huge opportunity, especially now with AI, and I'm sure we'll get to that, to really change how we do these things for the better. The goal of like, if you think about CEO, sorry, cfo, it's like actually what they're there to do is optimize enterprise value. So if you start with that and then you work backwards, it's like, okay, well where can technology update these processes? With that as the objective, how do we drive more enterprise value? Sorry, I've gone on a, a long roundabout.

Speaker B: Appreciate that. It was a great answer. I, I, I liked it. I was, I uh, was definitely listening close and even thinking in some areas. What would mine be for my business? I have goals. I don't necessarily call it bhag, but it's a little different when it's one person versus you know, company, but same idea. You need that unifying. What's the key to getting a team to achieve those? You've obviously hit the revenue. I think I had a goal of 100 million at one point. You're now past that. You're heading big 500 was the goal I saw on LinkedIn that you said, but how do you keep the team focused and unified? Obviously as the leader you need to help keep them focused on the goal. And there's all kinds of distractions. We, we all deal with them. So how do you make sure people remain focused on that kind of big goal when there's so much in between that has to be done.

Speaker A: Yeah, that's a great question. Let me answer it in two ways. One, look, I'm not an FPA practitioner, but I love fp. And A, I spend most of my time with my FPA team. Uh, so it's like, okay, how do we have a big goal that we don't have? It's open ended from a timeline perspective. But then how do we start backing into okay, how do you take a bhag and break it down into five or six individual measurements and then how do you as a long term success measures? And then how do you take those success measures and say, okay, well how do you roll that into like a three to five year plan? And obviously year three, four, five are kind of really high level tops down. But year one and two are very granular. Bottoms up. And and then again, how do you reassess the success measures, the KPIs that you're going to measure along the journey so you can keep everything focused and relevant. And then how do you cascade those down through the organization? So that's step one in my mind it's like, how do you take a big bhag, start working backwards from that bhag and be like, okay, for that to be true, these six things have to be true. For these six things to be true, these are the things that we need to actually be working on. And you kind of go all the way down to like what has to happen next month. So that's kind of like step one, the step two, and this is the piece that I find organizations miss sometimes is like you have to be constantly communicating with the team. And so I do like every Monday, I do a town hall every Monday. And then quarterly we do really big town halls and a cascading management week. And I find like on uh, day, the day before the quarterly town hall, the team is at its most, uh, disorganized. Like everyone has kind of has splintered off and going in different directions. And the moment the town hall ends, that is the most aligned. The entire team is exactly to your point. Everyone gets like, right, right, right. This is exactly what I need to do. I'm charged, ready to go. Let's go. And then, you know, three months later it's like all these little things come in. This customer problem or this thing or this deal we're trying to close and then you kind of get frayed and it's like you got to bring everyone back into, back up to 30,000ft, get them aligned, get them focused and then let Them go again.

Speaker B: You know, you make it sound also simple there with that answer. I, I love it. But yeah, I, I heard kind of two things there. You got, like you said, the planning. You got to take it from the top and really figure how do you filter that down so people have something manageable they can focus on that helps them achieve the goal that you can measure. And then two, I'm going to simplify it. Communicate, communicate, communicate. You got to keep it front of mind 100%.

Speaker A: People are busy, right? Like, think about an FP and a team. How many times, how often is an FPA team Friday afternoon being like, I, uh, got nothing to do, let me go back to strategy and reflect. It's like, no, we're slammed.

Speaker B: Yeah, I'm going to go read the mission and vision again. It's been a few weeks. Yeah, no, I don't think I've ever heard somebody say it that way. So appreciate you sharing that. So I want to transition to a little bit of an AI discussion like we talked about. So as I mentioned before at the beginning, you know, you and I first interacted on a post and I'm sure you've heard this story before. I've heard it. This is how a CFO would not let AI touch the budget or any numbers for that fact, but was using it in other areas. So maybe can you set the stage by telling a little bit about that story? Your response kind of set it up and we'll go from there.

Speaker A: Yeah, for sure. Listen, I was on a call with a Profix customer, I guess this would have been a month or so ago now. And we're just chatting and catching up and uh, she's a pretty active FP and a team and they're pretty active. They're on the spectrum of good degrade. They're closer to the great side of an FP and a team. Anyways, we were chatting about AI and they were avid users of AI, uh, both in profix and all the capabilities that we have, but also just in general they're deeply committed to Claude and cowork and more on the progressive side. So we were chatting about what do you use it for and how do you get value. And so right away it was like, love you love using AI for reporting, for analysis, for creating narratives, for synthesizing content, for creating, you know, board update emails and so anything, uh, analytic or analysis centric. She was like, oh my God, I love it. Like my team is getting so much value from the capabilities. And I was like, oh, that's really great. So I'm making notes and I'm like, great. You know, uh, Profix had launched a budgeting agent in September of last year. And the budgeting agent I kind of love because I find business users don't like to actual budget. And when I dig into why, it's because they don't want to go into a spreadsheet and they don't want to go update numbers. And, and so the oftentimes what happens to a financial analyst, this is probably, uh, your team, your audience will resonate. It's like, there's like five laggard executives and you have to be like, fine, let's just get a meeting, get in a room together. Just tell me what you want to do and I'll do it for you. Right? So great. We're like, let's solve for that. We created an agent where you can just articulate all the changes you want to make up these numbers, down these numbers, add this headcount, give this merit increase, and then the agent can actually do all the work on your behalf. So I brought this up. I'm like, hey, you're using all this amazing reporting analysis capabilities. Like, why are you not using the budgeting capabilities? Right? And the entire tone of the conversation changed. It was like, oh, no, no, I would never let AI touch a number. And for me, it was actually an interesting moment. And I've been now going away and talking to as many people as I can to pressure test this. And there seems to be a divide. Don't get me wrong, there's super progressive CFOs that are like, currently a small minority, I think, of AI for everything. But there's a very big chunk of the CFO and FPA teams, which are the very apprehensive of AI, whether it's a budget, a forecast, or, you know, even on the closed side, it's like apprehensive around actually touching or modifying numbers. And it, it makes sense. If you think about where AI today is today, like, it totally makes sense. I was just surprised at first.

Speaker C: Yeah, no, I, uh, I'm not surprised that you, you got that response. I am surprised that how many and how big it is. Sometimes I think if you look back to the early days of AI, your first time you tried it, you asked it what two plus two was and it told you five. And you're just like, okay, no numbers again. Or the examples everybody would give is,

Speaker B: I asked that, you know, Sally was

Speaker C: 4 and Bob was 8.

Speaker B: What were they 30 years later? You know, the answer was Just like what?

Speaker C: And so I, I think a lot of people have a fear and obviously hallucinations. But as you and I talked, and as I talk to a lot of people, what I always try to say is, you know, and these tools aren't just using generative AI. I think people sometimes have the idea that, okay, it's all being done by generative AI. And I've talked to a lot of software vendors and almost every one of them says no, the numbers. We're using a deterministic process here. If you give me the same inputs, you're going to get the same output every time. It's no different than uh, I think, I mean different in a little ways, but very similar to almost every FPA tool that has an algorithm that will do a statistical model for you. There's a lot of that behind the scenes. You're just now using AI. And so that different term, a little bit of different technology and automation creates a real fear. What would you say to that? That's kind of how I think about it. Is that a fair kind of assessment here of how to think about a

Speaker B: lot of this technology with agents?

Speaker A: Yeah, for sure. Look, uh, AI as it stands today is, is a probabilistic engine, right? And I think, I think part of it is like not everyone really understands how. Well actually I take a step back, no one really understands how AI works like this. They're like, this is really great. It's, it's, if you think about AI at its really simplest core, it's, it's like grade 12 math. It's multivariable regression analysis at uh, like monstrous scale. It's matrix math. That's really what it is. And not many people, even the researchers that are building these capabilities, even understand why this math produces the output and outcomes that it produces. But it's good. And so we figured this out. Now you take that to the non technical finance person and there's just a lot of natural fear. One, it's probabilistic. So everyone has had a situation where they've gone to ChatGPT or Cloud, asked the same question three times and got three slightly nuanced answers. And it's like, well that, that's a problem. Like fundamentally a probabilistic models don't, don't work in finance. Second, everyone is afraid of losing information, getting, losing, like putting information out into a, uh, you know, and all of a sudden, you know, one of these frontier labs is going to be training off of your data, right? And so there's uh, a Opaqueness, they don't understand how to put guardrails and to protect data. And so there's fear that, like, if I start putting, putting in data, like it's going to go away and all of a sudden chatgpt will know all of my financial information, which, to be fair, is a legitimate concern. Like, there's things you can do to protect yourself, but it is a concern. Uh, so there's just this overarching fear. Right, but Your answer is 100% correct. And this is what I try to get across to people as well, is we're not asking a generative AI predictive model to do anything with your numbers. What we're actually doing is saying we are decomposing our technology into hundreds of thousands of little tools and skills that AI agents can use. And so the AI agent is not actually doing the math, it's just passing the right parameters to the tools that we have so that we can do the math and return the number. So the math is 100% deterministic. The piece that I think software vendors are missing still is the process has to be deterministic as well. And so if you think about how agents work today, it's, you know, first you query data, then you reason, and then you execute. And what happens actually is. And this, this brings up a whole nother conversation we can have is it's not efficiently done. So the querying is wildly inefficient. An agent will just bombard every access point it has with queries, and it'll collect as much information as it can, and oftentimes it's orders of magnitude too much. And then it tries to apply reasoning skills on top of that to figure out, okay, what do I do with all this? And then how do I reason out the steps? Now if you go to an FP and a person and you're like, hey, I need to do a four plus eight forecast. It's like, it can't be a different process than when you did the three plus nine. It actually, I follow some basic methodology. So even though process has to be deterministic, so if you think about going back to what the agent does, it bombards every system, every access point, collects as much information, then it tries to make sense of it all, and then come up with the steps that he needs to do, and then it executes. And so for what I think software vendors have to do, especially in FP and A, you absolutely have to make the calculation engine deterministic. You can't have it guessing and making mistakes. You have to create more context, have deterministic workflow processes, right? And then you can let it go off and execute.

Speaker B: That makes a lot of sense. And one thing I've seen that I like, I don't know if you've seen it. Uh, Copilot, they just released this week in Excel is they have a mode they call plan mode, where you tell it what you want and you build all that plan stuff and you agree on everything before it goes off. And the more that happens, and then you have deterministic tools as well, I think the better you're going to be. Because especially in Excel, if you're just in a spreadsheet, it doesn't have context. And then unless you've given it, you know, maybe reviewed your spreadsheet or whatever. But often when you're building Excel, there's no context. So having a plan to start with is a lot better than, hey, build me this model. And then you're done. And you're like, that's not what I wanted, or that has 37 mistakes. You're going to get a lot better output. Kind of like you're talking about those workflows. And so I agree. So what would you say to kind of the finance community out there? Where should they be skeptical? When should they be using it? How should they be thinking about this? Because it's, it's just all moving so fast. I think everybody's at a different point in their journey. Everybody's struggling with something, whether it be, hell, no, it's not touching my numbers, or, sure, I'll let it do everything, but if it screws up once I'm out, you know, to, I'm not using AI at all. I think we have a little bit

Speaker A: of everything for sure. Like I was just telling you, I got back from our. A big profix conference. We met hundreds of CFOs, and I was. Every conversation was about AI on everyone's mind, of course. And, uh, look, the, the people are at different places in the journey. There's definitely people that are like, we have a corporate mandate not to use AI. There are people that are so on the one end, right, don't use AI. And some people came to me and I was kind of surprised. People were like, we have an ethical concern around AI. You know, it's like, okay, walk me through your ethical concern. It's like, well, we really, we are really concerned about our stewardship for the next generation of employees. It's like, oh, okay, that's interesting. People have come to me with environmental concerns. Around using AI, like, we're really concerned about the kind of, you know, the energy consumption, the land footprint, et cetera. And it's like, okay, look, I'm not in a place to tell you what your moral compass should be. Right. First of all, I just want to acknowledge right? For all you, like, whoever's listening here, wherever you are on the spectrum, it's totally okay. Like, I, I. There's just too much rhetoric around AI, too much, like, hype around AI, and a lot of it is not real. And so it's okay. Wherever you are on your journey, okay, Just understand where you are and then start mapping out where you, where the next couple steps are for you. Right. Oftentimes when you're on the early end of the spectrum where you're like, I don't, I don't know if I should use it or I don't want to use it, some of it is like, just take some small steps. Like, don't try to go and be like, um, I have found this autonomous agent that we can start building or using. It's like, don't go up to the extreme. It's difficult. There's work that has to get done to do that properly. Small steps could be like, you know, we're going to start, we're going to get a corporate license of one of the frontier labs so we can have some protection and we can start playing around with it for analysis. You know, small steps could be like, hey, you know, there's, there's really specific workflows that are very inefficient. Our cfo, he's an interesting, right? He's a cfo, but he also works in a company that builds software for cfo. So he's like, on our product advisory board, he's a marketing guy as well. It's kind of a unique spot for him. But anyways, he, uh, and I were chatting, but he's like, oh, man, I need an AP agent that just helps me do accruals at the end of the month because it's like, you're looking at your expenses. And then it's like, oh, shoot, this vendor didn't send me the invoice in time. And all of a sudden there's a variant. And it's like, but we know all the vendors, we have all their contracts. We just need to reconcile it with which ones we got and make the accrual adjustment. And it's like, well, that's a very small problem, but it's easy to solve. And it's like, okay, so there's so many things that your organizations are doing that are manual, that are slow, that are like extraordinarily like, uh, repetitive. It's like, just take some of those and chew through them. See if you can experiment, get a tiny bit of value. Uh, just take little steps, and then before you know it, you'll see some value, you'll get more comfortable. Uh, you can build some confidence and capabilities in the team, and then you can start taking bigger steps. Sorry, Paul, I went again on a tangent. I don't know if that answered the original question.

Speaker B: I think that definitely helped a little bit. Is just kind of right. We're all at different points, and I think that answers some of it. I like the example of the CFO. So the CFOs. Everybody's at a different journey. Where do you think AI makes the most sense right now? Maybe step back a little bit there. So assuming everybody's comfortable with using it, which obviously we know they're not, where do you think it makes the most sense for finance right now? Because we hear the hype of use it everywhere and it can change my life. And it does everything. And there is amazing things it does. But we all know reality versus marketing hype are usually never at the same point. And so I'd love kind of your thoughts of where. Where do you think the best use cases are? Where does it make sense and where would you tell people? I'd have some real pause here, like, you know, proceed with caution or don't use it. I'd love to get a little bit your thoughts.

Speaker A: It's a tough question. I'm hesitating for a reason. Right. Because like, the technology is moving so quickly and my thoughts from like last week have changed from this week and they'll change again. Right.

Speaker B: So I recognize that it's a great caveat to put out there. By the time we release it, the answer could change. And that's so hard to talk about this.

Speaker A: Yeah, yeah, totally fair. Look, there's. There's definitely value on the reporting and analysis side. Like, if you think about generative AI, uh, generative AI is a content engine. Anything that's content related, it's a natural use case. So, you know, if you think about the biggest use cases that are being monetized today, it's like agentic code writing. It's agentic content creation. It's in the legal space, which is a lot of content. Like, how do you understand the terms of, uh, and case history, et cetera. And so that's a natural Use case. So anywhere in your workflow that you're creating or analyzing content, it's a hundred percent ready to go. And by the way, that fits with a lot of what CFOs are doing today, right? It's like, oh, I want to build some reports, I want to do analysis, explain what's happening, I want to build a board pack, I want to build a uh, variant and explanation. It's like those are all like slam dunk, take it to the bank use cases right now and they're getting better and better. If I then go from that point and start breaking away from that, your, your audience is more FP&A. So I'll focus a little more FP&A as opposed to the controller closed slide. Uh, it's really modeling. Now the next major kind of hurdle is like how do you do better forecasting? How do you increase the velocity of forecasting? How do you get more people collaborating on forecasts? Like AI is an enabler for all of this. So if you can use capability, uh, assuming you have really good data as a starting point, you know, you have, you have some decent technology. Like you said, it's like if you think about forecasting as well, like there's two schools of thought on forecasting. I think I'm curious to get your perspective on this, Paul. There used to be a really strong voice around machine learning. It's like, hey, how do you do more predictive, uh, forecasting, both uh, operational but also financial. And that's kind of died down a little bit. I, I don't think, especially for mid market organizations, I don't think machine learning based times time series forecasting is, is there yet, like there's just too much of a variance in terms of how accurate it is. You know, oftentimes with time series forecasting, 10, 15, 20% variation is okay, but it's like I've just never met a CFO who's like I'm totally good with 20% variance on my forecast. It's like that's just way too wide, right? The cool thing I think about generative AI is not about forecasting in terms of like, hey, let's produce models and the algorithms are going to spit out time series numbers. How do we facilitate broad scale collaboration and make it really, really easy? And I think agentic AI is actually really good for that. So I think that's another area that's emerging on the FPA side that I think is going to be great. Here's the analogy, right? Like right now everyone does an Annual budget. It's really time consuming. You got to get all these stakeholders, you got to wrangle them in and they got to do a plan. And then you come up with a plan takes whatever time it takes. Months potentially, by the time you get there to the end, you finish like Q1, you're like, this is all out of date now you got to do a reforecast. And it's like, well, I'm not doing that again. Like I'm not going back to a hundred people and wrangling them. They don't even want to be part of it. So then we go to these like tops down models and we're like, well, you know this, the uh, the CRO told me we're probably going to be around here, so let's go tops down and, and drive this out. And what I've been challenging people recently is like, well, what if we could just take that entire process of wrangling 200 people in a budget process and make it so easy that it could be done in a couple of days? Like would you then do a monthly forecast with everyone actually contributing and getting really focused and really clear accuracy? Because people still have a lot of institutional knowledge that's really important in these processes, but because it's so taxing, we don't bother including everyone. And I think generative AI is perfect for that.

Speaker C: Uh, yeah, and I appreciate that answer. So on the flip side, we talk about, you know, some of the goods, where are the real risk. Obviously we have hallucination, there's data risk, but what are the watch outs and the. Hey, pause and slow down a little bit. Maybe talk about that side for just a minute.

Speaker A: Look, one of the cool things about AI is that it's democratizing all knowledge, expertise, right? So you probably have people that are on the forefront, ah, your audience members that are on the forefront of kind of AI and they're often, they're like, oh my God, I'm using REPLIT to build tools. I'm using, I'm, I'm writing code with uh, Claude code and I'm building my own stuff and it's like, oh, I'm integrating it to Excel and I'm, you know, I'm doing this integration, I'm doing that integration and I think it's great, by the way. And I'm a huge supporter. Like, I love tools like replit. You know, obviously we're a massive cloud code organization. Uh, so I really think it's great. The challenge is you could really quickly build a whole bunch of Stuff that you don't understand and that has no governance or control. And now all of a sudden all the problems that we've been trying to avoid over the past, where it's like, uh, we're looking at different sets of data or the integrations are, the pipelines are not all clean, and we don't. It's like all of a sudden we can take problems and make them like, we can, we can explode them. You know, everyone's running around with their own really complicated Excel model that they built with Claude Cowork. And it's like, oh man, this was one of the problems, right? Like, we were trying to, like we were trying to harmonize everyone's one pane of glass. I'll built all these, uh, these applications using Claude code or replit or something like that. And it's like, actually I don't know how to maintain any of this stuff. And it's getting messy and it's getting complicated. So I think the exciting part is we're democratizing capabilities, we're democratizing technology, and we're putting it into everyone's fingertips, which is really good. The word of caution is like, there is structure and discipline that needs to be put in place to govern these things properly, to control them properly, to make sure that you're going to maximize your benefit not just immediately, but down the road. And it kind of touches on another thematic that we were talking about, but, like, building things is getting easier, but maintaining things is actually quite hard. And oftentimes we're allowing people to build things who have never had to build things in the past and don't necessarily understand the maintenance part of the equation yet.

Speaker B: It's like you talked about a little bit of the whole idea of, uh, AI tech debt in finance on LinkedIn. And I've talked about this a little bit from a different angle, but I think a lot. We're heading into what I call AI sprawl. We all went through SaaS sprawl, where all of a sudden, how did we go from two tools to 500 and from 30,000amonth to 500,000 or whatever? You're going to see the same thing with tools for AI, but now you're going to multiply that by the fact of not only can people are people adding a ton of AI tools, they can build their own tools. And they've never, like I said, they never maintained it. They've never had to manage the digital security, the governance, privacy rules. And you know, what are, what are going to be the ramifications of that in a couple years, there's probably going to be a lot of debt and there are going to be some companies that. It's going to be a cluster, for lack of a better word.

Speaker A: Look, it's not just finance. This is everywhere, right? So, and just to make it to take one step further, right, like this is a problem in engineering right now. Like, my, my engineers can write oftentimes up to 20 times more lines of code than they used to be able to. So just to put that in context for the effort, it used to take them, um, to write a hundred lines of code, they can now write 2,000 lines of code. So that's wonderful. We're writing way more lines of code. We're writing more, we're creating more applications, we're building new things. But what's happening is the velocity of build is being outpaced by the velocity and our capability of understanding and maintaining. But engineering already has that discipline and functions. We have dedicated QA processes, we have dedicated people that's job is to maintain. We have documentation people that all they do is write up exactly what's been doing and why and how. Now you go outside of engineering and you go to a finance function and you're like, hey, you can start building your own applications. But the muscle memory and the discipline around, well, let's document all this, let's have QA processes, let's actually understand governance of data. Let's, that's think through infosecurity, all these points you made. The discipline isn't there because they've never had to have that discipline. And so I think this is the word of caution, right? It's really easy to build things. It's really easy to just try things out and go. I think we're really going to have to be thoughtful around what we put into production, how much rigor we put around it. I think there's going to be new roles that we're going to hire. Like I'm convinced that, uh, FPA teams are going to end up having to hire engineers like you're going to need in your, in your finance ops team. You're going to have to go hire like actual engineers because you, there's benefit to do this. You just have to have the discipline to know how to manage all this stuff.

Speaker B: I hadn't thought of engineers per se,

Speaker C: but I've definitely thought of like kind of AI architects or a system person.

Speaker B: So what you're saying makes sense.

Speaker C: It doesn't, doesn't surprise me. I just hadn't Thought that far ahead

Speaker A: before everyone's like, oh, uh, the skill you're going to need is prompt engineering. Like, uh, you're going to need to know how to prompt really good, right? And it's like, well, that's, that's easier. That's getting easier and easier, right? You don't even have to know how to prompt it. You can even just ask Claude to prompt to create the prompt for you. Like, you don't even have to know things, right? Like, it's just. The danger is you lose the understanding of like, what is being done and why and how does it work. And that's the danger. I think the same thing, by the way, with Excel, right? Like right now you can create these like really amazing models in Excel quickly, right? And there's, and we're making it more robust. You can create plans so you can understand what's being built. But I'm worried that actually what's going to happen is these like, we're going to propagate more and more monstrous, cumbersome Excel models through the entire enterprise that are kind of disconnected. They're working off a little bit of different data, which is some of the problems you have when you're building large scale cross functional. Uh, take it with myself, by the way. I'm a little biased, right? So you should understand that. The audience should understand I'm tiny bias. But actually what I think is happening is you build these complicated models and it's easy to do and actually you're just gonna throw them away and every time you need, you just build a new one because it's too much to maintain. It's like. And remember, AI actually doesn't have memory. Like it doesn't train. Like everyone has this idea that it's like, oh, it's learning and it's gonna get better. And it's like, no, it doesn't. Actually. The only time really it trains is when a new version of the model comes out. So when you're on Opus 4 and it goes to Opus 4.6, that's when it trains, right? The only thing we are doing really is providing incremental context every time we prompt. We're not training it on anything. It's another tangent we can go on one day.

Speaker B: But I've heard that before when we were talking on Financial Model, I was like, yeah, it doesn't really train. Yes, you give it, there's context and you. Yeah, so I've heard that before. That could be a whole episode by itself, uh, around that whole thing. But when you mentioned the modeling. I'll share one thing here and then we'll move into the FP and A and get to know you section because I know we're coming up on time. We're, we're running here, so I don't want to keep you too long, but, uh, the one that I talked to a guy that works, you know, in modeling, and he's one of those trusted for the audit, the verification. When a big deal is happening, a lot of the banks and the big companies, they come to him to validate the model. He goes, I'm seeing a lot of stuff that concerns me. The way I have to validate is different now, different risk AI. Uh, he even mentioned, which I thought was really interesting, is a lot of the banks are struggling to find good modelers earlier in their career because I think they can just use AI and everybody thinks it's going to take away the job. They interviewed someone else that said, right now the banks haven't been able to eliminate any jobs. They're getting pressure to, but they really can't because the AI is not good enough yet. So there's this fear of 50% of all jobs are going away. And then there's reality. And yes, there are places jobs are going away, don't get me wrong, but I think you got to step back sometimes.

Speaker A: Uh, I a hundred percent agree with this, right? I don't think jobs are going away. There's gonna be disruption, right? Some jobs are gonna change.

Speaker B: There was with the Internet, there was with the computer. That's just life.

Speaker A: But let me give you a really good example, right? The number one use case for AI today is agentic code writing. It's like, it's where anthropics is making most of their money. And it's a great use case. Like I said, I can get, you know, 10, 20 times productivity lift on my engineers. I'm still hiring engineers right now. Like, I can't, I can't, I can't hire them fast enough. I'm like, let's go, I need more engineers, right? And so that's, that's like a, that's a litmus test for every other function. It's like, yes, AI is making us more productive or can make us more productive, but as humans, I feel like we have like an insatiable ambition. And so if we can actually produce more, we will just do more things. Like, if we're that much more productive, it's like, great, let's expand the scope of the number of things that we're going to do so I fundamentally believe, like it's not at least AI in its current format, in the underlying technology as today I don't think is going to wipe out. We're not going to have unemployment. It's just sensational hype building. 100% of jobs are going to be destroyed. 50% of white collar jobs are going to be gone. It's like, yeah, um, I just don't see that world.

Speaker B: I'm with you. I don't see that world. The idea of we're all going to be at home with the universal basic income. Do I think there's going to be disruption? Are there jobs? Are there companies that may be able to take out a lot of jobs? Are there industries that are completely changed? Yes, this is, it'll have massive disruption. I don't think it'll have massive job displacement, which I think are different things.

Speaker A: Your audience member, right, they're sitting in a corporate finance group and they're already understaffed. Like it's a, it's a, there's a. I can pull up all these stats, right. Like the corporate finance groups have been getting squeezed for years. Like they're already overrun. The world is more complicated. They're at requested to do way more sophisticated things. There's just not enough headcount today. The idea that we're going to use AI and then wipe out more people from FP and A, it's, it's, it's ludicrous to me.

Speaker B: You know, historically numbers have been roughly 75% of FPA's time is on non value add activities. So. And most you ask any company if they're getting as much as they would like out of FP&A, the answer is almost always no. Like if I asked you, would you like to get more out of your FP and A team?

Speaker A: Yes, 100%.

Speaker B: The answer is probably yes. Right. They can always do more. It's um, not that they're doing a bad job. And so, okay, if I take out the 70% now, I can get 75% more of what I want. Okay, yeah, there may be some companies that displace a person here and there. Yes, there'll be some of that. But the idea that 50% of FPA jobs are going away as ludicrous to me as well. I don't, I don't see those type of numbers. So.

Speaker A: Yeah, I agree.

Speaker B: All right, real quick. I have an FPA section to get to know you, so we'll run through these. What do you think in today's environment and kind of going forward is the number one technical skill for FP&A professionals to master.

Speaker A: Yeah, look, I, I will answer it slightly nuanced because I thought about this a lot in interest in kind of prepping for this. It's not necessarily a technical skill, but I think everyone really needs to up their AI literacy and that's not just playing around with AI. I think it's really important to understand what is being built, how it's being built, why it's being built. Like what are the mechanics? Like what does training mean? What is post training? What is context? Like how are tokens used? I just think if you don't understand these things, then you're just kind of running around in the dark trying to follow or catch up or, or you might trip yourself up because you fundamentally don't understand how this technology is what it's doing. So it's maybe not a technical skill, but I'll put it under that category. I think everyone would be, I'll give it to you.

Speaker B: I think it qualifies.

Speaker A: It's like if we can all up our, like just general AI literacy, listen to some podcasts, you can read about it and uh, honestly you could just ask AI to help you, which is, which is pretty incredible.

Speaker B: I'll put a plug in. You can listen to my future finance podcast about AI. Glenn Hopper is an expert. I learned from him all the time. So one thing I was going to say which interesting, your answer's similar. I've heard a lot and these are more smaller companies. The number one skill is starting to become system thinking, design data and data modeling. And those are answers from FP&A people that are getting a lot out of AI because they realize how important the data and the system and the design becomes. And so I think that kind of goes into your AI literacy. I think that all goes into what's that skill becoming. I don't hear near as much as I used to, which was almost always the answer six months ago and now it's a lot less. Excel and financial modeling still very important skills. I'm not going to say they're not uh, important. You have to know them. But it's been very interesting to watch the shift on the show of that answer.

Speaker A: I totally agree and I like the shift.

Speaker B: So what about soft skill or human skill?

Speaker A: Look, I'll answer it in two different ways. One, I think as AI helps us kind of streamline a lot of that 70% non value add, I think the real value add, um, and this is What I mentioned at the top, when it comes to like, you know, best in class, FPA tools is actually integrating with the rest of the business. And so this is around like, how do you speak the language of your business stakeholders? How do you understand and empathize for what they're doing and then how do you support and collaborate? So building the bridges outside of finance is going to be critically important. So I don't know how you want to categorize that as a soft skill, but I think that's number one. I think if as you free up more of your 70% of mundane time, really good FPA professionals are actually outside of the finance group working in the operations. They're working with their sales team, their marketing team. They're, they're in the plant talking to the, you know, the managers that are running the machines, et cetera. Right. And they're collaborating. And you can't do that as a finance person. You can't come here to like, you can't go to ah, our CRO and say, hey look, I think you can, I ran some numbers and I think you can squeeze up the quotas because of the number. Here's my book. And it's like they're not going to want to listen to you, but yeah, so you have to understand their world, what they're going through and then you have to communicate your analysis to them in the way in which they're going to understand and then they're going to partner with you on just as an example.

Speaker B: I mean it's a bad idea to hear the CRO and say we think you can raise all your quotas.

Speaker A: I, I, I ran a model and I think we can get a 18% price increase if we do it this way. And it's like, okay, but you're, you know, you have to empathize for them. They're the ones are going to have to be on the call with every customer trying to squeeze out an 18% price increase and then there's damp like, you know what I mean? So that, that understanding that empathy, by the way, as an aside, ambitious FPA professionals should leave FP and A for some period of their career and go work in the other areas. If you really want to go and become a cfo, it's like go work in sales operations for a year or two and understand what they're going through. You know, go work in marketing, go work in, you know, in a plant, understand what's actually happening. Like that context is invaluable. So there's just A side tip for people, if you're ambitious and you want to grow, like, get out of the function and then come back.

Speaker B: I have talked about that more than once of the importance of having some kind of operational. I worked in a business analyst role. I started my career in procurement. Procurement. And I've run my own business for four years. And I can guarantee you, if I had to go back to an FPA role today, I would be better than I would have been five years ago before I started my own business. Because of the experiences and seeing things from a different lens, you get an appreciation you just don't get, or, uh, it's very hard to get if you get it. But very few really get that appreciation without those type of roles. I'm with you. That was something. Our American Express cfo we had Jeff, I can't think Campbell, I think was his name. But he mentioned one of the best things he ever did was go into operations for a year and a half before he came back out as a cfo. All right, so we'll do. To get to know you questions. I'll let you go. I know we're running over on time. What's a book you'd recommend for our audience?

Speaker A: Yeah, you know, I thought about this one, too. I am. I'm, uh, going to go off on a tangent. I'm a. I'm an avid reader, but I read fiction.

Speaker B: I'm actually a big fiction reader as well. I don't do a lot of. I do some, but I start them and never finish them. Um, is my problem with the business books?

Speaker A: Yeah, I'm the same. And then honestly, I find I, uh, read for stress management and relaxation. So good. Reading a business book just winds me up more.

Speaker B: Well, we're in the same. I think we have a similar, uh, thing there.

Speaker A: So, uh, I'm gonna go fiction and I'm gonna go, uh. I'm reading it right now. It's the kind, uh, of adventure fantasy book, but it's, uh, the Stormlight series by Brandon, uh, Sanderson.

Speaker B: Okay. Yeah, I've read all Brandon Bolt stuff. I haven't read Brandon Sanderson. He's here. He's from Utah. Brandon Sanderson.

Speaker A: Yes. Yeah.

Speaker B: So I have a friend that actually is his, uh, like, chief of staff, runs everything for him. Used to live up here and moved to become his chief for. For his business.

Speaker A: He's just a super interesting guy, both as an author, but as an entrepreneur as well. And, uh, yeah, I think the first book in the series is called the Way of Kings. Okay.

Speaker B: That's been on my list to read some of, some of his stuff. He's one of the few I haven't because I do a lot of young. I call that kind of that young adult fiction category. It's actually probably what I read the most. So if you could go on vacation anywhere in the world tomorrow, where are you going?

Speaker A: My, um, my happy place is on a mountain. Like any season. If it's winter, it's skiing. If it's summer, it's hiking. Like me on a mountain disconnected from my phone is like, it's my happy place. And uh, the place that I've been trying to get to or get back to is actually in Peru. So I would say Peru, getting back to, uh, the Sacred Valley and the hikes and the mountains that you can do there. It's just something. I had the fortune to go there a couple years ago with my girls and we hiked the Machu Picchu Trail. And it just has something so magical about it. So I would get back to Peru, get back onto a mountain, get disconnected from, uh, my devices.

Speaker B: There is something magical about it. I've done a mountaineering course. I used to do Boy Scouts as a leader for years. I've done a lot of hiking. I've done a 50 mile backpacking trip for a week with a bunch of young boys. And I, I love the outdoors. So I, similar to you, I love a good, good hike in the mountains. So that's when people say beach or mountains. I'm uh, mountains.

Speaker A: Yep, I'm the same. And any season, if it's winter, it's cold, no problem. Then, then I ski, you know.

Speaker B: Funny, I never picked up skiing and I live in Utah. That's. That, that, that's a shame. But I never had money as a kid. I did, we did a ton of what we called extreme sledding. I don't think I've ever shared this on the show, but you'll appreciate it. My dad would take us up the mountain, drop us off at a spot and we probably went down over a mile and he'd pick us up at the bottom and take us back up and we'd ride our slide. There are parts we're going 30, 40 miles an hour. We'd have to walk across the road and keep going. I still remember we had. My sister broke her hand one time. We, we had some pretty good. It was some of the more extreme sighting I've ever seen. So that was.

Speaker A: My closest parents won't let their kids do anymore, like fly down the side of a mountain that goes down a mile with no helmet.

Speaker B: No.

Speaker A: No helmet. Helmet. That sounds amazing.

Speaker B: Uh, totally. But that. That was what we called extreme sledding. And we, we did quite a bit. Quite a bit of that as a kid. So that was my poor man's version of skiing.

Speaker A: Very cool.

Speaker B: All righty. Well, thank you so much for joining me. It's been an absolute pleasure to chat with you. I hope the audience enjoys the conversation as much as I did. So thank you for carving out some time alok. I really appreciate it.

Speaker A: Thanks, Bob. Topic that I love. Great conversation. I, uh, hope everyone got some value out of it.

Speaker B: That's it for Today's episode of FP&A Unlocked. If you enjoy FPA Unlocked, please take a moment to leave a five star rating and review. It's the best way to support the FP&A Guy and help more FPA professionals discover the show. Remember, you can earn C2CPE credit for 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 and A Guy, and I'll see you next time.