When AI Does the Analysis, What Makes an FP&A Professional Valuable with Valerie Martin

If you look at our new pull and your generation of FPNA, you know, how they're learning, I feel like AI accelerates their learning curve where they sound smart very quick because AI helps them win it very quick. However, the other side of Flip of the Coin is because AI sounds very smart. The answer really sounds really smart for this to make sense. And this is where we need to push Mark. So yes, we have the benefit of that learning curve, but how do you challenge it? And because obviously AI sounds smart. Hard-coded opinions were Glenn and I tell you what we're really thinking. All right, my hard-coded opinion for today is around communication. So much of communication we think is us speaking or writing, but that's not it. Communication is about the other side. It's about someone hearing and understanding not just the words we're saying, but the meaning behind it. And one of the things that really just drives me nuts, it's hearing people communicate in acronyms. We have this at every single company, right? Throughout our lives, people are talking in acronyms. Think about when you don't understand an acronym and how you feel about that. So when you're speaking to somebody and you're trying to say something, you say these acronyms, they don't understand the meaning of the acronym. You're basically losing the audience. And if you lose the audience, you might as well be speaking to them in Swahili, because they have no idea what you're talking about anymore. Now, I say that and this could be some members of the audience who actually know Swahili. So maybe let me change that to ancient Samarian, right? You might as well be speaking ancient Samarian because no one should really be speaking that language anymore. But the idea is that make sure that when you're speaking to somebody or you're writing something out, you do it in a way that the other person can not only, you know, to read and understand it, but they get what you're really trying to say. That's the whole point of communication. It's not about hearing yourself talk. It's about making sure that the other person is hearing and understanding the way that you are saying it and getting the exact same idea that you have in your head that they now have in theirs. That's the point of communication. So just watch out whenever you're communicating with acronyms. It's okay to use them, but define them first to make sure you're not losing your audience. And that's my hard-coded opinion for the day. All right. Now it's my turn to share my hard-coded opinion. You know, every so often you see people want to incentivize or tie bonus to forecast accuracy for FPNA. I always have thought that's stupid. Is there some validity to it? Maybe a little, but let's take a minute and think about it. Especially if you're a fast-growing business or you're a business where FPNA doesn't have the control, why should your bonus be tied to that accuracy? It's one thing to hit a forecast. It's another say you have to be within 2% or whatever. Okay. If you're a very mature business, things are very consistent, sure, or certain areas where you have all the historical and you should be able to be accurate. It's more about making good assumptions and sometimes taking risks. So I don't think it incentivizes the right behavior. I've never understood it. I've never liked it. And so my opinion is just don't do it. It's a bad idea. It's kind of the whole idea of token-maxing. It doesn't incentivize the right behavior. I don't believe forecast accuracy incentivizes the right behavior. So ask what behavior you're trying to incentivize and focus on that. So that's my hard-coded opinion today. I'm going to keep it pretty simple. If you're thinking of tying bonuses or performance or ratings to forecast accuracy, don't do it. That's it for me. Thanks. Welcome to another episode of FBNAOnLock. I'm your host, Paul Barnhurst. And this week, I'm thrilled to be joined by my co-host, Glenn Glen Snyder. How you doing? Doing great, Paul. How you doing? Doing well. Work treating you well. I know you got a big project. Everything going good? I don't really know what time zone I've been all the time, but other than that, yeah, things are going well. How many time zones are you in a week these days? Last week, I was in three. I was on the east coast, and then I came home for a day, and then I went to central time zone. It's going to say I know you've been in a lot of time zone, so hopefully you get some sleep this weekend. Yes, just don't ask me what time it is right now. Okay, middle of the day if that helps. All right, so we're really excited to have you all here for this episode on FBNALock. And we have a great guest with us today. So I'm going to go ahead and introduce our guest. We have with us Valerie, Valerie Martin. How you doing? I'm doing fantastic. Thanks for having me. Well, thank you for being here, and I'm going to let Glenn tell a little bit about Valerie, and she can add a little bit to Max. I think Glenn knows her better than I do. So Valerie and I have known each other now about four or five years. Yeah, I would see that. Yeah, we're both active in the San Francisco Bay area in finance organizations, and we've crossed paths and just really hit it off. So I was thrilled when Paul, when you and I, we were talking about the topic that we're going to be talking about today, which we'll introduce in a second. And I guess not Valerie would be a great speaker, but Valerie, you could speak to your background better than I can. So I'll let you do a little intro of yourself. Yeah, great. Well, it was a great meeting, yeah, Glenn, and still having you around. Well, I spend most of my career in FBNAL when I do more specifically is strategic finance. I let a lot of go-to-market and tech companies in the Bay Area. What I really like about FBNA is staying close to the business. So we make better decisions, and how do we actually influence to get it there faster? I recently spent a lot of time in the Bay Area around the AI space in last past year, understanding the noise versus the smoke, what's relevant or not, and how it impacts our teams. So excited to be here about this topic today and be introduced to you, Paul, today. So that's about me. All right. Well, thank you, Valerie. We're excited to have you, and I'll let Glenn share a little bit more about our topic before we jump in that. But before I do, I ask every guest this question. So we'll see where you kind of take it. We get some similar answers, but there's always a unique twist. What makes great FBNA? Well, for me, what makes great BFBNAL is really helping making better decisions and faster. So basically, of course, you know, FBNAL know the numbers and we're going to go and know what the forecast is, but we're really helping about, you know, the question of what's so what, what do we care and worry should we do things differently? I like to consider FBNA like co-pilot. So if FBNAs co-pilot to the business and we're flying the plane together, our job is not to really read the instruments, it's really understand where we're going. What's changing around us and how do we need to redirect and what's the opportunities and where do we change course? And you can be a good co-pilot if, you know, you don't know the context, you don't build trusts and you don't have judgment. So you need a business to trust you enough so you could actually make those decisions and that's what I think FBNA should be. I like it and don't crash the plane is to co-pilot. How know? Well, I actually, I so much you're spending a lot of time on airplanes recently. I love the analogy because I think a lot of times you have a starting point, you have an ending point, but there at some times there are storms that come up in the way and you've got to go and say how do we go over and navigate and change the direction a little bit to go over and reduce the amount of turbulence so that you could get to where you need to go in the right way and make sure our plane land safely. So I love the plane, flood the flying plane analogies. Great. Hi Sutton. Glenn, why don't you introduce our topic, let our audience know what they've won today. I mean, what we're speaking about. Yeah, the grand prize. So one of the things that I've noticed and I think everybody probably noticed is there's been some conversation around the stuff called AI, right? It's in the news. It's out there everybody saying you have to have it. You know, Paul and I have had guests that have basically said if you're not using AI, you're going to be left behind, all those types of things and there's a lot of truth to all of that. But there's a story that people are not telling and that is what happens to the skill set for people who are using AI and then how do managers need to change the way that they're managing their teams when their teams are using AI to make sure they're still going to have the right impact and the trust with the business. And so that's where we thought Valerie would be a fantastic guest to have here and to have that debate because it's the conversation to me that is just not being had. Everybody's talking about the efficiency of AI and all these things and granted now the conversations more about AI is going to, you know, kill all humans on the planet in the next 10 years. Okay, fine. Let's put that aside for the moment and we can't talk about a favorite terminator and all those type of movies. Yeah. And the funny thing that I always come back for years and Valerie may have heard me say this a few events. There's a couple different views of AI. One of them is we are we think we're going to be building commander data from start correct but sometimes you're not actually getting someone who's that ethical, right? But but really when you're thinking about if you're using AI, what's really happening? You know, what's the skill set? What's what what are your analysts doing in FPNA or analyzing the numbers? How close are they to the numbers that they can remember it and recite them in a meeting when they get asked a question that might have a little different twist to it? Do they have enough knowledge to answer the questions or do they have to go back to AI to get the answers? And if so, how does the business feel about that? And then what happens to that trust relationship between FPNA and the business and that trust relationship as you know is so critical because that's how FPNA really gets the insights to not only improve forecasting but to understand what's coming and trying to be proactive to head off some of the issues that could be arising in the future. So with all of that said, Valerie, why don't we start off kind of with your take on how you're seeing AI is kind of being used in FPNA? Set the stage around that. Yeah, no, it's a good question. I'm very excited about a topic. Um, I see AI moving from AI helping to actually AI really doing pieces of the work. So if you're looking at variance analysis or commentary or rebuild models or repeatable work cleaning up messy data or repetitive workflows, that's where I see a lot of AI. And I've seen an example where I think your company had 16 years of old spreadsheets and data and AI was able to do it and rebuild the history in 90 seconds. Now, where I see AI, a lot is like, that would have sounded crazy long time ago. It's working now, but it's not because we have that data that actually it turns as valuable data. It's more what do you want to do about it? So what I'm seeing is more and more of the capabilities of AI are increasing. People are using it, but now it's basically focusing on how can we actually bring value to those tools and what we're trying to do and which problem we trying to solve. And we cannot really, you know, look at the risk of confused. It's more efficient, but are we actually bringing more value and what we're doing with the time that we have saved? So that's kind of where I'm seeing a lot of the AI moving into testing phases, looking at stuff, I'm actually doing the work today. That is helpful. I'm curious. What do you think are the best use cases? Not where you're seeing it being used, but in your opinion, today, via the list of top three use cases, what would they be? You know, it's interesting because I've stopped thinking about where is AI use case to what's a good case, meaning which problem are we trying to solve versus putting a tool on it? And I think that's where I'm constantly repeating where people are seeing the fancy like the fancy tool, but where I'm silly, it works really, really well to answer your question, Paul, is like, where it's fairly bounded and repetitive, where the data is reason little good or the process is simple to automate. And then somebody in the room knows what good looks like. So it's easy to audit and validate. So the first pass of like a various analysis, just the first pass of it, a recurrent analysis or some cleanup of the repetitive work. And then some in the room that could say I could audit that and like this looks like, that I think it's a great case of AI. Another thing where I'm seeing a lot and I don't know if you should probably say not too is forget the tool. People have a lot of complex processes. So they think by putting AI in, it's going to fix the whole thing. So it comes back to, well, AI could actually simplify it, but or make it garbage it, garbage it out, but I think it's really looking at what we're doing today, go back to our teams, do we design why we have that process? How do we make it simpler? And then eventually get AI. And I think that's where it's evolving, but it's not very yet. But I think that's something that we need to really look at. Yeah, no, Valerie, I completely agree with you. I mean, one, you always got to go and also make sure that AI is producing the right results because 90% correct, but the 10% can really get you in trouble if you're if you're not catching it, right? So you have that. I think the other side of things though is everyone's talking about AI and it gets you alluded to. It's around production. You can produce things more, you could get your reports out faster, you could do the analysis, it could put together a deck for you, those types of things. But one big aspect of FPNA is the relationships that you have with the business. How are you seeing right now AI having an impact on that finance business partner role in those relationships? Yeah, well, I just need come back to a foundation. And I do see a lot of changes to be honest. I think this is a great question. Is that if we go back to a whiteboard, you know, the whiteboard days and we just look at how do we use it? How do we want to use AI? Which decision is trying to use and support it? And like, how can we eliminate or simplify it? I think that's kind of your baseline. And if you come back on partnership and AI, I'm like, okay, great. How do you earn that trust and how do you earn that partnership? Is it because you spent a report or because you know the report, you know the data, not necessarily all you built it? And then also do you have you earn that seat, you know, under table with the partnership? And like, how do you build that context, build that trust, you know, and get that context? That's what AI is today. AI could help you automate manual work, but you still need to earn that seat. And I think that's an important distinction point. Today's sponsor is Linios. Most finance teams I've talked to have built something impressive in Excel. Real models with years of institutional knowledge sitting in the formulas, then the data underneath go style, the moment someone posts the journal entry and somebody spends a morning rebuilding it. Linios provides reporting intelligence by connecting your ERP data directly to Excel. Reports refresh with current data, your formulas stay exactly as you built them and you can trace any number back to the transaction behind it. Everyone works from one version of the numbers, including whoever is building the board deck. Your team moves from assembling reports to analyzing them when you leave it to Linios. Learn more at www.insightsoftware.com/linios/solutions/reporting-intelligence. That's www.insightsoftware.com/linios/solutions/reporting-intelligence. I agree, I may think Paul, when we think back in, you know, through our careers, we've always talked about it's not the great FPNA isn't about the numbers you're producing or doing a budget or a forecast. It's having that impact on the business. It's having that relationship and that trust. And I think that's the thing that AI can't do because you might say, hey, I could trust AI, I could trust the system once you feel like you're actually getting really good results from it. But when it comes to, hey, who's looking out for me? How do we go over and, you know, as Valerie talked before about being that copilot, helping navigate around the turbulence type thing, AI can't really do that. And my concern is that people aren't adjusting the way that they're managing their teams in a way that they're so, you know, people manage oftentimes they teach those technical skills. How do you produce the report? How do you do the budget? But they're not shifting to say, how do we maintain those trust with the business? Paul, do you, you and I have had similar experience? I'm guessing you're in the same boat. Yeah, I mean, obviously, regardless, I'm trying to think exactly how I want to say this, right? AI is nothing but a tool. Although we keep hearing it, we could be setting it at some point, whatever that means, it could kill us all, but it's still a tool. All right, could resist. Like excels the tool, like modeling's a tool. And if you look at each of them over time, when the spreadsheet came out, did it really change the business partner relationship? No, when the computer came out, did it change it? No. AI has the potential in the sense that it, those tools were just different way of working. AI can do the work for us. And so if you don't keep that accurate control, now whether it gets it all right, we could have that whole, we could spend the rest of the time discussing how well it works for us. We know it works. We know people are getting results from it. We know it can help us. But if we don't still keep control of all the relationships and really owning what FPNA is about, we're going to start to lose some of the core things. And the core of FPNA really is business partnering. You know, whether we're building a model, whether we're writing an Excel formula, whether we're doing analysis, those are all designed to help the business make a better decision. If we don't partner work with and have relationship with the business, we can help them make those better decisions. So I think that that's kind of the lens I look through at all is we kind of always have to bring it back and say, okay, it's just a tool. How do I make sure that I'm able to maintain the appropriate relationships to drive my goals, regardless of the tool I use to assist me? I think that's fair. And I think the difference is sometimes people are getting very hung up on, look at what I can produce for you business versus look at the insights and look at the problems that we can solve together. And what I've seen is I think there's this natural shift of production, of where production comes from. Oh, I don't need an FPNA team of 10 people anymore. I could do it with five because I have AI. The production goes up, but that FPNA leader isn't recognizing when production goes up, how are you now making sure that the business is getting greater value, greater partnership and getting better insight if the production is coming from AI. And I don't think that shift in leadership is really happening. I think a lot of time people are just they're managing it the same way that thing always has. And that is eventually down the road can potentially deteriorate the trust between the business and FPNA because FPNA is not really doing the work and learning the skills and the data. I mean, Valerie, what's your take? I think this is But on, I think AI could help us get the answer faster, but you can't outsource accountability and getting the answer in the trust behind it. So for me, it comes back that AI raises the bar for fpn, let's just admit it. Data is more available faster and so on, so our value is not necessarily getting that dead anymore, but it's really understanding how do we answer the real questions, right? So how do we answer judgment? In order to get judgment, you need context, and you need to know which question to ask, and you need to challenge something, and you need to have that relationship as you're talking about, that hasn't changed. But what changes, like, how do we train our teams? 'Cause in the past, like, I think you mentioned that, Paul, is, you know, there were an excel, and an excel, you have to think about the architecture and how it's built and what's the assumption's gonna do, and then you kind of test them, you kind of understand the architecture behind it. Now, when AI, assuming AI does the whole thing, right? So as a leader, now, when you're actually challenging your analyst to do the job, like, you need to challenge them on assumptions. Have you, like, what's your assumption? What do you think? Which questions are you trying to answer? Okay, well, there's a great report. It's served for what? What's your judgment? So it's like, how do you lead and challenge your, you know, the reviewing part is the key part here. So how do you wear the work? And also it's like, how do you pull these people more into those conversation that we're used to? So bring them closer to the business university. You could be sitting in the room to have a debate and see why the questions are happening. An example that I like to say is like, imagine that you're in a room and there's a 10 million assumption on, I don't know, there's just a marketing spend, and you're like, you don't know where that's going. So there's debates in the room that said, is it 10 million or 20 million? There's trade-offs, what does it mean? And risk and so on. If AI is not sitting in the room, like AI will know, just spit a number. But if the leader does not bring the junior analyst or challenge it or learn it, you'll never learn the context. So for me, it's AI will bring the bar, but the leadership needs to challenge the assumptions even more after questions, make sure they make them think and also pull them in the business tweeners where they have that context. Because judgment is not going away. It's just how do you train that judgment that's different now? It's not going to be trying to AI. It's trying how we actually train them. So I think we need to rethink that. And we need to think out, we look at that learning from that talent pool. - I think you make a lot of good points. I think there's a ton of discussion right now. I think we're mostly seeing is around the education system. If AI is starting to do more and more of what was often the junior analyst's work, what we sometimes call grunt work, dirty work, whatever term you want to use, not trying to be negative, but it's often just the stuff that had to get done to get to that deeper level. And so if a lot of that's done by AI, there's, I think the conversations are happening. How does that change education? How does people get training? But there's also the question, how does that change the way we need to be a leader? And like you say, we need to think about our teams, we need to manage them. Those frameworks are going to change. There's also the question of how far do we let AI go? So I think there are two kind of two separate things. What I mean by that is, right now, there's a lot of marketing hype of autonomous finance. If you could have an entirely autonomous finance department, would you want that? No way, no accountability. There's no one, you know, who's going to go over, it's like, when mistakes are still going to happen, who's accountable for them, who goes into fixes them. If you don't have anyone there that's, yeah, here we go. Let's just say it makes no mistakes in the sense of the data. All the reports are right. Even if you make that assumption, my answer is still no. Absolutely, there's no insight. There's no, hey, tell me what I'm not thinking about. There's so much more value than just the data that we bring. Even though it can do that in some situations, even if I assume it could give me insight and ideas, someone still has to ask the right questions. Someone still has to decide if that's the right insight. Even if it could, let's assume it does all that, you know, does those things well, you still need a human judgment or a human layer here. That's why I cringe when I see autonomous finance. Are there areas that's low risk and it's automated? We already have autonomous finance today in some areas that it just does it and there's a spot check. And Fayette is areas where that makes sense. Continue to you, no problem with that, but this I give. Yeah, you almost don't need a CFO. It's like, there's two things you need to address. There's a queue, an element from judgment, and there's the leadership. Valorant. - I think this is spot on because I think FPNA leaders, we just need to manage differently or think about it differently. It's like, we need to redesign the work, not just to deploy the tool. AI is going to do it. So if you think about work, I see three pillars, right? There's the work itself. Does AI does it or does it augment it? Or do we leave it human-led? Like, that's the work. Then if you look at controls, 'cause we need to be sucks control and audit for all the above. It's like, what needs validation? Who owns the output? And what happened if it's wrong? You know, like, what's our risk here? And what's our risk tolerance? But then there's a talent pool, right? So what experience do people need judgment on? And where do we fit it? And how do we challenge it? So all this still exists. You just need to, we need to go back to those whiteboards again, or like, whatever it is, but it's like, which problem we're trying to solve. Where are the workflows? What are we on mining? And we're actually the judgment stakes, 'cause this is where we need to focus, how to make sure that we train them all and train to give them the context and the trust. And AI is not gonna do that. Leaders need to do it. - And Valerie, I'm gonna add a fourth pillar for you, which is gonna be. - Okay, okay. It's communication. - Communication. - Oh, yes. - Because how do you communicate the results? How do you package it? You gotta know your audience what's gonna work on it. Right? You can't go over. I've been in meetings where I've shown detailed spreadsheets to marketing leaders, and they're just like, I don't like looking at Excel. Give me a picture, you need to make a PowerPoint, right? - I could not sell in a meeting together, by the way. - Yeah, right, but I mean, I love marketing people, but a lot of times, marketing people, they don't want to get into data. That's not their skill set, that's not their comfort zone. They want to give them a PowerPoint deck with a graph and a picture that they could comprehend and see in a much better way. So how do you communicate that? Not to mention, in fact, if you're going over, you're having a meeting and you're communicating something to an accounting person. You could be in the weeds, in the numbers, and that's where they're gonna be comfortable. If you're presenting to a board of directors or you're presenting to an executive, you gotta bring that up. Well, what's the bigger impact? Not just in the details of the data. Well, how does that work within the confines of the company or the industry or the market that you're in? What's the overall impact? And the message could change based on your audience. So it's understanding how you not only to do the work and work through it and what the results are gonna be and who's gonna do what, but how it gets communicating at the end of the day, if you're not communicating it to the business and why you're doing it at all. - It's interesting 'cause that communication, I think it's a great pillar, and I'll add it on my floor. It is like, how FPNA is taking complexity and make it simplicity where people could understand it, and that's where a pillar of trust, if you think about it. Because how are the business leaders gonna actually trust what you do is when you put something in front of them and are like, okay, first, yeah, I trust it comes from you and you know and you did your validation, basic FPNA. But, too, do I understand it? Do I just like, do I just understand it? How does it work? What does it mean to me? Why should I care? How does it tie to the whole company picture? And I think you're spot on by saying storytelling or communication is the key criteria that we need to make sure our teams continue to focus on. And one little thing, you mentioned the trust aspect thing, I trust you have the data right, but when it comes to the individual from a business perspective, they're also saying, do I trust that you've analyzed it in the right context of my business that's going to add value? Because that's where the business leaders are really looking for it to say, hey, if you go to a business leader and you keep on putting ideas out there and there's like, do you not have any idea what we do here, it just doesn't make any sense, you're not going to have that trust in the business. You've got to make sure it's going to resonate with the people you're talking with. - And I think you're wrong, you're right, not wrong, right. Actually, it's like, it's like-- - You're wrong, you're wrong, you're wrong. - I had to put that out there, but I know you're right. It's like, we have to earn or see differently. It's like the same thing, but we just do to earn it saying, well, yeah, AI will raise the bar. If you get your number, yes, whatever, like, forget that. But it's like, it's exactly what you're saying is like, what is the context, where is the judgment? Knowing the business is super, super important, so that's what we also need to make sure our teams understand that, and which questions to ask, and you don't know if you're not exposed to those debates, or how to be courageous and ask those questions, like, it's not just accepting it as it is, like, just challenges, you know, the five wise, and having the relationship, like you just said, the actual influence of the decision, and that's the key point here. Today's episode is brought to you by our sponsor, Linios. Linios brings live ERP data directly into Excel, allowing your team to build reports with fresh data. No still files in sight. More on that in a few minutes. I think, you know, we've all kind of aligned, obviously, there's a huge judgment element that you have to maintain. There's partnership, there, [BLANK_AUDIO] will disrupt some of that. So let's talk about what do you do. Let's assume, you know, AI is not assumed. We all know AI is going to continue. It's not going away. It's going to continue to get used. So what should the leadership leaders be doing? How do they manage their style? How do you manage with, you know, with the team of AI? I've never done it. And running my own business now. I don't know, you know, yes, I use AI, but I'm not managing employees that are regular using AI and kind of an FPNA environment. I don't know that anyone's really knows the full answers yet, right? We don't know the full implications, but how do you think about it? What are things that maybe managers need to be careful about? Because managing an agent, even though sometimes they say, hey, treat it like an employee, there's obviously differences. And you have an employee, and I can see Glenn cringing a little bit. Why don't we have a hard-coded opinion in his head coming? Oh, that's a good idea. But no, Paul, you know what, you're spot on. I think, you know, some of the challenges with managing people who use AI is the production happens so fast, they want to move on to the next day. But you think, wait, no, you got to slow, one thing is you got to change, where you got to tell people to slow down. You know, great, the numbers came out fast. Make sure you're looking at it. Don't go through the numbers so quickly that you're not really catching those mistakes because those mistakes are going to lead to credibility questions. And so you have to slow down, also make sure that the analysis is thorough. A lot of people, they go over and it's so easy to do elevator analysis, right? Which is this one up and that went down. But that's not insightful, right? What's the underlying metric? How do you get there? What's the impact going to be? Why does somebody care whether that was over budget or under budget? Right? So you've got to be able to, and you can't just go over and write, you know, a half a page for everything that you're trying to explain. How do you do that succinctly? So those are some of the things that you have to coach people on. But the other piece is to go and say, hey, guess what? FPNA analyst, you have now a lot more time because you're not manually producing these reports. AI is putting that together. Good. So what do you do with your time? You should be learning more about the products. You should be learning more about the customers we have, markets that we work in, the government regulations that we have to abide by so that now you can take the numbers and put it in a broader context that people at a higher level can start using to make decisions. And it is a different kind of coaching. I think it's not so much about you got to get the numbers out, got to be on time, got to be right. And then, you know, that's the majority of the work. Now it has to be, it has to be in the right context and adding more value because you have on to put that in. That's my tape. Valerie, what do you think? I think this is right. It's like, AI is not going away and you can't be in the sidelines or it's there. So how do you use it? But balance the benefit and the risk. So I would say is like, time save is not also necessarily creating value. So how do you make yourself more valuable? I think that's your question, Glenn, is like, well, how do you invest yourself to be more available as well? If your information is cheaper and faster, then it's how do you create judgment? How do you are curious? How do you build business acumen? How do you, your communication pillar? Like how do you invest in storytelling? How do you build trust? I think AI could do the analysis, then what do we do? Well, we need to know which one matters. So challenge which matters and the so why. AI will create scenarios. Well, which one is worth discussing and bringing to leadership? Like your marketing example, they're like, okay, why should I care about that one? AI could draw for a recommendation, but, you know, is it really the right decision and does it make sense? Like, challenges scenarios and actually train all the above. I think at the end of the day, it's like, you need to put people in the room to answer the business question and defend the assumptions, basically defend assumptions and recommendations and not just the answer. I think it comes back to a skill that we've done in the past and it's even more and more and more important to do it today. Great point. I think, you know, you both have something Glenn, you really hit on something that is really important. As leaders, there can be a tendency. We got AI, so now we can pump out more, we can do more and more and more. That extra report probably isn't adding the value you think it is. What percentage of reports never get read today? Glenn, in your career, have you ever gone into a situation? You find out nobody's reading 80% of the reports FPNA is producing. Yep, Valerie. Happy all the time. Absolutely. Yes. I've been there too. So, there's now, you have to really think, how can they add more value? One, am I teaching my team to ask the right questions of AI? Because AI can give us an answer and sound confident with just about anything. The questions never been more important in my mind and the second thing I think you hit on, I really kind of think of three things. You got to be able to write, ask questions, you got to be able to audit. We're becoming more of auditors, whether we like it or not, and that is not as fun of a job. People have to realize that. Who enjoys auditing a model versus building it? Who has more fun auditing the model? Again, nobody raises their hand. Right? We didn't go, we're not auditors for a reason, but we have to do more of that. And so, some managers have to understand how they manage that. I think the next two or more, I think the most critical is one, how do you make sure they're deeply understanding the operations of the business? If you want FPNA to provide a lot of value, don't have them build another report. Use that time to help them better understand the business, the deep operations, and then the fourth, instead of so much training being focused on technical skills, and that's what we train people on. Make sure your learning and development is around the soft skills that allow them to be that partner that's going to drive the business forward. So those are my thoughts as I hear a kind of everything you've said and kind of bring it together. It's been trying to crystallize it the last 20 minutes in my head, because I haven't thought about it this way before. Yeah. And it's interesting, because another thing I think we need to think about is like, if you look at our new poll and your generation of FPNA, you know, how they're learning, I feel like AI accelerates their learning curve, where they sound smart very quick, because AI helps them when it versor quick, you know. However, the other side of flip of the coin is, because AI sounds very smart, the answer really sounds really smart, but doesn't make sense. And this is where we need to push back. And because obviously AI sounds smart, but it doesn't always make sense to me. Anyways, but it comes back as leaders of how do you make sure that you redesign how you teach them and how you challenge them? So really make sure like my like my ask for all these FPNA leaders is make sure that your analyst explains the output to you. Make sure you put them with business partner in your sound the contacts and make them defend their recommendation, not just produce the answer. And I think it's even more important today. Yeah, I agree. I think that the defending part is incredibly valuable because when you're presenting the data, you can be challenged on it. You've got to be able to defend it. You know, the funny thing is, Paul, when you were talking about, you know, the focus on soft skills and value, what you were just going to, I, it made me think of the progression. Someone has an FPNA, right? You start off as an analyst and 95% of your job is technical, 5% with leadership or soft skills. Then you go up to a manager level and you're about maybe 80, 20, 80% on the technical side, 20% on the the soft skills side. You go to senior manager and it's probably 70, 30. You get to a director is about 50, 50. And then you get to the VP level and it's about 20% technical, 80% on the soft skill. What's the moral of the story? Is that if you want to move up in your in your career, it's not the technical skills that's going to advance you. It's the soft skills. And one thing is it's always easier to teach technical skills. I mean, we've all had to think of people who taught us how to build models or how to construct something. And it's very technical and it's like, oh, yeah, here, how you could apply that over. But rarely do you get people to say, hey, let me sit down and talk with about how you should coach your team or how you could build out better trust with your business partner. That's stuff that you usually learn by making a mistake, getting yelled at and then not doing it again the next time. And that's how you sort of pick it up. But it is in a way, I think just for anyone who's out there who's managing people, that's the hardest part of management. It's easy to go and welcome somebody how you put a budget together. You can go look at the trend of this and let's go and hear how you forecast that out. Great. But to go over and explain to somebody why it's important, and make sure that they understand and how to apply it to help other people make better decisions. And how to go and build that trust and engage with people and how to speak up when you're not comfortable. Those types of things, those are the skills that are not really being taught, but that's what really needs to be emphasized because the production side of things is moving to AI. That's my take. No, it's your take, but let me make it even more realistic or scarier because that's the reality. No, I'm not going to talk about terminators, but I could talk about how you could be scarier. But if I assume that's right, AI produces similar sorts for everybody, everybody has the same tool, everything spits it out. Like, what's your differentiation factor? Think about it. So, cheap people have the exact same data, same report rate AI, the a better FPNA partner may ask different type of questions. And then when it comes back to what you said, right? What will differentiate you is where are you, your curiosity, your judgment, and the understanding of the business, that while she make you more valuable. And it sells well. - Yeah, and then again, I'd add that fourth pillar of how you communicate it out. - Okay, I will get it in after this time. - You got it. (indistinct) - Absolutely, yeah. - Glenn just likes to talk about communication. - Well, I think it's key. I think for me, it's a given. That's why I don't mention it. I think it's a given, but you know what, you're right. It's not a given, we need a minute. - It's the base layer. It's not a pillar. It's the foundation, how's that? - So I'm actually laying that foundation for my hard-coded opinion that's coming up. - I'm not surprised. Like, I'm excited to hear it. So there's a tease for everybody. Although, well, then I'll stick it at the beginning and everybody will be confused when they listen. (laughs) - What happened when you put the God of Order? - Yeah. - No, I know, put it at the end. I'm just happy. So at the end, there will be a hard-coded opinion for everyone. I'm just lost my dream of thought. This is really bad. I'm doing great on this episode. All right. So back to, I think there's a lot of great points everybody's making here as we kind of talk about this. And I think what it does is the soft skills have always been important. They become important earlier. And the hiding behind why just need to master my technical skills is gonna become less of an excuse and less of an ability. Not to say technical skills are not going away, at least in the short term. They're still important. You still need to learn how to use Excel and modeling and all those things. But there's a clear shift. And I'm seeing, you know, at all getting better and better. And I think the whole idea of producing similar results is a really good point. So I think we're all in alignment. AI is here to stay. I'm gonna guess that way. How often do you use AI today? Oh, every day. Like every hour. I'm always with you. What's something you can't do now without AI? You couldn't go back to the old way. It'd be really hard for you. Oh my god, I use AI for cooking. I think I picture in my fridge. And I say, what do I do today? And I'm like, they just give the recipe. So for me, I use AI for cooking. Yeah, I mean, great example. Glenn, is there something for you? I don't use AI as much. But part of it is is that the role that I'm in is more strategic in nature. And it's more about the discussion and being present in the discussion than producing the results. Anything in your personal life you're using AI for that you like, wow, is so much easier now. AI, the one thing that I do use AI where it's great is just doing research. Hey, I need to go and understand something. Go come to web, figure out what's, you know, give me some, you know, ideas about different things. And I think AI is great for that so much faster than Googling things with self. Yeah, I agree with everything he said, you know, mine is, I do a lot of podcasting. And when I started, I had to go listen to the episode to put together my script. I don't have to do that anymore. AI can summarize everything. It can write that first draft, it prompts my mind and all the mate. And then I go through and put my own, you know, spin and wording on it. But it saves me just hours and hours of time. And that's, that's the world we live in. It's going to continue here. So how do we, what are the kind of things they should, FP&A should really be watching out for to make sure, you know, they're getting the benefits without letting the kind of, the risks kind of take over. So any thoughts on, hey, it's here to stay. We're talking about leadership. Which is people, if the biggest thing they need to make sure they watch for, so they can take advantage of the benefit and minimize that risk. Valerie, what do you think kind of those, those things are? For me, so looking at our process and you decomposed it, certainly understanding what can AI will come and introduce and go watch. What do we need to get our control? So we don't spend more time validating AI than actually our cost of control. And then the rest is like, where do we need our judgment? And how do we retain that judgment? So if it's not building models like we used to do, like how do you make sure as leaders that we carve out the time that we have now, I've not doing research or cooking or whatever we need to do to challenge your team? And that's something that we're accountable as leader. It's part of our job is just taking more percentage points as Glenn mentioned that we did before. Yeah, you know, I mean, for me, I look at it as, how are you now strategically having an impact? Not with the data, but with the ideas. And how are you presenting it? You know, with one of the best compliments I ever had in my career was I had one of the executives that I was supporting who said to my boss and it showed up in my annual review, I want Glenn at the table while making decisions. That is, to me, that's like FPNA Nirvana to me, right? That's where you want to get to. And it was, you know, it was not only it's a great compliment, but it basically said, it's not about the data that Glenn is producing. It's about how he's showing up. And I know I'm talking to myself now in the third person. Can't feel like Ricky Henderson a little bit. I just say, Carm, wow, he's a third person guy. But, you know, it's about when you're listening to something and it's, okay, here's my thought. Here's what's coming into my head. It's not running it by AI. It's just, I'm engaged in the conversation and having that impact and having that value. And to me, that's the new measurement. The measurement is about the value, not the production. And it used to be about the production. - And it comes back to, you know, I think my originally what makes FPNA great, right? How you get closer to the decision and how do you get that, you challenge, you bring value and you're being that co-pilot. And the co-pilot is basically being in that room and having that debate. And I would say the real value is when the executive comes over or the pilot comes over and says, I want you to be the co-pilot versus just assigning yourself into that role, but having that acknowledgement that the person that wants you right there next to them. - You've earned that seat. - Exactly. - We've now hit Nirvana. - That's right. FPNA Nirvana, the great place to be. - All right, so as we wrap up, it's kind of going to bring a real practical ending to this. We'll go Valerie and then you'll go Glenn and I'll share a few thoughts. How do FPNA professionals, whether it's leader managers, wherever you want to take that, make themselves invaluable in this era? - I think it's to be curious. You know, like, it could be curious, ask questions, think differently, everything's possible, but actually lean in. Don't like eye control is which problem you're trying to solve and how can you actually improve and bring value? And you'll see by default by being curious, you'll see it's really like communication, the storytelling, the soft skills. You still need to understand your foundation, but the value is shifting of building reports to actually making an impact in the business and make sure that the results are valuable to them. - In fact, by the way, Paul, it's a little flashback when Valerie said be curious. First thing I thought about a year ago, we were talking about private equity in the impact and you asked a question, what's the number one thing that private equity looks for an FPNA person and the answer was curiosity. So I think there you go. I mean, and I think Valerie, that's absolutely, I mean, curiosity and I think it connection. I think that'll be the one for me. It's having that connection with the business where they feel like you're part of their business. They recognize that you have a job to do as well, but they want you there with them when they're making decisions. They want you part of the leadership team. They want you engaging with people and the products and the clients and so on that they're working with so that you can add even more value. It's that having that invitation to be a part of that group, I think that's really where the value add comes in because I mean, we can think about people in our lives. When you see somebody who's like, they give you great advice for something, you start thinking about what else should I be asking them? How else can I engage with that? And that's what you want with that natural expansion of that trust. - I'm going to go in a totally different directions. I think both what you said is really well said and I don't want to repeat it. One thing, especially right now, they recommend FPNA, especially early in their career learn, foundational understanding of data and workflow design optimization. 'Cause if you understand how the data works, you can understand the processes. You're going to be much more efficient and better with AI. I've had several guests say they see a shift to what the most important technical skill is to around that data thinking, the data foundation, the design. I think those have always been critical, but they've been a shortcoming of FPNA 'cause we learn financial modeling, we think from a financial perspective. And building a model and understanding how to normalize data are different tasks. They require a different way of thinking about data. And the more and more you're going to get benefit out of AI, data, workflow, optimization. So you need to understand how to be able to do those. If you want to get the most out of AI, so you can do all the other things that Glenn and Valerie talked about, where you can add that huge value. - Impal just to add to that. I think a lot of people today in their FPNA roles, they're spending a lot of time cleaning up data and they are maybe implementing a new system and aligning data dimensions. And if you don't have that understanding about how to create that clean data dimensions and how where the intersections need to be, you're not going to develop the right system. You're not going to have the right organization for your data, and that's going to have downstream impacts as well. So, 100 percent agree with that. You're not going to get good intelligence from AI because if you give a good context, the output is much better and good data. Complex layer is super huge, so but you need the data foundation and how it's architect to add the context layer for sure. All right. Well, I think we'll call it there. Any last thoughts? Anything you want to leave the audience with before we thank you and let you go to enjoy your weekend, Valerie. Well, enjoy this podcast, I hope everybody enjoyed the conversation, but I would say just be curious. I think there's so much to learn, and it's so much storytelling to do, and FPNA is amazing spot to be today. So, I think that's a great note to end it on. Any last words from our wise sage Glen? I would regret, although you should always be watching out for when AI is trying to do the extinction event, but regardless, you should be looking and making sure you're watching AI for just making sure that the data is going to be lining up because although it is going to be correct a lot of times, there are errors, and you got to remember that your name on there, not the AI is before in the reports and the analysis and so on. So, just you always kind of still got to keep an eye on AI, plus to make sure it's not trying to poison you in some other way. And my trucking skills. Thank you. Well, let's not know everybody watch AI. We hope we don't have an extinction event before this episode is released. And thanks for joining us. Bye. That's it for today's episode of FPNA Unlock. If you enjoy FPNA Unlocked, please take a moment to leave a five star rating and review. It's the best way to support the FPNA guy and help more FPNA professionals discover the show. Remember, you can earn CPE credit for this episode by visiting earmarkcpe.com, downloading the app and completing the quiz. If you need continuing education credits for the F-PAC certification, complete the quiz and reach out to me directly. Thanks for listening. I'm Paul Barnhurst, the FPNA guy, and I'll see you next time.