Is your FP&A team just reporting the past? A conversation with Persia Setna

Speaker A: Storytelling has just transformed my business partnerships and has enabled me to help with decision making. Storytelling builds trust because it shows the leaders that you know what you're talking about. You're not just regurgitating data, but you understand what the data means. I always love going into meetings with executive leadership with a recommendation and starting with the recommendation at the top and saying, like, we'll discuss it more, but this is what I think we should do. And I would love to hear your thoughts.

Speaker B: So today I'm joined by Persia Setner. Uh, she's an experienced FP and a manager. Uh, Persia, thank you so much for being with us today.

Speaker A: Yeah, thank you for having me. I'm really excited to speak with you and just thankful for this opportunity.

Speaker B: Thanks so much. I'm sure our audience is going to love this. Um, before we get started, I'm sure people would just love to know a little bit more about you, uh, how you got into finance, how you got into FP&A, and what drew you to it.

Speaker A: Yeah. Um, so I am based in Austin, Texas. Born and raised in Texas all my life. Uh, I graduated from the University of Texas at Austin and I actually went to school to become a neurosurgeon. So, uh, we can't major in pre med, so I had to pick a major and I picked finance. I got into the business school and I started to quickly realize that I loved my business classes a lot more than, uh, especially organic chemistry. M. I still know, get chills when I talk about it. Not in a good way. Um, and so I made the decision to just focus on business and finance. And. But I, I say that I went to school to study pre med for a reason and that's because I was really interested in solving problems. Right. I wanted to be someone that not only helped people, but really solved a core issue. And that's what drew me to that field and that's what inevitably kind of drew me to business as well. And when I graduated, I started off my career at Indeed, uh, on the FPA team there, supporting marketing, more specific marketing. P and L. And what I really love about FPA is that it's really the intersection between strategy and finance. So you can really be creative. And I, I grew up in a creative background. My dad is an entrepreneur and an artist and I grew up playing a bunch of musical instruments. And so I really wanted that kind of blank canvas and that flexibility to be creative with my business partners and not only solve problems, but be really strategic and forward thinking about them. Uh, and I think what's really cool. And what drew me to finance and what once, what is keeping me in FP and A specifically is that I really think it's one of the few roles that you can be in where you have a true 360 degree view of the business. So you work with not only, you know, your business partners with the P and L that you support, but you also work with procurement and accounting and hr and you talk to different leaders to understand what the core issues are in the business and you solve those issues. So um, I really like the autonomy that it gives you and also kind of enabling decision making at any level. But the decisions I was able to make as an associate starting off, you know, you don't have that kind of flexibility or that power in a lot of other different roles. So that really allowed me to grow and it's, it's ultimately what's, what's keeping me here.

Speaker B: So how do you think that that's kind of shaped you then? You mentioned that, you know, you've got to work with a lot of different people. How do you think that's kind of contributed to your kind of skill set or your decision making?

Speaker A: Yeah, I mean it's, it's fundamentally changed how I think as a, as a human being, but also how I work. So at, indeed I've supported many different P Ls from uh, product to our go to market P Ls. And what I really learned is that traditionally, um, FP and A and finance, we tend to operate in silos. So we only support the business partners that we support. And yes, we talk to HR and other, other folks to help our particular business that we're focused on. But very rarely had I seen FP&A partners work kind of across the business and try to solve issues across the business. And I, um, think that there's a lot of opportunity to do that and that's what makes us more effective partners. So as an example, you know, if you're um, a sales FPA person, you would talk to your sales leader and maybe your sales leader is like, hey, we're missing quota 3/4 in a row. Like we can't hit quota. And as an FP and a business person partner, your mind may go too well. Is quota setting too high or is there some sort of an issue? And so you talk to the sales leader and say, you know, what's going on? And they might say, well, quota is too high, our product is impossible to sell and we're having a lot of product issues and our leads, our Quality leads are not there. So we don't have people that we can reach out to and really get their business. And so when I hear that as an FPA business partner, I think, okay, I need to talk to our sales operations team who's setting the quotas and see what's going on. Is there macroeconomic factors? Do we need to revisit our quota setting process? And then I heard that there was product issues. So, you know, I want to talk to CS and see where in this product funnel are people having issues, what product? And then I need to go talk to the product team and say, hey, we need to, uh, fix this product. It's, it's hard for our sales team to sell. And then also quality leads, we can fix that with marketing. Right. So marketing investment. And I've just named like three or four other business partners that I can go talk to. And so I can work with my FP and A counterparts, I can work with the leaders of those organizations, um, to kind of signal that, hey, we're seeing issues on the sales side and it's X, Y and Z things. And I think that is what makes us really impactful versus just taking what the sales leader says and say, well, I hope those issues get fixed soon. Fingers crossed. You know, that's not, uh, really how that works. So I think as FP and a business partnership, we need to shift our focus and our lens to, from how did we do to what can we do and why are these issues happening? And I think that that is a shift that we're seeing now, especially with AI and automation, as we're able to focus our time on those things. But that's going to be key in kind of propelling the FPA organizations into the future.

Speaker B: Yeah, I think, I think everyone, regardless of their role, um, wants the opportunity to start thinking strategically and also to feel like they're actually making an impact. And I think when you're just kind of in a position where maybe you're just on autopilot or you're just putting out fires, uh, it's hard to feel like that. And it's also hard to feel that way when you're kind of, you're very much stuck in your silo and you don't have that macro view of everything that's going on. I feel like, you know, we talk a lot on Finance alliance about how finance leaders want to become strategic partners. Uh, well, they must become strategic partners. Um, and I think it, I mean, it's impossible to do, isn't it? If you're in a. If you're just in a silo, I mean, it literally does not make any sense. There's no way you could. You could do it. Uh, I don't think. Um.

Speaker A: Um, yeah, and I think. I think that, you know, there's. There's no way you can do it. And I. I see that, um, you know, in the FPA organization that I was in and. Sorry, someone was at my door. So you're at the doorbell in the background. Um, but I think is what that does, is it really stunts growth. Um, because like I said earlier is we as FPA business partners have the 360 view across the business. Right, good. FPA teams work with their FPA counterparts, and we can kind of get an inkling of what's going on. And so when we operate in these silos, we stunt the growth in the business. And the power of FP is being a bridge to different teams. And if we're not being that bridge, uh, you know, it's like there's. There's an analogy of, like, we see our. Our target and our goal, and it's in this island across the sea, but we have no boat to get there. And I feel like FP and A can be that boat, uh, if we, you know, kind of focus on being that bridge and really understanding the core needs of the business.

Speaker B: Uh, okay, great. Um, all right, so that brings us really nicely onto what we want to talk about today, then, um, which is, I, um, guess these are all kind of buzzwords that you hear. Uh, a lot of digital transformation, technological transformation. Uh, and of course, AI is the big thing that people are talking about all the time. Um, so I just want to. I want to get your kind of, um, personal perspective on this a little bit. You know, as somebody who's obviously been doing this a while now, like, what were your first thoughts on AI in your particular job function, and how do you remember how you were first exposed to it and kind of what your thought process was?

Speaker A: Yeah. Uh, well, Anthony, I am a class A skeptic. So when AI came onto the scene, I think I really started to hear about AI a lot. And more specifically, the teams were actually using it in applications. And, um, this was maybe back in 2023. And I remember thinking, you know, is this just a fad? Is this just a phase? You know, we. We, uh, you know, as a society tend to get really excited about digital transformations, and then they just kind of fizzle out or we focus on the next big thing. Um, but unbeknownst to me, AI was the next big thing. And it was here to say, um, but anyone that knows me knows that I ask 5 million questions before and accept an answer. So, um, you know, I didn't take it at face value. I remember actually in an off site, uh, back in 2023, I presented to the FP and a team about how we can use ChatGPT to, you know, write emails for us and kind of those basic tasks. And everyone was like, oh, cool. But at the end of that it was like, well, finance, it's, it's hard because there wasn't a lot of practical application at the time for finance to use the tool. It wasn't great at modeling. It was kind of a basic, you know, product and, and especially if you work at a private company, there's a lot of limits, uh, to what you can share with AI, you know, a machine learning model that information and stores it. Um, so there's privacy and security concerns around that. Um, but once I started using the tool, uh, I was like, wow, this is really powerful. And it actually can be a partner to me, to quote, unquote, kind of replace a lot of the tasks that I was doing that I didn't really care about or maybe weren't super value accretive. Right. So summarizing a bunch of notes into an executive summary for a presentation, maybe that would have taken me 15, 20 minutes to do. I could do it in seconds. Um, but what I love about that is that I can then focus my time on the skill sets that I wanted to develop and being a more strategic partner to, to use your words. Right. So, um, now I'm no longer a skeptic, but I am an avid learner. And um, I think that now there's a lot of progress, especially if you work for a private company. You can get these enterprise licenses that promise not to share information. There's still, you know, a lot of limitations on like revenue sharing, revenue figures, but like expense lines you can share, um, if you have like enterprise licenses with certain tools. Um, and that is really kind of transforming our business. So now we can use it for, for modeling and, and um, in addition to kind of the email automation and the things that I mention, um, I think there's still a lot to learn. I, I hate when people claim to be an AI expert because no one is an expert. I'm certainly not an expert. Um, but I think that it is starting to really revolutionize how finance teams operate. Uh, and we have to kind of become more strategic. Otherwise I think our Roles can become obsolete.

Speaker B: Yeah, yeah, absolutely. I definitely think we're in, we're in that stage now, aren't we? And I still hear like a lot of kind of doom and gloomers on, um, you know, online just saying kind of like, no, this is awful. We need to get rid of it. It's just. You feel like saying, like, look, guys, calm. Like at this point, the genie's out of the bottle. You know, you can't, you can't put it back in. Like, you have to at this point, learn to live with it and learn to be aware of its drawbacks and its hazards, just like anything else, you know?

Speaker A: Absolutely. And I think, like, you know, the, the number one question that I feel like leaders are getting in town halls or across different companies, is AI going to replace my job? And that is kind of a scary concept to think about. I do think it's going to be a much more slow burn than we are expecting. I think AI is going to replace a lot of the work that we do. But, um, there is still very much a need for human engagement and interaction, um, and especially with FP and a, to get data from our business partners. And no one knows the business better than you as an FPA partner. AI doesn't know the nuances. It only knows what you tell it. And so, um, I do think, you know, it. It is scary kind of the concept of AI replacing our work, but I do think it's going to be a much slower burn. And I think AI still has a long way to go before we kind of get to that point.

Speaker B: I, I think one of the dangers of it as well is that, um, it like chatgpt, especially a lot large language models, like, they lie so convincingly, they make things up so convincingly. And so I've asked it to, to my own detriment to do things for me in the past like, where I've asked it to pull information from the Internet for like a presentation. And then I look at it and I kind of go like, well, that all looks very convincing, you know, because it says it in such a convincing way. You know, it's such certainty. So you put it in there and then you present it to people. Then, you know, hopefully a lot of the people there aren't that well versed in the subject, so they kind of accept it. But now, but recently I had a moment where I was asking it about something that I actually did know quite a bit about. And, um, it was just coming, you know, most of the things it's saying were True. But there were some things that were just completely wrong, you know, and it was like, it's kind of. You have to understand that these, these systems don't think like people, you know, they don't think like, oh, I don't know the answer to that, uh, that there's a gap in my knowledge. So I'm just going to say, look, I don't know about this. I'm going to tell you what it will basic or I won't say, like, there isn't enough data on that. It will fill in the gaps when there isn't factual information by a lot of like, creative guessing essentially through like pattern patterns and things like that. It will choose the most likely thing, uh, rather than what is actually true. And I think people like, you know, they react to it and they sort of go like, well, why does it do that? It's like, because it's not, you know, it's not a person, it's a machine. That's how it's been sort of programmed to work, you know. Um, and that's definitely one of the hazards. And I think that's one of the reasons why we still need human people, you know, is that we, we need someone who's I think AI can pick up a lot of the slack, but ultimately you still have to use your own expertise, don't you? To look over it and say like, look, I know that to be false or that doesn't sound right. That really does not sound right at all, you know, um, but it's, it can be difficult, can't it? Because it's so, it is so impressive in so many ways that you, you get blinded to it, I think a little bit sometimes that it's not perfect. I think.

Speaker A: Yeah. And I think I would, I love that you did that. I would encourage people to, to talk to AI about a subject you are an expert in. And I think you will find exactly what you did, which is that, you know, there's still a long way to go. I was in like a battle with Gemini and Chat GPT the other day because I was like trying to build a model and I was like, no, this isn't right. And I would give it feedback and it's like, oh, you're right. So you should actually do this, you know, so it still relies on us to fact check it to learn and to give us a better output. So I 100% agree with you. I think it's hilarious.

Speaker B: The hilarious thing you just said is like, how agreeable it is as well. Like you Know when you say like, you uh, say like actually you're wrong. And he goes, do you know what? Uh, you're absolutely right.

Speaker A: Yeah, yeah. And then you feel you said a little bit better about yourself. No. Or, you know, the people that put the two phones and it's AI talking to AI and they just go back and forth, forth for 50 hours because they don't know how to stop. They're very agreeable. But yeah, no, I, I, I 100% agree. I think at the core, FPA especially, we still need that technical skill set and that understanding so that we can fact check AI, you know, in addition to giving it really good fundamental inputs. Um, because, yeah, I agree we're not at that point where it's going to completely replace the work that we do. And I think a lot of people have a lot of fear around AI and, and are kind of trepidatious and kind of hesitant to use it because they're like, I don't want it to do a better job than me. But those people are really failing to see that you shouldn't see it as a replacement, but you should see it as a partner and a tool that's going to propel your work forward. And I think that that is how I see AI. Right. It's kind of like a companion, a colleague that's sitting next to me in the office that it can bounce ideas off of in addition to automating a lot of the work that I'm doing. So uh, I think if you look at it that way, um, you know, that will make you just a more successful partner and more willing to kind of adopt ah, AI into your day to day.

Speaker B: Yeah, yeah, absolutely. I think that's generally like what, I think it's there to, to augment your skill set, isn't it? I think is the best way to think about like a really capable assistant, basically. Uh, really capable diligent assistant I think is a, is a great, healthy way to think about it, I think. Definitely. Do you notice that, do you think that there's like a bit of a generation gap in your field in terms of like. Because I had, I just asked him because I had another guest and uh, she'd been in finance for a very long time and she said that, you know, there was a bit of an old guard mentality in finance. She said almost people have been there for a long time and she said she'd seen a lot more friction with people like that, you know, about embracing new technologies and things, you know, I don't know if that was the thing that you've experienced or.

Speaker A: Yeah, yeah, I think it's really interesting. I mean, I work in the tech industry and actually, you know, I've been at indeed for eight years and I'm currently transitioning into a new role that I'm really excited to talk about soon. But, um, yeah, I think the tech companies and the tech industries are doing a good job of kind of mandating a lot of AI usage, so they're building it into their okr. So you kind of don't have a choice but to adopt. Um, but I do think, yeah, certainly there's a generational gap. I, um, think too like it. Change management and FPA is always tough because we have our roots really deep in Excel spreadsheets and kind of, you know, certain processes that we've been doing for 100 years and it's, it's really hard to get out of that. So I do think there's a generational gap, but I think across the board there, there is going to be that struggle with change management. You know, our model lives in this Excel sheet and how can I integrate it with these AI tools and can I integrate it? And I think a lot of, I think some people will say, well, this is more comfortable and this is what I know, and they won't adopt AI. And that's when you are going to be behind and you will be spending 30 more hours than the next FP and a business partner doing the same level of work and maybe not even as well. Um, but yeah, I think generation, I mean, I see it with my, my, my parents, they're like, what is this chat gpa? And they use a different letter every time, I swear. Um, but, you know, I think it is something that I think successful companies will have to mandate so that they don't get stuck behind. So build it into your okrs. I mean, indeed, they were tracking our usage with Gemini and saying, you know, are you using it effectively and how are you using it? Um, so I think that that is a way that we can kind of mitigate some of that. And um, you know, people will have to adopt it definitely.

Speaker B: So let's kind of, um, look in our kind of, uh, figurative crystal ball. Uh, and if you could say like, like let's say like, you know, in five years, 10 years time, like AI is just so normalized that it's almost like, like imagine it almost like how we use the Internet in our work. You know, we don't even realize that we're online, you know, anymore. Like It's, I certainly don't. It's just there all the time and you're always interacting with it. How do you see like, you know, the kind of FP and a person of five years, 10 years, how does their job look different from what it looks like today? If that becomes the case, what do you think?

Speaker A: Yeah, um, it's, it's a really good question. I think traditionally fpa, we have been kind of data centers. You know, if someone needs a report or data, go to FP&A. Or in our monthly meetings we would say, you know, this is what your performance was, this is your actuals versus forecast. So really again, focusing on the what happened. Um, and we can't do that anymore because I can do that in a matter of seconds. Right. So, so I use AI to do my variance commentary. Previously. If you're in an FP and a team, you know, the close cycle, you spend days combing through accounting entries and pulling, reporting and saying, you know, what drove this variance last month and days are spent on the past and we cannot do that anymore. Um, A.I. is going to automate that and we need to focus on the future. I think FPA has fallen into the trap of being very backwards looking. Um, so we will have to evolve. I think FPA's skill set and strength is going to come from storytelling and providing actionable insights through that data. Right. So more of our time should be spent on the future and also being able to translate what that data actually means to the business. Uh, because I can't tell you how many meetings I sat in in the past. And I would sit in front of a leader and say, well, this was a variance. And the leader would say, well, should I be worried? What does that mean? And uh, that is something that FP&A will have to answer and get ahead of those questions. Uh, and I think, you know, the storytelling capabilities are going to be important. I think it's really interesting because when I first, eight years ago, when I interviewed for my first job in finance, there was a lot of emphasis on technical skill sets. Right. Are you versed in modeling? What is your attention to detail? And when I was most recently interviewing for a new role, there was a lot more focus on the soft skills. Right. What does your business partnership look like? What are your storytelling capabilities? And part of my case studies, yes, we're modeling, but a big focus on it was presenting to executive leadership and translating data insights into actionable insights. You know, the, I take the WIFM approach, which is very sales. What's in it for me. So going into those meetings and being able to tell the leaders like, this is what's in it for you. This is what we're seeing and this is what we should do about it. So there is going to be a shift. FPA is going to have to have a seat at the table and really work in the different organizations to get that seat and show tremendous impact. Um, and I think it's going to be a transformational shift and will make us more effective business partners, but will also propel businesses, uh, to grow at an even more substantial rate.

Speaker B: Uh, can we stay with the, um, storytelling for a second? Um, because that's the really interesting word for me, especially in relation to FP and A. In your experience, like, what is the difference? Like what does it look like when you successfully have a kind of storytelling mindset? Um, what can you draw on your own experience there? Like presentations you've had to give? Um, what does it look like?

Speaker A: Yeah, um. So storytelling has just transformed my business partnerships and has enabled me to help with decision making. Whereas in the past, you know, there was a lot of layers to work through because you kind of had to figure out like how to explain the data differently to everyone. And it was, it was, it was hard. I think storytelling builds trust because it shows the leaders that you know what you're talking about. You're not just regurgitating data, but you understand what the data means. And if you go into a meeting, I always love going into meetings with executive leadership with a recommendation and starting with the recommendation at the top and saying like, we'll discuss it more, but this is what I think we should do. And I would love to hear your thoughts. And what I found is like 8 times out of 10, they'll usually go with the recommendation that I outlined at the beginning. Because when I storytell with the data throughout the, the presentation and say, well, this is what it's showing us, um, I think it builds trust and people really listen to what you have to say. Um, so I think in the past experience, you know, when I started off as an associate and I just gave variance commentary and wasn't giving a lot of insightful ideas, um, or, or recommend decisions, um, that business partnership wasn't as strong and I didn't have a seat at the table with decision making. Um, so I think, you know, the ability to present your findings, lead with recommendations, um, and do concise presentations as well. No one likes a 50 page slide deck, but really focus on the content and target your content to what the Leaders want to see or should see, um, is going to be absolutely key, um, moving forward. So I think successful examples of that, you know, I would meet with my leaders once a month, um, you know, supporting product and go to market panels and um, you know, I've really been involved in decision making as a result. Um, you know, even with like benchmarking data and saying like this is what we should do because we're not, uh, we're not being competitive with the way that we are operating, um, or even with like things like deal desk where we would do discounts to drive more business. Right. Um, being able to explain to different leaders what all of that means, um, is has gotten me a seat at the table and I think successful FPE business partners have to do that.

Speaker B: Yeah. Empowers you. Then it's feel like it's empowered you. Yeah, definitely. And I think it's just, it's knowing how to um, to sort of get that seat at the table is. It's about knowing to speak the language, isn't m it of people in the C suite? I think in a way what are they concerned about? Um, and I think that again fits into what you were talking before about having like a macro view, doesn't it? Being aware of not just what you're doing, but what other people are concerned about that will pay off. Because then when you have to do your cross gun functional collaboration, you know how to talk, you know how to speak a language that they understand, don't you?

Speaker A: Absolutely.

Speaker B: Um, yeah, yeah.

Speaker A: And I think that's where AI, like the natural language generation subset of AI is really powerful in that uh, because I think a problem that FP and a business partners run into is like being able to translate the story to different leaders. Like if you ever talk to an engineering lead versus like a marketing lead versus a sales lead, they all interpret the business very differently and focus on different things. So I think that's where um, AI has its superpower with natural language generation and being able to help FP and a business partners with that.

Speaker B: Yeah, definitely. I agree completely. Let's um, um. I don't want us to repeat ourselves too much, but I wonder if you could maybe just talk about um, maybe let's do a little bit of weighing up. So like, what do you think? What are some of the most like promising applications you've seen so far of AI that you've seen in action versus some of the things that you've seen that maybe you think it's not quite there? Should we do that?

Speaker A: Yeah. Absolutely. I think, um, the most promising applications probably come around forecasting. So the modeling that I was, um, speaking to earlier, for example, I just used uh, Gemini to help develop a bad debt expense forecast model using like percentage of sales and looking at historical data and things like that. And it gave me a pretty good solid foundation for a working model. Uh, again, it wasn't a final product, um, but it saved me hours and days from having to kind of clean up the data and um, put together kind of an initial recommendation. So I think with forecasting you can input, you know, macro like ask Gemini or ChatGPT. Hey, taking into account maybe the US jobs report, if you work at Indeed, or certain macroeconomic factors, um, what should this model look like and maybe what are some risk areas, uh, if we look at the next 24 months and uh, again, no one knows the business better than an FPA business partner if you're, if you're stacking it up against AI. Um, so you will need to collect a lot of information and give AI a lot of good information and good data to work off of. Um, but I think that's like, it provides a promising start. I will say I think too with performance tracking, what I mentioned earlier, natural language generation, um, again a key struggle in the business has always been, you know, translating across the business what performance means. Or if you're meeting with the CEO and you're like, ltv, CAC is this and CPRD is this. And they're like, okay, cool, like, what does that mean? Like what? Why should I care? Right? So being able to communicate that effectively, um, is also very promising. Uh, and I think around budgeting too, scenario planning. I mean any FP and a business partner that has been involved with scenario planning knows it takes a lot of time and a lot of input. And with all that time and effort, usually focus on just kind of three scenarios, kind of a conservative, aggressive, and then like a middle case scenario. Uh, and days are spent in putting three scenarios together. But AI in a matter of seconds can give you thousands of scenarios with probability weighted outcomes. Right. And so it just helps, helps us kind of prepare better for the future. Um, I think like at its core, the not so promising applications, I think there are not promising components for all three of those things that I just mentioned. Meaning that you cannot just give data to AI and say, give me a model and then go slap that model in front of leadership and say like, this is what I think we should do. Right? I told you I went into an argument and like a bickering match with AI for like an hour and telling it why it was wrong and what it needed to consider and things like that. So I think at its core, like, we still as FPA business partners need to be technical and we need to understand how models work. Right. I think we, uh, people that come in and just try to say, well, I don't want to learn financial modeling, like, I'm just going to have AI do it. They're not going to know when they get the bad outputs. So I think, um, there's still a large element of checks and balances that we need to look at. Um, because again, I think the not promising component is like, AI just doesn't know the nature of the business or the nuances like you do, or the politics in an organization like you do that could sway decision making. So I think, you know, there's still a long ways to go. At some point maybe we'll get there and really know the business in and out. Um, but those are kind of the three, you know, ways that I think FP and A partners should really, really use AI, um, successfully.

Speaker B: Yeah, there's been some, I mean, there have been some horror stories already, haven't there, about AI? I mean, I actually had a friend who, um, was using an AI agent, um, to deal with something quite sensitive, um, and ended up again, just putting too much trust in this agent, basically. And the agent ended up sending an email about this to his whole, literally list, like that is like friends, family and like colleagues, you know, doctor, uh, whoever, you know, literally the whole list. Um, so I definitely think, like, you know, these are kind of funny examples. You know, sometimes like there are like, AI faux pas, but there are definitely, um, that you need to take them seriously, I think. Definitely. I think if you put too much, too much trust in it, um, it can, it can be dangerous. And especially something like, you know, what you're doing with finance. I mean, you're dealing with very sensitive data as well. I think it's something you have to be very, very, very, um, cautious about and really scrutinize, I would say. Um, but, but I agree with you. I think overall, like, I'm sort of in the same position you are. I think it's like kind of like you as your kind of subject matter expertise. Uh, other people who look from the outside, you know, might say kind of like, oh, well, AI could do everything. Now, of course it's going to take the job of finance, but it's up to like, people like you, isn't it, who really understand FPA and how it works and all the different variables to say like look, no, like this might be acceptable, but it's not like what you said, product ready. Like I think, yeah, ah, definitely.

Speaker A: And, and I think too like body language is, is such uh, like reading body language and interpreting social cues and social situations is such a superpower too. As I mean not just as an FP and a person, as anyone. So AI can't really do that. Right. So if I'm talking to a leader and I interpret body language, if I ask a question and I sense something like I, I can use that to make better decisions or change the conversation in the future and AI can't, can't do that. So I do think like the human component is extremely valuable and I don't see AI replacing that part of the job anytime soon. But more so I think of it as being a partner in automating the tasks that we don't want to do so that we can focus on the things that we do want to do.

Speaker B: So people talk about like the importance of data, data readiness, but like sort of, what would you sort of say to organizations like how do they need to prepare their data infrastructure for something like AI driven, uh, FP and a. What would you say to people in that.

Speaker A: Yeah, so I talked a little bit earlier about change management, right. Getting out of kind of the roots of the Excel spreadsheets and being able to kind of adjust and adapt. Um, I think that that is one. Um, I think building a culture of data stewardship across the organization that every function is responsible for data cleanliness and making sure that their data is accur. Um, is going to be key because again, AI outputs are only as good as the inputs that you give it. Um, and I think too something that I've seen organizations kind of struggle with is consistent data definitions. So you know, if everyone is going to be kind of automating, we need to make sure that we're all interpreting the data in the same way. So if we say revenue, do we mean net revenue or gross revenue? If we say cost per revenue dollar what is going into the cost? Is it overhead, cost, direct cost? So really laying out the foundation and saying this is what these metrics mean, these are our definitions, I think is going to be key so that everyone's interpreting the data in the same way. Um, and I think a trap that people may fall into with AI is you get really like overexcited and eager. It's like going to a theme park and just opening the doors. You don't really know where to start and you want to go on the biggest roller coaster. Um, but you, I think the way to successfully adopt AI and become comfortable is just start small and prove the value in that way. So look for the low hanging fruit that you can automate first, um, and show impact. And I think that that is a great way to start and that allows you to get more comfortable with the tool. And so I think organizations should encourage that. Especially we talked about a generational gap, right. If we're mandating AI usage, encouraging people to really play for the tool, uh, play with the tool, excuse me, and encourage failure. I've certainly failed with AI and presented, you know, things that probably were not a great use case to use AI for and that's okay. Um, but I think that organizations should encourage that because that is the only way that we, we kind of learn and adapt, uh, moving forward.

Speaker B: Yeah, well, I've shared my AI horror stories with you, involved in a couple. I think it's a natural learning, uh, process, isn't it? It's these like kind of uh, funny stories. Hopefully not too catastrophic. Like it's kind of like. But it's the same with everything, isn't it? That's how you learn from your mistakes and you refine it next time. I mean, I would dread to think like, like looking back like what some of my like ChatGPT prompts were like, uh, early on comparison to like now I think we probably are much more like aware of what the limitations are. And I think, I don't know if you found this as well. Do you find yourself giving it more information now than you used to? Yeah, See I, I thought in the beginning that I was kind of expecting it to work magic, but now it's like, I'll make sure that it has like so much context to draw from or as much as possible, you know, because otherwise it's like it's only as good as what you give it, isn't it?

Speaker A: I think, yeah, I think my prompts went from being like two sentences to paragraphs now. Um, yeah, no, I mean I always, I feel like I give it more information than it probably needs or uses. Um, but yeah, I think that that is the only way to. That it will like learn and that you will learn and use it successfully. And I think um, two, you can kind of say like this is all the information that I have, but I don't know how to incorporate it or how to use it. Can you help me? So that's what I do sometimes, especially if I'M supporting a new organization and I don't know the metrics that are important. I don't know what to look at. You can kind of do a data dump and just say like tell me what's important and like what data I should use for this model or, or for this presentation. And uh, I think AI does that really well, especially like ChatGPT and uh, Gemini. I um, think too what I'm finding really cool is that a lot of systems that we use are incorporating AI. So if you use for example workday adaptive planning as ah, an FPA partner, they're starting to do trends based forecast using AI. So that time that we used to spend on looking at our T and E budget which tends to be kind of stable month over month or maybe there's spikes, spikes when something else spikes, you know, there's a dependency. Um, I think that is what is very powerful ah as well in kind of the usage of tools. So I would say like too, um, as a part of like this data stewardship and organizations, um, kind of pushing AI. I think also using tools that leverage AI successfully um, is another really great place to start. People are comfortable with the tool as is. You know, if you use adaptive and you know, adaptive it's, it's much uh, less of a learning curve to then use kind of the AI capabilities within that tool versus using something that's maybe brand new.

Speaker B: That's I think harkens back to what I was saying earlier about it kind of becoming increasingly invisible, uh, to the point where you know, all of the tools that you use will have AI integrated. Um, I think that's, that'll be a point where it almost like I imagine it'll be like we won't even be like oh, I'm going to use ChatGPT now. It'll be like that'll be part of the systems that you're using. Um, that's what I think is going to happen and I think that's, that's the point where it's going to become invisible. Um, definitely. And then we won't have a choice about avoiding it. Look, it's just here all the time.

Speaker A: 100%. 100%. Yeah. I remember for the longest time I thought when I was using the Amazon Chatbot that it was an actual person. Uh, and then I realized every time I would complain about an issue with my order they would just refund me. They didn't even like, like argue with me. And I was, and finally my husband's like, you know, it's an AI Chatbot. And I was like, but they have a real name. And he's like, no. You know, so I, I, I've already experienced that personally and it's a little bit scary. I think, like, we often make fun of our parents. Like, oh, you didn't know that was a scam. I feel like that's gonna be me in the future. Yeah, I'm not gonna know. And my kids will definitely make fun of me for it.

Speaker B: Yeah, it's, um, yeah, that's hilarious. Like, oh, wow, customer service is really great. Yeah, they're really great. So funny. Yeah. Well, that's the thing, isn't it? I mean, if you've used the voice function on um, chatgpt, like it's crazy good now, like how, how much it sounds like a real person and like the little inflections, the little, um, it's gonna get to a point where people really, and I think it might already be there for some, some people actually like, they really don't know anymore. Like, and that kind of like, that's exciting, but it's also kind of like, like dystopian future kind of territory where you're kind of like, uh, what, what is real and what isn't anymore? Uh, that's, that's a big, that's a, that's, that's another AI top discussion about like, you know, the video stuff and the deep fakes and all that kind of stuff.

Speaker A: But, you know, I think, I think, yeah, I've, I've certainly experienced when I was interviewing for my team that people were using ChatGPT in their interviews and they would record my questions, my voice, and then they would respond reading off of a, uh, whatever output Chat GPT gave them. And I think like, that shows, we talked about like the over reliance on, on AI. I think AI is a tool and a partner that can propel us to think more critically. But I think conversely it can also harm critical thinking. Right. If people are using it for, for everything. And that's kind of the stopping point. The output is the stopping point. I think it will totally crush critical thinking. So, uh, I think going back to over reliance and like making sure that there's a human element that, that, that's something that I would stress because, um, you have to understand the core of what you're doing to use the tools successfully. And I'm a firm, firm believer of that.

Speaker B: Absolutely. Uh, excellent advice, definitely. So finally, uh, let's talk about this, uh, is something we do with all guests. Can we talk about some misconceptions about FP and A, your role that you would like to Send to room 1013 if be possible.

Speaker A: Misconceptions. I would say, uh, I said earlier that FP&A is backward looking. Backwards, um, looking. Which maybe isn't a misconception. I think that a lot of FP and A teams do struggle with that because they're spending a lot of time on the, the actual results and not as much time understanding how that changes the future. I think we're, we're certainly historians because we have to be right. There's, there's data compliance things that we need to do. Um, but we are also, uh, scenario planners and that's where our superpower is. So, um, I think that is the biggest misconception. Um, I think too that FP and A just means budgeting, annual planning is just a very, very small part of what we do. Um, you know, budgeting is a small part of what we do. Uh, and that's more so to just kind of set a North Star or a target for us to compare our progress against. But, um, you know, budgeting is kind of stale the month after you get your budget approved. Right? There's so many things that can happen, so many factors that, uh, will change the business and change the growth that we're seeing. So I, um, think focusing our time on resource allocation, strategic guidance as things change is really our superpower. And that's where you need to bring your FPA business partner to have a seat at the table. Um, and I think too, like, uh, the third misconception is that we are just a cost center. Um, some people think of finance as an overhead cost to the business. And we are not, uh, we, again, we serve as a bridge to kind of align, uh, investment decisions or investment recommendations to strategic priorities. Um, and we need to make sure that we're generating value and we're not just an overhead cost. Um, and I say these three things are misconceptions. But I think, you know, finance teams that again, are not adopting AI or not kind of moving with the latest trend can get stuck in each of those traps apps. And it can very quickly not be a misconception, but fact. Um, so I think that, you know, we're not just the cost police, as I've heard that term 5,000 times in my career already. Um, we, we really have an understanding of the business and a pulse on what's going on. We, you know, we should be brought in to do ROI analysis and be a part of your business case. Um, creation And I think that that's what our superpower is. So if you aren't doing that with your off paying a business partner, do it now.

Speaker B: The dreaded cfo. No, that's, uh, something I've heard.

Speaker A: Yeah.

Speaker B: Had people say before. Yeah, it's. It's definitely a common, um, common m. Well, maybe that again, like you said, maybe there's a kernel of truth in it somewhere historically, but it's definitely something I think that people who are doing it well or looking to do it well need to get away from. Yeah. Thanks so much for your time today, Persia. It's been fantastic. I'm sure our audience are really going to love this.

Speaker A: Uh. Oh, thank you so much, Anthony. It's been a pleasure. Thank you again for the opportunity. And in the chat, um, I learned a lot from you and, um, bad AI use cases that I will be sure to stay away from. So thank you for that.

Speaker B: Always here to help. Okay. Thank you so much.

Speaker C: And that wraps up another episode of the two Cents podcast. Thank you to our listeners for taking the time to tune in. If you enjoyed this episode and want to hear from more finance leaders like today's guest, please subscribe to the show and leave a rating or review on your favorite podcast app. It would mean so much to me and my guests if you can show them some love and appreciation for sharing so many great insights with us on the show. Again, thanks for listening. See you in the next one.