Ep 51: The New FP&A Feedback Loop

Speaker A: This episode's made possible by Planful. Hello and welcome back to Planning Aces where we spotlight FP&A insights from finance leaders helping their organizations plan smarter and execute faster. This month we are featuring three very different CFO seats, but a surprisingly shared operating rhythm, disciplined data driven decision making that moves fast without abandoning rigor. You'll hear from CFO David Lee of Webtoon who frames growth through a sharper lens, looking beyond volume to the quality of engagement and and the leading indicators that signal future health for the company. Then we'll turn to CFO Christina Kim of Octora, who focused on building a methodical framework for evaluating opportunity using forecasting not as a periodic exercise, but as a feedback loop that learns from what actually happened. And finally, CFO Zane Roe uh, of Workday shares how to keep investment strategy agile in the AI accelerated market. Reallocating resources, validating progress and staying intensely focused on returns and customer value. I'm Jack Sweeney and as always I'm um, joined by our resident thought leader this time author, former CFO and tech strategist Glenn Hopper. Glenn joins us right after this message. Ready to plan confidently, close faster and report more accurately. Here's what sets Planful apart. Imagine uh, purpose built applications for every department from FP&A to accounting, marketing to HR, all with built in financial intelligence. It's easy to implement Planful in just weeks and with minimal IT involvement. So you can rapidly and seamlessly engage everyone across the business on your key financial progress. Best of all, Planful grows with you. Its unparalleled scalability ensures that no matter how fast your business expands, Planful can keep up. See why over 1500 customers worldwide choose Planful as their flexible, user friendly, end to end financial performance management platform. Go to planful.com CFO Thought Leader to see how you can reach peak financial performance with Planful. Hello and welcome to Planning Aces where we spotlight FP&A insights from finance leaders helping their organizations plan smarter and execute faster. I am joined today by our resident thought leader, finance author, former CFO and tech strategist Glenn Hopper. Glenn, thanks for allowing us to catch up with you once again.

Speaker B: Yeah, Jack, glad to be here. This is a second episode together actually if we go back, this is what we did. Um, we did a podcast together and then we did a, ah, Planning Aces together.

Speaker A: We did one Planning Aces too. That's right. Are you traveling this month? Are you homebound?

Speaker B: So I am home, um, until at least mid January, which is pretty exciting. I've been some really uh, cool places but um, it's good to be back home. And of course now after traveling so much, all the meetings that I missed while I was traveling are just back to back all day, every day. So it's.

Speaker A: And, uh, everything you did this fall is still floating around on LinkedIn. So I'm like, where is Glenn? Is Clinton? Yeah, it's like, where's.

Speaker B: You're everywhere.

Speaker C: Yeah.

Speaker A: You're omnipresent. So. Hey, so what did you think of our lineup as you listen to this month's planning Aces?

Speaker D: What?

Speaker A: You know, uh, the common commonalities or threads that emerged for you?

Speaker B: Yeah, I guess.

Speaker A: Jack.

Speaker B: For each of these CFOs, it came across that they've each built a system for disciplined data driven decision making. They're in very different industries, but they're all using data and forecasting to constantly reassess where resources should go. And it's, that's really, that's the promise that we've been hearing about data for a decade or more now, is that you making better decisions by making data driven decisions. And with these CFOs, uh, they're not, I guess the best way to say it is they're compressing the feedback loop. So they're not spending, you know, months and months building a model and then testing it, uh, and then rolling it out. They're actually learning and acting in real time. And I think all three of these CFOs have figured out how to move fast but without abandoning rigor, which is so important, especially in like Gen AI age. It would be very easy to try to dump everything into an LLM and have it answer for you. But I think each of these CFOs has a strategy and a plan. And I saw that consistently, uh, with all three of them.

Speaker A: I just thought they're showing us how to bring clarity or drive clarity forward and, you know, not slow down. You know, if you slow down, you'll be toast in a year or two. But by sharpening the lens on what really matters, they're helping the organization as a whole. But, uh, let's catch up with them, let's meet them. And, uh, first up, it's David Lee, CFO of, uh, Webtoon. This is. It likes to bill itself as the world's largest digital comics platform. I was surprised how many of my family members knew Webtoon. I did not. Uh, but their content is out there.

Speaker B: Same thing. I've got a bunch of comic book fans in the family and they all know, they all know Webtoon. Yeah.

Speaker A: But, uh, David Lee's career spans global media technology and data rich consumer platforms. Experiences that sharpened his focus on engagement, constant currency performance and the metrics that matter in uh, let's just say the creator driven world or marketplace today. When you see him, He's a former McKinsey consultant. I think that's what's pretty obvious about him as well. He sort of has that McKinsey way of communicating. Uh, but here is, let's uh, tee it up for you. Here is David Lee, CFO of webtoon.

Speaker D: I look at growth and I'll give you a couple of metrics. The quality of the user and creator experience and financial health. And I look at them in those, in those rank orders. So let's talk about growth. I'm, you know I mentioned already knowing that our English platform amongst our app users on Webcomics grew 19%. That's important because while we're 50% market penetration, where we started in Korea, we're only just getting going here in what we call the rest of the world. So that pure growth, number of new users on our app, ah, reading Webcomics in a new market like the English markets is important, but then the quality of the engagement is very important. You know, how much time are they spending? You know, we know that on average our users spend 30 to 60 minutes every day actually. Are we providing them great stories? You know, one stat I look at is something like 97% of our North America consumers. In a third party study they said that our experience was more fun than TikToks, Roblox, Netflix. Great, great alternatives by the way. So good company. And the reason is 77% said it's because they can't find these stories anywhere else. Uh, we look at that very closely because our lifeblood is the fact that we could provide access to stories that none of us could have imagined, um, or purchased in advance. We're not a store model, right? We have these great creators that provide new stories to the platform. But on the other hand, deals like Disney mean that if we can grow at a financial profit, if we can deliver adjusted ebitda, uh, that's positive, but still grow our revenue. And I look at things on a constant currency basis for revenue because we're global. And when martial law is declared in Korea last year or the yen is at a 30 year low at some point last year versus the USD, these don't really reflect financial health. The constant currency growth is what's important because our bottom line is somewhat insulated since we oftentimes pay costs in the same currency that we receive revenue. So you know, growth, uh, quality of engagement between customers and creators and financial health are the three areas that I look at um, every day. The great thing about this place, webtoon is that it's fundamentally data driven. You know, I don't have to guess there. We measure everything and I think that's important in this day and age where you know, everyone's talking, I was just at a conference in San Francisco, or agentic AI, you know, everyone's talking about agentic AI is going to destroy every industry. I don't see it that way. Like we're using AI and technology and data to um, promote human creativity to help our creators. And part of it is our business model is different. You know, since we share future revenue, ah, with our creators, we can root for them to be successful and they can leverage every advantage they can in our platform to help them be more productive. Uh, to fight piracy is another great example. Or to allow, we use AI to let consumers have the best, fastest access to the stories they're going to like. Um, so for me uh, these metrics are super important and the use of AI and data just ah, enables them to be more successful. It's not that I'm, you know, the unique genius that came up with these metrics. I think these metrics were identified by the team. But I think underlying the importance because we have many metrics, um, two come to mind. One is what I just described which is looking at webcomic app MAU, monthly active user growth by language is super important because if you look at total MAU, our total monthly active users, that includes businesses that we don't choose to monetize for revenue. We have a huge web novel business, uh, a business called wattpad and multiple languages. It's a wonderful business to allow UGC creators to create content that we really value that oftentimes turn into movies, et cetera. But the paid revenue part of that business is not the priority. So for example, double clicking as I say on um, the right metric that predicts really future revenue growth is really this kind of web comic app MAU, which is as I mentioned, nicely growing, uh, here in the US and other English, um, platform countries. I think the other one that I would highlight is about the platform I just mentioned, wattpad. You know we, we measure everything but reading time is super important because you know, when you allow anyone for free to look at all this content, what I really am interested in is when they choose to engage, how deeply do they choose to engage? And uh, unlike other platforms, I don't need to worry about them using up my content. Right. I have new stories arriving every day to the platform. But making sure they find content to engage on, I think is another important metric. And then, of course, we all look at the financial metrics, right? Obsessively. I don't think there's a lot of insight there. But those are two examples kind of in the area of what I mentioned, growth and the quality of engagement that I think we are increasingly highlighting externally, um, for our investors.

Speaker A: Okay, so that is David Lee, you know, the big tagline there. Engagement becomes the growth engine. At Webtoon, David, uh, highlights something powerful, I think. Um, growth isn't just more users. It's the right users engaging deeply with the right stories. And Glenn, what, what stands out to you about how he framed engagement sort of as a predictive metric?

Speaker B: Yeah, I mean, so I could go on at length, um, about his use of data. I love it because it is the types of metrics that they're looking at. And he mentioned Netflix as a, as a competitor is exactly what Netflix looks at. To the point where I think Netflix is actually even developing content with certain milestones and in place. They know they have to engage at a certain level and they're actually driving production operational decisions. But that's a, that's a whole other topic. I guess what really stands out from a finance perspective is that he's doing exactly what the best FP and A teams do. He's elevating leading indicators that actually forecast future health, and he's looking at data that matters. And I remember in my first CFO role, a lot of battles that I had to fight where I was trying to move for the first time outside of the GL and look at data that's in our, uh, CMS and other systems, project management systems, because you can use that data to better forecast and, um, understand your metrics and be able to. And not just forecast, but be able to actually find levers that drive the business. So he talked about engagement, depth, creator, uh, interaction and constant currency revenue, which is, are very interesting. Like you said, those are not traditional financial metrics, but they certainly are levers that they can pull that not just. It turns from this office of the CFO being an historical reporter to actually providing value to the business and saying, hey, this is what we've figured out about our users. And, um, they tell you whether the strategy is working really before the P and L catches up. Because P and L is a lagging indicator. We're trying to find stuff in real time. And I think in, even in that short clip, his background at McKinsey M comes through. And you, I, uh, know from his background too, he's led transformations at what, Zynga and Impossible Foods. So I feel like he knows how to find the metrics that matter. And at Webtoon, um, you know, 150 million monthly users engagement is that metric. And I think he's completely nailed it. So I loved everything he said there.

Speaker A: He seemed to be something of a skeptic, uh, as far as AI disrupting creator base. I mean, what did you make of that? Because I was, I don't know, I think it's kind of ripe for uh, AI to come in and shake up the space. But.

Speaker B: Yeah, but he. Shake up, yes. And he also, I think he mentioned specifically augment and assist the base. So it's kind of like, and I'm guilty of this sometimes too, it's like when you feed sort of a messy draft email and do an LLM and you, uh, get an output. It's very passable, especially in sort of a corporate business setting. But there's something, it's not quite human. There's going to be, you know, bold words sprinkled out, em M dashes put in and things. It's going to be watered down. And I think that instead of any. If you hear a lot of creators talk, they'll say, um, you know, I, I will never use AI But I think that that's the generation that's there now. I think the generation that's coming up behind them is going to realize, okay, this is still my story, my content, my ideas. Let me. And I think I would be put in a stranglehold by any, any developer or uh, uh, content developer right now for saying this, but I do think we do see strengths there. And like I mentioned before, Netflix is using analytics to figure out where they need to put plot points and things to keep users engaged. If you're using it, it's like any other tool. Um, you know, I could trace a picture of a superhero for a comic book and it wouldn't be the same as if I were, uh, creating that originally. And, but there are ways you can use the existing tools to be better. And I, and I think that it's going to find a space. And I, I would say I agree with them. And I, I think that certainly as you know, AI, uh, video generation and art generators get better. People could create content with AI but it's gonna, uh, AI has to make A lot of advancements before that doesn't ring hollow. So I'm in agreement with him on that. And I, But I also, I don't think, you know, become Luddites about it and avoid it. Whether it's in finance or in, uh, creative endeavors, I do think, um, that it is going to help if used appropriately and judiciously. That was a long rant. That was nothing to do with finance.

Speaker A: All right, you're on his team. All right. Hey, uh, let's tee up our next planning ace. Who is Christina Kim, CFO of OCORA, a company transforming structured credit training, uh, trading. Excuse me. Through next generation automation, Christina's built her career across global banking and fintech, giving her a front row seat. And she saw this opportunity ripen and she said, now's the time to jump over to the operations side. Interesting interview that, uh, she shared with us. And let's tee it up for you, Christina Kim.

Speaker C: Oh, it's going to be. Yes, absolutely, Jack. It's something that already, even before coming in, uh, to this seat, something that, um, the CEO and I really thought about. How do we become more disciplined about how we make these decisions? Um, how do we become more methodical? Because as the company grows, more opportunities are coming our way every day. So at the beginning, right, and I think that you're very familiar with this, but as the company is growing, you're really just focusing on delivering the product that you promised you were going to deliver. Now you're a couple of years into it, you're doing very well, you're excited, accelerating, you are growing very fast. So things are coming your way. Hey, somebody wants to meet with us because maybe there's a potential partnership opportunity. Hey, somebody might be interested in doing more business with us from a partnership acquisition by perspective. And the more we start seeing that, we realize that we really need to become, uh, we need to be prepared to make those decisions in a method, methodical and in an efficient way without getting too bogged down with each opportunity. And so that's something that I really credit our CEO for having thought about it a lot and for really asking me from the first moment that we met. That's something that we really, that he would like me to be part of. And so it's going to be absolutely a key part of what I do. I think forecasting is going to be, uh, very important for us, and I think it's going to be incredibly helpful for me. Um, I think that for the finance function also, uh, just in helping us, I think, going back to your question, how do you think about capital allocations, resource allocation? I think going back and being able to use some historical data on what uh, what we had aimed to achieve by for example launching certain products. What was it that we really wanted to achieve? What was it that we really got out of it? And not just at the top sort of high level numbers, that's something that we already have today, but really getting much more deeper in terms of uh, client engagement, in terms of what really happened after we launched a product and how can we, we use that for future uh, expansion and growth? I think that's going to be also absolutely, um, very, very key for us.

Speaker A: I liked speaking with her. She was, as we discussed, she was at uh, an investment banker really for nearly two decades. At the same time she was so excited about getting into the operational data like it had been kept from her all these years. It was the numbers that investors saw. Now she really wants to dig in deep and use AI to become a more effective uh, forecaster. Uh, but just pounding on decision discipline and how it accelerates, will accelerate smart expansion. She had that mantra. She talked about the need to become methodical, to become methodical and evaluate every partnership, product and resource decision, um, and using again a framework, a consistent framework. But um, so great, ah, discussion throughout this particular episode. Glenn, but what did you find, what did you interpret the role of forecasting, uh, that she's sort of addressing there in the system she's, she's describing?

Speaker B: Yeah, I guess before I say that it's funny because I spent most of my time as a CFO in private equity backed companies and I always thought man, I really want to be on the other side of this. And it's always interesting to me when you hear um, whether it's VC or investment bank or private equity, when they say I want to get to the operation side, it feels like it's one of those grass is always greener. But I think the interesting thing is just by working with private equity groups or with investors and with investment bankers, you do learn a lot and you learn a different way to think. So I think it's actually a great transition for her. And um, and that was a great story behind that. But I think what I loved, she's using forecasting as a calibration tool, not just a planning tool. And what I mean by that, and I don't want to be overly reductive here, but really what she's talking about is a perfect feedback loop. So first you compare what you intended to what actually happened Learn from it and then feed that back into your next decision. So it's, I mean it's forecasting for forecasting sake, whether it's for investors or management or the sales team or whatever, that's fine, that's an exercise we all go through. But when you take that forecast and you can understand, again, it's about the levers and it's not just, and this is what separates and I think this is how people are able to advance in finance. Early in your career, it's just the numbers. Later in your career when the stakes are higher and you've been exposed to it, you understand that what you're doing with the forecast is not just an exercise that you tick the box. There is something you can learn from each of them and get better at it. And um, you know, I think because of that time at J.P. morgan, she knows what institutional rigor looks like. And I think a lot of times maybe in a, uh, in a fast moving startup, you wouldn't have that kind of experience and background in there. So now she's taking what she got from her background, building that same discipline in a, in a much faster moving environment. And that's not easy to translate. And I think that that's where she's adding real value there. And I loved everything she said as well.

Speaker A: So before I, uh, go any further, I, uh, I just want to make sure you mentioned uh, J.P. morgan. I said Morgan Stanley. So Christina's from J.P. morgan. Okay, so uh, no, let me ask you something about forecasting. As far as uh, AI and forecasting is concerned. When we look at the finance function, the different areas where finance leaders are really, you know, experimenting with AI and M looking to execute forecasting. Only the most perhaps advanced or uh, the most AI savvy CFOs are willing to go there. Whereas, and again I'll speak in generic terms, many of them, when we ask about how they're using AI, it's usually automation, it's usually efficiency they'll start talking about. You get to forecasting, they come up, uh, we're looking into a number of things. Am I right about that or what do you see happening as you have your own conversations and clients? Um, what, what's is forecasting at the back of the bus?

Speaker B: No, but here's, here's the thing. Right now the majority of people, when you say AI, they immediately think ChatGPT, Gemini, Claude, the new generative AI. What, what gets overlooked and forgotten there is, those are probabilistic and there are issues with them. And you can use them and we could expand more on that. But when I think about forecasting with AI, um, I'm thinking about classical machine learning and this has been available for years. However, the barrier to entry would be instead of just having people that are good at Excel, where you can do advanced forecasting in Excel, it's just, it's a lot like if you're doing a, I don't know, like a seasonal autoregressive integrated moving average, which is a mouthful. But if you're doing something more complex, you know, with seasonality in it and something more complex than just a standard regression forecast, um, you can do it in Excel. It's just a lot of work if you can use machine learning algorithms and you can bring in more features, more data, uh, and have a more accurate forecast. And this is, this is what my first book was about. So, and that was in 21, before uh, ChatGPT was, was around. And it was a really niche book because a lot of finance people are not going to be, you know, machine learning engineers. But so when we talk about forecasting that is exactly the kind of thing that machine learning is very good at. However, prior to generative AI, you had to know Python and have data science background and all. Now you can prompt generative AI, do this and it'll under the hood, write all this code for you and do a complex forecast. The problem is if you don't understand kind of the data science and time series analysis, the fundamentals of it, that forecast, you can't explain how it was created and what the it'd be like somebody handing you a forecast without seeing the assumptions and drivers in Excel of it. So if you're trying to tell the story around it, it's really tough. So that's classical AI. Machine learning is built for forecasting, assuming you have enough historical data and features and everything that can drive it. It is, and I think people are, by people I mean SaaS companies are building this into their platforms. So we're going to start seeing more and more that it's just built into your ERP or into your FP and a tool and all that. But right now, as long as maybe not as important for a startup, but I would, that I would still say pretty important. You can't just say the AI created this and talk about the magic black box, that you can't explain it. If you're using these tools for forecasting, you need to understand how they work and you need to be able to reproduce it, which is a problem with generative AI sometimes. I'm on a lot of soapboxes today, Jack. I can't stop talking.

Speaker A: I'll give you one more because we've heard how, I mean all the transcripts of past earnings calls are going into these large language models. And uh, just imagine if you're a particular analyst on these calls. Suddenly the CFO is so much more aware of what's likely to be asked. He's also, or she is also much more informed as to which analyst will likely go there first and just the nature of the questions and how they're, you know, who advances on the call. So the storytelling piece of this large, uh, language models are really, uh, producing uh, information to the investor relationship folks. And they're coaching their finance leaders like never before. It's the obvious place, but it's right there with storytelling where the finance leader is picking and choosing. Here's where you got into trouble in the past on these calls. It's always when we kind of venture off here and it's always these three analysts, you know, what can we do to keep the conversation in the lane that we want to. Here's what we can do anyways. Just kind of interesting to think about in that respect. Do you want to touch that, Glenn?

Speaker E: Do you want to.

Speaker B: Actually, yeah, I mean, I, uh, yes, because LLMs are incredible at. Think about it. And uh, I've used it for, you know, asset purchase agreements for long contracts taking or Q's and K's. Think of uh, an annual report From a company, 100 and something pages with all that information. If you're an analyst, you still have to read that. However, taking one of these LLMs and having it summarize it and then being able to chat with that document is huge. And so think about IR teams as they're prepping their CFO for these calls. Instead of going through just with like keyword search and looking at specific analysts and the types of questions. I mean, how much work that is, kind of work that in a lot of companies probably wouldn't have even been done where now you can take whatever, however many, you know, however many will fit in the context window or within your rag application and dump all these earnings call transcripts into an LLM and have it isolate. This analyst always asks about, you know, EBITDA or whatever, whatever it is they're asking. And then it can even do sentiment analysis on the earnings calls and, and like you said, identify the areas where this is where you struggled, this is where it didn't make sense or whatever. So the Prepping for this. This is. This is not AI, Uh, taking an IR Person's job. It's giving them superpowers to do their job better. So I think you're spot on with that.

Speaker A: Yeah, just. Just interesting to think about as they handicap and try to identify blind spots. What are these analysts not asking that we would like them to begin asking about? What is it that we don't want them to go near? You know, um, all of these things can now be finessed in a way, as they structure the call as they choose, cherry pick the analysts who ask the questions. It's always been done that way, to be honest, but now they just have so much more information, and they can do it over the last hundred calls. Here's, you know, here's, uh, the anatomy, uh, of a call gone wrong. Here's the anatomy of one that's gone right. All right, enough already. I'm on my box today.

Speaker B: Here we go.

Speaker A: Finally, we want to turn to Zane Rowe.

Speaker E: Whoa.

Speaker A: CFO of workday. Why not a leader? Uh, this is a, uh, finance leader whose career spans aviation, enterprise tech, and high velocity cloud innovation. Um, Zane brings a disciplined but flexible approach to investment, always evaluating timelines, adjusting resourcing, and leaning into opportunities where workday can accelerate customer value. You know what? That sounds awful.

Speaker B: Good.

Speaker A: He's really such an impressive communicator. Let's play the clip.

Speaker E: A lot of that conversation is around where. Where the next change can happen, and collectively, where are we putting the. The investments and are we getting the returns that we expect out of that? And then as it relates to timelines, there could be some things where I don't mind investing, and we collectively, as a company don't mind investing in something now that we know will only pay off a few years from now. But then I want to hear about progress. Are we hitting our KPIs? Are we really thinking about those initiatives? And has the market changed or has that opportunity changed where we can either dial back a little bit or otherwise lean in more and say, look, it was two years, but now we need to either acquire a company to help us accelerate that, or acquire talent to accelerate that, or just, you know, put more resources in a certain area or on a certain initiative to drive that a little bit faster? So it's really, as you think about the marketplace changing and with the evolution of AI being so fast and with what we can do with our technology changing so rapidly, even as we think about agents and which ones our customers are asking us more about versus not how do we Then redeploy our resources to make sure we're getting to the customers faster and helping them on their journey. And it's a lot of that dialogue because you're always sort of altering or changing things just a little bit. And then in some cases you say, look, let's pause on this initiative because it'll still be there a year from now, and let's really accelerate another initiative or otherwise. Let's think about how we, how we, you know, we're always inquisitive on the acquisition side. What are the technologies or the people and the talent that we feel like are out there and could be more helpful to us being a part of our workmates. I mean, we've got a great roadmap. We've got really talented people, but how do we augment that and go even faster because customers are really wanting more and more, and how do we do that in a smart way? We've always had a very high bar as you, as you, as you allude to Jack. We've always been inquisitive. We've had a very high bar on, is it the right technology? Is it the right cultural fit? Do we have the right team and can we really drive change with it and have that become part of this portfolio? And with the platform we have and the last acquisitions that we've made, we're very, very confident that what we're doing here is not only helping our teams internally, but again, with our customers recognizing value coming out of this platform. So it's exciting stuff that we're doing. And it's not like you acquire a company and say, hey, just give us a year or two, and you'll see change. In many cases, you know, we're counting it in months and quarters as far as the types of change that the customers would recognize and benefit from, you know, from the, the technologies and the. And the incredible teams that we've, um, that we've acquired. So thrilled to, you know, continue to grow the family here.

Speaker A: Investment rigor meets AI driven agility. And again, another finance leader laying out a framework for, you know, constant reassessment, uh, asking whether initiatives should be paused, accelerated, or augmented as they go along. Uh, but they're not slowing down. It's like, you know. So, Glenn, how does that mindset shape FP and a AT scale?

Speaker B: Yeah, I mean, I think we're. We're seeing a theme through each of these. And I would say, again, just like with Christina, this is. It turns planning into a living process when, when AI is reshaping what customers need, you know, week by week. The assumptions you built your plan on can go stale pretty fast. So you're constantly asking, like he said, do we pause this, accelerate it or find a different path through acquisition or talent. And I think it's funny uh, you mentioned he came, uh, came through airline finance and you've got to think that that's volatility is the norm there, right with it, with everything going on in the airline industry. So that probably you know, helped shape his instinct around that some. But what he's describing at workday is the version that every CFO is going to need as AI sort uh, of compresses planning cycles, I guess, no matter what industry you're in. I think that that is, gives voice to that mindset that we need to have with this because we're going to have quicker decisioning, quicker access to information and we need to, you know, planning process happens once a year, once a quarter, whatever, whenever that is. But we need to be a lot more agile and nimble around that.

Speaker A: And I like the way he kind of gives some detail as to how he doesn't want to lose even uh, short term visibility. His emphasis on measurable progress, hitting KPIs, validating assumptions, adjusting timelines. Again, he was in some ways repetitive. He's given the pep talk to us.

Speaker B: So it was kind of, he was on message for sure.

Speaker A: So across these three conversations you can hear the through line finance leaders are building systems for smarter decisions. You know, something we hear, but this was so consistent. Whether it's audience engagement, product performance or uh, you know, AI era investment pacing, they're using data and these discipline frameworks to guide where the organization focus needs to go, where they need to lean in next.

Speaker B: Yeah. And I think, hopefully I'm not reading too much into this, but I felt like hearing each of them talk, one of the things that connected them was they're not waiting for perfect data. I hear that so much because we're the, the accounting side of us wants everything to be perfectly aligned before we go and do our analysis. But with, with AI and with when you're moving from monthly or quarterly data to more real time data, you can't wait for that perfect data there. And that's what I saw in them. They're not waiting for that and they're committing to a framework and then running the system, letting it show them where the gaps are. And I think that's the discipline you understand, we're getting directionally correct. We're honing it in more and more. And we don't have all the answers out of the gate, but we're going to iterate and get better at it. And we're building that process, you know, building the plane while we're flying in some instances, but we're having that process that surfaces the right questions, and we can refine from there.

Speaker A: So what is it, um, anything else you want to reflect on for us? Just in regards to what these three have in common or don't have in common or.

Speaker B: Yeah, we're seeing now, we've been talking for years about the transformation of the cfo, but this is finance leading the business, not just reporting on it. And that's what we talked about, really, in each of them is going through testing, finding those levers, not just. I'm going to report this to you, I'm going to tell you what we need to do to change the direction of the company. It's much more strategic. And the through line for me is that all three of them have built systems that force those hard conversations early, before the numbers make the decision for them. So it feels like really moving that decision point up closer to real time, which has been a goal for years.

Speaker A: Well said. Uh, this is finance leading the business. I like that way, uh, of saying it.

Speaker B: Yes. And I feel like for our video viewers, I should point out that I've been sipping cold brew, not beer, in case anybody thought it is from a local brewery. But I haven't been pounding beer while we've been talking, Jack.

Speaker A: And here I thought you were putting your own spin on, uh, a dry January. Glad. And Hopper, thank you for, ah, allowing us to catch up with you.

Speaker B: Always a pleasure, Jack.