Scaling FP&A at RingCentral: Dan Newman

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Speaker B: This is FPNA Today. Foreign.

Speaker A: Welcome to FP&A today. I'm your host, Glenn Hopper. Our guest today is Dan Newman. Dan has spent more than 25 years in finance across consulting, startups and SaaS. After earning his MBA at University of California, Berkeley, he moved into operating finance at Salesforce, later helping build FP&A functions at OSRA and School messenger, and including acquisitions in both environments. The longest chapter of his career has been at Ringcentral, where he joined when revenue was about $250 million and has now spent more than a decade helping scale finance across revenue go to market, product and corporate FP&A today, he is the SVP and head of FP&A at a $2.5 billion public SaaS company, navigating the shift from high growth to profitable cash generative maturity while also integrating AI into the next phase of the platform. Dan, welcome to the show.

Speaker B: Thanks. It's great to be here, Glenn.

Speaker A: So I'm always excited to get to AI, but I'm going to hold off on that because we got a lot of career to get through before that. I love your background. You spent the early part of your career moving across consulting startups and different finance roles before deciding SaaS. FP&A was where you wanted to stay. And I'm wondering, the early career, uh, you know, development phase, what did it teach you and what point did you realize? I'm kind of liking this SaaS thing.

Speaker B: That wandering phase ended up being very valuable because each stop taught me something different. Consulting gave me structured problem solving, communication, and startups taught me speed and resource constraints. We didn't have much budget to go around in those days. Then I pivoted to, uh, some time at Wells Fargo to kind of a safety net. And that gave me some exposure to scale and process discipline. But my first true FP and A role was at Salesforce, and that showed me how finance could sit much closer to operating decisions. And after that, the clearest turning point for me came later at Osra. I was a solar thermal power startup and I led FP&A after the company was acquired by Areva. I think that confirmed for me FP&A was the right long term fit, but also that highly capital intensive businesses were probably less aligned with what I enjoyed most. And so after I'd been at Salesforce I wanted to get back into SaaS and worked for a uh, School Messenger, a startup in this uh, or a private equity backed company in the SaaS space and thought that moving back into SAS felt like the right fit because that recurring model gives you more visibility I think than some of the other businesses that I saw and there's still a lot of drivers underneath it.

Speaker A: Yeah. And going back to the startup phase, uh, I guess it depends on when you join the startup but I spent a little bit of time there and honestly for me being in a startup because there weren't, you didn't have staff and departments for everything, you really learn more about integration with the other departments and a lot of times you have to wear different hats and everything. Did you, did you find in the startup space that you were doing things outside of, you know, probably stuff you'd never do in finance again or what was, what was that like in your time there?

Speaker B: Well, uh, my first startup was, was at an online dental supply distributor and I was building the online catalog so I don't think I'd ever be doing that in a finance role again. But I learned a lot about data building databases. So even that was a great kind of foundation for my role in understanding data and how to leverage it to do analysis on it in finance.

Speaker A: It's really amazing when you get, when you start to look back at your career and I think a lot of it is what you learn in each role you sort of develop. This is what I understand, this is what I like, this is what I'm good at. This is how I can use what I pulled in from the previous uh, experiences to help this job. So it's, it is interesting when we look back, you know, those of us who've been uh, at this a long time, sort uh, of the lessons and the things you take away from each

Speaker B: space that's right at the time you don't necessarily know how things might connect. And it's not a straight, it wasn't, definitely wasn't a straight line for me. But uh, I look back on it and I think each, each of those experiences uh, really form and shape what I'm able to do today.

Speaker A: Yeah. And then you go, so I spent probably too long in the startup space. In those early stages it gets, it grinds on you. But then at school messenger you move uh, to a larger company where there's a little bit more stability. You're not in that waiting for the inflection point mode. I think when you were, uh, with School messenger, you came in there a $50 million PE backed company at that point, so already had some, some maturity, but no FPA department. So you come in, build FP and A, um, and when we talked before the show, you said, you know, you didn't know that they were planning to sell when you got there. So looking back at that experience, you're coming in, you're sort of thinking initially, okay, I'm building out something for the long term here. And then you find out, you know, we're building this thing to sell. Did that change anything, your approach to what you were building and what you were doing there?

Speaker B: Well, that's right. I joined thinking that I was building the FPA department, the function for growth, but very quickly found myself working directly with the CEO because my manager went out on maternity leave my first day. And we were basically locked in a room rebuilding the financial model. And I didn't fully understand why there was so much urgency around making the model so robust. That was a great experience though. And later when an operating partner from private equity firm came in to work with me directly, it became clear that the company was being positioned for sale. And that explained why the model mattered so much beyond just internal planning. And uh, once that became clear, the model suddenly had to work for a different audience, not just for an internal use, uh, but for potential buyers externally. And that kind of changed how I thought about the model design. Had to explain the business clearly at a high level, but it also had to hold up to detailed diligence if somebody wanted to go deeper on the assumptions or the drivers. I remember later walking the acquiring company through the model during diligence, realizing how important it was that finance could communicate at multiple levels at once.

Speaker A: That's another great learning point when you think about going through due diligence and working with private equity. I spent a lot of time in PE backed companies and you start to, I mean, if you were just building FPA for that internal team, you sort of, you know what the metrics are. And it feels like, okay, we're just, we're building this out. These are the metrics. We're trying to beat metrics and, and um, you know, beat plan. But we want to have the model as accurate as possible. But once you get into that M and A phase and you go through due diligence, you understand, I don't it's almost like having that outside sort of pressure and influence that only PE can give you or, or a buyer in a case of a sale or whatever where the numbers start to be more meaningful because it's like oh, these aren't just KPIs, these actually truly impact the valuation of the company. Did you pick up a lot? Like it's, it's almost, it's just looking at the same thing with a different lens.

Speaker B: That's right. And every number, especially on the top line, you know, a couple points of growth here and there can make a huge difference on valuation. If you can figure out a way to drive a few more points of profitability, that's a big lever too. So it really magnifies I think every action that you take and every assumption that you make and any of the variability. And then now at Ring Central we've made some tuck ins over time so I've seen the other side of it as well and I, I wish I could in a way go back and tell myself what I know now. Uh, then uh, I might have been even a little bit better prepared but, but things worked out at least so.

Speaker A: And you, I mean just kind of stair stepping with your career. So you go from startup to that PE back $50 million company and then you join Ring Central they're at 250 million, a pretty healthy run rate. When you joined had a six person FPA team so probably more help, uh, more help than you'd had in previous roles. But since you've been there, what 10x growth now, uh, now at like two and a half billion. Um, so I'd love to know, I mean and especially where you had come from. The scaling of FP and a function, how has it had to evolve as the business went to now uh, a behemoth.

Speaker B: Yeah, right. When I joined RingCentral we had a lean FP and a team like you said, highly capable, I mean really amazing team for, for six people. But uh, we were operating pretty reactively, growing 30 plus percent a year because the company was at that stage of growth. I remember the first task I had was trying to get a 60 meg Excel revenue model to stop crashing. And that was a good signal that the systems needed to catch up uh, with the rest of the business. And as the business expanded up market we built out a channel program, added uh, major strategic partners. The underlying business was just becoming so much more complex and so finance had to be kind of matched that level of complexity and sophistication and over time the function I Think evolved from producing numbers to creating more structured insight and expanding the metrics we tracked. Reviewing them more frequently was a lot about building frameworks and helping executives, the CFO investors be able to think about the business in a more structured way. Uh, and the scale really required adding all that structure. But we couldn't lose speed at the same time.

Speaker A: Yeah, and you mentioned metrics and I have to imagine that the metrics change when you go from that rapid growth phase of the business to the maturity stage. And at a SaaS company of this scale, imagine the metrics change. But are there, are there key metrics that you look at that define whether the business is healthy? And how much has that changed from just that, you know, 30% growth year over year to now? Uh, you know, growth has slowed, the business is in a different phase. Are there different levels of importance to metrics? Maybe that used to mean a lot more. And what are the key ones you're focusing on now?

Speaker B: Yes, the metric set has really evolved as the business has matured. So in the high growth years, my first seven years, we were growing 30 plus percent and the focus was primarily on ARR bookings, retention pipeline. And we looked at those as the business became more complex across route to market, uh, and segment because the main, the question was where was the growth most durable? We were absolutely looking at unit economics and the deal margins for larger upmarket customers as well. But those were more supporting metrics at that stage I think rather than a uh, primary lens. And as growth moderated, I think those secondary metrics though became much more central. Cac, payback, contribution margin, productivity, operating leverage, they all moved higher in priority because the conversation became less about maximizing the growth and more about balancing the growth with efficiency. And today I'd say it's even broader. Free cash flow, we've expanded, we've quadrupled or so free cash flow over the last few years. Stock based compensation's been a big focus for a lot of companies in SaaS the last few years? Gap, profitability. And now even with AI, uh, adoption metrics around AI enabled features because those are influencing the retention, expansion and ultimately capital allocation. So I'd say those metrics really evolve from growth to efficient growth and to capital allocation.

Speaker A: Yeah, ah, that makes, makes complete sense. And I guess, you know, everything feels so urgent when you're in that hyper growth phase. But once, once you've hit the maturity level, it's like the sense of urgency around the growth doesn't go away. But now, uh, does the mood feel the same? I mean the metrics are still important and you still have to um, you know, you have to hit those efficiency metrics and everything. But does it feel, is it different that it now feels more stable and predictable and uh, you're able to like dig down into a, a deeper level than you were before or what's the difference in, in tone from that hypergrowth to, to steady state?

Speaker B: I'd say we've become more sophisticated. We've, the frameworks we've built, the thinking about the business, it's all become much more sophisticated. The tools we've kind of built around that to be able to dig into the details of every slicing and dicing every aspect of the business and we've got it more automated. So instead of, I think we had a weekly dashboard that might have not aligned with the one that another function produced. So we were uh, trying to figure out, you know, which one to show in an executive meeting and that had a, you know, maybe five, 10 slides and that took me till late on a Friday to put together. Now it's you know, 100 slide deck. It's automatically produced and it goes into every aspect, you know, at least on the, from the booking side. So we really get a much better understanding of the, of the top line across various, uh, various parts of the business and can really flip to another slide when another, a follow up question comes up. So I think we're much more on top of how we look at things and more aligned too.

Speaker A: Yeah, and it sounds like in that, that obviously data maturity has come a long way and I think about, and all companies are structured a little bit differently. But thinking about data governance, the data dictionary, how you define the metrics, making sure everybody's singing from the same sheet of music as Ringcentral has gotten more, more mature in that how involved has FP and A been? And I don't know, I don't know if you guys have data people on your team or if you work with the data team, meaning the data scientists and data governance folks. Can you tell us a little bit about that structure and how FP&A has been involved with sort of the data foundation and helping bring data along to that maturity level?

Speaker B: Yeah, I think the, the data from a top line perspective I'd focus on, um, that's really where I mean we, from the, from the vendor span, I break out into three parts, sort of top line bookings, headcount and vendor spend. Those are kind of the three main areas and we really own vendor and uh, headcount. So it's those are the easier areas. I think the, the more challenging area has been on the top line on the bookings and getting alignment on definitions. Both. There's a, there's a data team kind of that owns data for the company they own Snowflake for example, uh, and the BI data that goes to there, there's a go to market analytics team that we partner very close with and they do a lot of the reporting and then we leverage that data for our forecasting. So the three of us have worked, partnered very closely on over time aligning to a single source of truth I think has been number one. There have been challenges with that and on the definitions and on the product catalog we have hundreds of thousands of SKUs. So it's hand in hand, week by week there's something new. Especially as we've expanded to become much more of a multi product company platform. Uh, there are new challenges now in that regard over the last year and we've had to work pretty closely together to make sure we kind of get to a single source of truth and we don't lose it.

Speaker A: Yeah, yeah, that's uh, and it's in our conversation today and in our conversation before uh, we even got on this. It's, you can tell that uh, you guys have a very robust data. I mean you've got a lot of data and uh, are using it effectively and that's the foundation for AI and for where we go forward and for probably a lot of the success of your team is that you've been able to build out that really uh, robust data foundation.

Speaker B: Yeah, absolutely. I think the less time we can spend debating the data and the more time we can spend analyzing it, applying judgment to it, uh, the happier and more impactful that will be.

Speaker A: I will say it's amazing though because I work with a lot of enterprise um, level clients and you would think, oh, you know big companies, they should have their data sorted out. But a lot of times their data setup is worse than what you'd see at a mid cap or smaller company just because maybe it's a lot of legacy systems and they haven't and they just never did the full digital transformation. I almost hate that word. I feel like we've been saying digital transformation for three decades now. But it's uh, it does sound, everything you're saying, it does sound like you guys have really got that foundation and I know that's key and really helps in what you're doing. You've mentioned Topline a couple of times and looking back at Your career at ring Central Revenue has kind of followed you through each step of the phase. And um, you said before the show that the CFO had built trust with you on it specifically, I'm wondering on that because it's such a key metric. What does it take to build that level of trust around the most sensitive number in the business? Really?

Speaker B: I think revenue trust really builds over time. It's through consistency more than anything else. Probably consistency in the assumptions, the methodology, and really making sure there are no surprises. You know, revenue is the most sensitive number in the business because it drives credibility both uh, internally and externally. So the key is making sure leaders understand your assumptions before the outcomes arrive. So there's no debating after the fact. Uh, and I think a big part of that is depth. We spent a lot of time understanding the mechanics underneath revenue behavior, kind of by customer, a different slicing of dicing route to market, partner performance, product mix, uh, because even in SaaS, revenue is rarely as simple as it looks from the outside. And we've been known to build 50 plus slides per month just on revenue and ARR analysis, uh, to make sure we weren't missing anything important. And we would go through slides one by one, you know, with the cfo, because that's really where the focus has been for a long time. And over time I think that that trust comes when you surface the risks early, you uh, explain the uncertainty clearly and that you show that you understand both what is happening and why.

Speaker A: Thinking about building that trust. And as the business has matured and it's changed modes and we've hit on this a little bit, but I'm thinking now in relation to business partnering and working with the rest of the company. So the, when the company's growing at 30% a year, you're helping tell the story externally at analyst days and you're, you have the messaging there. But now that you're in the mature phase, growth has slowed and you're, you're really focusing on holding people accountable for every dollar. So the way that you talk to other departments is going to be different and sort of that, the back and forth, uh, with the teams that you partner with is different. Did that shift, did it change your relationships with the other business leaders and the other divisions? How did that look then versus now?

Speaker B: Yeah, so when we were growing around 30%, a lot of the conversation with the business leaders was centered on how to sustain or accelerate that momentum, where to invest, how quickly to expand up market, how to build out the channel program, uh, how to support strategic partnerships I think efficiency mattered, but growth really carried the premium. So the tone was naturally more about expansion. And then once growth slowed, the relationship changed because the conversations became much more centered on trade offs and accountability. Not everybody likes to have those conversations, so it can be challenging. But instead of asking, uh, how much can we add? We were often asking what delivers the highest return and what can we push out?

Speaker A: Ah.

Speaker B: And so that meant we in finance really had to go deeper and earlier in the operating decisions. And I'd say in that first major year, that shift, we expanded operating margin by about 7 points to 19%. Uh, and that required a lot of difficult but productive conversations. And so in many ways FP&A, I think became more indispensable during that period because business leaders rely on us not just to challenge spend, but to help solve how these ambitious targets could actually be achieved.

Speaker A: On the soft side of that, that's gotta be a different feel. And I, uh, think back to the beginning of my career. I don't even know if anybody ever said the words business partnering to me. So there was always, whenever finance showed up to a meeting, it was kind of like the early days of pmo. Whenever someone from outside of that group shows up to a meeting, everybody's like, oh great, here's the cop, he's going to come, they're going to come and yell at us about something we're doing. Are there, are there things that you've seen or. I'm almost thinking about this like an advice for um, FP&A professionals who may be listening and are in a similar boat with partnering with other teams. Is there an approach that you take or any tips or guidance you have where it's not, where you have to come in and have these difficult conversations about margin and about potentially, you know, where spend is and where it needs to be reduced and how it, how it all fits together. Any, any tips or advice you have around that?

Speaker B: Yeah, so I, I think I've, I've entered many rooms where uh, it was kind of, it was that type of reaction, let's put it that way. And, and often it was joking though. And I, I think it became that way because I try to figure out some self deprecating sort of way to, you know, have a little fun and, and have a kind of jovial relationship, but also being there as an advisor and at the end of the day you're, I'm trying not there to tell them, I'm not there to tell them what to do. It's not my job to, it's my job to give them options. It's often to, it's to provide targets. And it's not necessarily these people think they're finance targets, but they're not finance targets, they're the company targets. We all own the same stock. These are shared goals. And some, some of these goals require tough decisions and we're here to help them think through the decisions with analysis, uh, and help them figure out what the best trade offs are to get to the answer that's best for them and best for the company. So that's, I think the lens and the type of attitude I try to bring and I think it works more often than not. But not always. Sometimes you just, sometimes it just doesn't work.

Speaker A: Yeah, that's well said. And that's something to think about too. It's, you know, finance is a step down from, there's the annual plan, the two year plan, the five year plan, whatever it is. But if we're going to hit the plan, we have to then figure out what are the numbers that we need in order to hit the plan and then, okay, who's going to track these and make sure that we're, we're on plan. So it's really, it's not finance just coming up with, hey, here's a magic number. I need you to hit it. It's all based on the, what's feeding it and what the company's trying to do. And I, I love your, you know that we, we all have the same stock, we all have the same goals here. This is what's going to be required to hit it. And that is, that is a great framing.

Speaker B: Yeah, I think it's, the more that you make it, you can't make it adversarial. It's really got a, it's a team effort. There's a business partner. Partner has to truly be a partnership for it to work. And I think it, it usually can if you have that mindset and you can kind of get your partner to think the same way and show them why they should think the same way.

Speaker A: Yeah. And it keeps things interesting as you, uh, yeah, and on that note, I mean you've been at ring Central now 11 years and I'm sure there were other options and opportunities for you to go elsewhere along, along the path or, or your chances at least to have that consideration. But you know, the sense of ownership that comes with being there and being part of that growth and everything, um, I'm sure holds you there. But is there something about the SaaS model specifically that kind of just, even after all these years, the work stays intellectually interesting for you. There's always a new challenge and a new approach. Um, how do you stay motivated and stay interested and find these new challenges, um, in your role today?

Speaker B: I think at RingCentral, the main reason it never felt repetitive is that the company just kept entering new chapters and at the same time my role kept evolving with it. And I think what keep SaaS interesting is that the revenue model looks simple from the outside, but the economics underneath it are very dynamic. You know, you've got your revenue for next quarter is pretty much your ARR from the last quarter, plus or minus a little bit. I think there's a lot going on under the covers. You can have a strong bookings quarter without immediate revenue impact in that quarter, or uh, you can improve margins in ways that hurt growth later. So there's constantly thinking about those trade offs. And I think that those trade offs, especially over the last few years in particular, where they've been, I think more distinct even have kept the work pretty engaging. And even revisiting the same function feels different at a two and a half billion dollar company than it did below a billion.

Speaker A: Yeah. And now you have, uh, now you've got the, as head of fpa, you've got the kind of the full picture of everything that I'm sure as you came up, you know, you had your slices that you were working with, but now you see a broader picture and the company is at this, um, size and scale and I'm. Is there something now in this capacity that you're focused on something new to build or something to change? What sort of shifts are you looking at in sort of growth opportunities for the department?

Speaker B: I'd say that the biggest thing I'm trying to build is more capacity for judgment. We still spend too much senior time producing first drafts of regularly recurring work that should be more system supported. I think the opportunity is just shifting finance from reactive. I think we've become more proactive over time, but even further along that spectrum, surfacing risks earlier and framing the trade offs before we even asked. That's really kind of the top of the pyramid type of FP and a. The goal is not just about efficiency, but it's creating more space for that, for that decision support. That's uh, those are kind of the things that I'm focused on right now. And it aligns well with a lot of the things we've been talking about with data and process and a little bit about AI we talked about before too.

Speaker A: You're already using AI for your FPA ChatGPT, Claude Copilot, and they're incredible. But here's the thing. AI is only as good as the data you feed it. Right now you're getting confident sounding guesses that you'd never dream of presenting to your board. Now imagine typing a prompt and getting a board ready dashboard backed by your real numbers or a P and L that you'd stake your reputation on. Finance OS consolidates your erp, CRM, hris and spreadsheets into a governed data layer. Every AI output now accurate, governed, repeatable and auditable. Find out why nearly 2,000 FPA teams run on Finance OS, with hundreds joining every week. Learn more at, uh, datarails.com finance os as we're going through this conversation, I keep picturing the framework of where you go from the analytics framework. You go from descriptive to predictive to, to prescriptive and then to whatever the next level is that we can get with AI. And it feels a lot like that's sort of the capabilities that you're building in and building towards, even enhancing further.

Speaker B: Yeah, I think so. I mean, uh, for me the direction's clear. We want to get to touchless forecasting basically. But to get there you need trusted structured data as a starting point. So I think if the all the key metrics on bookings and headcount vendor spend are all over the place with different definitions, then you're just not going to be able to automate because you're just automating inconsistency. So like we talked about, I think a lot of that work has been moving the key data sets into snowflake and aligning around a single source of truth. And then you can automate those drafts a lot faster and apply judgment and I think get to that vision where we're really doing a lot more thinking applying judgment. Uh, and I think we're going to get to pretty close to that place, but I'm pretty hopeful about it. Toward the end of the year, things are going to look pretty different.

Speaker A: How is AI factoring in either right now into that touchless forecast? And with AI, I guess we could address not just generative AI, but classical machine learning and all that as well with forecasting. And I don't know if in Snowflake if you guys are using any, anything like Cortex or anything to access and, and work with the data, but um, is AI factoring into that at all or how is it working into your, your plan for the future?

Speaker B: It's not there. Yet I think once we get all of the data kind of connected we, we've just started using Claude. I think that's, that can be an option to help us connect all the data we've been leveraging it on. We're starting to build the use cases for that. We're trying to use I think put Sigma on um, top of Snowflake for revenue. We're trying that out to give us revenue forecasting at a customer level and then being able to apply some AI to that. So that's one of the options we want to get automated headcount planning or at least a draft of that. So we're kind of preparing the data and getting that in a place where we can put it can infuse AI there. So I think we're still early phases of AI, but right now a good example is that we're about to do a hackathon uh, in a couple weeks to identify some use cases that we can help with some of the recurring work and how that can dovetail into some of these overall forecasting processes to make things a lot to reduce the friction in them.

Speaker A: Yeah, I love the idea of a hackathon for this because it's, you know that used to be just a, uh, what, what coding geeks would do but now with generative AI the use cases and what it opens up are, are so much more vast because you just have so many more people that can do it. And I think that hackathon is really the right way to think about it because whenever I talk to a company about AI implementation I always see it as, as two levels. One is, we'll call it the top down level where you're, you're going to build internally or have an external company come in and build some kind of tool that the whole company is going to use AI, it's going to connect systems, it's going to pull from systems, it's going to be automated workflows and all that. But then maybe the more impactful and currently maybe the harder to measure though is that bottom up implementation of AI where the employees have, there's a generative AI usage policy so they know what they can use and what environment they can use it in. Here's the company approved tools that we're using. They have the training on it and then it is how can you make yourself more efficient and uh, free yourself from these sort of the more mundane kind of mindless tasks and increase to that value add in companies where they haven't broadly adopted. Okay, we're going to lean in and we're going to try to use AI. Uh, you end up with these um, Ethan Mollick calls them secret cyborgs where there's just a handful of people who are using AI and nobody else is and they don't realize it and they're you know, 10xing their productivity and how are they using that time? Or maybe they're you know, even if they're using that time to do more things for the company, it's kind of happening in the shadows and I think a hackathon pulls it out and it's a, it's a good approach. So how did you get the employees sort of skilled up to be able to use these tools? What's their reaction to it been and what's kind of the environment around using AI tools on your team and maybe even more broadly.

Speaker B: We started with AI tools last year and uh, I think they weren't quite at the place that they added a lot of value yet. So maybe it was a bit of a false start. We use them for some things and people uh, sponsored a training for everybody to learn and most of the team took me up on that. So I think the excitement and the interest is there and also emphasizing in terms of career development there's almost nothing else that you can do that's more important right now. So I think people get it. And then I think lately when we started using Claude, I don't know, my first day I just couldn't stop playing, playing around with it, expanding models, reconciling numbers. It was fun. And watching it do the work and thinking about how I used to take me hours to do all these things it was doing in minutes. So then I would, something that I did, I would do something that maybe somebody on the team was supposed to do and would take them hours. I just, I expanded a model for our long, long range plan out a couple years. Then I sent it to the team saying it, saying I did it cause I had the cloud license first. And so uh, I, I do this and I show people here's, here's how I'm thinking about using it and here's an output of it and here's how long it took me and here's all the insight I got from it. It was amazing. So I think it's sharing the excitement and stressing the importance and making sure people don't forget about that. It doesn't take much and some people may be more embracing of it than others but I think you've really got to show as a leader in this situation that you can't just expect other people to do it. You've got to also walk, uh, the talk in some way.

Speaker A: Yeah. And man, uh, and I have no affiliation with anthropic or anything, so I'm not. This isn't a commercial here, but I think like everyone else in finance and in other domains, I have been what just in the last three to four months like I was fortunate enough to get an early preview of Claude, uh, for financial services, both the first version and 2.0 that came out last year, but just the off the shelf CLAUDE that's out there right now and with the Excel plugin. So the past two years I predicted so 24 and 25, I predicted, okay, this is going to be the year of the agents. And I was dead wrong on both of them because agentic didn't get anywhere. People started calling random things agents, but they weren't truly agentic. But in 2026 I didn't. I was like, well, fool me, fool me twice. You know, I'm not doing this again. But the long horizon work that Claude, Cowork or Claude in Excel can do right now and thinking about, like you mentioned, the, the sort of fits and starts early where you had to do chain of thought prompting and go step by step and everything you did and the plugins just weren't there yet to now what we can do in finance is pretty amazing. And you mentioned building out the model yourself and so it was interesting. Opus 4. 6 came out and then 30 days later, uh, ChatGPT 5.4 came out and it also has an Excel plugin. I actually just did a little Bake off between each of them. I had a fairly complex thing and I went through and I did both of them on the web interface and both of them in Excel and I had a scoring rubric and all that. But I'm wondering, Claude seems like they're really jumping ahead in the enterprise. Well, just in the professional space where prior to 5. 4 I thought, I guess maybe ChatGPT is going to go consumer. Um, but 5.4 is. Was a. It surprised me in the Bake Off. It did better than I thought it thought it would. I'm still, I'm still leaning towards Claude, but I'm. Are you guys now. Are you sort of testing both? Is everything sort of in a test and pilot mode? Or are you at a place where you're thinking we need to commit to something and not having people bounce around between different tools or it's a great Question.

Speaker B: I got most of my team ChatGPT last year and the usage was kind of mixed like it was good for some things, but it wasn't. You know, I think when I started using Claude, I think a month or two ago, you know, that was a game changer for me. And so I think the next day I asked uh, it to get everybody on my team a license because regardless of, I mean I think there, there will be comparisons but there's some benefit I think just to committing and I think the functionality of it now far surpasses anything I could have imagined. And the impact I think it'll have on our, on us day to day is such that it's, it's worth just kind of making the decision because I know it's good enough is not, it's probably, are probably not the right words here because it's so good. Ah, uh, even if ChatGPT comes becomes a little bit better, I want to at least commit to something, get people, get people up to speed on it over that learning curve and see how far we can go. And then if ChatGPT comes, comes back over time and far surpasses what Claude's doing, we'll take another look. But if they're pretty close, then might as well just go full uh, steam ahead.

Speaker A: Yeah. And that's been, I mean really as think about how early we are in this technology and how fast everything is moving and just how crowded and noisy that whole space is. And I, I, I know people who just chased one model after the other and I Really, I rode ChatGPT for a really long time and finally I just thought, okay, we're switching to Claude now and it's uh, but I, you know, I, I think it's just you, you can feel the bubble, but you, you got to hope that these big players will be there and then the way the technology is moving, you know, even if in a couple years we do have some, you know, where a lot of the competitors have been wiped out, I think the capabilities are, it's just a, uh, you know, it, it's a horse race, one might get ahead by a nose and then the next release, the next one passes it and all that. But I think there is, there is something to say for consistency and to your point, the amount that can be done right now, even if the technology never improved again from here, I mean this is completely game changing.

Speaker B: That's right. I'm pretty satisfied.

Speaker A: Yeah. And you know what? We need to get back together in another 12 months and see, boy, were we dumb then. We didn't. We had no idea what I was going to be.

Speaker B: It's changing so quickly. You just don't, uh, you don't know. But I'll, I'll, uh, I'll take whatever I can get at this point. Point. I'm pretty.

Speaker A: Yeah, yeah. And I. So obviously AI is a part of it. And you mentioned on, on your team how, you know, we all, we all have to learn this technology, but also there's sometimes you can be blinded or I, I can. And I think a, a lot of people fall in this, but everything seems so different because we're in this new AI world, but there are soft skills and technical skills and parts of our job that, you know, knock on wood, are not going to be replaced overnight by AI. And I'm looking back at your steady career progression and how you've moved through. For our younger listeners tuning in, are there any skills or habits or things that you've learned along the way that were important to your career growth up to now and that would translate even in this new AI age? I guess. What would you tell someone in their, say, early or middle stage of their FP and a career right now? What do they need to focus on? How do they need to be positioned so that they can move up?

Speaker B: Sure. Well, technical skill gets you in the room, but judgment and initiative, uh, are really what expand your career. Curiosity has probably been the most important habit for me in FP&A. You can't just be the messenger. You have to understand the business deeply enough to be able to explain the full picture and anticipate the next question. That's how you create leverage for your manager and the company. So for people earlier in the career, I'd focus on learning how experienced leaders think through decisions. And right now, especially taking initiative with AI to support that because it's one of the few areas where people can differentiate themselves very quickly.

Speaker A: Yeah, great guidance on that. I do think that's one of the things that you have to have that domain expertise and understanding and have that judgment. That's what's going to separate us from what AI can do. AI can throw out possibilities, but if you are or even answers and say, this is what I think is the best recommendation. But it's. Instead of treating the output from AI, um, like what you'd get from a calculator or an Excel formula, it is. You're a thought partner. That's your opinion. Let me now take it, digest it, use my own judgment and discernment, and come up with What? Just as if you would if you had a junior person on your team advising you, you know, or even appear. It's like, if I'm going to make a decision, let me just evaluate that and that. So to your point, I think you're, you're spot on. And that that, uh, judgment and discernment that are found, that are built on what you actually know in your domain and about your business, that's going to be the real differentiator. Yeah, that's well said.

Speaker B: And it becomes, I don't know, I think as I've learned through my career, some things just kind of become sub subconscious or accessible to me just because of the experience. So experience compounds over time. So it builds like a muscle and I think it just becomes easier without having to think about it too much, but really work, work on that. Building that muscle, basically.

Speaker A: Um, all right. I've not figured out. I've been doing this show for a couple of years now, and I've never figured out a good transition from those questions to our questions we ask every guest every week. So I'm just going to stumble right into them. We have two questions, uh, that we ask everyone and, uh, uh, the first one is what is something that not many people know about you, something they wouldn't Learn from your LinkedIn profile or social media.

Speaker B: So with my team, we do monthly birthday celebrations and we play two Truths and a lie as a way to get to know each other. So my recent one, uh, last month was one, I sold one sold cutlery door to door. Uh, two was I had a press credential for the super bowl. And three, I once performed standup comedy in front of a full room. And then we, we let people guess the lie. I don't know if you have any you would like to guess.

Speaker A: I, I had some friends in college who sold Cutco knives. So I feel like that's possibly that that could be true. The press credentials to the super bowl seems like that would be really hard to get. So it's either a, a really good one to throw in there as a, as a red herring. What was the last one?

Speaker B: I once performed standup comedy in front of a full room.

Speaker A: I'm going to go with the lie is the super bowl credential.

Speaker B: You got it right. Yes. The lie was the Super Bowl. It was actually the NFL Pro bowl when they were doing it back in Honolulu. So it wasn't quite the super bowl, but it was still a pretty amazing experience.

Speaker A: And I guess if you had a press credential at The Pro bowl where everybody's just chilled out. You probably got to talk to some people, too.

Speaker B: I got to talk to a lot of people. Uh, Joe Theisman. I was actually in the NFC locker room after the game and Michael Strahan was in there. Tiki Barber. It was pretty memorable experience.

Speaker A: That's awesome. That might be better than the super bowl where it's just so many people and so much going on and the chances of actually being able to talk to someone versus, uh, everybody just chilled out in Honolulu at the Pro Bowl. So that's pretty cool.

Speaker B: I think you're right. There's a lot fewer media and yeah, I think the attitude, the relaxation is much different. So, yeah, I think you're probably right about that.

Speaker A: Yeah.

Speaker B: All right.

Speaker A: Well, that was fun. Actually. I kind of want to change the question now and have everybody do two truths and a lie.

Speaker B: You learn a lot about people, uh, that way. Uh, each month we have a few different people who have a birthday. So I think we've, we've gotten to bond, get a little more connected over things that we didn't know. Otherwise. It's been fun.

Speaker A: I love that. I'm going to run it by the producers. Let's see if we can change that last question. That'll be fine. So, all right, now, everyone's favorite question. And it's, um. I always say this when I ask people in leadership positions in finance. I'm always, I always feel like they're going to stumble over this a little bit because I know you get to a point where you're not writing as many formulas as you used to. Maybe you're more of a recipient of these, uh, giant models and everything. But I feel like, I feel like you're staying in, staying in pretty close. So I'm anxious to hear your answer. Um, what is your favorite Excel function and why?

Speaker B: Well, I'd say that earlier in my career it probably would have been sumifs because it forces structured thinking around data and a lot of FP and A starts there. And I think still in a way I can follow it more easily than some of these other formulas that I see in the files that I, that I'm trying to review. But I think like we talked about before, more recently, the tool I probably used. I don't know if you can count as a formula, but I'll say it anyways. A function is the Claude add in and because, I mean, I use it to review models and simplify logic. Uh, somebody just sent me multi year bookings model many tabs and thousands of lines. And Claude just summarized it beautifully and called out some things that helped me figure out save me so much time was the bottom line and the thinking that it ah, helped me with. So I'd say. And that shift probably reflects how my role has changed from a model builder to reviewer and decision partner.

Speaker A: Yeah, yeah. And that's uh, I feel like we're going to start hearing that answer more and more often because we're going to, I mean you're just, everybody's going to be offloading tasks like that to Claude and interacting it more more as a thought partner than as a stare and compare or you know, running your own uh, sorting and everything, looking, looking back through the data. So yeah, I think you're uh, you're ahead of the curve right now. But it's ah, a, it's, the wave is coming fast and I'm, I'm going to start tracking that and seeing how many people, whether it's Claude or the Excel plugin or one of the, any of the other 19 companies out there that, that now have plugins and I'm guess I'm not going to take a dig at Microsoft for how bad Copilot still is. Yeah, well, Dan, this has been, this has been a lot of fun. I really appreciate you coming on and I love um, hearing all your insights and kind of watching your career progression along with the growth at Ring Central. I mean it's, it's, it feels like they're in lockstep and I think that our listeners got a lot out of this. So I really, really appreciate you coming on.

Speaker B: Yeah, Glenn, thanks for having me on. I've had a lot of fun.