The Missing Layer Between Insight and Action ft. Zone CFO, Chad Wonderling の全文書き起こし

the core zone order to cash billing products. Okay. And but what became clear to me from being customer at zone is zone always left me wanting more. I knew that there are more products that they had more products that that there was more offerings, there was more solutions, and I wanted more. And so when I came to zone, of where I put my focus from the outset was, ⁓ how can we provide more customer value to the existing install base? Okay. And so I think that's from like the company perspective. And then really from the finance perspective, you know, with that as kind of like the thesis. for zone and the value creation around the customer, where I saw the biggest opportunity from a finance and from a team perspective was turning finance from a team and from a position of just reporting on the business into something that helped run the business. Okay. And I know that we'll kind of get more into that, but I think those are really the two key things. speaker-0: Yeah. was that? When you button seat, what was the visibility like when you got here? What dashboards were already produced? What things did you need to get produced to get those insights? speaker-1: Yeah. And I would say this is, you when I got in, wasn't, nothing wasn't necessarily fundamentally broken, but it was just, there was a lot of fragmentation. So data lived in different places, different systems, different teams owned different parts of different workflows that led to that, that kind of housed and led to key data. And so a lot of time was spent reconciling really in terms of, instead of deciding. And so I think, The opportunity wasn't necessarily to rebuild finance, but really it was to connect it with the rest of the business to work to remove that fragmentation and then push it forward into something more operational. speaker-0: Yeah, makes sense. And did you find some fun formulas in your ⁓ operating model and your forecasting model as you dug in to those Excel files as well? speaker-1: Yeah, and I think, you know, it kind of speaks to joke about it, but I think it speaks to to kind of, you know, the visibility, you know, as a whole, because, you know, it's in and we're I'm sure we'll get we'll get more into it. But it's like, you know, data is like the baseline and is really the table stakes. And so it's like, visibility wasn't necessarily the challenge, not in the way that kind of, you know, people usually think about it. Like we had the data, we had the reports, but it was, we just didn't have the confidence to kind of know what to do once we tied it all together. So I think making the holistic meaning out of, you know, all the data and then going and taking action to then make change. so there's a phrase I use, you know, in finance that's kind of relevant to what we're talking about today is, and I use it with my team a lot. And that is really three things, three questions. What's the what, so what, and then now what? And when I got in here at Zone is, we were OK with the

what, but I think we struggled to get to the so what. But then we definitely were not at the now what. And I think from a finance perspective and the role finance plays, it's getting the what down. You must have that. You've got to have the credibility and the insights to know so what. But then it's like ⁓ what makes a great finance and elite finance team is ability to get to the now. speaker-0: Yeah, yeah. And then now what the the weights on the now what could change, depending on what environment you're in, what you're in. So yeah, really excited to dive into that transformation. So you get in, you know the what you can't you kind of know the so what and what do do? What is what are the first things you do about the now what like what processes did you put in place? What additional data did you go and collect to drive those decisions? Yeah, so what are the some of the first bigger processes and systems you decided to take action on to impact the now what. speaker-1: Yeah, yeah, exactly. And so I would say a couple things, at least from my approach was it was a combination of identifying the real quick wins, kind of the low hanging, the proverbial low hanging fruit, while then ⁓ sort of allocating the resources, but then also prioritizing within the team some of those longer term, higher impact ⁓ items and tasks. And so, you know, I think the other thing and you kind of alluded to it, Rose, from the outset is like, anytime you bring a CFO in, there's an element of change that is expected. But I think what's critical is that, you you need to be really intentional. And I think we tried to be as a team and our leaders, we tried to be very intentional, ⁓ not just to change things for change sake. And so if something was working, great, leave it alone. And I think we, where we really leaned in, was as you think about systems and workflows and process, we're really at the handoffs. Places where work and tasks and data moved between teams or systems. think those areas, for example, ⁓ the order to cash process was a big one and how we get the data from Salesforce into NetSuite. That integration was a big piece. Another term here at Zone was around approvals and spend visibility. It wasn't that we didn't have controls. It was just that they weren't happening early enough in the process to give finance a line of sight. so I think by tightening some of those workflows and bringing that visibility together, we didn't just improve reporting, but we reduced some of the surprises and increased the velocity of decisions. speaker-0: Yeah, no, that really speaks to I think the data element as well, right? Like the data lives in different pockets of teams. And one, you have to collect that data. And then two, you have to have that data translated to how it impacts finance, how it impacts the company as it moves downstream, ⁓ and hopefully not be caught a month out from it and be caught in real time or be able to tackle it in real time. So which gets us to this next section of the conversation here as you're thinking through systems. and process changes for your team as well as the company in the last few months for sure, probably years, there's been more AI brought into the finance team to help you and your team tackle these projects that are coming up. As you already mentioned, there's no shortage of work for finance to do. Even if you feel like you're ahead on hiring, there's somehow everyone's staying up, working a little bit later, getting a file out. So as you mentioned, you don't want to change just to change, but how have you thought about, you know, maybe some examples of things you actually didn't change as you got into zone and you saw that was working well for that time, you've got enough information and the processes were, you know, there are other fish to fry. What are some things that you actually didn't change as you were going through the first few months? speaker-1: Yeah, because finance is one of those functions where, ⁓ probably one of those two functions where stability really does matter. And I think if something is working, ⁓ there can be risk in really disrupting it. And so I think a lot of those things that maybe, or some of those things that I didn't change from the outset. is, you know, I talked about spend visibility and approvals is, you know, I wanted to get and I wanted to see for myself how the company went about buying things internally. Okay. It took time to eventually get to a place to, change, you know, or to define and to provide some good, good rigor around say, know, our purchasing process or procurement process. But one thing that I wanted to understand is, from a finance perspective and from a business partnering perspective, we wanted to meet the business where it's at, not necessarily try to pull it to where we think it needs to be. We wanted to meet it where it's at. So I think giving it some time, not changing for change sake, but just observing and seeing how other functions and how processes kind of naturally flowed and kind of went about their business. before introducing some of that change. And it's a little bit of slow change too, as opposed to kind of the shot-caller approach as well when we implemented that. speaker-0: Yeah, and I think part of this conversation is also sort of giving permission and acknowledging that it's actually not efficient to change everything all at once. You do have to prioritize the change of both the systems and the processes for the maximum impact and adoption by the company. And so even though there are systems and so much ability for financial agency and bringing insights into action, I think it's also responsibility of the great CFOs like you to actually decide what is reasonable for the team and what can be adopted by the team. That being said, I'm sure you are adopting a ton of AI and changing some of your processes to incorporate more AI. So if, could you share some examples of that approach over the last few months

or years with your team? You know, what are you actually leveraging AI for? What processes have you changed on the team? speaker-1: Yeah, and I think as we talk about change and look to identify kind of where to introduce change in the system kind of broadly is, I think when it comes to the AI piece, I think the key question is not necessarily where we can use AI or what we've developed on the team here at Zone is not necessarily where we can use AI, but I think first identifying where the friction is. And so I think observing, seeing, watching, at least from my seat, I look for friction points. And ⁓ from a change perspective, hey, if there's no friction, leave it alone. But there's plenty of friction that can be identified. And then we can look and see where and how AI might be able to be introduced into some of those friction points. So I think more specifically, and as we think about introducing AI, The approach we've taken is let's look at our workflows, all the workflows that we do, think about it from lead to cash, kind of procure to pay. And it's not just about rolling AI out broadly and hoping it sticks, but identifying specific problems. ⁓ So some examples where we've identified and introduced AI is really around a lot of those high volume repetitive tasks, the bottlenecks. Okay, certain bottlenecks, ⁓ whether it's some approvals, okay, as an example. And then also places where people are spending kind of a lot of time, but not necessarily adding a whole lot of value. ⁓ And I think the other thing, like we've looked at test it as well in our workflows, identify some of those friction points, but then test it in very kind of small surface area, kind of. ways where there's clear outcomes and objectives and then kind of expand, expand off of that. speaker-0: Yeah. Do you feel like it's been very broadens up from innovation from the team or bringing up some of these friction points or just even assessing what friction points are coming in more often and really applying? It seems like a very grounded, realistic view of how to apply AI into those workflows. speaker-1: think a couple things is... I think there's an element of like, it has to be introduced by folks that are actually close to the work and doing the work. I think why that's important, or what allows for that to happen is I think as a team, AI has to be an operating mindset in an operating culture. And so, we celebrate it, we talk about it. We use examples when we come together, you know, in our team all hands for other people to learn from, you know, other people's failures or successes. Where did it not work? Not just where did it work? Because that's how we can learn. ⁓ And so, you know, it has to be people that are really close to the work to introduce it. But in order for that to happen, it really has to be a cultural mindset. And we have to be okay with... with failing and having it not work as well. Now, I think the other thing in terms of the AI piece is, which I think is

critical in terms of, you talked about kind of a grounded approach in reality, is the thing that I've learned in trying some of these small experiments with our team is it really comes down to respecting constraints. And what I mean by that is like, We in finance do not operate in a vacuum. Okay. Like we operate obviously in a of control. speaker-0: Literally all the data is connected. speaker-1: Yeah, like in controls and audibility and accuracy and so, you know, think environmental context really matters. And so I think it's, you know, being aware of that. And I think that's why the human and the judgment element is really critical. So we think about AI as something that really augments workflows, not necessarily just, you know, full scale just replaces them. speaker-0: That's a lot of great insights there, Chad. All right, let's transition into your systems. So I have had the pleasure of going through three different NetSuite implementations, really up-leveling the data, tracking the system of record. I know you've gone through several as well. ⁓ once the system, once you have NetSuite up and running in a new company or a company that's growing, what in your experience has worked well? Where have you seen yourself or your team sort of then push the next step in terms of opportunity for both data insights as well as then getting into this embedding AI opportunity system of agency? speaker-1: Yeah, yeah. And I think in the flywheel of the what, the so what, the now what that I previously brought up is the ERP in a system like Enanswee is very good at the what. And I have a lot of respect for the ERP and the strong foundation that it provides a business. And obviously, when implemented very well, I think it can do exactly what it's supposed to do, be a strong system of record. and really that strong kind of foundation for business. But I think where we've spent some time as a team thinking and experimenting is I think that there's a lot that can sit in the ERP and around the ERP that can facilitate and expedite workflows to really complement the ERP as being a system of record. Okay, from, and I think some example of some of these things are the approvals and in the coordination and the routing and the decision making, you know, because I think, you know, that's critical. So that hasn't changed kind of the approach of the ERP, but I think, you know, it's to keep NetSuite as really the source of truth, but I think to layer in some of those workflows and some of that AI. in and around it. speaker-0: Yeah, thanks, Sunzen. As you've evaluated those decisions the last few months across, ⁓ from the revenue side all the way down to the expense and other operations, could you share maybe one or two examples of how you've sort of elevated and leveraged the system of record, but then elevated that with a new process or a new technology your team has used to ⁓ introduce more AI into the operations? speaker-1: Yeah, think a couple things is, and this is, we've seen a lot of the AI and finance kind of target the accounting side of the house, say versus FP &A, just as a function of the repetitive tasks and the high volume transactions. So, one thing that we've done recently is ⁓ we've been using some AI. ⁓ In conjunction with our Solution 7 ⁓ financial reporting tool that integrates directly with NetSuite, but the team uses Solution 7 as an, they're within Excel and they're using Solution 7. And one thing that we're doing is to get our arms around to ensure completeness of expense accruals, okay? By department, by vendor, by month. we're using AI ⁓ to do some data analysis around our expenses by vendor and match that up against purchase orders and then ⁓ expense recognition frequency by vendor to see if there's any gaps or any holes. we've seen some success in terms of accrual accuracy and completeness. And then also too, I think where that then leaves the team is, They're not doing the analysis and the reconciliation. They're reviewing it, they're checking it, but then they're applying the judgment on the other side of that to then determine, do we indeed need this? And that's higher level work for a frontline staff accountant. That's pretty high impact. speaker-0: Yeah, first do we need to make the accrual and then do we actually need this item that we're accruing for? And that is a much broader conversation, but you have to have that data surface to even get to that point in those decisions. So, you know, super, super amazing. And I think we're going to see more of that come up as the data gets more even interconnected, right? From the what data to the future data and like the live, even email data or meeting data that people have booked. And so how do you incorporate all of that? into the process. It's going to be a fun time. So from systems to then the data visibility, the type of data that you actually need these days changes and the opportunity to get even more granular. Sometimes GL account data isn't sufficient. You actually need to go into the transactions. You need to see the vendors. You need to see the specific revenue skew that was booked. How has that level of data visibility changed for you? in these past few months and with this technology, how are you thinking about visibility now? speaker-1: Yeah, I think that there's a lot that can be done there. ⁓ You know, in terms of how you cut the business and how you segment the business. And I think what it allows finance teams to do is get better insights from different angles to really pressure test, you know, the insights that you previously had or that you've held onto, those some of those beliefs, like you can test some of those beliefs. For example, think on the go-to-market side with selling and how you think about your go-to-market motion and segmenting the business, ⁓ whether it's around sales cycles, ⁓ customer size, and what that means for your sales reps, but then also what that means for how you're going about selling your

products and ⁓ how you're thinking about retention. And there's just a lot of different angles. that you can come at it from given, one, obviously you have to have that clean data, but then given the speed to analysis, to analyze that data. And so it's definitely up-leveled and the bar has raised. speaker-0: Yeah, and that building that system of financial agency, you really need to start getting more granular data and supplementing that with other data, may not be in your ERP even. And so really interesting to see that sort of come together for your team and how others are adapting to that visibility as there are systems of agencies being built on top of the ERP as well. speaker-1: Yeah, and I think, you know, that's what's, that's what's, think, really interesting. And as we think about, you know, you know, six months ago to a year ago, you know, I think the one thing, you know, back about eight, nine months ago, kind of the one trend that I was really watching, I think was really two things within sort of finance technology was kind of the evolution from Finance technology being dashboards to decisioning. That's one. And then secondly, how, you know, what I called and termed at that time, co-pilots would evolve within the ERP and within the workflow tools from not just generating insights, but to initiating action. And I think it's the execution that is key. So if we go back to kind of my, kind of the flywheel for our team, around the what, the so what, and the now what. It's, know, finance of the system of agency is really all about the now what. And I think that's where finance teams have to live in order to enable a business, but really impact the business kind of in the full holistic way. And I think we're beginning to see that. Where the execution in the and the decisioning is happening. speaker-0: Yeah, and even broadening the opportunity for that to be done autonomously. Right? Yes. You already have emails being sent out on invoices or reminders and things like that. And that's some level of agency that's already there. But now it's like, OK, what is the what am I going to allow you to send out an email? What is the agency this system is going to have? And I think that's the super exciting part that we're ⁓ getting into here. All right. in terms of, know, we've definitely talked a lot about the finance teams and certainly your accounting team being able to leverage this technology. Now as you're hiring, what do you ask folks that you're hiring? You know, there's so much that's changed in the last few years. I'm sure there are some questions and there are some things that are constant and especially those more strategic items. But how are you interviewing and thinking about recruiting and talent retention for your team? in this new age. speaker-1: Yeah, I think the technical skills definitely still matter. ⁓ The finance, core, nuts and bolts technical skills matter. But what I've found, at least with myself lately, is putting more of an

emphasis on problem solving and really adaptability and really assessing candidates when it comes to the rate at which they solve problems. and how they go about solving those problems and how they've grown and evolved in various environments that they've been in to kind of serve as an indication of how adaptable they are. So, you know, I think what stands out now is how people think and how they can approach problems. I think that's what matters most. And then I think, you know, the other element to it that I think is very underrated in the you know among the tactical skills and among the ai is is ⁓ you know how human you indeed still have to be ⁓ yeah speaker-0: feel like I'm talking to you a in this interview. No, hopefully not. ⁓ speaker-1: to work across the business, but also to get ⁓ the, to really, to really get things done. So I think those are kind of the core attributes and how we're thinking about it. speaker-0: Yeah, no, that's amazing. think that it's being agile, even in the interview and a talent assessment process, right? It's a super important here. And then, all right, so they're on your team now. And I've spoken with a couple of folks in your team who have been very kind to offer a few minutes in preparation for this conversation. And it seems like they're leveraging AI to up level their own skills on SQL or other things. And so as they're on your team, you mentioned before, the work needs to come from the people actually doing it. But I'm sure you're playing a key part in the strategy and the innovation on both what's being done in the business, but then also for the talent. And so how have you thought about ⁓ encouraging or drawing lines on how much AI exploration your team is doing for themselves and their own career building? speaker-1: Yeah, think it's, you know, it's got to be an operating model. It's got to be an operating culture. So you need to talk about it. You need to kind of shine a light on it, put a spotlight on it, reward it, recognize it and, ⁓ you know, really rally around it from sharing examples to doing screen shares. And like I previously said, I think one of One of the things that has been most eye-opening for me is ⁓ folks talking about some of their failures, where ⁓ in terms of, I had this expectation that AI would help me do this, but look what it returned. And I think the reality is, like, of all the AI that we use and all the large language models that we're using for the rest of our life, today's version is going to be the worst version that we use. It still has a long way to go, but it's not perfect. And so I think that's why from our team is, you know, we talked about talent. I think the one thing, the one thing among the three others that I previously mentioned. think the other key thing is we've got to lead with curiosity. We've got to lead with curiosity. And, and I think as part of leading with that curiosity, you know, we've got to turn And the phrase that I use with our team is turn the doers into reviewers. And that's what we want our team to do. And we put a lot of effort around it. And ⁓ we talk about it a lot. It's part of our talent assessment and our talent evaluations. How are you implementing and utilizing AI? What else do you need in order to make it happen? speaker-0: Makes sense. And can you talk a little bit more? I love that mentality. ⁓ I think what I've seen out in the wild, if you go on the LinkedIn, you scroll, the level of reviewing, maybe people need to refine their skills or up level their skills as they're getting more output. There's just a lot out there. And so how have you guided the team on the level of review that's needed? The models are not perfect, right? They're gonna give you things that don't make sense sometimes. So how have you guided them on the review process of that output? speaker-1: Yeah, and I think what it comes down to is like, as a leader and as someone that develops talent, I think it comes down to, you know, coaching and helping individuals develop a framework of judgment. it's, ⁓ you know, another thing that we're using AI for is to help us sort of quantify and think through how we should think about a bad debt reserve and an allowance for an outful account. speaker-0: I love a real example. speaker-1: AI is not going to just like, hey, what's our allowance for golf or comfy and then spit it back and then we go book the entry. It's all about the prompt. I think looking at the data, segmenting the business, looking at it by product. And when I get in and spend time with the team, it's not about the imbalance of what it is. It's about how we got there. And it's about the process that we use and the judgment. that we applied. Okay, how does this compare to history? How do we think about this by geography? How do we think about this by customer segment, whether it's industry or customer size? And so it's about helping individuals kind of develop that sense of judgment. And, you know, what might be kind of a fairly senior thing to do in terms of, you know, kind of the valuation policy of allowance for doubtful accounts. A frontline staff accountant has the opportunity to do that so long as he or she puts the right prompts in and really thinks through the judgment elements, which is some pretty high level work. speaker-0: Yeah, and that's where the repetition and seeing that experience and seeing those accounts either actually pay out or not, that's the experience and the data that the teams get more exposure to. I think having those types of prompts in that review, it's super helpful to think through what can you have the system do for you and then where can you improve on your own review and your own final output. Hopefully, the numbers you get in your email inbox to approve are solid and they have a really robust explanation behind them as well. Let's talk about AI product. You're in such a unique and I think such a fun position to be CFO of zone and having all these great financial

products that are bringing AI agency into finance teams. In your experience, obviously, we've talked about how accounting teams have already leveraged AI in many different ways for their work just based on the volume and based on the opportunities that they have. ⁓ But you're also seeing the full P &L. You're seeing tokens being used. You're seeing different third parties being incorporated as you're bringing out more AI technology. How have you been thinking about the margins? What's going on in the CFO chatter rooms? ⁓ speaker-1: Yeah, yeah, because, you know, it's easy to talk about AI. It's easy to, you know, no disrespect to any of our marketing friends, but it can be easy to market it, you know, not as easy to build it. ⁓ But I think the key thing and the hardest thing out of all of it is realizing and seeing the customer value. So I think as we think about the margin piece of it. And granted, I've had to stop many times and pause, call friends, call colleagues, call other folks. In a very fundamental sense, it's untraditional. The rules of the road from enterprise software don't always necessarily apply here with AI. And so, while a lot of us have gotten to the place in our career where ⁓ we can apply pattern recognition, this is a completely different puzzle that we're working through, to your point, Rose. And I think the key thing, at least, how we are thinking about it at Zone is it's all about, I would say, one word, if I were to kind of encapsulate it all. And that one word is adoption. So I think... If we optimize for adoption, whether it's adoption in our product, okay, adoption in how we build the product, adoption in how our customers use AI within our product, then once we get that adoption, then we can step back and look at kind of, okay, what is the margin profile on this? What sense can we make out of it? Okay. And then I think as part of that, we've got to have, in order to drive adoption, I think what's really critical, again, going back to the customer experience and customer journey is having the line of sight and the visibility into those key product data metrics that are signals for that adoption. speaker-0: Mm-hmm. Mm-hmm. Getting back to the visibility. Yeah. I love how you reframed that because sometimes when people talk about margin, they're assuming that the product's going to be adopted and the growth is there for that margin to be a concern. It's almost like if you have margin as your concern, it's a great place to be because your product's being adopted and you're growing and you have revenue on this new line of business. All right. Chad, I think the adoption thing is really speaker-1: Yeah, that's right. speaker-0: strategic about how to think about it. And now that you've made these investments, let's say you get the adoption, how do you assess the ROI? How do you, where do you pour more investments into in different products? How do you think about speaker-1: Yeah, so I think it's, you know, as probably the general market and how fast we're moving today, you know, we've got a lot of companies out there that are identifying their use cases, where they can kind of utilize ROI. We've got, you know, software providers and technology providers kind of providing the supply to really sort of meet that market demand, okay, and that's happening. And, you know, the eye is becoming clear in terms of return on investment. Okay, that's always the easiest one to kind of quantify is what is that investment? I think, you know, if we go back six to nine months ago, as we talked about, there was, you know, a lack of clarity around, you know, kind of a system of agency within finance and and how ⁓ workflows and solutions can be provided to provide ⁓ an element of agency to the finance function. Well, I think we're in a similar place now ⁓ when it comes to overall ROI in AI, where it's like, hey, we know the investment. We've got some use cases identified. We're experimenting. But now it's about how do we deploy those? at scale to really capture and realize that return. So I think that the variable sort of in that equation right now is what is the return. And then what is the time horizon going to be over that ⁓ to identify and quantify that return. So I think a lot to still be figured out there, ⁓ but it will certainly become clear over time. speaker-0: Yeah, and I'll actually tie in what you said about the team before, because if you think about the ROI, the R also comes from how embedded your finance team is with your business partners, right? And as you expand that pie and that opportunity, then the ROI for the full system of agency doing that work and actually impacting the business becomes even larger. So yeah, a lot more to be seen in these next few quarters and years here on the ROI. All right, we're going to flip things. So Chad, your CFO zone, which also means you get to decide on what zone buys and what zone invested from an AI technology perspective, not just in financial technology, but across the board. ⁓ In a previous conversation, you had mentioned from an outbounding strategy, if someone connects with you on LinkedIn, share some information and then start sharing more about the product and then going into those conversations about. How have you thought about being a buyer in this world and being a buyer for your own team for technology as well as for the company? speaker-1: Yeah, I would say now is the, I think the hardest time in history to buy technology solutions. And I think it's hard in a few different respects. One is, you the analogy I use when it comes to buying finance technology is it's like a puzzle. And, you know, I was luckily my, for me, my children are much better than I ever was at puzzles, but I was never a puzzle guy. Okay. But when it comes to financial technology, I've, I've, I've had to sort of master that jigsaw puzzle. But the way that I sort of have developed an approach to do it is I want to do, I want to put the puzzle

together of finance technology, order to cash, procure to pay, record to report, using the fewest number of puzzle pieces. And so, you know, there's a lot of kind of the market today having an offering that is kind of equivalent to where cable television has gone, where there's a lot of sort of, you know, plug-in interconnected. That's a lot of puzzle pieces. Okay. With that comes risk. And so, you know, I think the way that I think about it is, you know, one is, you know, how wide and broad can that solution kind of cover, okay, from a workforce standpoint? How does that solution sort of remove, eliminate friction that we're encountering today, okay? And then what level of automation or AI does that solution offer to then get my team to be, to spend more time on the not what, you know? And I think that that's not easy. And I think that kind of encapsulates why today in 2026 is the hardest time in history to buy finance technology. speaker-0: not only just for finance, but you're also making decisions on behalf of the company too for all the other departments as well and how it all interconnects. ⁓ Fascinating. All right. The last few minutes here, I want to focus on the builder behind this builder financial agency and focus on you, Chad. As I believe you grew up in Wisconsin and some great volleyball there. Yeah, talk to me a little bit about how much technology were you exposed to growing up? How you think about making technology investments given your background both on the accounting side, finance side, and the exposure to technology these days? speaker-1: Yeah. You know, I think a couple different sort of angles on this or a few things that come to mind. I think one is there's always in finance been kind of this... I don't know if I'll call it self-awareness, but I think this sense of in finance, of kind of the finance function, not being the most innovative of all the functions and disciplines out there. And so, there's always a push, at least from early in my career to get to the E audit, the electronic audit to pushing A now it's... pushing AI within workflows when, you know, a year ago, you know, a lot of those use cases weren't necessarily proved out very much. And so, you know, I think it's about continuing to push the limits, but I think it's led through at a very micro and individual level, it's led through curiosity, okay? And I'll never forget some of the feedback, some of the most impactful feedback I ever received. So. I went the traditional accounting route, went to big four accounting, got out of accounting, was a controller. And at one point when I was a controller, I got feedback, was given feedback. And I think it was via 360 review. it was, you know, some of my direct reports gave me feedback, other peers in the business and more superior people. And one commonality among the feedback that they gave me was I wasn't very innovative. And so, you know, that stuck with me and that was pretty jarring. And I think, you know, in finance, we can be very traditional and buy the book, but I think we have to lead with the curiosity, try things, do new things and different things in new ways, not the same old way, and experiment and fail along the way. speaker-0: Yeah, thanks for sharing that. For what it's worth, think chat is very innovative in my interactions with you in these last few months. So, ⁓ you know, seems like you've come a great way from that one whenever you received that feedback. All right, last two questions here. What are you concerned about right now with this technology with AI being brought in? speaker-1: Yeah, we talked a bit about like the human judgment, the human element of it, ⁓ developing kind of a framework for good judgment. I think the other thing, some of the use cases, and we can take FP &A because within accounting, we talked about how AI is developing quite nicely in some of the workflows and the disciplines there. I take AI as, or take FP &A, excuse me, AI in FP &A as an example. One thing that I've been thinking a lot about is, you know, we can take a financial model, upload it into one of these big LLMs and ask it a number of different questions. And, you know, it can find holes in the model. It can find inconsistencies. And so what that has really led me to think about is like, you know, how are young finance people or people earlier in their career that have an opportunity to learn about the business, to learn about the financial process, how value is created in the business. If they can do all this with the LLMs, how are they going to build kind of that contextual foundation that leads to strong corporate finance? Because it's going to be different. It's going to be very different. so, you know, from, you know, speaker-0: Yeah. speaker-1: sales capacity and quota model building. And so, you what I've been thinking about is, is if this is the way things are going, how can I, where can I meet them? How should I meet them at such a place that is, you know, not necessarily rooted in how I learned, but a combination of maybe how I learned, but then also where the world is going in terms of, in terms of AI, but still ensuring that they do have that strong contextual foundation. speaker-0: Yeah, it's like so much of what I learned as well as like through the model building and through the pain and the grind of getting that model together. But now how do you learn through the prompts? How do you teach through the prompts as well? That's right. All right. Ending on this note here, what are you most optimistic about moving forward? speaker-1: I think it's about the opportunity for those professionals that are very close to the work and that are doing the work of the opportunity that they have to ⁓ take not only just take on more, but take on a bigger role and make bigger decisions and have a bigger impact and have more influence. So we talked about developing the allowance for for doubtful account process and how we utilize AI ⁓ to help us kind of develop some of that. I mean, that's one, just one minor example. But that's something that, you know, in the past I would do as a VP or a controller or see done by VPs and controllers. And now we're having kind of an accountant, a staff accountant develop that. That's a big opportunity. for that individual. So I think it's about how some of your frontline folks can really have an impact and be elevated and work on more higher level and sophisticated tasks. speaker-0: Yeah, and get really great opportunities very early on in their careers. speaker-1: That's right. Yeah. And I would say the other broad element too, that I'm pretty optimistic about, think secondly, is like where this is going. I felt, you know, and probably it was a little bit just me, but I felt about maybe about nine months to a year ago that we were kind of kicking up some mud and going a little bit in high speed neutral with some of the use cases in finance. And I feel like now, you know, a lot of these things are pretty well defined. are getting to the market. And so I'm pretty optimistic as well as in terms of where the technology is going and the rate of it, but also the usefulness of it, which is very key. speaker-0: Yeah, getting out of that neutral here and actually getting that high speed on drive or even faster. All right. Well, Chad, thank you so much for being my first guest here on Builders of Financial Agency. ⁓ For those of you who are joining us for the first time or want to continue this conversation, if you found this conversation useful, please follow us on Apple, Spotify, podcast and subscribe to the newsletter on LinkedIn. And if this resonates with you, please feel free to reach out. Love to continue the conversation. Thank you for joining me. speaker-1: Thanks, Ross.