Ep 50: Discipline at the Heart of Innovation

Speaker A: Hello and welcome back to Planning Aces. On this episode, we'll be spotlighting FP and ah a insights and commentary from three finance leaders who are each rethinking how innovation gets managed. Inside the business, CFO Chris Sands of Invoice Cloud, CFO Steve Sutter of Soligo, and CFO Niels Boone of Scent. I'm Jack Sweeney, your co host and I'll be joined by finance author, former CFO and our resident thought leader Glenn Hopper as we explore what our planning aces are teaching us this time around. In different ways, each of these leaders is wrestling with the same challenge. How do you lean into AI new data tools and aggressive go to market investments without abandoning discipline. You'll hear how they're structuring AI efforts like real business functions, hardwiring finance into sales strategy and treating AI bets as portfolios of experiments rather than one big roll of the dice. Glenn joins me to connect the dots and help us surface the practical takeaways after this. Hello, welcome to Planning Aces where we spotlight FP&A insights from finance leaders helping their organizations plan smarter and execute faster. I'm joined today by yet another resident thought leader. We're really happy to have him with us. Finance author, former cfo, tech strategist Glenn Hopper. Glenn, good. Good to have you with us, Jack.

Speaker B: I'm really glad to be here. I hope I can match uh, your energy today. I'm a little jet lagged. Had a long trip across the Pacific and uh, trying to bring the energy today. Trying to match yours at least.

Speaker A: Oh no. Where, where were you? Where, where are you coming back from?

Speaker B: Yeah, so I'm calling this my 2025 AI world tour. And I just uh, I just went about a 31 hour flight from Singapore, but I did the Southeast. This is my to Southeast Asia and this time I was in Brunei and Singapore, so wild trip. And then 31 hour trek back was uh, a lot. Nothing like that long a travel will make you feel old. And you know there's a lot of things that I do that I'm like, yeah, I'm still, I'm basically 30, but man, losing sleep and all that. I'm, I'm dragging a little bit today.

Speaker A: Hey, um, Singapore, quite a few AI users. Did I see some report or stat on them? I, and I don't mean to put you on the spot but um, I was surprised to see how widely used AI is in the nation of Singapore.

Speaker B: Well, I'll tell you, Southeast Asia in general, I've had, I've turned down quite a few Just because of that long haul. But, um, it feels like that whole region and maybe all of Asia. This is anecdotal from my side without stats to back it up, but, um, I just sold the rights to my latest book in China. Um, and uh, I also, uh, am getting a lot of speaking requests there. I think they are. And if you look at what uh, China is doing in the AI space as, as well, um, I, I think that, you know, unlike maybe the EU where, uh, uh, there are a lot of privacy protections and things slowing it down, um, Asia is really leaning in. So I'm getting a lot of requests there. And, and based on the conversations I had in Singapore, you know, again, anecdotally, it, it. I would, I would say that your assumption there is probably right.

Speaker A: No, I, hopefully. I don't know if you had a chance to review some of our planning aces for this episode. We have just three of them who just sharing sort of their mindset when it comes. They touch on AI, most of them. Um, but any commonalities, any themes you see coming forward?

Speaker B: So first off, yes, I did, obviously I did check out, um, all three of them. And I loved what each of them had to say. And I guess that I don't want to overly simplify it, but discipline comes to mind, I guess when they talk about the way they're running innovation, it's not, um, just a wild, throw everything at the wall sort of experimentation. It's they're running innovation like a managed process. And I talk about this all the time and where I'm, you know, you hear all these stats about. And I know they talked about more than just AI, but right now everybody's talking about AI. So you hear about all these projects that fail, but what these CFOs were saying, it. That's the projects that succeed. So they're running it like a managed process. They've got formal ownership, clear metrics, tight feedback loops. And really what I hear is build the function when it's warranted. Don't just. I hear so many people, you know, there's sort of this idea that you can just sprinkle some AI on anything and make it better. But they, they were looking at, you know, tune, go to market with real unit economics, pilot new data tools in small steps. Um, and I, and I, I think it was Chris that said, you know, finance isn't known for creativity, but there is. And I don't mean Enron level creativity, but in finance there is creativity in how we manage this. And I think creativity with controls and moving upstream and not just being the reporting function. I just, I loved everything that they were saying this month.

Speaker A: Well, let's, let's tee up the first clip. And that will be Chris, referring to Chris Sands, CFO of, uh, Invoice Cloud. Um, this company, uh, likes to tell us they're reshaping digital payments and customer engagement for utilities and service providers. Just a footnote on Chris. Before joining Invoice Cloud, uh, Chris held senior finance roles across a variety of SaaS and enterprise software firms and experiences a sharper focus on scalable data driven operations. Okay, here's Chris.

Speaker B: But maybe it's not so obvious, right?

Speaker C: I mean, you said it well, there's a lot of AI tools out there that you're like, that's cool. Okay, great. Like what, what do we get for, for the coolness? Right. And so I think, uh, you know, we don't want to be in a situation where there's lots of cool opportunities to use AI. You know, leadership is saying we need to be AI forward. And so it's like a binary, hey, we're using AI and it's cool. And we check the box because people want us to use AI. We need to find the most impactful use cases. And it's, you know, using AI is not the end, it's the means to the end. Right. Um, which is increased efficiency or, uh, growth. And you know, we're certainly building features and functionality into our product to leverage AI. So if you get the growth, you get the efficiency, then, um, you know, you start talking about rule of 70, rule of 60 instead of rule of 40, which is so 2015.

Speaker B: Uh, a little bit of that, but

Speaker C: it was very deliberate. So, uh, the team is cleverly named aiops. Um, we don't, you know, we don't. The creative stuff doesn't come from finance, so I'm not gonna, uh, take any flack for that. There were definitely in the early stages of AI, we saw folks that were, um, power users. They had the genuine interest. It's very clear who these people are in the org because they're way out on the forefront, pushing the boundaries, trying new things. Um, but we had to make the deliberate decision to, okay, let's find these people, let's bring them together, let's take them off their other jobs, right? So we literally took people and we're like, we're going to backfill the role that you were doing with someone else because this is your new job. Um, and so we put them on a formal team. We gave them a Formal mandate. Um, and that's how we structured it. Right? If you're going to be, uh, AI forward, as we strive to be, I think you need to be deliberate about it and you need to put structure around it and, um, make sure that, you know, you're managing it as you would any other new function that you're standing up in the org.

Speaker A: Um, I have to say what I loved about this is he had an answer. I'm trying to, uh, with every interview that we do, discover how, if there's an approach, are they taking steps to, you know, identify their champions within the company and leverage that sort of enthusiasm that's out there. Uh, and finally Chris had a story about how they did just that. So I was kind of excited by that.

Speaker B: My favorite thing he said, and he's preaching to the choir when I'm listening here, that AI is not the end in and of itself, it's the means to an end. And I think with the hype cycle, with where we are with AI right now, everybody's saying do some AI without a definition of what that is. But understanding we're not doing AI for the sake of AI. We're looking for that efficiency and growth that he talked about. And, um, what I really liked is if you can change management is hard. And it's. You can do all the technical things right, but you have to bring the team along. So they're. One of the great things they did was identifying those power users early. Uh, you know, these become your evangelists. And because they're passionate about it, they're going to drive that forward. And the fact that they took them out of their standard role and made this their job, that's. That's set up for success. So they, they pull them out of their role, give them the team, and they treat it like a, uh, real. Any other function in the business. They give them a mandate, a budget, KPIs, accountability. And when you have people that are passionate about their jobs. Because I see this, whenever I come into a company, I'm always. I'm looking for those evangelists and those small wins, because that's how you're going to help get that change management move across the entire team. So, yeah, 100%. I love what they're doing.

Speaker A: This, uh, I also thought, he's kind of a progressive finance leader. Isn't this unusual that a CFO would say, yes, take my people, take this person? Yes, they're quite competent, what they're doing. Yes, we need competent people, uh, doing that. But no, we need him as an AI champion today, uh, he's very high

Speaker B: in the early adopters. And it's funny, there's going to be a lot of people who attempt these early adoptions but fail, uh, because they have the wrong approach or wrong understanding. And everything I heard from him, he gets it. And the fact that you know, finance, um, we're, we're sometimes um, our own worst enemy where we're making our budget so tight that we can't innovate and we can't do the things we need to do. And Chris is doing the exact opposite of that. And the fact, you know, realizing this is the future and we need to, to dedicate some time and resources to it, understanding that that means there's a higher expense in, in the finance function. But knowing again what he talked about, that efficiency and growth that comes out the other side, I love seeing people invest in that. And I, I would say I uh, I talk to people so often who want to do this but they, they keep getting, they keep stopping themselves from kind of the fear of the unknown. So when you hear about a finance function, about a CFO who's leading like this, that's rare. But that's what it's going to take to, to get to the other side of this. It's, you've got to sort of, and I don't mean overcome your fear and just go bleeding edge out blindly into that cold dark night and, and hope for the best. Because everything else he said is around strategy too. So yeah, he's, he's definitely a front runner here and I love, I loved hearing what he.

Speaker A: Our uh, next planning ace. Steve Sutter is CFO of Soligo, an automation and integration platform connecting business applications through IPAAS technology. Steve's background includes multiple growth stage roles and reputation and a reputation for operational discipline during scale ups. Here is what Steve shared with us

Speaker D: in terms of long term strategy. So when you're executing towards a business model that makes sense, you're going to realize that I need to invest in sales and marketing, uh, maybe at an excessive rate for some duration to establish traction. But you need to know ultimately what's the efficient go to market strategy look like. And you need to be planning to seeds with your CMO and your CRO about where they need to take it, right? So hey, we can hire maybe a new class of sales rep at a new ote. But I'm coaching my CRO to keep in mind for instance the uh, uh, quota the OTE ratio, right, because it can maintain an intelligent ratio oh, you want to pay your sales rep more, but that's fine, uh, because we needed people who uh, have more depth and experience in the sales motion. But uh, that Maybe means higher ASPs leading to higher quotas. And that ratio of quota to ote, uh, on target earnings, um, is something that I'm establishing with the CRO as we explore enhanced go to market strategies, uh, within the teams that support that, the ratio of sales engineers to sales reps, um, the various streams that we're building, uh, our pipeline through and the unit economics for each of those streams, um, uh, with our CMO and planting those seeds such that you've established the path to where you're going to get the business, uh, to, and that makes it sustainable and that makes it profitable. And then, and then you can start to fine tune. But in particular, I would say as you're scaling through a hundred million, you're going to make a lot of mistakes, you're going to have to play some bets, you're going to have to figure it out. Um, you know, one of the things I would, uh, you know, I try to remind myself is, you know, acknowledge failure, right? Because as soon as you've acknowledged failure, you can move on to something that will likely be successful. Okay. And so keeping that in mind, with all of these initiatives that didn't work, okay, we're going to move on, we're going to shut that down and we're going to move on to the next possible solution that's going to scale for us. So acknowledging failure is something that I like to keep in mind. Uh, and as we march towards a uh, logical business model, I think a really exciting use case that we're doing is uh, our customer success team. Before they make a call to one of those 4,000 customers, they're like reeducating themselves on this account. What did they license, when did they license it? Um, how are they using the product? Um, how many endpoints are engaged? They've licensed 10 endpoints. They're using eight. How many flows are running, how many, uh, et cetera. All that data is located in netsuite, in Salesforce, in our own database. And we're aggregating that into integrator IO. We're pushing it through, um, another large language model with specific instructions. The output of them, of that data set in the LLM is literally a PowerPoint presentation that lays out, you know, they've licensed this. On slide number one, they are utilizing this against those entitlements. On slide number two, the volume of flows they're running uh, over, you know, the last 18 months has done this. And it's really just a complete picture of that account that is built uh, on the fly by integrator IO and then you can just imagine how much time that took to build previously by logging into Salesforce and Netsuite and another system. Uh, and so that's a very exciting one because I mean it's like tripling the efficiency of customer, uh, success manager and the accounts that they can address on a daily basis.

Speaker A: So that was just one of a number of uh, use cases he uh, supplied us with. But I love the scaling smart means failing fast. I've heard that so many times. But when he said it it just really put an exclamation mark behind it and learning along the way. And of course he links financial modeling directly to the go to market execution of Soligo. He's sharing with us some of the own, uh, Soligo, uh, technology advances as well along the way, which is perfectly fine. But hey Glenn, what stood out for you?

Speaker B: Yeah, I mean he is exactly what you want in a scale up cfo because scale up is such a hectic and chaotic time. And Steve sounds like the voice of reason there. And it's finance again, it's that discipline. But it's also again moving finance upstream and making it part of the strategy. Um, everything he said about setting guardrails with the quota to ote ratios, payback and CAC to ltv, all that, um, it can be easy if you're not paying attention in that scale up time to kind of lose, lose track of where your investment's going and, and how you're spending your money as you're just chasing that growth. And the way that he talked about balancing talent quality with expected output, um, and shaping that resourcing mix, um, you know that what is the sales engineer to rep ratio the pipeline sources, unit economics of each channel. I mean that's you, you have to scale smartly as you grow and everything he said you just see this sort of um, durable motion from it where you're not going to kind uh, of get out over your skis. So uh, that kind of discipline in that kind of environment is so key and critical to success for finance leaders

Speaker A: who are trying to achieve what Steve just shared, which is this, this great partnership that he has with the, the go to market folks, the sales folks that's already in place here. He lined that up. Uh, uh, you get a sense that he arrived and he explained how, how it works with everybody. Here are the meetings I expect to

Speaker B: be part of it's, it goes back to that discipline and it is, you have to be, you have to move upstream, you have to be proactive, you have to understand what the end goal is with the strategy and, and not throw good money after bad. And I liked again he said acknowledging failure quickly to protect capital and time because it's, there's so much money being spe as, as you're in that scale up phase. So the idea of name the myth, shut it down, redeploy to what's compounding and just that cadence will kind of speed that discovery up. So um, the approach he's taking there is the only way to successfully get through this and sort of keep everything in line with. Yes, we have this rapid growth, we're keeping cost in check. I know it's a time to spend but um, we, we need to um, we need to do it rationally and control and guide that growth through the spend.

Speaker A: Finally, we turn to Niels Boone, CFO of CINT Group, the global insights technology company enabling data connection collection at scale. Niels began his career in corporate finance and strategy before joining SINT to guide its growth and digital transformation. Here's Neil.

Speaker E: Yeah, I think that uh, the biggest differentiator or difference with these AI investments compared to the regular ones, let's say, is that there's even more uh, uncertainty around what exactly it will bring. Right. So there are many ideas, but then the adoption is maybe high but like the effect of it, maybe not so much if you read articles about it as well. I think like 80% is not paying off yet or it's not fully adopted yet, uh, even though it's rolled out on paper. So it's difficult to have some certainty and uh, reliability around that. So you need to work with a bit more uncertainty. And of course you can look and take things in certain steps, right? Um, that you say first milestone. If that's working, then we invest more. So one example is we um, launched a tool, uh, in HR actually where employees can ask their HR questions that you normally have, like where's my payroll? Or can I have paternity leave? Or uh, like whatever it is that you have for hr. We now have an AI bot that was trained, uh, with a lot of data by the real HR team, uh, let's say, and that's answering now a lot of those questions directly. And this is really successful. And now we're thinking about rolling this out for any questions you might have, for example to the legal team or to the finance team. That can be handled by this as well. So, like that, um, we didn't say half a year ago, let's just invest X and hope that all the teams will adopt it. We now know what are the best practices from this first team that we tried it with. It seems to be successful. Okay, great. Now we can roll it out with other teams as well. So that's one way to kind of mitigate, um, the risk there. And then we have a couple of, like the examples I gave for the finance team. They are very specific, um, to a certain workflow where you can actually see that if it works, you can, you know how much time it will save you. Right? So you can do a demo, you can run it. For example, the balance sheet reconciliation, you can see how good the quality is of that. And then when you're satisfied, you can roll it out. And also those, they don't have to cost millions. It can be smaller tools as well. And like that, uh, you can take it step by step as well. So there is not one size fits all tool. For example, even within finance, it's different ones. And then for the different teams, um, there will be different solutions possible. For example, on the sales side, the go to market degeneration, filtering through, making sure that you talk with the clients at the right moment. That can all be, um, made faster with AI tools, uh, out there. So that is great. And then the biggest one, as I mentioned around synthetic data, that's the big bet. So there, there's basically nothing you can, you can calculate. We just have to decide together with the board how much do we want to set aside to have a big bet here. And if it becomes a big thing with synthetic data, then we want to be the, um, number one authority in that space as well. Also defining the quality level of synthetic data. So how do we know that it is actually working? Can you compare it with real humans? What are the metrics to look at? So we as synth, want to define those things before it becomes an industry. But no one knows. Maybe it never becomes an industry. Right. So this is, uh, the big bet that we have. So there is more about how much money do we want to reserve for that. So that's not even an ROI calculation.

Speaker A: All right. Neil's talking about piloting AI tools in small contained steps, building proof before scale. But, uh, Clint, how did. Does this mirror smart capital allocation in your mind?

Speaker B: Yeah. So Jack, think about what he's doing there. I mean, as I was hearing that, I was thinking he's running these investments like a portfolio um, you've got small pilots, clear hypotheses, stage gates and then the results are going to unlock the next tranche. So it's um, it's not all your eggs in one basket and it is diversification. He talked about starting with hr, which makes sense because there's a lot of documentation around HR and that's a great one to build on first but then you take your lessons from that, you apply it to the next. He talked about legal and finance and what I see in, in that approach to technology, think of it as like classic FP and a discipline applied to innovation. So I loved hearing their approach there.

Speaker A: And when he mentions the big bet on synthetic data investments with uh. Yeah, well, uncertain roi, how should finance think about those?

Speaker B: Yeah, I mean that's an R and D investment and it's a, ah, think about you know, a big capital investment. So you separate it from the business as usual. We don't know what the ROI is going to be. Um, so you cap your exposure, you ring fence the team and you watch the leading indicators of um, you know, data coverage, model accuracy on target tasks, time to pilot, graduation from, you know, from pilot to scale, early commercial signals. And I think that you just in there's, there's so many studies out there and all this noise around ROI from capital investments, I mean from um, from AI investments. And to think about it as what can we do operationally and then what do we do? That is kind of the bet on the future. And that's, that's R and D and you've got to treat it separately. Like you said, we don't know what the market's going to be. There may not be a market at all, but if we want to be at the forefront of it, we have to invest now. So I loved his, his approach to that.

Speaker D: Yeah.

Speaker A: So, and each of the three seem to be designing a system that involves learning where data uh, structure and uh, well, accountability, uh, let this type of experimentation scale.

Speaker B: Yeah, exactly. I mean it's fine. You know there's the old cf, no idea. But finance isn't there to slow things down. The ideas make innovation sustainable. But you know, you put the right guardrails and feedback in place and you move forward there. And I think looking at these, you know, the ones that need to be treated like capital investment projects, treat them that way. And then on the other side if it's, I think about this with uh, companies that are rolling out, you know, enterprise wide licenses to chat, GPT or whatever, that's, that's not an investment. That's a software cost. So you have to treat them differently. So I, he, I think he's, he's right on, on spot with the way he's looking at it.

Speaker A: I don't want to. Well, let's boil it down to one word. What would that be? If you were, and maybe you already revealed this to us, but what would that be? Terms of these three. What is the sort of the word that, uh, uh, capsulizes their mindset? Their shared mindset?

Speaker B: Yeah, it goes back to discipline. Like we said at the beginning, that's the job. It's guide innovation without losing rigor and, you know, keep funding what the evidence proves.

Speaker A: At the same time, I gotta tell you, I thought Chris Sands stands out with that, uh, again with the SWAT team. I guess that's my phrase, not his. But somehow that visual of, uh, just reassigning people who are really psyched to understand better how this, uh, you know, AI technology is going to, you know, innovate their business models or what have you, that's an exciting place to be. So you got, you can get caught up with what, uh, Chris was sharing with us, I suppose.

Speaker B: Yeah. And even outside of finance, that's just smart management and smart change management

Speaker A: get the people who are excited more involved so they can, uh, sort of spread the, uh, excitement, you know. So, hey, tell us something about, uh, like some of the trips you're taking.

Speaker B: The amount of time I'm spending talking about AI right now versus the number of projects that are going is kind of interesting. And I think what it represents is the Gartner hype cycle. I would say we are at the peak of inflated expectations around AI and I think some people have kind of caromed off the, off that peak going to the other side. And we're, you know, some are slipping into this trough, uh, of despair because it's, it's everything we talked about in the show. Um, we don't know how to do it. We don't know what the capabilities are, what the limitations are. We're getting pressure to do it. So some of these projects aren't being funded, so. Or are being funded, but aren't succeeding. And I think the reason I'm talking about it so much right now is because people want to hear that there is light at the end of the tunnel or that there's some kind of promised land on the other side. And what does that practically mean? So all my travels right now are. I'm talking to, um, everyone from government organizations to private company or to public companies, but to individual companies. Um, and it's a mix of kind of keynote speaking where, let's talk about what's possible and what you need to do and the guardrails you need to put in place and all that. And then it's a lot of training as well. Um, so this week I'm in Austin, um, at the Chief Executive Group, uh, Leadership Summit doing. People are choosing the day before the summit starts. They can either go play golf or they can come listen to me ramble for eight hours. And people are going to come here, you know, a full, full day training session on AI. But the thing is, we have to understand this technology to move forward in it. So I love when leaders are actually investing the time and effort to say, okay, I understand my domain expertise is finance, but, um, it's incumbent on me to understand this technology so that I can help guide and shape strategy around it. So I think the demand for talking about this and training on it is so high right now because people are still trying to figure it out, figure out what's possible. And to not be part of that 80% or, uh, you know, depending on whatever study you're looking at, of the projects and investments in AI that are failing right now because there are ones that are successful. It's just a matter. It's not the technology. It's a matter of the team that's implementing it and how it's being implemented that's driving the success or failure of it. So a lot of people want to hear about it right now. And, um, yeah, it's driving me around the world and I'm here for it.

Speaker A: Yeah. Yeah. Hey, uh, just a question about your book. You mentioned, uh, I think that you had just sold the rights Inside China. Um, tell us something about what that means. This is a book that you've already published in the U.S. and you've been on the circuit with. Is that right? Or is this, uh, a different book?

Speaker B: Yeah, no, this is my second book. This is AI Mastery for Finance Professionals. And, um, when was that published? October of 24. So, um, a little over a year ago.

Speaker A: Uh, a book on AI that came out a year ago. When is the sequel? When's the next. When's the next one? So you must have a few clues as to that.

Speaker B: Uh, well, yes, thank you for asking, Jack, because this is an early promo. I'm currently, um, under contract with Wiley Finance for my third book, which is going to be called the AI Ready cfo. And I'll be done with my part here in the next couple of months. But it. It's looking like the publication deadline. We may not see that for close to a year from now, just by the time it works through all the publication process and everything. And I, you know, I used to worry, oh, if it's going to be published that late, then, um, it's. It's going to be, um, uh, you know, it's going to be not relevant. But this one in particular is about everything that our guests were talking about. It's about that change management, it's about how to roll these projects out. And truthfully, we are still, as far as companies that are successfully pulling this off, we're still very much in the early adopter, innovator phase of who's rolling this out.

Speaker A: So one last question. Just about the rights in China. Will that have a payoff? A big payoff? Uh, China's notorious for, uh, you know, uh, when it comes to intellectual property. We're not. I don't know, it's kind of murky in my understanding.

Speaker B: Um, and, you know, I. It wasn't a priority and it wasn't even something I considered. Honestly. It was. The publisher reached out to me and said, hey, um, you know, China's interested in the rights. And I. You get in this whole thing around maybe the political side of it or, or whatever. And I thought, I don't. You know, ultimately, um, like we talked about at the beginning of the show, uh, Asia is leaning very much into AI and there is huge interest there. Um, uh, what ends up happening with, you know, who comes out on top. If you hear some people talk, it's an existential crisis about, you know, east versus west on who wins this. Um, but it is a global consideration right now. And, um, I think that it just opens up a new market for me. I don't know. I mean, this is these kind of books. It's not like I'm writing, um, you know, a bestseller fiction book. These are, ah, very neat publications. So nothing is going to be a retirement plan for me. But it is, ah, it is interesting, this one, because my book is. It's. It's a textbook. It's not, um. I mean, it goes through and it talks about generative AI, but not M. It mostly just talks about classical machine learning and applications across everything from private equity, venture capital, uh, portfolio management to corporate finance. So it is. It's a deep in the weeds book. And it's this. I'm m. Maybe not selling my book here, but it's not for people who just want a, uh, casual understanding of what AI is. And it's because I really believe that for us to drive innovation in this next round and use this technology, we have to have more than just a cursory knowledge of it. So, led me to write 100,000 word book on, uh, how the technology works, specific to finance and what finance applications would be.

Speaker A: Well, that's wild. Glenn Hopper, thank you, uh, for kicking off, uh, helping us kick off this episode and, uh, hopefully quite a few more, uh, of planning aces. Uh, we look forward to having you join us on a regular basis. Glenn, thanks for being part of this.

Speaker B: Thanks, Jack. This was a lot of fun.