Roper Technologies Inc (ROP) September 9, 2026 Conference Transcript

All right. I think we're ready to kick it off. Day two of the Communicopia and Technology Conference. My name is Joe Ritchie. I cover the multi-industry sector at Goldman, also co-head our U.S. Industrials and Materials Research. Really happy today to have Roper with us today. We have Neil Hun, President and CEO, as well as Jason Connolly, CFO. Thank you guys for joining us. Thanks for having us. Great to be here. So, Neil, you're three at the Communicopia Technology Conference. So, at this point, you would think that folks would be pretty familiar with the Roper story, but for those that aren't, maybe just provide a little overview on your value creation framework, and we'll take it from there.

Yeah. Real briefly, think of us as a durable, steady, low range of outcomes, cash flow for share compounder. That's the mindset of the organization. In terms of the profile of the assets in the business, about $8.5 billion in revenue, 40% EBITDA margins, low 30% free cash flow margins. Perhaps interesting for investors is the portfolio businesses are hard for investors to own individually because they're small. They're tiny businesses that are leaders in small markets. So we're the leader in a small TAM. So we have the scale advantage locally, but we compete on intimacy with customers. We have strong product market fit, strong gross retention, and collectively, the cash flow generative capacity of the enterprise is quite compelling. Now, standalone, each business would not be particularly uniquely productive in how to deploy the cash flow they generate to their own individual purpose. So, therefore, we take all the cash flow up to the center, the free cash flow, excess free cash flow, and deploy it to what is, we think, the next best opportunity, whether it's a new platform, a token acquisition, a buyback at the enterprise. And you put that together, the cash flow compounding per share capacity is in the mid-teens, and we go to work every day trying to find a way to get that to the high teens so the rate of double goes from every five years to every four years. So low range of outcome, durable cash flow per share compounder, one of the largest, if not the largest vertical market software businesses that's publicly traded. That's a collection of 29 businesses underneath the hood. So I'll stop there. We can take where you want to go to.

Yeah, super helpful. Before we get into the whole AI discussion, which I'm sure will dominate a lot of this discussion, we've talked historically about you being very focused, niche markets, vertical software. Why is the disruption risk low when you think about the horizontal players that are out there potentially coming into your markets? How do you think about the defensibility of the businesses that you're in?

Yeah, I think there's several defensive aspects to it. I mean, the first, and this is proving to be the case even in the AI era, is the size of the prize is quite small. When I say our TAMs are small, our largest singular TAM for one of our business, our biggest TAM is about $4 billion a year. This is pre-AI. Most of our TAMs are a billion or less. So the size of the prize is just quite small. It's already very, very well vended, particularly by us, and a very small number of competitors we have in each one of our verticals. That is the case. I think 18 of our 21 software businesses work in a regulated end market. Oftentimes, the regulatory regime is why you need very specialized software in a vertical, whether it's education or insurance or government contracting or whatever it may be, health care for sure. And that specialized in-market, there's something unique about the way that in-market operates that isn't suited particularly well for horizontal software. Every one of our businesses were created sort of in the nooks and crannies of the horizontal. So from the day that they were born, the businesses were born, they had to create differential value from the generic application of horizontal software. And those are some of the reasons why it's historically durable. Going forward on an AI front, if you think about, in our opinion, what are the attributes for an AI winner? Well, first, you have to be able to take this probabilistic outcome, the deterministic. That's largely done with the context and data, knowledge graphs that we have across all of our businesses. Then you have to apply that in a workflow with essentially no latency, and you have to have the customer trust to be able to do the first one of those. And so it's the aggregation of those that is, I believe, an incumbent's advantage in AI, which I'm sure we'll get into as we get further into the questions today.

Yeah.

So let's start.

So since last year, you have given a ton of examples on earnings calls in particular about how AI adoption is accelerating across the portfolio. Maybe just level set us on what parts of the portfolio are already monetizing AI and like bookings or retention ratios and how you're monitoring it.

Yeah, so just a step back in a little bit of context. So when the AI gun went off a couple years ago, our decision at the enterprise level was we want to take all of this goodness of these tools and drive into the product stack to drive customer value, to drive growth and play offense. And so from the very beginning, that's been the mantra and the mindset, not how can we do what we do substantially, you know, more cost effectively and take it to margin. So we're playing sort of a growth game knowing that over time we'll get the productivity and maybe there's some margin of benefit, but we want to take all the productivity gains and go play offense. So what that manifested is all of last year and this year, it's been how do we develop these agentic SKUs that are doing these task replacement sort of things across a large swath of our portfolio, not domiciled in like one or two of our businesses, but essentially all 21 of our software businesses. That's been largely successful in the first wave, and now we're in the commercialization sort of learning phase of that broadly across the portfolio. Now, when you have a portfolio of our size, you definitely have companies that lead the pack and ones that lag. So your question, like, are there early adopters and where you've had real success in autism therapy, legal, increasing success in some of our other healthcare IT franchises, early success in insurance, early success in pharmacy automation, and just to name a few. But I call it early. Like, we've got to scale this. It's got to get scaled across, you know, dozens of our, a dozen plus of our software businesses, not just the product releases, but the commercialization, the retention. But the signs are encouraging early, but it's too early to call the trajectory of that.

So maybe, you know, you have a disparate group of 21 software companies, right? What is it about the ones where you've had early success versus maybe more kind of like you think the adoption is going to be a little bit more gradual? What are some of the reasons why these companies, whether it's Central Reach or Adderant, is having a little bit more success than others?

I'll start, and Jason can add some color. And so I'll paint two distinct points on this map, and then I think most of our adoption will live in the middle. So in autism therapy, there is a huge demand for the service in the United States. There's something like 800 million therapy hours that are demanded every year. There's 300 million therapy hours supplied. There is a line outside the door of every autism therapy clinic for families that need these services. And so our three categories of agent excuse basically allow the therapist to have more hands-on physical therapy time with the learners. So instead of 15% administrative time, it goes down to like 5%. So they see more learners. The line gets shorter. More families receive the care. The practice makes more revenue. And so it's a win-win-win. It's just a win all the way around. So that's been a very fast adoption, faster adoption pace for us. It has been. On the other side of the coin, same conceptual setup. In freight matching in the spot market, we own the business that organizes the spot freight market in North America. It's called DAT. Roughly 750,000 unique loads are advertised on our network every day between freight brokers and carriers, and the capability that we bought and built now can automate entirely the matching process of a freight broker to the carrier. Today, it costs between $100 and $200 of manual labor to do that. There's about 10 phone calls that happen to each one of those matches. Are you available on Thursday? Can you do it for $3,000? Can you drop it off on Friday by three? This happens back and forth. I need your insurance. We collapse all of that, do it automated, charge $40 to $50 to do it. That rate of adoption has been slower because the person pressing the magic button is like, that's my job, right? So there's a human adoption that sort of has to sort of go through. Now, the unit economics are so compelling, it will happen, but the rate of adoption is going to be a little bit slower. I think the reality for most of our agentec tasks are going to live in between those two because in the case of DAT, it's not entirely placing a freight broker's job, but a big swath of it, it is. Most of what we're doing, whether it's pharmacy automation or insurance automation, we're replacing and augmenting individual tasks. And so I think the propensity to sort of, yeah, I don't like doing this reconciliation task, or of course I don't like to confirm every script every day for four or five hours of my day. If the software can do that, I'll let it do it, so then the people can go do higher-value-added tasks. So I'd expect the rate of adoption for the bulk of what we're doing to be between the two, not super fast, not super slow.

The only thing I'd add is, you know, we are in small markets, and so what we've seen at least on cloud adoption as an example, I'll pick on legal, we thought it was going to be the slowest to adopt cloud, and then when COVID hit, you had this tipping point, and then when law firms knew that others were implementing it, you had it. It's sort of an acceleration. I do see parallels here where you're going to come back a year from now at these user conferences, you're going to have reference customers, and they're going to show the benefits of it from a productivity standpoint and say, I got to get – so there's a lot of sort of conversations that happen within the industries, and so I think we just need to cede enough of those to get that tipping point. That's right.

Super, super helpful example. Thank you for that. So last year at this conference, I asked you about disruption risk. You mentioned Foundry as a potential portfolio company that could be disrupted maybe sometime down the road. And then this year, you know, Foundry returned to growth. You launched more AI tools. So has anything changed about the long-term risk with Foundry? And then as you think about the broader portfolio, any further thoughts on whether there are other parts of the portfolio that could be disrupted?

On Foundry-specific, the context here, just so everybody has it, Foundry's less than 1% of our revenue. So if we're wrong in this statement, it's not hugely impactful to Roper. What Foundry does, by the way, is it has a very clear market leadership position in post-production, High dollar per minute Netflix streaming theatrical releases that takes the live action and the computer generated graphics and composites that into a single frame. So think the Game of Thrones dragon and the humans putting it into a single screen. It all happens in post-production called compositing, and we have a very, very strong market position there. If you believe that all high dollar content is going to be completely generative, then there's not a post-production step to do. So that's like the existential threat for that business. The current course of speed is that it's not what's going to happen. AI is being used to generate more CG, and more CG means more compositing. And what Foundry is doing is making the compositing step even more automated. And so in the intermediate term, you know, multi-year term, it feels like there's wind at the back of Foundry, but we can all draw individual conclusions on where the future, you know, 10, 15, 20 years of high-end, like, million-dollar-plus-per-minute sort of releases will be in terms of content creation. That's sort of the framing of the existential risk. For the balance of the portfolio, we feel very comfortable. Like, if you think that's ultimately with Foundry, it's like, does the end customer exist, yes or no? There's a compositor exist. For the other 20 software businesses, we feel very convicted that the end customer exists. There's going to be an autism therapist. People are going to go to church. Insurance is going to continue to be sold for complex sort of business insurance. Like, the customer exists, and it's then our job to automate as much of that customer's activity to sort of service that end market as possible, so we feel very comfortable with that opportunity. It tilts very in favor of growth versus contraction.

Very helpful. Think about, maybe talk a little bit about the pricing models that exist, right? Because I'm sure they're disparate across the different portfolio companies, so.

Yeah, no, and it is disparate. Some of that is inherent with whatever the business model was before, so we have a couple businesses like parts of DAT where it's automated already, it's based on per transaction, or a soft writer is based on per script, so the agentic charge meter is going to be similar to that, and customers are used to that consumption, But that's more of an exception. I think what's starting to be apparent, and I think everyone's heard this, is that as a CFO, especially, I want to have certainty on my budget. So something like a credit system is what we think is going to be probably the preponderance of what's going to be out there. It's something that Vertiforce put in place in the market. So essentially, if you're a customer, you can draw down credits based on the task that whatever agent you want to choose, there's different meters based on different tasks. And so that's good because it's a use it or lose it type policy. We get recurring revenue out of that, more certainty to the customer. And so that's where we see things headed. Got it.

Okay, great. And then we talked a little bit about some of these efficiency gains. You talked about DAT and maybe some of the slower adoption. One of the things that we've talked about historically has been that AI could reduce some seat in some of the workflows. Have you seen any kind of seat compression today across your portfolio or now?

We've not. Gross retention continues to be very strong, mid-90s at the enterprise level. It's the number we sort of talk about, earnings calls. We obviously monitor that. We also just inventoried the pricing model. This goes back a year and a half, two years ago. There's only three of our businesses that have a pricing meter that we might potentially have to adjust the meter over the media mark. Nothing we have to do like we change it in a nanosecond. You know, and the reason there's only three is think like, like healthcare IT, to the extent we have seats, it's like the total number of healthcare workers. So we're not, we don't need to change that. And insurance is the one that we have a very clear line of sight, property and casualty insurance on how we need to modify the pricing meter. We have the plans in place. We have the telemetry that's being built, the first ways that'll go out in the first part of next year. It's completely aligned with the customer. We're not trying to price grab or change total price. We're just changing the underlying meter from a seat to something that's more indicative of the growth and the unit economics of the insurance agency itself. So that's the one that is sort of front and center. The next one would be our legal business, which we charge on the number of fee earners. But this is not sort of once you hit a fee tier, then you basically can't drop down. Drop down. So we might sort of introduce a secondary pricing meter with that. We may or may not. And those are the two that we have to sort of work on, and I'll call it the shorter term, and then possibly over a multi-year period of time, maybe modify what we do with Dell Tech, but not something in the short term. Got it.

Is your customer changing who you're actually targeting within each organization?

So interesting on this is it's hard to generalize across all 21 One software business is, but I'll take a stab at it. Our customer profile is typically a medium-sized business. Think a three-facility daycare center or a four-facility closed-door pharmacy or a 500-person insurance agency. We certainly sell to large enterprises in their government contracting business, and they're very large insurance companies for sure. But the preponderance of the number of customers that we deal with, we are the IT budget. Like, they don't have Salesforce administrators. They don't have, you know, big IT departments. We kind of, like, we are the IT department, if you will. And so in that regard, the customer is not really changing because we're generally selling to the principal or a very close proxy to the principal. You know, maybe the CFO of a large insurance agency, maybe not the owner or the CEO. And so in that regard, no, it doesn't change that much. At the enterprise level, like we do sell to larger customers, I would say that we have typically sold – that's where it might change a little bit more to the end user than the IT department, where we're sort of selling like a tool to make the user more efficient, then they have to take it and sort of, you know, we will work with the IT department. But that's like a minority of the sales motion that we have.

That's helpful. You mentioned earlier, Neil, that you're trying to take all your productivity gains to play offense, right? So maybe discuss, like, where you're seeing the biggest kind of efficiency gains within your own portfolio.

Okay, so like I said earlier, a couple years ago when the gun went off, it was like, how do we go drive in product velocity and play offense and get first products to market and sort of accrue the AI agentic learnings at an accelerated pace and compound that? And so without question where we've seen the most productivity in the organization is where the emphasis has been in the software development lifecycle. And it's all – it's directly – it's direct – it's straight-up offense. You know, as we get – we have more product velocity, and this is not just using the models to code faster. There's a whole workflow. Coding is but a small portion of the software development lifecycle. It's the whole software development lifecycle that we've got a very clear model on what it looks like, The way you organize your people, smaller teams, closer to customers, solve problems versus build features to drive product velocity, and we've seen really encouraging, super encouraging results in pockets of enterprise and are on track to sort of be fully agentic SDLC across all 21 software businesses by the end of this year. So like not 30% to 50% improvement, but multiple fold, like 3% to 5% sort of product velocity sort of releases. So that's great. We just now have the organizational sort of mandate to get other functions together to sort of share best practices about what they're doing on the AI productivity. Finance teams, sales teams, support teams, services teams, implementation teams. There's pockets of real goodness, because the incentive is like, hey, if you can drive productivity to go put more and to go to market a product, then every company's incentive to do that. It's just how do we sort of get the competitive juices flowing inside the portfolio? Like, oh, if that company's doing it, I want to do it better. I want to at least adopt what they're doing. And we're just like literally this month getting the other functions together, sort of tap into that.

I'm going to ask one more question, and I'll turn it over to the audience before we move beyond this topic. But just in terms of contribution, right? So I think you've said AI is not going to be a material revenue contributor this year. You're going to see it flow in bookings first and then into some of your recurring revenue streams. How are you thinking about potentially the 27 contribution, and when would you expect it to really kind of inflect for you?

Well, I think the second half will be telling in terms of the early signs of bookings, and then from there, I think it's just adoption. So, you know, I think adoption is the ultimate question. I think we're getting signal from customers like, wow, this is really going to kind of revolutionize what I'm doing, or, you know, so I think the signal's really strong, but in terms of, like, when it's actually going to materialize is the, you know, $100 billion question, and we're just not there yet.

Okay, fair enough. I'm going to turn it to the audience, see if there's any questions before we move on. Keith, just give me one second for the mic to come.

Thank you. So in terms of the value proposition of playing offense by increasing productivity in AI, maybe how much growth do you think you can get by executing this strategy and changing our trajectory from now on?

Again, that goes to the same question. I mean, what we know is, we know it's TAM expanding. I mean, I think that is, that went from theory to real. I think that's clear and obvious for us in our portfolio. What's less clear and obvious is the ultimate magnitude of growth and then the pace to get there. We're a, you know, we're a sort of a 6 to 5 to 7, 6 to 8 percent sort of organic growth business. I think this is, you know, hundreds of basis points, but it's not a doubling of the growth rate of the enterprise. I think we can say that just to put big parameters around it. But, again, we're going to be very cautious on, like, what the ultimate destination is and the pace to get there until we have more, like, we see it in the trailing numbers because we're speculating on, like, two different parts of a trajectory that we don't quite know yet.

Question right here.

Do you feel now is a good time for shopping for more M&A targets, given like everyone else is quite depressed?

Jeremy, you want to start? Yeah, the question was, what's the M&A environment? I think it is a great time to shop. We just need more products on the shelf. So I think there's lots of conversations about products that are going to be on the shelf, but they're not there yet. And what I mean by that is we still have this kind of bid-ask spread between where the public markets are trading and sort of where private equity thinks their assets should trade. And so, but there's a lot of, I think, tailwinds for us in terms of, you know, LPs need liquidity. There's some credit cliffs coming in the next couple of years, what we've seen in the public markets. And then, and so I think all those things, and we've been having very constructive conversations with sponsors over the last, well, really two years, but I think they've gotten increasingly more constructive, like really wanted to, you know, to transact. And so we do think, though, that because there's been so much volatility in the public markets, they're probably going to try to wait this out as long as possible. And it's probably going to be more of a 27 where we'll see, like, kind of meaningful platform-sized deals that will come to market.

But also just look at the decision, like, the actions we took in the last earnings call. So from November to June, we bought 8% of the company back in eight months, essentially. And then we said we're going to prepare the balance sheet and build M&A capacity on the prospect that what Jason just said is going to happen. So the early signal that there's some beginning stages of seller capitulation, the private markets appear real. And it takes more than a month or two to prepare the balance sheet. But the returns that we could potentially drive to the enterprise and the cash flow compounding that could accelerate to the extent we can buy the businesses we bought 18 months ago for 30%, 40%, 25% lower prices are quite compelling. So the option value for that is worth exercising. Yes, sir?

Following up on the M&A question, how does a rising rate impact your M&A decision, and at what point does it become prohibitive for you?

Yeah, so cost of capital is, I mean, it factors heavily, and it goes to ultimately the underwrite case. What we try to do in the underwrites is we know that in the cash flow compounding, what we need in terms of capital deployment to drive the cash flow per share compounding we need, we need to underwrite somewhere in the 10 times year five EB to EBITDA as a baseline. So tax effect debt and inverse that formula is sort of get year five ROIC. That's what we underwrote, too, with a year and a half ago. With the asset prices coming down, prospectively, that will help sort of underwrite substantially below 10 times. But then cost of capital will push that up a little bit. So it's just it all goes into the math equation of where it's best to deploy the capital. So certainly increasing cost of capital will put some deflationary pressure on the asset prices for sure.

And that impacts sponsors more than it does us. They can't put as much debt on the assets. Mezzanine capital is not available. So that should drive down prices.

Any other questions from the audience? Just continuing along with the M&A side for a second. In the pipeline that you see today, are you seeing more opportunities in earlier stage, faster-growing companies,

So, again, just to reground everybody, in 2022 timeframe, we made a modification to our cap employment framing, which is more value to be created, more long-term compounding for the shareholders instead of buying a nearly perfected asset. So, buying it from being the last owner of a long string from venture to mid-market to sort of scaled private equity to us. We essentially want to buy the same asset, a leader in a small market, high gross retention, strong product market fit, but being an owner or two earlier. So, we buy more from mid-market. So, we're catching businesses as they're still growing a little bit faster, still have margin opportunity to improvement opportunity. So, more value for our shareholders to go, for us to go execute the graph for our shareholders. So, that is still the archetype that we lean. So the businesses in the portfolio definitely lean more growthy relative to the pre-2022 time. Now, whether or not it's, you know, I wouldn't distinguish too much if they're 10 or 20% growth, but they're not like mid-single-digit growers because we're just not tuned in that part of the portfolio. The other is to do, if we could deploy all of our capital in tuck-ins, it would be a great outcome. Just the time and motion study of that doesn't allow us to do it. We don't have a portfolio that's large enough, and the tuck-ins are small dollars. So we'd love for that to be a quarter to a third of the capital appointment, bless you, over a long arc of time. And that's always the first call of capital for us is the tuck-ins. And the portfolio is always a good mix. I mean, Janet and her team do a great job of looking at every portfolio of every sponsor, meeting with all the companies, having them in various sort of quality quadrants, you know, high business quality, attractive end markets, spend more time, obviously, in the box that's attractive and attractive. Active, cultivating the relationships, understanding the timing of those. So that has substantially improved over the last three or four years in terms of understanding the actionability and sort of having us be an attractive owner of those businesses.

Yeah, and you've had some great outcomes so far with central reach and subsplash, maybe a less favorable outcome thus far with ProCare. I guess when you think about this shift, what's been the hardest thing to underwrite? Is it the management capability? Is it go-to-market? Is it scaling? What's been difficult?

Thinking about this, we had to almost like through process elimination, like what's easiest, what's hardest. And so what we've been able to prove, and this is a learning sort of process. We're always learning, by the way. So we're never perfect in any deal. We're always trying to learn what we did well, what we did not do well. So we apply that to the next one, both internally and externally. We game film everything. Every deal of every size goes to our board for a postmortem after the first full year. If it's not good, it goes to a second year, and so there's discipline around this. I would say the easier parts for us to diligence is sort of market attractiveness, competitive intensity, market growth rates generally. That we generally do a pretty good job with. On the business-specific things, we've done a pretty good job on the tech stack, understanding if it's a hornet's nest, understanding the stability or scalability, extensibility of the tech stacks, obviously the product market fit, the right to win, the customer attribution of why they buy and why they don't. And so almost by process elimination, like what's left is like the go-to-market, and I think it's, and generally the commercial function, and this has like been the most difficult, challenging part of ProCare, and it's what is, I think the hardest part is like what's driving the historical success? Is it like a cult of personality of a leader that's driving it? Is it like real process, repeatability? Sometimes those things in a diligence process can be masked a little bit, or you can't get to like the real root of that. You can see the pipeline. You can sort of vet the pipeline, but how did the pipeline get built? And so that's been, almost by definition, what's left over, it's been the commercial parts.

Yeah, that makes a ton of sense. Jason, maybe turning to you, and you talked about maybe the opportunities being more kind of like 2027, but your leverage right now is about 3.4 times net levered. You guys have talked about having $5 billion of annualized capital deployment capacity. Like, what would you be willing to lever up to in this current environment?

Yeah, I mean, look, it's over, you know, past 10 or 15 years, we've levered up a couple times for some larger assets, and we've always de-levered after that. You know, we generate a lot of cash flow, so obviously we're mindful of the environment, but if there's, back to the point of shopping, if there's just some very unique opportunities to compound free cash flow for share, then we're going to do it. But we're also committed to delevering shortly thereafter. So we want to stay investment grade, of course. That's a table stakes for us.

And then, Neil, you mentioned buying back 8% of your shares this year. How are you thinking about that decision, whether to continue to buy back, where your shares are today versus deploying capital?

Right, wrong, or different? We think about the capital allocation is just math. There's no emotion attached to it. It's just math. Now, our math is what's better on a cash flow pressure compounding basis, five and seven years in the future, not like in the next 12 months. And so we're constantly evaluating, is it better to buy our stock back? Is it better to do capital allocation M&A, this bolt-on? And so that's the way we think about it. Now, mathematically, as we all know in the room, the numerator compounds faster than the denominator. So mathematically, M&A has an advantage, it just compounds faster. And so that's how we think about it. It's just a capital allocation tradeoff. Put in perspective, what we did a year and a half ago, the central reach and subsplash deals, those were, as I mentioned, they're like 22 times NTM, obviously different valuation era than we are in today. We underwrote those like nine or 10 times year five that I talked about earlier. At the time, we could have bought our stock back at roughly 15 times year five. So there's like a 50% risk premium, which I think is extraordinary risk premium. We don't need that 50% risk premium. We just bought our stock back at nine or 10 times or something, call it 10, around the 10 times, something like that. So that's where we can buy our stock back-ish today. So we need to underwrite to a risk premium that's 20%, 30% better than that. So we need to underwrite to six, seven, eight times EVD bidon year five to make the math work on a risk-adjusted basis. That's like in today's valuation environment. This is very dynamic because where we can buy our stock back versus that private prices, I mean, it's always just a teeter-totter balancing equation of capital allocation math.

It makes a ton of sense. So you talked earlier about like, your kind of like organic growth framework. And, you know, today, it's kind of like mid single digit plus, but we've talked in the past, you know, something closer to like high single digits, you know, clearly, this whole discussion around AI is going to be an enhancer over time. You're, you're buying businesses right now that are growing faster than the portfolio. Um, is there potentially more addition by subtraction in the portfolio today? And then, like, how are you thinking about the other pieces to get to that, like, high single-digit, longer-term growth profile?

So on the portfolio construct, um, you know, we've been pretty aggressive over the last, you know, six, five or six years on the portfolio. I mean, we sold 40% of our revenue in 21-22, basically exiting our industrial and project-based businesses. And so we're certainly not afraid to do something if there's value to be unlocked for a shareholder. The math, though, is we're not a public-private equity firm. We own things on the rope and balance sheet. From a tax perspective, there is leakage when things exit the portfolio. But we would, under certain circumstances, we would, if there's a buyer who'll pay X for a price, we have the tax leakage, we can redeploy it and put the shareholder back in a better position. When we started, we would consider that. It's a math exercise like I went to before. Unfortunately, that math is quite hard. And so it just doesn't pencil out that frequently. It basically never is penciled out, but conceptually it could. On the growth side, I would say it's interesting that I'm simultaneously encouraged and frustrated on the organic growth. I mean, we have the potential, very clear potential, pre-AI to be a high single-digit, consistent, organic growth business. It has been frustratingly difficult to get our chin on that. We're doing all the right things in the portfolio. The leadership teams, the leadership CEOs, the teams themselves, the product velocity to go to market commercial execution is all sort of theming in the right direction. And AI is going to be on top of that. And so if this is, you know, we just have to demonstrate that across a broad swath in a portfolio. Last year was frustrating because, you know, 17 or 25 of the businesses did what they needed to do, and then three or four of the larger ones had market sort of challenges. And so it looks like there's a macro thing when underneath the hood there's a lot of good stuff happening. And so I feel very comfortable with the lever of the organic growth. It's just taken us a little bit longer to get there than we wanted to get there. It's about systematic structural repeatability, not just flash-in-the-pan growth.

So we're bumping up on time. Assuming we're here a year from now, what are you hoping to be able to say to us a year from now based on where you see the business trending over the next 12 months?

I think it'd be fantastic if we could say, you know, answer the question the gentleman asked in the audience, like, what is the rate of growth acceleration going to be in organic growth? Because of AI, you know, we can be, hey, we got these 18 or these 12 businesses that are doing this, which adds up to that, which means there's X basis points of growth on the fleet enterprise growth rate. It'd be great to be able to say that in a year.

Hope you can.

Good to see you guys.

Thanks for coming.