Good morning, everyone. Thanks for joining us. I'm Larry Adams, one of the software analysts here at Rosenblatt, and I'm happy to have Pegasystems with us here today. We've got Don Schuerman, CTO, over there, as well as Peter Welburn, Head of Investor Relations. So welcome, gentlemen. We've done this before. There's Peter. Don, maybe just for starters here for some of the people listening in this morning, just give us a bit of a background on or an overview on Pega and a bit of a background on your role at Pega. That would be helpful. Excellent. So Pega provides enterprise software to really some of the largest organizations and government entities in the world. What we're really focused on doing is helping them in three areas of their business. One, streamlining their core operations. So the operations really run around their core products, core customer missions, optimizing the way work gets done, the processes, and the workflows to drive that work. The second big area that we are used in is the customer servicing space. So automating and managing customer service requests, especially as customer service becomes much more of a digital play, not just a contact center play. So managing how work flows in from self-service into the contact center and across the continuum. And then the third area that we are used is to improve engagement with customers by using AI Decisioning to optimize every interaction that you have with a customer to drive the right and most effective conversation. That might be to recommend the right product. It might be to move the customer into the right service area. It might be to retain a customer that you're at risk of losing, but applying AI in real time to make that happen. And that's all happening on a cloud-based platform. Pega is very much a platform company that drives AI Decisioning and workflow automation across enterprise scale. So just to give you a couple of examples of this, we just wrapped up our 4,000-person user conference in Las Vegas. On stage at PegaWorld, we had the U.S. Department of Veterans Affairs talking about how they're using Pega's workflow technology to move the way that they service the families of United States veterans from being very paper-based and cumbersome to being digital, to being automated, and being easy and efficient across the business. We heard from T-Mobile and National Australia Bank about how they are using Pega in that customer engagement space to really optimize and ensure that every conversation they have with the customer is driving value for both the customer and the business, really central to both of them in terms of how they grow their brands and drive customer loyalty and ultimately revenue. And then we heard briefly from Elevance Health, who's using Pega in the servicing space, both from digital servicing, but they're actually using us on the desktop across their customer service agents. They're doing things like using AI to automatically transcribe the call in real time so that the agent doesn't have to fill in fields. The AI actually does that all for them automatically. I think just a couple of examples of some of the clients working with us. That's great, Don. You've been in your role as CTO for a number of years now, I believe. Yeah. I have a joke that I refer to myself, and I think this is becoming increasingly true across the industry, where CTOs are always going to have to be technical, but a lot of our job these days is what I call being the chief translation officer. There is so much happening in technology right now that a lot of what we need to do and my team needs to do is make sure that, one, we're translating that to our clients and putting it in business value terms. So it's not just about, "Hey, Jimmy, I have this magic tool," but, "No, here are pragmatic use cases you can bring to your business," and then also acting as kind of the ear to the ground of our clients, really understanding what they're doing, what their business problems are, and making sure that it makes its way back into our roadmap. So my experience at Pega has always been sort of one foot firmly planted in our client side and one foot firmly planted in our technology. For sure. Chief Translation Officer, I think, is perfect, especially with the amount of change and innovation that's happening across the board with AI, which has really been around for a long time, but has certainly stepped on the accelerator in terms of profile in the last few years. So I think give us a little bit of a background on just prior to GenAI, what was sort of Pega's experience, and how have you been doing in AI, and then maybe bring us up to date as to where you are today. Because you guys have, with your Infinity 24.1, have a number of new capabilities that have been rolled out that were, well, previously unimaginable a couple of years ago. Yeah. Maybe it's worthwhile stepping back. At our conference that we just did, and by the way, if you go to pega.com, you can see some of the client conversations I mentioned, watch replays if you're interested in digging in. Those have all been uploaded. But Alan, our founder and CEO, broke AI down into sort of three forms that it takes. And the first is what we've been calling AI Decisioning. So this is using Blair, as you said, the AI that's been around for over a decade. And for us, that started over a decade ago when we acquired a company called Chordiant. And they brought an AI Decisioning capability. We did a lot of engineering work to integrate that directly into our platform. We basically rewrote the Chordiant software into the core data platform. That AI Decisioning, which is really a form of statistical AI, I like to think of this as left-brain AI. It's analytical. It does math. It calculates probabilities. So it can predict the likelihood that a customer is to churn. It can predict the relevance of a particular offer to a specific individual customer. It can predict that the workflow is likely to miss a deadline and therefore lead to a regulatory fine, right? So it's AI that's driving predictions. And we've been working with that for well over a decade here, in some cases at pretty massive scale. Some of our clients in the decisioning space run billions of customer interactions through that on a daily, weekly basis. So we've had that background and have had that experience of doing it at the enterprise level, which means building in explainability, building in transparency, building in things like bias testing so that you can show that you're actually not reflecting cultural biases in your AI decisions. That left-brain AI has now, over the past year or two, and I'm glad we finally got into a GenAI conversation, has now emerged into what we call right-brain AI, right? Generative AI, AI that creates these large language models that are really, really powerful but bring with them some of their own challenges. They're not as predictable. They don't have as much explainability tied to them. So organizations are looking for ways to exploit the power while being safe and ensuring that they're protecting both their customers' privacy but also their own reputations. We see use cases for GenAI splitting into two areas. One is what I would call productivity use cases. Being able to do things like summarize workflows and cases that are in the Pega platform, being able to coach a user who might be working on a particular process towards the best practices that are going to help them get it done faster. We've launched a product called Knowledge Buddy, which basically allows an organization to point GenAI at their internal knowledge base and content and get answers from a GenAI bot that are completely tied to their documentation and their content, not hallucinated or made up in some form. Those productivity use cases are really, really powerful, but I think over time they will become table stakes across organizations and, frankly, across vendors. But there's a third area of GenAI or AI use that we're really excited about, which is how can we actually help accelerate the innovation, the transformation that organizations are going through? And that transformative AI will maybe eventually I'll pull up a demo and show everyone a product that we have called Pega GenAI Blueprint. We really think that is going to accelerate the rate at which our clients drive their transformation and adopt Pega as sort of a strategic platform in that strategy. That's great. There's a lot of concerns or questions around monetization of AI, whether you're adding it into your products or creating new products. What's Pega's sort of approach to monetization? Yeah. So we've talked historically about there's going to be three levels of monetization with AI. I think first and foremost, we believe that AI will ultimately drive more usage of the core Pega platform. The way we license Pega is our clients put more decisions or more cases, more workflows on the Pega platform. That's driving more value to them, and that's driving more ACV into our business. So a lot of the design time tools that we built with GenAI, including Pega GenAI Blueprint, we've made free and available to our clients because we believe that that actually drives and accelerates their utilization of the Pega platform. And so that really monetization takes the form of us driving more caseloads and more volumes onto Pega, which in our subscription kind of consumption-based model grows the amount of usage we have on Pega. The second level for growth for us is move to cloud, right? So as we accelerate the move of our client base onto Pega Cloud, that also moves them into a better recurring revenue stream for us. And frankly, with the complexities of GenAI, for most of our clients, the only way they're going to actually be able to do this in a way that is scalable, safe, secure, or maintainable over time is to move into a cloud-based operations where we're handling all of the operations for them. We're starting to overload the operational complexities that a lot of clients can bring to bear. So the move to GenAI is just helping accelerate a move that was already happening of our clients onto Pega Cloud. And then the third area, there are capabilities, that knowledge-based capability I mentioned, our GenAI Coach, which can advise users who are working on a particular process. We will have upcharge. We do have upcharge for those particular products. But I do think the primary monetization for us in the near term is driving more volume onto Pega and driving more of our clients onto Pega Cloud. Maybe take us back to Blueprint a little bit because I think that's the one that I think is one of the more interesting ones that you guys have brought to market in that it is a way to really accelerate overall digital transformation within your customer. Yeah. So probably the best way to talk through Blueprint is to just show it to folks. So I'm going to pop up a quick demo because I think this is really the best way to explain it. Easiest way to understand it. Yeah. So this is Pega Blueprint. This is actually available online at pega.com. So you just go to pega.com/blueprint, give us your email address. You can try it out. We had this set up in kiosks, little kind of physical kiosks at PegaWorld for clients to try. And we had a couple of clients actually ask us if they could buy the kiosk, get a kiosk to bring home because they're kind of excited by what this lets them do. And what Blueprint lets you do is really dramatically accelerate the transformation and the build-out of an application centered around your workflows and your core business process. So if I'm a bank and I'm thinking about, "Okay, how do I change and I transform my lending?" So I can go in, I select myself as banking, and then Blueprint says, "Oh, okay, you're trying to do the banking application. Got it. I understand that. And then Blueprint's going to say, "Well, I need some more information," right? Banking is not just one thing. It's asset management. It's corporate banking. It's investment banking. It's retail banking. So what area of the bank are you working in? I may say, "Okay, now I'm building a retail banking application." So I've given Blueprint some more specificity. But I need to even go further. Well, inside a retail bank, I've got collections, servicing, financial crimes, lending, onboarding. So I'm going to select lending here. And keep in mind, we're pulling all of these options out of our own library of best practices, us and our partners having worked with our clients over many years to have some pretty good industry understanding. Based on that, I may decide that, "Hey, I want to do retail loan origination." And again, some of these in Blueprint are flagged with a little Pega, which indicates that we're bringing to bear some of our own history and best practice into this area. Once I select retail loan origination, what Pega is going to do is dynamically go through our library of best practices, as well as some of the general wisdom that you get from GenAI, large language models on the internet, and actually recommends now, "Oh, if you wanted to do a retail loan origination app, you would need to have, in this case, it's going to be three workflows or case types that you would need to have. You would need to do home loans. You would need to actually manage unsecured loans as well as secured loans." And this is all dynamic. So maybe this bank says, "Well, we're not actually interested in dealing with unsecured loans." So I can remove it from my Blueprint. And what you'll see is that actually even disappears from my little preview of the application here. So I'm dynamically seeing how the experience evolves. But not only do we tell you what workflows would be in the application, we actually give you a starting point for the workflow itself, right? So we've now actually laid out for a home loan application the best practice, optimal process a home loan application would go through. So rather than have to spend weeks ideating and bringing in subject matter experts and debating what steps we need to have, we're giving our clients an immediate starting point so they can now start thinking about, "Okay, well, maybe what needs to change, right? If they don't agree with this, they may decide that they don't actually need to do an existing customer check. Great. We'll remove that and we'll take it out of the step." But they can evolve this themselves. And the powerful thing in Blueprint now is that not only can a client see the workflow, they can actually preview the end user experience. So they can see that if I were a loan processor, here's what my system would look like. Here are the various fields. Here's how the fields would currently be laid out. If I wanted to see what that would look like, say, on a mobile experience, I can go to a mobile experience and get that experience. If I wanted to see what it would look like for a customer in a self-service way or maybe a contact center agent taking calls about the loan. And all of this is possible because Pega's designed our architecture to work independent of the different channels and front ends that our customers have. We even can, for more technical users, show them the underlying API that the workflow is exposing that makes all of this possible. And as I step through the various parts of the Blueprint, I can do things like see the fields, right, that are used in the case. Again, all of this is editable. So if I don't care, for example, about things like the employment details for my customer, I can remove it from here, right? And as I remove it, again, it just sort of disappears from my preview so I can see the change happening dynamically. We're capturing the other data models. So this is where the interfaces might need to live, right? Every workflow's got to plug into the customer's data environment. So this is sort of laying out where those interfaces live, who the various people are that need to be involved. And then once you finish this, you can take this Blueprint, import it into Pega's design environment, and we will automatically build out about 50%-60% of the application for you. This saves a huge amount of time for customers building new projects or working on new projects. Huge amount of time. Huge amount of time. Yeah. And is that, Don, is that? I mean, you've been talking about this for a couple of months. Is this in the market for customers now, or? Yeah. So this has been in the market for customers since April. As I said, you can go to Pega.com/blueprint and you want to try it out yourself. It's really also dramatically changing the way us and our partners engage with our clients because it really accelerates the point at which you go from a theoretical conversation of, "Hey, here's what Pega technology could do," to, "No, no, no. Let me pull up your business and show you what your business would look like in Pega." So we almost, in the first meeting or even in the first marketing outreach, we're having a conversation with our client about a specific problem Pega could solve and what their business would look like if we powered it on the Pega platform. And so not only does it accelerate the way we deliver, but it actually accelerates the very sales conversations that we have. Just one thing, Blair, in terms of clarity. We made this available to partners in April. We made it available to clients at the end of March. It's been out for several weeks, just on the timing, just to make that clear. Right. Right. And then presumably, once customers start to use Blueprint and connect it into their existing systems, it becomes easier and easier to do the next and build the next process. That's right, right? And the other thing that Blueprint works with is it works with the existing library of reusable assets the clients have, right? So the other thing that we see, Pega is designed and built as a platform. And what that means is, as clients build one use case on Pega, they're actually building out libraries of reusable assets, for example, interfaces to some of their core systems, interfaces to their security protocols that can then be reused in the next application. So they're getting economies of scale and keeps on building on the next. If we look at your end market, your existing customers, as an example, how far in do you think they are into their digital transformations at this stage? Just how much more work needs to be done? I guess, therefore, how excited are they about this kind of automation? Two comments there. One, so they're pretty excited, right? Again, I just—and I'm a little energized because I just spent a couple of days talking to 2,000 clients and seeing their faces light up and some ideas of what we can do with some of this stuff. One thing I would say, I think clients are realizing that digital transformation is not a journey with an end goal, right? So there has been, I think, a realization. There's no done for digital transformation. Digital transformation is really the process of becoming continuously optimizing, improving. We talk about this idea of an autonomous enterprise with our clients. What that really means is I've applied AI and automation in such a way that everything is being continuously automated and constantly surfacing up insights to the business of where things could be improved, where things could be made better. So one is this realization that maybe I'm not trying to get to an end with digital transformation. I'm just trying to get into an accelerating cycle of continuous improvement. That said, everybody's been on that journey in one way or another. But all of our clients still have, we operate at the enterprise scale, and these clients have legacy systems. They have technical debt inside of their architecture. So one of the things that we're pretty excited about is the potential of Blueprint and some of our other technology to totally change the speed and shift the curve on how clients begin to drive some of that legacy transformation and legacy modernization. So being able to pull best practices with embedded in their legacy systems into Blueprint so that they can get a Blueprint that would be the Blueprint to take a legacy workflow system that is hard to change, not very plugged into digital channels, accelerate its movement into Pega so that you now get something that you can plug into self-service and digital experiences. You can provide better employee experiences. You can use AI to make it smarter on an ongoing basis. That's that sort of—I think we're at this inflection point of acceleration. That's great, Don. Maybe just help us understand a little better the competitive landscape for Pega kind of at the moment and how some of these technologies are changing things or enabling you to differentiate in any way. Yeah. I mean, so the competitive landscape for Pega remains a lot of the same players that we see. If you look across those three areas, on the core operations side, we see folks like ServiceNow who are a little more on the employee side. We tend to be a little bit more on the customer-facing workflow side, but there's obviously places we overlap a little in the middle. On the customer service side, we jump into Salesforce and Microsoft Dynamics. Again, we often, though, coexist with Pega as the core workflow platform for customer service. And sometimes Salesforce is the front end. I just talked to a client who's had big sort of deployment in that way. And then on the marketing side, we see Adobe just managing stuff there. They tend to be the front end for a lot of the marketing experiences for our clients. Again, quite often, we're pretty integrated together with Pega as the decisioning engine and Adobe as the marketing front end. The other big competitor that we see across the board is clients who want to build stuff themselves, like clients who want to unleash engineers on the problem. We think, especially with what we've been able to do with Blueprint and the ability to accelerate a vision forward, we can do some of the rapid prototyping and business collaboration consideration in a way that you just can't do when you're trying to hand crank code and use GenAI assistant code. We can operate at a speed and a level of business engagement that I think clients really appreciate. So that's been a big kind of help for us in that space. The robotic process automation is an area you guys have been in for a long time. I know it's not a huge part of your business. What's happening in that area? Is there any impact from AI in that area? So we continue to see clients with an interest in RPA. I think in another conversation, the metaphor I used is RPA is kind of about filling the potholes, right, in a workflow. And we continue to have workflows that we work with our clients where that might be an initial stage to smooth things out a little bit, prove some value, maybe generate a little bit of internal funding to move to the next project. What I do see is with GenAI, and if we can continue to build on what we think is the promise of Blueprint, we're going to be able to accelerate repaving the whole road to the point that you might not even need to spend much time paving the potholes. If we can come in and pave the whole thing faster than you can get potholes fixed, well, why wouldn't you put down new tarmac across the entire experience? So I think RPA will continue to be an important, but I increasingly see it being adjacent to a broader, more strategic workflow redesign and digital transformation. I don't think RPA itself can carry the weight of a full enterprise transformation. Right. Right. Okay. Excellent. Excellent. Just a reminder, if anyone has any questions, they can send them to me at the bottom of your page, and I will get to them here on my screen. Something else that you guys introduced last year was Pega Launchpad, which is really an app development platform. Maybe just tell us how that's going, how you continue to innovate around that. Yeah. So we continue to drive that. We actually had a—it was almost like a speakeasy inside of our conference, a little hidden area that was the Launchpad room itself. And we had a couple hundred clients and partners really participating in that. We've seen really great interest from our partners. Launchpad is really positioned as the tool for partners of ours who want to offer their own fast-paced solutions. So who themselves want to offer a fast-paced solution to market, to build up that solution and then take it to market. And over the past year, we've seen our initial providers go live with Launchpad. We've seen some initial subscribers come onto it. So we're pretty excited to continue to build on that momentum and really drive that new area of growth into the business. Great. Great. And another area we were talking about in a prior call was around fraud detection for a lot of your banking customers in particular, I guess. Can you maybe give us a sense of what's the state of the art for you guys there, and what's the opportunity? Can AI help you here as well? Yeah. What I would say is I want to be really clear. I don't think Pega is going directly into the transaction-chain fraud detection business, right? They're providers who operate at sort of that high-volume fraud detection. We kind of come in at the next level of triage where we can take the signals that are coming out of those tools and begin to do separation of the signal from the noise. And what we're really doing is applying a lot of the same decisioning technology that in many cases those banks are using to optimize their marketing, sales, and servicing conversations with customers. Well, we can use that same customer understanding coupled with analysis of the sort of fraud signals, the potential fraud signals that are coming in, to help an organization identify what's really the true instances you need to address, what's the noise, where are the most priority ones based on customer impact, potential, etc., and then to connect that to the case management and the workflow. Because at a certain point in the fraud space, once you've detected something, you have to be able to demonstrate that you've taken the right actions to do that. And having a really nice structured workflow that has high degrees of governance and auditability built into it gives our clients real power to be able to say, "Look, this is the action we took. We did the appropriate steps. And by the way, we automate as much of that as possible so that we're doing it at the most possible efficiency for our client. You touched on compliance and governance. How does Pega approach how you're helping your clients with that? And the other side of the equation is there's been an uptick in regulatory legislation in Europe, in particular, their AI Act, which has just come into being. Any impact on you guys and potential follow-on in the U.S.? Yeah. So I think Europe has definitely been a little bit more aggressive or a little bit more functional than the U.S. has been in being able to put together more legislative approaches to AI or to AI. We'll see how the U.S. can keep up. The nice thing is we already have a pretty good baseline experience for this, right? So we have from the AI decisioning stuff, the legacy Chordiant technology, the experience of operating with AI and predictive models in regulated industries like banking, healthcare, telecommunications, employing the right degree of audit, transparency, explainability, testing in order to get comfort and regulatory proof against those models. So I think GenAI is a little bit different because the models aren't actually owned by our clients. They're more often purchased from OpenAI or AWS, Google. And what we've done there is try to put all of the similar kind of guardrails, controls in place. I also think for the near term, most of the GenAI use cases are going to be human in the loop or employee in the loop. In other words, I'm going to use GenAI to recommend something, but I'm going to put it in front of an accountable employee to actually validate it and move to the next step. So even with something like Blueprint, it's great because it's actually using what could be a bug of generative AI. The fact that it doesn't actually consistently give you the same answer. It kind of can have some randomization baked into the algorithm. We're actually exploiting that as a good thing because in Blueprint, we want GenAI to throw ideas up to a user to get them to think differently about a process. But then ultimately, the employee has to go like, "Yeah, this is what I actually want it to be. I'm going to move this over here. I'm going to take that." So we're leaning into these employee-in-the-loop use cases where GenAI comes in as either a productivity assistant or as almost like a creative innovation push to nudge the employee towards thinking differently about some of the things we're doing. Great. Great. How do you get this to customers? Some of these new functionality like Blueprint, Knowledge Buddy, Coach, some of this new technology you guys have introduced, what's the go-to-market approach here and how are your partners reacting? So with Blueprint, we made the explicit decision to put it on pega.com. Again, it's pega.com/blueprint. You can just go check it out. We can make it free because we actually want clients to adopt it. As we say, we made it available to clients. Peter, I'm going to correct you. I think it was in early May we made it actually available to clients. We had it for partners in April so they could kind of preview it and see what's coming. But we want to drive adoption. I think by last count, we've had close to 35,000 Blueprints created. We've had over 500 organizations participate with Blueprint. It's frankly the fastest adopted piece of technology we've had in the history of our business. So we're pretty excited about that. We've also been building the other GenAI capabilities we have to take advantage of the, I think, operational sophistication and ease of access that we've built into Pega Cloud. So for example, Knowledge Buddy, which is our GenAI tool that allows you to take an enterprise set of content and generate trustable answers from it, say, to help a customer service agent go into content about how to process claims and ask those questions pretty directly. We've made Knowledge Buddy available on Pega Cloud so that we can stand it up for a client in a couple of days. And we've done it in such a way that Knowledge Buddy is a standalone cloud service that plugs into their existing Pega applications, new Pega applications, non-Pega applications. Because we think for a lot of this stuff, we need to make it really easy for the client to consume and experiment with. I don't think anybody thinks that we have completely cracked the GenAI nut and we know where all of the use cases are. So we have, I think, pretty strong hypotheses. We get good feedback from our clients on our hypotheses. I think we're at the stage where this technology we need and want to see our clients using it, getting value from it, and then giving us the feedback so we can partner with them to make it better. Great. Great. Excellent. Well, I've got a question that's just come in here, and it's kind of more of a Peter question. I'm just going to read it off directly. Peter, can you describe your capital allocation strategy? It appears that you have been repurchasing shares and issuing dividends. How do you trade this off with your investments in your business? Sure, Blair, thanks. So we've been doing a really nice job expanding margins and continuing to grow the company. So we did record free cash flow at the end of the year, which was great, over $200 million. In terms of the capital allocation strategy, we have a $500 million convertible debt obligation we need to take care of in March of 2025, so that's coming up. We do pay a modest dividend as well. And we're going to continue to take a look at, as we move forward with the business, where we're going to go with capital allocation. I'd say that all options are on the table for us, but right now, a big priority is going to be paying off that convertible in March of 2025. That's a consideration. As we move forward and get closer and closer to that date, we'll make a decision on what we do there. That's definitely something that's top of mind for us in terms of capital allocation strategy. Okay. Investing in the business, maybe just highlight your current view on your R&D investment levels and your go-to-market investment levels. Yeah. So as we talked about at the investor session that we did earlier this week, we're going to continue to get a little bit of operating leverage on the R&D line. We'd like to improve that maybe another percentage point as we move forward to get to a longer-term guide on that. And then in terms of the overall business, we continue to make improvement on making sales and marketing more efficient. We've had some great momentum there. We'd like to get R&D to be at about 30% of revenue, and we're continuing to make progress along that as well. And certainly, we want to grow the business too, and that's a big focus area for us. So if you think about a conceptual framework for us, it's Rule of 40, which would be the combination of free cash flow margin in addition to growing our ACV growth rate. Okay. Great. Thank you. Don, we've got a couple of minutes left here, but I wanted to ask you another product area question really around Process Mining and maybe just describe a little bit about sort of the state of the technology and Process Mining at Pega. And I don't know if there's any opportunities or implications with AI in this area that monitoring it could help with this problem. Yeah. So as some may know, we did an acquisition a couple of years back, probably two years, Peter. You will know the exact dates. I think it's 2022 that brought process mining into the portfolio. As with every technology that we acquire, our preference is to integrate and in some cases even rewrite rather than bolt on. So we took in the process mining technology. We spent about a year integrating it really tightly into the core Pega Platform, into Pega Cloud so that it sits inside the same core architecture and operating model. And then we've spent the last year really driving some of our initial adopters of process mining. We're really, I think, increasingly thinking of it as process intelligence because the other thing that we had prior to the acquisition is a form of process mining that is called task mining. What Task Mining does, while Process Mining tends to look at what's happening in the system logs, right, it kind of is a back-end-centric view. Task Mining looks at what the users are actually clicking on the desktop. So it's sort of a front-end-centric view. And we had that Task Mining through a product called Workforce Intelligence. We brought in the Process Mining. And so we now have this interesting convergence of both the front-end information of what's happening and the back-end information that allows us to get a much richer perspective on what the process is. The way we view all of this process intelligence, and I had some interesting conversation with some analysts about this at PegaWorld, we don't look at it as a one-time thing that you do as a process discovery exercise so you can figure out what your process you want to implement is. We actually think with Blueprint, we can show you what the process you want to implement is pretty quickly, and we can do it based on best practices. What we see process mining as is a continuous improvement lever then on the back of that, continuously monitoring your processes and surfacing up new opportunities, right? In the near term, we've actually introduced the ability in Blueprint to import some of the outputs of process mining so that I can take the output of process mining, feed it into Blueprint, and then I'm starting to go and move on my Blueprint stuff. We also are using GenAI to give people the ability to almost talk to their process intelligence, ask them process questions. Where am I seeing bottlenecks? Where do people end up doing workarounds? What opportunities are there to improve? Because ultimately, we think process mining is about part of our continuous improvement life cycle so that our clients aren't just implementing a process and forgetting about it. They're actually then using that data that the process generates to get smarter and smarter and more efficient over time. Really interesting. It's definitely taking steps towards what you guys have coined as the Autonomous Enterprise where you're continually monitoring and then also adjusting using technology. So this has been really great to get to see the innovation that's come out of you guys in the last year, and the customer response seems to be very positive. So that's great. So appreciate your time, and we'll catch up with you again soon. All right. Thank you all. Thanks, Don. Thanks, Peter.