Pegasystems Inc.

Over to Peter Welburn, Vice President of Corporate Development and Investor Relations. Please go ahead.

Thank you, Ellen. Good morning, everyone, and welcome to PEG Systems Q3 2026 earnings call. Before we begin, I'd like to read our safe harbor statement. Certain statements made during this call and the accompanying materials may be construed as forward-looking statements as defined in the Private Securities Litigation Reform Act of 1995. Words such as expects, anticipates, intends, plans, believes, will, could, should estimates may forecast and similar expressions are intended to identify forward-looking statements. These statements speak only as of the date the statement was made and are based on current expectations and assumptions. Because these statements relate to future events, they are subject to risks and uncertainties that could cause actual results to differ materially from our current expectations for fiscal year 2026 and beyond. Important factors that could cause actual results to differ materially are described in the company's press release announcing our Q3 2026 results and in our filings with the Securities and Exchange Commission, including our annual report on Form 10-K for the year ended December 31, 2025, as well as other recent SEC filings. Investors are cautioned not to place undue reliance on these forward-looking statements. Except as required by law, we undertake no obligation to update or revise any forward-looking statements to reflect subsequent events or circumstances. In addition, non-GAAP financial measures discussed on this call should be considered in addition to and not as a substitute for financial measures prepared in accordance with GAAP. Constant currency measures are calculated by applying the September 30, 2025 foreign exchange rates to all periods presented. Reconciliations of GAAP to non-GAAP measures can be found in our earnings press release. With that, I'll turn the call over to Alan Trefler, founder and CEO of Pega Systems.

Thank you, Peter, and thank you to everyone joining us today. Before Ken covers our results, I want to just touch on the AI market context and why it's a major long-term opportunity for Pega. Our technology is differentiated and very relevant to the times we're in. However, candidly, our execution this year has not kept up. We are making real changes to better capture the opportunity and Ken will be providing more detail in a moment. I would say the market status is confusion and a search for value. Enterprise leaders are facing tsunamis of AI hype. This is the most chaotic and confused technology market I've seen in my career. It makes the internet bubble of 2000 look like it unfolded in slow motion. There is real promise in AI and occasionally real delusion. This quarter, Salesforce announced CloudForce, essentially tying its future to Anthropic, a company that has said publicly it is going after all of Salesforce's TAM, their targeted addressable market. And then in September, Anthropic called for a pause in AI development. And leaders at OpenAI and other frontier labs chimed in to support the idea. But in the month since, Anthropic has released three major cloud model changes. OpenAI has also released three major GPT models. Google has released a new Gemini model. That's not counting the new models from Mistral, Quan, DeepSeq, or model labs that are using things internally they haven't released yet. So I guess this is what a pause looks like these days. No wonder everyone is confused. The leaders I spoke to at businesses have actually stopped chasing the latest model. They want AI connected to business operations in ways that deliver measurable value while maintaining governance and controlling costs. The market is full of ingredients and enterprises need a recipe. That's what Pega delivers. And the changes that Ken will talk about are focused on getting that recipe directly to the customers and senior leaders who need it. Now, much of the AI industry is selling models and tokens. Pega is offering something different, a structure for AI that delivers predictable outcomes and predictable costs, built on how enterprises really operate. Our architecture harnesses the power of LLMs and our own statistical AI to transform how enterprises design, build, and run the workflows and decisions that power their businesses. Now models are an important agreement, but the recipe for AI demands structure you can put around the model to provide operational context that tells it what to do. Our workflows wrap AI in a control and governance structure that feeds exactly the knowledge and context it needs at the right time. As Forrest has put it in his very recent report on enterprise AI platforms, workflows, not insights, are the value driver for agentic AI. We apply that structure at every stage of AI transformation. At design, we help clients reimagine their workflows and decisions for an AI world. We build to help create and maintain the applications in a way that's understandable and accessible. And we run them to create agentic workflows that behave predictably at scale. So leaders I speak to They want to be designed the way they work. And Blueprint harnesses AI to solve a problem that no one else has, giving business and IT teams a way to reimagine their operating model together at AI speed. It takes the raw power of generative AI and gives it a structure built on decades of Pegas workflow expertise and industry best practices. Blueprint combines the raw power of models like Claw, GPT, and Gemini with the decades of our knowledge from the work we've done with leaders in various industries. It helps establish the rules, workflows, policies, and operating model upfront before an application goes into production. The result is a repeatable recipe for how businesses should run, translated directly into working systems. And here is what much of the AI play misses, that 95% plus of work a business does each day repeats. Processes themselves will be transformed by AI, but once you figure out how to process a dispute or best onboard a new customer, you want to do it the same way at scale. Letting AI reasoning run roughshod over that repeatable work is risky and expensive. Blueprint uses AI to capture those repeatable processes. We designed them to be far more efficient and then run them consistently at massive scale without a massive token bill. Not only is this far more cost effective, it is exactly the right approach for AI governance. By creating the workflows you plug directly into the cloud or GPT or AI tool of your choice with MCP connectors, You're building workflows that tell agents exactly what they're allowed to do, what data they can access, and what rules they must follow. We believe the right way to govern AI is to design it into the processes from the start. Now, this is exactly what Blueprint does and the packet technology does. And while others are selling control towers or kill switches to manage problems after they happen, we consider that madness. It's addressing the problem too late and in the wrong place. Our clients can't afford to rely on a kill switch. With our approach, they never need one. So the key here is structure. Now, by helping clients finish design their recipes in days and load Blueprint into Infinity to build the application, they are able to be able to run with agents like Copilot, Cloud, and Codex. We also let clients bring their own model. If for some reason they have something, either an activity or they have a preference. And we have added into our development environment more than 50 agent skills. So the developer's environment and the Pega environment operate in a collaborative and comprehensive fashion. So developers can get the speed of agentic development, but we provide the structure that makes it understandable, Reusable and Scalable. Now, we understand that one of the pitches AI companies are making is, you know, code it yourself. And the reality, we've competed against code it yourself for decades. And of course, AI coding tools are powerful. We use them ourselves. And we've built them into the AI assistance in Infinity Studio. But this doesn't change the underlying question. AI agents generate lots and lots of code, millions of lines of code than we've ever seen before. And much of that code becomes very hard to understand and even harder to change six or 12 months later. That is at the heart of the Pega difference. In Pega, the logic stays visual and transparent, not buried in code so dense that only agents can read it. Business and IT users can look at the workflows, the rules, and the configuration in Pega and see exactly what their application does. This is what it means to build for change. Now, at runtime, Pega applications run agentic workflows that orchestrate agents with control and governance. In Infinity, any agent, a customer service chatbot, an employee assistant, even when outside of Pega, can discover and run Pega workflows and decisions. They don't need to re-reason through every step. Blueprint has already done that for them at design time. And we've put the human in the loop, blessing the design, as opposed to sticking them in every time a transaction runs. Pega agentic workflows call for agents to perform specific tasks, like reading a document or researching a case or client history. And the workflow sets the task constraints So each agent is controlled in what it can do and what it can see. This small focused agent approach obviates hallucination risk and uses far fewer tokens. That's how we can offer a fixed case price with no token charge. And I'm getting a lot of enthusiasm from clients as to this approach. Now, at PegaWorld, I compared this approach to a chef using a pet and many more. At runtime, the system executes those recipes as predictable workflows, calling agents only at the steps where they add value. Deterministic work stays. By the way, this is also hundreds, maybe thousands of times more efficient than running a continuous reasoning session on graphical processing units. Clients reimagine the process and then run a successful recipe thousands or millions of times. Design once, reuse many. AI gets the workflow right at design time and is invoked discreetly at runtime. with structure and governance building. I think the industry is recognizing our differentiated approach. Just in the last several weeks, Forrester was named a leader in the Forrester wave. Pega was named the leader of the Forrester wave for AI platforms, sitting next to companies like Amazon and Google. As I mentioned in that report, they wrote that workflows, not insights, are the value driver We strongly agree. And last month, Gartner again named Pega a leader in the magic quadrant for business orchestration and automation technologies, or what they call BOTE. I think you're going to hear that term a lot going forward because it really reflects the power that AI needs to bring to organizations. Gartner built this category on the idea that the combination of process orchestration Workflow orchestration and energetic AI create the foundation for predictable enterprise AI outcomes. And in terms of these two recognitions, Pega was the only company named as a leader in both. You can find both reports on pega.com. But the most important recognition is what we get from helping clients to maximize the value of their current AI investments and their goals. Together with our partners, we're delivering predictable outcomes that candidly just weren't possible before, all with predictable costs. At a large government agency, Pega workflow turns unstructured information into structured work, automatically processing documents and surfacing only the exceptions that require human judgment, helping people work more effectively while maintaining enterprise-grade governance and control. At a major U.S. bank, Pega's government workflows and specialized AI agents has cut complaint resolution times from 10 days to 10 minutes. In a customer critical and highly regulated process, Pega agentic workflows orchestrate our agents and third party agents to intake documents, classify requests, summarize histories, perform research, and determine next best action recommendation. Employees have embraced this new workflow right away. They report higher productivity and enthusiasm for capabilities that give them more time for judgment and customer outcomes rather than administrative. So these results confirm to us that enterprises want AI and that having AI that delivers predictable outcomes and predictable costs with governance built in is absolutely the right way to go. And you know, it's interesting because the market is chaotic and there's more hype, more tokens, more models. being discussed and feared by organizations. But with Pega Blueprint, with Pega Infinity, we can deliver outcomes, and we think that's incredibly important. I've also learned something from the dozens of successful clients I've spoken to in the last quarter. We have the right technology, but in a market this noisy and fast, we need to do a better job at directly helping our clients find clarity and keep up Success happens when we have deep executive and technical engagement, a clear message that cuts through the market values, and technical experts on the ground. So while the quarter showed improvement from the first half, we don't think the growth of the quarter anywhere comes close to reflecting the opportunity in front of us, and we take responsibility for that. Buying decisions have become more complex, and we can be more consistent in helping clients We're acting on what we learned. We've moved accountability closer to clients with a regional go-to-market model. We've made steps to unify our message, and we are creating a dedicated team to accelerate adoption of the latest AI capabilities, bringing operations teams together to simplify and improve how we plan and operate. These are execution changes, not a change in strategy. We remain focused on helping enterprises apply AI to workflows and decisions, and helping I'll hand it over to Ken to walk through the quarter's results and details of these operational changes.