Welcome. Thank you for joining Unisys for our 2026 Investor Day. We're broadcasting live today from our headquarters in Blue Bell, Pennsylvania, where our leadership team is gathered together and is excited to give you a look at our solution portfolio, our innovation, delivery, and go-to-market. Let me walk you through the agenda for today. We'll start with our Chief Executive Officer, Mike Thomson. He'll share some of the trends he's seeing in the market today and talk about our strategic direction overall. We'll hear from Chris, our Chief Operating Officer, who'll talk about the delivery transformation here at Unisys. He'll also speak to you about our approach to artificial intelligence. After Chris, we'll hear from some of the leaders of our segments, starting with Sean, who leads our Enterprise Computing Solutions segment, which includes our ClearPath Forward ecosystem. After Sean, we'll hear from Manju, who leads Cloud, Applications & Infrastructure Solutions. We'll take a quick break before we're joined by Patrycja to talk about Digital Workplace Solutions and some of the exciting market opportunities there. After Patrycja, we'll hear from Joel, our Chief Commercial Officer, and Teresa, our Chief Marketing Officer, who'll speak about our partnership strategy and go-to-market. We'll wrap up presentations with our Chief Financial Officer, Deb McCann. She'll go through our financial strategy and some of the medium-term targets we hope to achieve over the next few years. After Deb, we'll have a live Q&A session hosted by Mike, Chris, and Deb, and we'll be answering questions that you submit to the function on the side of the webcast portal throughout today's event, and we encourage you to do that throughout the event as questions come to you. Anything we don't have time to get to, we'll follow up on, and you'll hear from someone on our Investor Relations team. In between sessions, we'll also be sharing compelling stories from our clients, and we're really excited for you to hear about some of the breakthroughs we're driving for them every day. Before we get started, I want to direct you to the disclaimer slide in the presentation for today's event posted on our investor website. As a reminder, today's event contains estimates and forward-looking statements within the meaning of the securities laws. Current expectations, assumptions, and beliefs underlying these statements include factors that may be beyond our ability to control or precisely forecast. Future results could differ materially from our expectations, and we do not assume an obligation to update these statements in the future. For more on these items and a discussion of risk factors, please see our recent SEC filings. We may also reference certain non-GAAP financial metrics today, which we provide to give you a more complete understanding of our financial performance, but they are not meant to be a substitute for GAAP. With that, I'll turn it over to Unisys' Chief Executive Officer and President, Mike Thomson. Hello. Welcome, and thank you for your interest in Unisys and participation in this event. Many of you, we've been engaged with Unisys for quite a while, and we talk to you on a regular basis. Several of you are new to our story and are trying to understand better who we are and what we do. Good news is we have something for everyone embedded in our discussion today. Client focused and future ready is more than a rallying cry for our team. It's the essence of the strategic objectives that we established in 2021 and the basis for the strategic decisions we've made around our people, process, and solutions. The majority of the conversations that I have with clients, from Fortune 500 CIOs to government CTOs, come back to four basic things. They want AI embedded into service delivery from day one, not bolted on afterwards. They want results that tie to measurable business outcomes, not just activity metrics. They want a partner they can trust to navigate complexity over the long term, and they need the ability to move faster than their competition. These are not aspirational ideas. They're commercial imperatives, and they're reshaping IT services right now, and exactly what we've spent the last three years positioning ourselves to deliver. Today, you'll hear from our leadership team how these imperatives are reflected in our portfolio and our go-to-market approach and in our delivery. I hope you'll consider these imperatives as you listen to today's content. Just taking a look here at my agenda for today. As I've mentioned, we have some new folks joining us today, and we want to orient them and provide a refresher for everyone else as we're talking about who we are as a company. As far as progress is concerned, I think we're in about the middle innings of the full implementation of our strategy, so we'll share what we've accomplished and what's left to do. We'll illustrate the alignment of our strategic objectives and how they've evolved. We'll also illustrate the importance of technology implementers and orchestrators, as we believe that's the missing ingredient for full realization of agentic AI. Lastly, we'll give you a few takeaways to evidence the opportunities that are in front of us. Before we get started here, maybe just a little look into who we are. Our roots trace back over 150 years, which means we have a long track record of navigating change and creating and applying innovation. The current construct of Unisys was formed in 1986 through a combinations of Burroughs and Sperry, two of the most influential names in computing history. We're approximately 15,000 associates strong, operating in over 100 countries. We're not a startup making promises. We're a global enterprise with a long track record of delivering securely at scale in the most demanding environments in the world. The recognition we've earned reflects the work our people do every day. It comes from many sources, including industry analysts, clients, and business publications. Why does this heritage matter for today's AI conversation? Clients aren't looking for just experimentation partners. They're looking for partners with proven experience. They're looking for a trusted institution that they can bet their careers and business on, that's who we are. We have deep engineering roots and experienced hands in everything from hardware procurement, installation, and break fix, skills and solutions for asset management and service desk, proven experience in infrastructure digitization and application design and creation, a strong security reputation with the basis for that what we build into our solutions and services. We provide managed services that cross the continuum from mainframe to agentic computation. Coming out of our last Investor Day, we established a foundation in 2023. We've committed to a multi-year transformation. Today, we'll illustrate that foundation has been set and that we're in execution mode. Furthermore, our strategy is applicable today as it was three years ago, even though the market's changed considerably. First, we believe we've reset how the market sees Unisys. We're now positioned as a company with AI-enabled solution and as an outcome-based partner. That reset is validated externally by our climb up the analyst rankings into leader or challenger positions in our priority solutions, and you'll see evidence of that in Chris's presentation. We believe this is a significant first step to growth, as the analyst rankings are often the basis for getting invited to client RFPs. Second, we've launched a new generation of AI-enabled solution and stood up our agentic application factory that creates production-grade agentic apps and agents in weeks rather than quarters. We've upskilled at scale and continue to invest in educating our associates on the latest and greatest set of AI tools. Application of that tooling continues to augment and amplify our associates' capabilities. It avails them of a digital workforce to streamline operations and create capacity for scale. Our capital structure continues to improve based on strengthened profitability obtained through transitioning clients to higher value work, the application of our automation-first strategy, and a disciplined approach to allocation of capital. Lastly, we have a defined path for free cash flow, which inflects positive as pension headwinds are reduced and stabilized. The strategy that we've evolved from 2023 to 2026 really takes us from defense to offense, and we believe it positions us for growth. We're now evolving that framework to operate an AI-first mentality. As I mentioned, we think the missing ingredient for the full realization of agentic AI is the application of implementation expertise. From an industry perspective, we have the compute power and the tooling we need to create a new operating model. From an adoption perspective, companies don't know how, where, and when to use the tools, how to embed them into their existing hybrid ecosystem securely and effectively, or what the total cost of adoption means, and that's where we come in. Our addressable market has evolved into new adjacencies. Implementation of a digital workforce via the use of agentic solutions and AI-enabled workflows are opening new doors and new opportunities. Our solutions have evolved into AI-enabled and not bolted on. AI is now considered a default operating layer across the hybrid technology stack, and our focus around the ClearPath Forward ecosystem has expanded beyond services and industry solutions. We've witnessed firsthand from our clients that the ClearPath Forward ecosystem is a pillar of enterprise compute and an AI-first data backbone for mission-critical workloads across highly regulated industries. Our land and expand strategy is evolving into precision targeting, and we're expanding the use of our partnership engine using focused named account selling and doing it alongside our alliance partners, OEMs, hyperscalers, and frontier model layers of the technology stack. Margin expansion opportunities continue to evolve due to the agentic-first workforce transformation. A mix of digital and human labor ultimately delivers higher quality and enhanced experience without proportional headcount supports to continue that margin growth. Operational excellence evolves into unlocked free cash flow and provides a pathway to deeper investments into the business. This, of course, would be on the heels of our expected structural payoff of the U.S. pension, a potential debt refinancing, which we believe we could do more effectively with improved EBITDA and leverage ratios, and a continued disciplined use of working capital. Net net, the strategy we outlined in 2023 built the foundation for what we're discussing today, which we believe will help us realize a return to growth. The continuation or sharpening of our strategy based on what we've learned and the changes in market conditions are powered by an AI-first delivery model that's already in motion and accelerating. Client-focused and future-ready, again, more than a tagline. It's the basis of our strategic decisions regarding people, processes, solutions, and how we deliver value to our clients. AI is reshaping what clients expect from their IT service partners, not just faster delivery, but better and smarter outcomes. We believe companies that embed AI into their engagements and measure success by results will set the standard for service delivery of the future. What's required are solutions with embedded AI and an AI-first delivery service team. Embedded AI means accelerating delivery, reducing defects, and freeing talent for higher value advisory work, and not just in the delivery of IT services. That's what makes agentic AI different. It's the democratization of technology beyond IT, and it extends the application of technology into client delivery and to other functional disciplines. Outcome-based results baked into dynamic XLAs are becoming increasingly mainstream and rewarding partners who tie pricing to results, not hours. Trusted strategic partnerships with deep roots and a long history of operating and executing BAU run is required to augment operational execution with agentic features. We've got a deep engineering moat to leverage that AI with an operating stack that needs to work securely and in unison with the existing technology. Our approach also seeks to include a roadmap for continual improvement. Simply put, firms that demonstrate continual improvement and measurable ROI win renewals and expansions. Why now? I want to be direct about what we believe is happening in our industry. Agentic AI is not the next iteration of AI. It's a fundamental step change, the most significant market expansion event in the history of IT services. We believe that, and we think the market data backs that up. As you can see on this slide, analysts are calling for $450 billion of economic value to be created by agentic AI by 2028. 1 in 3 enterprises' software applications will contain some form of agentic AI. Usage of agentic AI is expected to increase tenfold. The agentic AI market is expected to grow at a 44% CAGR over the next decade. Enterprise clients need a trusted partner to help build, deploy, and orchestrate intelligent agents at scale. We believe companies that position themselves as architects of the agentic enterprise will define the next generation of IT services. The question for the room, and the question I'm asking every day, is who earns that role? We believe Unisys is one of the very few companies positioned to win it, and you'll hear that evidence throughout what we go through today's presentation. The timing of this event matters. We're not here to tell you about a plan. We're here to show you a business that's already executing this strategy with paying clients measurable outcomes, a commercial model that's evolving to capture newly created value. The debate is over. Three years ago, clients were asking whether or not to invest in AI. Today, the question is how to do it, how to do it fast, safely, and at scale. That shift has fundamentally changed the sales conversations. We've organized our entire approach to AI around a 3-stage framework, Develop, Transform, and Orchestrate. This is not marketing language. This is how our teams think about client engagements and how we measure progress. Develop is where we help clients build the foundation, data readiness, AI governance frameworks, security architecture, and the infrastructure required to run AI workloads reliably. You can't run before you walk, and a lot of our clients are discovering that their data environments and governance structures need to work before AI can deliver at scale. Transform is where the business value starts to become visible. Additional influxes of agentic workflows, intelligent service delivery, and AI-augmented workflows are all examples of that. This is where incident resolution goes from hours with multiple engineers to minutes with a single engineer. This is where our clients start to see the ROI of AI. From an orchestrate perspective, the destination is running at scale. Enterprise-level companies securely and responsibly with an eye towards continuous optimization is the right end state. Adopting AI as an ongoing process, not an event. In a perfect world, this is self-funding, and the advantages continue to compound, which allows clients to operate faster and cheaper than their non-AI competitors. Today, you'll see examples of how each of our business units map directly to this framework and how it plays out with real clients and real outcomes. Lastly, I want to call out a few items that hopefully you can help to listen for today's content, and we'll tie in some objectives that we're trying to obtain. These points should stitch together one Unisys. You'll see the continuity of our strategic objectives. First, we're in the midst of a multi-year transformation, reinventing ourselves as a leader in the future state, mission-critical IT solutions built for an agentic world. Trust, scale, security, and depth of enterprise knowledge is a combination most AI-native competitors simply don't have. Trust takes decades to build. We've earned that. The market opportunity is real and now. The agentic AI is not a 2030 story, it's a today story. Second, our AI go-to-market model, develop, transform, and orchestrate, gives us access to a larger addressable TAM. Every client engagement has the opportunity for new scope and expansion, which is how we expand wallet share without expanding headcount proportionally. You'll see specific proof points of this from each of the business units. Third, our people are the engine for our AI-first delivery mindset. We continue evolving our workforce through an AI-first capabilities and delivery mindset. AI-trained associates, future-ready solutions, and the digital plus human delivery mix are how we scale repeatable outcomes without proportionally scaling costs. Fourth, we're at a financial inflection point. We believe the pension liability that's hindered our capital structure is on a defined path to resolution. Margins are expanding, new business TCV is growing, and we believe our strengthened balance sheet can now be used to fuel growth. With that, you'll hear from Chris Arrasmith on future-ready solutions and how we're building an AI-first delivery organization that scales without proportional headcount growth. Sean Tinney's going to explain how our ClearPath Forward ecosystem with five nines availability and AI at its core is modernizing without disrupting $56 trillion in transaction flows. Manju Naglapur is going to show you how Cloud, Applications & Infrastructure is becoming the delivery engine for the future of agentic enterprise. Patrycja Sobera will speak to how Digital Workplace Solutions is giving employees back time and making infrastructure and physical AI a real competitive advantage. Our commercial team with Joel Raper and Teresa Poggenpohl will show you how we're rewiring our commercial engine to close faster and go broader. The through line here is simple. Unisys is not transforming for transformation's sake. We're evolving our transformation to better serve our clients, grow our business, and create sustainable value for our stakeholders. I'm confident you'll see evidence of this throughout today's presentations. I'd now like to hand it over to Chris Arrasmith, our Chief Operating Officer. Thanks, Mike. Thanks to those of you with us today for investing some of your day with us. Really happy to be with you. We've got some ground to cover. Let me briefly review the agenda with you. First, we're going to talk about our AI transformation journey at Unisys and the building blocks of the operational transformation that Mike was just alluding to. We're going to talk about our capabilities and solutions, our AI-powered workforce, the convergence of human and digital labor, our growing ecosystem of strategy around partnerships and alliances, and finally, some elements that we believe make up what we call the Unisys Edge. When we think about the pillars by which we define our AI transformation, we've been on this journey for some time, and you wouldn't be surprised to hear that some of the elements of that are really baked into the foundation, not as an add-on, but core to the mission of the company and interwoven throughout all of our solutions and services. We start by thinking about moving in an agentic workflow orientation from operating systems of record, in some cases that have been around for decades, in other cases, more recently developed, but moving from those systems of record to creating systems of action, taking advantage of the data sitting inside of those systems. We're creating that future with our workforce. I mentioned the convergence of human and digital labor, really operating in a mode whereby we enable our associates at Unisys to have the skills that are required to operate new tools in a new paradigm driven by AI, such that they can work together with digital workers, the digital labor footprint of the future that will bring our solutions to life. Our partner-led ecosystem has been growing over time. We anticipate that it will continue to grow as time passes. We'll talk in a couple of places today about tools of today and tools of tomorrow. Lastly, all of what we are doing is really built for scale. We've talked already about the length and breadth and depth of our expertise in industry. We are operating in a phase of repeating and continuously scaling our capabilities over time to bring value to clients and not just in pilots. When we think about all of the solutions that we bring to the market, the portfolio has an array of AI-infused capabilities. In fact, we also understand the way that clients rely on us to bring forward additional capabilities as well. We are answering for today's problems and helping clients to anticipate what we believe are their next series of problems to solve. In some cases, that relates to the fundamentals of the foundation by which they establish their AI strategy. How they think about the data that's nested in systems and the ways that they need to bring that data set forward to take action against it. In fact, there are likely workflow transformations and processes that have to be reimagined, redefined, and re-engineered. We need to consider the way that we give agency and create new agentic capabilities for the operation of those transformed workflows. We need to ensure that we've got an operating model that includes the infrastructure requirements in what is expected to only be an ever-expanding hybrid mode of operating, and that we do so responsibly and securely over time. Again, while we infuse those things into our defined solutions of today, we offer clients alternating and differing journeys based on where they find themselves in their own transformations. When we talk about the pillars of our go-to-market strategy for AI, you've already heard about develop, transform, and orchestrate. We want to give a couple of examples about what we mean inside of each of those capabilities. When we think about data and the development of data environment readiness, we are applying that in one instance to a large university data system, unlocking the power of that data to make that longstanding knowledge base available to all of the constituents that that university system is serving. In addition to that, for a multinational banking client, we're taking advantage of the similar view of long resident data in systems to create pathways to the future, unlocking the potential of those systems for customer experiences and the propagation of skills across that client. In transformation, we are looking at agentic workflow deployments in a growing number. We believe that the future of agentic is only going to expand. Today, we look at that from a proactive issue detection and resolution to ways that customers are operating and interoperating with those systems on an ongoing and daily basis. In some instances, what are previously thought to be well-worn workflows as it relates to complex infrastructures are achieving new heights of efficiency and reliability and security, thanks to agentic operating models. In orchestration, when we think about continuous optimization, you wouldn't be surprised to hear that we expect to be better tomorrow than we are today at operating complex systems and infrastructures and ecosystems. Importantly, as I mentioned previously, the tools of today are not necessarily the tools of even tomorrow, and we believe that a strong and growing ecosystem enables us to create optimization opportunity on an ongoing basis, driving value for our clients. When we think about the workforce of the future and, again, the convergence of human and digital labor, we consider that past roles, such as analysts, customer success leaders, systems architects and engineers, testers, subject matter experts, they're all necessarily part of a new reality, and they need to be recontextualized in the face of that new reality. When we think about the way that those roles I just mentioned become the future AI strategists, the outcome-based leaders, the architects of new transformed workflows, the validators of AI-based systems development and testing, and lastly, the translators of expertise and depth in domains, and you couple that with real-time data insights that are available through the use of today's AI tools, we are talking about a workforce that is prepared for making real the promise of the solutions that we have been developing over these last several years. It's that convergence of the solution readiness, its applicability to the market, and the ways that our associates drive value through those AI tool sets and create new roles for themselves that we believe expands value for our clients well into the future. I want to be clear that we are upskilling across our entire global team of associates. We expect every Unisys associate to be learning and improving upon their AI skills over the course of time. I mentioned earlier as well the necessary expansion of a partner ecosystem, and our approach says that we don't have to build everything ourselves at Unisys. We can bring the best of what's available forward to help solve client problems in their own context. Our strategic partnerships continue to extend our reach and capability and our alliances across hyperscalers, across infrastructure, software providers, model providers, the list goes on, is only going to expand. It enables us to have a reach and a depth that only augments some of the reach and depth you've already heard about today from Mike, and you'll hear about as we go forward. Just know that we anticipate fully that our partner ecosystem is not static and will be growing over the course of time. When we think about the Unisys Edge, we've got several key points that I want to visit on with you. First, when we think about end-to-end AI, we mention here the idea that AI at the edge is just as important as it is in the hybrid cloud and data center future, and the propagation of public and private AI models is a great example of that. We are positioned at scale globally to provide service and solutions that touch all of those elements of a vastly expanding ecosystem. In addition to that, security and governance have long been parts of Unisys' proud history, and we carry that into the future. We believe that security and governance will be a need to be built into every layer of the operating model, the solutions, the services, platforms, and products that are brought forward. We have a proven enterprise delivery model at scale. We've been doing that for a long time. Our depth of industry and process knowledge that are a result of that proven scale are unmatched and we believe are a significant differentiator for our future. Lastly, that agile and practical AI says that we meet clients wherever they might be on their journey. Solutions that are well-fit for specific problems they may have today, and roadmaps and paths that enable them to envision their next horizon and the one after that, so that they and we can help them to drive real-world enterprise outcomes. As a brand, we promise to power breakthroughs. You heard before some of the industry validation that we have been garnering over these last several years, we wanted to take a moment to show you some of that recognition, from global rankings to our presence on a growing number of global reports across a variety of providers. As well, we wanted to make sure to tell you that we're also focused on ensuring Unisys continues to be a compelling place for associates to work and build a career. You'll see that we have also been garnering a variety of recognitions for the depth and quality of the experience that we provide to our associates over time. Thank you for the opportunity to share more depth around our strategy and how we're making it real. Before I go, I would like to give you a look into our largest global delivery center in Bangalore, India. Like I tell our team every time I visit, there is no other place at Unisys quite like it, and I'd like for you to see some of it now. Enjoy. Hello. Welcome to Unisys, India. I'm Moses, and you're in the Bangalore Delivery Center. Let me take you through behind the scenes. This center is a powerhouse for breakthrough thinking, an epicenter of how Unisys drives artificial intelligence innovation to our clients. Let me walk you through. As you enter in, you can feel the intensity and the momentum and energy. Every corner here is built to break silos. It's designed to foster spontaneous collaboration and fast-paced problem-solving. Here we are at the collaboration hub, a dedicated space for hands-on client interaction. Innovation is all about connecting people. Rahila, can you tell us how your team makes that happen? We are working on a very advanced AI modeling project, Moses, wherein we are trying to bridge the gaps in terms of our client executive, sales executive, finding the real critical skills, the emerging technology skills, complex problem-solving skills in a jiffy. How we do this is we enable it through a very vibrant ecosystem where we are having real-time information, and we have built a system on an intelligence layer where we are able to give what the client executive wants in terms of even the surrounding information when it comes to successful client stories and provide to them expertise at their fingertips in a flash. This is helping Unisys build agility, build impact, and deliver at scale in very few seconds what used to probably take days before. Wonderful. Thanks for sharing. That's wonderful. Great. There is more. Just keep following me. There's a lot of focus on agentic frameworks. Let's talk to Sanket. Sanket, how are you doing? Hi, Moses. Tell me what's happening in your world. Well, multi-agent workflows, orchestration, tool integrations, and all as part of the agentic framework. We have been trying to connect all our disparate systems using agentic tools, and we've been successful in it. The other thing is a Service Experience Accelerator that leverages our Unisys capabilities and expertise in service management, incident orchestration. We are giving a conversational layer to the service desk agents to be able to find resolutions faster and in a much accurate way. Along with that, we also give them a knowledge curation tool, which uses AI and machine learning algorithms to find gaps within the knowledge base and also create articles that fills those gaps. Exciting stuff ahead. It is exciting. We've been winning awards and recognitions all across just because of these. Yeah. Thanks for all the work that you do, Sanket. Thank you very much. Thanks a lot. Great. That brings us to me talking about all these frameworks and me taking you to the place where these are actually implemented. There you go. Here we have Amit. Amit, how are you driving the future? Perfect timing, Moses. We just shipped a feature that enables our agents to respond with interactive components and not just text. Let's say, for instance, you're a salesperson and you're selling our managed meeting room solution. You might want to walk the client through the ROI, right? Before, you needed to sit down and create an Excel spreadsheet. It's just a lot of work. You can now just ask our agent for an ROI calculator, and it'll go through all of our sales material and build you one. You can turn the knobs, change the numbers, and walk through the client in terms of the ROI. That's a magical use of agentic AI. Of course. Love to see how these agents are integrated with our Unisys enterprise systems. More on it now. Hey, Kayi. How are you doing? Hey. How are you? Hey, Moses. Doing good. Thank you. Good. What's going on at ClearPath Forward? We have now a strategy to bring secure enterprise AI onto our ClearPath technology ecosystem, letting our client innovate with AI while keeping trust and security at the center. We have done a couple of innovations and prototyping around this strategy. Yes, we are working towards AI-ready access to ClearPath data, wherein our solution securely enables ClearPath data for modern languages and AI application to consume the data naturally. That's beautiful. That's wonderful, right? As we go forward, this is amazing stuff that we are taking it forward. This center is a relentless engine of creativity, building value to our customers 24 hours a day, seven days a week, all through the year. It is here that our engineers aren't just building AI solutions, but we are making them useful. Happy to have hosted you. Super exciting things happening all the time. Until next time, see you. Hello, I'm Sean Tinney, Senior Vice President and General Manager of Enterprise Computing Solutions at Unisys. I'm here to give you an overview of our ECS business and discuss the impact of AI on our ClearPath ecosystem. What you're going to see over the next 20 minutes is a business that's not only durable, but one we believe is accelerating, and how AI is not migrating workloads away, but rather driving more workloads onto it. By the end of this section, I want you to leave with three convictions. First, this revenue stream is sticky. Second, that it's growing in relevance. Third, that it has the opportunity to become more profitable as AI reshapes how our clients consume it. Let's take a look at how we're going to spend our time today. I'm going to walk you through our solution portfolio and the market opportunity behind it, how we're driving AI at the core, a demo of our Digital System Administrator to show AI operations in action, and we'll close with some client values and outcomes. First, let's start with what we actually sell. Our ClearPath ecosystem is not a single product. It's a three-legged stool of recurring revenue. At the core of this, 68% of that revenue is our ClearPath license and support business. These are our core operating systems, our products that drive our clients' mission-critical systems, and operate under long-term recurring revenue contracts. 21% of this revenue is our ClearPath managed services. These are the standardized services we deliver around this product ecosystem, managing and operating these systems on behalf of our clients. This also helps expand our wallet share with this client base. 11% of our portfolio revenue is our business process outsourcing services that allows us to move up the value chain with our clients and into the business processes themselves. Three things I want you to internalize here. First, the majority of this revenue stream is contracted under long-term recurring contracts. Second, the risk of migration is extraordinary, both in terms of $ and risk. Third, our standardized services layer drives operating leverage. Now let's take a look at what's going on in this ecosystem and around this ecosystem. The premise of this slide is simple. Mainframe workloads have doubled over the last 15 years, and they're poised to double again with AI as the accelerator. If we look on the left-hand side, what the research and analyst community is telling us, the mainframe market is expected to grow at roughly 6% a year over the next five years through 2033, with AI as the accelerator. 90% of IT leaders are planning on deploying AI directly onto the mainframe. MIPS growth, MIPS being a unit of workload consumption, are predicted to grow two times over the next two years at midsize enterprises. With all this transaction volume growth, 70% of that work growth running through these ecosystems. That's the profile of a growth industry. There are several structural reasons why this is happening. Data gravity favors the core. Enterprises want their AI inferencing happening near their system of records, not away from it. That is driving this AI inference onto the platform, co-located with the transactional data. These new agentic and AI and code assistants are spinning up in the ecosystem, driving that workload growth. The added requirements for data sovereignty and post-quantum security are driving more workloads, not less, into these ecosystems. How are we poised to capture it? We have AI integrated around the core, hybrid consumption models, greater than five nines availability, all on a globally secure platform. Quite simply, this market is running towards us. To put it plainly, the structural tailwind here is real, it's externally substantiated and accelerating, and we believe our portfolio is positioned to benefit from it. If the previous slide talked about the structural forces around this market, this slide talks about the buyer behavior. Now, for over a decade, the CIO narrative was cloud first, modernization by migration. That era is over. The market has decisively moved to cloud smart. Modernization without disruption. Look at where it matters. High-volume, data-sensitive industries like retail and banking. These are industries that can't afford the cost of disruption, the latency, the risk. Highly regulated sectors like financial services, healthcare, and government, where these distributed cloud-based architectures can't meet the audit requirements. Complex manufacturing environments where workloads quite simply run more economically on the ClearPath ecosystem. There are forces that are driving this. New technology creates the threat of new bad actors, creates more regulatory and auditability pressure, drives more need for cybersecurity and cyber resilience. The cost of these distributed ecosystems continue to rise, surprising many CIOs. You're also seeing performance inconsistency on workloads that are running there, these non-critical workloads. Business cases are upending. As Mike pointed out earlier, ecosystem flexibility, the need to maximize your investment and modernize without disruption, is becoming the need of the day. Why do we win? All of these factors play right into what has been at our core for years, mission-critical workloads in a secure and resilient environment. Perhaps most critically, we're built to modernize and integrate with these cloud AI and digital channels and not compete with them. How do we capture this? It's really three mechanisms. Develop. This is our client growth engine. AI at the core, modernizing on the edge, continuing to provide workload and performance balancing. This is how we extend the runway of our ClearPath Forward ecosystem with our clients. Transform. Tying our technology and products to the business outcomes and the industries that our clients operate in, allowing them to drive faster application creation, using ClearPath as the data hub and not the transaction engine. This is how we allow our clients to do more with ClearPath and not with less. Orchestrating the future of work. Ensuring we have the right skills at the right level of experience, supported with the right technology to help shape the future of work for our clients. That's how we drive accretive services revenue. Underpinning all this are five key advantages that are core to our business unit. Consistent and low TCO, consistent and incremental modernization, the access to emerging technologies like artificial intelligence and modern languages as a function of the estate, and a secure and reliable ecosystem. This is why our clients renew, this is why they expand. If we take a look at the roadmap that's driving all of this, it's anchored by three core tenets, security, performance, and interoperability. Security. We're in the phases now of crypto discovery, getting ready for post-quantum readiness, and into predictive security policies. Why this matters commercially is we're staying ahead of the post-quantum transition that's going to hit every one of these regulated financial and government institutions in the coming years. This justifies our premium pricing in regulated industries. On performance, creating a framework for hybrid computing, native language support, and continuous dynamic workloads. The system is always on, it's always performative. This is what drives workload growth. This is what drives workloads expansion. Interoperability. Ensuring we can work with the AI applications of today, setting the foundation to work for tomorrow, and solving the problems of the future with quantum and classical integration. This is what allows us to layer accretive services revenue around this. All of this underpinned by emerging technologies, enabling our clients to modernize one quarter at a time. We're going to talk about something that we believe fundamentally changes the economics of this business, and that's artificial intelligence. AI is not a feature we bolted onto the ClearPath. It operates in four reinforcing loops around the core. Number 1, AI-driven integration. Ensuring our ClearPath ecosystem can work with these emerging AI models, but doing so in a way that does not expose security and data flaws at the core. This extends the runway and the life cycle of our ClearPath ecosystem. Number 2, AI value realization. Ensuring when they're running these models, that they're validated against structured data, taking the validated business rules that is in this system. This increases speed to value, reduces hallucinations, and justifies our pricing power. AI data sovereignty. We're our clients' digital backbone of today and setting it up to be the digital and AI backbone of the future. This justifies our premium pricing. Ushering into AI-enabled operations. Orchestrating with structured datasets the operations of tomorrow. Driving AI into the operational mechanism, supporting them with the skills they need. More integrations drive more interoperability, which drives more platform value and more revenue per client. This is the flywheel. We're going to take a look now at a demo of our ClearPath Digital System Administrator. Three years ago, Chris Arrasmith, as the general manager of this business, stood here and talked to you about our Endurance Program, how we were training the next generation of ClearPath engineers on skill sets and coding languages that they didn't receive at university. How are we bringing them up to handle this ecosystem? The evolution of that Endurance Program has evolved into AI-based knowledge management, into AI-enabled operations, and our ClearPath Digital System Administrator. We're going to look at this through the hypothetical scenario of incident resolution. This isn't the same incident resolution that you and I have when we submit a ticket about our computer not working. These are mission-critical incidents. My website is down. I can't reconcile my transactions. Traditionally, these incidents come in and it's all hands on deck. You have a team of engineers, each with a different discipline, level of experience, skill set, all trying to understand the root cause of this problem. What is the application layer like? What is the infrastructure layer? What is the database? What is our partner ecosystem? Test the solution, retest the solution, retest the solution. Finally moving something into production. The whole time, cost to serve is going up, resolution time is going up, satisfaction is going down, and the potential for revenue leakage is compounding. In an AI-enabled scenario, a single cross and upskilled engineer working with a team of digital engineers behind them, proactively managing this situation. Same scenario, vastly different cost to serve. Let's take a look at our digital system engineer, or DSA. DSA instantly converts complex raw system data into clear, actionable insights, allowing a single administrator to seamlessly oversee multiple systems without needing command line expertise. By shifting workflows from reactive to proactive, DSA highlights critical alerts before they disrupt performance. It automatically captures expert system knowledge, upskilling administrators on the job by explaining technical anomalies in real time. Administrators receive guided step-by-step instructions backed by intelligent system guardrails. To mitigate risk, the AI agent performs automated mandatory safety checks before suggesting any critical changes. With no complex syntax required, admins can now query DSA and receive comprehensive technical answers in plain English. Here, DSA verifies that the proposed resolutions will not cause system downtime, maintaining service continuity. The administrator confidently hands off execution to a team of digital engineers trained across multiple coding languages that perform a final impact assessment and prompts a human in the loop for approval before running commands. Every action is logged, providing full forensic visibility for compliance and auditing. Once resolved, the admin can instruct DSA to conduct an immediate root cause analysis. DSA thoroughly analyzes the incident, logs the findings, and outlines hardening steps from ever happening again. By committing this RCA to institutional memory, the core AI models improve with every interaction. Finally, the AI agent automatically applies new hardening measures, securing the environment against future disruptions. Okay, let me close the loop with some numbers that really matter, because these define our mode. Greater than five nine availability. This is what our clients expect, and this is what ClearPath delivers consistently. 700 million airline passengers processed in 2025, 150 million payments reconciled per year. $56 trillion in interbank U.S. payments processed. $450 billion mortgages managed at over 1 million transactions a minute. These are not nominal transactions. These are the transactions that power global economies. On the right, perhaps the most important number on this page, zero. Zero incidents of compromised ClearPath user data. That amplifies every number you see on the left-hand side of this screen. What this also tells you is the cost of switching off ClearPath is not measured in dollars or emerging technology. It's measured in systematic risk to global operations. Now I'm going to bring Manju Naglapur up onto the stage, and he's going to tell you about our Cloud, Applications & Infrastructure business. Before I do that, I want to leave you with three takeaways and one client story. First, this revenue stream is durable. 68% of our portfolio is recurring licenses on clients who run some of the most mission-critical workloads on Earth. Switching costs are extraordinary, both in terms of dollars and risks, and we've had zero incidents of compromised user data at five nine availability. These are not commodity workloads. They're sovereign scale. Second, this market is growing. The mainframe market is growing at 6% a year through 2023, driven by the power of artificial intelligence. The market has shifted from cloud-first to cloud-smart, and IT leaders are deploying AI directly into the mainframe. This is a tailwind. Thirdly, this revenue stream has the potential to be margin expanding. AI-enabled operations compresses our cost to serve, effectively translating into operating profit. ClearPath is client-focused, future-ready, and scalable for clients to build our futures on. I'm going to let you hear from one of our clients building their future on ClearPath. Our relationship with Carnival Cruise Line goes back nearly 50 years, starting with one ship at one port and evolving into their entire operations. It's a story of partnership. It's a story of modernization. It's a story of growth. Please listen to Carnival Cruise Line. At Carnival, our mission is to bring more fun to more people. We've partnered with Unisys for 47 years and counting. Unisys helps Carnival connect our millions of guests to moments of fun. Whether it is our mobile application or whether you're in a dining room ordering, the Unisys platform is at the center of our e-commerce universe. Unisys handles nearly six billion transactions a year, so we can focus on the guest experience. We take about six million guests a year. We travel to 19 different countries. Our guests expect a seamless real-time experience, instant availability, immediate confirmation, and increasingly personalized offers. Unisys understands in detail our technical needs today and help predict where we're going tomorrow. They have been essential to how we deliver the guest experience at scale. Unisys helps keep fun flowing seamlessly and is how we keep breaking through. Hello, everyone. Good day. I'm Manju Naglapur. I'm the SVP and Global General Manager for C&I, Cloud, Applications & Infrastructure business unit. C&I lives in the core of the enterprise. We work at the intersection of hybrid cloud, applications, security, data, and AI. Not just to modernize technology, but to build organizations that are resilient, intelligent, and ready for the future. The market has moved past AI experimentation. You heard from Mike and also from Chris in the opening sessions. The pilots are done and dusted with. The real challenge now is secure, scalable execution in real environments. Most enterprises lack the foundational elements to operationalize at scale. AI is won or lost on foundations, and the gap between ambition and readiness is our next big opportunity. We're aiming to close that gap through our solutions by orchestrating transformation end to end, connecting strategy to measurable outcomes. At the end of the session, I want to leave you with three takeaways. One, we operate the foundation. The work we do sits in the core of enterprises trusted and mission-critical. The market now exactly needs what we operate. Two, we orchestrate the enterprise landscape. Taking advantage of these foundations, we are expecting to drive autonomous agent-driven actions at the enterprise. Three, we help enterprises reimagine for the new world of AI. We close that gap as an AI-first services organization, combining our AI framework and delivery model to embed intelligence into operations and delivery. Today, we'll show you how that drives outcomes across our solutions. In this session, we'll start with the market opportunity, then walk through the strategy and delivery approach. From there, we'll dive into our solutions portfolio and our AI framework, and finally, we'll transition to AI orchestration and action and client outcomes. Let's start with the market opportunity and growth. The traditional TAM for C&I was growing a little over 5% in a $1 trillion plus market. Let's say mid-level CAGR. AI just rewrote the math. The AI market is $2.6 trillion over the three-year timeframe from 2026 to 2029, growing at around a 30% CAGR. This is an opportunity at a much bigger scale. Out of this, we can focus our efforts on $1.6 trillion AI growth opportunity across infrastructure, cloud, applications, orchestration, and security with a projected CAGR of 26%. By aligning our capabilities to these segments, we are aiming to capture this projected market growth by delivering differentiated value. We are not chasing all of it. We're targeting four segments where our moat is the deepest, as you can see at the left of the screen: AI-driven infrastructure demand, enterprise AI orchestration, AI-enabled application transformation, and security and governance for AI. What gives us the right to play in this $1.6 trillion pool? First, our engineering moat. Unisys has spent years building, transforming, and operating some of the world's most complex enterprises. Second, agentic execution. Unisys is aligning our organization around an agentic operating and execution model. Three, enterprise and domain context intelligence. This is a core differentiator and our more defensible asset. Beyond foundational models, Unisys brings deep accumulated understanding of client environments and industries that gives real execution advantage. Four, trusted governance as a North Star. Clients don't just want AI that works. They want AI that can stand behind. We understand that governance isn't an afterthought. It's foundational to how our solutions are designed and delivered. We are aiming to become a resilient AI-first partner, combining enterprise context, governance by design, deep engineering, and agentic execution to consistently deliver outcomes in a complex real-world environment. We are now going to be talking about the alignment to the market evolution. We have been through multiple evolutions in the past 16 years, cloud and accelerated digital transformation until 2024. We are in a new phase of this market that many are calling the intelligence super cycle. That's driving a much more significant transformation in how we operate. The market expectations have significantly changed. Technology investments to business outcome expectation is getting close to real-time due to AI. We are aligning our business to this new reality. As you can see on the right side of the screen, demand is now market pulled with clients seeking faster time to value, which we are addressing through our paid Rapid Value Assessments that identify and prioritize impact faster, or we call them as RVAs. As an example, when we go into a prospect or a client, we are not spending months in terms of due diligence on workloads or application terrain or even security. We are able to provide assessments within two to three days. I think this is a game changer today, which is also helping our commercial division in terms of getting to better prospecting. Now if you think about value measurement, which is shifting from unit-based milestones to our outcome-based models, we are beginning to align delivery to business outcomes, not just SLAs and KPIs. At the core, we are moving towards a repeatable operating model that blends human expertise with an agentic digital workforce. Three things tie this together, what you all see along the bottom, Context-driven intelligence, every decision informed by client-specific environment, Nonlinear speed, outcomes that don't scale with headcount, measurable value, every engagement tied to a number the client cares about. That's the evolution. It's not an incremental upgrade, but a fundamental shift in how the business creates value. What is our transformation strategy with all these changes happening around the world? AI is becoming a part of nearly everything we deliver, not a side project bolted on later. The difference shows where it matters. Faster delivery in projects, leveraging our AI framework with a clear ambition to lead the market. As a result, we industrialize delivery, driving compounding value well beyond initial implementation. We have simplified our commercial models and linked them to client outcomes. Nobody cares how many tickets we close, but they want to know what measurable outcomes we delivered. Last, this is the one I care about the most, we have been building AI fluency in our own teams. This is where artificial intelligence meets human intelligence. We have been upskilling our associates so they can operate and scale with digital labor and not just talk about them. How do we reimagine our portfolio for the future? Our portfolio focuses on hybrid cloud applications and security, core domains for enterprise transformation. This reimagined portfolio is built on our experience with Unisys as client zero, combined with hard-won client credibility and moves it decisively up the value chain toward higher growth and margin opportunities in the new AI economy. It's how we help our clients develop, transform, and orchestrate. Three things should matter to you. First, aiming for higher mix shift. Our AI-first, AI-infused portfolio, intelligent operations powering hybrid cloud, data foundations that activate enterprises, and agentic applications that move the needle. This carries structurally higher margins than traditional services. As that mix shifts, these new services grow as a share of revenue, and a blended margin rises with them. Second, the moat. It's the Unisys Intelligence Accelerator, or what we call as UIA, the AI framework that powers the entire portfolio. It works two ways at once. It makes us sharper and more efficient across our installed base, and it lets us compete aggressively for greenfield wins in the markets we did not play before. Third, delivery. We have reimagined how the work gets done. Human plus agentic teams, forward-deployed engineering, and AI-native talent. This decouples revenue growth from headcount growth and allows us to scale. We believe we have the right-sized engineering team and deep industry expertise for the new AI world. The takeaway is simple, a trusted foundation, a portfolio moving up the value chain, and a delivery model through a framework built to expand margins as we grow. I talked about our AI framework, UIA, but I really want to dig deep into this for at least a few minutes. If you think from a delivery standpoint, we expect UIA to execute two things at once, a margin engine and an accelerant into new business. It captures the blueprint, agent, and the industry client context, making it reusable. Each engagement in an industry is cheaper to deliver than the last. We're converting a linear cost services business into one with software-like operating leverage. Why can't a competitor copy it? The technology anyone can build. What they can't build is what UIA accumulates and delivers as a compounding value. Deep proprietary context on mission-critical enterprises we run for decades. The frontier model is rentable by anyone tomorrow. Our understanding of these environments is not. That's the moat, and it continues to evolve. We treat models as interchangeable and focus our differentiation on intelligent routing and enterprise context. This brings FinOps discipline to AI consumption, selecting the right model on the right compute for each use case. That approach lowers token cost, strengthens margins, and gives us the confidence to make more aggressive AI-led investment bets in new pursuits. One thing to live with, UIA transforms a services business into a compounding one, where reuse drives margin, proprietary context builds defensibility, and model commoditization works in our favor. To show the power of this UIA framework, I want to share a demo that illustrates the agentic model actively driving decisions and workflows with human-in-the-loop governance ensuring control. Let's watch the demo. Enterprises are modernizing fast, keeping a complex multi-vendor environment running smoothly takes discipline. Meet Alex, a Unisys site reliability engineer responsible for monitoring and managing a critical client environment. Alex's day starts in a unified operations dashboard that aggregates data across the client's IT estate so he can see application performance, infrastructure health, and service availability. This is powered by Unisys Intelligence Accelerator, our AI framework that integrates data from third-party tools and partner technologies to give Alex a single operating view. Continuous AI monitoring surfaces issues early before performance problems reach end users or affect business operations. Always-on agents filter noise and deliver actionable insights so Alex stays focused on what matters. When something goes wrong, Alex isn't flooded with alerts. The Intelligence Accelerator pinpoints root cause, business impact, and risk in seconds, delivering clear, prioritized insight. It surfaces a recommended fix, transparent, auditable, and aligned to Unisys change management standards. Before anything is executed, Alex can see every proposed action and rollback step governed by enterprise policy and partner controls. Once Alex approves the recommended fixes, automation does the rest, updating code, restarting services, or correcting configurations. With every resolution, the Intelligence Accelerator captures what happened and builds it into future recommendations to proactively prevent recurring issues and resolve similar issues faster the next time. For Alex, that means less time chasing alerts and more time focused on strategic improvements. For the organization, that means fewer incidents resolved with less effort, lower operational costs, and more consistent application performance and infrastructure health. For Unisys, it's a scalable, governed approach that delivers measurable operational value and a repeatable model for AI-powered managed services. I hope you enjoyed this demo. The true measure of this approach is client outcomes. We are delivering measurable outcomes at enterprise scale across industries. Clients see faster modernization, greater resilience, and reduced operational complexity. Let me highlight a few examples. First, let's explore a client story for National Instruments. National Instruments, now a part of Emerson, is a leader in automated test and measurement systems. The initial challenge we faced a year and a half ago when we were working with this client was it had a large portfolio of legacy applications, which was slowing down modernization, and it was increasing operational complexity with mounting legacy debt. When we think about the leanings in terms of GenAI, we were able to embed GenAI into development lifecycle, automating code conversion, automating testing, reverse engineering code, and also documentation, and also providing an outcome where the client was modernized in months, not years. This kind of a project would have typically taken years with the same size team, and we were able to modernize upwards of 90-plus critical applications, improving speed, quality, and overall delivery efficiency. This demonstrates how AI accelerates modernization and reduces tech debt at enterprise scale. Let's transition to a client story about Cushman & Wakefield. They are a global commercial real estate service leader. The challenge that Cushman & Wakefield had was their rapid growth, which led to fragmented systems and inconsistent operations across 400 locations, driving inconsistency and operational inefficiency. The approach we took was to create a unified cloud platform with AIOps and AI-enabled automation and ServiceNow-based orchestration across operations. The outcome we generated here was we helped them save about $35 million in savings over a five-year period. There were a lot fewer incidents, faster resolution, and improved availability and operational resilience. This illustrates AI-driven operations delivering cost savings, stability, and improved enterprise performance. I would love to tell you about CaixaBank, a bank and a financial services company representing a vast majority of market share in Brazil's market sector. The challenge here was it was a highly complex, regulated environment requiring integration across systems and always-on reliability. They were on a modernization path and existed in two worlds, legacy and new. They wanted highly complex integrations to happen across the enterprise. The approach we took here was to integrate these 90-plus critical systems and also 280-plus APIs or Application Programming Interfaces, embedding DevSecOps automation and security controls that was needed for this highly regulated environment. The outcome here was we are today supporting 120,000 transactions per minute and 27 million monthly contracts at enterprise scale. This shows how automation and orchestration enable mission-critical operations in regulated environments. Now let me talk about how we lead with partners and how we are recognized by the analysts. We don't build AI in isolation, we don't ask you take our word for our capabilities. On the partner side, we deliver with 30-plus of the leading names in AI, the hyperscalers, the foundation model providers, and the platform players. In fact, we just won Dell's Data Center Partner of the Year, highlighting our ability to deliver across complex environments. This matters. We're not locked into one stack, so we architect what's right for your business. On the analyst slide, if you look to the right, 23 independent reports have evaluated our capabilities, and we have earned 10 leader rankings with 32 additional leader rankings in subcategories. What makes it so impactful is that it's external validation, confirming we deliver at scale. In closing, I want to share a very special client story, CSU, California State University. They've been a client and a partner for over two decades. Let's hear from their chief information security officer, George Vansor, as he tells his story about our journey together. Thank you for your time today. I'm joined, and have the pleasure of being joined today by George Vansor. George is the Chief Information Systems Officer at California State University. George, welcome. Thanks, Mike. A pleasure to be here. Look, we've seen a ton of change together over the course of your tenure and our tenure. What's top of mind for you right now? What's top of mind, I think, is AI is here. It's real. A large portion of our institution is really trying to think and grapple with how do we prepare the next generation of the workforce to be ready for this new world. Our goal would be to focus on eliminating the friction points that our students experience in their journey. We're definitely looking to figure out how we can make that a lot easier for our students to navigate the system as a whole. We have a lot of resources within the system. Those resources may not be located at any one institution. How do we make that available to all our students so that they can have access to all those services, those support programs, those sorts of things. We are a system of 22 universities. We are an aggressively federated environment. Each institution loves to be very independent. That's for a purpose. They serve different communities. How do we leverage the data that we have in our systems and really activate that data so that we could start to reduce those friction points, those barriers, and start to identify early on in the journey where somebody may not be perhaps ready, meeting those goals and objectives that they have for themselves? We want to help them support. Really, this focus on data fabric and utilization and analytics of that data is critical, right? Just having that data foundation is really a precursor to the utilization of the emerging tech when we think about how we're going to be using AI, et cetera. Maybe we can touch on a little bit of that infrastructure change that you've gone through over the course of the last year or two, really. You touched on a couple things there. Really that work that we've done recently here was working on optimizing how we're delivering this service. Right? Again, given that we're an access institution, we want to keep our tuition and fees as low as possible. We need to find a way to do that important work in a more cost-effective way or cost-optimized way. That's been the journey that we've been on for the last 20 years. We're very different from the client server days of 20 years ago, to where we are today and our ability to leverage public cloud services. At the end of the day, a lot of those systems are important, but they're not the reasons that our students show up. As we think about application of AI in a production environment, I can't think of a better way to really bring that data fabric together to get to those incoming students to help with that transition into the system, right. It's really addressing macros that you're facing today, right. If I think back to starting this relationship decades ago, you probably didn't have to do a lot of marketing and getting folks in. 20 years ago, we were probably turning students away. That was a reality for us. The nation is going through what they're calling the enrollment cliff. We're seeing declining numbers in college-age students, as well as other reasons for kids to not go to college. There is intense competition for those students. We need to change. The enrollment cliff is steeper and more pronounced than elsewhere in the nation. These are all things that are impacting us. For us to be an access university also means we need to be around, we need to stick around. We do need to figure out ways for us to attract students, keep students, and really start to tackle other markets, right? These are all opportunities for us and leveraging technology is frankly, probably the only way to address some of these issues. You also have the responsibility and the budget pressures on running an efficient infrastructure to not only support that student experience, but just to support the multitude of universities that you have and the faculty, as well as innovation and technology, innovation in how you bring technology to your students, but also in the run. Let's talk about the operational efficiency side as well, because that is, in my mind, two-pronged. One, to meet these tighter budget constraints, and two, to avail yourself of the technology in the education platform itself. Yeah. Operationally, we continue to look at opportunities that are brought forward by Unisys, areas where we can continue to optimize, continue to refine, largely through automation. Those are things that we can reinvest in the things that do matter to our students. That's extremely valuable to us because there's a lot of things that we're not doing today that we need to figure out how to do. That's where a lot of the innovation work that Unisys has brought forward to date and continues to bring forward is really helping us with. How do we free up our time, things that we no longer need to do that are still important? We can't let those systems go unsecured. They still need to run. We're able to do that, provide better service, better security, because we're able to bring in various components of automation through some of your teams. When we think about our client relationships, I share a statistic with many clients and prospective clients that for my top 50 clients, we've serviced them on average about 20 years. We become family, more so than that supplier relationship. We've been through tremendous amounts of change. What do you think is the glue that really keeps this relationship strong and enable us to continue to grow together? Yeah, from the onset, I've never felt that this was a customer-supplier relationship. It very much felt like a partnership where Unisys took time to understand what's important to us. As we're student-centered here in the CSU very clearly, the team that supported the CSU from a Unisys perspective is very student-centered as well. It's something that we feel, and our teams work together on that. It's one team that's focused on our customer, which is our students, faculty, and staff. That's been the glue that has kept this relationship tight. It's really about the client engagement around their core strategy. We're really there to support that. We don't start with our outcome. We start with your outcome. Absolutely. It's day in, day out for the last 25-plus years. That's how we've operated and why we've been so successful. Unisys is probably the handful of suppliers that I consider a partner. There is such that strong focus on the outcomes that matter to us. I think the most important thing in the relationship is that it's a partnership. It's a true partnership focused on common outcomes that are important to us. We operate as one unit in support of the CSU, in support of our students. Well, George, I'd like to personally thank you on behalf of Unisys for your wonderful relationship that we've had for decades here. We look forward to continuing to support you and the CSU in their mission of bringing a better engagement and experience to your students. Yeah. Thank you, Mike. I appreciate it. Again, thank you for your team. Your team has been tremendous. You really feel like part of my team. Helping support our CSU. Welcome back. My name is Patrycja Sobera, and I run Digital Workplace Solutions business unit here at Unisys. Well, before I start, a quick reminder of the Q&A. We would love to hear from you, so please use the chat on the side of the webcast portal. Well, you've already heard about our enterprise strategy and our AI framework and how my peers, Sean and Manju, are bringing them to life. Now, let's shift to focus on our Digital Workplace Solutions business unit and how we're evolving the space, not just from digital standpoint but also physical and human aspect. Agenda-wise, Weston, who leads our solution management, will start with market opportunities, key trends in digital workplace. We'll then bring it to life with a demo. We'll also walk you through our solution portfolio and its evolution, as well as examples of actual outcomes we're delivering to our clients every day. Finally, we'll close with where we're taking DWS next in terms of growth. Let's start with market trends. Well, over the past 12 months, four market trends have really accelerated. Number 1, every CEO out there is asking for ROI on AI investments. Not just use cases, but how do I get employees skilled up in AI? How do I drive adoption of the AI? How do I avoid the problems like hallucinations or data leakage? Secondly, the endpoint economics are under tremendous pressure. The cost of memory is driving volatility in PC pricing, and the CIOs feel stuck between investing in AI-ready devices versus deferring that cost to later. Thirdly, most enterprises are actually investing in employee experience, and they understand how it links to customer experience and ultimately to business performance. They're still struggling to connect it all, especially with telemetry in a metric-driven way. Lastly, the geopolitical situation in the world has introduced complexity and additional regulation demanding much stricter data governance. These four trends alone show us the workplace is becoming more complex, more critical, and really important to running any business operation successfully. According to forecasts from Gartner and Everest, we see a steady growth trajectory in this market, backed up by demand to invest in modernization of digital workplace, which we believe gives us great opportunity to grow. Weston, what's our strategy to capitalize on this? Well, maybe I could start with that first trend that you mentioned, that ROI, getting an ROI with AI. I loved it how earlier Manju talked about AI for the enterprise and bringing AI security to the enterprise as well. I think for us, Patrycja, the focus is on the human side of AI. There's a couple of strategies that we bring about. First of all, we know you can't just deploy tech and expect it to just magically work. I mean, that's especially true with AI. We are providing professional services that help our customers' employees figure out where to use AI, when, how, and even sometimes why they need to be using AI. The second aspect of our strategy is we all know that data is key to be able to make your AI working properly. I mean, we know these LLMs seem to have sucked in every bit of the internet, but do they have your corporate knowledge? Do they have your key business processes baked into it? In most cases, they don't. Hopefully, they haven't. What we are doing is to provide integration between the corporate data and these LLMs that you've purchased. Going beyond that to find the missing data, that needle in the haystack. Patrycja, the second trend that you mentioned was the high cost of PCs. I mean, that's a killer, right? We have a three-pronged strategy for that. First of all, can we keep PCs longer? We have a beautiful intelligent PC refresh program that looks at the reliability and the performance of every PC in the estate, and we determine how long the PC can be kept before it starts becoming a problem, often keeping it longer than the warranty. The second aspect is extending the life of the devices, physically doing something to them. I mean, that could be something simple like sending a technician around to blow out the fan so your CPU is running a little bit cooler, or even remotely wiping and reloading the operating system so it's running fresh again. The third prong of our approach there is to ask this question: Does everybody even need a PC? I mean, through our persona-based workshops, we're finding that some individuals might need a PC, some might need an AI PC, and others might get away just perfectly fine with virtual desktops, and others, maybe just an enterprise browser. Now, Patrycja, what about proactive experience, that third trend? What is our strategy there? Absolutely. Look, a subject really close to my heart. Since the inception of our experience management office five years ago, we haven't looked back. Experience really underpins all of our services. We haven't stopped transforming from reactive to proactive, predictive, and preemptive, telemetry-driven, insight-driven environment for our clients. Our priority is really to remove all that digital friction before it translates to a support call to a service desk. We're also a founding member of the XLA Institute, and we've created a framework of XLAs, experience level agreements, 20 of which are actually in production environment today, looking at device, looking at application, looking at persona, and now even physical workplace like meeting room XLA. All of this experience ethos really supports our services technicians and field services technicians as well with deep telemetry, with one-click automations. We're really always customer zero when it comes to our own innovation. Lastly, the geopolitical situation and all that instability in the world has created an increased demand for data sovereignty and governance. This is really why we designed our Service Experience Accelerator to run in-tenant with security in mind and to be third-party independent. Our clients' data is housed 100% within their domain, so we can actually still provide the full set of services, but without compromising any of our clients' data. It's super important to every client out there, especially on my side of the pond. Look, we made some really big claims, huge amount of buzzwords. Let's see how it works in reality. It's time for a demo. You'll see here how we're bridging the digital and physical workplace to really deliver value to our clients. Every day, Propentia, a global logistics company, moves thousands of packages through its production lines. Unisys Service Experience Accelerator watches over every machine, so when something goes wrong, the response is already in motion. Today, its telemetry detects abnormal power consumption in a critical packaging machine and fires a real-time alert that Frank, the lead warehouse manager, receives. He opens the Unisys Digital Assistant, describes what he's seeing, and uploads photos of the machine. agentic correlation ties his input to device telemetry, and the root cause becomes clear: a faulty voltage regulator. agentic AI orchestrates a repair path and surfaces a workaround to keep production running while the fix is underway. Guided by step-by-step instructions, the warehouse team applies a temporary fix, avoiding a costly shutdown. To fully fix the voltage regulator, Unisys frontline support services activate. Service Experience Accelerator orchestrates the response using agentic AI, powered by Salesforce, to schedule the most qualified technician, fully brief them, confirm the needed parts are available, and dispatches them. On-site, the technician is guided by AI every step of the way, working faster and with greater precision and no guesswork. Every detail, from detection to resolution, feeds back into the knowledge base. That's how Unisys shifts IT service delivery from reactive support to a continuous engine of operational resilience. Great. Well, let's unpack what we just saw there. With deep AI-enabled telemetry, we predicted issue before it impacted a live production environment. In this case, we're able to maintain operations at 80% capacity by reducing the load of that failing voltage regulator whilst we're orchestrating a permanent fix, which was actually using agentic AI to help us dispatch the right technician with the right skill, with the right certification, in the right location, tracked just like you would track an Uber. What's also important here is that clients who combine our offerings, like services operations, like field service, will really benefit from that continuous knowledge loop and really will get the maximum value from our solutions. Weston, what's the full scope of our digital workplace services? How are we evolving these? Well, maybe we could take a look at the left side of this slide here, because these are the five priority solutions that our clients are telling us, "This is what's essential to have a modern digital workplace." We've talked a lot about experience, we have an experience as a service offering. What is that exactly? What we do is we proactively monitor every device in the client environment and proactively detect and fix the issues, especially for people that are suffering in silence, we fix those before they even call the service desk. Now, of course, there are going to be issues that come into a service desk, when you need more help, we deliver an agentic service desk. We saw, for example, in the demo there, that last part of that video, that knowledge graph where we're collecting all that information. That's being supported by our knowledge curation tool that finds the needle in the haystack, that one last bit of data that's hidden away somewhere that's absolutely essential to fix a key problem in your environment. Thirdly, when in-person support is needed, we've invested heavily in that space. Our field services engineers, I like to say we give them superpowers with the AI that we are feeding them to help them solve their problems. We talked about the high cost of PCs, Patrycja. Our device subscription service is key to helping our customers reduce the cost and the complexity of managing their PC environment. Fifth, unified endpoint management, and I just want to be clear here, this is not unifying the management of just endpoints. It's unifying the management of everything in the digital workplace. What does that include? Here we are in a smart conference room. There are smart buildings. More and more sensors are showing up. Smart factories, smart restaurants. We're looking to manage all of that environment in the Digital Workplace. Well, let's now move to the right side of the graph here. These are the five growth services that we put forth. All of them are using AI in one form or another. The first one, IoT and connected devices. There's sensors showing up everywhere. We just talked about smart buildings, smart factories, smart offices, smart restaurants. All of these devices need to be installed, managed, configured, supported, and that is a big growth area that we see. You remember in the demo, that knowledge map that we saw at the very end? Absolutely needed for an IT service desk, finding that needle in the haystack. I'd like you just to imagine going to a single holistic help desk in your organization and asking it any question about your company. Not just IT, but maybe HR, travel, finance, maybe even some of your key business processes. All of that is backed up by this knowledge management. Thirdly, we've talked at length about data centers exploding everywhere. There's three being built in my state alone. They all need liquid cooling and high-end storage services, and we've been ramping up our services and training for our field engineers to take on this growing area. The fourth area, ESM, enterprise service management. That's like ServiceNow, those sort of platforms. You may say, "Hold on a sec. ESM, that's not new. What are you talking about there?" True, do you remember Mike talked about how these platforms are all embedding AI in them, and that's true with these ESM platforms. Our customers are saying to us, "Hey, these bots are showing up everywhere. What do I do with them? Can I standardize? Can I reduce my costs?" We see this as another great opportunity that we're already delivering for our customers. Lastly, sustainability. Europe's been on the sustainability bandwagon for quite some time. This energy crisis is now causing our U.S. clients to ask us, "How can I reduce the cost of my energy consumption?" To be clear, this isn't just a single offering. This is something, Patrycja, we're baking in across all of our offerings to be able to tap into that telemetry and make it possible. Hopefully, if we take a step back, the strategy should be clear. We're building on our strengths, key strengths we've highlighted here. We're now tapping into some key growth adjacency markets. Maybe we now could take a look at some case studies. To be clear, we're not just going to talk about what we're doing for our clients, but I think more importantly is what's the measurable business outcome for the clients? We see that especially in this very first client that we're going to look at, a European construction client. They had an admirable goal. They're wanting to get an ROI out of their AI. They got 10,000 employees. They want to get them all up to speed. Of course, we're going to provide training and data-driven OCM to figure out who's using AI, who's not using it, why are they not using it, and how to get them up to speed. I really love how this client said, "You know what? Let's start with the executives. Let's get all the executives in a room." We did. We had this prompt-a-thon workshop where they learned how to use AI properly. They saw the power of it themselves. The outcome, they came out and they said, "That was an awesome workshop. We can now walk the talk, and when we tell our employees to get on board, we can mean it." Now, Patrycja, what's an example of a client where experience management come into play? Yes, absolutely. Love this example. Well, look, this is a global manufacturing firm to whom we gave back 49,000 productive hours over a period of one year. How did we do it? Well, by driving proactive, predictive, again, data-driven insights leading to the removal of PC or application issues before they occurred. Which if you think in the context of this organization, their most important persona is the R&D engineers. Time back in productivity means that they are able to focus on product development and not calling the service desk about R&D application performance issues, which then contributed to an increase in the number of products they launched in Q4 by four times. Four times. Essentially, we removed 50,000 hours of digital friction to enable this organization to focus on what they do best, which is create products. Look, we just covered some great examples of value we deliver to our clients, whether it's AI enablement, whether it's knowledge curation, experience management. We want to shift gears a little bit and focus on field services. This is actually the largest part of DWS business and the largest expansion area for us. Let's play a video and see how we're infusing our field service with AI as their frontline companion. Your business never stops. The technology powering it runs on physical infrastructure, on devices, inside data centers, and in the environments where work gets done. Someone has to install it, manage, and support it. That's what Unisys' AI-enabled frontline support delivers. Our work has evolved to span every environment. From traditional devices to infrastructure-centric services, our global operations translate data into proactive support automatically. One team, every environment. Behind every dispatch, agentic AI analyzes patterns and matches the right technician to the right job. Powered by Salesforce Agentforce, AI handles scheduling, routing, and delivers real-time knowledge to the technician's device before they arrive, including predictive maintenance insights on what to watch for on-site. Over 1 million appointments are managed annually through AI, contributing to a 95% first-time fix rate. Unisys Service Experience Accelerator brings knowledge management and AI together, getting smarter with every service call and resolution. Less friction, more focus. Our 7,300 technicians work across more than 120 countries with real-time translation in nearly any language. We complete up to 4.2 million field calls annually and resolve 26 million issues through automation, orchestrating enterprise AI at scale to keep systems running and our people productive wherever they are. This is how we operationalize AI in the real world. Future-ready, AI-powered frontline support. What you just saw there is how we are enabling our frontline worker with agentic AI, making every technician faster, smarter, more effective in real time. That's exactly what's allowing us to move up the value chain. This is how we're actually expanding field services into higher value opportunities. At the lower end of the market, traditional OEM repair and workplace support are actually becoming a little bit more commoditized. Price pressure is high here. As you move up the stack, the dynamics change. In areas like multi-site environments or high-end infrastructure, the cost of failure is actually significant. Here, clients are willing to pay a premium for expertise, for consistency, for global execution we're known for. Overall, this AI infrastructure space with data center growth on the back of AI is huge, and it gives us great opportunity. This is actually where the demand is. Whether it's liquid cooling, whether it's high-end storage, as an example, this is exactly what we focused on in terms of growth. With our global footprint, our AI-enabled workforce, our execution, we can win these high-value segments. Look, I'll wrap up. I hope that you can see our passion and how excited we are about DWS growth with our experience-led delivery, reducing friction, and driving value to our clients every day with our investment in AI across services and client environments. It's not just the momentum for us, it's the momentum supported by analysts, advisors, the industry, and also the communities we're part of. Our funding membership are increasing. Excellence Institute, as an example, where I also have a pleasure of serving as a member of their advisory board, as well as Service Council. With everything aligned, the market, the technology, the community, we strongly believe that we're positioned here for growth and continued success. With that, all that remains to say is thank you for being part of this journey with us. Hi, I'm Joel Raper, Chief Commercial Officer here at Unisys. I'm Teresa Poggenpohl, Chief Marketing Officer at Unisys. Today, we're going to share a few highlights about our go-to-market strategy. First, I'd like to discuss our sales strategy and how AI solutions that you've heard about today from all the great presenters will be key enablers for growth. Next, I'm going to talk about how we have infused AI into our sales process. Last, Teresa is going to discuss how our marketing strategy is driving awareness and consideration for Unisys at our clients and prospects. Let me start first by saying how exciting of a time this is, Teresa. I, as a technologist nerd, we're living in a magical world, and it's really exciting to be part of where we are with AI that you've heard about all day today. Hopefully you've heard that AI is not an initiative at Unisys. It's about an operating at the core of Unisys. The winners in this market won't just be companies that talk about AI. It'll be the ones that fundamentally change how work gets done. For the first time in modern times, at least as my modern times, large enterprises are slower to implement AI at their core. They're struggling with things like governance and security and process changes and non-native AI employees that must adapt and learn about the new world that we're in. That's exactly what we're addressing here at Unisys. We're not layering on AI into existing processes, but kind of rewiring them through the strategic ecosystem of our partners, including Dell and AWS and Microsoft and many more that you can see here on the screen today. These partnerships accelerate how fast we can take the capability to market, and while Unisys differentiates on integration, execution, and outcomes. AI through us is an important part of that. Simply put, automate before you add effort into our delivery models. With the Salesforce Agentforce that you just heard about from Patrycja, we're already running one of the largest AI-driven field services operations globally, pretty exciting. Autonomously routing over 1 million tickets annually, auto-scheduling the appointments, and delivering industry-leading first-time fix rates across more than 100 countries. With Azure OpenAI, we're transforming service delivery into an agentic model, the biggest struggle that enterprises face today. Automating triage, resolution, knowledge creation, while self-enabling self-healing operations. AI with us is how we work alongside with us. This is how we help clients move from experimentation to real outcomes. A big struggle in the enterprise. AI-first model effort is no longer the proxy for value, the outcomes of what it delivers is. We're starting with focus industry-specific use cases tied directly to real enterprise challenges, whether that's application modernization, service operations, or engineering productivity. We have started to do this alongside with Microsoft, where we're developing more bespoke AI applications to solve very specific industry challenges in higher ed and healthcare, manufacturing, and financial services. Embedding agents built to solve very specific outcomes, and you heard a couple examples today of that. That model, combined with partner platforms and models with our understanding of the uniqueness of enterprise environments, we can quickly deploy and prove value early and solve real operational constraints from day one. That's about creating fast, credible entry points that scale into long-term engagements with our clients. AI for us, this is something very dear to my heart as I have kind of gone on this trajectory within sales organization, is we're running Unisys as client zero. Again, something you've heard in every one of our business units today, whether it's embedding Microsoft Copilot into the corporate functions or using GitHub Copilot with Claude into how we build and modernize our applications. We're using continuous structure upskilling programs to build AI fluency across our entire organization. I'll talk more about this shortly, but let me close one really important point. This is very deliberate. It's a model that position us to win consistently. Unisys partners with global enterprises managing complex distributed environments, mission-critical industries where resilience is no longer negotiable, as you heard from Manju, and mid-market organizations seeking enterprise-grade outcomes efficiently. These environments face fragmented systems, high cost to serve, limited visibility into the value, that's exactly where we believe our model is designed to win. Using AI-driven automation, intelligent operations, and outcome-based delivery to deliver these measurable results is the answer. That's what makes this repeatable, targeted growth strategy for Unisys. Let me show you how we're translating the AI first operating model into a modern, scalable sales engine. The sales engine 2.0 is how we build leading sales organizations in an AI first market. One that is repeatable, instrumented, improved productivity quarter after quarter. This market has already validated the shift. AI enablement is expected to significantly accelerate sales velocity. With the key point is simple. This is not about tools. It's about how you operate, how you go about it, what's the mentality of the people that are using this data. It requires a shift in their mindset completely from selling as an individual effort into running a system where intelligence is embedded and scaled right in front of you. Let me show you a little bit about that. First, guided selling with embedded intelligence. We're embedding AI directly into the cadence of selling. That could be meeting preparation, call plans, executive ready messaging, risk identification, and more importantly, next best actions. What is the anticipation of what's coming? At the same time, we're simplifying. We're going to just deconstruct our sales process, evaluate our tech stack, so sellers spend less time in administrative work. We've taken actions to reduce processes and tools, but we're still filling each one of our sellers with more data information to make us create this Sales engine 2.0. We spend more time where it matters with clients, building pipeline, and driving velocity. AI-enabled whitespace discovery is something that's big for us. You've heard Mike talk about the growth that we have within our existing clients, understanding what is it that they have needs for, what are their interests, and what are the things that make the opportunities for our solutions to be valuable within those companies. This is creating a pipeline that has more predictability to it, we're using data and AI to systematically identify unmet needs with those expansion opportunities. We're no longer reliant solely on the individual insight, but augmenting with it in a more complete data-driven view of each one of our accounts. Last, the Rapid Value Assessments, which you heard a little bit from Manju. Most of our enterprise clients know that they need to act now. Many of them are struggling with the way. We're creating these Rapid Value Assessments to help them with the stalled decisions that they have. Shared understanding of where the value that these Rapid Value Assessments can create. Our RVAs address that directly. The sales motion is done by identifying the ideal client to go after based on specific signals that they have in the market, and it's quickly structured for an engagement that identifies where that value is trapped, and quantifying the impact of that value and translating that into a clear prioritization for a roadmap for each one of those clients. Fourth, outcome-based pricing. You heard about that, I think, from most of the earlier presenters. We historically have been in a world where I think customers always wanted an outcome base but never could pull the trigger because of the complexity of setting that up. Now AI is changing that completely. Mike touched on this. Historically, our model, much like the rest of the industry, has been tied to effort. We're paid by the amount of people and things that we bring to the table. Well, we would price early and then spend weeks validating the solution before we ever got to delivery. Now, AI has fundamentally changed that dynamic. As AI removes the manual effort and reduces incidents, the question becomes, does the price go down or does the value increase? Well, our view is very clear on this. The value increases. We're involved. We have evolved our model towards value-based and outcome-aligned pricing, including elements like gain share and revenue share. As we remove cost, the complexity from the client's environment, their costs go down, and our margins can improve. That creates a much stronger alignment focused on outcomes, not activity. This is a key part of how we differentiate in the AI-first market. Net Sales engine 2.0 is not about tools. It's AI fluency within our people. It's value creation within our offerings, and it's a durable commercial model that the improvements continue to compound. Let me pass this on to Teresa, who's going to talk a little bit about the marketing efforts and how we're targeting our customers. I really love hearing Joel's story about how our commercial team leverages AI and tech tools. It's really, really powerful, as you heard. In marketing, we too leverage AI and leading-edge tools to find and nurture relevant clients and prospects at their key decision points. Digital channels are the backbone of our marketing strategy and enable us to be very, very targeted, but also to be very efficient with our dollars. I'm going to share four examples of how we're using tech for smarter marketing. First, intent signals. We harvest intent signals from activity across the internet and our website, and it helps us identify people who are exhibiting strong interest in a particular topic. For example, we might be able to see that 10 people from a specific company are all searching on various terms related to application modernization. These intent signals strongly suggest that they are in market to purchase services in this area. Second, precision targeting. We need to find these people without waste. AI really does a great job of enabling us to target our audiences in the right place at the right time with the right message. If you stay with the example of the 10 people that were researching various AI or app modernization terms, we can target them directly on the websites that they frequent and serve our ads on the topic to them. In real-time as they're researching, we're sending them our thought leadership, client stories, et cetera, on that topic. That enables us to position Unisys as a real leader in app modernization as they're doing their research and making their decisions. Third, personalized journeys. When these people get to our website, we show them tailored content that is based on their intent data. Pop-ups would show the next piece of content based on what we know they viewed before. Again, it is just storytelling and adding and evolving the story based on what they do. Fourth, predictive analytics. We use AI to optimize all of our campaigns, to prioritize target accounts, and to focus on the prospects with the highest intent to buy. All of this, and everything in marketing that we do is powered by AI, which we really call a force multiplier. An example, we do content creation. It helps us with writing. We develop ads with AI, we do research with AI, and we also optimize our campaigns using AI. What I'm showing here is a range of campaigns that we have in the market, ranges from thought leadership campaigns, lead generation campaigns, account-based marketing campaigns. They're all really focused on a few goals. One is to drive awareness for Unisys and our key solutions. The second one is to generate leads and really help Joel and his organization have something to work with when they go to market. Finally, help influence our target audience to help the commercial organization win when they're selling. These ads are driving real momentum for us in the market. We want to get our clients and our prospects to our website to consume our content, to learn more about us, and these ads do just that. For example, if you find one of these ads, might be on LinkedIn, they might be on The Wall Street Journal or Forbes, as our target audience is consuming information. We want to get their attention and have them click in to learn more. We're really, really proud that these ads are driving a click-through rate that is three times higher than the industry average. Again, you got to hook them, have them click, then go and learn more. Our web visitors are up 54% year-on-year. Asset downloads, that means they find a piece of thought leadership and they download it, up 52% year-on-year. We're seeing really strong engagement with an AI chatbot that we have on our website. We launched it a couple of years ago, and we weren't sure really how it would perform. What the chatbot does, it's AI-enabled, but it enables our visitors to ask specific questions and get an answer, to be directed to specific content, or even book a meeting with a subject matter expert. We've seen continued year-on-year growth in meetings booked, and the chatbot itself has become a key channel for lead generation. Again, we're trying to make it as easy as possible for our target audience to engage with us. There's nothing like hearing from our clients about how we deliver breakthroughs for them. You've heard a few stories as we've shared them with you today, we leverage these client stories in our advertising. We have a wide variety of client stories that we promote externally that we're really proud of, including what you see here, Benjamin Moore. We have Cushman & Wakefield, Carnival you saw, California State University, and I encourage you to go to our website, check them out, and learn more. These ads really do drive tangible impact for us. The ad that you see on the right is a digital ad. Again, something that you might see on The Wall Street Journal or LinkedIn. Again, the idea is to hook them so they click in and really want to learn more about what we did for Benjamin Moore. We have different formats for these ads. We have longer form Benjamin Moore ads that feature the CIO, and you can hear directly from the CIO about the powerful work we did to help their business. If you focus just on this Benjamin Moore ad, the stats are really powerful. 18 times the click-through rate from the ad to our website versus other content that we deliver. The campaign itself, just for Benjamin Moore to date, has driven more than 73,000 visitors to our website to learn more. Once they're on our site, they stay four times as long, which is exactly what you want them to do. I just share a fun story. After this ad launch, I think it was only in market for a couple of weeks, another company in the industry actually reached out to us after seeing the ads because they too wanted to learn more about what we could do to help them. It's exactly what you want to have happen. Account-based marketing is also a key pillar of our marketing strategy. The goal of account-based marketing is to influence key stakeholders with curated content that aligns with what we're trying to sell. Ultimately, we want to expand our share of wallet, and we want to win deals, and we can use our content delivered at the right time to help influence those buyers during their decision-making journey. AI itself has enabled us to significantly scale our account-based marketing over the past two years by creating content, helping us optimize and do all the things I talked about earlier. On this slide, I'm sharing an example from a biotech company that has been a client since 2016. We ran a personalized campaign for this client that included digital ads like the one that you see on the right side of this slide, which they might find when they're visiting LinkedIn or consuming media like Wall Street Journal, as I explained. That drives them to a custom landing page that has been created just for them. That landing page would share things like our specific credentials in certain areas. It would share and remind them who their account team is, who are other Unisys people that support them that they might not know. It would have case studies that are relevant, but all dynamically updated. When you look at this specific example on the bottom half of the slide, what we're really trying to do is drive consistent, high-value engagement with the senior leaders at that client. Again, getting all that content in front of them as they're considering who they're going to hire as a services provider. What we know in this particular case, that we touched 25 IT and senior leaders with their engagement on this site, and they were consuming information, learning more about us, again, influencing them through their decision journey. In this case, we know that the CIO and the VP of IT downloaded key content, and we know that in real time. We also know that we used physical touchpoints to help engage with them as well. Their CIO attended a Unisys-sponsored roundtable on a content relevant to what we were trying to sell them. Again, all those engagements are really, really high quality. The results are clear. When we run an account-based marketing program, we see significant higher wins. In closing, our intent data and tech tools enable our marketers to deliver the right messages at the right time to the right people, influencing them to help our commercial team be more successful. I think for us, this is the magic of AI. These insights and the account intelligence that we get now, they're critical for use as I presented with the Sales engine 2.0. It enables us throughout our sales process to give a leg up and ultimately win in the market. Hi. My name's Deb McCann. I'm the Chief Financial Officer of Unisys. When I looked at the list of those of you attending, I saw lots of familiar names, and I really want to thank you for your ongoing interest and support in Unisys. I also saw lots of new names. For those new investors, welcome, and thank you for your interest. Always feel free to reach out to our VP of Investor Relations, Michaela Pewarski. If you want to set up some time to meet with us, we're always happy to do so. First, I'm going to quickly walk through the agenda of the items I'm going to cover. First, before we move to our future targets, our Investor Day targets, we're going to look at what we told you in 2023 that we'd achieve and how we did against those. I'm going to move into the elements of our 2026 Investor Day targets and run through those. I'm going to talk through our capital allocation priorities, and then we're going to wrap up talking about our investment thesis and the milestones that we believe we are heading towards to unlock value for stakeholders. Before I dive in, I just want to orient you with our reporting segments and a terminology change that we're making. Our reporting segments are unchanged, and those are Digital Workplace Solutions, Cloud, Applications & Infrastructure, and Enterprise Computing Solutions. Within Enterprise Computing Solutions, we have on the far right on the bottom, ClearPath, and that consists of our operating system and the support that goes along with it. That makes up about two-thirds of ECS. In the past, this was called L&S for license and support. Going forward, we're going to call that just ClearPath. This consists of, from a total company revenue perspective, about 20%. Everything else to the left, we're going to now refer to, it used to be ex-L&S, now we'll be referring to it as Technology Solutions and Services. There's no change to the solution classification within these categories and no change to segments. We just thought these new names simplify our terminology to be more intuitive for our stakeholders. Next, we're going to talk about the goals we laid out in 2023 at that Investor Day and how we did against them. For ClearPath, on the top row you can see we had said the 3-year average revenue would be about $360 million a year, and we ended up averaging about $425 million per year. The margin we thought would be about 65%, and it ended up being 70%. This was due to higher consumption, longer-term deals, and additional integrated system purchases than we had planned on. This was strategy and execution, not just luck. As Sean went through, our ClearPath platform has unmatched security, speed, and resilience. The holistic ecosystem of ClearPath, we've made significant investments and we're benefiting from an increased use of data and AI by our clients. Next is our Technology Solutions and Services revenue. When we set our targets, the market was growing mid to high single digits, and the industry as a whole since then has underperformed that expectation and was growing low single digits. Overall, while we did not achieve our revenue targets, due to some of the things we've talked about on our earnings calls, such as pressure on public sector, lower PC field service volumes, and again, just that slower market environment, we were still pleased we grew basically within line with the industry, but disappointed we didn't hit our targets we set out. In light of that muted revenue growth, though, the good news is we focused on profitability, given some of those market factors that we couldn't control as much. From a Technology Solutions and Services margin perspective, we had targeted about 100 to 150 basis point improvement of margin per year, and we ended at the top of that range at 150 basis points. Regarding SG&A, we had targeted over the three-year period to reduce about $50 million in SG&A, and we ended up reducing $70 million SG&A. This was through streamlining corporate costs, looking at our real estate portfolio and rationalizing that, and centralizing IT. From that perspective, we beat what we had set out to do. Our operating profit margin, the algorithm may have been a little different, but we were still able to be in the range that we had laid out when we talked to you in 2023. Regarding pension, we had talked a lot about reducing liabilities, reducing volatility, and we did just that. We reduced liabilities 28% in the three-year period, and we were able to substantially remove all volatility in our pension contributions, which makes it easier to model and understand the cash flows we have going forward. From a pre-pension free cash flow perspective, the range we had given was about $150 million-$175 million. Although we achieved $72 million, it was a little bit not apples to apples in that since then we had made the decision to refinance and borrow an incremental amount to help reduce our pension contributions. The additional interest on that, plus the higher interest rate on our existing debt, was increased interest about $50 million. There was a $30 million environmental receipt from some previous payments we had made that we expect some recovery from. We thought those would come in in 2026, but will likely come in closer to either 2028 or 2029. Although we didn't beat the number, we came in at the low end of that $150 million operationally, if you adjust for those items. Next, before I get into the medium-term targets, I wanted to just set the baseline. For 2026, we're pleased to update our guidance on a constant currency revenue, the guidance for the constant currency revenue growth, narrowing the range and bringing that midpoint from -5.5% to -4.25%. This is really based on two things. One, increasing our ClearPath revenue. We had set that at $415 million. We're increasing it to $425 million. Then within Technology Solutions and Services, we've seen a modest improvement in client volumes and timing, and levels of short cycle work. This is relative to our original assumptions. We're pleased that we were able to update that revenue growth. Turning to our 2026 Investor Day targets, I'm going to start with revenue and specifically focus on Technology Solutions and Services revenue and the growth opportunity there. You've heard a lot from the previous speakers how we've reset Unisys' market perception, we've climbed in the analyst rankings, and all of the exciting opportunities in the market and our sales and marketing strategy to capture those opportunities. The alleviation of some of those macro headwinds, all of this translates into top-line growth. The growth is concentrated, as you see on the right, in solutions where demand is the strongest. These are riding on real agentic AI tailwinds while displacing more traditional work in our mix. This excludes, just a note, and it's footnoted at the bottom, these numbers exclude the potential impact of our U.K. joint venture wind down. This has about a 200 basis point impact on growth. This revenue is typically at a 0% margin, therefore not a driver of our cash and profit goals. I'm going to turn to gross margin. I'm going to focus on Technology Solutions and Services. Over the past three years, we've improved that gross margin by 560 basis points, a very significant improvement. The improvement, although we see a lot of opportunity, that rate decelerates over the next few years, mostly because we've done a lot of the heavy lifting already. Although we see a lot of opportunity, it's not at the same pace as these past few years. We still do see an opportunity for 200 basis points of improvement in gross margin over the next three years or about 70 basis points per year. It won't always happen in a straight line from year to year, that's our overall improvement, is that 200 basis points. The key drivers of that are on the right-hand of the slide. As you can see, mix shift, the higher margin field services at our data centers as opposed to traditional field services, AI adoption and automation, AI-augmented delivery, and improving operating leverage as we scale. Also, our outcome and consumption-based contracts as we sign those to support price, they really reflect the enhanced client value, and if we improve our productivity, that drops to the margin. Lastly, the future skilling on the existing base to support our associates to be future-ready. Okay, on the next slide, we're talking about SG&A. We already talked about over the past three years, we've reduced $70 million by rationalizing real estate, IT, and our corporate functions. We're really focused more on improving our SG&A productivity. Because a lot of the heavy lifting has been done, we still are looking at transforming, deploying AI, lots of efficiencies we see, and also shifting our resources to ensure they're focused on the highest impact areas that we're focused on, which is growth and profit. We're targeting 150 basis point improvement in SG&A as a% of revenue over this three-year period, which translates to about $10 million-$20 million reduction in SG&A on a dollar basis. Kind of bringing it all together. All of these numbers that we're setting out for our 2026 Investor Day targets, here's a summary of all of them. If you look on the lower left, we already talked about the Technology Solutions and Services, three-year CAGR of about 3%-5%. Assuming ClearPath, similar to what we've said in the past, about $400 million per year on average. All of that translates to total company of about 2%-4%, and that's a 3-year CAGR from the 2026 guidance midpoint. From a gross margin perspective, we expect total company 100 basis points of expansion. This assumes that 200 basis point improvement in Technology Solutions and Services we talked about and maintaining the ClearPath gross margin at around 70%. With the additional improvement in our SG&A productivity, this translates to a non-GAAP operating margin of 12%-14%. This translates to an adjusted EBITDA margin of about 17%-19%, which would be approximately $365 million of EBITDA in 2029, an increase of about $75 million, compared to what we're expecting in 2026. All of this translate, if you look at the far right, to our free cash flow target for 2029 of about $50 million or $110 million pre-pension. This assumes a similar level of items such as tax payments, CapEx, as we've assumed in 2026. Next, I'm going to move to our capital allocation priorities. As we generate more cash, our near-term capital allocation priorities will remain balanced between leverage reduction and growth in investments. We're successfully bringing down leverage, which should improve our credit rating and position us to continue reducing and ultimately removing our U.S. pension plans. With lower leverage and pension contributions, we can invest more in growth, and in the longer-term roadmap, we will consider a capital return program after we've de-levered and funded growth. The timing will depend on reaching sustainable positive free cash flow with a liquidity cushion of cash on the balance sheet. Let's talk a little bit more about de-levering, since it's such an important focus of our capital allocation over the next few years. Driven by a combination of increasing adjusted EBITDA by approximately $75 million and significantly reducing our pension deficit, on the right you can see an estimated trajectory of total debt. The dark green lines on the bottom are our debt, our actual secured notes. The two, the light green and lighter green, are the pension deficit for the U.S. and the non-U.S. plans. A portion of cash contributions as we make our pension contributions each year go toward that deficit reduction, though not dollar for dollar. As you can see, by 2029, we expect our pension contributions to translate to $240 million of deficit reduction from where we were in 2025. Net leverage, as you can see on the bottom right, going from 2.9 times to slightly under 2.0 times of leverage by the end of 2029. About two-thirds of that is from the deficit reduction, and the remainder is from the profit improvement. Achieving this target gives us the improved credit profile and capacity to finance the removal of the U.S. pension plans if we decide to do so. Continuing with our pension strategy, I'm going to talk about, a little bit, the benefits of last year's debt raise to refinance and to contribute $250 million to our U.S. plans. By doing that, as you look on the upper right, by partially funding our plans, it allowed us to provide more certainty to our investors, our investor forecast, by shifting asset allocation to remove substantially all of our contribution volatility. It also enabled us to resume annuity purchases that cost-effectively remove portions of the overall liability. It was cash flow accretive. We reduced contributions by more than the interest on the incremental debt we borrowed to make that $250 million contribution. Some additional steps we're taking are, on the bottom right you can see, we're executing another annuity purchase as our expectation in the second half of this year to remove additional liabilities later in the year. This lowers the premium cost for a full removal, which is based on the deficit plus a premium on the remaining liabilities. We're also undertaking a rigorous process to cleanse pensioner roles to bring down premium costs as a percentage of liabilities toward the lower end of that 10%-15% range we estimate. We're also evaluating the potential removal of one of our U.K. plans over the next few years. Before we move to Q&A, let's quickly talk about why we believe Unisys is a compelling investment. Today, we are being perceived in the market in a whole new way. If you look on the left-hand of the slide, these are things we really find ourselves today as a transformed Unisys. We're a recognized leader. We have a diversified client base with a $60 billion TAM. We've enhanced our profitability, and we've stabilized our pension. In the near term, we foresee growth inflection. We're working on scaling our digital workforce, and we see many AI tailwinds and TAM expansion, and that'll happen this year and into next. In the medium term, through 2029, we see a sustained growth step-up. We see that 200 basis points of Technology Solutions and Services margin, and we're seeing de-leveraging by about one time. Also the potential of the pension removal, plus that $30 million environmental recovery receipt that we should be getting in in that timeframe. Where does this all add up? If you look on the far right, this is where we believe we'll be unlocking shareholder value. You can see that $200 million of targeted net debt reduction and that $75 million targeted increase in adjusted EBITDA, before any multiple expansion, we see as providing value of about an additional $3 per share for the debt reduction and about $4 for the EBITDA. Really at the end, and this is before any expansion in our valuation. What we really believe is that Unisys at this point, we're a stronger Unisys. We're a highly competitive player with solidly positive growth and cash generation and enhanced flexibility. With that, I'm going to turn to Q&A. Thank you. Welcome to our Q&A session. Look, I'd like to open it first thanking Deb and the entire leadership team for what I hope you found to be a very informative set of presentations, and enjoyed it as much as we enjoyed giving it to you. Deb and I have the pleasure of speaking with all of you on a regular basis. For us, it was a pleasure to have you really introduced to the entire leadership team, and I hope that the passion that this team has for not only winning but delivering for our clients came through in the presentations. I really thought it did, the continuity was there. Happy to open this up now for some Q&A. We had a handful of questions already put into the queue, Michaela, I'm going to turn it over to you to tee up our first question, please. Thanks, Mike. The first question is: frontier models are launching deploy codes. Are they a competitive threat to Unisys? Yeah, it's a great question. I would like to say they would think they're a competitive threat to Unisys. I'm not sure that fact exists. I think about their push for deploy code, I believe that is really a push to drive more traffic to their LLMs, right, essentially, and utilization of their tool. I think you've heard throughout the day today the component pieces that are really required for that level of deploy code, I'll say continuity into our client base. We've talked a lot today around the trusted environment that we've built with our clients, in many cases over decades of support. Understanding of that technical environment is critical to actually application of AI into that environment. Clearly, there is a deep expertise from our teams for the clients we support, industry expertise. We've been running these ecosystems for decades. This is not a matter of just applying technology, and turning it on, and to use Joel's term, "It magically works." Right? This is really about an understanding of that ecosystem, and having the trust that our clients put in us to apply the technology to that ecosystem and orchestrate it. I agree wholeheartedly that what's missing in the environment for the full adoption of AI is the implementation and orchestration. You've seen that come through, really in each one of our business segments discussions, as well as our commercial and go-to-market discussion of how we think about that. Really driving that orchestration and implementation requires deep-rooted knowledge of the ecosystem because they're extremely complex and no one is vibe coding a new CRM system. Could you do it? Sure. Should you do it? Probably not in the construct of an enterprise environment. Chris, I don't know if there's any color you'd like to add to that? Yeah, happy to. Thanks. I guess just to reinforce a couple of points you were talking about, you alluded to reliability and scale and performance and security characteristics that don't just show up, and in fact, require the depth of expertise that you've just been describing as it relates to the engineering, the tooling, and the orchestration of the tooling, and the process and workflow expertise that we really have in significant depth across a number of industries, from our experiences. In addition to that, as we talked about earlier today, the tools of today are no longer the same tools as tomorrow or next week or next month or next year. When we think about positioning for expansion of tool set usage and the dedication of those tools in concert, orchestrated and fit for purpose for a specific client's challenge, that's where our capabilities would really come forward and shine. It's that combination of scale, performance characteristics, expertise in depth, and then orchestration potential over time. You just don't get that with AI native, right? We agree that's the hole in the market, and we think we're well-positioned to fill that hole. Thanks. The next question is: There are reports that companies are not getting a worthwhile return on AI investments. Are you seeing cases of clients abandoning projects or seeing issues with AI performance, and how is this impacting the pace of adoption? Yeah, look, another great question. Clearly there has been, I'd say probably for the last year, maybe with the exception of the last three months or so, everyone has been in kind of experimentation phase when it comes to AI, and it's changing so fast. I think that the enterprise clients significantly misunderstood the complexities of their ecosystem. I really feel like there was a little bit of a pause on everyone wanting to be first to apply AI, but not necessarily knowing where, how, when, and really what the cost is. Really the complexity and the understanding of the complexity of the ecosystem, making sure the right guardrails are there, making security a component piece of the application of AI. I think we've matured pretty quickly in that evolution. I think folks are realizing that this is not an event, this is a process. Just like every other process that you're adding to your software development life cycle or your ecosystem, whether that's adding hardware, whether that's software applications, agentic workflow, regardless of what the application is, you have to do it really thoughtfully. I go back to our previous question, the understanding of that ecosystem is critical to the application of that. I think what's happened in the last maybe three to six months is a bit of a pause, a slowdown, which I think was warranted, and really an understanding of how and when to apply AI, and really looking at it through that incremental gain point of view, as opposed to completely replacing the ecosystem. I think that's probably the stage we're in. We are in really early innings here, as far as I'm concerned, in the deployment of AI in an enterprise scale environment. We have a long way to go. You saw many of the TAMs we're talking about, $450 billion of economic value over the course of the next decade, 4x market growth there, high 30% CAGR growth. There's a lot to be done. Again, it has to be very pragmatic in the approach, and I think people are realizing that at this stage. I think really question one and two tied together nicely in that regard. Again, Chris, I don't know if you want to add any additional color there. Yeah, sure. I think the considerations you're talking about are really important in that they are a result of, to nobody's surprise, a few early stumbles with regard to deploying AI at scale. What are the ramifications for the enterprise? What about all these costs, the token costs, et cetera? What's really great for us is the opportunity that we see with some of our existing solutions to show returns from results with clients we're already deploying against, and we gave a couple of examples of that throughout today, and to help clients who aren't maybe yet that advanced in their journey with the other capabilities we talked about. When we couple that with the expertise that we've illustrated over time with the depth around the platforms that are executing so many high volume transactions over years and years, what comes with that is really some special insight that's going to help bridge that gap between expectation and reality as we go forward. For us, the positioning and the timing is just right. Yeah, look, just maybe one last point on that. Clearly, there's been value in the application of this. We gave you two great stories here with CSU and Cushman & Wakefield. Tens of millions of dollars saved. Patrycja talked about thousands, tens of thousands of hours saved. It's there. The technology's real, and we just have to be pragmatic about how we apply it, when we apply it, and I'm a firm believer that we're going to look back three years from now and see all of these small incremental changes that we've made to the infrastructure show tremendous value to our clients. Next question. At a recent conference, Mike mentioned Unisys has the skills to support quantum computing. Kindly double-click on this and what skills Unisys has that overlap in quantum, and are any of them being used now? Yeah, great. I would love to answer that question, but I happen to be sitting next to the gentleman who not only ran that business but is one of our experts in quantum. Chris, maybe I'll just have you field that one. Yeah, happy to. Thanks for the question. I'll answer that in a couple of ways. Firstly, as it relates to expectations around post-quantum cryptography futures, that's one of the key early areas where we deployed engineering depth and talent in order to protect and defend our ClearPath estate and all of the clients who have been using it for a long time. What that created was the motion for us that we've continued to increment against to develop and expand the capabilities of security around those ClearPath environments, and then extending those in the form of advisory and capability assessments as services to additional clients who may not be using ClearPath. In that way, we're protecting what's important for us from a proprietary platform usage across all those great deployments we've talked about with you, and extending the value out to other clients in a post-quantum crypto world, so they can iterate and improve as that horizon comes ever closer to us. That's number 1. Secondly, as it relates to the practical application of quantum computing, we have a growing number of quantum engineers at the company who are expanding on use cases with a variety of prospects and clients in early-stage capability assessments, use case studies, as well as early proofs of concept in a few targeted areas that are going to prove out the hypothesis that a quantum-based solution can in fact solve problems better than in a practical computing application. It's early days there, but we think that's a really important part of the enterprise computing story at Unisys. We've been doing that for a long time, and so we just consider that a next chapter. Coupled with the engineering depth that I've just talked about, we're developing and have in fact already developed several key assets and tools and accelerators that will help clients along that journey. Whether it's from advisory services or the actual creation of solutions on the ground and all of the algorithms that are expected to be a part of that, and in fact, the patents that we've already secured in that space, we think there's a significant opportunity as we head into the future in both of those dimensions. That was a heck of a lot better answer than I would have given on quantum computing. Thanks, Chris. Yeah, you bet. Next question. Your revenue growth targets have a footnote excluding the impact of a U.K. joint venture. Can you provide more detail about that? Sure. I'll start and then maybe Deb, I'll ask you to give a little color here. We've been involved for probably close to 15 years or 20 years with a venture called iPSL. It's a joint venture with three of our large banks in the U.K. It's a BPO for check processing, essentially. It's something, as we all know, check processing has been winding down year-over-year. Really over the last 10 years, this business has declined by more than half. It actually creates a pretty strong headwind for us as far as revenue growth is concerned. It's a zero-margin business. Frankly, we're in the business a lot more because of the client partners that we have in that business. They also happen to be clients of other aspects of our business. One, in particular, is a ClearPath Forward client. The engagement there has really been in support of those clients in a much broader relationship. This is something that over the course of, again, the last decade has been declining. We're at a position now where there's a benefit here to winding this venture up. We've gotten to a point really where the pension scheme that was aligned to that environment is also in a point where we can fully fund that pension and ultimately diffuse that as well. It's a good time for us to actually exit that. It does create a headwind, and maybe Deb, you can just give us some of the financial aspects of the. Yeah the headwind it creates for us. Yeah. In 2026, that number was about $90 million of revenue. That included, as we talked about on Q1 earnings, there was also a portion we got from iPSL to help fund that iPSL pension. It's a little higher in 2026. That has been steadily declining over the past few years, and that will start to really wind down, starting at the end of 2026. Primarily, that wind down will be in 2027. That's why when we give those three-year CAGRs for both the Technology Solutions and Services, which the other segment is within that, where this is, and then also total company, those CAGRs include that. It's about a 200 basis point impact in that three-year CAGR. That's why it's important. We wanted to lay that out, and we'll continue to give that differentiation, so that as we're reporting our numbers, you can see how we're performing against it. Yeah. Thanks, Deb. Next question. Given you expect a 30% CAGR in AI market growth, do you expect your growth forecasts might be modest? Well, look, I would start that with the 30% number was really an industry number, not specific to Unisys. We fully expect to participate in that market CAGR growth. There's also the aspect of mix when you think about the application of that growth, the size of those deals. We are, as most of you know, a large recurring base and have longstanding contracts. When you look at application of this new technology, that becomes incremental, and it takes some time for that to actually outpace the legacy base. Probably early days yet for us to be thinking about whether our forecast or guidance is conservative or not. We feel really good about the opportunities that the TAM represents. We feel really good about what that new business signings look like. We think we've got the right offerings to participate fully in that TAM, and I think you've seen and heard a lot of that today. I would say, again, early days, but certainly more of a tailwind than a headwind as far as we're concerned when we talk about forecast or profitability. I don't know, Deb, if you want to add any additional color there. Yeah, no, I think you covered it. Okay, great. Great. Next question. Several speakers referenced outcomes-based pricing. Are you seeing success in moving clients to these contract structures? Absolutely, we're seeing success in doing that. I think for those of you listening to our investor calls on a quarterly basis, last year, we had one of our biggest renewal years. We signed roughly $1.7 billion worth of renewals in our Technology Solutions and Services portion of our business. When you're signing that kind of work on a renewal basis, you don't get a real opportunity to do a lot of project work. It's really solidifying that base. We have had some good success as it pertains to outcome-based pricing. I would say we're not having any conversations with clients that are not starting with outcome-based pricing. One of the biggest barriers, frankly, to doing it is not our willingness to do it. It's the client's willingness to do it. Many of the procurement organizations at our clients are still trying to apply legacy methodologies to what they're doing. If I look at what you saw today in Service Experience Accelerator as an example, if we're successful in deploying that technology to our existing client base, clearly, that's going to have significant savings for us. We're talking about revenue share with clients on that. It's a little bit of a short-term headwind when you talk about the application of that technology, but it's margin accretive from our perspective. That's a great example of the deployment of that technology is really about deflections, and it's about preventive maintenance. Those are the types of outcomes that we think our clients want, and we think that adds value to them. When you talk about the number of hours saved as, again, Patrycja mentioned in her prepared remarks. There's lots of goodness there. I think the conversations we're having at every client really start with this outcome-based pricing. We've had good success with it, and we think it's going to continue to expand. Next question. How do you expect advancements in AI and AI code generation to impact ClearPath? Is it a risk or an opportunity? Well, I'll do a quick start on that, and then I'll punt that over to you, Chris. Clearly, we think it's an opportunity. There was some great dialogue here today. Sean gave you some wonderful statistics around how we're embedding AI into our ClearPath platform. We've seen 3 years now of consumption increases in that ecosystem, roughly $40 million a year. We just raised guidance again on the top line. A portion of that was related to an increase in that ecosystem. Clearly, ClearPath, from a consumption perspective, is benefiting from AI. Chris, maybe you can give a little more depth there. Yeah, happy to. You're talking about the continuing use of the data in these platforms for the creation of yet untapped value, that really speaks to the consumption patterns that you were just referring to, Mike. We anticipate and believe that that's going to continue as we head into the future. That's been our experience. In addition to that, we also consider that the continuing evolution of the platforms and the ecosystem surrounding ClearPath are also beneficiaries over time of the implementation and embedding or infusing of AI inside the platforms themselves. Consider the execution of AI native workloads inside the platforms. Those are parts of anticipated new capabilities in the platforms as we go forward. Those are parts of continuing to stay current and ahead of where our clients want the platforms to be, and we see continuing demand for that. Lastly, just as it relates to the other things that you would expect in terms of development efficiency, testing efficiency, comprehensiveness, et cetera, those are infused inside the engineering function as well. There's a multifaceted benefit chain here that we are, in some cases, in the early days of already experiencing, and in other cases are embedding in our roadmaps for the future. There really is confidence from our perspective about future paths here that are continuing some of the trends we've been seeing. Yeah, look, and I think Sean touched on as well, the part of the question was around code generation. The entire Project Endurance that Chris started and Sean picked up the mantle on really solidifies that engineering base, that ability to use code, convert code. We've been refactoring code for probably a decade in that environment. We feel strongly that we've got a digital answer to that question, and again, I think the engineering moat that's embedded in our ClearPath ecosystem speaks for itself. Mike, I guess I just would add to that, too. I'm so happy you mentioned that Endurance capability. Really one of the core questions about long-term serviceability and part of our ClearPath 2050 initiative over these last several years now has been about ensuring that effective engineering depth and support is available. It's not just in the creation of the platform code or the updating of that code for new capabilities. It's as much about the long-term lasting necessity for that engineering depth and expertise that's also benefiting from the infusion of AI technologies, like you just said. Thanks. Next question. There is significant capital being allocated to the data center build-out. Can you expand upon the data center services you provide across your portfolio and size those opportunities? Well, the sizing piece of that's going to be extremely difficult to do. As we all know and see every day in the news, billions upon billions of dollars are being invested into building out that data center. I don't want to limit our exposure there from a TAM perspective to be just a build-out of an AI data center. I think the opportunity there is large, and we continue to, I'll say, shift our marketing focus to aligning to the builders of those data centers, both as the users as well as the physical folks that are constructing those centers because we do think there's opportunity for us to play in that space, whether it's part of a consortium or as a sole proprietor kind of in there, racking and stacking and taking care of that. Our data center opportunity goes much, much deeper than that. We've already seen and have accomplished a transition of a lot of our front-line engineers. You heard Weston and Patrycja talking about the opportunities for those engineers inside of the data center, right? That goes to things like high-end storage and networking capabilities, training our front-end engineers into a multitude of skills. Takes them out of some of the lower cost commoditized work that they're doing and pushes them up stack. You heard Weston talk in detail around IoT devices, sensors in devices, conference rooms. If you looked in the journal today, you saw some NVIDIA discussion around AI chips in PCs. Dell had a very similar announcement earlier in the week regarding their chipset. It's not just data center anymore. AI is and will be more and more at the edge, at the desktop level, in servers, on-prem, in the cloud. I go back to that long-standing legacy of having hybrid infrastructure management, both from a field services perspective as well as embedded in our CA&I business unit where the orchestration of that and the application of how workloads get moved, done, executed. That's all, in my mind, data center support, both from a services execution perspective as well as an install, breakfix, and ultimately a managed services. Yeah, sure. Look, you've talked about Edge, you've talked about data center, you've talked about hybrid infrastructure and the private and public kind of handshake that's going on there. That's only going to continue to grow. Part of our mission is to ensure that the scaled global field services population, that some of you would be familiar with, have the right skills at the right time, delivered in an experience that they can absorb, usually via a mobile device, to go to the site, have the part, meet them there, and replace it successfully on the first try. Because the impact of missing out on that is pretty tough when you're talking about data center workloads, for example. Those are the exact investments that we told you about today. We're really well-positioned to take advantage of those investments and extend them across that entire ecosystem, that entire chain from the end user with their own device to the data center where they're sourcing the query or the response to their query in the data center. Yeah. I think we just, on the webpage, just put a whole new section on how we're targeting this market. Yeah. So. Great point. Yeah. Thanks, Deb, for bringing that in. Yeah. I think there's a multitude of data out there for the site- Yeah. Mm-hmm and what it means to us. If you're interested. Yeah check out our website. It's a new page for data center in particular. Yeah. Next question. Thank you for the disclosure around your de-leveraging path, pension detail, and EBITDA expansion. Would it be fair to think that pension-oriented de-leveraging could be pulled forward should capital markets provide you the opportunity? You want to take that? You want me to take that one? Sure. Sure. Yeah. I can't commit to anything right now, but clearly based on the execution, our profitability, and the way we see capital markets in the next year or two, that could be a potential. I think we'll have to see. Also in about two and a half years, the non-call period ends on the notes. We would assess, right? Does it make more sense to pay some of that down? Does it make more sense to borrow, make a pension contribution, or does it make sense at a certain point down the road to pay down the whole U.S. qualified pension, that $225 million-$275 million we've said it would cost? As we look at our execution on profitability and cash flow and we look at the capital markets, we'll make that determination. Yeah, I think at the end of the day. Yeah we look at that through a lens of cash. Right. Mm-hmm. What is the most advantaged cash position? Right we can put the company in to continue to reinvest and do other things with that capital? Exactly. Next question. Do you have any workforce reduction plans given the emerging AI technology? Yeah. Certainly, a question that's pertinent in the market, and we've seen many of our competitors having significant layoffs, unfortunately, in the market. I would say our position is and has been that we have really thinking about upskilling our workforce to have that workforce ready for scale. Chris has a program in our organization really aimed at delivery excellence, and we feel like it is a better investment from our perspective to lean into that, to upskill as many associates as we can. Our thoughts are really about making those associates much more productive, right? When you think about the productivity index, you hear everything from 6 to 10 times from a productivity perspective with the application of this technology. Our goal here is not just to meet our clients' expectations, it's to exceed and delight our clients, right? I think from our point of view, that doesn't mean we're not going to have normal trimming of the workforce where maybe there's a client attrition or something that we can't absorb. Our goal and what we've really instituted over the last 18 to 24 months was really a concerted effort to upskill really every associate in the company. Chris is spearheading some of this, so maybe you can give a little color there as well. Yeah, happy to. Our view around this productivity index that Mike is talking about is really core to a couple of important factors here. One is that for us to be AI first as a company, we need to ensure that our associates, who have been and continue to be the lifeblood of how we do things for clients, have the skills. That's really where our attention is focused now. Similarly, as Mike was talking about, there are places where we decide to make adjustments and changes, as any business does. Our expectations around delighting clients with the use of the newest technologies, with the implementation of our newest solutions that have been widely recognized in industry as leading by this time, is really where we're focused and where we're asking for our associates to focus. Next question. You laid out a number of seemingly significant growth opportunities. Which one or two are you most excited about? Wow. For me, the two that stand out the most are the opportunities within that data center ecosystem, if you will. I think we've got deep experience there. The competition, I think one of the stats we put out today, 7,300 field services agents that we've got. We've got a very deep and broad skill set that I think differentiates us in that space. I think there's some real opportunity for us to lean in there and expand that data center beyond field services and into management. The other area that's just jumps off the page from my perspective is agentic orchestration embedded in CA&I. When you think about the application of a digital workforce into this environment, it's something that we're seeing, and frankly, it's amazing. Things that we've delivered to clients already in days that would've taken months, if not quarters, to do with half the talent or a third of the talent to actually deliver it. The technology is really incredible there. Those are typically going to be smaller deals with quicker passthrough time. That'll get us likely some more in-year revenue, quicker recognition on those types of things, and really establishing a little better cadence in project work. Deb and I have talked for years that we'd like to see that mix shift between that 80/20 of recurring revenue and project work shift a little bit, because the project work is typically a little better margin profile. I think that aligns really nicely to those two opportunities that I would pull off. Again, either of you have anything you're thinking of? I was just going to say, I think Service Experience Accelerator, so within DWS, I think is an exciting opportunity where we already have a lot of clients on it, plan on shifting a lot of our other clients onto it, I think is the one that jumps out for me. Yeah. I know you're going to go ClearPath. I suppose I ought to round us out, but I do think you talked about this idea that we are touching compute capability and the necessity for participating in that evolving ecosystem, again, from end-user device, IoT device at the edge, into the data center, all the workloads happening in a hybrid way between private, public cloud, and in those data center environments. We are really positioned to touch all those. If I could have one big one, that's one for sure. Really where that comes to life is with our team, our associates, and we talked earlier today about the changes in roles, past and future. Future roles enable our folks to actually take the experience that they have and think about and answer questions differently with the tools we're putting in their hands. What happened yesterday? What happened last hour, such that we can make changes in our decisions for what to do in the next hour? That compression of time and enablement of decision-making is just a massive step change for our people. In that context of how we touch and fix and create new value across that compute ecosystem, that's a really big deal for me. It involves our people, which I'm really passionate about. Yeah, the last thing is clearly the ever-enduring value, and yet to be untapped value that exists inside that ClearPath client base. The ClearPath team and the clients are ever more involved in partnering to find new ways to unlock that. That's really exciting to me, too. We have time for one final question. Any comment on the stock price increasing from $1.97 to $4.60 in about two months? Yeah, keep it going. That would be my comment for the stock price. No, look, I really feel like, and I'll tie it back into the very first question that we've got. There, in my opinion, was a market knee-jerk reaction when certain of these models came out and if you think about what happened to deploycos when the models came out, SaaS providers got hit pretty hard. Solution implementers got hit pretty hard. We, in that industry in general, I think got hit pretty hard from a valuation. I like to think that this is really just a return to norm. It's interesting that the same positioning that caused the market deflection. Yeah Now coming out with deployco is actually causing the inflection. Yeah Again, do I think that this is the valuation that our company should be at? No, I still think there's room. Deb gave you some great examples of what that short-term investment could look like without even changing our valuation. We're happy to see a little bit of the return to the norm, and hopefully it's a realization from a market perspective that Unisys is here to stay. I'd like to just maybe close with a couple things that I started with and maybe close with. There are a couple things that I'd like you to take away from this. First, the strategy that we put in place, again, middle innings as far as I'm concerned, but the foundation is set, and I think you've seen throughout the day how that strategy continues to evolve. Right? We didn't luck into the position that we're in right now, and I think we're really set for success and building from a strong foundation. Second, if I look at the opportunity in the market, TAM has opened up for us in a couple of areas, and we just mentioned maybe three or four or five between us, that I think we've yet to see the benefit of that TAM. Hopefully, we'll continue to win in the marketplace, and we'll continue to grow the business from that front. Three, and the thing I'm probably most proud of and I think most important to us is a comment that Chris made. It is embedding into our workforce this AI-first strategy. We've spent the last several years embedding the AI into our technology and solutions, but those solutions really only have value if we can deploy those solutions in a meaningful way to our clients. Really, the fact that workforce is up to speed with the latest and greatest tool sets, and I think that's really going to help our clients achieve their value as well. Lastly, that the work we've done over the last seven to 10 years around continuing to strengthen our balance sheet, continuing to strengthen the profitability of the company kind of puts us in a solid position. We have a good cash balance. We've got working capital improvements, et cetera. Looking forward to what we can achieve together, and frankly, doing that probably for the first time in a long time with some tailwinds. Seeing the market kind of reflect a little bit of positivity in this space is really helpful to us. If nothing else, you should take away from today that Unisys is an AI future-ready company. Client-focused, future-ready is not a tagline to us. It's really important, and we're positioned to take advantage of that in the market. Thank you all for your time. Thank you for your participation. I hope you took as much away from this as we did, frankly. We really enjoyed bringing it to you, and look forward to talking to you probably at the next quarter. I'm joined today to have a conversation with a gentleman that I've worked with for almost a decade, Simon Springett, who is the Chief Technology Officer at Cushman & Wakefield. Simon, pleasure for us to have this conversation with you today. Pleasure, Mike. Nice to see you. Maybe I could just ask you to give a little background of yourself and Cushman & Wakefield and what you do and what some of the issues you're facing from a technology perspective are? Sure. Thanks, Mike. I guess my job here as CTO is surrounded by technology and myth and magic. Fundamentally, my job is to make life simpler and easier for our colleagues. We have 53,000 incredible colleagues distributed all around the world. Our job within the office of the CTO and across technology at Cushman is to simplify our tech landscape, make it more resilient, make it more secure, and build on our foundation to enable AI. Predominantly make sure that data and data governance is at our heartbeat, because if you get the basics right, everything else is possible. You've got a very pragmatic, realistic approach about adopting technology, and I think it comes through in our partnership. Maybe just by way of background, could you really expand on the issue we're facing today, with this AI first strategy is where's the ROI? Yeah, I think, AI ROI is a very interesting concept. Maybe we're looking at things slightly differently, and I know we've been in conversations as well, is how do we become agile? How do we look at opportunities? How do we stop looking at maybe cost or cost save, but look at true benefit in automation and in people? Try and get away from the normal model of financial ROI. Now that doesn't mean, of course, you go and throw tens of millions of dollars at these solutions and tell everyone it's a great idea. As with Unisys and with some other large and very public partnerships that we've created recently, we're really enabling our colleagues, every colleague from the brokers through to the most important janitors that we've got everywhere, to enable AI to make their jobs better. One of the things for us is investing in data. AI tools are fantastic, but they're also incredibly fast at serving up the wrong information. At Cushman, we've got a huge ethos around data security, data structure, and of course, cybersecurity. I think let's stay grounded in what's useful. We're looking at practical AI, so things like knowledge assistance, things like serving up policies, processes, procedures to people, rather than just buying the latest new thing. As I've mentioned, the biggest part of that is consolidating the opportunities into a handful of enterprise-enabled solutions. A theme that keeps coming back is AI is not an event, it's a process. I think you personally and your company take a really good approach to how to apply that process. Obviously, there's still some gray area, I think, between AI and automation. It feels like unless you throw AI into a conversation nowadays, you sound like you're behind the game. Automation is still very, very important to every business, not just CRE. Making sure that you're leveraging what you have within your automation portfolio. The other part is not forgetting what is your core business. AI is really great at enabling parts of functions. For us, what is still incredibly important is the expertise, the experience, and then the marketing aspect, the people that can take that information that's now delivered at pace and probably more broadly than ever before, to a device that they can be very mobile with and leverage the relationships that they've got with those clients to serve up industry-leading data to allow our clients, same as we're a client of yours, but to allow our clients to make really informed decisions. Ultimately, CRE, there was all the talk about why need CRE? We can just go and lease a building on Copilot. Nobody is going to be in that phase with the billions of dollars that we're talking about commitment. It's the people relationship that still matters. It's no different to the relationship we have with you at Unisys. Could you give a little insight into your experience with us and our view of experience when helping you shape your environment? We're a very distributed organization with regards to locations, but we're a very centralized, small organization when it comes to culture and people, and those are two really important values. They do sit very close to the values that Unisys came to us with the original, "Hey, we can be your partner." Way back when we were significantly younger and started this relationship, I really value the fact that we started it on a personal foundation. We said that trust is key, openness is key. One of my mantras is, "I don't like surprises," and Mike, you've heard me say that to you on a number of occasions. I have. We are surrounded by incredibly smart technical people. The partner piece was really important to all of us here, and it wasn't just you wanted to be a service provider. You came to us wanting to be a partner, and I think you came to us with some experience of what we were doing. You didn't come to us as the experts in the field and didn't pretend to be. We have enough expertise in everything that we're talking to you about that we bring, I think, some thought leadership and value. We also are smart enough to know that we don't know everything, and we certainly don't know your business as well as you do. I think we try to take an approach that says we're going to grow together. We're going on a journey together. I think genuinely, we can't, and we shouldn't underplay the value of the true partnership and in the way everybody shows up. Everybody shows up, I believe, with wanting to make the place better. When I say the place, I'm talking Cushman & Wakefield, and Unisys are coming there to make it better. That is for everyone's experience. The people who are hourly paid, who it's absolutely imperative are not hampered by taking too long trying to have technology do the basic functions they need technology to do. They need to work. We've got a great help desk, we've got a great technology tool, we've got great agentic solutions that are being deployed right now. We've got really quick pace to allow people to service. We go all the way through to the really technology-based SMEs, the network teams, the cyber teams, and all the people that we've just discussed, really partnering with our retained organization. Very lean. You know that, Mike. We're very narrow with regards to people, but we've got some incredible expertise. Put those two things together, I genuinely think we've delivered some outcomes that are way bigger than both of our joint sizes. I think the relationship is key. We talked about it already. Honesty and trust and transparency are hallmark for that relationship to grow. I think you've kind of just illustrated that, and in a nutshell, I think have illustrated our partnership together, right? I believe it starts with the relationship at the core and starts with us putting our heads together and essentially on a whiteboard, thinking about what is the process that we're trying to affect change. I think too often we all sit in a room, and we all chase innovation with the big capital letters because that's the dream. That's where we need to be playing. One of the things that I feel works really well with us as a partnership and as open dialogue is often we're looking at innovation with the lowercase I, but it's the stuff that's real. It's the stuff that we can sit in and get away, create cleanness in the noise. For me, hygiene is really important in everything we do across our technology disciplines. From the most important piece of the leavers and joiners process, because if you get that wrong, forget everything else. Together, we've sat down and helped automate where we can there. We've partnered with compliance, regulations. We've partnered with HR, we've partnered with your teams, all of the product sets. Everybody looks at that as just meat and potatoes. That sets the tone. For me, the most important interaction that anybody gets when they come to Cushman & Wakefield is day zero. If we can make people operational from the start, you gain trust. It's no different to our relationship. You gain trust. I'll close with a macro question on what's next, Simon, for you. What is the main theme or the main strategic objective that you want to bring to Cush Wake that not only that Unisys can help with, but clearly from an IT initiative? I'm glad you left the big question to last. I think there's a bunch of opportunities that we've got, and then there's what's real from a timeline point of view. I think for me, and certainly for my leadership and the board and where we're taking the direction of the organization is we have to understand our biggest opportunity is connecting everything. Modernizing our applications, strengthening data, and making AI sit across all of that environment to then make the surfacing of that data as quick and as real-time and as accurate as is physically possible to service every single one of our colleagues that need to use it. Making sure that we are investing, which we are, in governance frameworks, security process, just making sure we get the posture right for everything that we bring into the organization. I think we need to extend our service experience across the business. We need to accelerate what we do with data and app modernization. Ultimately, if we get those right, we get the trust of the end users, we get the trust of the colleagues. We can start to see a big difference in how AI is genuinely driving our business and ultimately our profit forward by not just obtaining new business but retaining existing business. The ROI is on the heels of that, right? It is a tremendous amount of expertise that needs to be applied in the orchestration of that technology. I'm happy that you're looking to us to help with some of that because of our experience in how the rest of your ecosystem works, right? I think I'm super proud of the partnership that we've got. It continues to grow and expand. We started another five years from November last year. Yeah, congratulations and thank you. We'll do this again in our 20th year anniversary or something. No, look, I really want to thank you for your time today, your partnership, your leadership, both with Cush Wake as well as supporting our team from a Unisys. We're proud to be part of your organization and wishing you the best success, and I know we've got some great things coming. Thanks for today, Mike, and thanks for the partnership.