Hewlett Packard Enterprise Company (HPE) Earnings Call Transcript & Summary

Good morning. Wow, that's a big group. It is great to be here with you. HP Discover is where we showcase what is next and shine light on the ambition and innovation shaping our industry and our lives. Today, we are witnessing 1 of the largest technology platform shifts in history. Workloads and applications are moving from being driven by end users, but now being driven by both end users and AI agents. Agents that will fundamentally transform how we design and build how we serve our customers and how we operate our businesses. I have always been done to how things work, how systems are built and how they evolve over time. In fact, if I have not become an engineer and a CEO, I will have become an architect. Architecture like engineering teaches you to think in systems to build for today and for the needs of tomorrow. You don't design a building around a single room. You design a structure that allows the whole system to flow and adapt over time. Architect and for AI demands the same focus and discipline fundamentally, AI is only as strong as the data foundation be needed. If the foundation is not robust, nothing else holds. Across networking, cloud and AI, HPE is delivering the essential building blocks that make your AI-ready foundation possible. Networking to connect to your infrastructure and workloads at scale. Cloud to enable you with a hybrid operating model to run new world loads and application where they belong and AI to turn your data into intelligence and put it to work. Architecting for AI starts with your network. For years, HP Aruba networking has helped you deliver secure connectivity across campus, branch and the edge, creating a digital on-ramp that connects your users, devices and data. With the addition of Juniper Networks, we have extended that leadership into the data center and across the critical networks, connecting the AI era, scale up, scale out and scale across. With our combined HPE networking organization, our goal is to deliver the best user and operator experience possible. We will do this through our next generation of secure self-driving networks across every domain. They fix problems securely before they impact the experience. we make new rollouts faster and easier and fundamentally transform how you manage your network whether you are on the HP Aruba Central or HPE miss, you get the full benefit of our accelerated innovation on both nobody is left behind on the road to self-driving. But the phrase self-driving may have caused some confusion with the Mercedes Formula 1 drivers. Let's take a look. [Presentation]

I had the opportunity to talk to both drivers this past weekend, and they told me they have fun to in that video. But reality is that they really are very, very keen and interested to understand technology. So congratulations to the Mercedes team for a fantastic year so far. Every AI architecture needs more compute. In the AI era, your servers need to operate more efficiently than ever. With HP ProGen 12, you can the performance to run everything from enterprise or lot influence in a much smaller, more efficient solution. As AI moves from generating content to taking action, the demands on the compute are increasing. Agent AI requires fast orchestration, continuous evaluation and real-time access to data, which shifts more pressure to the host CPU. That is why we are expanding our Pro Line portfolio with the new HPE ProLiant compute DL3394 Gen12, powered by NVIDIA Vera CPUs. Vera provides the low latency memory access bandwidth and coherence required for agent AI, reinforcement learning and other CPU-intensive workloads. It does so with the security needs of management, you back from ProLiant, AI moves beyond the data center, compute needs to move with it. We recently expanded our proline age portfolio, bringing secure AI-ready, compute to rag and distributed environment so that influence can happen closer to where decisions are made. AI also needs to reach the always-on systems at the core of digital business. With HP nonstop, we are bringing AI-powered fraud detection and autonomous operations into high-volume payment processing. This helps financial institutions detect fraud faster, automate compliance monitoring and keep transactions moving safely. The most advanced servers in the world are only as valuable as the data they can access, which make storage a critical part of your AI foundation. The HP Electra storage MPX makes your data accessible context-rich and ready to continuously feed your AI data pipelines. Across the full life cycle from ingestion training inference and continuous learning. The X10000 now supports native file and object storage on a single architecture. It is also the first object storage platform validated through MBDA certified storage for enterprise AI. And for the mission-critical applications around your business, the Electra storage MPB 10000 continues to deliver. The beat and towers is the fastest-growing all-flash bulk storage array in the market. Sitting across the top of the stack, of course, is software as AI advances enterprise environments are becoming more distributed and complex. The pine virtual machines, containers, AI infrastructure and of course, public and private clouds. At the same time, rise in virtualization costs are pushing many of you to look for more flexibility and choice. With HPE Cloud's op software, we have brought together HP morphis, Osram, Zerto and our broader cloud portfolio capabilities into 1 unified operating experience. This enables you to modernize on your own terms while simplifying how you provision, observe and protect your hybrid multi-vendor environment. Security and resilience must be built into every layer of the stack of your architecture. As AI change the speed and the scale of cyber threats, new risks continue to emerge. Resilience is no longer just a set of isolated tools. It is a converged strategy across your systems, your data and your network. With HPE Hilo silicon retotrust, we provide secure at the station from the silicon to cloud, helping verify that your infrastructure is trusted before your world lots run. And with Zerto and Cyber Resilience Bolt, we help protect your critical data so you can recover faster and minimize disruption. Networking and security are also converging as AI becomes more distributed, the network after is the first place to see what is happening across your enterprise. With 0 trust architectures and integrated Sasi, the network becomes an active security layer, enforcing policy detecting threats and reducing risk from edge to cloud. We bring all the elements of your AI foundation together with our Green Lake cloud. Green Lake gives you a unified cloud native experience across your entire hybrid estate with the flexibilities around workloads across public and private clouds, colors and of course, at the edge. With Green Lake Intelligence, we bring agentic AI to hybrid IT operations helping you see across environments, act faster and continuously optimize performance. From simplifying network operations to streamline in virtual machine migrations, GreenLake intelligence makes your infrastructure more adaptive, more autonomous and easier to manage. So you can spend less time managing tech and more time managing and advancing your business. So let's take a look. [Presentation]

It's an amazing advancements we brought with HPG. But by the way, what you saw here is just the beginning. In the Discover showcase, you can see how GreenLake Intelligence brings agent AI operations to life. So I will encourage you to go and experience yourself. Architect and AI takes more than technology. It also requires the right people, processes and partnerships. Our services team is here to help you through your AI transformation. With the HPE Financial Services will help you modernize with confidence and better economics, including lower upfront capital investment. And with our IT life cycle management program, you can retire legacy multi-vendor technology and tune that value into funding what is next. This week is an opportunity to explore our full stack AI foundation firsthand with demos, sessions and in conversations with your peers. Discover is 1 of the few moments where we have the full power of the HP community together in 1 place. During the rest of our time this morning, I want to elaborate on 2 core tenets of our strategy. First, the networks that they are at the heart of today's AI data center build-outs. And second, how we are enabling your transformation into an agentic enterprise. In the AI era, the network is both the essential enabler and the main bottleneck for performance. Nobody understands what is a stake more than our customers who are at the leading edge of AI. Customers like Volta, the world's largest privately held hyperscaler. Let's take a look at what HPE and Vulture are building together. [Presentation]

Thank you to the entire Vulture team for being such a great partner. I'm excited to share that this partnership continues to expand as we work together with NVIDIA to support Belcher's next phase of growth. What Vulture is building at hyperscale points to a truth that every architect notes that there is always 1 core element of your infrastructure that touches everything. With AI, that core element is the network. The performance of our entire architecture depends on it. Every bite, every token, every decision, all of it crosses the network, which is why, today, we are bringing the HPE Juniper network into our AI data solutions, enabling more efficient, high-performance AI environment. Whether you are a hyperscaler service provider neocloud or a large enterprise, you have more choice in how you connect and secure your largest AI investments. Let me show you how it all comes together starting with a model training use case. In AI data center design and 1 purpose, turning data, inter-intelligence as quickly and as efficiently as possible. At this scale, performance depends on how tightly computer networking work together for customers building AMD-based systems with Helios we are introducing the industry-first HP Juniper networking scale-up switch purpose built for the AMD Helius architecture. The QFX 50250 brings scale up performance into an open Ethernet fabric design for AI at scale. It connects 72 GPUs into a single rep delivering 260 terabytes per second of aggregate scale-up bandwidth. You get the low latency performance required for large-scale AI or loads with the openness of standard-based Ethernet Sonic OS support and Juniper AI automation. But 1 rack is just the beginning. The largest models trained across hundreds, even thousands of racks operating as 1 cohesive cluster. Multiply a small delay across hundreds of thousands of GPUs over weeks of training and your network can mean the difference between training a new model in 90 days or 30 days. Think about that. It is the difference between chasing a breakthrough or making one. That is why the scale-out network is so important. The Juniper QFX family is built for this next generation of AI scale-out connectivity. Our newest addition to the portfolio is shipping today. The QFX 50 to 50 is the word highest performance, 100% direct liquid cooled ultra Ethernet transport ready switch. Powered by Junos OS, it moves data across massive AI clusters. It achieves this through low latency congestion control and operational simplicity required to keep hundreds of thousands of GPUs working together. Increasingly, AI data centers are expanded beyond single sites. Let's find multiple data centers in regions, sometimes hundreds or even thousands of miles apart, that puts new pressure on the network between these environments. That is where the HP Juniper PTX routing family excels. PTX is built to carry massive volume of traffic across the data center interconnect, core net networks that connect today's distributed AI infrastructure. Our PTX 12000 series is an ultra dense routing platform designed specifically for AI fabrics. It enables 800-gig routing, 1.6 terabit ready scale and ZR, ZR Plus coherent optics to connect data centers across sites without compromising performance. And to protect that connection, we have our HP Networking SRX family, including our most popular firewall, the SRX 4700. It is 1 of the fastest quantum-safe firewalls on earth, delivering up to 1.4 terabits per second of security performance into -- in a single rack unit. It helps you secure modern data centers without slowing down the application and AI or loads you depend on it. But when we talk about a complete portfolio for a train, we support every layer of a modern networking architecture from scale up and scale out to scale across and secure data access all in a single coherent architecture that is secure and fully automated. With the introduction of the HPE AI grade with NVIDIA at GTC in March, we extended this integrated network even further. Built for service providers, the AI grid combines NVIDIA accelerated computing and AI networking, including Spectrum X, ConnectX and BlueField with Proliant Compute and Juniper routing, security and unified orchestration across the full stack. Together with NVIDIA, we are enabling a wide range of new real-time AI services from conversational agents and interactive media to hyper personalized experiences across hospitality, health care and retail. But the real value of AI increasingly come comes from inference. When intelligence moves closer to your users, applications and data, that requires a network built to extend AI to the edge locations like regional data centers and service providers. sites where the Juniper MX family of edge on-ground routers is top of the class. Our MX-301 brings the proven performance and flexibility of the MX family into a small form factor, 1 argue, power optimized platform. It is purpose-built to move influence out of the cloud and closer to where the data is processed for inferencing so we can accelerate decision-making. Powered by Juniper's sixth Generation 3 silicon has near infinite flexibility to meet your networking needs today and into the future. To build your influence environment, you also need a high-performance switching. That is why today, we have introduced the new HP Juniper network in QFX 5140 inference switch, purpose built for distributed AI deployments. Also in 1 RDU, the QFX 5140 delivers up to 16 terabytes per second of switching capacity, connecting GPUs and inference infrastructure with the AI optimized load balancing and end-to-end congestion control to maximize performance. The 5140 gives every edge location. The local intelligence to host AI world loads closer to where inferences needed for faster AI responses and better experiences. Look, the bottom line is to win in the AI era. You need a network build for the full AI life cycle from training at the core to inferencing at the edge. AI is also transforming the demands of the campus and branch networks. They still need to securely connect people and devices. But now they also need to support AI powered workflows that depend on real-time access to data without compromising speed, security and reliability. That level of complexity cannot be managed through reacted troubleshooting alone. Your network has to see more, understand more and do more. Self-driving networks move IT from reactive to overshooting to proactive assurance, understanding experiences, identifying root causes and resolving issues faster. In the race to self-driving, HPE continues to lead. In fact, we were recently recognized as a leader in the Gartner Magic Quadrant for both wire and wireless line for the 20th consecutive year. Position, highest in the execution and furthest invasion. What bothers most is what this capability means for customers like the Milano Cortina Winter Olympics, where HP helped deliver flawless network performance across a very complex environment, spanning 15 venues hundreds of miles apart. HPE miss adopted the network in real time. helping ensure seamless secure connectivity for everything, from broadcast, live streams to event operations and fine engagements. Every moment could be viewed by millions. I hope you watch the Olympics while organizes operated with confidence, knowing the self-driving network was working behind disease to maintain a broad solid performance. Today, we are extending this self-driving experience across our Aruba networking portfolio with 2 new announcements. First, Aruba CX with switching is coming to HPE mess. You gain AI native assurance, faster troubleshooting -- yes, you can applaud, and automated operations across your campus and branch environment. And second, which is why we talk about cross-pollinate, right, with Rami, Marvis actions is common to HP Aruba Center. Marvis is the first network assistant in the industry to bring conversational AI to networking. So your network can move from reactive to self-driving with the native operations that are continuously improving. So you can see how much progress we have made with Juniper in such a short period of time. We are really proud of the progress we're making for you, our customers and our partners. But across industries, customers are making the switch to HP Networking and discovering that the self-driving network is a quiet network because it just works. That is the experience we want every 1 of you to have. If you are considering a change from your current networking provider, you know who they are, I encourage you to start with a single site or even a single floor, experience what a self-driving network can do in your environment. You would be amazed of how simple the experience is. And I will ask you to not miss Rami Rahim's general session later today, plus our 4 networking spotlights throughout the week to see how our self-driving capabilities are coming to life across every single domain. We have talked about the networks that make AI possible and how HP is delivered in the next generation of self-driving networks, that are self-healing self-protecting and self optimizing. Now let's turn to the next major shift in the AI era, the rise of the Agentic enterprise. AI is no longer just a tool for finding answers. It is a critical part of how work gets done. Agents now reason across data applications, models and workflows. They help you make decisions, automate processes and are increasingly taking action on your behalf. Soon, IT will be responsible for thousands of agents that are part of your enterprise workforce operating across every function. But today, much of that innovation still have been in local clients in the hands of developers and small teams, often outside formal IT oversight. That speed of adoption is exciting, but also creates a real challenge, the shadow cost of an agentic workforce that now must be managed at scale, we have never seen before. Agent AI demands a new set of enterprise requirements. Agents need to be secure and governed. We clear guardrails for what they can do, what system they can act on and most importantly, what data they can access. They need to be trained with trusted enterprise data because the agents are only as good as the data and context behind them. And then to they need also infrastructure that can scale as demand grows without runaway costs. When we introduced our HPE private cloud AI 2 years ago. We give enterprise a turnkey AI factory that simplified AI adoption and provided more control. It brings AI to your data, not the other way around. So today, we are enhancing private cloud AI for the next generation of agentic workloads have been new govern agents, ground them in trusted data and scale our inference initiatives. Let's unpack what is new, starting with agented governance. You can now register agents built in any framework and wrap them with security controls to protect API calls, identity and encryption with 0 code changes required. A new 3-tier identity model verifies the user, governs the agent and enables human approval for sensitive action. Today, we're also announcing our new capabilities for secure Agentic operations with NVIDIA Open Shell and Nemoclo. Openshall provides a model and active run time for advanced private agents with policy enforcement built into how agents run. Each agent operates in its own isolated environment with gaterails for what data can access, what systems you can interact and what actions we can take. And with Nimoclo, you get an open source of reference stack and blueprints for govern agenetic workflows, helping you move faster, while maintaining their control and accountability enterprise AI requires. As agents operate across production environments, they also need a new class of operational risks, introduce a new class of operational risks. And that's why we are bringing Zerto to your Agentic enterprise. If an agent makes a mistake, Zerto helps you quickly roll back to a clean state reducing downtime and helping you protect your businesses. Governance in your agentic enterprise is paramount, but governance alone is not enough. Your AI agent are only as smart as the data you use to train them. Traditionally, that data requires custom preparation for every use case, a month of building the right AI data pipelines, but not anymore. Private cloud AI helps make that data you already have ready for agent enterprise for agent with a governed data layer and integration with the MBDAI data platform, you get a unified way to access, prepare and manage enterprise data across your existing environments. Now with Electra storage MP X1000 as the storage layer for private cloud, AI, you can build on a high-performance data foundation designed for modern AI. The X1000 adds real-time metadata enrichment in native MCP support. So your agents and applications can retrieve the right data and context faster across structure, and unstructured data. That means less customer integration work and 7 to 12 x months faster time to value compared to what you normally do, which is yourself builds the whole environment. Once you have governed agents, and train them with the right data, it is time to scale across both agentic AI and new broader enterprise inference for loads. Private cloud AI can now serve larger models across multiple systems with multi-node influence, so capacity grows with the math. A new unified gateway simplified access to frontier and open source models. This gives you team 1 unified API for mobile access with centralized credentials, budgets and policies. We're also expanding private cloud AI with new configurations that scale up to 256 GPUs, including the new ProLiant 394 with media beta CPUs, designed specifically for implant. And for long context worlds, we're also introducing shared KB cash capabilities that reduce the need to recompute context over Enova. This delivers significant cost benefits to first token and massive performance gain in compute capacity. With private cloud AI, you have now the foundation to build your agentic enterprise with confidence. And what is making us stronger is the ecosystem we have built around it. We continue to expand the HPE unleash AI program are curated ecosystem of validated partners, blueprints and orchestration frameworks for private cloud AI. With more than 60 partners and hundreds of use cases, unleash AI helps you find trusted solutions for scale and AI across your enterprise, from securing agents and models with partners like Craftstrike and Fortanex to expanding where you can deploy AI to Digital Realty and Equinix, private cloud AI and the Alisha AI ecosystem help you move from AI ambition to real-world impact faster. Let's take examples of that for St Jude's Children Research Hospital, that means bringing AI closer to doctors and researchers accelerated life-saving discoveries while protecting highly sensitive medical data. for Blue Star operations, the business behind the dollars car voice, it means reducing lower value work streams and advancing strategic decision-making across football and business operations. And for the rider cap, it means being able to power real-time event intelligence from crowd management and concessions to voluntary assistance and operational planning. In fact, the Redcar organization are now leveraging the private cloud AI to turn the next event in a massive success by really using a digital twin approach, so they will help them architect the 2027 turned experience. But look, these are just a few examples of how we are giving you a faster, more structure path to AI adoption that will transform how you run your business. And there is more to come. Tomorrow, Fidel Maruso will share additional news in her general session and go deeper in how our latest cloud and AI innovations help you build a new operating model for your agent enterprise. AI today is about moving faster from ambition to AdCom, accelerating time to token, reducing execution risk, ensuring your environments are ready to perform from day 1. Our AI factory solutions are designed to do exactly that with validated architectures, agent operations and enterprise great support. They also meet you where you are designed for your unique operative models, governance needs and scale. For enterprises, I share how private cloud AI is a secure and govern prepackage factory for your agentic enterprise. For model builders, service providers in Neoclouds are AI factory at scale is built for large multi-tenant AI environments. And for governments, regulated industries and sovereign entities, our AI factory for sovereigns enables you to deploy AI alike to local data, security and compliance requirements. Across our AI factory portfolio, our deep collaboration with media helps you build the latest accelerated computing platform like NVIDIA Beta and Beta Ruby. NVIDIA Beta CPUs is on our latest proline servers are powering now agent loads across enterprises. In supercomputing, NVIDIA Vera and Perarubin architectures are advancing our Crave portfolio for both HPC and AI. -- and AI factories at scale, NVIDIA Perarubin MDL72 is driving the next frontier of rack-scale solutions. Compared to NVIDIA Blackwell, Perarubin MBL72 delivers AI training with 1/4 of the GPUs and AI reference influence at the 1/10 of the cost per million token. So think about that, the massive gains you can get to that token faster. So whether you are building for the enterprise or training from tier models, HPE gives you a path to build and scale on the latest NVIDIA accelerators. As AI scale, scales across more users, more data and, of course, critical operations, trust must be built into that foundation. That is why we are making confidential computing standard across the full HPE AI portfolio, helping protect sensitive data models and allows while they are in use. With NVIDIA confidential computing, AI workloads run in trusted execution environment that are the hardware protected layer of security across the stack. And for organizations operating in a most sensitive environment. We are taking that trans foundation even further. Our sovereign factories now include the fed's great security hardening, federal compliance readiness, validated encryption standards and global data protection requirements all built in. So if you are a defense government or financial services, this is the sovereign AI architecture you have been waiting for. Architect for AI requires looking ahead, anticipating and designing for the constraints that will shape the future. But there is 1 challenge we all need to overcome, not just for our industry but for our society and our planet, and that is power. Every model, every lead, every agent depends on power because at its core, an AI factory is doing 1 thing, turning electrons into token. The U.S. is on track to have a 19 gigawatt power gap by 2028. That's roughly enough electricity to power 16 million homes. In data centers are expected to account for nearly half of the U.S. electricity demand through 2030. One customer, Siemens Energy is tackling this challenge head on helping build the energy infrastructure the AI era requires. They are doing it by applying AI to their own business. with HP helping deliver the AI foundation across networking, storage and compute. Let's take a look. [Presentation]

I want to thank the Siemens Energy team for working on such an important challenge. We are proud to support your ambitions. Initiatives like this underscores a bigger point. As AI scale, the future will not be defined by computer alone. It will be defined by how efficiently we can power it cooled and connected. That is why research become so critical. For 6 decades, HP Labs has helped shape the future of enterprise computing. Your next-generation infrastructure will need to operate with much greater intelligence, efficiency and transparency. Today, our researchers are applying AI to improve AI systems themselves, making them more scalable and more sustainable. This is what HP has a unique advantage. We engineer the compute -- the complete architecture from compute servers, obviously, networking, storage, software and security. And we apply that system expertise across the full stack. With innovations like GreenLake Intelligence, we are developing predictive cell driving intelligence that can learn world patterns and place data where it needs to be before an application asked for it. Across your broader data center environments, we are using AI to improve resource management, identifying idle patterns and reduce energy and water consumption without compromising performance. We also advanced the next frontier of computing through our work in Quantum. With initiatives like the Quantum scale in Alliance and our work in distributed quantum simulation, HP is helping bring Quantum out of the lab into the real world. You can see that feature taking shape right here at this cover where a quantum handle here sits along an original HP grade 1. It does a great reminder that how far high-performance computing has come and whether it is heading next. As network in HPC and AI and quantum converge, progress will depend on how effectively we bring these technologies together at scale. Yesterday, we took another major step forward with the announcement of an expanded industry collaboration to advance hybrid Quantum. Together with these leading companies, we are building a full stack hybrid quantum platform that extends our world-class HPC and AI infrastructure and moves Quantum closer to real-time and real-world deployment. Quantum advancements like this are accelerating the path to faster, more efficient solutions for most of the world's complex scientific and industry challenges. These are the challenges that inspires our HP Labs. Ultimately, it all comes back to 1 simple mission to advance the way people live and work. It is a guided force behind that innovation, our people and our long-term strategy. Today, we have covered how architecting for real starts with your network and how we can help you transform into an agentic enterprise. You have seen the powerful outcomes that results from people with bold ambitions, they are matched with the right technology and the right partners because none of this happens a lot. Our partners help HP bring these ideas to life with expertise, reach and execution, customers depend every single day. They help us extend our impact, bring an innovation closer to the customer and communities we serve. Many of our partners have generously sponsored this week. Discover is only made possible because of you, like us, believe in the ambition and the power of pursuing it together. So I want to thank all our sponsors and now our partner. We appreciate you very, very much. We take tremendous priority knowing that our innovations are a catalyst for your success, driving outcomes to propel new opportunities for you and your customers. As you experience all Discovery has to offer this week, keep these things top of mind. First, architect deliberate. The choices you make today will define your success tomorrow. Second, start with the network. Make your network the core foundation of your AI and cloud solutions, and finally, choose HPE as your partner to bring the full stack to help you build your AI future with competence. This week is an invitation to think bigger, to move faster, to architect the future you want to lead and for the world. And remember, you don't have to build this future alone. We are here to provide the intelligence foundation so you can move boldly with purpose and unlock your ambition. Thank you very much. I hope to see you on the floor. Enjoy the rest of the week.

Thank you for joining us at HPE Discover's Networking General Session. Please welcome to the stage, Executive Vice President, President and General Manager Networking, Rami Rahim.

Hello, everyone. Welcome to HPE. Discover Las Vegas. My very first Discover Las Vegas, could not be more excited about being here. Okay. Thank you. Thank you. So I got a story for you. A few years ago, in San Francisco. San Francisco became home to 1 of the most famous engineering, cautionary tail in modern construction history, and it was all about the Millennium Tower. This is a name, 58 story luxury skyscraper in the heart of the city, beautifully designed, technologically advanced built to be truly iconic. But over time, something unexpected started to happen. The building began to sync and then it began to tilt. Not because the structure above ground was poorly designed. But because the foundation underneath it was built -- wasn't built to handle the long-term realities of the environment around it. And as the demands on the building increased over time, the weakness underneath became impossible to ignore. So right now, companies everywhere are racing to build intelligence applications, autonomous operations, real-time experiences and entirely new business models powered by AI. But AI places enormous new demand on the infrastructure, massive data movement, constant influence, real-time responsiveness, explosive scale and if the underlying foundation isn't designed for that new reality, eventually the strain starts to show up. So to succeed in networking today we have to think differently because AI is changing everything. One of the clearest messages from this morning's keynote was simple. AI is reshaping every part of the enterprise. But none of that happens without the right foundation underneath it. And that foundation starts with the network. The network is no longer infrastructure sitting quietly in the background. It's become a strategic platform for how organizations operate, innovate and scale. Why? Because the demands on it are exploring more users, more devices and more applications, more data and entirely new expectations for real-time experiences across every industry from digital payments, connected stadiums, health care, research, media and AI-driven services, the network is what makes those experiences possible. That's why leading organizations are treating the network as core strategic infrastructure, not just to keep up, but to unlock what comes next. Now the ones that embrace the shift we'll be better positioned to innovate faster and to compete more effectively. The ones that don't will increasingly find themselves left behind. But the good news is this, while AI is placing unprecedented demands on the network, AI is also becoming the answer to how the network adapts, scales and withstand that pressure because the old model of networking, static, manual, reactive, simply cannot keep up with the speed and complexity AI introduces. What's required now is a network that can learn, a network that can predict a network that can optimize and heal itself in real time. In other words, the future of networking will not just support AI it will run on AI. Now we see this in 2 really powerful ways, AI for networks and network for AI. First, AI is changing how networks are operated as environments become larger, more distributed and more dynamic, manual operations are just not going to keep up anymore, that's where AI for networks become the true game changer. The payoff is significant, better uptime, better user experiences, fewer tickets, faster remediation and more time or IT team to focus on strategic work instead of constantly troubleshooting. And second, AI is redefining what the network itself must deliver. So the reality is this AI innovation can only move as fast as the network allows. You can have massive compute power and millions, if not billions spent on GPU, but if the network introduces latency and bottlenecks and instability, your limiting performance, slowing down outcome and giving up ground to the competition. That is why the network has become essential infrastructure or the AI era. And none of this works -- none of it worked effectively at least, unless security is built into the foundation itself because the network is now both the connected fabric for the business, and unfortunately, increasingly 1 of the main pathways that attackers use to target it. That means the network has to be a core part of the security strategy with AI-driven anomaly detection, automated response, role-based access and enforcement and 0 trust approach that helps protect users, applications and data everywhere. And just as importantly, it has to deliver that protection without adding friction that flows users down or piles more complexity on to IT teams. That is the real shift here security and user experience can no longer be trade-off. Now all of these point to a new era of IT, 1 where self-driving network are no longer optimal they are essential because AI scale infrastructure cannot practically be operated manually, the networks of the future must be able to sense, to learn, to optimize Supertech and heal themselves in real time. And let me be clear about this. HPE has made more progress than any other company in these areas. We are bringing together AI native hardware, software, silicon security and agentic AOP into a closed loop system that operate at speed and scale that humans alone simply cannot match. The result is a network that delivered better performance, stronger resilient, simpler operations and better user experiences and that's what the self-driving network is really all about moving IT team for manually operating infrastructure to accelerating the business. So today, I want to explore what this next era of networking actually looks like and how secure needed self-driving network are solving real problems for our customers. Now some of these customers are going to be joining me on stage to share how their organizations are navigating ideal world problems today. And you're going to be seeing demonstrations of how HPE ACE networking helps organizations overcome these problems and move at the speed of innovation with confidence. Because you know what, the organizations that modernize their networks now with the right architecture and the right intelligence, and the right operational model are going to be much better positioned for what comes next. With that said, enough from me. Let's hear from the people out there building and operating these environments in the real world. So please join me in welcoming my first guest CIO of the Ohio State University, Rob Loudon.

Thank you. Thank you very much.

Rob. Thank you so much for joining us at HPE Discover Las Vegas. Maybe just to get started, introduce yourself and tell us a little bit about your role, you're responsible for managing a very large campus, thousands of students, tons of connected devices and what a great school is data. So tell us a little bit more.

Absolutely.

I think there's some alma mater in the audience.

Any grads out there. We have 650,000 living alumni. So there's got to be a few of them here. So thank you for the opportunity to share a little bit about the Ohio State University. Not only is it the namesake Land Grant University, small city within Columbus, the capital is state of Ohio, we have 66,000 students and 8,500 faculty. We're the fourth largest university in the country and networks not a lot to us, as you've already alluded to. So on our Columbus campus alone, we have 22,000 HPE wireless access points, indoor and outdoor across numerous acres on campus. We have 15 colleges. We have a hospital. We have a comprehensive cancer center. And maybe a few of those folks out there know that we also have a football team.

I have heard, yes. And I think there are some rivalries out there that people care a lot about. Higher education institutions are probably some of the most demanding network environments around because it's pretty much everything you just stated. What are the biggest challenges and demands that, that places on your infrastructure.

Absolutely. So 1 that's unique to the [indiscernible] that we just heard. We have a stadium, some of you might have heard of, it's the Horseshoe. Not the Horseshoe casino, all those similar to the Horseshoe casino, the house always wins at Ohio State. So at the Horseshoe she can host 100,000 participants viewing us crushing maybe that institution to the north that we don't refer to too often. But that creates a unique networking challenge and fan experience, ticket, check-ins, everything being electronic. Last fall, we hosted a team from down south, and they were ranked #1 when they came to Columbus, but maybe they left a little lower. And that was the largest broadcast in NCAA history of any sporting event. So there's a lot on the line. During the event, there can be another 100,000-plus people outside the stadium. So it's not uncommon for a home day game for us to have 200,000-plus people around that. with the partnership with HPE as we speak, engineers are working together, collaborating on refreshing our WiFi networks there. And it's critically important to us, not just to ensure that, that fan experience is there. A few hundred yards away, we have a $7 billion hospital system, comprehensive cancer center, 2,000 beds in that facility. And that's going on next to 200,000 people enjoying a game. So we're putting in, I believe, the single largest implementation in the country, 2,000-plus wireless access points HPE Juniper missed [indiscernible] super excited about.

But like let me ask you, with that kind of complexity, I would imagine this is where AI Fit and self-driving automation. Yes, leading questions can be quite useful. Would it -- just comment on that a little bit.

Absolutely. AI ops is something that we absolutely are excited to have in place. We have 100,000 managed devices. We're expectations for us on bring your own devices is substantial add tens of thousands of more through in a game like that, we need AI ops that's provided to crunch all of that data in real time. And we've seen this, in effect, move us from scenarios where problem resolution can take hours sometimes, you just can't afford that with the various operations going on at the institution. So it's literally moving us from absorbing that threat intelligence, applying the AI ops and giving us answers to resolution, and we're seeing things gain result in minutes versus hours.

Awesome. Look, Rob, we so thoroughly enjoy having you as a customer. We become a better technology company as a result of patent a customer. Thank you so much for joining us. Thank you, Robbie. . Okay. I am pretty sure that what Rob just shared resonates with many of you because what you described isn't unique to 1 university or 1 industry, we hear the same thing from customers everywhere, more users, more devices, more applications and now AI adding an entirely new layer of demand and complexity. And at the same time, IT teams are being asked to move faster, simplify operations, strengthen security and deliver a flawless experience. And that is exactly why the cell driving network matters. And if the self-driving network can handle an environment as dynamic and demanding at Ohio State, it can handle just about anything, now you all know, we deliver those AI-native autonomous capabilities through 2 industry-leading Agentic AI platform, HP Aruba Central and HP Amit. Each platform brings unique strength and serve different customers and is trusted every day by organizations around the world. And let me be clear about this. We are committed to innovating and innovating aggressively on both of these platforms, including bringing the best capabilities from each platform on to the other. That both platforms continue to get stronger, and both are here to stay. So to show you what the self-driving capabilities look like in practice, please welcome [ Simelini ] from HPE's campus and branch business.

Hi, Rami. Hello, everybody. Hello again. Welcome. As you said, we are innovating in both platforms to deliver a consistent self-driving experience. This is possible due to 2 things: micro services and a common Agentic AI framework. With micro services, we are able to develop self-driving innovations once and deploy them on both the HPE Aruba Central and HPE Mist platforms, much like a single app experience is delivered on both iPhone and Android. So no matter what Agentic AI Ops platform you are on, you are going to be enjoying the benefits of a self-driving network. And with a common Agentic AI framework, we are able to accelerate self-driving capabilities at a much faster pace on both platforms with trusted actions that deliver measurable value. Our Agentic framework, Rami, is unique in the industry because of these key foundational pillars. First, we use real live experience data, every user, every minute, which HPE has uniquely validated against real customer support cases and enriched with digital twins to maximize the efficacy of our AI-driven insights. Second, we have an API-first approach, which means all the data is available via our APIs. This makes our MCP server and tools extremely powerful, delivering agentic automation at scale. Third, a powerful set of AI agents and skills analyze all data sets and apply reasons from HPE [ Marvis Minis ], which serve as digital twins of user experience to agents analyzing packet captures, logs, knowledge-based articles, security vulnerabilities and all the support data and signals we get. They all work together to proactively diagnose and autonomously root cause problems impacting users without inundating operators with data. And fourth, not the end yet. The fourth aspect of what makes us unique, models take the analysis and curate it to understand and analyze post connection issues. For example, [ Marvis ] has a large experience model or LEM, as we call it fondly, that identifies the cause of bad Zoom and Teams calls and predicts future problems to prevent them when possible. We bring all of this together into the Agentic framework, resulting in a system that is continuously observing, reasoning and analyzing with autonomous actions that optimize user experiences. This is a self-driving network.

So [ Simelini ], most vendors talk about AI assistant and agentic AIOps, but still rely on reactive, human-driven troubleshooting. But if humans still have to fix the problem, where exactly is the self-driving in that? So can you provide us an example of why real self-driving network operations actually matter?

Of course. We all know that today's networks need to cater to high-density requirements while also meeting the performance expectations of every user and application. But most networks are not designed to meet intermittent surges in peak traffic. Think of an all hand in an office building, a crowded classroom during a popular university lecture or even this very room right now where thousands of you are eager to see the best networking solutions ever. Operators try and accommodate these situations with a combination of static boundary parameters and on-demand changes. But often, that just isn't enough. Let me show you a self-driving network powered by real Agentic AI and how that handles this problem.

They can't wait. Let's do it.

If we look at [ Marvis ], we see that all the users in this office building are currently happy. But was this the case all of last week? No, it wasn't. So what happened? Hard and unhappy users become happy. Let's take a look. Last week, [ Marvis ] detected that over 6% of user minutes were bad, which may not sound like a lot, but it impacted hundreds of people. Marvis has a self-driving action for dynamically fixing capacity issues. This action was enabled and was able to autonomously fix the problem by enabling dual band 5 gigahertz. This reduced the fleet utilization from 90% to 54%, enabling a better experience for users that were unhappy. But how did Marvis know what to fix and when to fix it? The unhappy minutes in the past week triggered various models and agents in the HPE Agentic framework to reason and analyze and root cause the problem. For example, one model analyzed the service level experiences. [ Marvis Minis ] agents were activated to test the network using digital twins and other skills went into effect. Based on the reasoning and analysis, Marvis determined that the wired and WAN was not the problem. The wireless network was, but what in wireless wasn't working? Coverage was fine, roaming was fine, wireless capacity was bogged down on a few APs that were functioning at 90% peak utilization. This is when Marvis went into full self-driving mode. It changed the RF parameters and validated the service expectation of the users to ensure they had 100% satisfaction. Rami, this wasn't manual tuning. This wasn't chart an error. This was a network optimizing itself to deliver the best user experience. And this is available right now in HPE Mist.

Today, like right now?

Today, right now.

Amazing. So let me just think about what I just saw there. The network identified the issue, right? The network understood the root cause, it determined the right action and resolved the problem automatically before any user even had a chance to complain about their experience. No emergency troubleshooting, no IT team scrambling to diagnose, no help desk tickets. I mean that sounds like real value for IT teams and also for end users, [ Celini ], right?

Absolutely.

But that's just one example. I suspect you've got more.

Well, let's find out. We heard from our Mist customers that Marvis Actions, which automatically identifies network issues and proactively resolve them is mission-critical to daily operations. So that's why HPE Marvis, our industry-leading AI engine is coming to HPE Aruba Central. It has all the simplicity and all the impact that customers have come to expect from Marvis. This is experience-first AI in action, and it is a perfect example of how we are able to develop self-driving innovations once and deploy them on both the platforms, Aruba Central and Mist seamlessly, thanks to our Micro services foundation and the common Agentic framework. That's how we are bringing Marvis to HPE Aruba Central right into the global [indiscernible]. Behind the scenes, Marvis does all the heavy lifting, correlating logs, alerts, signals across the entire stack. Your morning cup of coffee view will now showcase end user impacting issues across wired, wireless and SD-WAN with recommended actions. Missing VLAN, MTU mismatches, negotiation failures, they're all coming to Central. In addition, what we call the Marvis Trust list is also coming to HPE Aruba Central. These are actions that you can choose to be fully autonomous. When enabled, Marvis not only finds the root cause, it fixes it for you. Imagine a security camera connected to a wired port in a bad state. No camera feed, that's a real problem. With Marvis in self-driving mode, that port is recovered automatically and expeditiously and the camera is now working again. That is the power of bringing Marvis into HPE Aruba Central, not just more visibility with highly accurate actionable recommendations, but a better end-user experience, thanks to self-driving capabilities.

Okay. That's like a pretty awesome example of just how quickly innovation can move when we bring together the best of HPE Aruba Networking and HPE Juniper Network, bringing Marvis into HPE Aruba Central is a -- not a little, a huge step forward. That being said, Marvis truly is the AI engine behind the self-driving network, and now Marvis will be available across everything, both platforms. So our mission is simple. It's not easy to do, but it's simple to say, bring the best innovations to both platforms so that every customer in every industry gets the same powerful self-driving network no matter which platform they choose. Okay. Now [ Selini ], we did this by promising both software and hardware cross-pollination. You just demoed some compelling examples of common software. What can you tell us about hardware? Have we made any progress on that front?

Yes, Rami. We have made quite a bit of progress. A few months ago, we made a commitment on common hardware by announcing the first dual platform access point, the [ 723H ], which is now generally available. Within the first year of the Juniper acquisition, not only have we cross-pollinated AI models and Agentic frameworks, we have delivered an access point that works with both the Mist and Aruba Central platforms. And on that same theme today, I am super excited to announce that we are doing something very similar with switching. Our world-class HPE networking CX portfolio, which previously was supported by Aruba Central will also very soon be supported by Mist for day 0, day 1 and day 2 operations.

It's a big deal.

It's a big deal. Let me give you a sneak peek of what you can expect in just a few -- few weeks, sorry. Let's start by showing how easy it is to onboard the HPE networking CX switch into the HPE Mist platform. It starts with a simple scan of a QR code of the CX switch from within the Mist installation app. Once onboarded, the devices show up in the inventory and they are ready to be configured with templates that enable you to centrally define and apply consistent configurations at scale like pushing VLANs and port profiles to hundreds of switches. With telemetry coming into the Mist platform every minute for every wired client, combined with high-efficacy AI/ML models from Marvis, we can measure preconnection and post-connection wired SLEs and not just a port is up. For example, the successful Connect SLE measures preconnection experiences and the bandwidth congestion and throughput SLEs will give us insights into post-connection experiences. With the SLEs, the IT administrators can quickly identify where an experience is bad, including interface anomalies and other issues but we don't stop there. All the goodness we get from Marvis Actions will also be available for CX switches. This includes proactive recommendations for missing VLAN, MCU mismatches, bad cables and more. And the best of all, a self-driving trust list will also be available, starting with the ability to autonomously fix stock ports to remediate wired clients in a bad seat. This enables real-time closed-loop self-healing, reducing mean time to repair, eliminating manual troubleshooting and delivering great user experiences at scale. This is huge because we all know the network being up is not the same as users having a great experience. So for our CX customers, no matter what agentic AIOps platform you choose, you get simpler operations, actionable and proactive recommendations and a real self-driving network.

Let me just pause here and say like what makes this so powerful [ Similini ] is not just what you just saw, what all of you just saw, but how fast we made it happen. In just a few short months after the close of the acquisition, our teams came together to deliver real software and hardware cross-pollination. Honestly, I could not be more proud of you and the team [ Similini ] because this is -- yes, yes, clap for [ Similini ] and the team. Mad respect because this is exactly what innovation at scale should look like, moving fast, bringing the best ideas together and delivering value to customers with speed. And the result is incredibly powerful. You all have a self-driving network with the flexibility to choose the platform experience that works best for you with the confidence that your investments are fully protected. And [ Sunalini ], the market, I think, is taking notice of this, right?

That's right, Rami. The best evidence is the new Gartner Magic Quadrant for wired and wireless access published just 4 weeks ago. Hold the applause, it shows HPE as a leader, applaud. And even more so, we are [indiscernible] right for vision and highest in the ability to execute. In my humble opinion, it is clear who the overall leader is. What do you think, Rami?

Look, I could not agree more. Thank you so much. Thank you so much for joining us again.

Thank you, Rami. Thank you, everybody.

I really think that says a lot. Last year, the industry looked at HPE Aruba Networking and Juniper Networks as 2 separate leaders. This year, the market is recognizing what happens when you bring those strengths together. One team, one vision, one innovation engine and most importantly, one clear direction towards AI-native self-driving network. Now of course, this doesn't just apply to wired and wireless networking. It also extends into security, which happens to be the next topic I'd like to talk to you about today. And to help me tee it up, I'd like to welcome from the Royal Bank of Canada, one of my favorite countries, by the way, [ Marlin Grumman ].

A lot of people right?

A lot of people.

Please have a seat.

Thank you.

Thank you so much for joining us, Marlin. I know you're at RBC, an iconic bank. Just maybe tell me a little bit about your role at the bank.

Sure. My name is [ Marlin Grumman ]. I run -- I'm Senior Director, I run engineering and automation and by extension, AI, fifth largest in North America. And just sort of lay the context of -- so there's difficulty from a threat perspective, about 4,000 endpoints from the SD-WAN and the Edge Connect. But if you took the entire scope of the threat landscape, it's probably double or triple between cloud, trading, every business line. So it is quite extensive, and it's extremely difficult for a legacy bank. And I'm not going to -- I don't mean that in the sense it's truly legacy, but very, very difficult to do.

I would imagine you're dealing with massive volumes of not just data, but sensitive data. So security, I suspect, is a bit of a consideration for you, right?

Yes, it is job #1, I think, for the most part. I don't think -- I think if you're a bank and you're regulated like we are in 29 countries, you have all of the, I'm going to say, the environmental from a regulatory perspective, you have to deal with for every single country. It is a very difficult process. So security for us is job #1. We actually don't have any other job other than protecting our client data. And not only that is a blueprint to actually how we operate. We feel that it's our competitive edge. You have 13 million clients, you're in lots of different countries. That data in itself forms the basis for decisions and all exactly our competitive edge. So that we protect with everything in our pri5bery.

So test me on this. One thing I think every malware has in common is that it has to use the network to do security work. So I always said that an effective security strategy must also leverage that same network to better detect and to better enforce policy. I mean, what do you think of that?

Absolutely. I think like we think about this a lot, and this is primarily to sort of keep the services up. I know you talk about self-healing and -- but for the most part, in general, you troubleshoot at the network layer. That's the first -- that's the only place that you can get some immutable evidence to be able to identify what's going on. So we've always lived on that mantra. So for the most part, how we sort of detect and manage troubleshoot, protect is all at the network layer. So being able to sort of identify when somebody is knocking out the door, that's very important. And the only way you're going to really see that is whether it be lateral movement in the network and so on and so forth. So you want to make sure that, that side of it has -- is fully covered part of the resetting the stage for the 4,000, at least what we can identify and speak about clients is the Edge Connect and that platform using CPI engine helps us sort of create, I would say, a persona or a personality for a user. Anything outside of that becomes an anomaly. And that's something that allows us to now be able to go look, something is going on and so forth. But it's part of the intel that we need to be able to sort of shore up our threat intel.

Makes sense. I know you're working on some big AI transformation projects. I mean maybe just share with us a little bit about how these AI projects are geared towards network operations in particular?

So as you know, I know it's public knowledge, we are AI first and AI everything right now. We've pledged to our Investor Day shareholders that we will generate $1 billion in revenue just using AI. So we've embarked on this incredible effort. So the [ AIification ], I guess, if you want to look at it, of the entire bank. So that's a significant effort. The operational side of it, I think is sort of table stakes sort of -- I don't want to say it's the easy piece, but it certainly stretches all the way through every business line, AI being sort of the horizontal in which you kind of build the framework. But the operations side is near and dear because that's where we actually can make a difference at least on our side. And we're sort of extracting telemetry. We're building the harnesses and creating a framework to be able to take that data you take the Connect, for instance, that's one place we also collect data to be able to sort of put into whether it be a [indiscernible] or through some sort of [ MITE ] framework to be able to sort of mitigate the threat response but -- or create a threat response. But the whole idea is that this will be an AI everywhere, collecting data, mining it, normalizing it and vectorizing it and using that data to be able to help us solve the AIOps problem.

Thanks so much for sharing that with us. Fun fact for you. Everybody, I built my career at Juniper and now HPE. Many people think that was the only job I've ever had. Actually, my first job was serving ice cream downtown Toronto. And the very first paycheck I got, I deposited at RBC.

I have to tell you...

So I am proud to have you...

Absolutely. Absolutely. And I have to tell you, I hear it a lot from a lot of people. We're sort of that cradle-to-grave kind of bank. We want to make sure that you come in early and stay forever.

Thank you so much for joining us. Appreciate it.

Thank you, Rami.

What Marlin just described is really the reality for [ ITT ] is pretty much everywhere in the era of AI, increasing scale, more complexity and a nonstop wave of evolving security threats, which is why networking and security can no longer operate separately. Security has to be built directly into the network. And that's exactly how we designed the AC self-driving network because the truly self-driving network doesn't just optimize and heal itself, it protects itself. So to show you what that looks like, please welcome product leader for SASE and security, Madani.

Good afternoon, Las Vegas. I'm excited to be here.

We're excited to have you. We're excited to have you. Okay, listen to me [indiscernible]. Attackers are already using the network as their weapon of choice, as you all know. And with the AI making threats faster, smarter, more sophisticated, defenders need to use the network as part of their defense. That's why security can no longer sit beside the network. It has to be built into the network. How do we help the good guys do this?

Well, Rami, it starts with a Zero Trust approach to network security. This assumes that no user or thing is trusted by default and requires continuous verification of every access request. This would be like a Blackjack dealer making a check a background check before ever dealing a single card and then kicking it off the table in the case of cheating. Wait, I think they actually do, do that. We successfully implementing a Zero Trust approach requires 5 core elements: visibility into all connected users and things, policy-driven orchestration, ubiquitous policy enforcement, real-time detection and automated AI-driven responses. Now you do have to have the right mix of protected -- protective layers in the network to deliver autonomous protection for the strongest defense possible. And HPE has a full security portfolio, which includes firewalls with industry-leading efficacy and performance, NAC with comprehensive access control with consistent enforcement across every type of device or user on the network, SSC with agent or agentless deployment options, supporting a broad set of applications with intelligent routing. And last and certainly not least, SD-WAN with integrated application performance, which is -- and security, which is optimized for any environment.

So okay, we clearly have all the core security pieces in place and all of the elements of a winning hand, you might even say we have a full house. I'm sorry. But ultimately, what's important is how these capabilities come together, Madani. And nowhere is that more important than in SASE, right? Where networking and security operate as a single system to securely connect users, applications and data everywhere. I think you have a pretty interesting announcement to make on that front.

That's right, Rami. I am very excited to say that we have now combined our [ EdgeConnect ] SD-WAN and our SSE into a unified SASE orchestrator with one console, consistent Zero Trust policy and AI-driven operations for simpler, faster and more secure connectivity.

Very cool.

Now what do you all think about that? But Rami, I can't just come to stage and talk about it. I need this audience to actually see it and to demo it live.

Okay. Let's do it.

So let's check it out. All right. The new SASE orchestrator is a powerful solution combining both networking and security under one umbrella. Here, you see the dashboard page. This is where an administrator would get sort of a quick snapshot of everything that's going on within the environment for the last 1 hour to up to 7 days. But this isn't the special sauce. Let's go ahead and check out the business intent overlays. To me, that's really where EdgeConnect shines. This is a great example where EdgeConnect has been self-driving for many, many years already. It autonomously delivers the best quality of the experience while also handling the worst kind of links for any type of application. Now let's jump out of this a little bit and talk a little bit about security. We'll navigate over here and check out the global firewall policy rules. This -- what's great about this feature is the fact that these firewall rules are written here, but automatically distributed across the entire SD-Wranch -- SD-WAN fabric. Next up, let's take a look at the SSC policy rules. This is the new capabilities that are part of the unified orchestrator. Now being in Vegas, Rami, Blackjack is on my mind, which is probably okay. But visiting a gambling side of my company laptop, probably not so much. All right. So let me show you how quick and easy it is to ultimately deploy like a web filtering policy for our [ ZTNA ] users. I'm going to attempt to type up here in front of a live audience, and hopefully, we'll get this right. I'm block gambling, come down here to the destination, and I'm going to look for gambling again. We've made sure that it sets a block and I'm going to go ahead and save it. Now what's going to happen is it's going to push this out. As you can see, essentially added that policy rule, fairly easy, even a product manager can do it. I'm going to go ahead and apply it. And all at once, we're now pushing out that policy for all of our users. And again, this policy could -- making these changes is all done directly here from that one place. Now -- that's what's really exciting about the fact that what used to be done in 2 different products is now possible in the single SASE orchestrator.

Super simple.

Super simple. Now before I leave, there's one more thing I want to take a look at, which is the new SASE Copilot, where ultimately, a user can ask a variety of different questions. We also have some prepopulated ones. And ultimately, what's great about it is it allows them to resolve issues and minimize risk to their environment very quickly.

I think this is a pretty big deal, right?

Absolutely.

So our SD-WAN and SSE solutions are already like excellent on their own. But now we're taking things a major step forward, bringing SD-WAN and SSE together into a deeply integrated solution. What else you got for us?

Well, I've got a few more demos, I do want to tell this audience that this is coming out just later this year. All right.

Let me actually -- before you go on, it's clear that SASE Orchestrator is like a great step towards agentic AIOps and Agentic SecOps with a network that is not just self-driving, but also self-protecting the Madani, right? But security is also critical to AI itself. Organizations need a way to harness the power of AI without compromising their data. So what are we doing to help customers on this front?

Well, Rami, we are all over it. Our AI aware firewall lets you safely embrace AI by giving you governance over how AI is used with real-time visibility, #1 rated efficacy and threat detection and simplified security operations. That means that you can protect sensitive data without slowing innovation, okay? Let's take a look. All right. So here, we have the Security Director Cloud, which manages our SRX hardware and virtual firewalls. Up at the top, we have a new capability, the Security Director Copilot, which lets our administrator ask different types of questions and can also select pre-populated ones. So this little exercise, I'm going to try my luck again at typing. And let's see here. We're going to show the latest threats to my environment. All right. We'll go ahead and do that. And now the Copilot is going to spend a little bit of time thinking, doing a little bit of crunching of data. And what's happening behind the scenes is that we're pulling information from all the SRXs that are in this particular topology. We're also leveraging threat intelligence from our HPE Threat Labs. And ultimately, what we should see here any moment now as it finishes up its thinking is that it's going to give us some very specific insights about the different types of threats that we're seeing, as you can see here. I'm going to go ahead and scroll up just a little bit here. We can see a variety of different pieces of information, user names, source IPs, [indiscernible] and so forth. But we really get some real detailed information in terms of things like what the threat is, industries that are being targeted and what countries. Now at the bottom, we also have a great set of recommendations, right? It's not just about showing the information, it's also being able to help supercharge our administrators. All right. Let's go ahead and we'll jump out of that. Now for this next little segment, I'm just going to put a little bit of background on what we're going to do for my last demo. Taking a look at some of the policies that we have here. And I'm looking specifically around some AI governance rules that we put in place. Now there are 3 types of rules that we've implemented. One is for sanctioned AI apps, which are AI apps that are allowed by the organization. Unsanctioned AI apps, which that's just a fancy way of saying we're going to block it and tolerated AI apps, which are AI ops that have some guardrails. Now sanctioned and unsanctioned is pretty straightforward, but I would say that tolerated is a little more nuanced, and it's about having the right types of guardrails in place. So let's just take a quick look at what that is. So here, we have a few different types of things that we're looking for. We're going to be looking at upload protection rules, prompt protection rules and last but not least, some keyword protection rules. Now let's see this in action. So I'm going to jump over. Now we've switched over to another view. This would be the end user view, if you will. I happen to be here on hp.com. And you know what, I'm going to go ahead and try to go to ChatGPT. Immediately, I get a block message, right? This is one of the apps that we've actually flagged as not being allowed. It's an unsanctioned app. Now let's go ahead and try plot as an example, same thing, just to make sure that everyone kind of saw that happen, we'll go to Google, and we'll bounce back, same effect. Now let's see what happens when we try a tolerated app. So I'm going to go over here to Gemini. And remember those guardrails I spoke about, Rami.

I remember clearly.

Okay. So I'm going to attempt to upload a corporate file, a company report, probably has some information, maybe it's an internal content. I don't necessarily think I should probably uploading it into this tool. And as we can see, Gemini is struggling a little bit. It's attempting to pull this file. But what's happening behind the scenes is our firewall is going and blocking this. And so ultimately, what we'll see here in a mere few seconds is that lo and behold, the upload fails. All right. Now let's try a different example. I'm going to grab some text here that has some unique words like restricted secret encrypted. I'm just using this to facilitate my typing. And what I'm going to do here is I'm going to say, "Hey, let's summarize this information." I'll go ahead and paste that and let's go submit lo and behold once again. Jim and I struggled. Can't figure out what to do, can't process this information because I was doing what he needed to do. All right. Now as I mentioned before, this is a tolerated application with some specific guardrails. And so I want to basically show to the audience that I haven't completely crippled this. So let's see what happens as I say, summarize HPE Discover, and I go ahead and submit that. Look, already, we see a little bit of a change in behavior here. It's searching the web. It's going to figure this out. And yes, it is our flagship annual conference. So very cool. As you can see, how we would use this technology in a real-world case.

That's awesome. That's really, really cool. And I would imagine it's super, super powerful, right, Madani.

Absolutely. And to me, this is what makes this solution so robust, so powerful, if you will. It proactively detects threats, simplifies operations with guided insights and enforces granular real-time controls to safely govern AI application usage.

You know what I really like about our AI firewall is that it gives customers the ability to see, govern and protect how AI is being used across their entire organization without slowing down their businesses because at the end of the day, nobody wants to choose between being secure and moving fast. And what you just saw were 2 powerful examples of something bigger, right? At HPE, AI, networking and security are no longer separate domains. They are converging into a single intelligent self-driving system. Madani, thank you so much.

Thank you, Rami. Thank you, everyone.

Okay. I want to shift gears a bit and talk a little bit about something that sits at the very core of networking. You know what that is? Routing. This is what Juniper was originally built to do. And honestly, routing has never ever been more important than it is right now because no matter what kind of network you are building, AI fabrics, data centers, campus environments, WAN, cloud connectivity, security architecture, service provider network, blah, blah, blah. Keep going, routing is foundational to it all. It is the connective tissue. There are some routing fans here, okay? It is the connective tissue for the modern infrastructure. And as networks become more distributed, more dynamic and more AI-driven, the demands on routing are growing dramatically. So to help me kick off this important topic, I'd like to welcome the Director of IT at [ Centerra ] Health, Tom Johnson. Tom, How are you?

Great, Rami.

Thank you for joining us. Tell us a little bit about yourself and also about Centerra Health for those that are not familiar.

Absolutely. So I've been with Centerra Health for 29 years. And during that time, I've witnessed and been a part of many major technology transformations. We are one of the largest not-for-profit integrated health systems in the U.S., Mid-Atlantic and Southeast. Over $14 billion in operating revenue, 35,000 employees, 12 hospitals in Virginia and Northeastern North Carolina. We have over 200 connected sites with 400-plus total points of care. We have a health plan division that serves almost 1 million insureds in Virginia and Florida. It all keeps us busy.

So I would imagine with that many different locations, employees and then patients and insurance business as well, you have a lot of sensitive data that you are dealing with on a daily basis. How does that impact your infrastructure and networking decisions?

Well, we moved massive amounts of data across the organization, and it directly impacts patient care. That's why our network has to be resilient, secure and always available because when data is delayed, care is delayed. Our clinicians rely on real-time data for decision-making at the bedside. And that requires instant, reliable delivery everywhere. Areas like radiology, where images from some of our systems are always pulled fresh with no caching. Latency simply is not an option.

Okay. So get it. You mentioned latency as an important consideration or requirement for your network. Now with many AI applications that require that, I get it. But what kind of AI applications are you typically deploying in health care? And what does that mean? What else does that mean for your network?

There are a lot of opportunities for AI. One example where we've seen real success is using ambient AI to capture patient conversations. It generates the clinical notes and even helps identify potential care gaps. It allows clinicians to spend less time focused on documenting and more time focused on the patient. But for those kinds of capabilities to work, our network has to deliver that data in real time, reliably, securely and without latency. So clinicians, they can trust it at the point of care.

So we were talking backstage and discussing this a little bit. But looking ahead, I know you have ambitious growth plans and digital transformation plans in particular. What can you share with us about how this is -- how HPE is helping you on this journey?

Well, I could spend a lot of time talking about our initiatives. Health care has got tons of them. But to enable them, our infrastructure has to be ready to grow with us. We need to be able to expand our operations and embrace new technologies while maintaining performance at scale. And HPE is a trusted partner that has given us the solutions we need now and into the future. And we love the AI capabilities that this gives us. You've heard about some of them today already. But we would love to see that extended across all the networking domains.

I hope you're paying attention to the keynote that I'm delivering here because that's exactly what we are delivering, my friend.

Excellent. I can't wait.

Listen, thank you so much. You are on an important mission, and we're proud to support you on it.