Today we’re speaking with Vladislav Zaimov, a seasoned specialist in the telecommunications sector with deep expertise in enterprise networking and the risk management of critical network deployments. The recent $14 billion acquisition of Juniper by HPE has sent ripples through the industry, creating a new titan to challenge established players. We’ll be exploring how this new entity is proving its capabilities in the most demanding environments imaginable, from major sporting events like the Winter Olympics, to the complex strategic and technical integration of two industry giants, and what the future holds for the AI-driven, self-driving network.
Deploying temporary networks for major sporting events is a massive undertaking. Beyond publicity, what specific technical challenges do these high-stakes environments present, and how does success there build credibility with enterprise customers when competing against established rivals? Please share a key lesson learned.
These events are the ultimate trial by fire. You’re not just setting up a network; you’re building a city’s worth of critical infrastructure across a vast, 22,000-square-kilometer area, only to tear it all down weeks later. The pressure is immense because failure is public. You have miniature drones transmitting live footage, thousands of journalists, and operations staff all depending on flawless connectivity. The key challenge is that the story must always be what happens in front of the camera, never the technology behind it. When we successfully manage thousands of access points and switches in this chaotic, high-density environment, it’s the most powerful marketing imaginable. It demonstrates reliability under extreme pressure, which resonates far more with a CIO than a standard sales deck. A key lesson is that these deployments are a masterclass in logistics and rapid execution; we love the “circular economy” aspect, but the precision required to install and then dismantle a network of this scale is a massive operational feat in itself.
With the integration of Aruba and Mist being critical, can you outline the technical roadmap for combining their strengths, such as extending the Marvis AI engine across all HPE networking? What are the key milestones, and how will you ensure this process is invisible to existing customers?
The beauty of this integration lies in the foundation of the two platforms. Both Aruba Central and Mist were developed using modern microservices, not legacy monolithic code. This is a game-changer. It means we don’t have to perform a painful, rip-and-replace style merger. Instead, we can treat the integration like assembling building blocks, taking the best of each platform and applying it to the other. A pivotal milestone is the expansion of Marvis, which was born in the Mist ecosystem. Marvis is now being positioned as the single AIOps engine across the entire HPE networking portfolio. This means its powerful predictive maintenance and chatbot-like operational capabilities will become available to Aruba customers as well. We are making this process invisible by focusing on API-driven integration behind the scenes. For a customer, their existing platform simply gains new features and capabilities over time without any disruptive migration.
Networking now drives over half of HPE’s operating profit, while the Juniper deal carries a $600 million cost-saving target. Can you break down how you will achieve these savings while simultaneously retaining key talent and accelerating innovation in the combined networking group?
It’s a delicate balancing act, but the strategy is clear. The $600 million savings target is a necessity of a merger this size, and we’ve already seen workforce adjustments, with almost 5,300 roles eliminated to bring the combined total to about 67,000 employees. However, this isn’t just about cutting costs. The primary driver is synergy. By eliminating overlapping functions and streamlining operations, we free up capital. That capital is then reinvested into the areas that are driving the future, specifically R&D in AI-driven networking. Because the networking unit now accounts for more than 50% of HPE’s operating profit—nearly 52% in the last quarter—it’s the company’s engine. The leadership understands that starving this engine of talent or innovation would be catastrophic. The focus is on retaining the engineers and architects who built these complementary platforms and giving them the resources to accelerate the integration and build the next generation of “self-driving” networks.
Given that the legacy Juniper portfolio was sufficient for the vast scale of the Winter Olympics, what specific customer problems can you now solve with the combined HPE portfolio that were previously out of reach? Please provide an example integrating servers with data center switches.
The Olympics deployment was a testament to the strength of the Juniper portfolio, which handled the event’s needs perfectly with its routers and roughly 1,500 EX-branded Ethernet switches. However, that was a pure networking play. The true power of the combined company is the ability to offer a complete, end-to-end solution from the server to the network edge. Before the acquisition, a customer would buy their servers from HPE and their data center switches and routers from Juniper or another vendor. This creates complexity in procurement, management, and troubleshooting. Now, we can walk into a customer’s data center and offer a unified stack. For instance, we can provide the server equipment and the high-performance Ethernet connectivity inside the data center as a single, integrated, and optimized solution. This simplifies everything for the customer and allows for deeper integration and automation that just isn’t possible when you’re stitching together products from different companies.
What is your forecast for the evolution of the AI-driven, “self-driving” network? What specific manual tasks for network engineers do you expect tools like Marvis to completely automate within the next five years, and what new skills will those engineers need to develop?
My forecast is that the “self-driving” network will move from an aspirational goal to a practical reality in most enterprise environments. Within five years, I expect AI tools like Marvis to completely automate the most time-consuming manual tasks. This includes things like proactive troubleshooting before users even report an issue, root cause analysis that currently takes hours of log-sifting, and the automated configuration and optimization of Wi-Fi networks based on real-time usage patterns. The days of engineers manually adjusting settings or hunting for a needle in a haystack will be largely over. Consequently, the role of a network engineer will fundamentally shift. They will need to evolve from being mechanics to being architects and data scientists. Their new skills will focus on understanding business intent and translating it into network policy, interpreting the insights generated by AI, and leveraging automation tools and APIs to orchestrate the network on a strategic level, rather than managing it device by device.
