Artificial intelligence is not only transforming applications and data centers, but it is redefining the very role of the network. That was the central message from Rami Rahim, former CEO of Juniper Networks and current Executive Vice President and Head of HPE Networking, during his speech at HPE Discover 2025. In an event with strong symbolism—the first edition where Juniper Networks is fully integrated into HPE's strategy—Rahim issued a direct warning to networking professionals: traditional models are no longer sufficient.

From Silent Infrastructure to Strategic Platform

One of the key axes of his speech was the changing role of networking within the organization. According to Rahim, the network has gone from being an invisible component to becoming a critical element for the business. To illustrate this, he used a striking analogy: the Millennium Tower in San Francisco, a skyscraper that began to lean because its foundations were not prepared for the passage of time. The message is clear: current networks are not designed to support the loads of AI.

Massive data movements, continuous inference, real-time response times, and extreme scalability are factors that stress infrastructures designed for a different paradigm. Hence his warning: "You can invest millions, even billions, in GPUs, but if the network introduces latency or bottlenecks, you are limiting performance."

For network engineers, the message implies a shift in focus: designing with east-west traffic, minimal latency, and deterministic behavior in mind, rather than traditional north-south models. And above all, aligning technical metrics with business outcomes, such as model training times or user experience. This approach recalls the need for runtime verification that modern critical systems demand.

The End of Manual Networking: Toward Autonomous Operations

This statement summarizes HPE's vision: the traditional network cannot keep up with AI. According to Rahim, "the old, static, manual, and reactive model cannot handle today's speed or complexity." The alternative lies in AI-native networks with autonomous operations, supported by platforms like Aruba Central and Mist, and technologies like Marvis or the so-called "Marvis Minis," within an agentic AI approach.

Sunalini Sankhavaram, Vice President of Product at HPE, reinforced this idea by explaining how the company is evolving toward experience-centric AI. This model is based on real usage data—"every user, every minute"—enriched with digital twins and validated with support cases. In one of the demonstrations presented, the system automatically detected degradations in user experience, identified the cause in saturated access points, and applied a correction without human intervention. "The result is that the network identifies the problem, understands the root cause, and resolves it before the user complains," summarized Rahim.

This approach implies a profound change in the role of professionals: from reactive operators to supervisors of autonomous systems, responsible for defining policies, limits, and SLAs. A change similar to what is being seen in other areas, such as software development, where code must be regenerated, not maintained.

Unification: A Single Architecture for the Entire Edge

Another strategic announcement was the progress toward a unified platform that integrates Juniper and Aruba technologies. The vision involves combining Mist and Aruba Central, wired and wireless networks and routing, and a single AI engine. "We are innovating on both platforms to deliver a coherent autonomous network experience," explained Sankhavaram, who highlighted the use of microservices to develop features once and deploy them across the entire ecosystem.

Among the new features, the integration of Marvis into Aruba Central stands out, including functionalities like the Marvis Trust List, which allows automating complete actions—for example, recovering a failed device—without human intervention. In parallel, HPE has already launched hardware prepared for this approach, such as dual-platform access points, and is working to extend its CX portfolio to Mist-managed environments. According to Rahim, "the mission is simple: bring the best innovations to all customers, regardless of the platform they use."

A Clear Roadmap for Network Professionals

Beyond technology, Rahim's speech sends a direct message to industry professionals: networking enters a new stage where AI will be the central axis. This implies designing networks specifically for AI workloads, adopting real AIOps models (not just monitoring tools), speaking the business language based on user experience, and defining policies for autonomous systems instead of operating manually.

In short, networking ceases to be a support and becomes a direct enabler of business value. And, as Rahim made clear, those who do not evolve with this change risk being left behind: legacy networks are simply no longer prepared for what is coming.

Designing for Platform Flexibility and Automation

Beyond architecture, HPE's strategy introduces a key concept: platform optionality. That is, assuming that over the hardware lifecycle, the management layer can change. For network teams, this means betting on infrastructures capable of operating with different ecosystems, such as Mist or Aruba Central, without requiring complete replacements.

In parallel, the use of digital twins gains prominence. Rahim and his team emphasized that these should no longer be limited to vendor demonstrations but should be integrated into real pre-validation and change management processes, through synthetic tests and experience simulations. Another pillar is automation. HPE insists on an API-first approach, which facilitates programmatic access to both data and network actions. In practice, this means professionals will need to strengthen skills in areas such as Python, CI/CD, or infrastructure as code.

Networks and Security: Inevitable Convergence in the AI Era

One of the most repeated messages by Rahim was the growing convergence between networking and cybersecurity. According to the executive, both areas can no longer operate independently. "Attackers are already using the network as their primary tool," he warned, adding: "And with AI making threats faster, smarter, and more sophisticated, defenders need to use the network as part of the defense."

This vision aligns with market evolution, where more and more network engineers are taking on security responsibilities. Customers themselves corroborate this. Marlon Drummond from the Royal Bank of Canada was blunt: "Security is our top priority. We have no other job than to protect our customers' data." In his case, detection relies on the network layer, using technologies like SD-WAN and DPI to build user behavior profiles and detect anomalies.

At the product level, HPE used the event to announce a unified SASE orchestrator that integrates EdgeConnect SD-WAN with its SSE stack in a single console. Additionally, it presented the concept of an AI-aware firewall, capable of distinguishing between approved, unauthorized, or tolerated applications, applying specific controls over payloads, prompts, or keywords. In Rahim's words, the goal is clear: to allow organizations to "see, govern, and protect the use of AI without slowing down the business."

For network professionals, this scenario implies taking on a broader role: managing zero trust policies and AI governance within the network fabric itself, using the network as the primary security sensor (analyzing telemetry and lateral movements), and ensuring that automated decisions respect segmentation and security policies. In short, the role evolves toward a hybrid profile between network engineer and security architect. This trend aligns with the challenges posed by post-quantum cryptography in corporate environments.

Experience-Centric Networks at Real Scale

Customer cases were probably the most illustrative part of the presentation, showing where complexity and scale push networks to the limit. This is the case of Ohio State University, whose network operates as if managing a small city: 66,000 students, thousands of faculty, tens of thousands of access points, and massive events that concentrate hundreds of thousands of people. In this context, the use of AIOps reduces incidents from hours to minutes.

In the healthcare sector, Tom Johnson from Sentara Health highlighted how network performance directly impacts patient care. "We move enormous amounts of data, and when it is delayed, so is care." AI technologies that generate clinical notes in real time are already in production. In his view, this requires reliable, secure, low-latency networks.

Disney, meanwhile, illustrated the challenge in the content industry. According to Ben Croy, global head of networking, a single production can generate massive volumes of data, with hundreds of simultaneous projects worldwide. In this context, the network must be "fundamental, but ideally invisible," so that creative teams can focus on content.

These examples reinforce a common idea: traditional metrics are no longer sufficient. What matters is measuring user experience and application performance, from videoconferencing to clinical systems or visual effects pipelines, and optimizing the network based on these indicators. Additionally, tools like digital twins and synthetic tests allow anticipating problems before they occur, validating complex deployments or large-scale events. This anticipation capability recalls advances in supercomputing and AI that are redefining data centers.

Conclusion: Networking Enters a New Era

Rahim closed his speech with a clear message: the traditional model of network operations has reached its limit. "The scale is too large, the complexity too high, the pace of change too fast, and AI is accelerating everything," he stated. In this context, autonomous networks are no longer a future promise but a real necessity.

For industry professionals, the challenge—and opportunity—lies in leading this transformation: designing, governing, and evolving these new infrastructures before someone else does. Because, as the speech implicitly concluded: AI will not replace network engineers, but those who know how to leverage it will. And on that path, business productivity and operational efficiency will be the great beneficiaries.

Original source: ComputerWorld. Analysis and adaptation by ForgeNEX.