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The goal of every digital transformation initiative is to drive leaner and easier business operations, create better UX and enable faster decision-making. Every transformation project relies on networks. However, many organizations still operate without a complete view of their entire network stack. This isn't because companies aren't investing enough in modern cloud deployments, AI tooling or advanced applications. Rather, companies don't fully understand what runs on their networks.

In the past, copper-based cables underpinned WANs worldwide. Fiber optics have largely replaced these lines. Fiber, composed of thin strands of glass or plastic, delivers data as light pulses at speeds much faster than electrons in copper. Today, fiber links corporate headquarters, branch offices, extended offices, remote locations, partners, vendors, employees and data centers. The enterprise network infrastructure now operates on a decoupled layer of fiber and software-defined virtual networks.

AI continues to accelerate the shift from legacy, manual network management to self-optimized, self-healing environments. As enterprises adopt AI to improve speed, efficiency and resilience, CIOs and other IT leaders must shoulder new oversight responsibilities. AI-driven networking requires governance frameworks and automation guardrails to balance innovation with control and human accountability.

At last June's HPE Discover 2025 conference, there was talk of networking as an essential pillar of AI infrastructure, along with announcements about new HPE Aruba networking products. But the pending Juniper Networks acquisition and the Department of Justice's opposition to it were muting factors. A short time after that conference, the DOJ dropped its opposition, and the deal proceeded. I shared my perspective on what all this meant last July.

LAS VEGAS -- HPE wants its networking customers to get comfortable in the passenger seat. HPE introduced a variety of "self-driving" network capabilities at its Discover user conference Tuesday, continuing to make networking and AI a focus as it takes on fellow IT infrastructure giants such as Cisco, Dell and Huawei. The latest updates further HPE's integration of technologies from prior acquisitions, such as Aruba Networks and Juniper Networks, into its overall product set.

With the rise of AI, the need for specialized low-latency processing to rapidly produce and render images, videos and 3D graphics is surging. GPUs, which can run thousands of mathematical operations, are foundational to facilitating the development and deployment of AI-driven applications. But global demand for GPU-centric computing has exceeded hyperscaler capacity. Specialized providers emerged to deliver GPU-as-a-service platforms, known as neocloud.

Cisco Live 2026 is now in the books, and it was a testament to the Cisco team's execution. The event revealed advances across the board, aligning with the vision and objectives detailed at last year's event. Most announcements and presentations focused on helping organizations deploy and adopt AI. Additionally, attendees saw important examples of how Cisco integrates AI within its products and services to foster and maintain progress.

AI is continuously changing, evolving and upgrading. While these advancements benefit organizations, they put a strain on the networking infrastructure. During a keynote at Cisco Live 2026, Denise Lee, vice president of engineering sustainability at Cisco, evaluated this challenge and presented Cisco's response: Energy Networking Systems.

Many IT organizations are turning to AI as they plan to replace the tools they now use to monitor and troubleshoot their networks. Enterprise Management Associates (EMA) recently surveyed 352 network management professionals for the 2026 edition of its biennial research report, Network Management Megatrends. The research found that 33% of IT organizations are very likely to replace their network observability tools over the next two years.

No matter the level, most network professionals feel the same way about AI. From networking architects to day-to-day engineers, network professionals experience the same struggles and successes with AI in network operations. As the field of AI evolves rapidly, it's important to understand how network professionals are integrating the technology into operations.