AI is raising the bar, and your network has to rise with it

We’ve all seen technologies arrive with a bang and disappear into obscurity just as quickly. AI is different. Certainly it arrived with a bang, but it’s not going anywhere. In fact, just the opposite: It’s evolving in real time, building on itself faster than we can write about it. In all my years in networking, I have never seen anything quite so transformative.

AI is changing how organizations operate, innovate and compete. It’s also redesigning the entire network infrastructure, and that’s where things start getting complicated, because this is more than an upgrade — it’s a complete reinvention.

Let’s examine why AI is becoming the primary driver of network modernization and how tools like agentic AI are transforming how organizations invest in modern networks, prove their value and justify their network as measured against experience level agreements (XLAs).

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AI’s demands on a network are relentless

To keep up, your network needs to become faster, more scalable and more secure. Here’s why:

Datasets are colossal

Let's be honest: the sheer “data gravity” of AI is staggering. Whether we’re training complex models or running real-time inference, we’re talking about colossal datasets that need to flow seamlessly between edge devices, clouds and data centers. The legacy networks many of us still manage simply weren’t designed for this kind of relentless, high-volume traffic. To keep AI running the way it should, low latency and high bandwidth are not negotiable.

Automation is fundamental

For AI to truly thrive, it needs a dynamic, adaptive environment. Our traditional, static, hardware-based networks are just not keeping pace. I’ve seen firsthand how the shift to software-defined, programmable infrastructure has become a necessity. It’s the only way to enable the continuous optimization needed for AI-driven automation, anomaly detection and predictive analytics.

The always-present challenge of security

AI also introduces entirely new threat surfaces — think data poisoning, model theft and sophisticated adversarial attacks. This goes beyond patching vulnerabilities; it’s about fundamentally rethinking our security posture. Modern networks are now integrating AI-native security models, like zero trust architectures and real-time threat detection, to stay ahead of these evolving risks.

Distributed demands

AI workloads now run everywhere, from the edge to the cloud — across devices, locations and environments. However, traditional networks stumble when intelligence moves between endpoints. Modernized architectures are being built from the ground up to handle this complex, distributed intelligence.

Rising costs and inefficiencies

Legacy networks are, frankly, expensive, rigid and a massive drain on resources. Countless hours can be lost to manual troubleshooting. This is where AI truly shines. AI-enabled automation is about tangible cost reduction — optimizing bandwidth, preemptively fixing failures and maximizing uptime. The efficiency gains are too significant to ignore, making modernization an economic imperative.

Competitive differentiation

The network is no longer just “plumbing.” It’s a strategic asset and a platform for growth. AI-ready networks are accelerating the deployment of new services, dramatically improving customer experience and securing a crucial first-mover advantage in their industries. This is where true competitive differentiation happens.

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The taskmaster is also your teammate

AI might place a lot of demands on your network, but it’s also a powerful enabler — the kind of partner that pushes hard, then helps you deliver.

AI is reshaping how networks are managed in five key areas:

1. Problem-solving

A self-healing network — one that fixes itself — is part of the promise of AI. These systems can automatically detect anomalies, pinpoint root causes and even initiate corrective actions, often before anyone notices there’s an issue. This predictive, proactive capability is a game changer for network resilience. It drastically reduces downtime and speeds up recovery, moving network teams from reactive firefighting to proactive prevention.

2. Real-time intelligence

For years, we’ve been drowning in network data but starved for insights. AI changes that. It gives us deep, real-time visibility into traffic flows, performance bottlenecks and, crucially, the actual user experience. AI-driven analytics transform data into actionable insights. This is where we start getting ahead of problems, not just reacting to them.

3. Security

AI continuously analyzes patterns, spots anomalies and blocks threats, often in real time. This is an adaptive, scalable defense — essential for distributed environments. With AI, zero trust becomes practical, not theoretical.

4. Human–machine collaboration

AI doesn’t replace network teams — instead, it augments them. By removing repetitive work, offering intelligent recommendations and streamlining escalations, the technology frees people to focus on strategic innovation and complex problem-solving.

5. Scalability to support increased adoption

Managing distributed networks across hybrid, multicloud and edge environments is too complex for human teams to handle efficiently. AI-enabled management tools deliver the scale needed to keep networks resilient, efficient and secure as adoption grows.

Agentic AI: The real power player

I mentioned earlier that AI is evolving — well, this is part of the evolution. Agentic AI is where automation becomes autonomous. It doesn’t just follow orders; it can understand, decide and act across complex network environments without human intervention. By combining telemetry, user experience data and performance metrics, it identifies issues, tests potential fixes and executes the right one in real time.

This is how networks become self-healing — adapting to demand, countering threats and configuration drift as it happens. With continuous learning built-in, agentic AI refines operational efficiency, reduces mean time to resolution and enables our network teams to focus on high-value work instead.

Not all numbers tell the same story

Service level agreements (SLAs) have their place, and they do what they’re meant to do well — measure technical metrics like latency, jitter and availability.

But that’s only half the story.

The other half — and perhaps the most important — is how people actually experience the service: How productive they are, how quickly they can work and how smoothly applications perform. And this is what XLAs measure.

XLAs shift the focus from infrastructure stats to real outcomes, such as employee productivity, customer satisfaction and the quality of the overall digital experience. And guess what? AI is the bridge that finally connects these two worlds. It takes the vast ocean of network telemetry, user feedback and application data and transforms it into tangible insights into real human experience.

It also predicts when performance is about to drop and flags issues before they reach users. Instead of fixing things after a failure, teams can intervene earlier, improving service quality and reducing disruption.

Turning insight into accountability

If a performance issue is detected, AI-driven observability can pinpoint the root cause — whether it’s in the local area network (LAN), wide area network (WAN), cloud or related to a software as a service (SaaS) provider. With the facts at hand, the problem can be resolved quickly with none of the ubiquitous finger-pointing — and with a clear understanding of how the issue affects real outcomes such as collaboration, quality, customer flows or operational throughput.

This shift lets IT leaders demonstrate value in terms the business understands and justify network investment based on outcomes, not just infrastructure metrics.

As technology gets smarter, your network needs to get stronger

More than a trend, AI is a dual force. It’s the catalyst pushing us to modernize our networks, and it’s the core enabler making those networks resilient, intelligent and secure.

Enterprises that invest now in AI-ready, software-defined and inherently secure architectures are supporting today's AI workloads while laying the foundation for everything that comes next.

From my perspective, the question is no longer if our networks need to modernize for AI, but how quickly organizations can act to seize the advantage.

The future of networking is here, and it's powered by AI. Let us help you build a stronger, more resilient network.

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