Your AI Strategy Is Failing in the Seams

There’s a certain comfort in the glow of your network operations center (NOC) dashboards. For some time, the sign of a well-run NOC was that sprawling bank of screens, each dedicated to a different domain. One for the WAN, showing link status. Another for the data center, tracking backbone health. A third for cloud consumption, pulling metrics from your provider. Each screen is a neatly bordered kingdom, diligently monitored by its own set of tools. As long as the lights are green, all is well.

This approach has served you for years. But for the AI-driven future you are now building, this comfort is a dangerous illusion. The very structure of this siloed monitoring strategy, which once provided clarity, is now creating the critical blind spots that will undermine your most ambitious AI initiatives. Your AI applications don’t live in isolated boxes, so why would you still be trying to manage them that way?

This isn’t a theoretical problem. The nature of AI workloads makes it a practical certainty. A recent research report from Enterprise Management Associates (EMA) found that when AI applications enter production, they need to access corporate data from everywhere, all at once. For most companies, this data resides simultaneously in public clouds (71.4%), private data centers (70.6%), and at the enterprise edge, in locations such as branch offices and industrial sites (60.6%). There is no single source of truth; the data is fundamentally distributed.

Where the finger-pointing begins

Think of your monitoring tools as a series of security cameras. One has a perfect view of the parking lot. Another has a feed of the building’s lobby. But there is no camera covering the doorway between them. Your siloed tools operate just like this. They may give you a perfect picture of what’s happening within the WAN or inside your AWS environment, but they go dark in the transitional spaces—such as the internet, the cloud interconnects, and the tunnels connecting the edge to the data center.

This is precisely where problems are apt to hide. When an AI training model starts pulling terabytes of data from an edge location, across the WAN, and into a public cloud for processing, its performance depends on the seamless integrity of that entire chain. Congestion on a cloud interconnect or a sudden spike in latency at the WAN edge can starve the model of data. This can cause the training job to be corrupted or fail outright.

Typically, the finger-pointing begins when these issues arise. The cloud team’s dashboard looks green. The WAN team’s dashboard looks green. Each team, looking at their own isolated kingdom, can honestly say that the problem isn’t on their end. They are all telling the truth, and yet, the multi-million-dollar AI project is grinding to a halt. The problem exists in the seams—the operational blind spots your fragmented tools have created. The EMA report confirms this is a widespread challenge, with IT leaders identifying a need for improved observability across public cloud infrastructure, cloud interconnects, data center fabrics, and the WAN edge in almost equal measure.

From siloed metrics to actionable intelligence

Adapting to this new reality requires a fundamental shift in thinking. You must move from a patchwork quilt of disparate data points to a single view of your entire end-to-end service delivery path. This doesn’t necessarily mean throwing away all your existing tools, but it does mean abandoning the notion that they can be operated in isolation. You need a way to correlate the data from all of them, transforming a collection of siloed metrics into unified, actionable intelligence.

This unified view follows the life of an application. It shows you the performance of the network path, not just through the internet, but through the cloud provider’s backbone and to the specific virtual private cloud (VPC). It connects a user’s complaint about a slow query to a specific API call and the network latency between the data center and the inference server.
The goal is to eliminate the gray areas where problems fester and accountability evaporates. When you can see the entire journey, there is nowhere for performance degradation to hide.

The temptation is to believe that the primary challenge of AI is the algorithm itself—the complexity of the model or the quality of the training data. But for many, the real, tangible threat is far more mundane. It is the silent failure of the infrastructure that supports AI, an infrastructure you are likely observing through a dangerously incomplete lens. The most pressing question you should be asking your teams is not whether the dashboards are green, but whether your collection of tools is providing a true picture of your network, or just a collection of nicely rendered, dangerously incomplete fragments.

Find out how a modern network observability strategy can provide the visibility needed to support your AI ambitions. Explore what's possible by visiting our Network Observability by Broadcom page.