Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network.
Most organizations rely on institutional knowledge to bridge the gap between what their tools are showing them and how the network is actually behaving. There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks. This leaves AI systems with a view of the network that’s riddled with gaps that it doesn’t know about. And when this is the case, how can any enterprise confidently act on what their AI is telling them?
Why AI Fails on Fragmented Data
While much of the AI discussion focuses on models and frameworks, NetOps teams are finding that the more difficult problem has less to do with the AI system itself and more to do with the quality and reliability of the data used to train it.
AI systems inherit many of the same limitations as human operators; like humans, AI systems can only reason from the information that’s available to them. If infrastructure data is outdated or missing critical dependencies, the resulting analysis will only reflect a fragment of the operational reality. These systemic gaps may result in an AI agent taking actions without considering the effect it’ll have on network behavior as a whole, which can put operations, security, and compliance at risk.
The Rise of Network Digital Twins
Given the hype around AIOps — and the desire to solve this data problem — enterprises are looking for a way to maintain a complete, contextualized view of the network. Rather than relying on fragmented operational views stitched together from diagrams, spreadsheets and disconnected systems, teams are exploring approaches that create a more unified understanding of configurations, connectivity, routing behavior and dependencies.
This is where network digital twins are gaining traction. Network digital twins provide an interactive model of the network at a given point in time, giving both human engineers and AI systems access to a normalized, end-to-end understanding of the network’s state and behavior. This shared context becomes especially valuable in enterprise environments, where teams are pushing dozens of changes per day, and any one of those changes carries the potential for downstream risk.
Building a Foundation for Network Autonomy
In today's complex environments, there is no shortage of information; the real difficulty lies in trying to piece fragments of the network together. Most network data is trapped in engineers’ heads, and until organizations find a way to document and validate that data, AIOps will be forever out of reach.
While many organizations are chomping at the bit to automate, the key to long-term success is simple: go back to basics. While it’s important to understand the configuration and state data for each device, it’s even more important to understand the relationships between the devices, which determine how the network actually behaves.
In other words: focus on the data first. Then when you’re confident that your data is an accurate representation of network behavior, you’ll be ready to take the first step towards automation, AIOps, and eventually, autonomy.