AI Is Forcing a Reset in Network Observability

For years, network observability was a tools discussion. Which platform collects the broadest set of telemetry? Which agent covers my more obscure devices? Which architecture will perform best at scale?  At which points on the network should we capture packets? That conversation assumed the network was relatively stable and change was incremental.

It isn’t anymore.

AI-driven workloads are increasing traffic variability, as AI adoption accelerates across the enterprise. Recent research shows that 88% of organizations now use AI in at least one business function. Hybrid architectures stretch across cloud, data center, WAN, and edge. Security and performance signals now overlap in ways they didn’t five years ago. And the business expects faster resolution, fewer outages, and clear accountability.

Under that pressure, the current approaches to network observability are failing. Not because teams lack skill, but because the architecture underneath observability hasn’t kept pace.

This isn’t about adding more dashboards or capturing more data. It’s about recognizing that observability must evolve from a collection of tools into a coherent data foundation. That foundation is what will allow network operations (NetOps) teams to leverage AI for network observability and intelligence.

Here’s how to think about where you are and how to move forward.

Where are you on the maturity curve?

Research from Enterprise Management Associates (EMA) showed that only 46% of IT leaders believed they were fully successful with network observability tools. Most of the complaints are well known, with tool sprawl, alert noise, and poor data quality making the list.

EMA’s 2025 report, Network Observability Maturity Model: How to Plan for NetOps Excellence, also identified five distinct stages of maturity:

1. Ad Hoc and Reactive
2. Fragmented and Opportunistic
3. Integrated and Centrally Managed
4. Intelligent and Automated
5. Optimized and AI-Driven

Today I want to focus on the middle three stages, which is where you’ll find most organizations, before describing the path to the final stage.

Fragmented and Opportunistic

You have multiple observability tools. Often three or four. Industry research reflects the same pattern, with 87% of NetOps teams now relying on multiple observability tools, yet only 29% of the alerts they generate are actionable. Coverage exists, but it’s uneven. Engineers act as the integration layer, pivoting between consoles and mentally correlating events. AI may be present, but it operates within silos. Teams work hard in this stage, but the architecture works against them.

Integrated and Centrally Managed

You’ve achieved strong monitoring coverage across infrastructure and traffic. There is some integration between systems. Dashboards are standardized. You may have early automation for common incidents.

But root cause analysis still depends on manual stitching. Predictive insights are limited. AI accelerates analysis, but it doesn’t fundamentally change how the network is understood.

Intelligent and automated

Telemetry is real-time where it matters. Flow, packet, and configuration data are correlated. Alerts are contextual, not threshold driven. AI supports anomaly detection, capacity forecasting, and guided remediation. Automation is introduced deliberately and within policy guardrails. Only organizations with ample resources are at this stage.

A smaller group of best-in-class organizations has reached the final stage of maturity, Optimized and AI-Driven. Tooling alone will not help you evolve.

From Intelligent and Automated to Optimized and AI-Driven: what to do next

Modernizing network observability does not require ripping out what you have. It requires a shift from tools to data.

1. Start with data coherence, not more AI

Before expanding AI initiatives, ask yourself a question: is our network data clean, consistent, and connected across domains?

Inconsistent telemetry formats, blind spots in cloud or SD-WAN, duplicate IP space, and stale inventory records undermine AI outcomes more than most executives realize. If telemetry cannot be reliably tied to identity and context from authoritative addressing, correlation remains probabilistic rather than definitive.

This is where foundational network services matter. DNS, DHCP, and IP address management (together known as DDI) form the authoritative map of the network. Every device, workload, and connection intersects with that layer.

When observability telemetry is enriched with authoritative identity and addressing intelligence, analysis becomes grounded. AI can distinguish expected behavior from true anomaly with greater confidence. Root cause analysis happens faster. Automation becomes safer.

2. Reduce tool sprawl through deep integration

Most enterprises will continue to operate multiple observability systems. That’s not the main problem. The problem is shallow integration.

Embedding one dashboard inside another or sharing basic data exports does not create coherence. Mature environments integrate at the data layer. They coordinate telemetry collection, correlate alerts across domains, and enable workflows that span tools rather than remain trapped inside them.

When integration reaches that level, consolidation becomes rational instead of political. Redundant systems are easier to retire. Overlapping telemetry is easier to rationalize. AI operates on unified context rather than stitched-together fragments.

3. Modernize in phases to avoid disruption

The fear of destabilizing legacy environments is legitimate. Nobody wants to break production while pursuing architectural purity. A phased approach reduces that risk.

Phase one: Overlay intelligence

Stream telemetry into a shared analytics layer. Enrich it with identity and policy context. Use AI for detection and recommendation, not autonomous enforcement.

Phase two: Standardize and rationalize

As correlation improves and noise decreases, identify redundant tools and retire those that cannot participate in the unified architecture.

Phase three: Introduce guard-railed automation

Begin with low-risk automation scenarios. Let agentic AI suggest remediation before allowing execution. Expand gradually as confidence and governance mature.

This isn’t about flipping a switch. It’s about increasing coherence without sacrificing stability.

The strategic shift: moving to Optimized and AI-driven

Observability is no longer a collection of monitoring tools. It is core AI-driven infrastructure that requires a new baseline. When organizations anchor observability in unified data architecture and authoritative network intelligence, AI becomes anticipatory.

Predictive analytics moves from theory to practice. By analyzing historical and real-time telemetry together, AI can identify early signals of capacity strain, configuration drift, or abnormal behavior before they escalate. Instead of racing to repair outages, teams intervene before users notice degradation. This is especially significant because large-scale IT outages can cost organizations up to $2 million per hour.

Capacity planning becomes dynamic rather than periodic. Resource exhaustion and service saturation can be projected in advance, enabling proactive optimization instead of reactive scaling.

This is what’s on the horizon.

If your data is fragmented, AI will expose it.

If your foundation is coherent, AI becomes leverage.

The question isn’t whether you’ll adopt AI-driven observability and intelligence. The question is whether your architecture is ready for it.