Network Automation Creates Visibility Crisis Despite More Data

The paradox at the heart of modern networks

Enterprises have invested heavily in network automation—intent-based networking, AI-driven operations tools, zero-touch provisioning—expecting faster incident response and clearer visibility. Instead, many organizations now face what experts call an "observability paradox": more data than ever before, but less understanding of what's actually happening when things break.

According to UK government research, roughly one in six UK companies currently use AI in some capacity. The result is an explosion of telemetry data—metrics, logs, flow records, traces—streaming into multiple dashboards across vendors and teams. Yet root-cause analysis has become harder, not easier.

"As an industry, we have successfully solved the data collection problem but worsened the context problem. We've created a context deficit," says Dray Agha, senior manager of security operations at Huntress.

Why it matters

This isn't just a technical nuisance. Slower troubleshooting means longer outages, eroded customer trust, and higher operational costs. In regulated industries like healthcare and manufacturing, delayed investigations can trigger compliance violations and substantial fines. More fundamentally, the abstraction layers that make networks "self-managing" are quietly eroding the diagnostic skills engineers need when automation fails—creating institutional knowledge gaps that surface during the worst possible moments.

How automation created the problem

The shift toward autonomous systems has introduced multiple layers of opacity. Software-defined networking centralizes decisions, removing direct device interaction. Intent-based networking translates high-level policies into machine-executed actions with limited traceability. AI-driven remediation platforms identify issues and reroute traffic automatically, but the reasoning behind those decisions often remains opaque.

"Most enterprises do not have an observability shortage anymore—they have a context correlation problem," says Ken Herron, co-founder of VCONify. "Telemetry volume has exploded across SD-WAN, hybrid cloud, edge infrastructure, SaaS platforms and security tooling. But operational context remains fragmented across tickets, chat systems, calls, dashboards and tribal knowledge."

The fragmentation is compounded by overlapping and sometimes contradictory AI insights. Herman Errico, senior product manager of technical research at Vanta, puts it bluntly: "Visibility without governance is just expensive noise. The core problem is that ingestion is being treated as the goal rather than detection."

The human cost

Beyond operational inefficiency, the observability crisis carries significant human consequences. Engineers spend more time reconciling conflicting dashboard views than actually diagnosing problems. Alert fatigue sets in. Teams apply temporary patches to recurring issues they can't fully understand.

Perhaps most concerning is the erosion of fundamental troubleshooting skills. Junior engineers never see raw network behavior or learn from failures. Senior engineers grow dependent on platform outputs rather than device-level understanding.

"Every auto-heal nobody investigates is a future incident nobody will know how to debug," warns Viktoriia Moskalets, senior data analyst at Sigma Software Group. "Every auto-heal should leave a question behind for a human to answer later, or it shouldn't run at all."

Rebuilding observability

Several practices can help restore meaningful visibility. Explainable AI systems should provide human-readable audit trails showing why automated actions were taken. Organizations need to consolidate dashboards, eliminating redundant tools. Every alert should map to a specific decision someone must make—alerts without owners should be removed.

Unified telemetry pipelines and dependency graphs can give teams consistent views across infrastructure layers. Regular testing of human overrides and explicit authorization requirements for remediation actions can maintain accountability.

"If your automation can't explain what it did and why, it should not be trusted with high-impact decisions," Errico cautions. "The organizations getting this right treat observability as a product with a roadmap, a dedicated team, defined internal users and success metrics."

These details were first reported by Computer Weekly in a feature examining the observability challenges created by network automation.