AI Isn’t the Future of NetOps. Trusted Network Data Is.

According to new research from Enterprise Management Associates (EMA), enterprises don’t have an AI problem in network operations; they have a data problem.

Artificial intelligence has officially crossed the line from experiment to expectation in network operations.

EMA’s latest research, based on a survey of 458 IT professionals across North America and Europe, reveals that AI-driven NetOps is now viewed as critical to improving performance, strengthening security, and increasing operational efficiency. But here’s the uncomfortable reality: Only 35 percent of enterprises say they are completely successful in applying AI to network management. This gap between ambition and execution is more than a technology story; it’s a strategic one.

From AIOps Hype to Autonomous Expectations

Less than a decade ago, AIOps meant event correlation and anomaly detection. Today, large language models (LLMs), AI agents, and embedded intelligence are pushing vendors and enterprises toward autonomous operations.

EMA observes a decisive mindset shift:

AI capabilities now influence vendor selection for nearly 60 percent of buyers

Engineers increasingly view AI-driven insights as table stakes

Strategic platforms are judged by their ability to automate confidently and explain decisions clearly

Skepticism has largely been replaced by expectation. But expectation doesn’t equal execution.

The Real Barrier Isn’t AI, It’s Data Readiness

When enterprises struggle with AI-driven NetOps, the root cause isn’t model sophistication. It’s foundational.

Only 44 percent are fully confident in the quality of their network data

Data silos persist across network and security domains

Security and compliance risks limit AI automation

Network complexity continues to expand

AI outcomes are only as strong as the telemetry data feeding them. If your AI is ingesting incomplete, sampled, delayed, or siloed data, you’re not building intelligent operations. You’re inferring rather than verifying.

Network Data: The New Competitive Advantage

EMA’s findings make something clear: High-fidelity network-derived telemetry data is emerging as the decisive differentiator in AI-driven operations.

Consider:

87 percent of enterprises are building IT and security data lakes

For more than one-third, AI-driven operations is the primary driver

Deep packet visibility, scalable telemetry ingestion, and cross-domain integration are becoming foundational

This reinforces a simple market reality: AI cannot compensate for blind spots in the network. Those without complete visibility will likely remain stuck in supervised AI. Organizations investing in enriched network telemetry, with deep packet visibility and full coverage across encrypted, East-West (lateral) and North-South traffic, are better positioned to:

Trust AI-generated insights

Reduce false positives

Automate remediation safely

Align NetOps and SecOps decisions

Move toward autonomous operations

The Security Paradox

Security is both a catalyst and a constraint in AI-driven NetOps.

The research shows:

49 percent say AI-driven NetOps reduces security risk

46 percent actively use AI to address evolving threats, including AI-driven attacks

Yet security and compliance concerns are cited as the #1 barrier limiting AI value

This paradox is critical. AI can dramatically improve threat detection, lateral movement analysis, and performance degradation tied to malicious activity. But without trusted data, automation introduces risk rather than reducing it.

Why This Matters

The competitive landscape is shifting. AI-driven network management is now operational, not experimental. It’s a buying criterion, an operational mandate, and increasingly, a board-level expectation.

EMA’s strategic takeaways for IT leaders are clear:

AI is influencing vendor selection and long-term platform strategy

Data readiness, not AI ambition, is the primary limiter of success

Network visibility and data fidelity are prerequisites for scalable AI

Cross-domain integration between network and security teams is essential

The organizations that win the next phase of NetOps won’t necessarily have the most advanced models. They will have the most complete, trustworthy, and actionable network-derived telemetry data.

Check out how the Gigamon Deep Observability Pipeline can help. And to explore the full research findings and detailed data from EMA’s January 2026 report, access the complete report.

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