Read this data-driven exploration of the visibility, AI readiness, and operational challenges currently facing network teams. The 2026 NetOps Imperative offers a strategic blueprint to evolve NetOps practices, detailing how to transform fragmented monitoring into a unified observability pipeline that powers AI-ready operations.


The eBook's research findings clearly demonstrate that IT leaders must transition from fragmented, reactive monitoring to a unified observability strategy. This checklist provides a strategic roadmap to achieve AI readiness and close critical visibility gaps.

Phase 1: Establish Foundational Visibility

  • Audit External Blind Spots: Identify the "segments of the network delivery experience" where your team currently lacks data, specifically focusing on Public Cloud (50%), ISP last-mile (38%), and transit networks (44%).

  • Implement End-to-End Path Analysis: Shift from device-centric monitoring to hop-by-hop analysis that can track traffic across provider-owned segments the business does not own.

  • Deploy Synthetic Testing: Proactively simulate user traffic to identify performance regressions in remote work and SaaS environments before they impact productivity.

  • Unify Telemetry Streams: Consolidate disparate data sources—metrics, events, logs, and traces—into a single observability pipeline to enable cross-domain correlation.

Phase 2: Bridge the Automation Maturity Gap

  • Standardize Workflows: Move away from manual processes (currently 3%) and basic scripts toward "Standardized Automation" with repeatable playbooks used across all teams.

  • Integrate with CI/CD Pipelines: Align network operations with modern software development practices by integrating automated tests into deployment workflows.

  • Automate Change Validation: Utilize automation to run pre- and post-change checks, ensuring network configurations remain in a "predictable state" after every update.

  • Centralize Configuration Management: Establish a "Network Source of Truth" to reduce the risk of configuration drift and simplify compliance reporting.

Phase 3: Prepare for AI-Enabled Operations

  • Cleanse Data for AI Readiness: Ensure the telemetry feeding your AI models is "trustworthy," as AI cannot succeed without consistent and accurate data inputs.

  • Establish AI Governance: Define clear policies for AI usage to address the "Trust Factor," given that 71% of teams currently do not fully trust AI-based remediation.

  • Focus on Predictive Triage: Once visibility and automation foundations are mature, deploy AI for anomaly detection and root cause analysis to reduce Mean Time to Resolution (MTTR).

  • Upskill the Workforce: Transition traditional NetOps roles toward "Automation-First Engineering" and "Cloud Networking Fluency" to manage the increased complexity of 2026 environments.

The 2026 State of NetOps report makes one truth clear: while monitoring ran yesterday's networks, observability will fuel tomorrow's. Bridging existing visibility and automation gaps is no longer optional; it is the mandatory framework required to achieve AI-ready operations and scale human expertise. Embracing this new operational imperative is the only way to successfully operate the networks that power today’s digital business.

 

Frequently Asked Questions

What is the current "Visibility Crisis" in network operations?

The visibility crisis refers to the growing inability of traditional tools to monitor modern network paths. Currently, 87% of organizations report blind spots in their internet and cloud environments, a number that has increased by 7% since 2024. These gaps are most severe in public cloud environments (50%), transit/peering networks (44%), and remote work segments (43%).

Why are traditional Network Management Systems (NMS) failing?

Traditional NMS tools are device-centric and cannot see across domains the business does not own, such as SaaS, SD-WAN fabrics, and ISP paths. This lack of end-to-end visibility makes troubleshooting slow and unpredictable. To solve this, organizations must shift toward holistic delivery path observability that includes hop-by-hop analysis and synthetic testing.

What are the primary prerequisites for AI-enabled NetOps?

AI cannot operate effectively in a vacuum. According to the 2026 report, AI readiness requires a three-step maturity evolution:

  1. Trusted Telemetry: High-quality data from across the entire observability pipeline.

  2. Consistent Workflows: Standardized and repeatable automated processes.

  3. Predictable Network State: A stable environment where AI can accurately perform predictive triage.

How mature is network automation in most enterprises today?

Most organizations remain in the early stages of automation maturity. Only 27% of organizations currently report "mature" automation (advanced or autonomous stages). The vast majority are still utilizing basic scripts (20%) or standardized playbooks (22%) that are not yet fully integrated into modern CI/CD pipelines.

Why is there a lack of trust in AI for network operations?

Trust remains a significant barrier, with 71% of operations teams reporting they do not fully trust AI** today. This skepticism stems from the fact that AI-driven autonomous remediation can only be as reliable as the underlying telemetry and automation workflows, both of which are currently facing significant maturity gaps in the enterprise.