AI First? Why CIOs Are Putting the Cart Before the Horse

The 2026 NetOps Reality: You Can't Modernize with AI Until You Fix Visibility and Automation
Jeremy Rossbach
Learn more about Broadcom

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand.

The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible.

The Core Problem: AI Needs Good Data, and Networks Aren't Providing It

The report reveals a troubling pattern:

  • 87% say cloud and internet usage creates critical blind spots.
  • 95% lack visibility into at least one major segment of their delivery chain.
  • 39% say insufficient visibility is directly impacting AI success.
  • 71% don't fully trust AI in network operations.

Why the mistrust?

Because AI is only as reliable as the data you feed it, and the data today is incomplete. Public cloud paths, remote work connections, ISP transport layers, and peering networks remain black boxes, yet companies are trying to deploy AI-driven triage and AI-driven automation on top of them. It's the equivalent of asking a self-driving car to navigate with half a windshield.

Before We Talk About AI, We Need to Talk About Order

There is a simple, non-negotiable sequence for modern networking success:

1. Visibility Enables Trust

You cannot automate what you cannot see, and you cannot trust AI-driven decisions when the data behind them is partial or ambiguous. The report makes this clear in no uncertain terms: cloud-to-cloud visibility, real-time path validation, ISP metrics, and public cloud telemetry are all required. Yet only 5% of respondents say they receive the ISP data they need, underscoring a simple truth that without visibility, trust is impossible.

2. Automation Enables Scale

Once you have reliable telemetry, patterns emerge: including baselines, repeated bottlenecks, behavior over time, capacity trends and known-good configurations. These patterns form the backbone of automation. However, today, only 27% of companies have mature automation practices. A full 70% are still early to mid-stage. Why so low? Because automation depends on accuracy. And accuracy depends on visibility. Automation cannot scale when your data is fragmented, when paths are unknown, or when you're still troubleshooting ISPs manually.

Which brings us to the final step.

3. AI Enables Foresight

AI is the multiplier. AI is the prediction engine. AI is the triage assistant that cuts MTTR and solves problems before users feel them. But AI is the last step and not the first. The report's adoption numbers prove enterprises are trying to skip ahead. 92% plan to use AI-enabled network operations solutions, but only 23% have anything deployed and a full 71% don't trust AI yet.

Why not? Because AI recommendations can't be trusted until the data is complete, the telemetry is continuous, the automation workflows are consistent and the visibility is fully end-to-end. AI doesn't magically fix missing data. AI makes missing data more dangerous.

The Right Path: Visibility → Automation → AI

Every CIO and NetOps leader should use this sequence as the modernization roadmap:

Step 1 - Expand Visibility - Goal: create trustworthy data

Expanding visibility means achieving full-path insight from the user all the way to the application, across both overlay and underlay networks. It requires comprehensive coverage of cloud environments, the public internet, and ISP infrastructure, combined with real-time and historical perspectives.

Step 2 - Scale with Automation - Goal: create consistent, repeatable execution

Take the high-fidelity telemetry and build: automated troubleshooting, automated remediation, automated policy deployment and automated performance baselining.

Step 3 - Apply AI for Foresight - Goal: create proactive, intelligent operations

Once trust and scale exist, AI becomes: predictive, accurate, reliable, safe and transformative.

But again — in that order.

The CIO Takeaway

AI is not your starting point; it is the reward for doing visibility and automation right. The message from the 2026 data is not subtle: AI initiatives are outpacing the network's readiness to support them. Until visibility gaps are closed and automation matures, network teams will continue to struggle, budgets will be strained, and enterprises will underperform on AI ROI. Your network observability stack is the AI data engine, your automation layer is the AI execution engine, and your AI platform is the intelligence engine. Mixing up this order doesn't accelerate progress; it sabotages it.

Final Thoughts

If enterprises want AI to truly deliver, powering autonomous triage, proactive remediation, intelligent routing, predictive detection, and real-time optimization, then the sequence is clear: visibility enables trust, automation enables scale, AI enables foresight and the companies that follow this order will win the AI era of NetOps.

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AI First? Why CIOs Are Putting the Cart Before the Horse

The 2026 NetOps Reality: You Can't Modernize with AI Until You Fix Visibility and Automation
Jeremy Rossbach
Learn more about Broadcom

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand.

The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible.

The Core Problem: AI Needs Good Data, and Networks Aren't Providing It

The report reveals a troubling pattern:

  • 87% say cloud and internet usage creates critical blind spots.
  • 95% lack visibility into at least one major segment of their delivery chain.
  • 39% say insufficient visibility is directly impacting AI success.
  • 71% don't fully trust AI in network operations.

Why the mistrust?

Because AI is only as reliable as the data you feed it, and the data today is incomplete. Public cloud paths, remote work connections, ISP transport layers, and peering networks remain black boxes, yet companies are trying to deploy AI-driven triage and AI-driven automation on top of them. It's the equivalent of asking a self-driving car to navigate with half a windshield.

Before We Talk About AI, We Need to Talk About Order

There is a simple, non-negotiable sequence for modern networking success:

1. Visibility Enables Trust

You cannot automate what you cannot see, and you cannot trust AI-driven decisions when the data behind them is partial or ambiguous. The report makes this clear in no uncertain terms: cloud-to-cloud visibility, real-time path validation, ISP metrics, and public cloud telemetry are all required. Yet only 5% of respondents say they receive the ISP data they need, underscoring a simple truth that without visibility, trust is impossible.

2. Automation Enables Scale

Once you have reliable telemetry, patterns emerge: including baselines, repeated bottlenecks, behavior over time, capacity trends and known-good configurations. These patterns form the backbone of automation. However, today, only 27% of companies have mature automation practices. A full 70% are still early to mid-stage. Why so low? Because automation depends on accuracy. And accuracy depends on visibility. Automation cannot scale when your data is fragmented, when paths are unknown, or when you're still troubleshooting ISPs manually.

Which brings us to the final step.

3. AI Enables Foresight

AI is the multiplier. AI is the prediction engine. AI is the triage assistant that cuts MTTR and solves problems before users feel them. But AI is the last step and not the first. The report's adoption numbers prove enterprises are trying to skip ahead. 92% plan to use AI-enabled network operations solutions, but only 23% have anything deployed and a full 71% don't trust AI yet.

Why not? Because AI recommendations can't be trusted until the data is complete, the telemetry is continuous, the automation workflows are consistent and the visibility is fully end-to-end. AI doesn't magically fix missing data. AI makes missing data more dangerous.

The Right Path: Visibility → Automation → AI

Every CIO and NetOps leader should use this sequence as the modernization roadmap:

Step 1 - Expand Visibility - Goal: create trustworthy data

Expanding visibility means achieving full-path insight from the user all the way to the application, across both overlay and underlay networks. It requires comprehensive coverage of cloud environments, the public internet, and ISP infrastructure, combined with real-time and historical perspectives.

Step 2 - Scale with Automation - Goal: create consistent, repeatable execution

Take the high-fidelity telemetry and build: automated troubleshooting, automated remediation, automated policy deployment and automated performance baselining.

Step 3 - Apply AI for Foresight - Goal: create proactive, intelligent operations

Once trust and scale exist, AI becomes: predictive, accurate, reliable, safe and transformative.

But again — in that order.

The CIO Takeaway

AI is not your starting point; it is the reward for doing visibility and automation right. The message from the 2026 data is not subtle: AI initiatives are outpacing the network's readiness to support them. Until visibility gaps are closed and automation matures, network teams will continue to struggle, budgets will be strained, and enterprises will underperform on AI ROI. Your network observability stack is the AI data engine, your automation layer is the AI execution engine, and your AI platform is the intelligence engine. Mixing up this order doesn't accelerate progress; it sabotages it.

Final Thoughts

If enterprises want AI to truly deliver, powering autonomous triage, proactive remediation, intelligent routing, predictive detection, and real-time optimization, then the sequence is clear: visibility enables trust, automation enables scale, AI enables foresight and the companies that follow this order will win the AI era of NetOps.

Related Links

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The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

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While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

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Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

AI Is Making Incident Management More Complex - Bad Data Makes It Harder

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

MEAN TIME TO INSIGHT Podcast - Episode 26: Network Compliance

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