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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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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