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

Hot Topics

Related Vendors

The Latest

Crisis Communications: When the Outage Hits, Your Communications Can't Be "Investigating"

Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

OpenTelemetry: A Complete Telemetry Framework that Grows with You

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

The Quiet Power of Milliseconds in High Traffic Systems

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

Are AI "Autonomous Coworkers" Ready for Payroll?

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI Reaches Pivotal Inflection Point: From Automation to Agentic Intelligence

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The Real Reason Enterprise AI Stalls: It Doesn't Fit in the Box

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

AI's Inflection Point Will Redefine Enterprise Readiness in 2026

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Turning Foresight into Resilience: Reclaiming Prevention in the Age of Exposure

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

MLOps Meets DataOps: Creating Unified Backbone for AI

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

2026 Network Monitoring Trends

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

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

Hot Topics

Related Vendors

The Latest

Crisis Communications: When the Outage Hits, Your Communications Can't Be "Investigating"

Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

OpenTelemetry: A Complete Telemetry Framework that Grows with You

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

The Quiet Power of Milliseconds in High Traffic Systems

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

Are AI "Autonomous Coworkers" Ready for Payroll?

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI Reaches Pivotal Inflection Point: From Automation to Agentic Intelligence

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The Real Reason Enterprise AI Stalls: It Doesn't Fit in the Box

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

AI's Inflection Point Will Redefine Enterprise Readiness in 2026

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Turning Foresight into Resilience: Reclaiming Prevention in the Age of Exposure

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

MLOps Meets DataOps: Creating Unified Backbone for AI

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

2026 Network Monitoring Trends

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...