AI First? Are You Sure That’s the Right Approach?

In enterprises, the conversations about AI have changed fast—moving from if to how soon.

But according to Broadcom’s State of Network Operations 2026, many organizations are trying to move faster than their network operations (NetOps) allow.

The result?

AI implementations that are built on incomplete data and fragile automation, and that yield untrusted outcomes.

Why isn’t “AI first” the right strategy for NetOps success?

AI depends on data—not aspirations.

Yet, the Broadcom 2026 research shows a persistent gap between AI ambition and network reality:

• 95% of teams lack visibility into at least one major delivery segment.
• 87% say cloud and internet usage create critical blind spots.
• 39% say insufficient visibility is already having a negative impact on AI success.
• 71% don’t fully trust AI in network operations.

That mistrust isn’t cultural. It’s technical.

AI is only as reliable as the data it is being fed.

Why does today's network complexity demand your attention now—before your AI-first initiative?

Today’s teams are confronting several critical gaps between AI aspirations and modern network realities:

Here’s why this matters now:

• AI doesn’t fix blind spots. It scales them.

If your network data is incomplete, an AI-first strategy doesn’t create foresight—it fosters misplaced confidence and faster mistakes.

Why does the order of your AI-ready NetOps strategy matter?

There is a non-negotiable sequence for modern NetOps. The data from Broadcom’s 2026 State of Network Operations Report make this clear.

Step 1: AI-driven NetOps requires end-to-end visibility because AI can’t generate reliable insights from data it can’t access.

You can’t automate—or trust—what you can’t see.

High-fidelity visibility requires:

• Full-path telemetry, spanning from the user to the app
• Cloud-to-cloud visibility
• ISP performance metrics
• Continuous, historical path data

The problem is that only a small fraction of teams report getting the ISP and cloud telemetry they actually need.

Without visibility, AI recommendations are guesses—not insights.

Step 2: AI-driven NetOps requires scalable automation. That’s because automation lets you scale impact, taking what works for one team or workflow and multiplying it across the entire organization, without adding complexity.

Once visibility is reliable, patterns emerge:

• Baselines
• Repeated bottlenecks
• Capacity trends
• Known-good configurations

Those patterns are what automation runs on.

However, respondents in only 27% of organizations report having mature automation practices today. Nearly 70% are still in early or middle stages.

Why? Because automation collapses when data is fragmented, paths are unknown, or ISP troubleshooting is still manual.

Step 3: AI doesn’t just tell you what already broke. AI gives you foresight, uncovering patterns early so you can see what’s coming, act faster, and stay ahead instead of reacting.

AI is the multiplier—but only after trust and scale exist.

Despite the hype, these are the realities:

• 92% plan to use AI in network operations.
• Only 23% have anything deployed.
• 71% still don’t trust AI outcomes.

Until data is complete and automation is consistent, AI can’t be predictive, safe, or reliable.

AI belongs last—not first.

Broadcom’s 2026 State of Network Operations: What the data is telling us Across NetOps teams, the same patterns keep appearing: Adding more tools hasn’t reduced mean time to resolution (MTTR)—fragmented visibility still blocks root cause analysis. Network uncertainty is slowing AI adoption more than bandwidth limits. ISP networks remain one of the delivery paths with the least accountability; service levels are often tracked by tickets instead of proof. These gaps don’t show up as outages. They result in hesitation, mistrust, and stalled AI initiatives.

The CIO takeaway

Your observability stack is the AI data engine.

Your automation layer is the execution engine.

Your AI platform is the intelligence engine.

AI is not the starting point.

AI is the reward.

Visibility creates trustworthy data.

Automation creates scalable consistency.

AI creates foresight—but only after visibility and automation are in place.

Change the order and you don’t accelerate progress—you sabotage it.

Final thoughts

The winners in AI-driven NetOps won’t be the fastest adopters.

They’ll be the teams that understood the correct sequence:

1. Visibility enables trust.
2. Automation enables scale.
3. AI enables foresight.

Everyone else will be debugging predictions they never should have trusted in the first place.

Frequently Asked Questions

1. Why isn’t “AI first” the right strategy for NetOps success?

Because AI amplifies whatever foundation you give it—and most NetOps foundations aren’t ready. Building AI on top of noisy data and broken visibility just intensifies confusion instead of accelerating outcomes. NetOps success comes from establishing visibility first, trust second, and intelligence last—not the other way around.

2. Why does the order of your NetOps strategies and successes matter?

NetOps maturity is sequential, not additive. Automation and AI only work when they are built on a foundation of trusted, end-to-end observability. Skip that foundation and you get misplaced confidence, not progress. Teams that get the order right gain speed and resilience; those that don’t just move faster in the wrong direction.

3. What are teams lacking when it comes to preparing for AI-ready NetOps success?

Teams in enterprises don’t lack AI—they lack operational readiness. Layering AI on fragmented visibility and inconsistent data guarantees shallow insights and low trust. AI-ready NetOps starts with clean telemetry, shared operational truth, and disciplined fundamentals—before AI ever enters the picture.