NetOp AI Tackles the Network Readiness Gap for AI

Most enterprises want to run AI at scale. Most of their networks aren't ready for it. That gap between ambition and infrastructure is something Cisco and its partner ecosystem are now attacking head-on—and a company called NetOp AI is building one of the sharper tools in that fight.

NetOp AI's AI Network Assessment Reporting platform, highlighted this week on Cisco's partner blog, gives IT teams and managed service providers automated visibility into whether their networks can actually handle what AI demands. It's not a monitoring dashboard replacement. It's a diagnostic layer that identifies the specific bottlenecks—aging hardware, configuration drift, firmware exposure, capacity limitations—that will break things once AI workloads hit production.

The Numbers Behind the Readiness Problem

The disconnect between AI adoption and network capability is well-documented at this point. A Broadcom-sponsored survey of 1,350 IT professionals found that while 99 percent of organizations have cloud strategies incorporating AI, only 49 percent believe their networks can support the bandwidth and latency those workloads require.

The specific pain points are telling. Network congestion tops the list at 46 percent, followed by insufficient visibility into network behavior (39 percent), congested traffic flows (38 percent), and latency (37 percent). Meanwhile, 95 percent of respondents admitted they lack adequate visibility into their network segments, with public cloud environments being the biggest blind spot.

Separate research from The Register reported that over half of enterprise AI projects stall on infrastructure problems. And a Deloitte analysis warned that the shift from episodic AI experiments to always-on production systems is widening the gap between what leaders expect and what their infrastructure can deliver.

This is the problem NetOp AI is designed to surface before it becomes a crisis.

How NetOp's Assessment Works

The platform connects to Cisco networking APIs—covering Meraki, Catalyst, and hybrid environments—and automatically discovers devices across the network. From there, it collects configuration data, firmware versions, traffic patterns, and performance metrics without requiring manual audits or new hardware installations.

The AI-driven analysis layer does several things with that data. It correlates signals to identify deviations from normal network behavior and traces them to root causes. It flags end-of-life and end-of-support equipment that would fail under increased load. It maps capacity utilization against error rates and traffic patterns to predict where congestion will emerge. And it assesses wireless coverage for signal weaknesses and interference that would degrade AI-dependent applications.

The output is automated reporting—executive summaries for leadership, detailed diagnostics for engineering teams—delivered without anyone having to manually compile spreadsheets. NetOp claims the approach cuts alert noise by up to 90 percent, letting teams focus on the issues that actually threaten AI readiness.

For MSPs in particular, the platform offers multi-tenancy support. A managed services provider can run assessments across their entire customer base from a single pane, identifying which clients need infrastructure upgrades before their AI initiatives stall. That's a revenue opportunity wrapped in a genuine service need.

Where This Fits in Cisco's AI Strategy

NetOp isn't operating in a vacuum. Cisco has been building its AI infrastructure story across multiple fronts this year. At the AI Summit last week, the company unveiled the Silicon One P200 chip delivering 51.2 terabits per second of throughput, along with AI Defense for securing AI models and AgenticOps for governing autonomous agents.

The company also launched the Cisco 360 Partner Program in January, specifically designed to help partners deliver outcomes around AI-ready data centers and digital resilience. NetOp AI's assessment tool fits neatly into that ecosystem as the first step in a partner-led AI readiness engagement: before you can sell the infrastructure upgrades, you need to show the customer exactly where the gaps are.

At Cisco Live Amsterdam, NetOp was listed among exhibiting ecosystem partners alongside companies like IP Fabric and BlueCat, and the conversations there about AI policy and government adoption underscored just how urgent the infrastructure question has become.

The Security Angle

Network assessment isn't just an operations play—it's a security one. Every end-of-life device, every unpatched firmware version, and every misconfigured segment that NetOp flags represents a potential entry point for attackers. That concern is front-of-mind for Cisco's security org, which has been vocal about the risks of organizations rushing into AI deployments without adequate security controls.

NetOp maintains SOC 2-compliant infrastructure with end-to-end encryption for the assessment data it collects, which matters given that the tool essentially maps an organization's entire network topology and its weaknesses. That's sensitive information.

Broadcom's chief technical evangelist Jeremy Rossbach framed the priority order clearly in a recent interview: "Maturing your network observability practice" needs to happen before organizations adopt AI operations tools. You can't govern what you can't see.

What IT Teams Should Take Away

If your organization is planning AI workloads—and statistically, you are—the network readiness question isn't optional. The assessments NetOp provides aren't groundbreaking in concept, but automating them through Cisco's API ecosystem removes the excuse that nobody had time to check.

For Cisco partners and MSPs, the tool represents a concrete way to start the AI readiness conversation with customers. For enterprise IT teams, it's a way to get ahead of the demand curve before an AI deployment fails at scale and everyone starts asking why the network wasn't ready.

The gap between AI ambition and infrastructure reality isn't closing on its own. Tools like this are how it starts to shrink.