While enterprise IT leaders have spent the past two years focusing AI infrastructure discussions on GPUs, cloud platforms, and data centers, new Cisco research suggests that enterprise networks may not be ready for the next phase of AI adoption.

A Cisco and Foundry survey of 3,472 IT and networking leaders across 15 countries found AI is already changing traffic patterns across campus and branch environments and exposing capacity, security, and visibility gaps that many organizations aren’t prepared to address.

“We have entered a networking supercycle, because the network is so central to all the AI infrastructure the world is building now,” said Jeetu Patel, Cisco president and chief product officer, in a statement.

The findings reveal that enterprises may need to expand AI readiness planning beyond data centers and cloud environments and pay more attention to the networks connecting employees, applications, and devices. This issue will become more significant as enterprise organizations move beyond generative AI pilots and begin deploying AI agents that communicate continuously with other systems and applications, according to the report.

Key findings from the Cisco survey

The Cisco survey found:

• Organizations reported a 34% increase in AI-related campus and branch network traffic over the past 12 months.
• Traffic is projected to climb 209% over the next three years, with companies broadly deploying AI expecting total network traffic to triple.
• 73% already face, or expect to face, campus and branch network capacity constraints within the next two years.
• 67% said AI workloads are increasing east-west traffic between internal systems and applications.
• 80% said AI has expanded their attack surface.
• 61% said they are delaying additional AI deployments until they gain more confidence in their security posture.
• 85% expect moderate or significant growth in AI agent deployments over the next two years.

Changing traffic patterns inside enterprise environments are causing additional pressure for enterprise network teams. (See also: AI traffic is radically reshaping WANs)

“Usually, networks are designed for consistent traffic, like SaaS and CRM traffic, and there aren’t a lot of unpredictable traffic patterns,” said the head of AI strategy for global IT and network engineering operations at a large U.S. technology company who participated in the research. “Suddenly, three AI agents are trying to talk to each other and solve a problem. That is going to be a big thing … how do we support increased east-west traffic?”

Cisco defined aggressive AI adopters as organizations with broad generative AI deployments across the enterprise, but only 30% of those organizations said they are fully prepared to support projected AI growth across their networks. As a result, 93% of IT decision makers said they are accelerating network modernization efforts.

Observability and security gaps

The report also highlighted an observability challenge that could complicate future deployments. As employees and business units increasingly experiment with AI tools, IT organizations may not know what is actually running on their networks.

“Right now, we don’t even know what the AI-driven demand is,” the AI strategy executive said. “Observability is a huge gap. There is experimentation going on all over the place, and there is no way for us to really identify if somebody is deploying some kind of service on our network, whether it is a genAI solution or an agentic solution.”

Security is also emerging as a barrier to AI expansion as organizations struggle to govern rapidly growing numbers of AI tools and workloads.

“The issue from a security standpoint is that it’s hard to create the guardrails for every possible AI tool that your organization must use,” said the vice president of infrastructure, network, and end-user services at a U.S. retail enterprise interviewed for the report.

Implications for enterprise network planning

The AI readiness conversation has often centered on data centers, but AI applications operate where employees work, devices connect, and business processes run. That means campus and branch environments may become just as important to AI success as the infrastructure supporting AI models.

The Cisco research shows that AI infrastructure planning can no longer focus only on back-end systems if enterprises expect to scale AI deployments over the next several years. Patel said in the statement: “Eventually there will be only two kinds of companies: those that are AI companies, and those that are irrelevant.”

To support the rapid growth of AI traffic, organizations must reassess their network architecture, invest in higher-capacity switches, adopt intent-based networking, and implement robust observability tools. The survey underscores that the network is not just a passive pipe but an active enabler of AI outcomes. Without modernizing campus and branch networks, enterprises risk bottlenecks, security breaches, and missed opportunities from AI agent deployments.

Historical patterns show that the rise of cloud computing similarly stressed enterprise WANs, leading to SD-WAN adoption. Now, AI's insatiable demand for low-latency, high-bandwidth connections between distributed endpoints and central AI models is driving a similar, but more urgent, need for network transformation. The networking supercycle Patel refers to is already underway, and organizations that delay may find themselves unable to compete as AI becomes embedded into every business process.

Industry analysts have long warned that AI workloads require predictable, low-jitter networks, especially for real-time inference and agent-to-agent communication. The Cisco survey provides concrete data that this is not a future problem but a present reality. For example, the 34% year-over-year traffic increase is twice the rate of traditional traffic growth, and the projected 209% three-year jump dwarfs typical network expansion expectations. These numbers suggest that even well-provisioned networks will face strain.

Furthermore, the security dimension cannot be overlooked. The 80% of respondents who say AI has expanded their attack surface points to the need for integrated security policies that extend to AI tool usage, data flows, and device identity. Zero-trust architectures that enforce least-privilege access for AI agents will become critical. Organizations must also address the observability gap: without knowing which AI applications are running, IT teams cannot allocate bandwidth appropriately or detect anomalous behavior.

The Cisco research presents a clear call to action. Enterprises must prioritize network modernization not as an IT cost but as a strategic investment. The 93% of IT decision makers already accelerating modernization efforts indicate that the message is resonating. However, the gap between those planning and those fully prepared—only 30% of aggressive adopters—shows there is still significant work to be done.

In summary, the Cisco survey reveals that AI is reshaping networks from the campus edge to the data center core. Traffic patterns are shifting, capacity limits are approaching, and security and observability challenges are mounting. IT leaders who act now to upgrade their network infrastructure and implement AI-aware management tools will be better positioned to harness AI's full potential. Those who wait risk falling behind in the new era of pervasive, agent-driven AI.

Source: Network World News