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. The study, conducted in partnership with Foundry, surveyed 3,472 IT and networking leaders across 15 countries and found that AI is already changing traffic patterns in campus and branch environments, 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 survey findings

The Cisco survey uncovered several critical data points:

• 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.

These numbers paint a clear picture: the network edge is under new and unprecedented strain. Typically, enterprise networks are designed for consistent traffic patterns—SaaS, CRM, email—with relatively predictable bandwidth demands. But AI workloads, especially those involving real-time inference, model updates, and agent-to-agent communication, introduce bursts of east-west traffic that legacy architectures struggle to handle.

"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?"

Campus networks as the new AI bottleneck

For years, the AI infrastructure conversation has centered on data centers: GPU clusters, high-speed interconnects, and cloud backends. But the Cisco research suggests that AI's real-world impact will be felt most acutely at the edge—where employees work, devices connect, and business processes run. Campus and branch networks, often built for a pre-AI era, are now becoming the choke point for AI adoption.

Only 30% of aggressive AI adopters—defined as organizations with broad generative AI deployments across the enterprise—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. This modernization wave will likely include upgrades to Wi-Fi 7, increased wired bandwidth, SD-WAN improvements, and network segmentation to handle AI traffic.

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 and governance hurdles

Security is also emerging as a major barrier to AI expansion. 80% of respondents said AI has expanded their attack surface, and 61% are delaying additional AI deployments until they feel more confident in their security posture. This caution is understandable—AI tools, especially those that are user-provisioned or shadow IT, can introduce vulnerabilities that traditional security controls are not designed to address.

"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. This sentiment echoes a broader industry challenge: as AI becomes more pervasive, network security must evolve from perimeter-based models to zero-trust architectures that can handle dynamic, AI-driven traffic flows.

The rise of AI agents

Perhaps the most forward-looking finding is the expected growth in AI agent deployments. 85% of respondents anticipate moderate or significant growth in AI agent deployments over the next two years. These agents—autonomous software entities that perform tasks, communicate with each other, and make decisions—represent a step change in network demand. Unlike traditional web traffic or API calls, agents can generate continuous, bursty, and unpredictable communication patterns that stress both network capacity and monitoring tools.

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 avoid irrelevance, enterprise IT teams must now look beyond the data center and treat campus and branch networks as first-class citizens in their AI strategy. This means investing in network visibility, automation, security, and capacity planning now—before the next wave of AI traffic arrives.

Source:Network World News