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 survey, conducted by Cisco and Foundry, polled 3,472 IT and networking leaders across 15 countries and found that AI is already changing traffic patterns across 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 found 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.

Changing Traffic Patterns and Network Design Challenges

Changing traffic patterns inside enterprise environments are causing additional pressure for network teams. “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?” This shift from traditional north-south traffic (client-to-server) to east-west traffic (server-to-server) is a fundamental change that many network architectures were not designed to handle efficiently.

Historically, campus and branch networks were optimized for predictable patterns: employees accessing cloud applications, checking email, and running standard business processes. AI workloads, especially agent-based systems that require real-time communication between distributed services, create bursts of unpredictable traffic that can overwhelm legacy switching and routing infrastructure. The survey indicates that 67% of organizations are already seeing this increase in east-west traffic, and the trend is accelerating as AI agents become more autonomous and interconnected.

Capacity and Observability Gaps

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. 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.”

This lack of visibility is particularly troubling because rogue AI deployments can quickly consume bandwidth, introduce security vulnerabilities, and lead to compliance issues. Without robust network performance monitoring and traffic analysis tools, IT teams are flying blind. The survey's finding that 80% of organizations say AI has expanded their attack surface underscores the urgency of improving observability. Security is also emerging as a barrier to AI expansion. “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 is echoed by the 61% of respondents who are delaying additional AI deployments until they gain more confidence in their security posture.

Implications for Network Modernization

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.”

Network modernization is not just about adding bandwidth. Organizations must rethink network architecture to support dynamic traffic flows, implement zero-trust security principles, and deploy AI-powered observability tools that can automatically detect and mitigate anomalies. Software-defined networking (SDN) and intent-based networking (IBN) become critical as they allow networks to adapt in real time to changing conditions. Additionally, as AI agent deployments are expected to grow significantly—85% of respondents anticipate moderate or significant growth—networks must support low-latency, high-availability connections between agents operating in different locations.

The shift toward distributed AI processing, where inference and even training occur closer to the edge, will further stress campus networks. For example, retail chains deploying AI for inventory management or customer service will need branch networks that can handle video analytics, natural language processing, and real-time decision-making without relying solely on centralized cloud resources. Similarly, healthcare organizations using AI for diagnostics or patient monitoring must ensure that campus networks can securely transmit large volumes of sensitive data without bottlenecks.

The Cisco survey also underscores the need for cross-functional collaboration. Network teams, security teams, and AI/data science teams must work together to plan capacity, define security policies, and implement monitoring solutions. Many organizations still operate in silos, which slows down AI adoption and increases risk. By breaking down these barriers and investing in modern network infrastructure, enterprises can turn AI from a potential liability into a competitive advantage.

As the report concludes, the next few years will be critical. Enterprises that fail to modernize their campus and branch networks may find themselves unable to scale AI efforts, while those that proactively upgrade will be better positioned to capitalize on the AI revolution. The networking supercycle is here, and the time to act is now.

Source: Network World News