Enterprise IT leaders have spent the past two years focusing AI infrastructure discussions on GPUs, cloud platforms, and data centers. However, new research suggests that enterprise networks may not be ready for the next phase of AI adoption. A survey of 3,472 IT and networking leaders across 15 countries found that AI is already changing traffic patterns across campus and branch environments, exposing capacity, security, and visibility gaps that many organizations are not 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 a senior executive at a major networking company involved in the study. 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 organizations move beyond generative AI pilots and begin deploying AI agents that communicate continuously with other systems and applications.
Key Survey Findings
The research 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. Furthermore, 73% already face, or expect to face, campus and branch network capacity constraints within the next two years. Sixty-seven percent said AI workloads are increasing east-west traffic between internal systems and applications. Additionally, 80% said AI has expanded their attack surface, and 61% said they are delaying additional AI deployments until they gain more confidence in their security posture. Finally, 85% expect moderate or significant growth in AI agent deployments over the next two years.
Changing Traffic Patterns and Network Pressure
Changing traffic patterns inside enterprise environments are causing additional pressure for network teams. Typically, networks are designed for consistent traffic, like SaaS and CRM traffic, and there are not a lot of unpredictable patterns. However, as one IT executive noted, “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?” East-west traffic refers to data moving between servers and applications within the same data center or campus network, as opposed to north-south traffic that flows in and out of the network perimeter. AI agents, which are autonomous programs that perform tasks and interact with other systems, dramatically increase the volume and unpredictability of east-west traffic. This shift challenges traditional network architectures optimized for north-south traffic patterns.
Modernization and Observability Gaps
The study 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. However, a significant observability challenge 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. One survey participant stated, “Right now, we don’t even know what the AI-driven demand is. 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 means network teams cannot proactively manage capacity, security, or performance.
Security as a Barrier to AI Expansion
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 a vice president of infrastructure at a retail enterprise interviewed for the report. The expansion of AI workloads widens the attack surface, introducing new vulnerabilities such as insecure API endpoints, data leakage, and model manipulation. Many organizations lack the policies and tools to monitor AI traffic effectively, leading to hesitation in scaling deployments. The survey highlighted that 80% of respondents said AI has expanded their attack surface, and 61% are delaying further AI adoption until security confidence improves.
The Importance of Campus and Branch Networks
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. Unlike data center networks, which are typically well-controlled and can be upgraded with high-bandwidth technologies like 400G Ethernet, campus and branch networks often rely on older equipment and are subject to less centralized management. These networks must now handle the bursty, high-volume traffic generated by AI agents and generative AI tools used by employees. The research indicates that enterprises can no longer focus AI infrastructure planning only on back-end systems if they expect to scale AI deployments over the next several years.
To address these challenges, organizations are rethinking their network architectures. Software-defined networking (SDN) and intent-based networking (IBN) can provide the flexibility and automation needed to adapt to dynamic AI traffic patterns. Upgrading to Wi-Fi 6E and 5G can improve capacity for mobile AI applications. Implementing robust network observability tools can close the visibility gap, enabling IT teams to detect and respond to AI-driven demand in real time. Additionally, integrating security into the network fabric through Secure Access Service Edge (SASE) or Zero Trust Network Access (ZTNA) can help create consistent guardrails for AI tools regardless of where they are used.
The networking industry is evolving rapidly to support AI. Major vendors are introducing AI-native networking platforms that leverage machine learning to optimize traffic routing, detect anomalies, and automate troubleshooting. These platforms can help bridge the gap between current network capabilities and future AI requirements. However, adoption remains uneven. Many organizations are still early in their modernization journeys and lack the budget or expertise to implement advanced networking solutions. The survey underscores the urgency: with AI agents expected to multiply rapidly, even moderate growth could overwhelm unprepared networks. Enterprises that delay network upgrades risk falling behind in AI adoption, potentially losing competitive advantage.
In summary, the research paints a clear picture: AI is not just a data center phenomenon. Its impact is spreading to the edges of the enterprise network, challenging assumptions about capacity, security, and management. Campus and branch networks must be modernized to handle the volume, speed, and unpredictability of AI traffic. Observability must improve to give IT teams visibility into what is happening. Security must be rethought to protect against the expanded attack surface. As one executive noted in the study, “Eventually there will be only two kinds of companies: those that are AI companies, and those that are irrelevant.” The network, often taken for granted, will play a pivotal role in determining which category an organization falls into. The time to act is now, as the next wave of AI agent deployments is already on the horizon.
Source: Network World News