Cisco: Optimizing Campus and Branch Networks for AI: A Strategic Imperative

Artificial intelligence (AI) is rapidly transforming enterprise operations, extending its influence beyond the data center to the network edge. The Accelerating Impact of AI on Campus and Branch Networks, new research from Cisco and Foundry reveals a critical challenge: a widening gap between AI ambition and current network readiness. Despite acknowledging the need for modernization, even aggressive AI adopters report that their campus and branch networks are not adequately equipped to support the dynamic and demanding nature of AI workloads. This creates significant operational, security, and competitive risks for organizations that delay strategic network upgrades.

The AI Imperative: Reshaping Network Traffic and Capacity

AI workloads fundamentally alter traditional network traffic patterns, placing unprecedented strain on existing infrastructure, particularly in campus and branch environments. These locations are where users, devices, applications, and autonomous systems converge, making them critical for AI adoption. The Cisco and Foundry research, which surveyed over 3,400 IT and networking decision-makers across 15 countries, highlights several key shifts:

• Evolving Traffic Patterns: AI systems generate diverse traffic types. For instance, 67% of respondents observed increased east-west traffic – the lateral device-to-device or server-to-server communication essential for AI agents to exchange data. Both continuous automated traffic (61%) as well as bursty, spike-driven traffic (49%) have also seen substantial increases, demanding networks that can handle unpredictable fluctuations in real time.
• Accelerated Capacity Demands: The report indicates a significant surge in network traffic attributed to AI workloads. Organizations experienced an average 34% increase in campus and branch network traffic over the past 12 months, with an additional 96% growth anticipated in the next year. Projections suggest traffic will reach three times current levels within three years, compounded by the expansion of generative AI, agentic AI, and physical AI systems. This translates to severe capacity limitations, with 73% of organizations already facing or expecting to face such issues within the next 24 months.
• Latency Sensitivity and Real-World Impact: AI systems, especially agentic AI capable of autonomous action, rely on low latency, continuous responsiveness, and reliable real-time interactions. A retail example in the research illustrates this directly: an AI loss prevention system became “pointless” due to a five-second network latency, allowing individuals to leave the store before the system could effectively act. This directly impacts key CX metrics such as time-to-resolution for AI-assisted customer service and operational efficiency for automated processes. Critically, 50% of this AI-driven demand is concentrated in wireless networks within campus locations, underscoring the need for robust Wi-Fi infrastructure.

What this means: Current network architectures, often designed for consistent SaaS and CRM traffic, are ill-suited for AI’s dynamic, distributed, and latency-sensitive demands. Proactive network modernization is not merely an IT upgrade but a strategic business imperative to avoid operational bottlenecks and maintain competitive relevance.

Security and Observability: Fueling Trust and Mitigating AI Risks

Security complexity emerges as the primary barrier to scalable AI adoption. Unlike previous technology waves, security concerns around AI are not a parallel consideration but a direct impediment to operationalization. Without trust in the systems, visibility, and controls surrounding AI workloads, enterprises are hesitant to broaden their AI initiatives.

• Expanded Attack Surfaces and Shadow AI: As AI adoption expands beyond generative use cases, 78% of organizations anticipate increased security risks, with 77% reporting that AI has already expanded their attack surface within the last 12 months. The proliferation of “shadow AI” – unauthorized or ungoverned AI deployments within departments – creates blind spots, contributing to the 69% of respondents who describe growing visibility gaps. These unmanaged AI initiatives can quickly introduce vulnerabilities across distributed networks.
• Evolving Threats and Inadequate Controls: A significant 71% of IT leaders believe AI-driven threats are evolving faster than existing controls can adapt. Traditional security models struggle with the operational dynamism of AI workloads, where agentic systems communicate constantly and AI-enabled workflows can automatically trigger actions across applications and networking infrastructure. This makes it challenging to establish consistent guardrails for every possible AI tool.
• The Network as an Enforcement Point: Given these challenges, IT leaders increasingly view the network itself as a critical enforcement point for managing AI-related security risks, ensuring visibility, and enforcing policy at scale. An integrated network and security posture becomes essential for monitoring, segmenting, and securing AI traffic flows effectively. This is particularly relevant for highly regulated sectors such as financial services or healthcare, where data governance and compliance are non-negotiable.

What to do / What to avoid:

• What to do:
• Implement AI-aware Network Segmentation: Dynamically segment network traffic to isolate AI workloads, applications, and devices, limiting the blast radius of potential security incidents.
• Enhance AI Observability: Deploy advanced network analytics and telemetry tools that provide real-time visibility into AI traffic patterns, resource consumption, and anomalous behavior. Integrate AI-driven telemetry into existing Security Information and Event Management SIEM and Security Orchestration, Automation, and Response SOAR platforms.
• Establish Centralized AI Governance: Form a cross-functional AI Infrastructure Steering Committee comprising network engineering, cybersecurity, data science, and business unit leaders. Define clear policies and guardrails for AI model deployment, data ingress/egress, resource allocation, and real-time monitoring.
• Prioritize Integrated Security: Leverage network architecture that converges security and networking functionalities, making the network an active policy enforcement point for AI workloads.
• Conduct Red-Teaming Exercises: Regularly test AI-driven attack vectors and potential vulnerabilities within your network infrastructure to proactively identify and mitigate risks, particularly for agentic AI deployments in critical operational areas (e.g., manufacturing, supply chain logistics).
• What to avoid:
• Treating AI Security as an Afterthought: Do not assume existing security frameworks will suffice for AI.
• Permitting Unmanaged “Shadow AI”: Implement policies and technical controls to prevent unauthorized AI deployments at the branch or departmental level.
• Relying on Static Security Policies: Avoid static rule-based security for dynamic, rapidly changing AI workloads.
• Delaying Network Modernization: Waiting until a significant security incident occurs will result in higher long-term costs and reputational damage.

Strategic Network Modernization: Differentiating for AI Success

Aggressive AI adopters are approaching network modernization strategically, recognizing that AI fundamentally transforms operational behavior rather than just presenting a temporary traffic surge. This proactive stance distinguishes them from early-stage adopters. For example, 55% of mature AI adopters are aggressively upgrading their networks to support AI capacity demands, compared to only 26% of early-stage adopters. Similar gaps exist in meeting compliance requirements (53% vs. 32%) as well as staying ahead of competition (51% vs. 26%).

Delaying network modernization for AI carries substantial business risks, extending beyond technical challenges to directly impact an organization’s competitive posture:

• Financial and Operational Impacts: 75% of IT leaders agree that reactive upgrades and remediation will incur higher long-term costs. This inaction can lead to decreased operational efficiency (71%) and an inability to meet customer expectations (73%), directly affecting CX metrics such as customer satisfaction and renewal rates.
• Competitive Disadvantage: 72% of respondents anticipate missed business opportunities, and 67% fear falling behind competitors if they fail to adapt their networks for AI-driven demand. This can manifest as slower time-to-market for new AI-powered products or services.
• Reputational Damage: 70% of IT leaders are concerned about business reputation risks stemming from network outages or inconsistent policy enforcement in AI environments.

Operating model and roles: Effective AI network modernization requires a shift in operating models. A dedicated “AI Network Readiness Task Force” should be established. This task force includes stakeholders from Network Engineering, Cybersecurity, Data Science, and relevant business units (e.g., CX, Product Development, Operations). Their mandate is to define, implement, and monitor AI-specific network policies, performance thresholds, and security controls. Escalation paths for AI-related network incidents must be clearly defined, with SLAs tied to the criticality of the AI application. For instance, for AI-powered fraud detection systems in financial services, the network team must operate with an incident response time of under five minutes.

Immediate priorities (first 90 days):

1. Conduct an AI Network Readiness Assessment: Perform a comprehensive audit of current campus and branch network infrastructure to identify bottlenecks, capacity gaps, and security vulnerabilities specific to AI workloads.
2. Establish AI Deployment Protocol: Implement a mandatory registration and approval process for all AI initiatives and model deployments across the enterprise. This will surface “shadow AI” and ensure alignment with network and security policies.
3. Develop a Phased Modernization Roadmap: Outline a multi-stage plan for network upgrades, prioritizing areas critical for initial AI scale. Define clear Key Performance Indicators KPIs such as latency thresholds (<50ms for mission-critical AI applications), Wi-Fi capacity (e.g., 10Gbps per access point in high-density areas), and packet loss rates (<0.1%) to guide and measure progress.

What ‘good’ looks like: An AI-ready network environment ensures the infrastructure dynamically adapts to AI workload demands, guaranteeing low latency and high throughput. Integrated security policies are enforced consistently across campus and branch locations, offering real-time visibility into AI-driven traffic. Business units can confidently deploy and scale AI solutions, knowing the underlying network infrastructure is robust, secure, and reliable, thereby enabling faster innovation, improved customer experiences, and sustained competitive advantage.

Summary

The accelerating impact of AI on campus and branch networks necessitates immediate, strategic action. The insights from Cisco and Foundry’s research underscore that network modernization is no longer an optional infrastructure project but a fundamental prerequisite for operating and competing effectively in an AI-powered economy. Organizations must move beyond reactive measures and invest in an AI-ready secure network architecture now, or risk being outpaced by competitors that have embraced this strategic imperative. Delaying these critical upgrades will result in higher costs, compromised security, and a diminished capacity for innovation, directly affecting an enterprise’s ability to capitalize on AI’s transformative potential.

Reference: Cisco & Foundry. (2026). The Accelerating Impact of AI on Campus and Branch Networks. White paper.