Preparing campus and branch networks for AI demand

AI workloads are reshaping how campus and branch networks operate. Data volumes are rising, traffic patterns are more complex, and the security perimeter now spans clouds, devices, and locations. Yet many enterprise networks carrying this traffic were designed and deployed more than a decade ago for a very different set of demands.

AI agents already help triage support tickets, analyse documents, assist contact centre staff, and provide real-time decision support. Each new use case introduces additional east–west traffic, more API calls, and more flows between data centre, cloud, branch, and campus locations.

Networks that once carried relatively predictable traffic must now support latency-sensitive AI interactions. User experience depends on whether those interactions feel instant and reliable, or sluggish and unreliable. When the underlying campus or branch network cannot keep up, AI projects stall or deliver inconsistent value across sites.

Skills gap

This technical pressure lands during a sustained IT skills shortage. Research from Gartner indicates that by 2026, 64% of enterprises expect to face a shortage of IT labour. Many organisations already experience this reality in day-to-day operations.

IT teams are responsible for more sites, more devices, and more applications, while headcount growth remains limited. Specialist skills are harder to find and retain. Teams still carry responsibility for uptime, performance, and security across the full campus and branch footprint.

In this context, operational simplicity becomes critical. Visibility, automation, and a consistent management experience across locations are core requirements. Tools that reduce manual effort and shorten time to diagnose issues help teams keep pace with AI-driven complexity.

Legacy campuses

Over recent years, many enterprises directed investment towards data centres. Servers, storage, and core networking have been upgraded to support GPU clusters and AI training workloads.

Campus and branch environments often tell a different story. Switches, wireless access points, and routing hardware may have been running for over ten years. These networks were designed for a pre-AI world, with fewer cloud applications, fewer connected devices, and less demanding digital experiences.

Such environments may continue to function for basic connectivity. When asked to support AI workloads, stronger encryption, and a growing variety of endpoints, they struggle with throughput, latency, or security. The result is a widening gap between what AI applications require and what the existing campus or branch network can deliver.

Unified platform

One response to this challenge is a unified networking platform that brings campus and branch infrastructure under a single approach. Cisco has aligned the Catalyst and Meraki portfolios into a common stack, so customers can deploy a single class of hardware and choose the management model that best fits their operations.

This current generation of campus and branch hardware is built for higher throughput, richer telemetry, and more stringent security requirements. It can support heavier traffic loads associated with AI use cases while giving IT teams better insight into how applications and devices behave on the network.

Security sits at the centre of this approach. As AI agents proliferate across environments, the attack surface expands and the perimeter becomes harder to define. Applying security controls at every layer of the network is one way to respond. That includes secure device-to-cloud connectivity, support for advanced cryptography such as post-quantum approaches, and inspection of device-level information to understand what new AI agents are doing across the campus.

MSP opportunity

For Managed Service Providers, the shift to cloud-managed networking creates operational and commercial advantages. Many MSPs already use cloud dashboards derived from the Meraki heritage to manage small branch networks from a central operations centre. As more of the Catalyst portfolio becomes manageable through the same interface, that model extends to large campus environments.

A single pane of glass allows MSPs to view and manage multiple customer networks from one platform. Standardised configurations, centralised monitoring, and remote troubleshooting reduce the need for onsite visits for routine tasks.

This model supports managed network offerings that span both branch and campus sites. MSPs can design AI-ready architectures, oversee deployment, and then operate those environments under defined service levels. That creates ongoing service revenue and additional gross margin on top of project work.

Partner actions

For partners and MSPs, a practical starting point is a focused AI-readiness conversation with customers. The core questions are straightforward: can existing campus and branch networks handle AI-related traffic, and can current tools manage the added complexity and security exposure.

Assessments can map out where bottlenecks, blind spots, and security gaps exist today. From there, partners can define a staged upgrade path that aligns with business priorities, budget cycles, and risk appetite. Some organisations may prioritise modernising core campus sites first, while others focus on high-value branches or locations where AI pilots will start.

Vendors and distributors support this work by providing training, design guidance, and access to the right technologies. Cisco, Ingram Micro, and partners in the broader ecosystem are investing in enablement programs that cover unified platforms, security capabilities, and cloud management features tailored to AI-era requirements.

The scale of change under way is clear. AI is altering traffic patterns, security exposure, and operational expectations across campus and branch environments. As many technology leaders now recognise, this is a once in a decade shift in technology, and every organisation should ask whether its campus and branch networks are ready for AI.