AI-related traffic is putting more pressure on business networks. More often than not, the initial response we have seen from organizations is to buy more bandwidth.
That can help, but it doesn’t actually solve the larger problem. If the firewall and other equipment at your site can’t handle the traffic, your business pays for speed it can’t use. Applications may still lag, fail, or deliver an unreliable experience.
This article explains how to find network constraints that can undermine AI performance and plan the right upgrade. If you want a deeper exploration and discussion on this topic, check out our webinar replay.
The assumption, “We have a two-gig circuit. We’re fine,” sounds reasonable at first glance.
However, bandwidth only tells you how much data the connection can carry. It doesn’t tell you whether the equipment behind that connection can process it.
A customer can order a gigabit circuit and still be limited by the firewall. A gigabit Ethernet port doesn’t guarantee gigabit performance across the network. Every part of the path has to keep up.
Before calling a provider, you need to understand what sits behind the circuit. If the firewall or another piece of your equipment is the bottleneck, your organization may pay for capacity it can’t use. You need the right upgrade that addresses the actual constraint.
If your firewall is limiting performance, improve the firewall. If traffic is moving inefficiently within a branch, upgrade local switching or Wi-Fi. If delays come from sending AI processing to the cloud, move that processing closer to the users or devices. More bandwidth only helps when the connection itself is the bottleneck.
Many networks were designed around predictable traffic from SaaS, CRM, and office applications.
AI tools change this predictable pattern. They can communicate with other systems without a person initiating every exchange, creating activity that is harder to predict and manage.
Some of these exchanges happen inside your branch or local network rather than between a user and an internet or cloud service. Cisco and Foundry’s survey highlights this shift, finding more internal, time-sensitive, automated traffic and more traffic arriving in bursts.
AI exchanges often involve many small, rapid messages. The resulting strain may show up in firewalls, switches, memory, or processing capacity even when your overall bandwidth looks manageable.
Preparing for AI requires you to understand how AI traffic behaves, because that traffic can create different levels of pressure depending on where exchanges happen, how quickly they arrive, and how your network handles them.
The network strain caused by AI traffic patterns becomes a business issue when a delay changes what an application can do.
Consider a retail organization losing millions each year to theft. It built an AI loss-prevention system that used store cameras to recognize when someone concealed merchandise or became aggressive. But a network latency of about five seconds made the system ineffective. By the time it flagged the event, the shoplifter could already be out of the store with the merchandise.
Basically, the network couldn’t move the camera data to the AI model and return a theft alert quickly enough for the store to act.
To solve this, the organization upgraded the local network and moved the AI video analysis to a server at the branch instead of sending it to the cloud. With this, the AI processing the footage was closer to the capture points, which helped the system detect incidents and alert the store in time.
This is a clear-cut example of how a network bottleneck can become a failed application.
When this happens, your business ends up paying for the gap between what the application promises and what your environment can realistically deliver.
You don’t necessarily need to predict every future AI use case. You do, however, need a clear view of the network you have today and a practical plan for the next 24 to 36 months.
That plan should leave room for backup capacity, resilience, and easier upgrades. You may not know the next workload, but you can avoid locking your organization into an architecture that cannot adapt.
Start by checking current traffic and utilization. Review the circuit, firewall, local network, Wi-Fi, network separation, security controls, and where workloads run. Determine whether an unusual traffic pattern is a one-time event or a sign of a larger problem.
Before ordering your next circuit, identify what is limiting performance today. A network readiness assessment can provide the information needed to choose the right upgrade and build a more predictable path forward.
If your team is rethinking how your network will support AI workloads, we can help.
A Bluewave network readiness assessment can help you identify where performance is most likely to break down and which upgrades should come first.
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