What makes a network AI-ready? A practical guide for enterprises

Have you noticed that AI isn’t waiting for your network to catch up?

Across every industry, AI projects are moving at speed. Models are getting larger. Data volumes are exploding. Inference is happening everywhere; from cloud cores to edge locations to branch sites. And suddenly, the network is no longer just infrastructure. It’s the deciding factor between AI that delivers value and AI that stalls out under its own weight.

An AI-ready network infrastructure means building a connectivity foundation that can handle significantly more data (and protect it), unpredictability, and scale globally, without becoming the bottleneck.

This guide breaks down what that really means, and how enterprise leaders can build AI-ready networks that move at the right speed.

Why does AI success depend on the network?

AI adoption is accelerating faster than network transformation ever has.

Enterprises can spin up AI platforms in weeks. Networks, by contrast, are often the result of years of incremental decisions. They’re built from different providers, different contracts, different architectures stitched together over time. That gap is now painfully visible.

AI workloads expose network limitations immediately. According to IDC, 45% of enterprise IT leaders see network performance limiting their AI projects. Training jobs saturate links. Inference demands consistent low latency. Data pipelines amplify packet loss and jitter that legacy applications could tolerate. What used to be “good enough” internet connectivity simply isn’t good enough anymore.

This is why connectivity has become a board-level AI risk. When AI underperforms, the issue is rarely the model alone. It’s the network underneath it with latency, congestion, or outages that undermine outcomes and inflate costs.

Put simply: you can’t out-optimize a weak network with better algorithms.

What is the role of networking in AI-ready architecture?

Why AI success now depends on the network

AI characteristic	Network impact
Large data volumes	Saturates best-effort links
Bursty workloads	Exposes congestion and jitter
Real-time inference	Intolerant to latency spikes
Distributed models	Requires consistent global performance
Continuous retraining	Amplifies packet loss

Put simply, the network is the foundation of AI performance, security, and scale.

Every AI workflow, from data ingestion and training, to inference and model updates, depends on moving data quickly, predictably, and securely. Networking decisions directly impact how expensive AI becomes to run, how reliable outputs are, and how confidently teams can deploy AI across the business.

In an AI-ready architecture, the network isn’t passive. It actively shapes outcomes:

• Poor connectivity increases retraining costs and delays insights.
• Inconsistent latency degrades inference accuracy and user experience.
• Fragmented security exposes AI data to unnecessary risk.

Without the right connectivity layer, even the most advanced AI stack will fail to deliver consistent value. That’s why AI-ready network infrastructure is now a core architectural concern.

AI-ready network infrastructure vs traditional enterprise networks

Traditional enterprise networks were built for predictable, human-driven traffic: email, SaaS, video calls, ERP systems. AI breaks those assumptions.

Legacy WANs and best-effort internet connections struggle under AI workloads because they were never designed for bursty, data-heavy, latency-sensitive traffic. They optimize for peak speeds on paper, not predictable performance in reality.

AI-ready network infrastructure flips that model. It prioritizes consistency over headline bandwidth. Predictability over averages. Control over best effort.

That’s where performance, resilience, and security start to diverge. AI-ready networks are engineered to deliver outcomes.

What are the network requirements for AI?

According to CATO Networks’ Greg Duffy:

AI needs a strong network with carefully structured overlay and underlay.

Performance that scales without bottlenecks

AI traffic does not behave like normal business application traffic. AI training jobs move large volumes of data in short periods of time. Demand can spike suddenly and consume significant bandwidth. AI inference traffic is more dynamic. It rises and falls based on user activity, API calls, and automated system triggers.

If the network does not actively manage this traffic, these spikes create congestion. Congestion leads to packet loss. Packet loss increases latency and causes inconsistent application performance.

This is where Enhanced Internet plays a role.

Unlike best effort access, Enhanced Internet continuously measures global path performance and dynamically routes traffic over the optimal path. This reduces latency, avoids congestion in real time, and stabilizes performance during AI demand spikes.

Improving connectivity and raw performance, on its own, does not fully solve the challenge. AI workloads must not only move quickly, but they must also move securely, consistently, and under centralized control across users, sites, clouds, and data centers. This is where a managed Secure Access Service Edge architecture becomes essential. SASE contributes in four key ways:

1. Intelligent traffic steering
   Application-aware routing prioritizes latency sensitive AI training and inference traffic over lower-priority flows.
2. Secure, distributed access
   AI users, branches, cloud workloads, and data centres connect through a single cloud-delivered fabric, avoiding backhaul bottlenecks.
3. Elastic security inspection
   Security inspection scales in the cloud rather than at fixed perimeter appliances, preventing AI spikes from overwhelming on-premise firewalls.
4. Unified policy control
   Performance and security policies are enforced consistently across sites, clouds, and remote users, reducing operational firefighting.

The result is a network that does not merely react to AI demand. It anticipates, prioritizes, and scales with it.

Predictable, low-latency connections

AI inference depends on consistency, not averages. A “good” average latency means nothing if spikes ruin response times.

For globally distributed AI platforms, consistency is the difference between usable insights and frustrated users.

Clean throughput with no congestion

Packet loss and congestion don’t always show up as outages. They quietly corrupt AI workflows, forcing retransmissions, degrading model accuracy, and slowing pipelines.

The result you want is clean, reliable throughput that AI systems can trust. 

Packet loss and congestion do not always show up as outages. They silently disrupt AI workflows, triggering retransmissions, reducing effective throughput, degrading model accuracy, and slowing training or inference pipelines.

Enhanced Internet helps prevent this by continuously monitoring internet path quality and dynamically routing traffic away from congested or unstable routes. By selecting the best-performing path in real time, it reduces packet loss and stabilizes throughput during demand spikes. The objective is clean, reliable throughput that AI systems can trust, not intermittent performance that undermines results.

Resilience built in, not bolted on

AI workflows don’t tolerate downtime. When pipelines fail, automation stalls, decisions freeze, and costs escalate fast.

Multi-path, technologically diverse architectures build resilience into the design using different, providers and connectivity solutions to eliminate single points of failure.

Security built for AI workloads

Legacy security stacks add friction and latency, exactly what AI doesn’t need.

Implementing managed SASE is a way of securing AI traffic without slowing it down. It enforces zero-trust access, protects data in motion, and applies security consistently across users, devices, applications, and AI platforms, without forcing traffic through inefficient choke points.

As CATO Networks’ Greg Duffy states:

How to develop an AI-ready network architecture

Step 1: Start with performance: Enhanced Internet

AI workloads aren’t equal. Training, inference, and data movement all have different performance needs.

Start by aligning AI workloads to performance tiers. Design for scale, not just today’s pilot. Enhanced Internet provides the predictable foundation AI needs to grow without repeated re-architecture.

By analyzing all the available networks every 5 seconds, Enhanced Internet improves consistency of application performance by improving metrics for latency, packet loss and jitter. 

Step 2: Secure AI traffic with Managed SASE

AI data moves everywhere: cloud platforms, edge environments, APIs, users, partners.

Managed SASE enforces zero-trust access across that entire surface area. It protects AI data wherever it moves without adding unnecessary latency or complexity.

Step 3: Design for resilience, geographic scale and data sovereignty

If you’re a global enterprise, your AI does not live in one place. Models train in one region, infer in another, and serve users everywhere. That reality raises the stakes. Performance still matters, but control matters just as much.

An AI-ready network architecture supports this by design. It delivers resilience and scale without losing sight of where data is allowed to live, move, and be processed.

That means building for:

• Diverse paths to avoid single-region and single-provider failures
• Regional optimization to keep traffic local where regulations demand it
• Predictable, global performance without forcing data to cross borders unnecessarily
• Policy-aware routing that respects data sovereignty requirements by design, not exception

When networks ignore sovereignty, teams compensate with workarounds. Latency spikes. Risk grows. Innovation slows.

Step 4: Simplify operations through managed services

AI evolves fast. Networks that require constant manual tuning and access to specialized AI and networking skills won’t keep up. The talent and skills gap is impacting organizations across the board. IDC data shows that finding AI, data and automation skills is a challenge for 33% of enterprises, while 39% struggle finding and retaining networking talent.

Managed Network-as-a-Service providers provide access to specialized skills and regional expertise, reduce operational drag, centralize accountability, and free teams to focus on innovation instead of incident response. That’s how infrastructure keeps pace with AI; not by adding complexity, but by removing it. It’s become the go-to model for 45% of enterprises outsourcing their networking requirements.

What are examples of efficient networking strategies for AI-ready architecture?

Efficient AI-ready architectures don’t rely on a single technology. They combine performance, control, and security into a cohesive model. This is where Enhanced Internet, SD-WAN, and Managed SASE work best together.

Efficient AI-ready network strategies focus on outcomes, not components:

• Using Enhanced Internet to support training, inference, and high-volume data movement with predictable, SLA-backed performance
• Prioritizing AI traffic using SD-WAN policies so AI workloads perform as expected without degrading business-critical applications
• Applying Managed SASE to secure AI access, APIs, and users consistently without adding latency or friction
• Supporting global AI rollouts with the same performance and security standards everywhere, regardless of region
• Ensuring your network is capable of handling more data when you need it without having to switch providers

These strategies mean your network can scale with AI needs instead of constraining it.

Bringing it all together: AI-ready networks don’t happen by accident

AI success isn’t determined by models alone. It’s determined by whether the network underneath them can deliver consistent performance, built-in resilience, and security without compromise.

That’s why an AI-ready network infrastructure isn’t about swapping one technology for another. It’s about combining the right layers:

• High-performance, global internet to deliver predictable connectivity for AI workloads
• SD-WAN to intelligently steer and prioritize AI traffic as demand shifts
• Managed SASE to secure users, applications, and data everywhere AI operates

Together, they turn the network from a constraint into a control plane; one that lets enterprises scale AI with confidence, not caution.

Most enterprises already have pieces of this architecture in place. What’s missing is alignment: performance engineered for AI, security designed for movement, and operations that can keep up as AI evolves.

Talk to an expert about building an AI-ready network infrastructure that delivers performance, security, and scale at the speed your business demands.