How AI Demand is Forcing Enterprises to Rethink Network Planning

Artificial intelligence is no longer a fringe tool. It's a central to how enterprises operate, compete, and deliver services. But while most organizations focus on the compute side of AI (e.g., GPUs, model training), many overlook a critical enabler: the network. As AI workloads surge, they are reshaping how enterprises must plan, provision, and pay for their networks.

The New Shape of Enterprise Network Traffic

Traditional enterprise network traffic was largely predictable - email, file transfers, SaaS, VoIP. AI changes that. Whether it's real-time video analysis at the edge, LLM inferencing in the cloud, or massive data transfers for training, AI introduces:

• High-throughput demands: AI needs to move large volumes of data, often across hybrid environments.

• Burstiness: AI training and inferencing can spike sudden and unpredictably.

• Latency sensitivity: Use cases like autonomous operations or real-time analytics require ultra-low latency.

For network planners, this means old models for forecasting traffic and capacity simply don't hold up.

SD-WAN Is Necessary but Not Sufficient

Most large enterprises have adopted SD-WAN for its flexibility, cost savings, and centralized management. It allows companies to dynamically route traffic across MPLS, broadband, and wireless links. But in the AI era, SD-WAN must evolve:

• AI-aware routing policies: Not all traffic is created equal. AI workloads may need to preempt bulk traffic.

• Integration with edge computing: AI processing is shifting to the edge, requiring local breakout and low-latency paths.

• Security and segmentation: With sensitive models and data moving around, fine-grained security becomes essential.

According to Gartner, over 70% of network operations teams will rely on generative AI for SD-WAN configuration and optimization by 2027. Enterprise network teams must plan now to integrate this level of intelligence.

Carrier Networks Are Evolving Too

Telecom providers are feeling the pressure. Enterprise clients are demanding AI-friendly networks with:

• Dynamic scalability: Bandwidth that flexes with AI load.

• SLAs for AI traffic: Guarantees around latency, jitter, and throughput.

• Edge POPs and MEC zones: To support inferencing and low-latency AI use cases closer to end users.

Carriers like AT&T and Verizon are already experimenting with AI-enhanced interconnects and 5G-based edge compute zones. Enterprise customers need to engage with their providers to understand roadmap alignment.

Pricing Is the Wild Card

AI traffic could break traditional pricing models. Enterprises may face:

• New cost structures: AI-intensive sites might require SLAs or usage-based models.

• Volume-based tiers: Similar to cloud egress fees, network pricing may scale with AI data volumes.

• Incentives to consolidate traffic: Carriers may encourage funneling AI workloads through preferred POPs or backbone routes.

Enterprises must model these pricing impacts now. It's not just about adding more bandwidth - it's about adding the right kind of bandwidth.

Recommendations for Enterprise Network Teams

1. Audit your AI footprint: Where is AI being used? What traffic patterns does it generate?

2. Map workloads to network capabilities: Do edge locations have the latency and throughput required?

3. Revisit SD-WAN policies: Can your fabric identify and prioritize AI traffic?

4. Engage with carriers: Ask about AI-readiness, edge capabilities, and dynamic pricing models.

5. Forecast for agility: Assume traffic patterns will shift. Plan networks to adapt, not just scale.

Final Thoughts

The rise of AI is a double-edged sward for enterprise IT. It unlocks huge value, but only if the supporting infrastructure can keep up. Network planning is no longer just about pipes and packets. It's about intelligently supporting a new class of applications that redefine performance, cost, and control.

In this AI-driven future, the smartest enterprises won't just deploy AI. They'll build networks that think ahead.