Insight and analysis on the data center space from industry thought leaders.

AI Is Moving Faster Than the Networks That Support It

To realize AI's full potential, enterprises must modernize their network infrastructure to be cloud-first, elastic, and high-performance.

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Misbah Rehman , VP of Product Management and Compliance, Alkira

November 10, 2025

4 Min Read

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Artificial intelligence is having a profound impact on nearly every industry. Whether it's drug discovery, fraud detection, supply chain optimization, or customer engagement, enterprises are doing everything in their power to incorporate AI into their operations. Between faster innovation, smarter decisions, and a notable competitive edge, the promise of what AI can deliver is tremendous.

But there’s a problem that many leaders don’t anticipate until they are too far into their AI journey to turn back. What should be a lightning-fast transformation is instead defined by long delays, spiraling costs, and months of infrastructure work before a single model begins delivering value. Enterprises expect AI speed, but too often they don’t get it.

Why AI Moves Faster Than Networks

The root of this issue is in the networks that tie everything together. Traditional enterprise networks were never designed to support the demands of AI.

AI workloads are different from conventional applications in almost every way. They depend on moving vast volumes of data, much of which is unstructured, across distributed environments. Training and inference rely on clusters of high-performance compute that must be fed with low-latency, high-throughput connections. Workloads often span hybrid and multi-cloud architectures , spreading data and compute across regions, providers, and even on-premises facilities.

This is not the world that yesterday’s networks were built for. Legacy networking was optimized for branch-to-datacenter traffic, not for training large language models across thousands of GPUs or rapidly scaling inference in multiple clouds. Now, enterprises attempting to adopt AI suddenly find themselves struggling with endless network redesigns, long provisioning cycles, and expensive hardware refreshes.

When Weeks Become Months

Consider what it takes today for many organizations to support an AI project. Before a single pilot can even begin, teams may spend months re-architecting their WANs, deploying new circuits, configuring complex routing policies, and securing traffic across multiple clouds. Each of these steps involves multiple vendors and manual trial-and-error processes.

What should be measured in weeks too often stretches into months. And in the fast-paced world of AI, where competitors are introducing new products and experiences at an incredible speed, those delays can be fatal.

The irony is that AI technology itself is advancing at a faster pace than ever before. Model architectures evolve every few months. Cloud providers constantly release new AI services. Open-source communities iterate daily. However, networks remain the slowest part of the stack, creating a bottleneck that enterprises can no longer ignore.

The Strategic Cost of Slower AI Deployments

The costs of this drag are strategic. Business leaders promise AI-driven innovation but face credibility gaps when implementations stall. Data science teams lose momentum, stuck waiting on infrastructure instead of freely iterating on their models. As timelines extend, budgets spiral with unexpected expenses from re-engineering and re-architecting the network. Most importantly, enterprises risk missing the competitive window of opportunity, while faster rivals bring AI-powered innovations to market. In this landscape, where speed determines leadership, this is a fundamental obstacle to long-term success.

Why Fixing the Network Comes First

To realize AI’s full promise, enterprises must confront the network issue head-on. But this can’t mean yet another incremental cycle of re-engineering, patching, or waiting on hardware refreshes that take over a year to implement. Instead, networks must evolve with principles that reflect the realities of AI as they exist today. They need to adopt a cloud-first design, which ties seamlessly into hybrid and multi-cloud environments without requiring lengthy, complex integration projects. They must be elastic, scaling capacity dynamically as workload demands rise or fall, without requiring manual intervention at every step.

Performance requirements, such as low latency and high bandwidth, are non-negotiable, but must be delivered in a way that avoids complexity and overhead for already-stretched IT teams. Security must be built in from the ground up, ensuring that sensitive data is protected across global jurisdictions and multi-cloud architectures. Above all, the network can no longer move at the pace of traditional infrastructure. It must keep pace with the rapid growth of AI innovation, ensuring that infrastructure never becomes the rate-limiting factor in business transformation.

A Call to Action

The race to AI is not slowing down anytime soon. In fact, it’s speeding up. Enterprises that figure out how to deploy faster will shape industries, define customer expectations, and set the pace for everyone else. Those who remain stuck in long cycles will struggle to catch up.

At its core, the solution is not about chasing every new model or GPU cluster as it becomes available. It’s about recognizing that the foundation of AI success is the network infrastructure. Modernizing the network to be adaptable, scalable, and elastic unlocks the ability to scale AI confidently and without delay.

The enterprises that succeed in AI will be the ones that invest in the infrastructure that makes it usable at scale. They will ensure that the story of AI in their business is written at the speed of opportunity, not stalled at the speed of a legacy network.