To Get AI Right Tomorrow, Modernize Your Network Today

Editor’s Note: This blog post was originally published in October 2024. It was updated in March 2026 to include the latest information.

A large regional healthcare provider was looking to make more data-driven decisions aligned to their mission of providing compassionate care. They started exploring AI initiatives to enable more personalized patient experiences and better diagnostics. The organization’s IT leaders had already adopted a hybrid multicloud architecture and experimented with some AI services in the cloud.

However, when they started assessing the infrastructure requirements for successful production-scale AI solutions, it became clear that their aging network was a blocker. They were already struggling with application latency, so launching new AI applications would have put further strain on their network. They needed to modernize their network to enable their AI strategy.

Networking might not be the first thing companies think of when planning their AI initiatives, but getting reliable, high-performance connectivity to distributed AI resources like models, ecosystem partners and data sources is crucial. AI requires massive quantities of data to be transferred to and from applications while avoiding bottlenecks. In technical terms, AI workloads require:

• Low latency during development and deployment.
• High throughput to finish model tuning and development faster and transfer huge datasets without delay.
• High reliability, since many AI applications require near-instantaneous processing.

Traditional network architectures—like the one our example healthcare provider had built decades ago—weren’t designed to move data over the shortest, most efficient path. Now, the rapid growth of data and distributed AI solutions is increasing network traffic every year, putting further strain on aging infrastructure. Fixed broadband internet traffic reached an all-time high of 7.3 ZB in 2025, and has grown an average of 16% annually since 2021.[1] There will no doubt be much more growth in the coming years as the AI boom continues.

By modernizing their networks, enterprises can achieve a range of business benefits, from improving network reliability and security to cost optimization. For companies eager to adopt more AI technologies, the time to update and optimize their networks is now.

Why is networking so important for AI?

AI creates new challenges because of the sheer volume of data required, but also because of the distributed nature of the workloads. Data needs to be collected from many sources and processed in many places. You can’t always bring AI processing to where data is generated, or vice versa.

AI applications are increasingly using edge computing to process data closer to the source. This helps reduce latency and bandwidth usage and make AI systems more responsive and efficient. However, networking is still vital for connecting edge devices and ensuring seamless data flow across an enterprise’s distributed AI infrastructure.

Another consideration is moving inference from the far edge to the metro edge level. In some instances, this location represents the sweet spot for optimal performance, privacy and efficiency. But enterprises need an optimized network to fully utilize this architecture.

The healthcare provider in our example has more than 20 hospitals and 500 outpatient facilities spread across three U.S. states. They have a centralized data center in a major city, but they collect data from many dispersed locations. When transferring diagnostic and patient data for processing, their centralized network architecture was a bottleneck that kept them from adopting AI for mission-critical applications—the very applications where AI could help them the most.

With the right network architecture, the healthcare provider would be able to accelerate data transfers, optimize for lower latency, reduce data transfer costs and uncover new possibilities for AI in diagnostics and personalized medicine.

Network modernization helps you tackle AI head-on

Traditional network architectures weren’t designed and optimized for distributed AI workloads. This can present several challenges, such as:

• Insufficient network capacity.
• Traffic routing that adds latency and potentially increases costs.
• Inability to scale quickly as projects grow or demand increases.

To address these problems, many organizations are rearchitecting their networks to be more decentralized. They recognized the need to support AI workloads that are distributed across different locations and cloud environments. Network modernization initiatives might include any of the following:

• Utilizing network architecture strategies to account for distributed AI.
• Optimizing cloud connectivity to improve application performance.
• Implementing software-defined networking (SDN) to improve speed and agility.
• Beefing up network security to protect enterprise data and increase reliability.
• Deploying SD-WAN to simplify network management and orchestration.

A modernized network architecture is more adaptable, making it easier to scale network services up or down as AI projects progress. It can be also designed with more built-in resiliency, to help companies prepare for unexpected interruptions.

Multicloud connectivity is a critical AI success factor

Many companies turn to AI services in the cloud for their earliest AI projects, since they’re convenient and quick to deploy. Low-latency access to services from multiple providers is essential to AI success. Therefore, multicloud connectivity is becoming a critical discipline for organizations to master as they launch their AI journeys.

We know enterprises are already using multicloud architectures to help them access specific capabilities, optimize costs and increase resilience by diversifying vendors. “Multicloud” doesn’t refer only to hyperscalers, either: Enterprises are increasingly including neoclouds and SaaS specialists in their AI ecosystems. In one recent survey, 97% of enterprise IT leaders reported that their organizations use more than 10 cloud and SaaS providers.

The main reason that most enterprises are using a multicloud approach to AI is that their data is already generated and stored across disparate cloud environments. Also, clouds and other service providers are racing to invest in further developing their AI capabilities. Enterprises are designing their networks for flexibility so that they can take advantage of new AI capabilities, no matter which provider they come from.

Most organizations using multiple clouds are, in fact, using hybrid multicloud models. That’s because some workloads require greater privacy, security and control. A hybrid cloud approach backed up by optimized network infrastructure ensures that workloads that belong in the cloud can go to the cloud, while those that don’t belong in the cloud remain on private infrastructure. It also provides the level of control that enterprises need to meet their digital sovereignty requirements.

Modernize your network for AI with Equinix

When the healthcare provider was ready to upgrade their network, they opted to deploy virtual network functions (VNFs) from the Equinix Network Edge portfolio, including upgraded virtual routers, SD-WAN devices and firewalls. They deployed edge nodes in their facilities across the region to collect data on location. And to optimize their multicloud connectivity, they’re using Equinix Fabric® Cloud Router, our virtual cloud-to-cloud routing solution. Now, they’re ready to implement AI-powered diagnostics and personalized treatment plans.

With our global footprint of data centers, Equinix is helping customers modernize their networks for greater efficiency and resiliency. Deploying at Equinix gives you access to a vibrant ecosystem of cloud, networking and security service providers, as well as our growing AI ecosystem of data and model providers, neoclouds, hardware and chip manufacturers, and more.

Our on-demand enterprise connectivity services, including Equinix Fabric and Equinix Network Edge, are great for building adaptable networks that can easily scale to address the needs of your distributed AI projects.

Network modernization should be a priority for every organization. If you’ve already updated your network, you’re likely in a good place to launch AI initiatives today. But if you haven’t, you have no time to lose. In the era of AI, companies simply can’t put off optimizing their networks any longer.

Learn more about how enterprises are simplifying multicloud connectivity using Equinix solutions. Access our solution reference designs below:

• Equinix Fabric and Equinix Fabric Cloud Router for secure, agile global connectivity
• Equinix Network Edge for on-demand virtual network functions from top vendors