5 Ways the Right Data Centers Can Accelerate Your AI Strategy

Our partners at Cisco wrote a blog post about the five signs your data center is holding your AI strategy back. They make a good point. Many enterprises have AI ambitions that outpace their infrastructure readiness. At Equinix, we help enterprises accelerate their AI strategy by providing the proximity, capabilities, and connectivity they need to go from AI strategy to AI results.

Enterprise AI adoption requires both a strategy with clearly defined objectives and the infrastructure to execute that strategy. One without the other is not enough. As Winston Churchill once said, “However beautiful the strategy, you should occasionally look at the results.”

Today, CIOs are laser-focused on AI strategies, with the assumption that they’ll figure out the infrastructure later. The time to “figure it out” has come: According to a recent survey from Omdia, 76% of respondents said that their organizations were either in the “mature production” or “early production” stages of AI adoption.[1]

Let’s look at five different characteristics that define AI-ready data centers. If these characteristics sound familiar to you, then you’re on the right track. If not, it’s time to rethink your approach.

1.    Ensuring proximity to models and data sources

Today’s AI workloads can run just about anywhere, which means you’ll need infrastructure just about everywhere. This is especially true now that many enterprise leaders are shifting their focus to AI inference. Unlike training, inference is very latency-sensitive, and it therefore benefits from running closer to users and data sources. In the Omdia survey, respondents were asked to name their network requirements for deploying AI technology. About 58% said they needed to improve performance to reduce latency, the single most common response.

Placing data centers in the right locations is also essential for addressing emerging digital sovereignty requirements. Most leaders face regulatory challenges, such as GDPR in the EU and LGPD in Brazil, but there’s much more to sovereignty than just compliance. Real sovereignty is about control: control over where data’s stored, how it moves, and how it’s used for AI purposes. Sovereign AI starts with local infrastructure that you fully control, deployed within specific jurisdictions. It also requires intelligent networking solutions to balance your global AI ambitions against your local sovereignty requirements.

For these reasons, where you build a data center is just as important as how you build it. Distributed AI infrastructure is the new standard, because enterprises are placing training where compute is abundant and running inference workloads near data sources.

2.    Enabling connectivity across hybrid multicloud environments

“Distributed AI” doesn’t just mean geographically distributed workloads. You’ll also need to run workloads across different cloud and on-premises environments to get the best mix of performance, reliability, scalability, data privacy, and cost-efficiency. In short, you’ll need a hybrid multicloud architecture.

True hybrid multicloud doesn’t just mean running siloed workloads across different clouds. It means consistent connectivity across different environments, so that workloads and data can move between clouds and on-premises data centers whenever the need arises.

Enterprises that have invested in GPUs are now learning that poor data movement is a bottleneck that limits GPU utilization and prevents them from realizing a return on investment. According to a recent study from Viavi Solutions, even a modest 1% packet loss rate in the network would result in a 33% drop in GPU utilization.[2]

That’s why you need to deploy data in a vendor-neutral environment to remove the risk of lock-in and keep data moving freely across distributed systems. The right data center also provides access to private, low-latency connectivity solutions like Equinix Fabric®, allowing enterprises to establish virtual connections in minutes, not months.

3.    Balancing coordination and raw compute power

AI success is less about pure processing power and more about coordination across distributed systems. The emergence of agentic AI underscores this. To support always-on AI agents, many enterprises are reevaluating their hardware mix: They’re still using GPUs for workloads that require high-throughput parallel processing, but they’re increasingly using the humble CPU for things like orchestration, sequencing tasks, and managing memory.

The network is the foundation that ties AI processors together, helping both CPUs and GPUs fulfill their roles and perform as a coordinated execution environment. As enterprises incorporate CPUs into their agentic AI strategy, they’ll need networking that’s as agile as the agents themselves. That’s why it’s essential to deploy inside interconnected data centers that offer intelligent networking solutions with built-in automation capabilities.

4.    Optimizing power and efficiency

The impact of emerging AI hardware on local power grids is widely recognized. This hardware has already far outstripped the power density found in data centers just a few years ago, meaning that it uses much more energy in the same data center footprint.

Higher power density also makes advanced liquid cooling a prerequisite for AI success. This is why you want a data center operator that provides these capabilities for enterprise customers, removing the cost and complexity of doing it in-house.

Using cleaner energy and using energy more efficiently both play a role in enabling reliable data center services in an uncertain world. The right data center provider prioritizes both.

At Equinix, we reached 96% renewable coverage globally in 2025 and are on track to achieve 100% renewable coverage by 2030. We also lowered our global annualized average power usage effectiveness (PUE) to 1.37, a 5.3% improvement from 2024. We achieved this via a $36 million investment in efficiency across 79 sites, including advanced cooling, intelligent monitoring, and optimized design. These investments help enterprises explore AI innovation without undoing the sustainability progress they’ve already made.

5.    Gathering AI ecosystem partners

Neoclouds like CoreWeave, Groq and Nebius are essential partners for organizations that need rapid access to AI hardware, tools and services. Deploying near these neoclouds and other AI ecosystem partners makes it quick and easy to connect whenever the need arises.

Vendor-neutral colocation data centers make ideal interconnection hubs because they gather many different ecosystem partners in the same locations. Over time, the density of the partner ecosystem acts like a magnet to draw in new participants, with each additional participant adding new value to the ecosystem.

If you deploy inside a colocation data center that’s home to a vibrant partner ecosystem, you may find yourself in the same physical building as the neoclouds, hyperscalers and security specialists you need to drive your AI strategy forward.

Building the future of AI infrastructure today

Enterprises ask a lot from their AI infrastructure, and a data center isn’t truly AI-ready unless it provides the kinds of capabilities named above. CIOs who currently lack these capabilities may feel behind on executing their AI strategies, with a lot of ground to make up.

It’s time for enterprise leaders to stop asking themselves how their data centers are holding them back, and start thinking bigger about what’s possible when they have the right data centers, with the right partners, in the right locations. With Equinix, organizations can access all of this under one roof. Only Equinix offers:

• The global reach needed to support distributed infrastructure at the edge
• A vendor-neutral foundation to enable hybrid multicloud networking
• Intelligent connectivity solutions that enable orchestration for AI agents
• Sustainability and efficiency leadership
• A dense AI ecosystem that includes all major neoclouds and hyperscalers

Every day, Equinix data centers help enterprises bridge the gap between AI strategy and AI execution. See what that could look like for your business.

Learn more about what really happens inside AI-ready high-performance data centers. Read the white paper, “Behind closed doors: The incredible digital architecture powering your daily life.”

[1] Automated Networking in the Distributed AI Era, an Omdia white paper commissioned by Equinix and distributed under license from TechTarget, Inc. March 2026.

[2] Martin De Saulles, Can your enterprise network keep up with its agents? CIO, March 16, 2026.