Why Enterprise AI Runs on Coordination, Not the Cloud Alone

Enterprises are looking to capture the power of AI, and many of them are turning to cloud services to help them do it. But in this context, the cloud itself isn’t the thing that actually matters. What matters is that they’re able to coordinate across their different cloud and on-premises environments. That’s because AI is distributed by nature, so a cloud-first approach to AI doesn’t always work. Enterprises need flexibility to ensure that the right workloads end up in the right places.

Businesses that have been cloud-first for years are now learning this the hard way. They’re stuck paying a “coordination tax” that they never planned for. This could mean literal costs driven by egress fees and difficulty optimizing for cost-efficiency. It could also be a metaphorical tax, in the form of lost control or missed opportunity.

Many of these enterprises rely on AI ecosystems that increasingly extend beyond hyperscalers to include neoclouds and other major providers. Bringing together all these different partners requires vendor-neutral physical infrastructure such as colocation data centers, as well as interconnection solutions for reliable, low-latency connectivity.

According to Gartner®, spending on data center systems and Infrastructure as a Service (IaaS) is on track to grow 62.5% and 29.3% respectively during 2026. This makes them the two leading growth sectors in the Worldwide IT Spending Forecast.[1]

The growth in IaaS speaks for itself: Organizations continue to see scalable, flexible cloud resources as a valuable part of their AI strategy. And while cloud hyperscalers are partially responsible for driving increased data center demand, enterprises are also acquiring physical data center capacity for their own private use. This indicates a growing preference for hybrid multicloud infrastructure in their AI strategies, rather than a return to cloud-first thinking.

Let’s take a closer look at why cloud-first doesn’t work in the current AI landscape, and why leaders should focus on cloud coordination instead.

What enterprises actually achieved with cloud-first

The early move to the cloud happened for a reason, and it’s important we don’t lose sight of everything businesses gained from that move. It helped them achieve agility and flexibility that they couldn’t have with physical infrastructure alone. It also helped them access new services from leading providers on demand. AI adoption requires new infrastructure and new capabilities, so it only makes sense that many enterprise leaders saw the cloud as a quick and convenient way to start their AI journeys.

Now, many of those same leaders recognize that the cloud isn’t right for every workload, nor will a single cloud always provide the best results. This doesn’t mean enterprises are turning their back on cloud computing as a concept; it means they’re taking a more nuanced, strategic approach. They’re using a combination of different cloud and on-premises environments to get the best possible balance of performance, cost-efficiency and security.

Many leaders are facing pressure to justify their cloud spending. Even when businesses achieve the agility and scalability they wanted from the cloud, it can still feel like they’re paying too much to get it. That’s because lack of coordination makes it difficult to demonstrate how spending directly links to outcomes.

Coordination is the new infrastructure challenge

Where you deploy AI infrastructure is certainly important. Distributed AI infrastructure is becoming the new standard because leaders recognize that different components are suited to run in different places:

• Sensitive data needs to reside in private environments where the enterprise can apply the appropriate security measures.
• Models may be hosted across multiple clouds to get the optimal mix of performance and cost-efficiency.
• Applications need to run close to users and data sources to keep latency low.
• Workloads may need to run in different regions or environments to meet a variety of governance and sovereignty requirements.

With all this in mind, success with AI isn’t just about what resources you deploy: Where you deploy those resources and how effectively you connect them are equally important. Before they work on scaling AI, enterprise leaders must first ensure they have a foundation that enables disparate environments to communicate, collaborate and operate as one.

Agentic AI demands a radical shift from legacy IT architectures. Gartner predicts that by 2029, at least 70% of organizations with production agentic AI in infrastructure and operations (I&O) will experience a material service, security or cost incident linked in part to insufficient runtime controls.[2]

AI agents need the ability to translate intent into coordinated action across hybrid environments. This could mean repositioning workloads, rerouting traffic or setting up new connections to meet goals and adapt to changing conditions in real time. To achieve this, agents need a network that’s just as agile and intelligent as they are.

What does it take to enable coordinated cloud infrastructure?

The need for coordination across cloud environments is clear, but the question of how to achieve it is less straightforward. Below, we’ll examine the three factors that all organizations need to account for in order to make the shift from cloud-first to coordination-first.

• Proximity to data sources: AI inference workloads are highly sensitive to latency. This means enterprises need to cut down the distance between where data originates and where it’s processed. This would be impossible using a cloud-first model, where the enterprise has essentially no control over where data is physically stored.
• Private, low-latency connectivity: Distance may be the inherent cause of latency, but poor connectivity exacerbates it. When enterprises use the public internet to move AI datasets, their traffic won’t follow the most direct route available, leading to unexpected delays that degrade the user experience for AI applications. Instead, they need interconnection solutions that move data traffic directly from point to point.
• Neutral ecosystem access: To find the right models, acquire GPUs or other AI accelerators, and share data with enterprise partners, organizations need to coordinate with a variety of ecosystem partners. This is difficult to do if they’re locked into a particular cloud provider.

With our global portfolio of colocation data centers, our Equinix Fabric®  virtual interconnection solution, and our ecosystem of more than 10,500 service providers and enterprises, Equinix provides the neutral foundation that enterprises need to connect their distributed clouds, AI infrastructure, data and partners.

What does this mean for enterprise decision-makers?

What enterprises need now is not a rip-and-replace of their existing cloud infrastructure. They just need to recognize that the cloud itself isn’t the answer to all their infrastructure needs. Coordination is what really matters, and enterprises need to tweak their AI strategies accordingly.

This means no more trying to stitch together different cloud services over the public internet. Instead, a dedicated coordination layer enables the business to easily change their cloud services whenever the need arises, access different AI models from different providers, and relocate workloads to wherever they run best.

For enterprise leaders that face the dual challenge of justifying their cloud spend and driving ROI from production AI applications, the answer is clear: Spend smarter, not harder. Focus your investments where they’ll have the biggest impact: at the coordination layer.

If you need help addressing your hybrid multicloud networking needs, start by using the Equinix Connectivity Advisor. This five-minute self-assessment helps identify gaps in your current connectivity model. You’ll get a customized benchmark report that compares your results to information we’ve gathered from more than 1,600 IT leaders.

Take the assessment today.

[1] Gartner press release, Gartner Forecasts Worldwide IT Spending to Grow 14.2% in 2026, Totaling $6.37 Trillion, July 27, 2026.

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[2] Gartner Infrastructure & Operations, The Infrastructure Enterprises Need to Support Agentic AI at Scale, July 8, 2026.