Is AI Your Single Point of Failure? Here’s What You Can Do About It.

Enterprises are already achieving great things with AI, such as improving productivity, efficiency and customer experience and unlocking new revenue streams. But in doing so, they’ve also quickly embedded AI dependencies throughout their business, often without fully considering the operational risks those dependencies create. AI adoption today is like the early days of cloud computing: Small proofs of concept often work their way into production systems, which can impact the business in unexpected ways.

Suppose that Company X uses Claude to run their operations. Now, imagine that Claude unexpectedly goes offline, as it did for thousands of users during a major outage last month.[1] This could render key capabilities inaccessible with no backup plan in place. What now?

Or perhaps Company X has found that Claude works very well as a frontier model for bleeding-edge business innovation. But they haven’t looked for an open-source model that’s more predictable and affordable for other use cases that don’t require heavy thinking. Translation: They’re leaving significant cost savings on the table.

Scenarios like these have started to expose AI as a potential single point of failure for enterprise infrastructure. As AI becomes embedded in more business processes, infrastructure resilience becomes business resilience.

All it takes is a quick look at the Status page for any of the major LLMs to know that these models aren’t bulletproof. In fact, there’s a paradox that sits at the intersection of AI and reliability. AI is often trusted with critical business processes, which could include predicting failures and automating recovery to minimize overall downtime. But what happens when the AI models themselves fail? It could lead to compounding outages throughout the business.

In a recent report from Splunk, a Cisco company, 100% of surveyed technology leaders said that their organizations have experienced some form of AI-related downtime.[2] The report also found that dependencies on third parties such as AI model providers are increasingly to blame: 63% of leaders said that they “often” or “very often” experience downtime caused by third parties, up from only 24% in 2024.

On the surface, there are some relatively simple rules of thumb that organizations can follow to protect themselves against the above scenarios, such as:

“Don’t put all your eggs in a single AI basket.”

Or:

“Not every model is right for every use case.”

But the truth is, the risks of enterprise AI have already proliferated in many unexpected ways. Let’s take a closer look at a few of these risks and explore best practices for mitigating them.

Sovereignty requirements complicate AI strategies

The U.S. government recently made headlines when they placed an export control directive on Anthropic, requiring them to revoke access to their Fable 5 and Mythos 5 models for all non-U.S. citizens. In Europe, this announcement provided both vindication and opportunity for providers that have long advocated for establishing sovereign AI models there, such as the French AI startup Mistral.[3]

While the export controls have since been lifted,[4] the whole affair demonstrates that it’s impossible to separate AI models from what’s happening in the world around us. In a world where governments can restrict access to models for particular sets of users, vendor lock-in isn’t just a technical issue anymore; it’s become a geopolitical issue as well. Therefore, enterprises must acknowledge that working with a single AI provider could impede their business continuity and digital sovereignty.

Imagine that Company X builds a core business practice around Fable 5, and the model is suddenly unavailable in Country Y for an undetermined period of time. The company needs a backup plan for how they’re going to continue serving users in that country.

The company’s AI backup plan needs to minimize the dropoff in capabilities and performance between their preferred model and any other models they may use to replace it. To do this, they need an infrastructure strategy built around distributed global infrastructure that optimizes choice and flexibility, empowering them to work with many different model providers across many different locations.

This is a business continuity best practice that enterprises should be following anyway. In some cases, they may even be legally compelled to ensure vendor diversity, like with the EU’s Digital Operational Resilience Act (DORA). The fact that many organizations haven’t diversified their AI ecosystems yet is likely because AI is still so new, and they’re figuring out their strategies as they go.

More trusted AI agents means more embedded dependencies

We see another specific risk factor emerging in agentic AI. As agents become more dependent on other agents, the fragility of the system compounds. Every new dependency increases the potential blast radius when something fails.

In this scenario, if one agent in the chain goes down, does the whole business process break? Does Company X only get partial output? What happens down the road if Company X continues to grant more and more capabilities to autonomous systems?

If the company starts piling more business trust into AI agents, building on what may already be a shaky foundation, the ultimate business outcome is tenuous at best. This doesn’t mean that enterprises shouldn’t adopt agentic AI; we already know that this adoption is inevitable. Instead, it means they need to double down on scaling security and reliability as they scale their agentic AI strategies.

To mitigate long-term risk, take control of your ecosystem

Put simply, there are still many unknowns when it comes to enterprise AI. We may assume that today’s leading models are unshakeable, or that their providers will be around forever, but no one can predict the future. The best that any enterprise can do is build their AI infrastructure on a resilient, flexible foundation, to ensure they’re ready for whatever comes next.

The organizations that will manage AI risk most effectively won’t depend on a single provider. They’ll build ecosystems that give them options when conditions change.

Equinix provides the dense AI ecosystem needed to achieve this resilience. Equinix data centers are interconnection hubs that bring together thousands of potential AI partners in the same places, including model providers, neoclouds and hyperscalers. This allows customers to:

• Connect to those partners on demand
• Avoid vendor lock-in to any particular model or infrastructure provider
• Adapt quickly as business, regulatory and technology requirements evolve
• Control their infrastructure so they can choose the right model for the right purpose

Equinix also enables a hybrid multicloud approach that’s well-suited to support distributed AI infrastructure. We empower customers to place AI workloads in different locations and environments based on latency, compliance, performance and cost requirements, helping balance competing infrastructure priorities.

The more control you have over your AI infrastructure, the more predictable your AI outcomes become. In this way, private AI infrastructure mirrors private cloud infrastructure: You acquire the models that you can control, and you put those models on the infrastructure that you can manage.

Equinix colocation offers the best of both worlds for your AI infrastructure: You can deploy a private environment that you control, while still having on-demand access to public cloud and model providers. In addition, our data centers are built with risk mitigation in mind. To help minimize downtime, we design for power redundancy, physical security and responsible resource consumption. Therefore, our data centers provide the ideal foundation to support AI workloads that have to stay online, no matter what.

Get started today with Equinix

For those who can’t manage AI infrastructure themselves long-term, there are easier ways to get started with deploying and managing AI-ready infrastructure. Equinix AI factories are turnkey solutions that take care of the procurement, deployment and connection on your behalf.

Through collaboration with partners such as Dell Technologies and NVIDIA, Equinix helps enterprises deploy secure, scalable AI infrastructure while maintaining the flexibility, security, compliance and sovereignty needed for production AI. Whether organizations choose integrated solutions or fully managed AI factories, they can accelerate deployment without giving up control over where and how AI runs.

These solutions point to a future where private AI infrastructure will be both safe and accessible. To keep up with the daunting pace of change, you no longer need all the expertise and staff to build, manage and operate your AI infrastructure, nor do you need to work alone to ensure resilience and limit risk in that infrastructure.

To be an AI-ready data center, a facility has to offer much more than just powerful compute. Equinix data centers help our customers prepare for the future of AI because they provide connectivity, ecosystem choice, and resilience by design. Therefore, deploying inside an Equinix data center could be an important step toward addressing the dependencies that often proliferate during rapid AI adoption.

The organizations that succeed with AI won’t simply choose the best model; they’ll build infrastructure that gives them the flexibility to adapt when models, regulations and business priorities inevitably change. Equinix data centers can help them do that.

To see what it really looks like inside an AI-ready data center, read the paper, “Behind closed doors: The incredible digital architecture powering your daily life.”

[1] Jake Peterson, Claude Is Currently Down, Lifehacker, June 5, 2026.

[2] Splunk, The Hidden Costs of Downtime: A $600 Billion Wake-Up Call, May 2026.

[3] Thibault Spirlet, Anthropic’s new models were restricted by the US. Europe’s top AI startup has been waiting for this moment., Business Insider, June 15, 2026.

[4] Hadas Gold, White House lifts export control on Anthropic that froze its most advanced models, CNN, June 30, 2026.