Enterprise AI has crossed a threshold.

What began as experimentation is now operational. Models are no longer confined to research environments or centralized hyperscale regions. They are being deployed into live business systems, embedded into workflows, and expected to operate in real time. That shift fundamentally changes how AI infrastructure must be designed, deployed, and governed.

Industry data underscores this inflection point. Global enterprise AI investment has tripled in just three years, reaching approximately $37 billion annually. At the same time, the center of gravity is shifting away from centralized training clusters toward metro-based inference environments, where proximity to data, users, and regulatory boundaries is no longer optional.

This is the moment the Digital Realty AI Infrastructure Solutions Program was built for. It represents the next step in the evolution of our partner strategy, building on the foundation established with the Digital Realty Innovation Lab (DRIL) to move from experimentation and validation into repeatable, production-ready AI infrastructure solutions.

How production AI is changing IT infrastructure

Modern AI systems are no longer static prediction engines. Increasingly, they are agentic systems composed of models, tools, and policies that plan, decide, and act across core business processes. These systems introduce new infrastructure requirements that traditional enterprise data centers and cloud-only architectures may struggle to meet.

Production AI demands low and predictable latency, high-density, and thermally efficient compute, dense east-west networking, and strong governance and sovereignty controls.

As these requirements converge, infrastructure decisions become tightly coupled to performance, risk, and business outcomes.

Analyst projections support this view. By 2027, nearly 50 percent of critical enterprise workloads are expected to run outside centralized public cloud regions, driven by latency sensitivity, and regulatory pressure. In practice, this means AI must live closer to operational data and end users, within metro environments that feel local even when supporting global enterprises.

Digital Realty’s strategy is to make that transition practical, repeatable, and fast.

The AI Infrastructure Solutions Program: Built for production, not pilots

The AI Infrastructure Solutions Program is designed to help enterprises move beyond bespoke pilots and into production-scale AI with confidence. It brings together validated infrastructure, partner-led solution design, and platform capabilities that reduce friction across the entire deployment lifecycle.

This is not a reference architecture or a future roadmap. It is a production-ready program anchored in four foundational pillars that help our customers operate and deploy in the areas that matter to them most: coverage, capacity, connectivity, and control.

Coverage: Where AI must live

Inference workloads concentrate where latency, data adjacency, and sovereignty intersect. Tier-one metros have become the control plane for enterprise AI, particularly in regulated industries and data-intensive environments.

Our platform strategy addresses the need for these enterprise priorities. We invest in the metros where AI systems must operate next to data and users, then interconnect those metros globally so enterprises can deploy locally while operating at scale. This approach allows organizations to meet performance and compliance requirements without fragmenting their architectures.

Capacity: Engineered readiness at scale

AI infrastructure is now pushing beyond the physical limits of traditional colocation models. Inference and mixed training workloads demand rack densities ranging from tens to well over one hundred kilowatts, along with liquid cooling architectures purpose-built for GPU-dense environments, advanced power delivery, and validated thermal design.

Digital Realty applies hyperscale engineering discipline to enterprise AI. Our AI solutions are designed to support high-density deployments with predictable power and cooling envelopes, significantly reducing time from onboarding to inference. In many cases, customers see deployment timelines compressed by months, not weeks.

That readiness is no longer a differentiator. It is a prerequisite for AI programs to scale, particularly in metros where power availability and deployment timelines have become the primary constraints.

Connectivity: The performance layer for AI

Inference performance is defined as much by movement as by compute.

Agentic AI systems depend on fast, deterministic east-west traffic between models, data stores, tools, and users. Interconnection has become a performance layer rather than a cost consideration. Latency, jitter, and network predictability directly influence model behavior and user experience.

Through ServiceFabric® and Private AI Exchange (AIPx), Digital Realty enables secure, high-bandwidth connectivity across clouds, networks, and ecosystems. ServiceFabric provides software-defined interconnection that enterprises can provision and scale on demand, while AIPx offers direct, private connectivity to leading AI platforms and model providers, removing the public internet from the inference path.

Customers deploying inference workloads within our interconnection hubs consistently achieve lower latency and more predictable performance, particularly for hybrid architectures that span private and public environments.

In real-time AI systems, milliseconds are now the standard.

Control: Built-in governance for private and hybrid AI

As AI systems move into regulated workflows, control becomes a gating factor. Enterprises increasingly favor private AI environments to maintain sovereignty over sensitive data, enforce security policies, and meet compliance requirements from day one. Private AI enables organizations to run models close to proprietary data while maintaining operational consistency and auditability.

At the same time, hybrid AI architectures are becoming the dominant model. Enterprises want the flexibility to connect private AI environments with public cloud services, specialized platforms, and distributed data sources without compromising governance or performance.

Our AI Infrastructure Solutions Program embeds these controls directly into the infrastructure and operating model. Governance, observability, and risk alignment are designed into deployments rather than added later, helping customers move faster through procurement and scale with confidence.

Designed to be partner-led, our AI Infrastructure Solutions Program collaborates with leading OEMs, systems integrators, and service providers to package, validate, and operationalize AI infrastructure solutions that can be deployed consistently across our global platform.

Operationalizing AI at enterprise scale

The defining shift in enterprise AI is operational.

Winning is no longer about building the largest cluster or running the most experiments. It is about operationalizing AI safely, repeatedly, and at scale. That requires validated infrastructure, predictable connectivity, integrated governance, and partner ecosystems built for execution.

The AI Infrastructure Solutions Program exists to do exactly that. It turns infrastructure into a platform, interconnection into a performance layer, and governance into an accelerator rather than a blocker.

As enterprises move from experimentation to execution, metro-based AI becomes the control plane for digital business. Our role is to ensure that control plane is ready, resilient, and repeatable.

Ready to move from experimentation to execution?

Whether you're planning your first production AI deployment or scaling existing infrastructure across metros, our team is ready to help you execute with confidence.

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PDx®: is your IT infrastructure AI-ready?