Artificial intelligence (AI) is transforming industries and businesses at an unprecedented rate. But the innovation driving it doesn’t happen in isolation. To move from siloed proofs of concept to large-scale implementation across the enterprise, organizations need a robust, interconnected digital foundation.

That foundation extends beyond raw compute power. It includes secure, scalable IT architecture, access to fast, flexible data across multi-cloud environments, and intelligent interconnection that ties everything together.

Wherever you are on your AI journey, in this blog, we’ll explore how the right kind of digital infrastructure enables you to unlock the full potential of your initiatives.

Legacy IT infrastructure: the hidden bottleneck

First, let’s focus on one of the biggest hurdles to AI deployment.

As AI adoption accelerates, many organizations encounter an unexpected constraint: their legacy IT platforms. While AI promises vast potential, success ultimately depends on the foundation that supports it.

AI workloads demand more than processing power. They need seamless interconnection between data sources, compute resources, and third-party services. Without that integration, even the most promising initiatives can stall.

Rolling out enterprise AI means dealing with complex, multi-layered infrastructure: high-performance computing (HPC), robust storage, ultra-fast networking, and advanced data management. These systems must deliver high bandwidth, low latency, and massive storage capacity - all while maintaining security and reliability.

Here’s an example to illustrate the point. While hyperscalers may train large language models (LLMs), most enterprises are focused on applying and operationalizing them.

Running AI inference at scale still requires significant compute capacity, low-latency data access, and highly optimized infrastructure. These workloads often rely on GPU-accelerated environments and high-performance storage capable of sustaining continuous, high-bandwidth access across distributed systems. Without the right digital foundation, enterprise AI initiatives can struggle to deliver real-time insights or scale effectively across the organization.

The critical role of interconnectivity

Modern AI workflows depend on interconnection. Data must flow easily between compute resources, storage systems, and external services - enabling models to learn, adapt, and improve continuously.

In practice, that means integrating diverse components: ingesting data from multiple sources, processing it efficiently, integrating or fine-tuning models, and deploying AI-driven applications in production environments that interact with other business systems. The smoother these interactions, the faster and more effectively AI can deliver value.

Physical location plays a pivotal role. Placing compute and storage infrastructure closer to data sources reduces latency and speeds up real-time processing. This is critical for AI applications that depend on instant insights, such as autonomous vehicles, healthcare diagnostics, or industrial automation.

Regulatory compliance adds another layer of complexity, making data locality and governance equally vital considerations. Intelligent interconnection ensures organizations can meet these demands without compromising performance.

The technical pillars of AI infrastructure

AI workloads bring specific technical requirements that define their infrastructure needs:

High-performance computing (HPC): AI models demand immense processing power, often running on GPUs and TPUs optimized for parallel workloads. NVIDIA’s V100 and A100 GPUs, for example, are industry benchmarks thanks to their high performance and memory capacity.
High-speed networking: Fast, low-latency connectivity is essential to keep compute, storage, and data services in sync. The right technologies provide the bandwidth and efficiency that AI workloads rely on.
Scalable storage: AI produces and consumes vast volumes of data. High-capacity, high-performance storage - such as all-flash arrays and object storage - ensures rapid access and scalability.
Advanced data management: From ingestion to governance, data must be organized, secure, and accessible. The most capable platforms provide flexible frameworks for managing and processing large datasets at scale.

Together, these technologies create an ecosystem capable of supporting AI’s evolving complexity - but as we’ve mentioned, only when they’re interconnected intelligently.

Metro-based inference: enabling faster interconnectivity

Metro-based inference is rapidly becoming a cornerstone of modern AI infrastructure. By processing data closer to its source, inference workflows reduce latency, enhance real-time decision-making, and optimize bandwidth usage.

Instead of routing everything to central data centers or the cloud, it brings compute resources to the network’s periphery - via data centers in strategic locations, micro-servers, or even IoT devices. This approach allows immediate analysis and response, especially in environments where milliseconds matter.

In industrial automation, for instance, edge systems can process sensor data locally to detect faults or predict maintenance needs in real time. In autonomous vehicles, AI interprets visual and sensor data in real time, enabling split-second decision-making and improved safety.

Ultimately, inference complements centralized infrastructure, creating a distributed framework that supports faster, more resilient AI operations.

The role of a trusted interconnection partner

Bringing these disparate elements together is no simple task. Many enterprises benefit from a trusted interconnection partner, one that can unify their digital ecosystem and support AI at scale.

Digital Realty is now performing this role for many of our customers. Our HD Colo facilities provide the secure, high-performance environments required for HPC workloads, complete with advanced cooling and connectivity solutions.

Our services also include:

Together, these advanced capabilities help our customers to deploy AI workloads with confidence - securely, efficiently, and at scale.

Case study: scaling AI through intelligent infrastructure

A real-world example shows how intelligent infrastructure accelerates AI innovation.

Macnica needed to scale its AI-powered hardware verification environment to support its new AI TRY IT NOW offering. Its existing data center wasn’t able to meet the high-power requirements of its new NVIDIA DGX™ H100 GPU server. Macnica deployed High-Density Colocation on PlatformDIGITAL® to enable 2x higher density and support their evolving line of AI offerings.

This success story highlights the power of interconnected infrastructure to turn AI potential into tangible business outcomes.

Maximizing the AI opportunity

While the promise of AI is immense, so are the challenges. Complex workloads require advanced infrastructure, massive compute capacity, and strict data security. Many organizations struggle to balance performance, cost, and compliance - particularly when scaling globally.

Yet each challenge is also an opportunity. By investing in intelligent, interconnected infrastructure, and embracing technologies such as edge computing, businesses can boost efficiency, speed up innovation, and future-proof their operations.

Building an AI innovation pathway

As we’ve outlined, AI innovation depends on a robust, interconnected digital foundation. Enterprises that understand the value of infrastructure, interconnection, and edge computing - and bring in the right partners to assist where needed - will be the ones that scale their AI initiatives successfully.

As AI continues to reshape industries, one thing is clear: intelligent, scalable infrastructure isn’t just a support system - it’s the engine driving the next wave of enterprise innovation.

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High-Performance Computing Deployment for Imperial College London