Returns on Sovereign AI Point to Edge Data Centers as a Pathway to Enterprise Growth

EDB’s comprehensive 2025 study of corporate AI strategies found that companies pursuing AI sovereignty as if it were mission critical are seeing up to five times higher ROI on agentic and generative AI than other organizations. EDB analyzed data from 134,000 companies and spoke with more than 2,000 executives around the world, deeply analyzing their strategic and tactical execution of generative AI. According to their study report, Sovereignty Matters, only 13% of enterprises analyzed were successful in their implementation of agentic and generative AI, and they all had sovereign AI as a priority in common. They also found that data sovereignty is a better than 90% predictor of an organization’s success. These numbers are consistent across regions around the world. “Real success comes from owning the infrastructure, the data, and the intelligence end-to-end,” EDB stated in an article for CIO.

Why Are Leading Enterprises Pursuing Sovereign AI?

Enterprises that have implemented sovereign AI have several advantages. For one, their data doesn’t live in silos, and they are able to access, govern, and secure data wherever it resides. They derive value from being able to access any data from across the organization at any time to securely guide corporate strategy. This efficiency allows for scalable agentic AI that is compliant by design. 

When a company has sovereign AI, they are more easily able to maintain compliance, data privacy, and security. This is especially important in the GenAI era as Large Language Models (LLMs) and AI agents pose a new and complex risk to corporate data privacy.

Using prompt injection attacks, attackers craft inputs to override LLMs’ initial system instructions or safety constraints. This can trick the models into performing unauthorized actions. Attackers repeatedly query models with crafted inputs to exploit LLMs’ tendency to memorize parts of their training data, causing LLMs to reveal personally identifiable information, corporate secrets, and other sensitive data. 

LLMs can also unintentionally expose sensitive information through normal operation. When employees use public LLMs and include confidential company data in their prompts, that data is shared with the external model provider, risking unauthorized storage or use. Malicious actors can intentionally insert corrupted or misleading data into the training set. If the text generated by the LLM is not properly validated and sanitized before being used by a downstream system (e.g., inserted into a database or used in a web application), it can introduce classic security flaws like Cross-Site Scripting (XSS) or remote code execution. All of this makes it difficult for companies to remain compliant with data privacy laws.

How Are Companies Architecting Infrastructure for Sovereign AI?

IT infrastructure is the foundation of data sovereignty. Being that data sovereignty is such an important factor, and possibly a determining factor of success, how should companies architect their IT infrastructure to support it? EDB recommends having hybrid control of private platforms in their Sovereignty Matters report. Without control of the infrastructure where data is stored and processed, it is impossible to achieve sovereignty. Some companies may have moved 100% of their data and workloads to public cloud services as part of their digital transformation strategies and will now have to consider owning some of their own infrastructure again. This doesn’t mean that all workloads and data have to move out of the cloud, as long as data can be localized, secured, and accessed by AI tools as needed.

There is a trend of companies moving toward a more distributed architecture due to data localization requirements and the nature of AI inference. For agentic AI to have maximum benefit, it must have access to metadata across the organization. It must have state persistence and failover mechanisms and must efficiently serve users while maintaining data sovereignty and privacy. While training AI models can be done in one centralized location, inference requires proximity and high-speed networking to answer users’ questions or perform tasks in real time while avoiding traffic bottlenecks. By distributing workloads across multiple locations, AI agents can efficiently serve users and honor data localization requirements.

Distributed, Edge Data Centers as a Solution for Sovereign AI

While companies will continue to use public cloud services where it makes sense, many will decide that owning some of their own AI infrastructure in multiple locations is the best path to ROI in the long run. This can be difficult in today’s market with power and data center space shortages (1.6% vacancy rate), and new construction projects usually take 2+ years to complete. Rather than building new data center sites in urban areas across the globe, it makes financial and environmental sense to reuse robust infrastructure in edge markets where it already exists. We define “the edge” as anywhere that users need to process data. RAEDEN does this by preemptively qualifying thousands of former industrial, commercial, and data center sites for AI/ML data center use in markets across the U.S.

Adapting Structures for Reuse as AI Factories

Not just anyone can identify and adapt existing buildings to meet high-performance computing requirements. As an infrastructure solutions provider with leadership that has built and operated data centers for some of the most innovative and high-growth technology firms of the 21st century, RAEDEN understands how infrastructure must adapt and modernize to accommodate change. Due to our unique combination of expertise in commercial real estate and data center operations, our teams are able to find and identify sites that have the fiber, power, and structural design needed to support AI/ML deployments. RAEDEN has the expertise to implement any high-density power and cooling configurations, including native liquid cooling, bridge power, and onsite power generation solutions. We source and deploy cooling equipment to support today’s and tomorrow’s computing needs, including:

Direct-to-Chip Liquid Cooling: A common and effective method involving attaching cold plates directly to heat-generating components. A coolant (often water or a specialized fluid) is circulated through the cold plates, absorbing heat at the source before being pumped away to a heat exchanger. This method can remove a significant percentage of the heat generated, often leaving a much smaller thermal load for the ambient air-cooling system to handle.

Rear-Door Heat Exchangers: These are liquid-to-air heat exchangers that are mounted on the back of server racks. As hot air exits the server, it passes through the heat exchanger, where it is cooled by the circulating liquid. This can be used in conjunction with traditional air cooling to handle higher-density racks without completely overhauling the data center’s infrastructure.

Immersion Cooling: This is the most extreme form of liquid cooling and is gaining traction for ultra-high-density applications. It involves the complete submersion of servers or individual components in a dielectric fluid. This fluid absorbs heat directly from all parts of the server, eliminating the need for fans and other air-cooling components. Immersion cooling can be either single-phase (the fluid remains a liquid) or two-phase (the fluid boils and vaporizes, then is re-condensed to repeat the cycle).

Start Your Journey to AI Sovereignty

Of course AI sovereignty isn’t all about infrastructure. It requires data management tools and processes, governance control, cyber security programs, and strict network access control, but RAEDEN helps enterprises secure their AI foundation. Contact us at sales@raeden.com to see how we can help you start your journey to sovereignty.