Preparing Data Centers for AI: The Future of Digital Infrastructure

As artificial intelligence (AI) continues to reshape industries and drive innovation, businesses face increasing pressure to ensure their data centers are equipped to handle the demands of AI workloads. Traditional data centers, designed for general-purpose computing, often fall short in meeting the intense processing, power, and cooling requirements needed to support AI. For many organizations, building entirely new data centers isn’t feasible due to time, cost, and land constraints. So, how can existing data centers be prepared to support the AI boom?

At TEECOM, we understand the complexities of transitioning traditional data centers to AI-ready facilities. Our approach focuses on five essential areas to ensure that our clients can meet the demands of AI workloads: facilities and design, pathways and cabling, racks, security, and adaptive reuse. Through these considerations, we help organizations prepare their data centers for AI while minimizing costs and accelerating time-to-market.

Facilities & Design: Reusing Existing Sites

One of the primary challenges organizations face is that their current data centers lack the infrastructure to support AI workloads. AI demands high-performance computing capabilities, which significantly increase power and cooling requirements. Building new greenfield data centers from the ground up is often impractical, especially for companies without access to new land or power resources. This is where TEECOM steps in, offering solutions that retrofit existing facilities to accommodate AI.

By reusing brownfield sites—locations with existing fiber, power, and cooling infrastructure—businesses can achieve a faster path to market. Brownfield sites allow both large and small organizations to upgrade their facilities without starting from scratch. Design strategies should prioritize flexibility, enabling clients to prepare their data centers for AI while coordinating with MEP engineers to deploy their systems in line with telecommunications requirements. This approach reduces downtime and accelerates deployment.

Pathways & Cabling: Expanding Capacity for AI

Another significant challenge is that AI workloads require vast amounts of data to be processed quickly and efficiently, which places a heavy burden on existing cabling and pathways. Most traditional data centers are not designed to handle this level of traffic, resulting in a need for expanded infrastructure.

It may be necessary to expand pathways infrastructure to accommodate the increased fiber necessary for AI operations. These expanded pathways must also support the added weight and structural demands that come with increased cabling. By ensuring that the design and installation of cabling are coordinated with the overall building design, we optimize the infrastructure to support the increased capacity AI demands.

Racks: Customized for AI Workloads

Most AI workloads require specialized hardware, including Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). As a result, the racks and cabinets that house this equipment must be customized to accommodate the specific power, cooling, weight loads, and additional cabling requirements.

Racks and cabinets can be customized to not only support the specialized AI hardware but also ensure that power and cooling systems, and cabling, can handle the increased demands. This level of customization ensures that AI data centers are optimized for performance, preventing costly inefficiencies and downtime.

Security: Adjusting for AI-Specific Risks

Security is another critical consideration in the transition to AI data centers. While many of the security risks are similar to those faced by traditional data centers, AI workloads—especially those involving machine learning and natural language processing—often handle sensitive data, requiring more advanced security measures.

Clients must assess the unique security needs of their AI data centers and implement tailored solutions. This can range from enhancing physical security to safeguarding the specialized mechanical systems required for AI workloads, such as advanced cooling systems. By addressing these risks early, we ensure that AI data centers are secure and compliant with industry standards.

Adaptive Reuse: Faster Deployment, Lower Cost

For many organizations, building a new data center to support AI is neither time-efficient nor cost-effective. This is where adaptive reuse becomes a game-changer. By repurposing existing data centers for AI applications, organizations can significantly reduce both the time and costs associated with new builds.

By leveraging existing infrastructure and optimizing it for AI workloads, it is possible to achieve faster deployment, often reducing go-live timelines by several months. In addition, cooling system upgrades become a critical aspect of these projects, as AI’s increased power requirements demand more sophisticated solutions.

TEECOM Can Help

Our mission critical team incorporates the latest technology trends, lifecycle expenses, and operational profitability as we drive designs forward. From site-specific requirements to understanding complex codes and regulations, we have you covered. To learn more about how our mission critical technology experts can support your project goals and objectives, contact us today.