Strategic Priorities for Modernizing Data Centers
The frenetic growth of AI workloads has exposed a critical bottleneck: The data center industry can’t build infrastructure fast enough to keep up with demand. In North America, for example, the vacancy rate for primary markets hit a record low of 1.6% in the first half of this year, despite the supply increasing by 43.4% annually. At this crucial tipping point, the ability to balance breakthrough AI innovation with the next-generation infrastructure is reshaping the competitive landscape. Organizations that treat their infrastructure as a strategic investment, not just a technical necessity, are quickly setting the pace for what’s next. In fact, the world’s leading hyperscalers are expected to invest a record $320 billion in AI infrastructure and data centers this year.
From energy efficiency to thermal management and modular design considerations, their infrastructure choices will have a direct impact on speed to market, the long-term cost and ROI of each deployment, and the performance of the AI workloads. Away from net new builds, modernization of legacy infrastructure has also become a key consideration, allowing organizations to become ‘AI-Ready’. This can include:
1 - Rack Configuration: AI workloads require precise planning to accommodate advanced cooling systems and the associated increased infrastructure footprint, driving the need for more compact and efficient designs. In turn, this is reshaping how whitespace is utilized, with a focus on optimizing rack densities and airflows to enhance systems’ performance. Large language (training) models are also driving a shift toward high-density computing, accommodating more servers in a smaller area, allowing for advanced computing power without increasing space.
2 - Liquid Cooling: The heat generated by AI-driven hardware is pushing data centers to adopt liquid cooling to maintain peak performance. Traditional air-cooling systems can’t keep up with the heat demands of GPUs, making liquid cooling vital not only to manage higher thermal loads more effectively but to extend equipment lifespan. Additionally, hybrid cooling solutions that combine both air and liquid cooling optimize performance and flexibility, allowing data centers to scale their operations in response to evolving workloads.
3 - Power: High-density workloads require robust power distribution systems, leading data center operators to seek advanced uninterruptible power supplies (UPS) and scalable power solutions for reliable energy delivery. Operators are also
equipping racks with advanced power distribution units (PDUs) that can handle higher loads, allowing for dynamic power allocation to support varying AI workloads. By collaborating closely with utility companies, data center operators can implement demand response programs to optimize energy sourcing, and leverage renewable energy for more sustainable and resilient power.
4 -Software: The combination of EPMS, DCIM, and BMS allows for real-time monitoring, enhanced visibility, and control of critical infrastructure, leading to mproved efficiencies, reduced downtime, and lower operational costs. Key benefits of integrating these systems include faster modernization, increased financial gains, streamlined processes, standardized system designs, and enhanced security. In the AI era, infrastructure is everything. Organizations that fail to evolve their data centers for high-density workloads will face rising costs, slower innovation, and shrinking competitive ground. Success won’t come to those who simply build bigger, but to those who build smarter.
