AI Reshapes Data Center Design: Balancing Sustainability, Scalability, and Workforce Needs

AI-ready data centers: how to integrate design, sustainability, and people

AI-driven demand reshapes data center design, sustainability, and workforce needs Artificial intelligence (AI) is not new - it has been evolving for over 70 years. However, recent advancements in compute power, the rise of AI applications like ChatGPT, the emergence of Agentic AI, and real-life use cases have created a perfect storm, driving unprecedented demand for data centers. Yet, this surge presents significant challenges for operators who must rethink infrastructure to keep up. A holistic approach to design is now essential to overcome operational inertia and ensure scalability. I recently chaired a panel at Data Centre World 2025, where industry leaders Niklas Lindqvist, Nordics General Manager at Onnec and Stefan Nilsson, Sales & Marketing Manager at Conapto shared their insights on the future of AI-ready data centers. Here are some key takeaways from our discussion. AI investment is accelerating, but traditional data center infrastructure is struggling to keep up. Unlike conventional IT workloads, AI tools are highly unpredictable and often require immense power and cooling capabilities. In some regions, such as Sweden, retrofitting existing sites is becoming increasingly unfeasible. Many Swedish facilities have been upgraded in recent years, making another wave of retrofits for AI impractical - both financially and spatially. For instance, AI requires increased height above racks for cooling solutions, which many legacy facilities simply cannot accommodate. Historically, data centers were designed for moderate-density, air-cooled environments, but AI demands high-density solutions with hybrid cooling methods. Operators must rethink their design philosophy, to ensure that new builds can support liquid cooling and other innovative technologies. Without this shift, retrofitting will remain costly, slow, and ultimately ineffective in supporting AI growth. A successful AI-ready data center must integrate both traditional and AI-specific infrastructure under one roof. Flexibility and scalability are paramount here. AI-ready facilities must accommodate a range of workloads, from low-density racks to 100kW+ liquid-cooled deployments. This is where a modular approach is key. Operators must be able to expand incrementally by adding power and cooling modules as demand grows. This strategy helps to align with customer-specific hardware needs and avoids unnecessary upfront costs. Operators must embrace a holistic design mindset. Power, cooling, and cabling cannot be treated as separate components - they must be integrated from the outset. Poor planning leads to capacity constraints, delays, and costly modifications. There is also a need for an "inside-out and outside-in" approach: operators must first understand computing needs and design the data center accordingly, while also considering space constraints and facility requirements. High-quality cabling is another crucial yet often overlooked element. Many operators prioritize speed to market and opt for second-tier cabling, but this short-term gain results in long-term operational headaches and frequent retrofits. Hyperscalers invest heavily in structured cabling strategies, and colocation providers must follow suit to remain competitive. Sustainability remains the most pressing issue facing the data center industry. Simply acquiring renewable energy certificates is no longer enough - operators must actively reduce their carbon footprint. One major consideration is location. Many data centers in the Nordics are built in remote areas where excess heat cannot be effectively utilized. In contrast, urban data centers offer opportunities to contribute to local infrastructure, turning AI into part of a circular economy. Many Swedish data centers are integrated with Stockholm's district heating network, selling excess heat back to the city. This approach is made even more efficient with liquid cooling and internal cooling loops that ensure sustainability without compromising operations. Regional climate also plays a role in determining the best cooling strategies. For example, in southern Europe, where temperatures remain high year-round, immersive cooling systems may be more effective than traditional liquid cooling. Best practices in sustainability extend beyond cooling solutions. Operators must prioritize genuine renewable energy sourcing, heat reuse initiatives, and battery storage solutions that enhance grid stability. Sustainability must be embedded in every stage of AI data center design - treating it as an afterthought will only lead to inefficiencies and missed opportunities. As AI data centers boom, the demand for skilled professionals in power, cooling, and infrastructure design is outpacing supply. This skills gap presents a serious challenge, as AI facilities require expertise in handling high-power densities, advanced cooling systems, and complex network architectures. Undertrained workers in these environments pose significant operational risks. The rapid pace of AI-driven expansion also increases workplace safety concerns. Shortened build timelines can lead to accidents if proper safety protocols are not enforced. Proactive risk assessments, rigorous documentation, and meticulous planning are essential to maintaining a safe working environment. Operators today must prioritize worker safety as much as they do technological innovation. Community engagement is another critical aspect often overlooked. Data centers frequently face local opposition due to misconceptions about their environmental impact and perceived lack of community benefits - think 'not in my backyard'. To counter this, operators must take an active role in educating the public about their role in digital infrastructure, sustainability efforts, and economic contributions. The rise of AI is transforming data center demands at an unprecedented pace, requiring operators to adapt quickly, and smartly. Meeting these challenges isn't just about scaling up power and cooling; it's about rethinking the entire approach to design. AI-ready data centers must be built with flexibility, efficiency, and resilience at their core, ensuring they can evolve alongside fast-moving technological change. Success will depend on more than just infrastructure - it will require a shift in mindset. Operators who invest in integrated, sustainable designs, prioritize skills development and safety, and engage with local communities will be best positioned to thrive in the AI era. Those who fail to take this holistic view risk falling behind in an increasingly competitive landscape. 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Scaling Data Centers with AI: Preparing for the Future of Hyperconnectivity

By Shrirang Deshpande The world runs on digital connections. From the moment we wake up, we are plugged into a vast network of data moving at incredible speed, powering financial systems, online learning, healthcare, and entertainment. Businesses rely on real-time analytics, AI-driven automation, and cloud infrastructure to function without disruption. But as data consumption skyrockets, the pressure on data centers is greater than ever. Hyperconnectivity is shaping how businesses operate, how consumers interact, and how data moves across the world. Every streaming service, financial transaction, and enterprise application depends on an infrastructure that must be faster, more secure, and endlessly scalable. However, traditional data center models are struggling to keep up. This is where AI becomes indispensable. It is the key to building data centers that can support the digital economy of the future. Global enterprises are making bold moves to integrate AI into their data center strategies, recognizing its pivotal role in scaling digital infrastructure. Oracle, for instance, is expecting to invest a staggering $500 billion over five years to its Stargate project, an ambitious initiative to build AI-driven data centers powered by 64,000 Nvidia GPUs. This level of investment signals a fundamental shift, with AI reshaping the very foundation of data centers. Let's look at the factors that make future data centers will look like. Ai-Driven Automation in Data Centers Scaling a data center is not just about adding capacity. Without intelligence built into operations, expansion leads to inefficiencies, higher costs, and more complexity. AI is changing this by enabling real-time automation that optimizes how data centers function. Energy consumption, workload distribution, and system maintenance are no longer managed through static, pre-set rules. AI continuously analyzes conditions, making real-time adjustments that improve efficiency and extend infrastructure lifespan. Cooling, for example, is one of the largest energy expenditures for data centers. AI-powered systems can predict cooling needs and regulate temperatures dynamically, reducing unnecessary power usage without compromising performance. The same intelligence applies to server workloads. Instead of overloading certain machines while others sit idle, AI distributes tasks based on demand, improving speed and reducing wear and tear. Enhancing Security and Data Integrity Data centers are a prime target for cyber threats, and traditional security models built around reactive measures are no longer enough. AI brings a predictive approach to cybersecurity, identifying unusual patterns before they escalate into breaches. Instead of waiting for an attack to happen, AI detects anomalies in real time, making security systems more adaptive and responsive. The complexity of compliance is another challenge AI is helping solve. Regulations around data privacy and security are constantly evolving, and staying compliant requires continuous monitoring. AI-driven systems can track and enforce compliance measures automatically, reducing the risks associated with human error and oversight. AI's Role In Sustainable Data Centers The demand for data is growing exponentially, but so is the pressure to reduce environmental impact. AI is proving to be a critical enabler of sustainable data center operations. By optimizing power usage, predicting maintenance needs, and integrating with renewable energy sources, AI is helping data centers reduce their carbon footprint while maintaining peak performance. Cooling systems account for nearly 40% of a data center's energy use, but AI-powered cooling solutions have been able to cut that down significantly by dynamically adjusting airflow and temperature settings. AI is also improving resource allocation, ensuring that energy-intensive processes are executed more efficiently. India's AI-Driven Data Center Ecosystem India is emerging as a major player in the global data center industry, with AI at the core of its expansion. Government initiatives, rising cloud adoption, and increasing investments in AI-driven infrastructure are accelerating the country's digital transformation. AI-powered data centers are not just supporting India's domestic digital economy but positioning the country as a strategic hub for global data processing and storage. AI is enabling hyperscale expansion without the inefficiencies that typically come with rapid growth. Whether through smarter energy management, enhanced security, or better workload distribution, AI is ensuring that India's data centers are built for the future, not just for today. The Future of Scaling Data Centers No business runs without digital infrastructure anymore. Every transaction, every interaction, every service we rely on is powered by data centers that need to scale, adapt, and stay secure. AI is no longer a choice in this equation, it is what makes modern data centers work. Without it, businesses are operating on borrowed time, trying to stretch outdated systems in a world that moves too fast for them. The real conversation is not about whether AI belongs in data center operations. It is about who will use it well enough to build infrastructure that keeps up with what the world demands. (The author is Shrirang Deshpande, Country Head, Strategic Programmes, Vertiv India, and the views expressed in this article are his own)