Around the world, the surge in artificial intelligence (AI) applications has sparked a debate: how do you balance AI’s potential to increase economic productivity with stricter regulation addressing ethical and societal concerns? Within that equilibrium is another core question, one of sustainability: how can we align AI’s ascent with environmental standards?

There’s no doubt: AI isn’t going anywhere. Gartner forecasts suggest global spending on AI software will increase at an 19.1% compound annual growth rate (CAGR) over the next six years, with a rise in adoption stemming from enterprises’ appetite for high-performance computing (HPC). AI’s prevalence will only continue to rise as organizations use it to develop innovative internal and customer-facing applications.

This makes sustainability even more pressing, considering AI’s increased energy demands and the need for advanced cooling systems in data centers to accommodate higher-density deployments. Data centers play a vital role in the integration of AI as the cornerstone of digital infrastructure supporting various cutting-edge applications spanning sectors. However, they encounter unparalleled hurdles in balancing the intricacies and demands of AI Operations (AIOps) with eco-friendly practices.

According to recent reports, the data center industry accounts for up to 2% of global greenhouse gas emissions, stemming from the growing electricity demand to run bigger, faster, and more complex workloads — while maintaining service quality. Amid the surge of AI, the green data center has emerged as a crucial tool in the ongoing hunt for sustainability. By embracing the benefits of AI, data centers can maintain service quality while lowering their carbon footprint.

Embracing innovation in the data center

AI hasn’t caught the industry by surprise. Conversations surrounding an increase in demand for AI and data analytics, as well as the associated environmental impact, have been going on for nearly a decade. All the while, it’s important for data centers to take proactive approaches toward innovation and enhancements.

For example, high-density colocation not only optimizes space for multiple customers' computing equipment, but also significantly improves energy efficiency and reduces resource use. When coupled with adoption of advanced cooling systems like direct liquid cooling (DLC), air-assisted liquid cooling (AALC), and rear door heat exchangers (RDHx), data centers can ensure digital infrastructure meets demands, all while reducing resource usage and carbon output.

Data centers can boast significant IT load capacity and be designed to accommodate cutting-edge technology, thanks to infrastructure tailored for AI deployments. Similarly, data centers equipped with DLC systems can adapt to evolving infrastructure needs, optimizing energy efficiency and reducing costs. Modular infrastructure features “plug-and-play deployments,” adapting to workload fluctuations and accelerating go-to-market times for enterprises, while preventing over-provisioning and reducing energy consumption. Simplifying AI adoption while promoting efficiency and sustainability within data centers has never been more possible nor more important.

AI’s role in building sustainable data centers

There’s no question that AI has a role to play in the design and sustainability efforts of the data center. It starts from the outset in the construction phase of a data center’s life cycle. To effectively minimize their environmental impact, data centers need to overhaul their architecture, prioritizing designs that eliminate dependence on water and fossil fuels.

Incorporating energy reuse systems and district heating networks, which redistribute surplus heat to neighboring communities, ensures that infrastructure not only fulfills the technological demands of customers but also seamlessly integrates with and contributes to the well-being of the communities where data centers operate. By focusing on renewable energy sources like solar, data centers can reduce their overreliance on fossil fuels and their significant carbon footprints.

AI also has transformative potential to optimize energy use in cooling, resource allocation, and energy consumption. While discussions about AI and sustainability often focus on energy and water consumption, it's vital to recognize AI's potential in fostering environmental progress.

For example, AI can be used to scrub through building management systems and investigate hidden energy inefficiencies. AI can help identify clogged filters, valves that were stuck open, and other issues deep in the cooling system that would be difficult to find through routine inspections. These adjustments to reduce parasitic loads can have significant impacts on the carbon output of data centers, enhancing sustainability programs consistently and incrementally.

Beyond steady improvements that make lofty sustainability goals achievable, AI can enhance efficiency and reduce environmental impact on a macro scale as well. In-house AI platforms can achieve significant energy savings across multiple sites, equivalent to powering over 1,600 U.S. households annually. Additional ways to enhance resilience and address capacity challenges include utilizing immersive 3-D visualizations and thermal optimization analytics to pinpoint and rectify airflow and cooling inefficiencies. Each initiative can cut cooling energy consumption by significant margins.

A greener AI future

While rapid growth of AI presents sustainability hurdles, it also presents solutions to environmental and societal concerns. As we make technological progress, efficient, sustainable infrastructure will be increasingly imperative for shaping a future where technology and environmental responsibility go hand in hand. Planning and setting ambitious goals for scope emission reduction in data centers and delivering tangible results can steer us toward a greener future for all.