Why Sustainability is Essential for AI Success

4 industry experts share insights on prioritizing sustainable AI and using AI for sustainability

Why Sustainability is Essential for AI Success

Enterprises can apply AI across two dimensions of sustainability: fostering sustainable AI deployments and using AI to solve sustainability-related challenges. Sustainable enterprise AI is about building responsible practices in AI deployments. On the other hand, AI for sustainability is about enterprises using the power of AI for good.

The amount of power and energy required to use compute-intensive workloads to train AI models and run inferences on those models has enormous implications for emissions and climate change. Enterprises need to focus on designing, training and deploying AI to reduce the carbon impact of these technologies. Examples of how to do AI sustainably include making more energy-efficient algorithms, models and forecasts, positioning AI workloads according to the local grid’s carbon intensity and sourcing renewables to address AI energy usage. Distributing AI infrastructure helps balance how enterprises meet the requirements of compute-intensive training workloads and latency-sensitive inference workloads.

When enterprises design and deploy AI sustainably, they can unlock the opportunity to help solve environmental and social challenges simultaneously while driving economic growth. Developments are underway to leverage AI to reduce operational carbon emissions and accelerate climate action programs. Whether optimizing airline flight paths to cut fuel usage or monitoring the success of biodiversity projects, AI can help speed up the adoption of sustainable business practices.

From a social perspective, companies need to identify and eliminate potential biases within their AI models since they are increasingly used to make decisions that impact people’s lives. It starts with the data enterprises feed into AI training models. When different subsets within datasets are over- or underrepresented, AI model outcomes will reflect that bias and be less accurate.

The Equinix Indicator

In the first volume of The Equinix Indicator industry experts share their thoughts on Digital Infrastructure and Private AI.

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We spoke with four industry experts to understand the significance of integrating sustainability into private AI strategies. Here’s what they had to say:

Balancing the demand and supply side of energy usage is at the intersection of AI and sustainability

Christopher Wellise, VP, Global Sustainability, Equinix – @cwellise

As we consider sustainable AI at Equinix, it requires focusing on the demand and supply side of energy in the data center. Using AI to optimize energy usage within the data center environment is critical to operating efficiently and reducing our carbon footprint. When enterprises look to data center services providers, they’re seeking like-minded businesses focused on balancing innovation and sustainability.

At Equinix, that means providing capacity for higher-density workloads in an energy-efficient way, including using advanced liquid cooling technologies. There are also opportunities to contribute to the circular economy as we partner with local economies to improve efficiency and reduce waste across the entire life cycle of our data centers.

The challenge to build sustainable AI brings digital infrastructure center stage

Sally Eaves, CEO, Tomorrow’s Tech Today – @sallyeaves

The intersection of AI and sustainability is an opportunity to create shared value. AI models hold promise to enable sustainable decision-making and accelerate ESG compliance. But for AI to deliver sustainability outcomes, it must first deliver on its own sustainability. In today’s “Age of (Gen) AI,” this challenge to build sustainable AI brings digital infrastructure center stage. From moving to liquid-cooled solutions to collaborating on secure and scalable standards, data and digital infrastructure are differentiators that will empower enterprises to design and deploy AI sustainably. I anticipate the next trend of enterprise AI will be a focus on sustainability considerations baked right in.

Driving economic growth and combating environmental and social challenges

Linda Grasso, Digital Creator & CEO, DeltalogiX – @LindaGrass0

In the quest for a digitally transformed future, businesses face the challenge of harmonizing innovation with responsibility. This involves embedding social and ecological awareness into the core of enterprise AI, paving the way for a future where technology propels business while protecting our planet and society.

Developing digital infrastructures that support advanced technologies like AI and are sustainable, efficient, and secure is essential. This requires investments in green energy solutions, optimization of digital resource usage, and robust data protection measures.

Such an approach transforms enterprise AI into a dual force: driving economic growth and combating environmental and social challenges. It fosters a holistic strategy that values innovation and sustainability, ensuring balanced and responsible technological advancement.

AI algorithms explicitly designed for sustainability are emerging

Helen Yu, CEO, Tigon Advisory – @YuHelenYu

Envisioning trends in sustainable AI involves anticipating innovations that reduce AI’s energy consumption and environmental impact. AI algorithms explicitly designed for sustainability are emerging, emphasizing both performance and energy efficiency. Additionally, expect advancements in hardware, like energy-efficient processors tailored for AI tasks. Staying ahead means continuously monitoring these trends and proactively adopting eco-friendly technologies and methodologies. At the intersection of AI and sustainability, standout areas include AI-enabled resource optimization. Through digital infrastructure AI-modeling, enterprises efficiently manage energy consumption and predict climate change impacts. AI-driven initiatives optimizing supply chains for reduced carbon footprint and waste management also hold significant potential in fostering sustainability.

Learn more about three strategies for enabling private AI

Driving sustainable outcomes for people and the environment at the global and local levels requires new ways of collaborating with customers, suppliers, partners and communities—wherever enterprises operate. Taking an ecosystems-based approach to sustainability and incorporating sustainable design principles into digital infrastructure promises to unlock huge potential as we all work together to enable sustainable AI and use AI for sustainability.

For more about how deploying digital infrastructure helps enterprises integrate sustainability with their private AI strategies, check out the Equinix Indicator. It’s where industry leaders also discuss data architecture and network connectivity trends in enterprise AI and approaches to implementing your private AI strategy.

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