As AI technologies advance, the increasing energy demands of data centers and AI processes necessitate that companies integrate energy-efficient practices into their AI and data infrastructure to align with sustainability goals and avoid regulatory and reputational consequences
The announcement of DeepSeek’s innovative and efficient approach to the development of its large language models dominated headlines in late-January. Since then, some have questioned the project creators’ assertion of the cost effectiveness and energy efficiency of this approach.
If DeepSeek’s claims of efficiency wind up being true, it is unlikely that the overall demand for energy will subside any time soon. In fact by the end of 2025, the convergence of AI and sustainability is expected to reach a critical tipping point, according to Nicola Acutt, Chief Sustainability Officer at NetApp. As AI technologies continue to advance and proliferate across industries, Acutt says their substantial computational requirements will shine a spotlight on the inefficiencies lurking within current corporate sustainability and data practices.
This collision of AI’s voracious appetite for resources and the imperative for environmental stewardship will force companies to harmonize their AI strategies with their sustainability objectives or face significant regulatory and reputational consequences. Acutt predicts that forward-thinking organizations will proactively integrate intelligent, energy-efficient practices into their AI and data infrastructure — including everything from data centers to storage solutions — and will emerge as trailblazers in this new landscape.
The energy, GenAI, and sustainability nexus
The energy-GenAI-sustainability nexus signifies a pivotal shift in which the growing energy demands of AI have significant implications for corporate sustainability goals. The surge in energy demand stems from AI’s reliance on vast data centers and computationally intensive processes, even in the wake of DeepSeek’s innovative energy efficiency. As AI adoption expands across various sectors, the strain on existing energy infrastructures will become evident, showing that it is ill-equipped for the digital age and highlighting the necessity for modernization at the federal level.
Indeed, the immense energy consumption of AI necessitates a more intricate understanding of data’s embodied energy costs, which is often an overlooked factor, according to Acutt, adding that at the corporate level, another underemphasized trend that is part of the overall inefficiencies is the “embodied energy” price of data management and storage. “The reality is that so much of the AI challenge is a data challenge,” Acutt says. “We’ve got this extraordinary explosion of data, yet without the requisite innovation in managing that data real estate.”
The energy-GenAI-sustainability nexus signifies a pivotal shift in which the growing energy demands of AI have significant implications for corporate sustainability goals.
This is why modernizing data infrastructure is crucial to support the growth of AI. The current infrastructure is outdated, inefficient, and leads to significant data waste, with nearly 70% of created data only being used once, according to an IDC-Seagate report. This inefficiency results in unnecessary energy consumption and costs. To address this, strategies like using data minimization, intelligent data-tiering, or data lakehouses will become paramount, as will leveraging tools to analyze a data estate’s energy footprint and using efficient storage solutions. By doing so, businesses can reduce their environmental impact, lower costs, and improve their competitiveness.
Essentially, the energy-GenAI-sustainability nexus is pointing us towards a future in which corporate energy and data strategies are intertwined. However, companies need to step up efforts and invest in a cohesive strategy that encompasses both successful AI integration and efficient data management.
Guidance for chief sustainability officers
To address the multifaceted challenges at the intersection of AI, sustainability, energy, and data, Acutt recommends corporate chief sustainability officers (CSOs) take several actions, including:
Build relationships between the company’s sustainability functions and its IT teams — We’ve previous discussed the growing role of chief information officers in sustainability and the need for data infrastructure modernization, which itself is another reason for fostering collaboration. In fact, pushing a company’s sustainability function and IT departments to align goals and integrate sustainable practices into the company’s overall IT infrastructure is an essential ingredient in this collaborative process. To do this effectively, CSOs need to conduct regular cross-departmental meetings to share business priorities and identify joint projects that focus on outcomes that benefit both functions.
Support using business cases to upgrade data infrastructure — Easy wins in this include highlighting reductions in the company’s energy consumption and carbon footprint that result from implementation of energy-efficient data centers, data storage, and cloud solutions.
Prioritize resiliency and future-proofing — All capital investment proposals need to demonstrate how they add to enterprise value. CSOs should develop strategies that enhance resilience amid environmental changes, which can help future-proof operations. This process should include investing in renewable energy sources, adopting AI solutions that support predictive maintenance, and ensuring business continuity in the face of climate-related disruptions.
Simplify internal processes and systems — CSOs should streamline processes to make sustainability reporting more efficient and accurate. By using digital tools and platforms to automate data collection and reporting, CSOs can ensure compliance with regulations and transparency in sustainability efforts.
Those companies that make themselves early adopters of these actions will not only mitigate their environmental impact but will also set new benchmarks for innovation. These actions will demonstrate that cutting-edge AI capabilities and robust sustainability initiatives can coexist and even enhance one another.
As this paradigm shift unfolds, it will become increasingly clear that the future belongs to those organizations which can masterfully balance the transformative power of AI with responsible environmental stewardship.
You can find out more about how companies are grappling with sustainability issues here
