As artificial intelligence continues to revolutionize how enterprises operate, many are eager to focus solely on the benefits: automation, speed, customization, and efficiency. Behind the scenes, the energy and resource costs of AI rise sharply, and so does its environmental impact.
In 2023, data centers consumed 4.4% of U.S. electricity, and that figure is projected to triple by 2028. If current trends continue, data centers could account for up to 20% of the world’s electricity consumption by 2030. Much of this surge can be attributed to large language models (LLMs), the engines powering generative AI (GenAI) tools and services. To build a truly sustainable AI future, we must rethink not only the models we deploy—but the way we manage the data that powers them.
The Environmental Impact of AI
Training and maintaining LLMs is among the most resource-intensive computing tasks, requiring thousands of high-performance GPUs and TPUs running around the clock. The result is massive electricity consumption, largely powered by fossil fuels which contribute to greenhouse gas emissions.
The environmental impact doesn’t stop at electricity. Cooling systems are used to keep AI data centers running, requiring fresh drinking water and often straining water supplies in areas that are already prone to droughts. According to industry projections, AI water usage could reach up to 6.6 billion cubic meters by 2027, about half as much water as the UK uses in a year.
In addition, the hardware used in AI training typically has a short lifespan, contributing to a growing pile of global electronic waste. Manufacturing this hardware also depends on rare earth minerals, extracted through ecosystem-damaging mining processes.
As AI grows in capability, so does its environmental footprint, unless enterprises change how they build and scale it.
Small Language Models: Efficiency Without Excess
One of the clearest paths to a more energy-efficient AI ecosystem lies in rethinking the models we use. Recent research from Nvidia and other industry experts shows that small language models (SLMs) are emerging as a powerful yet more sustainable alternative, especially for agentic AI systems.
Unlike LLMs, which are designed to handle a wide range of tasks, SLMs are purpose-built for narrow, repetitive functions. Think of them as task specialists rather than generalists. SLMs require far less computational power and can run efficiently on lower-cost, lower-energy infrastructure.
Deploying SLMs in place of LLMs can:
- Reduce latency and infrastructure load
- Lower cloud hosting and API costs
- Minimize electricity usage and cooling demands
A modular approach, where SLMs handle routine functions and LLMs are reserved for complex reasoning, can improve both performance and sustainability. As Virginia Dignum, professor of responsible AI at Umea University, put it: “You don’t expect your realtor to discuss philosophy, or your travel agent to be able to produce art.” Similarly, not every AI task requires the size or power of an LLM.
The Hidden Energy Cost of Information
Model size isn’t the only factor behind AI’s environmental impact. The quality and volume of data being used to train and operate these models is just as important. The storage, transfer, and management of massive datasets consumes considerable energy and adds to the operational burden.
Poor information hygiene like duplicate data, irrelevant records, and fragmented or siloed storage result in bloated training datasets, longer processing times, and higher compute requirements. It also forces models to work harder to deliver useful results.
For organizations deploying AI at scale, this is a hidden cost that can no longer be ignored. Enterprises need a unified solution to wrangle their siloed, unstructured data. Clean, governed, and well-curated data is essential not just for accuracy and compliance, but for energy efficiency.
Information Management: A Key Driver of Sustainable AI
Information management becomes a game-changer when it comes to resource-efficient AI. By optimizing how data is stored, accessed, and maintained, organizations can dramatically reduce the energy demands of their AI systems.
Effective information management practices include:
- Data deduplication to eliminate redundant records
- Content classification to surface relevant information quickly
- Lifecycle governance to archive records or dispose of obsolete data
With proper information management, organizations can ensure their AI models are trained only on the most relevant data for each project. Streamlining the datasets fed into AI models allows SLMs (and even LLMs) to perform more efficiently with less training time and computational overhead. In environments where real-time inference is required, efficient data pipelines are critical to reducing latency and infrastructure strain.
As AI use cases expand, organizations that prioritize information management and data governance practices will gain a competitive advantage—not just in performance, but in sustainability.
Building a Responsible AI Future
Small language models offer a practical, lower-impact alternative to LLMs for many enterprise applications. With effective information management, organizations can ensure these models are powered by relevant and well-governed data, maximizing performance while minimizing environmental repercussions.
In the race to scale AI, it’s time to take the smarter route; one that prioritizes both innovation and impact.
Sustainable AI starts with complete control of your enterprise data. Download our free brochure to learn how unified information management makes it possible.
