Here’s What Businesses Today Are Doing To Mitigate AI’s Environmental Toll

Posted on January 22, 2025 | Updated on January 22, 2025

Artificial intelligence (AI) has revolutionized industries by streamlining operations, improving decision-making and fostering innovation. However, its environmental impact is becoming increasingly evident. 

As AI adoption accelerates, the energy demands associated with training large-scale models and managing data centers are significantly contributing to carbon emissions. To address this, many companies are implementing innovative strategies to mitigate AI’s environmental toll. Check out some examples of companies who are prioritizing AI sustainability, and see what resonates with you and what you may incorporate into your own business efforts.

Why Sustainability Matters for AI

The environmental cost of AI is shown to be a growing concern for tech companies and consumers alike. This shift is driven by AI’s energy-intensive nature and the increasing demand for environmentally-friendly practices across industries. 

Today, more than 60% of consumers are willing to pay more for products with sustainable packaging, revealing a broader trend of eco-conscious consumer behavior. This sentiment is indicative of a larger expectation by the public regarding sustainability of products and services, including those powered by AI. 

As consumers become more environmentally aware, they demand more transparency from the companies they support, influencing purchase and subscription decisions and overall brand loyalty.

Further, AI companies will need to meet increasing regulatory standards for planet-healthy solutions. For example, 2024 saw the introduction of the Artificial Intelligence Environmental Impacts Act — a bill that addresses the need to regulate AI’s environmental impact. As laws such as these are introduced and eventually passed, companies that produce AI solutions could be fined or otherwise punished for having a large environmental footprint.

Challenges in Scaling Sustainable AI

Despite advancements, scaling sustainable AI practices remains a complex endeavor. The upfront costs of transitioning to renewable energy sources and developing less energy-intensive algorithms can be prohibitive, especially for smaller organizations. 

The lack of global standards for measuring and reporting AI’s environmental footprint further complicates efforts to achieve industry-wide improvements. Infrastructure challenges — such as the limited availability of renewable energy in certain regions — also hinder progress. 

Addressing these issues requires a collaborative approach that combines technological innovation with policy support and financial incentives.

Examples of Companies Taking Initiatives for Eco-Friendly AI

Innovative technologies are redefining what’s possible in sustainable AI. Neuromorphic computing, for instance, mimics the neural structure of the human brain, enabling faster computations while consuming significantly less power. 

Similarly, quantum computing promises to revolutionize AI by solving computational problems exponentially faster and with lower energy demands. Although still in its early stages, these advancements represent a promising path for reducing AI’s carbon footprint and meeting future energy challenges.

Beyond creating new technologies with fewer energy demands, the following are some specific ways technology companies are seeking to mitigate any environmental impacts of AI.

Life Cycle Assessment (LCA) of AI Models

A life cycle assessment (LCA) provides a comprehensive analysis of the environmental impacts of AI systems at every stage of their existence. From the energy-intensive training of machine learning models to the ongoing maintenance of servers and eventual decommissioning of outdated infrastructure, LCAs uncover critical opportunities for reducing ecological harm. 

For instance, training a single language model can emit over 600,000 pounds of CO₂, comparable to the lifetime emissions of five cars. By understanding these impacts, companies can make informed decisions, such as improving efficiency during the training phase or opting for sustainable disposal practices for obsolete hardware.

Regulatory and Policy Initiatives

Governments and regulatory bodies are stepping in to ensure the environmental sustainability of AI. The European Union has introduced guidelines for “green AI,” encouraging companies to disclose their AI systems’ energy use and environmental impact. Such policies incentivize organizations to adopt greener technologies and foster transparency.

Metrics and Reporting Standards

Standardized metrics are vital in assessing and mitigating AI’s environmental impact. One commonly used metric is Power Usage Effectiveness (PUE), which measures the efficiency of data center energy use. Other tools, such as carbon intensity trackers, quantify the emissions associated with specific AI workloads.

Transparent reporting frameworks — like those encouraged by the Global Reporting Initiative (GRI) — ensure accountability and enable industry comparisons. By adopting these standards, companies can identify inefficiencies and demonstrate progress toward sustainability goals.

Industry Collaboration for Sustainable AI

Collaboration among tech companies and research institutions is crucial for addressing the environmental challenges posed by AI. 

Initiatives like the Partnership on AI bring together stakeholders to develop best practices for sustainable AI development. By sharing resources and insights, these collaborations aim to create a framework for reducing the ecological impact of AI technologies.

AI-Specific Carbon Offset Programs

Many companies are turning to carbon offset programs tailored to their specific needs to counterbalance the emissions generated by AI technologies. Google, for example, invests heavily in renewable energy projects to neutralize the carbon emissions of its AI operations. 

Meanwhile, companies like Salesforce fund reforestation initiatives, recognizing that every tree planted contributes to carbon sequestration. These programs mitigate AI’s immediate environmental impact and signal a commitment to broader sustainability goals, appealing to eco-conscious stakeholders.

Case Studies in Sustainable AI Practices

Several tech giants are taking proactive steps to reduce AI’s environmental footprint in practical ways.

Google’s Renewable Energy Goals

Google has been at the forefront of sustainability efforts, with its AI systems powered increasingly by renewable energy. The company’s DeepMind division uses AI to optimize the energy efficiency of its data centers, reducing cooling expenses by 40%. This initiative showcases how AI can play a role in mitigating its own environmental impact.

Microsoft’s Carbon Removal Strategies

Microsoft has committed to being carbon-negative by 2030 by removing more carbon than it emits. The company uses AI to manage energy use within its operations, leveraging machine learning to forecast and optimize energy needs. 

Microsoft also collaborates with other organizations to develop AI models that improve resource efficiency in sectors like agriculture and water management.

IBM’s Focus on Energy-Efficient Models

IBM prioritizes developing AI models that require less computational power. By designing less energy-intensive algorithms, IBM aims to reduce the carbon footprint of AI applications. For example, their Green Horizon project uses AI to provide real-time insights into air pollution levels, helping cities manage environmental challenges more effectively.

The Path Forward for AI Sustainability

The environmental challenges of AI are significant but not insurmountable. Tech companies can mitigate AI’s toll through creative technologies, strategic collaborations and adherence to regulatory frameworks. By prioritizing sustainability, the tech industry can continue to harness AI’s benefits while ensuring a healthier planet for future generations.

About The Author

Eleanor Hecks is the Editor-in-Chief of Designerly Magazine, an online publication dedicated to providing in-depth content from the design and marketing industries. When she's not designing or writing code, you can find her exploring the outdoors with her husband and dog in their RV, burning calories at a local Zumba class, or curled up with a good book with her cats Gem and Cali.

You can find more of Eleanor's work at www.eleanorhecks.com.

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