Editorial
The Gist
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Carbon costs climbing. Generative AI systems use large amounts of energy, which makes emissions a growing issue for businesses.
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Efficiency strategies matter. Design choices like pruning and quantization can cut AI’s environmental impact from the start.
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Leadership must adapt. Sustainability should now be a core part of how companies govern and scale AI systems.
Editor’s Note: AI may be powering your customer experiences, but at what cost to the planet? Every chatbot interaction, personalized product recommendation and predictive routing feature burns energy behind the scenes. As CX leaders adopt generative AI to improve service and satisfaction, they now face a new challenge focused on sustainability. This is the subject of Scott's column today.
Artificial intelligence (AI) is no longer an emerging tech. It’s now embedded in how businesses operate, make decisions and engage customers. From accelerating R&D to reshaping user experiences, AI is fast becoming an engine of enterprise transformation. At the same time, its environmental footprint is becoming a growing concern.
AI’s rise brings with it a rising energy bill. The more capable the models, the more compute and the more electricity they demand. And with organizations scaling AI across functions and industries, addressing its carbon impact is becoming increasingly important.
Table of Contents
- What Is Net-Zero AI?
- The Energy Dilemma: Intelligence at a Cost
- 4 Pathways to Net-Zero AI
- Where AI Can Help the Planet
- What AI Leadership Looks Like Now
What Is Net-Zero AI?
Net-zero AI is the idea that powerful technology should not come at the expense of planetary health. It’s the practice of building and deploying AI in ways that align with broader environmental goals, including emissions reduction, water stewardship and ecosystem protection.
This means embedding sustainability into the design, development and use of AI, from model architecture to infrastructure decisions. As companies pursue aggressive decarbonization targets, they should bring the same discipline to AI as they do to other parts of their emissions strategy.
The goal isn’t to limit AI’s potential. It’s to use that potential more efficiently and sustainably. Let’s dive into what that may look like.
The Energy Dilemma: Intelligence at a Cost
Modern AI models, like those built on generative architectures, consume a tremendous amount of compute. That compute translates directly into energy consumption and rising operational costs, much of which is concentrated in data centers and cloud platforms. If left unaddressed, emissions from AI operations could soon make up a significant slice of corporate carbon footprints.
Generative AI systems can require significantly more compute than traditional machine learning models. The energy used to run AI models in production (known as inference) can, in many cases, surpass the energy used during training, especially when those models support high-volume or continuously running applications. And as business adoption expands rapidly, emissions tied to AI could rise in lockstep unless sustainability is proactively built into deployment strategies.
In other words, AI's environmental impact is no longer a technical footnote but rather a strategic issue that warrants C-suite attention.
Related Article: Is AI Stealing Earth's Lunch Money? The Environmental Impact of AI
4 Pathways to Net-Zero AI
Let’s explore the four key areas where businesses have the opportunity to act now.
Prioritize Model Efficiency From the Start
Use design strategies like model pruning (removing unnecessary parts of a model), transfer learning (reusing pre-trained models) and quantization (reducing numerical precision to cut compute needs) to help decrease computational overhead without sacrificing performance. These approaches can lower emissions, speed up results and reduce infrastructure costs.
Choose Clean, Efficient Infrastructure
Select cloud and data center providers that prioritize renewable energy and operational efficiency. The energy system is already showing signs of transformation, with increased investment in advanced nuclear technologies and scalable renewables. These shifts can benefit AI while helping to lower the carbon intensity of electricity for users, across industries and sectors. Businesses that align their AI operations with these cleaner grids will likely be better positioned to scale responsibly.
Measure and Report AI-Related Emissions
Incorporate AI’s energy use into enterprise-wide carbon tracking systems. This helps surface high-impact areas and identify where improvements can be made.
Consider the Full Lifecycle, Not Just Training
While training receives much of the attention, inference often represents a larger share of AI’s ongoing emissions. Deploying models on energy-efficient hardware can reduce long-term impacts.
Related Article: Confronting AI and Inequality in the Big Tech Era
Key Strategies for Sustainable AI
This table summarizes approaches to reducing the environmental impact of AI systems, from model design to infrastructure and reporting.
| Strategy | Action | Why It Matters |
|---|---|---|
| Model Efficiency | Use pruning, transfer learning, and quantization to reduce computational load | Decreases emissions, boosts speed, and cuts infrastructure costs |
| Clean Infrastructure | Choose cloud/data center partners focused on renewable energy and efficiency | Aligns AI operations with low-carbon grids, enabling scalable and responsible growth |
| Emissions Reporting | Integrate AI energy use into carbon tracking systems | Reveals high-impact areas and improvement opportunities |
| Lifecycle Consideration | Account for inference emissions and deploy models on efficient hardware | Addresses long-term energy use, not just initial training impact |
Where AI Can Help the Planet
It’s important to note that AI isn’t just part of the problem. It can also be part of the solution. When applied strategically, AI can enhance sustainability programs by improving energy forecasting, streamlining supply chains and reducing material waste. It can also help organizations surface insights faster and take action more effectively across the business.
Aligning AI with sustainability objectives can deliver dual benefits. It helps reduce the technology’s footprint while also using its capabilities to create new sources of business value.
Related Article: Energy Hungry AI: Is It Sustainable?
What AI Leadership Looks Like Now
As AI becomes central to business performance, sustainability should be built into its governance. Leaders who treat AI’s environmental impact with the same seriousness as data privacy or security will likely be better prepared for coming regulations and stakeholder scrutiny.
Key steps include making carbon transparency part of AI vendor and infrastructure decisions. It’s also important to drive alignment between IT, sustainability and operations teams. Finally, remember to take responsibility for both outcomes AI supports and the resources it consumes
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Organizations that lead on net-zero AI can set a new standard for responsible innovation, one where performance and sustainability reinforce one another.
AI will likely continue to define the future of work, industry and society. But that future should be low-carbon by design. Reaching net-zero AI can be a leadership choice, and the organizations that act early can lead not only in capability but in credibility.
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