We are living in a rare moment in human history where two powerful forces—artificial intelligence and climate change—are reshaping our future simultaneously. One promises exponential progress, the other demands urgent course correction.
Image of a metropolis surrounding by stormy clouds representing the twin forces of AI and climate change.
Photo by Nahil Naseer on Unsplash
Generative AI isn’t just about automating tasks—it is redefining who we are as workers, creators, and decision-makers. Futurist Ray Kurzweil predicts human-level AI by 2029 and a merging of human and machine intelligence within our lifetimes. But others, like Eliezer Yudkowsky—whose warning is captured in the book title If Anyone Builds It, Everyone Dies—fear the pace of AI advancement may outstrip our ability to align it with human values.
Meanwhile, attempts to treat climate change as mostly background noise is failing. NASA and the World Meteorological Organization (WMO) have confirmed that 2024 surpassed the previous record, held by 2023, making it the warmest year since global record-keeping began in 1850. We are now seeing more frequent and severe climate-related weather events, with far-reaching impacts on both people and the economy.
Each force—one environmental, the other technological—represents an inflection point. Can we afford to ignore one in pursuit of the other?
False Tradeoff: AI Or Climate Action
A World Economic Forum survey of 1,000 leading employers found that 86% expect AI to rapidly transform their industries. AI can accelerate climate solutions, from smarter energy grids to real-time pollution monitoring, circular economy design, and precision climate modeling. Yet AI’s role in contributing to energy demand and emissions through its vast computational requirements cannot be ignored. As AI adoption grows, so does the energy demand from training and operating large models like GPTs and BERT. According to the IMF, electricity usage from data centers—already comparable to that of Germany or France—could match that of India by 2030.
Climate impacts are already transforming supply chains, infrastructure planning, and customer expectations. A Deloitte survey of 2,100 C-suite executives across 27 countries found that 70% had experienced climate-related disruptions necessitating a business strategy reset. The European Union is responding with mandates like the Corporate Sustainability Due Diligence Directive (CSDDD), requiring large companies to address environmental and human rights impacts, including those related to AI. The tension is real: while AI drives innovation, it must be reconciled with sustainability goals.
As organizations race to adopt generative AI while striving to fulfil climate pledges, the two forces often seem at odds—one demands exponential growth in computing power, the other calls for restraint. Can business leaders navigate AI-based innovation balanced with sustainability?
Here are 4 tried and tested actions to consider.
MRV System for Carbon Emissions Monitoring Concept. Using a laptop with digital icons representing digital tools used in Measurement, Reporting and Verification (MRV) for environmental monitoring.
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1. Start with a Digital Carbon Audit
Every digital tool—from cloud storage to AI models—leaves a carbon footprint. By quantifying the impact of AI workloads, especially in model training and deployment, organizations can make data-informed decisions about what tools to adopt, scale, or sunset. Companies like Mistral AI are setting an example by publishing carbon audits for their models, detailing emissions from both training and inference. These efforts showcase that even frontier AI development can be paired with climate accountability and data-informed decision-making.
Tools like Climatiq can help organizations tackle Scope 3 emissions—the most complex and indirect category—by automating carbon data collection and analysis across procurement and digital operations. Google Cloud’s Carbon Footprint tool gives users a project-level view of emissions tied to their cloud usage, enabling IT leaders to track which services and regions are most carbon-intensive and optimize accordingly. This level of granularity makes sustainability a factor in everyday IT decisions—not just an annual report.
If unsure where to begin, consider open-source solutions like Cloud Carbon Footprint that offer a low-barrier entry point for estimating cloud emissions and identifying hotspots for improvement.
2. Consider AI solutions to Cut Carbon and Costs
AI isn’t just a business tool. From optimizing HVAC systems to improving energy forecasting, AI can drive significant reductions in emissions while saving costs.
Take the example of 45 Broadway, a Manhattan office building retrofitted with BrainBox AI technology. Using real-time data from sensors, the system proactively adjusts HVAC settings based on sun exposure, occupancy, and weather forecasts. Within 11 months, the building cut HVAC energy use by nearly 16%, saving over $42,000 and avoiding 37 metric tons of CO₂ emissions—all with a software upgrade, not new infrastructure. Research from Lawrence Berkeley National Laboratory confirms that integrating AI into building operations can reduce both energy consumption and emissions by up to 20% over the life cycle of the building.
Another strategy for cutting carbon and costs, is optimizing where and when AI runs. Carbon-aware scheduling is about running compute loads during times or in locations when the grid is cleaner—shifting workloads dynamically to reduce embedded emissions.
3. Right-Size Your AI for a Low-Carbon Future
Not all AI systems are equal when it comes to environmental impact. Decision-makers can seek out AI models and platforms that prioritize energy efficiency, transparency, and responsible design. Unfortunately, few models disclose complete emissions data and instead warn of trade‑offs: lower carbon translating to slower responses, less overhead capacity, or higher costs. However, there are new promising tools that are engineered to operate more efficiently, and thus have a smaller carbon footprint. For instance, authors of FrugalGPT show how performance-matching strategies can cut costs by 98% or boost accuracy by 4% without increasing emissions.
Organizations can opt for small domain-specific AI models rather than larger, general-purpose large language models (LLMs). Right-sizing compute requirements and infrastructure can support greater data center efficiency. And, flexible ‘pay as you go’ spending models too, can help organizations save on data center costs, while supporting sustainable IT infrastructure.
Digital generated image of glass human figures representing the linkage of generative artificial intelligence and innovative collaboration on climate action.
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4. Build a Culture of Climate-Smart AI Innovation
Creating a climate-conscious ‘techie’ organization calls for structural and behavioral reset. One major barrier to responsible AI is organizational siloing. Leaders should create hybrid roles—such as Responsible AI Officers or Sustainability + Data Strategists—to connect IT, executive, and environmental teams. These roles serve as bridges ensuring that AI development and digital infrastructure choices align with environmental goals.
Who you do business with matters. Supporting companies and working with vendors who publish emissions data and use renewable energy can help extend climate accountability across the supply chain.
Last but not the least, daily work habits matter. Most employees are embracing AI without any caution or guidance for measuring or mitigating the environmental cost of everyday AI use. Engaging in workplace conversations about potential but also perils of AI is the first step. Next, begin incorporating AI sustainability into digital literacy. The resulting insights such as avoiding fragmented or repetitive queries and favoring batching tasks to reduce unnecessary compute cycles can translate to climate-conscious AI habits.
The future belongs to professionals fluent in both AI and sustainability. Developing this dual fluency is essential as regulations, risks, and innovation all converge.
This Is the Leadership Moment
Using AI responsibly is not about rejecting innovation—it’s about harnessing it with foresight. Business leaders who act now will shape not only the future of work but the future of the world.
In the race to adopt AI, let’s not forget real progress is not just what we build, but how we build it.
