– Mr. Chandan Sharma, General Manager- Digital Media, Adani Group
Every day, I get up, and the first thing on my mind is how AI changed the world while I was asleep. Companies and governments are very aggressive when it comes to AI usage. Artificial intelligence is changing how industries operate, how people interact with technology & even how economies grow. The scale of adoption is remarkable, but so is the energy behind it. Training robust AI systems demands enormous computing resources & running them every day adds another layer of energy use. This has started to raise a difficult but important question—what is the real carbon footprint of AI & can it be managed in line with global climate goals?
The scale of the problem
Building AI models is not a small task. Large language models and deep learning systems need weeks of training on hundreds or even thousands of processors. Studies have shown that developing one of the biggest models today can release carbon emissions equal to those produced by several cars over their lifetime.
And training is only the start. Once the model is ready, it needs to be deployed. Billions of queries, conversations, or searches are processed every day. Each interaction on its own might use little energy, but when multiplied at scale, the demand is huge. Add to that the cooling required in data centres & the footprint becomes even more serious.
Why businesses should care
The carbon impact of AI is not just a technical side issue. It cuts across regulation, cost, reputation & strategy. Governments are moving quickly to make emissions disclosure mandatory. The EU’s new reporting rules are a clear signal of where things are heading.
Investors also want more transparency. They expect companies to demonstrate that innovation and sustainability can coexist. For customers, there is a risk of mistrust if a company promotes its green credentials but runs highly energy-intensive AI systems without addressing the impact.
There is also the cost factor. Energy prices are rising & inefficient AI workloads are not only unsustainable for the environment but expensive to maintain over time.
What companies are doing about it
The organizations are inclined to use more specialized models to get results, and they are ditching the bigger models with less efficacy.
Lighter, more specialised models can deliver good results without the same energy drain. Research into pruning and optimisation is opening new ways to reduce computational needs.
Cloud providers are investing heavily in renewable power for their data centres. Solar, wind & hydropower are being integrated to run AI workloads more cleanly. Hardware innovation is also moving forward—better chips, liquid cooling systems & improved energy management are all making a difference.
For businesses that depend on AI services, choosing partners and vendors with strong renewable commitments is starting to become part of procurement and vendor selection.
What leaders should keep in mind
Here, it helps to think in simple terms. The following points are shaping how executives respond:
- Sustainability cannot be separate from digital strategy. AI adoption must sit inside the broader climate agenda.
- Governance needs cross-functional input. CIOs, CFOs & sustainability leads should work together rather than in silos.
- Transparent reporting builds trust. Showing the emissions linked to AI use signals accountability.
- Efficiency can be a source of advantage. Offering or using greener AI services may become a point of differentiation.
The paradox of AI and sustainability
There is also an irony here. While AI has its own carbon costs, it can also be a tool to help reduce emissions elsewhere. AI systems are already being used to optimise supply chains, improve energy grid management & support precision agriculture. All of these applications can drive measurable sustainability gains.
This creates a balancing act. The challenge is not to stop using AI but to ensure that the value it creates for sustainability outweighs the emissions it generates. That means being selective, thoughtful & transparent about how AI is applied.
Looking ahead
The demand for AI will keep growing. Generative models, automation & real-time analytics are moving into more industries & with them, the energy required to support this technology will also rise. The key is not to slow down innovation but to scale it responsibly.
Companies that act early will be better prepared. Those who invest in greener AI infrastructure, in renewable energy sourcing & in transparent disclosures will find it easier to navigate future regulation and investor scrutiny. They will also gain credibility in markets where sustainability is a major concern.
Final thought
AI has incredible potential, but the environmental impact cannot be ignored. The carbon footprint of AI is now a strategic issue. While we struggle to keep the carbon emissions down, we also want to excel in the AI race.
It’s peculiar to see how the West is responding to this threat. While they have been very vocal about the carbon emissions through industrialization in developing countries, they are almost quiet on AI-emitted/caused carbon.
Leaders have an opportunity to demonstrate that technology and climate responsibility can progress in tandem. The ones who succeed will not only manage risks but also set themselves apart as real innovators in the next phase of sustainable growth.
