AI for ESG: Key Takeaways from the India–AI Impact Summit 2026

The India–AI Impact Summit 2026, held at Bharat Mandapam, New Delhi, marks a historic moment as the first global AI summit hosted in the Global South. It shifted the global conversation from abstract AI safety to "imagination into implementation."

The summit is anchored in three foundational themes that dictate how AI should be harnessed:

Build to Sustain is pleased to present the key takeaways and strategic highlights from the session "AI for ESG" conducted at the summit. The panel features a diverse group of experts from various fields, including law, academia and AI policy.

Panel Speakers
Speaker	Role
Dr. Manoj Bharatwaj	Moderator; Founding Faculty Member, Head of ESG Carbon Markets & Sustainability and Executive Education, Dhirubhai Ambani University School of Law
Ms. Sarith Felber	Senior Director, Office of Legal Counsel and Legislative Affairs (Department of Economic Law)
Professor Victor	Director, AI Centre at Volcani Institute (ARO)
Ms. Ayisha Piotti	Managing Partner, Horizon; Head of AI Policy Summit, ETH
Ms. Maya Sherman	Expert in Innovation and AI in India
Professor Dr. Avinash Dadhich	Founding Director, School of Law, Dhirubhai Ambani University (DA-IICT)
Dr. Vinni Singh	Associate Professor of Law, Dhirubhai Ambani University – School of Law

We have distilled the highlights of the "AI for ESG" session into an interactive Q&A summary. This format is designed to help you quickly identify the key trends, challenges and opportunities that defined the conversation.

1. What Is ESG for AI?

"ESG for AI" refers to ensuring that AI itself aligns with ESG principles. This means considering the environmental impact of AI technologies such as the energy and water consumption of data centres as well as the social and governance implications of AI development and deployment.

2. What Are the Three Phases of AI for ESG?

The speaker outlines three distinct phases:

• Meeting the ESG Agenda (Pre-Reporting): Using AI to directly achieve ESG goals before reporting, combating or reducing emissions (Scope 1, 2, and 3), creating metrics for social responsibility, and ensuring compliance with labour and social protection laws.
• ESG Reporting: The direct use of AI for generating ESG reports. The speaker acknowledges the question of whether this constitutes truly accurate reporting or potential "greenwashing."
• Monitoring & Verification: The third part of the MRV (Monitoring, Reporting, and Verification) ecosystem. AI can help verify third-party reports. For example, regulators using AI models to verify reports submitted by third-party verifiers.

3. How Does AI Aid ESG Measurement?

AI aids ESG measurement by addressing the issue of poor or inconsistent data, which often leads to greenwashing.

Specifically, AI is deployed to:

• Track supply chain emissions in real-time
• Detect greenwashing through data pattern analysis
• Monitor deforestation and biodiversity loss
• Automate ESG reporting for investors, regulators, and other stakeholders

4. How Can AI Reshape Sustainability?

AI significantly reshapes sustainability by providing tools for measurement, management, and market credibility:

Beyond reporting, AI also influences how sustainability is priced, regulated, and financed.

Globally, AI is recognised as a powerful tool to:

• Optimise resource use and improve efficiency across various industries, from smart grids and precision agriculture to waste management.
• Reduce waste and emissions by enabling predictive maintenance and optimising logistics.
• Enhance transparency in sustainability efforts and provide real-time insights for corporate decision-making.

5. Why Are Data Centres a Key Concern for AI and Sustainability?

The speakers highlight that data centres are a significant source of pollution associated with AI. They are major polluters and consume substantial amounts of energy and water. Specifically, they mention:

• Data centres contribute to filling the "carbon space".
• They consume a lot of energy and water, which is often overlooked as an abstract form.
• The infrastructure includes cables under the ground and in the oceans.
• AI itself, while a tool for sustainability, is one of the biggest polluters due to its reliance on data centres.
• Even simple interactions with AI, like asking a question to ChatGPT, consume energy in data centres.

6. What Are Socio-Technical Standards in AI?

Socio-technical standards are an emerging concept that integrates technical aspects with social contexts to ensure AI systems are responsible, fair, and trustworthy.

The speakers mention that in the policy, there is a shift towards developing "sociotechnical standards" for AI, which also consider the planet. These standards are:

• Being developed at organisations like ISO and NIST.
• Expected to be use-case specific rather than general across all AI applications.
• Seen as a way to avoid fragmentation in policy and governance across different geographies.

These standards are crucial for mitigating risks, enhancing adoption, and fostering responsible innovation in AI by balancing technical progress with ethical and social responsibility.

7. What Societal Values Influence AI Policies?

The societal values shaping AI policy include:

Societal Value	Influence on AI Policy
Mitigating Risks	Addressing and reducing potential negative impacts of AI
Building Trust & Adoption	Building public confidence essential for widespread adoption
Fairness & Equity	Ensuring benefits are distributed fairly among all
Supporting Local Innovation	Creating environments that foster domestic AI development
Balancing Innovation & Regulation	Reconciling technological advancement with risk prevention
Human Rights	Addressing AI's impact on fundamental rights
Environmental Impact	Considering the environmental footprint of AI, particularly data centres

8. Why Is There a Caution in Accepting New AI Laws?

Some experts suggest leveraging existing laws instead of creating new ones specifically for AI regulation. Ms. Ayisha noted that their approach involves examining laws that already exist, rather than developing entirely new legislation for AI. Similarly, Ms. Felber observed that legislation has aimed to be technologically neutral for years.

9. Why Is India's AI Approach Unique?

India's approach to AI is unique due to its focus on inclusion and infrastructure as key drivers. The country's AI summits are notable for inviting the "common man," making them more inclusive compared to exclusive elite gatherings in other nations like London and Paris. This approach aims to ensure that AI benefits everyone, promoting poverty alleviation, infrastructural growth, and overall economic development.

10. How Do Countries Balance AI Regulations?

• Balancing Innovation and Risk Mitigation: Ms. Ayisha emphasizes that policy frameworks and regulation aim to mitigate risks to build trust and ensure adoption of AI. This also involves ensuring fairness and equity in the gains from these technologies, and creating an environment that supports local innovation. She notes a "philosophical" challenge where some perceive regulation as misaligned with innovation, stressing the need to balance this.
• Diverse Global Priorities: Different geographies have varying values, objectives, and cultures, which influence their regulatory focus.

Region	Regulatory Approach
European Union	Regulatory-focused, emphasising human rights and building trust through regulation
United States	Market-led, private sector-driven, with a focus on national security
China	State-led, concentrating on control
India	Focused on inclusion and infrastructure as drivers

• Flexible Frameworks: Switzerland, for example, advocates for flexible frameworks that leverage existing laws rather than creating new ones, focusing on testing, red-teaming, and using AI as a tool for regulation.
• Sector-Specific vs. Horizontal Regulation: Israel's Ministry of Justice discusses a sector-specific approach to AI regulation, meaning they don't treat AI as a horizontal technology requiring overall regulation. Instead, they examine how existing regulations in sectors like finance can address AI's challenges and risks, while also ensuring they don't hinder AI adoption.
• Government Leadership and Experimentation: Israel's government leads by example, adopting AI in its own services and internal work, setting higher standards for itself to influence the market. They also encourage sandboxing and experimentation to gain a better understanding of the technology.
• Awareness and Trade-offs: Discussions also point to the need for awareness among the common public, politicians, and government officials about the impact of AI, particularly concerning energy and water consumption by data centres. Ultimately, this involves local-level trade-offs between the benefits of AI and its environmental and social costs.

The session underscored that the integration of AI into ESG frameworks is no longer a futuristic concept, but a present-day necessity for building resilient organisations. By transitioning from abstract safety discussions to practical implementation, businesses can leverage data-driven insights to meet rigorous global sustainability standards.

The speakers collectively emphasised that while technology provides the tools for transparency and efficiency, ethical governance remains the essential compass for long-term success. Ultimately, the journey toward a sustainable future requires a balanced synergy between human responsibility and artificial intelligence.