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Working Groups At The AI Impact Summit / Resilience, Innovation, & Efficiency

Resilience, Innovation, & Efficiency Working Group

Driving sustainable, resource-efficient AI systems that strengthen climate resilience and sustainability.

About The Working Group

The scale of resources consumed by modern AI solutions presents myriad challenges for the environment as well as exacerbates existing AI divide between nations. The Resilience, Innovation & Efficiency working group focuses on advancing resource-conscious and adaptable AI systems that operate effectively in low-resource environments without compromising accessibility or impact. It seeks to embed efficiency and resilience as central principles in AI innovation, ensuring that technological progress remains inclusive, sustainable, and responsive to the diverse needs of all nations.

Vision & Objectives

Building an equitable global AI ecosystem

The Working Group on Resilience, Innovation & Efficiency envisions a future where AI systems are designed to be efficient, sustainable, and resilient, capable of operating effectively in varied contexts and with limited resources. It aims to ensure that efficiency is not seen as a limitation but as a foundation for innovation and accessibility

Leadership

Mr Pankaj Agarwal

India Co-Chair

Secretary, Ministry of Power, Government of India

Shri Pankaj Agarwal, Secretary, Ministry of Power, Government of India, has been a key architect of India’s energy transition, driving reforms focused on sustainability, efficiency, and innovation. With extensive experience in public policy, infrastructure, and digital transformation, he brings a unique perspective to the Working Group—linking AI efficiency with energy resilience and national development, and reinforcing India’s commitment to responsible, resource-conscious AI that delivers sustainable impact.

France

Country Co-Chair

Key Issues

Understanding the global AI divide and our approach to bridging it

The Challenge

The increasing scale of AI development has led to growing environmental and accessibility concerns. Training large-scale AI models demands enormous energy and water resources, with global data centres projected to consume nearly 3% of the world’s electricity by 2030.

The Solution

AI’s energy intensity contributes to higher carbon emissions, while its water footprint adds stress in already water-scarce regions. These challenges make resource-heavy AI systems unviable for many parts of the Global South, deepening the global divide in AI innovation and adoption.

The Impact

Addressing this imbalance requires a shift towards efficiency as a design value and building AI systems that do more with less. By prioritizing lightweight architectures, optimized computation, and sustainable infrastructure, the Working Group aims to enable an inclusive AI ecosystem that is resilient, adaptable, and globally beneficial.

Meeting Schedule

16th-20th January 2026

Hybrid Meeting (Bengaluru) [Tentative]

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