‘Next-Billion AI Index’ to redefine artificial intelligence adoption in India
It argues that the metrics dominating AI development in the world are essentially useless for the billion-plus users across India and the Global South
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The Next Billion AI Index is developed on the basis of 10 measurable dimensions under three themes. | Photo Credit: PTI
A group of researchers have come up with the ‘Next-Billion AI Index (Nexbax): The compass for AI utility and adoption in the global majority’ .
This new study challenges the global AI industry’s obsession with benchmark rankings with its new framework Nexbax. It argues that the metrics dominating AI development in the world are essentially useless for the billion-plus users across India and the Global South.
The Nexbax is developed by Ambrish Rawat, Kush R. Varshney and Jessica He from IBM research, Subhabrata Majumdar from Indian Institute of Management Bangalore (IIMB), Claudio Pinhanez from University of Sao Paulo, Yann Le Beux from YUX Design, Satyapriya Krishna from Harvard University, Rahul Gupta from Amazon and Rumman Chowdhury from Humane Intelligence.
Even in India’s AI policy discourse, NITI Aayog increasingly recognises that readiness depends on affordability, multilingual access, and compliance-by-design. According to the researchers, the Nexbax gives that intuition a rigorous and a measurable form.
Subhabrata Majumdar, assistant professor from IIMB, said, “This framework is developed with coauthors not only from India but people spanning from across Africa, Europe, South America and North America. We are trying to evaluate if the existing AI systems can be deployed, trusted, and sustained under the constraints that define adoption in markets like India, because Indian market has diverse users using devices with intermittent connectivity, low-cost devices, multilingual and low-literacy users, and informal small-business workflows.”
He said, “The project was initiated during the India AI Summit in February 2026. The idea behind developing the framework was to guide the development of better AI systems so that these systems can solve the problems of people working at the grassroot level. For example, giving the most important health updates to AS
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