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Last updated: 13 Mar, 2025  

nandan.jpg Time to bring a billion Indians into AI-driven digital economy: Nandan Nilekani

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IANS | 13 Mar, 2025

There is an urgent need to build artificial intelligence (AI) solutions suited for India’s language diversity, to bring a billion Indians into the AI-driven digital economy, Infosys co-founder and Aadhaar architect, Nandan Nilekani, has said.

Open-source AI models, such as AI4Bharat, are working to create Indian language datasets that can power AI-driven services in agriculture, education, and healthcare, he said during an event by Arkam Ventures.

The government is planning an initiative called the ‘Open Agri Network’, which will use AI to deliver real-time agricultural insights to farmers.

According to him, another major shift will come from affordable smartphone access.

"This is a big unlock where we use technology, DPI, and AI to reach a billion Indians," Nilekani told the gathering.

Nilekani also spoke about the return of Indian startups to domestic markets. "It’s the perfect setup for more IPOs and rapid expansion," he noted.

On the growing Indian startup ecosystem, he said that successful founders are reinvesting in the next generation of entrepreneurs.

Nilekani said that while the Indian economy grows at 8 per cent, the number of startups would grow by a 20 per cent compounded annual growth rate (CAGR) and reach the 1-million mark in the next decade.

Additionally, he highlighted the need to enable 10 million MSMEs with better access to technology, markets, and credit.

India’s rapid technological leap over the past decade was enabled by digital public infrastructure (DPI) such as Aadhaar and UPI.

With over 500 million smartphone users and 530 million WhatsApp users, India has built an unprecedented digital base, he noted.

“There’s a need for India to go all-in on artificial intelligence (AI) adoption, ensuring its benefits reach a billion people. For this, key focus areas are Indian language accessibility, MSMEs, agriculture, health, and education,” he emphasised.

 
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