AI Adoption in India Advances With Local Language Support

India’s linguistic diversity presents both challenges and opportunities for artificial intelligence adoption, as local language support becomes a key factor in reaching a wider population.

With 22 scheduled languages and hundreds of dialects, India is considered one of the most linguistically diverse markets in the world. Speech and language barriers remain a central obstacle for broader AI usage, especially in regions where literacy rates vary and written communication is less accessible.

Voice-based AI technologies have emerged as a practical solution to these challenges. By enabling users to interact with digital services in their preferred language through spoken prompts, voice AI can help bridge gaps for those who cannot read or write fluently.

Several Indian startups and technology providers have focused on developing AI systems tailored to the country’s language landscape. Sarvam AI, established in 2023, develops large language models and multimodal AI tools for Indian languages, and has worked with UIDAI to add multilingual voice features to Aadhaar services.

What the numbers show

• 60% of AI users in India interact via voice at least sometimes.
• 59% have tried AI interactions in Hindi or a regional language.
• AI systems for low-resource languages can cost up to five times more than English equivalents.
• GPT-5 reached about 45% accuracy on a benchmark for 11 Indic languages.
• A bank using vernacular voice AI reduced call handling time by 50 seconds and raised satisfaction by 24%.

Government initiatives have played a role in promoting multilingual AI. Platforms such as BHASHINI, BharatGen, and Adi Vaani have been launched to support AI applications across public services, aiming to make digital tools more accessible in multiple languages.

Some companies report measurable improvements from adopting vernacular AI. For example, a bank using Gnani.ai’s voice AI system reduced average call handling time and saw a notable increase in customer satisfaction. A rural microfinance lender recorded an 82% engagement rate on collection calls, while another financial institution reported higher engagement and returns compared to text-based outreach.

Despite these advances, technical challenges persist. A study found that GPT-5 achieved only around 45% accuracy on a test covering 11 Indic languages, highlighting the complexity of developing effective AI for India’s linguistic environment. Additionally, sourcing and processing data for low-resource languages can be up to five times more expensive than for English, according to a UNDP-cited analysis.

India’s diverse language landscape has also made it a testing ground for AI solutions that could be adapted for other multilingual regions, such as Southeast Asia, West Asia, and Africa. Some Indian startups, including Sarvam AI, have stated that their optical character recognition and speech models outperform international competitors in sub-continental languages.

As AI adoption continues to expand, the focus on local language support is shaping how digital services are designed and delivered in India’s complex linguistic market.

* This article is based on publicly available information at the time of writing.