Voice AI moves from an add-on to the core for Indian companies

India’s push towards multilingual and voice-based artificial intelligence is beginning to reshape how enterprises and government agencies engage with users, especially outside English-speaking and urban markets.

As digital services reach deeper into tier-II and III cities, and rural India, companies are rethinking text-heavy and English-first systems in favour of voice-led and Indic-language AI.

“Nearly 80% of the country’s population does not use English as its primary language,” said Nikhil Malhotra, chief innovation officer and head of AI and Emerging Technologies at Tech Mahindra. As organisations digitise customer engagement, employee productivity and field operations, “the need for AI systems that understand regional context becomes fundamental rather than optional”, he said.


This shift is already visible in enterprise deployments.

According to Ganesh Gopalan, cofounder and chief executive of Gnani.ai, voice AI in Indian languages has moved beyond pilots. “Voice AI in Indian languages has moved from experimental to essential, and the use cases have expanded far beyond traditional customer support and collections,” he said. Companies are now using voice-led systems for ecommerce discovery, hotel bookings, HR workflows and internal automation.

The impact is becoming measurable in financial services. “Voice AI is transforming financial services by extending its use from simple collections to complex, negotiation-driven conversations,” Gopalan said.

In one non-banking financial company (NBFC) deployment, he said, voice AI agents improved collection rates by 4-6% and nearly doubled loan disbursals as compared to internal and outsourced contact centres. Adoption was strong even among rural customers because conversations felt more natural in local languages.

According to industry leaders, the real value of Indic AI lies in connecting language with action.

Aditya Sudhindranath, chief strategy officer, South Asia at Deloitte, said translation alone delivers limited returns. “Just having this translation will not be very powerful. You need to have translation with action and with a connected ecosystem,” he said. He pointed to use cases such as citizens applying for government benefits through voice, where language AI, authentication and automated workflows work together to improve last-mile access.

Sudhindranath said enterprises are changing the way they invest in AI. Instead of running multiple shallow pilots, many are now focusing on a few use cases and taking them deep into production. “People are moving from ten use cases, two feet deep, to two use cases, ten feet deep,” he said.

Data, however, remains the biggest bottleneck. Supporting low-resource languages and dialects is difficult due to limited public datasets and cultural nuances. “Right now, we are in a data poverty trap,” Sudhindranath said, noting that accuracy and trust will improve only with sustained investment in training data and governance.

Malhotra pointed to national initiatives like Bhashini as a step towards building shared language infrastructure. He also highlighted the need for models designed for Indian conditions, including offline use and low-connectivity environments.

Despite the challenges, the direction is clear, the executives said. As voice-led, multilingual AI becomes embedded in core workflows, Indic language models are no longer seen as an add-on. They are emerging as a foundational layer for how India’s enterprises and public services operate at scale.