As India's digital landscape evolves, artificial intelligence is emerging as the catalyst for vernacular marketing transformation. Companies are increasingly leveraging Indic language models to reach the country's diverse linguistic population, with recent developments showing significant momentum in sectors spanning banking, e-commerce, healthcare, and agriculture.
The shift towards vernacular AI represents more than technological advancement. It addresses a fundamental market reality. While English has long been seen as the language of business and technology, the reality is that only about 10% of the population is proficient in it. In contrast, around 90% speak regional languages, often in their daily interactions.
According to KPMG India, nearly 75% of new internet users prefer content in their native language. This preference is reshaping how businesses approach customer engagement across multiple sectors. The market size in the Artificial Intelligence market is projected to reach US$6.26bn in 2024, with an expected annual growth rate (CAGR 2024-2030) of 28.63%, resulting in a market volume of US$28.36bn by 2030.
Gunjan Khetan, Chief Marketing Officer at Perfetti Van Melle India, represents this strategic shift in his company's recent campaign targeting rural India. "In rural India, where TV reach is limited and smartphone usage is below 50 percent in states like UP and Bihar, conventional media cannot go far. These are strong markets for us," Khetan explains. His company partnered with WPP and Google to use Bharat GPT, engaging audiences without smartphones or internet access through AI-powered voice interactions in local dialects.
The campaign utilized tongue twisters to drive interaction, with the AI model capable of speaking hyperlocal dialects, including variations across Eastern and Western UP. "What made this work was the AI model's ability to speak not just Hindi but hyperlocal dialects, including variations across Eastern and Western UP. That level of contextual relevance made a huge difference," Khetan notes.
Vishnu Vardhan, founder of SML Generative AI, the parent company of Hanooman AI, emphasizes both the opportunity and challenge in regional language AI development. "AI offers a huge opportunity for regional languages but also presents a significant risk. In the coming decade, generative AI will become the norm. If we don't develop strong models for Indian languages, people will increasingly rely on English, threatening regional languages."
Ganesh Gopalan, CEO and co-founder of GNANI.AI, emphasizes the technical sophistication required. "Gnani.ai's 200+ top-tier customers in India, including major banks, insurance firms, BNPL (Buy Now Pay Later) providers, MFIs (Microfinance Institutions), and automotive giants will start leveraging highly accurate, low latency, efficient SLM deployments for impactful use cases."
The vernacular AI opportunity extends beyond basic translation. "We at Gnani.ai do the complete analysis of the language and ensure we have the entire context of what is being spoken to avoid errors during the process," Gopalan notes, highlighting the complexity of authentic regional language processing.
The market potential is substantial. Vardhan quantifies the opportunity: "India's regional language internet users, totalling around 500 million, represent a massive $20 billion market opportunity for AI-driven translation tools. E-commerce, for instance, could unlock $4 billion in growth, as 20 per cent of their market remains untapped due to language barriers."
The central government's commitment to vernacular AI has gained significant traction with BharatGen, India's first government-funded multimodal Large Language Model. Currently supporting 9 Indian languages—Hindi, Marathi, Tamil, Malayalam, Bengali, Punjabi, Gujarati, Telugu, and Kannada—the initiative targets support for all 22 languages listed in the Eighth Schedule of the Indian Constitution by June 2026.
Banking and financial services represent early adopters of vernacular AI solutions. Companies are implementing voice-first technologies to serve customers who prefer regional language interactions over English interfaces. Zomato exemplifies successful vernacular marketing implementation, delivering more than 150,000 orders a month via regional language versions of its app. Hindi accounts for 54% and Tamil for 11% of these orders, with other regional languages showing rapid growth. The food delivery platform has made its app available in Hindi, Bengali, Gujarati, Kannada, Malayalam, Punjabi, Marathi, Tamil and Telugu.
Retail brands are demonstrating measurable results through vernacular strategies. Hero Motors transitioned from traditional print advertising to digital campaigns, leveraging vernacular content to engage dealers and customers across India. This approach generated over 18 million impressions and 18,000+ leads at a significantly lower cost per impression. Panasonic's campaign for electric cookers used vernacular advertising to engage rice-eating communities across India, resulting in a 72% increase in organic reach and a 200% rise in brand engagement within a year.
The fintech sector shows particularly strong adoption. Khatabook collaborated with local and micro-influencers to create videos and posts in multiple vernacular languages. The campaign boosted brand visibility and increased the app's install volume by 60%, achieving 1 million downloads through vernacular marketing strategies.
Developing effective vernacular AI requires addressing unique linguistic challenges. GNANI.AI has announced a series of voice-first SLM (Small Language Models), meticulously trained on millions of audio hours of proprietary audio datasets and billions of Indic language conversations, capturing the rich diversity of dialects, accents, and linguistic nuances prevalent across the country.
Regional variations within states present both opportunities and challenges. Companies must understand that dialects often differ even within the same state or region. A brand operating in Tamil Nadu, for example, needs to be mindful of the nuances between Tamil as spoken in Chennai and in rural areas, requiring personalized campaigns tailored to specific localities.
Sarvam AI, a prominent Indian AI startup, has made significant strides in natural language processing with the launch of Sarvam-1. This innovative large language model, boasting 2 billion parameters, supports ten major Indian languages, demonstrating the technical feasibility of comprehensive Indic language processing.
Early adoption patterns indicate strong market response to vernacular AI solutions. According to a Google KPMG report, 70% of Indians find local language digital content more reliable. Furthermore, 88% of Indian language internet users are more likely to respond to digital advertisements in their local language compared to English.
The technology's technical accuracy presents challenges that native language models address more effectively. "Most translation tools first convert a language into English and then into the target language, leading to a loss of context and cultural nuances, especially in technical subjects. This can result in translations that are out of context or even change the meaning entirely," Vardhan explains. His company's approach involves building Hindi models with both English and Hindi tokens, allowing direct content generation in Hindi while capturing regional variations and mixed-language usage like "Hinglish."
Tamil and Hindi-speaking internet users have recorded the highest adoption rates—42% and 39% respectively—for internet services, indicating substantial market opportunity. Over 70% of Google searches in India happen in regional languages, making vernacular SEO optimization crucial for brand visibility.
Companies are expanding beyond customer-facing applications to include internal business processes. Organizations use vernacular AI for employee training, compliance documentation, and operational communications in regions where regional languages dominate workplace interactions.
The convergence of government support, private sector investment, and consumer demand suggests sustained growth for vernacular AI applications. Companies are expanding beyond basic language support to include voice biometrics, sentiment analysis, and predictive analytics tailored for regional markets.
Rural market penetration remains a key driver, with vernacular AI enabling services previously limited to urban, English-speaking populations. As artificial intelligence capabilities mature, the focus is shifting from basic language translation to comprehensive cultural understanding, positioning vernacular AI as a strategic enabler for reaching India's diverse population and making technology more accessible across linguistic boundaries.
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