“AI Made for India: Generative AI Needs Local Context — and Indian Software Companies Are Best Placed to Deliver It” highlights how India’s linguistic and cultural diversity demands AI systems trained on truly local data. As global, western‑trained models fall short in real‑world Indian environments, homegrown SaaS and enterprise solutions emerge as the natural leaders of this transformation. With deep familiarity of India’s languages, business workflows, and on‑ground realities, Indian software innovators are uniquely positioned to build AI that is accurate, inclusive, and relevant for the nation’s digital future.
As we are stepping into AI-driven and reshaped global industries, from content creation, to communication and business automation, one thing is very clear: AI will have to be contextual to be useful. Delving in a country like India, that can be as diverse as it can get in terms of both linguistics and culture, we cannot expect global deployments and western datasets to be imported and work alike. We need AI systems that are built, trained and equipped to work India’s culture, language and business realities, an area where local SaaS companies outpace others as clear leaders.
One of the promises of AI is that it helps in increasing productivity by automating the content creation process, streamlining operations, and improving customer engagement. However, the success of these tools rests upon the relevance and quality of data they are trained on.
“India’s AI future will be shaped not by imported models, but by intelligence rooted in our own languages, cultural logic, and business realities. When technology understands India as India truly is, it becomes not just smarter—but transformative. This is why the next wave of generative AI innovation must be built in India, for India.”
Kunal Singhal, MD & Founder, Eazy Business Solutions
Currently, most of the AI tools are built primarily on western datasets, making them insufficient for India’s multilingual and highly nuanced markets.
Consider language alone: India has 22 official languages and hundreds of dialects. Therefore, a sales chatbot that is designed and developed for English or Spanish speaking users may not be able to correctly interpret colloquial Hindi and Tamil, let alone the complex ‘Hinglish’ spoken in many parts of India. When the local developers train these AI models on multilingual datasets, they do this keeping in mind how India speaks, types and communicates. This is why localized training is extremely essential, so that the AI tools can actually understand what the Indian users are trying to convey.
We can apply the same argument to business workflows. Local Indian businesses, especially the MSMEs, operate rather uniquely. They may have a more informal functioning model, flexible pricing, and unique compliance needs. This is why AI systems that are built for international firms may fail to adapt to a local manufacturer operating in cities like Kanpur or Kota. Therefore, India needs homegrown enterprise software firms that are already experienced in tailoring ERP, CRM, and other automation tools for local markets, so that they lead India’s AI transformation.
According to a report, recent industry updates suggest that AI could add up to $500 billion to India’s GDP by 2025. To achieve this, India must transition from being a passive consumer of foreign AI tools to an active creator of Indian-trained AI models. A positive development is that we are already seeing this transformation happening. India is leveraging local technologies in areas like LLMs, AI-driven edtech platforms, agricultural analytics tools, etc. These solutions are not just more accurate but more inclusive too, allowing millions of first-generation digital users into the AI economy.
Government initiatives like India AI Mission and Bhashini are also much-needed policy support, promoting open datasets and multilingual AI research. But the private sector, especially India’s mid-sized software and SaaS companies, will play a decisive role in operationalizing
these technologies for real-world use. It’s because they are uniquely positioned at the intersection of innovation and implementation enabling them to integrate generative AI into finance, logistics, healthcare, and retail systems that power the Indian economy.
For major international players, India’s diversity might feel like a barrier. But for Indian players, it is not just an advantage but also an inspiration. Local developers have an understanding of diverse Indian languages, cultural logic, and on-ground workflows, they are uniquely positioned to make the tools relevant for Indian reality while also redefining global tech.
India’s next leap in AI innovation will not be led by scale alone but by relevance. The companies that understand India best will build the AI models that India needs.