There is a question every business intelligence vendor entering India eventually faces, usually after their first few pilots: why is it not working the way it worked elsewhere? The infrastructure exists. The willingness to adopt is genuine. And yet, somewhere between deployment and daily use, the insights stop connecting with how the business actually runs.
Context is the answer, and in India, context cannot be retrofitted. According to the Economic Survey of 2025-26, these businesses contribute 31.1% of our GDP. Account for 48.6% of our exports. A survey by PayNearby in 2025 found that only 7% of these businesses are using artificial intelligence tools. This is not just because they do not have the money to spend on these tools. It reflects a mismatch between what the tools assume and how Indian businesses actually operate.
India is not a single market
India is not one big market. If you look at the types of businesses we have here, you will see that they are all very different. For example, a textile exporter in Surat is very different from a dairy cooperative in Anand or a steel manufacturer in Jamshedpur. They all have to follow the rules and pay the same taxes, but they work in very different ways. They have schedules and make decisions in different ways.
Most global AI platforms were built for a different world: English as the default input, Western fiscal calendars, standardised ERP deployments, and a workforce comfortable navigating complex software at every level. India does not map onto any of those assumptions. What follows is AI that functions technically but misses the mark operationally, surfacing answers to questions nobody asked, through interfaces only specialists can navigate, in a language most users do not work in.
The language problem is bigger than it looks
India has 22 officially recognised languages and several hundred dialects. For the 63 million MSMEs that form the country’s economic backbone, business gets done in the mother tongue. A purchase manager in Rajkot works in Gujarati. A logistics coordinator in Coimbatore works in Tamil. Requiring English as the entry point into AI is not neutral. It redirects benefits toward those who already have access to English-language education and urban infrastructure.
India’s government drew the same conclusion when it developed Bhashini, a national multilingual AI platform built as foundational public infrastructure. Multilingual capability was treated as a core requirement, on par with compute and connectivity. For enterprises building or procuring AI in India, that framing matters.
GST, Tally, and the reality of Indian business infrastructure
The language barrier is one layer. Beneath it sits something equally consequential. In the back office of almost any mid-sized Indian business, Tally ERP manages the accounts. With over 80% market share in India’s SME accounting software segment, it is the financial backbone of a significant portion of the country’s commercial activity. GST compliance runs through every transaction and financial record in those same offices.
Most global BI tools treat GST integration and Tally compatibility as features to configure after the fact. Any AI built seriously for the Indian market has to treat them as starting conditions. Software that does not account for India’s fiscal year structure, GST input credit logic, or Tally’s data architecture will produce insights that are technically sound but practically unreliable—being context-aware means understanding the regulatory environment in which the data was created.
From dashboards to decisions
Enterprises that have resolved the infrastructure challenge often hit a third one: even with clean, integrated data, most of what AI delivers is still a dashboard. According to an EY-CII report, 47% of Indian enterprises run multiple generative AI use cases in production. Many are getting visualisations of information they already had. A real-time margin feed does not explain why margins moved or what to do next.
When a CXO sees margins have dropped 3%, they need to know what drove it, who owns it, and what can be actioned in the next few days. Tracing an outcome through contributing factors to its actual cause requires a different kind of analytical engine. The gap between a business that can do this and one still staring at charts is not data quality. It is the depth of contextual reasoning that the AI can perform.
Intelligence for everyone, not just the boardroom
The most significant upside in India’s AI story may sit below the leadership level. A store supervisor with real-time category data responds to underperformance without waiting for a weekly review. A collections executive working from a live cash flow projection resets priorities that morning. A procurement manager who can trace a cost spike to its source enters a supplier conversation differently. Distributed access to timely, relevant intelligence changes how fast problems surface and get resolved. Mobile-first design and multilingual interfaces are not secondary features here. They determine whether AI reaches every employee or only the few who already know how to use it.
India’s AI opportunity will not be won by adapting products designed elsewhere. The country’s linguistic range, its regulatory architecture and its infrastructure realities: these form the actual design brief, not complications to manage around. The builders who take that brief seriously are positioned to unlock what 63 million MSMEs could do with AI genuinely built for them. And in building for that brief, they will produce something no other market quite demands or can replicate.
Authored by Vipul Prakash, Founder & CEO of FireAI