The State of AI Adoption Among Indian SMEs: What the Data Actually Shows

Vi Business's ReadyForNext platform has tracked over 250,000 MSMEs across 16 sectors, and its Digital Maturity Index (DMI) for the segment has climbed steadily, from 55.9 in 2023 to 58.0 in 2025 and 60.8 in 2026. Within that broader digitization, roughly one in four MSMEs report they have already integrated AI tools into operations. A separate measure by CyberMedia Research in June 2026 puts 55% of surveyed MSMEs as actively exploring AI adoption right now.

Put plainly, AI adoption in this market is no longer rare or theoretical, but it's also not close to majority deployment. Most businesses sit somewhere between curious and piloting, and few run AI as routine infrastructure.

The optimism numbers stand out on their own. In the NASSCOM-Meta study of 300+ tech-enabled MSMEs across Gurugram, Bengaluru, Pune, Mumbai, and Hyderabad, 94% of respondents believe AI can significantly drive business growth, 87% are confident it can improve productivity, and 68% are optimistic about its role in developing new products and services.

That's a noticeably more bullish picture than Vi Business's broader 250,000-business sample, and the difference is informative rather than contradictory. NASSCOM's respondents were specifically tech-enabled MSMEs, a more digitally advanced subset than the general MSME population Vi Business surveyed. Across both studies the pattern is consistent. Belief in AI's potential is close to universal among businesses paying attention to it, but actual integration lags well behind that belief, even in the more digitally ready segment.

CyberMedia Research's June 2026 study found operational efficiency is the top business priority for 78% of MSMEs, ahead of revenue growth (71%) and customer acquisition (64%). AI adoption tracks that priority closely, since automation and AI are the most direct ways to move an efficiency number without adding headcount.

On the growth side, NASSCOM-Meta found MSMEs see the clearest AI potential in content creation and marketing (48%), customer engagement (46%), and new product or service development (68%). That mix of cost-saving and revenue-generating use cases matches what Joistic sees directly in client conversations. The first AI use case a business commits to is almost always tied to a number leadership already tracks, and rarely to a novelty feature.

The barriers are consistent across both studies, and none of them are about AI capability. NASSCOM-Meta found 65% of MSMEs cite limited awareness of available AI tools and resources, 72% say they need structured AI training programs, 59% cite budget constraints, and 91% called for more affordable, accessible AI solutions built for smaller businesses rather than enterprise pricing.

CyberMedia Research's numbers point the same way from a different angle: 26% of MSMEs say they lack internal digital expertise, and 20% struggle simply to identify the right technology for their needs. Demand isn't the issue. Awareness, cost, and integration support are, which is why scoped, done-with-you pilots tend to move faster than businesses trying to evaluate and build AI capability entirely in-house.

Regionally, Vi Business's DMI data ranks Telangana as the most digitally mature MSME ecosystem (a score of 68.3), followed by Karnataka, Maharashtra, Kerala, and Gujarat. These are broadly the states with the strongest existing tech-sector presence, which likely feeds MSME digital literacy through spillover talent and vendor ecosystems.

One data point deserves a mention of its own. In the same study, women-led MSMEs scored a marginally higher Digital Maturity Index (61.6) than male-led businesses (60.4). It's a small but real gap, and it runs counter to a common assumption about who's driving digital adoption in the segment.

Taken together, the data points to one practical conclusion. The businesses that move from believing in AI to actually running it don't wait until they've built full internal AI capability. They pick one measurable workflow, pilot it against existing tools, and prove the ROI before expanding. That directly addresses the two biggest barriers in the data, cost uncertainty and lack of internal expertise, because a scoped pilot makes the cost knowable and doesn't need an in-house AI team to get started.