India’s vertical AI startups gear up for enterprise shift amid cautious adoption

A fresh wave of Indian startups is betting that vertical AI—the application of artificial intelligence to specific industries—ought to be the next big frontier in enterprise technology, even as large enterprises tread cautiously in adopting these innovations at scale.

The VIBE50 report, released today, spotlights 50 emerging vertical AI startups founded by Indian entrepreneurs. These startups are tackling deep, domain-specific challenges across 12 key industries. From healthcare to manufacturing, financial services to logistics, these startups offer specialised solutions designed to embed AI directly into business workflows.

This comes at a pivotal time for enterprise tech globally. Corporations are shifting focus from broad, horizontal AI tools to specialized systems that promise measurable business outcomes. “The next $10 billion outcomes in AI will come from companies solving real workflow problems in specific industries, not from general-purpose platforms,” notes the report.

Yet, translating this vertical AI potential into enterprise-wide adoption remains an uphill task, say venture capitalists backing these startups.

“Surprisingly, even US enterprises are adopting AI slowly,” says Thiyagarajan Maruthavanan, co-founder at Upekkha, an AI accelerator for global Indian founders, in a conversation with Business Today. “When it comes to deploying AI where multiple people are involved, it’s still a slow grind.”

A key challenge is what Maruthavanan calls the “pilot-to-production” gap. Despite intense excitement around AI at the CXO level, as many as 90% of enterprise AI projects stall before scaling into production environments.

Adding to the hesitation is a wait-and-watch approach among enterprises as AI technology continues to evolve rapidly. “Many enterprises are saying: let’s wait another six months—maybe AI will do some new magic. They have the budgets, they’re excited, but they’re also cautious,” he explains.

For AI startups, this enterprise caution means they cannot simply sell software tools and expect clients to figure out their application. Instead, they’re being forced into deeper engagement with customers’ businesses.

“Founders can’t just build the tech and throw it over the fence anymore,” says Prasanna Krishnamoorthy, Managing Partner at Upekkha.

This shift is fundamentally transforming the traditional Software-as-a-Service (SaaS) model. In classic SaaS, software vendors sold tools, leaving adoption and impact to the customer. With AI, startups are expected to stay involved, ensuring the technology delivers tangible business value.

These dynamics are also influencing how investors approach funding vertical AI ventures.

“In the early days after ChatGPT launched, there was a lot of confusion. Investment into SaaS and applications slowed because people were unsure how AI would change the business landscape,” says Shekhar Nair, co-founder and Managing Partner at Upekkha.

However, clarity is emerging around vertical and domain-specific AI applications, prompting investors to return cautiously. “We’re starting to see investors come back, especially those willing to take risks,” Nair says. “But they want to know: how will you defend your solution if others build similar technology? Differentiation and defensibility are critical.”

Maruthavanan predicts the next few years will bring “dam-breaking moments” for vertical AI, where breakthroughs in specific industries create significant new markets. “The dam-breaking moment for developer AI happened in 2023. Our bet is that vertical AI will see those moments in the next one to three years,” he says.

India, he argues, is uniquely positioned to capitalize on this trend due to its rich pool of engineering talent, cost-effective R&D, and cross-border go-to-market experience. Upekkha has already made multiple pre-seed investments in vertical AI startups to help them navigate this emerging landscape.

Ultimately, while India’s vertical AI startups are poised for global impact, widespread enterprise adoption still hinges on bridging the gap between technical capability and real-world business outcomes—a challenge both founders and investors are eager to solve.