Why Indian AI Startups Achieve Recognition But Struggle to Scale-up

India’s AI startup ecosystem is thriving with ambition, but the path from recognition to true scale is strewn with hurdles. Being recognised by a government body may serve as a launch pad, yet few ventures transition efficiently to raising institutional funding, maturing products, and driving real revenue.

To answer why successful AI scale-ups remain uncommon in India requires one to assess the influence of government initiatives and analyse the obstacles hindering the nation’s deeptech ambitions. 

Recognition by the Indian Department for Promotion of Industry and Internal Trade (DPIIT) serves as a stamp of approval for AI startups. But, what comes next?

“Many founders report that after initial recognition, startups “develop well-structured pilot projects, solutions that demonstrate impressive potential in controlled environments, but often face significant hurdles when attempting to scale in real-world conditions,” said Reva Malhotra, a consulting director who partners with early-stage startups. 

She points out that deploying an AI model across multiple geographies “requires access to extensive networks, local language adaptation, and integration with government systems – all of which demand time, resources, and systemic support.”

In practice, while technical teams show capacity for scale, their progress is limited by the readiness of Indian enterprises to adopt emerging AI solutions. There’s a marked reluctance among large organisations to engage with early-stage startups, further delaying mainstream adoption.

Institutional Funding

Institutional funding remains elusive for many. Government programmes and accelerators have tried to bridge this gap, but the difference in outcomes is striking.

CV Farish, regional lead for AI startup programmes at Google, told AIM, “In the last cohort [Google AI First Accelerator], we facilitated 1,000 investor connections and the startups raised over $61 million funding within six months after the program. Overall, across all our cohorts, startups from our accelerator have collectively raised over $4.5 billion since they joined the program ($5.4 billion overall).” 

He cited SpotDraft, Kroop AI, and Merlin AI as alumni who successfully transitioned to securing substantial capital and scaling their platforms shortly after completing the program.

By contrast, DPIIT-recognised startups, particularly those reliant solely on government schemes, find it harder to attract follow-on investment. The enthusiasm triggered by recognition sometimes fails to translate into momentum. “While grants form an important part of the growth equation, they represent only one element in a far more complex framework,” Malhotra said. Without further support, market access, mentorship, and buyer introductions, many startups stall at the MVP or pilot stage.

“While India now has 115+ unicorns, access to growth capital, compute, and deep research remain ongoing priorities. India’s late-stage institutional funding is improving, but the Series A+ transition is still the critical drop-off point, underlining the importance of continued capital, global integration, and R&D incentives,” said Rohan Dani, a senior associate at the investment platform Blacksoil. 

Scalable Products vs Pilot Projects

The pilot-to-scale dilemma persists in the sector. Malhotra said, “Although the concepts are strong, transforming them into viable, widely adopted products remains a formidable task.” Infrastructure and network limitations mean that many projects remain at the demonstrator stage, lacking the connectivity and customer engagement required for nationwide adoption, she added.

Dani highlighted that the gap between seed grants and Series A+ funding indicates challenges within both the startups and the ecosystem. While over $780 million was raised in 2024 (a 40% increase), early-stage investments dropped by 37%, favouring late-stage firms. 

AI startups with strong traction and product-market fit attract funding but often struggle to convert validation into sustainable revenue, needing better IP, market validation, and monetisation strategies. Investors are wary of long gestation periods and limited exits. Domestic funding is increasing, with Indian venture capitalists (VC) and family offices investing $1.4 billion in H1 2025, yet issues like infrastructure and access to computing resources remain critical.

“Flagship successes like Qure.ai’s international deployments and Sarvam AI’s sovereign multilingual LLMs demonstrate potential when government infrastructure, market pilots, and VC capital align. Such cases are exceptions; the policy-to-practice gap narrows, but robust, scalable business models at Series A+ are essential for sustaining India’s AI growth and bridging funding gaps,” Dani added. 

Even for startups participating in government-grant programmes like Startup India Seed Fund Scheme (SISFS) or Credit Guarantee Scheme for Startups (CGSS), progress is hindered by fragmented support. An AI startup specialising in medical diagnostics, for example, may build an MVP funded through SISFS, but unless “healthcare institutions are prepared to trial or procure the solution, progress stalls,” Malhotra added.

Do Government Grants Drive Real-World Traction?

Government schemes like the SISFS and CGSS offer capital at early stages. However, founders argue that funding alone is insufficient if not paired with tangible market access and post-grant facilitation.

Dani said, “The value of government funds is real, but sustainable impact requires timely support for customer acquisition and technology scaling.”

He added that in India, the startup policy ecosystem is now a key driver of AI and deeptech growth beyond the seed stage. Major initiatives, such as the IndiaAI Mission with a budget outlay of ₹10,300 crore over the next five years, a ₹1 trillion innovation fund, and over 10,000 subsidised GPU units, enhance support for startups. 

The AI4Bharat program promotes open-source, local-language models, benefiting over 40 million students with AI-driven digital learning. Startups like Krutrim, Sarvam AI, Qure.ai, and AgNext showcase the effectiveness of these policies in healthcare, agri-tech and SaaS, Dani added. 

Why Indian AI Startups Pivot Globally

Founders in the AI sector report significant gaps despite national initiatives aimed at fostering innovation. Key challenges include the lack of actionable frameworks for data sharing, regulatory testing environments, and compliance with ethical AI standards, which hinder adoption and implementation, particularly evident in sectors like facial recognition and retail security. 

Additionally, many government programmes fail to provide ongoing connections to customers or the mentoring necessary for scaling businesses. 

A remarkable 93% of Indian startups in Google’s AI First Accelerator “are building for international markets, with their primary & secondary markets extending beyond India from day one,” said Farish. 

“Indian AI startups are increasingly setting their sights on international markets from the outset, demonstrating a bold and aspirational drive for global leadership.”

This global pivot isn’t only aspirational; it’s pragmatic. Domestic challenges, including a scarcity of high-quality data, slow enterprise adoption, long sales cycles, funding gaps for deeptech, and market readiness have compelled founders to look outward. “Addressing these issues within the ecosystem is crucial for strengthening the Indian market for AI startups,” Farish added.

According to Google’s accelerator programme, startups like SpotDraft used the opportunity to “reduce costs by 80%, increase accuracy by 30%, and reduce latency by 70%,” a leap only achievable with guidance and infrastructure.

How Can India Produce More AI Scale-Ups?

India’s AI ecosystem is at an inflection point. Private accelerators and multinational platforms (e.g., Google), by contrast, have demonstrated models of intense engagement, global market reach, and integrated mentorship that drive real-world traction, investor interest, and product scale.

Bridging India’s deeptech gap not only calls for better funding, but also end-to-end support and a readiness to address structural barriers. 

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