Why Most AI Startups in India Don't Succeed Startups that can overcome early challenges and build resilient, scalable, and explainable AI systems may still emerge as global success stories

India's startup ecosystem is keeping pace with more than 4,500 AI startups, 40 per cent launched in just the last three years. The country is fostering innovation across sectors, from healthtech and agritech to logistics and fintech. Many of these ventures are laser-focused on solving uniquely Indian challenges using AI, making their solutions globally relevant, as highlighted in BCG's recent report, India's AI Leap: BCG Perspective on Emerging Challengers.

All signs point to a tech transformation on the horizon. The next decade could bring mass adoption of AI across Indian industries, an adventure we're just beginning to witness. However, the road to scaling remains bumpy.

So, what's slowing down India's AI dream?

The Data and Compute Dilemma

One of the foundational challenges lies in the scarcity of high-quality, labelled data and affordable compute infrastructure which are critical for building and scaling AI solutions.

"Access to clean, labelled data is essential for training effective AI models," said Gaurav Kachhawa, Chief Product Officer at Gupshup. "Another challenge is that many AI applications require significant computing power, which can be costly. Cloud services help, but sustained usage often becomes unaffordable without investor support."

This is echoed by Rishi Verma, Head – AI Centre of Excellence, FSS, who noted that "less than 20 per cent of AI startups in India transition from pilot to production, held back by challenges like limited access to compute infrastructure, fragmented data ecosystems, and the complexity of enterprise integration."

The IndiaAI Mission, which promised large-scale GPU availability, is facing logistical bottlenecks. As Shailesh Dhuri, Co-founder & CEO, Decimal Point Analytics, pointed out, "Compute is on ration coupons. Long queue times, slow iteration, and therefore slow product-market fit, are key bottlenecks. One U.S. hyperscaler region already runs north of 40,000 H100s, while India is struggling to deploy even 15,000."

Capital Constraints and 'Free PoC Purgatory'

While early-stage capital is available, there's a shortage of patient, deep-tech investment needed to sustain capital-heavy AI ventures through scale.

Nirmit Parikh, Co-founder and CEO at Apna.co, shared, "Early-stage funding is relatively robust, but there is a significant shortage of patient capital for scaling infrastructure-heavy AI ventures." The costs associated with training large-scale models, especially GPU-heavy ones, can run into millions, well beyond what most Indian seed or Series A rounds can support.

Additionally, startups often get stuck in cycles of unpaid Proof of Concepts (PoCs). Nitin Lahoti, Co-Founder & Chief Sales Officer, Mobisoft Infotech, said, "Many enterprises want months of pilot work before they even consider a contract. By the time we deliver results, priorities may shift or budgets may freeze, leaving us with sunk costs and no closure."

Policy Potholes and Patent Gaps

India's policy framework, while ambitious on paper, has not yet provided tangible benefits to AI startups. For instance, the Production Linked Incentive (PLI) schemes have not yet extended meaningful support to deep-tech.

Dhuri pointed out, "The PLI schemes paid out barely INR 14,000 crore so far—almost all into mobiles, white goods and pharma. Deep-tech lines were sketched on a slide deck but never funded."

Intellectual property is another challenge, industry players find. Between 2014–23, India ranked fifth in generative AI patent filings behind China and the U.S. "The gap is not academic. VCs price 'defensible IP' into valuation models, and Indian founders are forced to sell services rather than platforms," he added.

Infrastructure and Market Readiness

Even with increasing demand, infrastructure readiness lags behind. Basic systems like telephony need to evolve to accommodate AI-driven services like voice bots. Akhil Gupta, Co-founder and Chief Product & Technology Officer, NoBroker, remarked, "Even today, basic ecosystem elements such as telephony systems need to evolve to support AI-driven solutions such as voice bots, which require real-time streaming capabilities."

Moreover, startups are often building for a fragmented market with diverse tech maturity levels. Gupta added, "Many AI startups are still at an experimental stage, lacking real-world deployment experience. Enterprises are seeking battle-tested, production-ready AI not just prototypes."

Long Sales Cycles and Reluctant Buyers

Enterprise adoption remains slow due to long sales cycles and risk aversion. As Parikh explained, "Slow enterprise sales cycles and risk-averse corporate procurement practices can delay market penetration, forcing startups to pursue offshore markets or rely on unpaid PoCs to demonstrate value."

Cultural resistance and ROI hesitancy among traditional businesses further complicate go-to-market strategies. Kachhawa said, "Many are hesitant to change existing processes or invest in new technology without clear ROI. Startups must not only build strong products but also educate the market—adding to their go-to-market burden."

Nevertheless, India boasts a large AI talent pool of over 600,000 professionals, which is expected to double by 2027. Additionally, the country's domestic AI market is projected to reach USD 17 billion by the same year, indicating strong future potential. Startups that can overcome early challenges and build resilient, scalable, and explainable AI systems may still emerge as global success stories.