India's AI Ambitions: Policy Push, Enterprise Adoption, and Investor Focus

Decoding The Policy Push Giving Wings To India's AI Ambitions

India is currently home to over 200 GenAI startups, which have cumulatively raised over $1.2 Bn in funding In 1956, a young mathematics professor at Dartmouth College brought together some of the brightest minds in computing and cognitive science to develop ideas about thinking machines and called the subject "artificial intelligence" (AI). Nearly seven decades later, the global AI industry is valued at $621.2 Bn and is expected to reach a market size of $1.3 Tn by 2030. From big tech firms like Meta and Google to banking giants like JP Morgan and Bank of America, and manufacturing majors such as Apple and Toyota Group, everyone seems to be going the whole hog to adopt AI into their day-to-day operations. In India, too, AI adoption is on the rise across the board. In the financial year 2023-24 (FY24), the rate of AI adoption was highest in the fintech sector at 68%, followed by the tech industry at 60-65%, healthcare at 52%, retail at 43%, and manufacturing at 28%. Despite this, India's large-scale enterprises such as TCS, Wipro and Infosys seem to be lagging behind their global peers in terms of AI adoption for reasons ranging from strict adherence to established processes to stringent compliance requirements. However, homegrown startups are increasingly integrating AI, particularly generative AI (GenAI), into their products and services, as per Inc42's 'The Rise Of India's GenAI Brigade' report. In an Inc42 survey of more than 50 VCs about GenAI adoption by non-GenAI startups in their portfolios, 43% said that AI or GenAI is now a key part of their product and service roadmap. India is currently home to over 200 GenAI startups, which raised more than $1.2 Bn in funding between 2020 and the third quarter (Q3) of 2024. According to industry body Nasscom, AI is expected to add a whopping $500 Bn to India's gross value added (GVA) by FY26. The government, too, is cognisant of the importance of AI adoption. Earlier this year, Prime Minister Narendra Modi said India should lead the AI revolution and called on the country's tech ecosystem to ensure this outcome. On its part, the government has taken a number of steps to further develop the AI ecosystem in the country. India's Policy Push For AI India's GenAI market, currently valued at $1.6 Bn, is expected to become a $17 Bn opportunity by 2030, which is expected to further drive the demand for semiconductor chips. As a result, the union government is actively wooing tech giants with incentives to set up electronics and semiconductor chip manufacturing units as well as data centres in the country. In March, the union cabinet approved the IndiaAI Mission with an outlay of INR 10,372 Cr spread over the next five years. As part of the initiative, the Centre aims to offer incentives and subsidies to private companies to scale up India's AI compute capacity. Under the IndiaAI Mission, the Centre plans to establish an AI compute infrastructure with over 10,000 GPUs through a public-private partnership. The initiative will also help in the development of AI foundational models with a capacity of more than 100 Bn parameters trained on datasets covering major Indian languages for priority sectors like healthcare, agriculture, and governance. Not stopping there, the government wants to integrate AI with India's digital public infrastructure. As part of this, the department of science and technology (DST) in October 2024 put into motion the BharatGen project, which is being touted as the world's first state-funded multimodal large language model (LLM) project. Not just this, the ministry of agriculture has also launched the 'AI For Agriculture' initiative to promote AI adoption in the agriculture sector through partnerships with industry and academia. Then, there is also the National Pest Surveillance System, which leverages AI and machine learning to detect crop issues in a timely manner. "The creation of a public digital AI infrastructure will unlock unprecedented opportunities for startups to innovate at scale, further positioning India as a global leader in AI-driven solutions," Anirudha A Damani, managing partner at Artha Venture Fund, told Inc42. While the AI-powered digital public goods infrastructure is being set up, priority will also be placed on developing an AI marketplace that offers AI-as-a-Service (AIaaS) and pre-trained models. Further, it will also pave the way for the establishment of AI research, development, and innovation centers across the country. Notably, the Centre's push for AI is already yielding results. Tech giants such as Microsoft, Amazon, Google, Apple, and NVIDIA are preparing to invest billions of dollars to boost the computing infrastructure in the country as they look to dominate the burgeoning GenAI market. As per reports, Amazon has agreed to invest $3.7 Bn for construction of data centres in India. This is expected to add 660 MW to its existing IT capacity. Further, Amazon has unveiled plans to invest $12.7 Bn in cloud infrastructure in India by the end of the decade. While GenAI use cases are numerous, there are also a number of issues facing the growth of the AI ecosystem in the country. A major headwind to the percolation of GenAI appears to be scarcity of talent. Talent Crunch A Key Hurdle Of the over 50 VCs surveyed by Inc42, 68% said that a lack of high-quality talent is the biggest roadblock to India's GenAI revolution. A recent Amazon Web Services survey also revealed that 79% of Indian businesses find it challenging to find AI talent. This talent gap persists despite India producing lakhs of computer science graduates every year. Startup founders that Inc42 spoke to previously said that outdated curriculum is the biggest reason behind India's ailing GenAI talent pool. However, the government aims to address these shortcomings through the IndiaAI Mission. As part of the initiative, it plans to introduce AI training in the existing engineering curriculum. Besides, the National AI Skilling Framework, introduced last year, aims to train over 1 Mn new data science professionals annually. Further, the government has also launched the IndiaAI FutureSkills initiative aimed at removing barriers to AI education by expanding undergraduate, masters and PhD courses and setting up data and AI labs in Tier II & III cities. Can Centre's AI Push Boost The Startup Ecosystem? While the Centre has pulled out all stops to build the necessary infrastructure to pave the way for the AI boom, it remains to be seen how much of the policy push percolates to the bottom of the funnel. Nevertheless, industry stakeholders opine that the Centre's schemes and sops will help create a vibrant GenAI ecosystem in the country. Government initiatives like IndiaAI Mission and Future Skills will position India as a global AI leader and an attractive destination for global investors, Anuj Srivastava, cofounder of fintech-focussed GenAI startup OnFinance, told Inc42. For startups, such policies will open up new opportunities, through public-private collaborations, access to government funding and lead to better innovation, accelerating growth in sectors such as BFSI, healthcare and agriculture, Srivastava added. While AI adoption is on the rise in India, it still hasn't matured as compared to other major economies such as the US and China due to a talent scarcity. Moreover, countries like the US have a far more advanced technology infrastructure and robust funding for startups, according to Pranav Pai, founding partner at 3one4 Capital. Pai believes that if the government prioritises budgetary allocations to strengthen India's indigenous AI capabilities, the country's academia and startups would rise to the challenge and deliver transformative outcomes. The track record of initiatives like Aadhaar, UPI and FASTag underscores the potential for such collaborative programmes to yield fruitful results, Pai noted. "If the Centre can effectively integrate research and commercialisation with the overarching goal of economic advancement, this approach could extend into key sectors such as energy, artificial intelligence, frontier technologies, defense, mobility, advanced materials, synthetic biology, and quantum computing," Pai said. Echoing similar sentiments, Artha Venture's Damani said that expanding grant programmes for AI-specific research and development and incentivising AI adoption in key industries can further accelerate growth. "Moreover, targeted policies that reward startups solving India-specific challenges in areas like logistics, climate change, and public health would create a fertile ground for sustainable AI-driven innovation," he said. Industry Calls For Sectoral Approach To Regulate AI Amid all the hullabaloo around AI, there continues to be one sticking point that has remained a thorn in the flesh of India AI startups - regulations. Currently, AI is governed by existing provisions on data privacy, cybersecurity and copyright laws, and there is no regulatory framework to govern AI in the country. This leaves the burgeoning AI ecosystem in the country with a big question mark that it has no answers to. Amid the regulatory uncertainty, startup founders and investors are seeking a sector-specific approach for AI regulation instead of a one-size-fits-all policy. "Existing laws on data privacy and cybersecurity are useful but don't address the unique challenges posed by AI, such as the risk of bias, lack of transparency in decision-making, and accountability for errors," OnFinance's Srivastava said. In order to address these gaps, the government needs to adopt a sectoral approach. For example, Srivastava said that the major regulatory focus for AI in the fintech sector should be on ensuring that existing models used for credit scoring and fraud detection are free from biases, which often puts low-income individuals at a disadvantage. "A one-size-fits-all regulatory approach would be inefficient for AI, as its applications across different sectors come with varying risks, requirements, and opportunities," he added. Artha Venture's Damani also said that a phased roll out of regulations tailored to specific AI applications in healthcare, fintech and other sectors would be more effective than a universal law. He added that stringent regulatory frameworks could stifle growth of early-stage startups.

Why Are Large-Scale Indian Enterprises Lagging Globally In AI Adoption?

While India's large-scale enterprises have been slower to adopt GenAI compared to global peers, the tide is gradually shifting At a time when AI adoption is on the rise around the world, India's large-scale enterprises seem to be trailing behind their global peers. As per Inc42's The Rise Of India's GenAI Brigade, Report 2024, 75% of startup investors believe that most large-scale enterprises in India are struggling to convert their AI use cases from proof of concept to organisation-wide deployment. While this may appear contrary to the image of Indian IT giants said to be riding the GenAI wave, it is only true to some extent. Well, companies like TCS, Wipro, Infosys, and several large banks have indeed adopted AI and GenAI technologies for various internal and external purposes, but their adoption is still slower compared to others. The findings of Inc42's latest report also suggest that startups have been more proactive in terms of adopting AI technology, including GenAI, as compared to traditional players. In addition, only 30-40% of GenAI proof of concepts by global capability centres (GCCs) and 15-20% by large domestic companies in India are progressing to production. In contrast, 66% of India's leading unicorns have started integrating GenAI into their offerings. In fact, GenAI adoption has truly become a new normal for Indian startups. As part of a survey done by Inc42 that asked more than 50 VC investors about the tech readiness of their non-GenAI portfolio startups in GenAI adoption, 43% said that AI/GenAI is now a key part of their product and service roadmap. Meanwhile, 27% of these VCs also said that their non-GenAI portfolio startups are already implementing the technology. Why Are Indian Enterprises Slow In AI Adoption? It is a known fact that India has been a little late to jump on the GenAI bandwagon -- whether it is about companies building foundational models and applications or implementing the tech. Large enterprises, with their larger teams, established processes, and stricter compliance requirements, naturally face more challenges when adopting new technologies, and this isn't unique to GenAI. Commenting on the matter, Vipul Patel, partner (seed investing) at IIMA Ventures, said that Indian enterprises have historically lagged behind their global counterparts in the adoption of technology for workflows during digitisation, cloud migration, and SAAS usage as well. "Even in the case of GenAI, since the adoption largely depends on the cloud migration and digital transformation of enterprises, Indian enterprises continue to witness a certain lag," Patel added. Sahil Chopra, VP of growth and marketing at Inflection Point Ventures, too, noted that enterprises have inherent challenges such as legacy systems, talent shortages, and infrastructure constraints, which have hampered adoption. However, Chopra believes these challenges are now being addressed via collaborative efforts in upskilling, modernisation, and strategic investments. "Indian firms are progressively preparing to adopt the tech, demonstrating their distinctive ability to innovate while facing hurdles with resilience... The rising recognition of GenAI's revolutionary potential is causing a shift in thinking, aided by government measures aimed at promoting AI use and a healthy startup sector," he added. GenAI adoption in enterprises is gaining momentum -- slowly but surely. TCS, which uses and develops AI and GenAI-based applications for its clients, has recently launched several platforms to serve various sectors. One of its latest innovations is TCS AI WisdomNext, a GenAI aggregator platform designed to help organisations quickly adopt next-gen technologies at lower costs, all while staying compliant with regulatory guidelines. Reliance is also developing a suite of infrastructure-level AI tools under the brand name JioBrain to allow industry and consumer adoption of the technology. But Is The Trend Shifting? GenAI's largest enterprise use case currently lies in conversational bots, particularly in sectors like BFSI, ecommerce, and telecom, where there is significant demand for customer support. For instance, Ganesh Gopalan, the cofounder and CEO of Gnani.ai, which is focussed on transforming the customer experience provided by enterprises, claims that its AI solutions help more than 150 enterprises achieve a revenue impact exceeding $6 Bn. "I have observed that Indian enterprises aren't just keeping pace with global enterprises in GenAI adoption but exceeding them in many ways. They seem to be more readily embracing GenAI solutions, likely driven by a keen understanding of the cost efficiencies and productivity gains these technologies offer," Gopalan said. According to the founder, this accelerated adoption in the Indian enterprise ecosystem in recent days is due to a modernised technological and infrastructure landscape. "Many global enterprises are grappling with legacy systems, while Indian enterprises have undergone a significant digital transformation in recent years," he added. AI-powered equity investment platform InvestorAI, which works with top domestic broking firms, including HDFC Securities, Geojit, JM Financial Services, and Axis Securities, believes that India is better placed than most in terms of businesses adopting GenAI. So, Will Enterprise Use Cases Rise Soon? The answer would be unequivocally affirmative. While India's large-scale enterprises have been slower to adopt GenAI compared to global peers, the tide is gradually shifting. Notably, any tech business falling behind in its adoption might also lose relevance in this swiftly changing world. Moving on, with advancements in digital transformation, the rise of AI-driven solutions, and increasing recognition of GenAI's potential, Indian enterprises have no option but to catch up to their global peers. Industry experts believe that with regulations around GenAI improving, ethical AI taking centre stage, and the launch of varied applications to solve niche industry-specific or department-specific problems, Indian enterprises will only accelerate the deployment of technology. Perhaps this promising opportunity is also the reason why 70% of GenAI startups are focussed on building solutions for enterprises today instead of consumer applications, as suggested by Inc42's report. As of now, the future looks promising, with significant opportunities for innovation and growth in the GenAI space, especially as the demand for personalised, enterprise-ready applications continues to rise.

Amid GenAI Mania, Investors Make A Beeline For Fintech-Focussed Vertical AI Solutions

"In the last 40 years, nothing has been this big. It's bigger than PC, it's bigger than mobile, and it's gonna be bigger than the internet, by far". This is how Jensen Huang, founder and CEO of NVIDIA, described generative artificial intelligence (GenAI) in November last year. These projections are indeed proving to be true for the US-based chipmaker. NVIDIA more than doubled its revenue year-on-year (YoY) to $60.9 Bn in FY24, thanks to the AI boom. Just like the globe, India too has been gripped by the AI fever. The buzzword seems to have opened new avenues of innovation and spawned a wave of new-age tech companies, catering to both B2B as well as consumer use cases from healthtech to SaaS. However, it is the banking, financial services and insurance (BFSI) sector that appears to have overwhelmingly embraced the emerging technology. As Inc42 reported earlier, large players like HDFC Bank, IDFC First Bank and startups like Policybazaar, Plum, and Fibe are leveraging AI to address multiple bottlenecks. This has spawned a number of vertical GenAI startups that are riding the AI wave to give the change-resistant sector a tech makeover. Treading judiciously, these fintech-focussed GenAI startups are steering clear of building cash-guzzling Indic large language models (LLMs) and are instead solving India-specific vertical challenges. And investors are rewarding this approach of startups for skipping general-purpose GenAI solutions. According to Inc42's 'The Rise of India's GenAI Brigade' report, the fintech sector is investors' top choice for vertical AI solutions. As many as 57% of the 50 venture capital investors surveyed showed the highest confidence for fintech-focussed vertical GenAI solutions. Speaking to Inc42, fintech-focussed GenAI startup OnFinance's cofounder Anuj Srivastava said that many financial institutions are still reliant heavily on outdated systems and manual intervention. This, he said, offers a white space for GenAI startups to enable financial enterprises to automate tasks and streamline operations. OnFinance is one of the over 200 GenAI startups that are transforming the fintech sector by automating key operational tasks like compliance, allowing financial institutions to scale their operations while reducing employee costs. While many startups in the space are offering full-stack customer communication solutions, others are helping businesses automate credit profiling and compliance for SME lending. Anirudh A Damani, managing partner at Artha Venture Fund, said that GenAI-led innovations can prove crucial for fintech companies looking to boost customer engagement and reduce operating costs. "GenAI is upgrading fintech by addressing inefficiencies across the credit cycle, from improving risk assessments to streamlining debt recovery. Beyond these applications, there is immense potential in fintech intersecting with niche areas to create innovative funding models," Damani added. Vertical GenAI startups are also revolutionising the way insurers approach fraud detection. A case in point is IDfy, which provides AI-based solutions for authentication, fraud detection and risk identification for both domestic and global markets. The startup claims that it verifies over 2 Mn individual profiles per day and counts HDFC Bank, Axis Bank, Paytm and PhonePe among its clients. Addressing The Fintech Pain Points India is the third largest fintech economy in the world. The homegrown fintech market is expected to become a $2.1 Tn opportunity by 2030. Over the last 10 years, fintech startups witnessed a staggering 500% growth and attracted investments worth over $31 Bn, Prime Minister Narendra Modi said earlier this year. However, India's fintech sector continues to be ailed by persistent pain points today, particularly those rooted in labour-intensiveness of manual tasks, high operational costs and the complexity of analysing huge amounts of financial data, as per OnFinance's Srivastava. "Creditworthiness assessment, for example, still relies heavily on traditional models that don't always capture the full range of risk factors, leading to inefficiencies in lending and underwriting. Similarly, compliance processes often remain manual, time-consuming, and prone to human error, exposing firms to regulatory risks," he said. Besides, the fintech sector also faces challenges such as systemic risks from overleveraged NBFCs and inefficiencies in credit assessments. By streamlining or automating these tasks, vertical AI can deliver tangible business value, allowing the fintech industry to unlock new efficiencies. Artha Ventures' Damani believes that vertical AI solutions have immense potential to alleviate these concerns. "AI tools that enhance creditworthiness assessments, optimise customer segmentation, and streamline compliance processes can improve profitability and reduce systemic inefficiencies," he said. The Road Ahead At the onset of this year, Inc42 predicted that 2024 will be a breakout year for vertical AI with growing enterprise adoption opening up opportunities for a new generation of entrepreneurs. And this is what appears to be happening on the ground. Unlike the horizontal approach taken by global tech giants like OpenAI and Anthropic, Indian fintech-focussed GenAI startups are solving industry-specific challenges. While vertical GenAI startups have been successful in alleviating some of the concerns faced by the fintech sector, gaps remain. The Indian fintech ecosystem still faces significant hurdles such as data privacy concerns, integration with legacy systems, and the need for more granular local solutions, according to Srivastava. "Looking to the future, vertical AI solutions will likely become even more integrated into the core functions of fintech companies. These solutions will not only automate existing tasks but also provide predictive capabilities, enabling institutions to proactively address issues like fraud, regulatory changes, and emerging financial risks before they escalate," Srivastava said. On top of that, there have also been instances of AI hallucinations, which have adversely impacted the reputation of this nascent technology and players deploying it. While Indian GenAI startups have a long road ahead of themselves to create trust among enterprises, the growing adoption of the emerging technology and its many use cases could pave the way for gradual ascent of the ecosystem in the future. For now, Indian GenAI startups will have to focus on high quality products that resonate with their clients and have applicability globally.

Why Are Investors Betting on AI Skyscrapers, Not Foundational Models?

Inc42's survey also suggests a majority of VCs want Indian entrepreneurs to build products for model fine-tuning As the adoption of GenAI grows in the country, varied applications, draped as horizontal and vertical solutions, are increasingly finding their use cases. Whether conversational tools, speech-to-text/speech-to-speech bots, text summarisation, or video generation, Indian startups seem to be taking charge of paving the future of this ingenious tech in the country In 2024 alone, several new-age tech startups, including the likes of Highperformr.AI, Ayna, Gnani.ai, Clodura.AI and Vitra.ai, raised funding from investors such as Inflexor Ventures, Info Edge, Venture Highway, and Bharat Innovation Fund, just to name a few. However, worth noting is the fact that 91% of the money invested in Indian native GenAI startups to date has gone to companies that are building business or consumer applications around GenAI technology, according to Inc42's latest report -- The Rise Of India's GenAI Brigade. Consequently, foundational solutions like LLMs and cloud infrastructure are seemingly less of a priority for the VC ecosystem. Substantiating this, a recent survey by Inc42 of over 50 VCs has revealed that 62% of the VCs prioritised startups building tools and applications on existing LLMs. Meanwhile, merely 11% see scope for startups in developing domestic open-source foundation models (LLM) and 19% in closed-source foundation models. This could be because building foundational models is expensive due to the high cost of graphics processing units (GPUs) and other expenses related to managing the infrastructure. In fact, earlier this year, many investors highlighted the capex-heavy nature of building a foundational model, making vertical applications a more attractive investment choice. For instance, Speciale Invest and 100X.VC, two of the leading GenAI investors in the country, stated that their relatively small fund and ticket sizes prevent them from making large investments in startups developing foundational models, which require billions of dollars in funding. But Is Money The Only Concern? According to the VP of growth and marketing at Inflection Point Ventures, Sahil Chopra, along with the capital-intensive nature of foundational GenAI models, there are several other reasons why VCs choose investing in the application layer. "Applications frequently meet immediate market demands, resulting in quicker ROIs and fewer technical development hurdles. They improve operational efficiency by integrating with current corporate processes and scaling more readily," Chopra said. In addition, he said that the startups building GenAI applications have a greater emphasis on user experience and the possibility of strategic alliances, which also increases the appeal of application-layer investments. "It also enables entrepreneurs to demonstrate the viability of their GenAI solutions rapidly and more efficiently, he said. Murali Krishna Gunturu, principal at Inflexor Ventures, too, believes that foundational models face monetisation challenges. According to him, in the presence of platforms like ChatGPT, Gemini, and Claude bringing out a significant differentiating factor that users would opt for is challenging. Offering a slightly different perspective, Sonal Saldanha, VP at 3one4 Capital, pointed out that model development faces the challenge of rapid depreciation. She explained that the market is very competitive, and margins are extremely fine, so it is tough to assume one will make money back on the cost of R&D without sustained investment and long-term strategy. According to Saldanha, like the earlier waves of the Internet and mobile, "infrastructure investments" will be fewer than "application investments." "In this context, applications also have better margin profiles and can be more economical to develop, distribute and establish. Overall the surface area for applications is very large, and this is now turning into the surface area for services as well, which expands the addressable market meaningfully." What Lies Ahead? As of now, while GenAI infrastructure tools such as GPUs, LLMs, and model fine-tuning capabilities are crucial for practical AI applications, less than 5% of funded Indian startups are focussed on developing these foundational tools. Meanwhile, besides building the foundational model and working with the application layer, there are a few other niche categories that are also getting increased investor focus. Interestingly, Inc42's survey also suggests that 51% of the respondent VCs want Indian entrepreneurs to build products for model fine-tuning. On the other hand, 46% believe there is scope for products in Retrieval Augmented Generation (RAG). "We are seeing domestic companies do interesting R&D work through distillation and fine-tuning, building tooling for ops and observability, building proprietary models for specific applications," Saldanha said. He gave the example of one of its portfolio startups Smallest.ai, which has built a text-to-speech model that generates 10 seconds of audio in 100 milliseconds and uses only 1GB of RAM. In fact, the small language models (SLMs) are increasingly finding more traction in the market, given they work with smaller datasets and can perform and excel in specific tasks while also keeping the costs lower. Recently, Sarvam AI also doubled down on SLMs with the launch of its full-stack GenAI platform. Hemant Mohapatra, partner at Lightspeed, said the VC firm's rationale behind investing in Sarvam AI stems from its belief that AI in India must be uniquely tailored to the scale and diversity of its population. He added that Lightspeed remains open to evaluating other companies building foundational models from any region and investing in the best teams. Overall, the investors' sentiment is clear -- while foundational model startups will continue to receive funding, the pace is expected to remain slow. Existing players will remain relevant only if they can consistently innovate and outperform competitors in global markets. Further, niche areas like SLMs and model fine-tuning are emerging as promising opportunities, which suggests that innovation tailored to India's unique ecosystem will be key to shaping the next phase of GenAI's evolution in the country.