Executive Summary 

Artificial Intelligence (AI) holds transformative potential for the Indian economy, boosting productivity and efficiency across sectors. However, realising effective value from AI-driven transformations is determined by the moving parts of the country’s AI ecosystem. This report synthesises current trends across AI adoption, investment, and innovation to provide insights for informed policy action and business strategy.

Key Takeaways

1. AI initiatives have a scaling problem. 

Most AI initiatives tend to be ad-hoc and project-based, or else at the proof of concept (PoC) stage with difficulty in scaling beyond that level. This can be attributed to systemic bottlenecks such as issues in integrating AI with legacy IT systems, inconsistent data standards and inadequate data architectures, lack of requisite talent, and unclear governance or compliance frameworks. Seventy-five percent of 500 small, medium, and large enterprises surveyed by Nasscom have a PoC-only strategy and 60 percent have ad-hoc or project-based funding. Meanwhile, only 13 percent have a dedicated AI talent pool for execution; and 18 percent have enterprise-wide data standards. Further, 62 percent of organisations surveyed by the International Data Corporation (IDC) and Qlik report the need to improve data governance and privacy policies.

2. India’s growth story suffers from a perception issue. 

India’s rapid economic growth and large market size is perceived predominantly as a consumption story with investors continuing to direct their capital towards commercial, application-specific, and late-stage ventures with defined market opportunities. In 2024, early-stage funding declined by 37 percent from the previous year. Patient capital for deeptech ventures that involve commercialisation of research-driven innovation is picking up pace but is comparatively limited.

3. AI is triggering a change in the investment landscape. 

AI, with its promise of cost and process optimisation, is forcing investors to rethink their investment approach. Investors have explored integration of AI at scale—e.g., with India’s outsourcing sector through strategic buyouts. Indian IT firms are also funding startups in a bid to pursue intellectual property-led growth. Further, with a view of data as a strategic resource, technology firms are consolidating their data capabilities through mergers and acquisitions.

4. AI innovation should not miss opportunities at the bottom of the pyramid. 

The adoption of indigenous LLMs is not at par with their foreign counterparts. Despite a low overall adoption rate of 31 percent, India is the largest user of the ChatGPT mobile app globally (13.5 percent of all users) and the third largest user of the Deepseek mobile app (6.9 percent). On one level, this underscores the need to understand India’s mobile-first, app-based consumption pattern; on another, it highlights the need to develop downstream innovation opportunities to facilitate the development of applications for India’s critical social sectors and last-mile, underserved multilingual population.

5. Edge computing demand is fuelling the next stage of infrastructure development. 

India’s data centre industry has been growing at a CAGR of 24 percent since 2019, with its edge data centre market projected to grow at a CAGR of 19.5 percent between 2025-2033, indicating strong demand for processing data close to source. India’s edge data centre market is currently dominated by IT & Telecommunications as well as BFSI (Banking, Financial Services, and Insurance). With comparatively high AI adoption in the manufacturing and telecom, and the media and entertainment sectors, it offers opportunities to diversify to Tier 2 and 3 cities close to sectoral demand hubs to take advantage of low costs and land availability.

6. Talent development is required across the AI ecosystem as a whole. 

India currently has a 51-percent demand-supply gap when it comes to the niche skills required for core AI development. In addition to building its talent pool in core AI development, India needs to develop skills and human capital to address current ecosystem bottlenecks with talent in data engineering, cloud, and compute.

7. Social sectors are getting left behind. 

Sectors like manufacturing, telecoms, and media and entertainment have comparatively high AI adoption, with investors privileging the BFSI sector for generative-AI startups. Social sectors like health register one of the lowest AI adoption rates. This potentially highlights the capital-intensive nature of AI adoption and the need to explore frugal business models and enable access at India’s last mile.

8. Public-sector transformation can have a boosting effect for the AI economy. 

AI integration in the public sector needs a comprehensive approach to improve government efficiency and service delivery. There is learning currently available through existing use cases and pilot projects that can be used to inform strategic action. A key part of public sector transformation can be preferential procurement for domestic innovation, thereby providing a boosting effect across the AI economy as a whole.

9. Innovation and governance are complementary forces. 

Innovation and governance are complementary, and not opposing forces, with unclear governance direction a key bottleneck for AI adoption. Ninety-eight percent of mature adopters have formalised governance frameworks. In the absence of national norms, frameworks, or methodologies, organisations are either adopting company-specific approaches or deferring to regional or global norms.

10. India needs to define its competitive edge. 

India ranks seventh in the world in the number of AI startups and third globally in terms of its startup ecosystem. However, the gap between India and the two leading AI economies of the United States and China is significant and it indicates the need to identify leapfrogging strategies to be competitive globally and ensure positive dividends for its citizens.

Read the report here

• Artificial Intelligence
• India
• AI adoption
• AI competitiveness
• AI in social sectors
• AI innovation
• ai investment
• AI talent gap
• Artificial Intelligence India
• data governance
• edge computing India
• indigenous LLMs

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