AI Start-ups In India

Prime Minister Shri Narendra Modi chaired a roundtable with Indian AI start-ups ahead of the India AI Impact Summit 2026. 

• The interaction highlights India’s strategic intent to position India as a global hub for ethical, inclusive and indigenous artificial intelligence innovation.
• A total of 12 Indian AI start-ups, qualified under the Foundation Model Pillar, participated in the roundtable ahead of the Summit scheduled next month in India.
  • The Foundation Model Pillar, a key component under the IndiaAI Mission aims to develop indigenous, large-scale AI models that can be adapted across many tasks such as language, healthcare, science, governance, and agriculture.

Key outcomes of the Roundtable Meet

• Observations by AI Start-Ups
  • Shift in Global AI Centre: Start-ups noted that the centre of gravity of AI innovation and deployment is beginning to shift towards India.
  • Rapid Sectoral Growth: The AI sector in India was highlighted as witnessing rapid growth with vast future potential.
• Prime Minister’s Vision for AI:
  • AI for Societal Transformation: The Prime Minister underlined the importance of artificial intelligence in transforming society and reiterated India’s role as a global technology leader through the upcoming Summit.
  • Start-Ups as Nation Builders: He emphasised that start-ups and AI entrepreneurs are the co-architects of India’s future, reflecting India’s capacity for innovation and large-scale implementation.
  • Made in India, Made for the World: The Prime Minister urged the development of a distinct Indian AI model that embodies the spirit of “Made in India, Made for the World.”
  • Ethics and Trust: The Prime Minister stressed that Indian AI models must be ethical, unbiased, transparent, and grounded in data privacy principles.
  • Inclusive and Affordable AI: He highlighted India’s potential to lead globally in affordable AI, inclusive AI, and frugal innovation.
  • Promotion of Local Content: Indian AI models should promote local and indigenous content, including regional languages, to reflect India’s cultural and linguistic diversity.
  • Assurance of Support: The Prime Minister assured full government support for the success and global scalability of Indian AI models.

Why need for AI Start-ups in India/ Role of Indian AI Start-Ups

• Technological Self-Reliance:
  • AI start-ups are essential for achieving technological sovereignty by reducing India’s dependence on foreign AI platforms and proprietary models.
  • Indigenous AI solutions help safeguard national data, strategic autonomy and digital security.
• Leveraging India’s Demographic and Data Advantage
  • India’s large population generates vast and diverse datasets that can be effectively harnessed by domestic AI start-ups.
  • Local start-ups are better positioned to develop context-specific AI solutions suited to India’s socio-economic realities.
• Promotion of Indian Languages and Culture
  • Indian AI start-ups play a crucial role in developing multilingual and Indian language AI models.
  • Language-inclusive AI ensures digital inclusion and prevents exclusion of non-English-speaking populations.
• Inclusive and Affordable Innovation
  • AI start-ups promote frugal and affordable innovation, making advanced technology accessible to a wider population.
  • Affordable AI solutions are vital for sectors such as healthcare, education and agriculture, particularly in rural areas.
• Economic Growth and Employment Generation:
  • AI start-ups contribute to high-value job creation in areas such as data science, research and engineering.
  • They enhance India’s competitiveness in the global digital economy and support the goal of becoming a $5 trillion economy.
• Sectoral Transformation:
• • In healthcare, AI start-ups improve diagnostics, medical research and disease prediction.
  • In industry and manufacturing, AI enhances efficiency through automation, predictive maintenance and simulations.
  • In governance, AI can support data-driven decision-making and service delivery.

Initiatives taken by India

• IndiaAI Mission:
  • The IndiaAI Mission is a national-level programme approved by the Union Cabinet in March 2024 to strengthen India’s AI innovation ecosystem.
  • It seeks to position India as a global leader in Artificial Intelligence (AI) by promoting research, innovation, computing infrastructure, and skilling.
  • Implemented by: Digital India Corporation (DIC) under Ministry of Electronics and Information Technology (MeitY).
  • Foundation Model Development: 
    • Under the Mission, support is being provided for the development of foundation models, including large language models and multimodal AI systems.
    • The initiative aims to reduce dependence on foreign AI platforms and promote technological self-reliance.
• National Strategy for Artificial Intelligence:
  • NITI Aayog released the National Strategy for Artificial Intelligence, outlining the vision of “AI for All”.
  • The core philosophy is to leverage AI to solve pressing societal challenges and improve the quality of life for its citizens. 
  • The strategy prioritises AI applications in sectors such as healthcare, agriculture, education, smart cities and mobility.
• Digital Public Infrastructure as AI Enabler
  • Data-Driven Governance: India’s Digital Public Infrastructure, including Aadhaar, UPI and DigiLocker, provides a robust foundation for AI-based innovation.
  • DPI enables large-scale data availability while promoting efficiency and inclusion.
• Kerala: Kerala AI Future Con:
  • Ahead of the India AI Impact Summit 2026, the Government of Kerala is organizing a one-day summit titled ‘Kerala AI Future Con’ in January.
  • This summit is organized under the India AI Mission, MeitY, and in collaboration with various local partners such as Kerala IT, Startup Mission, and Digital University.
• Tamil Nadu: Deep-Tech Startup Policy
  • The Tamil Nadu Government has unveiled a Deep-Technology Startup Policy with a Rs 100 crore outlay during the Umagine TN Technology Summit to support innovation and translate research into commercial projects.
  • The policy aims to foster deep-tech innovation, including AI, and supports Tamil Nadu’s shift from an IT services hub to a technology and innovation hub.

Global Initiatives

• Global Partnership on Artificial Intelligence (GPAI):
  • GPAI is a multi-stakeholder initiative involving governments, industry, academia and civil society to promote responsible AI innovation.
  • It supports AI start-ups through research collaboration, policy guidance and best practices.
  • India is a founding member, enhancing its role in shaping global AI governance.
• United Nations Initiatives on AI:
  • The United Nations promotes AI innovation through initiatives aligned with the Sustainable Development Goals (SDGs).
  • UN agencies encourage AI start-ups to develop solutions for healthcare, education, climate change and poverty reduction.
• European Union AI Strategy:
  • The European Union supports AI start-ups through research funding, AI sandboxes and innovation hubs.
  • The EU AI framework aims to balance innovation, consumer protection and ethical safeguards.
• Tech Giant-Led Accelerators:
  • Google for Startups Accelerator: AI-First: Global cohort based accelerator with expert guidance to tackle technical challenges.
  • AWS Generative AI Accelerator: Supports generative AI startups with up to significant AWS credits and intensive 8-week programme.
  • Google.org Accelerator: Generative AI: Open call for impact-focused AI solutions with funding (e.g., share of $30M), pro bono assistance, training, and cloud credits.
  • Ignition AI Accelerator: NVIDIA & Tribe collaboration focusing on scaling AI startups, particularly in Asia-Pacific.
  • Intel® Liftoff for startups: The program offers a mix of technical mentorship, cloud infrastructure resources, and access to Intel’s market ecosystem

Challenges for AI Start-Ups

• Data-Related Challenges:
  • Access to Qquality Ddata: AI models require large, diverse, and labeled datasets, which are costly and often proprietary.
    • Competition Commission of India study notes that 68% of AI startups cite access to large, high‑quality datasets as a major barrier
  • Privacy & Ccompliance: Adherence to data protection laws (GDPR, DPDP Act, etc.) increases compliance burden.
• High Cost of Technology & Infrastructure:
  • AI development requires high computational power, GPUs, cloud services, and continuous model training.
  • Dependence on Big Tech platforms may reduce autonomy.
  • Limited access to affordable compute infrastructure increases operational costs and restricts innovation.
    • Rising costs of AI chips and cloud credits strain early-stage startups.
• Talent Shortage:
  • AI start-ups face intense competition for skilled professionals such as data scientists and AI researchers.
    • By 2027, AI-related job openings in India could surpass 2.3 million, while the talent pool is expected to grow to around 1.2 million.
  • The migration of top talent to global technology firms leads to brain drain and raises hiring costs.
• Regulatory and Compliance Uncertainty
  • Evolving regulations on data protection, AI ethics and cross-border data flows create uncertainty for start-ups.
  • Compliance costs can disproportionately burden small and early-stage AI enterprises.
• Ethical and Trust Deficit:
  • Data bias & representativeness Concerns related to algorithmic bias, lack of transparency and misuse of AI reduce public trust.
  • Start-ups must invest additional resources to ensure ethical and explainable AI systems.
• Market Access and Scalability Issues:
  • Adoption Barriers: AI start-ups often face difficulties in scaling solutions beyond pilot projects.
  • Resistance to adoption by traditional industries and governments slows market expansion.
• Global Competition and Market Dominance:
  • Unequal Playing Field: AI start-ups compete with global technology giants that possess superior data, capital and market reach.
    • For Instance, AI-Tech giants like OpenAI, Anthropic, and Perplexity are deepening their presence in India.
  • Dominance of large firms can stifle innovation and reduce opportunities for smaller players.
• Cybersecurity and Data Privacy Risks:
  • Security Vulnerabilities: AI systems are vulnerable to cyber-attacks, data breaches and model manipulation.
  • Inadequate cybersecurity measures can undermine user trust and regulatory compliance.
• Funding Constraints:
  • Securing funding is a persistent challenge for AI start-ups, particularly amid economic uncertainty and rising interest rates. 
  • Venture capitalists are becoming more cautious, preferring businesses with proven models and clear profitability paths.

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Way Forward

• Strengthening Policy and Regulatory Framework:
  • Predictable Governance: India must develop a clear, stable and adaptive regulatory framework that balances innovation with accountability.
  • Regulatory sandboxes can allow AI start-ups to test innovations while ensuring compliance with ethical and legal norms.
• Expanding Access to Compute and Infrastructure:
  • Government-supported access to high-performance computing and cloud infrastructure can reduce entry barriers for AI start-ups.
  • Shared compute facilities under public-private partnerships can accelerate AI research and development.
• Improving Data Access and Quality:
  • Trusted Data Ecosystems: Creation of secure, anonymised and high-quality public datasets can support responsible AI innovation.
  • Data-sharing frameworks must ensure privacy, consent and representativeness.
• Building Skilled Human Capital:
  • Investment in AI education, research institutions and skilling programmes is essential to address talent shortages.
  • Collaboration between academia, industry and start-ups can strengthen applied AI research.
• Ensuring Ethical and Responsible AI:
  • AI start-ups should embed ethical principles, transparency and bias mitigation at the design stage.
  • Mandatory bias audits and explainable AI systems can enhance public trust.
• Enhancing Access to Finance:
  • Long-Term Risk Capital through government-backed funds and venture financing is necessary for research-intensive AI start-ups.
  • Incentives for deep-tech investment can reduce financial risks for investors.
  • Promoting Indigenous and Inclusive AI:
  • AI start-ups should focus on Indian languages, regional needs and local contexts.
• Inclusive AI can bridge digital divides and expand technology benefits to underserved populations.
• Others: 
  • Innovative Business Models: AI start-ups should explore cost-efficient solutions such as federated learning, open-source tools, and decentralised AI architectures to reduce operational costs, especially in the early stages.
  • Policy Advocacy: Engaging with policymakers to shape regulations that promote responsible AI development while ensuring that innovation is not stifled is essential for AI start-ups.
  • Investing in Talent Development: 
    • Developing in-house training programs and partnering with educational institutions will help bridge the talent gap and ensure a steady supply of skilled professionals for the AI sector.
    • Fostering entrepreneurial talent through mentorship and expert networks will further support start-ups’ growth.

• Role of States:
  • State governments can promote AI start-ups through state innovation policies, AI hubs and sector-specific pilots.
• Cooperative federalism can enable AI solutions tailored to local governance challenges such as health, agriculture and urban planning.

Conclusion

The sustainable growth of AI start-ups depends on a holistic ecosystem that integrates innovation, ethics and inclusivity. By strengthening institutional capacity, promoting responsible AI and leveraging its demographic and digital strengths, India can enable AI start-ups to become key drivers of technological leadership, good governance and inclusive national development.

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