PM Modi Advocates for AI Democratisation in India

Q: What does a “human-centric and democratised AI” framework mean in the Indian context?

Human-centric and democratised AI refers to an approach where artificial intelligence is designed, deployed, and governed in a manner that prioritises human welfare, equity, and inclusion over mere technological advancement. In the Indian context, this means ensuring that AI benefits are diffused across regions, genders, socio-economic groups, and linguistic communities rather than being concentrated among urban elites or early adopters.The Prime Minister’s articulation of AI resting on three pillars—sovereignty, inclusivity, and innovation—highlights this approach. Sovereignty implies domestic capability in data, compute infrastructure, and semiconductor manufacturing. Inclusivity stresses multilingual AI systems, diverse datasets, and applications in agriculture, health, and education. Innovation ensures that India moves beyond consumption to creation, aspiring to become a top AI superpower.For example, the use of AI by Amul to provide real-time cattle health guidance in Gujarati to 3.6 million women dairy farmers demonstrates how AI can dissolve divides instead of deepening them. Thus, democratised AI is not about universal access alone, but about meaningful empowerment aligned with India’s developmental priorities.

Q: Why is AI governance and global cooperation crucial at this civilisational inflection point?

AI represents a civilisational inflection point because it can significantly enhance productivity, automate complex tasks, and reshape economic structures. However, without proper governance, it can also amplify misinformation, bias, surveillance risks, and socio-economic inequalities. Hence, governance frameworks are essential to ensure that innovation does not undermine social foundations.The risks identified include algorithmic bias related to gender, language, and socio-economic status, as well as threats such as deepfakes, cybercrime, and misuse by terrorist networks. Given AI’s transnational nature, unilateral regulation is insufficient. Global cooperation is required to establish shared norms such as human oversight, safety-by-design, transparency, and strict prohibitions on malicious use.India’s proposal for a global compact on AI reflects its aspiration to shape norms rather than passively adopt them. By advocating balanced safeguards while promoting innovation, India seeks to ensure that AI accelerates development globally, particularly for the Global South, instead of reinforcing digital colonialism.

Q: How can the convergence of Digital Public Infrastructure (DPI) and AI drive inclusive development in India?

The convergence of Digital Public Infrastructure (DPI) and AI represents the next frontier of inclusive growth. DPI platforms such as Aadhaar, UPI, and DigiLocker have already demonstrated how scalable digital systems can provide universal access to services. When integrated with AI, these platforms can enable predictive governance, targeted welfare delivery, and improved public service outcomes.For instance, AI layered over health databases can assist in disease surveillance and preventive care, while AI-enabled agricultural advisories can use weather and soil data to provide customised guidance to farmers. Similarly, AI in education platforms can personalise learning content in regional languages, bridging urban-rural divides.India’s DPI experience offers a replicable model for the Global South. By combining open digital architecture with AI-driven insights, India can create scalable, cost-effective solutions that empower citizens while maintaining strong safeguards. This convergence ensures that technology becomes a public good rather than a private monopoly.

Q: Provide examples of how AI is being used to bridge socio-economic divides in India.

One prominent example is the use of AI by Amul, which provides real-time cattle health and productivity guidance in Gujarati to 3.6 million women dairy farmers. This demonstrates how AI can empower grassroots producers, enhance incomes, and promote gender inclusion.In healthcare, AI-driven diagnostics are being deployed for early detection of diseases in remote areas through telemedicine platforms. In education, AI-powered adaptive learning systems provide personalised content in regional languages, ensuring that students outside metropolitan centres receive quality learning support.These applications show that AI can dissolve divides when aligned with local contexts. By prioritising multilingual access, rural connectivity, and sector-specific innovation, India is attempting to transform AI into a tool of social equity rather than exclusion.

Q: As a policymaker, how would you design a framework to ensure safe, inclusive, and innovative AI deployment in India?

A comprehensive framework would rest on four pillars: innovation, safeguards, inclusion, and capacity-building. First, encourage R&D through public-private partnerships, support for startups, and investments in domestic semiconductor and cloud infrastructure to ensure sovereignty.Second, institutionalise safeguards through mechanisms such as the IndiaAI Safety Institute, mandatory watermarking of AI-generated content, and strict penalties for misuse including deepfakes and cybercrime. Ethical principles like human oversight, transparency, and fairness must be embedded in system design.Third, promote inclusion by developing diverse datasets representing India’s linguistic and cultural plurality. Expand AI access to agriculture, healthcare, and education, especially in rural areas. Finally, strengthen skilling ecosystems through Centres of Excellence and curriculum reforms. Such a balanced approach would align with the principle of Sarvajan Hitay, Sarvajan Sukhaye, ensuring AI serves as an engine of equitable national development.

Emphasizing inclusivity and sovereignty, PM Modi aims to position India among the top three AI superpowers globally.

Surya

•February 18, 2026•6 mins read

AI for All: Human-Centric Tech Vision

Not Started

1. Human-Centric and Democratic Vision of AI

Artificial Intelligence is increasingly viewed as a transformative, general-purpose technology with economy-wide implications. At the AI Impact Summit in New Delhi, the Prime Minister emphasised that AI must be “human-centric” and its benefits should not be confined to early adopters or urban elites.

India’s AI strategy is anchored in three pillars: sovereignty, inclusivity, and innovation. The stated objective is for India to emerge among the top three global AI superpowers, not merely as a consumer but as a creator of AI technologies. This aligns AI development with the broader goal of Aatmanirbhar Bharat and technological self-reliance.

The Summit, positioned as the first such event hosted in the Global South, seeks to amplify under-represented voices and development priorities. The framing around “people, planet and progress” signals an effort to integrate ethical, developmental and sustainability concerns into AI discourse.

“Technology exists to serve humanity, not replace it.” — PM Narendra Modi

By embedding AI within a human-centric and development-oriented framework, India aims to prevent technological concentration and social exclusion. If AI adoption is left purely to market forces, it risks deepening inequality and eroding social trust.

2. AI, Employment and Skilling Imperatives

AI-driven automation has generated concerns about job displacement, especially among youth. The Prime Minister acknowledged these fears but argued that technological change historically alters the nature of work rather than eliminating it.

“Preparation is the best antidote to fear.” — PM Narendra Modi

The government’s response focuses on skilling and re-skilling to prepare the workforce for AI-driven transformations. AI is described as a “force-multiplier” capable of expanding productivity and creating new categories of employment, particularly in digital and technology sectors.

AI market projections: India’s IT sector could reach $400 billion by 2030.
Growth drivers: AI-enabled outsourcing and domain-specific automation.

Enterprise-grade AI adoption remains concentrated in specific sectors, implying that established IT firms continue to play a crucial intermediary role in complex business problem-solving.

Labour market adaptation depends on proactive skilling policies. Without workforce preparedness, AI-driven disruption could widen unemployment and income inequality, undermining inclusive growth.

3. AI for Inclusive Development: Bridging Divides

India’s approach emphasises AI as a tool to bridge urban–rural and socio-economic divides. The Prime Minister highlighted AI applications in agriculture, healthcare, education and heritage preservation.

A key example is the dairy cooperative Amul, which uses AI to provide 3.6 million women dairy farmers across thousands of villages with real-time guidance in Gujarati on cattle health and productivity. This illustrates AI’s potential to enhance grassroots productivity.

AI integration with Digital Public Infrastructure (DPI)—including Aadhaar—has been presented as a scalable development model for the Global South. The convergence of DPI and AI is described as the “next frontier” of inclusive growth.

“Technology must serve every citizen, regardless of geography, gender or income.” — PM Narendra Modi

When integrated with public digital infrastructure, AI can democratise access to services and opportunities. However, if benefits remain urban-centric, technological advancement may reinforce structural inequalities.

4. Bias, Diversity and Ethical Challenges

As AI adoption accelerates, risks also scale. AI systems can perpetuate biases related to gender, language and socio-economic background, particularly in a country as diverse as India.

India’s linguistic and cultural plurality creates unique vulnerabilities. Systems trained predominantly on English or urban datasets may underperform for rural users or speakers of regional languages, reinforcing digital exclusion.

The government has indicated efforts to address these challenges through:

Development of diverse and inclusive datasets
Emphasis on AI tools in regional languages
Growing academic research on fairness and bias

The Prime Minister called for global cooperation to address systemic AI bias and ensure equitable outcomes.

Unchecked algorithmic bias can institutionalise discrimination at scale. Inclusive dataset development and fairness research are essential to prevent AI from replicating structural inequalities.

5. Institutional Framework: IndiaAI Mission and Capacity Building

India has adopted a structured approach through the IndiaAI Mission. The strategy integrates infrastructure, skilling, research and safety mechanisms.

4 Centres of Excellence in healthcare, agriculture, education and sustainable cities
5 National Centres of Excellence for Skilling
Expanded support for data centres and cloud infrastructure in Union Budget 2026–27
Continued push for semiconductor manufacturing and electronics PLI

These measures strengthen both hardware (compute capacity, semiconductor ecosystem) and human capital foundations.

AI leadership requires simultaneous development of compute infrastructure, research capacity and skilled manpower. Fragmented policy efforts would limit competitiveness and technological sovereignty.

6. Safety, Regulation and Global Governance

The Prime Minister underscored the need for effective human oversight and safeguards, calling for a global compact based on:

Human oversight
Safety-by-design
Transparency
Prohibition of AI use for deepfakes, crime and terrorism

India has launched the IndiaAI Safety Institute (January 2025) to promote ethical and responsible AI deployment. Additionally, rules have been notified requiring:

Watermarking of AI-generated content
Removal of harmful synthetic media

The emerging risk framework addresses national security concerns and harms to vulnerable groups, including women, children and the elderly.

Balancing innovation with regulation is central to sustainable AI governance. Over-regulation may stifle innovation, while under-regulation risks societal harm and erosion of public trust.

7. Global South Leadership and Strategic Positioning

India positions itself as a representative voice for the Global South in AI governance. Hosting the AI Impact Summit reflects an effort to shape global norms around equitable access, inclusive datasets and developmental applications.

By linking AI to “Sarvajan Hitay, Sarvajan Sukhaye” (welfare for all), the government frames technology as a tool for collective progress rather than concentrated power.

This strategy enhances India’s soft power while integrating AI into broader geopolitical and economic objectives, including digital sovereignty and strategic autonomy.

Norm-setting in emerging technologies confers strategic influence. If India fails to engage proactively in global AI governance, regulatory frameworks may evolve without reflecting its developmental priorities.

Conclusion

India’s AI strategy seeks to combine innovation, inclusion and safeguards, positioning AI as a catalyst for economic growth and social equity. With projections of a $400 billion IT sector by 2030, expanding infrastructure and institutional frameworks, the country aspires to global leadership while emphasising ethical oversight.

The long-term success of this approach will depend on effective skilling, inclusive datasets, robust safety mechanisms and sustained international engagement, ensuring that AI advances both national development and global equity.

Quick Q&A

Everything you need to know

Human-centric and democratised AI refers to an approach where artificial intelligence is designed, deployed, and governed in a manner that prioritises human welfare, equity, and inclusion over mere technological advancement. In the Indian context, this means ensuring that AI benefits are diffused across regions, genders, socio-economic groups, and linguistic communities rather than being concentrated among urban elites or early adopters.

The Prime Minister’s articulation of AI resting on three pillars—sovereignty, inclusivity, and innovation—highlights this approach. Sovereignty implies domestic capability in data, compute infrastructure, and semiconductor manufacturing. Inclusivity stresses multilingual AI systems, diverse datasets, and applications in agriculture, health, and education. Innovation ensures that India moves beyond consumption to creation, aspiring to become a top AI superpower.

For example, the use of AI by Amul to provide real-time cattle health guidance in Gujarati to 3.6 million women dairy farmers demonstrates how AI can dissolve divides instead of deepening them. Thus, democratised AI is not about universal access alone, but about meaningful empowerment aligned with India’s developmental priorities.

AI represents a civilisational inflection point because it can significantly enhance productivity, automate complex tasks, and reshape economic structures. However, without proper governance, it can also amplify misinformation, bias, surveillance risks, and socio-economic inequalities. Hence, governance frameworks are essential to ensure that innovation does not undermine social foundations.

The risks identified include algorithmic bias related to gender, language, and socio-economic status, as well as threats such as deepfakes, cybercrime, and misuse by terrorist networks. Given AI’s transnational nature, unilateral regulation is insufficient. Global cooperation is required to establish shared norms such as human oversight, safety-by-design, transparency, and strict prohibitions on malicious use.

India’s proposal for a global compact on AI reflects its aspiration to shape norms rather than passively adopt them. By advocating balanced safeguards while promoting innovation, India seeks to ensure that AI accelerates development globally, particularly for the Global South, instead of reinforcing digital colonialism.

The convergence of Digital Public Infrastructure (DPI) and AI represents the next frontier of inclusive growth. DPI platforms such as Aadhaar, UPI, and DigiLocker have already demonstrated how scalable digital systems can provide universal access to services. When integrated with AI, these platforms can enable predictive governance, targeted welfare delivery, and improved public service outcomes.

For instance, AI layered over health databases can assist in disease surveillance and preventive care, while AI-enabled agricultural advisories can use weather and soil data to provide customised guidance to farmers. Similarly, AI in education platforms can personalise learning content in regional languages, bridging urban-rural divides.

India’s DPI experience offers a replicable model for the Global South. By combining open digital architecture with AI-driven insights, India can create scalable, cost-effective solutions that empower citizens while maintaining strong safeguards. This convergence ensures that technology becomes a public good rather than a private monopoly.

Concerns about AI-driven job disruptions arise from automation replacing routine and repetitive tasks across sectors such as manufacturing, IT services, and customer support. As AI systems become capable of performing cognitive tasks, fears of redundancy among youth and mid-career professionals have intensified.

However, historical evidence from previous technological revolutions suggests that while certain roles diminish, new categories of employment emerge. The key lies in proactive adaptation. The government’s strategy focuses on skilling and re-skilling through National Centres of Excellence for Skilling and sector-specific AI Centres of Excellence in healthcare, agriculture, education, and sustainable cities.

For example, while AI-based automation may reduce manual data-processing roles, it simultaneously increases demand for data scientists, AI trainers, cybersecurity experts, and domain specialists. Thus, the nature of work evolves rather than disappears. Addressing disruptions requires investments in human capital, continuous learning ecosystems, and industry-academia collaboration to ensure workforce resilience.

India’s ambition to become a top three AI superpower reflects a strategic shift from being a technology consumer to a creator. Strengths supporting this goal include a vast IT talent pool, expanding digital infrastructure, semiconductor manufacturing push, electronics PLI schemes, and initiatives like the India-AI Mission. Budgetary support for data centres and cloud infrastructure strengthens compute sovereignty.

However, challenges persist. Enterprise-grade AI adoption remains concentrated in select sectors, research funding is lower compared to global leaders, and there is dependence on imported high-end chips. Additionally, ethical risks, regulatory uncertainties, and uneven digital literacy could slow inclusive adoption.

Balancing ambition with safeguards is critical. If India successfully integrates innovation with inclusion—leveraging DPI, multilingual datasets, and targeted skilling—it can offer a distinct development-centric AI model. Failure to address capability gaps or ethical concerns, however, may widen inequalities and undermine public trust.

One prominent example is the use of AI by Amul, which provides real-time cattle health and productivity guidance in Gujarati to 3.6 million women dairy farmers. This demonstrates how AI can empower grassroots producers, enhance incomes, and promote gender inclusion.

In healthcare, AI-driven diagnostics are being deployed for early detection of diseases in remote areas through telemedicine platforms. In education, AI-powered adaptive learning systems provide personalised content in regional languages, ensuring that students outside metropolitan centres receive quality learning support.

These applications show that AI can dissolve divides when aligned with local contexts. By prioritising multilingual access, rural connectivity, and sector-specific innovation, India is attempting to transform AI into a tool of social equity rather than exclusion.

A comprehensive framework would rest on four pillars: innovation, safeguards, inclusion, and capacity-building. First, encourage R&D through public-private partnerships, support for startups, and investments in domestic semiconductor and cloud infrastructure to ensure sovereignty.

Second, institutionalise safeguards through mechanisms such as the IndiaAI Safety Institute, mandatory watermarking of AI-generated content, and strict penalties for misuse including deepfakes and cybercrime. Ethical principles like human oversight, transparency, and fairness must be embedded in system design.

Third, promote inclusion by developing diverse datasets representing India’s linguistic and cultural plurality. Expand AI access to agriculture, healthcare, and education, especially in rural areas. Finally, strengthen skilling ecosystems through Centres of Excellence and curriculum reforms. Such a balanced approach would align with the principle of Sarvajan Hitay, Sarvajan Sukhaye, ensuring AI serves as an engine of equitable national development.