The recent India AI Conclave marked an important moment in the country’s technological trajectory. Public discussion around the summit was intense, enhanced by crowded social media spaces and competing narratives. While some avoidable events received disproportionate attention, a deeper analysis reveals that far more significant developments emerged beneath the surface. The major impact was of extraordinary enthusiasm – strong participation from the technology industry, energetic policy conversations and a strong signal of long-term commitment to AI-led transformation.
It is estimated that India could attract investments worth $200 billion in the coming years, which shows that the country’s AI ecosystem is entering a decisive growth phase. This scale of capital investment has the potential to reshape the technology landscape, from research and development to infrastructure, skills and deployment across sectors. At the same time, AI itself is also evolving. The transition toward agentic AI – systems capable of autonomous reasoning, decision making and adaptive task execution – signals a qualitative shift. As these systems become embedded in governance, commerce, and everyday life, their social implications will deepen. It appears that India is determined to move rapidly through the gradual stages of AI development. However, rapid progress must be accompanied by equally rigorous reflection.
One of the central topics at the conclave was sovereign AI. The question of technological self-reliance—control over data, compute infrastructure, underlying models, and governance structures—has become strategically important. In a geopolitically fragmented digital order, sovereignty in AI is being viewed as essential to national security and economic competitiveness. Yet sovereignty must be conceptualized carefully. It should not be limited to just data center expansion or a race for computational scale. The debate should also discuss whether India will emerge as a global innovator shaping AI norms, or primarily as a large consumer market and infrastructure hub, valuable for the land, power and water resources that enable global data storage and processing.
While technology experts and policymakers will set these strategic directions, it is essential that the public discussion move beyond competitiveness and address structural equity. Three interrelated dimensions require sustained attention: access, utilization and empowerment.
Access is fundamental. Who has meaningful access to AI infrastructure, high-quality data, capital investment, advanced research opportunities, and decision-making spaces? Without deliberate intervention, AI developments risk reinforcing existing hierarchies – between the global North and South, between metropolitan centers and rural areas, and critically, between men and women. Accessibility is not just about connectivity; This includes representation in technical education, leadership in AI enterprises, participation in regulatory bodies, and ownership of intellectual property.
A notable difference during the conclave was the marginal integration of a gender perspective into mainstream AI discussions. The discussion on gender and AI was largely limited to the few institutions already involved in these issues. The broader technology narrative remained predominantly gender-neutral in tone, which in practice often translated into gender-blindness. This kind of division is problematic. Gender is not a peripheral topic to be addressed in special sessions; It is central to how AI systems are designed, trained, deployed, and controlled. Without systematic gender analysis, bias may be structurally embedded.
Usage is the second dimension. AI deployment has transformative potential in public services—welfare delivery, predictive policing, health care diagnostics, agricultural advisory systems, credit scoring and financial inclusion. India’s digital public infrastructure provides a strong foundation for such integration. However, the consequences of algorithmic decision making in high-risk domains should be carefully evaluated.
When AI systems are embedded without transparency, explainability, and contextual sensitivity, they risk codifying historical inequalities. Women, informal sector workers, caste and religious minorities, persons with disabilities, and economically marginalized groups may experience large-scale exclusion. Data sets often under-represent marginalized communities; Algorithmic models can replicate discriminatory patterns present in historical records. Without independent audits, grievance redressal mechanisms and participatory oversight, technical efficiency may mask structural bias.
Therefore, the ethical imperative is to include accountability mechanisms within AI governance frameworks. This includes mandatory impact assessments, public disclosure standards, algorithm audits, and cross-sector collaboration involving civil society, academia, and affected communities. Technical sophistication must match institutional security measures.
Empowerment represents the third and most far-sighted dimension. Protection from harm, while necessary, is insufficient. The key question is whether AI governance frameworks enable excluded communities to actively shape the technological future. Are women and marginalized groups participating in AI design, research, entrepreneurship and policy making? Are investments being directed towards digital literacy, data rights awareness and leadership development programs that shift structural power?
Empowerment requires moving from representation to influence. There is a need to ensure that women are not only users or subjects of AI systems but also architects of innovation. This includes continued investments in STEM education for girls, mentorship pipelines, inclusive funding ecosystems, and institutional reforms that address gender bias in technology sectors. It also needs to recognize and address technology-facilitated gender-based violence, which has expanded in scale and complexity in digitally mediated environments. AI-powered moderation tools, predictive systems, and platform governance mechanisms should be designed with gender-sensitive safeguards.
The discussion on digital sovereignty connects directly to these concerns. Sovereignty should not become a shield for surveillance, exclusion or centralized control over citizens’ data. A human-centered approach to AI governance requires openness, democratic oversight, and adherence to rights-based principles. The public interest must remain at the center of national AI strategies. Transparency, accountability and proportionality should guide the deployment of AI systems, particularly in contexts that impact civil liberties.
Equity in AI is not a secondary or optional consideration. This is fundamental to democratic legitimacy and long-term stability. AI systems deployed on a large scale could increase inequality by machine speed. Conversely, if designed with inclusive principles, they can expand access to services, increase transparency in governance, and create new economic opportunities. The standard architecture established today will shape the distribution of electricity for decades to come.
Despite legitimate concerns, there is strong reason for optimism. India has a vibrant start-up ecosystem, a large pool of engineering talent, expanding digital infrastructure and a demographic dividend that positions it well for tech leadership. If capital investment is strategically combined with inclusive capacity-building, ethical standards, and participatory governance, AI can become a tool for social change rather than exclusion.
The future of AI in India will ultimately depend not just on computational capacity or market size, but also on moral foresight and institutional courage. Policy makers, industry leaders, researchers and civil society must engage in sustained, evidence-based dialogue. Women should participate as equal stakeholders in shaping regulatory frameworks and innovation pathways.
The integration of AI into human life is inevitable; Whether this deepens democratic values ​​or increases inequality is a matter of governance. With a deliberate commitment to equity, transparency and social justice, India can create a model of AI development that is technically ambitious but ethically based. Such a model would not only strengthen national resilience but also contribute meaningfully to the global debate on a responsible and inclusive AI future.
This article is written by Ranjana Kumari, Director, Center for Social Research, New Delhi.
