India is emerging as a global AI leader, not just in IT services, due to targeted policy, digital public platforms, and a strong, capable workforce, with Ivana Bartoletti underscoring its pivotal role in shaping global AI norms.

With a population over 1.4 billion, India generates a significant share of the world’s data, houses the second-largest AI talent base, and has hundreds of millions of digitally connected citizens, underscoring its influence at international AI governance forums.

The global AI debate should acknowledge increasingly embedded AI technologies shaping various facets of society and economy.

S. Krishnan of MeitY urged timely strategic steps for the Global South to invest in AI to harness the coming shift.

The Global Business Summit 2026 signals a structural shift from AI experimentation to large-scale adoption and systemic embedding of AI.

Open societal transformation and strong institutional capacity enable scalable tech adoption in India, moving governance from risk categorization to lifecycle accountability.

Governance should enable sustainable growth and trust, shifting from mere compliance to strategic capabilities that can keep pace with rapid tech evolution.

Foundation models and AI-enabled systems will impact sectors like drug discovery and autonomous agents, while addressing misinformation and its uneven effects on women and vulnerable groups remains critical.

Risk management alone is insufficient for AI governance; readiness and institutional preparedness are essential as governance must adapt to fast-moving advances.

A four-part risk-management framework includes: setting a clear national AI direction, designing for trust with transparent data flows and human oversight, using inclusion as a performance metric to boost robustness, and democratizing AI by enabling broad participation in design, governance, and standard setting.

A four-part governance framework emphasizes national direction and prioritizing sector risks, trust through transparent data and accountability, inclusion to reduce bias via diverse data and reg inputs, and open participation to co-architect AI through open ecosystems and capacity-building.

The AI revolution will hinge on robust institutions around AI; governance must anticipate interdependencies in infrastructure and geopolitics, and progress will favor those with strong institutional foundations and lifecycle accountability.