India AI Impact Summit 2026

building-indias-ai-governance-architecture-from-frameworks-to-implementation

7 min read

TRACK:Safe and Trusted AI
SPEAKERS
  • Abhishek Singh, Meity, GoI
  • Abhishek Singh, MeitY, GOI
  • Dr. Vivek Raghavan, Sarvam AI
  • Keita Nishiyama, University of Tokyo
  • Mr. Amandeep Gill, United Nations
  • Mr. Amitabh Kant, Ex-GoI
  • Mr. Gaurav Aggarwal, Reliance Jio
  • Mr. Hugo Valadares, Government of Brazil
  • Prof. Ramesh Raskar, MIT Media Labs
SESSION OVERVIEW

This session will explore how India can build mission driven institutions to govern artificial intelligence at national scale. It will examine emerging approaches to AI governance and expert mechanisms, drawing lessons from successful public digital institutions. The discussion will focus on balancing innovation with accountability, clarifying institutional mandates, and enabling sustained collaboration across government, industry, startups, and research to translate AI strategy into effective, responsible execution.

VIDEO RECORDING

Building India’s AI Governance Architecture: From Frameworks to Implementation

Detailed Summary

The moderator opened by stressing three policy imperatives: affordability, multilingual accessibility, and social benefit. A “Digital Public Infrastructure for AI” (DPI‑AI) was presented as a way to make AI trustworthy, interoperable, scalable and usable by MSMEs, women and people below the poverty line.

To address the breadth of the issue, the session was split into two 30‑minute panels:

  • Panel 1 – Outward look: how other nations are structuring AI institutions and trust frameworks.
  • Panel 2 – Inward look: concrete steps India can take to translate those principles into a functioning architecture within the next 12‑18 months.

2. Panel 1 – International Perspectives

2.1 Japan (Keita Nishiyama)

Key Insights

InsightExplanation
Process‑based, feedback‑centric governanceJapanese AI policy emphasises agile feedback loops rather than rigid, rule‑by‑law approaches. AI strategy and law are designed to evolve with real‑world usage.
Education for the general publicBeyond technical curricula for engineers, Japan promotes “AI passports” (through the IPA) and certificates for non‑specialists. The University of Tokyo’s “GCI” program opens data‑science training to business professionals and high‑school students, encouraging AI literacy for all users.
AI‑manufacturing synergyAI is linked to the country’s manufacturing base, with concrete use‑cases in construction and automotive factories where complex, site‑specific problems are tackled with AI tools.
Gap – Structured DPIJapan lacks a national DPI for AI; it sees an opportunity to learn from India’s experience with DEPA and other DPI mechanisms.

Recommendations

  • Institutionalise a national feedback system that continuously integrates user data into policy revisions.
  • Replicate the “AI passport” model to certify everyday AI users.

2.2 United Nations (Amandeep Gill)

Key Insights

InsightExplanation
DPI principles = interoperability, openness, modularityThese create a sovereign public‑layer that private innovators can plug into, yielding both digital sovereignty and global cooperation.
Model repositories & APIs as public goodsProviding open model libraries, benchmark datasets, and standardized APIs lets SMEs focus on innovative applications rather than rebuilding foundations.
Global scientific assessmentsAn independent international AI panel can supply neutral, evidence‑based guidance for all countries, reducing duplication of effort.
Capacity‑building & shared enablersExamples include joint climate‑data sets, resilient‑agriculture datasets, and compute‑sharing schemes that keep talent in the Global South.

Recommendations

  • Align India’s DPI‑AI with the emerging Global Digital Compact while preserving national control over data‑sovereign layers.
  • Leverage UN‑facilitated AI commons to accelerate adoption across low‑resource settings.

2.3 Brazil (Hugo Valadares)

Key Insights

InsightExplanation
Sovereignty + ecosystemBrazil couples a sovereign data/compute infrastructure with an open ecosystem that invites private start‑ups to use public super‑computing resources.
Public‑supercomputing & data assetsBrazil is building a national supercomputer and makes public datasets (justice, health, education) available for research and commercial use.
Inclusion‑focused AI policyThe Brazilian AI plan (launched by President Lula) embeds inclusion as a core principle, ensuring AI benefits are not limited to large firms.
Multi‑stakeholder collaborationPartnerships with other nations and regional bodies are already in place to share expertise and resources.

Recommendations

  • Replicate Brazil’s model of public‑supercomputing + MSME toolkits to enable smaller firms to access high‑performance AI resources.

2.4 United States / MIT (Prof. Ramesh Raskar)

Key Insights

InsightExplanation
From “factory” to “bazaar” AIThe early AI era was dominated by a few large providers (the “factory”). A shift is underway toward a decentralised “bazaar” where micro‑AI agents run on edge devices, owned by individuals.
Network‑centric infrastructureThe next competitive edge will be the network that connects these agents (identity, authentication, DNS‑like services), not just raw compute power.
Role of DPI‑AI as the networking layerDPI‑AI can provide the identity (Aadhaar, UPI, Digital Locker) and trust framework that enables seamless interaction among diverse AI agents.
Emphasis on health & educationAfter the initial hype, focus should move to sector‑specific AI for public good (e.g., health diagnostics, personalised learning).

Recommendations

  • Invest early in AI‑agent networking standards (identity, trust, discovery) to become the “Cisco” of the AI era.
  • Promote open‑source, low‑compute models (50‑70 B parameters) tailored to multilingual, local contexts.

3. Panel 2 – India‑Centric Implementation

3.1 Government Perspective (Abhishek Singh, MeitY)

  • Described India’s Digital Public Infrastructure (DPI) as already delivering “affordable, multilingual, open‑API” services (Aadhaar, UPI, DigiLocker).
  • Emphasised that government’s role is an enabler, providing the sovereign layer (identity, payments, data) while private sector builds applications.
  • Highlighted the India AI Mission (granting GPUs to research labs, supporting model development).
  • Stressed the need for continuous feedback loops—the same principle raised by Japan—to keep policies responsive.

3.2 Public‑Private & Civil‑Society Collaboration

  • Amitabh Kant (former minister) pointed to the whole‑of‑society model: government, private industry, and civil society (e.g., iSpirit, HighSpirit) co‑create solutions, as seen during COVID‑19 (Aarogya Setu, CoWIN).
  • Gaurav Aggarwal (Reliance Jio) argued that Jio’s deep‑pocketed “Jio‑GEO” platform can be a catalyst for large‑scale AI disruption, especially if it integrates with DPI layers.
  • Dr. Vivek Raghavan (though not recorded speaking) is noted as an AI‑industry leader; his organization Sarvam AI is part of the broader ecosystem receiving GPU allocations.

3.3 Talent, Education & Curriculum

  • Consensus that AI literacy must start early (class‑3 onward) and span all disciplines—humanities, economics, medicine, law.
  • Recommendation to upgrade university curricula to include model‑building, prompt‑engineering, and agent‑centric thinking.
  • Public‑sector should provide sandbox environments, toolkits for MSMEs, and open datasets (climate, agriculture) to lower entry barriers.

3.4 Future‑Facing Challenges Discussed

ChallengeDiscussion Points
Compute vs. SoftwareAmandeep Gill warned that relying on massive GPU farms could lock India into high‑cost infrastructure. The panel agreed that software optimisation, model pruning and low‑compute models are vital for affordability.
Sovereignty vs. Global CooperationThe UN representative reiterated that a modular DPI lets India retain sovereign control while still interoperating globally.
Inclusivity of AI BenefitsBrazil’s speaker highlighted the need for public procurement and sandboxes to help MSMEs gain AI capabilities. Indian speakers echoed this, citing AI‑commons and DPI‑enabled marketplaces.
Collaboration with Other NationsParticipants noted the G20 AI commons and a global DPI repository as mechanisms for cross‑border co‑development of models and datasets.

4. Audience Q & A Highlights

  1. Compute Power Concentration – Question on whether AI will become a commodity dominated by a few hardware‑rich firms.

    • Panel response: Emphasised decentralisation (micro‑agents), low‑parameter models, and the need for software innovation over raw compute.

  2. India’s “Late” Entry – Audience asked why India appears behind other nations.

    • Abhishek Singh answered that being a third mover can be advantageous, allowing India to leverage global lessons, optimise software, and focus on inclusive, low‑cost solutions.

  3. Governance Model – Public vs. Private – Query on the ideal balance of regulation and market‑driven innovation.

    • Amitabh Kant stressed a tripartite ecosystem (government‑private‑civil‑society) where government enables, private builds, and civil society validates.

  4. International Collaboration – Question on cooperating with other Asian or global partners.

    • Amandeep Gill highlighted the global AI commons and G20 DPI repository as pathways for collaborative model‑sharing and joint capacity‑building.

  5. Talent Pipeline for Start‑ups – How to ensure start‑ups have the expertise to build high‑end AI capacity.

    • Abhishek Singh proposed exposure programs, curriculum updates, and hands‑on labs where engineering graduates work directly with AI agents and models.

5. Closing Remarks

The moderator thanked the panel for the “global and international perspective” and announced the transition to the India‑inward segment. After a brief interaction with the audience, the session concluded at 4:30 pm.

Key Takeaways

  • Feedback‑Centric Governance – Japan’s agile, feedback‑loop model should be embedded in India’s AI policy to keep regulations adaptable.
  • DPI‑AI as Sovereign Public Layer – Interoperable, open‑source APIs and model repositories (as championed by the UN) enable both digital sovereignty and global cooperation.
  • Public‑Supercomputing + MSME Access – Brazil’s approach of a national supercomputer coupled with toolkits for start‑ups offers a template for scaling AI capacity while preserving inclusion.
  • Shift from “Factory” to “Bazaar” – The next AI wave will be decentralised micro‑agents; India must invest in the networking stack (identity, discovery, trust) rather than just raw compute.
  • Whole‑of‑Society Ecosystem – Effective AI deployment requires coordinated action among government (enabler), private sector (builder), and civil society (validator).
  • Early, Broad AI Literacy – Embedding AI concepts from primary school and across all academic streams is essential to build a skilled, inclusive talent pool.
  • Software‑First, Compute‑Light Strategy – Prioritising model optimisation, low‑parameter models and software innovation will keep AI affordable for MSMEs and the poor.
  • Global AI Commons & DPI Repository – Participation in G20‑led repositories will accelerate model sharing, reduce duplication, and foster cross‑border collaboration.
  • Regulatory Balance – Neither pure deregulation nor heavy-handed mandates work; a modular, modular‑policy framework that evolves via stakeholder feedback is optimal.
  • India’s Strategic Position – Being a “third mover” allows India to learn from Japan, Brazil, the UN, and MIT, applying those lessons to craft a uniquely Indian, inclusive, and multilingual AI governance architecture.

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