From February 16–20, 2026, the Government of India will host the India–AI Impact Summit 2026 at Bharat Mandapam in New Delhi. Framed around “People, Planet, Progress” and positioned as a Global South–anchored convening, the Summit signals a timely ambition: responsible, inclusive and impact-driven AI at population scale.

But India’s real opportunity in 2026 is not merely to showcase models, it is to demonstrate how a democracy can operationalise trustworthy AI at scale, using hard-won lessons from India’s own Digital Public Infrastructure (DPI) journey.

Case study 1: DPDP as the “trust layer” for AI

The Digital Personal Data Protection Act, 2023 provides the legal backbone for how digital personal data should be processed in India by explicitly balancing individuals’ right to protect personal data with lawful uses. For AI, this is foundational: training and deploying AI systems without robust purpose limitation, consent, safeguards, and grievance pathways is not innovation, it is an invitation to backlash.

Crucially, India is moving from statute to operational detail. The Digital Personal Data Protection Rules, 2025 emphasise practical compliance expectations such as clear points of contact for data principals, and heightened obligations for Significant Data Fiduciaries including independent audits and impact assessments. These are not bureaucratic burdens; they are the scaffolding for AI systems that can be defended, audited, and corrected.

A meaningful Summit outcome would be to align AI deployment in high-stakes domains (credit, insurance, hiring, education, welfare delivery) with DPDP-aligned controls: documented data lineage, privacy-preserving defaults, and transparency when AI meaningfully influences decisions.

Case study 2: UPI—trust through interoperability and rules, not monopoly

India’s best-known DPI success is Unified Payments Interface. What makes UPI a governance lesson for AI is not just scale, it is design: interoperable rails, open participation, clear standards, and a rules-based ecosystem. Government sources note that UPI connects hundreds of banks and serves hundreds of millions of users and tens of millions of merchants.

Trust emerges when systems are interoperable and contestable, and when accountability is not “hand-waved” to a single platform. If India wants “UPI-style” AI, the answer is not one national model. It is shared infrastructure + transparent standards: evaluation protocols, incident reporting, and auditability, especially for generative AI embedded into public services.

Case study 3: Account Aggregator—consent-based data sharing as AI fuel

India’s Account Aggregator framework offers a concrete template for “privacy-by-design” innovation: a consent-based system for sharing financial data across sectors, officially launched in 2021 and rooted in regulatory directions.

For AI, this matters because the most valuable models in finance, lending, insurance and MSME enablement are often data-hungry and high-risk. AA shows how to structure data access so that consent, purpose, and traceability are not afterthoughts. The Summit can elevate this pattern beyond finance: consented data exchange architectures that allow innovation while preserving user control.

Case study 4: DigiLocker and Aadhaar offline e-KYC—verifiable identity without over-sharing

Two less-discussed, but deeply relevant DPI elements are DigiLocker and Aadhaar’s paperless offline e-KYC. DigiLocker, a MeitY flagship, enables citizens to store, share, and verify authentic digital documents. Aadhaar Paperless Offline e-KYC enables voluntary identity verification in a paperless manner while emphasising privacy and security.

In an AI context, these are not just “digital governance wins.” They are building blocks for verifiable credentials and data minimisation and quite essential for reducing fraud, controlling access, and enabling accountable AI-enabled workflows in both government and industry.

Case study 5: ONDC—open protocols for market access, now with AI

Open Network for Digital Commerce (ONDC) is explicitly positioned as an open-network initiative to democratise digital commerce, launched in April 2022. The AI connection is straightforward: if discovery, recommendation, and conversational commerce become AI-mediated, open protocols can prevent “AI gatekeeping” where a few systems determine market visibility. ONDC-style openness, combined with DPDP-style safeguards, is how India can make AI-enabled markets more inclusive—not more concentrated.

The Summit’s real test: move from principles to proof

Globally, frameworks like the NIST emphasise managing AI risks across the lifecycle. India can go one step further: prove that responsible AI can be engineered using DPI patterns relating to interoperability, consent, auditable processes, and citizen-centric grievance mechanisms.

The IndiaAI Mission’s compute and ecosystem investments can power this shift by widening access to foundational capability. But India’s true global contribution in 2026 will be a replicable blueprint: AI that scales because trust scales legally, technically, and institutionally.

The India AI Impact Summit 2026, hosted by the Government of India, aims to showcase how a democracy can operationalize trustworthy AI at scale. By leveraging lessons from India's Digital Public Infrastructure, the summit will focus on responsible, inclusive, and impact-driven AI, emphasizing trust through legal frameworks, interoperability, and consent-based data sharing.

 (The writer is a Dean, Academics at IIM Visakhapatnam). The views expressed are personal.)