By 2035, artificial intelligence will add $1.7 trillion to India’s economy highlighting its role as a fundamental driver of productivity and public value.
This is not a call to replicate India wholesale, but to adapt a core philosophy: AI must serve public values, respect civil rights and be accountable to democratic institutions. (AFP)At the same time, CERT-In reports that cyber incidents have doubled (29.44+ lakh in 2025)² as in 2023.
Therefore, the million dollar puzzle for Indian policymakers is no longer about adopting AI, but how to do so responsibly, at scale and with public trust.
Intelligent governance from digital foundationsThe accelerating AI journey in India was fueled by a decade-long investment in building a reliable consumer internet connectivity highway; Connectivity usage has increased by 4 times (ie 96.96 crore in 2024 as against 25.15 crore in 2014).
India’s approach to AI has been pragmatic rather than speculative. Instead of focusing narrowly on frontier models, the emphasis is on applied AI to solve real governance and service-delivery challenges at the national, state, and city levels.
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Public digital systems have built rails for AI to deliver measurable results.
For example, Digilcar crossed 53.92 crore users by June 2025, enabling authentic digital documents and verification at scale – reducing paper dependency while improving service efficiency.
In digital payments, unified payment interface shows what population-scale adoption looks like in reality: April 2025 recorded 1,867.7 crore UPI transactions ₹24.77 lakh crore, underscoring how trusted digital rail can transform everyday life on a national scale.
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AI has found its footprints in various public utilities such as:
a) Healthcare – AI-assisted screening and decision support can strengthen early detection and care pathways
b) Agricultural, AI-enabled suggestions based on local data can improve input efficiency and resilience.
In cities, AI-powered systems are being deployed for flood forecasting, environmental monitoring, traffic management and quick complaint resolution.
The Trust Deficit: Risk Management in the AI-Driven EraThis digital trajectory has significantly improved accessibility and inclusion but also exposed the core fabric of the country to the insidious emerging pill of ‘cyber-incident’.
Union Budget 2025-26 Allocation ₹782 crore accelerates AI journey to boost digital confidence in the country.
The proliferation of AI-generated content, particularly hallucinatory information and deepfakes, has sharpened concerns about privacy, identity protection, and data integrity.
The enactment of the Digital Personal Data Protection Act, 2023, along with its rules, established a robust privacy framework based on consent, purpose limitations, accountability and grievance redressal.
Thus emphasizing that digital systems must not only be technologically advanced, but also trustworthy by design.
Trust as the foundation for scalable AIAs AI systems interact with increasingly sensitive citizen data, its sustainability depends on trust.
In a democratic system, AI-assisted decisions must be explainable, contestable and defensible, especially where the results affect livelihoods, rights or due process.
Algorithmic opacity is not merely a technical limitation; This represents a governance risk.
This growing debate on trust, accountability and scale will be the focus of the upcoming India AI Impact Summit 2026 (February 16-20 in New Delhi), envisioned as a global platform to align policymakers, technologists, industry leaders and civil society on a responsible AI path.
The summit aims to move the discourse beyond innovation hype to privacy-by-design, interpretability and risk-aware deployment of AI in public systems.
By foregrounding trust as a governance imperative, the summit reflects India’s intention to shape not just domestic AI policy, but global norms for ethical and scalable AI adoption.
Privacy and accountability must therefore be considered infrastructure enablers for AI, not regulatory afterthoughts. India’s policy direction, anchored in valid data use, auditability and institutional oversight, reflects an emerging global consensus. That is a prerequisite for the trust scale.
This approach is reinforced through strategic investments. Cabinet-approved IndiaAI Mission, under ₹10,371.92 crore over five years, includes creation of public AI compute infrastructure with 10,000+ GPUs, support for sovereign foundation models and development of “safe, trusted, and ethical AI” tools.
This is not just an innovation program; This is a clear signal that India sees trusted AI as a strategic imperative for governance, economic competitiveness and national resilience.
Lessons for the Global SouthIndia’s three-pronged approach, viz. a) AI infrastructure, b) AI talent, c) ‘Gamification for AI amplification’, has helped it successfully overcome common challenges such as uneven digital infrastructure, varying digital literacy, and institutional capacity constraints shared across countries in the Global South – Africa, Latin America and parts of Asia.
This is precisely why India’s experience resonates: AI progress does not depend on unchecked data extraction, opaque surveillance or reliance on externally controlled platforms; Instead, context-aware AI built on the fence of sovereign digital infrastructure and clear public interest.
During India’s G20 Presidency, the Digital Public Infrastructure (DPI) agenda was advanced to achieve tangible results. This global recognition empowers Global South countries to adopt scalable, interoperable and rights-respecting digital practices as a foundation for AI.
Federated approachAI-first, cloud-native, mobile-first and presenceless, privacy-by-design, federated architecture (IndEA Architecture) serves as the Minimum Viable Reference Architecture (MVRA) for deploying accelerated AI solutions on India’s national level framework.
These central values and structures are replicated in nature at scale in grassroots administration at the state and city/village levels.
The National Digital Health Mission’s (NDHM’s) health stack is a classic example of a centrally designed architecture federated up to state and city/village level data for local service delivery aligned with the National Framework.
This federal scalability is important because many high-impact AI use cases are inherently local: municipal services, disaster response, public health surveillance, education outcomes, and policing.
A trusted model is one where a national framework enables digital security, privacy compliance and interoperability, while state and city administrators maintain the autonomy to execute, trust and innovate service engines.
A Blueprint for ‘Responsible AI’ – Global SouthFor governments across the Global South, the opportunities are clear. By adopting policy-based AI policies, investing in digital public infrastructure, and embedding privacy-by-design into AI systems, nations can decouple experiments and move directly to responsible scale.
This is not a call to replicate India wholesale, but to adapt a core philosophy: AI must serve public values, respect civil rights and be accountable to democratic institutions.
As AI becomes the operating system of modern governance, trust will be its most valuable currency. India’s experience shows that it is possible to build AI systems that are robust yet principled, innovative yet accountable and transformative without being draining.
In a world increasingly driven by algorithms, rethinking privacy and trust is not optional, it is the foundation of inclusive and sustainable AI progress for the Global South.
The author is Managing Director and Chief Executive Officer of Digital India Corporation
