The Economic Survey 2025-26, tabled in Parliament by Union Minister for Finance and Corporate Affairs Nirmala Sitharaman, frames Artificial Intelligence (AI) as a developmental and economic strategy, not a prestige-driven technological race. The Survey examines how AI is reshaping the global economy and outlines a pragmatic pathway for India, one that is grounded in real-world deployment, institutional capacity, and long-term economic resilience.
Rather than pursuing scale for its own sake, the Survey advocates a bottom-up, application-led AI strategy, aligned with India’s structural realities of capital availability, energy constraints, institutional capacity, and market depth.
AI Deployment Rooted in Real-World Needs
The Survey notes that India’s demand for AI is emerging primarily from practical, high-impact problems, rather than speculative frontier use cases. Across healthcare, agriculture, education, urban governance, disaster preparedness, and public administration, AI adoption is gaining traction where it:
- Operates on local or low-cost hardware
- Functions effectively in resource-constrained environments
- Reduces costs and compensates for structural bottlenecks
Examples include early disease screening, precision water management, farmer market access, classroom analytics, disaster response tools, and regional-language digital interfaces. These use cases demonstrate a large and scalable market for frugal, application-focused AI, tailored to India’s socio-economic context.
The Survey cautions that a premature push toward capital-intensive frontier models could create fragile dependencies on external compute, energy, and proprietary ecosystems, undermining long-term resilience.
Aligning AI with India’s Structural Realities
A central message of the Survey is that AI adoption must reinforce, rather than strain, India’s economic foundations. Technology choices must account for:
- Capital and financing constraints
- Energy availability and sustainability
- Uneven institutional capacity across regions
- Varied levels of market depth and digital readiness
In this context, decentralised and task-specific AI systems are preferred over large, centralised models. Smaller models deployed across sectors allow innovation to diffuse more evenly, lower entry barriers for firms, and better accommodate India’s economic diversity.
The Survey highlights open and interoperable AI systems as force multipliers that encourage collaboration, shared innovation, and strategic autonomy.
National AI Mission: Enabling Scale Without Centralisation
The National AI Mission is envisaged as an enabling framework rather than a centralising authority. Its role is to support scale by providing:
- Shared compute and digital infrastructure
- Common standards and governance frameworks
- Targeted funding and institutional support
Crucially, the Mission is designed to preserve local creativity, enabling start-ups, municipal bodies, research institutions, and community organisations to develop and deploy context-specific AI solutions.
Local Ingenuity and Frugal AI in Practice
India’s AI ecosystem is already evolving from the ground up. The Survey highlights several examples of locally deployed, frugal AI solutions:
- AI-enabled thermal imaging for early breast cancer screening in low-resource settings
- Portable, low-cost AI-assisted oral cancer screening tools at primary healthcare centres
- AI-enabled agricultural networks improving price discovery and logistics for 1.8 million farmers across 12 states
Initiatives such as Bhashini (MeitY) and AI4Bharat (IIT Madras) illustrate how language- and voice-first AI systems can extend digital services to populations historically excluded by literacy, language, or device constraints. These systems operate on low-cost devices and in native languages, aligning inclusion with scale.
Human Capital and Education in the AI Era
The Survey emphasises that AI-era preparedness requires a shift away from narrow technical specialisation toward foundational human capabilities, including:
- Reasoning and judgment
- Communication and adaptability
- Reading, interpretation, and contextual understanding
It calls for experiential learning, early workplace exposure, and flexible education pathways, including “earn and learn” models co-designed by academia and industry. The National Education Policy 2020 is highlighted as aligned with this approach.
The Survey also stresses the need to map labour demand beyond white-collar roles, noting shortages in sectors such as nursing and geriatric care, where AI can augment human labour rather than replace it.
Data Governance and Regulation: Proportionate and Sequenced
On data governance, the Survey argues for accountability and value creation over isolation. Trusted data flows, transparency, and auditability are seen as more effective than rigid localisation in ensuring domestic economic gains while maintaining global interoperability.
Regulation is envisioned as risk-based and proportionate, with obligations scaled to potential harm and systemic importance. The Survey stresses careful sequencing:
- Build coordination
- Develop institutional capacity
- Apply binding regulatory leverage
This approach avoids premature lock-in or regulatory overreach while allowing innovation and institutions to co-evolve.
AI Safety and Institutional Architecture
The Survey supports the establishment of an AI Safety Institute, as proposed under MeitY guidelines, to analyse emerging risks, conduct scenario-based testing and red-teaming, and build institutional capacity.
International cooperation with bodies such as the UK AI Safety Institute and US NIST is encouraged to enable joint evaluation of high-risk models and promote interoperable global safety standards.
Proposal: AI Economic Council for India
The Survey proposes an AI Economic Council, distinct from a governance council, to align AI deployment with:
- Labour market realities
- Education and skilling systems
- Resource constraints
- Development priorities
Core principles include:
- Human primacy and economic purpose
- Labour-market sensitivity by design
- Sequencing over speed
- Co-evolution of technology and human capital
- Public-interest and ethical non-negotiables
The Way Forward
The Survey concludes that India’s strengths lie in application-led innovation, productive use of domestic data, depth of human capital, and the ability of public institutions to coordinate distributed efforts.
A bottom-up AI strategy, anchored in open systems, sector-specific models, and shared infrastructure, offers a more resilient and inclusive pathway than a narrow pursuit of scale. Regulation, data governance, and safety frameworks must evolve alongside deployment, not after it.
If aligned with India’s developmental priorities, AI can become a tool for broad-based productivity growth, dignified employment, and long-term economic resilience, rather than a source of dependency or exclusion. (Source: PIB PR ID 2219975)
Economic Survey of India 2025-26 dated 29/01/2026