BRICS Industrial Intelligence

Executive Summary

The National Strategy for Artificial Intelligence, branded "#AIforAll," was published by NITI Aayog (India's premier policy think tank) in June 2018 as India's foundational AI strategy document. The paper positions India not merely as an adopter of AI technology but as a potential global leader in developing AI solutions for emerging economies -- the concept of India as an "AI Garage for 40% of the world." Anchored in the dual objectives of economic impact (AI potentially contributing $957 billion, or 15% of current gross value, to the Indian economy by 2035) and inclusive social development, the strategy identifies five priority sectors -- healthcare, agriculture, education, smart cities, and smart mobility -- and proposes the establishment of research centres, a common computing platform, workforce reskilling programs, a national AI marketplace, and an ethical AI framework. Unlike many national AI strategies that focus primarily on competitiveness, the #AIforAll approach explicitly centres on using AI for broad-based societal benefit across India's 1.3 billion population.

Key Provisions

Five Priority Sectors. The strategy identifies five focus areas where AI can deliver maximum transformative impact for India:

Healthcare: Addressing the acute doctor shortage (1 per 1,700 people vs. WHO-recommended 1 per 1,000), enabling early disease detection, personalized treatment, drug discovery, telemedicine, and predictive outbreak analytics.
Agriculture: Serving 120 million farming households through crop yield prediction, pest detection, soil analysis, precision agriculture, weather forecasting, and supply chain optimization.
Education: Transforming learning for 250 million students via personalized adaptive content, automated assessment, virtual tutors, learning disability identification, and multilingual translation.
Smart Cities: Leveraging AI for traffic management, energy efficiency, waste management, public safety, and urban planning as India rapidly urbanizes.
Smart Mobility: Deploying AI for autonomous vehicles, traffic optimization, public transport management, logistics, and road safety.

Research Infrastructure. The strategy proposes three tiers of research institutions:

Centres of Research Excellence (COREs): Located at leading academic institutions, focused on fundamental AI research with top researchers.
International Centres for Transformational AI (ICTAIs): One for each priority sector, established through public-private partnership, focused on applied research and deployment.
Centre for Studies on Technological Sustainability (CSTS): Dedicated to ethics, privacy, legal aspects, social sustainability, and global competitiveness of AI.

AIRAWAT Platform. The strategy proposes establishing the AI Research, Analytics, and Knowledge Assimilation Platform -- a common cloud computing infrastructure connecting all COREs, ICTAIs, and academic institutions via the National Knowledge Network, providing shared compute resources for AI research and development.

National AI Marketplace (NAIM). A three-module marketplace comprising a data marketplace, a data annotation marketplace, and a deployable model/solutions marketplace. NAIM is designed to reduce barriers to AI adoption by providing accessible resources to startups, researchers, and government agencies.

Workforce and Education Transformation. The strategy recommends comprehensive skilling interventions across all levels: incentivizing new AI value chain jobs (data annotation, image classification), recognizing informal training institutions, creating open learning platforms (MOOCs, NASSCOM Future Skills), providing financial incentives for employee reskilling, transitioning schools to skill-based STEM education, promoting Atal Tinkering Labs, and introducing bridge courses in AI for non-computer science postgraduates.

Responsible AI Framework. The strategy addresses three pillars of responsible AI:

Ethics: Tackling built-in biases, assessing impact, promoting explainability and transparency.
Privacy: Establishing a data protection framework with legal backing, sectoral regulatory frameworks, benchmarking against GDPR, promoting privacy-preserving research (differential privacy, privacy by design, multi-party computation).
Security: Proposing negligence tests for AI damages, safe harbours for appropriate design, proportionate liability frameworks, and actual harm requirements.

Goals and Timelines

The strategy sets a 2035 horizon for its economic impact projections ($957 billion GDP contribution), while institutional recommendations are designed for medium-term implementation. Specific timelines include:

Establishment of COREs and ICTAIs as near-term priorities.
AIRAWAT platform deployment as a foundational infrastructure initiative.
National AI Marketplace creation for accelerating adoption.
Workforce reskilling as an ongoing program aligned with existing skilling missions.
Annual "AI Readiness Index" across states for continuous monitoring.

The strategy does not prescribe rigid deadlines but rather frames recommendations as catalytic interventions requiring sustained government commitment and multi-stakeholder engagement over 5-15 years.

Implementation Mechanisms

Government as Catalyst. The strategy assigns the government multiple roles: fiscal supporter (funding COREs, ICTAIs, AIRAWAT), regulator (data protection, ethics frameworks), facilitator (marketplace creation, industry-academia connections), and early adopter (grand challenges, preferential AI procurement by government agencies and PSUs).

Public-Private Partnership. ICTAIs are explicitly designed as PPP entities, potentially structured as Section 8 companies (Indian non-profit corporate form). This model leverages private sector efficiency and domain expertise while ensuring public interest orientation.

Multi-Stakeholder Coordination. The strategy calls for collaboration among five ecosystem pillars: policymakers, large companies, startups, universities, and multi-stakeholder partnerships (including trade bodies and venture capitalists). The "AI+X" paradigm promotes cross-disciplinary collaboration between AI researchers and domain experts.

Data Ecosystem Development. The government is tasked with opening government datasets in machine-readable formats, co-funding India-specific annotated datasets, and creating crowdsourced annotation platforms. This addresses the fundamental data bottleneck that constrains AI development in India.

International Engagement. The strategy proposes a "CERN for AI" -- an international collaborative framework for supra-national AI research, positioning India as a convener of global AI cooperation rather than merely a participant.

Industry Impact

The #AIforAll strategy has had a formative influence on India's AI ecosystem development. By framing AI as a tool for inclusive development rather than purely commercial competitiveness, the strategy created political consensus for AI investment that might otherwise have been contentious in a country with pressing development priorities.

Startup Ecosystem. The strategy's emphasis on incubation hubs, grant funding, and marketplace infrastructure catalyzed growth in India's AI startup ecosystem. By 2024, India had become the third-largest AI startup ecosystem globally, a trajectory partially attributable to the policy environment this strategy initiated.

Academic and Research. The COREs and ICTAIs concept influenced subsequent government programs and attracted renewed attention to AI research in Indian academic institutions. While implementation of these centres proceeded more slowly than envisioned, the strategic framework guided resource allocation decisions.

Government AI Adoption. The strategy's emphasis on government as an early adopter of AI has manifested in numerous e-governance and public service AI projects across central and state governments, particularly in healthcare diagnostics, agricultural advisory, and document processing.

Data Ecosystem. The focus on data marketplaces and annotation infrastructure anticipated the data challenges that would become central to AI development globally. However, India's data ecosystem development has lagged behind the strategy's ambitions, partly due to the delayed passage of data protection legislation (the DPDPA was enacted only in 2023).

Ethical AI Leadership. India's early articulation of responsible AI principles positioned the country as a credible voice in global AI governance discussions, particularly in forums representing developing nations.

Critics of the strategy have noted that while comprehensive in scope, it lacked specific budget allocations and implementation timelines, functioning more as a vision document than an actionable roadmap. Many of its recommendations required subsequent policy instruments -- such as the IndiaAI Mission (2024) -- to translate into funded programs.

Amendment History

The National Strategy for Artificial Intelligence (#AIforAll) was published by NITI Aayog in June 2018 as a strategy paper (not legislation), and as such has not been formally "amended." However, it has been supplemented and operationalized through subsequent policy instruments. In 2020, NITI Aayog published "Responsible AI for All," a follow-up paper deepening the ethical AI framework. The IndiaAI Mission, approved by the Cabinet in March 2024 with Rs. 10,371.92 crore funding, represents the most significant operationalization of the 2018 strategy's recommendations, establishing concrete programs for compute infrastructure, foundational models, datasets, skilling, and startup financing. The original strategy document remains the foundational reference for India's AI policy discourse.

National Strategy for Artificial Intelligence: #AIforAll