India’s AI Applications Stack: From Model Power to Social Impact

Why in the News?

The Economic Survey 2026 highlights Human Primacy and Economic Purpose as guiding principles for AI adoption, arguing that India’s AI leadership will depend less on large GPU clusters and more on real-world applications that improve everyday life.

Rather than competing purely in foundation models, India’s comparative advantage may lie in building an AI Applications Stack focused on:

• Public health
• Agriculture productivity
• Education reform
• Urban governance
• Disaster management

Background and Context

Globally, AI competition has centred on:

• Large language models
• High-end semiconductor access
• Massive data centre infrastructure

However, India’s structural strengths include:

• Large public digital infrastructure (Aadhaar, UPI, DigiLocker)
• Grassroots governance networks (ASHA workers, Krishi Vigyan Kendras)
• High mobile penetration
• Strong startup ecosystem

The Economic Survey 2026 emphasises that AI must align with:

• Welfare objectives
• Inclusion
• Developmental priorities

This shifts focus from “AI for scale” to “AI for social transformation.”

AI in Healthcare: Expanding Access and Early Detection

1. Niramai – Early Breast Cancer Screening

Niramai has developed a non-invasive AI-based thermal imaging solution:

• Effective across age groups
• Works for dense breast tissue
• Portable and affordable
• Enables screening in rural and semi-urban regions

This reduces dependency on costly mammography infrastructure.

2. Qure.ai – Rapid Imaging Diagnostics

Qure.ai uses AI to analyse X-rays and CT scans within seconds.

• Detects 35+ conditions
• Supports tuberculosis, lung cancer, heart failure detection
• Useful in districts lacking radiologists

Improves triage speed and treatment efficiency.

3. AISteth – Remote Cardiac Diagnosis

AISteth offers an AI-powered stethoscope:

• Converts heart and lung sounds into visual waveforms
• ~93% diagnostic accuracy
• Assists frontline health workers

Strengthens primary healthcare delivery.

AI in Agriculture: Smarter Farming, Lower Costs

1. Neoperk – Instant Soil Testing

Neoperk uses spectroscopy and machine learning to:

• Deliver soil health results in under five minutes
• Analyse 12 key parameters
• Reduce chemical fertiliser overuse

Promotes climate-smart agriculture.

2. CottonAce – Pest Management

Developed by the Wadhwani Institute for Artificial Intelligence, CottonAce:

• Allows farmers to upload pest images
• Provides instant, localised pesticide advice
• Helps manage pink bollworm threats

Enhances crop yield and profitability.

3. Niqo Robotics – Precision Spraying

Niqo Robotics deploys AI-driven robots:

• Detect pests and weeds in real time
• Reduce pesticide use by 60–90%
• Lower environmental damage

Improves sustainability and farmer margins.

4. Cropin – Digital Farm Ecosystem

Cropin offers:

• Farm monitoring
• Credit analytics
• Climate prediction tools

Transforms fragmented agriculture into data-driven systems.

AI in Education: Personalised and Inclusive Learning

1. PadhaiWithAI – Boosting Math Outcomes

PadhaiWithAI provides personalised learning tools for government schools.

• Improved pass rates within six weeks
• Enhanced performance among high achievers
• Scalable model for rural education

2. Rocket Learning’s Appu – Early Childhood Development

Rocket Learning developed “Appu”:

• AI-based WhatsApp learning companion
• Supports foundational literacy and numeracy
• Engages parents in early childhood education

3. Belagavi Smart City – Adaptive eBooks

Under the Belagavi Smart City initiative:

• AI-enabled eBooks adapt storylines in real time
• Improved engagement
• Increased reading speed by 12% in two weeks

Government as Ecosystem Orchestrator

The government can catalyse AI scale-up by:

• Procuring empanelled domestic AI solutions
• Creating demand across hospitals, schools, and agriculture departments
• Establishing clear standards for AI safety and ethics
• Promoting interoperable digital infrastructure

Such orchestration reduces uncertainty and builds trust.

Building the India AI Applications Stack

An India AI Applications Stack would:

• Integrate tested AI solutions
• Ensure interoperability
• Enable national scalability
• Offer export-ready AI products

International collaboration platforms such as the Global Partnership on Artificial Intelligence can support outreach.

A governance framework aligned with global standards (e.g., GDPR-like safeguards) can enhance credibility.

Significance

1. Inclusive AI Development

Moves focus from elite tech capability to mass welfare impact.

2. Cost-Effective Innovation

Leverages frugal engineering and public digital infrastructure.

3. Rural Empowerment

Brings AI tools to farmers, ASHA workers, and government schools.

4. Global Export Potential

India-tested, scalable solutions can serve Global South markets.

5. Strengthening Digital Sovereignty

Reduces overdependence on foreign AI platforms.

Challenges and Way Forward

Challenges

• Data privacy concerns
• Digital divide
• Regulatory uncertainty
• Ethical AI governance
• Integration across departments

Way Forward

• Develop robust AI governance framework
• Promote open datasets with safeguards
• Invest in digital literacy
• Encourage public-private partnerships
• Standardise procurement and benchmarking