AI for Economic Growth and Social Good | AI for All: Driving Economic Advancement and Societal Well-Being

Abstract

The panel examined how artificial intelligence can be turned into a catalyst for India’s pursuit of “Viksit Bharat” by 2047. Each panelist highlighted a sector where AI could generate scalable economic value and inclusive social impact – agriculture, manufacturing, banking, urban governance, education, healthcare, and cybersecurity. The discussion progressed from sector‑specific opportunities to cross‑cutting challenges such as data fragmentation, model explainability, cultural resistance, and the need for robust policy and skill‑development frameworks. Audience questions probed deeper into financial‑inclusion fraud mitigation, rural‑area digital literacy, and the scaling of pilot projects to national‑level deployments.

Detailed Summary

• Moderator (Sachin Tayal) welcomed the panel and introduced each participant, noting Dr Avik Sarkar’s role in drafting India’s first AI strategy at NITI Aayog and the presence of senior leaders from banking, industry, and cybersecurity.
• He framed the central question: If each panelist could choose one sector that would most accelerate the “Viksit Bharat” vision, what would it be and why?

1.1 Mohit Kapoor – Agriculture & Manufacturing

• Agriculture: 50 % of India’s population is rural. AI can integrate weather, soil, crop‑health, fertilizer, and farmer‑level data to lift yields and nutrition.
• Manufacturing (Make‑in‑India): AI‑driven quality control, agile supply‑chain optimization, safety monitoring, and predictive maintenance can make Indian factories globally competitive.
• Emphasised that AI is now feasible, viable, desirable, and affordable—a shift from a decade ago when the technology was only aspirational.

1.2 Rajeev Mishra – Banking & Financial Inclusion

• 65 % of India’s population lives in rural/semi‑urban zones, where 65 % of bank branches are located, contributing ~ 18 % of GDP.
• AI‑enabled last‑mile banking (e.g., automated land‑record checks, credit‑scoring from satellite imagery) can bring formal finance to the underserved “Bharat” segment, a prerequisite for inclusive growth.

1.3 Dr Avik Sarkar – Governance & Education

• Urban Governance: AI can automate city‑service monitoring (garbage collection, road maintenance, encroachment detection, traffic‑violation identification) via CCTV and sensor data, improving livability for rapidly expanding cities.
• Education: Progressive AI curricula from primary to tertiary levels are needed so citizens can understand, apply, and innovate with AI tools.

1.4 Dr Gulshan Rai – AI as a Productivity Tool

• Stressed that AI must be viewed as a productivity enhancer, not a goal in itself.
• Highlighted the need for balanced development—AI should augment human capability while being governed responsibly.

1.5 Dhrubabrata Ghosh – Healthcare

• Identified two healthcare AI thrusts:
  1. Drug discovery & disease diagnostics (AI‑driven image analysis, predictive pathology).
  2. Rural outreach via conversational AI that can triage patients when doctors are unavailable.

2. Deep‑Dive: Sector‑Specific AI Use‑Cases

2.1 Agriculture – Multi‑Modal AI & Product Platforms (7:00‑15:00)

• Multi‑modal AI (voice, video, text, sensor data) will let illiterate farmers interact with AI through cameras or speech.
• Nidan App (Mahindra) – Farmers photograph a crop; AI identifies disease and recommends treatment.
• Krishi Platform – Satellite‑guided sugarcane harvest scheduling (micro‑grid advice) improves quality‑based pricing; similar models yield 21 % quality gains for chili growers in Telangana.
• Future unlocks:
  1. Multilingual AI – dialect‑level models to overcome language barriers.
  2. Smart wearables – hands‑free AI interfaces for field workers.

2.2 Manufacturing – Safety‑First Autonomy (15:00‑20:00)

• AI‑enabled predictive safety systems can detect imminent vehicle failures before a human driver perceives them.
• Mahindra’s autonomy focus is safety, not driver replacement; the goal is a zero‑accident road environment.

2.3 Banking – Data‑Driven Lending & Real‑Time Fraud Prevention (20:00‑35:00)

• From document‑based to database‑driven lending: Satellite imagery, crop‑cycle data, and transaction histories replace PAN‑card/KYC checks for farmers, enabling instant loan sanction (“minutes”).
• Scale of transactions: Bank of India processes ~3.5–4 crore (35–40 million) transactions daily.
• Fraud detection:
  • Real‑time AI models (AFRMS) monitor transaction velocity and behavioral patterns, flagging anomalies within milliseconds.
  • Identification of “mule accounts” (rented accounts used for money‑laundering) through pattern analysis.
  • Integration with I4C (government body linking banks, regulators, and law‑enforcement) has already blocked/ saved INR 5,800 crore of fraudulent flow.

2.4 Cybersecurity & Geopolitics – Strategic AI Gaps (35:00‑45:00)

• Dr Rai highlighted how AI is becoming central to modern warfare (e.g., cyber‑operations in the Russia‑Ukraine conflict, Iranian internet shutdowns, and the strategic importance of semiconductor supply chains).
• The panel warned that AI‑enabled cyber‑espionage can precede kinetic conflict; thus, a robust national cyber policy with AI components is essential.

2.5 Governance & Public‑Sector AI (45:00‑55:00)

• Dr Avik underscored AI’s role in smart city management (garbage‑collection monitoring, road‑condition detection, traffic‑violation automation).
• He argued that governance AI directly improves citizens’ daily life and thus is a high‑impact sector.

2.6 Education & Skill Development (55:00‑65:00)

• The panel debated when AI education should start:

  • Dr Avik advocates early exposure (class 3) to AI as a tool, not to code‑writing.
  • Mohit points to a shift at class 6–10 where applying AI to real‑world problems becomes the focus.
  • Dr Rai cautions against displacing fundamental maths learning; AI should augment rather than replace basic literacy.

• Mahindra University / Mewki World School (Mohit) integrates AI labs with industry partners (NVIDIA, Google, Microsoft) and offers project‑based work that translates research into industrial solutions (e.g., intelligent tractors, safer cars, renewable‑energy optimisation).

2.7 Scaling AI Pilots to Population‑Level (65:00‑75:00)

• Sachin Tayal (Protiviti) outlined common barriers:

  1. Data fragmentation & governance – pilots often use clean, curated datasets; real‑world roll‑outs encounter inconsistent, sensitive, or incomplete data.
  2. Explainability & auditability – regulators and auditors demand transparent model behavior.
  3. Cultural resistance – fear of job loss and mistrust in AI outputs hinder adoption.

• Solutions suggested:

  • Establish data‑governance frameworks, PII redaction, and security layers (as emphasized by Dr Rai).
  • Top‑down leadership combined with grass‑roots AI literacy (driven by education and up‑skilling initiatives).
  • Indigenous language models (e.g., Paramtoo, Sarvamai) to overcome the English‑centric limitation of many global AI tools.

3. Audience Q & A (75:00‑95:00)

Questioner	Theme	Key Points from Panelists
Mike (Delhi)	Rural‑urban divide, AI for village‑level skills	Dr Rai: universal service delivery via satellite & broadband; AI‑driven video tutorials for basic repairs (e.g., tire fixing, fridge maintenance).
Prakriti (Secretariat)	Policy gaps, AI literacy, deep‑fakes	Rajeev: existing policy frameworks (civil‑score, Aadhaar, Digi‑Yatra) provide a base, but continuous refinement is needed; AI‑driven fraud‑prevention models require transparent guidelines.
Dolly Hussain (Consultant)	Aspirational districts – transformational AI	Mohit: focus on finance, healthcare, education; AI can bridge infrastructure gaps and drive inclusive growth in the most backward districts.

• The panel reiterated that policy, data‑governance, and skill‑building are the three pillars needed to translate pilots into nation‑wide impact.

4. Closing Remarks (95:00‑End)

• Moderator thanked the panel for “insightful” contributions and called for collective action to realize Viksit Bharat by 2047.
• A final round of applause signaled the end of the session.

Key Takeaways

• AI as an Enabler, Not an End – Across sectors, speakers agreed AI must boost productivity, safety, and inclusion rather than replace human roles.
• Agriculture & Manufacturing are Immediate High‑Impact Sectors – Multi‑modal AI, satellite data, and language‑localised models can dramatically increase yields and factory efficiency.
• Banking Can Reach the Last Mile via Data‑Driven Lending – Real‑time AI credit scoring using satellite and transaction data enables instant loans for rural farmers and fishermen.
• Governance AI Improves Urban Livability – Automated monitoring of waste collection, road health, and traffic violations translates to tangible citizen benefits.
• Education Needs a Paradigm Shift – Early exposure to AI as a tool and later emphasis on application (prompt engineering, critical evaluation) are essential; fundamental maths should not be sacrificed.
• Healthcare AI Must Combine Drug Discovery with Rural Outreach – Conversational agents and diagnostic image analysis can bridge the urban‑rural health gap.
• Scaling Pilots Requires Robust Data Governance and Explainability – Fragmented data, policy uncertainty, and lack of model transparency are the principal obstacles.
• Cybersecurity is a Strategic National Priority – AI‑enabled cyber threats are now integral to geopolitical tensions; national policy must embed AI safeguards.
• Cultural Adoption is as Important as Technical Viability – Top‑down leadership, continuous AI‑literacy programmes, and assurance of job security are needed to overcome resistance.
• Indigenous Language Models are Critical for Wider Adoption – Projects like Paramtoo/Sarvamai aim to provide multilingual AI capabilities essential for Indian conglomerates and government services.

These insights collectively outline a roadmap for leveraging AI to achieve sustainable economic growth and inclusive social welfare in India.

See Also:

• ai-for-inclusive-societal-development
• ai-for-impact-skilling-inspiring-and-empowering-the-next-gen
• harnessing-the-ai-revolution-for-social-empowerment
• ai-for-economic-development-and-social-good
• from-evidence-to-scale-testing-financing-and-operationalizing-technology-and-ai-for-development-and-humanitarian-action
• ai-for-fraud-prevention-and-financial-inclusion-in-bfsi
• democratizing-ai-resources-in-india
• democratizing-ai-resources-and-building-inclusive-ai-solutions-for-india
• the-future-of-work-for-the-global-south-skilling-for-opportunity-and-social-mobility
• responsible-ai-at-scale-governance-integrity-and-cyber-readiness-for-a-changing-world