India AI Impact Summit 2026

ai-for-esg-responsible-innovation-for-people-planet-and-progress

6 min read

TRACK:Safe and Trusted AI
SPEAKERS
  • Dr. Manuj Bhardwaj, Dhirubhai Ambani University – School of Law
  • Ms. Ayisha Piotti, RegHorizon
  • Ms. Maya Sherman, Embassy of Israel in India
  • Ms. Sarith Felber, Ministry of Justice, Israel
  • Prof Victor Alchanatis, Volcani Institute, Israel
  • Prof. (Dr.) Avinash Dadhich, Dhirubhai Ambani University
SESSION OVERVIEW

AI's growing role in economies and institutions makes its alignment with ESG priorities critical. While AI can strengthen climate monitoring, risk management, governance, transparency, and accountability, poorly governed use may amplify bias, inequality, and mistrust. This panel frames AI as a leadership and governance challenge, not just a technology, examining responsible, ethical, and policy-aligned deployment. It explores how AI can deliver inclusive, transparent, measurable ESG outcomes, especially for emerging economies and the Global South contexts.

VIDEO RECORDING

AI for ESG: Responsible Innovation for People, Planet and Progress

Detailed Summary

  • Welcomed participants, acknowledged logistical hiccup with his name tag, and thanked India AI Mission for logistical support.
  • Positioned the session as a “leadership and governance challenge” rather than a purely technical discussion.
  • Outlined three concrete AI‑for‑ESG pillars: Measurement, Management & Risk Forecasting, and Market Credibility.
    • Measurement – AI can clean noisy ESG data, detect green‑washing, monitor deforestation, and automate real‑time supply‑chain emissions tracking.
    • Management – AI models can stress‑test climate transition risk, forecast carbon exposure, and predict ESG‑linked credit risk.
    • Credibility – AI can bolster audit integrity, enable real‑time compliance monitoring and third‑party verification.
  • Raised the reciprocal question: How can AI itself be aligned with ESG values? and highlighted AI’s own carbon and water footprints (data‑centre energy consumption).

2. Opening Remarks – Ms. Ayisha Piotti (RegHorizon)

  • Emphasised that policy is the conduit that translates responsible AI innovation into trusted ESG outcomes.
  • Identified three policy goals: Risk mitigation → Trust → Adoption, and Fairness & equity in benefit distribution.
  • Described divergent geopolitical regulatory cultures:
    • EU – heavily regulatory, human‑rights‑centric.
    • USA – market‑led, security‑focused.
    • China – state‑directed, control‑oriented.
    • India – inclusion‑ and infrastructure‑driven.
  • Advocated for flexible, “testing‑and‑red‑team” frameworks rather than top‑down mandates, and suggested that AI itself can be a regulatory tool (e.g., compliance‑checking algorithms).
  • Mentioned the emerging policy issue of data‑centre environmental impact and the need for standards around energy and water use.

3. Government Perspective – Ms. Sarith Felber (Israeli Ministry of Justice)

  • Framed ESG/AI as a classic technology‑policy tension: regulators must balance risk‑prevention with fostering innovation.
  • Highlighted that AI will affect human rights and labour markets, urging policymakers to define values‑based priorities rather than purely legal technicalities.
  • Cited the EU’s “trust‑worthy AI” approach as a rapid, albeit imperfect, benchmark for protecting safety, environment, and labour.

4. Agricultural & Food‑Security Lens – Prof. Victor Alchanatis (Volcani Institute)

  • Described the environmental sustainability dilemma in conventional agriculture (over‑fertilisation vs. yield loss).
  • Presented precision‑agriculture & digital agriculture as AI‑driven pathways to reconcile food security with environmental stewardship.
  • Enumerated AI use‑cases:
    • Soil‑property mapping, disease early‑detection, variable‑rate fertiliser/pesticide application.
    • Decision‑support advice to farmers (currently limited by data scarcity and lack of trained models).
  • Stressed that data‑sharing policies and intellectual‑property frameworks are needed to unlock AI’s potential in farming.

5. Industry & Innovation View – Ms. Maya Sherman (Embassy of Israel)

  • Positioned ESG and AI as complementary “verticals” that together enable measurable, cross‑sector standards (e.g., gender parity, crop‑monitoring).
  • Cited Israeli agri‑tech startup Cropix as a concrete example of AI‑enhanced crop‑monitoring that feeds ESG metrics.
  • Argued that AI can turn high‑level ESG ambitions (e.g., 50‑50 gender representation) into trackable, data‑driven deliverables.

6. Legal‑Academic Reflection – Prof. Dr. Avinash Dadhich

  • Traced the historical emergence of “AI‑law” (the concept was ridiculed in 2016).
  • Discussed the dual‑sided narrative: AI is portrayed either as a utopian catalyst or a dystopian threat, creating confusion and anger among scholars.
  • Offered a philosophical framing of ESG:
    • Environment – natural world (pre‑existing).
    • Society – human community.
    • Governance – artificial organisations (corporations) that wield power.
  • Viewed AI as a third artificial entity (non‑human intelligence) that will reshape ESG dynamics, noting the energy cost of AI inference (e.g., ChatGPT).
  • Compared the AI “gold rush” to the historic California gold rush: the race is for profit and efficiency, not purely altruistic goals.

7. Technical & Governance Overlap – Prof. Dr. Vinnie Singh

  • Highlighted data‑protection, human‑rights, and human‑centred design as intersecting points of AI governance and ESG.
  • Reviewed commercial ESG‑reporting platforms (IBM, Microsoft) that automate data collection, while warning about fragmented data sources that jeopardise report reliability.
  • Re‑emphasised the cycle: responsible AI → trustworthy ESG reporting → better ESG outcomes → reinforced AI governance.

8. Structured AI‑for‑ESG Framework – Dr. Vinice Singh

  • Proposed a three‑stage categorisation:
    1. Pre‑reporting implementation – AI to achieve ESG targets (e.g., emissions reduction, labor‑law compliance).
    2. Reporting – AI‑enabled automated ESG disclosure (with risk of green‑washing).
    3. Monitoring, Reporting & Verification (MRV) – Regulators using AI to verify third‑party ESG audits.
  • Stressed the need for sector‑specific standards and technical solutions that bypass geopolitical frictions.

9. Audience Q&A (Selected Exchanges)

QuestionRespondentSummary of Answer
How can we avoid policy/governance fragmentation when AI and ESG evolve rapidly?Ayisha PiottiPush for international socio‑technical standards (ISO, CEN‑SENELEC, NIST) that are use‑case specific. Encourage cross‑standard‑body dialogue and industry‑driven technical solutions that are geopolitically neutral.
What regulatory approach does Israel adopt for AI?Sarith FelberSector‑specific regulation – adapt existing law to each domain (finance, energy, justice) rather than a blanket AI law; promote sandbox experimentation and government‑led AI deployments to demonstrate responsible practice.
How can AI‑driven agricultural science translate to ESG policy?Victor AlchanatisFirst, create data‑sharing incentives and clear IP rights for farm data; then develop policy instruments that reward precision‑ag tech adoption, thereby aligning food‑security, climate, and ESG goals.
What role does industry play in forming a “coalition of the willing”?Maya ShermanIndustry should engage regulators early, showcase AI‑enabled ESG solutions (e.g., agri‑tech startups), and co‑author regional standards to avoid fragmented global regulation.
How should emerging markets (India) adapt Israeli best practices?Sarith FelberLeverage Israel’s sandbox model, embed AI in public services to set high‑standards, and adapt sector‑specific regulations that respect India’s inclusion‑focused ESG agenda.

10. Closing Remarks (Moderator)

  • Thanked all panelists and the audience.
  • Noted the synergy of AI and ESG: AI not only improves ESG measurement and compliance, it also reshapes how sustainability is priced, regulated, and financed.
  • Invited participants to continue the dialogue in the summit’s networking areas.

Key Takeaways

  • AI is a double‑edged tool for ESG – it can dramatically improve data quality, risk modelling and auditability, but its own energy and water consumption must be governed.
  • Policy is the linchpin: effective AI‑for‑ESG requires risk‑mitigation frameworks that build trust, ensure fairness, and support local implementation; flexible “testing‑and‑red‑team” approaches are preferred over rigid top‑down rules.
  • Geopolitical regulatory cultures differ (EU, US, China, India); a sector‑specific, standards‑driven approach (e.g., ISO socio‑technical standards) offers the most pragmatic path to global coordination.
  • Precision agriculture showcases AI’s ESG potential: AI can reconcile yield, environmental stewardship, and food‑security, provided data‑sharing policies and IP safeguards are established.
  • Commercial ESG‑reporting platforms exist but suffer from fragmented data sources; trustworthy AI‑enabled reporting must be coupled with rigorous verification (MRV) mechanisms.
  • Sandbox experimentation and government‑led AI deployments are effective ways for regulators to understand technology and set responsible precedents.
  • International coalitions of the willing (regional alliances of regulators, industry, and civil society) are emerging as a realistic mechanism to develop shared AI‑ESG standards in the face of diverging national interests.
  • Three‑stage AI‑for‑ESG framework – pre‑reporting implementation, automated reporting, and AI‑driven monitoring/verification – provides a practical roadmap for organisations.
  • Education and awareness are critical; many stakeholders still conflate “AI law” with either utopian optimism or dystopian fear, leading to confusion that hampers constructive policy dialogue.

Prepared by the AI Conference Summarisation Team (All language has been cleaned, clarified, and organized while faithfully preserving the substance of the original discussion.)

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