India AI Impact Summit 2026

women-in-climate-and-ai-bridging-the-gender-gap-in-innovation

7 min read

TRACK:Inclusion for Social Empowerment
SPEAKERS
  • Jui Joshi, CCF
  • Ms. Pooja Kini, Salesforce
  • Samhita R, Resilience AI
  • Shalabh Tandon, IFC South Asia
  • Vaishali Nigam Sinha, Renew Foundation
  • Vrushali Gaud, Google
SESSION OVERVIEW

The objective of this panel is to highlight the importance of gender and it's necessity as the core for climate success. This session will focus on two priorities: changing the big systems (how money and rules work) and valuing local expertise (how women's hands-on knowledge makes AI smarter).

VIDEO RECORDING

Women in Climate and AI - Bridging the Gender Gap in Innovation

Abstract

The panel examined how gender considerations can be woven into climate‑focused AI solutions and financing mechanisms. Representatives from Salesforce, IFC, Resilience AI, and UN Women highlighted tools that surface gender‑disaggregated data, gender‑tagged investment criteria, and inclusive data‑collection practices. The discussion moved through systemic credit gaps for women‑owned enterprises, the cost of gender‑intentional modelling, the role of philanthropy, regional scaling of solutions, and technical approaches to reduce AI bias. Audience questions probed data gaps, credit‑worthiness evidence for women entrepreneurs, and methodological safeguards against gender bias in AI models.

Detailed Summary

  • Moderator (Zoe) & Co‑moderator (Jui Joshi) opened the session, captured a quick group photo, and framed the discussion around two priorities from the agenda:

    1. Changing systemic levers – how money and regulations operate.
    2. Valuing local expertise – leveraging women’s hands‑on knowledge to make AI smarter.

  • Zoe highlighted stark gender disparities in South Asia: 30 % women labour‑force participation and women 30 % less likely than men to own or use mobile phones. These realities set the stage for exploring gender‑intentional climate‑AI tools.

2. Salesforce’s Net Zero Cloud & Agent Force (Pooja Kini)

  • Net Zero Cloud provides a bird’s‑eye view of gender representation within organisations:

    • Basic metric – percentage of women in the workforce.
    • Segmentation by hierarchical level to surface “glass‑ceiling” drop‑offs.
    • Pay‑gap analytics (board, senior, mid‑level, entry) to surface implicit bias.

  • Agent Force is a customizable investment‑scoring tool:

    • Allows investors to assign gender weighting and set guardrails (e.g., “Did we invest enough in women‑led firms this year?”).
    • Supports risk‑adjusted scoring that can influence capital allocation toward gender‑inclusive projects.

  • Pooja emphasised that gender‑related data must be explicit, otherwise biases remain hidden. She noted that gender gaps in leadership directly affect product design and market outcomes.

3. IFC’s “She Wins” Accelerator & Gender‑Tagged Investing (Shalabh Tandon)

  • Problem context:

    • $1.7 trillion global credit gap for women.
    • < 3 % of venture‑capital funding goes to women‑led startups.
    • In India, 64 million MSMEs, of which 20‑25 % are women‑owned, and 90 % rely on informal credit.

  • IFC’s approach:

    • Internal target – 60 % of all investments/advisory services must be gender‑tagged.
    • Gender‑tagging means embedding gender‑impact KPIs (jobs for women, women in leadership, care‑economy safeguards, technology access, credit access).
    • Avoiding “gender‑washing” by requiring measurable outcomes rather than mere statements.

  • De‑risking mechanisms:

    • Develop ecosystem‑level tools (data platforms, guarantee structures) before rolling out financial products.
    • Partner with fintechs and VC funds that use cash‑flow‑based, non‑collateral lending to expand credit to first‑time women borrowers.

4. UN Women’s Institutional Solutions & Regional Programming

  • Mandate: Gender equality and women’s empowerment across political participation, violence prevention, peace & security, and economic empowerment.

  • Institutional solutioning:

    • Work with governments and private sector to embed gender lenses into policies and programmes.
    • Emphasise women as knowledge‑holders, not just beneficiaries.

  • Recent initiatives:

    • ASEAN‑wide entrepreneurship support for women in climate‑tech (some projects have AI components).
    • WEPS framework (seven principles covering women in leadership, workforce, marketplace, community).

  • Data & intentionality:

    • Ensure gender‑inclusive data capture during ESG due‑diligence fieldwork.
    • Use gender‑tagged data to design future interventions (e.g., impact on women, not just “women reached”).

  • Five regional pillars (briefly listed):

    1. Regional vision & enabling environment – align country policies via ASEAN guidance.
    2. Affordable finance access – expand credit for women‑owned enterprises.
    3. Innovation ecosystems – strengthen entrepreneurship, research, and data‑census mechanisms.
    4. Talent pipeline – develop diverse, region‑specific skilled workforce (including transgender representation).
    5. Evidence, accountability & learning – embed monitoring and evaluation from the outset.

5. Resilience AI: Women‑Led Disaster‑Risk Platform (Samhita R)

  • Product: Resilience 360 – a machine‑learning tool that maps exposure to natural disasters (e.g., floods) within 30 minutes for any structure (schools, offices, hospitals).

  • Team composition: Four women (urban planner, architect, technologist, literature practitioner).

  • Gender‑inclusive modelling:

    • Data collection is human‑assisted; women and men collect field data to ensure gender‑balanced datasets.
    • Three‑layer approach:
      1. Recognition layer – identifies high‑vulnerability hotspots (schools, Anganwadis, community centres, construction sites).
      2. Prioritisation layer – uses ground‑truthing surveys capturing socioeconomic vulnerability (income, livelihood loss).
      3. Action layer – delivers actionable, 24/7 accessible software enabling decision‑makers to act on insights.

  • Cost & scalability: Acknowledged that gender‑intentional data gathering is expensive, but highlighted funding from Kalari Capital, Vanicola, Colossa Ventures, and others that are increasingly earmarked for women‑led climate‑tech.

  • Ecosystem advice: Emphasised the need for equal representation across genders in the workplace, including transgender inclusion, and stressed that execution (“the trick is in the doing”) matters more than rhetoric.

6. Financing, Credit Data Gaps & AI‑Enabled Solutions (Amal/Amar)

  • Fintech & cash‑flow lending: AI can analyse transactional data (UPI payments, phone usage) to build credit profiles for women entrepreneurs lacking collateral.

  • Data gap: Lack of granular, gender‑disaggregated credit performance data hampers evidence‑based lending.

  • Evidence from banks: Some banks (e.g., HDFC) report higher repayment rates for women borrowers, but systemic data is still scarce.

  • Opportunity: Building a data stack for women’s credit behaviour would attract fintech demand and enable non‑collateral, behavior‑based lending.

7. Philanthropy’s Role (Pooja Kini)

  • Salesforce 1‑1‑1 model: 1 % of time, 1 % of equity, 1 % of product contributed to social causes.

  • Subsidised tools: Salesforce offers its AI‑for‑Impact suite at reduced rates to non‑profits and educational institutions, with gender‑weighted selection.

  • Beyond software: Philanthropy should also fund child‑care support, gender‑inclusive data collection, and capacity‑building to ensure women can fully participate in AI‑driven climate solutions.

8. Audience Q&A

8.1 Data Gaps & Credit‑Worthiness (Shalabh)

  • Credit gap: $1.7 trillion globally for women.
  • Evidence: Female borrowers often have better repayment profiles, but comprehensive data across sectors is limited.
  • Call to action: Encourage startups to build gender‑inclusive credit data stacks; fintechs are ready to consume such data.

8.2 Flood‑Risk Modelling & Urban Heat Island (Samhita)

  • Data philosophy: Treat data as foundational; use proxies where direct measurements lack, but achieve ≥ 95 % parity.
  • Three‑step method: Ask right questions → know where to look → accept data limitations and use ground‑truthing for refinement.
  • Performance: Achieved 96 % confidence on urban heat‑island mapping across 8.9 million structures in 200 locations.

8.3 Mitigating AI Bias (UN Women Representative)

  • Intentional data: Build gender‑tagged datasets from the start.
  • Methodologies:
    • Sentiment/EQ analysis to capture non‑numeric bias signals.
    • Constitutional AI (rule‑based guardrails) to guide model outputs.
    • Ongoing research on bias detection and correction.

8.4 Scaling Regional Solutions (UN Women Representative)

  • Regional vision: Align policies via ASEAN, create enabling environments, and replicate successful models (e.g., flood‑risk tool) across South‑East Asia.

9. Closing Remarks

  • Moderator thanked the panel and audience, noted time constraints, and invited attendees to continue conversations offline.

Key Takeaways

  • Gender‑disaggregated data is essential for both climate impact assessment and investment decisions; tools like Salesforce’s Net Zero Cloud make such data visible.
  • IFC’s “She Wins” accelerator embodies a 60 % gender‑tagged investment target, using KPIs that go beyond superficial metrics to avoid “gender‑washing.”
  • UN Women stresses institutional solutions and a five‑pillar regional strategy that couples policy alignment, finance, ecosystem building, talent pipelines, and evidence‑based learning.
  • Resilience AI’s three‑layer model demonstrates how AI can be human‑assisted, gender‑inclusive, and actionable for rapid disaster‑risk mapping.
  • Fintechs can leverage everyday digital transaction data (UPI, mobile usage) to create non‑collateral, behavior‑based credit scores for women entrepreneurs, narrowing the $1.7 trillion credit gap.
  • Philanthropy (e.g., Salesforce 1‑1‑1) plays a pivotal role by providing subsidised AI tools, gender‑weighted program selection, and supporting ancillary needs such as childcare and capacity‑building.
  • Bias mitigation requires intentional dataset design and algorithmic guardrails (e.g., constitutional AI, sentiment/EQ analysis) to prevent the perpetuation of existing gender biases.
  • Regional scaling is feasible when a common vision, enabling policy environment, and shared data standards are in place, allowing solutions built in India to be replicated in neighboring countries.
  • Data gaps remain—especially around women’s credit performance and localized climate impact—as a barrier to evidence‑based financing; filling these gaps is a priority for fintechs and investors alike.
  • Execution matters: The panel repeatedly emphasized that “the trick is in the doing”—moving from rhetoric to concrete, gender‑intentional actions across finance, technology, and policy.

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