India AI Impact Summit 2026

the-future-of-work-for-the-global-south-skilling-for-opportunity-and-social-mobility

6 min read

TRACK:Human Capital
SPEAKERS
  • Abhineet Kaul, Access Partnerships
  • Abhishek Singh, IndiaAI Mission and Ministry of Electronics and Information Technology (MeitY)
  • Angelica Salvi Del Pero, OECD (France)
  • Anjali Kaur, Chair on India and Emerging Asia Economics, Center for Strategic and International Studies
  • Chenie Yoon, Google
  • Naina Subberwal Batra, AVPN
SESSION OVERVIEW

This high-level panel explores how AI-driven workforce transformation can unlock economic opportunities and social mobility across the Global South. Convening policymakers, industry leaders, and practitioners, the session examines critical skills gaps, regional priorities, and actionable frameworks for inclusive AI upskilling. Drawing on India's policy leadership and cross-country insights, participants will engage in dialogue to shape future-proof roadmaps that ensure equitable access to AI skills, empowering vulnerable populations and strengthening workforce readiness across emerging economies.

VIDEO RECORDING

The Future of Work for the Global South: Skilling for Opportunity and Social Mobility

Detailed Summary

Moderator (Abhineet Kaul)

  • Thanks the audience repeatedly, then outlines the panel’s purpose: to move beyond the adoption of AI and focus on the skill gap that limits that adoption, especially in the Global South.
  • Cites two research‐backed facts:
    1. AI adoption is roughly twice as high in the Global North as in the Global South.
    2. AI‑skilling programs lag behind AI adoption, leaving companies unable to find talent that understands not only engineering but also ethics, implementation, and AI literacy.
  • Warns that AI can exacerbate existing inequalities (gender, digital divide) if not deployed responsibly.
  • Highlights Indian policy leadership: the India AI Mission (2024), Shram Shakti Niti 2025 (labour‑side policy), and the broader VIXIT India 2047 vision.
  • Introduces the “AI for All Workforce Scaling Policy Toolkit” (QR‑code available). The toolkit is positioned as a commons‑sense guide to:
    1. Identify AI‑related skill needs (currently unknown).
    2. Design measurement approaches (survey fatigue is a problem).
    3. Build an iterative implementation‑monitoring cycle.

Key Insight: Without a clear, data‑driven understanding of what skills are needed, any skilling effort risks being mis‑aligned.

2. Emerging AI‑Related Skill Gaps – OECD Perspective

Angelica Salvi Del Pero (OECD)

PointSummary
Skills as a barrierA 2022 survey of manufacturing & finance firms found 40 % could not adopt AI because of missing skills; a 2025 SME survey reported 50 % facing the same barrier.
Changing skill compositionAI raises the educational bar – jobs most exposed to AI (where AI complements workers) see faster employment growth, driving demand for higher‑educated workers.
Training as the main leverOnly ≈ 50 % of workers reported receiving employer‑provided training; those who did reported better job quality and performance.
Beyond codingOnly ~ 1 % of the workforce will become AI specialists. The majority need AI‑augmented literacy: data understanding, tool usage, limitation awareness, and ethical considerations.
Modular, flexible pathwaysTraining must be portable, stackable, and accessible to informal, platform, and temporary workers—not just full‑time employees.
Recognition of credentialsMicro‑credentials need standardised certification so employers can recognise them nationally and internationally, enabling mobility along the AI value chain.

Recommendation: Invest in life‑long, modular training that is recognised across borders to move workers from low‑value AI tasks to higher‑value AI‑enhanced roles.

3. Structural Realities of the Global South – CSIS View

Anjali Kaur (CSIS)

  1. Three structural constraints

    • High informality of labour markets.
    • Labour mobility (frequent sector switches).
    • Limited institutional capacity for rapid curriculum updates.

  2. Foundational AI literacy at scale

    • Focus on short, mobile‑first, locally‑language modules that teach “how to use AI tools safely” rather than deep coding.
    • Emphasise stackable credentials that travel with the worker, not with a specific employer.

  3. AI embedded in everyday tools

    • WhatsApp Business AI, AI‑enhanced digital payment apps, AI crop‑advisory systems — these are infrastructure, not stand‑alone products. Workers will adopt AI indirectly through tools that already solve immediate business problems (pricing, inventory, credit).

  4. Productivity‑focused skilling for MSMEs

    • 70‑80 % of employment in the Global South resides in micro‑ and small‑medium enterprises (MSMEs).
    • Proposes public‑private partnerships with fintech, digital‑tech startups, and regulatory co‑financing to embed AI tools in MSME workflows.

  5. Institutional agility

    • Existing Industrial Training Institutes (ITIs) were built for slower industrial cycles.
    • Calls for annual skill‑gap reviews, instructor upskilling every 18 months, and contextual sector strategies (the AVPN toolkit).

Policy Choice: Adopt low‑friction, embedded AI tools and agile, data‑informed training ecosystems to boost productivity and income resilience.

4. Ecosystem Approach & Gender Lens – AVPN Perspective

Naina Subberwal Batra (AVPN)

  • Ecosystem mindset: AI skilling must simultaneously involve governments, NGOs, private sector, and philanthropies; it is not a linear pipeline.
  • Local relevance over “global best practice”: Tools that work in a specific country must be scaled locally before being exported.
  • Partnerships & funding: AVPN’s AI Opportunity Fund (Phase 2), co‑funded by Google, the Asian Development Bank, and US‑based partners, targets 27 M USD across Asia‑Pacific.
    • India alone expects 174 000 beneficiaries (rural, low‑digital‑literacy communities).
  • Gender inclusion: Only < 10 % of AI technologies reach women. A gender‑responsive design is essential—from content to delivery channels.
  • Offline village training model:
    • Partnered with People’s Action for Development (PAD) to deliver fully offline, face‑to‑face AI training to 4 000+ villagers.
    • Trained participants used Gemini (Google’s LLM) via voice‑based, local‑language queries for agriculture, weather, health, and government schemes.
  • Micro‑finance linkage: Embedding AI tools within micro‑finance networks accelerates adoption by MSMEs, especially women‑owned enterprises.

Key Takeaways

5. Private‑Sector View – Google’s AI Opportunity Fund

Chenie “Jenny” Yoon (Google)

  1. AI Opportunity Fund Overview

    • $27 M grant programme across the Asia‑Pacific, with Phase 1 (train‑the‑trainer) and Phase 2 (sector‑specific curricula).
    • India: 174 000 beneficiaries targeted; Gujarat pilot delivered 52 000 workers a 15‑hour, sector‑specific curriculum (food processing, beauty, micro‑entrepreneurship).

  2. Narrative of augmentation vs. replacement

    • AI augments rather than replaces jobs. “Someone who knows how to use AI will out‑compete someone who doesn’t.”
    • Emphasises “human‑in‑the‑loop” thinking: critical thinking, creativity, and contextual judgement remain essential.

  3. Real‑world success story

    • College students produced a short film entirely with AI, saving tens of thousands of dollars in production costs—a concrete illustration of AI lowering entry barriers.

  4. Learning outcomes & productivity

    • Only a minority of respondents reported less office time after AI use, indicating that skilling must be problem‑oriented (e.g., creating logos, price lists, packaging designs).

  5. Scalable design principles

    • Relevance: curricula built around existing business tools (e.g., voice‑based AI in Gujarati).
    • Modularity & flexibility: short, targeted modules that can be blended into daily workflows.

  6. Future‑focused recommendations

    • Standardised digital credentials (Google Career Certificates) integrated into India’s Skill India Digital Hub.
    • Agile governance to keep policy and training in step with rapid AI advances.
    • Regional mobility frameworks for cross‑border recognition of AI‑skill certifications.

Actionable Insight: Industry can provide widely‑recognised, market‑aligned credentials and co‑design sector‑specific curricula that leverage existing tools, thereby shortening the learning curve for underserved workers.

6. Q & A – Themes and Debates

Question / PromptMain Points Raised
What roles are transforming most rapidly? (Moderator to “Jenny”)AI tools are permeating white‑collar, blue‑collar, and informal work; the speed of transformation varies, but human judgment remains crucial.
Women’s participation in AI training (Anjali & Angelica)Women are not less likely to enrol; the gap lies in topic relevance and the type of jobs they hold (often lower AI exposure). Emphasise non‑coding, tool‑use training.
How to reach underserved MSMEs? (Anjali)Four design levers: 1) Self‑help groups & cooperatives; 2) Voice‑first interfaces in local languages; 3) Gender‑disaggregated income tracking; 4) Micro‑finance integration.
Labour data blind spots (Angelica)1) Informal sector invisibility; 2) Gender invisibility (unpaid care, home‑based work); 3) Outcome distortion (certification without tracking income mobility).
Minimum data for AI policy (Angelica)Blend quantitative (real‑time job vacancy analytics, transaction‑level data) with qualitative (worker narratives, employer feedback); use labour‑data twins and annual industry consultations.
Measuring long‑term societal impact (Google)Three pillars: 1) Standardised digital credentials; 2) Agile governance & monitoring; 3) Regional mobility frameworks for cross‑border credential recognition.

7. Closing Vision – What Should Be Achieved by February 2027?

Each panelist offered a concise “one‑thing‑to‑solve” statement:

SpeakerAspiration for 2027
Abhineet KaulClear research‑backed AI‑skilling objectives and a coordinated, evidence‑based rollout across Asian economies.
Anjali KaurPortable AI literacy that seamlessly bridges formal and informal sectors.
Jenny (Google)Scalable voice‑first AI tools (e.g., Health Bonnie) that deliver essential services in native languages.
Angelica Salvi Del PeroCertified micro‑credentials for the top‑50 AI‑augmented jobs, accepted by employers globally.
Naina Batra (AVPN)Greater gender representation on panels and in skilling programmes, ensuring women’s voices shape AI policy.

See Also:

Graph View

Backlinks