India AI Impact Summit 2026

empowering-the-human-edge-building-a-future-ready-workforce-in-the-age-of-ai

9 min read

TRACK:Human Capital
SPEAKERS
  • Aparna Ganesh, Tata Sons
  • Idris A. Rai, Inter University Council for East Africa
  • Jay Krishnan, Beyond Next Ventures
  • Phani Nagarjuna, Telangana AI Innovation Hub
  • Roma Datta Chobey, Google India
  • Sidharth Madaan, BCG
  • Tomoyuki Yamada, JICA
  • Vani Kola, Kalaari Capital
SESSION OVERVIEW

Bringing together governments, enterprises, and academic leaders, this session will spotlight how large-scale AI skilling and reskilling initiatives are being designed and delivered; the gaps emerging in AI talent supply; and what it takes to build a workforce that can adapt and thrive in an AI-driven economy. The discussion will surface practical lessons and scalable approaches for building inclusive, future-ready AI talent ecosystems.

VIDEO RECORDING

Empowering the Human Edge: Building a Future-Ready Workforce in the Age of AI

Abstract

The panel examined how large‑scale AI up‑skilling and reskilling can be operationalised across governments, enterprises and academia. Participants discussed the paradox of teaching AI‑augmented skills while still assessing without AI, the need for domain‑specific AI fluency, and the importance of change‑management at organisational scale. Case studies from Telangana’s ICOM initiative, Google’s nation‑wide skilling programmes, the East‑African university network, and venture‑capital perspectives illustrated practical pathways and pitfalls. The conversation also touched on gender inclusion, the role of curiosity‑driven mindsets, and the long‑term race to build an AI‑native workforce that is both inclusive and globally competitive.

Detailed Summary

Moderator (Matthew) opened the session by posing a series of questions about how organisations can “up‑skill” their workforce for an AI‑driven future. He highlighted a recurring tension:

  • Paradox of Assessment – learners are taught to use AI tools, yet examinations and performance reviews often forbid AI assistance.
  • Tool‑Dependency vs. Skill‑Retention – the danger of “teaching with a hammer and then asking the student to build without it.”

Key Insight – Ethical AI education must explicitly address how and when AI should be used, not merely that it should be used.

2. Real‑World Example: Empowering a Teacher through AI Learning

Matteo (Speaker not on the formal list, but a panel participant) recounted a personal anecdote about a 35‑year‑old UK sixth‑form teacher who, after completing Matteo’s Coursera AI course, struggled to translate knowledge into action. Through a brief coaching call, the teacher discovered that he could be the catalyst for AI adoption in his school by organizing an AI discussion panel for staff and students.

  • Lesson – Structured AI learning can unlock agency; the solution often already exists in the learner’s mind and merely needs a catalyst.
  • Recommendation – Provide mentorship platforms that encourage learners to become “agents of change” rather than passive consumers.

3. Educator Concerns: AI as a Source of “Frantic” Workloads

An audience member (AJ, lead of digital health & data science for an NGO) raised a common fear: AI expands the scope of work, making educators feel overwhelmed. The panel responded that resistance to change is a human constant and varies by career stage.

  • Sanjay (likely Sidharth Madaan, BCG) noted that senior faculty may be slower to adopt AI, while younger professionals tend to be more flexible.
  • Illustrative Example – A professor at IIT Thirupati now uses “Notebook LM” (a large‑language‑model‑augmented notebook) to guide undergraduate research, showing that the right tooling can reduce mundane tasks and free time for mentorship.

Key Insight – Successful AI integration hinges on psychological readiness and incremental benefit (i.e., showing faculty that AI saves time rather than adds to it).

4. Change Management at Scale: Tata Sons’ Perspective

Aparna Ganesh (Tata Sons) described how a conglomerate of Tata’s size approaches AI transformation:

  1. AI Champions Programme – Mid‑level managers are trained as “AI champions” to bridge senior leadership vision and technical execution.
  2. Continuous Upskilling Loop – Because AI tools evolve faster than a typical training cycle, the company institutes rolling learning modules that are refreshed quarterly.
  3. Three Pillars of Success
    • Domain‑specific AI fluency (e.g., finance + AI, manufacturing + AI).
    • Leadership “Art of the Possible” workshops to expose senior execs to emerging use‑cases.
    • Technical talent pipeline that is kept current through partnerships with academia and specialist startups.

Challenge Highlighted – The rapid tech churn means the organisation is always playing “catch‑up,” necessitating a culture of perpetual learning.

5. State‑Level Coordination: Telangana AI Innovation Hub

Phani Nagarjuna (Telangana AI Innovation Hub) announced the launch of ICOM (Integrated Centre of AI Operations), an autonomous body created to unify all AI initiatives across the state.

  • Mandate of ICOM – Align skilling, startup acceleration, financing, co‑creation with industry, and joint research with global partners.
  • Strategic Rationale – AI is a “runaway train” and a national security matter; fragmented efforts would dilute impact.
  • Concrete Actions –
    • Consolidated funding streams for AI startups.
    • Established a “trainer‑multiplier” model (train‑the‑trainer) through the Gemini Academy, reaching over 1 million educators.
    • Partnered with Internet Saathi (digital literacy programme) to broaden reach into rural communities.

Outcome Metric – Early pilots have already generated ~26 000 jobs via Google‑backed accelerators, signalling a tangible economic multiplier.

6. Scaling AI Skilling in India: Google’s Ecosystem Approach

Roma Datta Chobey (Google India) outlined Google’s multi‑pronged strategy to democratise AI learning:

PillarDescriptionImpact
Lowering Entry BarriersFree, multi‑language tools (Gemini LLMs in 9 languages, expanding to 22).Reaches students in small towns and regional languages.
Application‑Centric LearningCurriculum links theory to real‑world problem solving (e.g., startup‑school projects).96 % survival rate for growth‑stage startups that completed Google’s accelerator.
Trainer‑Multiplier Effect“Gemini Academy” up‑skilling of >1 M teachers, professors and community trainers.Enables a cascade of AI education to millions of learners.
Deep PartnershipsAlliances with NGOs (Internet Saathi), government (India AI Mission), and philanthropic arm Google.org.Leverages funding, technology, and policy alignment.

Key Data Point – Over 30 000 participants across 700 + Indian cities have completed Google’s AI‑focused programmes.

7. Higher‑Education Coordination in East Africa

Idris A. Rai (Inter‑University Council for East Africa) explained the council’s role in harmonising AI education across the eight member states of the East African Community (EAC).

  • Strategic Objectives – Use higher education as a platform for regional integration, policy advocacy, academic mobility, and joint research.
  • AI‑Centric Initiatives –
    • Annual AI conference (June 2023) – First regional AI‑focused academic gathering.
    • Teacher‑Training Programme – Collaboration with UNESCO to up‑skill faculty on AI‑enhanced pedagogy.
    • Federated Centres of Excellence – Networked university nodes co‑creating curricula and joint research projects.

Challenges Identified –

  • Severe skill‑gap in AI expertise among faculty.
  • Limited infrastructure (computing resources, broadband) in many institutions.

8. Venture‑Capital View: Market Dynamics & Inclusive Growth

Jay Krishnan (Beyond Next Ventures) and Vani Kola (Kalaari Capital) offered a market‑oriented perspective on AI talent and startup ecosystems.

  • Value‑Chain Insight (Jay) – Jobs are not eliminated; tasks are displaced. Successful professionals must identify the one task they excel at (e.g., high‑level decision‑making, accountability) and augment it with AI.
  • Capital vs. Talent (Jay) – In a globalised capital market, access to capital increasingly outweighs sheer talent supply. India’s advantage lies in data diversity and the potential to build public‑good AI infrastructure (e.g., finance‑as‑infrastructure).
  • Risk of Concentration (Vani) – If AI development remains dominated by a few large firms or countries, wealth may concentrate. However, the panelist stressed that long‑term historical patterns (land → oil → tech) show technology eventually democratises wealth.
  • Ecosystem‑Led Skilling – Emphasised that outcome‑focused programmes (jobs, not just certificates) are essential; they require alignment of startups, investors, industry, and academia.

9. Inclusion & Gender: Raising Women in AI

Jeanette (Research Scholar, New School) posed a question on gender parity in AI. The panel responded:**

  • Roma (Google India) highlighted multilingual LLM access (Gemini LLMs in 22 languages) that enables girls in remote regions (e.g., a teenager in Sreerampur) to query AI in their native language, lowering barriers to learning.
  • Vani (Kalaari Capital) underscored the importance of role models and targeted mentorship to increase women’s participation in AI design and decision‑making.
  • The panel announced an “AI for Her” session the next day dedicated to women leaders, signalling a concrete follow‑up initiative.

10. Audience Q&A – Key Themes

QuestionSpeaker(s)Summary of Answer
How to prevent AI from making work “more frantic”?Sanjay / Sidharth MadaanEmphasise AI as productivity‑enhancing, start with small pilots, provide clear ROI to faculty.
What does AI‑native organisation look like for large firms?Aparna GaneshAI champions, senior‑leadership vision workshops, continuous upskilling loops.
How can smaller economies (e.g., Africa) replicate India’s scale?Idris A. RaiLeverage regional policy harmonisation, build federated centres, partner with global bodies (UNESCO).
How to ensure AI initiatives stay inclusive and not just for elite towns?Phani Nagarjuna / Roma Datta ChobeyTrainer‑multiplier model, multilingual tools, community‑driven programmes (Internet Saathi).
What concrete steps to institutionalise curiosity, critical thinking and creativity?Jay Krishnan / Vani KolaEmbed prompt‑engineering labs in curricula, project‑based learning, assess by outcomes, not certificates.

11. Closing Remarks & Announcements

  • Moderator thanked the panel and audience, noting the need for robust change‑management across organisations.
  • Mementos were presented to each speaker (a symbolic gesture of appreciation).
  • Transition – The summit moved to a “AI for Her” dedicated session on women’s participation in AI.
  • Future Outlook – Panelists concurred that AI will continue to evolve as a runaway train; the decisive factor will be how quickly nations can mobilise talent and data at population scale.

Key Takeaways

  • AI Upskilling Must Pair Domain Knowledge with Tool Fluency – AI alone is not a standalone skill; professionals need complementary expertise (e.g., finance + AI, computer science + AI).
  • Paradox of Teaching vs. Assessing – Ethical AI curricula should explicitly define when AI assistance is permitted, avoiding a mismatch between learning and evaluation.
  • Change Management Is Critical – Senior leadership vision, mid‑level “AI champions,” and continuous learning loops are essential for large organisations to keep pace with rapid AI evolution.
  • State‑Level Coordination (ICOM) Demonstrates Scale – Telangana’s unified AI body showcases how governments can align skilling, funding, and research to create an AI‑ready ecosystem.
  • Google’s Multilingual, Trainer‑Multiplier Approach Reduces Barriers – Free tools in regional languages and a cascade‑training model reach millions, especially in underserved areas.
  • East African Higher‑Education Network Highlights Regional Collaboration – Harmonising curricula, faculty training, and research across borders accelerates AI adoption in the Global South.
  • Venture‑Capital Perspective: Focus on Tasks, Not Jobs – Professionals should identify the high‑value tasks they excel at and augment them with AI; capital access and data diversity are key competitive advantages for India.
  • Inclusion Imperative – Gender disparity remains stark; multilingual AI access and dedicated women‑focused sessions are concrete steps toward greater inclusivity.
  • Outcome‑Based Skilling Over Certificates – Effective programs tie learning to employability and real‑world impact rather than merely awarding diplomas.
  • Long‑Term Innovation Race Relies on Talent‑Data Mobilisation – Nations that can simultaneously scale skilled human capital and leverage diverse data will lead the next AI wave.

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