India AI Impact Summit 2026

ai-driven-digital-transformation-for-india-building-innovation-an-ecosystem-through-collaboration

7 min read

TRACK:Democratizing AI Resources
SPEAKERS
  • Abhilash Soundararajan, PrivaSapien
  • Akhil Gupta, Convozen AI and NoBrokerHood
  • Akhilesh Tuteja, KPMG
  • Anisha Singh, She Capital
  • Dr. Tapan Sahoo, Maruti Suzuki
  • Rohan Chhatwal, Maruti Suzuki
SESSION OVERVIEW

This session brings together leaders from government, industry, startups, and academia to discuss democratizing AI infrastructure for innovation. The session explores how accessible AI infrastructure can empower startups, MSMEs, and researchers, and how collaborative national models can sustain growth. It highlights the value of open repositories, shared use cases across sectors, and open-source platforms, while emphasizing ethical, inclusive AI design that prioritizes privacy, fairness and accessibility to help India lead global AI innovation.

VIDEO RECORDING

AI-Driven Digital Transformation for India: Building Innovation an Ecosystem through Collaboration

Abstract

The session opened with a welcome note that framed AI as a universal “leveler” and positioned speed as a new strategic capability. A short video traced AI’s evolution and highlighted Maruti Suzuki’s AI‑driven transformation across manufacturing, quality, and customer experience. Dr. Tapan Sahoo then detailed Maruti’s ambitious growth roadmap (aiming to multiply production six‑fold in 21 years) and outlined the company’s AI initiatives—responsible AI, smart factories, customer‑centric services, and a structured startup‑engagement program that has onboarded ≈ 6,200 startups (200 + active, 60 + paid POCs, 32 + business partners). Akhilesh Tuteja (KPMG) reflected on the nature of innovation, warning against feature bloat, echo‑chamber AI, and “fake” data, while urging a mindset‑centric, “vibe‑coding” approach. The core panel—moderated by Rohan Chhatwal and featuring leaders from She Capital, NoBroker/Convozen AI, T‑Hub, and PrivaSapien—debated how AI‑enabled startups can accelerate India’s journey to a “Viksit Bharat” by 2047, focusing on funding gaps, product‑market‑fit, patient capital, and the role of ecosystem collaborators. The session concluded with logistical announcements and a group photograph.

Detailed Summary

  • Theme framing: AI is a “great leveler” that has reset the competitive landscape; everyone—from seed‑stage founders to CEOs—must embrace it.
  • National vision: References the India AI Mission’s theme “Happiness for All, Welfare for All” and links it to the aspirational goals of “Aspiring India, Abundant India.”
  • Collaboration mantra: Cites internal discussions with “Dapan‑san” (Maruti senior leadership) that stress collaborate with all parties—big‑tech, startups, consultants—because no single entity can master AI.
  • Analogy of a refrigerator: AI is the “refrigerator”; the real value lies in what you store inside (data, insights), not in the hardware itself.
  • Speed as strategy: Compares AI‑driven execution to landing a fighter aircraft on a carrier—speed and precision become competitive weapons.
  • Transition: Announces a short audiovisual (AV) showcase of Maruti Suzuki’s AI journey.

2. Video Showcase – “Maruti Suzuki’s AI Journey” (≈ 2 min)

  • Traces AI from Alan Turing’s 1956 concept to today’s pervasive applications (navigation, language, health, digital services).
  • Highlights India AI Mission’s ₹10,300 cr public investment in GPUs, startup financing, datasets, and indigenous foundation models.
  • Shows Maruti’s AI uses: fault‑prediction in manufacturing, AI‑augmented quality inspection, customer‑service personalization, and mobility‑as‑a‑service (e.g., on‑demand drivers, parking, amenities).

3. Leadership Address – Dr. Tapan Sahoo (≈ 12 min)

3.1 Maruti’s Growth Ambitions

  • Long‑term vision: Aligns Maruti’s trajectory with India’s “Amrit Kal” (vision to 2047).
  • Scale targets: From ~4 M vehicles/year today to 24‑30 M by 2030, implying a 6× volume increase over the next 21 years.
  • GDP impact: Automotive sector currently contributes 7.1 % of India’s GDP (≈ 49 % of manufacturing GDP) with a goal to double its share by 2030.

3.2 Role of AI

  • Strategic enabler: AI is the linchpin for coordinating thousands of plants, touch‑points, and 40‑45 M customers.
  • Responsible AI framework: Already implemented to ensure ethical, fair, and privacy‑preserving deployments.

3.3 Concrete AI Initiatives

AreaExample
Customer JourneyIntegrated mobile platform (“clicker”) for seamless service, on‑demand driver, real‑time amenity finder.
Smart FactoriesConnected IoT ecosystem delivering real‑time visibility, predictive alarms, and automated controls.
Startup CollaborationFour‑stream program: Incubation (IAM Calcutta, IAM Bangalore), accelerator, mobility challenge (T‑Hub), and dedicated fund‑raise support.
Metrics6,200+ startups screened; 200+ engaged; 60+ paid PoCs; 32 became business partners.

3.4 Insight on Organizational Agility

  • Cites Tom Chizrite’s observation on the “high‑frequency change” of technology vs. slower human/organizational adaptation.
  • Positions startups as the agile “organ‑resistance” required to keep pace.

3.5 Closing Vision

  • Calls for a democratized AI ecosystem that fuels mobility innovation for every Indian—from Bengaluru coders to Punjab farmers.

4. Industry‑Expert Perspective – Akhilesh Tuteja (KPMG) (≈ 10 min)

4.1 Historical Context of Innovation

  • Mentions the first autopilot (1914) and earlier inventions (wheel, luggage trolley) to illustrate that “everything one could invent is already invented.”
  • Emphasizes that innovation is not unpredictable; rather, it’s about predictable, purposeful change.

4.2 “Vibe Coding” & AI‑Generated Code

  • Introduces “vibe‑coding” (2025 Word of the Year) – the notion that anyone can generate code by providing the right “vibe” to an AI system.

4.3 Three Cautionary Stories

  1. Remote‑Control Bloat – Feature creep leads to unusable products; incremental zero‑cost additions create “35‑button remote” syndrome.
  2. Echo‑Chamber AI – AI models that amplify biased or fake data, leading to homogeneous recommendations (e.g., Instagram reels).
  3. Fake‑Data Illusion – Over‑optimistic AI claims (“100 % accuracy”) that are substantiated by only a handful of real cases.

4.4 Mindset Over Skillset

  • Argues that skill‑set value declines as AI automates routine coding; mindset (domain expertise, curiosity, adaptability) becomes the decisive factor.

4.5 Call to Action

  • Encourages founders to focus on problem‑first thinking, avoid over‑engineering, and leverage AI as an accelerator, not a crutch.

5. Panel Discussion – “Accelerated Innovation for Nation‑Building: The Role of AI Startups in India’s Growth Story for Viksit Bharat by 2047” (≈ 30 min)

Moderator: Rohan Chhatwal

5.1 Funding Landscape – Anisha Singh (She Capital)

  • Current state: 200 k+ startups in India; 75 % are early‑stage (seed).
  • Challenge: Limited follow‑on capital; ≈ 80 % of startups fail to reach Series A/B.
  • Observation: Deep‑tech and AI‑focused ventures need patient capital; VCs are increasingly demanding performance (traction, margins) rather than just ideas.

5.2 Deep‑Tech Scaling – Kavikrut (T‑Hub)

  • Signal vs. Noise: Emphasizes rigorous product‑market‑fit (insane customer love, distribution channel, sustainable economics).
  • Early‑stage risk capital: Scarcity of funds for “pre‑Series A” deep‑tech; suggests leveraging government SISF grants and aggregated angel investments.
  • Ecosystem role: T‑Hub’s watch‑tower analogy—monitoring trends, facilitating rapid iteration, and encouraging founders to build the plane while flying.

5.3 AI‑Enabled Speed – Akhil Gupta (NoBroker & Convozen AI)

  • Disruption timeline: AI can compress a 10‑12 year growth trajectory into 2‑3 years via vibe‑coding and rapid prototyping.
  • IPO outlook: From 8 tech IPOs in 2021 to ≈ 41 projected in 2025; AI will accelerate this trend, fueling a thousands‑of‑companies future for 2047 India.

5.4 Startup‑Corporate Collaboration – Abhilash Soundararajan (PrivaSapien)

  • Real‑world problem: Startups must solve actual on‑ground business problems, not just speculative ideas.
  • Ecosystem need: Corporates (e.g., Maruti) should provide paid PoCs and clear use‑case briefs, enabling startups to demonstrate value and secure follow‑on business.

5.5 Women‑Led Entrepreneurship – Anisha Singh (re‑visited)

  • Focus: She Capital backs women‑led/focused ventures, viewing them as critical contributors to the broader consumer market.
  • Message: Perseverance, not AI hype, is the key differentiator; AI should augment founders, not replace the resilience required to succeed.

5.6 Risk‑Capital Models – Panel Consensus

  • Government SISF fund (₹10 000 cr) and grant mechanisms identified as primary sources for high‑risk deep‑tech capital.
  • Angel aggregation and HNI‑driven funds suggested as complementary routes.

5.7 Closing Remarks (Moderator)

  • Summarized three actionable take‑aways: patient capital, performance‑based funding, and problem‑first startup ethos.

6. Session Close & Logistics

  • Attendees thanked; brief housekeeping announcements (exits, next session location, group photograph).

Key Takeaways

  • AI as a Leveler & Speed Enabler – AI resets the competitive playing field; rapid execution (“fighter‑carrier landing”) is now a strategic advantage.
  • Collaborative Ecosystem Required – Maruti’s model of working with all tech partners (big‑tech, startups, consultants) is presented as the blueprint for national AI adoption.
  • Responsible AI Framework – Ethical, privacy‑preserving AI is mandatory before scaling across millions of customers.
  • Startup Program Success Metrics – Maruti’s four‑stream startup engagement has screened >6,200 startups, with 200+ active partners and 32 business collaborations.
  • Innovation Pitfalls – Feature bloat, echo‑chamber data, and “fake‑accuracy” claims must be avoided; focus on real problems and usable outcomes.
  • Mindset Over Skillset – As AI automates coding, founders’ domain expertise and adaptability become the decisive success factors.
  • Funding Gap in Deep‑Tech – Early‑stage AI/startup ventures need patient, performance‑driven capital; government SISF grants and aggregated angel funds are key to bridge the gap.
  • AI‑Accelerated Growth Trajectory – AI can compress a decade‑long development cycle into a few years, potentially increasing tech IPOs from <10 (2021) to >40 (2025) and fueling the “Viksit Bharat” vision.
  • Women‑Led & Inclusive Entrepreneurship – Targeted funding (e.g., She Capital) and inclusive policies are essential for a balanced AI‑driven economy.
  • Actionable Blueprint for Founders – Solve real problems, secure paid PoCs, be agile, seek early risk capital, and maintain ethical AI practices to thrive in India’s AI‑centric future.

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