India AI Impact Summit 2026

scaling-enterprise-transformation-how-india-can-leapfrog-in-the-ai-economy

7 min read

TRACK:Economic Growth and Social Good
SPEAKERS
  • Arpan Sheth, Bain & Company
  • Chinmaya Sharma, InfoEdge Ventures
  • Karthik Reddy, Blume Ventures
  • Mihir Shukla, Automation Anywhere
  • Sravanth Aluru, Avataar.ai
  • Tejeshwi Sharma, Peak XV
SESSION OVERVIEW

As AI adoption accelerates, enterprises are realizing that the next wave of transformation looks very different from early hype. This session brings together investors, founders, and transformation leaders to examine where enterprise AI creates real value, where it falls short, how companies can move from pilots to scaled impact, how operating models must evolve for the new "AI worker," and what this means for India Inc's opportunity.

VIDEO RECORDING

Scaling Enterprise Transformation - How India Can Leapfrog in the AI Economy

Abstract

The panel examined how AI is moving from experimental pilots to large‑scale enterprise transformation, with a particular focus on India’s unique position. Arpan Sheth opened with data showing AI’s rising priority among global firms and the rapid growth of AI‑centric revenue streams. Panelists then debated the distinctions between “signal” and “noise” in the market, the shift from seat‑ or token‑based pricing to outcome‑driven models, and the execution challenges that keep many pilots in a “purgatory” stage. The conversation broadened to address the human and change‑management dimensions of AI adoption, the role of Indian talent and services firms in delivering applied AI, and strategic opportunities for venture capital and public‑policy (e.g., the proposed Unified AI Interface). The session closed with consensus that Indian enterprises must act now, leveraging AI to create new economic value rather than simply cutting costs.

Detailed Summary

  • Welcome & moderator introduction – Arpan introduced the panel, noting the blend of founders, venture capitalists, and transformation leaders.
  • Brief on Automation Anywhere – Highlighted 5,000+ global customers, >400 million bots in production, major Indian banking clients, and the firm’s ecosystem with over $850 million AUM.
  • Avataar.ai snapshot – Mentioned the company’s focus on “applied AI, not models,” its participation in the India AI Mission, $55 million raised from Sequoia & Tiger Global, 10 patents, and an academic partnership with UC Berkeley.
  • InfoEdge Ventures perspective – Chinmaya’s role as lead AI partner, exposure to Indian‑scale AI founders (e.g., Ghani).

Key Insight: The panel brings together the “full‑stack” of AI transformation – product builders, capital providers, and consultancy expertise.

2. AI Adoption Landscape – Global & Indian Trends

Arpan displayed three slides summarising Bain’s quarterly AI survey:

Metric (global)Observation
Priority ranking20 % of firms name AI as top priority; 53 % place it in the top‑three.
Budget allocationAI spend is rising as a share of overall IT budgets; budgets are being re‑allocated from legacy areas to AI.
Revenue of AI‑centric vendorsOpenAI, Anthropic, Palantir each forecast 20 bn revenue for FY 2024, signaling rapid enterprise penetration.
  • India‑specific note: Early data suggest Indian firms are already among the most intensive users of Anthropic models.

Key Insight: AI is moving from experimental “toe‑in‑the‑water” projects to a strategic priority that commands sizable, growing budgets.

3. Signal vs. Noise – What “Enterprise AI” Really Means

Mihir Shukla (Automation Anywhere)

  • Enterprise AI ≠ ChatGPT – Treating a large‑language model like an English teacher leads to “hallucinations” when it is given engineering or finance problems.
  • The “Enterprise Worker” model – Automation Anywhere repurposes its 420 million bots into domain‑specific agents that understand corporate processes.
  • Outcome‑first mindset – The breakthrough firms (≈10 % of Indian enterprises) focus on outcomes rather than the promise of AI.

Karthik Reddy (Blume Ventures)

  • From seat‑based SaaS to outcome‑based pricing – Traditional per‑seat models are being replaced by pricing tied to cost‑reduction or revenue generation (e.g., replacing a $100k human resource).
  • Illustrative case – Confido Healthcare – An AI receptionist replaced a full‑time staff, driving a 10× revenue growth in a year.

Chinmaya Sharma (InfoEdge Ventures)

  • Cash‑flow impact – AI can unlock 400 M inventory savings for manufacturers, and $250 M IT‑software spend reduction for ticketing platforms.

Key Insight: The differentiator for successful enterprises is a clear, quantifiable outcome (cost‑avoidance, revenue uplift, risk reduction) that can be linked to AI‑driven automation.

4. Pricing Paradigms – From Tokens to Value Sharing

Chinmaya Sharma

  • Token‑based pricing limitations – Enterprises rarely understand “tokens” or “API calls”; they care about business results.

Karthik Reddy

  • Usage‑vs‑Outcome – Usage metrics (tokens, minutes, tickets) measure quantity, not quality; customers now ask for “how many loans originated and closed” rather than “how many minutes we talked”.

Sravanth Aluru (Avataar.ai)

  • Risk‑sharing model – Avataar.ai adopts a hybrid approach: a modest fixed platform fee plus a performance‑based component that captures a percentage of the value created (e.g., P&L cost‑savings).

Key Insight: The market is shifting toward value‑sharing contracts where AI vendors are financially aligned with the client’s ROI.

5. From Pilot to Scale – “Purgatory” and Execution Challenges

Arpan Sheth

  • Pilot fatigue – Many firms have built proof‑of‑concepts that achieve 70‑80 % accuracy but fail to deliver at scale.

Sravanth Aluru

  • Avoid over‑emphasis on incremental ROI – Instead of chasing a modest 5 % cost cut, frame AI as an investment that, if omitted, could jeopardize competitiveness.

Karthik Reddy

  • Human impact & change management – Replacing “dumb” jobs (e.g., repetitive data entry) is socially acceptable, but transformation must address the human side (re‑skilling, empathy in loan‑recovery, union negotiations).

Key Insight: The greatest barrier is execution – aligning technology, process redesign, and workforce transition to move beyond pilots.

6. The Human & Organizational Dimension

  • Compassionate dialogue – Example of a UK hospital that saved 4,000 lives via AI; a European union case where AI eliminated error‑prone roles, prompting a joint labor‑management solution.
  • Strategic messaging to CEOs – Emphasise AI as a future‑proofing lever rather than a pure cost‑cutting tool.
  • Population as an asset – Sravanth suggested using India’s large, digitally‑connected populace as a training‑data reservoir for reinforcement‑learning models, turning “population liability” into a strategic advantage.

Key Insight: Successful scaling hinges on transparent, empathetic stakeholder communication that balances efficiency gains with societal impact.

7. India’s Strategic Role in the Global AI Ecosystem

Mihir Shukla

  • Applied AI focus – India should concentrate on efficiency‑driven, high‑volume use‑cases rather than chasing frontier model research.

Karthik Reddy

  • Service‑to‑product transformation – Traditional IT services firms must evolve into AI‑transformation partners; otherwise “the big six” risk losing relevance.

Chinmaya Sharma

  • Local data advantage – India’s massive digital footprint (1.3 bn people, billions of UBI transactions, ONDC commerce data) offers a unique foundation for building domain‑specific AI solutions.

Sravanth Aluru

  • Foundational infrastructure – Building Indian‑owned accelerators, high‑bandwidth memory, and data‑centers is essential for a self‑sustaining AI ecosystem.

Mihir Shukla (closing remarks)

  • Unified AI Interface (UAI) – The government‑backed mission aims to create a “UPI‑for‑AI” that standardises data exchange, fostering rapid, interoperable AI deployments across sectors.

Key Insight: India’s comparative advantage lies in applied AI, talent depth, and data assets, which can be leveraged through a coordinated public‑private push (e.g., UAI) to become a global AI service hub.

8. Closing Remarks & Audience Takeaways

  • Call to action for CEOs & promoters: Adopt AI now; early adopters already showcase dramatic productivity gains (e.g., Bajaj Finance’s surge in call‑center capacity).
  • Panel consensus: AI transformation is an existential imperative for Indian enterprises; the window of competitive advantage is narrow.
  • Acknowledgements: Thanks to Bain for organizing, to the audience, and a brief announcement of a memento giveaway and the next session with Bharat Jain.

Key Takeaways

  • AI is now a top‑priority strategic initiative for the majority of global enterprises, with budgets growing rapidly.
  • Outcome‑first thinking separates the 10 % of Indian firms that are “break‑away” leaders from the 90 % still stuck in pilot mode.
  • Pricing is moving toward value‑sharing models (fixed fee + performance‑based component) rather than seat‑ or token‑based licensing.
  • Pilot “purgatory” is a execution problem, not a technology problem; scaling requires process redesign and workforce re‑skilling.
  • Human and change‑management considerations are essential—successful AI adoption must address employee impact, union dynamics, and societal expectations.
  • India’s competitive edge rests on its massive data assets, deep talent pool, and the ability to provide applied AI at scale rather than frontier model research.
  • The Unified AI Interface (UAI), modeled on India’s UPI success, could become the digital public infrastructure that accelerates AI adoption across sectors.
  • Venture capital sees AI as a catalyst for new business models (outcome‑based SaaS, AI‑native services) and is betting on rapid transformation of traditional IT services firms.
  • CEO mantra: “Don’t ask how much you can save today—ask what you cannot afford to miss tomorrow.”
  • Population as an advantage: Leveraging India’s large, digitally‑connected user base can fuel model training and reinforcement‑learning, turning a perceived liability into a strategic asset.

See Also:

Graph View

Backlinks