Accelerating India’s AI Growth – A Blueprint for India’s AI Success

Abstract

The session introduced a practical “AI Blueprint” that outlines how India can scale AI infrastructure, build a future‑ready workforce, and embed responsible governance. Dell Technologies, in partnership with the Indian government and the broader ecosystem, framed three strategic pillars—Invest, Innovate, Evolve—and explored concrete policy levers (e.g., GST waivers, tax incentives), data‑center expansion plans, and the need for a trustworthy, “zero‑trust” AI architecture. A round‑table panel dissected the challenges for MSMEs, trust building, institutional capacity, and strategic autonomy, while a concluding fireside chat highlighted public‑private collaboration as the engine for inclusive, sustainable AI adoption across India.

Detailed Summary

Host (Mridu Bhandari) welcomed the audience, framed the session as a call to action aligned with the summit’s goal of “bridging the global AI divide”.
She announced a 55‑minute “journey” that would map the execution pathway for AI adoption from an industry perspective.

2. Keynote – “Architecting India’s AI Leadership: A Blueprint for Transformation”

(Delivered by Dr Vivek Mohindra, Special Advisor, Dell Technologies Global)

Pillar	Core Message	Illustrative Data / Recommendations
Invest	Build sovereign, scalable compute and data foundations; ensure energy supply for data‑centers; democratise access for MSMEs.	- Projected AI compute growth in India > 10 XFLOPs. - AI workloads expected to grow 30 % CAGR over the next few years. - Current GPU inventory: ≈ 40,000 vs. estimated need ≈ 200,000.
Innovate	Close the AI skills gap from school to enterprise; foster public‑private collaboration for a “future‑ready workforce”.	Suggestion – when Minister Chaudhary joins, the discussion will dive deeper into skilling pathways.
Evolve	Establish agile, responsible governance; balance regulation with innovation; maintain an “agile, trusted AI” ecosystem.	Emphasised that regulations must be “agile” to keep pace with rapid AI evolution.

Strategic Insight – The blueprint is a public‑private partnership (PPP) model that marries public resources (policy, data, compute subsidies) with private innovation (hardware, platforms, services).
Call to Action – Attendees were urged to read the full blueprint, provide feedback, and collaborate on implementation.

3. Panel Discussion – Translating the Blueprint into Action

Moderator: Mridu Bhandari (continued).

3.1. Unlocking Compute Access for Start‑ups & MSMEs

Speaker: Raj Gopal A S (NextGen Cloud Technologies)

Current Landscape – India’s AI Mission offers subsidised GPU access, yet only ~40 k GPUs are available vs. an estimated 200 k needed.
Policy Levers Proposed
- GST Waiver on Imported Servers: Removing upfront GST would cut initial capital cost by ~18 %.
- Income‑Tax Incentives for AI service providers that host workloads domestically.
- Tax Holiday already in place for export‑oriented services; similar relief needed for domestic AI services.
Industry Example – NextGen helped the Election Commission de‑duplicate 90 crore photos in 51 hours, showcasing AI’s scale‑up potential.

3.2. Trust & Non‑Technical Bottlenecks

Speaker: Prof. Bhaskar Chakravarti (Tufts University)

Trust Infrastructure – Defined as a mix of data governance, privacy, transparency, grievance redressal, and digital literacy.
Bottom‑Up Trust Gap – While Indians display high digital trust, institutional mechanisms vary dramatically across districts (e.g., Telangana vs. Jharkhand).
Key Trust Pillars
1. Transparency – Explainable AI, open model disclosures.
2. Redress Mechanisms – Clear pathways for users to contest AI decisions.
3. Digital Literacy – Up‑skilling citizens to understand AI outputs.

3.3. Aligning Industry, Academia & Policy (Innovation Pillar)

Speaker: Manish Gupta (Dell Technologies India)

Stressed the synergy of skilling (large talent pool) with infrastructure (data‑centers).
Highlighted energy‑efficiency & sustainability in Dell’s data‑centre designs.
Cited NextGen’s work on ultra‑efficient data‑centres as a model for democratized compute.

3.4. Data‑Center Strategy for Nationwide Scale

Speaker: Raj Gopal A S (continued)

Geographic Expansion – Plan to deploy ≈ 100 MW of data‑centre capacity across six states (beyond the Mumbai‑Chennai concentration).
Use‑Case Drivers – Education, healthcare, citizen services demand high‑performance AI workloads.
Interconnectivity – Leveraging India’s railway, power, and telecom networks for low‑latency, distributed compute.
Investment Needs – Multiple billion USD required; open‑source stacks can dramatically reduce per‑compute cost, enabling affordability for the bottom 90 % of the population.

3.5. Institutional Capacity for Inclusive Growth

Speaker: Prof. Bhaskar Chakravarti (continued)

Identified three “high‑impact” sectors: Agriculture, Skilling, Healthcare.
Stressed Institutional “trust infrastructure” – e.g., a farmer must trust a phone‑based pest‑diagnosis app via explainability and local language support.

3.6. Building Strategic Autonomy – Domestic Capabilities

Speaker: Manish Gupta (continued)

Three Foundational Capabilities for the Next Decade
1. Developer‑Centric Talent Pipeline – Move from 1 billion users to 1–10 million AI developers.
2. “Trusted‑in‑India” Hardware & Software – Partner with organisations such as AI Safety Institute (AISI) for governance.
3. “UPI‑of‑AI” – A national, open, low‑cost API layer that aggregates datasets (≈ 7 000) and compute for any stakeholder.

3.7. Speed vs. Caution – Regulating AI

Speaker	Position on Regulation
Raj Gopal	Regulation should not stifle innovation; treat AI as a utility – adopt a “light‑touch, outcome‑focused” approach, iterate as risks emerge.
Prof. Bhaskar	Analogy: a Ferrari on a pothole‑riddled road – speed is irrelevant without a safe infrastructure (trust, transparency, job‑impact safeguards).
Manish Gupta	“Agility and security are complementary”; existing frameworks (DPDP, DEPA, AISI) provide a solid foundation; focus on real use‑cases and monetisation pipelines to move pilots to production.

4. Fireside Chat – Public‑Private Partnership (PPP) for AI Scale

Participants: Hon’ble Shri Jayant Chaudhary Ji (Minister of State for Education & Skill Development) & Dr Vivek Mohindra.

4.1. PPP as the Engine for Infrastructure & Trust

Minister Chaudhary highlighted the “second‑mover advantage”: rapid top‑down policy push combined with open‑access compute (≈ 38 000 GPUs already deployed, target > 100 000 by year‑end).
Emphasised human‑centric AI: democratise access, ensure equity, and embed AI in education, up‑skilling, and citizen services.
Cited Sarvam (IIT Madras incubator) as a PPP‑driven, ultra‑low‑cost compute platform (₹65/hr vs. a cinema ticket).

4.2. Dell’s Role in Skills & Tier‑2/3 Enablement

Dr Mohindra outlined Dell’s three‑tier skilling model: school → college → employment.
Commitment to AI apprenticeships, future‑skill labs, and partnerships with the Ministry of Skill Development to reach tier‑2/3 towns.
Noted the price advantage of Indian GPU clouds as a catalyst for mass up‑skilling.

4.3. Zero‑Trust AI Architecture at National Scale

Speaker	Key Points
Minister Chaudhary	Zero‑trust means verifiable, auditable data pipelines – anonymised datasets released for research, national AI stacks for education & skill portals, audit trails (potential CAG reports).
Dr Mohindra	Extend zero‑trust from data → models → deployment → IAM; propose a national risk registry, real‑time observability, and mandatory incident reporting.

4.4. Closing Call‑to‑Action

QR code displayed for downloading the Dell Technologies AI Blueprint.
Audience thanked; note that no audience Q&A was possible due to time constraints.

5. Closing Remarks & Acknowledgements

Mridu Bhandari thanked all panelists, the Minister, and the audience.
A brief photo‑op and applause marked the end of the session.

Key Takeaways

Three‑pillar Blueprint – Invest in sovereign compute & energy, Innovate through massive skilling, Evolve with agile, responsible governance.
Compute Gap – India needs ≈ 200 k GPUs; current supply (~40 k) is insufficient. Policy levers such as a GST waiver on servers and tax incentives were proposed.
Public‑Private Partnership (PPP) is the core delivery model: government‑funded compute, industry‑built platforms, academia‑driven research.
Trust Infrastructure – Transparency, explainability, grievance mechanisms, and digital literacy are essential non‑technical foundations for AI adoption.
Zero‑Trust AI Architecture – A national framework that verifies data, models, access, and provides auditability (e.g., a CAG AI audit) is envisioned.
Decentralised Data‑Centre Strategy – Deploy ≈ 100 MW across six states, interconnect via rail, power, and telecom networks to serve education, health, and citizen services.
Skill Pipeline Goal – Shift from a billion‑user base to 1–10 million AI developers, leveraging low‑cost compute and partnership with the Ministry of Skill Development.
Regulatory Balance – Speed should not be curtailed by heavy‑handed rules; instead adopt a light‑touch, outcome‑focused approach that iterates as risks materialise.
Strategic Autonomy – “Trusted‑in‑India” hardware/software and a “UPI‑of‑AI” open API layer are required to avoid dependence on external AI ecosystems.
Immediate Action – Stakeholders were urged to download the full Blueprint, provide feedback, and begin co‑creating sector‑specific pilots that align with the Invest‑Innovate‑Evolve framework.

See Also:

India AI Impact Summit 2026

Explorer

accelerating-indias-ai-growth-a-blueprint-for-indias-ai-success