From Volume to Value: Role of AI in Redefining Indian Pharma’s Leadership for Viksit Bharat 2047

Abstract

The panel explored how artificial intelligence can help India’s pharmaceutical sector move from a volume‑driven “generic‐supplier” model to a value‑driven innovator aligned with the Viksit Bharat 2047 vision. Speakers discussed AI‑enabled talent creation, IP generation, enterprise‑wide deployment, data‑infrastructure gaps, regulatory frameworks, and collaborative ecosystem building required to scale affordable, high‑impact medicines for the country and the world.

Detailed Summary

• Priyanka welcomed participants and outlined the session’s focus.
• She described India’s current pharma stature: ~20 % of global generic medicines and ~60 % of world vaccines supplied to 190+ countries.
• The Viksit Bharat 2047 ambition is a USD 500 bn pharma industry, requiring a shift from volume to value (i.e., innovative NCEs/BNEs that capture > ⅔ of global pharma value).
• Recent Union Budget “Biopharma Shakti” policies emphasize biologics, advanced manufacturing, deep‑tech integration, and AI.
• AI is framed as an enabling layer across discovery, development, manufacturing, patient engagement, not a standalone solution.

2. AIRO Vision & Early Initiatives – Dr. Amit Sheth

2.1 Mission Statement (Three‑point summary)

1. Create world‑class AI talent in India (counteracting the talent shortage).
2. Generate original AI‑driven IP and move prototypes quickly to market (PPP model).
3. Provide an ecosystem for startups and corporates to leverage AI cost‑effectively and accelerate commercialization.

2.2 Talent Development

• AIRO has recruited senior AI academics (e.g., Dr. Neogi – UT Austin; Dr. Vasant Honar – Penn State; Dr. Srinivasan – Ohio State).
• Trained ~190 PhD‑level researchers; aims to match US‑grade AI expertise within a few years.

2.3 Startup & Corporate Collaboration

Initiative	Description
Startup Funding Pipeline	Partnered with investor Juhi Bhattnagar; broad pre‑seed to growth‑stage funding available; AIRO supplies core AI development, reducing costs and raising success odds.
PPP with Corporates	Short‑term, high‑impact projects delivering measurable economic benefit; pharma highlighted as top R&D‑per‑capita investor.

2.4 Concrete AI Opportunities (Near‑term, 1‑2 years)

1. Enterprise‑wide Knowledge Graph – world’s largest pharma KG (latest version refreshed 2 years ago). Enables question‑answering, rapid cross‑linking of efficacy, toxicology, and mechanistic data.
2. Neuro‑symbolic Drug Design – diffusion models guided by KG for small‑molecule design (driven by Dr. Honawar’s work).
3. Clinical‑Trial Data Quality – AI‑based workflow monitoring to enforce FDA‑style data completeness, reducing missing‑data penalties.
4. Manufacturing Quality & Root‑Cause – sensor‑driven anomaly detection, automated re‑planning, and rare‑event identification.
5. Regulatory Compliance Automation – transform regulatory documents into structured KG, auto‑populate filing forms, and flag non‑compliance.

2.5 Collaboration Roadmap with IPA

• AIRO will engage the 30‑plus IPA members to surface priority use‑cases, develop shared projects, and define a year‑one pilot pipeline.

3. Zydus Lifesciences – AI Roadmap (Dr. Sharvil Patel)

3.1 Enterprise‑wide AI Adoption

• Zydus has historically run piecemeal pilots; the new goal is AI as a core enterprise platform (akin to SAP rollout).

3.2 Immediate High‑Impact Domains

Domain	AI‑enabled Value Proposition
Regulatory & Quality Documentation	Language‑model‑driven drafting, automatic compliance checks, faster variation filings.
Manufacturing & Operations	Predictive analytics for “golden batch” identification, proactive deviation prevention, 30‑50 % productivity uplift.
R&D (Long‑term)	Lead optimisation, multi‑indication exploration, faster hypothesis testing; recognizes that ROI will be visible over longer horizons.

3.3 Shift from Reactive to Preventive

• Emphasis on first‑time‑right manufacturing, reducing re‑work, and integrating AI into quality‑by‑design.

3.4 Scaling Strategy

• Identify quick‑win AI use‑cases, build ownership structures, and embed AI change‑management to gain user adoption across functional silos.

4. Eli Lilly Global Perspective (Mr. Winslow Tucker)

4.1 AI as Accelerator & Personalisation

• AI speeds innovation to market and enables patient‑centric experiences across the value chain.

4.2 Global Capability Center – Hyderabad

• Newly opened center focuses on building data foundations, governance, and AI infrastructure that can be exported globally.

4.3 Commercial & Real‑World Data (RWD)

• AI can map patient locations, needs, and preferences, improving sales targeting and medical outreach.
• Need for interoperable, high‑quality RWD in India to satisfy payers, regulators, and researchers.

4.4 Infrastructure Challenges in India

• Current data ecosystems (health records, hospital‑pharmacy networks) are fragmented; public‑private partnerships are essential to build a unified data layer.

5. Government & Policy Outlook (Mr. Sudarshan Jain)

5.1 Current Landscape

• India supplies 20 % of world medicines, yet innovation lag behind.
• Budget: launch of 1,000 clinical‑trial centres, emphasis on AI in healthcare and open data systems.

5.2 Key Gaps

1. Clinical‑Trial Participation – India conducts only 1 % of global trials despite 17 % of world population; enrollment time ~12 months vs. 3 months in China.
2. Data Fragmentation – Scattered datasets across IITs, ISI, pharma houses; need for a national knowledge‑graph and data‑integration platform.

5.3 Strategic Priorities

• ICH Harmonisation for regulatory alignment (2‑year timeline for trials, 5‑6 years for quality).
• AI Platform via IPA‑IRO collaboration: shared best‑practice repository, regular progress reviews, and a public portal for start‑up matchmaking.

6. Trustworthy AI & Neuro‑Symbolic Methods (Dr. Amit Sheth – second turn)

6.1 Data Quality Enhancements

• Recent OCR advances (e.g., Sarvam) improve digitisation of legacy data across languages.

6.2 Explainability & Safety

• Neuro‑symbolic AI merges deep‑learning pattern recognition (neuro) with symbolic knowledge graphs for human‑readable reasoning.
• Example: Mental‑health co‑pilot – symbolic constraints prevent AI from asking suicidal‑risk questions, ensuring safety.

6.3 Alignment with Regulations

• Symbolic layer encodes clinical guidelines, guaranteeing AI outputs remain within regulatory limits (non‑hallucination, auditability).

7. Organizational Challenges & Enablers

Challenge	Speaker Insight
Data Trust & Silos	Dr. Patel: “People assume data exists; reality is fragmented, untrusted, and not democratized.”
Pilot Addiction	Dr. Patel: “Too many pilots without owners → expensive demos.”
Talent Gap (Translators)	Dr. Patel: “Need AI‑business translators; not just engineers.”
Leadership & Change Management	Winslow Tucker: “Clear AI vision, transparent roadmaps, and workflow integration are critical.”
Measurement & ROI	Winslow Tucker: “Define metrics early; iterate or abort based on evidence.”

8. Ecosystem‑Building Wishes (Collective)

• Democratise AI tools: shared regulatory intelligence, quality‑knowledge bases, and open‑access AI labs (Zydus pledge).
• Identify 50 high‑impact AI use‑cases with measurable time, quality, compliance, cost outcomes (IPA goal).
• Create a central IPA‑AI platform for best‑practice exchange, start‑up vetting, and coordinated pilot rollout.
• Establish trust through explainability, validation, and governance (highlighted by Dr. Sheth).

9. Audience Q&A Highlights

Question (summarised)	Respondents / Key Points
Start‑ups entering pharma	Mr. Jain: Need an IPA‑run portal to funnel start‑up proposals; corporate leaders will later reach out.
Speed of innovation vs. regulatory constraints	Panel: Simplify clinical‑trial processes (budget focus), new CDSCO scientific cadre, leverage Aadhaar‑derived datasets for real‑world evidence; AI can accelerate but must respect privacy & regulation.
Data fragmentation & where AI will advance fastest	Winslow & Dr. Sheth: Manufacturing data is most structured → quickest AI gains; clinical‑trial & post‑marketing data hold next‑level potential once integrated.
Measuring AI impact	Winslow: Define clear KPIs, monitor adoption, and adjust pilots accordingly.
Policy‑maker education	Panel: Ongoing training on AI toolkits for regulators; new “deregulation committee” chaired by Rajiv Gaba aims to streamline guidelines.

10. Closing Remarks

• Moderator thanked panelists, noting optimism about moving from pilots to large‑scale deployment.
• Audience encouraged to continue discussions in breakout rooms.

Key Takeaways

• AI as a strategic enabler: The consensus is that AI must be embedded across the entire pharma value chain, not treated as a standalone project.
• Talent & IP creation: AIRO’s mission to build world‑class AI talent in India and generate AI‑driven IP is seen as a cornerstone for sovereign innovation.
• Enterprise‑wide rollout over pilots: Both Zydus and Eli Lilly stress the need for clear ownership, change‑management, and measurable KPI frameworks to move beyond fragmented pilots.
• Data infrastructure is the bottleneck: A national, interoperable health data ecosystem (including Aadhaar‑linked health IDs, open‑access knowledge graphs, and RWD platforms) is essential for scaling AI.
• Regulatory alignment & explainability: Neuro‑symbolic AI offers a pathway to trustworthy, guideline‑compliant systems that satisfy both clinicians and regulators.
• Government support is growing: Budget allocations for clinical‑trial hubs, AI‑healthcare initiatives, and ICH harmonisation signal policy momentum.
• Collaboration over competition: IPA’s vision of a shared AI platform, open‑access tools, and 50 high‑impact use‑cases aims to lift the whole industry while preserving competitive product development.
• Short‑term quick wins: Manufacturing quality, regulatory documentation, and predictive analytics are the most data‑ready domains for immediate AI impact.
• Long‑term innovation: AI‑enabled drug design, multi‑indication expansion, and real‑world evidence generation will drive the shift toward a value‑centric, USD 500 bn pharma ecosystem by 2047.

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Prepared by the AI Conference Summarisation Team.

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