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Tuesday June 2nd 2026 | 10.00am - 5.00pm
*** This session is not open to everyone. It is an invitation only event ***
Sponsored by:
Pivot from Proof of Concept to Proven ROI
AI is driving the next wave of innovation in drug discovery, accelerating target identification, drug design and response prediction by streamlining complex, multi-modal data.
It promises to cut costs, reduce labour and prevent late-stage failures. Yet, as with any emerging technology, adoption comes with challenges, particularly in managing expectations during this transformative phase.
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10.30 Strategic AI Investment: From Proof to Impact
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Are we moving beyond proof-of-concept and delivering measurable ROI, leading to increased executive buy-in and scaled adoption of AI in drug discovery?
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Managing expectations: AI won't shrink timelines from 10 years to 2, but it can optimize decision-making, derisk assets, and improve pipeline productivity. How do we separate hype from actionable opportunity?
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How to build and manage an AI investment portfolio across the R&D pipeline—from early discovery to clinical validation—to ensure value at every stage.
Moderator: Rohit Arora, Associate Director, External Innovation Strategic Assessment - Data Science & Digital Health, Johnson & Johnson
11.15 Quantifying AI Success: KPIs That Matter
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How do we define and align AI-related KPIs with core business goals: Is success measured by user adoption, model performance, or validated clinical impact?
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Where is the real value of AI in drug discovery and how do we track it?
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Avoiding vanity metrics by shifting focus to meaningful outcomes like reduced failure rates, faster decision-making, and enhanced pipeline productivity.
Moderator: Emily Rose Holzinger, Senior Director of Statistical Genetics, Bristol Myers Squibb
12:00 Building AI Scientists Can Trust — From Experimental Nuance to Transparent Decisions
Every scientist arrives at AI with trust issues, earned from years of messy data, fragile assays, and tools that confidently produce the wrong answer. As agents move from demo to deployment, how do we build ones scientists actually want to use?
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How do AI platforms earn — and keep — scientific trust? What does transparent, traceable, and auditable need to look like in a domain where ground truth is rarely clean?
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Every team has its own flavor of the same multi-modal integration problem. Where do AI agents genuinely help unify omics, structural, and assay data, and where do they introduce new failure modes?
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How do we capture the unwritten knowledge of the bench — which assays we trust, which protein preps felt "off," what makes a result feel real — and feed it into AI platforms so they don't make confident mistakes on top of noisy biology?
Moderator: Ada Shaw, Partnerships and Scientific Collaborations Lead, Mithrl
2.00 Unlocking Innovation Through Pre-Competitive & Startup Collaborations
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Pre-competitive partnerships as a mechanism for risk-sharing, learning, and algorithm enhancement without compromising IP.
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How to effectively collaborate with startups. What makes partnerships succeed or fail? How to create win-win feedback loops?
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Building shared benchmarks, synthetic datasets, or testbeds that accelerate algorithm validation across the ecosystem.
Moderator: Sudeshna Fisch, Senior Distinguished Scientist, Sanofi
2:45 Training AI: Data, Metadata, and Strategic Feedback Loops
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Building AI-ready data products: Addressing fragmented, incomplete, or noisy datasets through better metadata, annotation and standardization.
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Creating robust feedback loops between AI developers and users to enhance algorithm performance and contextual relevance.
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How to prioritize datasets across therapeutic areas to align with high-value AI use cases (e.g., target identification, patient stratification).
Moderator: Madhu Sevvana, Principal Scientist, Structural Biology and Protein Design (LMR), Sanofi
3.30 Building Future-Ready AI Teams in Pharma
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As AI becomes core to R&D strategy, how are pharma org charts evolving? What new roles, hybrid skills, and inter-team collaborations are emerging?
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How to effectively hire and develop professionals who understand biology, statistics, and AI and how to scale their impact quickly.
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Upskilling within pharma constraints: What’s working, what isn’t, and lessons from the field.
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Centralized AI teams vs. embedded domain experts: How do you strike the right balance for innovation and operational efficiency?
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What critical skill sets will define success in AI-enhanced drug development over the next 5–10 years?
Moderator: Vishal Thapar, Director, Product Manager, Applied AI Biomarker Development, AstraZeneca
4.15 Closing remarks
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Everyone to summarise what three things they’ve learned or want to take home to implement at their company.
** Please note that the names, job titles and company names of confirmed attendees will be shared with prospective participants and attendees at the PharmAI Leaders Exchange. If you would prefer not to have your name, job title and company name shared, please let me know when you confirm your attendance.