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How AbbVie accelerates drug discovery with Claude

Sarah Nam, VP of AI Strategy and Partnerships at AbbVie, and Anthropic’s Ivy Weng discuss how AbbVie is transforming pharmaceutical research and development with AI. ​​Sarah shares how AbbVie uses Claude for Life Sciences to reimagine drug discovery, from analyzing multimodal biological data to optimizing clinical trials with smarter patient stratification and adaptive protocols. They also explore AbbVie’s approach to bringing AI skills to employees across the entire company. Learn more about what Claude can do for life sciences: https://claude.com/solutions/life-sciences

Sarah NamguestIvy Wenghost
Oct 20, 202510mWatch on YouTube ↗

CHAPTERS

  1. AI as a once-in-a-generation shift for pharma impact

    Sarah Nam frames AI as a transformative technology capable of reshaping every function in pharma, accelerating progress and patient impact. The conversation sets the tone: AbbVie is pursuing AI as a broad, enterprise-level transformation rather than isolated experiments.

  2. Sarah Nam’s remit: enterprise AI strategy + AI partnerships

    Sarah explains her role leading AbbVie’s newly created AI Strategy and Partnerships function. She outlines two responsibilities: defining cross-enterprise AI priorities/enablers and driving external innovation/business development related to AI.

  3. Value-chain approach: mapping AI priorities across the biopharma lifecycle

    AbbVie organizes its AI efforts by identifying high-value use cases within each function. Sarah describes how this method helps prioritize deployments systematically rather than opportunistically.

  4. Drug discovery focus: scaling biology understanding and therapy design

    Sarah details how AI is being applied to understand human biology and to improve design-make-test-validate cycles at scale. She highlights optimization challenges across efficacy, safety, and pharmacokinetics for both small molecules and biologics.

  5. Expanding indications and precision medicine with multimodal data

    AbbVie is pursuing AI to drive indication expansion and combination studies by integrating clinical, genomic, and other multimodal datasets. Precision medicine is highlighted as a growth area, starting with digital pathology and expanding toward more personalized delivery of medicines.

  6. Clinical development: smarter trial design and execution automation

    The discussion shifts to clinical development, where AI can improve trial design (criteria, adaptive designs, responder subpopulations) and automate trial operations. Sarah also notes AI-driven support for regulatory document workflows and ongoing clinical data surveillance.

  7. Two AbbVie use cases with Anthropic: Genesis for sales effectiveness

    Sarah highlights Genesis, a generative AI tool designed to improve sales call planning by integrating and surfacing Salesforce-related tools and insights. Early results indicate meaningful gains in sales force efficiency and effectiveness.

  8. Gaia for clinical document authoring: large efficiency gains

    Sarah describes Gaia, an LLM-powered clinical development authoring tool that automates drafting of study documents. Starting with NDA and PSUR documents, the vision is to scale across thousands of document types, with reported 40–60% time savings.

  9. Enterprise transformation realities: change management beyond technology

    Ivy and Sarah discuss that deploying AI at scale is as much about people and processes as it is about models. Sarah emphasizes winning “hearts and minds,” redesigning processes, and ensuring adoption across the enterprise.

  10. AbbVie’s change-management playbook: upskill, prove ROI, empower champions

    Sarah outlines AbbVie’s approach: AI training at all levels, generating early wins tied to key business metrics, and building domain champions via small AI teams embedded in functions. This structure helps sustain momentum and broaden adoption.

  11. How AbbVie evaluates AI partners: four-pillar diligence framework

    Sarah shares a structured framework for assessing AI partnerships, especially in AI-driven drug discovery. The four pillars are strategic fit, technical foundation, management team quality, and external validation/benchmarks.

  12. Advice to pharma leaders: start simple and build momentum

    Sarah’s guidance is to begin with a small set of high-impact, achievable use cases that create quick wins. Demonstrated ROI then catalyzes broader transformation and can self-fund the next wave of initiatives.

  13. Next 3–5 years: generative design, agentic multimodal reasoning, stratification

    Sarah closes with what excites her most: generative models for de novo design of molecules/biologics, agentic systems that reason over multimodal biomedical data, and improved patient stratification for both discovery and trial design. These advances could reshape how therapies are discovered and developed.

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