CHAPTERS
- 0:00 – 0:23
AbbVie’s view of AI as a once-in-a-generation pharma shift
Sarah Nam frames AI as a transformational technology capable of reshaping every function in pharma. She emphasizes AbbVie’s focus on accelerating progress and improving patient impact through AI adoption.
- •AI can reimagine end-to-end pharmaceutical functions
- •Framing AI as a unique, generational opportunity
- •Primary goal: faster progress and greater patient impact
- 0:23 – 1:28
Sarah Nam’s role: enterprise AI strategy plus external innovation partnerships
Sarah explains the new AbbVie function she leads, covering both internal AI strategy and external AI business development. The remit includes strategic priorities and cross-cutting enablers like data, architecture, and change management.
- •Leading AbbVie’s enterprise AI strategy and priorities
- •Building enabling foundations: tech architecture, data modernization, change management
- •Running AI-focused business development and external innovation partnerships
- 1:28 – 1:58
Value-chain approach to deploying AI across AbbVie
AbbVie organizes AI priorities by function across the pharma value chain. The goal is to identify where AI can deliver the most value in each area and then deploy targeted use cases.
- •Function-by-function prioritization across the value chain
- •Mapping AI use cases to core business priorities
- •Focus on scalable deployment rather than isolated pilots
- 1:58 – 2:29
Drug discovery priorities: biology insight, better design, and optimization at scale
Sarah details how AI is being applied in discovery to understand human biology and accelerate the design-make-test-validate loop. AbbVie also targets multi-parameter optimization to improve efficacy, safety, and pharmacokinetics across modalities.
- •AI to deepen understanding of human biology
- •Scaling the design–make–test–validate cycle
- •Multiparametric optimization for efficacy, safety, and PK
- •Applying across small molecules and biologics
- 2:29 – 2:59
Expanding indications, combination studies, and precision medicine with multimodal data
AbbVie is using AI to integrate diverse data types—clinical, genomic, and other modalities—to drive indication expansion and combination strategies. Precision medicine efforts start with digital pathology and extend toward more tailored treatment delivery.
- •Integrating clinical + genomic + multimodal datasets
- •Using AI to support indication expansion and combinations
- •Precision medicine initiatives starting with digital pathology
- •Aim: more precise medicine delivery for patients
- 2:59 – 3:29
Clinical trial design: criteria optimization, adaptive designs, and responder subgroups
In clinical development, AbbVie applies AI to improve trial design decisions, from inclusion/exclusion criteria to adaptive trial approaches. A major focus is identifying patient subpopulations more likely to respond in heterogeneous diseases.
- •AI-informed inclusion/exclusion criteria
- •Exploring adaptive clinical trial designs
- •Identifying responder subpopulations for heterogeneous diseases
- •Improving trial effectiveness before execution
- 3:29 – 4:20
Clinical execution and surveillance: automation, regulatory authoring, and monitoring incoming data
Sarah highlights opportunities to automate operational clinical trial processes and generate regulatory documents more efficiently. AbbVie also uses AI for data surveillance to monitor trial data and adjust programs as needed.
- •Automation of clinical trial processes
- •AI-assisted authoring for regulatory submissions
- •Scaling support across many document types
- •Data surveillance to monitor signals and adjust programs
- 4:20 – 5:43
Two AbbVie–Anthropic examples: Genesis for sales effectiveness and Gaia for document authoring
Sarah shares two concrete deployments: Genesis for sales call planning via generative AI, and Gaia for clinical document authoring with LLMs. Early results show meaningful sales productivity gains and 40–60% time savings in writing key documents.
- •Genesis: genAI-powered support for Salesforce call planning
- •Reported improvements in sales efficiency and effectiveness
- •Gaia: LLM-driven automation for clinical/regulatory writing
- •Initial focus on NDA and PSUR documents, expanding to thousands
- •Estimated 40–60% authoring time savings
- 5:43 – 6:10
Change management reality: people, processes, and technology must move together
Ivy and Sarah discuss enterprise adoption challenges and why transformation isn’t just a model or tooling issue. AbbVie emphasizes aligning hearts and minds and updating processes alongside technical decisions.
- •AI transformation is not purely a technology challenge
- •Process management is critical at enterprise scale
- •Winning adoption requires cultural and behavioral change
- 6:10 – 7:37
How AbbVie drives adoption: upskilling, early wins, and function-level champions
Sarah outlines AbbVie’s change management playbook: organization-wide training, prioritizing near-term ROI use cases, and empowering champions in each function. Small domain AI teams help sustain momentum and scale impact.
- •AI upskilling programs across proficiency levels
- •Focus on early wins tied to ROI and “golden metrics”
- •Use cases that demonstrate patient and business impact
- •Empowering champions via small AI teams in each function
- 7:37 – 9:01
Partner evaluation framework: strategic fit, technical foundation, leadership, and validation
AbbVie uses a four-pillar diligence framework when assessing AI partners. It balances alignment to strategy with technical differentiation, strong bilingual leadership (domain + AI), and evidence-backed external validation.
- •Strategic fit with AbbVie’s objectives
- •Technical differentiation and data generation capabilities
- •Management team strength and domain/AI “bilingualism”
- •External validation via benchmarking and case studies
- 9:01 – 9:43
Advice to pharma leaders: start simple and let early ROI fund the journey
Sarah recommends starting with a small set of high-impact, quick-win use cases. Demonstrated ROI can then self-fund broader initiatives and unlock deeper organizational transformation.
- •Start simple rather than boiling the ocean
- •Identify quick wins that demonstrate impact
- •Use ROI to self-fund expansion
- •Early successes catalyze organization-wide change
- 9:43 – 10:36
What’s next (3–5 years): generative design, agentic multimodal reasoning, and stratification
Sarah shares the frontiers she’s most excited about: generative models for de novo design, agentic systems that reason over multimodal biomedical data, and improved patient stratification. These advances could reshape both discovery and clinical trial design.
- •Generative models for property prediction and de novo design (small molecules + biologics)
- •Agentic models to reason over genomics/proteomics/transcriptomics/clinical/RWD
- •Integrating multimodal insights to solve biological problems
- •Patient stratification to improve therapeutics and trial design
- 10:36 – 10:49
Closing remarks
Ivy thanks Sarah and reiterates enthusiasm for AbbVie’s work. The conversation ends on the shared momentum around AI transformation in pharma.
- •Wrap-up and appreciation
- •Reinforces excitement and momentum in AI-driven pharma transformation
