CHAPTERS
AI as a once-in-a-generation shift for pharma—and AbbVie’s patient-impact focus
Sarah Nam frames AI as a technological shift capable of reimagining every pharma function. AbbVie views this as an opportunity to accelerate progress and increase impact for patients.
Sarah Nam’s remit: enterprise AI strategy plus external AI partnerships
Sarah explains her dual role: defining AbbVie’s enterprise AI strategy and building cross-cutting enablers, while also leading AI-related business development and external innovation. This sets up how AbbVie aligns internal execution with an ecosystem approach.
Value-chain approach: prioritizing AI use cases across AbbVie functions
AbbVie takes a value chain-based approach to identify AI priorities in each function and deploy targeted use cases. Sarah previews the main areas where AI is being prioritized, starting with discovery.
Drug discovery: scaling biology understanding and therapy design
In discovery, AbbVie focuses on using AI to better understand human biology and to scale the design–make–test–validate loop. The goal is to increase speed and quality in generating candidate therapies.
Optimizing molecules and expanding indications with multimodal evidence
Sarah highlights multiparametric optimization (efficacy, safety, PK) across small molecules and biologics, plus AI-driven indication expansion and combination studies. Integrating clinical, genomic, and other multimodal data is central to this effort.
Precision medicine: starting with digital pathology and moving toward tailored delivery
AbbVie is investing in precision medicine, beginning with digital pathology. The broader aim is to deliver medicines more precisely for individual patients.
Clinical development: AI for trial design and patient subpopulation discovery
In clinical development, AI is used to improve trial design, from inclusion/exclusion criteria to adaptive designs. A major focus is identifying patient subpopulations more likely to respond, especially in heterogeneous diseases.
Clinical operations: automating trial processes, regulatory authoring, and data surveillance
Sarah describes opportunities to automate clinical trial processes and accelerate document creation for submissions and regulatory requirements. AbbVie also emphasizes AI-driven surveillance of incoming clinical data to enable program adjustments.
AbbVie + Anthropic use case: Genesis for smarter commercial call planning
Genesis uses generative AI to surface and unify Salesforce-related tools, improving call planning for sales reps. Early results indicate meaningful gains in commercial efficiency and effectiveness.
AbbVie + Anthropic use case: Gaia for clinical document authoring at scale
Gaia applies large language models to automate authoring of clinical development documents, starting with NDA and PSURs and expanding to many document types. AbbVie reports substantial time savings from this approach.
Change management: people and process as equal partners to technology
Sarah emphasizes that AI transformation is not just a technical challenge; it requires winning hearts and minds and redesigning processes. AbbVie prioritizes upskilling, proving early ROI, and empowering function-level champions.
How AbbVie evaluates AI partners: a four-pillar diligence framework
AbbVie uses a structured framework to assess AI partnerships, especially in AI-driven drug discovery. The pillars cover strategic alignment, technical differentiation, leadership capability, and external validation of impact.
Advice to pharma leaders: start simple, prove ROI, and let wins fund scale
Sarah’s guidance is to begin with a few simple, high-impact use cases that generate quick wins. Demonstrated ROI can help self-fund broader AI transformation and build organizational momentum.
The next 3–5 years: generative design, agentic multimodal reasoning, and patient stratification
Sarah is most excited about AI pushing drug discovery frontiers through de novo design of molecules and biologics, agentic reasoning over multimodal datasets, and improved patient stratification. These advances could reshape both therapeutic discovery and future clinical trial design.
