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.
- •AI can transform the entire pharmaceutical value chain
- •AbbVie positions AI as a generational opportunity
- •Primary north star: faster progress and greater patient impact
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.
- •Building a new AI Strategy & Partnerships function
- •Enterprise AI priorities across functions
- •Enablers: architecture, data modernization, change management
- •AI-focused business development and partner innovation
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.
- •Value-chain mapping of AI opportunities
- •Function-by-function prioritization of use cases
- •Deploying AI against core business priorities
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.
- •AI to deepen understanding of human biology
- •Scaling design–make–test–validate cycles
- •More effective therapy creation through AI-enabled workflows
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.
- •Multiparametric optimization of efficacy, safety, and pharmacokinetics
- •Applies to small molecules and biologics
- •Indication expansion and combination study support
- •Multimodal integration: clinical + genomic + other data types
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.
- •Precision medicine as a major AI priority
- •Digital pathology as an entry point
- •Expanding toward more precise patient targeting and delivery
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.
- •AI-informed inclusion/exclusion criteria
- •Adaptive clinical trial design support
- •Identifying responsive subpopulations
- •Improving trial success 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.
- •Automation of clinical trial operational processes
- •AI-assisted authoring for submissions and regulatory needs
- •Ongoing surveillance of incoming clinical data
- •Adjusting programs based on AI insights
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.
- •Genesis: generative AI for Salesforce workflows
- •Improves call planning for sales teams
- •Reported significant efficiency and effectiveness improvements
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.
- •Gaia: LLM-powered clinical document authoring
- •Initial focus: NDA and PSUR documents
- •Expands to thousands of document types
- •Reported ~40–60% time efficiencies
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.
- •AI transformation requires people + process + technology alignment
- •Upskilling and training across proficiency levels
- •Demonstrating early wins tied to key business metrics
- •Empowering champions via small AI teams in each function
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.
- •Pillar 1: strategic fit to AbbVie objectives
- •Pillar 2: technical foundation and differentiation (models, data generation)
- •Pillar 3: management team ‘bilingualism’ (domain + AI/ML depth)
- •Pillar 4: external validation via benchmarks and case studies
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.
- •Start with a small number of high-value use cases
- •Prioritize quick wins and visible impact
- •Use ROI to self-fund additional initiatives
- •Early wins unlock broader transformation
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.
- •Generative models for de novo small molecule and biologic design
- •Beyond property prediction to true design assistance
- •Agentic models reasoning over multimodal data (omics, clinical, real-world)
- •Better patient stratification impacting discovery and trials
