Accelerating Scientific Discovery with FAIR Principles & Agentic AI

AstraZeneca is transforming R&D by combining foundation models, AI agents, and FAIR data to accelerate scientific discovery and clinical development. This session explores how the company operationalizes agentic workflows, integrates multimodal data for biomarker research, and ensures trust and compliance through strong data governance and human oversight.

Key Takeaways:

1. AI agents are reshaping how scientists ask and answer complex questions by automating data discovery, evidence synthesis, and context-aware analysis across R&D and clinical workflows.
2. FAIR data principles and federated data architecture are key enablers that allow analytics and AI models to scale while maintaining trust, traceability, and audit readiness.
3. Multimodal data and foundation models are unlocking new possibilities in biomarker discovery and trial optimization, all while keeping human experts in the loop for critical decision-making.