AstraZeneca’s Perspective on Why AI Value Depends on Reliable, Governed Data

AI can shorten development and commercialization timelines by 20–40%, but only when data is reliable, governed, and fit for purpose. This session shares AstraZeneca’s perspective on why data quality is the real bottleneck to AI value, with insights from Victor Kim, R&D Reference and Master Data Director. Using real risk and value scenarios, it shows how poor data quality, weak lineage, and unclear ownership amplify bias, regulatory exposure, and costly failures. Attendees will learn what “AI-ready data” means in practice—from trusted identifiers and shared data models to quality SLAs, privacy by design, and human-in-the-loop validation—and why leaders must fund the data foundation before scaling AI.