How J&J MedTech and Takeda Scale Data & AI

This session features thought leaders from Takeda and J&J MedTech on building governed AI foundations. Takeda shows how pharma AI stalls on disconnected data governance, not model quality. Learn how they're extending one governed Lakehouse on Databricks across commercial, R&D, and manufacturing—turning descriptive analytics into generative science across generative chemistry, adaptive trial design, digital twins, and agentic workflows where the platform is the advantage. Surgery is high-stakes and deeply personal. J&J MedTech will then explore how the OR's richest data—surgical video, room video, and robotics—is unstructured, massive in scale, and deeply sensitive. Learn how their AI and engineering team uses Databricks to de-identify surgical video at scale, govern lineage in Unity Catalog, and share datasets via Delta Sharing—unlocking a new generation of multimodal surgical AI that bridges scene understanding and situational awareness in the OR.