Operationalizing AI in Drug Development: From Infrastructure to Impact

Leaders from Johnson & Johnson, AstraZeneca, and Dash Bio discuss how AI can move from theory to impact in drug development. They explore the infrastructure, governance, and collaboration required to operationalize AI in regulated environments, sharing real-world lessons on interoperability, user trust, and measurable ROI.

Key Takeaways:

1. AI at scale requires more than models. Successful deployment depends on platforms that enable reproducibility, compliance, and day-to-day usability for scientists.
2. Interoperability is the bottleneck. Disconnected systems and proprietary formats are blocking progress. Open standards and shared environments are the way forward.
3. Trust is built into the workflow. Features like human-in-the-loop validation, confidence scoring, and explainable outputs are essential for AI adoption in clinical settings.