De-Risking What Matters

In this episode of Innovation to Impact: Ruminations & Ramblings, Szczepan Baran, Brian Berridge, and Nick Kelley tackle one of the biggest problems in modern drug development: we keep adding more technology, more data, and more complexity, yet clinical attrition remains painfully high. Across discussions on AI, NAMs, digital biomarkers, animal models, translational science, and organizational culture, they argue that innovation fails when tools become the strategy instead of serving clearly defined patient-centered decisions. The conversation explores why reverse translation matters, how AI should function as a decision-support system rather than a magic oracle, why “decision warranties” may become essential in AI-enabled science, and how the industry continues to confuse activity with progress. This is a candid, often uncomfortable discussion about predictivity, accountability, translational learning, and what it would actually take to build a drug development system optimized for patient outcomes instead of platform hype.