Leveraging AI for Better Outcomes Across Drug Development - with Patricio La Rosa of Bayer

Today's guest is Patricio La Rosa, Head of End-to-End Decision Science at Seed Production Innovation in Bayer Crop Science. With over 20 years of experience developing AI and data science solutions across healthcare and agriculture, Patricio joins Emerj Managing Editor Matthew DeMello to explore how machine learning can drive better outcomes across the drug development lifecycle, from research design to clinical deployment.

Patricio discusses how AI is optimizing early trial planning, improving participant engagement, and supporting ethical, human-centered decisions at scale. Drawing lessons from both agriculture and life sciences, he emphasizes the importance of connecting technical models with real-world workflows.

The conversation also delves into key barriers to AI adoption in clinical settings, including behavioral friction, model transparency, and challenges in orchestrating decision-making across global teams. Patricio offers a grounded perspective on what it takes to move from experimentation to enterprise impact in AI-driven R&D. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on Emerj's flagship 'AI in Business' podcast!