Pharma executives and managers are racing to apply AI across drug discovery and development. But while the possibilities of AI advances can be very promising, many efforts falter before bringing home dependable, real‑world performance. This session outlines five essential elements for preparing your data, systems, and scientific workflows for AI success. You’ll learn why strong data governance, complete metadata, interoperable platforms, resilient model‑management processes, and scientific validation are critical for trustworthy, reproducible results. By aligning these elements early, you'll enable faster decisions, fewer surprises, and you'll develop AI tools that deliver more consistent value across the drug development lifecycle.
Piyush Agrawal, Data Science Manager, American Chemical Society