Govern first, scale faster: what trustworthy AI actually takes in life sciences

Life sciences organizations can scale AI more effectively by embedding governance throughout development rather than treating it as a final approval hurdle, since regulated uses in discovery, clinical operations, manufacturing, regulatory affairs, medical affairs, and pharmacovigilance require defensible evidence of data provenance, intended use, oversight, and ongoing performance. The discussion highlights Good Machine Learning Practice principles, which emphasize representative data, lifecycle management, human-AI team performance, and clear documentation, alongside evolving requirements from regulators such as the FDA, EMA, and EU AI Act. It recommends assigning risk tiers and accountable owners during ideation, maintaining portfolio-level use case registries, building traceable and reusable data assets, applying agent guardrails and meaningful human review, and defining predetermined change-control plans for drift, retraining, revalidation, or suspension. It argues that these practices reduce delays caused by reconstructing evidence after development and concludes by presenting Dataiku’s data catalog, model evaluation, agent controls, monitoring, and governance capabilities as tools for supporting such workflows.

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