This article explores whether the industry needs a more structured framework for determining when validated AI has earned greater operational trust. It considers how model performance, validation, reviewer behavior, governance maturity and risk could help organizations confidently reduce routine human oversight while maintaining compliance.

The article explores why successful AI adoption in pharmacovigilance depends on more than validated models and high accuracy. It examines how transparency, governance and human accountability are essential to building the trust required to embed AI into regulated safety operations

ArisGlobal’s Jason Bryant and Bristol Myers Squibb’s Samuel Wallis argue that pharmacovigilance has an unspoken problem: reviewers who trust the paperwork more than the model.

Together, CNI and ArisGlobal have implemented innovative commercial‑off‑the‑shelf (COTS) and custom-built solutions that significantly improve the FDA’s ability to intake, process, analyze, and share safety data across drugs, biologics, vaccines, cosmetics, tissues, and human cell products.

Hans discusses why Regulatory Affairs should sit at the center of pharmaceutical product data management, and how trusted, connected data will underpin future AI-enabled operations and global standards such as IDMP.

Lucinda explores why signal management workflows remain rooted in legacy processes and how AI has the potential to fundamentally reshape pharmacovigilance by removing traditional case-processing bottlenecks.

Lucinda discusses how autonomous pharmacovigilance and AI-driven workflows could transform signal management, enabling faster identification, assessment, and response to emerging safety risks.

The article explores how advances in AI and automation are creating an opportunity to rethink adverse event case processing and broader pharmacovigilance operations, moving beyond incremental improvements toward more fundamental transformation.

Based on the Life Sciences AI Exchange podcast panel discussion, the article explores why data transformation remains one of the biggest barriers to successful AI adoption in life sciences. The discussion examines the industry's ongoing challenges with fragmented data, the realities of preparing organizations for AI at scale, and the steps companies must take to create a stronger foundation for future innovation.