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If your statistical computing environment was designed before R became a viable language for submissions, before cloud infrastructure had the prominent position it has today, and before "data engineering bottleneck" entered everyone's vocabulary you're probably already feeling the pressure. It's obvious that to overcome some of these challenges, a serious modernization effort needs to ensue. The questions that remain are how, how fast, and whether to build, buy, or partner. To answer those questions, Appsilon interviewed statistical computing leaders at top pharmaceutical companies. We coupled this with internal expertise and released a comprehensive report last year: The Anatomy of Modern Statistical Computing Environments in Pharma.

The conversation around AI readiness typically centers on data quality, talent acquisition, and executive buy-in. While these factors matter, they overlook a more fundamental question: does the organization have the technical and cultural infrastructure to operationalize AI at scale? 

Novo Nordisk, Roche, GSK, Johnson & Johnson: the biggest names in pharma have moved off SAS. This post breaks down what's actually driving that shift, what open source delivers beyond cost savings, and what separates a successful transition from one that stalls.