Call for Papers for the Validated AI for Pharma Summit is officially open. Details: appsilon.com/validated-ai-fo… If you are building, validating, or deploying AI in a regulated pharma environment, we would love to hear what you are learning. We are organizing a free, virtual summit in September, and we are looking for practitioners with real lessons to share: what worked, what broke, and what you would do differently. Topics we are particularly interested in: 🔹 Clinical trials and data-driven drug development 🔹 AI validation and regulatory compliance 🔹 Open source tooling in pharma 🔹 Getting AI from experimentation to production We are especially interested in talks that get into the specifics: real decisions, real constraints, lessons other teams can actually use. CFP closes July 31, 2026. Decisions go out by August 15. Submit your talk here: docs.google.com/forms/d/e/1F…

Teams tend to assume a GxP-compliant SCE is a multi-year build, the kind of project you know you need but keep pushing down the roadmap. This clinical-stage biotech had six months. No existing environment to build on, and active clinical programs already in motion that left no real option to wait. Six months later, regulated analyses were running in production. What made that possible wasn't speed for its own sake. It was building compliance into the architecture from the start instead of trying to add it later. Containers for reproducibility, Git for controlled change, and audit trails generated automatically as part of the deployment process. We broke the approach down in this carousel, so swipe through if you're thinking about SCE modernization for your own team 👇 Full details are in our 2026 AI-Ready SCE guide: appsilon.com/ebooks/modern-s…

SAS, R, and Python in one IDE? Walk into a pharma stats team and you'll probably find SAS still running the validated workflows it's handled for years, quietly doing its job in the background. Right next to it, new work keeps landing in R and Python, and nobody wants to rip one out just to make room for the other. So the question isn't which language wins. It's how to let them work together without adding more complexity. Running SAS, R, and Python in the same IDE makes that possible. Teams work with the same data, compare outputs side by side, and adopt open source at their own pace instead of betting everything on a full migration. That's what Altair SLC on Positron Pro makes possible. appsilon.com/post/altair-slc…

It is that simple. Before you build any AI agent, ask the user(s) what they want it to answer. That one step tells you what data and evaluation work has to come first. More from our webinar on what it takes to make pharma data usable for AI 👉 youtube.com/watch?v=4llFFQE1… Psst, join us on September 24 at the Validated AI for Pharma Summit, a free virtual event: appsilon.ai

*beep*