Bringing GenAI Governance to Pharma: Lessons from the Field
The hardest part of a GenAI initiative in a regulated industry usually isn’t the model. It’s answering, clearly and durably, three questions: who approved this model for this use case, how do we know it’s still behaving the way it was approved to behave, and what happens the moment it doesn’t.
Standing up a GenAI governance framework — model approval workflows, audit-ready documentation, responsible-AI review gates — sounds like a compliance exercise, and part of it is. But the more useful way to think about it is as a delivery discipline, because the teams that treat governance as a separate track from delivery are the ones who end up bolting it on after a model is already in production, which is exactly the wrong order.
The practical version looks like this: every model use case gets scoped with its governance requirements alongside its functional ones, before a single sprint touches it. Model approval isn’t a signature collected at the end — it’s a checkpoint built into the same cadence as sprint reviews, so that by the time a model is ready to move toward production, the governance conversation has already happened in pieces, not all at once under deadline pressure.
Cross-functional coordination matters more here than in almost any other kind of delivery I’ve led. A GenAI initiative touches IT, QA, Compliance, Risk, subject-matter experts, and the AI/ML team itself, often simultaneously, and none of them speak quite the same language about what “ready” means. Part of the Scrum Master’s job in this context is translation: turning a Risk team’s concern about model drift into a concrete acceptance criterion an engineer can build against, and turning an engineer’s technical caveat into language a compliance reviewer can actually sign off on.
The payoff for getting this right isn’t just passing an audit. It’s speed, counterintuitively. Teams that build governance into their delivery cadence from day one move faster later, because they’re not re-litigating foundational questions — what counts as evidence, who has approval authority, how changes get tracked — every time a new use case comes up. They already answered those questions once, and now they’re reusing the answer.
GenAI is still new enough in pharma that most of these frameworks are being built, not inherited. That’s a real opportunity: the teams doing it now get to decide whether governance is a brake on innovation or the thing that lets innovation move responsibly. I’ve found it can be both fast and careful — but only if governance is designed in from the start, not bolted on at the end.