Establishing a Generative AI Governance Framework and Scoping Use Cases for a Biopharma Company
In this generative AI governance framework case study, Clarkston Consulting equipped a life sciences organization to adopt AI deliberately. Read a synopsis of the project below or download the full case study.
Download the Generative AI Governance Framework Case Study Here
As generative AI moves from experimentation to enterprise adoption, life sciences companies face a distinct challenge: how to capture productivity gains without increasing risk to regulated data. For a biopharmaceutical company preparing to roll out Microsoft Copilot, that question had to be answered before adoption could scale. The company engaged Clarkston Consulting to create a practical foundation for safe, effective AI adoption across three connected priorities: governance, workforce enablement, and operational value.
Clarkston drafted a four-document governance framework designed to work as a single system, comprising an acceptable use policy, a tiered AI use-case risk matrix, a human-in-the-loop validation guide, and an incident escalation playbook. The framework established clear boundaries around patient-level data, clinical trial data, regulatory submissions, quality records, and pharmacovigilance information, while defining a central principle for everything else: AI assists, humans own the output.
With the guardrails in place, Clarkston developed and delivered Microsoft Copilot training to approximately 70 employees. The program moved beyond feature demonstrations to practical judgment, teaching people not only how to prompt effectively and build their own AI agents, but how to decide whether a given task is appropriate for AI at all, how to validate what comes back, and what to do when something goes wrong.
The engagement’s third pillar turned enablement into value. Clarkston facilitated an AI Use-Case and Business Value Workshop across five functions, then developed a weighted prioritization model that scored each opportunity on business value, implementation effort, build complexity, and data readiness. The analysis produced a ranked backlog of 29 use cases and revealed that the individual requests could be addressed through a smaller set of reusable AI capabilities. Rather than treating each idea as a separate project, the company can now build capabilities once and apply them across multiple functions.
The result is an organization equipped to adopt AI deliberately: with governance that enables rather than obstructs, a workforce that understands both Copilot’s potential and its limits, and a prioritized roadmap for translating AI investment into measurable operational value.
