Every significant advancement in science starts with a fundamental inquiry: can this treatment enhance longevity, restore equilibrium, or significantly transform patient care? At Ipsen, we are adopting a similar methodology in the realm of artificial intelligence, questioning whether it can aid our teams in analyzing data in innovative ways, minimize repetitive tasks, and facilitate quicker, more informed decision-making focused on purposeful scientific outcomes.
To explore this potential, we have initiated a pilot program in collaboration with the Claude team specializing in healthcare and life sciences. This initiative aims to test theories within actual research and business processes, assess impacts, and gain insights swiftly while embedding a sense of responsibility from the ground up.
Launched in May 2026, this pilot involves more than 40 team members from Ipsen's Research & Development and Medical divisions, who are engaged in a specific set of use cases. These encompass external innovation evaluations, synthesizing scientific literature, generating knowledge, and enhancing clinical development workflows. The focus is on high-value tasks that require teams to sift through significant volumes of intricate data while consistently delivering high-quality results. Participants are utilizing resources such as Claude Science and Claude Code to explore the platform's capabilities.
When it comes to healthcare, the application of AI is just as critical as its functionalities. Accordingly, this pilot emphasizes strong governance, appropriate access controls, and responsible usage of these tools to ensure that teams engage with them ethically.
This initiative is not about adopting AI for its own sake. Ipsen's goal is to expedite the process of delivering medications to patients. The pilot serves as a deliberate examination of how AI can sustainably support this objective, grounded in scientific rigor. While we recognize that not every aspect of medication delivery can be hastened, optimizing decision-making processes and alleviating challenges in complex analytical tasks can lead to significant improvements.
