Jim Swanson

We have three main layers of AI deployment. The first layer is about improving productivity around our enterprise capabilities. The second layer is centered around enabling our end-to-end processes, whether it be drug discovery, clinical operations, getting a product to market, or engaging with customers. The third layer is about working to embed AI into the core of our products and services.

First, we have created two councils, an AI Council and a Data Management Council, which allow us to properly mature the technology stack and apply these tools to use cases that matter. These bodies help ensure we adhere to ethical standards and have a clear understanding of how to use these technologies in ways that are scalable.

Third, we’re placing a heavy emphasis on embedding AI tools directly into employees’ workflows. For our sales representative, these capabilities are integrated directly into their CRM platform. When a representative prepares for a visit with a provider, they can get information on the kinds of patients the provider usually sees, the interactions we’ve had with them previously, the types of advances or insights they might want to know about, the channel the provider prefers to be engaged on, etc. Similarly, with our drug safety teams, we have embedded these tools into their workflows so they can better identify and resolve cases.

To defend against threats, we’ve created secure enclaves within the company for our large and small language models, and use vetted, curated data. Additionally, we created policies that educate our employees on the risks and restrictions around use of public Gen AI and direct them towards use of our internal generative AI application. We also review every proposed Gen AI use case through a process that includes security and architecture experts.