The Importance of Data Strategy and Infrastructure in AI's Potential

Pharmaceutical Executive: How important is data strategy and infrastructure in realizing the full potential of AI in pharma, and what best practices have you seen for ensuring AI-driven insights are accurate and actionable?
Raj Indupuri: Yeah, so that's an excellent question, and that's in terms of data infrastructure. That's kind of foundation without data, there is no AI, right? So to bring these AI capabilities that users can trust. In terms of adoption or scale, it has to start with strong data foundations. So that's another differentiator that we have as a company, because we start as a data company, we build infrastructure so that we can bring all this patient data together, and then we can embed this intelligence on governed data, right? So having those controls, those guardrails, the safety practices around our own data, right, is incredibly important if you want to benefit from the use and adoption of AI and also the performance of these models, right? So the Tech has evolved so much that it's with prompts you can build this intelligence now, so you don't need to be a data scientist anymore, right? That's good and the bad, right? So the bar to build AI capabilities have reduced significantly that on the other side right for us to build responsible AI. So you got to ensure that there are a lot of guardrails. So going back to your question, strong data foundations is an imperative if you want to have any chance to have success with AI.