Overcoming the Challenges of AI Adoption

Pharmaceutical Executive: What are the primary challenges pharma companies encounter when adopting AI, and how can they overcome them?
Raj Indupuri:. So I think there was research as well in q1 from McKenzie, in terms of how much has been spent on AI, and it increased exponentially, but terms of the value creation or the ROI is questionable, and lot of companies are again, in general, are being challenged to scale up from this pilots to using it at scale within their organizations. So I believe in our space, it's more common. Pounded in terms of the challenge, because it's regulated, and also you are dealing with patient data, right? So you need to focus a ton on guardrails, safety, managing risks. So the approach we have been taking, is, how do you balance the infusion of these AI models, or how we can embed into our platform, combined with our existing automation and deterministic workflows that we already built in over a decade? Right? So if you can find a way to add AI as a layer and also, now again, everyone is we are actually also investing into agents, right? We can talk about that as well. So if you can build these capabilities in a way that it's pervasive, embedded into workflows for different personas within clinical development who is using these applications or software, but also ensure that they're strong guardrails, they're combined or they coexist with existing, powerful, deterministic ways of how work is being done. So we believe that can yield to significant results and also gives confidence and trust.That's another big fact in terms of why that option is lagging to users and that approach, we believe will yield results, and we also think that we are at an inflection point where all these learnings over the past few years can be applied now to take advantage of the advancements with all the tech that's happening. So I think the next two, three years are going to be quite again. We are incredibly excited in terms of how we can take advantage of opportunity and really transform clinical development. And again, like I mentioned earlier, we are all around data, and we believe, as a company we are, we are on a journey to transform all this data chaos into data intelligence for life sciences so that we can help patients.