Peter Gassner
Analyst · Rishi Jaluria with RBC. Please go ahead
Let's see. So the first part of it is the disruption, what we mean by that. So if you look at a year or so, a little more than a year ago, AI really burst upon the scene with Gen AI. And that causes, it became very accessible. You saw it on 60 minutes, you could log on and try it yourself, it could answer a question. So that caused a lot of pressure in our larger enterprises. On the IT department, hey, what are we going to do about Gen AI? What's our strategy as a large pharmaceutical company, biotech, about AI and that would land in the IT department of these companies. Now for the smaller our smaller SMB customers doesn't land so much. They have other things to think about, other more pertinent, very stressful things. But in the large companies, with tens of thousands of people, they're looking for these operational efficiencies that they could potentially get through AI and they have a budget to kind of get ahead of that game. So that by the word disruption I meant that through a competing priority into our customers, hey, we had some existing plans. Now this AI, we have to plan for what we're going to do on that, where are we going to spend on innovation, on experimentation, who's going to do that, what budget would we use that type of thing. So some of that would take an impact onto us which is core systems. Now those core systems, when we get that type of impact, it will delay a project, but it won't stop it because these core systems are things you need, you can delay them, but all that does is create somewhat of a pent-up demand. I guess, Rishi, there was a good parallel list with COVID, the pandemic a few years ago. That created a whole different set of dynamics with vaccines and therapies and work from home, priorities, all types of things that created a disruption, which then, okay, take the focus off of the core systems a bit and then it came back again. So that's the first part. That's the answer there. In terms of the AI strategy, our strategy is to really enable customers and their partners to develop AI applications because they're going to be very specific AI applications, Gen AI applications for very specific use cases, whether it's field information, pre-call planning, next best action, what have you. They're going to be very specific applications. That innovation has to come from everywhere. And one of the things it needs is clean data. All of these AI applications need clean, concurrent fast data. So one of the things we did started about two years ago, actually, is put in a new API on the Vault platform called the Direct Data API and that was just released this April and that provides concurrent consistent data about 100 times faster than normal API. So that's I think that's going to be a great thing. Now when we look to the long-term, some of those applications will be developed by our customers, some by partners and over time, probably some by Veeva as well. But we're concentrating on the foundation versus the APIs for the data, the core system because that's really what -- that's really only Veeva that can deliver that for our customers. So that's what we're focused on.