 and cloud community and welcome back to the desert. We're here in fabulous Las Vegas, Nevada, day one of Google Cloud and Next. Our coverage for theCUBE goes for the next three days. Totally action packed. My name is Savannah Peterson, joined by Rob Strecce. Rob, we are cruising through the afternoon. We have a really exciting conversation that actually affects real lives coming up next. Yeah, I was going to say, this is the applicability of AI, which is really, I think, where the rubber meets the road and a lot of fun to talk about. Yeah, absolutely, and we've been talking about it. 2024 is the year that we make AI real, not just hypothetical, so I'm very excited to welcome Chris and Sherob to the show. Thank you both for being here. Thanks for having us. Thank you. How's it going so far? This has got to be an exciting show for you, I would imagine. Yes, it is. Yes, it is. It's day one still, so. And I see you've got a pin on your shirt there. What does that represent? That represents part of the year. We got three awards this year, so very happy about that. That's exciting, yeah. That's got to be very exciting, and it's nice that you get to show off here at the event. Insmed, we're talking life sciences. Obviously, Gen AI, the hottest topic of the show, without question, probably the hottest topic of the year, quite frankly. Chris, tell us how Gen AI is affecting innovation at Insmed as you tackle rare diseases and some of the real challenges that affect all the people in this room. Well, I got to tell you, I'm excited to be here, and I never thought, you know, in my career I'd be sitting at a Google conference talking about technology and with Google. But let me tell you, the life sciences industry has been, we've been developing drugs for a long time, and it's a long process. If you look at the lengthy process from discovery to developing to commercial, it could be 10 to 15 years upwards of a billion dollars. So when you look at that, you really need to say, how can we change that paradigm when you look at how can we get products to patients faster? And our motto at Insmed is the patient is our North Star. So if you think about the past two years, how Gen AI has taken us by storm, we started to look internally and say, how can we adapt our business process to help accelerate the drug development process, the discovery, and eventually get product to patients? So we started basically testing and learning. We said, you know, what does this mean to our industry? And if you look at, when you look at the process in general, it takes a long time to find that molecule. It takes a long time to run that product through a development process, which generates a lot of paper, a lot of passing of information back and forth. And you say, how can we streamline that? And we started to look at it, this is a perfect opportunity for really looking at leveraging Gen AI. And our focus is really to look at, how can we use Gen AI to find that next best molecule? How do we get it into our clinical development process a lot faster and basically accelerate that? So if you look at shaving off four weeks, eight weeks, 12 weeks, doesn't seem like a lot of time, but think about it if you're suffering from a rare disease. It's going to say a day can be a long time, yeah. Moving that needle by four, eight, 12 weeks. So moving it through the clinical development process. Once we get product approved and then the commercialization process, how can we accelerate and get the product out to the patients a lot faster? Knowing the physicians that are treating those patients, knowing who those patients are who are suffering from those diseases. So we have a host of projects that are looking across the whole life cycle from discovery, development, and commercialization. And we're just on this journey. And we knew, yes, we could test and learn, but we needed a partner. And Google approached us, we had conversation, and what a better partner to have is Google. And then getting introduced to Quantify to help us move that process along has been a great partnership. So we're excited. We still got a long way to go, but when you look at really changing the paradigm and accelerating this, that's what we're all about. I think one of the things that we look at is it's the time to innovation, right? And I think that's big in the life sciences. But how is Quantify really looking at this, customer-specific and domain-specific to help organizations really accelerate that time to value? And how is Quantify really being specific about that and really helping? So like Chris mentioned, having the right partner, whether it be a cloud service provider or a service provider is very essential for any customer to basically go through their transformation journey, right? From Quantify perspective, over the last 10 years, we've worked with 25-plus life sciences customers on some of the cutting-edge solutions like molecular design, protein synthesis, toxicity testing, et cetera. Now with Generative AI, like Chris mentioned, there's been a lot of advancements and we believe the entire clinical trial process can be shrunk to a large extent, right? Now, if I talk from Quantify perspective, what investments we are making to help customers like Insmed, I think the first one is around having a dedicated Applied AI Research team. We have 100-plus researchers who are continuously looking for latest research coming in the academia and testing it out in publicly available data to see what is available for commercial use, right? To give you an example, we have team members who are working on currently protein 3D modeling. We have people who are working on how to take medical imaging and embed it into software devices, right? Even prior to Generative AI, becoming a buzzword that it is today, we were using knowledge graphs to do entity matching extraction from large copies of data during the COVID time frames to basically help our customers. Now, this helps us stay cutting edge, get patents, secure trust of the customer. So that's one. The second is around investing early into product expertise that Google Cloud launches. So over the last eight years of our partnership with Google Cloud, we've developed deep relationships with product teams and that helps us get early access to new technology like Alpha Fold that we plan to use with Insmed, right? Is one of the areas. And then we also work with Google on coming up with new solutions together in life sciences. So that helps us stay at the cutting edge. So that's the second aspect that we are investing in. The third one is industry depth or industry subject matter expertise. The new solutions, leveraging the technology like Generative AI can only be solved by having the right people be part of the project who understand how this industry functions, right? How does the world work today? Whereas while we are changing using technology, how will it work tomorrow? So doing this investment, I believe like these three aspects are helping us move forward and help customers like Insped. At Quantify, we believe in solving what matters. It's not just using technology, getting customers to the business outcome that can see the real world light. So that's been our approach towards customers. I mean, I think that's great. And so I mean, you were probably ready for this moment, for this market moment that we're having. You've been anticipating this in the space for a long time. Chris, I'm curious because this is the first conversation we've had on theCUBE about Gen AI and Life Science. Gets me very excited, I'm so here for it. What is your advice to other companies as they approach this journey themselves, as other Life Science companies start to play in the sandbox? Just test and learn, that's the first thing. I think we started less than a year ago and we focused a small team to really focus on some, just simple stuff. Just prove that this Generative AI is real. Can it really help us? And we were very successful in a very short period of time. Getting buy-in from your executive team, from your board of directors, early on, educating them on the value that this is not just hype, we need to get behind it. And really starting to, as you're testing and learning in parallel, build your strategy, build your roadmap. And one of the things we did, we enlisted the entire organization from discovery, development, and commercial. And we cohesively built a roadmap and really laid it out over, let's say the next two to three years. But we didn't want to, again, look two to three. We want to say what in a short period of time in the next six to 12 months can really make impact to the process? And we prioritize six critical initiatives that are going to impact clinical development, going to impact our commercial process. So by the end of the year, we would hope to have come to demonstrated that we did move the needle and show that this has impact. And we're not going to stop because we want to look at the entire process and continue. So you start small, get buy-in from the top, build your roadmap. And for us, like I said, less than 12 months we're, I think, well positioned. And it sounds like the team was all on board. Or at least you were able to get that buy-in pretty quickly too. There's, you know, it's interesting. There's a lot of question, excitement is this real? And I think by testing and learning and educating, it really proved again to get the buy-in. And again, having a partner like Quantify bringing the life science expertise, helping us understand our problems along with the Google Cloud Platform. It's been a perfect collaboration. Love to hear it. Yeah, I mean bridging off of that because I mean, you guys are no stranger to AI. I mean, AI has been in life sciences for quite a while. But how are Quantify really helping other customers and helping clients navigate and overcome the challenges that they might run into? Like getting buy-in and getting the right use cases figured out. How is Quantify really helping with that? So from our perspective, right? For any solution to go into production from AI perspective, right? It takes three things. Which is first is around customer buy-in, right? And from the customer buy-in perspective, what we do is we build a small prototype on the customer's data to prove out the technology. So that's the first piece. And once the pilot has been implemented, we basically go on, build out a business case, right? How will this particular solution get to the business outcome that customer is seeking? How will we be able to get to the success aspects? This helps us get into a business case like which is a CFO proof business case. You can take it and show what ROI will be there. That helps us fast track that particular step. The second piece when it comes to basically putting solutions into production is around user adoption journey. Any technology change brings about disruption in terms of how work happens today, right? So understanding who is going to use it, right? And then basically making sure they understand and there is minimal disruption in their day-to-day work actually helps us. To give you an example, if I expect a clinical researcher to go through a generative AI course, learn about prompt engineering, they will most likely go back to doing the work that they used to do, right? But if you are able to design the workflow using the technology in a way that causes minimum disruption, you're likely to get better buying. And the last part, which I believe is very, very important when it comes to AI solutions, is around getting buying from your security and change management teams from compliance and risk perspective. Because you get on the journey towards adoption and you realize you've basically missed some of the checks and balances from risk and compliance perspective. And lastly, which I believe is foundational, I know I've said three, right? But the fourth one is having the right champion on the customer side of things because there will be a lot of different teams that need to come together to make the solution get adopted into the customer side. And that's, we need a champion and Chris has been the person for us at InSmet. You talked a little bit about the regulation side. I mean, life science is one of the most regulated industries around the world for good reason. Sometimes I think for a little bit of a slow, slowing of innovation reason occasionally. How is that community, and you kind of touched on this, but I'm curious because you're both so in it. How is the community at large receiving AI and generative AI as a tool for, whether that be drug development or just life science innovation in general? Well, I think the regulatory agencies, I mean, it's still TBD, we're still waiting as far as kind of their position. And they know, we know it's coming. We know, but I think again, the more we start to build, we'll work closely with the regulatory agencies. Again, there's enough to be done, as far as the easier stuff to tackle, which still will shorten the time before we get to a point when we press a button and filed with the FDA. We're not there yet. Right, that'd be so nice. So gosh, I just gave me goosebumps to even think about. Yeah, the easy button, but for the FDA, that would be amazing. That would be outstanding. But I think there's enough incremental movement along the way, and I think that gives the FDA time to kind of learn as well. And again, we're anxious to see where we go with that. Yeah, it's a really, it's a super exciting time. I'm curious for both of you, you started a year ago, where do you hope you are a year from today? I'll start with you, Chris, and then I want to hear what you have to say, Sherob. What you can say about your customers or the solutions or whatever that might be. I would love to sit here a year from now and say we actually did shrink the clinical development time by four weeks. We actually did help our commercial team find the right physicians or target the right patient population and help our commercial team. And again, come back and say that with our discovery, it did help weed out maybe certain genes or molecules that maybe they were spending a lot of time. So it's small wins, but it does move the needle that in a relatively short period of time that we did make a small improvement to the process. Well, if you fine-tune every aspect of this cycle, you're actually having a huge impact. So I don't think you're selling those small steps a little short, just because everyone's talking about crazy and sane numbers here doesn't mean that stuff doesn't matter, especially when it comes to healthcare and life science. What about you, Sherob? What do you hope you can say in a year? So from our perspective, I would say if I look a year forward, in context of Generative AI and Life Sciences more so, we want to be top partner for Google Cloud from Generative AI perspective when it comes to Life Sciences, right? I come from the Life Sciences background when I started my career. I was working for one of the pharma companies. So that thing is very close to my heart as well. And I've seen the clinical trial process, the entire marketing operations, et cetera. So we want to be the top partner for Generative AI from Life Sciences perspective. Currently, we are seeing close to a third of the solutions or pilots that we are running go into production. I would love to see that go to like two thirds because we want to continue to test and learn. So there will definitely be a lot of testing, but more solutions going into production is something that we would like to see from quantified perspective. Fantastic. Well, we can't wait till we can share those numbers with you next year and that you're doing two thirds. A third is pretty remarkable as well. And you know, now that everyone's seen you interviewed on theCUBE, I suspect you'll be attracting a few more partners and players in this space. Do you think that Generative AI has the potential or will save lives as a result in the Life Sciences space? I mean, listen, if it could help us accelerate drug development and helping people with serious and rare diseases and serious medical conditions, that's what we could hope for. I think if we could speed up the process. I think last year, 2023, there was 55 drugs approved. If we could increase that with great efficacy, great safety and improve that, it's a win for everybody. It is a win for everybody. I'll say it. I think it will save lives. I think it's great. What do you think, Sharob? I think so it will save lives. It's a matter of time. The technology needs to mature a little bit. A lot of solutions need to go in production. But I truly believe that this technology will change our Life Sciences as an industry operate and eventually help save lives. I love it. Perfect sound bite to close on. Sharob, Chris, thank you both so much for being here. This is a fantastic partner conversation. Love that we got to dive deep on the Life Sciences. Cannot wait to see where you're at a year from now at the next at Google Cloud Next. And Rob, thank you so much for your fabulous insights and great questions as always. I'm just being a lot of fun. And thank all of you for tuning in to our three days of live coverage here in the desert from beautiful Las Vegas, Nevada. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.