Morning, everyone. Thank you for coming to this session of the Lyriant Global Health Care Conference. My name is Mike Cherney. I'm the Health Care Tech Distribution Analyst. It's my extreme pleasure to have with us the IQVIA management team, Ari Boosbibb, CEO, Kerry Joseph, Gustavo Peron, who run the IR team.

Mike Philok, our new CFO.

I didn't even see Mike there. I apologize, Mike. So we had a whole squad here. I'm glad that Ari didn't bring any slides, because I got plenty of questions to keep us busy. But I think it's important, I think, to start with the topic of the day, which is all things AI. So I'm going to keep this very high level. As you think about where AQB is positioned across your organization, what do you see as your AI-oriented strengths? I think that's the piece that maybe has been a bit overlooked and your perception on the perception versus reality debate that's currently undertaking the market.

Yeah, well, thank you, and thank you for inviting us. Look, I think obviously that AI and its application to our industry are very largely misunderstood. We are a services company. We provide services to life sciences mostly, and, you know, we got lumped into the services bag. these AI companies are spending a massive amount of capex on developing their models. I think someone told me it's as a percentage of GDP, you have to go back to the Louisiana purchase to have that kind of level of spend. And when they're being asked, why are we spending so much money and you know how do you support such valuations and the answer is well we're going to capture half of the 13 and a half trillion dollar services industry now why it's misunderstood in our industry it's because the industry itself is quite unique this is not any services industry and secondly our company in particular within that industry has a very unique position and a unique mode it starts with the data but it's not just the data AI for us and I can say this after having punched the bag many many times internally is a very strong net positive very strong net positive whether it's on the clinical side or on the commercial side you're aware we two large businesses. We help our clients develop drugs and we help our clients commercialize the drugs. And in many aspects, not everything, but many aspects of what we do, AI will be an enabler, a facilitator, a driver of efficiency, as well as a net revenue generator for us. So that's the the general question, I can go into more detail if you'd like.

Yeah, and maybe I think some of the dynamic at play that we've all seen is that we see something come out from a broader multi-scaler LLM and suddenly it's okay, what can they replace? But I think sometimes what gets missed is what's already being done. And so are there real world examples, no pun intended with real world evidence basis, but that you can give us on where AI encapsulates both within the clinical and commercial side right now?

Well, first of all, most of what our clients are doing on AI, most of it, I'd say 90%, is in early stage discovery. And in early stage discovery is the more frustrating part in pharma because you have to sort through so many molecules and try to decide what's the most likely one that will be successful and it'll be a successful outcome. And the fact is there already are before the event of AI many many tools and simulations and models that enable you and algorithms that enable you to sort through that. In fact we did some work with one of the top tier pharma company on this and benchmarked okay let's do it with the existing tools and let's do it using the most advanced French first name tools possible that applies to early stage discovery and let's see what the outcome is and the AI model got it right one out of six times once out of six times. And that's using the brightest, most advanced PhDs to do the prompts. Now, you can say, well, over time it will. But the answer is no, because it doesn't have access to all that data. And so I want to start with that. You have to step back and understand that about 70% of all the data used by pharma from early stage discovery through clinical development, through commercialization is IQVIA data, 70%. That data is not out there, not on the web, not in a particular company. It's not accessible. Once everything, after everything is said and done, you can have the most advanced AI model that's been trained on whatever you want it to be trained on. If you do not have the right ingredients, you're just not going to be able to generate any meaningful outcome. That's number one, that the data is proprietary. Number two, that data is dynamic. It's not static. It changes daily. It's updated. Number three, it's got to be hugely compliant with a gazillion regulations, privacy. They vary country to country, et cetera. number four even if you had theoretically access to all that data it is not usable you got to work it you've got to curate it you have to bridge it to code it which is the work that we do and that magic sauce we don't sell to clients we sell the final products the clients can't just take that data and then use an AI model and put it to work. So that's a huge mode that people do not understand and it's not the only mode. We've got the analytics, we've got the domain expertise, we've got the workflows and how those workflows are embedded in the business models of our clients at every stage is really a quadruple mode that people don't fail to understand. To your question about specific examples, whether we talked about early stage discovery, but even when you get to start a trial, so protocol design, site identification, site startup, patient enrollment. These are all activities where we, several years ago, when we merged the largest clinical trial organization in the world with the largest provider of data and analytics on the commercial side, that's exactly what we set out to do. we set out to base those processes on data and evidence and optimize those workflows to make them more efficient. So if you could put AI agents on top of those, then you make those even more efficient. But again, bear in mind, you got to have the data and you got to have the optimized workflows. Otherwise, it doesn't help. So, you know, there's a massive amount of documentation that's exchanged between regulators, sponsors, investigator sites, the CRO when there's one involved, when you want to start a site. There is a massive amount of documentation involved in informed consents between the sponsors and the investigator sites and the patients that are enrolled. All of that in a traditional process involves many interactions. With all the white spaces that are involved, those interactions, you got to wait for a response, et cetera. So over the past year and a half, working with NVIDIA, made available to us all their foundation models, we built over 150 agents that we've deployed already in our workflows. and those agents by the way we have filed over 90 patents in AI so when you step back and you look at the universe of AI in healthcare we feel obviously I'm outing my own book here but you should feel free to go and verify by talking to clients we are the AI company in the life sciences industry and we've been at it for a while and as you know the further away you are the the the the faster you go and the harder it is for us to catch up because our ai agents are trained at a much more advanced stage so that's on the clinical side and on the real world evidence side you ask you know real world evidence requires you to sort through massive amounts of scientific literature which obviously is done in a much better way with ai agents but it's not just the available scientific literature it's all the data we have data on over 1.2 billion patients worldwide you know deep granular um privacy uh compliant data that is simply not accessible to people who are not within the four walls of our company. And on the commercial side, again, we launched a number of agents. We have the IQVIA AI assistant, which enables a sponsor to model and simulate the entire launch of a new authorized product and literally, you know, be able to make decisions on where to launch, which channels, which demographics, how do you promote the drug, how do you price the drug in a matter of a few days versus many months. And you have to understand pharma employs thousands and thousands of people to do these launches and then they ask us to help with analytics and with advice and so on so we can now provide those ai agents enabling pharma to generate huge efficiencies internally and to us it generates incremental revenue i mentioned before that we um that about 70 percent of the data used in life sciences is IQVIA data but there's another 30 percent and that's company specific data that's third party data how do you integrate all of that within the walls of a particular a pharma company so we launched a product called das data as a service plus and i think you know pharma companies are are are not always willing to have us publicize what we do for them but i think we had a press release a week or two ago with of the launch of this product with bohringer ingelheim and there'll be there'll be more um this is a an ai agent that enables you to integrate those various sources of data in a seamless fashion and be able to connect the global regional and local data so you have one single version of the truth and enables a lot of decision making in a fluid manner whereas in the past you would have needed a lot of interactions with people involved bear in mind when i speak about ai agents or agentification that means that you could have i've seen long chains of emails you know please produce this analysis for me can you get me that data um how should we price for that demographic and And at no point in time in that email chain is there a human. It's all agents speaking to each other and going through routines and workflows that we have already AI'd, so to speak, or agentified. Now, you always need a human in the loop to oversee things, but 80% of some of these processes, we already are deeply involved in doing them. So that helps us generate efficiencies for our clients. And it also will help generate top line growth at a higher clip than we are today.

So just along those lines on that last point, in terms of the offerings you have now and the ability to utilize your partnerships. I appreciate you bringing up NVIDIA as a partner to drive value. You're basically, will it be sold as new tools, new, I mean, the data as a service is a clear one, but will it be something that filters into the way that you compete on price and RFPs within the clinical site? How should you see it mathematically working?

No, we sell this as increment. Obviously, when you introduce a new product, the pricing is always, you know, the first client that adopts it, second, et cetera. But we have, like, for this DAS Plus, we have a huge pipeline of opportunities. We're asking ourselves, what's the right pricing? It's not like it's cannibalizing anything else. It's giving our clients the opportunity to generate savings within their own organizations by utilizing that which we already sell to them, the data, the analytics, etc., in a better fashion. So it's really for our clients to decide, you know, I want to use this and replace a thousand people. Okay, so what we go to clients with is, look, the savings you could generate. And obviously, we're going to charge for that. It's not cannibalizing something that we already do. Now, I don't want to be just positive. There are negative aspects to it. Not everything we do necessarily utilizes advanced, sophisticated, curated data. Not everything we do utilizes advanced knowledge, which is not replicable easily by AI agents. There is, we have on our commercial side, on the commercial side, we have, what about, for this year, we said about $7.3, $7.4 billion of revenue. About 20% of that is what we call analytics and consulting. So that's kind of more analytics and advisory work. And we estimate that about 5%, which is about, let's round it up, about $100 million of existing revenue that potentially can be displaced over time. The paradox is it's actually growing faster this year than any product. But this is what happens when you have a substitution. It takes a lot of time for it to happen. But again, I said before, it will be a net positive because that will be replaced over time by all this AI agents revenue that is growing. So it's not going to happen overnight, but we feel that that's kind of the most threatened, if you will. On the clinical side, it's the basic task, some of the most simple medical writing, some basic stuff that sometimes clients do themselves, sometimes for convenience, they outsource, potentially can be done with more advanced AI models. But again, it's a small portion of what we do. And that's why I said and I emphasize it's a net positive for us.

I did bring a lot more questions. So maybe jumping past the AI discussion, that was all very helpful. Last couple years have been volatile on the clinical side from demand perspective. There's been ebbs and flows on RFP flow, on RFP wins, on cancellations. Where do you think the health of the market is right now? And for IQVIA's position in the market, what has vacillated up and down in terms of your ability to win the representative share that you're pushing for?

Yeah. Look, a lot of what has happened is macro-induced. It was the IRA, it was the post-COVID bubble, you know, COVID created, I mean, our company grew, I don't remember, quarter was like 20 to 30 percent growth and for anybody who was involved in this industry the post-covid period is a deflationary period because people overspent and when you overspend you kind of tighten the belt and after that you spend less now the interesting part is you look at very large companies that we you know we compare ourselves to or people like Thermo Fisher Danaher and some of these very very good companies and very high performance companies they experienced negative growth as a result of that post-covid deflationary environment we didn't even though we grew at 20% plus over that short period of time we continue to grow after that not at the same pace obviously and that is because the clinical trial business is a long cycle business it's not like you could just decide that you're gonna you're in the middle of a trial it continues and it continues to generate revenue so that's number one the number two factor with this IRA which introduced the notion of price negotiations and people sort of our clients started pausing decision making and then you had the Trump administration with you know the MFN the the the tariffs the changes in the at the agencies and frankly the let's call it unstable environment that was created as a result of all those pronouncements the good news is all of that is behind us and from what we can tell our interactions with the agencies with our clients with administration things have been returned to a more stability as a result of which our clients have accelerated re-accelerated decision making and you've seen that in the RFP flows the growth of of our bookings and it has been you know we think the trough which was probably in the 24 25 first part 25 time frame between you know middle of 24 and the middle of 25 uh is behind us and things have started going back up uh you can see that in the numbers um and the momentum you know continues as far as we can tell i can't make predictions i don't know how people can forecast their their bookings we can't we we i only find out at the end of the quarter or a week or two later what where are we you know a lot of the decisions are often done at the end of the quarter or or you can slip one quarter to the other so i've often said you've if you've been listening to me for a while you know that i hate that metric called book to bill and i feel that it is a disservice to investors because people get excited when it's a high book to bill and they get the disillusion when it's not and it doesn't really mean anything it's a long cycle moving business and you got to look at your uh at your bookings over over long time periods and and the growth of your backlog um so i think you know the environment in a nutshell is a lot more stable um our clients are more confident funding has returned yeah to biotech you can look at the numbers that's uh available and um and people always you know they say well you know r d spend is not growing as fast it's two percent it used to be five it's only two and i may or may not be true for large pharma but people forget biotech that's the single you know largest driver of growth over the long term EBP R&D investment grows high single digits eight percent nine percent ten percent and that's that's a significant growth factor you can see it from we have there's a tiny tiny not so tiny but a CRO that's 100% focused on on biotech and you see their numbers so that gives you a sense for uh why biotech is a big driver and funding has returned um so that that's again all of that makes me more optimistic about demand for for

for clinical trial services i can safely tell you we here at larynx don't forget about the biotech funding environment um with that being said though on biotech and ebp how do you feel that iqb is positioned now and given that this tends to be much more of a full service market for some of the fsp you've seen on pharma how are you making sure to prioritize resources so that your win rates on the ebp side can continue to remain high and potentially grow yeah i mean the win rates on

on the ebp side are not as high as on large pharma because our shama we have preferred relationships by and large, with maybe one exception, the top 25 pharma companies only work with three people. I mean, you know, us and two other large providers. And the rest, they fight it out for biotech. So our win rate has continued to improve. We have dedicated resources, therapeutic experts that accompany the assets earlier in the journey. We didn't do this before. We've been a little bit more aggressive in terms of taking on work. We, from a commercial point of view, are extremely conservative in EBP historically. and we tended to be scrutinized at a very extreme degree the scientific validity of the molecule and the financial viability of the company. Many times these EBP companies are 15 people or 10 people and they might be very well funded and it could be a $50 million clinical trial but they don't have any resources and you want to make sure that you know you're gonna get paid really and and we we've been a little bit more forthcoming going to these companies a much earlier we've had a strategy actually of investing in funds in biotech funds very tiny positions a few you know five million dollars a year from now so we are invested in I think 40 different funds and this is a recent activity so we get a first look and then finally internally we've organized we have IQ via biotech we have dedicated resources so all of those actions are helping us grow our biotech business and

on the large pharma side have you seen any change in tenor cadence of how large pharma wants to partner with you you talked about strategic partnerships i know it's been a big part of your growth but are they changing what they're asking from you from that partnership side i think about this against the backdrop of your the early discussion on the agentic ai rollout like like how does that factor into the continued expansion of these partnerships yeah well you know as you know

just giving you AI for a site for a minute for a moment um over the past two three years pharma large pharma swung the pendulum a little bit more towards fs fsp right just resourcing which is you know lower margin and you control less of the clinical trial this uh often when it happens is because the industry demand is shrinking and pharma companies appropriately so want to use their own resources as opposed to outsource the trial um and we spoke before about all the drivers that uh sort of reduce the demand at large pharma historically this pendulum swings back why does it swing back because the very reason we exist is because no pharma company in the world is going to make the investments that are required to have the full therapeutic expertise and and maintain all the resources that are required to run clinical trials even a large pharma company doesn't have you know we we are running at any given point in time 2500 trials so we've got a lot of scale and footprint and resources a pharma company a large pharma company may be running 20 trials 30 trials they often grow by acquiring biotech assets in which they may not have therapeutic expertise. So there are many reasons why the pendulum swings back. And for us, we saw FSP creep up from 15% approximately of our backlog to 16%, 17%, 18%. But then we saw, you know, last quarter, I think, in our bookings, FSP was like 7% or 8% of the total bookings. So it's already going back. And, you know, there are many reasons why economically it doesn't make much sense for a pharma company to just do everything internally but we partner with our clients and whatever it is that we can help them do this is what we are here for you know we are here to as an extended partner of the broader enterprise of our clients and we try to make ourselves unavoidable we're gonna

to run out of time quickly, but I do want to touch on the Cedargate deal. IQVIA has a long history of being acquisitive, lots of token acquisitions. The platform lends itself to doing that. Cedargate being the most recent one and somewhat of a notable asset, what does Cedargate bring you relative to the platform in terms of your expansion potential

and opportunities? Yeah. So Cedargate is a little bit outside the usual. That's why you bring it up and and i thank you for that the it's it active in the payer provider space so um we have a payer provider business it's tiny but it's all overseas europe middle east in particular and we sell platforms analytics platforms that help connect payer providers and the patient Increasingly, we have pharma companies have become patient-centric. In fact, I didn't mention one of the AI, the most successful AI tool that we've launched is called PRM, patient relationship manager, which essentially uses natural language to facilitate the interaction between the patient and the caregiver and the pharma companies. so this is to increase adherence to improve outcomes so we never found an asset in the US that would enable us to augment this patient analytics and you know because of circumstances in the market it became affordable it's a great company it's about 140 million dollars in revenue I'm gonna say it's got great margins maybe 20 million dollars in EBITDA thereabouts and it's growing very nicely it's essentially a patient adjudication it's got a lot of data on patients we have synergies and it helps expand the the set of capabilities with respect to patient patient issues patient data patient claims it connects very well with our

real-world evidence business awesome we'll look forward to seeing that build and the rest of our business Ari thank you so much thanks everyone being here