3 Actions Giving Biopharmas an Agentic Commercial^™ Edge

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Video & Transcript Credit: “How Will Pharma Win in the AI Era? Veeva GM Tristan Theurier Answers the Tough Questions” by Amplifiz - Pharma & MedTech Innovations Podcast is licensed under CC BY 4.0. Original video available on YouTube. Transcript generated from original audio.

Chapter 1: Introduction (00:00)

Guillaume Villard: Artificial intelligence is transforming healthcare at a rapid pace. It supports healthcare professionals in their day-to-day work and some of the clinical decisions they make already. Ultimately, AI changes how healthcare is and will be delivered. Today, we will explore what this new reality means for pharma commercial organizations. Joining me is Tristan Theurier, the General Manager at Veeva in Asia Pacific. We will discuss how AI is reshaping healthcare and how pharmaceutical companies can successfully evolve their commercial models for this new era. Welcome to Amplifiz podcast. We are very proud to be the number one pharma and medtech innovation podcast on YouTube. So, thank you so very much for your support, and please make sure to subscribe if not done yet. It helps us tremendously. Hello, Tristan. Welcome to the show. How are you?

Tristan Theurier: Hello, Guillaume. I'm good. Thanks for having me.

Chapter 2: Patients' landscape is changing (01:05)

Guillaume Villard: AI is empowering both patients and healthcare professionals, as I mentioned. So from your perspective, what are some of the biggest behavioral shifts?

Tristan Theurier: You probably used AI already this morning at some point. I did. So throughout the day, you always do. And patients and doctors are consumers as well, so that's impacting them. For patients, I think one thing is in the past, for example, they were googling information. They maybe were going to specialized medicine websites to get information about some conditions they had. Now they go to AI. ChatGPT even has a ChatGPT health version now. So that is changing because they have more questions for the doctors. There are also two sides of the coin. There was a recent medical publication that showed that 50% of the AI-generated answers to health questions were found problematic and 20% of these answers were actually harmful. So you've got kind of these two sides, right? You've got more information, but with that more information and better access, you also have questions about the relevance in these cases. So that's for the patients.

Chapter 3: Healthcare professionals' landscape is changing (02:27)

Tristan Theurier: For the doctors, it's not about having access to more information. The problem doctors are facing right now is they have access to too much information—information overload. And there is that concept called medical knowledge doubling time, which is how much time it takes to double the volume of medical knowledge. In the 80s, it was taking seven years to double that medical knowledge. In 2010, it was taking 3.5 years. And you see where we are going in the 2020s, now medical knowledge is doubling every two months. Every two months you double the medical knowledge.

And so I was thinking about a medical officer going through their training, from the moment they start training to the moment they become associate consultants. Think about how many times the volume of information doubles and they are still just studying. So think about the doctors that are already practicing. That's huge. And what does that mean? AI is helping clearly in that case. Solutions like Open Evidence are for doctors and help with medical search and clinical information. Now it's not about accessing information, it's about doing the triage. How do I get the essence of what I need, and am I able to ask complex questions as a practitioner where I can get the response? So that's a big shift, I believe. You have AI solutions that help them be more productive.

You also have solutions that start doing part of their job. For example, in the state of Utah in the USA, they recently got approval to get AI solutions to refill certain prescriptions without human intervention for chronic diseases. Of course, you've got a lot of guardrails, it's for a certain class of medicines, but if you know you need to renew certain prescriptions, AI can just generate that prescription.

The last point maybe is science is getting more complex. If you think about the history of medicine—and I'm not a medical specialist, so I'll be careful about what I share—but if you think about the history, a century ago there were small molecules. Simple to manufacture, and they were solving smaller problems, like paracetamol pretty much. In the 80s, you start seeing what we call large molecules, more complex to build and solving bigger problems. And in the last 20 years, we have more of what we call targeted therapies or precision medicine. That's not the chemotherapy you had before, but more like a sniper. Very specific therapies that will go deep and solve one specific problem in the body. And what that means is not only do doctors have more information, they also have a more complex way to treat patients, right? That means personalization of treatments. You don't treat lung cancer anymore. You treat a very specific subtype of lung cancer. That means you may have to combine drugs. You may have to switch treatments at some point.

So that's kind of where we are going. AI is the technology that is changing how patients and doctors think, act, and work together. And at the same time, you've got this environment, this science that is moving at a super fast pace.

Chapter 4: HCPs access & relevance of the SalesRep model in question (06:15)

Guillaume Villard: But what are your takes on the key pointers that pharma commercial leaders should really be mindful of?

Tristan Theurier: AI is to some extent changing and forcing the industry to adapt, and on the other side, it's accelerating a trend that was already there. It's not that it's just changing everything, sometimes it's accelerating. One example I have in mind is access to doctors, to HCPs. We've seen a decline over the last 15 years; this is something that at Veeva we measure. For example, in the USA, 70% of HCPs were accessible by the industry 15 years ago, and now we're down to 45%.

Guillaume Villard: And define accessible?

Tristan Theurier: It's the ability for the pharma industry to engage with the doctors. Most of the time what that means is a rep meeting face-to-face, but that could even be using different channels like sending emails. So all that together is what we call access. So 45% in the US, even lower in certain regions like APAC for example.

So you've got this big trend that is happening, and my guess is that it will amplify with AI as access reduces. There's a bit of a question if a rep can meet a doctor 9 to 5, but now the doctor has access to Open Evidence at any point in time, on their phone, at 10 p.m. when they have a question back from work. There's a question about whether there is an inadequacy between the needs of the doctors and the model that is in place for the pharma industry.

Now I can give you a very specific example. I happen to be married to a doctor. So I've got some information. It does not represent the entire society of doctors, let's be careful. But she was giving me a very specific example last week. She said, "Hey, I met a rep and I had a very specific question." She's a hematologist, so it tends to be more specialty. So quickly you get into clinical and innovation. The pharma rep did not have the ability to provide answers. So what the person says is, "Hey doctor, it's a good question. Let me introduce you to the MSL." And so that person will reach out to help. The reaction of my wife was, "Okay, great. But actually, I'll get the response in one hour on my phone."

Guillaume Villard: By the time the introduction is made, I've already found what I was looking for. Of course.

Tristan Theurier: Yeah. And so that's the thing. Everyone is moving into 24/7 accessibility to information, and maybe the old playbook of the role of the pharma rep to educate the doctors on innovation, on clinical trials that were not at the same pace, needs to change. That model probably does not work anymore.

Guillaume Villard: That is very clear. The traditional rep-led face-to-face interaction has been declining over the last 15 years, so it's nothing new, but AI is probably accelerating that evolution. So that's one element that is important. Now, making that statement is one thing, but being able to identify the action items that need to be taken out of it is another story altogether.

Chapter 5: How Field Reps will evolve (09:49)

Tristan Theurier: Maybe I can start with how I think about where we are going before giving solutions. If I think about the new commercial model, I think there are four components: the field team, the digital, the content, and the agents. And agents is a new one, so I can talk about it.

When I say field, I believe the number of sales reps will reduce over time, and we will see more specialized roles. MSLs have been discussed for the last 10 years, but even more specialized roles as clinical experts. The way I see that—and it's maybe a simple analogy—is a parallel with the history of medicine that I shared earlier. Where we are today is more the large molecule. You still have the sales reps, but you also have the MSLs, the market access team, and it's more a combination of that. But it's still very field-centric. Targeted therapy means personalization, precision. And I think that's where the commercial model should go. How do we make sure that we personalize that information, that it is on-demand when the doctors need it, and what does that mean to be very specific, very personalized? So I think that's where we are going.

Guillaume Villard: I like the analogy.

Chapter 6: Shaping Content for humans & AI (GEO...) (11:03)

Tristan Theurier: So part of it is the field team. Part of it is the agents. If we say the doctors go on Open Evidence now to get information, what do we need to think so that Open Evidence and other tools can get the relevant information from my drug, from my clinical trials, from my publications?

A big thing, for example, that we hear a lot these days is GEO. How do we optimize our content for AI solutions? The model is moving from the pharma industry influencing KOLs and HCPs to influencing the AI that is influencing KOLs and HCPs. So there's a big thing about the influence of GEO.

Guillaume Villard: Interesting. So, GEO, Generative Engine Optimization as opposed to SEO, Search Engine Optimization. I really love the idea of not directly influencing KOLs per se or HCPs at large, but going through that lens of AI wherever they are going for information.

Tristan Theurier: Because the content needs to be ingested by both the human and the AI, we need to rethink how the content is designed. So there's a big thing behind which is, what is our content strategy? That's a big change because that's going to completely change the process. How do we get the content that can be easily absorbed by AI solutions and not just the cognitive mechanism of a human? So that's a big thing. You can influence to a certain extent, but you cannot control the narrative the way you used to in your previous model.

Guillaume Villard: Of course. You're not creating a website page where you control the narrative. That's for sure. That's a big difference.

Tristan Theurier: Exactly, Guillaume. Actually, where I was getting to was the websites. We see something happening as well, which is revamping the brand websites just to make them more relevant to the needs today. If you think about a doctor visiting a brand website in the past, you could probably see where they were clicking and with that you were trying to guess, or maybe find some search words somewhere, to figure out why they came to your website. What were the questions? And you tried to guess whether they got the response, but it was not clear.

One big thing we see now is conversational AI on the brand website. That's AI into the brand website, and actually that's changing everything because now you've got access to the full journey. What was the question? Why did the doctor come to my website? What did they want to achieve? Did they get the right response? And what we see is that brand websites using conversational AI get 38% more requests for a follow-up with a rep than brand websites without conversational AI.

That means we still tend to think it's either digital or it's field. But what you can see is actually it's the opposite now. It starts digitally, but if we are smart, if we have the right tools, then it triggers a follow-up with a rep. Then the rep becomes more demand-triggered. It's less "I'm going to meet Dr. Guillaume five times." Now, it's more like, "Dr. Guillaume will need me after visiting a website." So that's changing the dynamics as well. Not only the different categories you have in your commercial model but the flow as well. Digital to rep, instead of rep to digital that was in the past.

Guillaume Villard: So earlier you touched on four main topics, and let's go back to the content aspect.

Tristan Theurier: I think we talked about responsiveness as a big change of AI, and I think that's also impacting content. If you think about content right now, we have a 12-month marketing campaign or brand plan strategy. You cascade down that content through the regions, and it's hard to actually be super iterative. I think the constraints were primarily because of compliance and different things that did not allow the industry to move fast enough. I think we are beyond that now. Where I'm coming from is I think the content side of the industry can be a lot more iterative and a lot faster in creating content.

We have what we call Falcon MLR, which is AI agentic labor. It's a solution that, based on the risk of the material, will get through the approval of medical and legal without human intervention. The goal and the statement are quite clear: we think we can reduce the time for MLR by 70% within five years.

Guillaume Villard: So MLR, the medical legal review which is the critical process...

Tristan Theurier: Right, what it takes to review and approve promotional content, for example.

Guillaume Villard: So having an MLR accelerated process or enhanced process thanks to AI is one thing, but the point is that if you accelerate the MLR process, you are by virtue creating even more content than ever before. And you said it yourself, doctors are overwhelmed with too much. So what is the end goal here?

Tristan Theurier: If we don't change the strategy, there's no point being faster in creating more content. The only way to solve that is creating more content that is more targeted. I take the parallel again with precision medicine. Where we want to go is targeted therapies and personalized treatment. And this is how we should think about the engagement with the doctors. Can we create smaller content that is addressing one specific need? What are the unmet needs of the doctors, and what could be the content that we can create quickly to manage that objection or to address this barrier to adoption treatment, for example.

Guillaume Villard: That's very clear, and I think that's a good reminder of focusing on quality instead of quantity.

Chapter 7: How AI helps understand HCPs unmet needs & barriers to adoption? (17:18)

Guillaume Villard: You talked about the unmet needs. So I'm tempted to ask you about this because everybody talks about it and obviously has various ways of trying to understand them. But how can we leverage AI to better understand unmet needs that maybe we were assuming for many years without having clear evidence?

Tristan Theurier: The answer for Veeva is Commercial Evidence. Commercial Evidence is getting the real context of the interactions at the field and the brand level. What does that mean? I'm going to give you a very specific example. We talked about the brand website with conversational AI. Now we have the exact narrative and the exact journey of the doctor. We know which questions they asked, why they came on the website, what were their doubts, and the responses. This is something that we could not get until now. This is commercial evidence. If you get the questions the doctors are asking on the website, then you understand where they come from, and that's a big change.

The big one we see as well is in the face-to-face interactions between the doctor and the pharma rep. What we can do now is capture all that information in our CRM. We call that the Agentic Call Report. For anyone that has been in the industry dealing with CRM, they will know that the way we capture information between the rep and the doctor in the CRM systems has not really changed in the last 20 years just because compliance was refraining that. I met Dr. Guillaume. We talked about drug A, and the main topic was about efficacy. What do you do with that? It's hard. But the main reason is we couldn't actually get more. Now we have technologies that are able to understand language, check for compliance based on that language, and derive meaning. And so that's all what commercial evidence is.

Very specific examples with our early adopters: we had 65% of the interactions that identified a barrier to adoption treatment. So 65% of interactions identify barriers to adoption treatment. It's not anymore that Dr. Guillaume talked about the efficacy of drug A. Now we know exactly why Dr. Guillaume does not want to prescribe, and that's information that was there before because it was just a discussion, but it was never in the system. It was never centralized, and therefore for a company, it never existed. Now that's a big change because, in my view, that's completely changing the role of everyone. The role of the rep is not to meet Dr. Guillaume five times. The role of the rep is to understand the main barriers to adoption and to work with their managers and the marketing team on how we address these unmet needs.

And so the role of the marketing team, the brand plan, is different. Now it's like, okay, we have these three main barriers to adoptions. How do we address that? We have a better vision on how we need to solve that at the hospital level or at the healthcare organization level. So it's changing all the dynamics, and it starts by getting good information.

Guillaume Villard: The way we track performance should also possibly be redesigned.

Tristan Theurier: The industry was—and I think about the field team—really towards activity-based metrics. Because there were actually not many other ways. You have the activities on one side, and you have the sales of your drugs on the other side. But in most countries, you don't have sales at the level of Dr. Guillaume. So we have a bit of triangulation, and we try to correlate efforts on activities to results on sales. But that's pretty much all we could do.

Now, if you've got more insights from the discussions between a rep and a doctor, you can actually be more outcome-driven. Going back to that example I had with the number of calls per day, now it's going to be more about how many barriers to adoption treatment are you identifying every week? How are you solving them, and how many are you solving? That's going to be more outcome-driven in terms of HCP sentiment, adoption ladder, which is another big thing, or barriers to adoption. That will mean it's going to change the role of marketing. It will even change the role of managers, the field-level managers, the coaching. All that will need to be reconsidered because it's just a different playbook on how we engage with doctors.

Guillaume Villard: We talked about the fact that Veeva is a longstanding partner for the industry. But as an organization now, what does it mean from a strategy perspective?

Tristan Theurier: Today we talked about commercial, and quickly I talked about CRM. Just to be clear, Veeva is a lot more than a CRM company. We do solutions, software, AI, data, and consulting across the entire industry. Actually, more than half of our revenue is on the drug development side—so solutions for clinical, regulatory, quality management systems—more than actually on the commercial side.

Guillaume Villard: That is very impressive. So you are really helping your customers throughout the entire drug life cycle.

Tristan Theurier: We are. Actually, 85% of new drugs approved were touched by a Veeva solution at some point.

Guillaume Villard: Wow. 85%.

Tristan Theurier: 85% of new drugs, whether it's in the clinical, regulatory space, or commercialization space. When we think about Veeva as a long-term industry partner, this is how we think. Long-term is very important to us. What I always share is it takes 10 to 15 years to develop a drug from the inception in the lab to the drug approval. You can't claim to be a partner of the industry if you are geared towards short-term goals. You have companies investing billions into a new drug. So we have to take that as well. We are always committed to the long-term vision. Some of the decisions we make, we know might take time, and that's okay because we're here to stay and we're here to help.

Chapter 8: Why Veeva moved away from Salesforce platform? (24:20)

Guillaume Villard: Let me be a bit controversial here because if you touch 85% of the drugs that were launched last year, there is a lot to lose when you are leading so strongly, right? Your organization has made a pretty bold move. When was it, one, two years ago now?

Tristan Theurier: Three years ago.

Guillaume Villard: I'm sorry, three years ago to change platform, not leveraging the Salesforce platform anymore, having your own and so on. Looking at it as an outsider, I'm thinking this is a pretty bold move. What were the motivations, and tell us a bit more about where you guys are right now.

Tristan Theurier: We announced that we will move to Veeva's own platform for our CRM solution three years ago. Now, this is CRM again. CRM is not Veeva. This is actually now a small portion, and the reality is all the solutions pretty much of Veeva are on Veeva's own Vault platform, not CRM. CRM was the last one. We are indeed dominant in that market. But the main reason was really about where we think we can still help is to really bring sales, marketing, and medical together. And the only way to get that is to get a full CRM suite that is life science industry-specific.

I’ll give you a very specific example. When we announced that we are moving to the Vault platform, so Vault CRM, we also announced Campaign Manager, which is the solution to run campaigns in the industry. This is not an industry-agnostic solution. This is just for the industry. For example, a key thing is the ability to coordinate your field teams as part of your marketing campaign. What's happening today is that you have different solutions. The marketing team has one solution with one view of Dr. Guillaume, the field rep has a different system with one view of Dr. Guillaume, and then behind the scenes, there is a massive effort to combine information of Dr. Guillaume from version A with Dr. Guillaume from version B. We want to really simplify that.

The other big thing is we talk about Dr. Guillaume. Dr. Guillaume is a practitioner. You can also be a principal investigator. You can be involved in clinical trials, right? You might have a case of pharmacovigilance. So it made sense to actually bring everyone into the same Vault platform, where we have now more than 50 applications that support only the life sciences industry.

Guillaume Villard: Interesting. And a quick disclaimer for those watching us: I am actually not a medical doctor at all. I hear a lot "Dr. Guillaume," but it is an unreal persona for the sake of this conversation.

Chapter 9: Will AI kill the SaaS business (Software market)? (27:10)

Guillaume Villard: One of the promises that I've heard multiple times, if not an endless number of times over the last three years of AI booming, is that nowadays we are all our own developers. What does it mean for SaaS? Software as a service, which is typically the industry I would imagine we classify Veeva as part of. What does it mean for you guys as a leading software partner, to now see your pharma clients possibly create their own AI solutions by themselves?

Tristan Theurier: You mentioned SaaS, I would segment that a bit more. We have consumer applications and we have enterprise applications. What we're looking at now is that Veeva is doing enterprise applications. So the client is the entire enterprise. There is a lot of debate on whether GenAI will make applications obsolete. I do think that enterprise applications are not going away. The reason why is enterprise applications at the end of the day are the operating model of your company. Everything is there. This is where your business rules are hardcoded. Your enterprise applications actually coordinate the work of people. So I don't think they are going away, but it's clear that they need to evolve.

I had this speech about the pharma industry having to adapt to engage with humans and with AI. Everyone has to do the same in a way. Enterprise applications will use enterprise agents. AI agents will use enterprise applications, and these applications now need to coordinate people, but they also need to coordinate agents. So I think that's where we're going. It's more about how we make these applications work with both.

Chapter 10: The price of AI, the consumption model & expectations (29:08)

Guillaume Villard: That makes a lot of sense. And so, as AI has become that enterprise-grade solution more and more, one of the questions that has been discussed more by pharma leaders, but also by procurement professionals, is the price of AI. Because we get a very big buzz and excitement from many professionals in any industry about AI, and then you realize, "Hold on, I have all these tokens that I'm supposed to pay." What is your take on that pricing pressure, and how can we make sure that this truly scales in enterprises and they can afford it?

Tristan Theurier: You're right that it's something we hear more and more, and again it's changing very fast. Six months ago it was about everyone needing to get on AI, and now it's like, everyone is on AI, but hold on, there's a cost associated with that. So I'm tempted to answer with "it depends," and I'm going to explain a bit more, but it also depends on what we mean with AI.

For example, I explained that at Veeva, we do two types of AI. We do agentic labor, which is really automating something with AI, and humans are only involved for escalation. There is the agentic MLR, for example, that I explained. So it's pretty much full automation. Or you may have AI that is more about driving productivity of a human person. Veeva at heart is still a product company, and so what we do is we have requirements or problems of the customers, and we design something with one intent in mind. And we try to really approach AI this way. We don't do AI; we do specific agents for specific use cases, or we have agentic labor to address very specific things. From the moment you have the outcome in mind, then it's a lot easier.

So one thing that we start pitching is ROAI, the return on AI. It's not return on investment; it's return on AI. But it's really to drive that mindset of what are the outcomes that you want to drive, and are we able to quantify that? I think if we get into that direction, then we will still have a question on the consumption, but we can relate that consumption to something a bit more tangible.

Chapter 11: The 3 Key-takeaways of our conversation & closing (31:40)

Guillaume Villard: So Tristan, we touched on a lot of very valuable guidance. The market and the industry are evolving fast. Give me clear takeaways that our audience should take from today's conversation as pharma commercial leaders. What would they be?

Tristan Theurier: I would say three things, and they are more like short-term.

Number one is Commercial Evidence. Get on board with Commercial Evidence. It's too big to be ignored now because that will unlock a completely different model of engaging with doctors.

Number two is content. Is your content ready to be ingested by both humans and AI?

And number three is ROAI. Do you have that mindset of outcome-first, outcome-driven intent when you build AI, so that you move away from this discussion about consumption only?

Guillaume Villard: Tristan, thank you so much for accepting the invitation. I've learned a lot about you, about the industry, about Veeva as well.

Tristan Theurier: Thank you. It was great, really. Thank you.

Guillaume Villard: Thank you all very much for watching Amplifiz podcast. Make sure to subscribe, give us some likes, ask questions. We will be very happy to answer all of them on YouTube. You can also listen to Amplifiz podcast on Spotify, Amazon, Apple and so on. If you would like to follow Tristan from Veeva, his LinkedIn profile is in the description of this video. Thank you all very much once again and see you soon. Bye-bye.

Video & Transcript Credit: “How Will Pharma Win in the AI Era? Veeva GM Tristan Theurier Answers the Tough Questions” by Amplifiz - Pharma & MedTech Innovations Podcast is licensed under CC BY 4.0. Original video available on YouTube. Transcript generated from original audio.