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How AI Is Reshaping Pharma’s Commercial Operations with Punit Kumar
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The way I look at Intel is what are the use cases where we can actually execute on it? So my firm belief is on the execution excellence. We can talk about it as much as we want, but the real value added is when we actually execute on it. So I'll give you an example. Earlier in my experience, commercial operations across functions, whether it is targeting, call planning, incentive compensation, reporting, analytics, there was a lot of manual effort into it. Yes, we were able to leverage systems and tools and technology. However, in my experience, I have seen a lot of time and effort and resources invested into the generation of the outcome. I think in some of my recent experiences, I have led initiatives where we have been able to deploy AI agents and we have been able to free up a lot of the manual effort because the AI agents have been able to deliver a better quality outcome in significantly less time.
SPEAKER_00Hello, hello, and welcome to Pharma Sessions, a place for pharmaceutical leaders to come and learn from each other. I'm your host, Jonathan Kaskey. Technology and market trends are bringing change at an ever-accelerating rate, and no person, team, or company can afford to be left behind. Here, we dive into the strategies and tactics that our guests use to tackle these challenges and create new opportunities and how you can do the same in your own organization. This episode of PharmaSessions is sponsored by Xunt, makers of the X1 reporting platform. On today's episode of Pharma Sessions, I'm thrilled to welcome Punit Kumar, Executive Director of Commercial Operations for the U.S. Enterprise Portfolio at Novartis. With more than two decades of experience across companies like Novartis, Regeneron, and Atsuka, Punit has helped launch multiple blockbuster therapies, build commercial operations capabilities that support many of the industry's fastest growing brands. What I think makes Punit's perspective really compelling is his track record of building fit-for-purpose commercial operations from the ground up. This is scaling globally. This is driving major transformations, focused on efficiency, speed, decision making across entire portfolios, really. So today we're going to talk about that and what it really takes to build launch-ready organizations. Of course, how AI is reshaping commercial teams and concept of staying comfortable with being uncomfortable and why that might be the most important skill in pharma today. So with that, welcome to the show, Bunit.
SPEAKER_01Thank you so much, Jonathan. It's really a pleasure and thank you so much for taking the time and the effort. Appreciate it.
SPEAKER_00Oh, absolutely. So it is uh very late February. It's Friday. I'm I'm looking at two feet of melting snow on my driveway. How much shuffling have you done this week? Tremendous.
SPEAKER_01I can't even tell you how much snow I have seen. As much as I love the white envelope outside and it's so beautiful. Yet the other side of it, which I had to deal with, getting the snow on my driveway, has not been a pleasure.
SPEAKER_00It's a beautiful thing. And when it's found the day after the blizzard, somebody in my neighborhood has basset hounds, which you know are the tiny, low, stretchy dogs, and watching them try to romp through it was great. And about a couple hours after that, I was ready to ready for summer. All right. So let's get into it, right? So you supported multiple launches across different therapeutic areas, different companies. I guess we could take a step back in in your career. What first drew you into the commercial operations side of pharma? Absolutely.
SPEAKER_01So I think what first drew me is the ability to make an impact. And in commercial operations, we are enablers. We enable the launch of a product, we enable its growth post-launch. We enable how the life-saving medicines, the life-altering medicines can reach the patients. We take a lot of pride as enablers of that. And the impact that we make in doing so is tremendous. And across the commercial operations functions, if you think about it, the impact is tremendous. We partner very closely with the sales teams who are actually on the ground engaging with their customers, helping drive that impact and to help strengthen them, to help arm them with the right actionable insights, with the right tools and systems so that they can do their job effectively to help the medicines reach the patients. I think it's very fulfilling, very rewarding. And that has been the core of my career as well.
SPEAKER_00It sounds very collaborative when you describe it like that, really, where you are, I'm assuming you're getting involved well pre-launch, right? So you're dealing with commercial teams, probably medical teams, you're trying to understand the needs of your sales organization. Does that particular aspect appeal to you?
SPEAKER_01Absolutely. I think within a pharma company, it is a highly collaborative environment. And I personally believe in creating a win-win situation. Uh, there should be no intergroup rivalry or competitiveness. I think the best outcome which I have personally seen in my career is when we all work as one team where we can help each other out, support each other, and really create that win-win partnership. We all can be successful together, all boats can rise with the tide. And there is so much of fulfillment because the impact can be amplified. We can understand each other's business needs, we can help out one another, we can be proactive. I think when we build it together, we can truly amplify the impact. So collaboration is, I would say, a very critical must-have, not a nice to have.
SPEAKER_00So if you're in pharma, because most of my audience is in pharma, and you're looking around and you're wondering, am I doing a good job of collaborating or not? How can you tell? Or like what are some indicators that you're creating that collaborative team environment? And what are some indicators that things might need a little bit of attention?
SPEAKER_01Absolutely. I think it's the focus on the right metrics that can measure how the objectives are being met. Each subgroup, each function, like even within commercial operations, we have several subgroups, and each subgroup has objectives. Similarly, our stakeholders have objectives, the brand team, the medical affairs team, the market access team, the analytics team, and so on and so forth. I think if we think about meeting of those objectives, all those objectives come together to drive the brand success, pre-launch, post-launch. And we cannot look at them in isolation, in silos. We have to look at them together. So if we look at them together, we can clearly see how we can collaborate for joint success. And if, let's say, market access team has certain objectives, commercial operations team has certain objectives, we can help each other, we can be proactive and we can help each other meet those objectives. And that's how the true collaboration is. And that's an opportunity of driving the thought leadership, the proactiveness in helping each other meet those objectives collectively for collective success.
SPEAKER_00That makes a lot of sense. Because at the end of the day, right, by sharing your metrics and your objectives with each other, you're really understanding what's important, each person and what they, quite frankly, in their own expertise, say that they need in order to be successful. So then you can start to have a much better understanding of, hey, if what I'm asking might be taking away from that, there probably going to be some natural resistance, right? And you can start to think creatively. But you had started on the consulting side, right? So do you think that that background kind of helps you take that step back and see the whole picture? Absolutely.
SPEAKER_01I would say that, and selfishly so, I might be sounding like I'm promoting myself, but I think the functional skills are very critical. In this day and age, we are constantly being asked to deliver more with less. And in doing so, the functional and the technical skills are so important. It's not that each of us would be constantly asked to roll up our sleeves and execute. Those opportunities might be far and few in between. But having those functional skills helps us in our understanding of the various situations, helps us being more proactive, helps us drive the required thought leadership, helps us not just be a coordinator or a PMO, but helps us being a true leader of our function. So I would say functional skills are very important. And that's, I would attribute that to the ongoing learning and development as well, part of upskilling ourselves. I would, and if that would be one message I could send to my peers and colleagues, that would be to focus on their individual learning and development to constantly upskill them on the functional skills of their respective functions. So they could be true thought leaders.
SPEAKER_00And that's incredibly helpful. That makes a tremendous amount of sense because one of the dangers, especially in pharma, is like we're doing this way because that's just the way we've always done it, right? This is how we do a launch. And it's like you really can't launch a drug the same in 2026 that they can't that you could in 1996 or even probably 2016, right? Things things are changing very, very rapidly. Let's come back to this idea of upskilling because I think that's a whole thread that we need to pull. But you have talked about building a fit for purpose commercial operation, which I think is kind of along the lines of what we're talking about. So, what does that actually look like in practice? If let's say a company is preparing for launch.
SPEAKER_01Absolutely. So, what I heard you ask is what exactly is fit for purpose and how do we bring it into play? And interestingly enough, I think this is common across whether it's a small farmer or mid-sized farmer or a large farmer. There will always be constraints on the resources, limitations on the time, limitations on the budgets, and we are constantly asked to do more with less. And that's where the beauty of fit for purpose comes into play is how we can truly understand what the business needs are, how we can really prioritize what's critical, what needs to be delivered today, what needs to be delivered three days after, what needs to be delivered a week after, a month after, and how we can prioritize and how we can invest our resources and our energies on what can make the maximum impact, and then kind of line up everything after. This prioritization, I think, really plays a key role in designing a fit-for-purpose infrastructure. Because whether you are a small farmer or a big farmer, you have to constantly evolve, you have to constantly reimagine yourself, and you have to constantly do more with less. And that's where I would love to talk about AI as well, because how we can leverage technology, how we can leverage the best practices. And to your previous point, it's hard to constantly execute to the same playbook how things were done 20 years ago, because how things are evolving so fast, how can we take on more work and at the same time deliver a reduction in the resources which are consumed to deliver on that workload, how we can leverage technology, how we can leverage AI. This all kind of goes into how we can continuously deliver on fit for purpose.
SPEAKER_00Somebody who was influential in my career, he was a Swiss guy who led one of the companies I worked for, and he said that efficiency is not doing things faster, it's it's figuring out what not to do. And I think that part of that falls into the AI components, where some of it is like, what can I, as a human, no longer need to do anymore? Right. And so we're looking for these wins where we can rely upon it to deliver good results and to deliver things faster and do what it's good at while still maintaining some oversight. So, how do you let's just dive right into that? How do you look at AI and separate hype from reality and where to get real practical value today?
SPEAKER_01I think you're asking a great question. There is indeed a lot of hype about AI. And as I read about AI, there is indeed a lot of hype, there's a lot of excitement, and rightfully so, because it's a new technology and a lot of people are benefiting from it. The way I look at Intel is what are the use cases where we can actually execute on it? So, my firm belief is on the execution excellence. We can talk about it as much as we want, but the real value added is when we actually execute on it. So I'll give you an example. Earlier, in my experience, commercial operations across functions, whether it is targeting, call planning, incentive compensation, reporting, analytics, there was a lot of manual effort into it. Yes, we were able to leverage systems and tools and technology. However, in my experience, I have seen a lot of time and effort and resources invested into the generation of the outcome. I think in some of my recent experiences, I have led initiatives where we have been able to deploy AI agents and we have been able to free up a lot of the manual effort because the AI agents have been able to deliver a better quality outcome in significantly less time with significantly less resources. And the human effort we have been able to free up from that, and we have been able to reinvest the human effort in actually analyzing the actionable insights, working with our customers to translate those actionable insights into the execution and to deliver the real impact on the ground. So I would say the human effort, how it can be freed up from all the manual tasks, and how it can be reinvested into really focusing on the actionable insight and translating that into execution, that is where I think a great use case of AI is, and that's where the execution excellence is.
SPEAKER_00I totally agree. And I think there's a couple of things you said that I just kind of want to call out. But one is AI tends to work best in areas where you already have deep expertise because you're able to ask it the right questions, you're able to probe on it, you're able to understand when it's giving you exactly what you want versus something that may look close, right? So where I've always where I've had the best experiences and my team and others that I've talked to is in that when you're kind of using it within your wheelhouse. When you're at the fringe of that, that's where it can get a little bit harder to manage. But the story that you're saying, if I can just share a quick story, it resonates so well because the other piece of it is you are dealing with people, right? And there's fear out there of like, is AI going to just eat my job, right? And so we were just, I was talking to a client and I actually watched mental progression happen where it some of the analysts at start had some reticence where they're like, well, we spent kind of spent all day programming Power BI reports, right? And if we're no longer programming Power BI reports, like what is our job? And their leader was very supportive and just to kill any suspense, their team is actually growing, it's not shrinking. But basically, he was able to bring the team on board that actually that's not your job as an analyst. Your job is to surface actionable insights, right? And so, in experimenting with this tool that we were able to bring in and understanding, like, oh, actually, I can ask probably five levels of questions or maybe more in what before I was fighting with filters and waiting for reports to build. It was a real kind of like light bulb moment that this can actually increase our value to the organization as a team rather than be a threat.
SPEAKER_01So I hear you. So what I heard you say is that people being apprehensive about if AI is going to do this, then what will my job will be? I might get eliminated. And that's where the insight you mentioned is your job is not to generate to develop the Power BI program, but to instead focus on the insights. I think, and there are a couple of other things you talked about is in the fringe where we don't have deep expertise, how can we leverage AI there as well? So I think these are brilliant concepts that you are asking. I would not lie to you, I had similar apprehensions about AI when I was ignorant about it. But as I try to study more and as I try to upskill myself, and I'm still learning, I think AI is massive, uh, the application of it. The one thing which I have found very, very helpful is that what is more fun to me. Am I going to have more fun in developing a Power BI program, or I'm going to let an AI agent develop it for me in one tenth of the time, one hundredth of the time, and I can actually see what all insights I can call out from it and I can work with my customers to see where all those insights can be found helpful, where all it can unlock growth opportunities and the impact which I would make as an individual, wouldn't that be more fun for me? So I think it's a reframing of the perspective where AI is actually enabling us to reframe our perspective from a more boring, mundane, repetitive, less fun thing to a more high impact, high strategy, highly productive work. So I'm not going to get eliminated. It's my manual work that has gone and it has freed up so much of my time. I think that perspective has helped me a lot. And that's what I would love to share with my peers and colleagues and my teams, is just to reframe the perspective.
SPEAKER_00I promise I'm not trying to be, you know, polyannic, because I know that legitimately there are jobs that are impacted. I mean, it's in the news around just today, block, which is Jack Dorsey's company, laid off like 40% of the programmers, right? So it is, there are impacts that we're going to have to figure out how to deal with as a society, right? But I think the idea that it's a defense to try to put up blockers, that's a really tough one to hold on to. Yours is a much more optimistic attitude, and like you said, probably a much more fun and much more valuable attitude of learning new skills and kind of learning to operate at the top of your pay grade, right? With all these tools.
SPEAKER_01I think that's an interesting comment. I read that in the news as well. And not just Blog, but several other companies which are releasing workforces. And let me tie that to your previous comment about on the fringe. And it's about the risk-taking ability. We we are constantly in experimentation mode. We are experimenting what works, what doesn't work, and we are constantly providing solutions to the real world problems, and that's a part of us being enablers. Now there's a concept which I love, which is called fail fast. What AI does is it enables us to experiment quickly, fail quickly, learn from it so that the next experiment that we conduct, we are better informed, we exactly know what not to do, and then using this iterative model, we can get to our desired state faster and with better productivity. Now, if we put this in the perspective of what you just mentioned about the companies releasing workforces, I kind of draw a corollary with when we moved from horse-drawn carriages to steam engines. Imagine a company that had employed a thousand people to drive the horses, and when the steam engines came, it let go 90% of its workforce. I think at that point the conversation is how can I quickly learn how to operate a steam engine? Because that can get me from point A to point B faster, better, and it can be so much more productive. So that is the power of the upskilling. That is the power of quickly realizing that the horse-drawn carriage is no longer efficient. So that's what I call failing quickly, failing fast. That's what I call the need to upskill ourselves. That's what I call the need to constantly evolve, because this is a win-win for everyone. As we upskill, it's a win for us, it's a win for our community, because a steam engine is going to deliver far more value. It's going to make a much bigger impact than a horse-drawn cannons.
SPEAKER_00If you've been enjoying the conversations here on Pharma Sessions, you should know they're made possible by the team at Xunt. XSunt helps life sciences companies turn complex data into clear, actionable insight. For years, XSunt has made complicated datasets simple to help commercial, medical, and operations teams map what's happening, predict what's next, and make stronger decisions faster. Now there's an added AI layer that makes everything work so much better. I was actually pretty jaded about some of the AI approaches I'd seen, but when XSunt showed me theirs, I actually left my job to come work for them. It's really awesome. So if you want to understand your market, your customers, or your performance with more clarity than ever, check out XSunt.com. That's XSUNT.com. All right, let's jump back into the episode. As we were talking about failing fast, right? This is a concept that is I very much believe in, but I also believe there needs to, you need to create an environment where one, that's seen as acceptable. But also I think that AI offers some opportunities to almost sometimes fail without consequences. So what I mean by that, like our company, there are many companies that are dealing with this concept of digital twins, right? So let's say you're training your reps. Why not have them on the phone with an AI oncologist, for example, and let them fail in an essentially a meaningless training scenario, right? Before they're out there failing in the in the field. So it's it's all the possibilities are really one of our developers was basically saying that now, and even more so six months from now, really the possibilities start and end with your imagination around where you want to take things. So you've talked about this idea of being comfortable being uncomfortable. And this, I think change is a lot of fun. I think change is very exciting. Change can also be really uncomfortable, right? So, how do you get yourself that and how do you instill that mentality amongst your team?
SPEAKER_01Absolutely. I think you're talking about great things about being uncomfortable, being comfortable being uncomfortable and failing without consequences. I think it's a mindset shift and it's a mindset about the continuous evolution, continuous progress. And as we try to upskill ourselves, as we try to evolve, as we try to constantly unlearn what worked earlier and what may not work today, as we try to unlearn that and as we try to learn new technologies, new processes, new ways of working, as we try to learn AI implementation. That's what I think could potentially make us uncomfortable because we all love to be in our comfort zone. I love to be in my own comfort zone. I want to keep doing what I have been doing past several years. But how can I challenge myself to come out of my comfort zone, to embrace the discomfort, to learn the new, to unlearn the horse-drawn carriage, but to learn how to drive the steam engine. And so, how do you do that? How do how do you force yourself to do that? What is what are you doing? I think I think we have to overpower our skepticism. We have to over-identify and overpower our insecurities, and we have to really try to look at the fun aspect of it. Driving the steam engine can be so much fun, it can go fast, we can feel the wind, we can feel the impact. I think it's the fun. The fun is what keeps us going. And every time I have tried to learn something new, the instant I have started to feel the fun of it, it has really kicked off my intellectual ability, it has really brought in the energy. So I would associate that with having fun with the new, and that's what can make us comfortable coming out of our comfort zone. And I think as we do that, I'm not saying that success is guaranteed in the first iteration. But what I'm saying is that if we are able to leverage AI in an appropriate context, it can help reduce the consequences of it, to your point. And I'll give you a live example. Across pharma companies, we train our sales forces how to engage with their customers. If we can leverage AI to simulate that real-world scenario, and let's say if I am a rep, the AI can watch how I'm messaging to a doctor. AI can really coach me on things I should and should not do. AI can coach me on the best practices because AI has been able to crunch a large amount of data to see what works and what doesn't work. AI can take in all the analytics, all the insights, all of the omnichannel insights in terms of how the physician's mind operates. And AI can really coach me on what are the right levers to pull to really make an impact with my doctor in a couple of seconds, a short period of time. And that can really help hone in my skills. I can fail with an AI when there are no consequences, but I cannot fail in front of a doctor because there's a greater impact there. So I think that's a good example. And there are so many use cases where AI can be so helpful for us.
SPEAKER_00And because there is so much data and there's so much information available. Like even in that scenario, you might have the doctor's Twitter feed, you might have their academic associations, it might know who they co-published with on a paper and where they were author number 11 13 years ago, right? It can know this massive amount of detail and help make these connections and serve that to you to elevate your salesperson in the conversation or your medical science liaison or whoever is having that actual conversation with the physician, if it's done right and appropriately. And hopefully in that scenario, it can be done in a way that is not feeling invasive to the doctor, right? It's just helping helping you actually make much better use of the limited time that they're providing. Exactly.
SPEAKER_01So what I heard you say is that yes, I'm sorry for the technical, but I'm just trying to kind of translate that into what how my some of my peers may relate to it. We have always talked about pre-call planning. We have talked about dynamic targeting and dynamic call planning and generating triggers, and all of it is just to for the field forces to identify and prioritize the right doctors. And what I heard you say is that AI can take in all of that information and help generate the right actionable insights for us much more quickly than what a home office team can take. A home office can team can take a week to generate a dynamic call plan and to generate those triggers. A home office team can take two weeks to create an army channel strategy. But AI can do all of that instantaneously if the agents are built the right way. And that way an AI can understand the real world situation, understand the competitive landscape, understand the market access landscape, understand the ever-changing government policies, and can create those actionable insights in real time much more quickly, that we can then action on it and create a much bigger impact better and faster.
SPEAKER_00100%. I mean, thank you for synthesizing my ramble eggs. But uh but I think at the end of the day, it almost gets back to what you were talking about at the very beginning about understanding different people's goals. One statistic that I'm sure every sales leader knows is their average calls per day, right? How many calls? And I was talking to one the other week, and she was basically like, we've been stuck at two and a half to three calls per day forever, or people. And she's like, and when you go on a ride along, oftentimes the first thing that a rep is doing is they might pull into a Panera and they're spending an hour planning out their day and who they're going to do and who they're going to talk to. And she's basically like, if we could have them maybe do that via voice while they're driving around, if we can get from say three calls to three and a half calls per day on average, okay, it's 125 calls more per year across 200 reps, right? Like that's it's the equivalent almost of having another 20, 30, 40 reps in the field just by getting your calls per day up this much. So I think it's interesting with this, all this futuristic stuff, but then at the end of the day, it kind of comes back to these numbers that pharma has been focused on for a long time for really good reason. And it's like if it's done right, these can actually drive your core things that you know move the business.
SPEAKER_01Yep. I think you're touching upon something very important. You know, pharma has for long been measuring these metrics, which are more quantity related in terms of the reach and the frequency. I love to look at it from the perspective of the impact and the quality. As you mentioned, AI can enable more number of calls and better quality calls. So, what you're essentially saying is more impactful calls that could deliver better outcomes. Number one. Number two is as a ref, you can have you can save a lot of effort from the pre-call planning. So you can reinvest that into having more number of calls. You can deliver the same impact with lesser resources, smaller field team, or with the same size of field team, you can deliver a much bigger impact, better quality impact. I think the use cases are tremendous. And I think as for any pharma company, as the pipeline grows, as we launch more product, it will not be possible to constantly increase the resources in proportion to that. I think as we face more challenging dynamics to include more challenging government policies, to include more pricing pressures, to the constant need to do more with less, AI can be everyone's best friend, where we can do more with less, faster and better. I think the use cases are tremendous. And I think it's really up to us how we embrace it, how we upskill ourselves, how we really use it as one of our best friends and not treat it as a competitor.
SPEAKER_00So let's go forward looking here. So if we're let's say we fast forward five years, it's now February 27th, 2031. What do you think commercial operations and pharma will looks like compared to 2026?
SPEAKER_01I think that's a great question. I think there could be a couple of significant changes. One is that the amount of resources that we currently invest into it, I think in terms of the actual resources, would be significantly less because AI would be able to take over a lot of that work and would be able to deliver that with better quality and faster. I think where we would need is the experts who have the functional expertise who are able to take the outcome of the AI tools and translate that into meaningful outcomes. Those experts will be needed to basically translate the insights into execution. I see a lot of that happening five years hence. So less on the actual generation of insights and more on the execution of insights.
SPEAKER_00I love that. I don't know. I think this is so interesting. I'm curious, uh, the way you were describing learning new skills, it sounds to me like somebody who's maybe played an instrument or has some deep hobbies. What do you do on the on a personal level as far as skills building like that?
SPEAKER_01My most favorite thing is very weather dependent. So I have a motorcycle, which I love to ride during summers. I have a Harley Davidson street bob. I enjoy swimming. I'm a great movie buff, so I enjoy watching movies. It's a great de-stressor as well after long days of work. If I can catch a good comedy movie, it helps me de-stress and and laugh a little bit. So yeah, those are some of the things uh I enjoy.
SPEAKER_00I love it. Well, I'm sure with swimming and with the Harley, I'm not so sure about the movies, but that what you're describing of you get out of things what you put into them, right? That is, I think most people's experience with almost anything that they try. It's what I try to tell my as they're experimenting and trying to figure out what they like and what they don't like. It's like, well, the more one of my daughters loves playing basketball. And what she's found is the more she plays, the more she loves it, right? Then same thing with the drums or with other instruments. Like, and I think my honest take on what personal habits help you get comfortable being uncomfortable, because I think that's a great, great concept, is like just don't be afraid to try. Like, I was talking to somebody else who is not a programmer, he all sorts of collectible cards and stuff like that. He actually used Claude to build an app to organize all this stuff so that he has it. I'm working my right now. My speaking of launches, this is not a drug launch, but my wife is launching a little retail shop, a yarn shop, wool and yarn shop. I'm playing around with building an expense tracker because it's such a pain in the butt to find something that is like not Oracle overkill that just does what she needs. And honestly, I haven't been successful yet, but it doesn't really matter, right? It's like I'm trying, I'm learning, I'm kind of having fun actually seeing something that I'm typing in come to fruition or maybe not. We'll see.
SPEAKER_01Yep, absolutely. I think as of right now, I'm having so much fun in thinking about different real life use cases where AI can be leveraged. And I use, for example, ChatGPT. I have subscribed to the Plus version of it. There are so many practical use cases where I'm just having so much fun with it. For example, my elder daughter, she's a freshman in high school, she's pursuing biology. I have not studied biology, I was a maths major, and I have created an agent which is called my biology tutor, and that is such a lifesaver. It is able to translate very complicated biology concepts into a very simple, easy to understand. To an extent, I have started having fun with biology myself. That's one. Second is, you know, uh, I recently tried creating an agent called My Financial Advisor, and it is helping me with different tax saving tips and different strategies for investments and whatnot. I don't claim to have become an expert. There are definitely financial experts out there whom I would go to, but its ability to really take real-world concepts and translate and give it to you in a very simple, concise manner is really stupendous. So I've created a couple of different agents for different things. There's an agent I create for real estate. It's just keeping me abreast of what's happening across the country as the real estate prices are going on a top C turve ride as the mortgage rates are falling. It's so very interesting. The thing that fascinates me about AI is that it is so applicable to the real life use cases. And we don't really have to be a technical expert at it. We just have to focus on where we can use it, and it is there to help you with it.
SPEAKER_00I love it. All right, well, let's leave it there. I had a lot of fun talking with you. I think people are gonna like it a lot. So let me just say thank you for joining me. Thank you so much, Jonathan.
SPEAKER_01I think you asked amazing questions and very thought-provoking. It's really a pleasure. Thank you.
SPEAKER_00This episode of Pharma Sessions is sponsored by Xunt, makers of the X1 reporting platform. And that's a wrap on today's episode of Pharma Sessions with me, Jonathan Kaske. If you enjoyed today's conversation, don't forget to hit follow or subscribe and share it with someone else in the pharma world who might need to hear it. For more on pharma trends, career growth, and business strategies, connect with me, Jonathan Kaske, on LinkedIn. Until next time, thanks for listening.