Transcription
Hello everyone. This is a great moment when we start another episode of Pharma Talks. And here I am [music] Natalia Andrichuk, a usual guest of all of our podcasts. [music] And I have a surprise, a nice conversation for you. The guest of today's is Alejandra Jerma who is not only data scientist but a specialist in pharma domain with the vast experience and data analytics and data science for market access for commercial and medical fields. And today we'll be having an amazing conversation about how datadriven approach is changing pharma and how pharma survives in the age of AI. Alejandro welcome to the studio and would you play please tell tell us a little bit about yourself.
>> Hi Natal thank you for the invite. It's great to be here with you and having this great you know uh conversation about analytics AA and data in the pharma space as you say I have a lot of years of experience supporting everything from consulting to major roles in commercial analytics forecasting market access analytics and I'm just excited to be here and share what I have learned and also learn from uh from your your questions and sharing a lot of information.
>> It's amazing. You know in pharma talks we always take the most vivid the best topics which are worrying the biggest audience of ours and take these topics to the people who are the heart of our industry who are working hard dayto day bringing the science to life. And um the first question will be quite a provocative one. So you know how from the dashboards to decisions we jumped and how has the role of commercial analytics evolved from simple dashboarding to influencing the real business decisions. I always say the data used to describe the past now it shapes our future as the future gold as everyone is saying today and pharma needs to see analytics as a decision making engine not as a reporting function. What do you think?
>> Yes. So back in the day when I first started in commercial analytics, most of the work was about reporting, finding relationships and looking back in time. But today given the technology and given kind of quick advance in technology is more about analytics driving business strategy. So we are moving more from a scenario where we will look at the past to a scenario where we are more predicting the future and basically analytics having a seat on the table kind of driving this important discussions about business opportunities and challenges. So you can think of analytics now being more like a GPS GPS for the business to help on that uh journey of discovering and grow. So in today's world it is interesting because a lot of times people think about now about AI machine learning as something completely new and what is new is the speed and the technology but basically a lot of of the methods that we have used on the past you know are being leveraged by technology. So it's more about a scale and a speed to make predictions to be proactive and to drive this business decisions.
>> It's absolutely amazing. I love this metaphor as a GPS. This does mean that everyone today has a GPS. That means everyone today needs to have understanding in basics of data of being able to surf with data or just be a skilled user of the result right because we are all the GPS users in the end so that's that's amazing how you see say that I love the metaf you you're absolutely right so you know uh if if we will uh turn a little bit our angle to understand what is the real value and what is hype right because there is so much talk about AI in our industry we're talking a lot so we are all about how we bring to life those agentic architectures what we will you know weaponize with AI so what do you believe are real tangible use cases and where should companies be careful not to overpromise you know like even ourselves at Vizer when we are working a lot with the modular content and omni channel journeys so we can see that AI is bringing like amazing value huge value and in the business processes in the personalization aspect making the segmentation clear bringing this content engine to life but it is not the magic IC we cannot you know rely on overpromising. So what is your opinion where should companies be careful not overpromise on the AI perspective?
Yeah, that's a great question and indeed AI is definitely the buzz word of the moment, right? And kind of going back to the GPS analogy, when we think about GPS, it's like with the GPS uh we can go anywhere, we can find locations, we can find uh restaurants is so amazing at any point in time. But really what defines the use of GPS is having a destination and a journey. And what is happening today with AI is that we all talk about AI and everyone talks about AI and and it's like it's so general because AI is made up of so many components. So it is important for us to define what is AI as a set of computational technologies including the traditional methods with new machine learning, natural language processing and you know other systems that allow reasoning reasoning and to take information, digest and prepare information to a scale to make business decisions. And we can spend the entire time talking about the different components of AI. Um, and that's why it is important when we talk about AI, we talk about what we want to accomplish, right? It's like not just think about AI, about getting the genie out of the bottle kind of to solve everything, every challenge, provide every solution to every business problem that we have. But it's like what are the components of AI that will help us move the business the business forward in a more efficient, more personalized, more predictive, right? but being very cautious about overpromising. So in other words, AI won't replace thinking but it will change the way that that we think and there are like a lot of examples online of companies that have implemented AI processes and AI technology over time. Like for example, there are uh case studies from at Canana looking at the top 10 uh bioarma how they have been able to drive uh increase in sales. Also there is a a market research study by by Merc where they model and train about on 300 K records to improve prediction and adherence behavior for the road and when I mean records I mean patient data, doctor data, provider data to improve adherence and also there are also the risk of AI where is overpromised. For example, relying on AI to make predictions and thinking that we are going to get perfect predictions, right? also looking at uh legal compliance when using AI especially in the pharma industry. We got to protect uh patient privacy, HIPPA rules, and we just have to make sure that whatever data we use through these AI tools and and platforms, right, the process that we share the data is compliant, right, with the existing regulations. And also we have to understand that AI is so powerful that allow us to identify other opportunities that in the past it was harder like for example with regards to equity let's say equity in healthcare but at the same time you know if if we are not careful and if we are just looking at AI as a way just to get answers and to get solution there is a risk to get bias with regards to equity gaps. For example, if we have launched a product and we look we see that the product is doing very well among the population that has commercial insurance, right? But we see that there are some adherence gaps in the Medicaid population, right? And therefore sales of the product are not as high as expected in the Medicaid population but they are the product does very well in the commercial population meaning the people that have a job and have insurance through their jobs. You know one may think oh then we should focus because the data and AI is giving this analysis that these sectors of the population don't adhere to the product right where the the people that have insurance use the product and are compliant using the treatment then therefore we should spend more resources expanding our market in that population where our product does really well. if that's kind of what the data will tell you. But in that case, it is biasing that equity gap that instead of that also we should look why the people let's say receiving Medicare are less compliant and what can we do to help them be more compliant and improve their health and indeed improve our business. So, so we have to just be careful where we go automatically when getting results through AI. There has to be reasoning and there has to be a lot of good judgment to move the business but also to to deliver on the value of treatments uh to the entire population.
>> That's so true. That's so true. Many of us today are uh talking about human and human alone to be in the uh you know orchestration of entire AI instrument which we are using for our our work daily and this is not the pure questions answers and the results. Yes absolutely absolutely true. So where we mustn't be really uh only you know relying on the AI also uh there are still there are strong guard rails from uh you know HIPPA and GDPR and all the compliances issues and the AI regulation which is coming up. with this strong regulatory environment. So where we need to be very careful but nevertheless the recent Auvia data says that we are having uh today like over 44% of pharmaceutical companies are willing to invest into AI in 2026 despite all these you know risks we are imagining and almost like 80% of their IT budgets are going into AI investments uh and investments on the data integration. So that means like we are going there slowly and surely. I want to touch in the next question a very dear topic to my heart which is personalization. So I think this dear topic is a nightmare from the others nightmare dreams but personalization it is. So how can AI and data support true personalization for pharma and how we can do this personalization supported by AI without over overwhelming uh teams in the fields and violation of the compliance. This is this is really what is bothering me. Do you have a recipe for us?
It is hard to have a recipe, but I think that you touch in a very important point that is personalization because usually when we talk a lot about AI, the first thing that we think is about technology, data, software, infrastructure, platform and and then in that journey we miss the human piece and the human piece come with personalization and what really matters is that the information that we provide to AI or the solutions are relevant to the people that it intended to be used and those can be patients for example with patient service programs ensure that when we use AI capabilities let's say in in the area of customer support it is personalized to meet the patients needs. It may be providers processing claims or even uh deciding on to which be best treatment option is uh for the patient but also it can be for the sales reps when when they are on the field and they have only a few minutes to talk to a to a physician or to the or to the staff in the office. is very important you know that that the information that is provided to that sales rep to be shared with the physician is also is personalized for the goal of the sales rep to be able to discuss what is meaningful and what is useful for the office to provide better treatment uh to the patients. So the the nice thing about AI is that now it allow us to build those capabilities. So the use of data and results can be personalized for everyone needs. So instead of now providing the sales rep with a lot of of reports and graphs about the patients about the office that he's visiting, we are able to tailor a message that will be relevant and that will provide value to the doctor's office and will allow the rep to use uh his time more efficiently. So personalization done right is in about more complexity but it's about more clarity and AI helps to simplify these decisions focus the engagement and ultimately strengthen throws with both providers and patients.
>> That's that's totally amazing. this personalization things is only you know um uh becoming more powerful with AI and talking about personalization if we can look at this on the a little bit different angle is on the emotion angle because every every persona has um their their very own type of the digesting of the information. Some of us as people as humans we are more the conic focused like to have the precise communication. Some of us are more I know philosophic have make some preamles in communication something something extra. So I think the simulation of the field force behavior with the doctor is an important part of this AI AI simulations which are built by different companies because definitely we cannot we cannot have this uh personalized to the level of the person without the consent from the person of course. But then if we will have this uh simulation of the behavior of field force but regarding the type of the persona or the personality of the doctor. So it helps to train field force for the behavior to be more efficient in the field and to jump into that short attention spot of the doctor. the short window to deliver the message to be efficient, effective, to be proactive and to be not, you know, scared or to be out of the field because of the uh not understanding how to act um efficiently over there. That's a that's a true thing. So this is very powerful and uh all of that is changing a leadership perspective, right? So we do understand that the next generation of farmer leadership might be changing and changing and evolving and what skills or mindset will define the next generation of pharma marketing and strategy leadership. So I believe the future leaders will combine empathy and analysis as an impossible combination but they will manage that emotional intelligence and data fluency must go hand in hand as the only way to connect meaningfully the both HCPs and patients as the as like the both sides. So from your perspective how the leadership in pharma will change?
>> I think the next generation of of pharma marketing and strategy leaders will need to bring together three things. One is analytical fluency. They have to be familiar with the methods and the tools of analytics. H they have to have crossf functional agility, the ability to work in teams to uh to be able to set up h common goals, common strategies and also they have to have human center leadership which has to do with humility to be able to do good stakeholder management towards work cross functionally. The data literacy is very important and I don't mean literacy in a sense that the leader has to be 100% knowledgeable of all the insights in and outs of the data or that they have to code in Python or R or or or whatever technology but they do need to understand what AI and advanced analytics can do and where what their limits are so they can ask the right questions and translate insights into actions. Secondly, they will need to work across silos. They will need to be able to reach out to different teams, reach agreements, answer questions and and of course listening. A lot of times due to technology, due to other priorities, sometimes uh leaders don't listen to to others, but they just want to implement their strategy and their vision. And it is very important to listen to others and be willing to to be challenged even if if we disagree. is so important to listen and understand where people are coming from, what are their objectives, what is their reasoning and in that way have a valuable conversation and have the humility to say you are right if if if we agree that their approach although it may be different than uh our approach you are right and and explore and be open to explore uh other methods methods. And third, you know, it's important to to be curious. We may think that AI has all the answers for every single question that we have, but AI requires good judgment, good understanding uh and and good communications to to drive a successful strategy. So in short, tomorrow leaders won't just be storytellers with data. They will be bridgebuilders who can turn complexity into clarity and strategy into patient impact. But I want to stress the boat with true humility.
This is amazing how this collaboration across functions and being able to connect people and to avoid these rabbit holes of silus built along the organizations. So that's that's how this transformationary the new leadership role is and also it looks like a little bit like to be a translator. So sometimes there is this famous saying we lost in translation. So it's not the language which is the blocker. It's kind of the internal language where marketing talks brand analytics talks models sales talks stories. We need the translators. So we need like connectors translators people who can connect the dots across the silus. So you have been working across analytics, marketing, sales and market access. What's the key to making these functions work together in a data first model?
>> Yes. So the the key is aligning around a share goal and a share vision. It is very important to be aligned about those two aspects and and that takes time also on building relationships with the different stakeholders and and and team members. So analytics, marketing, sales, market access each operates with their own priorities and data sets. And in a data first model, the real power come from connecting those pieces into a single study to be able to crossfunctionally talk and prioritize what is important, understand what are the the challenges and also move in into action. So that means that marketing isn't just measuring campaign clicks. It is working with access teams to understand coverage barriers. Sales isn't just looking at collectivity, but it is leveraging analytics to prioritize the physicians and those patients that face affordability issues. And analytics is in sitting on the sidelines. Actually it is translating data complex data into actionable insights that each function has can trust and and use. So so there is kind of a glue to this communication and that is trust right and trust is derived from people knowing that let's say the analytics team brings value and that you are willing to listen. You are flexible and that you are bringing value and also you are bringing value from a sound methodological perspective and also with uh with with good judgment that you are not just someone needs to know uh some important information to launch their marketing campaign and then you understand what they need and then analytics goes does the analysis and kind of throws it over the fence. That's not the best way to work. The best way is to understand what are the goals, what are the objectives, what are the business impact and understand how that analysis and door results uh driven through traditional analytics or AI will help the business and sometimes that involves sometimes challenging the assumptions of your of of your business partners. So the real key to making analytics, marketing, sales and let's say market access and other organization work together in a data first model is alignment not just on the data itself but on the goals and the questions that data is meant to answer.
Alejandra, that's a great great summary and a great takeaway. So that to become the truly datadriven company, it's not all about the technology, it's about the culture. So there might be the best platforms and the smartest data scientists but the the leaders don't trust the data teams don't know how to act on this overprotective cannot understand the technology the impact of this technology will be limited. The companies that succeed are the ones that with the data into everyday decision making. Not just treat it as a special project that we need to make a decision. We look into the data to make this decision. That means setting clear metrics everyone aligns on telling the story behind the numbers in plain language. Empowering people at every level to use insights and bringing confidence to organization. This is this is what is really important and then the real transformation will happen and the data will stop to be like the exclusivity. It will become the commodity the result of collaboration the driver for the smartest choices and the decisions. In short, it's not only about the tools, it's more about the trust and collaborations and acting on that. So, if you would give the advice to the young marketeers and to the commercial IT leaders, what your advice would be?
Well, my advice will be don't just learn the tools and don't focus only on the technology, right? Uh learn the business, right? See how the technology move the business uh forward and in that way, you know, when you learn the business, build relations, you will be able, you know, to contribute more. A lot of times companies and it's kind of it has to do with the a lot of hype of AI are eager to uh try AI technologies or or or AI tools and in that process the focus can be become very technology technological oriented and they may miss the other part that is the real value is connecting technology to business problems to solutions to empower organizations and that requires collaboration. So you cannot do the job and deliver results alone with technology. You have to work with others and those others a lot of times are not familiar with the technology but are their own subject experts in their particular area of expertise whether is marketing sales market access and is the ability to bridge that gap between business needs knowledge and technology to produce results. I will say it also is always stay curious you know as why how when challenge the status quo and be a a team player indeed to succeed in today's world is not only about technology actually technology is getting easier with time thanks to AI things that will take a long time and that require very specific specific knowledge expertise in the past is becoming easier because AI is evolving. Now we have generative AI you know that provides a lot of answers to our questions that provide a lot of chat bots AI agents and and in some cases there are platforms that have uh chat bots and AI agents out of the box uh to be used for analytics, project management, customer support, you name it. So it's becoming easier to use technology and to use advanced technology. So the important aspect now is kind of to be that uh that bridge with other business units with other team members you know to ensure that technology delivers on its value. So in short, just don't be a person who runs the numbers, runs the reports, looks at the data. Be the person who helps the team act on the results and contribute to the share success, you know, of the company.
That's an amazing advice and I'm so grateful Alejandra that you was the you were the gu of our studio today and you shared all of that thoughts and all that expertise with us. So I believe our listeners will be happy to continue a conversation with you and therefore there there is the ability to connect via linkadine or don't hesitate to reach out to us van to connect with Alejandra. Alejandra has an amazing page on linkadine with a lot of published works and a lot of projects. So that's available. So, thank you so much for you being with us today and uh having all of these insightful conversations and we will meet with all the subscribers in the next episode of Pharma Talks.
>> Thank you Natalia for having me. This has been a great conversation and I just want to add you know to your audience that there are a lot of case studies on the use of AI and on the success and examples. If they want they should check them online. Studies by Iubia by by Mercer in the public uh domain CS associate there is a lot of uh published work there that we can read and learn you know before we start kind of our own AI journey. Again thank you for for the opportunity you know to [music] have this great discussion. So, thank you so much and see you in the next episode. Thank you and goodbye.