The Role of Technology in Biopharma with GSK

Speaker 1:
Welcome to TD Cowen Insights, a space that brings leading thinkers together to share insights and ideas shaping the world around us. Join us as we converse with the top minds who are influencing our global sectors.
Charles Rhyee:
Hello. My name is Charles Rhee, TD Cowen's Healthcare Technology and Distribution Analyst. And welcome to the TD Cowen Future Health podcast. Today's podcast is part of our monthly series that continues TD Cowen's efforts to bring together thought leaders, innovators, and investors to discuss how the convergence of healthcare technology, consumerism, and policy is changing the way we look at health, healthcare, and the healthcare system.
And when we think of the biopharmaceutical industry, we naturally think of innovation in terms of discovery of novel medicines, treating any number of diseases and conditions. But the innovation occurring in the pharma industry today isn't limited to drug discovery, but also the application of technology in pursuit of developing new therapies. And in this episode, we dive into how the pharma industry thinks through the development and deployment of novel technology. Technologies such as clinical operation software, AI, machine learning.
All of these have increased the efficiency of every stage of the drug development process. This embrace has helped drive the discovery of new therapeutic classes and the development of drugs for targets and diseases that were previously thought untreatable. And to discuss this topic, I'm joined by Shobie Ramakrishnan, GSK's Chief Digital and Technology Officer. Before joining GSK, Shobie held senior technology leadership roles in organizations including AstraZeneca, Salesforce, Genentech, and Roche. Shobie, thanks for joining us today.
Shobie Ramakrishnan:
It's a pleasure to be with you.
Charles Rhyee:
Maybe to start, could you provide sort of an overview of your career leading up to GSK?
Shobie Ramakrishnan:
Sure. I started out my career as a software engineer and I've spent most of my adult life in Silicon Valley. And about half my work life has been working with high-tech companies during key points of inflection for them. Apple back in the day, and then later Salesforce for many years. The other half of my career has been working with life sciences companies, starting with Genentech, which then later became part of the Roche ecosystem and then AstraZeneca and now GSK, of course.
And across all these amazing experiences, I think a common thread has been, Charles, to help realize the big opportunity and as you said rightly, that digitization and data and in the past decade, AI offers to really drive innovation and growth as well as still have a better customer and employee experience as well.
Charles Rhyee:
And how would you then frame that evolution, particularly in the biopharma industry as you think about that industry's approach towards IT infrastructure?
Shobie Ramakrishnan:
If we look way back, I would say pre-2010, the industry was just making slow but very short pivot towards adopting the modern techs of that time. A big push to the cloud was a theme of the time, starting with software as a service companies that are dedicated to life sciences like Viva Mobile adoption with iPhone. When I was at Genentech back in the day in 2007 is when we launched the first iPhone and we were already beginning to build apps like employee directories on the iPhone. And also social was just getting adopted in marketing, et cetera.
But there was also a recognition of the opportunity with big data as [inaudible 00:03:34] back then. But the past decade has seen an enormous focus on expanding the data sets we have access to particularly R&D, but across the business. And an enormous uptake in data-driven customer engagement, adopting AI and machine learning across the board, as you said.
But none of this would be possible without the underlying infrastructure, IT infrastructure becoming exponentially faster, better and cheaper as well. And we have this incredible ability to scale and move fast with the adoption of cloud. And we are seeing the industry also build reliable and connected data platforms to have access to clean, connected data at pace. So we have invested in two data platforms and I'm happy to talk about those to enable our digital and now AI-led tech adoption.
One is called Onyx, which is dedicated to research and another called Code Orange where we house-clean connected enterprise data at scale. And I think there's a distinct change of pace in our sector. And it is an incredibly exciting time to be working on data technologies in our industry given this incredible potential it has to help us develop new and innovative medicines and vaccines.
Charles Rhyee:
And then definitely, I'm sure we'll definitely touch on a couple of those as we go along here. Maybe just step back real quick, because you mentioned you were at Apple, at Salesforce and then you made this pivot to Genentech. What kind of precipitated the switch from traditional technology into healthcare?
Shobie Ramakrishnan:
First, I would say that we all have patients in our lives, don't we? And I think, in my case, I lost my mom and a very dear aunt to breast cancer. And when I first joined Genentech in South San Francisco, I was just struck in complete awe of the science that had the potential to prolong lives of people with cancer or bring precision medicines based on genetics and other factors that have the potential to actually cure cancer.
So all of that has become real and we now have access to medicines that my mother didn't, as a result of the work we do in this industry. And so it's very special and very personal. So my experience is a technologist, being of service to accelerate scientific innovation is super exciting. And we've now frequently hired amazing data and tech talent from even big tech frequently. And a key differentiator always comes down to that sense of purpose and personal connection to why we do the work we do. And this industry provides an opportunity to apply technology to enhance human health. And not many sectors can definitely offer that.
Charles Rhyee:
Yeah, for sure. Then as we got to 2018, you joined GSK. What attracted you to coming to GSK?
Shobie Ramakrishnan:
I think it was a company that was sort of at the cusp of an important transformation in both the pipeline and its performance. So I think we can, hand on heart, say in the past five to seven years, the company has really pushed forward the agenda on scientific innovation as well as on, as you recently saw with the launch of our vaccines for RSV, et cetera. But also just the performance, the commercial performance has been quite impressive over the years.
And we've had to drive this turnaround in very pragmatic ways. Some of it is management 101, other things are absolutely new innovation that none of us had embarked on. So doing this work with this team at this time was definitely very exciting for me.
Charles Rhyee:
You've spoken in the past about how technology can be an enabler of a company's purpose and strategy. Maybe help us understand what you mean by that.
Shobie Ramakrishnan:
I think this is truly a transformational moment for healthcare broadly, as you said earlier, and life sciences industry specifically. And significant advances have occurred in two important areas that I think are driving forces behind this. One is our understanding of biology. I think the digitization of biology has gone through tremendous levels of innovation. And two, there are powerful new technologies that can make a sense of enormous amounts of scientific and clinical data at scale and at pace.
And this means that we can truly reimagine what's possible for patients and society in fairly profound new ways. And at GSK, we place enormous emphasis on three things as we go on this journey. And first and foremost, we will not do tech for tech's sake. We want to pursue only those opportunities that'll have a transformational impact on the health of patients through the novel medicines and vaccines that we bring to life.
And I'm very optimistic that the next decade of innovation in our industry, Charles, will be shaped by our ability to use data and AI to understand and engineer biology and thereby change the course of disease entirely in many cases. And while R&D is our big focus area, we see an enormous opportunity. And you said this really well in your opening. We see an enormous opportunity to use technology to help get our products into more patients around the world.
Our second emphasis is on talent. So it turns out that engineering biology is a cool thing to say, but is an enormously hard thing to do and there aren't that many people who are proficient at it. So we work very, very hard to be a workplace where world-class talent can come join us on this quest and do their best work. And we also try to invest and power all our people across the company with data and digital fluency so that they can solve some of the most complex problems better and faster.
And a third element of doing this work is that we need to realize we are part of an ecosystem and we have to be a trusted and capable partner because the use of data comes with enormous responsibility, which then requires expert capability and a deep care for what we use and handle to progress our mission.
Charles Rhyee:
If we think of these three pillars in your thinking, how does a chief digital and technology officer come in day one and put together a strategy for really a company as large and complex as GSK?
Shobie Ramakrishnan:
It's very important to recognize that no matter where we want to go, we all have to start where we are as a company. And I think this is one of the things that's almost always missed in transformations because we are so excited about the target state, you forget where people are in the company. So listening and learning from what people and the organization is saying on what's working well, what do we need, where are the opportunities and where can we accelerate what we need to help people with, I think is a super important thing to do.
And I would say first and foremost, that leadership and ownership of this agenda, really, across the company matters. In our case, we said when we spun off our consumer health business and we set up new GSK as a standalone biopharma, we said that our purpose is to unite science, technology, and talent to get ahead of disease together. So that was a very intentional statement to say we're placing technology at the core along with our people and our science.
So our entire executive leadership team, including our CEO Emma Walmsley, have been committed to making AI and tech adoption a differentiator for our business. This means that we have shared objectives at the executive committee level and goals to really make it real for the company. And I think that's incredibly important. It becomes very hard for the teams to prioritize this if the leadership doesn't own and activate it.
And our priorities, plans, investments and value measurement, all just stuff follow from that. And you'll see that I didn't say tech strategy here because we believe that it's not a tech or a digital strategy. It is our company's business strategy to embed technology into what we do and how we work because we are going into an incredibly increasingly digital world as well.
So the second thing I would say in terms of setting the compass is that it's important to keep an eye on key signals of disruption and opportunities or threats that we see from those. And patients when they access healthcare experiences, they experience it through an entire healthcare ecosystem with all its idiosyncrasies. And we are just one very important component of their experience.
So being mindful of how we work together with the other partners in the channels, in the way with our HCPs, with the provider ecosystems to remove friction and deliver a better experience, I think, is very crucial. And we are very excited about our plan and how it can drive the company's performance and pipeline. And our aim, of course, is to is that this will be a differentiator in helping us achieve our goal of impacting the health of 2.5 billion people by the end of the decade.
Charles Rhyee:
It's six years in, how have you seen... Because clearly as you mentioned earlier, this is a really dynamic period in time, I think, in healthcare and particularly with new technologies emerging, if we think about large language model AI, for example, but beyond that as well. How have you seen the strategy change or what kind of pivots have you been, I don't want to say forced to make, but have chosen to make as you've seen the environment change around you?
Shobie Ramakrishnan:
I think we recognized earlier on our transformation that investing in foundational things like cloud, et cetera, are going to be super important to scale and move fast and deliver things in weeks, not waiting for a server to be delivered in order to progress our agenda. So we tried to think about what are the foundational things we need to put in place. We recognized that data was going to be a big differentiator because unlike a digital banking experience or a digital retail experience, we don't go to a web experience to have or a mobile experience to have the entire company's delivery capabilities organized.
We focus on research, we focus on development. These are all fairly distinct and substantial functions in itself. So we recognized the data is the golden thread, and therefore we made investments for, at that time six years ago, for being a more digital company. And then when GenAI happened, those things all provided this massive jumping tool for us to be able to embrace AI without any delay because we had invested in all these data infrastructure that drives our digital capabilities in the company.
Charles Rhyee:
You mentioned earlier, obviously GSK's ambition is to positively impact the health of two and a half billion people by the end of the decade. GSK, obviously, has a very long and storied history, particularly in vaccine development and innovation among other areas, of course. Can you talk about how technology has enabled GSK to go beyond clinical development, streamlined the real world vaccination process? I think you have some tools like EasyVax. Maybe talk about that because you mentioned earlier. It's sort of how it's incorporated into the whole patient journey.
Shobie Ramakrishnan:
Yeah, it's really important. As you probably know, we have a big focus on prevention as a company. And I'll talk about the EasyVax example here. When we are talking to healthy people who don't want to get sick, we really wanted to understand, simplify, and remove friction from their personal experience to get access to vaccines so that they can manage their health risks effectively. And our U.S. commercial and digital teams have worked together to build a first ever vaccine scheduler in the U.S. for all recommended adult immunizations regardless of manufacturer, actually, to improve vaccination rates and protect people and health systems from the burden of preventable diseases.
So right from a QR code in the doctor's office, patients can schedule their vaccination appointment at any pharmacy from their smartphones very quickly. And we believe that this digital tool has the potential to reduce the nearly 50% of patients who never make their vaccine appointments after leaving the doctor's office. So that's an example of a tactic we've deployed that's a purely digital tactic to enable our patients to access vaccines that's available to them, whether it's us or anybody else.
So we made smart investments like that, but I think that's an example that you can see across. That's a commercial example, but we have examples in scientific engagement and we have examples in supply chain, et cetera. So it's been embedded throughout the company well beyond R&D.
Charles Rhyee:
That's interesting and makes a lot of sense. One thing I want to ask you about, you guys talk about it often, is this. Explain the concept of a digital twin and how GSK uses that particularly, I guess, in the manufacturing process mainly.
Shobie Ramakrishnan:
I think a digital twin is simply a virtual replica of a process, a product or a service. So when applied to producing medicines or vaccines, it's a mechanism to collect real-time insights from the outset by combining the virtual and real realms of development and manufacturing in a closed loop of sorts. I think of it as a real-world experiment in forming a computer-simulated experiment and vice versa in a closed loop so that both become as effective and as efficient as possible.
And this has been something that's helped us get medicines and vaccines to people much faster and much more effectively. And in one of our new vaccines launched last year, which has so far protected nearly eight million people since its approval in May 2023, we used a digital twin to simulate manufacturing processes and anticipate some issues for the vaccine deployment. And using what we learned to really accelerate the manufacturing process, we were able to increase production yields by double digits than we had anticipated and were able to make more doses using the same inputs, thereby reaching more people who needed our vaccines and eliminating the supply constraints where there was an incredibly high demand as well.
You asked about the six-year journey. And we had zero digital twins six years ago, and we have over 50 digital twins deployed across multiple products and manufacturing sites to optimize various parts of our manufacturing process. And our teams are now asking themselves, "Why would you launch any asset without digital twins embedded wherever it makes sense to do so to continuously optimize the process?" So that's sort of how it's been a really simple idea, but we have been able to implement it at scale. So we like this principle of thinking big, starting small and then scaling fast when some ideas work. And digital twins is a good example of that for us.
Charles Rhyee:
And it's interesting, you talked about the example of the vaccine last year. When you are modeling that out and simulating, are you also simulating that all the way beyond just your own manufacturing, but distribution down then to the pharmacy as well? Are you able to model that entire chain as well, the whole supply chain?
Shobie Ramakrishnan:
This is probably the most profound and obvious application of digital twins. But we think about, like in our research process, we think about digital twins as well to help us understand biology better. So we think about applying digital twins as a concept, but these are specific manufacturing supply chain digital twins, but we are going to be assimilating processes. The concept of simulation I think is something that we will be applying to all parts of it in supply chain planning, and obviously wherever there are logistical complexity that needs to be undone, I think, it lends itself very well to those problems.
Charles Rhyee:
You talked earlier about how everything is becoming more digitized and sort of being able to be ready for that and to leverage and move quickly. Obviously as healthcare has become increasingly digitized, we already prescription and claims data, we've had that for years, but now increasingly clinical data from electronic health records. How has GSK managed that ingestion and processing of this ever-expanding breadth and depth of healthcare data that's out there?
Shobie Ramakrishnan:
I think patient phenotyping and using real-world evidence and data that we have access to both internally and from all the data that we are able to access through our partner collaborations or data that we buy from companies like Optum is a critical and crucial part of our R&D data tech strategy. When we put that together with the research data sets that we either generate or we have access to, it gives an incredibly holistic set of data to train our AI and AI models on.
And what we have tried to do is build very robust data platforms for both research and development so that it makes it easy to clean and provision the data in one place to help drive our drug discovery, but also our clinical development life-cycles. It is continuously the concept of using real-world data and bringing in the translational bits of research has become a core part of our strategy. It's not a cute innovation thing that we do on the side. It's the main entree now in how we think about research and development.
Charles Rhyee:
And I think you have a number of data partnerships such as those with UK Biobank, FinnGen, Tempus. How have those helped accelerate new discoveries, particularly if we think about precision medicine?
Shobie Ramakrishnan:
That's a really good question. I said this before, I think data is an essential fuel for our research engine. And that's such an obvious thing to say, but it's important to focus on this as the data we work with is really a key differentiator for our success. And our specific scientific problems will point to the need for very specific data sets that we need access to. So it's not just a free for all conversation, it's very specific to the scientific problems that we want to solve and are our priorities, et cetera.
So we are really keen to be a responsible partner of choice and offer research and development, scalability, acceleration, and life-cycle value generation that's powered by both our tech and our talent. And our collaboration with organizations like UK Biobank, FinnGen, Alliance for Genomic Discovery and other very specialized partnerships that we have had in the past or we are continuing to drive, is primarily to give us access to meaningful genetic data sets that deepen our understanding of disease and can help us make sense of the disease much more proactively so we can design our medicines better to solve these unmet needs.
And our unique ability to integrate these data sets with other data sets, including our own generated data as well as patient data and applying AI/ML tools is what's giving us a significant advantage in advancing our drug discovery at pace. You asked about precision medicine, in oncology we're working with Tempus which is a precision medicine biotech as well as King's College London to get better at matching the right patient to the right treatment at the right point of disease.
And all of this wouldn't be possible without these incredible collaborations. And that's why we emphasize on the partner of choice bit. And we are replicating clinical conditions using tumor models alongside digital pathology and AI to increase our speed and probability of success earlier and earlier in the development cycles.
Charles Rhyee:
That's really interesting. How discriminating are you when it comes to picking data partners? Because we kind of heard it elsewhere that, particularly the largest pharma companies are really actively out there trying to take in as much data that's available and then internally decide, "Okay, well how do we use this? How can we use this? What's useful?" So that obviously we've seen so many companies trying to figure out how to monetize the data that they sit on, particularly and face it towards the pharma industry. I'm just curious, when you're evaluating potential partners or vendors, what are the criteria that you consider?
Shobie Ramakrishnan:
I think it's very important to recognize that of course quantity matters and I think a variety matters, et cetera. But we have been very specific about the quality of the data that we want to go after. And that we have a data strategy that's anchored to the scientific problems that we want to solve at this moment in time. So we have been very specific to start with our big scientific questions first, where the opportunities lie, where's our capabilities in disease areas that we want focused on?
So while we have public data banks and biobanks and data from them, the uniqueness of what we do comes from the specific data we are able to generate internally to the questions we are asking in our labs and solving. And I think that is what is the guiding principle. That's how you focus your energy on what matters.
Otherwise, you could be easily distracted, as you said, with everything that's becoming available. And there are so many companies just booting up with all kinds of interesting data offerings. So we are, as you said rightly, we are incredibly discerning. Our BD teams focus and have expertise to really think through and be aligned with our scientists on what problems we want to solve. And then we go make very niche or sensible, either data partnerships, data acquisitions, or in some cases, platform acquisitions, which are scientific platforms that then give us the ability to generate our data later on. So it's targeted and focused on the priorities that we care about.
Charles Rhyee:
That's really helpful. You mentioned at the start, a couple of the research data platforms that you operate. Can you talk about the role of research data platforms and how that feeds into GSK's approach to data engineering and data insights?
Shobie Ramakrishnan:
Yeah, our ambition is to build a comprehensive data and machine learning ecosystem so that everyone across GSK, of course R&D, but everyone across GSK has access to a world-class data experience and the right knowledge at hand when they need it. That's the underpinning goal. And the scientific and business problems we solve are so complex and it is really important for me, personally, that we take as much of the pain out of finding and wrangling data as much as we can so that our talented people can focus their energy and their capabilities on solving the bigger problems, the higher order problems from a business and science perspective.
So this is a journey and not a destination, if I can say so humbly, Charles, as the next set of challenges are continuously arriving at our shores in the dynamic world of data with the regulatory challenges and everything else. So what we have tried to do is set a solid foundation to tackle this with two interconnected data platforms at GSK. The first one is a company-wide data. It's not focused on research, it's a company-wide data platform where most of our data, including some of our clinical data, some of the real world data and our commercial data, our enterprise data, our sales data, all of these things reside outside of our core transaction systems and we call it Code Orange.
And the digital twins I talked about earlier leverages this data platform. Some of our clinical and commercial teams use this data platform for their data needs and driving their digital innovation at pace. And our other niche data platform, which is dedicated for research and it's very specialized because the research data has very unique footprint, very unique challenges, latency issues, volume issues. So we've created a platform called Onyx, which is uniquely focused on research data. And we use that primarily to allow our scientists to play with data, bring in data, mesh the data that they need, and then discover new medicines,
Charles Rhyee:
How incorporated is that into the everyday.... Is that now foundational in everyone's sort of day-to-day operations?
Shobie Ramakrishnan:
100%. Even when we bring in a strategic consultant to come do a strategy project for us and do a data and analytics insights project, we insist that they use the data from the core data platform and leave their models and information back in that data platform. So we promote reuse and I think that's an important part as well. Otherwise, you do the same thing over and over and over again.
So this has been embedded. In fact, this is one of the things... We are generally federated. We allow a lot of creativity and innovation, but this is one area where we lay down the ground rules that you can only use this data, these two data platforms to drive innovation when it comes to data and analytics and AI. And it's worked. It's worked so far.
Charles Rhyee:
And is that set up so that now if you had some group within GSK wanting to then build, let's say, a model using some sort of new AI tool, that would then just be layered onto the data platform, right? It'd be pulling from it?
Shobie Ramakrishnan:
Yeah. In fact, we had a place where we wanted to think about how we drive better optimization of our commercial resources and resource allocation and how do we optimize our SG&A. The project team came together and we said, "You've got to use Code Orange." So they brought in data that was not in Code Orange into Code Orange.
They build the models on top of that. Now, the data is pipelined in and it's available for everyone to use even if we are solving a future problem with it. So the analytics can vary, but the data then comes in and stays current. It's not perfect, but it's, I think, as close to effective as I've seen in my long career.
Charles Rhyee:
That makes a lot of sense. When we think about then applications, obviously a lot of focus in the last several years has been around AI. GSK has been an early adopter of AI and machine learning. I think you were one of the first to fully invested in-house team for AI. Can you talk about how the use of these technologies has been more embedded across the company?
Shobie Ramakrishnan:
Yeah. As you say, the sector is at an inflection point, and I think we're starting to see what this means for the development of innovative vaccines and medicines and impacting the health of people at scale. And I think the stories will come through, the proof points will come through in time. I have no doubt about that. And GSK was one of the first biopharma companies to fully invest in multiple in-house AI/ML teams, starting with our scientific AI/ML team across every functional domain in the company.
So we now have a dedicated data science and AI team for every functional domain in the company. And we are then using them to embed AI as a driver for innovation, performance, productivity, but within the context of those distinct priorities in our R&D, commercial, and supply chain organizations, as well as capturing enterprise-wide opportunities by adopting a people-centric approach to AI that aims to empower all our people and their work.
And as an integral part of this work, we are committed to the responsible use of data and AI as well, and are always meeting the highest standards of security, privacy, and ethics. And we were one of the first companies to set up a dedicated team focused on AI ethics and policy back in 2018, just as we were setting up our first AI/ML team much before all this new gendered AI and responsible AI hype.
Charles Rhyee:
Yeah, we can go down a whole rabbit hole talking about the dangers, but I'm sure we can leave that for another time. When you talk about how it's embedded across the company, I think what's also interesting is how you guys are also applying that to understand patients that you're treating as well and how they're responding to treatments. Maybe if you could talk a little bit about that as well.
Shobie Ramakrishnan:
I think this is a crucial and important thing. It's a really thoughtful question. I'll probably use an example to answer that, Charles. I think if I think about chronic hepatitis B, it's a horrible disease and it has a massive human impact. And existing oral treatments can help control infection, but they very rarely clear the virus completely. Only 5% of patients achieve functional cure. And most patients will require lifelong treatment, and it also is linked to risk of liver cancer. So it's just bad news all around for the patient.
It's also a disease that's notoriously difficult to treat because it behaves differently in different patients depending on their genetic makeup or immune system. And not all patients respond to the same treatment as expected. So when we had this asset moving through the pipeline in very, very early stages, our AI/ML teams built models to help us understand why the virus behaves the way it does, how the disease develops, and which patients are most likely to respond to certain treatments.
And that has actually informed the design of our clinical trials, which are underway right now, and I hope they're successful. We believe that we will be reaching and curing more patients as a result of doing this type of work upfront to model the disease, which we did not have the skills or the ability or the data to do in the past. And therefore, it's such a profound thing, isn't it? It's so profound that this can have an impact on the clinical trials being designed to be more effective, reach more people and be more curative. I think that really brings it home for me on why we should keep at this with everything we've got.
Charles Rhyee:
Yeah, absolutely. Really to kind key in and be able to be more effective across the board. So makes a lot of sense. And we will obviously wait to see how the results of those trials come out. Maybe just to close out here, looking ahead, what kind of excites you? Maybe let's keep it maybe three trends, I think, in healthcare and in technology that you're excited about maybe that also investors should also be excited about in the coming years.
Shobie Ramakrishnan:
It's a great question and I spend a lot of time thinking about it. I think it's probably worth re-emphasizing just how unprecedented this moment is for the biopharma industry. And this ability to understand disease and engineer biology, I think the next 10 to 20 years are set to be incredibly exciting for the discovery of development and new medicines and vaccines.
And I think we'll make a lot of mistakes. There'll be a lot of, "Show me, don't tell me," moments, but eventually all the energy, effort, investments, talent that's going into this is going to result in a lot of failing forward and it's going to have a breakthrough. And it's almost, for me, analogous to mRNA. 10 years ago, people wouldn't have thought mRNA was cool, but it literally saved the world when COVID hit. So I think this will be a differentiator in the long time.
And I'm quite hopeful and confident that the pivots that the life sciences industry is making powered by big tech will all ultimately make a dent on R&D productivity, which is what we talk about all the time in our industry. But we should remember that the path will not be linear. And at least that's my experience having lived in Silicon Valley my whole life. Is, like all other innovations, there'll be pivots, there'll be setbacks, and then there'll be learnings but there is usually no going back, isn't it?
So that's definitely my top trend. And I think the second thing is, as populations age, the healthcare trend that I want to talk about is populations age and pressure on the health systems increase, it's likely to be a growing focus on preventing disease before they even start. And we are seeing a lot of residents for our messages on that front with the regulators we talked to, with the government entities that we talk to across the world and not just in the bigger countries.
And GSK is well-positioned to disrupt with our expertise in infectious diseases and vaccines and our focus on data and platform technologies as we call them. And I think the third trend I would say is, I should probably say that AI/ML is still in its relatively early stages in delivering impact and not just in terms of its maturity. We've got the tech. I think it's in terms of delivering impact, I think we've still got some work to do as broadly as an industry, not just us and including the tech bios who are going at it with all they've got as well.
And only in the coming years will we see its full potential unleashed similar to the start of the internet years in some ways. And companies like GSK, we need to remain curious and strategically collaborate to ensure that we have access to and harness the latest technological advances. And my view here, Charles, is that we will always overestimate what can happen in the short term and we will underestimate what can happen in the long term. So it's really important that we keep going and keep the focus on this.
Charles Rhyee:
And I can't help but ask, you mentioned with an aging population, being more focused on preventative care. It seems to me that this is exactly the area where AI can really be impactful. To your third point, be more predictive and to look at these large population sets and maybe be able to provide more predictive insights to allow healthcare professionals to intervene earlier.
I feel like healthcare, we're generally reactive. Someone comes in feeling unwell and gets tested and we find something. How do we reverse that and be ahead of it and predict something and intervene beforehand? And so, it seems like we're at that. Perhaps we're right now at the cusp of that to be there. It seems like you guys are doing a lot of good work in that area.
Shobie Ramakrishnan:
Yeah, we are very, very excited about it and I think it's what fuels us.
Charles Rhyee:
Great. Well, Shobie, I really appreciate all your time and your insight. It was really informative and I am sure that the listeners here will really appreciate your thoughts here. And so, thanks for being here. Really appreciate it.
Shobie Ramakrishnan:
Thanks, Charles. I enjoyed talking.
Speaker 1:
Thanks for joining us. Stay tuned for the next episode of TD Cowen Insights.

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