Why the digitalization of the life sciences is so unique – Transcript
To read the digitalization podcast’s show notes, click here.
John Nixon: Well hello! Welcome to the Industry Forward Podcast. Where we explore the key trends, transformative technologies and real-world innovations that are reshaping fields from aerospace to energy and beyond.
I’m your host, John Nixon, Global Vice President of Process Industries at Siemens Digital Industries Software.
Today, we are talking about the digitalization of the life sciences industry and how Siemens can help that industry catch up with others that have already embraced tools like digital twins, AI and simulation.
Our guest today is Patrick Ansems, the Global Head of Life Sciences at Siemens Digital Industries Software. Pat, let’s start with data generation.
Across industries, we’ve gone from managing megabytes and gigabytes to terabytes and petabytes. Now we’re firmly in the world of zettabytes, of data being generated and stored.
But in life sciences, that growth isn’t just about volume. It’s about complexity and interdependency because you’re not just dealing with production data or supply chain data. You’re dealing with biological systems, clinical data, formulation data, regulatory documentation. I mean, all of this, it’s all highly interconnected, but it’s often managed in completely separate environments.
And that’s where I see a very, I’ll call it a recognizable pattern when I compare life sciences to industries like consumer-packaged goods (CPG) or chemicals. I mean, data is fragmented across the functions. Knowledge is trapped in documents instead of models and organizations struggle [to really] create a single trusted thread of information.
The difference is, again, the stakes and the variability. Let me come back to CPG. In CPG, you might be optimizing for cost, sustainability or speed to market. In life sciences, you’re dealing with systems where the underlying science is still really being discovered today.
So, I think the challenge becomes, how do you bring structure to something that is inherently complex and it’s evolving. I mean, this is where technologies like the digital twin, data platforms and AI-driven analytics start to play a critical role. And not just to manage the data, but to connect it, contextualize it and then make it actionable across the life cycle.
And ultimately, I think that is the shift we’re seeing. right? I mean, moving from a world of data accumulation to a world of data orchestration and insight generation.
Patrick Ansems: Yeah, I absolutely agree with you. It’s all about the ability to orchestrate that information.
We don’t struggle or we’re not depending on not having enough information or enough data. In this industry, we’re [generating] more information [daily on] things that we [could] not do before.
We kind of spoke about … sequencing in one of our earlier podcasts, where some of these things were just not part of our technological assets in the past. [But,] they are right now. And the cost of all of these, let’s say, measurements are also decreasing. So, they become much more readily available.
So, [we’re] generating [more] data. We have [many] more layers of information from genomics to proteomics to transcriptomics. So, we have much more information about the individual.
We have much more information about the process in which we’re making drugs and the process of how we’re going to validate and regulate that.
We have information from the patients, right? And we think about all the people that are wearing these wearables where we have continuous streams of heart rates, blood pressures, right?
So, the volume of data is not necessarily the bottleneck. It’s not the velocity. It’s the variety and veracity of that information. So that I think is the daunting challenge that we have as a life sciences industry is how to actually … generate [a] model around all that information. [One] that is specific [to] the individual and that [is] therefore, based upon that information. [One that] can [determine] what’s the best treatment based upon similar individuals with a similar disease stock.
I mean, we’re still a little bit away from that. But I can only imagine that if, like I’m [sure] that by the time when I go [on] retirement. And I’m 45 years old today, so I still have a good 15 or 20 years to go. By the time when I go [on] retirement, I’m hopeful that my legacy has been that I’ve been contributing to a world where if you go to a doctor, you kind of get your personal makeup profile information based upon your environment that you live in or the genetic information that you have. And that you can get [treatment] for [a] disease [with an] efficacy of over 95% rather than the 40% that we have today.
And if we were able to do that as a society and as Siemens, I would be extremely proud of ourselves as a company, but also of myself as a human being.
John Nixon: Well, and we all look at the world we live in. We consider our families, our friends, our children and we always ask ourselves the question, “am I leaving the world that was given to me in a better place?”
And as you were talking, you mentioned something that resonated with me. You said “volume, variety and veracity.”
And so that, if I were, as a takeaway, [to] look at that and I realize: as you consider each one of those, how, (again, I hate to reuse the word daunting) but it truly is daunting. But we have [also], finally the computational power as a species to manage that volume and to address the variety and the veracity, right? The contextualized veracity we need for the digital twin.
And that hearkens me to a question I want to pose to you. If you think about life sciences and you think about what we talked about in our last episode and in this episode. How would you score the digital transformation (digitalization) of the life sciences industry compared to other industries?
I mean, what role? You mentioned Siemens and how proud we are to be here working in this company to drive solutions in this space. What role can Siemens play to get those industries up to speed, to get life sciences up to speed?
Patrick Ansems: Yeah, so the first question that you ask is like, how would you score ourselves?
I think this is an industry that is evolving rapidly. If you look at only a short period of time, like three to five years back. AI was not really a thing. We talked about what we call FAIR data principles (findable, accessible, interoperable, and reusable). But we never had a clear real purpose for that. Now with AI, we [do]. And now you’re also already starting to see some incremental improvements around how AI is affecting our [industry].
Look at AlphaFold as an example. Or look at other tools where they’re utilizing AI to predict efficacy or binding or how a drug will perform. So, there’s a lot of pockets where AI is already providing instrumental value.
But overall, as an end to end, that’s what we are still maturing. What I’m really happy about transitioning through my entire career and now ending up at Siemens is that if you look at companies with the breadth and width that we have as a product portfolio like Siemens, where we’re not only providing solutions in R&D, or process development, or tech transfer or in manufacturing. We also have an extensive simulation portfolio. We have knowledge-graph capabilities and agentic AI.
We, as part of our toolbox, have everything that we need, together with partners, which are system integrators, but also technology providers like, for instance, NVIDIA, with partners like Accenture or CAP. We have in our arsenal and the tools as well as the partnerships and the people to make a tremendous impact.
I’m [happy] that Siemens has also entered the world of life sciences. You hear our CEO talk about it [at] every opportunity that he gets. He’s really committed. I see our company being extremely committed. I see a lot of very powerful technologies that I wish that I had as part of my arsenal when I was still working in the laboratories.
So, we have everything in the right place to [make] a real impact [on] this industry. As I mentioned before, that’s something I’m extremely proud of, but also something I’m looking extremely forward to.
When we start pulling [all] these capabilities together, together with the people and the processes, I think as a company or as a life sciences industry, we can make a phenomenal impact.
Not only for the companies that we serve, but also the patients that they [serve]. And that is something that gets me up every morning super excited and ready to do another, let’s say, day and provide that impact to our customers.
John Nixon: So Pat, as you’re talking about this, I want to add to … the maturity discussion.
If I were to “score life sciences,” and let me kind of provide you my thoughts and I’d like to hear what you have to say. If I were to score life sciences on, let’s say, digital transformation (digitalization) maturity, relative to the industries we work in here at Siemens, I’d say it’s about mid-pack, but with the highest upside.
Industries like automotive or electronics, they’ve moved faster because they’ve had, now this is a relative comparison, fewer regulatory constraints and more standardized data environments.
Life sciences on the other hand [have] been understandably more cautious, but that’s led to fragmented data and slower adoption of integrated digital platforms. I mean, I had the privilege in the patchwork quilt of life. I had the opportunity to work at a life sciences company. Specifically, we were working on monoclonal antibodies, developing those as a treatment for peanut allergies.
And so, I’ve seen from that experience and then coming forward and now working with you, this journey of maturity. And I think the opportunity now is [about] catching up on digital maturity, moving from document-driven processes, which I was very familiar with back then, because it was all about creating an electronic document management system and getting everything right for clinical trials.
Now we’ve moved to a connected model-based environment. And this is where I think things really get interesting. Technologies like synthetic data can play really a key role here, helping organizations simulate outcomes, augment limited data sets, and accelerate learning without the same regulatory burden on real world experimentation. You know, that’s the consummate, you know, dry lab versus wet lab.
So, from a Siemens perspective, It’s about bringing together the digital thread, the digital twin and this scalable compute … so companies can move faster with more confidence while still meeting the strict requirements of this industry.
Patrick Ansems: No, you’re [right].
I mean, there were a couple of comments in your question or conversation I would like to click on a little bit. As we were talking before and already quite extensively, the fragmentation of information is really the key crux in this industry.
And I agree also with [you] that the level of maturity is mid-pack if you kind of compare that to other industries within Siemens. But I would say it’s the most rapidly growing and evolving industry.
And you really see that at conferences or at publication, you can’t open any type of news outlet today where AI and life sciences are not mentioned in one single article, whether it’s the entry of some of these technology giants into that space or whether it’s an acquisition of an AI company by a pharma organization or whether it is companies like Recursion or [Isomorphic Labs] that are almost utilizing AI to do their end-to-end, let’s say, discovery and clinical projects.
So, It is the industry where I see the most rapid growth. The fact that Siemens has recognized that and has done [several] let’s say strategic acquisitions over the last two years to kind of also enter that space, not to play along, but to really kind of execute and win, this is something that I’m extremely looking forward to.
There are already tools in that space that we can leverage. We kind of brought up the phrase “synthetic data,” which also will become at some point in time part of that conversation. In life sciences, we often deal with sensitive patient data and limited data sets. So, extending that further out with synthetic data can help us test and verify models and explore scenarios.
Because to me, we will see in the next few years a tremendous change in how drugs are being brought to market. Where currently we’re doing, let’s say, R&D to generate models that we can utilize for downstream analysis. I think the world will fundamentally flip upside down where AI will give us the leading direction and will utilize simulation to verify all the conditions and the accuracy of the model.
And we need to use [data] from the real world, meaning the data that’s being generated by R&D scientists or the manufacturing process or in the end even the patients to kind of influence that direction.
The word that I kind of utilize for that is what I [call] “pharma in the loop,” right? Where you have real data, synthetic data, model and simulation information to really build a digital twin of the people, the process and the product, right? The 3Ps that I typically talk about.
And showcase how that actually will lead to much better development, much quicker development of drugs, but also a much more beneficial commercial model for the organizations that are trying to bring this product to market, as well as the kind of healthcare system to make sure that the drugs that we do bring to market, we actually have a really good understanding which patients will benefit from that specific treatment and therefore will reduce cost in that entire healthcare system.
Because that is another very important angle. But if we don’t fix that, healthcare will become unaffordable globally. So, we as a company need to help our customers to bring better drugs on the market faster and quicker in order for them to help the patients, in order to help us as a human race to make sure that we stay healthy and long living and build out a world that we can leave behind for the next generations to follow.
John Nixon: Agreed.
Well Pat, thanks for joining us today.
One of the biggest takeaways from our conversation is that the future of life sciences isn’t about collecting more data. We’re swimming in it! It’s about connecting and contextualizing it.
As AI, simulation, synthetic data and digital twins continue to mature, the opportunity to transform fragmented information into insights can accelerate innovation and improve patient outcomes.
Life sciences may still be catching up in its digital transformation (digitalization) journey, but its potential impact is enormous. By bringing together the people, processes and products in a connected digital ecosystem, we can help deliver better therapies faster, more efficiently and with greater confidence.
Thanks again, Pat. And thank you to our listeners out there for joining us on this episode of the Industry Forward Podcast. Be sure to join us next time as we explore technologies and trends that are shaping the future of industry.
Until next time, I’m John Nixon, helping you process the process industry. HA! HA! And goodnight.
To learn more about digitalization, read: What is the digitalization process?
John Nixon – Global Vice President of Process Industries at Siemens Digital Industries Software
As Group Vice President for Process Industries at Siemens, John leads a global team that helps process industries leverage digital solutions that enhance efficiency, accelerate innovation and achieve sustainability goals.
John has over three decades of experience in strategy, operations and technology deployment for energy, chemicals, life sciences and CPG. He is well versed in the operational and business pressures of industry, including regulatory demands, decarbonization, talent gaps and the push for innovation.
Patrick Ansems – Global Head of Life sciences at Siemens Digital Industries Software
As Global Head of Life Sciences at Siemens, Patrick helps pharmaceutical and medical device organizations build comprehensive Digital Twin technology that spans development to manufacturing, and beyond. These tools accelerate innovation and deliver better therapies to patients.
Patrick has 25 years of experience ranging from wet labs to digital transformation. He understands the complexity of scientific workflows and how to translate them into strategies that deliver business outcomes.
Connect with Patrick on LinkedIn
All trademarks are property of their respective owners.
