Life sciences industry’s big win: last in the digitalization race – Transcript
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John Nixon: Hello and 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 continuing a discussion of the digitalization challenges seen in the process industries, but this conversation will focus on the specific challenges experienced by the life sciences industry.
Our guest today is Patrick Ansems, the Global Head of Life Sciences at Siemens Digital Industries Software. Pat, welcome back to the podcast.
So, I got to ask. What digital transformation (digitalization) challenges are specific to life sciences, Pat?
I mean, what challenges does it share with other process industries?
And does this all relate to the life science industry’s digitalization maturity level compared to other industries?
Patrick Ansems: Yeah, so digital transformation (digitalization) in life sciences has a few challenges that are specific to the industry, but it also shares a lot of, let’s say, challenges that we hear from other industries as well.
So, the most obvious one in life sciences are the challenges around regulation. It’s a necessary component. [When] making products that go into patients, … quality, safety, traceability, and compliance are … non-negotiable.
But it does mean that change is slower compared to other industries. And you can’t simply replace a system and change the process or introduce AI to such a highly regulated workflow without proving that it’s controlled and validated as well as reliable.
The second challenge that we see is, fundamentally: “how knowledge is being managed.” A lot of this industry, while people in pockets are using very sophisticated tools, whether it’s in research or in development or manufacturing.
But when we start thinking about handovers from group to group, [that] still runs on Excel and PowerPoints and documents and a lot of institutional memory. So that data [exists] within the organization, but it’s always very fragmented across [systems, instruments, teams, and partners].
So very often that context is lost. So, we know the results. We know the outcome because that’s in a report or in a patent somewhere, but we don’t always know the full story of how that result was generated and under which of the conditions, with which assumptions. So that makes reuse and decision making much harder because all that institutional information is not part of the data set.
John Nixon: As you were talking about that, … Patrick, it really reinforces something that I’ve seen not just in life sciences, really, but across process industries. It is particularly acute here, though.
In life sciences, the regulatory burden and the reliance on disconnected tools like Excel, PowerPoint, PDFs, and the list goes on, they’re not just inefficiencies. They [shape] how this innovation happens. They slow the ability to connect as you’re talking about all this federated data to reuse knowledge and to ultimately to scale insights across the organization.
What I find interesting, this isn’t entirely unique. If I look at industries like chemicals or even parts of consumer-packaged goods, I see very similar patterns, right? Fragmented data, siloed processes, a heavy dependence on legacy workflows that weren’t designed for today’s level of complexity.
And where [the life sciences industry] stands out though, in my mind, is the consequence of getting it wrong. The cost of failure, whether that’s patient safety, regulatory compliance, liability, [is] so high that it naturally makes organizations really a lot more cautious. And I would think that lends to slowing digital adoption.
And with that said, I [see] this as a signal of what, maybe a massive upside. Because once you start connecting the data, once you move from documents to, as you say, structured, contextualized information, you really do unlock a completely different level of speed and insight.
It does remind me of a conversation we had on the podcast with Bill Hahn. We [talked] about the explosion of data across process industries. And what we’re really seeing now is that life sciences [is] really at the center of that shift. It’s arguably facing the most complex version of it.
Patrick Ansems: I’ve listened to the podcast that you and Bill [did] together, it was really nice. [If] people haven’t listened to that, I would highly recommend to kind of go back and listen to that podcast as well.
But both of you are [actually] right, in the context of the data that we’re generating in life sciences is enormous. And all of that is highly regulated and misses a lot of contexts. What we hear over and over happening again from our customers, right, because we just looked at it through the lens of the patient, but if you look at it through the lens of the organization, that they’re spending a lot of time and investment now on these AI initiatives as a kind of silver bullet to untangle with that.
And to me, it’s great, right? I mean, I see how technology companies are now looking at life sciences as fundamentally the next iteration of what we had in the past around other industries, right? There’s now a lot of focus and investment of these technology giants or AI companies in life sciences. So that is great. And the models that they’re building are phenomenal.
But if that’s done on a very wobbly foundation, then most of these initiatives don’t necessarily result [in] an impact. And that’s what we’re seeing now in the industry, right? There’s a lot of these AI initiatives that are happening and they’re all relatively successful, but where they all fail is when it starts to scale to kind of an end-to-end workflow.
John Nixon: Right.
Patrick Ansems: That is really the issue in life sciences, right? Is that that information might sit somewhere, but it’s very – as we mentioned a couple of times, right? It’s really across systems, instruments, documents, places, people’s intellectual brains. But it doesn’t necessarily connect to each other.
John Nixon: When you talk about that, every day when I look at the magnitude of the explosion of data, right? That’s one thing our species does really good. We create a lot of data.
But the complexity, it lends to this effort of a life cycle, right? As you were talking, kind of like as we go from, you know, lab to the market itself, when you look at the federated data that’s throughout an organization, really throughout the market too.
I mean, how has this explosion of data and complexity really affected life sciences? But I mean, let’s get specific here.
Patrick Ansems: Yeah, so I mean, there [were] a lot of, let’s say, situations where we saw that volume of information and why it has been already providing a real impact when we started thinking about, let’s say, digital twins.
So, … let’s name a few examples for our listeners. We have Terumo America, which is kind of a global leader in medical devices. And they wanted to move away from paper-based manufacturing and quality processes and build a more, let’s say, connected and digital factory environment.
And by digitizing the manufacturing execution and quality workflows, they created a much stronger digital thread across production. And that impact was significant. Material batch releases went from taking a full day to just taking 30 minutes. And other implementations helped reduce [non-conformance] and complaints by 40 to 60%.
And let’s take another example. Let’s look at Johnson & Johnson, Innovative Medicine group (J&J), where digital process twins [are used] to optimize active pharmaceutical ingredient production. And instead of relying on physical trial and error, teams can simulate, test and refine parts of the process virtually before they are starting to apply some of these changes in production. And one published example that they have is that they reduce solvent time by about 30% and overall cost by about 35%.
And there are other examples as well, right? If we think about the frontier, not only in manufacturing, but also refer that back to the lab, Many of the organizations that we speak to are now asking [a] bigger question. They ask where they can build kind of like a digital mind for the lab.
And what I mean by that is not just a system that stores experimental data, but also [one that] understands the context around that. What was the hypothesis, which method was used, which instrument generated that data, and what was learned?
And this is really an example of the influence of digital threads, AI and digital twins starting to come together. And the goal is to move away from isolated and static reports to a learning system. The system where knowledge from research and manufacturing can flow into development. And how I would like to call it, comes from the world of R&D.
So, one kind of important sentence that we always talk about is kind of like lab in the loop. But it’s [interesting] to see if we now start [to] connect information, whether we can also build a pharma in the loop.
We now see companies already on that journey where they’re predominantly utilizing, let’s say, AI and computer-based systems that [builds] the next generation of drugs.
So, it is extremely interesting to see how quickly this industry [has been] evolving in the last year or so. I can only imagine what we will be able to do when we take, let’s say, two or three years, let’s say when we look two or three years in the future.
So, I’m really looking forward to being part of that journey as part of Siemens. But also, as part of this industry, to see what type of drugs we’ll bring to the market and how we will [start] making an impact to the patients, right? Which are people that you and I have, dearest at our hearts.
John Nixon: That we do.
Well Pat, thanks again for joining us.
Three things stand out from our discussion today.
- First, the life sciences industry faces unique regulatory demands that make digital transformation (digitalization) both more challenging and more critical.
- Second, while the life sciences industry creates an enormous amount of data, too much of the knowledge remains trapped in disconnected systems, documents and workflows.
- And third, the promise of AI will only be realized when it’s built on a connected digital foundation that preserves context and enables information to flow across the enterprise.
What’s encouraging is that we’re already seeing the impact. From digital threads and digital twins to smarter, data-driven decision-making, leading organizations are proving that connected data can accelerate innovation, it can improve operations, and ultimately it can help bring better outcomes to patients.
Pat, thank you for sharing your insights. And thanks to all of you for listening to the Industry Forward Podcast. I hope this podcast helped you process your process industry knowledge.
Oh! Always puntastic. All right, have a good one.
To learn more about digitalization, click here.
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.
