I’m working as the “Global Lead for Digital Lab Services & Infrastructure” at the Roche Pharma Research and Early Development organization. Roche is currently aligning our R&D units more closely. This triggers multiple activities in the digital landscape that keep me busy. It also brings opportunities to bundle our collective power and expertise across all R&D units to tackle long-term challenges around the “end-to-end lab data connectivity” and their link to our computational science activities, including AI.

The trigger to why I expressed my interest in talking at the Lab of the Future conference were multiple discussions I had during last year’s event in Amsterdam. I exchanged with multiple representatives of other Life Science companies about the gaps preventing us from applying the “hype topics” of FAIR data and GenAI in R&D. We came to the conclusion that we often see that the basic infrastructure and process needs are missing. For example: one representative mentioned that “they don’t even have WiFi in their R&D labs”. With the D-WoW project (digital ways of working) that has been triggered as a related project to our new R&D research facility here in Basel (The pRED Centre, introduced during the LotF 2024 in Amsterdam) we had the opportunity to improve this basic Digital Lab infrastructure. Together with one of our user representatives, Anke Kurt, we will talk about the implemented solutions, our learnings, recommendations and the basics of a digital lab infrastructure.

I see multiple challenges, but also many opportunities. However, my personal view is that: “Technology is not the problem!” I mainly see challenges in applying the existing and emerging technology to the reality of our R&D lab environment.

The major four challenges are:
• Cross-functional alignment - The full chain of scientific data from the lab instruments (raw data) to scientific insights (analytics) involves many disciplines. Aligning the strategy, priorities, investment, efforts and gaining a common understanding of the needs is challenging, but required to make this happen in a scalable, efficient and compliant manner.
• “Boil the ocean” - The digital transformation at R&D labs often seems to be considered a one-time, large investment to make it happen rather than embedding the activities as a continuous, iterative, long-term process.
• Scattered application landscape - While our vendors provide new applications and lab devices with their best intent to add most value to the end-users, the applications and data sets regularly lack from interoperability. This results in a very scattered application landscape that absorbs a big portion of the available resources that are then missing to boost new technologies.
• Standardization vs Specialization- A great mentor of mine once mentioned: “you can kill research by over-standardization”. Scientific innovations rely on great ideas from our teams and individuals. And they need to get a certain level of freedom to work in the way that fits best with their needs. On the other side, value in the digital space comes from scaling. Finding the right balance between standardization and specialization is challenging.