Tomorrow, Today: Experts Discuss the Lab of the Future

The modern R&D laboratory is in a state of constant evolution, always striving to deliver the best possible science from the tools at its disposal.

In recent years, the biopharma and drug discovery sectors have seen dramatic technological advancements. These “lab of the future” technologies – such as powerful artificial intelligence tools, robotics and software solutions – are already accelerating research efforts. But the adoption of these technologies in laboratories has not been without challenges, and there is still room to grow.

The Lab of the Future Congress Europe 2025 brought together scientists, thought leaders and experts from leading companies and research institutes to discuss how these challenges may be addressed and share their vision for what the “Lab of the Future” could look like in the coming years.

To learn more about the challenges and opportunities facing today’s R&D labs, Technology Networks spoke with a range of experts attending the congress.

Technological advances are shaping the role of the modern scientist

One central theme that came up in almost every conversation at the event was, simply: people. Though technological advances are critical to improving scientists’ capability to deliver much-needed therapeutics at pace and scale, experts stress that it is equally important not to lose sight of the human scientists at the heart of the research ecosystem.

“I think, too often, when we look at change, we lead with technology. But fundamentally, it is about the people. For scientists, the rate of change is through the roof,” said Mark Fish, VP & GM of digital lab solutions at Thermo Fisher Scientific.

“One of the things that really resonates with me – and some of the talks here are talking about this – is understanding the mindset of the organization. I think a lot of organizations might start with considering what we need to do differently, not the why,” Fish continued. “I'm very passionate about that human element of technology transformation.”

So, why might these human scientists be interested in implementing “lab of the future” technologies?

“[These technologies are] actually helping them get back to doing more science,” said Rob Brown, global VP and head of the scientific office at Sapio Sciences. “It’s helping them be more efficient.”

At Lab of the Future Congress Europe 2025, Sapio Sciences introduced attendees to Sapio ELaiN – the world’s first 3rd-generation ELN, which leverages agentic and generative AI to help plan, design and analyze experiments. Acting as a type of “co-scientist”, ELaiN is equipped to support R&D domains such as cheminformatics, bioinformatics and structure-based design.

“In many companies today, you have one or two people in the company who can look at a molecule and just know how to make it. That’s the result of years and years of know-how and they're very special people. But imagine if everybody in your company who you hire as a chemist could do that? It’s not just a case of [increased] productivity, but also innovation, because everybody will be raised to the level of being an expert.”

This concept – that “lab of the future” technologies are not just improving efficiency, but accelerating innovation – is also reflected in the results of the latest Lab of the Future Survey, conducted by the Pistoia Alliance.

According to the survey results, the top reported benefit of digitizing and automating the lab by respondents (largely biotech/biopharma and large pharma firms) has shifted in recent years, from improving the efficiency and effectiveness of R&D to accelerating innovation. In the 2025 edition of the survey, 53% of respondents cited accelerating innovation and new breakthroughs as a top benefit, with 49% citing the lowered barriers to interoperability, data sharing and collaboration. Improving efficiency fell to third place in the survey, with 47% citing this.

Bustling exhibition hall at Lab of the Future Congress Europe. Credit: Lab of the Future Congress Europe 2025, Unmask Photography. 

What barriers remain to the adoption of new laboratory technologies?

In a testament to the open and collaborative direction of the industry, experts were also keen to not shy away from discussing the various challenges or areas for improvement that must be dealt with to realize the ideal “lab of the future” model.

“[The goal] is an end-to-end, enter-data-only-once approach. And to do that, you need open APIs [application programming interfaces] between instruments and systems from different vendors,” said Christian Baber, PhD, chief portfolio officer at the Pistoia Alliance.

“Even simple things, like balances, are not always good. We’ve seen hard-coded sample IDs, for example, where if you start using a different sample ID, it doesn't work anymore. The interoperability of data within the lab to allow seamless end-to-end flow will require vendors to open up their ecosystem – and I think they are heading there.”

With digital transformations, self-driving labs and AI-powered technologies set to play a key role in the future of R&D, many are also looking for reassurance regarding the security and reliability of these technologies.

Regarding the use of AI-powered scientific chatbots and co-scientist tools, Brown points out: “There is still a lot of concern about security. I think we have good answers to addressing that security, but the first reaction of a lot of people is “You are taking my questions and sending them to Meta or Google or Grok!” But no, it's all contained within a secure place and it’s all validated in the same way. But security is certainly a key talking point.”

“Consistency is the other one,” Brown continued. “There is a lot of concern about perhaps not getting a consistent answer [from scientific AI assistants].”

To address this issue, Sapio ELaiN – and other AI tools – are keen to be as transparent as possible. This can involve introducing ways for scientists to review and audit how the AI has conducted its search and where its answer has come from.

“You have to show the scientists the working, because once they can understand the working, then they can accept the answer,” Brown explained.

“Even though it's a large language model, which is variable, it's calling our API, which isn't variable. That's deterministic; so as long as it gets the right command, you get the right answer.”

Another key challenge – also highlighted in the Lab of the Future Survey – is the increasing demand for AI education. This “AI skills gap” is quickly becoming a bottleneck to the adoption of AI technologies, with growing numbers of survey respondents expressing a desire for best practice guides, educational courses on AI and machine learning topics and specific skills training in these areas.

“People are naturally fearful of change,” Fish said. "There’s a lot of discussion about how AI might lose jobs, and some of the talks here have done well to address that point. [AI assistants are more] like a new team member who's going to help you be more productive – I think that's a good way of starting to communicate that.”

“We have to focus on user experience as well,” Fish adds. “Traditionally, for software products in the lab, you would need to go on a training course to use them. But if I had downloaded something from the App Store and then needed to go on a training course, I'd look for the next app! So think about your user experience. People shouldn't need training if you provide the right interface for your tools.”

The future of biopharma R&D

While the focus for the conference is the lab of the future, it is clear that these emerging technologies are already being implemented in the labs of today.

Together with a more digital mindset shift, these are having measurable impacts on R&D labs around the globe, helping change the science of drug discovery and biopharma R&D.

Looking ahead, experts anticipate even more positive change over the coming years.

“A lot of companies still talk about ‘AI readiness’, and I think in a year from now, we'll be talking about AI,” predicted Yuri de Lugt, global director of field marketing at Sapio Sciences. “I think that's the biggest shift that we can expect in the next year.

“Nowadays, a lot of people say that they have to prepare their data first, then they can start using AI. To that I say, the algorithms are there. You don't have to stop for that. At the very least, your own data might not be ready, but there are a lot of data and capabilities that are ready. In a year from now, I hope that this data readiness issue is either resolved or irrelevant.”

The importance of data to the future of R&D is echoed by Fish: “The shift towards FAIR data is already well underway. That lays the foundation for using things like machine learning and generative AI models.”

“Now, I think those barriers are coming down,” Fish continued. “The data is FAIR. We have digital assistants to help us with data science. I think we'll see a big shift where scientists do both dry lab work and wet lab work, without handing data over to other people. I think we'll see continued innovation, more people becoming digital citizens in addition to being physical lab researchers and hopefully, we will see a lot more collaboration in the digital world as well.”

As for what the landscape of R&D will look like in five years' time, it’s a question that few are willing to pin an exact prediction on. If the trends identified in the Pistoia Alliance’s survey hold true year-on-year, we can expect to see the continued adoption of AI, ELNs and cloud computing in the lab. The survey also highlighted a decrease in reports of challenges with data silos and unstructured data, which is encouraging. However, for these opportunities to be capitalized on, overcoming institutional resistance to data sharing and securing backing from management will be key.

“The rate of change is such that we're going to see huge improvements in five years. [The landscape] is going to change hugely, and it's going to be because of the mindset change of the scientists,” said Baber. “There are a lot of people who have been in the industry for a long time and are not digital natives. Gen Z and the younger millennials are digital natives, but the next generation is AI-native. We are going to see more of them, work with them, and hopefully recognize and promote them as well. That’s going to move things quickly.”

“I strongly believe the technology isn’t rocket science. The science is really, really difficult, of course, but the technology isn’t. The real issue is the people; you get the right people doing the right job, you incentivize them correctly and we will get there.”