For decades, scientific discovery has been constrained in part by the time and expertise required to develop and prioritize hypotheses. AI is beginning to change that equation. 

As Samantha Paoletti framed it, AI can potentially propose enormous numbers of molecules, targets and experimental designs. But those ideas still need to be tested in the physical world. The bottleneck may therefore be shifting from generating hypotheses to executing experiments. 

Much of the underlying automation technology already exists. The bigger challenge is that it often exists in isolation. As Christoph Jung described it: 

“The way we built things was in islands of automation.”  

Individual workflows may be highly automated, but connecting them into a coherent research environment remains difficult. Throughout the discussion, connectivity and data quality emerged as two particularly important barriers. 

Different instruments have traditionally been built around proprietary software, interfaces and protocols, making connectivity one of the most immediate challenges facing the lab of the future. 

Jung acknowledged that vendors themselves have contributed to this fragmentation through proprietary systems and argued that the industry now needs to move towards more open APIs and interfaces. The longer-term goal is to make automation accessible not only to specialist engineers, but directly to the biologists and chemists conducting the science. 

Robert Zechlin similarly emphasized the importance of common standards. With thousands of laboratory devices in use across the industry, individually building and maintaining integrations is difficult to scale.

“The only way we get to autonomous labs, to self-driving labs, is if the vendors actually adopt standards.”

Other industries offer a useful lesson. In automotive manufacturing, interoperability accelerated when customers demanded common connectivity standards from suppliers. A similar approach could help laboratory automation move from bespoke integrations towards infrastructure that can be deployed more easily across organizations.

The ultimate vision is compelling: AI proposes an experiment, automated systems execute it, the results are analyzed, and those findings inform the next experiment – creating a continuous learning cycle with limited human intervention.

Parts of this closed-loop experimentation model are already feasible. Jung pointed to bounded workflows such as protein or DNA purification, while stressing that scaling the concept across entire laboratories is much more complex. 

“When you talk about autonomous labs and the closed-loop workflow over a couple of labs, I think that is still in the very early stage.”  

Practical issues quickly emerge. How are reagents and consumables replenished during experiments running for days? How does a system recover from an unexpected equipment error? And how does it distinguish an error from a scientifically meaningful result? 

The distinction between closed-loop and fully autonomous laboratories is therefore important. Progress is more likely to happen workflow by workflow, with systems gradually taking responsibility for clearly defined decisions.

For Zechlin, this also means focusing on making today’s automation reliable and useful before becoming too distracted by the promise of fully autonomous labs. Many existing work cells still operate well below their potential, making reliability, connectivity and return on investment immediate priorities. 

“Let’s get to stuff that runs first.” 

Rather than treating AI-driven autonomy as the starting point, the discussion pointed towards a more pragmatic progression: build reliable automation, connect systems and data, and then layer increasingly intelligent capabilities on top. 

For organizations to hand greater control to AI-driven systems, technical capability alone will not be enough. As Cutler said:

“Trust is something that… is earned.”

That trust will need to be built through controlled experiments, reproducibility and clear boundaries. An autonomous system must not only perform an experiment successfully, but recognize when something has gone wrong, determine whether it can recover safely and know when human expertise is required.

This suggests a changing role for scientists rather than their removal from the process. Humans are likely to remain responsible for setting objectives and guardrails, validating outcomes and intervening when systems encounter unexpected situations.

Science also presents another challenge: serendipity. Unexpected findings can be enormously valuable. Teaching systems to recognize when an unexpected result may be scientifically interesting, rather than simply an error, will be an important frontier.

If AI is to make increasingly sophisticated experimental decisions, the quality and context of the data feeding those decisions becomes fundamental. 

Simply producing more data is not enough. Results need context: temperatures, timings, calibration data, instrument status, reruns, errors and potentially sensor or camera feeds. As Cutler put it:

“You can have a ton of data, but if it’s not good enough data, it’s not going to feed the model.”

This is particularly significant for organizations with decades of historical research data. Volume alone does not make that information AI-ready. Without sufficient annotation, metadata and quality controls, potentially valuable datasets can be difficult to reuse effectively. 

Jung similarly emphasized the need for contextualized data that allows a system to assess not only a result, but the conditions under which it was produced. For organizations, this makes data governance part of the scientific infrastructure required for AI-enabled research. 

Despite the focus on AI and robotics, the discussion repeatedly returned to one distinctly human challenge: adoption. 

Connected, automated laboratories require investment, new skills, governance and changes to established ways of working. In large organizations, fragmented ownership of automation, software and informatics can make this particularly difficult.

Cutler argued that greater centralization and a willingness to invest and take risks will be important.

“You can’t implement things piecemeal across 20 different places and then coordinate it.” 

Smaller companies may have an advantage. Their ability to make decisions quickly, establish common infrastructure from the outset and experiment with new operating models could allow them to demonstrate approaches that larger organizations subsequently adopt. 

Technology adoption also requires people to understand its purpose. As Jung emphasized, organizations need to communicate not only the risks of new technologies, but the opportunities and benefits they create. 

So, what will the laboratory look like five or ten years from now? 

The panel offered different perspectives on the speed of change, but there was broad agreement on the direction: laboratories will become increasingly connected, automated and AI-enabled. 

We are likely to see more closed-loop workflows, greater orchestration between instruments and software, and scientists moving away from repetitive experimental execution towards supervision, interpretation and decision-making.

But the pace of technological development may not ultimately determine how quickly this future arrives. As Cutler observed:

“The technology is going to be there way before we are.”

Standards, regulation, cybersecurity, governance and organizational readiness will all influence how quickly new capabilities can move from experimentation to widespread adoption. 

Ultimately, the lab of the future will not be created by AI, robotics or automation alone. It will emerge at the intersection of these technologies – bioconvergence in practice – and through collaboration between scientists, technology providers, pharma companies, startups and regulators.

The technology is moving quickly. The challenge now is ensuring that the infrastructure, organizations and people around it can move with it. 

Watch the full panel discussion on the Basel Area Business & Innovation YouTube channel to hear the experts explore the future of labs.  

This year’s applications were evaluated by a Selection Committee of senior leaders from pharma and diagnostics, investors and innovation partners, combining scientific, commercial and investment expertise. 

Following an initial review, 33 startups were invited to Selection Days, where founders presented their technologies, answered questions from the committee and explored how their solutions could advance Pharma R&D. 

After two days of presentations, interviews and deliberation, the committee selected a diverse cohort of founders from across Switzerland and Europe developing technologies across the Pharma R&D value chain. 

The cohort will begin their three-month acceleration journey with the in-person Kickoff Bootcamp in Basel in September. Thanks to weekly cohort and 1:1 sessions, they will then keep working with mentors, pharmaceutical partners and industry experts to shape their value proposition, strengthen their technologies, validate use cases and explore collaboration opportunities. 

The program will conclude with the Innovation Showcase in early December, where the startups will present their progress to the Basel innovation ecosystem. 

Up to three startups will then be selected for DayOne NEXT, the post-accelerator support program providing up to CHF 100,000 in funding and in-kind services, as well as 12 months’ desk space at Switzerland Innovation Park Basel Area.

Interested in joining the next cohort? 

If you’re an early-stage TechBio or HealthTech startup developing solutions for Pharma R&D, we’d love to hear from you. The next Call for Applications opens in March 2027. Learn more about the DayOne Accelerator and express your interest here. 

Many organizations are already experimenting with AI. That is not the issue.

The issue is that too many of these experiments sit in isolation. Individual teams launch promising pilots. Technical groups test new tools. Business units look for quick wins. But the initiatives often remain disconnected from each other and from the strategic outcomes that matter most.

Petra described this as the “thousand flowers bloom” stage of AI adoption. It creates activity, but not necessarily impact.

For pharma and healthtech leaders, a pivotal question is “Which part of the value chain should we start to reinvent?”

That shift matters because clinical development is not held back by one single bottleneck. Delays come from handoffs, rework, fragmented data, sequential decision-making and limited visibility across the full development lifecycle. Automating one task can help. Connecting the system can change the performance of the whole process.

Petra made a sharp distinction between optimization and reinvention. 

Optimization improves the current process. Reinvention asks whether the process would look the same if it were designed today with AI, data and digital orchestration built in from the start. 

In clinical R&D, that distinction is critical. Industry pressure is rising through patent expirations, productivity challenges and more complex trials. Incremental improvements alone are unlikely to close the gap. 

For Petra, the answer sits across three dimensions: work, workforce and workbench. 

Work means moving from siloed tasks to end-to-end intelligent workflows. Workforce means preparing teams to work with AI agents, not just digital tools. Workbench means building the secure, interoperable digital backbone that allows people, agents, systems and data to operate together. 

Without all three, companies risk applying advanced technology to broken workflows. The result may be a faster version of the same problem. 

One of the clearest examples is the journey from IND to first in-human dose.

Today, this phase is heavily coordination driven. Feasibility, protocol design, site selection, vendor setup, contracting and activation often happen sequentially. Each handoff creates room for delay, rework and misalignment.

AI orchestration could change that pattern. Instead of waiting for one step to finish before the next begins, clinical teams could move toward parallel execution, continuous evidence feedback, adaptive protocol refinement and real-time visibility.

The benefit is not only speed. It is adaptability.

If a site drops out, patient recruitment slows, or a regulatory requirement changes, an AI-orchestrated workflow could support faster replanning and better decision-making. The system becomes less brittle because it is continuously learning and adjusting.

Petra also shared a concrete example of reinvention through AI: the idea of designing a study in one day. 

The concept brings together complementary AI capabilities. One solution supports evidence-based protocol generation. Another adds simulation, using computational models to test scenarios before a protocol is finalized. 

Together, these capabilities could create a closed protocol optimization loop: design, simulate, refine, generate, monitor against actuals and amend only when the evidence demands it. 

The potential gains are tangible. Petra pointed to examples including faster protocol development, faster implementation in systems such as EDC and eTMF, fewer amendments and scenario analyses that can be run in minutes rather than days. 

For clinical teams, this is more than a productivity story. Better upfront design can reduce downstream friction. Simulation can expose risks before patients are enrolled. Connected workflows can help teams make decisions with stronger evidence and less guesswork. 

The hard part regarding AI adoption is not just the technology.

Petra emphasized that agentic AI requires clear business alignment from the start. Business teams need to define the outcomes and decision boundaries. Technology teams need to build for real workflows, not abstract capability. Governance must clarify accountability, oversight and trust when agents support or take decisions.

Architecture also matters. Multi-agent orchestration and enterprise system integration are complex. They need to be designed deliberately, especially in regulated environments where data quality, traceability and human oversight are essential.

And then comes the practical test: will people use it?

For pharma and healthtech innovators, that may be the most important takeaway. Agentic AI will not transform clinical trials because the technology is impressive. It will transform clinical trials when it is connected to the right workflows, trusted by the right users and measured against the right outcomes. 

The next wave of clinical innovation will not be powered by pilots alone. It will come from clinical systems that learn, adapt and improve with every decision. 

Find about our upcoming Open Mics and other DayOne events by signing up to our newsletter here. 

About the expert

Dr. Petra Jantzer is Senior Managing Director and Global Client Account Partner at Accenture, where she advises CEOs and senior leaders across life sciences, medical technology and digital health. With more than 20 years of experience in consulting and life sciences, she focuses on digital transformation, growth strategy and innovation across the full life sciences value chain.  

Petra holds a Ph.D. in immunology and an honors degree in molecular biology. She is also President and Co-Founder of Advance, a cross-industry association in Switzerland focused on advancing gender equality in business, and represents Switzerland in the G20 EMPOWER initiative. 

Clinical trials are full of coordination work. Data must move between systems. Sites need to be selected and activated. Protocols, reports, queries and submissions need to be written, checked and updated. Vendors, sponsors, sites and internal teams all operate across different tools, formats and processes. 

That is why orchestration came up repeatedly. 

For Laetitia, the biggest opportunity is using AI as a “continuous intelligence layer” across clinical development. The point is not simply to automate individual tasks. It is to help medical writing, operations, regulatory and other functions work from a more connected view of the trial. 

Luca gave a concrete example from clinical data management, where teams still spend significant time aggregating data, reviewing lines of information, cross-checking against protocols and writing queries or reports. Agentic AI can break that work into specialized steps, with individual agents reviewing data, checking outputs and escalating what matters. 

The human role remains central, but it changes. 

That distinction matters. Agentic AI should not remove clinical judgment from the process. It should reduce manual burden so experts can spend more time on interpretation, quality, risk and patient impact. 

Robert pointed to one of the biggest barriers in large pharma: disconnected systems and inconsistent data. 

Clinical organizations often work across multiple platforms, vendors, standards and data structures. That creates friction for analytics, reporting and decision-making. It also limits the impact of AI, because poor inputs produce poor outputs. 

Agentic workflows could help by translating data between formats, reconciling site information, generating standardized outputs and making operational data easier to use across the organization. 

Robert described this as a strong starting point because the task is often clear: data goes in, data comes out, and quality can be checked. Human oversight remains necessary, but the workflow is easier to control than more subjective use cases. 

This is where agentic AI may create near-term value: not by replacing clinical expertise, but by cleaning up the infrastructure around it. 

Much of the agentic AI conversation focuses on large pharma. But the panel also highlighted a different opportunity for biotech and mid-sized companies. 

Large organizations have deep resources, but also deep complexity. They carry legacy systems, established processes, multiple decision layers and risk controls that make change slower. 

Smaller companies may be able to move differently. 

Rohit argued that many use cases are not yet proven at scale, which creates an opening for companies that can make decisions faster and avoid legacy complexity.

Laetitia saw the same appetite among biotech companies, especially where speed to market is critical. Smaller teams may be less constrained by existing structures and more willing to design workflows around AI from the start, provided regulatory and compliance expectations are built in properly. 

That does not mean every company should build its own technology. It means AI-native thinking could become a strategic advantage. 

The panel was direct about one recurring problem: too many AI initiatives are treated as technology projects rather than operating model changes. 

Pilots can prove that a tool works in a narrow context. But clinical trials operate across countries, functions, vendors and regulatory expectations. A proof of concept that works in one geography or workflow can become fragile when pushed into global production. 

Robert framed the main barrier clearly. Technology is no longer the limiting factor. The harder challenge is rethinking the end-to-end system. 

That requires process change, people change and technology change together. It also requires stronger value discipline. ROI cannot be an afterthought, especially when implementation and operating costs may outweigh benefits in some contexts. 

The success metrics themselves are not new: faster development, lower cost, higher quality and better data. The challenge is choosing the interventions that move those metrics meaningfully. 

Trust is not a soft issue in clinical development. It is a precondition for adoption. 

Internal teams need to trust that AI makes their work better, not just faster. Sites and clinicians need reliable information. Patients need safety, clarity and confidence. Regulators need traceability, control and accountability. 

Rohit emphasized that human oversight remains essential, especially in regulated contexts. The key is not whether a human stays in the loop, but how the process is redesigned so that human review is efficient, meaningful and focused on the right decisions. 

Robert added that trust will build progressively. AI systems will need close monitoring, quality checks and feedback from users. In some areas, AI may eventually make fewer mistakes than humans. But clinical development cannot assume that point has already arrived everywhere. 

The panel also touched on a strategic question facing every pharma and biotech leader: what should companies build themselves, and what should they buy or partner for? 

There was no single answer. Different companies will make different choices based on maturity, ambition, internal capabilities and competitive priorities. 

Luca put the principle simply: pharma companies are in the business of bringing drugs to patients. If proven technology already exists, there needs to be a strong reason to build it internally. That reason may be competitive advantages, unique data access or a highly specific need. 

Robert argued for a robust internal team, not to build everything, but to ensure the technology ecosystem works. Companies need governance, integration capability and enough internal control to combine external tools safely and strategically. 

Laetitia was even more direct: the question should always come back to return on investment and the patient. If agentic AI can make trials more efficient, quicker and more cost-effective, that is where attention should go. 

Agentic AI could help clinical teams move from fragmented work to connected, adaptive execution. It could improve data management, accelerate writing workflows, support study planning, strengthen feasibility and help sponsors respond faster as trials change. 

But technology alone will not do that work. The next phase depends on clinical organizations becoming more willing to redesign processes, upskill teams, connect systems and measure value from day one. It also depends on keeping the human role where it matters most: judgment, oversight, interpretation and trust. 

The opportunity is real. The advantage will go to teams that stop asking where AI can be inserted and start asking how clinical development should work now that AI agents are part of the workforce. 

Watch the full panel discussion on the Basel Area Business & Innovation YouTube channel to hear the experts explore where Agentic AI can create real value in clinical development, and what pharma, biotech and healthtech teams need to get right before it scales.

About the experts 

Laetitia Soudanas is Senior Director Clinical Solutions at Biorce, where she focuses on strategic pharma partnerships and AI transformation in clinical development. She has more than 15 years of experience driving growth in clinical and pharmaceutical settings, with a current focus on making clinical trials more efficient, trusted, explainable and impactful through AI. 

Dr. Rohit Pushparajan is Managing Director, Lifesciences R&D Lead EMEA at Accenture. A physician by training, he has more than 20 years of experience in pharma R&D and deep expertise in helping life sciences organizations rethink how R&D can become faster, more productive and more patient focused. 

Robert McGregor is Executive Director, AI Program Head at Novartis Development, where he works at the intersection of AI, technology and drug development. He describes his focus as developing and deploying novel, value-adding solutions, with a particular emphasis on innovation ecosystems and practical AI adoption in large organizations. 

Luca Finelli is VP and Global Head of Digital Endpoints and Patient Centered Solutions at Roche Pharma Product Development. His background spans digital endpoints, data science, analytics, AI and patient-centered evidence generation, with earlier roles in data and digital strategy at Novartis and research experience at the Salk Institute. 

We are excited to announce that Lemna Bio, a standout company from the DayOne Accelerator 2025 cohort, is the first startup selected to join DayOne NEXT, our long-term support program for the most promising DayOne founders. 

DayOne NEXT reflects a simple belief: the journey doesn’t end with the accelerator – it’s just the beginning. Designed as a hands-on, long-term venture builder, the program supports a small number of exceptional startups as they build, scale and anchor their R&D in the Basel Area. Participants receive a non-dilutive CHF 50,000 grant, up to 12 months of tailored operational and strategic support (valued at up to CHF 50,000), workspace at the Switzerland Innovation Park Basel Area, and deep integration into the region’s world-leading life sciences ecosystem.  

By working side-by-side with founders, DayOne NEXT provides not just resources, but sustained, highly personalized support – from fractional expertise and mentorship to ecosystem access and visibility opportunities – helping startups transition from early traction to long-term impact.  

Lemna Bio is a techbio company building a structural AI platform to decode and validate hard intracellular targets. While advances in chemistry are unlocking new ways to engage biological targets, failures in drug discovery are still largely driven by incorrect target selection or an incomplete understanding of how to engage them in a disease context. Lemna Bio develops the tools, intelligence and experimental data to help drug discovery teams de-risk target biology and disease mechanisms early – before committing to costly chemistry efforts. 

Commenting on the milestone, Moustafa Houmani, Co-founder & CEO of Lemna Bio, said, “DayOne provided us with rare, first-hand insights into how pharmaceutical companies operate – insights that typically take years to acquire. Being selected for DayOne NEXT is an exciting opportunity to continue building with the right support and ecosystem around us.”  

With Lemna Bio as the first company in DayOne NEXT, we are proud to deepen our commitment to the most promising founders in our community and to support them as they scale breakthrough innovations at the intersection of technology and pharma R&D. 

The journey doesn’t end with the accelerator – in many ways, it’s just the beginning. 

We are proud to announce the launch of DayOne NEXT, a long-term support program designed to help the most promising startups from the DayOne accelerator cohort build, scale and anchor their R&D in the Basel Area. It is a hands-on, non-profit venture builder for exceptional early-stage teams, where we provide capital (no equity, no fees), workspace and deeply embedded operational support – and we stay for the long term. 

Each year, up to three startups from the accelerator cohort will be selected to join DayOne NEXT and receive tailored, long-term backing to transform breakthrough science and technology into real-world impact. 

DayOne NEXT is designed around a simple idea: early-stage healthtech, techbio and pharma-enabling ventures need more than short-term momentum. They need time, focus, ecosystem integration and highly tailored support. We will work side-by-side with a small number of exceptional founders and actively help build the company.

What this means in practice…

For the startups we back, this isn’t just “access to benefits.” It’s real involvement. We embed ourselves within the team – working on strategy, fundraising, hiring, positioning and whatever is mission-critical. 

The program is intentionally selective. We select top ventures from the DayOne accelerator program with strong scientific foundations and a robust intellectual property position – or a clear path to defensibility. Companies should have an active R&D component, require technical, regulatory or clinical support beyond business development, and have raised limited external funding so that the grant delivers meaningful impact.

We also prioritize engaged, collaborative teams with a clear strategic rationale for locating R&D in the Basel Area and strong alignment with the region’s ecosystem.  

We deliberately work with very few startups so we can go deep. This is high-conviction, long-term venture building. 

DayOne NEXT represents a new chapter in our mission: not just to accelerate innovation, but to anchor it, nurture it and help it scale sustainably within the Basel ecosystem.

It is more than just follow-on funding. It is a long-term partnership – aligning capital, expertise and ecosystem access to help exceptional founders build enduring, globally impactful health innovation. 

Learn more about the DayOne accelerator program here.  

A few years ago, “doing omics” often meant generating a single layer of insight and handing it off downstream. The conversation made clear that pharma has moved on: multi-omics now matters most when it connects to validation and development decisions, not when it produces isolated signals.

The implication for innovators is straightforward: the value proposition is increasingly platform oriented. The winning solutions reduce uncertainty at a specific decision point (or across several), and they come with a realistic path to operationalization inside a drug development organization.

The panel converged on a pragmatic definition of value: multi-omics earns its keep where it is already close to routine use and improves decisions across the chain.

Three “value zones” surfaced clearly:

Target identification and prioritization, with a validation plan attached

Integrated datasets – including preclinical, patient and real-world sources – help teams move beyond the “crowding problem” in oncology (too many efforts converging on the same small set of targets). But identifying more targets is not the win; the win is identifying targets that can be validated and developed with a credible downstream plan.

Molecule optimization using functional readouts

Modern biologics are increasingly complex, and teams need functional readouts to guide iteration. Omics-derived signals can provide that functional feedback loop, especially when combined with robust experimental workflows.

Clinical development and trial design

Longitudinal, multi-modal data collected in trials (not only bulk RNA sequencing) enables a more granular understanding of patient populations – supporting better trial design, patient stratification, and next-generation product criteria.

A recurring reality check is that multi-omics models can be scientifically impressive and still fail the “clinical feasibility” test. If your biomarker requires a long list of assays, modalities and feature-heavy models, clinicians may simply be unable (or unwilling) to use it in real-world trial design.

This is not anti-innovation – it is the translation bottleneck in plain terms. The path forward discussed on stage was not “less ambition,” but better constraint handling: interpretability, feature reduction that respects clinical logic, and smarter proxies for expensive measurements.

One concrete direction highlighted was predicting richer omics layers from more readily available clinical inputs (for example, extracting spatial or proteomic signals from standard pathology slides). Not “there yet,” but viewed as a strategically important route to speed and adoption.

If there was one word that kept resurfacing, it was validation.

In discovery, “ultimate validation” (a drug succeeding in the clinic) may be a decade away. In trials, validation may require embedding new measurements or endpoints into prospective designs, which introduces friction: incentives, timelines, regulatory comfort, and infrastructure maturity.

In clinical settings, the bar becomes existential: decision support impacts patient outcomes, and the expectation shifts toward rigorous evaluation (including trial-grade validation) rather than retrospective benchmarks.

For startups, the practical takeaway is to define validation in a staged way: what “good” looks like now, what must be true for adoption, and what prospective evidence is needed to earn expanded scope.

Even the best model fails if no one uses it. Trust, the panel argued, is not a slogan – it is built through usability, transparency, internal dogfooding, and human-in-the-loop workflows that reveal failure modes early.

One particularly sharp nuance: “explainability” is not the same as forcing AI to mimic human reasoning. If a model outputs a story that sounds reasonable but does not reflect how it produced the prediction, that can create false confidence rather than real trust.

The discussion treated partnering as a strategic reality – but also a survival question for innovators: can you maintain differentiation as methods and tooling evolve quickly?

A key point from the pharma side: technical models alone are not enough to differentiate long-term. The stronger partnering case often combines:

• unique data access (or ability to generate new data),
• operational speed and execution capability,
• and a clearly scoped measurable outcome tied to drug development decisions.

From the startup perspective, partnering can also be a rational risk allocation strategy for pharma teams facing a fast-moving methods landscape, where internal reinvention every six months is neither practical nor desirable.

If you are building a multi-omics, data science, or AI-enabled solution that can help pharma R&D make better decisions – from discovery through clinical development – DayOne is here to help you accelerate the path from promise to platform. 

Explore DayOne’s startup support (e.g. the DayOne Accelerator), sign up for our newsletter to be informed about our upcoming Open Mic sessions, or get in touch to discuss how we can support your journey from early validation to scaled partnerships. 

Please note: This article was based on discussion during the recent Open Mic: Next in Health event held on February 09, 2026, titled “Multi-omics intelligence: Platforms accelerating precision drug development”.

A recurring theme was that Basel compresses the essential ingredients of healthtech into a small geography. This density is one of the defining features of the Basel Area Life Sciences Supercluster: from pharma to clinical care to academia, the distance to collaboration is short.

As Astrid Kiermaier put it, “in the healthcare ecosystem … we have in a very close proximity every player around, and it’s easy to connect.” Bram Stieltjes described arriving in Basel and seeing “the hospital, two big pharma companies … all in such a small place.”

Basel also benefits from being a trinational region with natural connectivity across borders, which expands the partner and talent surface area beyond city limits.

The discussion emphasized that collaboration is not an ecosystem nice-to-have – it is a requirement driven by how modern pharma innovation works.

Astrid Kiermaier highlighted that clinical trials generate only a small slice of the data needed for a comprehensive patient picture. If you want to understand patients holistically, you must learn from the much larger share of patients who are not in trials and the data that sits outside trial datasets.

This is where the structure of the Basel Supercluster matters. Collaboration happens across pharma, hospitals, startups and the public sector because proximity makes it easier to align around shared problems, not only shared events.

Basel’s healthtech opportunity is tightly linked to a shift in what “infrastructure” means. Buildings and labs still matter, but the panel argued that data frameworks and standards will increasingly determine the speed at which innovation can move.

A practical example is University Hospital Basel’s move toward openEHR, an open standard that enables structured clinical data capture and reuse.

Bram Stieltjes framed this as ecosystem design, not just a technical upgrade: creating a foundation that supports a regional health data environment, not a single institution’s database. He stressed that “a hospital alone is not good enough. You need to have data on a regional basis.” He also highlighted why openness matters: if software providers sit in the middle, they can control data definitions and access, centralizing power and reducing collaboration. Open standards help avoid that dynamic.

Basel’s advantage is not only that stakeholders want to collaborate. It is that they are actively working to remove the operational friction that makes collaboration hard at scale.

One challenge discussed is the request cycle problem: repeated back and forth to find out whether relevant patient cohorts exist, how many there are, or whether a hypothesis is feasible. Bram Stieltjes described why this does not scale if every enquiry requires manual processing.

The direction described is to enable more self-service feasibility exploration, so users can explore parameters without exposing patient-level data. Astrid Kiermaier also pointed to privacy preserving approaches such as synthetic data as one way to reduce governance overhead and cut rounds of paperwork that slow collaboration.

A key forward-looking note from Astrid Kiermaier was that constraints on medical data use are likely to increase, making it even more important to invest in ways of working that enable innovation under tighter rules, rather than defaulting to slower contracting cycles.

The canton’s role was framed as enabling conditions: infrastructure, innovation promotion programs and legal frameworks that make collaboration easier and keep the region competitive.

Karin Sartorius described Basel-Stadt’s latest response to increased global competition: the creation of the Basel Incentive Programme, also referred to as the Basler-Standortpaket, with up to CHF 400m annual resources allocated to support spending for R&D and high-tech production in Basel-Stadt companies.

She also pointed to a specific gap many ecosystems struggle with: funding mechanisms that directly encourage collaboration between startups, hospitals, universities and industry. Basel is explicitly planning support in this direction effective from late 2026.

The audience Q&A sharpened the future agenda. A recurring question was whether Switzerland’s federal structure creates inevitable fragmentation for health data and research collaboration. The response was nuanced: federalism does not have to mean a fragmented data space if standards and intent are aligned.

Two constraints stood out. First is standardization and harmonization: the more clinical and research participation models can be standardized, the less organizations need to navigate “20 different” approaches. Second is willingness to share: progress depends on shared intent, clear benefits and mechanisms that make collaboration feel safe and worthwhile.

Beyond Switzerland, the panel also pointed to emerging requirements for understanding the full patient journey. Astrid Kiermaier noted that this often starts with registries and cohorts, with approaches like tokenization offering potential but facing regulatory and legislative hurdles in Europe.

Basel’s story is not that the region has “lots going on.” It is that the ecosystem has built a practical advantage by reducing the distance between discovery, clinical reality, industry capability and public sector enablement. For pharma innovation leaders, that density is valuable because it makes collaboration easier to initiate, easier to govern and more likely to translate into outcomes.

The panel’s most consistent message is that modern innovation depends on combining complementary capabilities and datasets. In practice, that means building partnership models that can connect clinical trial insights with real-world clinical data, while staying compliant and earning trust. Basel’s trajectory is also a reminder that infrastructure is increasingly defined by data standards and interoperable foundations. Open approaches like openEHR matter because they reduce lock-in, encourage reuse and make it more feasible to think beyond single-institution datasets.

Finally, the discussion highlighted that ecosystem competitiveness is not only a private sector concern. Policy tools such as the Basel innovation promotion programs and the Basel Incentive Programme, plus targeted support for collaboration, can create the conditions for faster experimentation, stronger partnerships and long-term resilience in the face of tightening constraints around medical data use.

Visit our Contact us page and tell us what you are trying to achieve via the form. Whether you are exploring innovation partnerships, looking for validation environments, or assessing where to build your next healthtech initiative, the DayOne team will follow up with relevant pathways and connections.

The 2025 edition of our DayOne Accelerator program culminated on 10-11 December at our sold-out Innovation Showcase event, held in Roche Building 1 in Basel. Over 300 startup founders, pharma industry leaders, investors and regional ecosystem stakeholders came together to watch two pitching competitions, learn from expert plenary presentations and panel discussions, and to network. 

After completing their 3-month program involving expert mentorship, webinars, hands-on “pitch to pharma” practice sessions, networking events, and more, 10 of this year’s cohort of 14 healthtech and techbio startups pitched on stage during the event. 

Nives Rombini, CEO & Co-founder, Navis Bio; Anna Cichońska, COO & Co-founder, Harmonic Discovery; Cécile Tardy-Srinivasan, Portfolio & Acceleration Lead, DayOne; Angela Holmes, CEO, OmniScience

A jury of investors and industry experts in digital health and pharma chose Harmonic Discovery as the competition winner, who got rewarded with a cash prize of CHF 16,200 representing the total ticket sales for the event. Navis Bio and OmniScience deservedly took the runner-up spots and were awarded startup packages for health.tech global summit (valued at CHF 3,500 each). The jury included representatives from Debiopharm, Redalpine, Kurma Partners, Vi Partners, Cathay Innovation, Asabys, Kadmos Capital, CSL Behring, Novartis, Novo Nordisk and Roche. 

Feedback from attendees was overwhelmingly positive, and the event also received some media coverage – notably in an article published in the Basler Zeitung (German). 

Thank you to Roche for hosting the event, and to Canton Basel-Stadt and our event partners for helping make this event happen! 

Before the dust has finished settling from the Showcase, the DayOne team is already preparing for key activities in the coming year. 

First up is the selection of those accelerator cohort members who will move to our long-term support program, DayOne Next, where they will receive a CHF 50k non-dilutive grant, 12 months of high-touch mentoring and in-kind services, plus office and lab space, and investment opportunities from our partners. 

Second is the startup pitching competition that DayOne will be running at the health.tech global summit, taking place in Basel on 3-5 March. 15 startup founders will have the chance to pitch directly to investors on the main conference stage. 

And of course, at the end of March we will be opening the Call for Applications for the 2026 edition of the DayOne Accelerator. Watch this space for details!