From innovation to adoption: leadership perspectives on what makes life science tools succeed

I’ve built my career in the life sciences tools sector, focused on translating scientific innovation into commercially scalable technologies that enable research, development and production.

Over the past three decades, I’ve had the opportunity to work across several organizations in the industry, including leadership roles at Merck Millipore (MA, USA) and GE Life Sciences (now Cytiva (DE, USA)), as well as serving as CEO of Gemini Biosciences (CA, USA) and Mirus Bio (WI, USA). These roles provided exposure across the full ecosystem – from early research through to technologies supporting emerging therapeutic areas such as cell and gene therapy.

A consistent theme throughout my career has been bridging the gap between innovation and sustainable market adoption. Scientific breakthroughs are important, but long-term impact is created when technologies translate into reliable, repeatable solutions that can be deployed at scale in real-world environments.

That perspective is what attracted me to Abselion. The company is focused on addressing practical measurement challenges, with a clear path toward building technologies that can integrate into everyday workflows and deliver measurable value.

In practice, moving a technology from innovation to adoption is less a linear process and more about successfully navigating a series of constraints that determine whether the technology can translate beyond the development environment into scalable, real-world use.

The first is a meaningful scientific advantage that solves a real problem. Innovation alone isn’t enough – a new approach needs to demonstrate a clear and defensible benefit over existing methods, whether in the quality and consistency of the data it produces or the speed of its analysis. Deeply understanding how customers work and the challenges they face is essential to identifying where innovation can add value.

The second is operational fit. Technologies must function reliably across different facilities, users and sample conditions, and integrate naturally into existing workflows. In areas such as biopharmaceutical development and production, there are often additional expectations around data integrity, traceability and the ability to operate within regulated environments.

The third constraint is economic viability. Organizations are always balancing performance with cost, throughput and resource constraints. A technology that delivers excellent data but is difficult to scale operationally or economically will struggle to gain broad adoption.

In many cases, the underlying science isn’t the limiting factor. The breakdown is at the front end – insufficient focus on clearly defining the customer pain point and what the technology is intended to solve. Without that discipline, even strong innovations struggle to translate into solutions that deliver clear, adoptable value.

Beyond that, adoption is won or lost in real-world performance. A technology may show impressive results in research, but development and production environments often require data traceability, highly reproducible results, the ability to be validated and confidence that the technology can be relied upon at scale.

It also comes down to whether the technology meaningfully advances decision-making. Customers aren’t looking for data in isolation – they need outputs that help them move work forward in real-world settings, for example through platforms such as Amperia™, whether that’s selecting a cell line, optimizing a process, supporting process understanding or progressing a program.

Finally, speed matters. Getting results quickly contributes to bringing life-changing therapies to market faster and is essential to maintaining momentum in resource-constrained environments where timelines and capital efficiency are critical.

The technologies that succeed are the ones that are tightly anchored to a clear, high-value customer problem and deliver measurable and repeatable impact within existing workflows.

In practice, that shows up in a few ways. They make a meaningful difference, improving speed, reproducibility, cost or risk in a way that is easy to recognize and justify internally. Even incremental gains can be powerful if they compound across larger workflows or multiple stages of development.

They are also engineered for real-world use. Successful technologies perform consistently across users, sites and conditions, and integrate cleanly into established processes without adding friction.

Importantly, they directly enable better decisions. The output isn’t just data, it’s actionable insight that helps scientists move programs forward, whether that’s selecting candidates, optimizing processes or accelerating timelines.

Finally, they align with the broader ecosystem. Timing matters; adoption accelerates when the infrastructure, regulatory environment and customer readiness are in place, and when the value clearly outweighs the cost and effort of switching from existing solutions.

In practice, adoption often comes down to how well a technology fits into the day-to-day realities of the laboratory and integrates with existing workflows without introducing additional operational burden.

Researchers, developers and biomanufacturers typically operate within established experimental processes, so solutions that complement those workflows without requiring significant changes are much easier to adopt. Increasingly, this also includes compatibility with automated systems and digital infrastructure, such as data management or analysis platforms, which are becoming central to scaling laboratory operations.

Ease of use remains another critical consideration. Technologies that are intuitive to operate, require minimal training and produce consistent results across different users are far more likely to become part of routine work, particularly in environments with multiple operators or sites.

Speed is also increasingly important. Across research, development and production environments, teams are under pressure to generate data quickly and move projects forward. Technologies that simplify experimental steps or reduce turnaround time can have a meaningful impact on productivity and help teams progress programs more efficiently.

Practical considerations such as instrument footprint, resource efficiency and sustainability are also becoming increasingly important in laboratory decision-making. Instruments and methods that fit more easily within a typical laboratory bench-top setup, while also reducing reagent consumption, energy use or laboratory waste, can help organizations operate more efficiently while also supporting broader environmental goals, particularly as labs look to optimize space and resource allocation.

One of the key shifts is recognizing that developing a successful life science technology today requires more than solving a scientific or technical problem in isolation. The most effective development efforts take a broader perspective, considering how the technology will integrate across the full workflow and deliver value across multiple stages of use.

This includes thinking early about how data will be generated, interpreted and integrated with other processes, as well as how the technology may fit within automated systems, digital infrastructure and regulated settings, particularly as workflows become more connected and data driven.

Another important consideration is the diversity of settings in which technologies may ultimately be used. Solutions that can function effectively across different stages of the life science therapeutic pipeline, from research and process development through to manufacturing, quality control and bioprocess analytics, often have greater commercial potential and longer-term relevance.

Maintaining close dialogue with users is critical. Technologies often evolve significantly once they are exposed to real laboratory and production environments, and continuous feedback helps ensure that development remains aligned with how customers actually work, supporting both adoption and ongoing product-market fit.

From my perspective, one of the most exciting developments in life science today is how advances in measurement and sensor technologies, together with computational methods, are converging to create more scalable and information-rich approaches to understanding biology.

On the measurement side, we are seeing rapid progress in technologies that allow users to observe and measure biology with much greater resolution and context, enabling more consistent and reproducible data generation across workflows.

Alongside these advances, computational approaches, particularly AI, are becoming an increasingly important part of how scientists interpret and prioritize complex biological data, helping translate data into more actionable insights at scale.

What is particularly powerful is when these capabilities are combined with high-throughput technology and increasing levels of laboratory automation. Together, they allow scientists to generate richer biological data and do so much more quickly, while also supporting more efficient and scalable experimental workflows.

In practical terms, as technologies become more accessible and integrated into everyday research and development workflows, for example, through platforms such as Amperia™, they have the potential to significantly accelerate how scientists understand biology and translate discoveries into new therapies, with broader implications for productivity and development timelines across the industry.

I think one of the most important priorities for technology developers is maintaining a clear focus on the real scientific problems researchers are trying to solve. As workflows become more complex, the technologies that create the most value are often those that address meaningful bottlenecks, with a clear link to improving outcomes or efficiency, rather than simply introducing additional technical sophistication.

In our field, there can sometimes be a tendency to over-engineer solutions. While technical innovation is essential, new technologies only gain real traction when they provide a clear and practical improvement over existing approaches or enable scientists to do something they could not do before, in a way that is easy to adopt and justify within existing environments.

Another important consideration is the commercial and operational context in which technologies are used. Many promising ideas originate from strong technical or academic foundations, but without a clear understanding of how scientists adopt technologies in real laboratory environments, even very capable solutions can struggle to reach their full potential, both in terms of adoption and long-term market impact.

For that reason, successful technology development often requires balancing scientific innovation with practical implementation. Companies developing tools that stay closely connected to the needs of scientists, and that understand how technologies will be deployed, scaled and integrated into real workflows, are far more likely to create solutions that deliver sustained value and achieve broad adoption.

What attracted me to Abselion was the opportunity to support a company that is addressing a very real challenge in life science research, development and production: the need for reliable and accessible ways to measure and understand biological systems as workflows become increasingly complex.

Throughout my career, I’ve seen many examples of how enabling technologies shape progress in the life sciences. Scientific breakthroughs often depend not only on new biological insight but on the tools that allow researchers and production teams to generate reliable data and act on it with confidence. At the same time, I’ve also seen technically impressive technologies struggle when they are not sufficiently aligned with how scientists actually work in the laboratory. What appealed to me about Abselion was the clear focus on combining strong engineering with practical usability –  building technologies that scientists can realistically adopt and apply in their day-to-day work, with a clear pathway toward broader deployment.

One example of that approach is reflected in the Amperia™ platform, which follows the same philosophy. It represents an effort to rethink how certain biological measurements can be performed, with the aim of making them faster, more accessible and better aligned with modern workflows, while supporting more consistent and scalable data generation.

Equally important was the strength of the team and the broader ecosystem around the company. Abselion brings together experienced scientists, engineers and commercial leaders, supported by a strong group of investors and board members who understand both the science and the industry. That combination creates a strong foundation for developing technologies that can make a meaningful impact across the life sciences, and scale as adoption grows.

For me, joining the board was an opportunity to help guide a company that is not only developing innovative technology, but doing so with a clear focus on practical adoption and long-term value creation for the life science community.