Why does promising scientific innovation fail to launch?

I’ve spent the past 2 years working in Michigan’s scientific innovation space, helping promising breakthroughs in the lab get closer to achieving real-world impact. The pre-clinical life sciences technologies I’ve encountered are consistently promising, but, as is often the case with biotechnology innovations coming out of academic institutions, not all are successfully commercialized. The ones that do make it to market take a very unique path, a journey that relies on expert attention and carefully designed strategies.

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Clarity doesn’t always equal momentum.

I have presented customer discovery findings in early strategic meetings to the scientists behind a variety of different platform technologies, such as new therapeutic delivery systems. We began with a broad hypothesis about who might need or benefit from the innovation and gradually narrowed the field to a specific market. Through interviews, expert calls, and industry conversations, we learned that success would depend on being highly specific and targeted to enter a niche market and become a commercial reality. 

I’m not alone in this work. People in roles like mine aren’t the founders or the principal investigators. We are scientists and engineers, sometimes MBAs, recruited through fellowship programs specifically designed to help research teams take their technology beyond the university lab. Our role is to act as a bridge: to translate between scientific potential and commercial reality, and we do this often by turning interviews and data into a path forward. On paper, this is the point in the meeting where things are supposed to accelerate. With the insight we’re delivering to the team, we’re positioned to help guide their next steps towards a business plan. And yet, this is often where I see things stall. 

What comes into focus at this stage is the next set of hurdles the technology will face. The path to commercialization will need more hands - such as students or postdocs - to take charge. Because of the nature of biotech, the process will require more time and funding before scaled production beyond the lab bench is achievable. It will demand more effort than a full-time faculty member can realistically give, and the continuity that students can’t provide. From where I sit, as we inch the technology towards exiting the university lab doors, we also find ourselves staring down the next part of the process with no clear way to overcome these hurdles. I’ve learned that the clarity I and those like me can provide doesn’t always translate into momentum.

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Close enough to see the gaps, not close enough to fix them.

My two-year fellowship was a translational role at a university research institute. It was unique to me because I wasn’t running experiments or publishing papers, but rather consulting for early-stage biomedical technologies. Drawing on my background as a PhD-trained scientist, I was now operating in a middle space, where my colleagues and I often served as translators between scientists, industry partners, investors, and regional innovation programs. We sat close enough to the research to understand the science, and close enough to the broader ecosystem to see what it would actually take to move forward in commercialization.

From this vantage point, I noticed a pattern that was hard to ignore: many slowdowns in university-born innovation weren’t stemming from weak science, but from the hurdles they would face next, and consequently, the system gaps that emerge just as they should be accelerating. People in these translational roles (like myself) often see those gaps early and yet are rarely positioned to shape how the system responds. We’re not inventing the technology, and we’re not ultimately commercializing it. But we often see where the system starts to strain. And while we can see the gaps clearly, we’re not empowered to close them. 

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Different Midwest institutions with the same failure points.

Take, for example, colleagues I’ve kept in touch with from my graduate training and fellowship programs who now hold similar bridge roles across the Midwest: at other universities, on startup teams, or within innovation programs. Even though the setting might be different, the patterns are strikingly similar.

A former colleague from Michigan, now in a similar translational role in Indiana, has shared with me that similar issues arise with teams forming around academic tech. The teams complete training programs meant to guide them through customer discovery and value proposition development. They conduct interviews, analyze feedback, and refine their ideas. Yet the next step, which should be executing on all this data they’ve collected, often fails to materialize. The majority of the teams lose momentum, “fall off a cliff” when faced with logistical roadblocks, and the technology stays on the research bench.

Another scientist in our regional network manages operations for a faculty-led startup. They’ve been tasked with handling logistics and day-to-day execution, but are often blocked by key strategic decisions dependent on the overall direction of the company. Their progress routinely stalls while waiting on leadership who are likely still operating with an academic mindset, focusing primarily on exploration and curiosity, and secondarily on commercial success.

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Lately, as I’ve reflected on both my work and conversations with peers, these cases have stopped feeling anecdotal. The sticking points show up in the same places over and over: after customer discovery, at funding inflection points, or at regulatory checkpoints. Those of us working in translational roles see these patterns regularly, and these observations become especially relevant as we see more and more Midwest states and universities interested in strengthening their innovation ecosystems. 

I would argue that embedding feedback loops with the professionals operating in these translational spaces may be just as important as launching new programs or funds. If we want the startup system to operate more efficiently, we need individuals who understand the challenges to be more involved in the intentional evolution of the system. 

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The bridge-builders should help design the bridges.

If the Midwest innovation ecosystem is serious about building differently, we, the professionals working at the middle layer, could be the litmus test. Across the region, we have built consensus around designing smarter systems. Instead of copying other ecosystems, we want to create a model that is more durable, more connected to our industries, and more rooted in long-term impact. But this system design shouldn’t just focus on where the money goes or which new programs launch; it should also account for whose insight shapes and informs the process. 

These perspectives matter for practical reasons, not just philosophical ones. 

As I mentioned earlier, moving deep tech forward no longer fits neatly into a single job description. Incorporating scientific rigor, regulatory awareness, customer insight, partnership strategy, and operational planning requires adaptable skill sets. People in translational roles (often PhD-level scientists) are used to working across these boundaries. A PhD is essentially code for someone who can rapidly learn to navigate business, policy, and ecosystem dynamics. We’re trained to evaluate technical claims critically, ask uncomfortable feasibility questions, synthesize messy information, and translate between communities that don’t naturally speak the same language.

So what am I getting at? 

I believe we should be asking the people who fill translational middle-layer positions explicitly what they are seeing inside their own innovation systems. 

We’re entrenched between labs, startups, and ecosystem support programs, and we see where technologies consistently stall. We watch what happens after customer discovery. We see the funding gaps that follow even after promising technical validation. We see where regulatory complexity conflicts with academic resources, and where the necessary infrastructure simply doesn’t yet exist for highly novel biotechnology. 

Midwest universities and states are investing in new initiatives, funds, and institutes. Just as important as building the next center or accelerator, they need to create clearer feedback loops with people in these translational roles. The life sciences technologies coming out of Midwest labs will always be promising. The real questions are, first, whether we can design systems around them that can adapt as quickly as science advances; and second, whether the people working at the checkpoints between discovery and deployment are fully tapped as a source of the intelligence those systems need. 

We are after all the Midwest: our strength has always been in the middle. 

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Ruby Miller is a Biotechnology Regulatory Fellow with the National Academies of Sciences, Engineering and Medicine. She holds a PhD in Chemistry, has worked on the commercialization of cutting-edge biomedical technologies and has a keen interest in how these technologies are funded and come to market.