When Great Ideas Die in the Lab
Why systems thinking is crucial for successfully bringing scientific innovations from the lab to market
We don’t know exactly how many brilliant ideas have already died in the laboratory before they ever had a chance to change the world. One thing is certain: there are more than we realize. Studies show that a large portion of scientific findings never make the leap into practical applications — leaving enormous potential untapped [1].
Innovation is often reduced to technological breakthroughs. But a groundbreaking discovery is not yet a product, let alone a scalable business model. Without market validation, access to capital, regulatory clarity, and a functioning ecosystem, even the best technology remains without impact. According to the OECD, transfer processes between science and business repeatedly fail due to missing interfaces and incentive systems [2].
The real challenge, therefore, lies not in the laboratory but in the transfer. Ideas fail because they are not placed in the broader context: What problems do they solve? What partners are needed? What societal impact can emerge? Systems thinking provides the crucial leverage here. It connects scientific brilliance with economic relevance and societal benefit — thereby creating the foundation for turning research into real solutions for the future [3].
The Dilemma of Scientific Innovations
No one knows exactly how many promising scientific ideas never make it out of the laboratory. There is no global statistic that makes these losses visible — yet the problem is all too familiar to anyone working in the innovation environment. Researchers report that often only a fraction of their work ever makes the leap into application. A study by the European Patent Office [4] shows that less than 50% of granted patents are ever commercially exploited. This means: even behind already protected inventions lies enormous untapped potential.
The reasons for this gap are manifold. On one hand, there are structural barriers: missing funding mechanisms for the phase between basic research and market-ready product (“Valley of Death”), unclear responsibilities at technology transfer offices, or simply a lack of entrepreneurial experience in research teams. On the other hand, it’s also a question of culture: while science aims for validation and precision, the market demands speed and scalability. The result is a transfer problem that is felt on both sides but is rarely openly addressed.
This invisible graveyard of ideas is more than an academic problem. Every failed transfer also means a loss of potential societal benefit — whether through undeveloped medicines, unused technologies for emissions reduction, or missing solutions to global challenges. According to the National Academies of Sciences [5], only about 14% of biomedical research results are transferred into clinical application. Applied to other disciplines, this illustrates: the path from laboratory to impact is a bottleneck that urgently needs to be thought about systemically.
The Misconception “Technology = Product”
Many scientists and engineers are convinced: if a technology works in the laboratory, it will automatically conquer the market. But this assumption is deceptive. History is full of groundbreaking inventions that, despite technical excellence, never became commercially successful. Why? Because markets don’t buy scientific proof, but solutions to concrete problems, embedded in business models that are viable and scalable.
The gap between laboratory results and market-ready products is larger than it often appears. Research answers the question: “Is it technically possible?”. The market, however, asks: “Why should anyone pay for it?”. Without this translation, patents remain in drawers, prototypes in institutes, and research results in academic journals, instead of creating value in society. The difference between “being able to make something” and “being able to sell something” is the blind spot of many technical startups.
It’s not enough to have the best technology — it must be embedded in a narrative, economic, and user-oriented framework. Startups like Better Place, which once wanted to revolutionize the electric car, failed because they underestimated the market, infrastructure, and business model [6]. Excellent technology alone doesn’t prevent failure — rather, it requires the ability to think about market logic, stakeholder dynamics, and user needs from the beginning.
This is where another form of excellence comes into play: entrepreneurial excellence. Founders in the DeepTech world need far more than research competence — they must speak the language of investors, lead teams, develop business models, and anticipate markets. Many technologies fail not in the laboratory but with the founding team. Studies show that the most common reason for startup failure lies in the areas of team and execution, not in the technology. Excellent founders are interfaces — they connect the technical potential of their research with market logic, capital access, and user focus.
Another misconception: innovation is synonymous with commercialization. But while innovation describes the process of developing new ideas, commercialization requires a clear value chain, distribution channels, and willingness to pay. This explains why many scientific centers of excellence remain dependent on government funding — they generate knowledge but not market-ready business models. A look at innovation economics shows: only when research is translated into an entrepreneurial structure through design, business development, and capital does a product emerge.
This is also where the systemic challenge lies: research institutions and startups often operate in separate logics — one in the publication and funding logic system, the other in the market and competitive environment. Overcoming these breaks requires a new generation of founders who understand both: scientific depth and entrepreneurial breadth. Only then can technologies be translated from the logic of research into the reality of markets.
Lack of Systems Understanding: Why Founders Underestimate Partners as Secondary
As explained, founders often assume that a strong product alone is sufficient to conquer markets. But innovation never emerges in a vacuum — it is always embedded in a network of partners, regulators, investors, and users. Those who ignore this network miss crucial leverage for scaling. Publications by the World Economic Forum show that companies that rely on partnerships early on are significantly more resilient and growth-oriented [7].
Those who underestimate partners therefore risk not only market delays but also loss of credibility. Investors increasingly pay attention to whether founders understand and actively shape their ecosystem. A startup without strategic partnerships appears isolated and therefore fragile, while a startup with clearly recognizable embedding in the network builds trust. In short: systems thinking today determines not only speed but the survivability of innovations.
The consequence of lacking systems understanding is a dangerous decoupling: technologies are perfected in the laboratory while potential partners — suppliers, distribution channels, or pilot customers — are not involved. Particularly in the DeepTech and life science sectors, this leads to products that are technically mature but fail due to missing interfaces to the market. One example: Theranos failed not only due to questionable methods but also because they deliberately excluded their partner ecosystem, thereby preventing validation and integration into existing healthcare structures [8].
This means those who don’t involve partners early risk inefficient use of their own resources and leave valuable market opportunities unused. Every interface that isn’t considered — from the regulatory environment to production partners to pilot customers — can later become a hurdle that costs time, money, and credibility. Early partnerships are therefore not just a strategic add-on but a crucial leverage for scaling and for the trust of investors and stakeholders.
Science and markets follow fundamentally different logics. Research is about knowledge generation, reproducibility, and publications; markets, however, measure success by value creation, customer benefit, and competitive advantages. This difference not only creates misunderstandings, it also often prevents the early involvement of necessary partners. The OECD describes this as “fragmentation of innovation pathways,” which prevents knowledge from being systemically translated into value creation [9].
Those who ignore this break therefore run the risk that their innovation remains in the laboratory while competitors with a better understanding of market and ecosystem logic seize the opportunities. Founders must learn to speak both languages: they need an understanding of scientific excellence, but at the same time also the ability to navigate market mechanisms, investor requirements, and partner networks. Only then can technologies not only be developed but successfully scaled.
The missing systems understanding is not an individual problem but structurally conditioned: universities reward publications, investors returns. Successful founders are therefore bridge builders: they recognize early which partners are crucial for validation, market access, or scaling, and integrate them into their roadmap. Organizations like Fraunhofer Venture show how research projects become market-ready companies through systemic partner integration. Those who believe they can conquer the market through technological brilliance alone often remain trapped in the ivory tower of science.
Systems Thinking as a Bridge
In practice, the question therefore arises: How do sciencepreneurs build the bridge between vision and entrepreneurial reality? This is precisely where systems thinking comes into play. It enables viewing innovation not just as a product, but as part of a larger network of markets, people, and structures. Only those who understand these connections can avoid remaining in a technology-obsessed bubble and instead activate the relevant levers in the market early on.
The first critical lever is the question of the market. Many founders confuse proof of concept with product-market fit. While the former only proves that an idea works technically, the latter answers the central question: Is this solution actually needed and is someone willing to pay for it?
Especially with impact startups, there is a great risk of equating technical feasibility with impact potential. But a functioning product can ultimately remain systemically ineffective if it doesn’t find market access or misses the real needs of users. Systems thinking broadens the perspective here: It forces consideration of the innovation project in the context of value chains, customer realities, and regulations — and thereby recognizing the difference between a functioning prototype and a functioning business model.
Early market validation is therefore more than risk mitigation. It’s a reality check that shows founders whether their solution fits into the existing ecosystem or whether it will fail due to crucial resistance. Pilot customers, failed sales attempts, and initial pricing discussions are not secondary matters but proof that a vision can also hold up in market structures. Those who dare to make this bridge early build a startup not against but with the system.
Translator Roles: Founders as Interface Developers
Systems thinking only unfolds its impact when founders take on the role of translators. Scientific innovations exist in a field of tension between laboratory logic, market mechanisms, and societal expectations. Each of these worlds follows its own rules, speaks its own language, and sets different incentives. Those who fail to bridge these differences risk that even the best technology gets stuck in no man’s land.
In research, what counts are precision, methodological depth, and verifiability. In business, on the other hand, speed, scalability, and return on investment dominate. Both logics are justified, but without translators, a gap yawns between them. Founders must be able to communicate scientific excellence in such a way that investors, partners, and markets recognize the value — and at the same time reflect economic requirements back into scientific work so that research remains compatible. Studies show that most innovations fail precisely at this interface: they remain either too technical or too vague to be attractive to investors.
But the bridge doesn’t end at the market. Innovations today also face growing pressure from societal expectations: climate change, social inequality, digital ethics. Impact-oriented startups cannot afford to ignore these dimensions. Founders are therefore not only mediators between research and business but also translators between economic benefit and societal value. They must embed technologies in narratives that explain how an innovation solves concrete problems and why it is legitimate to claim capital and attention. Innovations without societal acceptance can hardly have an impact — no matter how technologically superior they are.
The role of translator is far more than communication work. It requires entrepreneurial excellence: the ability to reduce complexity, involve stakeholders, and make decisions that are scientifically sound and market-oriented at the same time. Successful sciencepreneurs are therefore not just inventors but also architects of understanding. They create shared images, shared goals, and shared language. This is precisely where their competitive advantage lies: they enable research, capital, and society to work not alongside each other but with each other.
Iterative Business Model Development: Impact First Strategy Instead of PowerPoint Illusions
Many founders start with a detailed business plan that promises market forecasts, revenue curves, and five-year strategies. But in reality, these plans are often nothing more than a beautifully designed illusion. No document survives the first encounter with real customers, real markets, and real resistance. Those who spend too long in the laboratory polishing Excel spreadsheets lose valuable time and risk developing past the market.
Traditional business plan thinking comes from a time when markets were more stable and innovation cycles were slower. Today, however, approaches are needed that acknowledge uncertainty and draw strength from it. Methods like Lean Startup [10] have demonstrated how rapid testing, feedback loops, and iterative adjustments can accelerate the path to product-market fit. But in the realm of impact startups, this isn’t enough. Here it’s not just about whether a product works, but also whether it makes a demonstrable positive contribution to ecological or social systems.
This is precisely where COSMICGOLD’s Lean Impact & Sustainability Assessment [11] comes in: an approach that combines the principles of Lean Thinking with systemic impact thinking. Founders test not only hypotheses about customer benefit and willingness to pay, but also about ecological and social effects. This makes impact an integral component of the business model and not an afterthought “add-on”. The Stanford Social Innovation Review emphasizes that this iteration logic is crucial for translating impact-oriented innovations from theory into practice.
Iterative business model development also means consciously seeking confrontation with resistance. Pilot projects, early pricing discussions, failed prototypes — all of these are not setbacks but data points. They show where assumptions don’t hold and where the system reflects back that adjustments are necessary. This learning process accelerates market readiness more than any business plan. Impact-oriented accelerators like Yunus Social Business [12] or the European Institute of Innovation & Technology (EIT) [13] have long relied on this logic: rapid testing, clear KPIs, iterative learning.
Instead of page-long business plans, dynamic models emerge that sharpen with each cycle. Founders learn that business models are not fixed plans but hypotheses that must be continuously tested — economically, ecologically, and socially. Our Lean Impact & Sustainability Assessment is therefore not just a method but an accelerator: It forces startups to think in real contexts rather than in PowerPoint slides.
What Founders Can Learn From This
Systems thinking is not an academic theory but an entrepreneurial survival skill. The good news is: systems thinking can be trained. Tools like systems mapping or causal loop diagrams help make interactions visible and uncover blind spots. With a value network analysis, one can trace which partners contribute what to value creation and where tensions or bottlenecks may arise. Institutions like the MIT System Dynamics Group [14] have developed practice-oriented approaches that go far beyond abstract models and enable founders to translate complex relationships into manageable decision options.
In addition to classic system tools, methods like the Business Model Canvas and the Theory of Change have become established. Both bring structure to thinking: The Canvas forces founders to relate customer segments, channels, and value propositions to each other, while the Theory of Change sharpens the impact logic along inputs, outputs, outcomes, and impact [15]. Together, these methods offer a double compass: economic and impact-oriented.
Systems thinking also means changing one’s own mentality. Instead of thinking linearly “idea → product → market,” the ability is needed to build in feedback loops, repeatedly test hypotheses, and consciously question assumptions. This is uncomfortable because it makes uncertainty visible. But precisely this openness to complexity creates the prerequisite for acting resiliently. The Club of Rome studies and current work on the circular economy approach [16] show that linear thinking has long reached its limits.
Why Transparent Learning Processes Are Crucial
Failure is omnipresent in research and entrepreneurship. But while in the laboratory it is often accepted as a necessary step toward knowledge, a completely different pressure prevails in the market. Founders tend to conceal or embellish failures in order not to unsettle investors, partners, or customers. This attitude costs time, resources, and strategic flexibility. Those who establish transparent learning processes, on the other hand, transform mistakes into valuable feedback and reduce the likelihood of failing at systemic challenges in the long term.
Transparency means that not only successes are documented, but also false assumptions, test results, and failed experiments. Tools like impact metrics, key performance indicators for pilot projects, or rapid prototyping make failure measurable and manageable. In practice, it becomes evident that teams that openly analyze failures adapt hypotheses more quickly and achieve product-market fit faster. Examples from practice are provided by initiatives like Lean Impact, which combine iterative experiments with systematic learning.
A culture that makes failure visible has additional effects: it promotes trust within the team, improves collaboration with partners, and signals to investors that the company is capable of learning. Studies prove that learning organizations act more resiliently, scale innovations faster, and address risks early [17].
It’s important that visible mistakes are not an end in themselves but directly feed into strategic decisions. Iterative testing, documented lessons learned, and open communication enable continuous improvement of innovations and actively involve the market and partner ecosystems in the development. Failure thus becomes a building block for sustainable success — instead of a risk that must be concealed.
Shaping the Future Instead of Just Building Products
Many innovations remain trapped in the laboratory because founders lose sight of the big picture. An excellent product is only the first step: it only develops impact when embedded in a functioning ecosystem. Systems thinking requires considering the long-term interactions of technology, market, and society. Founders thereby become architects of regenerative systems: they design processes, partnerships, and business models so that innovative capacity, impact, and economic success interlock.
It’s not enough to “attach” impact retrospectively to a finished product. Instead, founders must define early what systemic changes their innovation should bring about — ecological, social, and economic effects. Tools like Theory of Change and the Lean Impact & Sustainability Assessment help translate this vision into measurable results and integrate it into operational implementation.
This perspective fundamentally changes the role of founders: they become not just developers and managers but also strategic architects who orchestrate stakeholders, partners, and markets. They must understand interfaces, actively shape networks, and ensure that innovations don’t work in isolation but unfold regenerative effects. Organizations like the Ellen MacArthur Foundation show that systemically designed business models are significantly more resilient, scalable, and effective in the long term.
Founders who make the leap from pure product thinking to becoming architects of regenerative systems create not only economic value but influence entire markets and societies. The ability to plan, measure, and control impact becomes a central success factor and a competitive advantage that extends far beyond individual products.
Sources:
[1] https://www.nature.com/articles/533452a
[2] https://www.oecd.org/en/publications/oecd-science-technology-and-innovation-outlook-2021_75f79015-en.html
[3] https://www3.weforum.org/docs/WEF_Markets_of_Tomorrow_2020.pdf
[4] https://www.epo.org/en/results?search_type=website&q=Patent+commercilisation+Scoreboard+2019&filters=%5B%7B%22field%22%3A%22language%22%2C%22values%22%3A%5B%22en%22%5D%2C%22type%22%3A%22any%22%7D%5D&sortField=&sortDirection=&tab=all_results
[5] https://www.ncbi.nlm.nih.gov/sites/books/NBK373700/
[6] https://store.hbr.org/product/better-place-the-electric-vehicle-renaissance/IN1163?sku=IN1163-PDF-ENG
[7] https://www.weforum.org/meetings/the-davos-agenda-2022/sessions/technology-cooperation-in-the-fourth-industrial-revolution/
[8] https://www.technologyreview.com/2016/08/31/7265/theranos-makes-another-unforced-error/
[9] https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/03/oecd-science-technology-and-innovation-outlook-2023_fb6e6c20/0b55736e-en.pdf
[10] https://www.penguinrandomhouse.com/books/210088/the-lean-startup-by-eric-ries/
[11] https://www.cosmic.gold/lean-impact-sustainability-assessment
[12] https://www.yunussb.com/articles/the-man-impact-accelerator-opens-applications-for-a-third-batch
[13] https://www.eit.europa.eu/our-activities/opportunities/impact-funding-framework
[14] https://www.mit-alumni-systemdynamics.org/system-dynamics-in-industry-mit-iap-2025
[15] https://www.oecd.org/content/oecd/en/topics/sub-issues/development-co-operation-evaluation-and-effectiveness.html
[16] https://www.ellenmacarthurfoundation.org
[17] https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/raising-the-resilience-of-your-organization.
