Leveraging Scientific Research for Business

Successful startup founders think like scientists. As an entrepreneur, relying on intuition and gut feelings can be tempting. But if you want to increase your chances of success, you might need to think like a scientist. I recently read a Harvard Business Review article titled "Why Entrepreneurs Should Think Like Scientists." The article highlights a study showing that startups using the scientific method generated significantly more revenue and were more likely to pivot away from unviable ideas. For the top 5%, this meant earning an additional €492,000 compared to those who didn’t apply this approach. So, how can you integrate the scientific method into your startup? 1️⃣ Test your assumptions Don’t just assume your idea will work. Test it with real customers and gather feedback. At Google for Startups, we create small pilot programs to avoid costly mistakes by learning early what works and what doesn’t. 2️⃣ Be ready to pivot Flexibility is key. If something isn’t working, be prepared to change direction. I’ve experienced this firsthand—by pivoting based on user feedback, we’ve turned potential failures into successes. 3️⃣ Use the scientific method Follow a structured process of observation, hypothesis, experimentation, and analysis. This methodical approach helps make informed decisions and drive continuous improvement. For practical application: 👉 Create an MVP Develop a basic version of your product to test your assumptions with real users. 👉 Run A/B tests Compare different versions of a feature to determine what performs best. 👉 Track your results Monitor your metrics to understand what’s working and what needs adjustment. The bottom line? Experimentation isn’t just a safety net; it’s a path to discovering what truly works for your startup. Whether you’re just starting out or looking to refine your approach, integrating the scientific method can be transformative to your startup. What’s your experience with using the scientific method in business?

From my executive search desk this week - Episode 4. Something I keep seeing come up in food businesses lately, and it’s worth paying attention to. More and more, when we’re working on senior roles in food companies, nutrition and food science keep entering the conversation in a very real way. Not as a side function or as a nice story for innovation decks. But as people who are genuinely influencing what gets built, what gets funded, and what gets killed early. This feels new for food but it makes complete sense. As consumers move away from traditional snacking and more toward health, wellness, and functionality, companies are being forced to ask harder questions. Not just: Will this sell? But: Is this actually good for people? Can we stand behind it scientifically? And what happens if we can’t? That shift changes who has a voice in the room. I’m seeing nutrition and food science leaders shaping innovation pipelines, product claims, and long-term strategy in ways that would have been rare even five years ago. If you’ve worked in pharma, this won’t surprise you. Research-led decision-making has always been part of that world. In food, it’s becoming unavoidable. And this is where I think both companies and leaders can get it wrong if they’re not careful. For companies, innovation can’t sit purely with commercial or marketing teams anymore. If scientific thinking only comes in at the end, you’re already late. For leaders on the commercial side, this is not about becoming a scientist or going back to school. It’s about being able to work credibly with people who think very differently than you do. The strongest commercial leaders I see right now are the ones who: 1. Understand enough nutrition and science to ask intelligent questions 2. Know when to push and when to listen 3. Can translate complex research into real business decisions That ability to bridge science and business is becoming a real advantage. From what I’m seeing, the future of food innovation is being shaped at that intersection. And the leaders who get comfortable there early will have far more influence over what comes next. If you’re hiring, think about how early science has a seat at the table. If you’re building your own career, it might be worth expanding your exposure beyond the purely commercial lens. That shift is already happening. #executivesearch

Technology scouting is an essential, yet challenging task in industrial R&D. Among millions of research articles published each year, how can companies identify the rare few with real commercial promise? A new study led by Prof Sharique Hasan at Duke University demonstrates how AI can help estimate the commercial potential of scientific research at the time of publication. The team trained large language models on over 420,000 scientific abstracts to predict whether a paper would eventually be cited by a "renewed patent", one a company finds valuable enough to keep alive. This serves as a proxy for a company's early belief that the research could drive economic impact. To validate the approach, the researchers compared the AI-generated scores with real-world tech transfer outcomes at Duke. The results held up: articles rated highly by the model were more likely to move forward into patenting, licensing, or commercialization. Even better, they’ve made the tool publicly available: https://scientifiq.ai/ This is a great example of AI accelerating a critical part of the innovation process. As companies increasingly rely on external science for breakthroughs, tools like this can make scouting faster and smarter. 📄 Measuring the commercial potential of science, Strategic Management Journal, May 5, 2025 🔗 https://lnkd.in/eUvZ4Qxt

Most biopharma providers we’ve spoken to spend hours sifting through papers, patents and clinical trials, hoping to uncover commercial opportunities. Here’s the problem I see with that: > Humans process research linearly i.e., reading each paper in full to extract insights. > AI processes research contextually i.e., analyzing thousands of papers in seconds to surface the most relevant findings. Here’s why AI is changing the game for business development teams in life sciences: 1/ AI identifies patterns across thousands of documents > Humans can read a handful of papers a day. AI can analyze millions. > It recognizes recurring keywords, experimental techniques, and funding trends across vast datasets. > This means less manual review, more actionable insights. 2/ AI understands commercial relevance, not just science > AI doesn’t just summarize, it prioritizes findings based on business impact. > It can surface research linked to clinical-stage companies, industry collaborations, and commercial applications. > Instead of scanning endless publications, BD teams get a filtered list of high-value prospects. 3/ AI tracks emerging research in real-time > Manual research is static, AI research is continuous. > AI flags newly published papers, active trials, and emerging patents relevant to your business. > This means your team sees opportunities before competitors do. 4/ AI cross-references multiple sources > A BD rep might read a single paper and miss its connection to industry movements. > AI links clinical trials, patents, and publications to map the full competitive landscape. > This is how leading biotech firms identify rising players before they make headlines. Manual research is slow and reactive. AI is fast and predictive. The teams leveraging AI-powered research aren’t replacing their scientists, they’re making them exponentially more effective.

🟥 From an industry perspective, which scientific research findings hold the greatest commercial potential? For scientific research findings to truly transcend the laboratory, they must possess clear market value and potential for industrial transformation. From an industry perspective, the most commercially promising research findings often possess three characteristics: they address real-world pain points, are scalable, and possess clear intellectual property barriers. First, the life sciences and healthcare sectors remain the focus of investment and industry attention. Gene and cell therapy, regenerative medicine, mRNA drugs, and diagnostic technologies based on single-cell and spatial omics are reshaping disease treatment paradigms. For pharmaceutical companies, technologies that extend their pipelines and address unmet clinical needs are most valuable. For example, novel therapies for cancer and neurodegenerative diseases often achieve rapid market response. Second, artificial intelligence and data-driven research findings are becoming the new darlings of the industry. Whether it's an AI-driven drug discovery platform or an intelligent medical imaging diagnostic system, they can significantly shorten R&D cycles and reduce costs. For the industry, such findings not only improve efficiency but also foster new business models. Third, breakthroughs in new materials and engineering are also crucial. Degradable medical materials, smart sensors, advanced manufacturing technologies, and brain-computer interfaces are all areas of significant industry interest. Once these technologies achieve large-scale production, they will be widely applied in medical devices, consumer electronics, and industrial manufacturing, driving cross-industry transformation. In addition, research findings related to environmental protection and sustainable development also hold enormous potential. Innovations such as green energy technologies, carbon capture and utilization, and biodegradable plastics not only align with policy guidance but also meet the market's urgent need for sustainable solutions. Overall, judging the potential of research findings from an industry perspective hinges not solely on technological leadership but also on their ability to solve practical problems, enter sustainable markets, and establish competitive barriers through intellectual property rights and clinical or engineering validation. Only findings that combine scientific depth with market breadth can truly achieve the transition from "paper" to "product." Keywords: Research Results Transformation, Commercial Potential, Life Sciences, Artificial Intelligence, New Materials, Sustainable Development

Most university spin outs fail for reasons that have nothing to do with science. Every top research institution produces breakthrough discoveries. Yet a significant share of those discoveries never reach a paying customer. This is not a science problem.  It is a commercial readiness problem. Across multiple studies of academic spin outs, failure patterns repeat. Teams overestimate market pull. Founders rely on technical merit to carry them forward. Universities rarely provide the operational muscle required to convert early IP into revenue generating companies. Through collaborations with Cambridge, Oxford, Stanford, and Harvard, I have seen this pattern up close. The ideas are rarely the issue. The gap is almost always the transition from proof of concept to a functioning business. When founders lack customer discovery, go to market strategy, regulatory guidance, or operational mentorship, even strong intellectual property can stall. This is why we built the five stage pathway called From Lab to Launch. 1. Validate technical feasibility beyond academic requirements. 2. Test real customer pain and market urgency. 3. Assemble a team with both scientific and commercial capability. 4. Develop a minimal solution and gather early customer feedback. 5. Build scalable operations supported by investment and advisory partners. Strong science plus structured commercial support significantly improves outcomes. When academic teams receive operational guidance early, the odds of survival rise sharply. If you work in tech transfer, research leadership, early stage investment, or academic founding, I welcome a conversation. The goal is simple:- help more breakthrough ideas reach real markets and avoid the avoidable failures that hold back scientific progress.

🚀 𝗙𝗿𝗼𝗺 𝗟𝗮𝗯 𝗕𝗲𝗻𝗰𝗵 𝘁𝗼 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗜𝗺𝗽𝗮𝗰𝘁: 𝗪𝗵𝘆 𝗠𝗼𝘀𝘁 𝗕𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝘀 𝗡𝗲𝘃𝗲𝗿 𝗟𝗲𝗮𝘃𝗲 𝘁𝗵𝗲 𝗟𝗮𝗯 Innovation is often tied to tangible outcomes: a patent, a product, or a new company. But the real challenge and the real opportunity lies in translating groundbreaking research from the laboratory into real impact. This is not about incremental improvements or chasing trends. It is about creating a pipeline where curiosity-driven science meets rigorous entrepreneurial execution. 𝗔𝘁 𝘁𝗵𝗲 𝗵𝗲𝗮𝗿𝘁 𝗼𝗳 𝘁𝗵𝗶𝘀 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝘀𝘁𝗮𝗿𝘁𝘂𝗽. A startup is not simply a vehicle for funding or a marketing label. It is a disciplined engine for de-risking uncertainty, aligning technical breakthroughs with real-world needs, and iteratively building value under extreme constraints. In essence, startups act as the laboratory’s extension into society, carrying ideas that are scientifically validated but socially untested into the world, where impact is measured not in citations but in change. This process requires a framework grounded in three intertwined principles: 🔬 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗮𝘀 𝗮 𝗡𝗼𝗿𝘁𝗵 𝗦𝘁𝗮𝗿 – The rigor of the research must remain uncompromised. Any shortcut to market without robust validation undermines both the science and the venture. Startups that succeed translate, not dilute, the original insight. 🛡️ 𝗦𝘆𝘀𝘁𝗲𝗺𝗮𝘁𝗶𝗰 𝗗𝗲-𝗥𝗶𝘀𝗸𝗶𝗻𝗴 – Every technology carries layers of risk: technical feasibility, regulatory barriers, market adoption. Successful translation requires methodical mapping of these risks and designing experiments and business models to resolve them sequentially. Entrepreneurial thinking becomes a form of scientific method applied to uncertainty. ⚡ 𝗜𝗺𝗽𝗮𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 – Speed matters, but it must be intelligent. Iteration should be informed by direct feedback from end-users, partners, and the ecosystem. The ultimate goal is not simply a product launch but measurable societal, clinical, or environmental outcomes. By embedding these principles, research-driven entrepreneurship can be replicated beyond luck, hype, or prestige. The university laboratory, often confined by publication cycles and funding silos, can become a cradle for ventures that scale science into systemic change. 🌟 My vision is to position this approach as a reference point for world-class institutions: not through self-promotion, but through the clarity of thought, rigor of execution, and depth of insight we bring to every translation effort. In doing so, we transform startups from uncertain bets into precise instruments for impact. Science finds its ultimate validation not in journals, but in the lives it improves. Curious to hear your thougths, and feel free to reshare if it resonates! #Innovation #Entrepreneurship #Startups #ResearchTranslation #Impact #ScienceToSociety #DeepTech #AcademicFounders

Being a researcher is wild because you’re always balancing two worlds... 🔍 The 𝘢𝘤𝘢𝘥𝘦𝘮𝘪𝘤 𝘸𝘰𝘳𝘭𝘥, where precision, methodology, and rigor reign supreme. 💼 The 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘸𝘰𝘳𝘭𝘥, where speed, impact, and ROI matter more. The challenge? 𝗡𝗲𝗶𝘁𝗵𝗲𝗿 𝘀𝗶𝗱𝗲 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝘀𝗽𝗲𝗮𝗸𝘀 𝘁𝗵𝗲 𝗼𝘁𝗵𝗲𝗿’𝘀 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲. I’ve been in plenty of meetings where I knew the research was airtight, but if I didn’t frame the findings in a way that clicked with their goals, then forget about it. Here’s what can help bridge the gap: 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 '𝗦𝗼 𝗪𝗵𝗮𝘁?’: Before diving into the data, lead with the business impact. Answer... why should they care? 2️⃣ 𝗞𝗲𝗲𝗽 𝗶𝘁 𝘀𝗵𝗼𝗿𝘁 𝗮𝗻𝗱 𝘃𝗶𝘀𝘂𝗮𝗹: Swap dense decks for exec summaries with engaging takeaways and visuals that tell the story fast. 3️⃣ 𝗕𝗿𝗶𝗻𝗴 𝘁𝗵𝗲𝗺 𝗮𝗹𝗼𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗿𝗶𝗱𝗲: Involve stakeholders early, whether it’s through a kickoff call or sneak peeks during the process. It builds trust and reduces surprises. 4️⃣ 𝗦𝗽𝗲𝗮𝗸 𝘁𝗵𝗲𝗶𝗿 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲: Use terms your audience uses. Swap 'statistical significance' for ‘confidence' or 'what you can count on.' Balancing research rigor with business needs is messy, but when you get it right, it’s magic. ✨