Listening to the Market: Unclogging the Path from Lab to Commercialization

Beside basic research - the essential bedrock that every scientific ecosystem must fund and cherish - there exists another layer of research: one that targets the user from day one. This layer demands a fundamentally different approach and execution strategy.

Market-driven research does not mean only studying what can be sold instantly. Every scientific nation needs long-term, fundamental research - the kind that might only reveal its true applications ten or twenty years down the line. But parallel to that foundation, we need another layer: research designed from the very beginning with a crystal-clear image of the end-user. This is the cradle of products, solutions, and science-and-technology enterprises. It is also what restores societal faith that science does not sit idle.

This post outlines a practical approach to bridge this gap: (1)shifting the starting point of our research questions. (2)Learning from the NSF I-Corps model -the gold standard for pulling scientists out of the lab. (3)Walking through a 7-step market-driven framework alongside de-risking mechanisms that Vietnam can implement right now.

Shifting the starting point of our research questions.

In the traditional academic playbook, a research team usually kicks off with a scientific hypothesis: a new technology, a novel material, or an optimized process. The project's ultimate goal is to prove that this technology works, remains stable, and outperforms existing alternatives. While this linear path is vital for advancing human knowledge, hitting the market requires translating that initial question into an entirely different language: "Who out there actually faces this problem, how are they currently solving it, and are they satisfied with their current solution?"

It sounds deceptively simple, yet it is the exact step most research teams bypass. We routinely guess on behalf of the user: “Farmers surely need A, enterprises must lack B, and district-level hospitals are definitely starved for C,” instead of going out and asking them.

A market-driven approach demands that you do something counterintuitive to academic instinct: leave the laboratory, talk to potential users or buyers, and let them describe their pain points in their own words. If they don't view it as an urgent problem, it’s probably not a viable starting point for commercialization - no matter how scientifically fascinating it might be.

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I-Corps: Cây cầu buộc nhà khoa học phải gặp thị trường

In the United States, the National Science Foundation’s (NSF) I-Corps program was born out of a blunt observation: Why does so much promising lab technology fail to become a commercial product or a viable company? The answer wasn't weak technology; it was that scientists were never taught how to listen to the market.

I-Corps does not teach engineering. I-Corps teaches dialogue.

Under this framework, a research team - typically consisting of a Principal Investigator (the scientist), an Entrepreneurial Lead (often a postgraduate student), and a seasoned Commercialization Mentor is pushed through a rigorous process:

- They must personally interview dozens, sometimes hundreds, of potential customers within a matter of weeks.

- They must describe their technology in plain language that the end-user understands, stripping away all academic jargon.

- They must log real, unfiltered feedback, including the devastating phrase: "We don't need this."

- They must adjust their hypotheses. If customers care nothing about Application A but react enthusiastically to Application B, then Application B must be prioritized.

The most brilliant piece of the I-Corps model is its funding mechanism. The program doesn't say, "Go do your research and we’ll give you money to build a product later. Instead, subsequent funding only unlocks when the team proves they have found a validated market problem with people willing to pay for a solution. Funding is granted based on evidence of demand, not just scientific novelty.

Where does this approach de-risk the ecosystem?

- Public budget isn't poured into directions that yield no clear social or economic output.

- They don't have to launch a company prematurely just to "prove they are serious." They are given a safe space to learn and adjust first.

- Projects that have graduated from I-Corps typically enter the fundraising arena with relatively clear initial market data, rather than just blind "scientific faith."

In short, I-Corps does something fundamentally basic: it forces scientists to talk to end-users before receiving additional funding. This is a point that Vietnam can learn from very quickly. We do not necessarily have to copy the entire American model, but the philosophy of "funding after validation" - funding after initial market verification - can entirely be piloted at the ministry, university, or even provincial level. If executed correctly, this will be a new kind of de-risking mechanism: risk does not disappear, but it is spotted early.

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A classic case study from programs like I-Corps in the US: a biomedical research team believed their new technology would help hospitals save diagnostic time. When they stepped out to interview hospital administrators, they uncovered something no one in the lab had anticipated: hospitals didn't prioritize diagnostic speed above all else; they prioritized legal liability mitigation in case of a misdiagnosis. This meant the "value" the research team believed was crucial (faster) was not what the hospitals were willing to pay for (safer, legally bulletproof). That piece of data alone was enough for the team to recalibrate their priorities. 

This is precisely the first step of market-driven research: instead of proving that a technology is valuable in general, prove that the technology resolves a specific pain point for a specific group of people within a specific context. And to achieve that, you must listen to them speak in their own language, not yours.

Radical Honesty Regarding Market Scale and Conditions

Many research ideas sound incredibly logical on a technical level, but become highly difficult to deploy when dropped into real-world socio-economic contexts. A wastewater treatment solution for agriculture might perform beautifully in a lab, but if a small-scale shrimp farmer has to shell out an upfront capital cost that exceeds their entire seasonal profit, nobody will adopt it; or if the licensing procedures are more complex than the farmer's administrative capacity, the solution will stay on paper - not because it is bad science, but because it does not fit the existing cost-and-procedural structures.

Market-driven research demands a level of honesty that can feel brutal: not every problem is a commercial problem. There are severe societal issues that currently lack an economic model to sustain a market-driven solution. Those cases are absolutely still worth pursuing, but they should be viewed as public service mandates, free from the expectation of "generating daily sales." Conversely, for problems backed by genuine cash flow (for example: traceability for agricultural exports, energy loss reduction in factories, or remote health sensors for understaffed clinics), analyzing market size, regulatory frameworks, and existing competitors is the step that keeps a research team from driving into a dead end.

In other words, before running any further, you must be able to answer: "If this product exists, who will pay for it, out of what budget, and through what mechanism? "If you cannot answer this, it is a warning sign that you need to adjust, not a sign to give up. And adjusting your research direction at this stage is always far cheaper than adjusting after pouring three years of effort into developing the technology.

Bringing Market Analysis Back to the Drawing Board

This is where many scientists feel the most uncomfortable because it forces a change in thinking habits. We grew up in an environment where "changing direction midway" is often stigmatized as "lacking maturity." Meanwhile, a market-driven mindset views adjusting a project based on real-world data as completely normal - even a healthy sign.

In the US, teams participating in I-Corps usually go through this exact moment: they start the program with a fairly confident statement ("we are developing solution X for market Y"), and end the program with a completely different statement ("market Y doesn't really care, but segment Z is crying out for help and ready to pay; we will pivot technology X toward Z first"). If you think according to old habits, that is a "project deviation." If you think in the new way, it is precisely the optimization of the commercialization pathway: finding a real output for your knowledge.

This is also the time to start thinking about two factors that may sound "unscientific" but are actually critical: intellectual property and the value model.

Intellectual Property (IP) does not always mean "file a patent as fast as possible." Sometimes, rushing to register too early locks us into a specific implementation before we truly understand the right application. On the flip side, you cannot just freely share everything with a partner enterprise without any protective framework; because by the time the product begins to yield commercial value, the question of "who owns this technology?" becomes sensitive and can easily ruin the partnership. The approach of universities like UCL (University College London) or Cambridge is to sit down with scientists very early on, mapping out feasible scenarios for that technology ahead of time: licensing? forming a spin-off company? joint development with an industrial partner? And for each scenario, who holds what rights, how benefits are shared, and what the responsibilities are. Clarifying this upfront is not about applying pressure, but about building trust. When everyone knows how the pie will be shared beforehand, they are willing to go much further together.

Meanwhile, the Value Model is a question that sounds like "pure business" but is actually directly tied to the survival of applied science: specifically, what kind of value does your solution create for the user—cost savings? yield optimization? legal risk reduction? unlocking new export markets? increasing diagnostic accuracy? reducing operational manpower? Each type of value leads to a different pricing strategy. If you help farmers increase crop yields, the way you convince them will be entirely different from helping a hospital reduce the risk of lawsuits. Knowing what kind of value you create dictates how you tell your product's story. Telling this story is not a PR stunt; it is the ultimate bridge between the language of science and the language of the buyer.

When the three layers mentioned above - real demand, real market conditions, and real delivered value - are clear enough, only then is it worth stepping into the stage that many are eager to rush into: prototyping.

Building a prototype (a workable version, even if imperfect) is not about showing off achievements at a conference, but about putting it into the hands of potential users as early as possible. The goal of a prototype is not to prove that "it runs," but to observe "whether users actually use it." This is the step where scientists, engineers, and business-minded individuals should sit side-by-side. It serves as the first reality check to see if these three worlds can truly talk to each other. When a nurse at a provincial hospital says, "I don't have time to click through 5 steps like this, "that isn't a petty complaint—it is critical design feedback. When a shrimp farmer says, "You can't expect me to log data every day, I'm too busy, "that is a clear signal that the product needs automated sensors instead of manual input. At this point, the market ceases to be an abstract concept; it becomes a specific person holding your product and telling you the raw truth.

From prototype to commercial product lies a stretch of road that easily breeds illusions. Many teams think: "As long as the technology is good, an enterprise will swoop in, take over, and sell it for us. "Real-world data from elite global universities shows it is never that simple. Even highly professional units like UCLB in the UK invested nearly ten years of patient capital before yielding major spin-out deals, and throughout that entire period, the university still had to pay salaries to keep the technology transfer department alive. In other words, even under ideal conditions of capital, institutions, and markets, commercialization is never a matter of "just hand it over to a business and you're done." It is a journey of companionship.

This is exactly where many within the Vietnamese ecosystem feel hesitant: who will be the companion? The honest answer is: it cannot be just one side. The university cannot simply conduct research and push it out the door; the enterprise cannot demand a flawless product that perfectly fits its existing production line from day one; and regulatory bodies cannot simply demand "immediate proof of economic efficiency." If these three parties do not sit down together, the product will easily face an early death at the handover stage - not because the technology is bad, but because no one has the patience to walk alongside one another.

Therefore, there is a small but highly significant shift that many universities around the world have adopted: instead of telling lecturers and scientists to "go start a company," they invite them to participate in initial discussions with enterprises in the role of experts, advisors, or solution co-designers. The university steps in to underwrite legal liability, permits the use of the institution's reputation when signing consulting agreements, and streamlines the contracting process. This may sound bureaucratic, but it is psychologically vital: scientists no longer feel pushed out of their comfort zone; they feel protected when stepping outside. And it is precisely these instances of "stepping out with a companion" that forge the first linkages between the lab and the market - in a way that is far more natural and sustainable than administrative mandates.

If we look at the entire process just described - from identifying the right market problem, verifying whether that market truly exists economically and legally, adjusting the research direction based on real-world data, clarifying intellectual property rights and benefit-sharing mechanisms, building prototypes to listen to actual user feedback, and then sitting down with enterprises to map out the go-to-market path - we will see an important truth: all of these steps are risk-mitigation mechanisms. The following four de-risking mechanisms are those already utilized in mature ecosystems:

- De-risking Cognitive Perception for the Scientist

I-Corps-style programs create a safe space for scientists to learn the language of the market without being forced to "launch a startup immediately." This is a skill-building phase, not a period of being pushed out of the academic system.

- De-risking Finance for the State

Funding based on the principle of "post-initial-demand-validation" ensures public resources are not funneled into directions that nobody can use. The State does not need to inject massive capital from the start; it deploys small amounts to harvest authentic data, then decides whether to unlock major funding..

- De-risking Legalities and Intellectual Property

Clarifying IP ownership, benefit-sharing mechanisms, and equity splits when forming a spin-off company helps prevent bitter, late-stage disputes between scientists and universities. In the UK, UCLB endures sustainably not just because they have excellent biomedical technology, but because they have a benefit-sharing framework clear enough for all parties to feel respected.

- De-risking Societal Expectations

One of the critical lessons from universities like Cambridge or UCL is the need to be honest about the fact that commercialization is a multi-year, even multi-decade process. If we apply pressure for "immediate revenue," we will push scientists into a defensive shell, and they will close their doors. Conversely, if we treat dialogue with enterprises, initial consulting contracts, and solution co-designs as milestones with social value equal to "launching a company," we keep them in the game far longer.

And by doing so, cultural change will unfold naturally, rather than by coercion.

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Market-Driven Research: Seven Steps Not Just for Memorization, But for Operation

Market-driven research does not mean "go ask customers what they want and then build exactly that," because if we stop there, we will never achieve breakthrough innovation. The seven steps below should be understood as an iterative loop - where each step acts as both a technical activity and a risk checkpoint.

Step 1: Identify Market Need

Instead of starting with "I have technology X," the process begins with "Where is there a specific problem that is currently not being solved well enough?" It could be farmers needing to manage crop pests but unable to use high-residual pesticides due to export barriers; provincial hospitals lacking diagnostic equipment because current machinery costs are too high; or a seafood supply chain needing export-compliant traceability tracking, but current solutions are too complex for small-scale households.

This phase requires a healthy habit: interview, observe, and log data rather than speculating on behalf of the user. More importantly: write down that need using the user's exact words, completely free of translated technical jargon.

This is the first risk-mitigation checkpoint: if we cannot describe the problem in the language of the person suffering from it, it means we do not understand the problem yet.

Step 2: Market and Competitive Analysis

Once the problem is understood, the next step is to be honest about the surrounding economic landscape. How large is this market? Who is already trying to solve it? Are there regulatory hurdles, international standards, or licensing requirements that could hinder the deployment of the solution?

Many research projects freeze at the commercialization stage simply because the answers to this step were ignored. For example: an agricultural wastewater treatment solution might be brilliant technically, but if the installation cost per site is too high compared to the producer's profit margin, it cannot be called a "commercializable solution" - it does not fit the economic structure of the person who actually has to pay for it.

Step two serves as a financial and regulatory risk-mitigation checkpoint. It keeps a team from investing years into a direction that is nearly impossible to get approved, or impossible to sell because it fails to align with the cost framework.

Step 3: Adjust R&D Direction Based on Market Data

This is the most difficult stretch for many scientists: accepting a change in the research question.

The traditional approach is often: "We have come this far, please grant us more funding to finish the rest." The market-driven approach, however, asks: "Which part of this research actually hits a large enough demand to become a sustainable product or service? Which part should be temporarily set aside for deeper academic research that doesn't need immediate commercialization?"

This does not mean science is being entirely "commercialized." It simply means deployment resources - such as the research team's time, prototyping budgets, and opportunities to connect with partner enterprises - are prioritized toward the branches that possess the highest probability of survival in the real-world environment.

De-risking here means reducing opportunity risk: we stop pouring effort into branches that the market is not yet ready to absorb, focusing instead on the branch with the highest chance of survival.

Step 4: Protect Intellectual Property (IP)

In Vietnam, this step is usually either neglected or misunderstood.

Neglecting it is dangerous: without protecting IP at the right time, the research team loses control over their own technology when entering partnerships with enterprises. Misunderstanding it is equally dangerous: sometimes we rush to register patents too early or too broadly, wasting time and money, while the core technology's specific application remains unclear.

The true meaning of IP protection within a market-driven model is to:

- Establish clear boundaries regarding who owns what to prevent future disputes.

- Create conditions for transparent negotiations when entering collaborative pilot phases with enterprises.

- Build a foundation for future licensing, revenue-sharing, or contributing intellectual assets as equity into spin-off companies.

This serves as a relationship risk-mitigation checkpoint: clear IP preserves trust between scientists, universities, and enterprises.

Step 5: Develop and Validate a Prototype/Minimum Viable Product (MVP)

This is the phase that I-Corps highly values: there must be something tangible for users to touch, test, and provide feedback on. This prototype does not need to be perfect. In fact, it should not be perfect. It only needs to be sufficient for users to tell us: "Is it useful? Is it usable? Is it worth the money?"

At this stage, scientists, engineers, and ideally someone with a business mindset sit at the same table. This marks the first common language shared among three distinct worlds: research, engineering, and the market. For Vietnam, training research teams to successfully navigate this exact moment is invaluable.

Step five mitigates both technological risk and user acceptance risk. Instead of forcing an "unripe" product onto the market, we let the market participate in raising it.

Step 6: Architect the Commercialization Strategy

Many scientific projects stumble here, not at the technology level. The question shifts from "Can we build it?" to "Who pays whom, and through what mechanism?"

Three main pathways typically emerge:

1. Selling the technology usage rights to an enterprise that already possesses an established market.

2. Forming a spin-off company with equity shared among the university, the research team, and potentially a seed investor.

Collaborating with a large industrial partner - a path particularly common in sectors requiring massive infrastructure investment such as novel materials, medical devices, or energy.

During this stage, the research team must outline a go-to-market roadmap, pricing models, distribution channels, and fundraising plans. This is not just "the sales department's job," because if these answers remain unclear, the product can face an early death despite having excellent technology.

Step six mitigates commercial execution risk: it forces the entire system to look at financial realities instead of drowning in technological euphoria.

Step 7: Launch and Scale

Reaching this point does not mean the scientist's role is over. In fact, at many top-tier universities (in the UK, US, and South Korea), scientists continue to accompany the project during the initial post-launch phase, serving in at least three key capacities:

- Acting as the ultimate custodian of core scientific and technical integrity (ensuring the product is not "distorted" under intense commercial pressure).