Every spring, science fairs across America perform a familiar ritual. Tables are lined with poster boards. Judges walk the aisles. Ribbons are awarded. Somebody gets photographed with the dean, the vice president or the local newspaper. Nobody asks whether the volcano made from baking soda has a five-year revenue forecast.
That’s because the purpose of a science fair is to celebrate discovery. But here’s the problem. Sometimes we manage research departments the same way.
Universities and corporations employ extraordinarily talented researchers, occupy expensive laboratories, purchase specialized equipment and consume billions of dollars pursuing new ideas. In fiscal year 2024 alone, American universities reported $117.7 billion in research and development expenditures.
That is not a science fair. Its an industry. And perhaps we should start managing it like one.
The Difference Between Discovery and Impact
Research organizations are remarkably good at counting things.
Grant dollars, papers published, and patents applied for. Those large come from invention disclosures, departmental awards, and conference presentations. Things things matter. But none necessarily means that something useful happened.
A patent is not a product, just as a publication is not a customer, and a prototype is not manufacturing. A startup document doesn’t equal a company, and even a license agreement is really just somebody promising to try. The federal government recognized this distinction decades ago.
The policy behind the Bayh-Dole Act does not merely encourage universities to patent federally funded discoveries. It specifically talks about utilization, commercial collaboration and making inventions available to the public. In other words: Discovery isn’t the finish line.
Xerox Won the Science Fair
Few companies demonstrate this better than Xerox. At Xerox PARC, researchers helped create technologies that read like they accidentally discovered the next forty years of computing. Networked computers, graphical user interfaces (GUI), ethernet, and laser printers. If the goal had been to win the world’s greatest science fair, Xerox would need a larger trophy case.
The problem was what happened next.
Xerox struggled to convert many of those extraordinary inventions into businesses. Researchers could create astonishing prototypes. But they lacked an equally effective mechanism for deciding what should happen to these successful inventions afterward. The Xerox Alto is a perfect example.
Xerox demonstrated a computer in the 1970s containing many features we now associate with modern personal computing. But the company never successfully turned the Alto into a mass-market computer.
The lesson isn’t that Xerox executives were stupid. (Quite the contrary.) Xerox successfully commercialized some PARC technologies, (most notably laser printing), and research inevitably produces ideas that don’t fit the company creating them.
The point is that invention and commercialization are different capabilities. Xerox had built an extraordinary invention machine. It had not built an equally extraordinary conversion machine.
Kodak Invented Its Own Replacement
Kodak provides an even more fascinating example. In 1975, Kodak engineer Steven Sasson built one of the earliest self-contained digital cameras. Think about that for a moment.
Kodak didn’t miss digital photography - it largely invented it. The research department had done exactly what a great research department was supposed to do. Unfortunately, the invention threatened the business Kodak already had. Kodak made money selling film, photographic paper and chemicals.
Digital photography potentially eliminated much of that ecosystem. So the problem wasn’t technological. It was economic. Kodak created the future while enjoying enormous financial incentives to protect the past. That’s why commercialization cannot simply begin when somebody hands a patent disclosure to a technology-transfer office.
The potential consequences of an invention need to be considered while the research portfolio is still being managed. Sometimes the most valuable discovery reinforces an existing business. Sometimes it destroys one. Sometimes it requires a completely different customer.
Or manufacturing process. Or distribution system. Or business model. Or company. Research management should be designed to recognize the difference.
The Laboratory Has Customers
Calling research a business does not mean every laboratory needs to produce quarterly profits. It means recognizing that research has customers—even when those customers aren’t obvious yet. For a university, those customers might ultimately be companies, patients, government agencies, manufacturers, entrepreneurs or communities.
The return might be a drug, or a software platform, or a manufacturing process. That could mean a startup, a standard, or maybe a license. Or perhaps knowledge that enables somebody else to build something valuable. But somewhere inside the research organization there needs to be a theory of conversion.
Money and talent go into one end, knowledge comes out the other. Then what?
Which discoveries deserve more investment? Which need customer discovery? Which require regulatory expertise? Which should be patented? Which belong in startups? Which should be licensed? Which should be released openly?
And perhaps most importantly, which should stop? Businesses call this portfolio management. Research organizations sometimes find that conversation uncomfortable.
Stop Measuring the Parade
Universities naturally celebrate what is easiest to count. Grant awards make wonderful press releases.
So do patents, startups, and big checks. Commercialization is much messier. It can take years. Outside companies become involved. Markets change. Technologies fail. One successful therapy or manufacturing process may create more impact than hundreds of projects that never leave the laboratory.
That doesn’t mean we shouldn’t measure commercialization. It means we should measure the pipeline rather than the parade. Imagine looking at research as a funnel. At the top are research expenditures, projects and discoveries.
Then come validated problems, prototypes, disclosures and protectable intellectual property. Farther down are industry collaborations, licenses, startups and follow-on investment. At the bottom are products, services, treatments, manufacturing processes and measurable public benefits.
Most projects shouldn’t make it through. That’s fine. Good businesses kill weak projects too. The important thing is understanding why projects disappear from the funnel, and making sure the strongest ones move forward deliberately rather than accidentally.
Build the Conversion Machine
Universities already possess most of the pieces. Research administration, sponsored programs, industry engagement, and technology transfer. Then there’s entrepreneurship, economic development, faculty expertise, students, and labs, What we often don’t have is a system connecting them early enough.
Too often, technology transfer enters the process after the research decisions have been made, the grant money has been spent, the graduate students have graduated, and somebody has dismantled the prototype. Then a disclosure arrives. “Here’s what we invented.” And the commercialization team is expected to find somebody who wants it.
Now imagine reversing that. Researchers would still decide what questions deserve investigation. Basic research would remain basic research. Academic freedom would remain academic freedom. But researchers could gain earlier access to information about unmet needs, industry problems, regulatory pathways, manufacturing challenges, potential partners and startup capital.
Research leadership, meanwhile, could see which types of projects actually move from discovery to use. That feedback loop might change everything.
What Happens After the Ribbon?
There is nothing wrong with a science fair. They create curiosity, celebrate experimentation, and encourage people to show each other something interesting. Universities should probably do more of that. They simply shouldn’t confuse the fair with the enterprise.
The morning after the symposium, somebody needs to ask, ‘What happens next? Is there a customer? A licensee? A manufacturer? A startup? A clinical pathway? Does the project need more development? Or has it reached the end of the road?
Xerox showed us that an organization can invent the future and still fail to capture much of it. Kodak showed us something even stranger. An organization can help invent the future, and then discover that the future threatens the business it already has.
The lesson isn’t that research should become less adventurous. It’s almost the opposite. If we’re going to spend billions of dollars searching for extraordinary discoveries, we should build equally extraordinary systems for recognizing what happens when we find one.
The ribbon celebrates the discovery. The Big Idea is building what comes next.
