Editor’s note: This is the 23^rd article since May 20, 2026 in an ongoing series by Dr. Andrew Maxwell, the Bergeron Chair in Technology Entrepreneurship in the Lassonde School of Engineering at York University. Every week – and occasionally every other week – we’ll present a new article by “Innovation Doctor” Maxwell, in a series whose wide-ranging and incisive themes encompass: Canada and innovation policy; productivity and industry; innovation frameworks; AI and higher education; research and intellectual property; technology adoption; entrepreneurship and commercialization; universities and higher education; entrepreneurship education; and AI and the future of work. 

Over the past few weeks, I’ve been asked to review several proposals, commercialization programs and academic papers, all concerned with improving how research is translated into economic or societal value.

The proposals are often thoughtful, the research is frequently excellent, and the ambitions are worthwhile. But I’ve noticed something that increasingly concerns me.

Many begin with assumptions about commercialization that I believe are fundamentally wrong.

Some assume that developing better technology will naturally lead to commercialization. Others treat commercialization as something that happens after research is completed. Some focus on patents, licensing or startup creation as measures of success, while others propose new funding programs without adequately understanding why existing approaches struggle to turn promising inventions into innovations that people actually use.

The problem isn’t necessarily the proposed interventions. It’s the assumptions on which they’re based.

And if we misunderstand what commercialization is, how can we expect to design effective programs to improve it?

After more than twenty-five years working with researchers, entrepreneurs, universities and commercialization programs, I’ve come to see commercialization rather differently. It is not an automatic consequence of good research or superior technology. It is a journey of progressively identifying and reducing the uncertainties that stand between an invention and its adoption.

That journey requires us to understand users, their needs, their decisions, and the circumstances in which they might choose to change. It requires us to develop technology, explore business models, test assumptions and adapt our plans as evidence emerges.

So I thought it would be useful to write down ten principles that I repeatedly return to in my own work. They challenge some common assumptions about commercialization and might provide a useful starting point for anyone trying to improve commercialization outcomes.

They are not intended as 10 more steps in a prescribed process. Rather, they are 10  myths worth challenging before we design the process.

1. Invention is not commercialization. Adoption is.

An invention creates a possibility. Commercialization occurs when someone chooses to adopt and use a novel solution to create value.

Between these two points lies a journey of identifying and reducing uncertainties about the technology, the users, the business model and the conditions required for adoption. Patents, publications, licensing agreements, funding and startups may all contribute to that journey, but none independently demonstrates that adoption has occurred.

This distinction matters because we often measure the activities undertaken to commercialize research rather than whether commercialization is actually happening.

First adoption provides important evidence of success, while sustained use and diffusion determine the eventual scale of the benefits.

Commercialization is not about what we invent. It is about what people choose to use.

Further reading: If You Start Commercializing Your Research When It’s Finished, You’re Missing the Point

2. Commercialization shouldn’t follow research. It should help shape it.

The conventional approach assumes that researchers develop technology, and commercialization specialists subsequently find applications for it. But by then, many of the decisions that determine whether the technology is useful or adoptable have already been made.

Engaging with potential users while research is underway can reveal problems, constraints and opportunities that researchers might otherwise overlook. It can influence which technical questions are investigated, what performance characteristics matter, how much a solution needs to cost, and what conditions must be satisfied for it to be used.

Users may not know what technology they need, but understanding their experiences, motivations and challenges can reveal research questions we would never discover in the laboratory.

This does not mean every research project requires an immediate commercial application. It means that when adoption is an intended outcome, understanding users should inform research while there is still an opportunity to change its direction.

The opportunity is not simply to commercialize research earlier. It is to use adoption insights to conduct better research.

Further reading: Are We Asking the Wrong Question About Research Commercialization?

3. Technology doesn’t decide to adopt. People do.

We devote enormous effort to understanding how technologies work, but often surprisingly little to understanding why someone would choose to use them.

Adoption is fundamentally a behavioural decision. It depends on motivations, incentives, habits, perceived benefits, trust, uncertainty and the consequences of changing from existing practice.

Understanding these factors requires engagement with real users: listening to their experiences, observing their behaviour, understanding their circumstances and testing how they respond to possible solutions.

And listening does not mean asking users what technology they want. People may be excellent at describing their problems without being able to imagine unfamiliar solutions.

Technical expertise tells us what a technology can do. Behavioural understanding helps us discover what will motivate someone to adopt it.

Commercialization is not simply an exercise in engineering or selling. It is an exercise in understanding human decisions.

Further reading: The Trust-to-Adopt Gap

4. Better technology isn’t always the better choice.

Technological superiority does not guarantee adoption.

People do not evaluate new solutions in isolation. They compare them with what they already use, including the option of doing nothing. They assess not only potential benefits but also costs, risks, disruption, learning requirements and the difficulty of integration.

An innovation offering significantly better technical performance may still be unattractive if adopting it requires extensive training, expensive infrastructure, new organizational procedures, or unacceptable risks.

Sometimes the most valuable innovation is not another technical improvement. It is reducing the difficulty of adoption through simpler implementation, compatibility with existing systems, different pricing, or a less disruptive way of delivering the same benefits.

And we should remember that existing solutions are themselves competitors, even when they appear technologically inferior.

Successful commercialization requires more than a better technology. It requires a solution whose additional value justifies the effort and risks of changing.

Further reading: Perhaps Innovation Needs More Constraints, Not Fewer

5. The user may say yes. The system can still say no.

We frequently talk about finding the customer or understanding the user, as though adoption involves a single decision-maker.

In reality, the person who benefits from an innovation may not be the person who pays for it, approves it, implements it, or bears responsibility if something goes wrong.

Multiple stakeholders can influence adoption, each with different motivations, incentives, concerns and authority. An enthusiastic user may be unable to proceed because of procurement restrictions, regulatory requirements, incompatible systems, financial constraints, or resistance elsewhere in the organization.

This is why understanding the wider stakeholder system is essential. We need to identify whose behaviour must change, whose approval is required, who bears the costs and risks, and what evidence each stakeholder needs before they can proceed.

Their concerns are not necessarily obstacles to overcome through persuasion. They may reveal problems with the solution, business model, or proposed adoption pathway that need to be addressed.

Winning over the user is important. Understanding everyone who can enable or prevent adoption may be just as important.

Further reading: Two Ears, One Mouth

6. Technology readiness is not adoption readiness.

Technology Readiness Levels help us understand technological maturity. They do not tell us whether someone is likely to adopt the resulting solution.

A technology may be fully functional and still face significant barriers involving user motivation, economic value, competing alternatives, implementation, organizational decision-making, or regulatory requirements.

Adoption Readiness provides a complementary framework for examining these conditions. It brings together technical, behavioural, economic, operational, organizational and stakeholder considerations to identify which factors support adoption and which still create uncertainty.

The purpose is not to produce another score or checklist. It is to discover which unresolved factors matter most and determine what research, experimentation, engagement, or redesign might address them.

That could involve improving technical performance, but it might equally involve changing the business model, reducing integration costs, finding a more motivated first user, or resolving a regulatory barrier.

A technology can be ready for deployment while the conditions for its adoption are nowhere near ready.

Further reading: From Fragmented Funding to Disciplined Venture Creation

7. A great invention doesn’t necessarily need a startup. It needs a pathway to adoption.

We often assume that successful commercialization of university research involves creating a new business. But venture creation is only one of several possible pathways.

Licensing, partnerships, integration into existing organizations, and new ventures can all enable adoption. The appropriate choice depends on the technology, the users, the competitive environment, the capabilities required and the economic and organizational conditions involved.

The business model matters just as much. Selling a product, licensing its use, providing a service, offering a subscription, or charging according to usage can create very different incentives and barriers for potential adopters.

Choosing a startup too early can introduce additional uncertainties about financing, management, distribution and execution that might be avoided through another approach.

The objective should therefore be to explore alternative pathways and business models, gathering evidence about which will make adoption most likely.

A startup is one possible means of commercialization. It should never be confused with the objective.

Further reading: What If a Startup Is the Wrong Answer?

8. Assumptions are inevitable. Treating them as facts is optional.

Commercialization requires decisions under uncertainty, which makes assumptions unavoidable.

We assume we understand the problem, know who will benefit, believe the technology will work, anticipate how much users will pay, and expect particular stakeholders to support adoption.

The danger is not making assumptions. It is allowing them to become the basis for significant decisions without recognizing or challenging them.

A disciplined commercialization journey makes assumptions explicit, identifies those whose failure would most seriously affect the project, and designs experiments to test them.

Engaging users is essential, but listening to what people say is only part of the evidence. Observing what they do, testing actual behaviour, and securing meaningful commitments can provide stronger evidence of their willingness to adopt.

The purpose of experimentation is not to confirm that we were right. It is to improve our understanding so that we can make better decisions.

The most useful evidence is sometimes the evidence that changes our minds.

Further reading: Why Evidence Beats Intuition: Turning Innovation Assumptions into Actionable Insights

9. A commercialization plan isn’t a promise. It’s a hypothesis to test.

The journey from idea to adoption is rarely a straight line.

We begin with incomplete knowledge, make assumptions, encounter unexpected problems, discover new opportunities, and learn things that challenge our original plans. We may need to change the technology, the user, the value proposition, the business model, or the entire direction of the project.

A structured commercialization process helps us navigate this uncertainty. Its value is not in specifying an inflexible sequence of activities, but in helping us identify what we need to learn next and when we should continue, strengthen, pivot, or stop.

Many commercialization efforts will not achieve their original objectives. That is a consequence of tackling genuinely uncertain opportunities. But failure does not automatically produce learning.

We need to understand why something did not work and how that evidence should influence the next decision. Recognizing an unsuccessful approach early can save substantial resources and create knowledge that improves future attempts.

Successful commercialization is not about faithfully executing the original plan. It is about making better decisions as the evidence changes.

Further reading: Why Failure Is So Important — and So Hard to Admit

10. Commercialization isn’t a handoff. It’s a capability we need to develop.

Commercialization requires skills that are distinct from, but complementary to, those needed to conduct excellent research.

Understanding user needs, investigating adoption decisions, identifying stakeholders, developing value propositions, exploring business models, and making evidence-informed decisions under uncertainty are all learnable capabilities.

Yet our conventional approach often assumes that researchers can complete their work and transfer the results to someone else who will make commercialization happen.

This overlooks the value of developing commercialization capabilities within research teams themselves. Engaging with users, working across disciplines, experimenting with alternative approaches, and reflecting on evidence can improve research decisions while developing skills that contribute to future projects.

Not every researcher needs to become an entrepreneur or acquire every commercialization skill. But research teams need access to these capabilities, and opportunities to develop them through participation in the journey.

Embedding commercialization into research can improve the likelihood of adoption today while developing the people and capabilities needed to commercialize tomorrow’s discoveries.

Further reading: Beyond Patents: Rethinking Canada’s Approach to Commercializing University Research

If we misunderstand commercialization, how can we improve it?

These 10 myths are not an argument against research excellence, patents, technology-transfer offices, startups or commercialization funding.

All can contribute to successful commercialization.

The problem arises when we confuse these activities and inputs with the outcome we are trying to achieve, or assume that strengthening one component will automatically improve the entire journey.

This is what concerns me when I review proposals intended to enhance research commercialization. Too often, the proposed solution is clear before the commercialization problem has been properly understood.

More funding may help. Better intellectual property management may help. More experienced entrepreneurs, stronger incubators, or new commercialization programs may help.

But help with what?

If we have not understood why users are not adopting a particular innovation, what uncertainties remain unresolved, or which conditions are preventing adoption, how can we know that the proposed intervention addresses the right problem?

Perhaps we need to apply the same discipline to commercialization programs that we advocate for individual innovations: understand the problem, engage those involved, identify the critical assumptions, experiment with alternative approaches, and evaluate whether the evidence supports our decisions.

And we should recognize that this is a process we can study and improve. We can examine where decisions are made, where evidence arrives too late, why promising projects stop progressing, and how changes to the process influence eventual adoption. These are questions of process quality and effectiveness, not simply how many activities we undertake.

The challenge is not to create more commercialization activity. It is to improve the quality of the journey from research to adoption.

That means developing the capabilities of researchers and commercialization professionals, improving how we support the journey, and evaluating progress through evidence that uncertainties are being reduced and adoption is becoming more likely.

I wrote these 10 myths partly because I wanted a reference I could return to when reviewing proposals, working with researchers, or helping organizations rethink their approach to commercialization.

But perhaps they also provide a useful test for all of us.

Before developing another technology, launching another startup, or creating another commercialization initiative, we might ask:

What do we actually know about why someone would choose to adopt this innovation – and what do we need to learn or change to make that more likely?

Because commercialization is not what automatically happens when good research is finished.

It is what we achieve when we understand enough about the technology, the users, and the conditions for adoption to turn a promising possibility into something people actually choose to use.

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