Editor’s note: This is the 24^th 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. 

This is Part 1 of a two-part article. Part 2 will be published on November 18.

I’ve been encouraged by the growing enthusiasm for mission-driven research in Canada. The idea appeals to me enormously: start with an important problem, decide what improvement would make a meaningful difference, and bring together the knowledge and capabilities needed to achieve it.

Much of my own research work has been guided by that instinct. Yet I have a concern that recurs whenever we seek to improve innovation outcomes: if mission-driven research holds so much promise, what might get in the way of its adoption?

I suspect this involves considerably more than choosing new research priorities, selecting different proposals or developing new teams, but also transforming the research journey. Let me start by explaining how different mission-driven research is from much current research. Mission-driven research is framed by the broad impact we want to achieve, and refined to a more specific outcome, rather than pursuing a predetermined scientific or technological objective (with a predetermined approach). Instead, mission-driven research evolves as we gather evidence and learn more about the problem to identify possible pathways to impact.

As a result, the path of the research journey, as well as the nature of the research may need to change. These changes are profound, and have substantial implications for the researchers, institutions, funders as well as the research process itself.

This raises a familiar question: based on my experience in introducing more innovative approaches to organizations, what has to change, and why?

Begin with the desired strategic impact

Traditional research often starts with an intriguing scientific question or a technological opportunity, and only later considers where the resulting knowledge might be applied. That is a valuable and necessary way to advance knowledge. The commercialization process tends to start as the research is completed.

Mission-driven research has a different organizing principle: it begins with a desired consequential outcome, then investigates alternate ways in which the desired outcome might be achieved. This profoundly changes the potential of the research, the nature of the activity and how we improve the likelihood of success.

I will examine these changes to explain the need for a fundamentally different approach to mission-driven research.

Let’s start with an overview of the broad range of potential impacts possible from mission-driven research, and in the case of Canada how this includes at least six fundamentally different objectives. I have selected these six to highlight how different research fields can have fundamentally different strategic objectives: economic prosperity, security, sustainability, society, health and diversification. These are not six separate missions, but different directions in which research might contribute to national priorities, often simultaneously.

Economic prosperity can mean new products, more productive firms, new sources of value and better-paid employment.

Security includes defence and cybersecurity, but also resilience in food and energy supply. Sustainability can involve energy production and distribution, reducing emissions and waste, and the responsible use of land, water and other scarce resources.

Societal benefits may concern accessibility, equity and community well-being.

Health can include better prevention, nutrition, treatment and access to care.

Diversification can reduce Canada’s dependence on particular products, suppliers, export destinations and trading relationships.

These are reasons for acting, not missions in themselves. “Improve food security” or “strengthen Canadian competitiveness” expresses an important ambition, but it does not yet tell us what has to change, for whom, by how much or over what period. We need to move from a strategic priority to a specific, testable mission without rushing to embed our favourite solution in the challenge.

Because these directions concern different kinds of impact, they call for different measures of success, making comparisons between potential missions more challenging.

Choosing specific missions is a research task in its own right

I think we underestimate the challenge of identifying possible mission areas, let alone choosing specific missions within them. Before committing substantial resources, we need to understand the problem, its magnitude and strategic importance, who is affected, the consequences of inaction, and the potential impact and feasibility of different approaches. These judgments are particularly difficult when the benefits cannot be reduced to a single metric.

The process for choosing mission directions is a consultative and political process, to ensure balance, equity and expediency, and is complicated given the competing challenges facing the country.

However, even within a broad area, such as security, there are multiple missions that address competing priorities, leverage current resources or address critical vulnerability.

Starting with a list of potential missions and choosing focus areas involves a more sophisticated approach than we currently apply to many research initiatives and gets even more complicated when we start to focus on a specific issue. The choice of impact involves not only what is possible, but what is strategic and how various projects overlap with current or future research.

Importantly, mission-driven research has to focus on impact, which means that the likelihood of success and adoption have a direct influence on the choice of mission and the path followed.

This selection process challenges the way projects are chosen now. Rather than spending considerable time choosing the most promising project and then committing large resources to a fixed plan, mission-driven research is iterative, embedding prototypes that provide feedback to researchers and funders and enable pivots and course correction.

Early experiments (often just evidence gathering) clarify the problem and opportunity, test key assumptions and validate feasibility, identify adoption barriers, confirm anticipated impact, and start to address likely resource requirements (and the implications of use). Subsequent decisions, based on evidence gathered, help prepare for the larger evolving project, or suggest ways in which the initial plan might need to be reconsidered.

Such decisions and choices need to be equitable and transparent. Prospective users, industry partners, communities, public agencies and researchers may see different problems and possibilities. Involving them early can reveal important constraints, alternative solutions and consequences that would otherwise be overlooked. We should be transparent about these considerations rather than pretend every possible benefit can be maximized simultaneously.

Mission selection also raises questions about the overall research portfolio. Some missions reinforce one another; others compete for scarce resources or create trade-offs, where advancing one mission can undermine another. Existing capabilities, political priorities and changing circumstances will influence the choices we make. A mission may be ambitious, but its purpose, beneficiaries and measures of success must be clear enough to guide decisions along the way.

One industry, many potential missions

The development of Canada’s canola industry provides an illustrative example of how different forms of innovation can combine to create substantial economic and societal impact (average annual economic impact of C$43.7 billion, 2020/21–2022/23).

What began with plant-breeding research to improve the characteristics of rapeseed evolved into new crop varieties, agricultural practices, processing technologies, food products, animal feeds and industrial applications.

But scientific and technological breakthroughs were only part of the story. New quality standards, branding, business models, industry partnerships and international markets were equally important in transforming these innovations into a globally successful industry.

Seen as a journey, the industry’s progress can be understood as a succession of related mission-led initiatives rather than one predetermined strategic mission. Different innovations, disciplines, organizations and markets contributed as opportunities and challenges emerged, with each achievement opening possibilities for further work.

Suppose, today, we wanted to build on these achievements. Before choosing a research project, we would need to decide which particular improvement matters most, to whom, and how we would recognize progress. Even within the canola industry, different potential missions imply different beneficiaries, intended impacts and pathways to adoption. These might include:

• Agricultural productivity: Increase crop yields or reduce production costs and resource requirements while supporting resilient farms.
• Higher-value products: Increase the economic value created and retained in Canada through higher-value ingredients, products and processing.
• Supply-chain performance: Reduce losses, delays, bottlenecks and unnecessary costs between farms, processors, distributors and end users.
• Market diversification: Reduce dependence on a narrow set of markets by developing alternative customers, export destinations and applications.
• Food and feed security: Strengthen Canada’s ability to meet domestic demand for cooking oils and protein-rich animal feed, particularly during supply disruptions.
• Human health and nutrition: Improve dietary outcomes through appropriate uses of canola-based foods, including substitution of unsaturated oils for foods higher in saturated fats.
• Sustainability: Reduce emissions, energy and water use, waste and other environmental impacts associated with production, processing and distribution.

These seven potential missions illustrate different ways to pursue economic and societal impact within one industry; they are not seven predetermined research projects. Their benefits may overlap, they may compete for resources, and some may share research capabilities. But each seeks a different outcome and could require a different approach.

That brings us to another important implication of mission-driven research: before deciding which research projects to fund, we may first need to invest in understanding which missions are worth pursuing, the alternate pathways and different capabilities required.

Suppose we select the mission of improving agricultural productivity. That describes the result we want, not how we will achieve it. One approach might be to improve seed characteristics; the techniques we investigate could include conventional breeding or genetic engineering. Another approach might be to reduce losses at harvest, perhaps through changes in harvesting practices or equipment.

The intended outcome, the approach we choose and the techniques used to investigate it are distinct decisions. Adoption evidence from farmers and processors should help us choose between them. Importantly, each path may involve different research approaches, resources and capabilities, such as:

• Science and technology. New knowledge, crop traits, processing methods, ingredients and technical solutions; potentially involving plant scientists, food chemists and engineers.
• Processes and systems. Improvements in production, logistics, quality control, integration and coordination; perhaps requiring industrial engineers, operations researchers and data specialists.
• Business and delivery models. New customers, revenue models, commercial agreements and routes to market; bringing in business researchers, economists, firms and investors.
• People and organizations. The skills, behaviours and organizational practices required to make a solution work; involving educators, change specialists, managers and frontline users.
• Policy and incentives. Standards, regulatory decisions, procurement, trade rules and economic incentives that enable—or inhibit—change; requiring policy, legal and regulatory expertise.
• Infrastructure and capabilities. Equipment, pilot plants, testing facilities, data, shared expertise, technical services and the ability to access them when needed.

A mission does not correspond to one pathway, and a pathway does not prescribe a single research technique or discipline. We may need several approaches at once. We may also discover that adapting something already developed elsewhere contributes more than inventing a new technology. The mission should guide the selection of activities, partners, equipment, facilities and funding—not the other way around.

The research journey may need to evolve as we learn

Identifying promising pathways is only the beginning. What happens when the evidence tells us that our initial assumptions were wrong?

Imagine we begin a project to create a higher-value canola-derived protein ingredient. We might reasonably start with plant science and food chemistry, believing that the central challenge is improving the characteristics of the ingredient.

But early discussions with food manufacturers could reveal something different. Perhaps suitable ingredients already exist, but their cost, taste, processing requirements or regulatory status make them unattractive to manufacturers. A more sophisticated ingredient might not solve the problem.

The research that matters most could instead concern extraction efficiency, formulation, plant-scale processing or the economics of changing manufacturing lines. Engagement with customers might reveal another obstacle: consumers do not value the proposed product enough to justify its higher price.

These are not details to be addressed after the research is finished. Understanding why prospective users would adopt a solution, what they currently use, what would motivate them to change and what might prevent them from doing so can alter the research questions themselves. It can also tell us when a promising technology is unlikely to deliver the intended impact.

Positive feedback can justify expanding an approach; negative feedback may tell us to strengthen, redirect or stop it. This is why we need to identify potential adopters when defining the project and keep testing whether there is a credible path to use, even when the research itself is excellent.

The mission has not necessarily changed, but the work required to achieve it may have. We might need different disciplines, new researchers or industry partners, different experiments, access to a pilot-scale facility instead of a laboratory instrument, new data, regulatory advice, revised budgets or a longer – and sometimes shorter – timescale. Some activities should receive more resources; others may no longer justify investment. Sometimes a specialist’s brief intervention or access to an existing piece of equipment could prevent months of unproductive work.

This evolving work also calls for skills beyond the original technical expertise: reframing problems, discovering and observing users, testing adoption barriers, and exploring alternative business or delivery models. Those capabilities can be brought into the team as the evidence demands.

That poses a challenge for our conventional approach to funding research. We often select a team, agree on the questions, fix the budget, identify facilities and approve a sequence of activities before the work begins. Those commitments serve real purposes, including accountability. But when the evidence points toward a different pathway, faithful delivery of the original plan may be less valuable than a well-justified change. Mission-driven research asks us to manage an evolving, evidence-informed journey rather than merely deliver a predetermined program.

It also changes how we should assess research progress. Rather than relying primarily on the completion of planned activities and the production of conventional research outputs, we should examine what has been learned, which uncertainties have been reduced, whether prospective adopters are demonstrating greater confidence in the proposed approach, and whether the evidence strengthens our expectation of achieving the intended impact.

Sometimes the most valuable outcome of an experiment is discovering that we should pursue a different direction. Such decisions should be recognized as research progress, not necessarily as failure.

The two adoption challenges

This brings me to an insight from my work on Innovation Cartography. There are two connected adoption challenges here, and we need to distinguish them. The first is embedding the adoption decision into the research activity itself

In the case of canola, will farmers, processors, manufacturers, retailers, consumers or public agencies actually choose to use the proposed technology, process or practice? What benefits do they perceive, what alternatives are already good enough, what capabilities would they need, and what barriers might stop them? Without that understanding, we can complete excellent research and still fail to create the intended impact. Evidence about adoption must therefore shape the research as it unfolds, not merely the commercialization plan written at the end.

The second is adoption of mission-driven research itself. Will researchers, universities, research infrastructure organizations, funding agencies and their partners change the way they identify problems, choose projects, share expertise, access facilities, redirect resources and evaluate success? What motivates them to keep working as they do today, and what would make a different approach worth the effort and risk?

We should be cautious about assuming resistance is simply conservatism. Departmental structures encourage depth of expertise. Grant processes seek fairness and financial accountability. Research infrastructure must be scheduled, maintained and operated safely.

Universities reward outputs they know how to assess. Researchers make career decisions within those rules. These arrangements have good reasons for existing, but they can also make rapid reconfiguration of teams, methods, budgets, partnerships and facilities difficult—even when the evidence says a change is needed.

The mismatch is particularly clear when we consider what is measured. Publications, grant income, patents and licence revenue can tell us something important about activity and achievement, but none of them alone tells us whether a mission achieved its intended outcome. Nor do they necessarily recognize excellent problem framing, an intelligent decision to stop pursuing a weak pathway, or the colleague whose timely expertise enabled someone else to succeed. If we want people to behave differently, we need to understand how the system currently rewards and constrains their choices.

The connection between the two adoption challenges matters. We ask researchers to learn why users might not adopt their results, then adapt the research to improve the chances of impact. We should bring the same curiosity to the people and organizations asked to adopt a mission-driven approach. Changing the language of a funding call may be easy. Changing how the research system works – and why people within it choose to work differently – is another matter.

The implementation question

The changes in how we manage research, decide on research funding and measure impact are profound, and we cannot assume that we know how to achieve the potential benefits of increasing mission-driven research. Further, making such a change will encounter resistance and skepticism, with a higher level of accountability and inspection.

We need to adopt a prototyping and learning approach to the implementation of mission-driven research, so that, while pursuing specific missions, we are also learning how to improve the mission-driven process itself. As we start to implement, there should be two learning objectives: to achieve the declared societal outcome from a specific mission and to learn which changes to the research system actually make missions more effective. We should measure both, rather than assume a new label produces better results.

But that is a discussion I want to develop in Part 2 of this article. Before prescribing new funding structures, policies or research practices, we need to understand the motivations, constraints and capabilities of the people who would have to adopt them. That is the challenge we too often leave until after a promising idea has been announced.

I began by asking why, if mission-driven research holds so much promise, we are not already doing more of it. Part of the answer may lie in a system designed to support valuable research, but not always to change direction as evidence emerges. We are proposing to transform our approach to research so that decisions throughout the journey are calibrated against intended impact and evidence about whether people will adopt the results. We should apply that principle to the introduction of mission-driven research itself, treating it as an evolving learning journey with feedback from those asked to use it, evidence of adoption, and measurable impact outcomes informing how the approach develops.

Changing what we fund will not be enough. What will it take for researchers, universities and funders to adopt this way of working – and how will we learn whether it is succeeding?

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