Impact pathways are changing how research is imagined
For a long time, research evaluation was primarily organized around scientific excellence. The central questions were relatively clear: Is the science robust? Is it novel? Can it advance knowledge? Eventually, additional criteria such as publication metrics, patents, technology transfer became increasingly important. But the overall logic remained largely the same: research produced knowledge first, and societal effects would follow later. This is progressively changing.
Today, researchers and innovation actors are increasingly asked to explain not only what they are developing, but also what kinds of futures their work could help produce. Funding bodies, public institutions, investors, and innovation ecosystems now expect projects to anticipate societal relevance, environmental consequences, stakeholder inclusion, adoption conditions, and possible long-term effects.
This evolution explains the growing importance of concepts such as “impact pathways” within research and innovation systems.
At first glance, an impact pathway may appear to be just another bureaucratic tool. In practice, however, it reflects a much deeper transformation in the way research itself is imagined.
Because impact pathways do not simply ask researchers to measure outcomes after the fact. They ask them to project possible trajectories upstream.
From measuring impact to imagining trajectories
One of the recurring misunderstandings around societal impact is the assumption that impact evaluation is only about measurement. Of course, measurement matters. Once technologies, policies, or innovations are implemented, it is important to assess their effects and understand whether intended outcomes were achieved.
But in research and innovation, many important decisions happen long before measurable impacts exist.
Deep Tech startups, emerging scientific fields, early-stage technologies, or exploratory research projects often operate precisely in situations where direct impact data is unavailable. At these stages, what matters is not measurement in the strict sense, but projection: trying to anticipate plausible futures, possible applications, affected stakeholders, risks, dependencies, or unintended consequences.
This is where impact pathways become interesting.
In their best form, they are not simply reporting frameworks. They force innovation actors to make explicit the assumptions embedded within technological development itself. Thinking through an impact pathway means asking what conditions would actually be necessary for a technology to produce meaningful societal value, which actors would realistically benefit from it, what forms of infrastructure or governance it depends upon, and what kinds of environmental or social externalities could emerge along the way. It also means recognizing that technological choices are never isolated technical decisions, but always imply trade-offs, exclusions, and particular visions of what desirable futures should look like.
These questions are not peripheral to innovation. They actively shape technological trajectories.
Researchers were not trained for this
The difficulty is that most researchers were never trained to think this way.
Scientific training is designed to produce disciplinary expertise, methodological rigor, and technical specialization. Researchers learn how to formulate hypotheses, produce evidence, validate results, and contribute to scientific debates. They are not necessarily trained to think about stakeholder ecosystems, societal adoption, governance structures, public controversies, or systemic environmental effects.
And yet this is increasingly what innovation systems ask them to do.
Researchers are now expected to anticipate the social life of technologies before these technologies even exist. In practice, this often creates confusion and resistance. Many scientists perceive impact requirements as external administrative constraints disconnected from “real science.” Others feel uncomfortable making projections they cannot fully guarantee. Some worry that societal impact language remains vague, unstable, or politically instrumentalized.
To some extent, these concerns are legitimate. The language of impact can absolutely become performative, superficial, or bureaucratic when reduced to funding templates and standardized indicators.
But dismissing impact pathways entirely would also miss something important. Because these tools emerged in response to a real structural problem: innovation systems have historically been extremely poor at collectively discussing the broader trajectories of science and technology early enough for choices to still be possible.
The politics hidden inside technological development
One of the most interesting consequences of impact pathways is that they make visible something research systems often preferred to keep implicit: technological development is never neutral. Every innovation trajectory contains assumptions about what problems matter, which futures are desirable, who gets included, what forms of life are prioritized, and which risks are considered acceptable.
For decades, these questions were often treated as external to scientific work itself. Researchers produced knowledge. Society and politics would deal with consequences afterward.
This separation no longer holds. Climate change, biodiversity collapse, AI governance, resource depletion or public health crises, all illustrate the same issue: by the time technologies become fully operational, many structural choices have already solidified. Many decisions that will be critical for society are made during the research and development phase.
Impact pathways are therefore not simply reporting tools. They are attempts, even if imperfect ones, to reopen discussion around technological directionality before trajectories become locked in. In that sense, they subtly change the role of researchers themselves. Scientists are no longer only asked to produce knowledge. They are increasingly expected to reflect on the systems their work contributes to shaping.
A new literacy for research and innovation
Over the past decade, I have worked extensively with researchers, startups, public institutions, and innovation ecosystems on these questions. One thing has become increasingly clear to me: many actors are not opposed to societal impact at all. In fact, they often care deeply about the societal relevance of their work. The difficulty is rather that most of them have never been equipped with practical ways to collectively think through impact trajectories, stakeholder inclusion, unintended consequences, or the societal conditions necessary for technologies to generate meaningful outcomes.
This is why methodologies such as impact pathways matter when they are approached seriously. Not because they magically guarantee positive impact, but because they create structured spaces to reflect on the consequences, dependencies, assumptions, and strategic orientations embedded within innovation processes.
The danger is therefore not that research actors are increasingly asked to think about societal impact. The danger is that these practices become reduced to administrative exercises optimized for evaluation rather than used as genuine tools for reflection, deliberation, and collective orientation.
The future of responsible research and innovation will likely depend on which of these two directions ultimately prevails.