Every research program is a commitment to the future. However, scientists rarely examine the assumptions behind those bets. Decisions about subsidies, infrastructure, contracting, regulation, and training often assume that certain technologies will mature, specific skills will be needed, the public will accept the resulting innovations, and few risks will materialize. However, these assumptions are rarely stated, much less systematically tested or reviewed.¹.

The consequence is a mismatch between current research activity and future conditions, a gap that will only grow with the pace of discoveries and global change. If they do not think ahead, scientific institutions will be increasingly pressured to quickly review research priorities, training plans and infrastructure investments after a crisis arises for which they were not prepared.

Could agent AI bring down grant funding systems?

During the COVID-19 pandemic, for example, researchers have had to accelerate vaccine development, expand telemedicine, redeploy clinical staff, move education online, and manage public trust in surveillance and vaccination. They had to do it under crisis conditions and with little initial knowledge of the virus.².

The rapid rise of artificial intelligence is also difficult to confront. Institutions must make decisions about validation, assessment methods, workforce preparation, research integrity, and governance before long-term evidence is available.³.

What is missing is not perfect prediction, but foresight: a structured, forward-looking process for examining multiple plausible futures, identifying the important assumptions in all of them, and defining signals that can trigger changes in research direction, staffing, and funding priorities.

The field of ‘futures studies’ offers rigorous methods for analyzing trends, planning for a range of outcomes, and using ‘horizon scanning’ to detect future risks and opportunities. However, these methods are not common in scientific practice. It’s time for that to change.

Here, we ask that the methods of the future be connected to the core mechanisms of scientific development, to inform decisions about what science should be funded, built, taught, validated and evaluated. We call this concept translational forecasting. Adopting it would make the promises included in research programs explicit, verifiable, traceable and reviewable (see ‘Translational Foresight’).

Looking forward

Translational forecasting has parallels with translational medicine. At the end of the 20th century, molecular biology was advancing rapidly, but this knowledge did not reach the clinic.⁴. Expressions in medical research such as “from the bench to the bed” and “crossing the valley of death” highlighted this gap.⁵. In response, scientists reoriented their systems to link the discovery to patient outcomes.⁶. This was accompanied by the increase in training programs for clinical scientists.⁷changes in the funding model for translational research and the development of institutions dedicated to translational medicine⁸.

Similarly, the field of futures studies has established methods for exploring future trends, uncertainties, and alternative trajectories. These methods are not routinely used in scientific research contexts.

The Future Wheel is a method for graphically visualizing direct and indirect consequences (see ‘Tools for thinking about the future’). Some governments used it to map the cascading impacts of the COVID-19 pandemic, revealing shifts toward telemedicine, job strain, and changes in care delivery, for example.⁹.

Scenario analysis is another technique that has informed long-term strategy in industry and policy. For example, the energy company Shell has used it to respond to energy crises and governments have used it to develop national policies on climate, infrastructure and mobility.

Forecasting platforms, like the Good Judgment Project, led by researchers at the University of Pennsylvania in Philadelphia, harness the wisdom of the crowd to answer forward-looking questions across the political, economic, and social spectrum.

Without such tools, science relies on informal judgment, intuition, and short-term planning to anticipate its future implications.

Science in 2050: the future advances that will shape our world and beyond

Pandemic preparedness frameworks, for example, can identify biological and logistical risks, such as whether health systems have enough tests, vaccines, beds, protective equipment and emergency plans. But they rarely shape research priorities, workforce planning, or technology validation needs before a crisis begins.¹⁰.

Translational forecasting turns the preparation of a static checklist for emergencies into a system for testing and updating the assumptions on which future decisions depend. For example, funders could support the testing of remote care, home monitoring and rapid diagnostic systems before they are urgently needed. Health systems could define in advance trigger points – from intensive care unit occupancy to levels of public distrust – that would trigger a review of research, staffing or funding objectives.

Similarly, personalized, genomics-based vaccine research involves not only designing vaccines, but also making assumptions about the future world in which they will be used.¹¹. Scientists and funders implicitly assume who the target populations will be, how microbes will evolve, what genomic data will be available, whether regulators will accept personalized vaccine strategies, and whether the public will trust the vaccines. Recognizing these assumptions from the beginning will allow projects that can adapt if conditions change.

The Great University Restructuring: Four Charts Show How Global Higher Education Is Changing

The challenge lies in translating the results of future methodologies into research priorities, funding decisions, infrastructure planning and evaluation. Several barriers have prevented this translation from becoming routine.