GoKawiilEvery research programme is a bet on the future. Yet, scientists rarely examine the assumptions behind those bets. Decisions about grants, infrastructure, hiring, regulation and training often presume that certain technologies will mature, specific skills will be needed, the public will accept the resulting innovations and few risks will materialize. Yet these assumptions are rarely stated, let alone tested or revised in a systematic way1.
The consequence is a mismatch between current research activity and future conditions, a gap that will only grow with the pace of discovery and global change. If they don’t think ahead, scientific institutions will increasingly find themselves pressed to swiftly revise research priorities, training plans and infrastructure investments after a crisis emerges for which they were unprepared.
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During the COVID-19 pandemic, for example, researchers had to accelerate vaccine development, scale up telemedicine, redeploy clinical staff, move education online and manage public trust in surveillance and vaccination. They had to do this under crisis conditions and with little initial knowledge of the virus2.
The rapid rise of artificial intelligence is also hard to navigate. Institutions must make decisions about validation, evaluation methods, workforce preparedness, research integrity and governance before long-term evidence is available3.
What is missing is not perfect prediction, but foresight: a structured, forward-looking process for examining multiple plausible futures, identifying the assumptions that matter across them, and defining signals that can trigger changes in research direction, staffing and funding priorities.
The field of ‘futures studies’ offers rigorous methods for analysing trends, planning for a range of outcomes and using ‘horizon scanning’ to scope out future risks and opportunities. Yet these methods aren’t mainstream in scientific practice. It’s time for that to change.
Here, we call for futures methods to be plugged into the core mechanisms of scientific development, to inform decisions about what science should fund, build, teach, validate and evaluate. We call this concept translational foresight. Adopting it would make the promises embedded in research programmes explicit, testable, traceable and revisable (see ‘Translational foresight’).
Looking ahead
Translational foresight has parallels with translational medicine. In the late twentieth century, molecular biology was advancing rapidly, but that knowledge was not reaching the clinic4. Expressions in medical research such as “bench to bedside” and “crossing the valley of death” highlighted this gap5. In response, scientists reoriented their systems to link discovery to patient outcomes6. This was accompanied by the rise of clinician-scientist training programmes7, changes in the funding model for translational research and the development of dedicated institutions for translational medicine8.
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