Technology Foresight
Also known as: Foresight studies, Strategic technology forecasting, Future-oriented technology analysis
Technology foresight is a systematic, participatory process of looking into the longer-term future of science, technology, the economy, and society in order to identify the areas of strategic research and the emerging generic technologies likely to yield the greatest economic and social benefits. Rather than predicting a single future, it brings experts and stakeholders together to explore plausible futures, build shared visions, and translate them into present-day priorities and action.
Key highlights
- Integrates multiple methods—Delphi, scenarios, scanning, roadmapping—into a coherent process tailored to the decision at hand rather than relying on any single technique.
- Builds networks and shared understanding across the innovation system, so the 'wiring up' of actors is often as valuable as the forecasts produced.
- Connects long-term thinking to present-day priority-setting and policy, giving decision-makers a defensible basis for allocating research effort.
- Embraces uncertainty by exploring multiple plausible futures instead of issuing a single brittle prediction.
Intuition
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How it works
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When to use it
Use technology foresight when an organisation, sector, or nation must set long-term science and technology priorities under deep uncertainty and wants both better-informed choices and stronger links among the actors who must implement them. It suits situations where the relevant knowledge is distributed across many experts and stakeholders, where the horizon is too distant for simple extrapolation, and where building shared commitment matters as much as the forecast itself. Foresight assumes the future is open and shapeable, that participation improves both legitimacy and content, and that the process is iterative. It is less appropriate for short-term operational forecasting, for narrow technical predictions where a single quantitative model suffices, or where there is no genuine intent to act on the results.
Strengths & limitations
- Integrates multiple methods—Delphi, scenarios, scanning, roadmapping—into a coherent process tailored to the decision at hand rather than relying on any single technique.
- Builds networks and shared understanding across the innovation system, so the 'wiring up' of actors is often as valuable as the forecasts produced.
- Connects long-term thinking to present-day priority-setting and policy, giving decision-makers a defensible basis for allocating research effort.
- Embraces uncertainty by exploring multiple plausible futures instead of issuing a single brittle prediction.
- Outcomes depend heavily on who participates; unrepresentative panels can entrench incumbent views and overlook disruptive or marginal possibilities.
- Large national exercises are costly and slow, and their impact on actual decisions is hard to measure and frequently disappointing.
- Expert consensus can converge on the conventional, dampening exactly the radical signals foresight is meant to surface.
- Without genuine commitment to follow through, foresight reports become shelf-ware disconnected from funding and policy.
Common pitfalls
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Applications
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Frequently asked
How is foresight different from forecasting or prediction?
Forecasting tries to estimate what the future will be, often as a single quantitative projection. Foresight is broader and process-oriented: it explores multiple plausible and desirable futures, deliberately involves many stakeholders, and aims to shape present decisions and build networks. Its success is judged not by predictive accuracy but by the quality of the priorities set and the connections and shared visions created.
Why is participation so central to foresight?
Because the relevant knowledge about long-term science and technology is distributed across scientists, firms, policymakers, and users, no single actor can see the whole picture. Participation pools that dispersed knowledge, builds mutual understanding and trust across the innovation system, and gives the resulting priorities legitimacy and ownership—making implementation far more likely than for an expert report produced in isolation.
What methods does a foresight exercise typically combine?
There is no fixed recipe, but exercises usually combine several techniques: horizon scanning to detect emerging issues, Delphi surveys to aggregate expert judgement, scenario building to explore alternative futures, and roadmapping to link technologies to goals over time. The choice depends on the purpose—priority-setting, vision-building, or network-building—and good practice tailors the method mix to the specific decision rather than applying a standard template.
Sources
- 1.Martin, B. R. (1995). Foresight in science and technology. Technology Analysis & Strategic Management, 7(2), 139-168.
- 2.Miles, I. (2010). The development of technology foresight: a review. Technological Forecasting and Social Change, 77(9), 1448-1456.
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Cite this page
ScholarGate. (2026, June 22). Technology Foresight. ScholarGate. https://scholargate.app/science-technology-studies/technology-foresight