Technology roadmapping has become an important foresight tool for science, technology, and innovation (STI) policy and technology strategy development. There are, however, challenges in translating evidence from foresight into the strategies of STI agencies and the planning of research & technology development (RTD) organizations. While the foresight evaluation literature identifies methodological issues related to evidence granularity, scope, and stakeholder confidence, there is limited guidance on how to ensure roadmapping outputs are strategically relevant, appropriately detailed, and credible. This paper highlights the potential of using structured visual roadmapping frameworks to anticipate potential strategic foresight evidence failures and using the adaptive and iterative nature of roadmapping processes to address them. In this paper, we distinguish between: the roadmapping framework ‘canvas’; the foresight evidence captured on the canvas; the process of generating the evidence; and any final strategic plan developed using that evidence (with goals, milestones, actions, etc). We investigate efforts to use the roadmapping canvas as a research tool and diagnostic to explore emerging technology trajectories and innovation ‘pathways’. We demonstrate that key patterns of evidence distribution on the roadmapping canvas have the potential to reveal where further evidence may need to be gathered, or where further triangulation of stakeholder perspectives may be required. We argue that by adaptively addressing these patterns at key stages within the roadmapping process (and appropriately re-scoping, re-prioritizing, and re-focusing foresight effort and resources), the granularity, coverage, and consensus of the roadmapping evidence can be greatly enhanced. We conclude the paper by summarizing a set of novel principles for adaptive agile roadmapping, reflecting on the implications for foresight more generally, and outlining a future research agenda to test and refine this approach to agile foresight.
Agile Roadmapping: An Adaptive Approach to Technology Foresight
