Detecting and analyzing weak signals of change in futures research and foresight

Purpose This paper aims to outline current and emerging opportunities and challenges in applying collective intelligence methods to detection and analysis of weak signals of change. Design/methodology/approach The article builds on review of current literature on the topic and analysis of projects employing various methods of collective intelligence to scanning and sense making for signals of change. Findings The article points out possible roles of collective intelligence in analysis of weak signals, specific to the scanning and sensemaking stages in futures research. It identifies key variations of applying collective intelligence to weak signals that shape the result of the research process: selection of participants for collective intelligence (from controlled to open) and ways of collaboration in the collective intelligence process (from fully collective to switching between collective and individual mode). It also gives an example of a successful application of collective intelligence to weak signals analysis and suggests possible models that can be fitting for identifying and interpreting weak signals of change. It further discusses the current implications of AI for foresight and possible future implications of its development (“explainable AI” and human-machine collaboration). Originality/value The paper hypothesizes on the emerging challenges in the field of collective intelligence for weak signals of change and suggests a new framework for mapping the field. The paper has not been published before.