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New perspectives for data‐supported foresight: The hybrid AI‐expert approach

 
: Geurts, Amber; Gutknecht, Ralph; Warnke, Philine; Goetheer, Arjen; Schirrmeister, Elna; Bakker, Babette; Meissner, Svetlana

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Volltext urn:nbn:de:0011-n-6384482 (1.3 MByte PDF)
MD5 Fingerprint: 077d551b251f61dbdf54e826ea6ec364
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Erstellt am: 30.7.2021


Futures & foresight science (2021), Online First, Art. e99, 14 S.
ISSN: 2573-5152
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer ISI ()
AI; data supported foresight; framework; hybrid AI‐expert‐based approach; innovation management; strategic decision making; strategic foresight; technology management; trend detection

Abstract
This paper outlines new perspectives for data-supported foresight by combining participatory expert-based futures dialogues with the power of artificial intelligence (AI) in what we call the hybrid AI-expert-based foresight approach. To this end, we present a framework of five typical steps in a fully fledged foresight process ranging from scoping to strategizing and show how AI can be integrated into each of the steps to enable the hybrid AI-expert foresight approach. Building on this, we present experiences gained from two recent research projects of TNO and Fraunhofer ISI that deal with aspects of the hybrid AI-expert foresight approach and give insights into the opportunities and challenges of the new perspectives for data-supported foresight that this approach enables. Finally, we summarize open questions and challenges for future research.

: http://publica.fraunhofer.de/dokumente/N-638448.html