Robotic artistry: Four surprise pathways for generative artificial intelligence-assisted abductive theorization

Abstract

The role of generative artificial intelligence in qualitative research is subject to intense debate, with critics warning that it could undermine human sensitivity and contextual understanding. We argue that thoughtfully integrated artificial intelligence can enhance qualitative research by promoting discovery and surprise, both essential elements of theory building. Drawing on Picasso’s iterative abstraction in The Bull and Refik Anadol’s Unsupervised exhibition at the Museum of Modern Art, we treat reduction and synthesis as complementary engines of insight and identify four surprise generation pathways in generative artificial intelligence-assisted abductive analysis: multiplying lenses, surfacing absences, bridging levels, and testing categories. When paired with interpretive vigilance operationalized through four heuristics, meaning-making remains squarely in human hands. Using an empirical example of organizational future-making, we show how artificial intelligence’s pattern recognition combined with human interpretation reveals insights neither could achieve alone. Our framework positions artificial intelligence as a collaborative partner that amplifies researchers’ capacity for theoretical discovery while preserving methodological rigor and interpretive depth.