Design's most distinctive cognitive contribution — abductive reasoning — remains largely confined to the studio, the workshop, the project timeline. We theorize it extensively (Kolko, Zingale, Dorst), we recognize it as the engine of design synthesis, the mechanism through which incomplete observations become structured hypotheses, through which uncertainty generates possibilities.
Yet when it comes to collective contexts — communities navigating complex territorial challenges — abduction stays trapped in episodic formats, limited by the well-known pathologies of participation: who gets to be in the room, for how long, with what resources, and whose complexity gets actually processed.

This is the gap we explored in the paper presented at the Italian Design Society last June with Michele Zannoni and Flaviano Celaschi. The Systemic Relational Insight (SRI) framework, born from my doctoral research at the University of Bologna, proposes a hybrid intelligence process — community and machine — designed to scale the abductive dimension of sensemaking across broader publics, longer timeframes, and thicker layers of data and knowledge.
The core idea: integrate qualitative knowledge from situated workshops with quantitative data and scientific references, generate candidate insights and submit them to community validation (bringing scale in the formula to reach who didn't attend). An insight here is never a single statement delivered by an algorithm. It's a cluster containing multiple versions, each traceable to its genealogy of sessions, voices, and contexts, each carrying different degrees of community consensus, data support, and scientific consistency.
That simple.

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