Serendipity with Generative AI: Repurposing knowledge components during polycrisis with a Viable Systems Model approach

TL;DR

This paper demonstrates how generative AI can serve as a serendipity engine to discover, classify, and mobilize reusable knowledge components from documents, organized within a Viable Systems Model framework, to enhance organizational resilience during crises.

Contribution

It introduces a novel theory of planned serendipity leveraging GenAI, along with an empirical repository of components, patterns, and a process blueprint for organizational adoption.

Findings

Generated 711 components from 206 papers

Organized components into a VSM-aligned repository

Proposed links between repository use and reuse efficiency

Abstract

Organisations face polycrisis uncertainty yet overlook embedded knowledge. We show how generative AI can operate as a serendipity engine and knowledge transducer to discover, classify and mobilise reusable components (models, frameworks, patterns) from existing documents. Using 206 papers, our pipeline extracted 711 components (approx 3.4 per paper) and organised them into a repository aligned to Beer's Viable System Model (VSM). We contribute i) conceptually, a theory of planned serendipity in which GenAI lowers transduction costs between VSM subsystems, ii) empirically, a component repository and temporal/subject patterns, iii) managerially, a vignette and process blueprint for organisational adoption and iv) socially, pathways linking repurposing to environmental and social benefits. We propose testable links between repository creation, discovery-to-deployment time, and reuse rates, and discuss implications for shifting innovation portfolios from breakthrough bias toward systematic repurposing.