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

arXiv:2602.23365v1 Announce Type: cross 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

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Gordon Fletcher, Saomai Vu Khan · · 1 min read · 21 views
#cs.HC #cs.CL #cs.IR

arXiv:2602.23365v1 Announce Type: cross 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.

Executive Summary

This article proposes the application of generative AI as a 'serendipity engine' to discover, classify, and mobilize reusable knowledge components from existing documents. The authors apply their approach to 206 papers and create a repository aligned to Beer's Viable System Model (VSM). They contribute a theory of planned serendipity, a component repository, and a process blueprint for organisational adoption. The authors also discuss environmental and social benefits of repurposing knowledge components and propose testable links between repository creation, discovery-to-deployment time, and reuse rates. The study sheds light on the potential of generative AI to facilitate systematic repurposing of knowledge and shift innovation portfolios from breakthrough bias.

Key Points

  • ▸ Generative AI can operate as a 'serendipity engine' to discover and mobilize reusable knowledge components
  • ▸ The authors created a repository of 711 components aligned to Beer's Viable System Model (VSM)
  • ▸ The study contributes a theory of planned serendipity, a component repository, and a process blueprint for organisational adoption

Merits

Strength in Conceptual Framework

The authors develop a theory of planned serendipity that conceptually links generative AI to the Viable System Model, providing a novel framework for understanding the role of AI in knowledge repurposing

Empirical Contribution

The creation of a large-scale repository of reusable knowledge components and identification of temporal and subject patterns provide valuable insights into the potential of generative AI for systematic repurposing

Managerial Relevance

The process blueprint for organisational adoption provides actionable guidance for managers seeking to integrate generative AI into their innovation strategies

Demerits

Limitation in Generalizability

The study relies on a specific dataset of 206 papers, which may limit the generalizability of the findings to other contexts or domains

Need for Further Validation

The proposed testable links between repository creation, discovery-to-deployment time, and reuse rates require further empirical validation to establish their reliability and relevance

Expert Commentary

The study provides a compelling case for the application of generative AI in facilitating systematic repurposing of knowledge. The development of a theory of planned serendipity and creation of a large-scale repository of reusable knowledge components represent significant contributions to the field. However, the study's limitations in generalizability and need for further validation of the proposed testable links highlight the importance of future research in this area. The implications for organisational adoption and innovation strategy are significant, and policymakers may consider incorporating generative AI into their innovation strategies to promote systematic repurposing of knowledge.

Recommendations

  • ✓ Future research should focus on validating the testable links between repository creation, discovery-to-deployment time, and reuse rates
  • ✓ Organisations should consider incorporating generative AI into their innovation strategies to facilitate systematic repurposing of knowledge

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