用生成式AI挖掘组织文档中的可复用知识，提升危机应对能力
Serendipity with Generative AI: Repurposing knowledge components during polycrisis with a Viable Systems Model approach
- 用生成式AI从206篇论文中提取711个知识组件，按维利系统模型分类
- 每篇论文平均发现3.4个可复用组件，构建了可追溯的知识仓库
- 适合组织管理者、创新决策者参考，推动系统性复用而非盲目突破
组织在多重危机中面临不确定性，却忽视内部隐含的知识。本文展示生成式AI如何作为意外发现引擎与知识转导工具，从现有文档中发现、分类并调动可复用的知识组件（如模型、框架、模式）。基于206篇论文，我们的流程提取出711个组件（平均每篇约3.4个），并按贝尓的维利系统模型（VSM）组织成知识库。研究贡献包括：一、概念上提出有计划的意外理论，表明生成式AI降低了不同VSM子系统间的转导成本；二、实证上构建了组件仓库及时间与主题模式；三、管理上提供组织采纳的案例与流程蓝图；四、社会层面揭示知识复用与环境及社会效益的关联路径。我们提出可验证的假设，将知识库创建、发现到部署时间与重用率相联系，并讨论如何引导创新投资从突破性偏好转向系统性复用。
原文摘要 · Abstract (English)
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.
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