Emergent Strategy: Strategy Knowledge Graph & GraphRAG Application

Objective

Architect a Neo4j knowledge schema mapping the book’s guidelines, including strategic tests, diagnosis tools, and implementation task sets.
Design a GraphRAG (Retrieval-Augmented Generation) framework to provide context-aware, non-linear guidance via a chatbot.
Map the relational dependencies between theoretical chapters and practical guidebook examples to facilitate implementation.
Build a technical structure that allows users to identify specific strategic disqualifiers and bottlenecks through interactive queries.

Overview

We provided a comprehensive knowledge graph and expert advisory for a GraphRAG-based chatbot for "The Emergent Approach to Strategy". The system converts the book’s strategic frameworks into a relational engine to help users orient themselves and implement the strategy logic in real-world scenarios.

Problem

Static strategic theory often leads to execution failure, where organizations "over-plan and under-deliver". Traditional search and documentation fail to capture the complex relational interdependencies between core concepts like the Five Disqualifiers, Five Task Sets, and Bottlenecks, preventing users from applying the framework to their specific "killer problems".

Results

Converted the book’s IP into a relational knowledge base, transforming strategic theory into a queryable technical asset.
Defined the GraphRAG architecture necessary for a chatbot to provide framework-aligned, adaptive strategy coaching.
Established a data model that automates the identification of strategic bottlenecks and "disqualifiers" within user-provided business contexts.
Delivered a production-ready knowledge schema that operationalizes the Emergent Approach methodology for organizational scaling and reinvention.

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