Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics

TL;DR

This work addresses the challenge of unifying analytical strategic frameworks with decision heuristics by applying semantic NLP to map framework parameters to heuristic patterns, enabling cohesive, actionable guidance. It introduces a plug-and-play recommender architecture that uses vector-space embeddings, cosine similarity, and KL-divergence-based validation to connect models like the 6C framework with the Thirty-Six Stratagems, while constraining LLMs to explainative reporting. The contributions include a modular computational architecture, a semantic-integration methodology adaptable to multiple frameworks (e.g., SWOT, Porter’s Five Forces), and empirical validation via two case studies (hydrogen vs electric mobility; Commodore vs Apple) showing robust integration performance and practical implementation pathways. The approach scales to additional frameworks and benefits organizations by delivering rapid, evidence-based recommendations with transparent reasoning, supported by gamified simulations and human-in-the-loop AI explanations.

Abstract

We present a novel approach for recommending actionable strategies by integrating strategic frameworks with decision heuristics through semantic analysis. While strategy frameworks provide systematic models for assessment and planning, and decision heuristics encode experiential knowledge,these traditions have historically remained separate. Our methodology bridges this gap using advanced natural language processing (NLP), demonstrated through integrating frameworks like the 6C model with the Thirty-Six Stratagems. The approach employs vector space representations and semantic similarity calculations to map framework parameters to heuristic patterns, supported by a computational architecture that combines deep semantic processing with constrained use of Large Language Models. By processing both primary content and secondary elements (diagrams, matrices) as complementary linguistic representations, we demonstrate effectiveness through corporate strategy case studies. The methodology generalizes to various analytical frameworks and heuristic sets, culminating in a plug-and-play architecture for generating recommender systems that enable cohesive integration of strategic frameworks and decision heuristics into actionable guidance.

Figures (3)

• Figure 1: Computational Architecture Overview
• Figure 2: Strategic Implementation Pathways for HydrogenEngines, showing primary and secondary strategies with tactical implementations. Dashed lines indicate cross-strategy synergies.
• Figure 3: Strategic Implementation Pathways for Commodore vs. Apple, showing a primary focus on core capability development and a secondary focus on resource optimization. Dashed lines indicate cross-strategy synergies.