From Signals to Decisions: A Framework for Executive Signal Synthesis in AI Systems

This work introduces Executive Signal Synthesis (ESS), a methodological framework for transforming fragmented business signals into structured, explainable, executive-level decision intelligence. Modern organizations generate heterogeneous signals across customer interactions, internal discussions, operational processes, and market observations. Despite their abundance, these signals rarely reach decision-makers in a structured, actionable form. ESS formalizes the transformation of these signals through four stages: ingestion, classification, clustering, and synthesis. A research prototype is implemented to evaluate the feasibility of this approach using lightweight natural language processing techniques. Experimental results on a synthetic multi-source dataset demonstrate that heterogeneous signals can be grouped into coherent cross-source patterns and translated into actionable executive insights. This repository provides:- a conceptual research framework (ESS)- a prototype validation system- a synthetic dataset for experimentation- example executive decision outputs This research positions signal synthesis as a distinct intermediate layer between data processing and executive decision-making. The prototype implementation is available as an open-source repository.