Weak-Signal Intelligence: An AI-Enabled Intelligent Modeling Framework for Early Detection of Strategic Disruptions

Strategic environments are increasingly characterized by uncertainty, rapid technological change, and disruptions that may initially appear as low-visibility or ambiguous signals. Existing approaches to weak-signal analysis, strategic foresight, early-warning systems, and artificial intelligence address parts of this challenge but remain fragmented, particularly in connecting signal detection with interpretation and strategic assessment. This study develops Weak-Signal Intelligence (WSI) as an AI-enabled organizational capability for detecting, interpreting, integrating, and evaluating weak signals to anticipate emerging strategic disruptions. Using a conceptual theory-building and intelligent modeling approach, the study integrates insights from weak-signal research, strategic foresight, early-warning systems, artificial intelligence, sensemaking, uncertainty, and complexity. The proposed framework connects signal detection, interpretation, pattern recognition, uncertainty analytics, disruption assessment, strategic response intelligence, and adaptive learning. The model positions WSI as a continuous human–AI process that transforms low-visibility signals into uncertainty-aware strategic early-warning intelligence. The study contributes a conceptual foundation for improving strategic awareness and preparedness and provides directions for future empirical validation, measurement development, and cross-sector application.