Knowledge Discovery in Intelligent Business Systems：Current Research and Future Directions

Driven by scientific management in enterprises，knowledge discovery in databases plays a critical role in transforming enterprises’ data resources into knowledge capabilities，and further into decision-making advantages. This study centers around the technological evolution of intelligent business systems，systematically reviews the developmental trajectory of enterprise knowledge discovery capabilities and typical application scenarios across different technological stages. We reveal how emerging technologies，represented by pre-trained large models and multi-agent systems，are shifting enterprise knowledge discovery paradigmatic from static analysis to dynamic generation，and from decision-support tools to decision-making agents. In response to the new challenges and demands facing enterprise knowledge discovery，this paper proposes future research directions characterized by multi-agent collaboration，multimodal integration，multi-system orchestration，and multi-level governance. The study aims to provide theoretical insights for constructing a future-oriented intelligent knowledge discovery framework，helping enterprises capitalize on the digital-intelligence dividend and advance toward management innovation and organizational transformation.