| Abstract | Decision-making and management in large organizations are inherently complicated and multilayered processes. These tasks are often prone to cognitive limitations, human errors, and biases, making them challenging to navigate effectively. Recently, with the advent of advanced AI models–particularly, LLMs and Agentic AI–new opportunities have emerged with unprecedented capabilities to address the fundamental limitations inherent in human decision-making. These advancements have tremendous potential to revolutionize the landscape of organizational level decision making with more efficient, accurate, safe, and responsible outcomes. Such technologies allow organizations to create safe and responsible mechanisms that validate decisions and comply with established regulatory frameworks yet remain operationally efficient. Furthermore, AI systems can synthesize organizational knowledge, ontologies, and external insights to provide managerial awareness and sophistication. We propose an intelligent platform called the Shadow Management system, designed to implement a dynamic feedback loop. This system integrates internal organizational knowledge, such as meeting briefs, policy documents, and operational guidelines, with external data sources, including federal rules and regulations, business intelligence, etc. Unlike previous approaches, our system combines role-based agent architectures with organizational knowledge graphs and secure communication protocols to create a comprehensive decision support infrastructure. Our contribution includes: 1) a hierarchical multi-agent architecture that mirrors organizational structures; 2) a secure role-based access control mechanism for organizational knowledge; 3) causality-augmented decision support algorithms; 4) an evaluation framework for measuring AI-enhanced organizational decision-making; and 5) a standardized multi-agent communication protocol enabling diverse LLMs to collaborate. |
|---|
