(untitled) (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
In the era of digital transformation, efficient information exchange is a critical factor in the success of electronic business systems. This article explores various models of information exchange, emphasizing graph models, neural networks, and artificial intelligence (AI). The study investigates the structural and functional aspects of information flow within e-business environments, analyzing how different computational models enhance data processing, security, and decision-making. Using graph theory, we model the relationships between business entities, while neural networks optimize data-driven interactions. AI-driven approaches further refine exchange processes by enabling adaptive learning and automation. The research employs a comparative analysis of these models, supported by quantitative metrics, simulation results, and case studies. Findings demonstrate that hybrid AI-graph models significantly improve transaction efficiency and security, reducing processing time and enhancing reliability. The article contributes to the theoretical and practical development of intelligent information exchange frameworks, offering insights for businesses seeking to optimize their digital ecosystems. Future research directions include integrating blockchain and quantum computing to further enhance security and efficiency.