Predictive Analytics and Autonomous Decision-Making: AI's Role in Enterprise Network Management for Smart Buildings

Abstract

Artificial intelligence has fundamentally transformed enterprise network management within smart building ecosystems, creating unprecedented opportunities for automation, optimization, and security enhancement. This integration enables networks to autonomously classify diverse traffic patterns, apply appropriate quality of service policies, and detect anomalous activities without continuous human intervention. The symbiotic relationship between AI systems and network engineers facilitates intent-based configuration, where high-level objectives translate into granular network adjustments. Smart buildings particularly benefit from this technological convergence as network-generated data informs environmental controls, occupancy management, and energy utilization. The resulting dynamic infrastructure demonstrates greater resilience, adaptability, and efficiency than traditional network architectures, while simultaneously reducing operational complexity. As building systems grow increasingly interconnected, this AI-enhanced network intelligence serves as the critical foundation for next-generation smart infrastructure development, enabling buildings to respond intelligently to changing conditions while maintaining optimal performance.