AI-Driven Solutions For Enterprise Network Optimization

Enterprise networks have become increasingly complex due to the proliferation of connected devices, cloud services, and distributed workforces. Traditional network management approaches often struggle to maintain optimal performance, reliability, and security in such dynamic environments. AI-driven solutions offer a transformative approach to enterprise network optimization by leveraging machine learning, predictive analytics, and intelligent automation. This study explores the application of AI in network traffic analysis, congestion management, fault detection, predictive maintenance, and security threat mitigation. It examines how AI models can dynamically optimize routing, bandwidth allocation, and quality of service while reducing human intervention and operational costs. The paper also highlights the integration of AI with software-defined networking (SDN) and network function virtualization (NFV) to create adaptive and self-healing networks. Challenges such as data privacy, model interpretability, and integration with legacy systems are discussed, along with strategies to overcome them. The findings indicate that AI-driven network optimization enhances performance, reduces downtime, improves security, and supports scalable enterprise operations in increasingly complex network landscapes