AI in Network - the Inevitable Choice of Long-Termism

In today’s rapidly changing digital economy, enterprises are encountering unprecedented network complexity and increased competition. The traditional model for network operation and maintenance is no longer sufficient to handle the high complexity of enterprise IT services, the diverse needs of businesses, and the urgent requirement for efficient management and continuous optimization in the AI era. In this context, AI in Network—network management powered by artificial intelligence—is emerging as a critical component of enterprise network operation and maintenance. It not only ensures the efficient and stable operation of networks but also lays a strong foundation for the long-term strategic planning of businesses. This article will explore how AI in Network can serve as an essential strategic tool for the long-term development of enterprises through four key dimensions: visualization, intelligent troubleshooting, intelligent tuning, and sustainability in low-carbon practices.

01 Network visualization: a leap from data perception to intelligent decision-making

As the scale of enterprise networks continues to expand, it is particularly important to grasp the network operation status in real time and accurately. Network visualization technology, as an important part of AI in Network, provides the operation and maintenance team with an intuitive view of network health, which not only provides an overview of the overall situation, but also allows in-depth details to capture those fleeting abnormal signals. Taking H3C's panoramic operation and maintenance map technology as an example, it integrates multi-dimensional information such as network topology, traffic analysis, and device status to create a real-time and dynamic network monitoring platform for enterprises, greatly improving operation and maintenance efficiency.

Research shows that a network operation and maintenance platform that uses visualization technology can shorten fault identification time by 40%. At the same time, through in-depth analysis of visualization data, enterprises can more accurately optimize resource allocation and improve overall network efficiency. This technology not only helps enterprises respond to network events quickly, but also provides reliable data support for long-term network planning, enabling enterprises to predict future needs based on historical traffic trends, make more forward-looking network expansion decisions, and effectively avoid waste of resources caused by blind investment.

02 Intelligent troubleshooting: shortening downtime and ensuring business continuity

For enterprises, network interruption often means business interruption and huge economic losses. According to Gartner research, for large enterprises that are highly dependent on network operations, the cost of network downtime per hour may be as high as $560,000, which is undoubtedly a fatal blow to real-time service-oriented enterprises. Therefore, intelligent troubleshooting capabilities have become the key to ensuring high network availability for enterprises. Practice has proved that enterprises that deploy intelligent troubleshooting functions can reduce the mean time to recovery (MTTR) of network interruptions by more than 50%, greatly reducing the impact of failures on business operations.

AI in Network uses AI technology to realize automatic fault identification, analysis and repair, greatly shortening the time of manual intervention. The AI system can accurately capture anomalies from massive network data, quickly locate the root cause of the fault, and provide repair suggestions. For example, the terminal intelligent diagnosis function of H3C AI in WLAN technology introduces knowledge graphs and KDN architecture, and flexibly associates multi-dimensional information through machine learning and reasoning, which can find the root cause of the problem more accurately and quickly, and improves efficiency by 91% compared with traditional methods, thereby improving network reliability and business continuity.

On this basis, AI in Network can also predict network failures based on historical information, using big data and machine learning, thereby achieving "preventive treatment" and keeping the enterprise network in a healthy state at all times, thereby achieving business sustainability.

03 Intelligent Tuning: Efficient Utilization of Resources to Ensure Network Performance

The load and traffic of enterprise networks are always changing dynamically. Especially with the expansion of cloud services and the implementation of more AI application scenarios, the demand for network resources is increasing. Traditional resource management methods are no longer able to meet the needs of modern networks due to their lack of flexibility. Intelligent tuning uses AI technology to achieve real-time monitoring and dynamic adjustment of network resources, ensuring that the network is always in the best operating state.

AI in Network 's intelligent tuning functions, including automatic bandwidth allocation, intelligent routing optimization, and dynamic traffic management, can adjust resource configuration in real time according to changes in business needs. For example, the intelligent routing and scheduling functions of the wide area network can automatically select the optimal data transmission path based on application requirements and the status of the entire network to ensure efficient operation of the network. This not only improves the company's network performance, but also avoids network bottlenecks caused by improper resource allocation and speeds up the response speed of key businesses.

According to IDC research, after enterprises deployed intelligent tuning technology, they significantly improved resource utilization and, on this basis, reduced network management costs by 20%-25%. This means that enterprises can expand and optimize their networks without having to invest in additional hardware, and ensure the long-term stability of the network and continued business growth through intelligent tuning strategies.

04 Green and low-carbon: Intelligent energy saving to promote sustainable development

Against the backdrop of increasingly severe global climate change, companies not only need to focus on operational efficiency, but also actively fulfill their social responsibilities and reduce their carbon footprint. The green and low-carbon function of AI in Network effectively reduces energy consumption in network operations through intelligent device management and power consumption optimization. According to the International Energy Agency (IEA), by 2030, data center energy consumption will account for 13% of the world's total electricity consumption. Therefore, reducing energy consumption through intelligent energy-saving technologies has become an urgent issue for enterprises.

AI in Network significantly reduces the energy consumption of network equipment through energy-saving technologies such as intelligent fan control and automated port management, combined with the use of liquid cooling technology and new chip technology. This not only reduces the company's operating costs, but also complies with global low-carbon and environmental protection policy requirements and enhances the company's social image. In addition, intelligent energy-saving technology also reduces the company's equipment replacement and maintenance costs by extending the service life of equipment, thereby achieving operational sustainability.

AI in Network——A wise choice under the long-term strategy

In summary, the significance of AI in network management for the long-term strategy of enterprises is clear. AI technology offers companies more efficient and intelligent solutions for managing their networks through four key capabilities: visualization, intelligent troubleshooting, intelligent tuning, and green, low-carbon initiatives. As network demand continues to grow and technology advances, AI in networking will not only enhance the efficiency of operations and maintenance but also significantly lower operating costs. This will help enterprises maintain a sustainable competitive advantage in a challenging market.

Visualization technology provides businesses with comprehensive and real-time insights into their networks. Intelligent troubleshooting minimizes the impact of network disruptions on operations, while intelligent tuning optimizes network performance through dynamic resource management. Additionally, green and low-carbon technologies help organizations achieve their strategic sustainability goals while reducing energy consumption. The integration of these capabilities makes AI in networking not just an essential tool for modern network management, but also crucial for long-term success and sustainable development.