Network management AI is the entry ticket to the network future

Related Vendors

Juniper Networks GmbH

FAULHABER Drive Systems

Wuxi InfiMotion Technology Co., Ltd.

With the use of AI, companies can manage the growing challenges in network management. However, they need to pay attention to some important aspects. Five performance features should not be overlooked in a future-proof network environment.

Dieter Badmann works as Enterprise Sales Lead DACH at Juniper Networks.

Companies need to be flexible and responsive to new or changing market and customer requirements. The significance of edge computing environments is increasing and hybrid working remains a trend. Network environments are becoming more distributed and complex. Modern networks generate so many data that it is practically impossible to manually correlate them or gain real-time insights.

Network teams can meet these challenges by using AI in conjunction with automation to increase operational efficiency and shift from a reactive to a proactive or even predictive management approach.

When companies evaluate AI-based network solutions, they must first determine the effectiveness and value of a solution. According to Juniper Networks, the five key criteria for evaluating such solutions include:

A cloud-based concept: A network management system should be cloud-based. This allows solution providers to collect all necessary network data—both real and synthetic—across all network areas and all users, and use it to create accurate algorithms.

A platform approach that provides end-to-end transparency: A platform approach eliminates data silos and ensures that all data from wired and wireless technologies, software-defined WANs, switches, and routers can be collected and utilized across the entire enterprise environment. This includes data centers, home offices, and all campus, branch, and cloud networks, providing end-to-end transparency across the entire network.

A virtual assistant with a dialog-oriented user interface: Ideally, network operations teams are supported by a virtual assistant. It enables queries and responses in natural language, allowing for fast, easy, and accurate extraction of information. This makes managing complex networks accessible to a wider range of users. In principle, such an assistant reduces the time and cost effort of operation teams, for example, when learning vendor-specific command line interfaces.

The use of trustworthy, granular data: For accurate analysis and decision-making, deep insights into network operations are crucial. These are obtained through telemetry and metadata at the session and application level, with data collection enabled by cloud-based management. Furthermore, algorithms for network optimization must be precisely targeted at the right problems, for example, not just on up and down status, but also on connection times and other factors that can affect the user experience. This requires contextual data and, in some cases, even a combination of real and synthetic data generated by a network provider to ensure optimal configuration.

A bidirectional API ecosystem: Gaining transparency across an entire network is a first step. However, data can also be collected from other systems and tools or adjacent areas, such as from collaboration applications or private 5G networks. This requires APIs that support a bidirectional flow of data. An open and extendable API ecosystem allows for continuous improvement of network environments as well as a general increase in operational efficiency—such as through the integration of workflow automation and security tools.

The introduction of AI in the network area comes with several challenges. Key success factors include the right data, the right real-time response, and the right secure infrastructure. With an AI-native network platform that meets the aforementioned five criteria, these obstacles can be overcome, ensuring efficient AI-Ops usage. This enables companies to relieve their operations teams and provide them with opportunities for strategic initiatives and driving innovation. Ultimately, this venture with AI into the network future also benefits a company's competitiveness.