How artificial intelligence can solve network problems

Artificial intelligence (AI) is no longer just a buzzed-about word. The release of ChatGPT and Microsoft’s announcement that it will invest US$10 billion in AI indicate that artificial intelligence has entered real life from the “future”.

For networking professionals, there are two factors to consider with the rise of artificial intelligence. First, how will its traffic impact the network, and second, how can they use it to better manage their network?

Can you still control the network?

Over the past two years, the rapid shift to the cloud has thrown many enterprise networking teams into disarray. In some cases, teams lost control of the network as the core of the business moved from on-premises to hybrid cloud environments. The challenge for networking teams is that their traffic still flows to the data center the way it should. Network management and workflow automation need to be reimagined now.

While artificial intelligence can undoubtedly help monitor networks, it also adds its own strain to networks. Cloud-based AI tools require networks to manage and adapt to the high volumes of data traffic between internal and external environments as they shift and move them. In fact, AI is everywhere, in analytics tools, IoT and intelligent edge devices, spam filters, and even content creation tools. Since these require their share of the network, they can also create traffic surges and latency issues.

AI for mission-critical networks

Artificial intelligence-driven traffic management, network management and monitoring tools are maturing. However, while these AI-infused tools offer a lifeline to resource-constrained network teams, there remains some skepticism about how much control we can truly hand over to these systems to help manage increasingly vulnerable networks. For example, potential network outages spiral even further out of control.

The answer lies in the use of “explainable AI,” AI solutions that network administrators can still engage with and whose inner workings they understand. Trust begins to build when network teams understand how the AI ​​makes decisions and can use the team to provide regular feedback on whether the AI's findings are successful in improving or managing performance.

Embracing the power of AI in networking

But skepticism aside, enterprise networking has been one of the industries most aggressive in adopting artificial intelligence and automation. It is used by network teams for a variety of network functions, extending to performance monitoring, alert suppression, root cause analysis, and anomaly detection. For example, Juniper Networks Mist AI automates network configuration and handles optimization.

The main catalyst is that artificial intelligence can help improve customer experience. In a recent article, Juniper Networks Chief Artificial Intelligence Officer Bob Friday said, "AI's ability to adapt and learn from changes in client-to-cloud connectivity will make AI ideal for the most dynamic networking use cases."

One example of where artificial intelligence can help improve customer experience is wireless user experience. It can provide insights and better manage the spiderweb of wireless connectivity created by mobile devices and work-from-home use cases. In this case, AI can provide insight into circumstances that many network professionals cannot control.

Give some control to AI

One of the most common applications of artificial intelligence on the web is its role in search and chatbots. Network professionals can find their way through a pile of support tickets with the help of chatbots and virtual assistants built using natural language processing (NLP) and natural language understanding (NLU).

When these bots understand the questions posed by users, they can respond with information and suggestions based on the knowledge they gain from observing the network and the insights they have been trained on. This is a form of client-to-cloud insights and automation in which chatbots provide context and meaning to user questions, not just yes or no. And the longer they run, the more intuitive they become.

While using Juniper Mist AI and its Marvis chatbot, a global retail giant has been able to gather insights into potential issues with its network and how to fix them. Because Mist AI continuously measures baseline performance, it automatically issues alerts if deviations occur.

Preparing for Artificial Intelligence

In industries where skills are scarce, IT and networking professionals must embrace the idea that AI will free them from mundane, repetitive chores. They should also know that no business can expect network professionals to become AI experts overnight. They should prepare themselves for the inevitable exposure to AI-enabled devices and systems.

To better manage their networks, network professionals should determine how they can start using their brains to manage these networks, working with data scientists, developers, and IT departments to identify the AI ​​tools they need , and start using artificial intelligence in the network more effectively.

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