The benefits of AI in network operations

Use cases for AI in network operations

According to Bradbury, enterprises implement AI in their networks to enhance network operations, primarily for network automation.

"Network automation is about automating the network," he said. "As we think about AI, we're starting to realize AI is going to be about automating the automations."

Ways in which AI can enhance network operations include the following:

• Optimize network efficiency. AI can help network teams generate codes to automate configuration management.
• Improve network reliability. AI can help with root cause analysis to enable enhanced troubleshooting.
• Advance network security. AI can automate remediation capabilities to enforce cyber policies across the network.

Use AI to build a source of truth

Although AI can help network teams manage and automate complex network operations, Bradbury said network professionals remain skeptical of AI. Not only are they unsure of how to use AI, but they're also reluctant to trust it.

Even before the advent of AI, many network professionals were reluctant to consider network automation. Those feelings of doubt have only been exacerbated due to AI's development. But Bradbury said AI's ability to build trust through a source of truth is one of its most important capabilities.

AI can help facilitate the data already part of a network source of truth and make it more accessible to users for data analysis. This can help network professionals with network operations and enable them to accelerate network automation.

"A network source of truth is fundamental to any automation effort," Bradbury said. "Without a solid, objectively true underlying data set, the AI doesn't have a chance to be right. You've got to start with a network source of truth."

Once network professionals establish a source of truth with AI capabilities, the AI tool can provide several benefits to organizations. For example, when an AI tool has access to reliable data, it can make more accurate predictions and provide recommendations based on that data.

In addition, Bradbury said having a network source of truth enables organizations to use multivendor AI services. When organizations have a network source of truth, they can apply AI to the data, regardless of the vendor. This lets organizations choose whichever vendor platforms they want however they prefer.

Future of AI in network operations

As AI becomes a major part of the technology and enterprise landscape, Bradbury recommended organizations take the following steps to streamline AI deployment in networks:

1. Integrate AI into networks.
2. Make decisions -- such as establishing partnerships, figuring out how to apply AI and selecting suitable AI tools -- to plan for the AI wave.
3. Train and develop tools to prepare network teams for AI use.
4. Collaborate with industry partners to make decisions about AI.

These steps will enable organizations to redefine network management and unlock the potential of AI-powered networks, he said.

Deanna Darah is associate site editor for TechTarget's Networking site. She began editing and writing at TechTarget after graduating from the University of Massachusetts Lowell in 2021.