How to Manage an AI Network Automation

Imagine a network that fixes slowdowns before users even notice them and automatically reroutes traffic when there is an outage, all without anyone having to do anything. And that's the power of AI network automation. For smart infrastructure, this is now the rule.

But it takes more than just putting in AI tools to run this kind of system. This blog will break down the most important steps you need to take to handle AI-driven network automation well.

1. Understand the Scope of AI Network Automation

Have an understanding of the tasks that need to be completed automatically, such as managing traffic, finding issues, or making adjustments. Put in writing what happens on the network daily, as well as the locations where issues or delays frequently occur.

Take into consideration the size of the network as well as the types of information that it provides and receives. There is a possibility that larger systems will require more sophisticated tools.

2. Choose the Right AI Tools and Platforms

Choose tools that can grow with you and meet your needs. Others work on traffic or tech problems, while others focus on security. Choose a solution that is compatible with your current setup. Do not go big just because it sounds cool.

Try out tools by using them in real life. Make sure they work with the tools and devices that are already in place. Easy-to-use tools, helpful customer service, and adaptable settings make life simpler.

3. Set Up Data Collection and Monitoring Systems

Keep an eye on network traffic, device health, and reaction times. To get info in real time, use smart sensors or tools for logging. The AI stays smart with the help of data. Without data, automation can't happen.

See if the system lets you know when something goes wrong. The goal is early notice, not being able to respond late.

4. Define Clear Automation Policies and Workflows

Ensure that you instruct the AI on what needs to be fixed, when to raise an alarm, and when to remain silent. Clear processes keep things from getting confusing and help keep mistakes from happening. In order for the system to work, break down big jobs into smaller steps.

Make rules easy to understand and test them often. Find out what the system should do if this happens. Make sure your team can understand the reasoning. Automation works better and safer once the rules are clear.

5. Integrate AI with Network Management Systems (NMS)

You can use AI tools with the network management tools you already have. This integration allows all systems to communicate effectively with one another. No more switching screens or doing the same things over and over.

To make the bridge, use APIs or built-in features. Make rules that tell each system what to do and when to share information. When everything is in line, AI can make better decisions.

6. Monitor AI Performance and Fine-Tune Models

Monitor the actions of the AI. Do problems get found quickly? Does it fix things or leave them out? Keep track of outcomes as well as actions. Performance lets you know what's working and what needs to be fixed.

Retrain models with new data. AI isn't perfect, but it learns new things all the time. Take out steps that make it harder to understand or slow it down.

Let AI Enhance, Not Replace

AI makes hard jobs easier, cuts down on downtime, and helps your team focus on bigger goals when it's used correctly.

Check out how Zinkworks helps telecoms and CSPs with advanced automation solutions if you're ready to grow your network operations with reliable technology. AI should not replace your ability to handle things smarter, faster, and better. It should help you do it.