8 areas where AI is driving the operations of next generation networks.

In the digital age where businesses are evolving faster, operations teams are having to deal with more information and digital complexity adding to the already significant operational workload. Artificial Intelligence, using machine learning, is the ideal precursor to automation that is critical for reducing both complexity and operational workload but also increasing the effectiveness of the team to make quick informed proactive decisions.

In the world of NetOps, we have found 8 ways that AI is touching aspects of our client’s operations:

1. Virtual assistants use natural language processing (NLP) that could query a network link for statistics in plain language for example the error rate on a specific link. These 24×7 bots are simple and efficient ways to handle customer first line events and pass the more complex issues onto the 2^nd line specialists.
2. NLP also helps to identify language sentiment. For example, calls logged that indicate customers in severe distress which can help prioritise workload or escalate to management.
3. Machine learning can also be used to dynamically understand what the broad issues are that the operations team is having to contend with and create a heat map for management to direct their attention and support the operations teams.
4. Anomaly detection is an important machine learning tool in the operations AIOps toolbox. When events happen outside the normal, operations teams can be notified, or specific actions can be taken to stop the event becoming a bigger issue.
5. Capacity management, i.e., predicting trends and identifying future capacity constraints assist the operations team in planning and the sales team in proactively engaging customers that might run out of capacity well in advance.
6. Dynamic routing of traffic across a network is another area that machine learning is quite capable of managing, whether it is failure, lowest latency or increased capacity AI is adapt at improving application networks. This is particularly well done by the SD-WAN solutions prevalent in the market.
7. Service level management reporting is usually reported historically, however, predictive reporting using machine learning could identify problem links availability in advance.
8. The holy grail of AI driven NetOps is the fault prediction, isolation with proactive automated remediation. Machine learning’s deep learning using neural networks have evolved over time to make this a reality and with the onset of open APIs, automation is a line of code away.

There is no doubt that networks are becoming more complex, but we have found that AI is an integral part of the network operations, evolving and becoming more intuitive, driving automation and essentially being the conductor for software defined next generation networks of the future.

However, while NetOps AI can adapt and help operations teams become more efficient, it does not exist within a bubble and DataOps and MLOps are key areas that need to be mutually developed. Access to data, data storage, data analytics and data processing are also important pillars. The inevitable operationalising of NetOps, DataOps and MLOps needs to be developed in unison.

Let’s a have a conversation, contact us for a further discussion.

Simon Harvey

See author's posts