The Impact of AI/ML within the Core Network Activities

Artificial Intelligence and Machine Learning have the potential to impact telecommunications in almost every aspect, thanks in part to the enormous power of cloud-based computing. In a recent survey conducted by our team for one of the largest research firms in the world, we uncovered the impact of AI/ML within the core network activities. This article shares the fascinating possibilities of augmenting human intelligence with new technology.

In the most advanced self-organizing networks, AI and ML are already part of the core activity, which allows mobile networks to adapt in a fluid way to alter capacity and configure networks on-demand. This technology is making vast improvements to real-time analysis of customer activity and data by predicting the right service to offer at the right time and to anticipate and react to issues. 

Two Key Areas 

When deploying the potential of AI/ML within the core network activities, two key areas stand out as being impacted most by the benefits of this technology: 

• Customer Experience and Satisfaction – this area includes real-time analysis of customer-related activities and the data they generate, such as chatbots interaction and requests for care or services. Thanks to nonlinear algorithms employed by AI/ML, customer experience and satisfaction are improved immediately.
• Reliability and Optimization – this area includes the issue resolution through the ability of AI/ML systems to recognize the root cause of a service issue and the ability to take corrective action immediately. According to the survey responses, 70 percent of the executives believe that improving the reliability of their service for the customer is the biggest impact that AI/ML currently has within the core network activities. 

The Research

A survey was conducted on a group of executives in the telecommunications industry, with responses gathered through telephonic interviews and online surveys of the telecommunications industry. The data analyzed for the survey came from the 165 market leaders in 6 regions around the world that provided confirmed, verified responses. The goal was to understand AI and ML and how this technology is deployed with the goal of improving customer satisfaction. More than three-quarters of the executives surveyed agreed that AI/ML is a requirement for their systems going forward.

Improving Reliability 

One significant event on the horizon that AI/ML will have a significant contribution to is the rollout of 5G networks. Through nonlinear modeling of network activities impacted by subscriber growth, data expansion, and design alterations, AI/ML activities will assure market leaders of continuous adaptation of network services with zero downtime.

 Predicting future outages and constraints in service quality is an activity that AI/ML thrives in, which will be of critical importance to manage the 5G network and the complex parameters and configurations. By employing AI/ML to understand, adapt, and expand the network according to needed capacity, market leaders can assure customers of service reliability over the competition. 

Data Quality 

The survey also uncovered that the quality of the data gleaned by the AI/ML systems in place is a recurring challenge to the value of the system overall. More than half of the respondents said data quality issues are the biggest barrier to the company’s ability to draw actionable insights from data and act on them. The telecommunications industry can respond to these quality issues by taking steps to address them immediately. 

FAIR Data Principles 

Ensuring that data gathered by AI/ML systems comply with the FAIR Principles can provide confidence in the information, making the system more valuable. Data must be Findable, Accessible, Interoperable, and Reusable, according to the FAIR system. To break down these principles:

• Findable – Data (and supplementary data) is a unique, persistent identification.
• Accessible – Machines, as well as humans, can understand the data, which resides in a trusted place.
• Interoperable – Data is represented by a broadly applicable language that is shared and formal.
• Reusable – Clear usage licenses exist for the data and provide accurate information if qualified.

Overall, the use of AI/ML within core network activities is impactful only if the data is of a high standard, which can be solved by adhering to the FAIR data principals adopted by organizations around the world. Where most executives see the largest impact for their company is by improving reliability for the customer. One-third of the executives responding to the survey point to Network Infrastructure/Engineering as being the hungriest for adopting AI/ML at scale across network operations. 

Implementing AI/ML at scale is a long, but exciting process, full of possibilities and challenges. Embracing the culture of AI/ML throughout the organization is the first step, with careful consideration by the entire organization on where this technology can add value. Implementing AI/ML to process the vast amounts of data generated by a market leader is a way to introduce technology to the organization. Finally, deploying AI/ML as an augmentation of human intelligence is the ultimate goal, enhancing the capabilities of human customer service agents, engineers, sales reps, and more to efficiently use their time and serve customers better than ever before.