With 4G dominating the world and 5G networks around the corner, Telecommunications have to deal with ever-increasing data consumption. That’s why optimizing networks to withstand this type of increased data usage is becoming one of the main strategic solutions in the industry.

Telco enterprises implement Artificial Intelligence in two key areas:

  1. Customer service
  2. Network maintenance

The successful application of this technology can lead to staggering results.

AI in practice for network maintenance

Network maintenance is often considered a second generation of AI-powered solutions. It focuses on a software-oriented approach to self-maintenance, self-optimization, and self-learning networks (Machine Learning).

Network providers send field workers to sites to perform periodic infrastructure maintenance. This leads to frequent delays and errors that negatively affect the customer experience. This method is important and widely used today thus downtime can be avoided thanks to AI.

Today, algorithms can monitor millions of signals and data points within a network to detect threatening problems before they occur. Based on this data, the company can respond by balancing the load, restarting the software, or sending a person to fix the problem.

Here are some examples of AI-powered network analysis in action:

  • AI-powered systems that can restart cell towers based on their behavior, e.g. if they do not connect to the network.
  • Algorithms can identify parts of the network that need investment and would lead to the highest ROI.
  • Similarly, network operators can use AI to identify parts of networks with a large number of users that would benefit from network improvements, leading to greater profits.
  • Optimize network behavior based on weather data, daily traffic, and real-time data usage.
  • Improve network usage and customer satisfaction through dynamic resource allocation.

Some cases of using machine learning algorithms for sales and personalized user experience include:

  • Prepare personalized recommendations based on behavioral patterns and content preferences.
  • Making appropriate sales and cross-selling to the right consumers at the right time.
  • Decide which cellular and data package is best for different types of users, increasing sales success.
  • Detect and fix potential problems for customers, even before they are obvious to the end-user.
  • Analyze social media, brand coverage, and customer sentiment to learn what makes customers choose a certain service provider and what could make them leave.

Before you take the first step in implementing Artificial Intelligence in your company, we recommend that you consider the following matters:

  1. What are the main areas where you want to see improvement? Your department for customer service, sales, network operations, maintenance?
  2. Are you sure that AI is the optimal solution to your business needs and problems?
  3. Do you have the necessary data for the algorithms to learn from, or do you need to set up a data warehouse or lake first?

Create a roadmap for AI implementation

One AI model can accomplish a lot for a specific business unit within telecom, but several machine learning models working together can work wonders, which is why the roadmap for an artificial intelligence initiative is important.

In other words, the output of one model of artificial intelligence can contribute to another model by making them more powerful together.

Budgets and return on investment are important, so it is necessary to choose a suitable starting point. Think big, but start small in order to implement a model with a good ROI. But even more important is the Time-to-Value KPI – the sooner the better.

In that case, a specialized tool can be useful for simplifying the task. For example, the Power Platform can make AI intuitive. You can quickly simplify tasks using AI models like the ones we just mentioned:

  • Prediction
  • Form processing
  • Object detection
  • Category classification (visualization)
  • Entity extraction (visualization)

The roadmap creates a clear plan for the next machine learning models to be deployed and worked on, while others learn and improve. So, start with what you know – the company’s goals and current and future processes.