So, have you heard the news that created quite a commotion worldwide, especially in new technology industries?
Well, the rumor is that Google’s artificial intelligence has become a “thinking being,” with feelings, no less – according to the Google engineer, that was suspended pretty quickly after that. He states that the computer chatbot is now an entity that thinks like a human being. As you can imagine, this raised new questions about the secrets hidden behind the development of artificial intelligence.
“If I didn’t know what it was – and it is LaMDA, short for “Language Model for Dialogue Applications”, a computer program that we recently developed – I would say that I am communicating with a real child aged seven or eight.”, told Blake Lemoin to the Washington Post.
This inspired us to write about Machine Learning and Artificial Intelligence in Telco industry. But, before we start, let’s see the difference between those two. Artificial intelligence is a technology that enables a machine to simulate human behavior; we get that. But, what is ML then?
Machine learning is a subset of AI that allows a machine to learn automatically from past data without explicitly programming. We can conclude that the primary goal of AI is to make intelligent computer systems much like humans when it comes to complex problem-solving.
As stated in Britannica, Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that humans usually do because they require human intelligence and discernment.
Regarding the telecom industry, AI is used through its advanced algorithms. Those are applied to look for patterns within the data, enabling telecoms to detect and predict network anomalies so that communications service providers can proactively fix problems before customers are negatively impacted.
Are there any challenges in implementing Artificial intelligence in Telco?
Implementing AI and ML does not come without consequences. Before all, many organizations struggle with data collection as Artificial Intelligence algorithms require clean and well-structured data. Moreover, almost 80% of any ML project is dedicated to extracting, transforming, loading, and cleaning data. Consequently, establishing a suitable Big Data engineering ecosystem that can collect, integrate, store, and process data from diverse siloed data sources is of the utmost importance.
Besides that, there is a question of technical integration. Old legacy systems are the main reason many AI integration fails. When you add to that a constant lack of technical expertise, it is pretty clear that we have a long way ahead. Being a relatively new technology, ML and AI in Telecom are facing the problem of limited local talent. This can be resolved with a technical partner that can implement AI in telecommunications. But, as initiating AI in Telecom can be pretty high-priced, finding the right partner is crucial.
What are the most common use cases of AI for Telco?
- Predictive Maintenance
This new technology significantly improved the telecom industry’s ways of extracting valuable business insights. The very “nature” of the telecom business requires a massive amount of Big Data, and AI uses it to generate efficient and effective decisions through customer segmentation, predicting the lifetime value of any consumer. This enables better purchase recommendations.
Predictive analytics works by the principle of finding patterns in historical data. This makes possible the accurate anticipation and potential warning about possible hardware failures. But it doesn’t end there.
Its algorithms and data science models can even identify the reason behind every failure, allowing to fix a problem at its root. This type of preventive support will permit telecom companies to be proactive at maintaining their equipment, resolving issues before they occur, and minimizing support requests. No need to say just how much this will boost the customer experience!
- Network optimization improvement
It is not an understatement to say that modern communication networks are complicated and challenging to handle and maintain. Introducing the 5G will not make things easier. On the contrary, deploying AI&ML technologies could be a sort of cavalry!
This new technology is planned to help operators leverage advanced automation in their network operations to optimize their architecture while improving management and control. And hence – customer satisfaction, lowering customer service costs, preventing outages, and keeping good network quality.
This all can be a result of various data parameters that are to be collected from the customer and their devices. We are talking here about their requests, complaints, and service logs. These are all analyzed to help telecoms uncover trends and performance issues in different demographics, time zones, devices, and locations.
3. Network anomalies
Anomaly detection coming from the AI can effectively augment and automate early detection, predictions, and decision-making regarding operations and business processes where people can’t handle data volume or velocity.
When the time of detecting the deviation is improved, the resolution of incidents becomes quicker, reducing costs associated with the interruption of, while at the same time – the prevention of lost revenue and brand impact is improved.
Also, this type of anomaly detection can analyze multiple dimensions of data sources – looking at the cell, subscriber, and device-level KPIs, faults monitoring in network equipment and correlating alerts across domains for noise reduction and root cause analysis.
And just like that – you get a transparent view of network and service performance. And let us not forget the customer experience, as well.
These are only a few use cases of AI and ML in telecom. To that list, we can add fraud detection and prevention as anti-fraud analytical systems can detect suspicious behavioral patterns, immediately blocking services or accounts by processing call and data transfer logs in real-time.
Artificial intelligence can make faster and sometimes even better decisions than humans, as we all sometimes need a few seconds or even minutes to make a decision. But, simultaneously, a machine learning model processes a considerable amount of data items in a fraction of a second!
Also, expert knowledge can be used more efficiently because ML expert knowledge can be distilled into a model and applied more widely.
No wonder Nokia, Vodafone, and Deutsche Telecom have been making considerable investments in AI at various levels. The range of potential Artificial Intelligence use in telecom is surprisingly broad. We can be sure that market players will see increasingly intelligent automation systems rise to streamline day-to-day operations, delivering more and more value to customers!