Whether most of the verticals will be impacted, or, even, disrupted by the rapid rise of AI is no longer up for debate. Telecom would be no exception.According to Markets and Markets, the global market for AI software in the Telecommunications industry will have grown to nearly $ 2.5 billion from a modest $ 235.7 million in 2016.

Surprisingly, the onset of AI and Data Science will allow Telcos to not only make an investment and achieve better performance, but, also, to profit additionally, making the AI and Data Science transformation pay its way, at least, partially. Telecom operators will be able to create additional, meaningful revenue streams for themselves by tapping into the business niches they haven’t, previously, even, imagined could be profited from.
Read More: Opportunities In Telecoms

Let’s take a closer look at the three major possible areas of interest for Telecom businesses in the AI space, - improved performance, increased profitability of their service offering, and additional profitability and try to identify the more promising uses of AI and Data Science in the Telco sector.

Using Artificial Intelligence and Data Science to Improve Network Performance, Reliability, and Security
Just like most sizable enterprise companies, Telecoms have always had enough problems to deal with. Some of these problems have proven extremely resistant to any conventional means, and, obviously, require an utterly innovative approach to be disposed of. A plethora of recurrable issues, which can, potentially, crop up any minute, need to be monitored for and expeditiously detected and fixed if they, actually, occur. This is the exact reason why AI’s introduction in the Telco industry is underpinned by the concept of what one can call “a self-serving” (and, more specifically, self-regulating, self-healing and self-protecting) network.

The Сoncept of a "Self-Serving" Network
The Сoncept of a "Self-Serving" Network
Can we expect the present-day and near-future AI to be capable of making this concept a reality?

Read More: Telecoms with Artificial Intelligence
To answer this question, we’ll examine below some of the uses of Artificial Intelligence in Telecom that are becoming the driving force behind the Telco sector’s AI transformation. In our opinion, these uses of AI in Telecom prove that the technology is already mature enough to become a real game-changer.

Combatting Overload
With AI in place, you network will be able to respond automatically to any significant overload that may arise. It will become possible for the network to detect an overload, create automatically the number of virtual machines, required to handle the incoming amount of traffic, and funnel the excessive traffic via these virtual machines, promptly and without human involvement.

Optimizing Service Quality by Predicting Future Network Usage
The AI technology of Machine Learning can help you optimize your service quality.

You can use Machine Learning algorithms to predict how the usage of your network will vary across the different geographies it covers during a specific time period. It can be possible to factor in a host of criteria to achieve better optimization results, including time zone, hour, weather, national or regional holidays, and more.
Performing Predictive Maintenance
Without Artificial Intelligence, you can hardly, ever, preempt a hardware-related network problem, until it, actually, emerges to affect, or, sometimes, even, disrupt your normal operation.

Machine Learning is now turning the tables on you by giving you the ability to detect various alarming network signals (for example, those, emitted by cell towers or powerlines), that may be a precursor of a forthcoming network failure.
Read More: Telecommunications offer AI-driven services

In essence, Artificial Intelligence can take the robustness and reliability of your network to an entire new level.

Averting Malicious Actions
Machine Learning can reliably secure your network against malicious actions, such as, for example, DDoS attacks.

With Machine Learning, your network can be trained to identify a large number of similar requests, inundating it simultaneously, and make a decision on whether to deny these requests flat-out, or shunt them to another, less busy Data Center to be dealt with manually by your employees.