Artificial intelligence (AI) is group of methodology that focus on formation of intelligent machines with the help of human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. The main application of artificial intelligence in telecommunications is for network management. The two key technologies that are widely in telecommunication industry are expert systems and machine learning. However, AI is expected to be more beneficial in telecom industry, if the operators upgrade their networks to Software Defined Networks (SDNs), which leads to network virtualization and the deployment of relatively better cloud-based services.
Advent of The fifth generation of mobile networks (5G) and Internet of Things (IoT) technologies, to build future networks is expected to aid in integration of AI in telecom industry. Mobile networks have to deal with heterogeneous data coming from all over the world and from a huge variety of systems, retailers and network types and they should have the ability to act in real-time. So, the analysis of these huge data sets from all over the world is time consuming and somewhere it is next to impossible. In this case Artificial Intelligence plays a key role because it is used to predict and analyze issues faster than human. Artificial Intelligence will make the fifth generation of mobile networks more open enabling connectivity to predictability.
To solve these issues in the telecom industry, machine learning tools are used, which is used to tackle the churning prediction problem. The methods used for solving churning problem includes artificial neural networks, decision trees learning, regression analysis, logistic regression, support vector machines, naive Bayes, sequential pattern mining and market basket analysis, linear discriminant analysis, and rough set approach. Hence, the use of machine learning helps to detect fraudulent calls in mobile phones by examining the user’s calling behavior.
However, lack of availability of cheaper hardware to develop AI-enabled products is the major factor restraining the growth of the AI in telecommunication market.
On the basis of component, the global artificial intelligence market in telecommunication industry is segmented into:
On the basis of mode of deployment, the global artificial intelligence market in telecommunication industry is segmented into:
- Cloud based
- On - premises
On the basis of application, the global artificial intelligence market in telecommunication industry is segmented into:
- Traffic Classification
- Resource utilization and Network optimization
- Anomaly detection
- Network Orchestration
Market Outlook – North America held the largest share in global artificial intelligence market in telecommunication industry in 2016
On the basis of geography, global artificial intelligence market in telecom industry is classified into North America, Europe, Asia Pacific, Latin America, Middle East, and Africa. North America held a dominant position in the global artificial intelligence market in telecom industry in 2016, and is expected to retain its dominance throughout the forecast period. This is due to increasing adoption of AI solutions and consumers in the region been early adopters of new technologies. In November 2017, according to Genpact survey of working population across the developed economies that included the U.S., the U.K., and Australia showed high inclination towards adoption of AI in industries including telecommunication. Genpact is a global professional services firm delivering digital transformation.
In addition to this, the Asia Pacific market is estimated to exhibit the highest CAGR over the forecast period. This is attributed to rising adoption of machine learning and natural language processing technologies and several 5G pilot projects. This is expected to boost the AI market in telecom industry. For instance, China Telecom Corporation Ltd. established a new 5G base station in Lanzhou on December 2017 to expand 5G pilot projects in China.
The key industry players include Atomwise, Inc., Lifegraph, Sense.ly, Inc., Zebra Medical Vision, Inc., Baidu, Inc., H2O ai, IBM Watson Health, NVIDIA, Enlitic, Inc., Google, Inc., Intel Corporation, Microsoft Corporation, and others.