AI has become increasingly important in telcos' digital transformation strategies. TMTs are using AI-powered platforms that blend multi-source data with real-time sophisticated analytics to create hyper-personalized consumer engagements and differentiate themselves from competitors.

According to current research, the global AI in telecommunications market is expected to reach USD 14.99 billion by 2027, up from USD 11.89 billion in 2020, with a CAGR of 42.6% between 2021 and 2027.

For industry participants looking to win the race, we've compiled a list of the top application areas that telecom enterprises may use to redefine and claim their market share as technology-enabled frontrunners.

Predictive Maintenance

AI-driven predictive analytics, which uses data, ML techniques, and complicated algorithms, helps brands provide better services by forecasting future operational results based on previous data. This means that telecom operators can use data-driven insights to monitor the condition of their equipment and predict loss patterns.

Using artificial intelligence in telecommunications allows CSPs (Communications Service Providers) to proactively handle issues with communication hardware such as set-top boxes in customers' homes, power lines, cell towers, and data center servers.

Network Optimization

Beginning in 2019, 5G networks are estimated to service more than 1.7 billion clients globally by 2025, accounting for 20% of all connections. AI is required to build self-optimizing networks (SONs) for CSPs, allowing for this expansion. These allow network administrators to automatically improve network quality based on geography and time zone specific traffic data.

Advanced algorithms are utilized in the telecommunications sector of AI to search for patterns in data, allowing brands to detect and forecast network issues. Using AI in TMT helps CSPs to fix issues before they negatively impact customer experiences.

Digitized client service

According to a survey done by Salesforce Research in the United Kingdom, chatbots currently handle only 4% of emails and interactions, while AI handles 20% of chats and 5% of emails with human support. This is a significant cost opportunity for major TMT businesses that handle 100,000 or more interactions each week.

Intelligent bots are predicted to play an increasing role in automating processes such as sentiment analysis, translations, search query contextualization, and so on. This is in light of the COVID-19 epidemic, which accelerated the transition to digital-first services and the rapid developments being made in Transformer AI, such as BERT, GPT-3, a Deep Learning-based model for self-attention in the NLP area.

AI-Powered Cybersecurity

For TMT companies, cybersecurity and copyright piracy have proven to be significant challenges. The coronavirus epidemic increased US piracy rates by more than 40%. In 2020 alone, the average cost of a malware attack on a telecom company was almost $900,000.

According to recent research, Deep Reinforcement Learning (DRL) security techniques are being offered as applications to improve autonomous intrusion detections, cyber-physical systems, and multi-agent game-theoretic simulators of defense strategies.

Without the need for manual intervention, software tools powered by these AI solutions and linked via low latency networks will be able to react to significant events, security risks, and so on, as well as act on network alterations, patching, and predictive governance in real time.