10 ways telecom companies use artificial intelligence & machine learning

Artificial Intelligence (AI) and Machine Learning (ML) are seeping into the telecom sector in several different ways. For companies, AI adoption is not just about harnessing the power of data and Artificial Intelligence to improve their services and business operations but about holding the ground and surviving among their competitors.

Here are several major ways that telecom companies are using AI and machine learning in their day-to-day business to both flourish and survive.

1. Better Customer Service with Chatbots and Virtual Assistants

Changing market climate and evolving customer expectations make it difficult for all companies to identify and meet customer preferences and needs. No industry will survive if the customers are unhappy. Telecom is no exception. So, the first way telecom companies use AI is to improve their customer service by incorporating Virtual Assistants and Chatbots.

Bots can automate and streamline numerous backend processes and issues related to installation, maintenance, and troubleshooting that telecom companies face daily. These virtual assistants automatically deal with and respond to support requests, saving a substantial cost of hiring workforce. Unlike their human counterparts, chatbots can operate 24/7, and when equipped with machine learning, can learn and analyze customer requests, identify sales opportunities, route and escalate customer queries to higher authorities if necessary. They can also recommend customers about other products and relevant services based on their profiles and preferences. This ability to analyze data in a short time to provide better solutions or suggestions makes them far superior to their human counterparts.

With many chatbots already offering speech and voice services, they are not only becoming more ‘human’ but also accessible to people with disabilities. Telecom guides that ‘speak’ network names, show titles, time slots, etc. help customers with special needs, who need such speech assistance to navigate through their options more easily.

The use of AI and machine learning has already proved to be a massive success for customer service programs of large telecom operators like AT&T, Verizon, and Comcast. Vodafone, for instance, reported a 68% improvement in their customer satisfaction after incorporating a Chabot called TOBi. As technology evolves, these chatbots can become smarter and come up with more intelligent and cost-effective solutions for complex tasks, instead of companies hiring and relying on fallible human beings.

2. Predictive Maintenance and Network Optimization

AI-powered predictive maintenance isn’t in the spotlight yet, but it is an essential use case to prevent outages. It involves algorithms to monitor and anticipate equipment failures so that the maintenance managers can fix them in advance. Coupled with visualization tools, they allow operators to see what’s coming and direct their attention accordingly.

Since ML processes continuously learn and improve, we begin to see the rise of new technology – Self Organizing Network (SON) that can self-analyze and self-optimize, eliminating manual configuration of network during deployment, optimization, and troubleshooting. Improving network performance, SON can significantly reduce the cost of mobile operator services.

3. Robotic Process Automation (RPA)

The sheer volume of customers that a telecom company deals with every day leaves room for a variety of human errors. Automation of processes by incorporating AI reduces the margin of such mistakes, besides ensuring that all repetitive operations run far more smoothly and accurately than manual completion of tasks. RPA improves data quality, reduces average response time, and makes the entire operation more scalable and adaptable. Realizing the benefits of RPA, all leading telecom companies are making significant investments in RPA these days. For instance, AT&T has more than 200 types of bots, handling repetitive and mundane tasks such as entering information into their legacy system.

4. Predictive Analytics Leading to Quick Data-Driven Decisions

Telecom companies own a tremendous amount of customer data. To analyze and derive valuable insights from this data is a cumbersome task for people, but not for AI. Data analytics, armed with AI and Machine Learning algorithms, allows telecom players to gain a competitive edge by understanding the data quickly and effectively and make better and faster business decisions in real-time, saving both money and time. It also helps companies to build better products, understand patterns, resolve issues that crop up much faster and sometimes even prevent them from happening. All this eventually translates to better business decisions and increased customer satisfaction.

5. Fraud Detection and Prevention

Online fraud is increasing rapidly and poses the biggest menace to the telecom industry. Fortunately, a fraudster often leaves a digital trail. Machine learning algorithms follow this trail, learn to differentiate between regular and fraudulent activities and detect any such activities, including fake profiles, identity theft, illegal access, and much more. It is also known as Supervised Machine Learning, wherein each transaction or activity is tagged as either fraud or non-fraud. It goes through extensive data sets in a fraction of the time than a human analyst, detecting anomalies along the way. It can provide both real-time and pre-emptive responses to fraudulent or suspicious activity by understanding the behaviors of individuals, accounts, devices, etc. Adaptive Analytics continually update machine learning models based on the analysis of the fraudulent activities. It gives these Machine Learning algorithms an edge over future fraudsters and prevents such issues from cropping up.

6. Enhancing Endpoint Security

Cyber attackers, combining bots AI and machine learning tools to bypass endpoint security controls, raise significant threats for telecom companies. The risks are escalating so rapidly that traditional ways of securing endpoints based on hardware aren’t stopping attackers anymore. Sophisticated breach attempts are being made using AI and machine learning, and the time it takes to compromise an endpoint has been shortened down to just 7 minutes, after which the attackers gain complete access to internal systems and valuable data.

Thanks to cloud platforms; they can help AI-based endpoint security control applications to adapt dynamically to various types of threats. Data Security, Cloud Security, and Infrastructure Protection are the fastest-growing areas of security spending through 2023, and this is something that telecom companies are investing in. 80% of telecom companies are counting on AI to help identify threats and thwart attacks, according to research by Capgemini. $7.1 billion was spent on AI and machine learning-based cybersecurity systems and services in 2018. This spending will grow to $30.9B by 2025, according to a study by Zion Market Research.

7. Network Capacity Planning and Optimization

As the demand for data increases exponentially, telecom companies are under constant pressure to ensure their networks can handle the load efficiently. AI and ML are revolutionizing network capacity planning and optimization. These technologies analyze vast amounts of data from network traffic patterns, usage trends, and customer behaviors to predict future demand accurately. This enables telecom providers to optimize their network infrastructure proactively, ensuring high service quality without over-investing in unnecessary capacity. By leveraging AI for network optimization, telecom companies can deliver faster and more reliable services to their customers while managing operational costs effectively.

8. Personalized Customer Experiences

AI and ML enable telecom companies to offer personalized experiences to their customers. By analyzing customer data, including browsing history, service usage, and preferences, AI can tailor services and recommendations to individual users. This personalization extends to marketing campaigns, where AI-driven insights help create targeted offers that resonate with specific customer segments. For instance, AI can identify customers who are likely to churn and proactively offer them incentives to stay, thus improving customer retention rates. This level of personalization enhances customer satisfaction and loyalty, giving telecom companies a competitive edge in a crowded market.

9. Virtual Reality (VR) and Augmented Reality (AR) Services

With the advancement of AI and ML, telecom companies are exploring new frontiers in Virtual Reality (VR) and Augmented Reality (AR). These technologies require robust and high-speed networks, which telecom companies are well-positioned to provide. AI algorithms optimize VR and AR experiences by ensuring low latency and high bandwidth, making immersive experiences more seamless and accessible. Telecom providers are not only enhancing their service offerings but also exploring new revenue streams through VR and AR applications in gaming, virtual meetings, remote assistance, and immersive entertainment.

10. Energy Management and Sustainability

AI and ML are also playing a crucial role in helping telecom companies manage energy consumption and improve sustainability. Telecom infrastructure, including data centers and network towers, consumes significant amounts of energy. AI-driven energy management systems can monitor and optimize energy usage, reducing operational costs and minimizing the environmental impact. Machine learning algorithms can predict energy needs based on network traffic patterns and adjust power consumption dynamically. By adopting AI for energy management, telecom companies are contributing to global sustainability efforts and aligning with regulatory requirements for reducing carbon footprints.

As technology continues to evolve, the integration of AI and ML in the telecom industry will only deepen, driving innovation, efficiency, and enhanced customer experiences. The future holds immense potential for telecom companies to leverage these technologies in ways that were once considered science fiction.