AI IN TELECOMMUNICATIONS
What is driving the widespread adoption of AI by telecoms? AI has proven itself essential to the telecoms’ digital transformation strategy as it addresses the key challenges telecoms face today.
Nowadays, maintaining massive call centers has become a no-go for most companies throughout the world. Instead, customer service departments heavily rely on AI/ML solutions to process typical questions without human interaction. Such tools can also help classify the issue and connect customers with the correct support department faster. Automated AI/ML solutions increase the support quality and decrease processing time, thereby affecting the overall user experience.
Influence on business: Various chatbots and AI voice assistants significantly reduce support center costs and increase satisfaction.
1)Reduce customer service response time.
2)Assess each situation in detail, collect data and use it to improve your business.
3)Spend less on customer support while maintaining effectiveness.
Data Needed: Depending on the exact solution, such technologies may require specific dictionaries/terms or response rules. Quite often, client data is also required.
Price: Compared to traditional call centers with physical rooms and staff, AI solutions are not that expensive to invest in. Also, in the long term, such automated assistants are more cost-effective.
Examples: complex solutions from Mindtitan, Set of solutions by Inbenta.
Network Optimization and Management
AI-driven tools provide an invaluable insight into the network structure and its possible future behavior. Such tools can be used to visualize live performance over a vast number of devices or to interactively show better solutions. Complex Network monitoring and management solutions create an environment for stable and maintainable business.
Influence on business: With such tools, telecom operators can use their resources more efficiently. AI-based Network Optimization tools allow companies to provide better services without having to overly invest in infrastructure .
1)Such solutions allow engineers and those who maintain complex networks to have clear visual insights.
2)Network Optimization tools usually contain some forecasting functionality, which is impossible to undervalue.
3)Utilizing Big Data concepts, such solutions may consistently be implemented at scale without investing in personnel and hardware.
Data needed: All infrastructure information: Geographical data, maintenance information, etc.
Price: These solutions are not cheap to begin working with because they always have to be fine-tuned for a specific business need. However, many companies see an obvious benefit of using these tools.
Examples: All-in-one solution by Aria Networks.
In every industry, client analysis has made an enormous impact. Telecom is not an exception. Artificial Intelligence tools that analyze client profiles provide an insight into metrics such as customer satisfaction rates or topics that the client is not as happy about. There are numerous ways these metrics can be derived from clients: from reviewing natural language processing (NLP) to behavioral analysis and client clustering – many companies rely on AI/ML solutions to better understand their client base. In telecom, unique use cases include traffic monitoring, device detection, etc.
Influence on business: AI/ML-driven, customer analysis tools allow companies to determine business areas that they should focus on. These tools can also be used to monitor how certain business decisions project onto the client base.
1)Better understand your client base .
2)Assess what users value in your service
3)Improve the whole business model and operations according to the data.
Data Needed: Sales history, client profiles, usage statistics, etc.
Price: Heavily depends upon the size and complexity of the task. Specific businesses may introduce challenges that would require more time and effort to be spent.
Examples: CUJO with their custom telecom solution.
AI/ML tools are excellent for anomaly detection. These can process vast amounts of data within fractions of a second. For telecom companies, whose large networks produce infinite amounts of data, anomaly detection proves to be exceptionally beneficial. By utilizing anomaly detection tools, telecom can build better network maintenance solutions and detect possible failures sooner.
Influence on business: Automated anomaly detection allows companies to spend less on monitoring and maintenance while reducing human factors and decreasing reaction time. Overall, this leads to a more stable, powerful service.
1)Always keep your hardware stable.
2)Spot failure right on time.
3)Detect issues that would otherwise not be visible.
Data Needed: Depending upon the application, financial history, hardware metrics, or any other data that may be utilized.
Price: Building such systems from scratch is not possible for many businesses. However, subscribing to an existing solution may be both helpful and cost-effective.
Examples: Anodot services for the telecom industry.
Most telecom companies invest in anti-fraud solutions to make their services more reliable and secure. Some companies specialize in fraud prevention tools. Usually, these are a complex combination of strict rules and ML algorithms. Some are built to prevent known risks, while others are introduced to avoid suspicious activities. These issues exist not only in telecom but in almost any industry where the B2C model exists. Therefore, telecom benefits from other industries’ experience.
Influence on Business: Anti-fraud systems protect clients from possible attacks within the network. This creates a better company image and generally secures the company profile.
1) Maintain the security of your business by keeping client databases and business insights from others.2) Prevent clients from taking any risk when using your services.
3) Protect not only the privacy of a client but also your reputation.
Data Needed: Such systems are often data-hungry and require all possible data sources to be available.
Price: Depends on the size of the business, complexity of the interface, and existing safety precautions.
Examples: Complex fraud prevention toolkit from Subex.