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AI in Telecom: 6 Common Applications

The future of AI in telecom will evolve as big data tools and applications become more accessible and advanced.

Telecoms may anticipate continued, accelerated development in this fiercely competitive market by utilizing AI.

With the help of cloud, 5G, AI, and cognitive computing technologies, as well as consumer insights, it is now feasible to respond to a wide range of queries in the language of the client. 

Human customer-service representatives may, however, become obsolete in the future as companies get more at ease handing up client knowledge to computers, allowing customers to interact with virtual assistants and bots instead.

Additionally, it is anticipated that AI in telecom will go beyond handling information to forecasting customer behaviour and influencing corporate choices. As a result, their lifetime value should rise while expenses are reduced and customer satisfaction is improved.

There are a surprising number of possible uses for AI in telecom. Key industry participants will undoubtedly observe the use of increasingly sophisticated automation technologies to simplify daily operations and provide consumers with more excellent value.

Let’s look at some of the typical applications of AI in telecom. 

Applications of AI in Telecom 

ai in telecom

Here are some typical applications you will witness of AI in telecom. 

  1. Network Optimization 

Launched in 2019, 5G networks are expected to have more than 1.7 billion customers worldwide by 2025, accounting for 20% of all connections.

To enable this expansion, communication service providers (CSPs) must use AI to construct self-optimizing networks (SONs).

These enable network administrators to automatically improve network quality based on regional and time zone-specific traffic data.

Advanced algorithms are used in the telecom sector to search for patterns in data, allowing telecoms to both identify and forecast network problems. Using AI in telecom enables CSPs to proactively address issues before they have a detrimental impact on customers.

  1. Regular and Predictive Maintenance 

A common challenge faced by telecom providers is maintenance of mobile towers.

To ensure everything in these towers is operating correctly, including machinery and equipment, on-site inspections are needed. This is expensive and also requires a lot of management to get done. 

Companies can utilise AI-powered robots and video cameras at mobile towers in situations like these. AI in telecom may also assist in providing real-time alerts to operators in the event of hazards or other catastrophes like fire or storms, etc.

IoT sensors can be used at these mobile towers. These IoT devices employ several machine learning methods to help them evaluate the vast amount of data at their disposal. 

Maintaining a network gets more challenging as it expands and becomes more complex. It can be expensive and time-consuming to fix problems. 

Additionally, it may result in service disruptions and downtimes, which customers hate.

With predictive maintenance, AI has a significant impact. AI and ML (Machine Learning) systems may reliably predict and foretell potential hardware breakdowns by seeing trends in the past data. 

This enables carriers to manage their hardware in a more proactive manner, resolving problems before they impact the end user. 

Additionally, these algorithms can pinpoint the cause of every failure, allowing for root-cause analysis and practical problem-solving.

  1. Robotic Process Automation (RPA)

Each of the millions of daily transactions that CSPs’ enormous client bases participate in can be easily prone to human error.

A type of AI-based business process automation technology is called robotic process automation (RPA). 

By enabling telecoms to more efficiently manage their back-office operations and sizable quantities of repetitive and rules-based actions, RPA may increase the efficiency of telecom processes. 

By automating the execution of complicated, labor-intensive, and time-consuming activities like billing, data entry, workforce management, and order fulfilment, RPA frees up CSP workers for greater value-added tasks.

  1. Better Customer Services 

Telecommunications firms can automate customer service efficiently and give clients a more tailored experience thanks to artificial intelligence. 

Managing everything individually can be challenging. To be able to address consumer complaints, a sizable team is needed around-the-clock. Notably, the current pandemic highlighted the need for automating customer service jobs. 

Artificial intelligence helps in this regard it enables you to offer help around-the-clock. AI-powered chatbots have gained popularity. They can be seen transforming customer service in just about every business and industry you can think of.   

  1. Fraud Detection 

A simplifies designing algorithms that can recognise and respond to fraudulent network activity.

The computer learns to distinguish between erroneous and legitimate patterns and identifies abnormalities, once it examines the data it has gathered.

These developments enable the system to identify abnormalities as they happen in real-time. This proves to be more effective than using human analysts, even the most experienced and skilled of professionals. 

Real-time anomaly detection capabilities provided by AI and machine learning algorithms significantly cut down on telecom-related fraud, including bogus profiles and illegal network access.

As soon as suspicious activity is noticed, the system may instantly limit access to the fraudster, reducing the harm. This AI application is particularly pertinent for CSPs given that industry estimates show that 90% of operators are targeted by fraudsters on a daily basis, resulting in billions in losses per year.

  1. Increase in Revenue 

A wide variety of data, including data from devices, networks, mobile apps, geolocation, in-depth customer profiles, service consumption, and billing, may be combined and made sense of by AI.

Through intelligent upselling and cross-selling of their services, telecoms may raise their average revenue per user (ARPU) and subscriber growth rate using AI-driven data analysis.

Telecoms may provide the appropriate offer on the right channel at the right time by anticipating client demands using real-time context.

  1. Data-Driven Decision Making 

For any human employee, no matter their skills or expertise, will find it difficult to quickly examine data when they have the amount of data that is being gathered by businesses now.

AI in telecom can help. Ai makes it easier to make informed judgments using data, even if there is a lot of data at their disposal. 

They have the capability to understand and find necessary patterns in vast amounts of data. 

Challenges of Using AI in Telecom Market and Their Solutions 

ai in telecom

Even though the worldwide market for AI in telecommunications is expanding quickly, many firms still struggle to integrate it.

The most frequent difficulties encountered while deploying AI in telecom include the following:

Unstructured or Incomplete Data 

Without access to pertinent data, implementing an AI system is a pointless exercise. Because of a few frequent problems, data collection is difficult for many organisations:

  • Fragmented data. With no central database from which it can be retrieved, several systems collect and store data.
  • Unstructured data. Any AI system will not be beneficial with a large volume of uncategorized data lacking any context or explanation of what it is connected to.
  • Incomplete data. Using data that is lacking key elements can result in the AI system learning in an inconsistent or inaccurate manner.

Solution: Since AI algorithms need clean, well-structured data, data extraction, transformation, and loading (ETL) take up about 80% of the time in each ML project. To gather, integrate, store, and process data from various siloed data sources, it is crucial to establish a suitable big data engineering environment.

Technical Expertise Requirements 

Technology in the field of AI is still very young. Building an internal team can be time-consuming and fruitless when there is little available local talent.

It would be more beneficial to find a technical partner that could deploy artificial intelligence in communications on your behalf.

To construct an AI system properly, a supplier must have sufficient skill and expertise, which might be challenging to locate. It’s also essential to start your project with the correct partner because deploying AI may be extremely expensive.

Solution: Before deciding to work with a software provider, do your research. Look at their actual AI experience, and see what their clientele are saying about them. To assist you achieve your unique business goals, look for a technology partner with experience in ML/AI, Big Data, Cloud, DevOps, and Security.

Technical Integration 

One of the most common reasons AI integration projects fail is the use of outdated systems. Make sure your IT infrastructure can manage the project before committing to it.

Solution: You can do a number of things to get your system ready for the next AI project.

  • Don’t be afraid to completely overhaul your data collection and storage process if you notice that the collected data is disparate or unstructured.
  • Establish a unified database where all the data needed by the system will be stored.
  • Use data lakes, edge computing, and cloud computing to eliminate any problems that can arise when storing large amounts of data.

How DotNet Reports Helps You Implement AI in Telecom 

ai in telecom

Ad hoc reporting ensures insightful data, much as AI in software testing assures top-notch development. 

Businesses may increase sales and enhance customer service by using these valuable insights.

Ad Hoc Report Builder from Dotnet Reports has a number of built-in capabilities that make it easy to create reports for your end customers, such as automated drill down reports, scheduling reports, export to PDF, and more. 

Businesses may make better judgments if they have more significant business insights. The development of a data-centered culture results in well-informed and strategic solutions. 

Dotnet Report offers a variety of Reports, Charts, and Graphs that customers can simply create on their own, customise with filters, and run the necessary analytics on. Users may also design several Dashboards and organise the Reports anyway they like.

Dotnet is committed to offering other software developers a quick, easy, and secure reporting and analytics solution.

Final Thoughts 

The facts are indisputable, and AI’s explosive rise in the telecommunications business is evidence of its rising significance to the sector.

It is crucial to avoid falling behind as more businesses boost their investments in cognitive technology.

The capacity of telecommunications businesses to employ AI effectively as soon as feasible and create related software will be crucial to their success as they begin their digital transformation path. 

There may be no end to what AI can help us do with the data acquired by cognitive technology, trustworthy insights, and manual skills.

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