The use of AI in telecommunications is at its all-time high right now, with the future seeming even brighter. By the end of 2027, the global AI in telecommunication market is expected to reach an impressive $14.99B.
What is causing such a surge of interest in AI among telecoms? Global traffic and the amount of network equipment are growing dramatically. This makes network management complicated, expensive, and time-consuming. AI can optimize and automate networks, keep them healthy and secure, while at the same time reducing operational costs. Let’s take a closer look at how AI is used to improve the telecom industry, and how you can implement it while overcoming the most common challenges.
5 most common uses of AI in telecommunications
Many industries value AI for its exceptional ability to analyze big data. As an industry that has constant access to vast amounts of data, it is not surprising that telecom and AI go together better than peanut butter and jelly. Let’s take a closer look at the most common ways this technology is used in telecommunications.
As a network grows and becomes more sophisticated, maintaining it becomes increasingly difficult. Fixing issues can be a costly and time-consuming process. Moreover, it can lead to downtimes and service interruptions — something customers do not appreciate.
AI can make a big difference with predictive maintenance. By finding patterns in the historical data, AI and ML (Machine Learning) algorithms can accurately anticipate and warn about possible hardware failures. This allows telcos to be very proactive at maintaining their equipment, fixing issues before they occur, and affect the end-user.
Furthermore, these algorithms can identify the reason behind each failure, making it possible to fight the problem at its core. This is what happened with one of the world’s largest providers of in-flight connectivity and entertainment, Gogo. They partnered up with N-iX who improved the quality of their in-flight internet and made it possible to predict equipment failures. Moreover, data science models built by the N-iX team helped identify the main cause of ill-performing antennas. As a result, Gogo was able to solve the issue that was wasting costs and causing downtimes.
Another common use of AI in telecommunications is building self-optimizing networks (SONs). Such networks are automatically monitored by AI algorithms that detect and accurately predict network anomalies. Furthermore, they can proactively optimize and reconfigure the network to ensure that end-users enjoy the stable performance.
As companies realize the value of using AI in telecommunication network infrastructure, more and more are willing to invest in it. According to IDC, 63.5% of telecom companies are actively implementing AI to improve their network infrastructure.
Virtual assistants and chatbots
Conversational AI platforms are one of the biggest influencers on the growth of the AI in telecommunication market. These virtual assistants, or chatbots, as they are also known, can automate the handling of customer requests.
Long waiting periods are the bane of existence for good customer service and are something that human-operated call centers are very prone to. By scaling conversations to simple queries, chatbots can respond to massive amounts of customer inquiries with impressive speed. This, plus the ability to provide uninterrupted service 24/7, reflects very positively on customer satisfaction. Indeed, Vodafone saw an increase in customer satisfaction by 68% when they introduced their chatbot TOBi.
As virtual assistants develop and learn to handle more complicated requests, the need for human operators decreases. This can help companies greatly reduce their expenses. In fact, by 2022 the use of chatbots will lead to over $8b in annual savings.
Fraud detection and prevention
The fraud detection and prevention market reached $20.98B in 2020 with an expected CAGR of 15.4% during 2021-2028. Despite this, malicious attacks on businesses still cause over $3.6B of losses annually.
With AI’s excellent analytical capabilities, it is not surprising that many industries, including telecom, are finding it useful at battling fraud. The most prominent advantage of AI-powered fraud analytics is its ability to prevent fraud altogether. The system blocks the corresponding user or service as soon as it detects suspicious activity, not allowing the fraud to occur. All of this is done automatically, making the chances of not responding to an attack in time very slim.
Robotic process automation (RPA)
RPA is a form of digital transformation that relies on implementing AI. Telcos can use RPA to automate data entry, order processing, billing, and other back-office processes that require lots of time and manual work. This frees up your employees’ time, letting them focus on more important tasks, and reduces the number of errors that manual labor is prone to. As a result, your office runs smoother, your employees are more productive, and your customers enjoy error-free service.
With so much to gain, it is not that surprising that over 53% of all organizations have already begun their journey in RPA. Moreover, this number is expected to grow to 72% in the next 2 years, while in 5 years RPA will achieve almost universal adoption among businesses.
Top challenges of using AI in telecom and how to solve them
Even though the global AI in telecommunication market is growing rapidly, implementing it can still be tricky for many businesses. Besides being unable to recognize the need for AI or identify appropriate business use cases, the most common challenges of implementing AI in telecoms are as follows.
Unstructured or incomplete data
Implementing an AI system without access to relevant data is a fruitless endeavor. Many organizations struggle with data collection because of several common issues:
- Fragmented data. Data is collected and stored by different systems without a single unified database from where it can be accessed.
- Unstructured data. A big mass of uncategorized data without any context or explanation of what it is related to is not very useful to any AI algorithm.
- Incomplete data. Using data with missing components can lead to inconsistent or faulty learning by the AI system.
Solution. Since AI algorithms require clean well-structured data, around 80% of the time of any ML project is dedicated to ETL (extracting, transforming, loading) and data cleanup. Therefore, it is important to put an appropriate big data engineering ecosystem (based on Apache Hadoop or Spark) in place that will collect, integrate, store, and process data from numerous siloed data sources.
Need for additional technical expertise
AI is a relatively new technology. With limited local talent, building an in-house team can take a significant amount of time and yield little result.
A better option is to look for a technical partner that would implement AI in telecommunications for you. However, finding a vendor that has both enough competence and experience to successfully build an AI system can be a challenge in itself. Moreover, implementing AI can be quite pricey, so it is crucial to start your project with the right partner.
Solution. Do your research before opting for a partnership with a software company. Take a look at their practical experience with AI, and find out what clients are saying about them. Trusted platforms such as Clutch can give you a good understanding of whether a vendor will be able to deliver the results that you expect. Look for a technology partner with expertise in ML/AI, Big Data, Cloud, DevOps, Security to help you meet your specific business needs.
Old legacy systems are one of the most common reasons why many AI integration projects fail. Before committing to such a project make sure your IT infrastructure is ready to handle it.
Solution. The are several things you can do to prepare your system for the upcoming AI project:
- Set up a unified database where all the data required by the system will be stored
- Use data lakes, as well as edge or cloud computing to eliminate any issues that can occur when storing large amounts of data
- Do not hesitate to give your data collection and storage process a complete overhaul if you notice that collected data is disparate or unstructured
- Make sure that you have the required hardware and software to handle the new system
Find a reliable technology partner to conduct the audit of your legacy system and help you with the Discovery Phase to validate your ideas, choose the most suitable architecture and tech stack and prepare all the deliverables needed for the successful kick-off of the project.
The facts don’t lie, and the rapid growth of AI in telecommunication market reflects its growing importance in the industry. As more companies increase their investments in cognitive technologies, it is very important not to fall behind.
However, when dealing with complex or unfamiliar technologies, it is equally important to pick the right tech partner who will support you along the way. If you are looking for a partner that has over a decade of experience with AI and software development in telecommunications ― look no further!
How N-iX can help you take advantage of AI in telecommunications
- N-iX has extensive experience in forming partnerships with leading telecommunications companies, such as Gogo and Lebara, and helping them take full advantage of AI’s capabilities
- You can take advantage of the full range of our cognitive technology services, from Big Data and Data Science to Business Intelligence, Artificial Intelligence & Machine Learning.
- N-iX has been recognized by ISG as a Rising Star in data engineering services for the UK market and positioned in the Product Challengers Quadrant both in the data science and data Infrastructure & cloud integration services.
- Our talent pool has surpassed the 1,500 mark and includes over 80 experts in data analytics
- N-iX is trusted in the global tech market: the company has been listed among the top software development providers by Clutch, in the Global Outsourcing 100 by IAOP for 5 consecutive years, recognized by GSA UK 2019 Awards, included in top software development companies by GoodFirms.co, and others.
- N-iX complies with international regulations and security norms, including ISO 27001:2013, PCI DSS, ISO 9001:2015, GDPR, and HIPAA, so your sensitive data will always be safe.