Ever since the outbreak of COVID-19, telecommunication companies have acted as an adhesive keeping the world together and enabling individuals, teams, and entire companies to operate as effectively as humanly possible under the circumstances. Now that it seems that the work-from-home season is not ending anytime soon, telecoms will be dealing with the increased demand and growth challenges.
AI in telecommunications is currently viewed by many experts as the next big thing that will help service providers adapt to the rapidly changing business environment and thrive, as opposed to just surviving.
Companies will adopt AI — not just because they can, but because they must.
In this article, let’s have a look at what AI in telecommunications can bring to the table for the countless telecoms around the world, great and small:
- The added value of AI in telecom
- Open challenges in AI for telecom businesses
- Pain areas of applying AI in telecom
- AI for telecom: Real-life examples
- AI-powered telecom future
The added value of AI in telecom
The use of artificial intelligence in telecommunications can help solve a number of pressing issues and at the same time yield tons of added value to both consumers and operators alike. The latter have always been collecting substantial amounts of telemetry and service usage statistics, but most of it never got used in a meaningful way, due to the lack of the right software.
With AI, this massive array of previously unused data can be turned into fertile soil for growing new services, improving the quality of existing ones, taking customer experience to a new level, and optimizing business operations. According to a fairly recent study by Tractica, AI in telecom companies will be generating nearly 11 billion dollars by 2025 — a staggering amount that is likely to keep growing as the scope of AI applications expands.
AI in telecom software revenue by use cases
Source: Tractica
For customers, the application of artificial intelligence in telecom services translates into a much higher level of personalization (based on their history of orders, data consumption patterns, data profiles, call frequency and duration, etc.) going as far as custom service plans and offers, as well as improved quality of voice calls and data connections for online conferences.
From the operators’ perspective, the added value of AI in telecom industry is mainly concentrated on the backend side, where it helps streamline data transmission, dynamically adjust network settings, perform calculations on IoT edge devices, and draw actionable insights from big data lakes, among other things.
To sum it up, telecommunication companies adopting AI and ML at scale will take the lead in terms of operational effectiveness and the attractiveness of their service portfolio both for B2C and B2B segments. However, it is a complex effort that requires tight cooperation between highly skilled AI/ML development teams and business stakeholders at many levels.
Open challenges in AI for telecom businesses
COVID-19 restrictions are pushing telecoms towards finding new ways of engaging and serving their current and prospective subscribers. Prior to the pandemic, a fair deal of those initiatives had been on indefinite hold, but the unfortunate pandemic of 2020 became the catalyst for changes in the telecom industry.
Today, most Communication Service Providers (CSPs) sign customers up online and face fierce competition on local and global markets. With B2B revenues going down due to scarcely populated offices and paralyzed business travel, telcos need to adapt and come up with highly competitive service offerings focused on innovations — and artificial intelligence in telecommunications is at the very top of this list.
Global AI in telecommunications market share (%) by application, 2019
Source: Grand View Research
On the most general level, here are the main challenges that CSPs are facing during the pandemic:
- Alternating demand
As mentioned above, with office attendance being at an all-time low, the consumers’ focus has shifted dramatically toward the home office. Reliable, high-speed data connectivity and flexible pricing models coupled with deep personalization and options like teleconferencing tools and VPN access are what customers need from telecoms these days.
Most of these objectives can be achieved easier and faster using artificial intelligence and machine learning. AI in telecom can do a great job analyzing individual service usage patterns and matching those to the company’s service portfolio, allowing for very targeted marketing offers. As a result, subscribers will enjoy a wider gamut of services while CSPs will be able to sell more and reduce churn.
- Fluctuating supply
The disruption of supply chains has been the plight of telecommunication companies since the beginning of the year. At the same time, major market players have long been planning to start rolling out the long-awaited 5G networks and upgrading the corresponding infrastructure.
With equipment supplies sporadically becoming unstable and service delivery risks piling up, AI in telecom industry could be used to manage lean manufacturing processes, early-stage AI-based diagnostics (combined with predictive maintenance) on terminal devices, and even automation of procurement routines for expediting infrastructure upgrades and maintenance.
- Customer support
With call centers getting hit hard by the pandemic, things have been getting a bit out of hand for CSPs trying to provide the same high level of customer support as before the pandemic. AI-enabled customer support tools can effectively reduce the number of call center requests and therefore help telcos save on overheads.
Elements of AI in telecommunications can also contribute to the improvement of online help systems that prioritize search results and content presentation based on the user’s own previous requests and trending requests from other users. Intelligent chatbots, smart voice assistants and interactive voice response (IVR) systems, as well as other tools, help minimize human-to-human interactions, allowing CSPs to provide the best quality of customer support with what meager resources they may have at disposal.
- Business operations
While there can be multiple applications for AI in telecom, the ones pertaining to business operations mostly deal with deep business process automation and orchestration of various standard flows. AI-enhanced RPA is an important initiative that many telcos are embracing these days to minimize delays and boost operational efficiency.
Another lucrative avenue that a lot of telco execs are starting to look at are AI-powered decision-making tools that constantly monitor the key operational parameters of a business and highlight areas of concern or opportunities for improvement. Such analytical systems are especially important during times like these, when long-term forecasting may not be as reliable as it used to be.
Applying AI in telecom pain areas
Now that we’ve covered the main challenges in AI for telecom and outlined the ways in which AI could contribute to overcoming those, let’s take a look at some specific technical areas where the application of artificial intelligence really makes a difference.
Network optimization
Real-time traffic analysis and network reconfiguration is one of the things that AI can do extremely well. Intelligent AI-enabled traffic analyzers do a really good job recognizing malfunctions and bottlenecks long before they become visible to network administrators. And when it’s time to act, AI-enabled systems can modify network configurations and reroute traffic to healthy nodes in response to local equipment failures and bottlenecked channels.
Fraud prevention
One of the things that AI in telecom can do exceptionally well is fraud detection and prevention. Processing call and data transfer logs in real time, anti-fraud analytical systems can detect suspicious behavioral patterns and immediately block corresponding services or user accounts. The addition of ML enables such systems to be even more accurate and fast.
Optimization of financial operations
The use of artificial intelligence in the back office helps streamline and automate various business-critical processes, resulting in reduced overhead costs and more effective planning. With increased financial efficiency comes a higher ROI and more funds available for capex investments leading to greater customer satisfaction.
Preventive maintenance
Preventing issues is less costly than fixing them. In the telecom industry, the cost of hardware of software failures can be catastrophic, which means that investing into AI-based preventive maintenance systems always pays off in the long run. Artificial intelligence in telecom can be a game changer for companies that focus on 24/7 service accessibility.
Virtual assistants
Virtual assistants and AI-driven chatbots are gradually replacing live operators at telcos for cost-saving purposes and in order to offer customers a faster, more convenient way of getting answers to their questions and getting issues resolved. This is especially important in light of the pandemic imposing severe restrictions on the functioning of large-scale call centers.
Robotic process automation (RPA)
RPA has always been the number one choice for all digital transformation projects. If implemented correctly, it will deliver tangible value from day one by reducing document processing delays and accelerating business flows. With AI applied to RPA, the performance-boosting effect is even more profound, allowing for anomaly detection and (semi)automatic error correction.
Source: Digitalist Magazine
AI for telecom: Real-life examples
Having covered a number of challenges and application areas for AI in telecommunications, let’s now take a quick glimpse at some AI telecom use cases.
Verizon Communications
One of the largest CSPs in the world is investing heavily into AI/ML technologies to improve network performance and customer services. A recent partnership with mobile network operator Cellwize has resulted in the creation of a new intelligent platform that is facilitating the rollout of Verizon 5G sites and simplifies the development of network applications. Other AI-related partnerships include one with IBM and another one with Google, whose Cloud Contact Center AI service will offer Verizon’s customers a more intuitive and natural way of interacting with Verizon’s support service using advanced NLP features.
Vodafone
The British telecom giant Vodafone Group launched an assistant app called TOBi, a highly intelligent text bot capable of supporting users in dealing with issues, subscription management, and purchasing new equipment and services.
Deutsche Telekom
Deutsche Telekom has been making considerable investments in AI at various levels. From an AI powered chatbot called Tinka, capable of providing over 1500 answers to customers’ questions, to intelligent business planning tools, this CSP is actively embedding AI elements into its infrastructure and service portfolio.
AI-powered telecom future
The success of telecommunication companies embarking on a digital transformation journey will inevitably rely on their ability to put AI to good use as early on as possible and develop corresponding software. Those telecoms that have already managed to embed AI elements into both their customer-facing products and internal processes are now the ones with the solid competitive edge.
The range of potential applications for AI in telecommunications and AI telecom use cases is surprisingly broad and there is zero doubt that we will be seeing increasingly intelligent automation systems being rolled out by key market players to streamline day-to-day operations and deliver more value to customers.
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