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AI in Telecos by Dr Jassim Haji

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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.

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.

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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.

by Dr Jassim Haji

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