AI's Influence on Telecom: A New Era of Innovation and Efficiency

The telecom industry is poised to experience increased growth with the introduction of AI.

Huge and varied amount of data in Telco industry makes the use of AI even more interesting from networks, devices, IoT sensors, billing data, customer usage patterns and profiles.

The Telecom industry has seen disruptive changes in the last decade from being a basic telephony and SMS provider to a quad play operator under intense competition for media and hyperscale’s and OTT innovators. As the market is gearing itself for 5G high speed low latency networks, it has created multiple opportunities and revenue streams for Telcos, Enterprises, NEPs and SIs.

The telecom industry, which has historically been slower in adopting emerging technologies compared to some of its peers in the tech industry, is poised to experience increased growth with the introduction of these innovative technologies.

As an example, 5G is expected to be the strongest enabler for private networks and IOT industry using concepts of Network slicing.

According to analysts, the telecom sector with a current market size of USD 2000 billion in 2022 is expected to grow to USD 2500 billion by 2028. 5G subscriptions grew by 110 million during 3Q22 to around 870 million, and will likely to reach 1 billion subscriptions by the end of 2022. By the end of 2028, global 5G subscriptions are expected to reach 5 billion, accounting for 55 percent of all mobile subscriptions

Digital transformation redefining CX

The telecom operators have a huge urge to bring digital transformation and redefine the user journeys and improve the CX. One of the key enablers as a sub set of digital transformation has been drawing insights from massive amounts of data flowing in the networks and make telcos data driven. The adoption of AI across few telcos has been beneficial at proof-of-concept stage but yet to see it happening at a scale with AI ops and realising the CBA (Cost benefit analysis on the use case) for business.

The telecom sector is an industry where the dataset is large and can be harnessed in many possible ways. Huge and varied amount of data in Telco industry makes the use of AI even more interesting from networks, devices, IoT sensors, billing data, customer usage patterns and profiles. This surely makes Telco industry suitable to adopt AI. Telcos have started their journey on streamlining data governance, implementation of data lakes, store, process and analyse these massive volumes of data to draw actionable insights for a much better customer experience and reduce subscriber churn.

IDC predicts that by 2024, 60% of enterprises will have operationalized their ML workflows using MLOps. It is the scaling catalyst of AI for Telcos. Today, fewer than 5% of decisions in a Telco are driven by algorithms, but that will eventually change. With MLOps, 20 to 25% of decisions will be driven by algorithms in the next five years.

According to a recent Gartner survey, the average time it takes to take a model from proof of concept to production has dropped from 9 to 7.3 months. But 7.3 months is still high, so there are many opportunities for Telco CXOs to take advantage of MLOps. MLOps can be highly beneficial for CXOs, data scientists, and data engineers alike. C-suite leaders require fast, accurate, and unbiased predictions. They are also looking for an AI solution that can provide them with a clear return on investment. That has been challenging for years, but MLOps changes that forever by making it simple to highlight ROI on AI investments. By putting MLOps in place, CXOs can therefore utilize their energies into scaling AI capabilities throughout the organization while focusing on tracking KPIs that matter to each team and department.

Key challenges

Now let us understand some of the challenges faced by the telcos across departments:

CTOs today are facing challenges around the following areas as they upgrade their networks, devices and services.

  • Network availability
  • Network growth
  • ROI on Network investments
  • Network security & compliance
  • Network assets management and optimisation
  • Digital twin mapping of Networks
  • Network assurance and fault management
  • Network slicing monetisation

On the other hand, CIOs are planning or already implemented large IT transformation programs and face following challenges by business stakeholders and to innovate of different way of working

  • Information Security
  • Data privacy protection and regulatory compliance
  • Enterprise analytics and insights
  • Application availability, support, and maintenance
  • IT Infrastructure availability, support, and maintenance
  • IT infra-assets mgmt. and optimization
  • License costs optimization
  • Architecture consistency and modernization

CMOs are under pressure to redefine the Customer experience and bring in more innovative and differentiated yet simplified products to consumers and enterprises (B2C/B2B)

  • Subscriber growth
  • ARPU growth
  • Competitive differentiation
  • Customer Experience enhancement
  • Brand building
  • Product planning, design, and execution
  • Churn Management

CFOs are working very closely now with technology and business stakeholders to address the following headwinds around

  • Revenue assurance
  • Cost optimization
  • Cashflow mgmt.
  • Financial and accounting regulatory compliance
  • Fraud Management
  • Investor confidence management
  • Business Intelligence
  • Product profitability analysis

To address the above challenges, the industry requires intervention from all three angles namely People, Process and Technology and business KPI can be achieved by Telcos on key parameters around agility, costs, control and CX.

Let us explore the areas where Telcos can potentially use AI to their advantage and draw insights to stay competitive in terms of their products and offerings

Network related issues

As telcos upgrade their networks and services the complexity and cost of running the NOC starts increasing. It therefore becomes extremely critical to monitor every aspect of the network and nodes. The legacy ways of running the NOCs are probably outdated and do not provide real time monitoring experience and corrective actions. Also, it does not have ways to predict failures and correct them. This is an area where AI-ML can be harnessed processing the network data and performing analytics. Network data can be monitored in real time for traffic analysis, performance and latency issues across locations, time zones, devices and AI algorithms can be used to study and help in removing network congestion. Proactive alarm monitoring systems can supersede traditional ways of alarm generation for better network efficiency and optimisation. Anomaly detection on network data can also be potentially used to pre-empt any network related failures by using historical data. There are some innovative tools and technologies that are being used by Telcos from Network equipment providers for solving network issues.

Total global mobile data traffic (FWA) reached around 115 EB per month by the end of 2022, and is expected to reach 453 EB per month by the end of 2028. 5G will account for 17% of mobile data traffic by the end of 2022, and this share is forecast to grow to 69% in 2028. Video constitutes around largest share (70 percent) of all global mobile network traffic in 2022, followed by social networking & software download and updates. Video streaming from the top 4 social media platforms (YouTube, TikTok, Facebook, Instagram) makes up the largest part of video traffic in 4G/5G networks with 40–95 percent across Europe, Asia and the Americas. Global streaming video-on-demand traffic (Netflix, HBO max, Disney+, Viaplay) is in the 10–30 percent range.

AI has a huge role to play in the Net zero sustainability initiatives of Telcos by reducing carbon footprints and adopting energy savings measures.

Digital transformation, apps modernisation

In fact the CIO office of telcos can also potentially use the same concepts to understand better their application portfolio rationalisation and modernisation. AI plays a huge role there in modernising legacy applications and increasing its adoption and organisational level change management. Most of the telcos today are on a massive digital transformation journey led by chief digital officers who mainly come from the business side. Picking the right use case becomes very important to realise AI cost benefit analysis at scale as at times AI based implementations are expensive given the scale, tools and technologies involved and highly skilled data engineers and scientists.

AI can potentially be harnessed across business functions to make massive transformations in any business workflow in a Telco IT environment from Multi channel subscriber acquisitions, to billing provisioning and activation journeys.

Telcos can simplify their products and services based on past data on consumer segments and which rate plans, demographics etc. This is another area with AI can be used to create accurate data models and provide more insights from IT to business to help create differentiated converged offerings (Fixed line, mobile broadband video)

Differentiated and competitive products and services

Customer relationship management (CRM) is of utmost importance for any Telco. The CMO team has opportunities with the growing subscriber base, dynamic usage patterns and constant pressure to bring converged services as Telcos consolidate in the industry and there are growing numbers of M&A deals happening.

As and when the telcos release new products, the simplification of these so that they are better recommended and targeted at the right customer segments becomes very critical. There are options in which AI/ML algorithms can help the marketing team to develop Customer Relationship Management (CRM) strategies targeted towards specific segments.

Another area for application of AI is in the call centres. High wait time for a call in the call centre and lack of proper resolution leads to massive customer churns. It also adds to operational expenses to involve human operators for each call.

With the proper integration of AI-based chatbots, Telcos can guarantee 24*7 availability and quicker resolution to a query from customers. It has also helped reduce TCO for running call centres. It is important to link the chat bots integration with the back end business workflows so that end to end support can be provided and wherever human intervention is needed the chat bot is able to route the call based on underlying NLP capabilities.

Streamlining financial processes

The finance function is sitting on a goldmine of tightly governed data from ERP and other financial systems which is integrated well with other functions of the organisation.

AI can help automate many of the mundane financial processes and help in bringing better efficiencies. It can also help in forecasting costs by looking at past historical data, revenue forecasting, Sales forecasting and help in better financial management.

Data Models can be built around Revenue assurance, fraud management which are huge issues today for CFOs on revenue loss.

By proper cleansing of data CFOs can also do a per subscriber-based profitability analysis and bring in tighter controls for effective asset management as they do their financial planning and accounting exercise.

The Business intelligence function which requires huge amount of reporting at the end of the month can be further streamlines by building proper AI models and automating the whole process with less dependence on IT. Self-service and visualisation and reporting can be achieved by proper selection of tools and AI-ML ops.


AI can be potentially used across towers in a Telco with data governance to bring in more agility, reduce costs and enhance customer experience. By the right selection of tools/ technologies, process improvements and talent management AI has the full potential to achieve respective business KPIs. The best way to do is to start small with a prototype and achieve the proof of value and then incrementally do it “at scale” using ML ops.

Around 80% of data scientists' time goes into creating, training, and testing data. Therefore, Data governance is of utmost importance before Telcos undertake the AI journey. It should be the essence of an digital transformation journey. At times we tent to confuse digital transformation with AI transformation. In my view, digital transformation is needed to clean and govern your people , process and technology spectrum before you undertake the AI transformation to achieve the Cost benefit analysis and ROI on AI investments. In another POV I have tried to provide my views on how AI centre of excellence can potentially convert into Centre of Monetisation for the CXOs.


- The article has been written by Sandeep Sudarshan,CTO & Lead Telco Architect, Business Technology Services, Capgemini UK



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