There is more innovation going on in the telecommunications industry through AI and IoT than any previous period. Telcos are no longer just providing basic phone services, internet services, mobile capabilities and network services, they are instead fueling growth through AI. According to Fortune Business Insights, reports abound of how the IoT era will grow by more than 25% for telecom providers over the 2021-2028 period.
Telecom players are adding AI capabilities given their huge volume of data pools leveraging data from everywhere: mobile devices, networks, geolocation intelligence, customer profiles and log behaviors, services and service usage, data sales from communications with customers, invoicing, contracts, etc. etc The source areas for data aggregation and collective intelligence exploration are simply endless. The ability to create an intelligent customer profile that aggregates all service and usage patterns into an intelligent AI model to predict future share of wallet, upsell opportunities, and churn makes this industry one of most exciting to design, develop and deploy AI innovations.
However, a dilemma faced by administrators and C-level executives is to ensure that the companies they support in the telecommunications sector have a robust data aggregation strategy, authorizations and a maintenance infrastructure to support robust AI and machine learning (MLOps) operations. Note: “MLOps is the natural progression of DevOps in the context of AI,” said Samir Tout, professor of cybersecurity at Eastern Michigan University’s School of Information Security & Applied Computing (SISAC). “While it leverages DevOps’ focus on security, compliance, and IT asset management, the real focus of MLOps is on consistent, fluid pattern development and scalability.” (Note: Taulli, Tom – Forbes Blog)
From my perspective, I see many islands of projects focused on a single use case to solve a specific business problem versus a holistic architecture that connects all customer behavioral signals in the telecom industry. It’s a daunting prospect, but companies that get the AI infrastructure and capabilities right will outperform their competitors. With data so widely distributed in a Telco operation, it takes tremendous vision to bring all data sources together into a unified operating infrastructure/intelligence hub.
The value of using AI in telecommunications companies enables them to secure actionable insights, deliver better customer experiences, improve operations, and increase revenue, net new or renewals.
When you look at the global growth of connected devices – estimated at more than 30.9 billion, according to Statistica, this means that telecommunications companies are in an enviable position to unify intelligence on the usage patterns of connected devices and be in able to see patterns more deeply than most industries will be able to.
According to IDC, 63.5% of telecom companies are actively implementing AI to improve their network infrastructure. There has always been AI in network optimization, especially in areas of cybersecurity, which allow communication service providers (telco) to easily optimize and navigate traffic on their networks. The ability to predict anomalies (aberrant behaviors) in the network allows telecommunications providers to fix problems before they occur or automatically redirect traffic using AI monitoring systems. The growth of self-optimizing networks in telcos is increasing to over 50% CAGR, so a hot space to be in.
Some notable developments, Nokia launched its own machine learning-based AVA platform, a cloud-based network management solution to better manage capacity planning. It also predicts service degradations at cell sites up to seven days in advance.
More and more, innovative partnerships are being formed. For example, global provider of Smart City wireless solutions, eleven-x, has partnered with Canadian telecommunications leader SaskTel to help optimize information and communications technology networks in Saskatchewan. SaskTel is able to innovate and scale powerful AI solutions by bringing its expertise as a telecommunications provider to companies like eleven-x that have a strong foundation in AI. For more information, see SaskTel’s press release here.
AI applications in predictive maintenance is not a new field, but the ability to predict the future based on historical data, and being able to monitor equipment usage and predict points of failure is a very profitable investment of AI solutions. Opportunities to monitor complex communications hardware systems, from cell towers to cellular towers to set-top boxes in a customer’s home, provide increased opportunities to improve customer service and reduce operating costs. AT&T uses AI to support its maintenance procedures and has experimented with drone technology to expand its network coverage during natural disasters, using drones to analyze video data from cell towers to assess damage. and identify areas requiring prioritization of services, thereby improving resource allocation.
We are seeing more and more innovations in call center operations using AI and NLP practices to analyze notes sent by customers to call centers or notes taken by call center agents. calls to identify improvement opportunities. For example, KPN, a Dutch telecommunications provider, uses information from call center notes to make improvements to its Interactive Voice Response (IVR) system. More proactive uses of AI can also track customers’ home behaviors on their devices and automatically switch modem channels before a WI-Fi issue occurs.
Conversational Virtual Assistants
Juniper Research predicted that conversational virtual assistants would reduce business expenses by $8 billion per year. Here are some examples of innovation: Vodafone introduced virtual assistants and saw a 68% improvement in customer satisfaction. Nokia’s MIKA virtual assistant identifies network problems and solutions, leading to 20% to 40% improvement in resolution rates. Aura by Telefónica reduces customer service costs generated by telephone inquiries. Comcast has a voice remote that allows customers to interact with their Comcast system through natural voice. All of these areas are advancing human comfort by working with intelligent AI agents to solve business problems.
Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is a form of AI-based business process automation technology and improves operational efficiency allowing telecom operators to more easily manage their back-office operations and manage large volumes of repetitive and data-based actions. rules in areas such as: order fulfillment, workforce management, even mundane data entry. Additionally, the RPA market is expected to reach $13 billion by 2030, with RPA reaching near universal adoption within the next 5 years (Statistica).
A Forrester report suggests that RPA will be a $2.9 billion industry by 2021. Integrating robotic process automation (RPA) can help telcos simplify operational task management and generate sustainable revenue streams by providing fast, high quality and affordable services.
One of the innovative areas is to streamline customer order processing more efficiently by taking for example a well-structured workflow in Salesforce where there is a need to introduce more accurate and accessible customer data, from areas such as: e -email, company, demographic data, personal data. interests, psychographic profiling (communication style), relationships (links of social connectivity in visible social interaction networks), etc. All of these unified domains can aggregate and enable collective intelligence to optimize revenue acceleration in powerful and new ways. Companies like IntroHive are at the forefront of this use case and offer an AI-powered SaaS platform designed to help organizations take full advantage of their relationships and underutilized data in their business to increase revenue, employee productivity and improve customer experience management.
These are exciting times in the telecommunications industry and C-level administrators and executives need to be aware of the AI journey that needs to be undertaken and ensure they understand what the current reality of the vision is. of the architecture and continue to ensure that investments are made beyond domains. such as network management and ensuring RPA domains progress equally and continue to modernize and transform operations. You can find a chapter on AI in Telecom in my recent book, The AI Dilemma to continue learning more about the use cases of AI in the Telecom industry.