There is more innovation going on in the Telecom industry due to AI and IoT than any other prior period. No longer are telcos’ just providing basic telephony services, internet services, mobile enablements and network services, rather they are fueling growth using AI. Reports abound on how the IoT era will increase over 25% growth for telecom providers in the 2021-28 period according to Fortune Business Insights.
Telecom players are adding AI capabilities given their sheer volume of data reservoirs tapping data from simply everywhere: mobile devices, networks, geo-location intelligence, customer profiles and log behaviours, services and service utilization, sales data from customer communications, billing, contracts, etc. The source areas for data aggregation and mining collective intelligence are simply endless. The opportunity to create an intelligent customer profile aggregating all service and usage patterns into an intelligent AI model to predict future share of wallet, up-sell opportunities and churn makes this industry one of the most exciting to design and develop and deploy AI innovations.
However, one dilemma facing board directors and C Level executives is ensuring that the companies they support in the telecom sector have a robust data aggregation strategy, enablements and maintenance infrastructure to support robust AI and Machine Learning Operations (MLOps). Note: “MLOps is the natural progression of DevOps in the context of AI,” said Samir Tout, who is a Professor of Cybersecurity at the Eastern Michigan University’s School of Information Security & Applied Computing (SISAC). “While it leverages DevOps’ focus on security, compliance, and management of IT resources, MLOps’ real emphasis is on the consistent and smooth development of models and their scalability.” (Note: Taulli, Tom – Forbes Blog)
From my vantage point, I see many islands of projects focused on a single use case to solve a specific business problem vs a holistic architecture that connects all the customer behavioural signals in the telco sector. It’s a daunting perspective but the companies that get AI infrastructure and enablements right – will outperform their competitors. With data so widely distributed in a Telco operation, it takes tremendous vision to bring all the data sources together into a unified AI operating infrastructure /intelligence hub.
The value of using AI in Telecom companies allow them to secure actionable insights, provide better customer experience, improve operations, and increase revenue, net new or renewals.
When one looks at the worldwide growth in connected devices – estimated to be over 30.9 billion, according to Statistica, this means that telecom companies are in an enviable position to unify intelligence on usage patterns across connected devices and be 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 CyberSecurity areas, which allow communication service (telco) providers to easily optimize and navigate traffic on their networks. Being able to predict anomalies, (outlier behaviors) in the network allow telco providers to solve problems before they happen, or reroute automatically traffic using AI monitoring systems. Growth in self-optimizing networks in telecom companies is growing at over 50% CAGR so a hot space to be in.
A few noteworthy 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 on cell sites up to seven days in advance.
Increasingly there are innovative partnerships being formed. For example, global supplier of wireless Smart City solutions, eleven-x, has a partnership with industry-renowned Canadian telecommunication leader, SaskTel, helping to optimize Information and Communications Technology networks in Saskatchewan. SaskTel is able to innovate and extend powerful AI solutions bringing their expertise as a telecom provider with companies like eleven-x that has strong AI underpinnings. For more information see the SaskTel press release here.
AI applications in predictive maintenance is not a new area but the ability to predict futures based on historical data, and being able to monitor equipment usage and predict fail points is a very cost effective investment of AI solutions. Opportunities to monitor complex communication hardware systems from cell towers to cell 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 their maintenance procedures and has been experimenting with drone technology to expand its network coverage during natural disasters, using drones to analyze video data of cell towers to evaluate damages and pinpoint areas needing service prioritization’s, improving resource allocations.
We are seeing more innovations in call center operations using AI and NLP practices to analyze the notes send in by customers to call centers or notes taken by call center agents to identify opportunities for improvement. For example, KPN , a Dutch telecom provider uses the insights from call center notes to make improvements to their interactive voice response (IVR) system. More proactive AI uses can also track customer’s home behaviours of their devices and automatically switch modem channels before a WI-Fi issue occurs.
Conversational Virtual Assistants
Juniper Research has projected that conversational virtual assistants will reduce business expenses by $8B annually. Examples of innovation include: Vodafone has implemented virtual assistants and experienced a 68% improvement in customer satisfaction. Nokia’s virtual assistant MIKA identifies network issues and solutions, leading to a 20% to 40% improvement to resolution rates. Telefónica’s Aura, reduces customer service costs generated from phone inquiries. Comcast has a voice remote that allows customers to interact with their Comcast system through natural speech. All of these areas are advancing increased human comfort in working with AI intelligent agents to solve business problems.
Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is a form of business process automation technology based on AI and improves operational efficiency allowing telco operators to more easily manage their back office operations and handle large volumes of repetitive and rules-based actions in areas like: order fulfillment, workforce management, even mundane data entry. Also the RPA market is forecast to grow to 13 billion USD by 2030, with RPA achieving almost universal adoption within the next 5 years (Statistica).
A report by Forrester suggests that RPA will be worth a $2.9 billion industry by 2021. Integrating robotic process automation (RPA) can help telecom companies simplify the handling of operational tasks and generate lasting revenue streams by providing fast, high-quality and affordable services.
One of the innovative areas is on streamlining more efficiently sales order processing by taking for example a well-structured workflow in Salesforce where there are requirements to push in more accurate and accessible customer data, sourced from areas like: email, company, demographics, personal interests, psychographic profiling (communication style), relationships (social connectivity ties in social visible interaction networks) etc. All these areas unified can aggregate and enable collective intelligence to optimize revenue acceleration in powerful new ways. Companies like IntroHive are leading in this use case area, and offer an AI powered SaaS platform designed to help organizations realize the full value of their relationships and under-utilized data across their business to increase revenues, employee productivity and to improve customer experience management.
It’s an exciting time in the telecom Industry and board directors and C level executives need to be mindful of the AI journey that needs to be undertaken and ensure they understand what is the current reality of the architecture vision and continue to ensure investments are made beyond areas like network management and ensure RPA areas are advancing on par 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 to learn about AI use cases in the Telco Sector.