Generative AI has quickly gone from buzzword to mainstream to key differentiator. Businesses in nearly every industry are experimenting with ways to incorporate generative AI. Currently, 60% of organizations with reported AI adoption are utilizing Gen AI in at least one function.
In the competitive telecommunications industry, telecommunications companies (telcos) and communication service providers (CSPs) are embracing Gen AI to enhance various aspects of their business functioning, from increasingly operation efficiency and network performance to marketing. Generative AI accounted for approximately $151 million of the global telecom market in 2022 and is expected to reach $4.8 billion over the next ten years as companies become more fluent in Gen AI’s capabilities for data analysis, network optimization, product development, customer support, and other critical business functions.
Investments in generative AI are accelerating its development, promising more opportunities for growth, especially among early adopters. But there is still time for telcos and CSPs that have not yet embedded Gen AI within their functions to catch up. By understanding the true capabilities of Gen AI, enterprises can better understand how this technology can fit into their operations and deliver the greatest returns on their investments.
Why Is Generative AI Important for Telecommunications?
Personalization
Tailoring customer-specific features and services is easier with Gen AI. Marketing campaigns can be designed using customized content, both textual and visual, that targets individual customers. Gen AI can also be used to examine customer data such as purchasing patterns and browsing history to generate personalized recommendations, services, and offers, helping to drive sales, increase customer loyalty, and lower churn rates.
Self-service
Today, the best customer experience is one that facilitates self-resolution of issues. Powered by machine learning tools, Gen AI can respond to customer queries with accurate, real-time responses for quicker resolutions. With the integration of virtual assistants and conversational AI, Gen AI can also provide data-driven insights into customer preferences, trends, and challenges that can be utilized to optimize self-service options.
Data and network security
Massive data volumes and expansive networks are characteristic of telcos and CSPs — factors that expose them to security threats. Gen AI boosts network security by analyzing data to identify security vulnerabilities such as examining user behaviour and network traffic to discern patterns indicative of malicious activity (phishing attacks, hacking). Importantly, Gen AI aids in synthetic data generation by producing diverse and realistic datasets that emulate real-world data. This data is used for training and testing ML models aimed at enhancing data and network security, improving compliance standards, and boosting fraud detection.
Process efficiency and productivity
Gen AI can automate repetitive, time-consuming tasks relating to customer service, content creation, code generation, documentation and more. Using Gen AI, workforce planning can be enhanced, and employees across teams, particularly customer service, can be trained to improve performance using techniques like scenario simulation. Gen AI can also optimize resource allocation of critical resources such as bandwidth, spectrum, and network capacity, thereby enabling efficient resource utilization.
Network planning and monitoring
Building a resilient network infrastructure is key to offering the best service. By providing data-driven insights, predicting network demand, and strategizing network deployment, Gen AI can improve network planning. It can also enable better network monitoring by examining real-time network data and engaging in predictive maintenance, which minimizes the chances of network failure or breakdowns. Further, Gen AI can elevate network planning and monitoring through its code assistance capabilities by automating the generation of configuration scripts, streamlining network parameters, and assisting in troubleshooting. This can boost application development, improve network maintenance, and simplify cloud migration.
How Can Gen AI Enhance User Experience?
Intra-knowledge Q&A
Gen AI can help create an internal knowledge base for CSPs. This centralized digital repository can help employees find answers to process-related queries, access information relevant to their tasks, and encourage intra-organizational knowledge sharing.
Post-mortem (PM) search assistant
Gen AI can assist in conducting post-mortem analysis of outages, failures and other incidents within the network by analyzing incident reports, systems and application logs, and other relevant data. Gen AI can also identify the root cause of issues, provide recommendations for process improvement, and enable the formulation of corrective actions for better incident management.
Management of auto-generated network anomaly tickets
Through the analysis of network data and identification of potential issues, AI can help automate the management of network anomaly tickets. It can generate tickets, assign them to the appropriate teams, and provide insights based on historical data and patterns for faster resolution.
Media content discovery
Gen AI’s advanced natural language processing (NLP) can analyze the context, semantics, keywords, and other aspects of documents for improved search and summarization functions. These capabilities can improve the accuracy of search results, help teams locate specific information within large volumes of documents, and summarize information from the retrieved document. Gen Al can also power media content discovery platforms for CSPs and personalize recommendations by evaluating user preferences, behaviour, and content metadata.
Branded consumer interactions
Gen AI can elevate the quality of interactions between a CSP and its customers by offering more natural, supportive engagements with virtual assistants as well as personalized brand experiences based on customer preferences and behaviours.
Considerations when Adopting Gen AI
Deploying a Gen AI system is not an easy task — it is resource-intensive and requires advanced technological proficiency. To ensure a smooth deployment of use cases that will deliver real value to the business and its customers, telcos and CSPs must work with technology partners that understand the industry as well as they understand the technology.
An ideal technology partner will be able to quickly build prototypes of Gen AI solutions and test their viability. They should posses a strong understanding of the technology, its various models, architectures and algorithms (generative adversarial networks, variational autoencoders, transformer models), and be knowledgeable in the various datasets specific to the telecommunications industry that are integral to the Gen AI solution being developed. This is crucial as it affects aspects such as data pre-processing and model training that influence the efficacy of the use case. Technology partners should also be experienced in training AI models and possess a strong moderation team that can ensure the content generated is accurate, devoid of errors and biases, and adheres to ethical and legal demands.
The telecommunications industry has evolved significantly in recent years, from facilitating phone calls to providing internet connections to powering IoT. Generative AI offers many powerful tools that can help telcos and CSPs keep pace with a changing industry while providing the best possible customer experience. To make the most of these capabilities, and to truly stand out from the competition, companies will need to invest in strategy, enlist the support of an experienced technology partner, and develop a forward-thinking approach to generative AI adoption that aligns with the greater goals of the organization.
Authored by:
Jansylvette Rotger, Account Director, Americas
Lalit KashyapVice President and Cluster HeadUS Communication, Media and Information Services
