Practical use cases of Generative AI in Telecom
The global mobile data traffic is expected to multiply fivefold before the end of the decade. Thus, telecom companies report struggling to manage this surge, grappling with network congestion, maintenance challenges, and heightened customer expectations for seamless connectivity.
These challenges underscore a broader issue: telecom providers must deal with complex network infrastructures that require constant optimization and maintenance. They face stagnating revenues and the pressures brought on by deploying 5G technology, which demands more from already strained networks.
In response to these pressing issues, Generative AI becomes a solution. This powerful technology, backed by Machine Learning and neural networks, redefines how telecom companies operate, innovate, and serve their customers. Let's explore how Generative AI is changing the industry from automated customer service chatbots to dynamic network optimization
The telecom industry, burdened with outdated operating practices, can achieve new levels of profitability with Generative AI. In some cases, incremental margins can increase by 3% to 4% within two years and by 8% to 10% within five years through improved customer life cycle management and reduced operating costs.
Here are impactful Generative AI use cases in the telecom industry:
Network management
Generative AI connects multiple complex AI/ML models used across network planning and operations with large language models (LLMs). They understand network behaviors and create action plans in areas like network capacity planning and performance. Technology can train models with customer experience and sentiment data to build better prediction capabilities, significantly enhancing privacy, factuality, and relevance while protecting intellectual property.
For instance, AI can automate network management for 4G and 5G systems. The technology's ability to streamline complex processes through contextualized LLM is particularly valuable in the telecom industry, which manages one of the most intricate and data-intensive networks in the world.
Generative AI for telecom enhances performance by producing summaries and recommendations for network orchestration or coding. AI identifies network anomalies, provides intent-based development or code assistance, automates incident response, and supports field maintenance workers with step-by-step guidance. LLMs that excel in mathematical and logical reasoning are best suited for automating network operations.
Key network applications of Generative AI in telecom:
Customer service and support
Generative AI in telecom can analyze a customer's usage patterns and interactions with the provider's website and app to recommend personalized plans and add-ons. If customers exceed their data limit, AI can suggest a plan with more data and add-ons like international calling or unlimited texting. It can also recommend new devices compatible with upgraded plans, offering features like larger screens, better cameras, or longer battery life. The system might also provide special deals or discounts for users who renew their contracts and upgrade their plans or devices.
AI chatbots and virtual assistants provide 24/7 support, handling customer queries efficiently and generating human-like responses. AI chatbots can address various issues, from billing inquiries to technical support, and escalate complex problems to human agents when necessary. They can transcribe and summarize all voice and written client interactions, creating a smarter customer service knowledge center.
AI systems can predict and resolve common service issues without human intervention. If a network outage occurs, AI can automatically diagnose the problem and initiate corrective actions, reducing the need for manual intervention. From using raw log data to detect incidents to drafting texts for customer support requests or trouble reports and generating labeled clusters of categorized trouble reports for easy searching when similar tickets are raised, AI enables faster resolution.
Marketing and sales
Generative AI can create tailored content for different audiences and channels, such as blog posts, social media, landing pages, and email campaigns. Telecom operators can use AI to generate titles, summaries, keywords, or captions based on audience interests and actions. It can also classify and segment customers based on attributes, desires, preferences, and actions, using data from web analytics, CRM platforms, and social media, from customer interactions, and target customers using micro-segmentation derived from call insights.
Generative AI can recommend personalized products or services based on previous purchases, web history, and feedback. A telecom operator can suggest the best bundle, plan, or add-on for each customer, tailored to their budget, needs, and usage patterns.
The technology can consolidate all sales documentation, including product details and pricing models, into a robust knowledge engine. Innovative solutions like chatbots can provide instant answers to sales managers and representatives, integrating real-time insights from the entire customer experience value chain.
Fraud detection
As telecom networks handle vast amounts of sensitive data, security is a top concern. Generative AI in telecom can spot and counter security threats by detecting anomalies in network traffic in real time that indicate possible cyberattacks or unauthorized access. By using Machine Learning algorithms, AI can detect fraudulent activities such as SIM card cloning, call rerouting, and unauthorized billing with high accuracy. For instance, Generative AI models can continuously learn from new data, adapting to evolving fraud techniques and improving detection rates.
Telecommunications providers use Generative AI to protect against fraudulent activities by analyzing data, including call records and online transactions. The AI learns normal customer behavior patterns and detects anomalies, such as sudden surges in international calls or unusual data usage. It also identifies patterns of SIM card swapping or suspicious login locations, helping telecom companies swiftly detect and mitigate fraud.
Predictive maintenance
Generative AI for telecom analyzes vast amounts of data to predict equipment failures and network issues before they occur. The AI promptly raises alarms when anomalies or deviations arise, such as unexpected traffic spikes or equipment malfunctions. This proactive monitoring allows telecom operators to address potential issues through automated responses swiftly.
By analyzing historical and current performance data to identify system patterns and trends, AI creates predictive models that consider weather conditions, equipment age, and usage patterns. Telecommunication companies can be enabled to do proactive maintenance scheduling during off-peak hours, minimizing disruptions and ensuring consistent connectivity.
Billing
Generative AI refines billing inquiries by providing precise solutions and insights. AI algorithms can analyze usage data to calculate bills accurately, eliminating errors and enhancing customer trust. For example, AI can provide detailed billing explanations, helping customers understand their charges and reducing billing-related disputes.
Additionally, dynamic pricing models can adjust prices in real time based on market demand, customer preferences, and other external factors. For instance, AI can adjust pricing during peak usage times to manage demand and ensure network stability.
Synthetic data generation
Synthetic data generation for testing, training, and research involves creating realistic network traffic patterns to test and strengthen security systems against potential cyber-attacks. By simulating various attack scenarios, AI helps in identifying vulnerabilities and fortifying defenses, ensuring robust network security.
For example, Generative AI for telecom can expedite the resolution of fiber cut incidents, a common issue in telecom. These incidents generate data from monitoring tools and systems, including logs, alerts, event details, and diagnostic information. This flood of data can be overwhelming and make it difficult to identify critical information.
Keep reading: Generative AI use cases and applications
Integration with legacy systems
Telecom companies often operate on legacy systems unsuitable for advanced AI technologies. These systems, often built decades ago, lack the flexibility and scalability required to support advanced AI technologies. The incompatibility can lead to delays in deployment, increased operational costs, and potential service disruptions. A study by McKinsey highlights that 70% of digital transformation projects fail due to issues related to legacy system integration.
N-iX solution: We adopt a phased approach to integration, including identifying critical areas for AI implementation, developing custom APIs to bridge legacy systems with AI platforms, and gradually modernizing the IT infrastructure.
Data silos and quality
Telecom companies generate a plethora of data across various departments and legacy systems, including customer information, network performance metrics, and service usage statistics. This data often resides in silos, making it difficult to consolidate and analyze effectively. Ensuring data quality is another critical issue; poor data quality can lead to inaccurate AI predictions and unreliable outputs.
N-iX solution: We recommend investing in robust data management strategies, including adopting advanced data integration tools, implementing stringent data governance policies, and leveraging data cleansing techniques.
Security concerns
AI involves processing vast amounts of sensitive customer data, which must be protected against breaches and misuse. In addition to security, telecom companies must navigate complex regulations related to data privacy, such as the General Data Protection Regulation in Europe and the California Consumer Privacy Act in the United States. Non-compliance can result in serious fines and legal repercussions. For example, under GDPR, companies can be fined up to 4% of annual turnover for data breaches.
N-iX solution: We ensure robust data protection, which requires comprehensive cybersecurity strategies, including encryption, access controls, and regular compliance assessments.
High complexity
Implementing Generative AI in telecom involves significant complexity and risks. The development and deployment of AI models need precise calibration, continuous monitoring, and regular updates to adapt to changing network conditions and user behaviors. Any errors or inaccuracies in the generative AI models can result in service disruptions and financial losses.
N-iX solution: We apply a comprehensive approach that includes rigorous testing, validation, and quality assurance processes, thorough risk assessments, implementation of fail-safes and redundancies, and continuous evaluation of AI model performance.
To benefit from the impact of Generative AI, organizations need to move away from the labyrinth of proofs-of-concept and scale the AI technology. There are no shortcuts; successful implementation requires strategic investment in infrastructure, skills, and change management. Telecom service providers like Vodafone and Orange have already deployed dual customer and Business Support System chatbots powered by Generative AI.
Generative AI is becoming a central topic in every board and strategy meeting due to the telecom industry's combination of low margins and high IT expenditures. The time to scale and integrate Generative AI is now.
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