Introduction
Artificial Intelligence (AI) is driving transformative change across the technology, telecom, and services industries, reshaping the landscape with innovative solutions and enhanced efficiency. In the technology sector, AI is revolutionizing processes like automation, cybersecurity, and data analytics, enabling companies to stay ahead in a rapidly evolving market. Infosys’ recent research shows that Al in the telecommunications industry is expected to generate an impressive US $11 billion in annual revenue by 2025. AI in Telecommunication can be used to personalize services, optimize network management, and improve customer interactions, while the services industry is adopting AI-driven automation and predictive analytics to enhance operational processes and deliver more tailored customer experiences. The following list explores the compelling use cases of AI across these industries, highlighting how it is shaping the future and driving exponential growth.
Use Cases
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1. Improve network reliability and efficiency
Drones and AI are used to enhance mobile network maintenance by testing network performance, inspecting cell towers for physical issues, identifying obstacles that affect signal quality, detecting rust on equipment, and assisting in radio frequency planning. This ensures better network reliability and efficiency.
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2. Organizational memory preservation
Enterprise knowledge management systems facilitate the efficient storage and retrieval of data, preserving organizational memory. They enhance collaboration by ensuring that the right individuals have access to the relevant data, and they promote seamless integration across the organization through advanced knowledge management platforms.
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3. Develop intelligent CRM systems
Customers and their families often have multiple accounts (e.g., mobile, landline, internet) with a telecom company, stored in separate databases. This fragmentation can be frustrating as it prevents a unified view of services. Telecom companies are leveraging AI, particularly similarity matching, to develop intelligent CRM systems that consolidate these accounts into a single view. This unified view enhances customer experience, suggests next best actions, improves brand image, and reduces customer churn.
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4. Protection of employee communications
Employee communications, including emails and phone conversations, are protected using advanced multilayered cryptography integrated with AI models. This ensures that the company’s confidential data remains secure from corporate espionage, safeguarding sensitive information and maintaining data integrity.
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5. Predictive and prescriptive maintenance
AI systems are being trained to offer predictive and prescriptive maintenance, which helps in reducing the likelihood of failures. This is especially important as technology and telecom networks grow increasingly complex and critical for operations, such as network management and service delivery. Predictive maintenance anticipates issues before they occur, while prescriptive maintenance suggests actions to prevent these issues.
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6. Self-healing networks
Given the critical and complex nature of telecom networks, many companies are implementing AI-based self-healing networks. These networks leverage AI to optimize decision-making at all stages, from planning and construction to operation. AI systems enable self-healing by automatically detecting and fixing faults, and if a frequency is blocked or congested, they can switch customers to a less congested frequency, ensuring uninterrupted service.
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7. Enhanced cybersecurity
Enhance your cybersecurity capabilities to defend against various cyber-attacks by training AI and Machine Learning systems to respond efficiently. These systems can augment the work of security analysts, reducing the risk of human errors by providing greater autonomy in cybersecurity operations. Additionally, AI-backed systems can ensure compliance with security standards, further strengthening your defense mechanisms.
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8. Detect anomalies and automate emergency response procedures
To ensure 24/7 operation and maximum protection, use AI to detect anomalies in Information Technology and Emergency Response environments. AI can also automate emergency response procedures by providing instant notifications, ensuring rapid and effective responses to potential threats or incidents. This enhances overall security and operational efficiency.
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9. Enhance coding efficiency
Utilize AI systems, particularly GPTs and Large Language Models (LLMs), to assist IT developers in intelligently accessing coding knowledge from the web. By incorporating best practices for specific development tasks into these AI systems, developers can receive real-time feedback, enhancing their coding efficiency and ensuring adherence to industry standards.
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10. Cognitive customer service robots
Cognitive customer service robots are designed to converse with customers, provide advice, and answer questions, enhancing customer interaction. Similarly, chatbots are used to address customer queries, offering instant, on-demand solutions and assistance for both technology and telecom companies, thereby improving customer service efficiency and satisfaction.
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11. Gesture-based payments in VR
Gesture-based payments in a Virtual Reality environment enable users to log in to their payment accounts or complete transactions by nodding their heads or making specific gestures with a mobile VR headset and AI. For example, users can “smile to pay,” making the payment process more intuitive and engaging.
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12. Reconcile intercompany bills
AI can be used to learn formulas and processes to reconcile intercompany bills, particularly among international telecom companies, thereby reducing disputes. Currently, many telecom companies face challenges in matching data for calls made by individuals from other countries, often spending several months resolving these issues with the originating country’s telecom company. AI can streamline this process, ensuring accurate billing and faster resolution.
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13. Optimize base stations
Optimizing base stations in telecom companies is a critical task. The growing demand for high-speed mobile data services necessitates an increase in the number of base stations. However, the high cost of base station equipment and professionals can make some of these stations unprofitable. AI systems are used to determine the optimal locations for base stations and to optimize their power consumption and other key performance indicators, ensuring cost-effectiveness and efficiency.
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14. Predict customer churn
Telecom companies experience significant customer churn, particularly among transient customers who frequently move from one city or county to another. To address this, they use AI systems to analyze comprehensive customer data and predict churn. This enables them to adjust pricing strategies as needed, reducing churn rates and retaining more customers.
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15. Digital twins for network planning
As telecom carriers evolve and expand their service portfolios, they increasingly rely on AI systems for network planning and optimization. They often use digital twins—virtual replicas of physical networks—to simulate the characteristics of new networks before actual implementation. This allows for thorough testing and optimization, ensuring efficient and effective network deployment.