The telecommunications landscape has been significantly reshaped by AI, enhancing service quality, customer satisfaction, and enabling new products. According to Prianca Ravichander, chief marketing officer at Tecnotree, AI and machine learning (ML) applications are broad and diverse, yet their greatest impacts are often unclear. GenAI, a recent innovation, has notably improved customer experiences, network management, and service delivery. Abhishek Sandhir of Sand Technologies highlights the combination of classical AI with GenAI to elevate capabilities and growth strategies, offering new AI-related advances and business opportunities. AI’s ability to personalise customer experiences, driven by vast amounts of customer behaviour and network performance data, is virtually limitless. Joy King from Optiva notes that customising offers and automating customer needs can drive new revenue and reduce costs. AI-powered chatbots and virtual assistants are increasingly deployed for instant customer support, freeing human agents for more complex tasks.
Generative AI is revolutionising how CSPs build and launch products, leveraging natural language and image recognition to accelerate the process from concept to catalogue. Dominic Smith from Cerillion adds that this transformation allows CSPs to translate ideas into commercial offerings swiftly. AI’s role in holistic optimisation is crucial, particularly in addressing challenges like network congestion, competition, churn, fraud, and sustainability. Sandhir emphasises that AI’s effectiveness relies on a strong data foundation, which MNOs, being data-rich, must harness for optimisation and improvement. AI can evaluate and act on various issues, including fraud and cybersecurity risks, due to its proactive capabilities.
Network optimisation through AI involves more than simple adjustments; it requires a holistic view of network operations. Sandhir explains how AI can adjust azimuth, angles, and power levels based on real-time demand, prolonging equipment life and reducing energy consumption. AI predictive analytics can foresee network congestion and take measures to prevent it, as noted by Ravichander. Additionally, AI-driven resource management allocates bandwidth and other resources in real-time, preventing bottlenecks and ensuring optimal performance. AI’s ability to analyse back-office data rapidly can uncover new marketing strategies, reduce churn, and identify fraudulent behaviour.
Monetising networks amidst competition and declining core service revenues is a priority for telecom operators. Sandhir points out that AI can unlock business insights from existing data, identifying new monetisation opportunities. Despite the challenges in translating marketing ideas into BSS configurations, Smith notes that GenAI enables faster product and service launches by bridging the gap between marketing and operational teams. AI’s predictive capabilities can enhance customer satisfaction and reduce churn by anticipating high-interest usage areas and preventing slowdowns.
Innovative CSPs are collaborating with ecosystem partners to offer additional services and expand into new markets. Ravichander predicts that open ecosystems and partnerships will drive growth beyond connectivity. However, real-world AI adoption in mobile networks remains limited due to various challenges. Sandhir explains that the fear of the unknown and the need for experienced partners can hinder AI adoption. King highlights the demand for AI talent, regulatory scrutiny, and significant investment as additional obstacles. Ravichander points out that high implementation costs, outdated systems, and data quality issues also impede AI deployment.
Effective AI integration requires a clear definition of goals, robust data management, and an understanding of AI’s role in complementing human ingenuity. Sandhir emphasises that AI applications must be built on solid data foundations and supported by an operational mindset ready for AI integration. Successful AI deployment demands seamless integration of hardware, software, and networking technologies. Ravichander advises investing in scalable infrastructure and ensuring compatibility with legacy systems. King warns that relying solely on internal data limits AI’s impact; integrating external market data is essential for maximising AI’s potential.
AI and GenAI are transforming the telecommunications industry, offering numerous opportunities for CSPs to enhance services, optimise networks, and discover new revenue streams. While challenges exist, strategic planning, robust data management, and collaboration with experienced partners can help telecom operators fully harness AI’s potential, paving the way for a more efficient and innovative future.
Cerillion plc (LON:CER) is a leading provider of billing, charging and customer management systems with more than 20 years’ experience delivering its solutions across a broad range of industries including the telecommunications, finance, utilities and transportation sectors.
