Value vs. readiness FIGURE 2 Readiness (technology, data, compliance and people) LowLow HighHigh Product and service Network Security and operationsSales and marketing Customer service Tech and platformDigital twin planning SOC assistant Incident detection, analysis and response Vulnerability management Network decommissioningDesign automation Virtual assistant (voicebot) Real-time service conﬁguration Sales through serviceRAN energy savings assistant Customized service packages Data delivery life cycleNOC genAI assistant Field tech assistant Technology delivery life cycle Advanced troubleshooting Network engineering assistantFraud prevention Virtual assistant (chatbot) Deployment QA assistant Customer segmentation Transcript analysis Assisted customer care with sentiment analysis Sales genAI assistant Hyper-personalized content and customer journeys Optimizing service design and delivery B2B lead enrichmentValue (cost reduction, revenue uplift, customer experience, security) Note: The telecommunications industry is expected to be “ready” for these use cases within 18 months, with 10 identified as current top priorities. Due to the range of products and services that AI can enable, “AI-enabled products and services” are not included. 2.2 Emerging and future scenarios As AI capabilities advance in core enablers, automation and agentic architecture, CSPs can unlock new, transformative opportunities. AI-enhanced data monetization With advancements in computing, including scaled GPU-enabled processing, CSPs can monetize the vast data they generate. Open API technology simplifies data sharing with third parties, enabling them to sell AI-enhanced data monetization to B2B customers, with offerings across AdTech, insights marketplace and analytics-as-a-service. AdTech enhances customer profiling, segmentation, propensity to buy and pricing elasticity models to enable targeted advertising. Advertisers can use AI-driven insights to engage the right audience at the right time, boosting marketing performance. Insights marketplace provides actionable views on market trends using customer profiling and market assessments. AI-driven capabilities such as location intelligence, share-of-wallet predictions or market share simulations offer predictive insights. Analytics-as-a-service allows enterprises and SMBs to access anonymized, aggregated customer data for custom analysis and model development, combining telco data with third-party inputs. Data monetization must comply with regional and local regulations and align with responsible AI principles. CSPs must ensure that personal data is used responsibly, with robust measures to safeguard privacy, mitigate liability and maintain compliance. Sovereign AI The rise of AI brings significant societal and economic value but also introduces cybersecurity risks, particularly for sensitive data.26 The definition of “sensitive data” and the ways in which AI governance and regulations dictate its treatment varies by geography. Artificial Intelligence in Telecommunications 16

Artificial Intelligence in Telecommunications 2025