The AI Use Case Library equips telcos and the wider tech sector with proven AI applications that solve real operational challenges
AI implementation has been something of a success in the telecoms industry compared to other sectors. As the UK-based survey by SAS on AI adoption points out, telecoms ranked ahead of most industries in genAI adoption, with 70% of telcos having fully or partially implemented the technology. Telcos also plan to lead in investment too. According to the same survey, 89% of telecom operators expect to invest in genAI in the next financial year—the joint highest alongside the insurance industry.
The findings align with the GSMA’s recent Telco AI: State of the Market, Q4 2024 report. This research finds that 65% of operators have adopted an AI strategy, and 41% are in early-stage deployment, often running pilots or exploring where AI can provide tangible benefits. Yet many operators struggle with implementation, scalability, and crucially, understanding which use cases are best for them.
To help operators navigate these challenges, the GSMA AI Use Case Library provides a curated collection of industry AI applications. This resource compiles AI-driven solutions from operators, vendors, and technology providers across different markets, with concise case studies that help inform AI strategies.
Why the AI use case library matters
Unlike theoretical discussions on AI, the AI Use Case Library focuses on actual implementations, demonstrating where AI has delivered measurable impact. The use cases span network optimisation, customer experience, operations, sustainability, and security, reflecting AI’s growing role in daily processes.
By exploring these applications, organisations can identify scalable solutions, mitigate deployment risks, and accelerate AI adoption. The library contains contributions from a diverse range of organisations, providing a global perspective on AI deployment.
Key use cases from the AI Use Case Library
Network operations and optimisation
- Huawei and Shandong Unicom: AI-powered wireless intelligent agents improve network performance for VIP customers. AI identifies high-value users and optimises network resource allocation, achieving a 98% success rate in maintaining seamless service.
- Qualcomm: AI-driven mobility management optimises cross-vendor network performance using a dynamic neural network (DNN). This solution enhances mobility assurance across 124,000 5G cells, improving network efficiency without manual intervention.
- Nokia: AI assistant helps engineers resolve technical issues faster. It has led to a 40% reduction in response times and a 31% drop in assisted support cases. It has solved 20% more tickets at Tier 1 level.
AI-driven sustainability
- China Mobile: AI-driven energy-saving models reduce network power consumption by 24% per server, cutting annual electricity costs by RMB 202 million. The AI model predicts computing demand to optimise energy use without affecting service quality.
Enhancing customer experience
- Telkomsel and ZTE: AI network automation reduces latency for gaming by 47%, boosts video streaming speeds, and dynamically manages power consumption. AI enables real-time service prioritisation, optimising network performance without additional capital expenditure.
- Nokia: AI-driven customer-premises equipment (CPE) monitoring detects service issues in real time. This allows operators to proactively adjust configurations, reducing disruptions and improving customer satisfaction.
- Ericsson: AI-assisted customer support accelerates resolution times by recommending relevant technical documents. This optimises engineer workloads and improves response efficiency for telecom service issues.
- KDDI: Generative AI improves chatbot accuracy and intent recognition by summarising long customer inquiries and asking questions to clarify missing details. It also streamlines agent handovers by summarising chat history. Testing shows this can trim response times by 5 minutes, a 20% improvement. The technology also helps train chat advisors, improving customer satisfaction.
- Ericsson: AI-assisted customer support reduces response times by matching inquiries with relevant technical documents from internal databases. This optimises engineer workloads, improving resolution efficiency and streamlining support operations.
Understanding AI use cases are shaping the future of telecoms
The GSMA AI Use Case Library is an ideal resource for assisting organisations on their AI journey. By providing demonstrable deployment insights, it helps organisations understand AI’s impact, mitigate risks, and identify scalable solutions. Whether organisations want to analyse market trends, assess competitors, or get started with AI, the library offers valuable guidance.
As AI transforms telecom networks, customer interactions, and energy management, the need for proven, replicable use cases will only grow. For operators looking to adopt AI with confidence, the GSMA AI Use Case Library is an essential tool for making smarter investment decisions.