Asia’s telecommunications sector has long been a proving ground for innovation. As 5G networks mature and digital services expand, operators are turning to artificial intelligence (AI) to take connectivity and their offerings to the next level. AI is emerging as the strategic layer that powers automation, efficiency, and intelligent decision-making across the network.

Why Asia Pacific Operators Are Embracing AI

Telecom operators in the Asia Pacific face extraordinary operational challenges. These include massive subscriber bases, hyper-dense cities, and traffic patterns shaped by mobile-first digital behavior. The deployment of 5G, with massive MIMO radios, cloud-native cores, expanding IoT footprints, and edge computing workloads, adds a level of operational complexity that outstrips what human teams can manage using traditional manual processes.

AI addresses this gap by ingesting and analyzing billions of network events in real time, detecting patterns that manual monitoring cannot, predicting congestion and faults before customers notice them, and automatically tuning parameters across RAN, transport, and core domains. This transition, from rule-based automation to predictive, zero-touch operations, forms the operational foundation for future autonomous networks.

Read More: AI-RAN and the Rise of Smarter Towers

AI in Action Across Asia

Asia already operates some of the world’s most advanced AI-driven network deployments, with systems in production that influence live traffic and service quality.

China Unicom’s Intelligent Brain is one of the region’s most mature AI operations platforms. According to the company’s sustainability disclosures, the Intelligent Brain covers more than 1,397 automated operational scenarios and over 30,000 rule automations. The operator has reported an automation handling rate for network events of roughly 99.6% and the platform enables China Unicom to detect anomalies, diagnose their cause, and trigger remediation actions with minimal human intervention.

While this is important, AI’s impact goes well beyond performance gains. As energy costs rise, operators are increasingly relying on intelligence to balance sustainability with service demand.

Rakuten Mobile in Japan operates a cloud-native, virtualized network designed for automation from the outset. Rakuten Symphony has demonstrated AI and machine learning models interfacing with a RAN Intelligent Controller that can reduce RAN energy consumption in demonstrations by up to 25%. Future RIC-driven RAN optimizations are projected to range between 15% to 20% as the platform scales across real traffic. These results highlight the operational benefits created by combining cloud-native architectures with AI-driven orchestration.

NTT DOCOMO in Japan has demonstrated AI-driven wireless innovations, including outdoor trials that use real-time AI to adapt transceiver processing and improve throughput in 6G-oriented tests. DOCOMO also operates AI-based energy optimization programs that analyze traffic and site conditions to determine when low-power operation is feasible.

Meanwhile, Globe Telecom in the Philippines uses AI and machine learning across its base stations to dynamically forecast network demand and adjust power-saving parameters. In early deployments, Globe has reported energy savings in the range of 3-8%, with up to 187,774 kWh saved and 139 metric tons of CO₂ avoided in 2023.

Beyond performance, AI is reshaping customer interactions. Across Asia, operators are weaving AI deeper into both customer engagement and network performance, with True Corporation using AI to enhance customer experience across touchpoints, Singtel combining AI assistants with smart network analytics to correlate real-time service performance with customer sentiment, and Chunghwa Telecom leveraging Ericsson’s Site Digital Twin and GenAI to predict congestion, optimize capacity, and sustain smooth, reliable connectivity.

Interesting Read: China Unicom Transforms Customer Contact Center with AI Innovation

Building the Foundation for the Future

Achieving full network autonomy requires more than algorithms; operators must invest in unified data platforms, cloud-native architectures, and open APIs that allow AI systems to act across multiple network domains. Industry alliances, such as TM Forum and ETSI, are already working to standardize this transition.

However, there are still challenges along the way. Data governance, model transparency, and interoperability continue to test the limits of large-scale deployment. Equally important is preparing the workforce for an AI-driven environment through reskilling and upskilling initiatives, ensuring human expertise remains central to intelligent operations.

AI now underpins everything from predictive maintenance and energy optimization to personalized customer engagement and offerings. As 5G evolves and 6G begins to take shape across the Asia Pacific, success will depend on how effectively operators embed AI as a core network competency rather than an optional enhancement.