How China Mobile’s AIOps Evaluation Sets a New Benchmark for Intelligent IT Operations

Intelligent Operations (AIOps) applies artificial‑intelligence techniques such as machine learning and data science to IT operations, enhancing and partially automating core functions. Gartner describes AIOps as a loosely coupled, scalable approach that extracts and analyzes ever‑growing volumes, varieties, and velocities of IT data to support operations management products.

On December 26, 2022, the China Academy of Information and Communications Technology (CAICT) announced the latest batch of AIOps standards evaluation results. China Mobile Information Technology Co., Ltd. (referred to as China Mobile Information) participated with its “Management Information Domain Operation Management System” project. The fault‑prediction module of this project successfully passed the comprehensive‑level assessment, indicating that the company’s AIOps capabilities have reached a leading domestic level.

Interview with Project Manager Liu Yansong

Q: Please introduce yourself, your company, and the project you evaluated. Liu Yansong: China Mobile Information Technology Co., Ltd. is a wholly‑owned subsidiary of China Mobile Communications Group. I am a project manager, and the evaluated project is the Management Information Domain Operation Management System, with the fault‑prediction module as the assessment target.

Q: How do you feel after passing the CAICT AIOps standards assessment? Liu Yansong: It is our first time participating in the CAICT AIOps system and tool assessment, and we are delighted to have passed smoothly. The exchange with experts during the assessment was highly beneficial, and we thank all colleagues involved in the system construction.

Q: What considerations did your enterprise have when participating in the assessment? Liu Yansong: Since 2021 we have explored AIOps capabilities, building functions such as fault prediction and anomaly detection. We now monitor thousands of resources and configure tens of thousands of monitoring points, achieving cost reduction and efficiency gains while still having optimization space. We hope the assessment helps us learn industry standards, gain expert advice, and clarify future development directions.

Q: What changes has the assessment brought to your enterprise and team? Liu Yansong: The assessment prompted us to re‑examine our AIOps tools. Although our tool covers many capabilities, its depth still needs improvement. The process gave us new ideas and strengthened our confidence in building stronger AIOps tools.

Q: What are your next steps for AIOps work? Liu Yansong: First, we will deepen fault‑prediction research to improve accuracy. Second, we plan to expand other AIOps capabilities such as root‑cause analysis and automated fault recovery, broadening the tool’s functional scope.

Q: What is your view on the future development of AIOps? Liu Yansong: The intelligent operations industry is rapidly moving toward diversified scenarios, data‑centric contexts, refined services, and platform‑based algorithms. Future development will focus on quickly identifying abnormal hosts, networks, and call relationships, assisting users in rapid root‑cause localization, isolating faulty resources, and automatically repairing issues through a composable platform, ultimately forming an end‑to‑end intelligent closed‑loop monitoring system.

The AIOps Capability Maturity Model, led by CAICT together with the Cloud Computing Open Source Industry Alliance, Efficient Operations Community, dbaplus community, BATJ, and major telecom and financial enterprises, is the first domestic and international standard for intelligent operations. It has been approved by ITU‑T SG13.

Based on the “Cloud Computing Intelligent Operations (AIOps) Capability Maturity Model – Part 2: System and Tool Technical Requirements,” eight modules are currently open for assessment: anomaly detection, fault prediction, alarm convergence, root‑cause analysis, fault self‑healing, fault prevention, capacity prediction, and knowledge‑base construction.