How China Agricultural Bank Earned Top AIOps Rating – Inside the Evaluation

Intelligent Operations (AIOps) applies AI technologies such as machine learning to IT operations, extracting and analyzing growing volumes, varieties, and speeds of data to support operational management products. Gartner describes AIOps as a loosely coupled, scalable approach.

On December 26, 2022, the China Academy of Information and Communications Technology (CAICT) announced the latest batch of AIOps standards assessment results.

The Agricultural Bank of China’s Integrated Production Operations Platform – Data Analysis Platform (Kongming) passed the CAICT “Cloud Computing Intelligent Operations (AIOps) Capability Maturity Model – Part 2: System and Tool Technical Requirements” standard with an “Excellent” rating for the root cause analysis module, indicating a leading domestic AIOps capability.

The assessment ceremony was held on January 6, 2023 at the “2022 GOLF+ IT New Governance Leadership Forum” organized by CAICT.

Q&A

Q: Please introduce yourself, your organization, and the project evaluated.

Cai Shizhi: The Agricultural Bank’s R&D Center, a direct department of the head office, supports the bank’s digitalization and fintech innovation across retail, corporate, investment banking, fund management, leasing, asset management, and life insurance, serving over 800 million customers worldwide.

The AIOps Operations Data Analysis Platform is a key component of the bank’s integrated production operations system, built on a data‑driven, algorithm‑supported, scenario‑driven intelligent operations concept, addressing challenges of digital transformation and distributed architecture by creating a data marketplace, analysis engine, and intelligent operation scenarios.

Q: How do you feel about passing the root cause analysis module assessment?

Cai Shizhi: Passing the assessment validates our work and guides future optimization; we will continue to invest in intelligent operations and promote AIOps across the bank.

Q: What considerations led your enterprise to participate in the assessment?

Cai Shizhi: Rapid fault root‑cause location is critical for business continuity; we built real‑time health evaluation and AI root‑cause models to achieve fast fault analysis, using the assessment to benchmark against industry standards and identify improvement areas.

Q: What changes did the assessment bring to your team?

Jia Lei: The successful evaluation confirms our intelligent operations achievements, highlights gaps, and provides guidance for future enhancements; learning from the standard and experts has enriched our team’s knowledge.

Q: What are the next steps for AIOps development?

Jia Lei: We will expand AIOps capabilities to cloud platforms and micro‑services, enhance full‑link monitoring, analysis, and remediation, and focus on business continuity by strengthening “regulation‑control‑analysis” capabilities to detect risks early and ensure system stability.

Q: What is your view on the future direction of AIOps?

Jia Lei: AIOps will evolve from isolated solutions to a systematic platform offering data, algorithm, and scenario services; shift from reactive response to proactive prevention, emphasizing fault prediction and risk discovery; and extend empowerment to quality, efficiency, security, and management domains.

The “Intelligent Operations (AIOps) Capability Maturity Model” was jointly developed by CAICT, the Cloud Computing Open Source Industry Alliance, the Efficient Operations Community, BATJ, and major financial and telecom enterprises, and has been approved by ITU‑T SG13.

Eight modules are currently open for assessment: anomaly detection, fault prediction, alarm convergence, root cause analysis, self‑healing, fault prevention, capacity prediction, and knowledge‑base construction.