What the 2024 China AIOps Survey Reveals About Smart Operations Trends
The 2024 XOps Forum in Beijing showcased a new era of smart operations, unveiling a record‑breaking AIOps survey that highlights rapid investment growth, rising adoption of large language models, evolving maturity levels, and key challenges such as model accuracy and data quality across Chinese enterprises.
2024 XOps Forum Overview
The XOps Industry Innovation Development Forum was held on July 25, 2024 in Beijing, featuring a "New Era Smart Operations" session that focused on AI Ops, large‑model operations, SRE, FinOps, and cloud governance, gathering experts to share best practices and discuss future trends.
China AIOps Status Survey 2024
China Information and Communication Technology Academy released the "China AIOps Current Situation Survey (2024)" based on 3,218 valid online questionnaires, setting a new record for participation.
Key Investment Findings
More than 40% of enterprises invest over 50 million CNY annually in operations, with 47.16% at the highest tier and 40.12% in the 5–50 million range. Overall IT budget growth has slowed, and 70% of companies report AI Ops spending in 2024 remains flat compared to 2023.
Priority Technologies
When allocating operations budgets, 61.53% prioritize AI Ops, followed by business observability (37.54%) and SRE reliability practices (32.99%). Emerging interests include data governance, large‑model integration, and digital operation agents.
Large Language Model Adoption
Adoption of LLMs in operations has risen sharply; 51.81% of firms are still researching generative models, 6.20% have built their own LLM capabilities, and 16.96% have integrated vendor LLM APIs. Companies using vendor LLMs increased by nearly 10% year‑over‑year.
Maturity Model Progress
According to the "AIOps Capability Maturity Model" (L1‑L5), 58.42% of respondents have reached the L3 advanced stage, up over 20% from 2023, while 18.72% are at L2 and 9.72% claim full L5 maturity.
Challenges
Difficulty tracing model accuracy, relying on manual expertise.
Data quality issues affecting machine‑learning reliability.
High cost of maintaining and continuously optimizing models.
Future Directions
Nearly 50% of enterprises plan to develop limited large‑model capabilities this year, with 30% already building related scenarios. Top large‑model use cases are monitoring alerts (69.91%), automated script generation and task execution (57.55%), and operational agents (48.85%).
Enterprises are moving toward unified platforms for smart operations, with over 40% having deployed AI Ops tools, 28.13% in early practice, 15.2% with established platforms, and only 1.96% with full evaluation systems.
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Efficient Ops
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