Why Transformers? A Comprehensive Overview of Transformers in Artificial Intelligence for IT Operations
Binpeng Shi, Shenglin Zhang, Jingya Wang, Bowen Hao, Minyi Shao, Luo Yu, Wenwei Gu, Yongqian Sun, Sibo Xia, Yongxin Zhao, Dan Pei · ACM Transactions on Software Engineering and Methodology · 2026
AIOps (Artificial Intelligence for IT Operations) has emerged as a key strategy for operating large-scale systems. Meanwhile, the Transformer architecture, renowned for its strong performance and potential, is driving advances in AIOps. However, prior reviews leave gaps in explaining why Transformers trigger a major shift in academia and industry, with limited coverage of multimodal data (logs, metrics, events), insufficient analysis of structural and methodological connections between Transformers and AIOps, fragmented views of capabilities, and a lack of consolidated evaluation strategies. To bridge these gaps, our work decomposes “Why Transformers” into three questions. First, we review what roles Transformers assume in AIOps across 70 representative papers, using a structured taxonomy that proceeds sequentially from data to targets, principles, and approaches, which present an evolutionary trajectory of role changes. Then, to explain why Transformers excel, we introduce a capability framework comprising two basic and four advanced capabilities. As for how to evaluate Transformers in AIOps, we consolidate evaluation resources and strategies, including offline datasets, standardized questions, and real-time simulations. Furthermore, probable best practices are distilled, offering actionable guidance for real-world adoption. These explorations aim to clarify the internal rationale and unlock the practical value of Transformers in AIOps.