面向决策就绪型ITSM智能的AI流水线设计
Designing AI Pipelines for Decision-Ready ITSM Intelligence
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中文总结 AI 辅助
该研究遵循设计科学原则设计了结合LLM标准化、HDBSCAN聚类等的AI流水线，将ITSM工单数据转化为多级决策支持内容，经评估其四项决策指标平均得分超4.0，验证了其作为以人为中心IS问题的价值。
中文摘要 AI 辅助
IT服务管理（ITSM）系统积累了大量异构工单数据，销售及高管利益相关者难以将其转化为可执行的情报。本文遵循设计科学研究原则设计并评估了一种社会技术AI流水线，该流水线将原始ITSM导出数据转化为多级决策支持 artifacts。该流水线结合基于大语言模型（LLM）的模式标准化、HDBSCAN子主题聚类及层次凝聚聚类，生成面向高管的主主题与细粒度子主题。对6个artifacts及来自销售工程、客户成功角色的5名评估者开展的利益相关者评估显示，可解释性、可操作性、信任度、使用可能性这四项决策支持指标的平均得分均超过5分制的4.0分，其中信任度是最一致的信号。研究结果表明，ITSM分析是一个涉及转换、抽象及以人为中心设计的信息系统（IS）问题。
英文摘要
IT service management (ITSM) systems accumulate large volumes of heterogeneous ticket data that are difficult for sales and executive stakeholders to convert into actionable intelligence. This paper presents a sociotechnical AI pipeline, designed and evaluated following design science research principles, that transforms raw ITSM exports into a multilevel decision-support artifact. The pipeline combines LLM-based schema normalization, HDBSCAN sub-topic clustering, and hierarchical agglomerative clustering to generate executive-facing Main-topics and granular Sub-topics. A stakeholder evaluation across six artifacts and five raters from Sales Engineering and customer success roles shows that all four decision-support metrics, interpretability, actionability, trust, and likelihood of use, on average exceed 4.0 out of 5.0, with trust as the most consistent signal. The findings position ITSM analytics as an Information Systems (IS) problem of transformation, abstraction, and human-centered design.
