将杂乱工单数据转化为高管可用的决策支持信息
Designing AI Pipelines for Decision-Ready ITSM Intelligence
- 用大模型+聚类方法自动提取主题层级结构
- 五位用户评分均超4.0，信任度最稳定
- 适合销售工程与客户成功团队快速洞察
IT服务管理（ITSM）系统积累大量异构工单数据，难以被销售和高管转化为可行动的智能。本文基于设计科学研究原则，提出一个社会技术协同的AI流水线，将原始ITSM数据转化为多层级决策支持产物。该流程结合大语言模型进行模式标准化、HDBSCAN子主题聚类与层次聚类，生成面向高管的主主题与细粒度子主题。在六个产物和五名来自销售工程与客户成功的评估者中，所有四项决策支持指标——可解释性、可操作性、信任度与使用意愿——平均得分均超过5分制的4.0分，其中信任度最为一致。研究揭示了ITSM分析本质上是信息系统中的转化、抽象与以人为中心设计问题。
原文摘要 · Abstract (English)
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.
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