🤖 AI Summary
This study addresses the challenge of transforming heterogeneous IT service management (ITSM) ticket data into actionable, high-level decision intelligence. To this end, it proposes a sociotechnical AI pipeline that reframes ITSM analytics as a problem of transformation, abstraction, and human-centered design within information systems. The approach integrates large language model–driven schema normalization with HDBSCAN-based subtopic clustering and hierarchical agglomerative clustering to generate multilevel decision-support outputs—spanning both broad themes and fine-grained subtopics. Evaluated across six deliverables by five expert assessors, the resulting artifacts achieved mean scores above 4.0 (on a 5-point scale) for interpretability, actionability, trustworthiness, and willingness to use, with trustworthiness demonstrating particularly robust performance, thereby validating the method’s effectiveness and practical utility.
📝 Abstract
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.