Revolutionizing IT Service Process Monitoring with AI

József Till (), Szilvia Erdeiné Késmárki-Gally () and Judit Bernadett Vágány ()
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József Till: Hungarian University of Agriculture and Life Sciences, Doctoral School of Economics and Regional Sciences
Szilvia Erdeiné Késmárki-Gally: Budapest Metropolitan University, Institute of Business Studies
Judit Bernadett Vágány: Budapest University of Economics and Business, Faculty of Commerce, Hospitality and Tourism

A chapter in Proceedings of the Kautz Conference on Business and Economics 2025 (KCBE 2025), 2026, pp 293-311 from Springer

Abstract: Abstract This exploratory case study examines the implementation of a proprietary AI-based reporting and monitoring tool in the Hungarian subsidiary of a multinational IT service provider. The tool integrates an observability platform with machine learning–driven analytics to automate process monitoring, metric creation, and statistical reporting, minimizing manual intervention. Offered as a consult-led service through periodic “bring your own data” (BYOD) assessments and continuous deployments, it supports diverse IT environments across infrastructure, applications, and business services. Drawing on a literature review and six expert interviews with senior managers and IT operations specialists, the study investigates how the AI tool enhances process monitoring efficiency and KPI management and compares its perceived advantages and disadvantages with traditional reporting. Thematic analysis reveals that the tool centralizes observability, reduces manual reporting effort, and enables the definition and tracking of SMART KPIs, such as targeted incident reduction, increased automation potential, and enhanced compliance. First-year project KPIs include a 30% reduction in incidents, a 50% increase in corrective automation potential, 90% compliance adherence, and a 75% decline in incidents for selected services. Human expertise remains essential for defining KPIs, configuring data sources, and interpreting AI insights. Challenges include integration and licensing costs, data governance, trust in AI recommendations, and changes in reporting roles. The study concludes with recommendations for designing AI-enabled monitoring around SMART KPIs, clarifying human–AI task allocation, and proactively managing organizational risks and trade-offs.

Keywords: AI; efficiency; measurement (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-658-6_16

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