Agentic Automation and Work Flow Orchestration in Enterprise SaaS: Effects on Ticket Resolution Time and Employee Productivity in IT Service Management

Abstract

The author of this study discusses the influence of AI autonomous agents on the IT Service Management (ITSM) ticketing process, focusing on ticket resolution time, first-point-of-contact resolution rate, and worker productivity. The main objective is to determine whether AI-powered automation is effective in enhancing the leading performance indicators of ITSM over manual operations. The questions the research considered in the study are: how can the time required to resolve a ticket be reduced by training classification, triage, and routing agents, and what affects SLA compliance and agents' work distribution? Its design is a quasi-experiment, where results are obtained from retrospective datasets on HuggingFace and Kaggle to compare agent-aided workflows with manual workflows, and then implement performance-based indicators of time to solution for the ticket, the rate of first contact, and the agent's workload. The results show that the number of call-ins at the customer level is significantly reduced, and the workflow, with agent assistance, saves up to 13 hours per ticket compared to the manual processing-based workflow. The first-contact resolution rate also increased by 53%, and agent productivity increased by 22%. The study concludes that AI automation could help simplify ITSM functions by reducing operational costs, increasing SLA compliance, and enhancing employee productivity. The recommendations to improve AI models for multi-step, complex problems, broaden datasets to include more ITSM problems, and implement longitudinal research to assess the long-term impacts of AI on employee performance and satisfaction complete the list of research recommendations.