Agentic AI Reduced IT Ticket Triage Time by ~95% for a U.S. Semiconductor Manufacturer
Client Overview
Advanced chip manufacturing depends on precision at every stage. The company behind this engagement is a Fortune 250 semiconductor equipment manufacturer in the U.S., providing technologies used by semiconductor manufacturers around the world. IT teams had plenty of knowledge to work with, spread across the ITSM system and Confluence (a knowledge management tool). Each ticket had to be read, understood, routed, and matched against past issues manually. Triage could take 15 to 45 minutes, followed by another 20 to 40 minutes spent searching for similar tickets.
The Right Answer Exists, but Finding It Takes Time
The ITSM system and Confluence contained a wide range of solutions for common support issues. The challenge was mapping new tickets to the appropriate resolution data from similar past tickets and identifying which issues could be resolved at L1 without unnecessary escalation.
01
Manual Ticket Triage
Technicians spent 15 to 45 minutes interpreting each ticket before resolution could begin.
02
Routing Loops
New team members misrouted tickets 30 to 40% of the time, causing repeated reassignments.
03
Lost Resolutions
Past resolutions and institutional knowledge remained fragmented and inaccessible, limiting reuse for new issues.
04
Ticket Hunting
Engineers spent 20 to 40 minutes searching historical tickets before starting diagnosis.
05
Over Escalation
Junior technicians escalated L1-resolvable issues, consuming valuable L2 and L3 time.
06
Repeat Diagnosis
Recurring issues required fresh investigation because prior resolutions were hard to access.
Agentic Intelligence for IT Service Management
To modernize IT service management and eliminate manual ticket handling, the client sought to build an Agentic AI-powered solution that leveraged existing enterprise knowledge from the ITSM tool and Confluence (knowledge management tool).
The solution connected the ITSM system and Confluence with historical ticket data to improve routing accuracy, accelerate L1 ticket resolution, and enhance support operations across global teams.
01
Agentic Resolution Intelligence
AI agents powered by LLaMA 2 13B worked together using ITSM and Confluence knowledge to resolve unstructured and previously unseen tickets end to end.
02
Intelligent Ticket Prioritization
A triage and prioritization agent scored incoming tickets based on business impact, equipment criticality, and SLA proximity, helping high-priority incidents get attention first.
03
Automated Classification & Assignment
A classification and segmentation agent extracted key issue signals from ticket text and routed each ticket to the right resolver group on first contact.
04
Multilingual Ticket Intelligence
A multilingual understanding agent identified the ticket language and handled real-time translation across Japanese, Chinese, Korean, Thai, Filipino, and other regional languages.
05
Self-Learning Resolution Intelligence
A human-in-the-loop feedback mechanism captured technician corrections, improved the classification layer, and added validated resolutions to a centralized knowledge repository.
06
Real-Time Operational Analytics
A custom dashboard gave teams real-time visibility into ticket distribution, classification accuracy, resolver performance, and resource allocation for intra-day SLA management.
Measurable Change in IT Operations
The new IT support model brought down ticket handling time and helped teams resolve more issues at L1.
~95% Faster Triage
Ticket triage dropped from 15–45 minutes to under 60 seconds. Support teams could start resolving issues much sooner.
<5% Misrouting Rate
Misrouting fell below 5% with smarter ticket assignment. Tickets reached the right resolver group sooner, with fewer reassignment loops, thereby improving resolver efficiency from 30–40%.
40–60% Lower MTTR
RAG surfaced similar incidents and proven resolution steps in under 10 seconds. Engineers could diagnose recurring issues much faster.
25–35% Ticket Deflection
Common L1 issues were resolved automatically. L2 and L3 teams could focus on incidents that needed deeper expertise.
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