A large enterprise with a growing digital workplace footprint was handling thousands of IT service tickets each month across multiple channels—email, service portal, chat, and phone. While the service desk had an established ITSM platform and defined assignment groups, the frontline triage process remained heavily manual. Agents had to read every ticket, interpret the issue, select the correct category, identify impacted services, and route it to the appropriate resolver group.
As volumes increased, triage became a bottleneck. Misrouted tickets bounced between teams, resolution time increased, and user satisfaction declined. The organization also faced inconsistent categorization due to agent-to-agent variation and limited context in ticket descriptions.
Key pain points included:
Low first-time-right routing: Tickets frequently went to incorrect assignment groups, leading to reassignments and SLA breaches.
Longer response and resolution times: Manual triage consumed valuable service desk time, delaying first response and escalation.
Inconsistent ticket classification: Similar issues were tagged differently, reducing reporting accuracy and limiting trend analysis.
High operational cost: Skilled agents were spending time on repetitive triage rather than complex troubleshooting and proactive support.
Multi-channel complexity: Different channels produced different data quality—short chat messages, vague emails, and portal forms with missing details.
Knowledge trapped in people: The best triage decisions depended on individual experience rather than a consistent, reusable system.
The client wanted to automate triage without compromising control—improving routing accuracy, reducing ticket bounce, and accelerating resolution while maintaining auditability and human oversight.
Maayan Technologies implemented an AI-driven ticket triage automation layer integrated into the existing ITSM workflow. The solution combined natural language understanding, historical ticket learning, and deterministic rule controls to achieve high accuracy and predictable governance.
1) Ticket Understanding & Enrichment (NLP + Context)
We built an NLP-based classification model that analyzed short and long ticket text, subject lines, and structured fields. The model was trained on historical ticket data and tuned to recognize:
Issue intent (e.g., “VPN access,” “Outlook not syncing,” “password reset,” “laptop performance,” “SAP access”)
Key entities (application names, device types, locations, user roles, urgency indicators)
Similar ticket patterns from past incidents
Tickets were enriched with suggested category, subcategory, service, priority hints, and likely assignment group.
2) Hybrid Routing Logic: AI + Rules
To ensure governance and prevent “black box” behavior, we used a hybrid routing approach:
AI recommendations for category and assignment group
Rules engine for mandatory policies (VIP routing, location-based handling, after-hours support rules, security-related auto-escalation)
Confidence thresholds that determined whether tickets were auto-routed or sent for human review
This gave the organization control while maximizing automation coverage.
3) Human-in-the-Loop for Quality and Adoption
For tickets below a confidence threshold or with ambiguous text, the system presented agents with top recommendations and reasoning signals (keywords, similar past tickets). Agents could accept or correct the recommendation in one click. Those corrections fed continuous learning to improve accuracy over time.
4) Standardized Taxonomy and Assignment Group Mapping
We rationalized the client’s ticket taxonomy to reduce overlaps between categories and clarify assignment group responsibilities. A clean mapping structure was created so the automation did not inherit legacy ambiguity. This step significantly improved routing reliability.
5) Integration into ITSM Workflow
The triage engine was integrated into the existing ITSM platform to:
Automatically populate classification fields
Route tickets to the right group
Trigger notifications and SLA clocks immediately
Maintain audit logs of decisions (auto vs agent override)
6) Monitoring, Reporting, and Continuous Improvement
We deployed dashboards to monitor:
Routing accuracy and reassignments (“ticket bounce rate”)
Automation coverage (percentage of tickets auto-routed)
Top misclassification categories and confusion pairs
SLA improvements and backlog trends
A monthly tuning cycle was introduced to expand automation safely across new services and issue types.
The triage automation program delivered measurable operational and service improvements:
Routing accuracy improved to ~95%, dramatically reducing ticket reassignments and bounce between teams.
Faster first response times as tickets reached the right resolver group immediately.
Reduced manual triage workload, freeing service desk capacity for higher-value support and proactive initiatives.
Improved SLA compliance through fewer delays and reduced queue churn.
Better reporting and trend visibility with consistent classification—enabling root-cause analysis and problem management.
Higher end-user satisfaction due to faster resolution and fewer “please re-open with more details” loops.
Get in touch to learn more about our solutions and services tailored to help enterprises Scale at Speed.