AI-powered Root Cause Analysis (RCA) workflow built with n8n and OpenAI to identify recurring support issues, surface trends, and enable both executive reporting and human-in-the-loop validation.
Support teams often struggle to identify why issues are happening, not just resolve them. Root cause analysis is typically manual, inconsistent, and difficult to scale across large ticket volumes.
This project introduces an AI-powered RCA Engine that classifies support tickets into known root causes, detects repeat issue patterns, and separates unmatched cases for human review.
Rather than relying solely on automation, this system combines AI classification with human-in-the-loop validation, ensuring accuracy while continuously improving the RCA library.
This is Project 6 in a broader system:
- Project 1 → AI Ticket Triage
- Project 2 → Escalation Risk Detection
- Project 3 → SLA Breach Prediction
- Project 4 → Weekly Support Intelligence Report
- Project 5 → AI Response Suggestion
- Project 6 → AI Support RCA Engine (this project)
This workflow represents the intelligence layer of the support system, transforming resolved tickets into structured insights and operational actions.
This project is part of a multi-stage AI-powered support operations system designed to move teams from reactive workflows to proactive, intelligence-driven operations.
Each project builds on the previous one:
Classifies incoming support tickets, enriches them with structured metadata, and establishes a clean foundation for downstream automation.
👉 https://github.com/jesseautomates/ai-support-ticket-triage-automation
Identifies tickets likely to escalate by analyzing urgency, sentiment, and response patterns, enabling earlier intervention.
👉 https://github.com/jesseautomates/ai-support-escalation-risk-detection
Predicts which tickets are at risk of missing SLA before deadlines are breached, allowing teams to prioritize and act proactively.
👉 https://github.com/jesseautomates/ai-sla-breach-prediction
Aggregates support metrics and AI signals into a structured weekly report with insights, risks, and recommendations.
👉 https://github.com/jesseautomates/ai-support-intelligence-report/
Generates intelligent, context-aware response drafts to assist agents in resolving tickets faster and more consistently.
👉 https://github.com/jesseautomates/ai-support-response-suggestion
Classifies resolved tickets into root causes, identifies repeat issues, and routes unmatched cases for human review.
Together, these projects form a layered AI pipeline:
Triage → Risk Detection → SLA Prediction → Intelligence Reporting → Agent Assistance → Root Cause Analysis
This progression demonstrates how AI can be applied across the full support lifecycle.
- Analyzes resolved support tickets to determine root causes
- Classifies tickets into known RCA categories using AI
- Standardizes and normalizes root cause outputs
- Separates known vs unknown issues
- Aggregates RCA trends for reporting
- Generates a weekly executive summary
- Routes unmatched tickets for human review
- Enables continuous improvement of the RCA library
- Processes a dataset of resolved support tickets
- Standardizes ticket structure and prepares text for evaluation
- Evaluates each ticket against known root causes
- Assigns:
- Root cause category
- Confidence level
- Supporting reasoning
- Extracts structured AI outputs
- Standardizes root cause naming
- Reduces duplicate or inconsistent categories
- Compares AI classification against approved RCA library
- Determines:
- Known match
- Unmatched / low-confidence case
This is the key system design element:
- Matched tickets → Leadership reporting pipeline
- Unmatched tickets → Human review pipeline
- Aggregates matched tickets by root cause
- Generates:
- RCA breakdown
- Key insights
- Recommended actions
- Sends weekly HTML summary email to leadership
- Logs unmatched tickets for manual review
- Generates a review queue summary
- Sends alert to support manager for investigation
- Enables:
- New root cause discovery
- RCA library improvement
- Total Tickets Reviewed: 50
- Tickets Matched to Known Root Causes: 19
- Tickets Requiring Review: 31
Key insight:
- Known root causes accounted for ~38% of tickets, highlighting opportunities for targeted remediation
- 31 tickets flagged for human review
- Did not match existing RCA categories
- Requires classification, escalation, or RCA updates
Use Sample Tickets - RCA Build - Sample Tickets CSV in Repo
- n8n (workflow orchestration)
- OpenAI API (classification + reasoning)
- Optional integrations:
- Help desk platforms (Zendesk, ServiceNow, etc.)
- Slack / internal tools
- Data storage / logging systems
- Import the workflow JSON into n8n
- Add API credentials (OpenAI, etc.)
- Load sample ticket dataset (CSV or API source)
- Define known RCA categories
- Configure classification prompts
- Set up email nodes for:
- Leadership summary
- Support manager alerts
- Test workflow with sample tickets
- Tune prompts and RCA categories over time
- Demonstrates AI-powered root cause analysis at scale
- Combines automation with human-in-the-loop validation
- Enables proactive issue identification and trend detection
- Separates outputs for different stakeholders (exec vs ops)
- Provides a foundation for continuous improvement in support systems