Automated Intelligent Processing Workflow for Long-Unresolved JIRA Tickets
This workflow is designed to automate the handling of long-unresolved JIRA tickets. Through scheduled scanning and AI analysis, it achieves intelligent classification of ticket statuses, generation of solutions, and assessment of customer satisfaction. It can automatically send reminders to facilitate ticket follow-ups, reducing the chances of tickets being forgotten or stalled. Additionally, it supports Slack notifications to ensure that relevant personnel are promptly alerted to any anomalies. Furthermore, it can automatically close unresponsive tickets, thereby enhancing the efficiency of the customer service team and improving customer satisfaction.
Workflow Diagram
Workflow Name
Automated Intelligent Processing Workflow for Long-Unresolved JIRA Tickets
Key Features and Highlights
- Automatically performs scheduled scans of unresolved JIRA tickets that have been open for more than 7 days.
- Utilizes AI to comprehensively analyze ticket content and comment history, intelligently categorizing ticket status (resolved, awaiting additional information, pending response, etc.).
- Leverages knowledge base and historical similar tickets to automatically generate potential solutions and provide feedback within the ticket.
- Applies sentiment analysis to evaluate customer satisfaction and automatically triggers escalation notifications for negative feedback.
- Sends automated reminder comments to prompt follow-up on unresponsive tickets, reducing forgotten and stagnant cases.
- Supports automatic closure of tickets that are unresponsive or already resolved, enhancing customer service team efficiency.
- Integrates Slack notifications to alert relevant personnel in real-time about abnormal or negative tickets.
Core Problems Addressed
- Tackles the challenge faced by traditional customer service and technical support teams in timely handling and following up on long-pending tickets.
- Reduces ticket neglect and response delays, thereby improving customer satisfaction and support efficiency.
- Employs AI-assisted status determination and solution generation to lessen the burden of manual judgment.
- Enables intelligent lifecycle management and proactive reminders for tickets to prevent issue backlog.
Application Scenarios
- IT operations support teams needing automated screening and handling of long-unresolved technical issue tickets.
- Customer service centers aiming to accelerate ticket processing and enhance customer satisfaction through automated assistance.
- Product support teams utilizing knowledge bases and historical data to automatically assist in answering user inquiries.
- Cross-departmental collaboration environments where Slack notifications ensure abnormal tickets receive timely attention and are not overlooked.
Main Process Steps
- Scheduled Trigger: Daily scheduled scan for unresolved JIRA tickets created over 7 days ago.
- Retrieve Ticket Metadata and Comments: Extract ticket details and all associated comments to form a complete communication thread.
- Simplify and Consolidate Comment Content: Format and merge comment content to facilitate AI analysis.
- AI Ticket Status Classification: Use text classification models to determine if the ticket is resolved, requires additional information, or is awaiting a response.
- Sentiment Analysis: Conduct customer satisfaction sentiment analysis on resolved tickets.
- Knowledge Base Intelligent Response: For unresolved tickets, AI agents retrieve relevant information from JIRA historical tickets and the Notion knowledge base to attempt automatic solution generation.
- Execute Actions Based on Analysis Results:
- If a solution is found, automatically reply and close the ticket.
- If no solution is found and no response is received, send a reminder comment.
- If sentiment analysis indicates negative feedback, send Slack notifications for escalation.
- Automatically add closure prompts and close tickets with prolonged no response.
- Slack Notifications: Notify relevant personnel via Slack channels about abnormal tickets or negative feedback in real-time.
Involved Systems and Services
- JIRA Software Cloud: Ticket data retrieval, comment management, and status updates.
- OpenAI GPT-4o-mini Model: Text classification, sentiment analysis, and intelligent response generation.
- Notion: Knowledge base query tool supporting AI-generated solutions.
- Slack: Real-time notifications to customer service teams regarding abnormal tickets and negative feedback.
- n8n Workflow Automation Platform: Overall orchestration and automated execution of the workflow.
Target Users and Value Proposition
- Enterprise customer support teams, technical support engineers, and IT operations managers.
- Organizations seeking to improve ticket handling efficiency, reduce manual intervention, and achieve intelligent automation.
- Teams aiming to combine AI and knowledge base resources to enhance customer satisfaction and response speed.
- Modern enterprise teams pursuing digitalized, intelligent workflows to optimize customer service experience.
This workflow integrates AI capabilities with existing enterprise ticketing and knowledge management systems to enable intelligent diagnosis, automatic response, and closure of long-unresolved tickets, significantly boosting customer service team productivity and customer satisfaction.