AI-Powered Ticket Routing and SLA Breach Prediction in Support Teams

In every growing tech company, support teams are often overwhelmed not by the number of tickets, but by how those tickets are routed and escalated.

When I joined our support function, we had the same issue: important tickets were missed, SLAs were violated, and engineers were manually picking and updating issues.

I decided to solve this using a simple principle: Let machines handle the repetitive logic, so humans can focus on solving real problems.

This is how I built a practical, no-frills solution that combined JIRA, Python, and Slack without needing expensive tools or over-complicated AI.

🎯 The Real Problem

We had two major bottlenecks:

1. Ticket Routing

- All issues came into a single backlog.

- Engineers had to manually assign issues based on priority and type.

- Mistakes were common, and high-priority issues often waited too long.

2. SLA Breach Alerts

- There was no visibility into how long tickets were sitting idle.

- Even urgent ones often crossed the response SLA before being noticed.

- Managers only knew about it when it was already too late.

⚙️ My AI-Inspired Solution

Here’s what I implemented:

- A Python script that runs every hour and checks ticket status using the JIRA API.

- Based on priority, labels, and historical behavior, the script routes tickets to specific queues.

- Tickets that are about to breach SLA are flagged and sent as alerts to Slack channels.

- Simple logic like decision trees + if-else chains mimicked basic machine learning but with explainable rules.

🔍 Features Built

✅ Auto-routing based on ticket type and past assignments

✅ SLA timers monitored dynamically from JIRA timestamps

✅ Slack alerts for tickets with <30 mins left in SLA

✅ Priority tagging for leadership visibility

✅ Escalation dashboard using filters + custom fields

📈 Real Impact

After 4 weeks of implementation:

- Ticket routing accuracy improved by 60%

- Response time for critical issues decreased by 45%

- SLA compliance went up from 74% to 93%

- Engineers spent less time checking queues, more time resolving

📂 Repo and Resources

💻 GitHub Repo:

https://github.com/aroojjaved93/AI-Powered-Ticket-Routing-SLA-Breach-Prediction-in-JIRA

📄 Technical Journal (Zenodo):

[zenodo.org/record/xxxxx](https://zenodo.org/record/xxxxx)

🧩 The repo contains:

- Python code for ticket routing logic

- Dummy JIRA dataset (CSV)

- Setup guide

- Screenshot examples

- JSON rule exports for JIRA automation

🧠 Why This Matters

This isn’t just about automation. It’s about building resilient systems that scale with your company.

No team should manually check if a ticket is about to breach SLA not in 2024.

Smart, simple systems can save hundreds of hours a year.

🤝 Let’s Connect

Have you tried solving similar problems with JIRA, Zendesk, or custom systems?

Drop a comment or message I’d love to see your approach or help you get started with mine.

Written by Arooj Javed – Tech Support Engineer | Process Automation Advocate | Always building to solve.