Rework & Waste Tracking using AI

Rework and silent churn are among the biggest reasons teams lose predictability—yet most Agile dashboards never surface them clearly. This 1:1 working session focuses on building an AI-powered churn and rework detection system using your real Jira or ADO data.

During the session, we build a Python-based script that calculates Ticket Churn, helping you identify where work moves backward and where delivery time is getting silently wasted due to:

1. Ticket reopenings
2. Status reversals (QA → Dev, UAT → Dev, etc.)
3. Late-stage scope changes
4. High rework cycles inside a sprint

You’ll learn how to:

1. Quantify true delivery waste, not just velocity
2. Detect teams or workflows with high rework risk
3. Correlate churn with spillovers and missed commitments
4. Use churn as a leading indicator for delivery instability

✅ You’ll walk away with:

1. A working churn & rework tracking script
2. A clear understanding of how rework impacts sprint stability
3. A practical framework to learn and apply AI-based waste detection
4. Guidance to use churn insights for retrospectives, planning, and leadership reporting

⚠️ Technical Requirements (Read Before Booking)

1. Environment: VS Code must be installed with GitHub Copilot active
2. Access: API token access to your Jira/Confluence or ADO instance (or a sandbox environment)
3. Skill Level: Not for absolute beginners. You should be familiar with Agile workflows and comfortable reviewing Python code, even if you don’t write it daily.