Sprint Prediction using AI with Ajinkya Nalamwar

This 1:1 working session is designed for delivery leaders and Scrum Masters who want real, usable AI—not theory.

During the session, we securely connect to your Jira instance and extract historical sprint data. Using this data, we build a lightweight Python-based sprint prediction model that helps you:

1. Predict sprint success probability before execution
2. Identify early bottlenecks in backlog flow
3. Detect risk patterns in commitments vs delivery
4. Spot overloading, spillovers, and unstable velocity trends

You’ll walk away with:

1. A working sprint prediction script
2. A clear understanding of which data points influence delivery risk
3. A practical framework to learn and apply AI-driven forecasting
4. Guidance to understand and extend this for quarterly planning and leadership reporting

⚠️ Technical Requirements (Read Before Booking)

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