Quarterly Capacity Planning using AI

Quarterly planning often fails because capacity is estimated on intuition, static spreadsheets, or ideal team availability. This 1:1 working session helps you build a realistic, data-backed quarterly capacity model using your historical delivery and availability data.

During the session, we use:

1. Historical sprint velocity
2. Team composition
3. Planned leaves and holidays

to create a Python-based capacity forecasting model that helps you:

1. Forecast realistic quarterly output
2. Identify overcommitment risks early
3. Model impact of leaves and team changes
4. Create leadership-ready capacity views
5. Improve confidence in roadmap discussions
6. This shifts your planning from best guesses to defensible, data-backed forecasts.

✅ You’ll walk away with:

1. A working quarterly capacity forecasting script
2. A clear understanding of how velocity and availability shape capacity
3. A practical framework to learn and apply data-driven quarterly planning
4. Guidance to use this model for roadmap reviews and leadership conversations

⚠️ Technical Requirements (Read Before Booking)

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