Facing the reality of overcommitting to 60 engineers’ work with only 35 available, Atlassian Principal Product Manager Jensen Fleming developed a live, data-driven capacity planning system inside Jira Product Discovery to create visibility, prioritize effectively, and avoid burnout.
- Capacity planning replaces spreadsheets with live project data
- Visibility reveals front-end resource bottlenecks and overload
- Prioritization shifts from workload debate to outcome focus
What happened
Jensen Fleming realized her team had committed to the amount of work equivalent to 60 engineers, despite having only 35 engineers available. The tool they previously used, a manually updated spreadsheet, became quickly outdated and cumbersome. To address this, Jensen built a dedicated capacity planning view inside Jira Product Discovery (JPD) that pulls real-time data from projects and provides an up-to-date, clear overview of committed capacity against available resources.
This custom view is split by engineering team, with monthly columns showing engineers committed per project, as well as indicators to show whether work is resourced or not. With known team capacity targets, the system immediately showed that their commitments exceeded capacity, forcing a reprioritization of existing projects and better planning for future work.
Why it matters
The new capacity planning approach replaces subjective status updates and manual tracking with a live system of truth, allowing the team to make data-driven decisions about which work to prioritize. Instead of debating whether the team was simply overextended, the conversation shifted toward determining which projects matter most and should proceed given limited engineering bandwidth.
This method also surfaced specific bottlenecks, such as frontend development constraints. Jensen supplemented the capacity view with a timeline layout by engineer for frontend work, visually showing workload distribution and backlog assignments. This transparency helps avoid overload, ensures better resource use, and supports team health by avoiding burnout.
What to watch next
Jensen’s team now uses the capacity planning view monthly to assess commitments and adjust plans. The approach signals a shift toward more dynamic, data-driven capacity management in product teams, especially those scaling to accommodate competing priorities across engineering disciplines.
Other organizations may look to integrate similar live capacity monitoring, pairing aggregate roll-ups with granular, per-engineer views to quickly detect overload and balance workloads. This kind of tooling is expected to become foundational for agile, outcome-focused product delivery in complex engineering environments.