Assess team health
Track goal confidence, flow, predictability, dependency exposure, and quality readiness through transparent component signals.
Case study 02 · AI product intelligence
From retrospective reporting to early delivery decisions.
Scrum Health Intelligence converts fragmented delivery data into explainable health signals, dependency exposure, and action-ready leadership reporting.
- Role
- Product strategist & prototype builder
- Status
- Published · Open source
- Focus
- AI · Delivery risk · Product operations
The opportunity
Engineering organizations have abundant delivery data, but leaders still spend hours reconstructing what is at risk, why it changed, and where intervention will matter.
Short description
An intelligence layer, not another status system.
The concept connects Jira work signals with approved Confluence context to detect emerging risk before sprint and release commitments are missed.
It separates measurable analytics from probabilistic AI, cites the evidence behind each assessment, and gives teams a way to confirm, correct, or dismiss every signal. The result is decision support that improves transparency without turning delivery metrics into individual surveillance.
Use case
Insight to action
Designed for engineering and product leaders coordinating complex, cross-team delivery.
Expose dependencies
Rank cross-team relationships using schedule slack, downstream impact, ownership, and mitigation coverage.
Mitigate risk early
Surface material changes with confidence, evidence, an affected outcome, and the next accountable action.
Automate reporting
Generate a concise, role-specific executive brief from the same source facts instead of rebuilding status slides manually.
Tech stack
Built for an explainable AI workflow.
A dependency-light reference implementation demonstrates the full product loop without requiring cloud credentials.
The prototype includes a Python service, deterministic health-scoring engine, dependency-graph analysis, JSON APIs, responsive executive dashboard, sample Jira/Confluence-style data, and automated tests. Production Jira, Confluence, OAuth, persistence, and model-provider integrations remain explicit extension points.
PythonJavaScriptHTML/CSSJira Cloud APIsConfluence Cloud APIsExplainable scoringDependency graph analyticsRetrieval-grounded AI
Evidence of execution
Strategy translated into a product artifact.
What this demonstrates
“The strongest AI product starts with a decision worth improving—not a model looking for a use case.”
This work demonstrates product sense, analytical thinking, system design, responsible AI judgment, and the ability to translate an ambiguous delivery problem into a testable market concept.