Forum Discussion
Introducing AIVANA Decision Intelligence for Azure Boards
A backlog can contain all the right work items and still leave important questions unanswered. Does a user story have acceptance criteria? Who owns a blocked task? Which items have stopped moving?
I’m developing AIVANA Decision Intelligence, an extension for Azure Boards that brings these questions into a focused review of backlog quality and delivery signals.
The goal is to help teams identify items that need attention and understand the evidence behind each finding.
What the current version does
AIVANA Decision Intelligence reads work items from an accessible Azure DevOps Services project and applies explainable rules to highlight:
- Missing descriptions, acceptance criteria or assigned owners.
- Work items with identical titles that may warrant a duplicate review.
- Explicitly blocked or stagnant items.
- Overdue items where a due-date field is available.
Each finding includes its source and a suggested next step. Teams can inspect the underlying work item and decide whether the finding matters in their context.
An identical title, for example, is a reason to investigate—not proof that two requirements are duplicates.
Optional AI-assisted review
The extension also includes an optional AI-assisted review of selected work-item text.
This requires explicit approval to transmit the selected content and an active organization license. AI suggestions remain subject to review by the team. The extension does not automatically modify work items.
The current version focuses on backlog review and explainable findings. It is available as a technical preview, with live installation and commercial release checks still being completed.
A practical starting point
One way to evaluate AIVANA Decision Intelligence is during backlog refinement:
- Review the findings for a project.
- Check the source behind each relevant finding.
- Agree which items need clarification, ownership or follow-up.
- Compare the findings with the team’s existing review process.
The useful question is whether the extension surfaces issues that deserve attention without creating unnecessary noise. That is something I want to evaluate with real teams.
I’d welcome your feedback
How does your team currently check backlog quality in Azure Boards?
Which checks are useful, and which tend to produce false positives? I’m particularly interested in how teams define stagnation and handle acceptance criteria across different work-item types.
Website: https://www.aivana-gmbh.ai
Disclosure: I’m involved in developing AIVANA Decision Intelligence.