AI PRODUCT OPERATIONS · CASE 02
Work Aggregator
A read-only daily brief that connects work items, conversations, and tasks without changing the source systems.
01 / Business problem
The decision behind the work
Product work did not live in one queue. Azure DevOps contained planned work and hierarchy; Outlook contained decisions, mentions, and side conversations; tasks contained personal commitments. Reading each system separately made status look cleaner than it was.
I designed a read-only aggregator that groups work at the business-feature level, correlates available evidence, and asks one practical question: what needs to happen next?
02 / My product decisions
How the decision works
- 01Keep extraction deterministic. System reads, hierarchy rollups, and source preservation happen before AI interpretation.
- 02Start with the Product Owner’s decisions. The main output shows what needs a decision or follow-up, not every recorded activity.
- 03Show the source. Every recommendation points back to the work item, thread, task, or missing evidence behind it.
- 04Never write back. The operator reports and recommends; it does not update DevOps, send email, or close work.
Featured sample output
Product Owner daily topics
Synthetic sample digest · one line of sight across work and communication
Azure DevOps + Outlook
Data model issue affecting today’s refresh
- Evidence
- Active incident thread and linked work item disagree on the current owner.
- Recommended next action
- Confirm ownership, separate source anomaly from report defect, and set the next check.
03 / Evidence
What the demonstration proves
The synthetic sample digest demonstrates the Product Owner output. A separate Role Lens Demo shows how the same source evidence changes when the reader’s responsibility changes.
04 / Outcome
One product view
The system converts fragmented activity into a feature-level narrative with evidence, ownership gaps, mentions, and recommended next action.
05 / What I’d carry forward
Aggregation is not truth by itself. The operator must expose missing sources, contradictory signals, and the boundary between observed evidence and inferred status.