AI & Automation
Conversational Intake to Jira for Predictable Analytics Delivery
Client: Professional education and services organization
Situation
Analytics requests arrived through scattered emails and DMs to individuals, creating confusion, inconsistent requirements, and limited visibility into the backlog.
Task
Centralize intake and triage while improving request quality without forcing rigid forms on stakeholders.
Action
Implemented conversational intake using Microsoft Copilot Studio integrated with Jira, creating tickets that rolled into projects. Kept intake dialog-based to gather context naturally, with asynchronous follow-up for missing details instead of blocking submissions. Ran weekly triage meetings using the structured backlog to improve ability to pivot for urgent work without losing queue control.
Results
- Reduced person-dependent chaos in request routing
- Improved prioritization and organization of analytics work
- Enabled more predictable execution with structured backlog management
Transformation
Before: Request chaos
Analytics requests arrived through scattered emails and DMs to individuals. Requirements were inconsistent, nothing was prioritized, and the team couldn't plan capacity or pivot for urgent work.
- Requests scattered across email, DMs, and hallway conversations
- Inconsistent requirements and missing context
- No visibility into backlog or priorities
- Reactive execution with no capacity planning
After: Structured, AI-assisted intake
A conversational AI assistant gathers requirements naturally and creates structured Jira tickets. Weekly triage meetings use the organized backlog to balance priorities and plan capacity.
- Centralized intake via conversational AI
- Structured tickets with consistent requirements
- Visible backlog enabling capacity planning
- Weekly triage for balanced prioritization
Key Metrics
Request routing
Requirement quality
Backlog visibility
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