Epic Template for Jira: Structure Agile Roadmaps

A structured Jira epic template for breaking down large features into user stories. Includes epic description, acceptance criteria, dependencies, timeline, and linkage to stories.

Jira Epic Template

Use this template to define large features or initiatives in Jira. Epics break into smaller user stories that fit into sprints.

Epic Header

Epic Name: [Brief feature title, 5-10 words]
Epic Key: [Auto-generated by Jira, e.g., PROJ-123]
Status: [Backlog / Planned / In Progress / Blocked / Complete]
Product Owner: [Name]
Assign To: [Team lead or architect]
Start Date: [YYYY-MM-DD]
Target End Date: [YYYY-MM-DD]

Epic Description

Business Context

What problem does this epic solve? Why are we building it?

[2-3 sentences explaining the customer pain point or business opportunity]

Example:
"Customers are manually entering data from PDFs into our system, 
which takes 10+ minutes per file. This epic automates that process 
to save time and reduce errors."

Proposed Solution

What high-level approach will we use?

[1-2 sentences on the solution]

Example:
"We'll build an OCR-powered upload feature that reads PDFs, 
extracts structured data, and pre-fills forms automatically."

Success Criteria

How will we know this epic is done and valuable?

[ ] Feature is launched to production
[ ] 80%+ of users adopt the feature (tracked via GA4)
[ ] Manual data entry time reduced from 10 min to under 1 min per file
[ ] Customer satisfaction score is 4/5 or higher (NPS)
[ ] Zero critical bugs in first 2 weeks

In-Scope Features

What's included in this epic:

Out-of-Scope (Future Epics)

Deliberately NOT included:

Multi-language support (future epic)
Bulk file processing (future epic)
Mobile app support (separate epic)
Custom field mapping (future enhancement)

Technical Considerations

Architecture & Tech Stack

Backend: Node.js + Express
OCR Service: Google Vision API (or Azure Computer Vision)
Data Format: JSON to SQL mapping
Database: PostgreSQL (existing)
Frontend: React component library (existing)

Risks & Dependencies

Dependency: Google Vision API availability & pricing
Risk: OCR accuracy varies by PDF quality (need >85% accuracy)
Risk: Integration delays if PDF formats are inconsistent

User Stories (Breakdown)

This epic breaks into these user stories:

Story	Title	Status	Points
PROJ-124	As a user, I can upload a PDF file from my device	Planned	5
PROJ-125	As a system, I can extract text from uploaded PDF	Planned	8
PROJ-126	As a system, I can parse extracted text into structured data	Planned	8
PROJ-127	As a user, I can review and correct pre-filled data	Planned	5
PROJ-128	As a user, I can see upload progress and errors	Planned	3
PROJ-129	As an admin, I can view OCR accuracy metrics	Planned	5
PROJ-130	As a user, I receive email confirmation after upload	Planned	2

Total Story Points: 36
Estimated Timeline: 2-3 sprints (4-6 weeks at 12-16 points/sprint)

Acceptance Criteria (Epic-Level)

The epic is "done" when:

All linked user stories are marked "Done"
All acceptance criteria in child stories are verified
No critical/high bugs remain unresolved
Feature is deployed to production
Monitoring dashboards show healthy metrics (upload success rate >95%)
User documentation is complete
Team has completed UAT (user acceptance testing)

Dependencies & Blockers

External Dependencies

Google Vision API quota (need 10k calls/month)
Data privacy compliance (GDPR/CCPA for storing PDF content)
Design mockups from Design team (blocking PROJ-124)

Internal Dependencies

Database schema updates (DBA task, needed before coding)
API endpoint design review (arch review, blocking backend work)

Blockers (Current)

Design mockups not yet approved (ETA: May 31)
GDPR legal review pending (ETA: May 28)

Sprint Schedule

Planned Sprint Allocation:

Sprint	Week	Stories	Points	Goal
Sprint 31	Jun 3-14	PROJ-124, PROJ-125, PROJ-128	16	Upload & OCR integration
Sprint 32	Jun 17-28	PROJ-126, PROJ-127, PROJ-129	18	Data parsing & review
Sprint 33	Jul 1-12	PROJ-130 + bug fixes	5	Email notifications & polish

Metrics & KPIs

How we'll measure success:

Metric	Target	Tracking
Feature adoption rate	>80% of active users	GA4 event tracking
Time saved per upload	under 1 min (was 10 min)	User survey + analytics
OCR accuracy	>85% correct data extraction	QA testing + user feedback
Upload success rate	>95% (no failures)	CloudWatch monitoring
Customer NPS score	4/5 or higher	In-app survey

Rollout Plan

Phase 1: Beta (Week 1)

Deploy to 10% of users (internal team + beta customers)
Monitor errors & OCR accuracy
Collect feedback

Phase 2: Staged Rollout (Weeks 2-3)

Expand to 50% of users
Monitor adoption & issues
Address top user feedback

Phase 3: Full Release (Week 4)

Release to all users
Monitor adoption & support tickets
Create knowledge base articles

Related Epics

Other epics that tie into this:

Mobile PDF Upload Epic — Mobile app version of this feature (future)
Data Quality Epic — Validation & error handling across all imports
Admin Dashboard Epic — Reporting on PDF processing metrics

Success Definition (What "Done" Looks Like)

Engineering Done:

All user stories closed
Code reviewed and merged to main
Deployed to production
Monitoring alerts configured

Product Done:

Feature launched to users
80% adoption within 30 days
No critical bugs
User feedback is positive (4/5 NPS)

Business Done:

Revenue impact measured (e.g., +15% retention)
Customer support tickets reduced
Roadmap item marked as delivered

Review & Retro

Post-Launch Review (1 month after launch)

Schedule retro with full team
Review KPIs against targets
Identify what went well & what to improve
Plan improvements for future updates