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Path Steps

Follow these steps in order. Each one links to an EasyDNNnews article/video and gives you a quick, practical takeaway.

You’ll learn how to frame AI as a teammate that supports Scrum events and backlog work without replacing judgment or collaboration.
Do this exercise: Write a 3-sentence “AI usage policy” for your team (what you will use AI for, what you won’t, and what must be reviewed by a human).
You’ll learn repeatable prompt patterns to generate stories with clearer intent, constraints, and acceptance criteria.
Do this exercise: Take one messy request and prompt AI to produce (a) a user story, (b) 5 acceptance criteria, and (c) 3 key questions for the PO.
You’ll learn how to generate “plan options” (not commitments) and improve shared understanding of scope and dependencies.
Do this exercise: Ask AI for 2 sprint goal options based on your top backlog items, then pick one as a team and adjust wording together.
You’ll learn facilitation prompts that help teams extract insights, turn feedback into actions, and avoid “retro theatre.”
Do this exercise: Feed AI 5 bullet facts from the sprint and ask for (a) patterns, (b) 3 improvement experiments, and (c) 1 metric per experiment.
You’ll learn how to convert your best prompts and practices into a lightweight working agreement the team can actually follow.
Do this exercise: Create a “Prompt Library” page with 5 prompts: refinement, story writing, planning, review, retro—each with input/output examples.
 

Learning Path - Free

24 Feb 2026

Step 1: What AI Can (and Can’t) Do for Scrum Teams

AI is a productivity amplifier—not a Product Owner, not a Scrum Master, and not a Developer.

Used correctly, it accelerates learning, drafting, summarizing, and exploring options. Used poorly, it replaces thinking with automation theater.

This step helps your team position AI as a supporting teammate, not a decision-maker.

Author: Rod Claar
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24 Feb 2026

Step 2: Prompts That Produce Better User Stories

AI can help—but only if the prompt is structured.

This step introduces repeatable prompt patterns that improve:

  • Intent clarity

  • Constraints visibility

  • Acceptance criteria quality

  • PO alignment

Author: Rod Claar
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24 Feb 2026

Step 3: Backlog Refinement with AI (Without Losing the “Why”)

The Core Risk

When teams use AI in refinement, a common failure mode appears:

  • Stories get cleaner

  • Acceptance criteria get longer

  • Technical detail increases

  • Business intent becomes less visible

Scrum optimizes for value delivery, not documentation density.

AI must support the “why” behind the work.

Author: Rod Claar
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24 Feb 2026

Step 4: Sprint Planning Acceleration

The Key Principle

AI should propose:

  • Possible Sprint Goals

  • Possible scope groupings

  • Possible dependency flags

The team still decides:

  • What to commit to

  • What fits capacity

  • What aligns to product strategy

AI drafts.
The team commits.

Author: Rod Claar
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Learning Path - Member

 
 
✓ Featured Content

AI for Scrum and Agile Teams
Videos

A curated playlist of specific YouTube content.

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9 Mar 2026

Step 5: Backlog Refinement & Slicing Techniques

Author: Rod Claar  /  Categories: AI for Scrum POs Learning Path Members  /  Rate this article:
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Step 5: Backlog Refinement & Slicing Techniques

Objective

Large backlog items often stall teams. When work is too broad or vague, it becomes difficult to estimate, test, or complete within a sprint.

Common symptoms include:

  • stories that span multiple sprints

  • unclear scope during sprint planning

  • hidden dependencies discovered mid-sprint

  • difficulty demonstrating value in the sprint review

AI can help Product Owners break down large features into small, valuable, testable increments that are appropriate for sprint delivery.


Core Skill

Slicing Work into Sprint-Ready Stories

Effective backlog refinement focuses on splitting work into increments that deliver usable value, not just technical tasks.

Good slices should be:

Property Meaning
Small Can be completed within one sprint
Valuable Delivers user or business value
Testable Has clear acceptance criteria

Rather than splitting work by technical components, Product Owners should slice by user outcomes or workflow steps.


Common Story Slicing Techniques

Workflow Steps

Break a process into smaller steps that can be delivered incrementally.

Example:

Feature: Export analytics data

Possible slices:

  • Export basic CSV data

  • Export filtered dashboard results

  • Export scheduled reports


User Roles

Deliver value for one user role before expanding to others.

Example:

Feature: Dashboard editing

Slices:

  • Editing for administrators

  • Editing for standard users

  • Shared dashboard editing


Data Scope

Deliver functionality for a smaller data set first.

Example:

Feature: Reporting

Slices:

  • Report using last 30 days of data

  • Report using historical data

  • Custom date ranges


Complexity Reduction

Start with a simpler version of the feature.

Example:

Feature: Notifications

Slices:

  • Email notifications

  • In-app notifications

  • SMS notifications


Prompt Pattern for Backlog Slicing

Use AI to generate possible story slices.


 

You are assisting a Product Owner refining backlog items.

Break the following feature into smaller user stories that could fit into a single sprint.

Each story should:
• Deliver clear user value
• Be small enough to complete in one sprint
• Include a short description of the outcome

Feature:
[Paste feature or epic here]

This prompt helps identify multiple delivery paths for the same feature.


Exercise (Hands-On)

DO THIS EXERCISE

Pick one epic or large feature from your backlog.

Use this prompt:


 

You are assisting a Product Owner with backlog refinement.

Break this feature into 4–6 smaller user stories.

Each story must:
• Deliver user value
• Be independently testable
• Be small enough for a sprint

Feature:
[Paste feature description]

Review the results and ask:

  • Can each slice be delivered independently?

  • Does each slice provide user value?

  • Are acceptance criteria clear enough for development?

Remove or rewrite any stories that still feel too large.


Example

Feature (Epic)

Customers want to export analytics dashboard data.


Possible Story Slices

Story 1 — Basic CSV Export

Users can export dashboard metrics to a CSV file.


Story 2 — Filtered Export

Users can export data using the filters currently applied to the dashboard.


Story 3 — Permission Controls

Only users with analytics permissions can export data.


Story 4 — Scheduled Exports

Users can schedule a weekly export of dashboard data.


Why This Matters for Product Owners

Proper story slicing improves several aspects of Scrum delivery:

  • sprint planning becomes faster and clearer

  • stories are easier to estimate

  • work completes within a sprint

  • teams demonstrate value more frequently

AI helps Product Owners explore multiple ways to slice a feature, reducing guesswork during backlog refinement.


Practical Tip

During backlog refinement, ask:

“What is the smallest piece of value we could deliver first?”

If the answer still feels large, slice the story again.

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Author: Rod Claar
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