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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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Article rating: No rating

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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Article rating: No rating

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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24 Feb 2026

Step 1: AI Foundations for Product Owners: A Practical Mental Model

Author: Rod Claar  /  Categories: AI for Scrum POs Learning Path  /  Rate this article:
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Most Product Owners struggle with AI because they start with tools instead of outcomes.

Start with this simple mental model:

AI helps most in four areas:

  1. Discovery – Clarify user needs and surface hidden assumptions.

  2. Backlog Quality – Improve acceptance criteria and reduce ambiguity.

  3. Prioritization – Evaluate trade-offs using value, risk, and constraints.

  4. Stakeholder Communication – Turn complex ideas into clear narratives.

AI does not replace judgment. It amplifies thinking.

Here is a practical exercise for your next release:

  1. List your top 3 unknowns:

    • Users (Who are we really serving?)

    • Value (What outcome matters most?)

    • Constraints (What limits success?)

  2. For each unknown, ask AI to generate 10 clarifying questions.

You will surface blind spots before they become sprint failures.

Better questions lead to better backlog decisions.
Better backlog decisions lead to better business outcomes.

AI is most powerful when it sharpens thinking—not when it writes user stories for you.

Try this exercise before your next backlog refinement.
Comment “AI PO” and I’ll send a short guide you can use with your team.

 

#ProductOwnership
#AIinBusiness
#ScrumLeadership

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Author: Rod Claar
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