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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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27 Jan 2026

AI for Scrum Masters

Author: SuperUser Account  /  Categories: AI Training  /  Rate this article:
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Event date: 2/2/2026 11:00 AM - 3:00 PM Export event

About AI for Scrum Masters

With the right approach and expertise, you can seamlessly add AI to your team. AI can function as an agile co-pilot, operating effortlessly alongside team members to increase productivity, save time, and differentiate you as an AI-capable professional.

AI for Scrum Masters provides ready-to-use templates and tactics that are applicable across roles, including scrum teams, project managers, team leaders, and product managers.

Learn all of the ways you can integrate AI to assist with managing timelines, predicting risks, identifying metrics for team performance, and much more.

Plus, put your knowledge to the test in the final module of this course: Assume the role of a scrum master and use GenAI to complete tasks based on real-world scenarios.

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20 May 2026

Author: Rod Claar
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2 Apr 2026

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