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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.

Search Results

24 Feb 2026

Step 5 Fill Out the Workbook

Author: Rod Claar  /  Categories: Scrum Master Learning Path  /  Rate this article:
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Step — Fill Out the Workbook

Objective

Convert conceptual understanding into operational competence through structured, hands-on application.

Reading creates awareness.
Practice builds capability.

The workbook is designed to move you from intellectual agreement with Scrum principles to repeatable execution under real-world constraints.


Why This Step Matters

Most ScrumMaster development fails at the application layer.

Common failure modes:

  • Knowing Scrum theory but struggling in live facilitation

  • Identifying impediments but not resolving root causes

  • Running events but not improving outcomes

  • Adopting tools (including AI) without disciplined usage

The workbook addresses these gaps by forcing structured reflection and scenario-based problem solving.

 

What You Will Practice

1. Diagnosing Systemic Impediments

You will analyze real or simulated cases and:

  • Identify root causes vs. surface symptoms

  • Classify impediments (technical, organizational, process, cultural)

  • Propose removal strategies

  • Define measurable success criteria

This builds diagnostic rigor.


2. Strengthening Event Facilitation

Exercises will require you to:

  • Draft a Sprint Goal from ambiguous backlog items

  • Design a focused Daily Scrum intervention

  • Structure a high-impact Retrospective agenda

  • Convert Review feedback into actionable backlog refinement

You will practice designing events for outcomes—not compliance.


3. Applying Systems Thinking

You will:

  • Map dependencies

  • Identify bottlenecks

  • Analyze flow metrics

  • Recommend WIP adjustments

  • The emphasis is on understanding how small changes influence system-wide behavior.


    4. Practicing AI Prompting for Scrum Masters

    You will create and refine prompts to:

  • Generate acceptance criteria

  • Surface risk scenarios

  • Analyze retrospective themes

  • Draft stakeholder updates

  • Each exercise emphasizes:

  • Clear context

  • Explicit constraints

  • Defined output format

  • Iterative refinement

The goal is disciplined augmentation—not automation dependency

How to Approach the Workbook

  1. Use real examples from your current team whenever possible.

  2. Write answers in full sentences. Precision improves thinking.

  3. Define measurable outcomes for each proposed action.

  4. Revisit your responses after two sprints and refine them.

If your answers cannot be implemented immediately, they are too abstract.


Expected Outcomes

After completing the workbook, you should be able to:

  • Diagnose systemic constraints confidently

  • Facilitate ceremonies with measurable impact

  • Remove impediments at the root level

  • Apply AI tools strategically

  • Demonstrate improvement through observable metrics


Completion Standard

This step is complete when:

  • All exercises are filled out

  • At least one improvement experiment is implemented

  • Results are inspected within a sprint cycle

Application creates mastery.

Execution creates credibility.

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