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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

9 Mar 2026

Step 2 — Boundaries first: modules, seams, and dependency direction

Author: Rod Claar  /  Categories: Design Patterns Learning Path - Members  /  Rate this article:
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Step 2 — Boundaries first: modules, seams, and dependency direction

Goal
Learn how to design boundaries that keep change localized and make refactoring safer.

What this step teaches
Good architecture is less about clever patterns and more about controlling change. When boundaries are clear, one part of the system can evolve without forcing changes everywhere else. This is where modules, seams, and dependency direction matter.

A strong team asks:

  • Where does this responsibility belong?

  • What should change together?

  • What must stay independent?

  • Which direction should dependencies flow?

The practical rule is simple: dependencies should point inward toward stable policy, not outward toward volatile details.

Core ideas

Modules
A module is a unit of responsibility. It should have one clear reason to change.

Seams
A seam is a place where you can change behavior without rewriting the whole system. Interfaces, adapters, events, and service boundaries are common seams.

Dependency direction
High-level policy should not depend on low-level implementation details. Stable code should not depend on volatile code.

Why this matters for real teams

When boundaries are weak:

  • small changes spread across many files

  • testing becomes slow and brittle

  • refactoring feels risky

  • teams step on each other’s work

When boundaries are strong:

  • change stays localized

  • modules are easier to test

  • refactoring becomes safer

  • team ownership becomes clearer

Exercise

Draw a 6-box module map of your current system.

Label each box with a major area, such as:

  1. UI

  2. Application services

  3. Domain logic

  4. Data access

  5. External integrations

  6. Shared utilities

Then do two things:

  • Mark the highest-churn box

  • Propose one new seam that would reduce coupling around that box

Prompt for the learner

Use this template:

  • Highest-churn box: __________

  • Why it changes often: __________

  • What it is tightly coupled to: __________

  • New seam to add: __________

  • How that seam reduces change spread: __________

Example

  • Highest-churn box: Order processing workflow

  • Why it changes often: New pricing rules and fulfillment rules

  • What it is tightly coupled to: Payment gateway and reporting code

  • New seam to add: Payment adapter interface

  • How that seam reduces change spread: Payment changes stay behind the adapter instead of leaking into workflow logic

Completion outcome

By the end of this step, the learner should have:

  • a visible map of the system’s main modules

  • one identified hotspot of change

  • one concrete seam they can introduce to make future refactoring safer

Key takeaway

The first design move is not adding patterns. It is drawing boundaries so change has somewhere to stop.

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Author: Rod Claar
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