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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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Article rating: No rating

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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2 Apr 2026

Why Your AI Agent Fails 97.5% of Real Work — And the Fix Isn't More Code

Most AI agent projects fail not because of bad code or weak models — they fail because teams aim at the wrong part of the workflow. AI strategist Nate B. Jones argues that real work is only about 2.5% high-judgment "core" decisions, while the other 97.5% is mechanical edge work: data prep, QA, synthesis, handoffs, and packaging. Teams that try to automate the core first stall out fast. Teams that start with the edges — the boring stuff surrounding the valuable work — ship results in days, build organizational trust, and create a proven path toward eventually tackling the core. It's the same principle behind Agile: start small, deliver value fast, and expand from a foundation of demonstrated success. The fix isn't better AI. It's smarter strategy about where you start.

Author: Rod Claar
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Learning Path - Member

 
 
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8 Jun 2026

AI for Scrum Product Owners and Product Managers - June 23, 2026

AI for Scrum Product Owners and Product Managers - June 23, 2026

Author: Rod Claar  /  Categories: AI for Scrum Product Owners and Product Managers  /  Rate this article:
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Event date: 6/23/2026 9:00 AM - 1:00 PM Export event

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  • Attending: 0
  • Seats: 12
  • Remaining: 12
  • Price: $299.00
$299.00

A practical, hands-on 4-hour live course for Scrum Product Owners, Product Managers, Agile Coaches, and Team Leads. This course gives you the AI skills you need to sharpen backlog refinement, sprint planning, sprint review, and product decision-making — without replacing your judgment, accountability, or product ownership.

No prior AI experience is required. Every technique is grounded in real Scrum and Agile practice and is ready to use in your next sprint.

Course Modules:

Module 1 — AI as Assistant, Not Product Owner Replacement Understand where AI fits in the product role, where it does not, and how to keep accountability firmly in human hands.

Module 2 — Prompt Patterns for the Full Product Cycle Learn prompt structures for product planning, sprint planning, sprint review, dependency analysis, and meeting preparation.

Module 3 — AI-Assisted Product Backlog Item Refinement Use AI to draft, split, and improve user stories, then apply your product knowledge to make them sprint-ready.

Module 4 — AI-Assisted Product Backlog Ordering Let AI surface trade-offs, risks, and dependencies — then own the final prioritization decision yourself.

Module 5 — Ethics, Privacy, Hallucination Control, and Human Judgment Understand AI risks in a product context and build habits that protect your team, your data, and your decision-making integrity.

Module 6 — Hands-On Scenario Lab Apply everything in a guided lab using realistic product scenarios with group debrief.

What Is Included:

Full 4-hour live instructor-led session
All prompt templates used in the course

Hands-on scenario lab
Live Q&A with the instructor throughout the session
Certificate of Completion from AgileAIDev.com
No required software purchases

Who Should Attend:

Scrum Product Owners
Product Managers working in Agile or Scrum teams
Agile Coaches Team Leads with product responsibilities

Registration:

Selelect the insance of this class and complete the registration form to reserve your seat. Sessions are limited to 12 participants. Registration closes when the session is full or 24 hours before the session start time.

Cancellation Policy: Cancellations made 72 or more hours before the session are eligible for a full refund or transfer to a future session. Cancellations made fewer than 72 hours before the session may be transferred to a future session but are not eligible for a refund.

Questions: rod@AgileAIDev.com

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