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Learning Path

Certified Scrum Product Owner: From Vision to Value

Built for Product Owners and Product Managers who want a practical, repeatable way to turn ideas into outcomes—without losing alignment, clarity, or momentum.

  • Create a clear product direction that teams can execute without constant rework.
  • Build and refine a backlog that connects customer needs to measurable value.
  • Improve delivery decisions with better slicing, prioritization, and stakeholder alignment.

Path Steps

Step-by-step: From Vision to Value

Work through these steps in order. Each step links to a specific article or video post (EasyDNNnews item), includes a one-sentence focus, and (optionally) a small exercise to apply it immediately.

1

You’ll learn how to express a clear product direction that aligns stakeholders and guides real backlog decisions.

Do this exercise: Write a one-sentence vision + three measurable outcomes you want in 90 days.
2

You’ll learn how to clarify who you serve and what decisions they must make—so your backlog has purpose.

Do this exercise: List 2 primary user types and the top 3 “jobs” they need done.
3

You’ll learn a practical slicing approach to create small, testable items that still deliver real value.

4

You’ll learn a simple prioritization model that makes tradeoffs explicit and reduces thrash.

Do this exercise: Score your top 5 backlog items by Value, Risk, and Learning (1–5).
5

You’ll learn how to run refinement so teams leave with shared understanding—not just more tickets.

6

You’ll learn lightweight stakeholder habits that keep direction aligned while protecting team focus.

7

You’ll learn simple metrics that show whether you’re improving value delivery—not just shipping more.

Steps - Free

24 Feb 2026

Step 1: Start with product vision that teams can actually execute

If the team cannot use it to prioritize backlog items, it is not actionable.

Author: Rod Claar
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24 Feb 2026

Step 2: Identify customers, users, and the decisions that matter

If you cannot name:

  • Who you serve

  • What they are trying to decide

  • What “job” they need completed

Your backlog will drift.

Author: Rod Claar
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24 Feb 2026

Step 3: Turn outcomes into backlog slices (without giant stories)

If a backlog item cannot be completed inside a Sprint with clear acceptance criteria, it is not sliced—it is deferred complexity.

The goal is not smaller tasks.
The goal is small increments of validated outcome.

Author: Rod Claar
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24 Feb 2026

Step 4: Prioritize with Confidence: Value, Risk, and Learning

Prioritize with Confidence: Value, Risk, and Learning

This step introduces a simple, explicit prioritization model based on three dimensions: Value, Risk, and Learning (V-R-L).

Instead of relying on vague “priority” discussions, teams score each backlog item (1–5) on:

  • Value — business impact delivered

  • Risk — uncertainty reduced or exposed

  • Learning — validated insight gained

Making these criteria visible reduces backlog thrash, clarifies trade-offs, and exposes hidden assumptions. It also encourages earlier risk burn-down and faster validation of uncertainty.

The exercise requires scoring the top five backlog items and reviewing the ranking for balance. The goal is not mathematical precision, but strategic clarity.

AI can strengthen this process by stress-testing assumptions, surfacing overlooked risks, and simulating alternative rankings—while leaving final decisions to human judgment.

The broader outcome is disciplined, transparent prioritization aligned with strategy rather than habit.

For deeper capability, the next step is the AI for Scrum Product Owners class, which expands on using AI to refine backlog items, quantify value hypotheses, and improve decision quality.

Author: Rod Claar
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Steps - Members

 
 
✓ Featured Content

Scrum Product Owner Videos

A curated playlist of specific YouTube content.

Search Results

3 Jun 2026

From Retail to AI: Pattern Recognition Across 50 Years

From Retail to AI: Pattern Recognition Across 50 Years

Author: Rod Claar  /  Categories: AI Training  / 

My first job in tech was in a lumber yard. Seriously.

It was 1972. I was stacking two-by-fours and counting inventory.

My job was to spot patterns.

Which items were running low? Which suppliers were slow? Which seasons drove which sales?

I didn't call it data analysis back then. But that's exactly what it was.

Fast forward 50 years.

Now I teach Artificial Intelligence.

And here's what surprises my students every time:

AI doesn't do anything magical. It finds patterns.

That's it. Patterns in data. At massive scale. At incredible speed.

When I look at an AI model's output, I ask the same questions I asked in 1972.

What is this telling me? What's missing? What doesn't look right?

The tools changed. The thinking didn't.

If you've spent years solving real problems in the real world, you already have the most important AI skill.

Pattern recognition.

The rest is learnable.

Want to start learning? 👉 https://agileaidev.com/resources/ai-tips-and-tricks 👉 https://agileaidev.com/courses

What pattern-recognition skill from your past career helps you most today? ⬇️

#AI #ArtificialIntelligence #Agile #SoftwareDevelopment #AgileCoaching

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20 Jan 2026

Author: Rodney Claar
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