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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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Article rating: No rating

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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Article rating: No rating

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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Article rating: No rating

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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Article rating: No rating
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Steps - Members

 
 
✓ Featured Content

Scrum Product Owner Videos

A curated playlist of specific YouTube content.

Search Results

16 Apr 2025

Nvidia faces $5.5bn hit from Trump clampdown on AI chips

Nvidia faces $5.5bn hit from Trump clampdown on AI chips

Author: SuperUser Account  /  Categories: AI Finance  / 

Nvidia said it expects to take a $5.5 billion hit as President Trump clamps down on the sale of powerful artificial intelligence chips to China.

The US chip designer at the centre of the AI boom said the US government was introducing new restrictions on its chip exports over fears they could be used to help China build a supercomputer.

Supercomputers are the engines of a type of data centre created for the sole purpose of powering AI.

The US had already imposed export restrictions on more powerful Nvidia chips, including the Blackwell, to prevent them reaching China where they could be used for military applications and breakthroughs in AI.

However, Nvidia said the US government will now require licences for exports to China of its H20 chip, the most advanced Nvidia chip presently available in China.

Nvidia announced the $5.5 billion charge in a regulatory filing on Tuesday, sending shares in the company down almost 6 per cent in after-hours trading.

The latest government crackdown on chip exports comes after Chinese companies reportedly placed at least $16 billion in orders for Nvidia’s H20 chips in the first three months of the year.

ByteDance, Alibaba Group and Tencent Holdings are among companies that have been buying up the most advanced Nvidia artificial intelligence chips that are legally available in China under US export controls, The Information reported earlier this month.

The high demand for Nvidia’s H20 chips is believed to be driven by the Chinese start-up DeepSeek’s AI models.

Nvidia said the US government informed it on April 9 that the H20 chip would require a licence to be exported to China and on April 14 told Nvidia that those rules would be in place indefinitely.

Nvidia’s filing did not indicate how many of those licences the US government could grant.

The chipmaker said on Monday that it was planning to spend as much as $500 billion building supercomputers for artificial intelligence entirely in the US over the next four years.

 

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