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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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Article rating: No rating

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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Learning Path - Member

 
 
✓ Featured Content

AI for Scrum and Agile Teams
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A curated playlist of specific YouTube content.

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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  /  Rate this article:
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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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Author: Rod Claar
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