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Learning Path

Generative AI for Scrum Teams

Who it’s for: Scrum Masters, Product Owners, and Agile teams who want to use Generative AI safely to accelerate planning, facilitation, and delivery.

Outcomes

  • Create sprint-ready user stories faster with AI-assisted refinement (without losing clarity).
  • Run more effective Scrum events using repeatable prompt templates and facilitation checklists.
  • Add lightweight guardrails to reduce risk (data leakage, hallucinations, and inconsistent outputs).

Your Learning Path

Follow these steps to master Generative AI for Scrum Teams

  1. 1

    Understanding AI Fundamentals for Scrum

    Learn the core AI concepts every Scrum team member needs to know before diving into practical applications.

    Do this exercise
  2. 2

    AI-Assisted User Story Creation

    Discover how to use AI to draft, refine, and validate user stories that are sprint-ready and stakeholder-approved.

    Do this exercise
  3. 3

    Prompt Templates for Sprint Planning

    Get repeatable prompt templates to streamline sprint planning, capacity forecasting, and backlog refinement.

    Do this exercise
  4. 4

    Facilitating Scrum Events with AI

    Learn how to use AI to prepare agendas, generate retrospective insights, and capture action items efficiently.

  5. 5

    Building AI Guardrails for Your Team

    Implement lightweight policies to prevent data leakage, hallucinations, and ensure consistent, trustworthy AI outputs.

    Do this exercise
  6. 6

    AI for Product Backlog Management

    Use AI to prioritize backlog items, identify dependencies, and align work with strategic product goals.

  7. 7

    Measuring AI Impact on Team Velocity

    Track how AI adoption affects your team's velocity, quality, and overall delivery predictability.

    Do this exercise

Steps - Free

24 Feb 2026

Step 1: Understanding AI Fundamentals for Scrum

Before using AI in backlog refinement, Sprint Planning, or testing, every Scrum team member should understand a few core concepts.

Without shared understanding, misuse is inevitable.

Author: Rod Claar
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24 Feb 2026

Step 2: AI for Product Owners: Turn Customer Feedback Into Sprint Experiments

Customer & Stakeholder Discovery Prompts

This content explains how Product Owners can use AI to convert raw customer and stakeholder feedback into actionable sprint work.

Instead of treating interviews and notes as static documentation, the approach reframes them as structured inputs for rapid synthesis.

The model follows four steps:

  1. Input – Gather interviews, support tickets, surveys, and call notes.

  2. Clustering – Use AI to group feedback into meaningful themes.

  3. Risk Framing – Identify usability, adoption, and value risks.

  4. Experiment Design – Translate insights into 2–3 testable sprint experiments.

A practical exercise reinforces the method:

  • Paste 10–20 lines of real feedback into AI.

  • Ask it to cluster themes, surface risks, and propose three experiments for the next sprint.

The core principle: AI accelerates synthesis, enabling continuous learning and faster validation within the Scrum cadence.

Author: Rod Claar
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Steps - Members

 
 
✓ Featured Content

Generative AI 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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