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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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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
Videos

A curated playlist of specific YouTube content.

Search Results

20 Jan 2026

AI-Enhanced Scrum: Transforming Agile Development with AI Feb 25-27

AI-Enhanced Scrum: Transforming Agile Development with AI Feb 25-27

Author: SuperUser Account  /  Categories: Scrum & Agile Training  /  Rate this article:
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Event date: 2/25/2026 - 2/27/2026 Export event

Revolutionize Your Agile Development with AI-Enhanced Scrum

Are you ready to supercharge your Scrum practices with the power of Artificial Intelligence? Join this cutting-edge course that bridges the gap between traditional Agile methodologies and modern AI-powered development, featuring comprehensive technical specification generation that transforms your entire development workflow.

What You'll Master:

Transform your entire development pipeline from requirements to deployment with AI-driven processes that create a seamless, integrated development experience. Start with AI-guided requirements gathering, advance through revolutionary UI design generation, and culminate in comprehensive AI-generated technical specifications that synthesize all your project artifacts into actionable implementation guidance. Learn to leverage AI for complete feature development including detailed technical architecture, robust test-driven development practices, and quality assurance processes that ensure consistency across your entire project ecosystem.

Revolutionary Requirements-to-Code Pipeline:

Experience how AI can guide you through an integrated development process where each step builds upon the previous one. Watch as your PRD requirements, UI designs, UML models, and acceptance tests are synthesized by AI into comprehensive technical specifications that provide detailed implementation roadmaps for your development team.

Breakthrough Technical Specification Generation:

Master the art of AI-generated technical specifications that analyze and integrate your PRD requirements, UI designs, UML architecture models, and ATDD scenarios into comprehensive implementation guides. Learn how AI can generate database schemas from class diagrams, API specifications from UI flows, architectural recommendations from system complexity analysis, and detailed code implementation guidelines that ensure consistency across your entire technology stack.

Advanced UI Design Integration:

Continue leveraging cutting-edge non-traditional prompting techniques for UI generation, including role-based prompting, persona-driven design creation, and constraint-based accessibility compliance, now enhanced by technical specification integration that ensures design feasibility and optimal implementation approaches.

Complete Development Ecosystem:

Discover how AI maintains perfect synchronization between your PRD requirements, UI designs, UML models, technical specifications, acceptance tests, and code implementation, creating a living documentation system that evolves with your project while maintaining consistency and traceability throughout the development lifecycle.

Why This Course Matters:

In today's rapidly evolving tech landscape, teams that successfully integrate AI into their complete Scrum workflow—from requirements gathering through technical specification to code deployment—are delivering features faster, with higher quality, and with unprecedented thoroughness in their documentation, architecture planning, and implementation consistency. You'll learn to create AI-generated technical specifications that bridge the gap between high-level design and detailed implementation, ensuring your development team has clear, comprehensive guidance for every aspect of feature development.

Perfect For:
  • Solution Architects seeking AI-powered technical specification generation
  • Product Managers wanting end-to-end AI-enhanced development workflows
  • Technical Leads looking to streamline architecture documentation and implementation guidance
  • UX/UI Designers seeking integration between design and technical implementation
  • Scrum Masters modernizing their practices with comprehensive AI-generated documentation
  • Development teams ready to integrate AI tools across the complete development lifecycle
  • Organizations seeking to maintain quality while accelerating delivery through intelligent automation

Led by an Industry Veteran: Learn from an instructor with decades of software development experience, from the early days of C++ to the latest AI innovations. With expertise spanning custom development, core product development, and years of teaching Scrum, TDD, software design patterns, and system architecture, you'll receive practical insights that only come from real-world implementation experience across the complete software development lifecycle.

Class Price$698.60
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