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Free Download Scrum After AI What Stays, What Changes, What Disappears
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 4h 35m | Size: 1.69 GB
Rethink user stories, sprint planning, daily Scrums, reviews and team roles for AI-native product development.
What you'll learn
Sort every Scrum practice your team performs into Keep, Adapt, Remove or Add using a repeatable four-question test
Separate what the Scrum Guide actually mandates from what your organisation added - and prove it from the source
Redesign Sprint Planning, the Daily Scrum, refinement, Review and Retrospective so AI prepares and humans decide
Choose from seven backlog item formats instead of forcing every item into a user story
Write an AI-aware Definition of Done covering review, security, provenance, evaluation and human approval
Build a human-AI responsibility matrix that assigns every delivery activity to a human, an agent, or a shared boundary
Draft an agent escalation policy and governance checklist that answer an auditor's questions
Explain why AI-generated pull requests wait 5x longer for review - and what that means for sprint capacity
Requirements
Working familiarity with Scrum events, accountabilities and artifacts. No certification required.
Awareness of AI coding assistants such as Claude, Claude Code or Copilot. Hands-on use helps but is not required.
No programming is needed. The capstone is a design exercise, not an implementation exercise.
Description
This course contains the use of artificial intelligence.
If Claude, Copilot and autonomous agents can analyse Requirements, write code, generate tests, review pull requests and update documentation - which parts of Scrum still create value, and which have quietly become rituals?
Most courses on AI and Agile teach you to run your existing ceremonies slightly faster. This one asks the harder question:what should Scrum become when producing software is no longer limited by human execution capacity?
You will sort every practice your team performs into four categories - Keep, Adapt, Remove and Add - using a repeatable test, and rebuild your sprint around the answer
What makes this course different
- It is built on measured evidence, not opinion - DORA 2025, Sonar's 2026 State of Code survey, LinearB's analysis of 8.1 million pull requests, and the Scrum framework stewards' own practitioner research. Every figure is cited
- It covers what nobody else covers: the practices toremove, and the governance controls toadd that Scrum never specified
- It corrects a belief most Scrum teams hold. Search the Scrum Guide for "user story", "story point", "velocity" or "estimation" - none of them appear. You will verify that yourself in lecture three
- Roughly one lecture in four is a live demonstration, not an explanation - including watching a plausible, well-written, completely wrong AI result get rejected in review
You will build eight working artifacts
- A Keep / Adapt / Remove / Add assessment of your own team
- A redesigned sprint workflow
- A human-AI responsibility matrix
- An AI-aware Definition of Done
- A new Daily Scrum format
- An agent escalation policy
- A governance checklist
- An AI-native backlog written in formats an agent can build from
Everything is developed alongside a running case study: a 1,400-person healthcare software company twelve months into rolling out coding agents, whose output has tripled while its ceremonies have not changed at all - and whose clinical safety officer is about to ask a question nobody can answer
Who this is not for. If you want a pack of prompts for running a better stand-up, this is the wrong course. This is for people with the authority, or the ambition, to change how their team actually works
Who this course is for
Scrum Masters and Agile Coaches whose ceremonies have started to feel hollow
Product Owners spending more time on backlog administration than on product decisions
Engineering managers and tech leads running teams that already use coding agents
Delivery and PMO leaders who need to govern AI-generated work before an audit
CTOs and heads of product deciding how their teams should operate from here
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