Agenda
Framing: Why AI, Why Now, Why You
- Understand why using AI often fails to improve delivery
- Explore how AI works best as a tool to support Agile teams, rather than as a technical solution (for Scrum Masters & Product Owners)
- Learn what Scrum Masters and Product Owners can realistically achieve with AI
- Understand scope boundaries: GenAI tools and simple automation
- Outcome: Shared understanding of AI as an accelerator for Agile delivery
Foundation: Data Protection & Responsible Use in 2026 AI
- Understand how GenAI tools handle prompts, memory, and data retention
- Differences between public, enterprise, and controlled AI settings
- Common misconceptions around “AI training on your data”
- Practical guardrails for team-level AI usage
- Outcome: Confidence to use AI responsibly (with good governance)
AI for Stories, Backlogs, and Product
- Use AI to surface assumptions in user stories
- Story slicing and acceptance criteria refinement
- Identify edge cases and non-obvious requirements
- Shift backlog from activity to value
- Exercise: Improving a weak story using AI-supported prompts
- Outcome: Clearer, testable, value-oriented backlogs
Lightweight Automation with GenAI
- Role of no-/low-code automation for non-technical roles
- Examples:
- Sprint and iteration summaries
- Retrospective insight consolidation
- Stakeholder update drafts
- Designing automations that reduce cognitive load, not thinking
- Outcome: Practical understanding of automation as delivery support
From Insight to Practice
- Understand where AI adds value vs where it creates noise
- Sense-checking overuse risks
- Individual commitment:
- One delivery problem to address with AI
- One practice to pilot immediately
- One misuse of AI to avoid
- Outcome: Clear, low-risk experiments for immediate use