Launching AI-Native ARTs: the class board

Assess your ART against three success factors: outcome-driven product development, portfolio and investment strategy, and curated data. Rate, compare, reach consensus, then turn every gap into a launch plan on the roadmap.

2 days, 2 lessons, 18 exercises. Every class opens with the start-here strip: Trainyard intro, quick agenda, remote working agreements, team and challenge, parking lot, daily structure.

Lesson 1: Start and calibrate

  • Activity: Try the assessment scaleInstructions

Lesson 2: Assess the ART

  • Discussion: Why “outcome-driven” flow?Instructions
  • Activity: Compare workflowsInstructions
  • Activity: Evaluate an OKRInstructions
  • Assess: Outcome-driven product developmentInstructions
  • Discussion: Why did we evaluate differently?Instructions
  • Assess: Adjust your evaluation if neededInstructions
  • Discussion: Why the portfolio matters to the ARTInstructions
  • Activity: Does strategy reach your ART?Instructions
  • Assess: Portfolio and investment strategyInstructions
  • Discussion: Why did we rate differently?Instructions
  • Assess: Adjust your rating if neededInstructions
  • Discussion: What makes data important?Instructions
  • Activity: Who is responsible for great data?Instructions
  • Assess: Curated dataInstructions
  • Discussion: Why did we rate differently?Instructions
  • Assess: Adjust your rating if neededInstructions
  • Activity: Gap analysis reviewInstructions

Running it

  • Templates → Classes → Launching AI-Native ARTs → create one board per cohort. Participants open the link; no install.
  • The PI planning simulation uses the real planning room: ART board, team boards, story backlog with points, ROAM and the confidence vote.
  • Readiness checklist, feature canvases and WSJF exercises point at the live canvases, so what the class practises is what a train uses.
  • Have your own version in Miro? Export the board as CSV and import it under Bring your own boards; frames become lessons.
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