AI Kanban Policy Rewrites: Naming the Rules the Team Already Half-Follows

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-operations-AI-and-kanban-policy-rewrite-r7a2-adults

1. What is the main idea of "AI Kanban Policy Rewrites: Naming the Rules the Team Already Half-Follows"?

   1. AI can rewrite kanban explicit policies from observed behavior, but the team must agree to live by them.
   2. Use AI as the final authority for the whole decision
   3. Avoid checking the answer once it sounds polished
   4. Focus only on speed instead of judgment

2. Which concept is most central to "AI Kanban Policy Rewrites: Naming the Rules the Team Already Half-Follows"?

   1. explicit policies
   2. kanban policies
   3. WIP limits
   4. definition of done

3. Which use of AI fits this topic best?

   1. Make the team enforce WIP limits when leadership keeps adding priority work.
   2. Let the AI decide what matters without your review
   3. Infer current implicit policies from cycle-time and WIP-history data.
   4. Use the answer before checking whether it fits the situation

4. Which limitation should you watch for in this topic?

   1. Infer current implicit policies from cycle-time and WIP-history data.
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Make the team enforce WIP limits when leadership keeps adding priority work.

5. What should a careful learner remember about "Kanban policy rewrite draft"?

   1. Use AI to draft or organize ideas about kanban policies, then verify before acting.
   2. Skip the context so the tool can guess faster
   3. Treat the output as private even after sharing it online
   4. Use the answer without checking the source

6. You want to use AI after this lesson. What is the safest next step?

   1. Act immediately because the AI answer is written clearly
   2. Use AI as a workflow assistant, with human review for decisions that carry risk.
   3. Hide uncertainty so the final answer looks cleaner
   4. Use private or sensitive details before checking permission

7. How should AI output about kanban policies be treated?

   1. As proof that no other source is needed
   2. As a replacement for context, consent, or expert review
   3. As a draft or helper output that still needs human judgment and verification
   4. As something that becomes correct when it sounds confident

8. Name one way to verify an AI answer about kanban policies.

9. Which action would help you apply "AI Kanban Policy Rewrites: Naming the Rules the Team Already Half-Follows" responsibly?

   1. Replace the daily standup conversation that catches policy violations.
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Draft revised explicit policies with specific WIP limits and pull rules per column.

10. Which choice is a bad use of AI for this lesson?

    1. Replace the daily standup conversation that catches policy violations.
    2. Infer current implicit policies from cycle-time and WIP-history data.
    3. Ask for a plain-language explanation of explicit policies
    4. Compare the answer with a trusted source