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

End-of-lesson check

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

1. A team has been running kanban for three months. The flow data shows that work consistently piles up in the 'Review' column, causing downstream delays. What can AI reliably do with this information?

   1. Recommend firing the team members assigned to Review
   2. Infer that the team has an implicit policy of accepting unlimited work in Review
   3. Predict exactly when the project will be cancelled
   4. Delete the Review column from the board automatically

2. An organization wants to use AI to rewrite their kanban policies. They have collected two weeks of data from their board. What does the lesson indicate about their data readiness?

   1. Two weeks is enough to identify all team bottlenecks
   2. The dataset is too small for meaningful policy inference
   3. The AI will work better with less data to avoid bias
   4. The data is sufficient for AI to draft new policies immediately

3. A senior manager repeatedly adds urgent work to the board, bypassing WIP limits that the team agreed to. What does the lesson identify as AI's limitation in this scenario?

   1. AI cannot force the manager to respect WIP limits
   2. AI will automatically reprioritize the urgent work
   3. AI will notify the board of directors
   4. AI can delete the manager's account

4. A team publishes detailed WIP limit policies but never enforces them when violated. What does the lesson warn this behavior creates?

   1. A pattern where the team learns to ignore official documents
   2. Faster cycle times due to flexibility
   3. Improved trust between team and leadership
   4. A highly productive team culture

5. After analyzing 90 days of kanban data, AI suggests specific WIP limits per column with rationales. What should the team do before publishing these as official policy?

   1. Immediately post them on the company intranet without discussion
   2. Submit the policies to legal for approval
   3. Replace daily standups with the new policies
   4. Commit to actually enforcing the limits when violations occur

6. A team notices that despite having written policies, certain columns frequently exceed their WIP limits without consequence. What human process does the lesson identify as essential for catching these violations?

   1. Automated email alerts
   2. Daily standup conversations
   3. Quarterly review meetings
   4. Annual performance reviews

7. Which of the following would be identified by AI analyzing kanban data as an anti-pattern the team tolerates?

   1. Clear handoff documentation between columns
   2. Daily coordination between developers and testers
   3. Work moving backward in the workflow when requirements change
   4. Consistent WIP limit adherence

8. An AI analysis reveals that the team consistently pulls work from the backlog in LIFO (last-in-first-out) order rather than FIFO. What can AI do with this finding?

   1. Document this as an implicit pull policy and suggest making it explicit
   2. Delete the backlog column
   3. Recommend replacing the product owner
   4. Automatically reorder the backlog

9. Leadership wants AI to suggest policies but plans to continue adding emergency work whenever they want. What does the lesson suggest the team should do?

   1. Fire the leader who adds emergency work
   2. Use AI to block the leader's access to the board
   3. Publish the policies anyway since AI recommended them
   4. Either commit to enforcement or do not publish the policy

10. When AI analyzes cycle time data, what specific aspect of team behavior is it examining?

    1. The team's annual budget allocation
    2. How long work items spend in each workflow stage
    3. Customer satisfaction scores
    4. Individual developer coding speed

11. A product owner asks: 'Why should we use AI to tell us what we already know about our process?' What is the strongest argument from the lesson for using AI?

    1. AI makes implicit practices explicit and negotiable, creating shared understanding
    2. AI will make the team work faster
    3. AI eliminates the need for team communication
    4. AI is cheaper than hiring a consultant

12. The lesson mentions that with 90 days of flow data for a 7-person team, AI can draft several specific policy elements. Which element is NOT listed as something AI can draft?

    1. Pull rules between stages
    2. Definition of done per stage
    3. Employee performance review criteria
    4. WIP limits per column with rationale

13. What happens to the credibility of kanban policies when teams frequently violate WIP limits without any corrective action?

    1. The team becomes more motivated to follow them
    2. The policies become meaningless and ignored by the team
    3. The violations are automatically fixed by the system
    4. Productivity increases significantly

14. An AI tool analyzes your kanban board and reports: 'Your implicit policy is to allow maximum 3 items in Development, but you regularly exceed this to 5 or 6 during sprint ends.' What type of insight is this?

    1. A recommendation to hire more developers
    2. An observation of the gap between stated and actual WIP limits
    3. A complaint about the team's work ethic
    4. A prediction of project completion date

15. Why might a team choose to explicitly adopt a different policy than what the AI observes them actually doing?

    1. Because AI recommendations are always wrong
    2. To satisfy regulatory requirements that don't exist
    3. To correct a problematic behavior they want to change
    4. To avoid using the kanban board