AI and go/no-go launch decision memos: structuring the case before the meeting

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-business-AI-and-go-no-go-launch-decision-adults

1. Which of the following best describes the primary value AI brings to go/no-go decision memos?

   1. It surfaces kill criteria that stakeholders would prefer to ignore
   2. It makes the final launch decision on behalf of executives
   3. It automatically cancels launches that meet any downside scenario
   4. It replaces the need for human judgment in launch decisions

2. A properly structured AI-generated go/no-go memo should include which four sections?

   1. Market analysis, competitor review, risk assessment, and cost-benefit analysis
   2. Executive summary, financial projections, team capabilities, and timeline
   3. Project plan, resource allocation, stakeholder map, and success metrics
   4. Upside thesis, downside scenarios, explicit kill criteria with thresholds, and reversibility analysis

3. After writing each go-side claim in a go/no-go memo, what should AI be prompted to generate?

   1. A summary of executive preferences
   2. The strongest possible counterargument
   3. A supportive case study from industry leaders
   4. A list of alternative launch dates

4. Which limitation of AI in go/no-go decisions is MOST related to organizational dynamics?

   1. AI cannot pull historical postmortems for base rates
   2. AI cannot generate counterarguments to go assertions
   3. AI cannot generate the initial memo draft
   4. AI cannot override an executive who has already decided

5. Why are vague kill criteria like 'if adoption is poor' problematic in go/no-go memos?

   1. They violate regulatory requirements for launches
   2. They cannot be checked by AI systems
   3. They get rationalized away because they lack measurable thresholds
   4. They are too specific and limit flexibility

6. What does the reversibility analysis section of a go/no-go memo address?

   1. Whether team members can reverse their voting positions
   2. How easily the product launch can be undone or rolled back if it fails
   3. The financial reversibility of investment decisions
   4. Whether the launch decision can be changed after the memo is written

7. How can historical launch postmortems be used in AI-generated go/no-go memos?

   1. To predict exact future revenue outcomes
   2. To write the executive summary automatically
   3. To replace the need for kill criteria
   4. To populate base rates for downside scenarios

8. What problem does the lesson identify with momentum in launch decisions?

   1. Momentum ensures sufficient resources are allocated
   2. Momentum prevents AI from generating useful memos
   3. Momentum causes decisions to be made without structured analysis of risks
   4. Momentum leads to balanced decision-making

9. Which scenario represents the accountability gap in AI-generated go/no-go memos?

   1. The team agrees to kill criteria but ignores them three weeks later when adoption is low
   2. The memo lacks reversibility analysis
   3. AI fails to generate counterarguments
   4. AI generates a memo that no one reads

10. Why might AI-generated kill criteria miss critical production dependencies?

    1. AI always generates specific numerical thresholds
    2. AI doesn't know which dependencies are actually fragile in production
    3. AI cannot access production systems
    4. AI overestimates production risks

11. What is the relationship between kill criteria thresholds and rationalization?

    1. Only executive-approved thresholds prevent rationalization
    2. Specific numerical thresholds make it harder to rationalize away the criteria
    3. Thresholds have no impact on rationalization
    4. Vague thresholds make it easier to hold teams accountable

12. Which of the following is an appropriate use of AI in the go/no-go process?

    1. AI enforces kill criteria after launch
    2. AI drafts the memo structure including counterarguments
    3. AI makes the final go/no-go decision
    4. AI attends the executive meeting to vote

13. What underlying problem does the go/no-go memo process aim to solve?

    1. Decisions being made on momentum without structured risk analysis
    2. Lack of executive interest in launch decisions
    3. Too many launches being cancelled
    4. Insufficient funding for launches

14. What type of information should kill criteria include to enable future verification?

    1. Specific numbers and dates
    2. Qualitative descriptions of success
    3. General timelines without metrics
    4. Subjective assessments from stakeholders

15. What is the purpose of including explicit counterarguments in a go/no-go memo?

    1. To discourage teams from pursuing any launch
    2. To satisfy regulatory requirements
    3. To ensure the analysis is balanced rather than confirming existing preferences
    4. To replace the need for downside scenario analysis