20 multiple-choice questions across the three guides — the inclusion bar, the 10 findings, the operating habits, and the transformation reframe. Live score tracks as you go.
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01 · FoundationsWhat the Study Found0 / 7 correct
1. Which of these does NOT belong in Stanford's four-part "successful deployment" bar?
The four criteria: live in production, used consistently, measurable business value, capable of scaling. Marketing visibility is not on the list.
2. The dataset covered how many organizations and industries?
51 deployments × 41 orgs × 9 industries × 7 countries × 1M+ employees. Some orgs had multiple deployments.
3. The playbook's central thesis is best summarized as:
"The difference was never the AI model. It was always the organization." 42% of cases had interchangeable models.
4. Why does the 95% pilot failure rate matter to the playbook's framing?
If most pilots fail, the useful question is what the small minority did differently. The 51 are an empirical answer.
5. Identical use cases took weeks in some companies and years in others. The biggest single determinant of that gap was:
Same use case, same technology, vastly different timelines — set by exec cadence, infrastructure readiness, user willingness, gatekeeper behavior.
6. In what fraction of studied cases was the model interchangeable?
42% — the empirical case for treating model choice as substitutable infrastructure.
7. Your VP says "let's pick the best foundation model first, then design the workflow." Based on the playbook, what's the most accurate response?
The playbook's clearest PM-facing implication. Model lock-in budget goes to workflow, integration, and UX — not provider choice.
02 · AppliedThe PM Playbook0 / 7 correct
1. What percentage of AI implementation challenges does the playbook attribute to non-technical sources?
77% — change management, data quality, and process redesign. Not technology.
2. By the McKinsey figures cited in the playbook, what fraction of high performers redesigned workflows around AI vs. other companies?
55% vs 20% — the largest single separator across performance tiers.
3. Among fully autonomous, approval-based, and escalation-based operating models, which produced the highest median productivity gain?
Escalation-based won. Approval-based makes the human the bottleneck; fully autonomous surfaces risk too late.
4. Resistance to AI deployments overwhelmingly came from which group, per the playbook?
Counterintuitive. End users adopted; gatekeepers vetoed.
5. What distinguishes "operational" executive sponsorship from "strategic approval" sponsorship?
Operational sponsorship is a recurring cadence + blocker list + OKR alignment. Strategic sponsorship is a signature.
6. What percentage of successful deployments in the dataset had at least one prior failed attempt?
61% — failure-as-precursor is the norm, not the exception.
7. Your team's AI rollout is stalled. The end users love the tool but the rollout hasn't progressed in six weeks. Where do you look first?
A stalled rollout with enthusiastic users almost always points to a gatekeeper veto.
03 · AdvancedOperating an AI Transformation0 / 6 correct
1. The shift from "experiment mode" to "strategic integration" most affects which dimension first?
The reframe is structural: ownership, KPIs, control systems, failure handling, funding.
2. Productivity gains automatically translate to layoffs in what proportion of deployments?
45% reduced headcount; the rest redeployed or grew. Workforce strategy is a deliberate choice.
3. Which of these is the highest-ceiling AI value type, per the playbook's pattern?
Cost savings are bounded by the cost line; productization creates net-new revenue with the largest ceiling.
4. A team says "we can't start the AI project until we clean our data." By the playbook, this is most likely:
LLMs handle messy data well enough that "we need to clean it first" is now usually a delay rationalization.
5. Given 42% model interchangeability, where should a PM steer the architectural lock-in budget?
Keep the model layer thin and replaceable. Put institutional knowledge above it.
6. Which of these best summarizes the playbook's overall PM-facing message?
The moats are organizational, not technological. Operating skills, not technology choices.
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Final Score
Guide 1 · Foundations
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Guide 2 · Applied
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Guide 3 · Advanced
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20 questions · The Enterprise AI Playbook · self-learning library