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A Self-Learning Library · Stanford Digital Economy Lab, Apr 2026

The Enterprise AI Playbook

51 enterprise AI deployments that cleared a strict production bar — the reverse-engineered profile of the 5% that worked. Three guides, twenty questions, thirty-two concepts, grounded in the original Stanford report and four cross-validating syntheses.

Source: Stanford Digital Economy Lab · Authors: Pereira · Graylin · Brynjolfsson
3
Learning Guides
32
Concepts Covered
20
Quiz Questions
51
Deployments Studied
Practice — test & drill what you've learned
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Multiple Choice · All Guides
20Questions
3Guides
LiveScore

Full Quiz

All 20 questions in one sitting — live score tracks as you go, with a per-guide breakdown at the end.

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Flip Cards · All Guides
32Cards
3Guides
Shuffle+ Filter

All Flashcards

32 flip cards across all guides. Filter by topic, shuffle the deck, or flip everything at once.

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Curriculum — three guides, in learning order
01
01 · Foundations

What the Stanford Study FoundFailure rarely lives where you think it does

The strict four-part inclusion bar. 51 deployments × 41 organizations × 9 industries × 7 countries. The 95/5 backdrop. The central thesis: the model wasn't the moat. The "weeks vs years" gap and the 10 findings at a glance.

Inclusion barMethodologyCentral thesis42% interchangeability10 findings7 questions · 10 cards
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02
02 · Applied

The PM PlaybookSix operating habits the high performers shared

77% non-technical pain — plan for it. Workflow redesign (55% vs 20%). Default to escalation-based operating models (71% productivity gain). Triage legal / HR / compliance gatekeepers early. Operational executive sponsorship over strategic approval. Failure-as-precursor (61%).

77% non-techWorkflow redesignEscalation modelGatekeeper triageOperational sponsorship7 questions · 12 cards
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03
03 · Advanced

Operating an AI TransformationRubrics, workforce, revenue, model choice

Strategic integration vs experiment mode (ownership, KPIs, control). Workforce strategy is a deliberate choice — 45% reduced headcount, others redeployed. From cost-savings floor to revenue ceiling. Messy data is no longer a blocker. Where to spend the lock-in budget when the model is interchangeable. AI as organizational transformation problem.

Strategic integrationWorkforce choiceRevenue upsideLock-in budgetTransformation reframe6 questions · 10 cards
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How to use this library.
  1. Read the guides in order — Foundations sets the data, Applied is the operating habits, Advanced is the strategic reframe. Each takes 15–20 minutes.
  2. Drill with flashcards while reading — the deck has a filter for the current guide, so you can lock in the atoms as you go.
  3. Take the full quiz after all three — many questions are scenario judgments ("your VP says X…", "your rollout stalls…") that test whether the principles transfer.
  4. Use the 10-findings table from Guide 01 as your reference card — print it, paste it in your team's AI strategy doc, or keep it open during the next steering review.

Grounded in the original report + 4 cross-validating sources

  1. Pereira, Graylin, Brynjolfsson. The Enterprise AI Playbook: Lessons from 51 Successful Deployments. Stanford Digital Economy Lab, April 2026. (PDF)
  2. Jayaram, A. 10 findings from the Enterprise AI Playbook by Stanford University. Sanctorum, April 2026.
  3. Zhou, Y. A Stanford Study of 51 Successful Enterprise AI Deployments. Medium, April 2026.
  4. Heger, B. The Enterprise AI Playbook: Lessons from 51 Successful Deployments. BrianHeger.com, 2026.
  5. McKinsey. State of AI (workflow-redesign 55% vs 20% figure cited in the playbook).