Domain II: Identify Business Needs and Solutions — Study Game
Refresh-only. Per locked decision in CLAUDE.md, Domain II is a pointer game. Use this alongside you's existing source/CPMAI_Phase_I_Study_Guide.md Knowledge Check section.
How to Play
Pick a game mode and test yourself. Cover answers and try to recall before peeking.
GAME MODE 1: Rapid Fire Flashcards
Foundational Concepts (refresher)
Card 1 — Front: Three Ps of Intelligence?Answer: Perception, Prediction, Planning. Continuous loop.Card 2 — Front: Probabilistic vs Deterministic?
Answer: Probabilistic = variable outcomes (use AI). Deterministic = same input → same output (use traditional code).Card 3 — Front: DIKUW Pyramid?
Answer: Data → Information → Knowledge → Understanding → Wisdom. AI sweet spot = Knowledge.Card 4 — Front: Seven Patterns of AI mnemonic?
Answer: CR-PG-HAP — Conversational, Recognition, Predictive, Goal-driven, Hyper-personalization, Autonomous, Patterns/anomalies.Card 5 — Front: Go/No-Go Traffic Light Framework?
Answer: BDT — Business, Data, Technology — 3 lights each. Any RED = STOP.
ECO Tasks (II.1-II.10)
Card 6 — Front: What's the most cross-pulled Domain II artifact?Answer: II.8 success criteria — flows into III.7, III.8, IV.2, IV.6, V.4, V.5.Card 7 — Front: Domain II ECO task that involves persona definition?
Answer: II.1 — Identify problem to be solved (e.g., needs, persona).Card 8 — Front: Difference between Pilot and PoC?
Answer: Pilot = validate end-to-end value in real-world. PoC = prove specific technical capability is feasible.
AI Project Quality
Card 9 — Front: AI vs Automation rule?Answer: "Same way every time = Automation. Learns from data = AI."Card 10 — Front: Five GenAI Apps mnemonic?
Answer: CHEDVA — Content gen, Human augmentation, Efficient design, Data augmentation, Virtual avatars, Augment existing.Card 11 — Front: What's the AI sweet spot in DIKUW?
Answer: Knowledge layer (between Information and Understanding).Card 12 — Front: Should AI ROI include Trustworthy AI cost?
Answer: Yes — audit, bias monitoring, governance, contestability all have cost. Include in II.5/II.9.
GAME MODE 2: Scenario Showdown — What Should the PM Do?
Scenario 1: The Vague Goal
- Business unit asks for AI to "improve customer experience"
Reveal
Engage stakeholders to define specific problem (II.1) — need, persona, business outcome — before any solution is drafted.Scenario 2: The Vague Success Criteria
- Stakeholders define success as "improve customer satisfaction"
- No measurement specified
Reveal
Facilitate stakeholder engagement to define measurable, time-bound, business-tied criteria (II.8). Vague criteria fail downstream gates.Scenario 3: The Wrong Pattern
- Phase II reveals available data is structured tabular
- Phase I selected Recognition pattern
Reveal
Convene stakeholder review to revisit pattern selection (II.7). Pattern affects every downstream domain. Document revision.Scenario 4: The Automation Disguise
- Mid-Phase II, team realizes a rule-based automation would solve the problem
- Project funded as AI
Reveal
Loop back to II.1 to revisit problem definition. CPMAI iterative. Engage stakeholders. Don't continue with mismatched solution.Scenario 5: The Adoption Risk
- Stakeholders raise concerns users may resist AI-driven decisions
Reveal
Treat as II.6 adoption risk. Develop strategy: training, change mgmt, disclosure (I.2), feedback, contestability path. Document mitigation plan.GAME MODE 3: Pattern Match Challenge
| # | Scenario | ECO Task |
|---|---|---|
| 1 | Identifying the problem (need, persona) | II.1 |
| 2 | Evaluating AI feasibility (BDT framework) | II.2 |
| 3 | Conducting risk assessment | II.3 |
| 4 | Developing project scope statement | II.4 |
| 5 | Determining ROI (incl. Trustworthy AI cost) | II.5 |
| 6 | Managing adoption risks | II.6 |
| 7 | Drafting AI solution (pattern + persona) | II.7 |
| 8 | Defining success criteria (KPIs/metrics) | II.8 |
| 9 | Supporting business case creation | II.9 |
| 10 | Identifying project resources (people/hardware/contractors) | II.10 |
GAME MODE 4: Fill-in-the-Blank Speed Round
- Three Ps: Perception, Prediction, ________.
- AI sweet spot in DIKUW = ________.
- Seven Patterns mnemonic: ________ (CR-PG-HAP).
- BDT Go/No-Go: Business, Data, ________.
- Any ________ light = STOP.
- II.8 ________ ________ flows into III.7, III.8, IV.2, IV.6, V.4, V.5.
- ROI for AI projects must include ________ ________ cost.
- PoC vs Pilot: ________ proves technical feasibility; ________ validates end-to-end value.
- AI vs Automation: same way every time = ________.
- Probabilistic vs deterministic — AI fits ________ problems.
Reveal answers
- Planning
- Knowledge
- Conversational, Recognition, Predictive, Goal-driven, Hyper-personalization, Autonomous, Patterns/anomalies
- Technology
- RED
- success criteria
- Trustworthy AI
- PoC / Pilot
- Automation
- probabilistic
GAME MODE 5: True or False Lightning Round
| # | Statement | Correct |
|---|---|---|
| 1 | Phase I is all about defining the problem before solutions | TRUE |
| 2 | Persona definition belongs in II.1 | TRUE |
| 3 | Vague success criteria are fine if the team understands the intent | FALSE — fails downstream gates |
| 4 | Any RED light in BDT means proceed with caution | FALSE — STOP |
| 5 | AI is appropriate for predictable repetitive problems | FALSE — that's automation |
| 6 | II.8 success criteria flow into Domain V production monitoring | TRUE |
| 7 | Trustworthy AI cost is too small to include in ROI | FALSE — material |
| 8 | Adoption risk is solely the marketing team's concern | FALSE — multi-functional |
| 9 | A PoC without a follow-up plan is just a science project | TRUE |
| 10 | The PM Oversight Angle pattern doesn't apply to Domain II | FALSE — same oversight verbs |
GAME MODE 6: Mnemonic Speed Recall
| Mnemonic | Expand it |
|---|---|
| PPP Loop | Perceive, Predict, Plan (continuous loop of intelligence) |
| DIKUW | Data, Information, Knowledge, Understanding, Wisdom — AI sweet spot at Knowledge |
| CR-PG-HAP | 7 AI Patterns: Conversational, Recognition, Predictive, Goal-driven, Hyper-personalization, Autonomous, Patterns/anomalies |
| BDT | Business, Data, Technology (Go/No-Go Traffic Light) |
| CHEDVA | 6 GenAI Apps: Content gen, Human augmentation, Efficient design, Data augmentation, Virtual avatars, Augment existing |
| HIIPP | 5 GenAI Risks: Hallucination, IP, Inappropriate, Prompt injection, Private data |
| 3 Phase I Questions | WHY AI? HOW meet needs? HOW succeed? |
| 4D Tasks | Dull, Dirty, Dangerous, Dear (expensive) — ideal for AI |
Scoring Summary
| Mode | Score | Max |
|---|---|---|
| Flashcards | ___/12 | 12 |
| Scenarios | ___/5 | 5 |
| Pattern Match | ___/10 | 10 |
| Fill-in | ___/10 | 10 |
| True/False | ___/10 | 10 |
| Mnemonic | ___/8 | 8 |
| TOTAL | ___/55 | 55 |