refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-management-consulting
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title: Management Consulting
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [경영 컨설팅, Strategy Consulting, Mgmt Consulting]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [education, consulting, strategy, business]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: english
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framework: business
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---
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# Management Consulting
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## 매 한 줄
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> **"매 management consulting 은 hypothesis-driven problem solving as a service"**. McKinsey/BCG/Bain (MBB) 의 1960s codification — pyramid principle, MECE, issue tree, hypothesis-driven 의 four pillars. 매 2026 modern state: AI augmentation (Claude Opus 4.7, GPT-5) 으로 research/synthesis 의 80% acceleration, but human judgment + executive trust 가 still core.
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## 매 핵심
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### 매 four pillars
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- **Pyramid principle (Minto)**: top answer first, supporting reasons next, evidence below.
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- **MECE**: Mutually Exclusive, Collectively Exhaustive — partition framework.
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- **Issue tree**: top question → sub-questions, recursively.
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- **Hypothesis-driven**: form answer first, test against data — not bottom-up boil-the-ocean.
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### 매 typical engagement structure
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- Week 1-2: scoping, interviews, hypothesis tree.
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- Week 3-6: data gathering, model building, expert calls.
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- Week 7-9: synthesis, slide drafting, partner reviews.
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- Week 10-12: client workshops, final readout, implementation roadmap.
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### 매 modern (2026) augmentation
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- **AI research**: Claude/GPT for industry primers, expert call prep, public filings synthesis.
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- **AI modeling**: code-interpreter for forecasts, sensitivity tables.
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- **AI slide drafting**: rough layout from issue tree + key numbers; human polish.
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- **Still human**: client relationship, executive trust, judgment under ambiguity, internal politics navigation.
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### 매 firm tiers
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1. **MBB**: McKinsey, BCG, Bain.
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2. **Tier 2**: Strategy&, Oliver Wyman, LEK, Roland Berger, Kearney.
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3. **Big 4 strategy**: Deloitte Monitor, EY-Parthenon, PwC Strategy&, KPMG.
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4. **Boutique**: Veritas, Putnam, Analysis Group (specialized).
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## 💻 패턴
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### Issue tree as data
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```typescript
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interface IssueNode {
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question: string;
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hypothesis?: string;
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children: IssueNode[];
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evidence: Evidence[];
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status: "open" | "supported" | "refuted";
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}
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function leaves(node: IssueNode): IssueNode[] {
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return node.children.length === 0 ? [node] : node.children.flatMap(leaves);
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}
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```
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### MECE check
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```typescript
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function isMECE<T>(partition: T[][], universe: Set<T>): { mutually: boolean; exhaustive: boolean } {
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const flat = partition.flat();
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const mutually = flat.length === new Set(flat).size;
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const exhaustive = [...universe].every((x) => flat.includes(x));
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return { mutually, exhaustive };
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}
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```
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### Pyramid principle slide skeleton
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```markdown
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# [Action title: the answer in one sentence]
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- Reason 1: [supporting argument]
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- Evidence A
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- Evidence B
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- Reason 2: [supporting argument]
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- Reason 3: [supporting argument]
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```
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### Profitability tree (canonical)
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```
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Profit
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├── Revenue
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│ ├── Volume × Price
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│ │ ├── Market size × Share
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│ │ └── Mix × Discount
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└── Cost
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├── COGS (variable)
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└── SG&A (fixed)
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```
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### Expert call synthesis prompt (Claude Opus 4.7)
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```typescript
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const prompt = `
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You are a research analyst. Given these 5 expert call transcripts on [TOPIC],
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extract:
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1. Areas of consensus (≥3 experts agree)
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2. Areas of disagreement
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3. Quantitative anchors (market size, growth, margin)
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4. Open questions for further research
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Output as MECE bullets, max 300 words.
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`;
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```
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### 2x2 framework template
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```markdown
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| | High Impact | Low Impact |
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|--------------|-------------|------------|
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| Easy to do | DO NOW | Quick wins |
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| Hard to do | Strategic | DROP |
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| C-suite strategy refresh | MBB or Tier 2 strategy boutique |
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| Operational turnaround | Big 4 + ops specialists (AlixPartners) |
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| M&A due diligence | Bain (PE focus), strategy boutiques |
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| Digital/AI transformation | McKinsey QuantumBlack, BCG X, Bain Vector |
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| In-house build | Hire ex-consultant + AI tooling |
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**기본값**: Hypothesis-driven + issue tree + MBB-style synthesis. AI augmentation for research/modeling. Human for trust/judgment.
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## 🔗 Graph
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- 부모: [[Business Strategy]]
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- 변형: [[Strategy Consulting]]
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- 응용: [[Pyramid Principle]] · [[Issue Tree]]
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## 🤖 LLM 활용
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**언제**: industry primer, expert call prep, slide drafting, financial modeling, synthesis.
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**언제 X**: client relationship building, executive trust, internal politics, judgment calls under deep ambiguity.
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## ❌ 안티패턴
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- **Boil the ocean**: hypothesis 없이 모든 data 모음 → time/budget overrun.
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- **Pretty slides, weak answer**: aesthetics > insight 의 trap.
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- **Recommendation without data**: "we believe" without grounding.
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- **AI hallucination unchecked**: AI 의 fabricated stats 의 client-facing slide 의 disaster.
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## 🧪 검증 / 중복
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- Verified (Minto's Pyramid Principle, McKinsey/BCG/Bain public materials, 2026 industry observation).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — FULL spec rewrite with 2026 AI augmentation context |
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