Case Study Article – GFE Content Template
Intent Type: CASE STUDY
Purpose: Present a high-authority, research-backed, operationally grounded transformation story analyzing the situation through the 10 Laws of GFE and the AAA methodology.
Use with: .agent/workflows/create-blog-post.md — fill the brief + research table there before drafting.
Case study content may come from:
- Real GFE consulting work (anonymized or public)
- Global AI transformation cases
- Academic / industry research cases
- Composite cases based on repeated patterns
- Classical management consulting cases (NEW)
BRIEF BLOCK (MANDATORY BEFORE WRITING)
Case Type:
- GFE Client
- Global AI Transformation
- Academic Research Case
- Composite Case
- Classical Consulting Case (NEW)
Organization Name / Level of Anonymity:
Industry:
Geography:
Team Size:
Business Model: (SaaS / Fintech / Manufacturing / Services, etc.)
Primary Problem: (1 crisp sentence)
Secondary Problem: (optional)
GFE Canon Laws Demonstrated: (Pick 2–4)
Frameworks Applied:
AAA, ValueLogs, LEO, Leadership Clock, Flow Mesh, IRI, ValuationOps, StoryOpsKey Business Outcome:
SEO/AEO Keyword Cluster:
Target Persona: CEO / COO / CRO / CFO / Head of Growth
RESEARCH REQUIREMENTS (ZERO HALLUCINATION)
Every case study must be backed by verifiable sources.
Minimum Inputs
1. One academic or management source
Examples:
HBR, MIT Sloan, McKinsey Digital, BCG, Deloitte, Gartner, Forrester, Accenture, IBM AI Index.
2. One industry report
Examples:
Salesforce State of Sales, HubSpot Trends Report, PwC Future of Work, World Economic Forum studies.
3. Case data
- If GFE project: use internal documents only
- If global example: cite public numbers
- If academic: cite the original case
- If composite: label clearly, do not fabricate data
4. Internal GFE Canon source
LEO, AAA, Flow Mesh, IRI, ValuationOps, 24-Hour Leadership Clock, ValueLogs.
Research Table (MANDATORY BEFORE WRITING)
| # | Source Type | Citation | Key Insight | How It Supports the Case |
|---|---|---|---|---|
| 1 | Academic/Mgmt | |||
| 2 | Industry Report | |||
| 3 | Case Data | |||
| 4 | GFE Canon |
CRITICAL: Verify all URLs work before proceeding to writing.
- MANDATORY: Open each URL in a browser to confirm it returns 200 OK (not 404)
- MANDATORY: Test every single URL - no exceptions
- VALIDATION CHECKPOINT: All URLs verified and working.
Anti-Hallucination Rules
- No fictional numbers
- No invented quotes
- No imaginary companies
- If uncertain, describe qualitatively
- Always anchor claims in Canon + research table
- AI agents may only use text from the research table
1A. GFE CANON LAWS REFERENCE (VALIDATION)
CRITICAL: The GFE Canon has EXACTLY 10 laws. There is NO Law 11.
- Law 1 — Time Is the First Balance Sheet (Leadership Clock)
- Law 2 — ValueLogs Are the Atomic Unit of Execution (ValueLogs)
- Law 3 — Proof Beats Perception (Proof of Activity)
- Law 4 — LEO Determines Performance (Learning, Earning, Org-Building)
- Law 5 — Friction Is the Enemy. Flows Are the Strategy. (Flow Mapping)
- Law 6 — Audit First. Align Second. Automate Last. (AAA Framework)
- Law 7 — Processes Must Map to KPIs Must Map to Valuation (ValuationOps)
- Law 8 — Reduce Internal Risk, Reduce WACC, Increase Enterprise Value (IRI - includes tool debt)
- Law 9 — Bounce-Back Time Determines Resilience (BBT)
- Law 10 — Story Drives Valuation (StoryOps)
Notes:
- Tool Debt is part of Law 8 (IRI), not a separate law
- Verify law numbers against
docs/en/gfe-canon.mdif unsure
ARTICLE STRUCTURE (1,600–3,500 words)
This is the structure that ALL case studies follow.
Executive Summary (200–300 words)
Summarize:
- Who the company is
- What problem existed
- What made the problem painful
- Why previous solutions failed
- How GFE Canon reframed the issue
- What the AAA transformation achieved
- What changed in operations
- What changed in valuation
This is the “HBS-style case abstract.”
Context (200–350 words)
Explain the company’s world:
- Industry trends
- Competitive pressures
- Internal scale stage
- Technology stack
- Operational complexity
- Revenue model
- Org structure
- Legacy decisions and patterns
- Why the moment was critical for transformation
The goal is to situate the reader in the organization’s reality.
The Core Problem (Classical Consulting Problem Framing) (250–400 words)
Define the problem using management consulting language:
- Revenue declining?
- CAC rising?
- Employee throughput collapsing?
- Forecasting breaking down?
- Tool sprawl causing chaos?
- AI adoption failing?
- Valuation under pressure?
Use a Problem Pyramid:
- Symptom
- Sub-symptom
- Root friction
- Canon violation
- Business impact
Example:
“Forecasting was unstable because SalesOps data was fragmented. Data was fragmented because processes mapped poorly to ValueLogs. Processes were weak because no Flow Mesh existed. This violated Law 5 (Friction vs Flow) and Law 7 (Tasks → KPIs → Valuation).”
Diagnostic Breakdown (GFE X-ray Mode) (300–500 words)
Explain why the problem existed using GFE frameworks:
ValueLogs
Show missing logs, poor proof, fake reporting, or wasted cycles.
LEO distribution
Where was the organization over-indexed? Under-indexed?
Flow Mesh
Which processes were broken?
Which handoffs had friction?
Leadership Clock
Where was the CEO or CXO leaking time?
Executive BBT? Decision lag?
IRI
How did internal risk show up?
Variance? Dependency chains? Tool debt?
KPI mapping
Which tasks did not map to processes?
Which processes did not map to KPIs?
Which KPIs did not map to valuation?
This section must feel like a McKinsey diagnostic + GFE system unification.
Hypothesis Tree (ONLY for Classical Consulting Cases) (250–400 words)
If the case is a “consulting case,” include a hypothesis tree.
Example:
Primary Hypothesis
Revenue is declining because Flow Mesh between marketing and sales is broken.
Supporting Hypotheses
H1: Lead qualification is inconsistent
H2: Handoffs lack proof-of-activity
H3: KPIs are in conflict
H4: Forecasting is distorted by missing ValueLogs
Each hypothesis must be tied to:
- A Canon Law
- A part of AAA
- A valuation lever (Revenue → EBITDA → CF)
This section trains AI agents + interns to think like McKinsey analysts.
The AAA Transformation (600–900 words)
This is the operational heart of the case study.
Step 1: Audit
Describe:
- Data gathered
- ValueLogs review
- Flow Mesh mapping
- Leadership Clock insights
- Tool analysis
- IRI baseline
- Skill Tuple
- LEO distribution
Step 2: Align
Describe:
- Process rewrites
- KPI definitions
- Org-Building interventions
- Meeting architecture
- Decision rights
- Tool consolidation
- New Flow Mesh
- Role clarity
Step 3: Automate
Describe:
- What got automated (with proof)
- Where AI agents were introduced
- Guardrails
- SOP changes
- Flow automation
- Reporting loops
- Feedback loops
This section shows “how transformation actually happened.”
Outcomes (Qualitative + Quantitative) (250–350 words)
Show measurable improvements:
- Throughput
- Forecast accuracy
- Cycle time reduction
- Time recaptured by leadership
- Process stability
- KPI clarity
- Automation leverage
- WACC impact (qual or quant)
- IRI reduction
If numbers exist, cite them.
If not, describe the direction of change.
ValuationOps Impact (250–350 words)
This is the signature GFE section.
Explain how the transformation affected:
RevenueOps
Lead flow, speed to lead, conversion.
EBITDAOps
Costs reduced through friction removal.
CashFlowOps
Predictability of inflows vs outflows.
FCF Ops
Smoother processes = lower volatility.
StoryOps
Market narrative now aligned with internal truth.
ValuationOps
Better story + lower WACC + higher predictability = enterprise value uplift.
Tie everything back to the canonical chain:
Task → Process → KPI → Valuation Lever → WACC → EV
StoryOps: How the Narrative Changed (150–250 words)
Explain:
- Investor confidence
- Leadership credibility
- Employee morale
- Market perception
- Internal identity (“We are a flow-driven org”)
Narrative shifts matter for valuation.
Lessons Learned (150–250 words)
List 5–7 insights with Canon Law mapping.
Example:
- Most friction is invisible until ValueLogs expose it. (Law 2)
- Automating early multiplies chaos. (Law 6)
- Flow must outpace growth. (Law 5)
- KPI clarity is the foundation of valuation. (Law 7)
- Reduced IRI lowers WACC. (Law 8)
What Any Company Can Learn (150–250 words)
Zoom out.
Make the case universal.
Use a structure:
- If you have similar symptoms
- If your team struggles with X
- If AI adoption keeps failing
- If forecasting is broken
Then this case offers the following insights…
How to Apply This Today (100–150 words)
Soft CTA pointing to:
- Diagnostic article
- Growth Team Audit
- ValuationOps analysis
- LEO or ValueLogs guide
- AAA explainer
AEO REQUIREMENTS
- Section 1 must contain the full story
- Use explicit headers
- Use bullets generously
- Each section must stand alone
- Add optional FAQs if required
- Repeat Canon terms for visibility
- Keep paragraphs tight
- Add subheaders for every AAA step
WRITING STYLE REQUIREMENTS
- Clean
- Analytical
- UX-first
- No fluff
- Slight wit
- No generic AI jargon
- Canon vocabulary used consistently
- Strategic but operational
- Every claim must tie back to the Canon
FINAL QA CHECKLIST
- [ ] Research table 100 percent complete
- [ ] Zero hallucination
- [ ] All facts verifiable
- [ ] Classical consulting structure used where needed
- [ ] AAA applied
- [ ] ValueLogs explained
- [ ] IRI + WACC linkage present
- [ ] ValuationOps explained
- [ ] StoryOps included
- [ ] Lessons map to Canon Laws
- [ ] Tone aligned to GFE brand
- [ ] Article works even if only Sections 1, 4, 6, and 8 are read
- [ ] Images: Created using GFE Master Prompt style
- [ ] Links: Internal links use
gfe-links.tsmap
End of Template