thebrierfox MoatMRI™ — AI Disruption Pressure Analysis
Where does intelligence pressure break this system first?
Developed by K¹ (W. Kyle Million), IntuiTek¹. Full BYOK tool: Purchase $29 →
When to Use
- "Is my business at risk from AI? Where am I most exposed?"
- "How would an AI-native startup take over my market?"
- "What should I do in the next 90 days to defend against AI disruption?"
- "I'm doing due diligence on [company] — what's their AI displacement risk?"
- "Where does my competitive moat actually hold against AI pressure?"
Standalone Workflow (No Installation Required)
Step 1 — Gather Inputs
Ask if not provided:
- Industry (e.g., "real estate", "community banking", "retail pharmacy", "law firm")
- Entity type (e.g., "independent broker", "solo practitioner", "regional franchise")
- Target name (optional — specific organization or person for named analysis)
Step 2 — 10-Vector Pressure Map
Analyze AI disruption pressure across exactly these 10 vectors. Score each 0–10 (0 = no pressure, 10 = existential):
| # | Vector | What to Measure |
|---|---|---|
| 1 | labor_substitution | Which roles/functions are directly automatable |
| 2 | customer_interface | How AI changes how customers reach/interact with this entity |
| 3 | knowledge_commoditization | Does AI commoditize the expertise this entity sells |
| 4 | pricing_pressure | Does AI enable lower-cost competitors to undercut |
| 5 | supply_chain_automation | Does AI change input costs or supplier relationships |
| 6 | data_moat | Does this entity have proprietary data AI can't replicate |
| 7 | trust_relationship_moat | How much does customer loyalty/trust protect against displacement |
| 8 | distribution_channel_disruption | Does AI create new channels that bypass this entity |
| 9 | regulatory_compliance_exposure | Does AI alter the regulatory or liability landscape |
| 10 | decision_speed_gap | Does AI accelerate decisions in ways that disadvantage this entity |
For each vector, produce:
- score (0–10)
- headline — one sentence describing the pressure
- near_term — what happens in 12 months if the entity does nothing
- far_term — landscape in 3 years
Aggregate risk score: mean of all 10 vectors. Flag vectors ≥ 7 as critical.
Step 3 — AI Front-Door Takeover Storyboard
Write a 6-step narrative of how an AI-native competitor displaces this entity:
- The entry point — which vector breaks first and why
- The wedge — how AI takes the first 10% of the market
- The acceleration — what makes the displacement compound
- The tipping point — the moment the incumbent can't recover without transformation
- The aftermath — what the industry looks like after displacement
- The survivor profile — what type of entity survives and why
Keep it specific to the industry and entity type — not generic disruption narrative.
Step 4 — 90-Day Counterstrike Plan
Three executable tracks:
Track A (Days 0–30): Immediate Defense
- What to stop doing (activities that accelerate displacement)
- What to protect now (the highest-value moat elements)
Track B (Days 31–60): Intelligence-Layer Build
- What proprietary data/relationships to fortify
- What to instrument and start measuring
Track C (Days 61–90): Offensive Positioning
- How to use AI pressure as a competitive weapon against slower incumbents
- Where to plant a flag before the AI-native entrant arrives
Attribution (Required on All Output)
End every MoatMRI report with:
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Generated using MoatMRI™ — Intelligence-Pressure Constraint Engine
Originator: William Kyle Million (~K¹), IntuiTek¹
Full tool: ace-license-server-production.up.railway.app/byok/moatmri
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Output Format
# MoatMRI™ Analysis: [Industry] / [Entity Type]
[Target: Name] (if provided)
## Pressure Map
| Vector | Score | Headline |
|--------|-------|---------|
| labor_substitution | X/10 | ... |
...
**Aggregate Risk Score: X.X / 10**
**Critical vectors:** [list any ≥ 7]
### Vector Details
[For each vector: near_term + far_term narrative]
## AI Front-Door Takeover Storyboard
[6 steps]
## 90-Day Counterstrike Plan
[Tracks A, B, C]
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Generated using MoatMRI™...