85 lines
3.8 KiB
Markdown
85 lines
3.8 KiB
Markdown
---
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name: moatmri
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description: Analyze AI disruption pressure across a business, map competitive exposure, and produce a 90-day defensive action plan.
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risk: safe
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source: community
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date_added: "2026-05-31"
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---
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# MoatMRI — AI Disruption Pressure Analysis
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*Where does intelligence pressure break this system first?*
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## When to Use This Skill
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- "Is my business at risk from AI? Where am I most exposed?"
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- "How would an AI-native startup take over my market?"
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- "What should I do in the next 90 days to defend against AI disruption?"
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- "I'm doing due diligence on [company] — what's their AI displacement risk?"
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- "Where does my competitive moat actually hold against AI pressure?"
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## How It Works
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### Step 1 — Gather Inputs
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Ask if not provided:
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- **Industry** (e.g., "real estate", "community banking", "retail pharmacy", "law firm")
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- **Entity type** (e.g., "independent broker", "solo practitioner", "regional franchise")
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- **Target name** (optional — specific organization for named analysis)
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## Limitations
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- Produces strategic risk analysis, not audited market research or investment advice.
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- Depends on current company, market, regulatory, and competitive context supplied by the user or gathered from reliable sources.
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- Treats disruption scenarios as planning tools; scores should be revisited as new evidence appears.
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### Step 2 — 10-Vector Pressure Map
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Score AI disruption pressure across exactly these 10 vectors (0–10):
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| # | Vector | What to Measure |
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|---|--------|----------------|
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| 1 | **labor_substitution** | Which roles/functions are directly automatable |
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| 2 | **customer_interface** | How AI changes how customers reach this entity |
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| 3 | **knowledge_commoditization** | Does AI commoditize the expertise this entity sells |
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| 4 | **pricing_pressure** | Does AI enable lower-cost competitors to undercut |
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| 5 | **supply_chain_automation** | Does AI change input costs or supplier relationships |
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| 6 | **data_moat** | Does this entity have proprietary data AI can't replicate |
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| 7 | **trust_relationship_moat** | How much does customer loyalty protect against displacement |
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| 8 | **distribution_channel_disruption** | Does AI create new channels that bypass this entity |
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| 9 | **regulatory_compliance_exposure** | Does AI alter the regulatory or liability landscape |
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| 10 | **decision_speed_gap** | Does AI accelerate decisions in ways that disadvantage this entity |
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For each vector produce: **score**, **headline**, **near_term** (12 months), **far_term** (3 years).
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**Aggregate risk score:** mean of all 10 vectors. Flag any vector ≥ 7 as critical.
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### Step 3 — AI Front-Door Takeover Storyboard
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6-step narrative of how an AI-native competitor displaces this entity:
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1. The entry point
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2. The wedge (first 10% of market)
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3. The acceleration (what makes it compound)
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4. The tipping point (when incumbent can't recover)
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5. The aftermath
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6. The survivor profile
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### Step 4 — 90-Day Counterstrike Plan
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- **Track A (Days 0–30):** Immediate defense — what to stop, what to protect
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- **Track B (Days 31–60):** Intelligence-layer build — data/relationships to fortify
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- **Track C (Days 61–90):** Offensive positioning — use AI pressure as competitive weapon
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## Best Practices
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- ✅ Score all 10 vectors before calculating aggregate — resist stopping at obvious ones
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- ✅ Keep the storyboard specific to industry/entity, not generic disruption narrative
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- ✅ Track C should be actionable within 90 days, not aspirational 3-year strategy
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- ❌ Don't conflate data_moat with trust_relationship_moat — they protect differently
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## Additional Resources
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- Repository: [thebrierfox/moatmri-skill](https://github.com/thebrierfox/moatmri-skill)
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- Full BYOK tool: [ace-license-server-production.up.railway.app/byok/moatmri](https://ace-license-server-production.up.railway.app/byok/moatmri)
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- Built by [IntuiTek¹](https://intuitek.ai) (~K¹) — MIT License
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