FrancoStino MoatMRI — AI Disruption Pressure Analysis
Where does intelligence pressure break this system first?
When to Use This Skill
- "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?"
How It Works
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 for named analysis)
Example
User request:
Assess where this business is most exposed to AI disruption and produce a prioritized 90-day defensive plan.
Limitations
- Produces strategic risk analysis, not audited market research or investment advice.
- Depends on current company, market, regulatory, and competitive context supplied by the user or gathered from reliable sources.
- Treats disruption scenarios as planning tools; scores should be revisited as new evidence appears.
Step 2 — 10-Vector Pressure Map
Score AI disruption pressure across exactly these 10 vectors (0–10):
| # | Vector | What to Measure |
|---|---|---|
| 1 | labor_substitution | Which roles/functions are directly automatable |
| 2 | customer_interface | How AI changes how customers reach 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 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, headline, near_term (12 months), far_term (3 years).
Aggregate risk score: mean of all 10 vectors. Flag any vector ≥ 7 as critical.
Step 3 — AI Front-Door Takeover Storyboard
6-step narrative of how an AI-native competitor displaces this entity:
- The entry point
- The wedge (first 10% of market)
- The acceleration (what makes it compound)
- The tipping point (when incumbent can't recover)
- The aftermath
- The survivor profile
Step 4 — 90-Day Counterstrike Plan
- Track A (Days 0–30): Immediate defense — what to stop, what to protect
- Track B (Days 31–60): Intelligence-layer build — data/relationships to fortify
- Track C (Days 61–90): Offensive positioning — use AI pressure as competitive weapon
Best Practices
- ✅ Score all 10 vectors before calculating aggregate — resist stopping at obvious ones
- ✅ Keep the storyboard specific to industry/entity, not generic disruption narrative
- ✅ Track C should be actionable within 90 days, not aspirational 3-year strategy
- ❌ Don't conflate data_moat with trust_relationship_moat — they protect differently
Additional Resources
- Repository: thebrierfox/moatmri-skill
- Full BYOK tool: ace-license-server-production.up.railway.app/byok/moatmri
- Built by IntuiTek¹ (~K¹) — MIT License