Assess AI disruption risk across 10 competitive vectors

MoatMRI analyzes AI disruption pressure across a business, maps its competitive exposure, and produces a 90-day defensive action plan. Its guiding question is where intelligence pressure breaks this system first. The output is strategic risk analysis built in three parts: a pressure map, a takeover storyboard, and a counterstrike plan.

The workflow starts by gathering inputs, asking the user for the industry (for example real estate, community banking, retail pharmacy, or a law firm), the entity type (for example an independent broker, a solo practitioner, or a regional franchise), and optionally a target name for a named analysis.

Step 2 is a 10-vector pressure map. The assistant scores AI disruption pressure on exactly these ten vectors, each from 0 to 10:

1. labor_substitution: which roles or functions are directly automatable.
2. customer_interface: how AI changes how customers reach this entity.
3. knowledge_commoditization: whether AI commoditizes the expertise the entity sells.
4. pricing_pressure: whether AI lets lower-cost competitors undercut.
5. supply_chain_automation: whether AI changes input costs or supplier relationships.
6. data_moat: whether the entity holds proprietary data that AI cannot replicate.
7. trust_relationship_moat: how much customer loyalty protects against displacement.
8. distribution_channel_disruption: whether AI creates new channels that bypass the entity.
9. regulatory_compliance_exposure: whether AI alters the regulatory or liability landscape.
10. decision_speed_gap: whether AI accelerates decisions in ways that disadvantage the entity.

For each vector it produces a score, a headline, a near-term view over 12 months, and a far-term view over 3 years. The aggregate risk score is the mean of all ten vectors, and any vector at 7 or above is flagged as critical.

Step 3 is an AI front-door takeover storyboard, a six-step narrative of how an AI-native competitor displaces the entity: the entry point, the wedge (the first 10 percent of the market), the acceleration that makes it compound, the tipping point when the incumbent cannot recover, the aftermath, and the survivor profile.

Step 4 is a 90-day counterstrike plan in three tracks: days 0 to 30 for immediate defense (what to stop and what to protect), days 31 to 60 for building the intelligence layer (data and relationships to fortify), and days 61 to 90 for offensive positioning that uses AI pressure as a competitive weapon.

Use it for questions like: Is my business at risk from AI, and 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? What is the AI displacement risk of a company I am doing due diligence on? Where does my competitive moat actually hold?

Do not treat it as audited market research or investment advice. It depends on current company, market, regulatory, and competitive context supplied by the user or gathered from reliable sources. Disruption scenarios are planning tools, and the scores should be revisited as new evidence appears.

The inputs are industry, entity type, and an optional target name. The outputs are the scored ten-vector map with an aggregate score, the storyboard, and the three-track plan. An example request is to assess where a business is most exposed to AI disruption and produce a prioritized 90-day defensive plan.

Best practices: score all ten vectors before calculating the aggregate rather than stopping at the obvious ones, keep the storyboard specific to the industry and entity rather than a generic disruption story, make Track C actionable within 90 days rather than an aspirational three-year strategy, and do not conflate data moat with trust relationship moat, because they protect differently.

It names a public skill repository and a full bring-your-own-key tool hosted by the author. The skill was built by IntuiTek and is MIT licensed. Beyond that it has no runtime integrations.

It is for founders, executives, strategy teams, advisors, and investors who need a structured view of AI exposure, whether for their own business, a competitor, or a due diligence target.