The methodology is the product.
Anyone can list AI use cases. The value is not the list, it is the lens. Three disciplines no aggregator imposes on itself.
1. A public evidence level on every case
About 90% of the "AI use cases" in circulation come from the players selling the tech. We grade every case on an explicit scale, and we display it:
- Proof A Financial results, earnings call, court ruling: the hardest proof.
- Proof B Quantified platform/vendor case study: useful but biased, capped at B.
- Proof C Major press naming the brand.
- Proof D Statement at a conference.
Several concordant sources raise the level. Every figure links back to its source, dated and archived.
2. Verified live, at a date
Brands announce loudly and bury quietly. Every case carries a liveness status and a verification date. A case that dies does not disappear: it goes to the graveyard.
3. Mapped to the customer journey
Everyone else sorts by industry and tech. We cross industry × growth lever (acquisition, conversion, retention, monetization) × evidence, so the coverage matrix makes the blind spots visible, where no one invests yet.
Facts vs inference. What is sourced and what is our analysis ("how to replicate", typical-approach diagrams) are always visually separated. A single claim presented as a fact that is not one, and the whole index loses its value. Credibility is the product.