AMI (AI Mindshare Index) is a composite score for how a brand stands in AI answers: mindshare is stress-tested separately at the brand (L1), category (L2), and scenario (L3) tiers, then weighted into a single 0–100 number you can track over time and compare against peers.
Also known as AI Mindshare Index · Mindshare Index
What one number is for
AI recommendation rate and fact citation rate are each clear enough, but they measure single points. What a brand actually wants to know is something broader: where do I stand in AI’s understanding right now, and is that better or worse than last week?
A scattered set of rates can’t answer that. AMI exists to collapse them into one trackable, comparable number.
Three tiers
AMI doesn’t average every query together. Queries are first sorted by where the user is in their thinking, scored per tier, then weighted.
| Tier | What the query looks like | Where the user is |
|---|---|---|
| L1 Brand | ”How good is X?” “Is X any good?” | Already knows you, verifying |
| L2 Category | ”Which brand is best for this?” “A or B?” | Knows what to buy, choosing who |
| L3 Scenario | ”I have this problem, what should I do?” | Has a need, doesn’t yet know the category |
The point of splitting them is that difficulty runs in the opposite direction to what you’d expect.
The brand tier is easiest: the user named you, so AI will usually say something. Scoring above 90% there is normal and proves little. The scenario tier is hardest: the user never mentioned a category, so AI has to decide what kind of answer fits before deciding who to name. And the scenario tier is where the query volume is, and where new customers come from.
A brand that only scores well at the brand tier gets pulled down by AMI, honestly. That’s the entire point of tiering: don’t let the easy part hide the real gap.
These tiers weren’t invented for the index. Every vertical in our industry guides organizes its real query set by the same L1 / L2 / L3 structure, and the stress-test seeds come straight from there.
How it’s measured
Queries are run anonymously at volume, mentions are counted, results are grouped by tier and weighted. The essentials match how recommendation rate is measured:
- Fixed query set. Once the baseline is set, it doesn’t change. Change it and comparison breaks.
- Anonymous. No identifying signals in the session, so personalization doesn’t contaminate results.
- Multiple engines. Chinese and global platforms both covered; one platform’s noise isn’t the picture.
- Consistent cadence. Same conditions each round. You’re reading a trend, not a snapshot.
Where it stops
AMI is an index we defined and use. It isn’t an industry standard and shouldn’t be treated as an authoritative score. Its value is a consistent, checkable, trackable methodology, not the number looking good.
For the same reason, comparing AMI across categories is meaningless. Query sets differ wildly in difficulty between industries. Two brands in different sectors comparing AMI values are measuring the gap between their categories, not between themselves.
Related: Brand visibility and fact citation rates · What AI answer visibility is · What A2A Marketing is
How does AMI relate to AI recommendation and fact citation rates?
Those two are the raw rates: does AI mention you, and does AI cite your content. AMI sits on top of them. The query set is split into brand, category, and scenario tiers, each tier's mindshare is calculated separately, and the results are weighted into one number. The rates measure specific points; AMI measures overall standing.
Why split into three tiers instead of one overall recommendation rate?
Because the tiers differ enormously in difficulty and value. Brand-tier queries (someone asks about you by name) are easy to score well on; hitting 95% there proves little. Scenario-tier queries, where someone describes a need without knowing which category solves it, are the hard ones and the source of new customers. Blended into a single figure, the easy tier hides the real gap.
What counts as a good AMI score?
As with recommendation rate, there's no universal benchmark. Category competitiveness, query-set definition, and market maturity all move the absolute number. The useful approach is to set a baseline, then watch your own week-over-week movement and your relative position against peers on the same query set. Anyone promising a guaranteed score is overselling.
Is this an industry standard?
No. AMI is an index Daimonia defined and uses. We publish how it's composed and measured so clients can see where the number comes from and check it, not to make it look authoritative.