Your share of the answers AI gives.

Recommendation share, not rankings.

AI Market Share is the percentage of relevant purchase decisions in which a company appears as an option recommended by AI agents. It answers a competitive question SEO never could: when a shopper asks an assistant what to buy, how often are you the answer?

A single brand can hold, at the same time (illustrative):

— and that mix tells you something very different about its competitive position.

Being found vs. being chosen.

SEO measures whether you can be found. AI recommendation share measures whether you’re named and chosen when the assistant gives its answer. One is presence in an index; the other is being the recommendation. Different question — different moat.

Why does the assistant recommend it?

Measuring is only the start. The gold is discovering which signals make a product the one AI recommends. Take a product the assistants keep naming for a real shopping need — it tends to share a pattern of signals:

• detailed product pages — materials, sizing, care and real specifics
• independent reviews and editorial “best of” inclusion
• comparison and how-to content for the buying need
• clear, structured product data a model can parse
• third-party mentions across independent sites — press, forums, video, communities

The question then becomes measurable: which signals correlate with the recommendation? That is a new discipline — and a defensible one.

The Evidence Graph.

Google’s edge wasn’t more content — it was understanding the relationships between documents: PageRank. The AI shelf may have an equivalent, and it probably isn’t links. It’s the Evidence Graph — the web of corroborating evidence (mentions, citations, agreement across independent sources) that makes a product the answer. Mapping it is the long-term research question.