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Ahrefs breaks AI visibility into tagged prompt groups and nine specific plays, after Ahrefs's own data showed a 0.98 correlation between third-party mentions and AI visibility
Mateusz Makosiewicz at Ahrefs says a single AI visibility score hides the real work, and lays out a seven step workflow that groups prompts by business intent, picks one of nine named plays per diagnosis, and quotes a 0.98 correlation Ahrefs saw between third-party mentions and AI visibility growth.
Image: Ahrefs
Why it mattersA marketer told to "improve AI visibility" now has a workflow that reduces the problem to a diagnosis and one of nine concrete plays, rather than a dashboard reading that does not tell them what to do next.
A single AI visibility score hides the real work, Ahrefs says in a workflow piece published on 30 September. The author, Mateusz Makosiewicz, lays out seven steps that start by sorting a brand's prompts into business intent groups, pick a diagnosis against each group, and finish with one of nine named plays. Ahrefs calls them AEO plays, for answer engine optimisation.
The point of the piece is that a score averaged across every prompt tells a marketer nothing about where the gap is. Ahrefs's own working example in the post: a 96.2 percent mention rate for Ahrefs across SEO tools questions, against 8.6 percent across local SEO questions. One average would have smoothed those together and the local SEO gap would have been invisible on the dashboard.
Tag prompts by business intent
Makosiewicz recommends tagging prompts by the business question behind them, with examples including comparisons, recommendations, use cases, product facts, reputation and category expansion. A single visibility number then becomes a column of them, and the gap shows up on the tag with the lowest score. The piece names Brand Radar, Ahrefs's own tool, as the tracker it was built against.
The nine plays
Ahrefs lists nine plays and says to pick from them per diagnosis. Correct factual errors on cited pages. Improve how pages describe the brand. Request inclusion in relevant comparisons. Update outdated internal content. Publish pages that answer a specific prompt. Build topic hubs. Partner with trusted creators. Engage in community discussions. Learn from competitor content. Each one maps to a different reason a brand is missing from an answer: wrong facts, wrong description, missing from the list, stale pages, no page on the question, scattered coverage, no third-party coverage, no community presence, or a competitor doing the whole set better.
The 0.98 figure, and whose it is
The piece quotes a 0.98 correlation between the number of third-party pages mentioning a brand and the brand's AI visibility growth. That is Ahrefs's own figure, from Ahrefs's own data, which is also what Brand Radar is built to track. Treat the number as what Ahrefs measured against its own index rather than a market-wide average. The post also recommends checking that AI crawlers can read the pages a brand hopes to be cited from, and lists position-weighted share of voice, fact-accuracy tracking, citation stability and sentiment of negative narratives as the dashboard metrics worth keeping.
The piece's practical claim is that the volume of mentions is less load-bearing than the influence of the pages that already shape the answer: Makosiewicz suggests focusing outreach on frequently cited sources, correcting the facts on them, and letting the citations do the work.
Source
- Primary source: Ahrefs: AI visibility data paralysis, how to get unstuck, 30 September 2026
This item was written by an AI system from the linked source. Reveneau is responsible for what it publishes.
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