The Main Thing That Changed
When this post was first published, measuring AI search visibility in Search Console meant inferring it: finding queries where impressions were healthy, position was strong, and clicks were not, then reasoning backwards about what was absorbing the difference.
That inference is no longer the primary method. In June 2026 Google introduced Search Generative AI performance reports in Search Console, covering visibility within generative AI features on Search, with reporting for both Search and Discover.
Start there. It is first-party data from Google about your own property, which makes it strictly better than anything reconstructed from CTR patterns. The older workflow is still useful, and the rest of this post keeps it, but it has been demoted to what it is: a diagnostic for questions the report does not answer.
Step 1: Read the Generative AI Report First
Establish the baseline before forming theories. Which of your pages and queries are already appearing in generative AI features, and how has that changed over your reporting period?
Two patterns are worth looking for immediately. Pages with generative AI visibility but weak conventional performance are usually doing more for you than a traffic report suggests, and cutting them would be a mistake. Pages with strong conventional performance and no generative AI visibility are your clearest opportunity set, because relevance is already proven.
Step 2: Use the CTR Divergence Check for What the Report Misses
The old method still earns its place, because it catches something the report does not: queries where an answer is satisfying intent and you are not part of it at all.
In Performance > Search Results, over a 90-day window, look for queries combining high impressions, low CTR, and a position in the top five. That combination is a prompt to investigate, not a finding. Good position with poor click-through means something above the links is absorbing attention, and an AI Overview is one explanation among several.
Step 3: Verify Against the Actual SERP
Confirm before acting. Search the query, or run it through AIORadar's AI Overview Tracker, and record whether an AI Overview appeared, whether you were cited, and which domains were cited instead.
This produces three groups, and they need different responses:
- Answer appears, you are cited. Protect this. Its value shows up in exposure and branded search, not clicks.
- Answer appears, a competitor is cited. A content gap. Compare their page to yours on coverage and originality.
- Answer appears, a publication or review site is cited. A brand-presence gap. No amount of on-page editing fixes this one; it is earned off your site.
That third group is the one teams most often misdiagnose, spending months rewriting a page when the real problem is that their category's answers are sourced from publications they have never appeared in.
Step 4: Assess the Not-Cited Pages Honestly
For each page in the gap list:
Does it answer the query directly, near the top? If the answer arrives in paragraph four after a throat-clearing introduction, fix that. It helps every reader, not only the machine ones.
Is it comprehensive across the topic? Google's AI features use query fan-out, issuing multiple related searches across subtopics before synthesizing. Check the related queries and People Also Ask entries your page does not address, because each is a subquery you are not eligible for.
Is it a commodity? The hardest question and the most important. Google asks for content that is not merely recycled from what is already indexed and that a generative model could not have produced by itself. If your page has no original data, testing, or first-hand perspective, restructuring it will not make it the preferred source.
Is it current? Check the last modified date. If the topic has moved and the page has not, update it and say when you did.
Step 5: Find Topic Gaps, Not Just Query Gaps
In AIORadar's Search Console Intelligence module, the Query Clusters tab groups queries by semantic topic. Look for clusters where several queries show the divergence pattern and your coverage is a single page.
Given fan-out, this is the highest-leverage pattern in the whole workflow. Demand exists across a topic, your coverage is thin, and each supporting piece you add makes you eligible for more of the subqueries behind every query in the cluster.
Step 6: Review on a Sensible Cadence
Monthly is enough. Re-check the generative AI report and your citation status for the queries you changed.
Do not expect a clean, fast signal. Citation selection is probabilistic and re-evaluation does not follow a fixed schedule, so a single month of movement on a handful of queries is noise. Look for direction across a reasonable query set over a quarter, and resist the temptation to attribute every change to your last edit. Anyone offering you a precise expected uplift and a timeline is guessing.