Google Search Console will tell you that your page appeared inside an AI Overview.
It will not tell you what someone asked to make that happen.
That single gap is the most consequential thing in SEO measurement right now, and it has a workaround that most teams have not set up. This post covers what each platform actually gives you, a step-by-step workflow for extracting AI citation data you can act on, and — the part almost nobody is writing about — how to read that data without drawing conclusions it cannot support.
The measurement gap, stated plainly
On 3 June 2026, Google launched a dedicated Generative AI performance report in Search Console, with separate views for Search (AI Overviews and AI Mode) and for Discover. It was a real milestone: for the first time, site owners could isolate AI-surface visibility from standard organic data.
Then you open it, and you find the following:
- Impressions only. No clicks, no CTR, no average position.
- No query data. You cannot see what prompted the citation.
- No API access. Whatever you want to do with this data, you are doing it by hand.
- Dimensions available: pages, countries, devices (Search only), and dates.
There are also aggregation rules worth internalising before you interpret anything:
- Two results from your site inside the same AI answer count as one impression.
- An AI Overview occupies a single position, and every link inside it inherits that position.
- In AI Mode, each follow-up question counts as a new search with its own metrics.
- Links hidden behind an expandable element only count once the user expands them.
- A brand mention with no link generates no impression at all.
That last rule matters more than it looks. A large share of AI-surface brand exposure is invisible to Search Console by design.
So Search Console answers “did we appear?” It does not answer “why?”, “for what?”, or “how does that compare to last month?”
What Bing Webmaster Tools gives you instead
Microsoft shipped its AI Performance report in February 2026 — four months before Google’s equivalent. It covers citations across Microsoft Copilot, AI-generated summaries in Bing, and selected partner AI experiences.
On 16 June, four dimensions were added in preview, globally:
Intents: the grounding queries behind your citations, classified into categories including Informational, Commercial, Navigational, Learn and Solve, Research, Creation, and Local.
Topics: your citations grouped into thematic clusters, organised the way AI systems organise information rather than the way your sitemap does.
Citation Share: the portion of the citation space your site holds for a specific grounding query. Microsoft describes it as an observational metric: it does not expose competitor domains or traffic shares.
Compare: how all of the above moves over time, which matters because citation activity shifts with model updates, competing content, and freshness signals, not just with your own changes.
Bing still does not give you clicks or CTR for AI citations. Nobody does yet.
Why this is useful even if Bing sends you almost no traffic
The obvious objection: Bing organic is a rounding error for most sites, so why care?
Because you are not using it as a traffic channel. You are using it as a measurement layer.
The content characteristics that earn Copilot citations overlap substantially with those that earn AI Overview citations — structure, specificity, first-hand data, entity clarity, freshness. Different models, broadly similar grounding logic. So query-level citation data from one system is the closest free proxy available for the system that refuses to show you any.
This is a proxy, not a mirror. Treat it as directional evidence about what AI systems reward, not as a readout of your Google performance.
The workflow
Step 1 – Get verified (10 minutes)
Add your site at bing.com/webmasters. The fastest route is importing from Google Search Console, which takes about two minutes and carries your verification across.
Step 2 – Turn on IndexNow
Bing uses IndexNow as a freshness signal that feeds grounding. If your CMS runs Rank Math, Yoast, AIOSEO, or Cloudflare’s integration, this is a toggle. If you are on a custom stack, it is a key file at your site root plus a ping on publish.
Freshness is not a nice-to-have here. AI answers prefer recent sources, and grounding eligibility depends partly on Bing having your current version.
Step 3 – Baseline before you analyse
Open the AI Performance report and export everything. Record three numbers: total citations, total distinct grounding queries, and the date range covered. You need this baseline because every metric downstream is only readable as a trend.
Do not conclude in week one. You are establishing a denominator.
Step 4 – Read Intents first, not Citation Share
This is the step most people skip, and it is the one that produces actual strategy.
Pull the intent distribution across your citations. The pattern you are looking for is imbalance:
- Heavy Informational, thin Commercial — you are being cited as a reference but not as a solution. You are missing bottom-of-funnel content that shapes purchase decisions.
- Heavy Navigational — your citations are largely brand-driven. Good for brand strength, weak as a discovery signal.
- Strong Research or Learn and Solve — your explainer content is working. Consider whether it converts.
- Nothing in Local despite serving specific markets — a geographic content or entity gap.
Intent distribution tells you which part of the funnel AI systems currently trust you for. That is a content brief, not a metric.
Step 5 – Map Topics against your own clusters
Export the topic clusters and lay them next to your site’s actual content architecture. Three findings are possible, and all three are useful:
- A topic you rank for but are not cited in — a structure or specificity problem, not a coverage problem.
- A topic you are cited in but have not deliberately built — accidental authority. Usually your highest-ROI expansion.
- A topic mapped differently than you expected — your internal taxonomy does not match how AI systems cluster the space. Worth fixing in your information architecture.
Step 6 – Apply Citation Share with denominator discipline
Here is where most published commentary goes wrong.
Citation Share is a percentage. A percentage of a small number is noise. If a query produced twelve citations and you hold four, your 33% share is statistically meaningless and will swing wildly next month for reasons unconnected to anything you did.
Set a minimum threshold before you report on it. My working rule is that a query needs a citation volume in the low hundreds before share is worth acting on, and anything below that goes into a watch list rather than a recommendation.
Microsoft has also stated that some query data represents a sample of overall citation activity. Treat absolute counts as directional.
If your reporting cannot survive someone asking “out of how many?”, it is not ready to send.
Step 7 – Set a monthly cadence with Compare
AI citation data is only meaningful as a slope. Run this monthly, not weekly — model updates and competitive shifts introduce enough variance that weekly readings will have you chasing noise.
Track three things month over month: total citations, intent mix, and share on your threshold-passing queries.
Step 8 — Cross-reference with Search Console
Now join the two sides.
From Search Console’s Generative AI report, pull AI impressions by page. From the standard Performance report — or the BigQuery bulk export if you have it configured, which remains the best free unsampled search dataset available — pull clicks for those same pages.
The pattern to look for is rising AI impressions against flat clicks. That is the cited-but-not-visited signal, and it means the page is doing brand work while surrendering the visit. Whether that is acceptable depends on the page’s job. For a top-of-funnel explainer it may be fine. For a commercial page it is a problem you can address with better structure, a stronger reason to click, or content the answer cannot fully absorb.
Then overlay the Bing intent data onto those same pages. Now you have: which pages surface in AI, whether they earn visits, and what people were actually trying to do. That is a complete enough picture to act on.
Three caveats worth stating out loud
One. Both reports are new and both are in some form of preview or staged rollout. Google’s report backfills to 18 May 2026, so your history is short. Build trend lines, but hold them loosely.
Two. Absence of citations is not evidence of absence from AI answers. Unlinked brand mentions do not register anywhere. Your actual AI presence is larger than either dashboard shows.
Three. If you have anything automated against Bing’s APIs, note that the legacy SOAP and POX APIs retire on 31 August 2026. Migration to the REST APIs is required to avoid disruption. This is the most time-sensitive item in this entire post.
Where this leaves us
Search Console owns the traffic truth. Nothing else comes close to its unsampled, daily, query-level export of what actually drove visits.
Bing Webmaster Tools currently owns the AI visibility truth. It is the only free first-party source that will tell you what someone asked before an AI system cited you.
Neither replaces the other, and the teams that will read this environment correctly are the ones joining both rather than picking a side.
Most are not doing that yet. That is the opportunity.
How are you tracking AI citations right now – first-party tools, third-party trackers, or not yet? I am collecting informal benchmarks on what citation volumes people are actually seeing, because the honest answer to whether Citation Share is usable yet depends entirely on denominators nobody is publishing