Nearly every AI-search citation study reviewed for this site focuses on informational or commercial queries. Local-intent queries — "near me" style searches, a large share of all search volume — are almost entirely absent from the rigorous, disclosed-method research we could find. This is a genuine structural blind spot in the field, not a niche afterthought.
A structural blind spot
Every research field has blind spots that reflect what's easy to study, not what matters most. AI-search citation research is heavily weighted toward broad informational and commercial queries. Those are the kind that map cleanly onto a fixed query set run through a chat interface. Local queries often involve maps, business listings, and structured local data. They don't fit that pattern as neatly, and appear to have been under-researched as a result.
What is known
Google's documented query fan-out mechanism explicitly includes "locations" as one of eight sub-query type categories, confirming local intent is architecturally recognized in at least one major AI surface's retrieval design.
How citation patterns for local-intent queries actually differ from informational queries — across any engine — hasn't been measured with a disclosed method that we located.
Why local queries are architecturally different
Local intent likely deserves its own research track, rather than being folded into general citation studies. The reason is a genuine data-access asymmetry between engines. Google's AI surfaces can plausibly draw on Business Profile listings, Maps data, and location-specific review corpora it has built and maintained for over a decade. No obvious equivalent exists for an engine without that underlying infrastructure.
A general-purpose language model without native access to a comparable local data source would, in principle, have to rely more heavily on whatever local information happens to be described in ordinarily crawled web content: directory listings, a business's own website, third-party review platforms. That's a structurally different, and likely much thinner, source base for the same query.
The specific gap
Do AI engines cite differently for "best plumber near me" than for "how does a water heater work"? Does Google's access to Business Profile and Maps data given it a structurally different local-citation pattern than ChatGPT or Perplexity, which lack native access to that data? Nobody has published a direct comparison.
A worked hypothetical comparison
Here's the kind of comparison this gap calls for, using an invented scenario. Imagine querying "best coffee shop near [a specific neighborhood]" across multiple engines. A Google AI surface might plausibly draw its answer from a combination of Maps-listed businesses, ratings, and review snippets integrated directly.
A non-Google engine, lacking that same structured local dataset, might instead surface citations from a local news roundup article, a third-party "best of" listicle, or a review-aggregation site that happens to cover that neighborhood. That's a genuinely different kind of source entirely. Does this pattern hold consistently across many such local queries? How large is the resulting gap in coverage and citation quality? That's exactly what a real study would need to measure, not assume from architecture alone.
Local-intent queries are a large share of all search volume. AI-search citation research on them is almost entirely absent. That's a genuine structural blind spot, not a niche afterthought.
Share on XA study design, sketched
A local-intent subset of the Citation Index's query set, run across multiple metro areas and business categories, comparing citation patterns to the informational-query baseline already being collected. Registered as a candidate expansion rather than a currently running study.
Candidate hypotheses for a future study
These are candidates for a future pre-registration, not findings yet. First: Google's AI surfaces show a measurably higher share of Maps- and Business-Profile-linked citations for local queries than non-Google engines do. Second: non-Google engines rely more heavily on third-party aggregator and directory content for the same query type. Third: the citation gap between engines is larger for local queries than for the informational-query baseline the rest of the Index already measures.
None of these has been tested. They're stated explicitly to make clear what a properly designed local-intent study would need to confirm or refute, rather than leaving the direction of any future finding unstated.
Why this matters for local businesses
Local businesses represent a huge share of the web's total site count, and almost none of the existing GEO tactic guidance — built primarily on informational-query research — has been validated for their specific use case. If local citation dynamics differ meaningfully, current advice may simply not transfer.
What classic local SEO already assumes, unverified here
Classic local SEO has an established playbook: claim and optimize a Business Profile, accumulate reviews, ensure consistent name/address/phone data across directories. It's built around a well-understood Google Maps ranking system. A significant share of current "local GEO" advice appears to simply extend that playbook to AI citation by assumption, reasoning that the same structured data plausibly feeds both systems.
That extension may well turn out to be correct, particularly for Google's own AI surfaces. But it remains an assumption carried over from a different, older ranking system, not a claim independently verified for AI citation specifically. It says nothing at all about how a non-Google engine, without access to that data, might behave for the same query.
What a local business can do right now
You don't need to wait for this gap to close. Keep doing the classic local SEO basics well: an accurate, complete Business Profile, consistent name, address, and phone data everywhere you're listed, and a steady flow of genuine reviews. None of that work goes to waste even if AI citation ends up working differently than expected.
Beyond that, make sure your own website, not just your listings, describes your services in plain, specific language. That's the one source every engine can reach, regardless of whether it has access to Google's structured local data or not.
Limitations
- This page identifies a gap; it doesn't yet close it. No first-party data on local AI-search citation exists on this site today.
Namdev, R. (2026). Local AI search: what is actually known (v1). Retrieved from https://ritiknamdev.com/blog/local-ai-search-statistics Published under CC BY 4.0 — reuse freely with attribution.
Complements Google AI Mode statistics, which documents "locations" as one of the eight patent-defined sub-query types fan-out can generate.