AI Local Search Statistics 2026
Every figure on this page is our own measurement. None of it is quoted from another roundup. Each statistic carries its denominator, its collection date, a link to the study it comes from and the raw data behind it.
- Statistics
- 16
- Last measured
- 2026-08-28
- Sources
- 3 studies
- Licence
- CC BY 4.0
of the 4,657 businesses Google Maps returns for a local buyer query were named by any of four AI assistants. That is the headline number behind everything else on this page.
Cite this page
LocalSeen, "AI Local Search Statistics 2026", 2026-08-31. https://localseen.ai/research/statistics
Reusable under CC BY 4.0 with a link back. Every statistic below has its own permanent link and its own copy button, so a single figure can be cited without the rest of the page.
How visible are local businesses in AI answers
We asked four AI assistants for local recommendations in 48 US markets, then checked their answers against the businesses Google Maps actually returns for the same query.
Google AI Overviews named 8.07% of the Google Maps roster and Claude named 1.98%, a fourfold gap between the most and least generous AI assistant.
Method. 376 and 92 businesses of 4,657. ChatGPT 4.42%, Gemini 2.88%.
Google AI Overviews appeared for 47 of the 48 local queries tested, making it the most consistently present AI answer surface in local search.
Method. One AI Overviews capture per market, 48 markets, collected 2026-08-15/16.
Gyms were the most visible category in AI local answers at 14.38%, and hair salons the least at 9.71%.
Method. Pooled across all eight cities: 109 of 758 and 77 of 793.
Two independent collection runs of the same 48 markets produced 12.22% and 11.94%, a difference of 0.28 percentage points.
Method. Runs on 2026-08-15 and 2026-08-15/16 under rules frozen before collection. The second run is the one published.
How much the assistants agree with each other
The same dataset, a second question. Being recommended by AI is not one outcome but four separate ones, and the four rarely overlap.
In 36 of 48 local markets there was not a single business that all four AI assistants agreed on.
Method. A market is one category in one city. 12 markets had at least one unanimous pick.
72.5% of the businesses Google AI Overviews named were named by no other AI assistant, which makes it an outlier rather than a proxy for the others.
Method. 271 solo picks of 374 named. It reads a local results page while the chat models answer from what they learned.
A business owner who tracks only ChatGPT is blind to 63.1% of the local businesses AI recommends, and tracking only Claude leaves 83.5% unseen.
Method. ChatGPT named 204 of 553 businesses, Claude 91, Gemini 133, AI Overviews 374.
The closest pair of AI assistants, ChatGPT and Gemini, agree on 30.1% of their combined local picks, and no pair anywhere agrees on even a third.
Method. Jaccard index, shared divided by union, pooled across 48 markets. The furthest pair is Claude and Google AI Overviews at 8.1%.
Whether AI search volume tools measure local demand
This time the subject is not the assistants but the tools that claim to measure them. We sent the same local queries to an AI keyword volume endpoint and to Google advertising data.
7 of 12 "near me" queries were reported as zero volume by an AI keyword tool while Google records real demand for every one of them.
Method. Median real volume for these queries is 205,500 searches a month. A missing measurement is not the same as a zero, and these are missing measurements.
The query "coffee shop near me" gets 9,140,000 Google searches a month and the AI volume metric for it reports 1.
Method. Google Ads monthly volume against a DataForSEO AI keyword volume figure for the identical string, collected 2026-08-28.
The gap runs both ways: "best gym" carries 35,344 in reported AI volume against 5,400 real Google searches, which is 6.5 times higher.
Method. Generic "best X" phrasings inflate while "near me" phrasings collapse, so the error is directional and not a constant that can be corrected for.
How this page gets updated
- The year in the title tracks the measurement, not the calendar
- When January arrives, nothing on this page changes. The year moves only when the collection is actually re-run, because a refreshed date on unrefreshed numbers is the one thing that would make this page worth less than the roundups it is meant to replace.
- The address never changes
- This page keeps one permanent URL across every edition. Citations made today keep working, and the figures they point at keep their measurement date.
- The next edition will be a new baseline, not a continuation
- The data here was collected 2026-08-15/16, when the chat assistants were answering from memory. On 2026-08-20 our engine moved to real web search, so the same script run today would measure a different instrument rather than a changed world. We will publish the next run as a new baseline and say so, instead of drawing a trend line across two different methods.
- Old data stays where it is
- Every edition keeps its own dated dataset file. Nothing gets overwritten in place, so a figure quoted from an earlier edition can still be checked against the numbers it was quoted from.
What these numbers do not tell you
- One country, one language. Eight US cities and six categories for the visibility figures, twelve categories for the volume figures. Nothing here has been tested outside the United States or in a language other than English.
- One collection window. These are measurements taken over days in August 2026, not a trend. Models change, and that is a reason to keep measuring rather than to treat any figure here as fixed.
- Name matching is strict. A business counts as named only when its name appears as a phrase. An assistant that describes a business without naming it does not register, which is why the loose matching figure is published next to the headline rather than in place of it.
- Disagreement is a claim about coverage, not quality. Nothing in the consensus figures says one assistant picks better businesses than another. It says they pick different ones.
- A missing measurement is never reported here as a zero. Where a tool returned nothing we say it returned nothing, and we keep that separate from a measured absence of demand.
Editions
This is the first edition. When the next collection is published, the figures above will be replaced and this list will keep a link to every dataset that came before, so an older citation can still be checked.
| Edition | Collected | Dataset |
|---|---|---|
| 2026 (current) | 2026-08-15/16 and 2026-08-28 | Visibility · Volume |
The consensus figures are derived from the visibility dataset rather than collected separately, and the script that reproduces them is web/scripts/derive-consensus.ts. Normalising names across engines reduces the 556 named businesses in the raw file to 553 distinct ones, which is the denominator used in this section.