The Outsized Role of Google Business Profile in AI Search

Kevin Indig's early-2026 Growth Memo analysis of ChatGPT citations found that about 44 percent of AI citations come from the first 30 percent of a page, which means the source that lands inside the first slice of retrieval is the source that gets named. For a local service business, that first-retrieved source is not usually your website. It is Google. Specifically, it is the entity behind your Google Business Profile, the most reliably retrieved object in the whole local AI stack.

This article maps to Vector 10, Localize in Formative Digital's 12 Vectors: making your local entity unambiguous to every retrieval system at once, with the profile as the anchor that the other eleven vectors brace against. It leans on Vector 5, Cite, as well, because a profile only compounds when the same facts are echoed in the sources an engine trusts. To be clear on scope: this is a deep read on the profile itself as a retrievable entity, not the wider basket of ranking inputs covered in our piece on the local signals AI weighs for near-me intent, and not the answer-box mechanics covered in our Google AI Overviews optimisation guide. It is the one object, examined closely. We are going to argue the case with first-party evidence rather than the usual assertion, then give you an Ontario-grounded order of operations. The headline you should carry through every section: of every entity an AI engine could retrieve about your business, the Google Business Profile is the one it retrieves most reliably, which is precisely why it is the highest-return place to start.

The Google Business Profile is the highest-return lever in local AI search

The profile is the biggest lever because it is the one local entity AI engines retrieve with near certainty, and you cannot be cited from a source the engine never reaches. Every ranking guide on the internet asserts that "GBP matters for AI." Almost none of them prove it, and the published technical consensus actually points the other way, which we will get to. What follows is the proof, drawn from our own data rather than borrowed from a US study read in the abstract.

Think of an AI answer to "best HVAC contractor in Burlington, Ontario" as an assembly job. The engine retrieves a small set of sources, reads what they say about local businesses, and writes a shortlist. The question that decides whether you are on that shortlist is brutally simple: did any source the engine retrieved contain accurate, complete, recent information about you? For most local businesses, the source most likely to carry that information, and the source the engine is most likely to reach, is the same object: the Google Business Profile and the Maps and Knowledge Graph data it feeds. That is the entire argument in one sentence, and the rest of this page is the evidence behind it.

There is a stakes side to this too. SOCi's 2026 Local Visibility Index, built on more than 350,000 locations across 2,751 brands, found ChatGPT recommends just 1.2% of local business locations, and that only around 45% of the brands winning traditional local search also appear in AI recommendations. Read that twice. Most businesses are invisible to ChatGPT, and being a Google winner is not, by itself, a ticket in. The profile matters not because it guarantees a citation, but because it is the highest-probability entry point into the narrow set of businesses an engine will name at all.

google.com is the runaway source ChatGPT cites for Ontario local queries

In our scrape, google.com was ChatGPT's most-cited domain by a margin that is hard to overstate: 130 citations, against two for the next-ranked domain. Formative Digital ran the analysis through DataForSEO's LLM endpoints in May 2026, asking the live engines "who are the best {vertical} in {city}, Ontario" across nine Ontario cities and five verticals, and logging every source cited. For ChatGPT specifically, the source distribution was not a gentle curve. It was a cliff.

One domain appeared in seven of the nine cities and across all five verticals, dentists, HVAC companies, personal injury lawyers, roofers, and small businesses. Every other domain in ChatGPT's citation set was a single business website appearing once or twice: bergellaw.com twice, then a long tail of individual sites, westwooddentalgroup.ca, wmgreenroofing.ca, tenacityhvac.ca, stonegatedental.ca, each cited a single time for a single city. The structure tells the story on its own. ChatGPT is not reading a balanced spread of directories and review sites for Ontario local queries. It is leaning, hard, on Google, then occasionally surfacing a business's own website on top.

The mechanism behind that pattern is visible in ChatGPT's own answers. When we asked for the best HVAC companies in Burlington, ChatGPT returned ABW Air Systems with its exact Maps profile attached: "Open now, HVAC contractor, 4.8 (100 reviews), 1110 Heritage Rd #4, Burlington, ON L7L 4X9," followed by the business description. That block of data, the open status, the star rating, the review count, the formatted address, the category, is Google Business Profile data rendered almost verbatim inside a ChatGPT answer. The engine is not paraphrasing a directory. It is reading the structured local record and reciting it.

This is what gives the profile its pull. The fields you control inside your Google Business Profile, category, hours, review count, address format, become the literal sentences an AI engine writes about you. A blank or wrong field is a sentence the engine cannot write, or writes wrong. For a wider view of how this google.com dependence sits against the other engines' very different habits, our companion analysis of why four AI engines name four different local businesses breaks the cross-engine divergence down in full.

ChatGPT runs on Bing, yet Google still wins the answer

Here is the contradiction this article exists to resolve: the published technical consensus says ChatGPT cannot see your Google Business Profile, and our data says google.com is the first thing it cites. Both are correct, and the reconciliation is the interesting part.

The consensus is well documented. Search Engine Land's Damian Rollison, writing in May 2025, traced the mechanism precisely. ChatGPT runs a Bing search for a local query, gathers the top 20 to 30 web results, and re-ranks them with its own logic. It has, in his framing, no direct access to Google Business Profile; it references Google data only when Bing-indexed pages happen to cite it. Local Falcon and others in the local-search field describe the same routing. Taken literally, that should mean Google's local data barely features in ChatGPT's answers. Our scrape shows the opposite.

The resolution is that "no direct access" is not the same as "no access." Bing's index is saturated with pages that carry Google's local data: directory listings that scrape Maps, aggregators that embed star ratings and review counts, local news and business pages that reference a company's Google reviews, and Bing's own places data that mirrors much of the same public record. When ChatGPT pulls 20 to 30 Bing results and re-ranks them, the Google-derived facts ride along inside those pages and surface as a google.com citation. The profile reaches ChatGPT not by a private API but by sheer redundancy across the indexed web. This is why a complete profile still wins even on a Bing-powered engine: you are not optimising for Bing or Google in isolation, you are making one set of facts so consistent across the open web that whichever index an engine reads, it reads the same true thing about you.

As Matt Griffin puts it, the lesson is not to chase whichever engine is fashionable this quarter but to build the entity once and let every retrieval path find it: "People hear ChatGPT runs on Bing and conclude Google does not matter to AI. Our Ontario data says the opposite. Google's local record is so deeply mirrored across the web that it surfaces as ChatGPT's number one source anyway. You are not optimising for an index. You are making your business the same true entity everywhere, so it does not matter which index the engine happens to read. Truth, not tricks."

That reframing has a practical edge. It means the work is not "get into Bing" or "get into Google," it is entity consistency across both and the dozens of sites that mirror them. The profile is where that single source of truth is easiest to establish and police, which is the third reason it leads the priority order, after retrievability and stakes.

The profile fields AI engines actually read and reuse

The fields that feed AI answers are the structured, factual ones: primary category, name, address, phone, hours, attributes, services, and the review summary, because those are the fields that map cleanly onto sentences an engine can assemble without guessing. Decorative fields matter far less. The ABW Air Systems answer above shows exactly which fields travel: category ("HVAC contractor"), open status (from hours), rating and review count, and the formatted address. Get those precise and complete, and you are handing the engine ready-made, citation-safe sentences.

Primary category is the highest-impact field and the most commonly fumbled. Google offers thousands of categories, and an engine reading "HVAC contractor" versus the vaguer "contractor" or the wrong "air conditioning repair service" will surface you for different queries. Pick the single most specific category that genuinely describes the core business. This is also what Google's own LocalBusiness structured data documentation instructs for your website: use the most specific applicable sub-type. The profile category and the schema type should agree. When they do, the same classification is reinforced wherever an engine looks.

A complete profile beats a higher-rated incomplete one in AI answers, and the reason is mechanical rather than aesthetic. An engine assembling a shortlist needs fields to read. A profile at 100% completion, full hours, services listed, attributes set, description written, photos present, gives the engine more true sentences to work with and fewer gaps to fill or skip. A sparse profile with a 4.9 rating but no hours, no services, and a one-line description offers little for the engine to assemble, so it tends to reach for a competitor whose record is richer. Completeness is not a vanity metric here. It is the surface area an AI engine has to cite.

The description deserves its own note, because most are written for a human skimming Maps and not for a machine parsing facts. Write it the way an engine reads: state what you do, where you do it, and who you do it for, in plain declarative sentences near the top. "Family-owned HVAC contractor in Burlington serving Halton Region since 1986, specializing in furnace and air conditioner installation, tankless water heaters, and indoor air quality" is legible to a retrieval system. A paragraph of adjectives about passion and dedication is not. We treat this as part of the same discipline behind structuring a site so AI can actually read it: the format is the message.

Reviews, recency, and the prominence signal that compounds into AI answers

Reviews influence AI recommendations, and recency now matters more than raw total, because a recent review trend signals an active business while an old wall of stars signals a dormant one. BrightLocal's December 2024 study of 800 manual local-business searches in ChatGPT found business websites made up 58% of cited sources, business mentions 27%, and directories 15%. Reviews and the contextual mentions around them are a meaningful slice of what the engine assembles its answer from, not a side detail. A business generating fresh, specific reviews keeps feeding the signal; one coasting on volume from three years ago slowly goes quiet in the eyes of a recency-weighted retrieval system.

This connects to how Google itself describes local ranking, and the connection is the point. Google's official guidance names three factors: relevance, distance, and prominence. Prominence is partly built from reviews, with Google stating that more reviews and positive ratings improve local ranking, and that review recency and response activity are part of how visibility is earned. The same levers Google names for its own local ranking are the levers that carry a business into AI answers, because the AI answer is assembled, directly or at one remove, from that Google-prominent record. You are not optimising for two systems. You are optimising one entity that both systems read.

It also means the honest answer to "can I pay for this" is no. Google states there is no way to request or pay for a better local ranking. The position is earned through relevance, proximity, and prominence, and prominence is earned through completeness, genuine reviews, and a record kept current. That is good news for a disciplined business and bad news for one hoping to buy its way past the work. The compounding is real but it is earned: a profile kept complete and freshly reviewed gets more retrievable every month, which is the slow, unglamorous mechanism behind durable AI visibility. For the broader picture of how these near-me signals feed AI, our piece on the local signals AI engines weigh for near-me intent goes deeper than we can here.

An Ontario order of operations: what a local business should fix first

Fix the profile in this order: completeness, then primary category, then name-address-phone consistency, then a recent-review habit, then matching schema on your own site. The order is deliberate, ranked by how much each step moves AI retrievability per hour of work, and it is grounded in the same Ontario data this article is built on.

First, drive the profile to 100% completion. Every field filled is surface area an engine can cite, and this is the fastest gain available because it is entirely within your control and requires no third party. Hours, services, attributes, a fact-first description, and current photos all go in now. Second, set the single most specific primary category, and make sure your website's LocalBusiness schema names the matching sub-type, so the classification agrees wherever an engine reads it. Third, audit your name, address, and phone across your site, your profile, and every directory and listing that mentions you, and make them byte-for-byte identical. An engine that finds three slightly different addresses cannot tell which entity is real and may cite none of them. Fourth, build a habit of earning recent, specific reviews, because recency is now a live ranking and retrieval signal, not a vanity number. Fifth, mirror the whole record in LocalBusiness structured data on your site, so the facts the engine reads from Google are confirmed where it crawls you directly.

One caveat, stated plainly, because GEO work is money work and deserves honesty: outcomes depend on your industry, your competition, and your existing digital presence. AI visibility is not a switch that flips identically for every business. Our retail client Mattress Miracle in Brantford grew from roughly 1,000 to over 82,400 monthly organic visits (SEMrush, April 2026) through sustained structured-content and entity work, and as their owner Brad put it, "In 40 years of advertising I've never seen anything like this. It's a completely new business." That reflects one industry and one starting point; yours will differ, which is why we diagnose before we promise. We run this profile-first sequence through the Formative Forces, our orchestrated multi-agent system, so completeness, category, consistency, reviews, and schema are worked in parallel rather than one quarter at a time. The reason it is not a magic-dust pitch is that every step is verifiable against exactly the kind of scrape that produced this article's numbers.