Ask two marketers whether AI search matters for a local service business and you will get two confident, opposite answers backed by real data. That is not because one of them is lying. It is because "local search" covers two completely different behaviours, and AI has taken over one of them while barely touching the other.
Here is the short version. Ahrefs analysed 146 million SERPs and found that only 7.9% of local queries trigger an AI Overview, compared to 22.8% of non-local queries. Meanwhile Seer Interactive, working across 53 brands and 5.47 million tracked queries, found that informational queries containing "near me" showed an AI Overview 76.9% of the time. Those two numbers look like a contradiction. They are actually the answer.
The 7.9% and the 76.9% are the same finding
The reason the data looks contradictory is that most studies bucket "local" as one thing. It is not. Every local service business has two distinct streams of search demand, and they behave in opposite directions.
Stream one is dispatch. The water heater is leaking. The panel is tripping. Someone types "plumber near me" or "emergency electrician Calgary", looks at three map pack results, and calls the one with the most recent reviews. Four words, no reading, decision in under ninety seconds.
Stream two is research. The furnace is fourteen years old and making a noise. Someone spends two weeks asking whether it is worth repairing, what a replacement involves, how to tell if a quote is fair, and what questions to ask a contractor. Twenty-three word questions, several sessions, then a call to a name they have already decided to trust.
Google treats these differently on purpose. Seer's February 2026 breakdown across 49,353 distinct queries shows AI Overview prevalence by intent type:
| Query intent | AI Overview prevalence |
|---|---|
| Informational | 36% |
| Commercial | 8% |
| Transactional | 5% |
| Transactional "near me" | 1.5% |
And by query shape:
| Query type | AI Overview rate | Organic impressions |
|---|---|---|
| Comparison (X vs Y) | 95.4% | 79,436 |
| Review queries | 86.3% | 21,221 |
| Question (what/why/how/is) | 85.9% | 1,408,843 |
| Price / cost / buy | 83.4% | 192,911 |
| "Best of" | 81.3% | 105,279 |
| "Near me" (informational) | 76.9% | 188,606 |
| Single word | 27.3% | 25,042,440 |
Read those two tables together and the picture is unambiguous. Google is layering AI on top of the thinking part of local search and leaving the hiring part alone, because on a transactional local query the Maps result already is the answer. Seer's own analysts flagged the near me finding as "the unexpected" one, and made an explicit bet that AI Overview prevalence on transactional intent stays below 10% through Q3 2026.
By the numbers
Ahrefs, across 146 million SERPs: 99.9% of keywords that trigger an AI Overview are informational in intent. If a query's job is to produce a phone call rather than an explanation, it is very unlikely to be answered by AI today.
So the honest answer to "does AI search matter for my business" is a question back: what fraction of your revenue comes from customers who research before they call? A drain cleaning company that lives on emergencies and a basement finishing company that gets chosen after three weeks of reading are not in the same fight, even in the same city.
What AI search actually sends you: a distribution, not an average
The second thing that makes this argument circular is that people quote averages for a channel that has no meaningful average.
LovedByAI analysed GA4 referral data across 239 professional and local service properties over a single 30 day window and recorded 47,402 AI referred sessions. Here is how those sessions were distributed:
| Metric | Finding |
|---|---|
| Sites receiving at least one AI referral | 71.5% |
| Median sessions per site per month | 6 |
| 90th percentile site | 186 sessions |
| Top outlier | 8,025 sessions |
| Share from ChatGPT | 56.3% |
| Share from Brave AI | 19.3% |
| Share from Gemini | 9.9% |
| Share from Claude | 7.5% |
| Share from Perplexity | 5.8% |
| Traffic landing on the homepage | 53.6% |
Six sessions a month is nothing. One hundred and eighty six is a real channel. The gap between them is not luck, and it is not budget. It is whether the site is legible to the models at all, which is a solvable technical and editorial problem.
Two details in that table are worth pausing on. First, Brave AI at 19.3% is larger than Gemini, Claude and Perplexity combined in this dataset, which quietly kills the "just optimise for ChatGPT" advice. Second, 53.6% of AI traffic lands on homepages, not deep blog posts. When an AI recommends a local business it usually points at the business, not at an article. That has direct consequences for what you should fix first.
Watch out
Six sessions per month is the median, not the ceiling and not the floor. Treating a power law channel as if it had a typical value is how owners talk themselves both into overspending and into ignoring it entirely.
Your analytics is lying to you, in a predictable direction
The most common reason an owner concludes AI search does not matter is that they opened Google Analytics, filtered for chatgpt.com, saw a handful of sessions, and closed the tab.
That reading is structurally wrong. Data from Graphite indicates roughly 83% of AI usage happens inside mobile apps rather than browsers. When someone asks a question in the ChatGPT iOS app and taps a link, the handoff from app to browser strips the referrer. Google Analytics records it as direct traffic, which looks identical to someone typing your domain from memory. Nobody types a local contractor's domain from memory.
There is a good firsthand account of this on r/smallbusiness from an owner who spent roughly a thousand euro on Google Ads with zero attributed sales, then went looking in his raw server logs:
"I grepped my nginx logs and found the answer sitting there the whole time: ChatGPT's browsing agent (ChatGPT-User) was fetching my content around 30 times a day. Real people asking ChatGPT questions about my niche, and ChatGPT serving them my pages as the answer. Here's the kicker: this traffic is invisible in every dashboard. ChatGPT app clicks carry no referer, no UTM, nothing. It all lands in the 'direct' bucket."
He traced one buyer's first ever visit landing directly on a deep page he had never visited, with no referrer. Something handed that person the exact URL.
This is the measurement gap in one story, and it cuts both ways. It means "my analytics shows nothing" is not evidence of absence. It also means anyone promising you a clean attributed report on AI search leads is selling you something they cannot deliver. A practitioner in r/aeo put the workable version well: stop trying to treat chat windows like a Google Ads dashboard, and instead add an explicit intake field, a mandatory dropdown or a conversational question asking whether an AI tool, summary or forum thread pointed the customer your way, then combine that qualitative intake with your CRM pipeline. That is a job for your CRM, not your analytics.
The backdrop to all of this is that clicks in general are getting scarcer. SparkToro's June 2026 clickstream study with Similarweb found 68% of US Google searches in the first four months of 2026 ended without a click, up from about 45% a decade ago. Roughly 276 of every 1,000 searches now send a visitor anywhere at all. Judging any search channel purely by sessions is getting less useful every quarter.
We run the diagnosis in this article for you across ChatGPT, Gemini, Perplexity and Google AI Overviews, then show you exactly which of your job types are exposed and which are not. No retainer required to find out.
A twenty minute test: does it matter for your business yet
Skip the industry averages. This is the version you can run yourself this afternoon, and it is the same diagnostic we start a GSEO engagement with.
Step 1. Split your last fifty jobs into two columns. Dispatch jobs, where the customer had a problem today and called fast. Research jobs, where they compared options, asked questions and took days or weeks. Do not guess the ratio. Look at the actual jobs.
Step 2. Write down the five questions a research customer asks before they hire you. Not keywords. Real sentences. "Is it worth repairing a fourteen year old furnace or should I replace it." "How do I know if a roofing quote is fair." "Do I need a permit to move a panel in Ontario."
Step 3. Ask those five questions in ChatGPT, Gemini and Perplexity, once with your city named and once without. Read what comes back. You are looking for three things: whether any local business is named at all, whether it is you, and what sources the answer is built from.
Step 4. Ask the direct recommendation question. "Who are the best [your trade] companies in [your city]?" Then ask the follow up that almost nobody asks: "Why did you pick those?" The reasoning it gives you is a free audit of what the model believes about your market.
Step 5. Check whether you are readable at all. Open yourdomain.com/robots.txt. Broad Disallow rules written years ago for scrapers now block GPTBot, PerplexityBot and ClaudeBot. If you have server log access, grep for those agent names. No crawler visits means no citations, permanently, no matter what else you do.
Now score it:
| What you found | What it means |
|---|---|
| Mostly dispatch jobs, and you are already strong in the map pack | AI search is a watch item. Spend the next dollar on review velocity and answer rate. |
| Mostly research jobs, and AI names competitors but not you | This is live revenue leaking now. Highest priority in your marketing. |
| Mostly research jobs, and AI names nobody local | The category is unclaimed in your city. Cheapest window you will ever get. |
| AI describes your business wrongly (old address, services you dropped) | Fix the data consensus first. A confidently wrong answer costs more than absence. |
| Crawlers blocked in robots.txt | Nothing else matters until this is fixed. |
That last row is more common than it should be. A local SEO practitioner who audited over 150 local businesses reported broad, years old robots.txt Disallow rules blocking AI crawlers in a majority of the sites they looked at, alongside missing LocalBusiness schema and mismatched name, address and phone details across directories.
The signals that tell you it is already happening
Because the referral data is unreliable, the useful signals are indirect. An operator posting in r/localseo, trying to work out why GEO "seems real to some, invisible to others", listed the pattern they kept seeing across local businesses and agencies. It is the best informal detection list I have come across:
- Map rankings hold steady, total leads look flat year over year, but first discovery looks strange.
- Branded searches rise without a corresponding ranking improvement. Someone is learning your name somewhere else and then searching for it.
- Direct traffic rises with no campaign driver behind it.
- The answers to "how did you find us" get vaguer or stop mentioning Google.
- Decision-ready calls increase while research calls decrease. The customer already knows what they want.
That last one is the one owners feel before they can measure it. If your phone conversations have shifted from "can you explain how this works" to "I want the mid option, when can you start", the research step happened somewhere you cannot see. AI is one of the places it now happens.
What actually moves the needle, and what is theatre
If your twenty minute test says this matters for you, the work is narrower than the hype suggests. Most of it is unglamorous.
Reviews with sentences in them, not just stars
This is the finding that surprises most owners. A local SEO practitioner described a pizzeria client in r/localseo: ranked second in the map pack, a 4.7 rating, a massive backlog of four and five star ratings. Every LLM tested ignored it completely and recommended chain restaurants and newer spots with fewer reviews and lower ratings. The difference they identified was that the competitors' reviews contained real text, some of them paragraph long, while the client's said "great pizza" and "chill place".
That behaviour makes sense once you remember what a language model is doing. It cannot taste the pizza, and a 4.7 next to a 4.5 tells it almost nothing. What it can do is read sentences describing what happened. A review saying "they diagnosed a cracked heat exchanger the other guy missed and had it replaced the next morning" is usable evidence. "Great service" is not.
Practically: stop asking customers for a rating and start asking them what job you did. "Would you mind mentioning what we fixed and how long it took?" is a better ask than "leave us five stars", and it works for both audiences, since BrightLocal's 2026 survey of 1,002 US adults found 97% of consumers read reviews before choosing a local business and 41% now always do, up from 29% a year earlier.
Consistent facts across the sources these tools actually use
AI assistants do not have a private database of local businesses. They cross reference public ones. BrightLocal's research into ChatGPT's local sources found it draws on review information from Google and Google Maps, and analyses by several local SEO shops report that ChatGPT Search leans heavily on the Foursquare Places API for local business data, supplemented by Yelp, Bing Places and indexed web pages.
The practical consequence is boring and important: if your suite number is on one listing and not another, if an old phone number is still live on Yelp, if your Facebook page uses a different business name, you are giving these systems conflicting evidence. Conflicting evidence produces low confidence, and low confidence produces no recommendation. Consensus across sources is the actual ranking factor, and your own website claiming you are the best is the weakest source in the set.
Machine readable basics
LocalBusiness schema with name, address, telephone, opening hours, geo coordinates and areaServed. Server side rendered content, since a page that needs JavaScript to show its text is a blank page to a lot of crawlers. Service pages that answer one real question each, with the answer in the first paragraph rather than after four hundred words of history. None of this is exotic, and it is covered in more depth in our guide to generative engine optimization.
What to ignore
- llms.txt. No major AI system has committed to reading it. It is a proposal, not a standard.
- AI visibility scores sold as a product. Useful as a diagnostic, meaningless as a KPI. Being mentioned is not the same as being recommended, and a model can name you and recommend a cheaper competitor in the same sentence.
- Anyone guaranteeing citations. Nobody controls model output. If you want to sanity check a vendor's claims, run the checks in how to check if AI is recommending your business yourself before and after.
- Abandoning what works. Seer's 2026 update found that being cited in an AI Overview delivers 120% more organic clicks per impression than not being cited on the same SERP, but it still underperforms a SERP with no AI Overview by 38%. The queries Google has not put an AI Overview on are the ones users still click, and organic click through rate on those actually rose through 2025, from 2.93% to 3.97%.
So, does it matter?
Yes, conditionally, and the condition is knowable in an afternoon rather than arguable forever.
It matters if your customers research before they hire, because that is precisely the query space AI has taken over: comparison questions at 95.4%, question format queries at 85.9%, informational near me queries at 76.9%. It matters less today if your work is dispatch, because transactional near me queries sit at 1.5% and Google is still answering those with a map.
It matters more than your dashboard suggests, because most of the evidence is stripped before it reaches you. It matters less than the panic suggests, because the median service business is getting six sessions a month from it and 68% of Google searches were already ending without a click before AI arrived.
The businesses that get this wrong do so in one of two directions. Some cancel the marketing that is currently producing their leads because a headline told them Google is dead. Others dismiss the whole thing because their analytics shows nothing, and quietly cede the research stage of their market to a competitor whose reviews happen to contain sentences.
The work in between is not dramatic. Be findable by the crawlers. Be described consistently everywhere. Be reviewed in words. Answer the questions your customers actually ask, in the first paragraph. That is roughly the same work that has always produced local service leads, which is why doing it now is a low regret bet regardless of how fast the AI numbers move.