There’s a moment every local business owner eventually has: you search for your own business on Google, and the top of the page no longer looks the way you remember it. A large grey box now sits above the familiar blue links. It might mention your business. It might not. It might get a detail wrong.
That box is an AI Overview — and it is one of several ways that artificial intelligence is restructuring local search as we know it.
If you run a business that depends on local customers finding you online, the shift underway is significant enough to warrant a clear-eyed look at what the data actually shows, what is changing on Google’s search results pages, how large language models (LLMs) like ChatGPT and Perplexity factor in, and what specific actions will protect and grow your visibility. This guide covers all of it, without hype, using the most current research available.
The Real State of AI Adoption in Search: What the Numbers Say
Before adjusting a single element of your local SEO strategy, it helps to understand how widely consumers are actually using AI tools for search. The numbers are more nuanced than most headlines suggest.
An August 2025 Sparktoro study of American search behavior found that only 20% of Americans use AI tools ten or more times per month. Forty percent access them at least once per month. Despite the volume of industry commentary predicting Google’s decline, 95% of Americans still engage with traditional search engines like Google every single month.
Sparktoro founder Rand Fishkin wrote: “My takeaway is that traditional search isn’t going anywhere, even for the heavy adopters of AI. The more data we gather, the more I’m convinced the ‘AI vs. Search’ narrative is largely made up by media and influencers seeking attention, rather than an accurate reflection of reality.”
He added a detail that is often overlooked: the combined growth trajectory of AI tools — including ChatGPT, Claude, Copilot, Gemini, Perplexity, and DeepSeek — has not posted a 1.1x-or-higher growth month since September 2024. Adoption is growing, but the acceleration is slowing.
Perhaps the most counterintuitive finding comes from Semrush: when new users adopt ChatGPT, their usage of Google search actually increases rather than decreases. These platforms, at least for now, appear to be complementary rather than substitutional.
For local businesses, this means one clear thing. Google is not going away. The right strategy is not to abandon traditional local SEO in favour of chasing LLM visibility — it is to build a presence that serves both environments simultaneously.
How AI Overviews Are Reshaping the Google Local SERP
While AI adoption rates in general may be more measured than some predict, AI Overviews — Google’s AI-generated answer boxes that appear within Google’s own results pages — are a different matter. These are not something consumers need to opt into. They appear in standard Google searches, and the frequency with which they appear has changed substantially throughout 2025.
A Semrush analysis of more than ten million keywords tracked between January and November 2025 found that AI Overviews appeared for 6.49% of all queries in January, surged to a peak of 24.61% in July, and then pulled back to 15.69% in November. Google’s deployment of AI Overviews has not been a straight-line rollout — it has been exploratory and volatile, with the search engine expanding into new query types before sometimes pulling back.
The shift in intent coverage is the more commercially important story. In January 2025, 91.3% of queries triggering AI Overviews were informational in nature. By October, that figure had dropped to 57.1%. Meanwhile, commercial queries triggering AI Overviews grew from 8.15% to 18.57%, transactional queries from 1.98% to 13.94%, and navigational queries from 0.84% to 10.33%.
In plain terms: AI Overviews started as a feature for people asking general information questions. They are now appearing more frequently for people who are closer to making a purchase or visiting a local business. That is a meaningful shift for local brands.
A separate, local-specific analysis published by Whitespark in Q2 2025 (to which the Search Engine Land guide’s author contributed) provides sharper resolution on how this plays out in local search specifically:
- AI Overviews appeared for 68% of local searches overall.
- Local packs appeared for only 39% of queries.
- For simple local intent queries — searches like “tacos San Francisco” — AI Overviews appeared for just 15% of results, while local packs appeared for more than 90%.
- For informational-intent queries — such as “how long does an eye exam take near me” — AI Overviews appeared for 92% of results.
- For hybrid-intent queries — such as “average cost of dental implants in Phoenix” — AI Overviews appeared for 97% of results.
The pattern here is important. When someone is at the very beginning of a purchase journey, asking process or cost questions, they are far more likely to encounter an AI Overview than a traditional local pack. This is exactly the type of query a local business would want to appear in — and the rules for appearing in AI Overviews are different from the rules for appearing in local packs.
AI Overviews pull content from business websites, third-party review platforms, forums, and other sources. A local business that has strong content on its own website covering costs, processes, and common questions — and that has a healthy presence on platforms like Yelp, Tripadvisor, Reddit, and other sites — is better positioned to appear in these overviews than one that has only focused on its Google Business Profile.
The Click-Through Rate Impact: Traffic Is Moving
The emergence of AI Overviews at scale has had a measurable effect on organic click-through rates. A Seer Interactive study from September 2025 found that organic CTR for queries with AI Overviews fell 61%, dropping from an average of 1.76% to 0.61%. AI Overviews occupy substantial visual space on the results page. Their presence pushes organic listings further down and, in many cases, answers the user’s question well enough that no click occurs.
Bain & Company research found that nearly 60% of searches now end without a click — the user gets what they need directly from the search results page. This zero-click trend predates AI Overviews (featured snippets and knowledge panels have been pushing in this direction for years), but AI Overviews have accelerated it meaningfully.
For local businesses that have spent years building traffic via organic search results, this is a real change in the environment. It does not mean SEO is a wasted effort — the visibility and brand exposure still have value, and the local pack (discussed below) remains a strong click driver for transactional queries. But it does mean that the number of page visits generated purely from informational organic rankings will likely continue to decline, and a diversified visibility strategy is increasingly important.
The Local Pack: Still Alive, But Facing Pressure
Amid all the discussion of AI Overviews, it is worth being precise about the local pack — the familiar block of three local business results accompanied by a map that appears for many location-specific searches.
For pure transactional and navigational local queries — the kind where someone is simply looking for a nearby business to visit or call — the local pack remains the dominant feature. Google local packs appeared for more than 90% of simple queries like “primary care clinic Phoenix.” These searches trigger the map result, not the AI Overview. The local pack format persists precisely because it works: GatherUp data shows that 50% of consumers consider Google’s local platform to have the most trustworthy review content, and 98% of consumers consult reviews before choosing a local business.
That said, local packs are under real pressure from two sides. First, the growing prevalence of AI Overviews is eating into local pack traffic for hybrid and informational queries. Research by Andrew Shotland documented a measurable dip in local pack impressions coinciding with the rollout of AI Overviews. Second, some practitioners have observed — and local SEO expert Elizabeth Rule has highlighted — that for certain queries, Google is surfacing AI Overviews that include business mentions without surfacing a local pack at all, producing significant ranking fluctuations for businesses that had previously held stable local positions.
A third pressure comes from Google AI Mode, which is beginning to include local pack-like results within its interface. For a query like “tacos near Golden Gate Park San Francisco,” Google AI Mode returns a visual result that resembles a local pack. This indicates that Google is working to preserve the local pack functionality while integrating it into an AI-driven experience, rather than simply eliminating it — but the format, ranking signals, and user behaviour around these results are still evolving.
Google Maps and New AI Features
Google Maps deserves its own mention, because it is not simply a mapping tool. On Android devices, it is the default navigation application, which means it is the first place millions of people turn when they are deciding where to go locally. Since Q4 2024, Google has been rolling out a series of AI-powered features within the Maps app, including review summaries, “Ask about this place” prompts, and a “know before you go” feature.
These features use AI to process and synthesise the reviews, photos, and Q&A content associated with a Google Business Profile. A business that has recently accumulated specific, detailed reviews — reviews that mention particular menu items, describe the atmosphere, note parking availability, or discuss pricing — is more likely to generate useful, accurate AI-powered summaries than a business whose reviews are generic (“great place, highly recommend!”). This is a practical incentive to actively cultivate quality review content, not just review volume.
LLMs as Local Discovery Platforms: A Mixed Picture
When a local business owner asks “should I be optimising for ChatGPT?”, the honest answer right now is: be aware of it, track it, but don’t over-invest in it at the expense of Google optimisation.
ChatGPT, Perplexity, Claude, and Gemini are increasingly used by consumers to ask questions that might previously have gone to Google — including local questions. But the local search experience within most LLMs is demonstrably weak. An experiment replicated in the Search Engine Land guide asked ChatGPT for organic taco restaurants near Golden Gate Park and received a single result. When the user asked the tool to display a map of nearby options (as Google’s local packs do routinely), it returned photographs and an address rather than a functional map — while asserting that it was showing a map.
This isn’t a minor usability issue. It reflects a fundamental limitation: most LLMs, including ChatGPT, do not have access to real-time local business data. They rely on training data that can be months or years old, do not update for business closures or new openings, and cannot confidently confirm current operating hours, availability, or pricing. For a consumer trying to decide where to go for dinner tonight, this is a poor experience compared to what Google Maps or a traditional local pack delivers.
That said, LLM usage is growing, and certain categories of local queries — particularly research-oriented ones about costs, process, or comparisons — are being answered by LLMs with enough confidence to influence decisions. A study cited by Search Engine Land found that “AI is more likely to strongly recommend a brand when it’s mentioned on many cited source pages.” This means the same unstructured citation work that has always mattered for local SEO — getting your business mentioned on reputable third-party sites, forums, review platforms, industry directories, and PR-style content — is increasingly relevant for LLM visibility as well.
Semrush research into the most-cited domains in AI platforms found heavy reliance on sources like Reddit, Quora, Medium, LinkedIn, and established news sites. A local business that earns genuine mentions in these ecosystems — through PR, through community engagement, through real customers sharing experiences — is building the kind of digital footprint that LLMs draw on when generating local recommendations.
The Hallucination Problem: A Direct Risk to Local Brands
One issue that local business owners cannot afford to ignore is the phenomenon of AI hallucinations — instances where an AI tool generates false information that appears credible.
The Search Engine Land guide includes a striking example: a query about a well-known sporting goods retailer prompted an AI Overview to generate content suggesting the store might have a cockroach infestation. There was no source reporting such a problem. No evidence existed. But the AI generated a response that could plausibly be read as confirmation of an infestation, displayed prominently on Google’s results page.
For a local business that has invested significantly in its reputation, this represents a category of risk that did not exist five years ago. GatherUp research found that 67% of consumers are not rigorously fact-checking AI-generated information before choosing a local business. A false claim in an AI Overview or an LLM response, seen by enough local consumers before it is corrected, can cause real commercial harm.
The practical response to this risk is not passive. It involves monitoring how your business is being described across AI platforms, addressing inaccuracies through the available mechanisms (Google’s feedback tools for AI Overviews, structured data on your website that clearly states accurate information), and building a strong enough factual digital footprint that accurate information crowds out AI confabulations. Tools like Mangools AI Search Grader, Semrush AI Visibility, and LocalFalcon are now specifically designed to track how a business is being described across AI environments.
Citation Strategy in the AI Era
Citations — mentions of your business name, address, and phone number across the web — have been a foundational element of local SEO for years. In the AI era, their role is expanding rather than contracting.
Structured citations remain important. These are listings in directories like Yelp, TripAdvisor, the Better Business Bureau, YellowPages, and industry-specific directories where your NAP (name, address, phone number) appears in a consistent, machine-readable format. They signal to search engines that your business is legitimate, operating, and located where it says it is. Consistency across structured citations remains a ranking factor in both traditional and AI-driven environments, because LLMs that synthesise information about local businesses draw on these sources.
Unstructured citations — mentions of your business in blog posts, news articles, forum threads, social posts, review narratives, and PR coverage — are becoming a more prominent factor in AI visibility specifically. Unlike structured directory listings, unstructured citations carry contextual information: they say not just that your business exists at a given address, but what it’s known for, what customers say about it, how it compares to competitors, and whether it comes up organically in community conversations.
Data from Yext’s analysis of 6.9 million citations found meaningful correlations between unstructured citation volume and AI search visibility. Adding statistics to your content improves AI citation probability by 41%, while including expert quotes increases it by 28%, according to research compiled by Ziptie.dev.
The practical implication is that a content strategy focused only on a business’s own website is insufficient. Businesses need to pursue mentions in relevant third-party contexts: local news coverage, participation in community forums, profiles on industry association sites, responses in relevant Reddit threads, and genuine customer testimonials on multiple review platforms. This is not a shortcut strategy — it takes sustained effort — but it is the kind of digital footprint that performs across both traditional local packs and AI-driven discovery environments.
Google Business Profile Optimisation in 2026
Your Google Business Profile (GBP) remains the single most important individual asset in local search. It determines whether and where your business appears in local packs, it feeds AI Overview content, it populates the “know before you go” summaries in Google Maps, and it is a data source for some LLMs. Getting it right is not optional.
Core profile completeness matters more than ever, because AI systems prefer complete, consistent data. Every available field should be filled in accurately: business category, subcategories, service areas, hours (including holiday hours), service list, product listings where applicable, website URL, and business description. Incomplete profiles create gaps that AI can fill with guesses — and AI guesses are not always correct.
Review management has compounded in importance. AI Overviews on Google regularly pull from Yelp, Tripadvisor, and GBP reviews to generate summary statements about local businesses. GatherUp found that 98% of consumers consult reviews before visiting a local business, and 50% specifically trust Google’s local platform most. A business with a high volume of specific, recent, and responded-to reviews is better positioned than one that has a handful of old reviews and no engagement.
Photos and videos should be authentic. Google implemented quality filters in 2025 that penalised AI-generated visuals on Google Business Profiles and websites. Businesses that had added AI-generated images saw image traffic reverse when these filters rolled out. Real photos of your actual premises, team, products, and work perform better and carry less risk.
Posts and Q&A are underutilised by most local businesses. Regular Google Posts signal to Google that a profile is active and maintained. The Q&A section, when populated proactively with accurate answers to common questions, can directly influence the content that appears in AI-generated summaries of your business.
Schema markup on your website provides machine-readable information about your business that both traditional search engines and AI systems can use confidently. LocalBusiness schema, with accurate NAP, operating hours, service area, and review data, is not a nice-to-have — it is a foundational element of AI-era local SEO. Implementing deep, nested schema is a recommendation that appears repeatedly in recent best-practice guidance for this environment.
Reputation Management as a Strategic Asset
Reputation management has always been part of local marketing. What has changed is that the stakes are higher and the feedback loop is faster. AI systems are continuously scraping and synthesising review content from multiple platforms. A sudden spike in negative reviews — even from a single bad week — can influence what AI Overviews say about your business before you’ve had time to respond.
This is why reactive reputation management is no longer sufficient. The current environment calls for proactive systems: a process for routinely asking satisfied customers for reviews (using compliant methods), monitoring review platforms regularly, responding professionally to both positive and negative reviews, and using review management tools like GatherUp or Semrush Review Management to maintain visibility across platforms.
Review content from Yelp, Tripadvisor, the BBB, TrustPilot, Facebook, and Google itself all feeds the AI ecosystem. A business with a strong, consistent review presence across multiple platforms is harder for AI to misrepresent (because there is a large body of accurate information) and more likely to appear prominently when AI systems generate local recommendations.
Tracking and Tools: What to Measure Now
Many local businesses are measuring visibility using tools and metrics that were designed for a pre-AI environment. A broader measurement framework is needed.
Google Search Console remains the baseline for tracking impressions, clicks, and average position in traditional Google search. Use UTM codes in your GBP links to capture Google Business Profile-specific traffic data within Search Console.
Google Analytics tracks session and conversion behaviour — essential for understanding whether changes in traffic patterns are affecting actual business outcomes.
AI-specific visibility tools are a newer category that has emerged to fill the gap between traditional rank tracking and the AI environment:
- Mangools AI Search Grader tracks your business’s visibility across ChatGPT, Perplexity, Gemini, Grok, and other platforms.
- Semrush AI Visibility tracks your share of voice across AI platforms and identifies the prompts driving your visibility.
- LocalFalcon tracks visibility across Google AI Overviews, Google AI Mode, ChatGPT, and other environments.
Traditional rank trackers — including Semrush Position Tracking, Whitespark’s Local Rank Tracker, and BrightLocal — continue to serve an important diagnostic function for local and organic ranking positions.
Brand monitoring tools — including Semrush Brand Monitoring, Brand Mentions, and AlertMouse — track unstructured citations and brand mentions across the web, which is increasingly relevant to LLM visibility.
Monitoring across all these dimensions gives a complete picture of where your business is visible, where it is losing ground, and which platforms are driving the most valuable traffic.
A Practical Action Plan for Local Businesses
The data and research across all the major studies points to a set of specific, prioritised actions. Here is how to approach them:
1. Audit your digital footprint systematically. Start by searching for your business on Google, Google Maps, ChatGPT, Perplexity, and Gemini. Note what information each surface returns, whether it is accurate, and whether your business appears at all for relevant queries. This baseline audit is the foundation of everything else.
2. Complete and maintain your Google Business Profile. Every field, every category, every photo, every post, every Q&A. Treat it as a live channel, not a one-time setup. Google’s AI summarisation features draw directly from this data.
3. Implement LocalBusiness schema on your website. Make sure your website markup accurately reflects your business name, address, phone number, operating hours, and services. This is the structured data that search engines and AI systems rely on for factual assertions about your business.
4. Build a multi-platform review strategy. Identify the platforms most relevant to your category and implement a systematic process for generating authentic reviews. Respond to all reviews — positive and negative — promptly and professionally.
5. Invest in content that addresses process and cost questions. Informational and hybrid-intent queries trigger AI Overviews at rates above 90%. Content on your website that directly addresses “how much does [your service] cost,” “what happens during a [your service appointment],” and “how to choose a [your type of business]” is the type of content AI Overviews pull from.
6. Pursue unstructured citations actively. Get your business mentioned in local news, community blogs, industry forums, and review platforms beyond Google. PR activity, community involvement, and encouraging genuine social mentions all contribute to the brand footprint that LLMs use when generating recommendations.
7. Set up AI visibility tracking. Use one of the AI-specific tracking tools to monitor how your business is described across LLM platforms. Address inaccuracies through available feedback mechanisms.
8. Audit your NAP consistency across all directories. Name, address, and phone number must be identical across every listing. Inconsistencies confuse both search algorithms and AI systems.
9. Deploy IndexNow. This protocol, available via most CMS platforms and CDNs, enables real-time notification to search engines when your content is updated. It improves the speed at which fresh content is discovered and indexed, which matters as AI systems place increasing weight on recency.
10. Monitor, adjust, and repeat. This landscape is evolving quickly. Commit to a monthly review of your visibility metrics across both traditional and AI environments and adjust your strategy based on what the data shows.
Frequently Asked Questions About AI and Local Search
Q1: What exactly is a Google AI Overview, and how is it different from a featured snippet?
A Google AI Overview is an AI-generated response generated by Google’s Gemini AI that appears at the top of search results for many queries. Unlike featured snippets, which pull a specific passage from a single webpage, AI Overviews synthesise information from multiple sources — including business websites, review platforms, forums, and news articles — and present a generated summary. The AI Overview does not simply quote a source; it creates new text based on information gathered from multiple places. Featured snippets link directly to a source; AI Overviews cite several. Both appear above traditional organic results, but AI Overviews are larger, take up more screen space, and are generated rather than extracted.
Q2: Are AI Overviews replacing local packs on Google?
Not entirely, and the relationship between the two features depends heavily on query type. Whitespark’s 2025 research found that for simple, transactional local queries — “nail salon near me” or “emergency dentist Boston” — Google local packs appeared in more than 90% of results. AI Overviews appeared in only 15% of those same simple queries. However, for informational and hybrid queries — “how long does a root canal take near me” or “average cost of braces in Chicago” — AI Overviews appeared in 92–97% of results. The pattern suggests that AI Overviews are taking over the upper-funnel, informational layer of local search, while local packs continue to dominate for bottom-of-funnel, transactional queries. Some practitioners have observed cases where AI Overviews appear for a query without any accompanying local pack, which can cause significant ranking fluctuations. Google continues to test and evolve the relationship between these two features.
Q3: Does my business need to optimise for ChatGPT and other LLMs?
You need to be aware of your visibility in LLMs, but wholesale restructuring of your strategy around them is not warranted yet. A Sparktoro study from August 2025 found that 95% of Americans still use traditional search engines like Google every month, while only 20% use AI tools ten or more times per month. More importantly, LLMs currently provide a weak local search experience: they rely on training data that can be outdated, cannot reliably provide current operating hours or availability, and often return incomplete or inaccurate local recommendations. That said, LLM usage is growing, and the same strategies that improve your visibility in Google — strong unstructured citations, prominent review presence on multiple platforms, accurate and complete business information across the web, and PR-style mentions in authoritative sources — are the same strategies that improve your visibility in LLMs. The most efficient approach is to build a strong, accurate digital footprint across all channels simultaneously.
Q4: What are unstructured citations, and why do they matter for AI search visibility?
Unstructured citations are mentions of your business online that do not follow a standard directory format. They include references in blog posts, news articles, social media posts, forum threads, review narratives, podcast transcripts, and any other web content where your business is named and discussed. Unlike structured citations (directory listings with your name, address, and phone number), unstructured citations carry contextual and qualitative information: what your business is known for, how customers describe their experiences, and how your brand is discussed in relevant communities. In the AI era, this matters because LLMs like ChatGPT and Perplexity rely heavily on content from sources like Reddit, Quora, Medium, LinkedIn, and news sites when generating local recommendations. Businesses that have a rich network of genuine unstructured citations are more likely to be mentioned, recommended, and accurately described by AI systems. Research indicates that AI is more likely to recommend a brand when it is mentioned across many cited source pages.
Q5: What is an AI hallucination, and what can a local business do about it?
An AI hallucination is a factually incorrect statement generated by an AI system that appears plausible in context. In local search, this can manifest as an AI Overview stating incorrect hours, describing a business as having closed when it hasn’t, attributing negative characteristics to a business without any source, or making claims about prices, services, or features that are inaccurate. Because 67% of consumers do not rigorously fact-check AI-generated information before choosing a local business (GatherUp), a hallucination that reaches enough consumers before being corrected can cause measurable commercial harm. Local businesses should monitor their AI presence regularly using tools like Mangools AI Search Grader, Semrush AI Visibility, or LocalFalcon. When inaccuracies are found, use Google’s feedback mechanisms to report AI Overview errors, ensure your website content, Google Business Profile, and schema markup clearly state accurate information, and maintain a strong review presence across multiple platforms so that accurate information about your business is abundant and easy for AI to find.
Q6: How does Google Business Profile optimisation affect AI Overview visibility?
Your Google Business Profile is one of the primary data sources Google uses when generating AI Overviews for local queries. A complete, accurate, and regularly maintained GBP — with full category selection, detailed service listings, up-to-date operating hours, authentic photos, and active Q&A — gives Google’s AI more accurate material to work with. This both increases the likelihood that your business is mentioned in AI Overviews and reduces the risk that the AI generates inaccurate information about you. Review content on your GBP is also directly scraped and summarised by AI Overviews. A business with many specific, detailed reviews that mention particular services, products, or experiences generates more useful AI summaries than one with only generic reviews.
Q7: Is local SEO still worth investing in given the rise of AI search?
Yes — and the evidence suggests that local SEO investment is arguably more important now than it was before AI Overviews. The reason is that local SEO creates the structured, consistent, accurate digital footprint that both traditional local packs and AI systems rely on. A business with strong local SEO fundamentals — complete and verified GBP, consistent NAP across directories, schema markup on its website, a robust review presence, and quality content addressing local intent queries — is better positioned to appear in traditional local packs (which still dominate transactional local queries), AI Overviews (which now appear in 68% of local searches), and LLM responses (which rely on the same network of authoritative mentions). Neglecting local SEO in favour of chasing AI visibility specifically is a false choice. The strategies overlap extensively.
Q8: What role do online reviews play in AI-driven local search?
Reviews have become one of the most important data inputs for AI systems operating in local search. Google’s AI Overviews regularly synthesise review content from multiple platforms — including GBP, Yelp, TripAdvisor, Facebook, and the BBB — to generate summaries and assessments of local businesses. LLMs also pull from review content when asked about local businesses. The specific, descriptive language in reviews matters: AI systems draw on that language to describe what a business is known for, what its strengths are, and what customers consistently experience. Businesses should pursue a multi-platform review strategy, generating authentic reviews across the platforms most relevant to their category, and respond professionally to all reviews. GatherUp data shows that 98% of consumers consult reviews before choosing a local business — and those reviews are now being amplified and synthesised by AI systems that present them as answer content.
Q9: What query types should a local business create content around to appear in AI Overviews?
Whitespark’s 2025 research identified two query types with the highest AI Overview prevalence for local searches: informational-intent queries (92% prevalence) and hybrid-intent queries (97% prevalence). Informational-intent queries include questions like “how long does an eye exam take near me,” “what does a roof inspection involve,” or “what should I bring to a tax preparation appointment.” Hybrid-intent queries combine informational and transactional elements, such as “average cost of dental implants in Phoenix,” “best type of hearing aid for seniors near me,” or “how often do you need an oil change in cold weather.” Content addressing these specific questions in a clear, direct, well-structured format — with headers, factual statements, and schema markup — is the type of content Google’s AI systems are most likely to surface in AI Overviews. Most local business websites focus exclusively on “who we are” content and service descriptions. The businesses that are capturing AI Overview visibility have content that directly answers the questions their customers are asking in search.
Q10: What tools should a local business use to track visibility in both traditional and AI search?
A complete local visibility monitoring setup in 2026 includes tools from both traditional and AI-specific categories. For traditional search: Google Search Console (impressions, clicks, position), Google Analytics (sessions, conversions, behaviour), and UTM codes in your GBP links. For local rank tracking: Whitespark Local Rank Tracker, BrightLocal, or Semrush Position Tracking, all of which capture local and organic rankings across geographic areas. For AI-specific visibility: Mangools AI Search Grader (visibility across ChatGPT, Perplexity, Gemini, Grok), Semrush AI Visibility (share of voice across AI platforms, prompt identification), and LocalFalcon (Google AI Overviews, Google AI Mode, and ChatGPT visibility). For brand mention monitoring: Semrush Brand Monitoring, Brand Mentions, or AlertMouse. Using this combination of tools provides visibility into how your business is performing across the full search ecosystem — from traditional local packs to AI Overviews to LLM responses — and gives you the diagnostic capability to identify where visibility is being lost and which channels are driving the most commercial value.
Q11: How should a multi-location business approach AI local search differently from a single-location business?
Multi-location businesses face a compounded version of the same challenges. Each location needs its own complete, maintained Google Business Profile. Schema markup on the website should include location-specific pages with LocalBusiness schema for each branch. Review generation and management must be organised by location, since AI systems evaluate each location’s online presence independently. One common trap for multi-location businesses is inconsistent information — different phone numbers, slight address variations, or category differences between locations — which creates conflicting signals that AI systems struggle to resolve correctly. Centralised management of GBP profiles, a standardised local content template for each location page, and consistent NAP data across all directories is the baseline. Above that, location-specific content — covering local events, community involvement, location-specific services, and locally relevant customer stories — helps each location build the contextual signals that AI systems use to recommend specific branches for specific queries.
Q12: Will Google AI Mode eventually replace the traditional Google search interface?
This is a question the industry is actively debating, and a definitive answer doesn’t exist yet. What is observable is that Google AI Mode is being built with local search functionality in mind — it already returns local pack-style results for location-specific queries, and Google appears to be iterating toward a unified experience that combines conversational AI responses with local business listings. Whether AI Mode eventually replaces the traditional results page or exists as a separate opt-in mode alongside it is unknown. What is clear from Google’s behaviour through 2025 is that it is testing aggressively and that the results page will continue to evolve. The most defensible strategic position for local businesses is to maintain strong fundamentals — accurate profiles, quality content, consistent citations, active reputation management — which tend to translate across whatever interface Google ultimately settles on.
The decisions made about local SEO strategy in 2026 will have compounding effects. The businesses that build comprehensive, accurate, multi-platform digital presences now — not just on Google, but across review platforms, directories, community forums, and earned media — are the ones that will perform well regardless of which specific SERP features Google, ChatGPT, or Perplexity prioritise next. AI is adding complexity to local search visibility, but it has not changed the underlying principle: businesses that are genuinely known, genuinely trusted, and genuinely visible across the places people gather information will earn customers. The tools and platforms that surface that information are changing; the value of being known and trusted is not.
About ALM Corp
ALM Corp is a results-driven digital marketing agency helping businesses build and sustain local visibility in an increasingly complex search environment. As AI fundamentally changes how consumers discover local businesses — through Google AI Overviews, Google Maps AI features, and large language models like ChatGPT and Perplexity — ALM Corp’s team develops the full-spectrum strategies that these shifts demand: Google Business Profile optimisation, structured and unstructured citation building, reputation management across multiple review platforms, schema implementation, locally focused content strategy, and AI visibility tracking. Whether your business has one location or fifty, ALM Corp builds the digital footprint that performs in both traditional local packs and the AI-driven environments that are increasingly shaping how local customers make decisions. To learn how ALM Corp can improve your local search visibility in the AI era, visit www.almcorp.com.
