Local Visibility in AI Search: A Practical Guide for SMEs

Why AI Search is changing the game for local businesses

Until a few years ago, appearing on Google Maps and having a few reviews was enough to attract local customers. Today, the search journey has fundamentally changed. Users query ChatGPT, use Google AI Overviews, and rely on voice assistants that provide concise answers, not lists of links.

Consequently, a local SME might have a well-ranked website and still be absent from AI recommendations. This happens because language models don't just read the website: they aggregate data from business listings, reviews, editorial mentions, and social activity. Therefore, visibility in AI Search is the result of an ecosystem of signals, not a single optimized channel.

Jonathan Berthold and Kevin Chen of Moz analyzed this phenomenon in a in-depth article published on Search Engine Journal , identifying the operational levers that influence local recommendations in AI systems. The following guidance is based on that analysis, integrated with SHM Studio's perspective on the Italian market.

The starting point: the business profile as digital foundations

Google Business Profile is the first signal AI models consult to evaluate a local business. An incomplete profile or one with inconsistent information creates ambiguity in recommendation systems. Furthermore, inconsistency between the profile and other online sources—website, industry directories, social media profiles—reduces the model's confidence in the data's accuracy.

The fields to fill out carefully are: exact business name, main category and secondary categories, full address and verified phone number, updated hours (including holidays and seasonal changes), business description with terms relevant to the service offered, recent and quality photos.

In particular, the consistency of the name-address-phone (NAP) across all platforms is a technical requirement that AI systems verify by cross-referencing sources. We have already analyzed this risk in detail when discussing conflicting brand information and the risks in AI Search : inconsistent data between different sources can cause the model to discard the business or present it with incorrect info.

Reviews: volume, frequency, and active response

Reviews are the second pillar of local visibility in AI Search. Language models read them as a signal of reputation and thematic relevance. However, it's not just the average score that counts: the frequency of new reviews, the variety of terms used by customers, and the quality of the owner's responses also matter.

So, an effective review management strategy involves at least three ongoing actions. First, systematically request reviews after each completed transaction or service, using direct channels like email or SMS. Next, respond to every review — positive or negative — with text that includes references to the specific service and location. Finally, monitor industry platforms in addition to Google: Tripadvisor, Trustpilot, and vertical category portals.

This approach isn't just useful for online reputation: review texts become training corpora for AI models. Therefore, customers who describe the service with specific terms increase the likelihood that the model will associate those terms with the business when it receives a relevant query.

Authoritative mentions: the signal many SMEs overlook

Beyond listings and reviews, AI systems give weight to mentions of the business on authoritative external sources. These include articles in local newspapers, listings in industry directories, mentions in specialized blogs, interviews, or press releases. Similar to traditional SEO, the quality of the source matters more than the quantity.

For an Italian SME, concrete opportunities include: collaborations with local newspapers for press releases on events or openings, registration in recognized industry directories (Yellow Pages, category portals, trade associations), participation in local events with media coverage, sponsorships of initiatives with online visibility.

At SHM Studio, we integrate this activity of citation building within SEO strategies for SMEs, because the distinction between optimization for traditional search engines and optimization for AI Search is becoming increasingly blurred. Those who want to understand how to measure these signals can read our analysis on how to get citations and measure visibility in AI Search .

Social activities: consistency and local context signals

Social profiles are not a separate channel: they contribute to the overall picture that AI models build around a business. In particular, Facebook, Instagram, and LinkedIn provide signals of recent activity, geographic location, and interaction with the local community.

It's not necessary to be present on all platforms. However, active profiles must be consistent with the information on the business listing and regularly updated. Posts that include explicit references to the city, neighborhood, or local events strengthen the geographic signal that models use for local intent queries.

Furthermore, interactions — comments, shares, mentions by other local accounts — increase the perceived relevance of the activity within the geographic context. Therefore, a social strategy focused on the local community also affects visibility in AI Search, not just direct engagement.

The bigger picture: AI Overviews and the new local SERP

Google AI Overviews has significantly changed the structure of the results page. As we documented by analyzing the self-expansion of AI Overviews and its impact on SEO , AI-generated blocks occupy increasing space in the SERP, reducing the visibility of traditional organic results.

For local businesses, this means a concrete change: AI responses can include or exclude a business regardless of its organic ranking. Therefore, optimizing solely for traditional rankings is no longer enough. You need to build a profile of consistent signals that AI systems can read and cite with confidence.

The redesign of European SERPs also affects this scenario. Our analysis on Google redesigning SERPs in Europe shows how local verticals — restaurants, tourism, personal services — are among the most exposed to layout changes. Therefore, those operating in these sectors have an additional incentive to act now.

Metrics to monitor over time

A local visibility strategy in AI Search requires an adequate measurement system. Traditional metrics — ranking position, organic traffic — remain useful, but they don't capture presence in AI answers. Therefore, monitoring needs to be integrated with specific tools.

Parameters to keep an eye on include: Google Business Profile card views (impressions, clicks, direction requests), the frequency and sentiment of new reviews, the volume of online mentions through brand monitoring tools, and the business's presence in ChatGPT and Perplexity responses for relevant local queries.

Regarding measurement tools, Google Search Console has introduced specific AI reports that allow you to evaluate exposure in generated responses. We analyzed the impact of these reports in our guide on Search Console AI Reports and their effect on SEO audits . Furthermore, periodic qualitative research — directly querying AI assistants with your customers' most likely queries — remains the most direct method to verify your presence.

Common mistakes that compromise visibility

In practice, Italian SMEs make some recurring mistakes that limit their visibility in AI Search. The first is the inconsistent management of the Google Business Profile: information updated once and then forgotten for months. AI models perceive inactivity as a negative signal.

The second mistake is responding defensively to negative reviews or not responding at all. The owner's responses are part of the corpus that models read: a professional and constructive tone strengthens perceived reputation. Conversely, a lack of response signals disinterest.

The third mistake is treating digital channels as separate silos. Website, Google Business Profile, social media, and directories must form a coherent system. Therefore, any update — new address, new hours, new service — must be propagated across all platforms synchronously.

Finally, many SMEs underestimate the importance of textual content on the site. Pages that describe services generically do not provide AI models with the necessary context to associate the business with specific queries. A SEO copywriting strategy focused on local search intent remains a high-return investment even in the age of AI Search.

The recommended path to get started

For a small to medium-sized business starting from scratch or looking to revise its strategy, the most effective path follows a logical sequence. First, an audit of the Google Business Profile and major industry directories, correcting any NAP inconsistencies. Then, implement a systematic review collection process, integrated into daily operations.

So, building a minimal editorial plan for social channels, focused on local relevance and consistency with the services offered. Finally, launching citation building activities on authoritative sources in the sector and the territory.

This path doesn't require large budgets, but it does require consistency. Results in AI Search are built over time, through the accumulation of consistent signals. Those who want to explore the entire landscape of available strategies can consult our dedicated cluster for AI SEO and visibility , where we collect up-to-date analyses on the evolution of this landscape.

For SMEs that prefer to rely on an expert partner, our SEO services and the activities of Digital marketing include the integrated management of local presence in AI Search. It is possible contact us for a no-obligation initial assessment.