If you run a local business, you know the rush: a new five-star review drops, and you're riding high on social proof. But one bad review? It can feel like someone graffiti-tagged your storefront with "AVOID AT ALL COSTS." That one-star review will keep you up at night. 

Over the past two decades, we’ve gone from flipping through the Yellow Pages to scanning Google Maps to reading AI-powered summaries. In today’s search landscape, we’re navigating a maze of acronyms beyond SEO — from GEO (Generative Engine Optimization) to AEO (Answer Engine Optimization) to the emerging LLMO (Large Language Model Optimization). At the core, all of this is still SEO built for machines that synthesize information, not just for ranking pages.  

When I first published this post a year ago, the connection between your local reviews and what an AI assistant says about your business was a reasonable inference. Now, it seems there’s tangible evidence that live reviews will impact the AI recommendation engine, especially with companies like Yelp signing a licensing deal with OpenAI to provide reviews, ratings, photos, and business details. ChatGPT will now have a direct feed of local recommendation data, complete with Yelp branding and citations in responses. 

Today, local reviews are no longer a side benefit — they’re a central part of your overall local SEO and visibility strategy. They work hand-in-hand with other local signals, like your Google Business Profile (GBP), other local citations (all of which must be accurate and up to date), reviews, and more. In an AI-driven search environment, your entire online presence becomes data for machines to evaluate, including your online reputation and social proof.

This shift means that we’re no longer just trying to rank a website or local listings; instead, we’re also feeding machine learning models that synthesize information from your reviews, your site, third-party listings, social media, and more. These systems don’t just crawl and index like search engines. They also generate answers by predicting language patterns based on what they’ve seen across the web, including your reviews. They generate responses based on trends, sentiment, and context. What people say about your business, and how they say it, matters more than ever.

Let's break down the three core ways LLMs can use reviews to influence how and when your business gets recommended in AI-powered search experiences.

LLMs don't just compare each business’s average rating; they analyze the tone of reviews to understand customer satisfaction and trustworthiness. Authentic and positive sentiments are signals of reliability and customer trust, which can propel your business to a top pick for LLMs in summaries. These models detect emotional nuance: a heartfelt 4-star review raving about your service may outperform a terse 5-star review that says "Good."  

Consider this an exercise in semantic analysis of your customer testimonials. LLMs will think beyond “good” and “bad” to identify entities and connect them to sentiment descriptors:

• Positive Signals: "thrilled," "relieved," "impressed," "saved the day," "above and beyond," "gentle hygienist," "expert advice."
• Negative Signals: "disappointed," "frustrated," "ignored," "never again," "rude staff," "waste of money."

The AI Model uses this emotional nuance to build a complex profile of your business. It’s pattern recognition at a massive scale. For example, if a pattern of “unprofessional service” emerges across multiple reviews, your star rating will merely be an illusion. However, avoid trying to skip the line and resist the temptation to load up your listing with fake reviews, as Google will undoubtedly remove them. 

In fact, Google’s 2025 Maps Trust and Safety report, published in April 2026, revealed the extent to which some marketers exploited the goodwill of these public, free-to-use systems. Google ran a massive cleanup project that resulted in:

• Blocking or removing 292 million policy-violating reviews.
• Blocking 79 million inaccurate or unverified Google Business Profile edits.
• Placing restrictions on more than 782,000 policy-violating accounts.
• Removing over 13 million fake Business Profiles on Maps.

Let’s see this LLM review analysis in action, shall we? The screenshot below shows how ChatGPT has selected this dentist in London based on “reviews, recommendations, and local popularity.”

Pro Tip: Solicit genuine customer feedback, particularly from your brand loyalists. Monitor reviews on your listings to avoid being caught off guard by inaccurate testimonials or fraudulent spam from deceitful competitors. Specific industries can be cutthroat, so don't let spam tarnish your reputation. 

A consistent stream of fresh testimonials sends positive signals to LLMs about a business's reputation and reliability in a local market. LLMs prioritize businesses that feel active. A steady stream of recent reviews signals ongoing engagement, up-to-date service, and operational credibility. Creating a tapestry of recent social proof is crucial for models trained to deliver real-time, trustworthy recommendations.

AI Overviews often pull from listicles, but what the top recommendations share isn’t just placement — it’s a steady stream of fresh, high-quality reviews. Even more telling: the language in those standout Google reviews often matches the phrasing used in AI-generated summaries.

Over time, it seems like AI-powered conversational systems will continue to be integrated into everyday searches. For example, Google launched Ask Maps in March 2026, a Gemini-powered conversational layer within Google Maps. Users can now build an itinerary on the spot or ask the most on-the-spot questions, such as “my phone is dying, help me find a place to charge it and grab an espresso.” The integration of Ask Maps taps into the rich, up-to-date information Maps has about local businesses, personalizing options to each user’s specific needs.

Pro Tip: Delivering the best customer service is your most effective marketing tactic, so ensure you have workflows in place via email, SMS, and even in-person interactions to gather testimonials. Provide thoughtful and timely responses to all reviews, including those from angry customers who have written in all caps. Additionally, local marketing tools like Moz Local offer add-ons specifically designed to monitor reviews and respond to them in real-time. 

Here's a shocking fact: some of the best signals you can send to both Google and AI models about your business come from the content used in reviews. For example, your website might list HVAC repair as a service. Still, a review on your Google listing might go into more context, stating, "They repaired my broken AC unit during the middle of a heatwave in under an hour - exceptional service!" This type of review provides:

• A service term (AC repair)
• A product context (AC unit)
• A customer sentiment (“exceptional service”)
• A positive outcome (repair done in an hour, no complaints)

The added depth and content provide LLMs with richer, more useful context. In this case, the review doesn’t just reinforce your service; it effectively boosts your business listing by adding a layer of organic relevance, almost like tagging on an additional category.

These keyword-rich testimonials may contain copy that outperforms your website's published content. LLMs mine reviews for product mentions, service information, locations, and contextual clues that match specific queries. Highly detailed and relevant reviews can give you a competitive edge.

But before you start asking your customers to write reviews for your business, be sure you’re complying with Google’s updated policies. In an effort to combat spam and merchant-influenced reviews, Google added two clauses to the Maps user-generated content policy under the heading "Rating Manipulation" in April of 2026. In the past, it was common for small businesses to encourage staff to solicit customer reviews, even specifying the types of content to include, such as employees' names. However, Google now indicates that the following is part of its prohibited and restricted content scope:

• “Merchants requesting that staff solicit a certain number of reviews.”
• “Merchants requesting that staff solicit reviews that include specific content, including content that identifies a staff member.”

However, Google maintains that merchants may still solicit or encourage customers to share content that reflects a genuine experience, as long as they do not offer incentives, influence the content of the review, or request specific details to be included in the review. 

Pro Tip: While you can't put pen to paper for your customers, you can shape the narrative of their reviews by providing exceptional service and engaging them in ways that turn them into brand ambassadors. For example, a local fine-dining restaurant can focus on the overall presentation of their food, appealing to the inner nature of Instagram & TikTok foodies. Here are some policy-compliant ways to encourage better reviews:

• Ask open-ended questions that encourage users to be detailed without dictating the structure. For example, post-service emails that ask questions such as “How did we do?” or “Is there anything you would want another customer to know?” are an excellent way to invite customers to share their experience in detail.
• Showcase great reviews on your website or in an email to inspire a specific tone or style. 
• Offer QR codes or short URLs on receipts or signage that directly lead to your listings (e.g. Google Business Profile). For service-focused businesses, you can send the link via email rather than requesting a review on the spot, which can pressure customers into leaving one.
• Audit any existing programs run by third parties or staff to ensure compliance across the board. 

Looking for more ideas to encourage reviews? Check out our Local SEO courses in Moz Academy.

Avoid these common mistakes if you want both LLMs and users to trust your brand and recommend you:

• Publishing fake reviews: As previously mentioned, fake reviews can get flagged and adversely impact your brand’s reputation.  
• Ignoring complaints: Silence suggests neglect in this regard, so do your best to respond to users with valid complaints. 
• Focusing only on Google: It’s essential to collect reviews from a variety of sources, particularly those that are most used in your niche. With Yelp and OpenAI working together, the dynamics of local search are quickly changing.
• Over-engineering your review program. Over the past year, Google has implemented numerous safeguards to ensure that small businesses do not pressure customers into leaving heavily influenced reviews.

Search is no longer just about traffic and clicks – it’s about visibility, representation, and exposure to your target audience, no matter what platform they use. In an AI-driven world, a growing share of your customer base is likely to ask an AI assistant for a summary rather than use a traditional search engine. A year ago, I forecast how the future of local search might unfold, and three of those predictions have come to pass in the last 12 months.

How local search has evolved over the last year:

Prediction: AI chat interfaces continue to surge in popularity, offering users an additional entry point beyond traditional search engines. 

Outcome: AI chat interfaces did not just become an entry point for users, but they became a primary starting point on some platforms. Google merged the AI Mode experience into the default search surface globally, combining the traditional search experience with an AI-powered ecosystem.

What this means for local businesses: Your business’ public footprint needs to extend far beyond just search engines. Adoption of AI assistants is growing at a rapid pace, and if you’re not part of the conversation that potential customers are having with LLMs, then you’re letting your competition get the edge.

Prediction: LLMs will continue to improve and surface the most relevant recommendations based on prominence, customer sentiment, and contextual clues. 

Outcome: LLMs have improved at surfacing relevant recommendations, and will only continue to get better as data licensing deals shape the future of local marketing (OpenAI + Yelp).

What this means for local businesses: Optimizing your business across as many relevant listings as possible is more important than ever. As the battle for AI supremacy continues to unfold between OpenAI, Anthropic, Google, and others, the likelihood of future licensing deals could skew how customers find local businesses with AI assistants. As with social media, meet your customers where they are and focus your efforts on the review platforms that matter most in your industry. 

Prediction: Brand mentions and user-generated content across the web will become signals, signaling the importance of generating reviews on multiple platforms.

Outcome: Brand mentions became signals, and the prompt-tracking industry has exploded, prompting marketers to report on new metrics such as AI Brand Visibility, Citation Rates, and more. 

What this means for local businesses: Over the last 18 months, visibility for brands in AI answers has gone from a hunch to a science, driven by the boom in prompt tracking. You can now baseline where you stand relative to competitors, analyze sentiment of brand mentions, and even identify which sources are most critical in shaping the recommendations from LLMs. 

The future of local search may see further changes as the World Wide Web becomes more integrated with AI. Recommendation engines will eventually convert into full-funnel transaction engines. For example, Google rolled out agentic restaurant booking to eight additional markets in April 2026 and followed up by announcing the ability to order food, book hotels, and secure event tickets within Ask Maps in August. Google isn’t the only tech giant bridging the gap between discovery and transactions; Square introduced an integration in July 2026 that lets restaurants accept orders placed directly in ChatGPT and Claude, allowing users and restaurants to avoid potentially expensive aggregator fees that make online ordering cumbersome. 

At the end of the day, Local SEO won’t disappear. Platforms like ChatGPT, Gemini, and Claude just provide new interfaces for users to conduct research, and it’s vital to optimize the sources they’ll use most to recommend local businesses. 

LLMs will reward clarity, consistency, and credibility in the battle for local search. Although reviews are critical to maximizing your business's presence, don't expect them to be generated on autopilot. You can't (and shouldn't) game the system, but you can guide it. Focus on cultivating meaningful, descriptive, and timely reviews across all of your local listings. Here are some action steps you can implement:

• Audit your recent reviews: How many reviews do you have over the last 90 days? If customer testimonials have dried up, that’s not a good sign. 
• Respond to all reviews: Engage with your customers and demonstrate to Google and LLMs that you’re staying current with testimonials. 
• Encourage customers to share specifics: Provide a memorable service that naturally prompts them to share their experiences with others.
• Monitor all major review platforms: “To Google” has become part of the English lexicon, but don’t have a myopic focus on local marketing. Embrace reviews from all relevant platforms. 
• Monitor your brand’s AI visibility: Set up your own prompt tracking using a tool like Moz Pro to track how your brand shows up on popular LLM chat interfaces, as well as which sites are more likely to influence the recommendation engines. 
• Centralize your strategy: Using a tool like Moz Local can help you optimize your listings, monitor reviews, respond to customers, track your local rankings, and post to social media platforms all from one place. 

The next time someone asks ChatGPT or searches Google for the best [insert your service] in your area, make sure your business is the one recommended.