How AI Search is Redefining Local SEO and Why Star Ratings Are No Longer the Primary Metric for Business Visibility

The traditional hierarchy of local search, long dominated by high star ratings and the "Local Pack" of three top-rated businesses, is undergoing a fundamental transformation as artificial intelligence (AI) begins to mediate the relationship between consumers and local enterprises. Recent findings presented by industry experts Annie Jackson and Jason Wertham of GatherUp highlight a significant shift: AI-driven search tools are now prioritizing specific query matches and data accessibility over raw review scores. This shift was starkly illustrated by a case study in Norfolk, Virginia, where a car wash with a modest 3.3-star rating secured the primary AI-generated recommendation over higher-rated competitors. The reason for this anomaly lies in the AI’s ability to parse specific attributes—such as "no-touch" capabilities and "SUV clearance"—from various web mentions, effectively outranking the conventional star-rating metric in favor of functional relevance.

The Norfolk Case Study: Query Match vs. Star Rating

The emergence of AI Overviews, ChatGPT, and Ask Maps has introduced a new logic to local discovery. When Annie Jackson, Director of Revenue Operations and Growth at GatherUp, conducted a search for a "no-touch car wash that fits an SUV in Norfolk, VA," the results challenged long-standing SEO assumptions. Instead of a 4.5-star or 5.0-star business appearing at the top, Google’s AI returned a business with a 3.3-star rating.

The AI prioritized the business because it could verify the specific clearance height and the 24/7 operating hours required by the user’s highly specific query. In this new ecosystem, the AI functions as a digital concierge, assembling a comprehensive description of a location from a mosaic of sources, including reviews, official listings, and public web mentions. For the consumer, the convenience of a direct answer—confirming that their vehicle will fit and the facility is open—outweighed the general consensus represented by the star rating. This highlights a critical pivot in consumer behavior: the transition from "broad discovery" (e.g., "car wash near me") to "precision utility" (e.g., "no-touch car wash for my SUV").

Chronology of the AI Search Evolution

The shift toward AI-mediated local search did not happen overnight but is the result of several years of incremental technological integration.

1. Early 2023: The integration of Large Language Models (LLMs) into search engines began in earnest with the introduction of Bing AI and Google’s initial experiments with the Search Generative Experience (SGE).
2. Late 2023 to Early 2024: Google began rolling out "AI Overviews" to the general public, moving beyond experimental labs. During this period, the volume of indexed local data grew to include over 300 million places and 500 million review contributors.
3. Fall 2024: Consumer data indicated a tipping point. According to GatherUp’s research, 55% of consumers had consulted Google or Bing AI summaries for local business information, and 48% had specifically used ChatGPT to vet local service providers.
4. 2025 Projections: The "fall 2025" data cited by Jackson and Wertham suggests that nearly a third of consumers are now "power users" of AI search, consulting these tools multiple times to make a single purchasing decision.

This timeline reflects a rapid adoption curve that has outpaced many local businesses’ ability to update their digital presence.

The Technical Reality: Why Reviews Are Often "Invisible" to AI

One of the most significant revelations from the GatherUp session, led by Jason Wertham, Vice President of Review Defense Operations, concerns the "crawlability" of review data. There is a common misconception that because a business has thousands of reviews on Google or Yelp, those reviews are automatically fueling AI summaries.

In reality, major directory service providers like Google and Yelp actively block LLM crawlers from scraping review content directly from their business profiles. This creates a data silo. While those reviews still influence traditional search rankings and the "Local Pack," they are often invisible to tools like ChatGPT or Claude unless the business takes proactive steps to move that data into the public web.

"The major directory service providers… do not allow LLM tools like ChatGPT and Claude to scrape or crawl the review data on the business listing," Wertham explained. However, the moment a business republishes those reviews—whether on social media channels, through review widgets on their own website, or in blog posts—they become "fair game" for LLMs. This distinction is vital for businesses aiming to win "popular" or "highly reviewed" queries in AI search; if the content is confined to a directory, the AI cannot cite it as evidence of the business’s quality.

Supporting Data: What Modern Consumers Prioritize

The transition to AI search is being driven by a change in what consumers value most when reading reviews. The GatherUp data provides a clear breakdown of these shifting priorities:

• Recency Over Rating: 45% of users prioritize how recently a review was posted over the overall star rating.
• Depth Over Surface: 60% of consumers trust detailed, written reviews significantly more than "rating-only" (star-only) submissions.
• The 72-Hour Window: 70% of consumers prefer to receive a review request within 72 hours of their transaction, indicating that the window for capturing high-velocity, relevant data is small.
• Trust in Volume: Wertham noted that a business with 1,000 reviews and a 4.0-star rating is often perceived as more trustworthy than a business with only 30 reviews and a perfect 5.0-star rating.

This data suggests that "review velocity"—the speed and consistency with which new reviews are generated—is becoming a more powerful signal than the aggregate score itself.

The "Build, Manage, Defend" Framework

To navigate this new landscape, Jackson and Wertham proposed a three-pillar strategy for multi-location brands:

1. Build

The first phase involves establishing a foundation of consistent, accurate listings across all platforms. In the AI era, a single discrepancy—such as an incorrect phone number on a legacy Facebook page—can "poison the well" for an LLM trying to synthesize a factual summary. This phase also focuses on "evangelizing" reviews by moving them from third-party directories to the business’s own website and social channels to ensure they are crawlable.

2. Manage

Management focuses on the "72-hour response window." This involves not only responding to reviews to build engagement but also monitoring the "velocity" of incoming feedback. For franchisors, this is particularly challenging. Wertham suggested that brand-level oversight is necessary because one poorly managed franchise location can negatively impact the AI’s perception of the entire brand.

3. Defend

The "Defend" pillar involves protecting a brand’s reputation from policy-violating reviews and "burial tactics." Wertham highlighted a technique known as "review smothering," where businesses must actively generate new, positive, and factual content to push down outdated or irrelevant negative feedback. He also noted that keyword-heavy reviews from "Local Guides" tend to have a longer shelf life in AI summaries, making it necessary to occasionally dispute older, non-compliant reviews that may be unfairly weighing down a location’s profile.

The "Slot Machine" Effect and the AI Slop Penalty

A unique challenge of AI search is its inherent lack of stability. Jackson described AI answers as behaving like a "slot machine." Because LLMs generate responses based on probabilistic models and stored context, two different users asking the same question might receive different rankings or summaries. SparkToro research corroborated this, showing that results rarely return in the same order across different accounts or devices.

Furthermore, Google has recently intensified its crackdown on what experts call "AI slop." This refers to low-value, AI-generated content—such as generic blog posts or FAQ pages—designed solely to "game" the system. Google’s updated guidelines now include penalties for such content. Businesses that rely on automated, low-quality text to populate their websites may find themselves penalized, losing the very visibility they sought to gain.

Analysis of Implications for Local Businesses

The implications of these findings are profound for local marketing. First, the "set it and forget it" approach to Google Business Profiles is no longer viable. Businesses must treat their own websites as the primary source of truth, as AI tools update factual data (like hours and services) from websites much faster than they update "reputational" data from reviews.

Second, the role of the "Local Guide" and the "written review" has been elevated. Because AI looks for context, a review that mentions specific keywords—like "wheelchair accessible," "vegan options," or "fast Wi-Fi"—is infinitely more valuable for AI visibility than a simple five-star rating.

Finally, for large-scale enterprises and franchisors, the "consistency gap" is the greatest threat. If franchisees are left to manage their own profiles without a centralized playbook, the resulting data fragmentation will lead to inconsistent AI answers, confusing both the LLMs and the customers who rely on them.

Conclusion and Actionable Steps

As AI continues to synthesize the world’s information, the goal for local businesses is no longer just to be "the best," but to be "the most understandable" to a machine. To achieve this, the GatherUp experts recommend a monthly audit using specific prompts in incognito mode to see what ChatGPT and Google AI are currently telling customers.

The fastest way to influence an AI’s narrative is to ensure the basics—listings, hours, and core services—are identical across the web, and then to "socialize" customer reviews. By making private feedback public and directory-based reviews crawlable, businesses can ensure that when an AI "pulls the lever" on its results, their brand is the one that appears with the correct context, regardless of whether they have a perfect five-star rating.