The Evolution of Local Search: How AI Overviews and LLMs Are Redefining Business Visibility and Review Management
The landscape of local search engine optimization (SEO) has undergone a fundamental transformation, shifting from a simple ranking of star ratings to a complex ecosystem where artificial intelligence (AI) prioritizes specific query matching over traditional reputation metrics. This shift was recently exemplified by a case study involving a car wash in Norfolk, Virginia, which managed to secure the top AI-generated recommendation despite holding a modest 3.3-star rating. The instance, highlighted by industry experts Annie Jackson and Jason Wertham of GatherUp, serves as a critical indicator of how Large Language Models (LLMs) and Google’s AI Overviews are reshaping the way consumers discover and interact with local businesses.
When 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 assumptions about local search rankings. Instead of surfacing the highest-rated businesses in the area, Google returned a 3.3-star business. The AI summary prioritized the business because it directly answered the user’s specific requirements regarding vehicle clearance and operating hours, placing these factual answers above the star rating itself. This "query match" phenomenon suggests that for AI tools, the context of a business’s offerings and its operational details are becoming as significant—if not more so—than its aggregate review score.
The Shift in Consumer Search Patterns
The emergence of generative AI has led to a significant change in how consumers seek information. According to consumer data collected in the fall of 2025, the transition toward AI-assisted search is accelerating. The data reveals that 55% of consumers have consulted AI summaries on platforms like Google or Bing, while 48% have utilized ChatGPT to inquire about local businesses. Notably, 31% of users reported using these tools multiple times, indicating that AI-driven search is becoming a habitual part of the consumer journey.
Unlike traditional searches, which often utilize short-tail keywords like "car wash near me," AI-driven queries are increasingly conversational and specific. Users are asking full questions and, crucially, accepting the summarized answers provided by the AI. This behavior creates a "zero-click" environment where the customer may never visit the business’s actual website, instead relying entirely on the description assembled by the AI from various digital footprints, including reviews, directory listings, and public web mentions.
Jason Wertham, Vice President of Review Defense Operations at GatherUp, noted that these AI tools are also becoming more personalized. LLMs now factor in the "who, when, and where" of a query. For instance, if an LLM recognizes through past interactions that a user owns an SUV or a large dog, it may apply that context to future local queries automatically. Furthermore, the time of day can influence results; a query made at 2:00 AM may prioritize 24/7 businesses over those with higher ratings that are currently closed.
The Technical Barrier: How AI Accesses Review Data
A common misconception in the digital marketing space is that LLMs have unfettered access to all review data across the web. In reality, major directory service providers such as Google and Yelp actively block LLM crawlers from scraping review content directly from business profiles. This creates a strategic gap: while reviews on these platforms still influence traditional local rankings and conversion rates on the listings themselves, they do not automatically inform the narratives generated by ChatGPT, Claude, or Google’s AI Overviews.
However, this barrier is porous. Wertham explained that the moment a business republishes its reviews on other public-facing platforms, that content becomes "fair game" for AI crawlers. By embedding review widgets on a company website or sharing customer testimonials on public social media channels, businesses provide the crawlable text that LLMs need to categorize a business as "popular," "highly reviewed," or "expert in SUV detailing."
For multi-location brands, the implication is clear: relying solely on third-party directories for reputation management is no longer sufficient. To influence AI summaries, businesses must "evangelize" their reviews by moving them to surfaces that LLMs can index. This includes not only the review text but also the business’s responses, which provide additional context and keywords that AI tools use to build their summaries.
Metrics of the AI Era: Recency and Velocity Over Star Ratings
As AI summaries become the primary interface for local discovery, the weight of the traditional five-star rating system is evolving. In the audit examples presented by Jackson and Wertham, AI answers rarely cited a business’s average star rating. Instead, they consistently cited specific review content and factual data points.
Consumer sentiment appears to be following this trend. Current data suggests that 45% of users prioritize the recency of a review over the overall star rating. Furthermore, 60% of consumers trust detailed written reviews more than "rating-only" submissions, and 70% prefer businesses that request feedback within 72 hours of a transaction. This preference for "fresh" data is reflected in how users interact with Google Maps; many now manually switch the sort order from "Most Relevant" to "Newest" to ensure the feedback reflects the current state of the business.
Wertham argued that a business with 1,000 reviews and a 4.2-star rating is often more attractive to both AI and humans than a business with a 5.0-star rating built on only 30 reviews, many of which may be years old. For AI, a steady "velocity" of new reviews provides a continuous stream of updated information, allowing the model to remain confident in its recommendations.
The Build, Manage, and Defend Strategy
To navigate this new reality, experts propose a three-pillar framework for local reputation management: Build, Manage, and Defend.
- Build: This phase focuses on establishing a foundation of consistent listings and a steady volume of reviews. Businesses must ensure that their Name, Address, and Phone number (NAP) data is identical across all platforms. Inconsistency in these basic facts can lead to AI hallucinations or the exclusion of a location from search results.
- Manage: Managing the reputation involves monitoring feedback and responding within a critical 72-hour window. This not only satisfies the consumer’s desire for engagement but also provides fresh, keyword-rich content for AI to crawl.
- Defend: The defense pillar involves protecting the earned rating from policy-violating reviews, such as spam or harassment. It also involves addressing "review smothering," a tactic where negative reviews are buried or manipulated. Wertham’s team specializes in removing reviews that violate platform policies, even those that are over a decade old, to ensure the AI narrative remains accurate.
The "Slot Machine" Effect and the AI Slop Penalty
One of the most challenging aspects of AI-driven search is its inherent lack of predictability. Jackson compared AI queries to a "slot machine," noting that the same question asked by different users, or even the same user at different times, can yield different results. Research from SparkToro supports this, showing that LLM results rarely return in the same order across different accounts or devices.
Because of this variability, "position" or "rank" is becoming a less reliable metric for success. Instead, businesses should focus on "total citations"—the breadth and diversity of sources that mention the brand. The more often a brand is mentioned across various crawlable sites, the higher the probability it will appear in an AI summary, regardless of the specific device or account used for the query.
Furthermore, Google has recently intensified its crackdown on what industry insiders call "AI slop." With the update to its generative AI guidelines, Google is now penalizing businesses that rely on low-value, AI-generated content, such as generic blog posts or automated FAQ pages. This "AI slop penalty" means that businesses must prioritize authentic, human-centric content and genuine customer reviews to maintain their visibility.
Strategic Implications for Franchises and Multi-Location Brands
For franchisors, the shift to AI search presents a unique set of challenges. Often, individual franchisees control their own local profiles, leading to inconsistencies that can damage the national brand’s AI narrative. If one location has outdated hours or a string of unaddressed negative reviews, it can skew how the LLM perceives the entire brand.
The recommended approach for large organizations is to establish a centralized "playbook" that provides franchisees with white-labeled tools and best practices. By running regular "emergency audits"—using specific prompts to see what ChatGPT or Google AI says about each location—franchisors can identify weak links and coach franchisees on improving their local digital footprint.
Conclusion and Future Outlook
The case of the 3.3-star car wash winning the AI answer is a harbinger of a more data-driven and context-aware era of local search. As LLMs become more integrated into the daily lives of consumers, the "basics" of SEO—consistent listings, high review velocity, and authentic engagement—have become more critical than ever.
The move away from a pure star-rating meritocracy toward a system that values specific utility and recent feedback means that businesses of all sizes have a renewed opportunity to compete. By ensuring their data is crawlable, their reviews are recent, and their digital presence is consistent, businesses can influence the AI "narrative" and ensure they remain visible in an increasingly automated marketplace. The fastest way to change an AI’s opinion of a company today is not through a single viral post, but through the meticulous management of the facts and feedback that define the brand across the open web.
