AI Local Visibility Is 30x Harder Than Google Ranking: Report
▼ Summary
– Most brands successful in traditional local search fail to appear in AI assistant results, with only 1.2% to 11% of locations being recommended by platforms like ChatGPT and Gemini.
– Achieving visibility in AI recommendations is three to 30 times harder than ranking in traditional local search, such as Google’s local 3-pack.
– AI systems prioritize businesses with accurate data, strong positive sentiment (reviews), and clear differentiation, often excluding locations with average ratings.
– Strong Google local rankings do not guarantee AI visibility, requiring consistent brand data and reputation across the wider ecosystem AI systems rely on.
– AI visibility impact varies by industry, with retail showing low overlap and performance heavily dependent on consistent, trusted signals and complete profiles.
Achieving visibility in AI-powered search results like those from ChatGPT, Gemini, and Perplexity is proving to be a monumental challenge for multi-location brands. A new industry report reveals that securing a recommendation from these AI assistants is between three and thirty times more difficult than ranking in Google’s traditional local search results. While a brand might appear in Google’s local 3-pack over 35% of the time, AI platforms are far more selective, with recommendation rates as low as 1.2% on some platforms. This stark disparity highlights a fundamental shift in how businesses must approach their online presence.
The data shows a significant disconnect between traditional and AI-driven visibility. Fewer than half of the brands that lead in Google local search also appear among the most visible in AI results. In the retail sector, for instance, only 45% of the top 20 brands by conventional search visibility overlapped with the top 20 most frequently recommended by AI. This indicates that a strong Google ranking is no longer a guarantee of being surfaced by conversational AI tools, which rely on a different set of criteria.
A primary reason for this gap is data quality. The analysis found that business profile information was only about 68% accurate on platforms like ChatGPT and Perplexity. In contrast, Gemini, which is grounded in Google Maps data, showed 100% accuracy. AI systems filter aggressively and favor locations with accurate, consistent data across a wide ecosystem of trusted sources, including Google Maps, Yelp, Facebook, and official brand websites. They treat this information as a qualification hurdle; if the data is incomplete or inconsistent, a business may be excluded entirely from consideration.
Sentiment and online reputation play a disproportionately large role in AI recommendations. These systems consistently favor businesses with above-average ratings, using reviews as a critical filter. Locations recommended by ChatGPT averaged 4.3 stars, for example. In traditional local search, a business with average ratings can still rank well based on proximity or category relevance. In the AI landscape, those same locations are often omitted, as the algorithms prioritize confidence and risk reduction over simple breadth of results.
The impact of these factors varies considerably across different industries. In retail, brands like Sam’s Club and Aldi exceeded expectations in AI visibility, while others like Target and Batteries Plus Bulbs underperformed, underscoring the importance of consistent signals. The restaurant sector shows visibility concentrated among a small group of leaders; Culver’s achieved recommendation rates of 30% on ChatGPT and 45.8% on Gemini due to strong ratings and complete profiles. Financial services provide a clear case study: after improving its profile coverage, ratings, and data accuracy, Liberty Tax achieved high visibility on both Google and AI platforms. Conversely, financial brands with poor profile accuracy, average ratings near 3.4 stars, and low review response rates were effectively invisible in AI results.
This new paradigm represents a major strategic shift for local businesses. The focus is moving from optimization for search engines to qualification for AI systems. Success now depends on a foundation of impeccable data hygiene, stellar reputation management, and clear brand differentiation across all platforms. Weak fundamentals in data and sentiment now translate directly into zero AI visibility, making these elements more critical than ever for future growth.
(Source: Search Engine Land)
