In 2026, your customers don't move from "search" to "purchase" in a straight line.
They ask ChatGPT for a restaurant recommendation, then search Google to verify the location's hours. They also click through AI's cited sources, check a handful of reviews, and maybe scroll through Instagram before they commit. And if they're among the growing segment of high-income customers with real purchasing power, there's a good chance they've asked two different AI models the same question and compared the results.
As the discovery journey has fractured across these different touchpoints, so too has the conventional wisdom about where visibility strategy should focus. Some brands are investing heavily in AI optimization; others are doubling down on traditional SEO. And plenty more are doing both, hoping to cover enough ground to stay visible in every channel.
But Yext's research shows that the brands winning across all these channels aren't spreading their strategy too thin. They're building one foundation that works everywhere through structured, accurate data synchronized across every place customers — and AI models —look.
How do we know? Yext conducted three independent studies spanning 21.6 million Google search results, 17.2 million AI citations, and survey responses from 1,120 U.S. consumers. The findings tell the exact same story from three different vantage points.
Here's what each study reveals.
In Google search, data quality is the competitive edge
For all the attention AI has attracted, Google is still where most local searches begin. Yext's Elo Ranking Study analyzed 21.6 million search results across 30 industries using the same statistical method competitive chess uses to measure true player strength. This system was designed to filter out day-to-day volatility and surface sustained competitive performance over time.
The finding: brands actively managing and synchronizing their data rank 2.71 positions higher in local search within one mile of the searcher. In ultra-competitive markets (100 or more brands competing for the same keywords), that advantage climbs to 6.20 positions.
Six positions is the difference between page one and page two. For a restaurant competing against 120 others for "pizza delivery near me," the relevant number isn't the overall average. It's +6.20.
The advantage held across 19 of 20 industries with statistically meaningful sample sizes, including hospitality, healthcare, retail, services, and finance. That consistency matters. It's the difference between a category-level result and a structural one.
Scale doesn't change the picture. Small brands with fewer than five locations see a lift of +4.23 positions. Enterprise brands with 500 or more locations see a +3.41 gain, and that gain compounds across every location in the national footprint. The structured data advantage doesn't diminish with size. It persists.
Notably, what the higher-ranking brands had in common wasn't better star ratings. Managed brands averaged slightly lower star ratings (3.57) than their unmanaged counterparts (3.82), yet still ranked higher. The signal was profile completeness, data freshness, and active synchronization — not sentiment.
In AI search, no two models cite the same way
AI search presents a more complicated picture. Yext's AI Citation Research analyzed 17.2 million citations across Gemini, Claude, Perplexity, and ChatGPT and found that each model sources its responses differently. Sometimes dramatically so.
Gemini, grounded in Google Search, leans heavily toward first-party websites and owned content. That's consistent with how things have traditionally worked: Gemini synthesizes from Google Search results, so competing for visibility in Gemini still means excelling at traditional search optimization.
Claude tells a different story. Across all seven sectors in the study, Claude's reliance on reviews and social media runs 2 to 4 times higher than competing models. Ask Claude about a restaurant, and roughly one in four sources it cites is a review or social post. Ask Gemini the same question and that ratio drops to one in forty.
ChatGPT shows the highest variance by industry. In hospitality, it cites official hotel websites at 38.1%, roughly double the rate of other models in the same sector.
Perplexity is the most consistent across industries, with citation patterns that hold relatively steady regardless of category.
The practical implication: there is no single AI optimization strategy. A brand with strong visibility in Gemini could be nearly invisible in Claude, with no way of knowing the difference without model-level data. Optimizing for one model's preferences isn't an AI strategy. It's a partial one.
The common winner across all four models has the same key characteristic the Elo study identified: structured, accurate, brand-controlled data across every channel AI models draw from, including websites, listings, reviews, and social. More than 90% of AI citations trace back to brand-controlled sources. That means the content AI surfaces about a brand is largely owned or actively managed by the brand. Whether it's accurate, complete, and current is a data question — not an AI optimization problem.
In the search journey, customers still verify elsewhere before they act
The third study addresses the question the first two don't: once a customer gets an AI recommendation, what do they actually do with it?
Yext surveyed 1,120 U.S. adults in March 2026 about how they find local businesses, how much they trust AI, which platforms they verify against, and what drives them to act. The results clarify two things at once.
On trust: 73.8% of AI users rate their trust in AI local business recommendations at 4 or 5 out of 5. This means AI has earned real consumer confidence. Nearly half of all U.S. adults (47%) used an AI tool to find a local business in the past month, and 61% say they use AI as much or more than they did last year. Adoption is accelerating.
One signal stands out for brands focused on high-value customers. Among households earning $150,000 to $175,000, AI has already surpassed Google as the starting point for local searches (53.4% vs. 49.3%). At $175,000 to $200,000, the gap widens further. The customers with the most purchasing power have already made the shift.
On verification: only 6.7% of AI users act on a recommendation without doing any additional research. The other 93% take at least one more step, searching Google (61.8%), visiting the brand's website (57.7%), clicking through the AI's cited sources (52.4%), or checking reviews (31%).
What makes this finding significant is that verification rates are nearly identical across trust levels. High-trust users — those who rate their confidence in AI at 5 out of 5 — verify via Google at 62.0%. Neutral users verify at 63.1%. Trust level doesn't predict whether someone verifies. It only predicts how.
This is the visibility gap most brands miss. Earning an AI recommendation is not the finish line. The customer still visits the listing, the website, and the reviews. If any of those are inaccurate, incomplete, or out of date, that recommendation is lost. Not because the AI failed, but because the brand wasn't ready to deliver what the customer wanted or needed next.
And when customers verify, what influences the final decision? Review signals dominate. Five of the top six purchase influencers are review-related: star rating, word of mouth, review recency, review sentiment, and total review count. The conversion layer between an AI recommendation and an actual customer isn't ad spend or AI-specific optimization. It's the quality and freshness of structured brand data across every channel the customer checks.
Three studies. One answer.
Each study approaches brand visibility from a different angle. The Elo research asks: what separates the brands that rank higher on Google? The citation research asks: what makes a brand show up across multiple AI models? The consumer survey asks: what makes a customer choose a brand after an AI recommendation?
Different questions. The same answer.
In Google search, the brands that rank higher keep their data accurate, complete, and freshly synchronized. In AI search, more than 90% of citations come from brand-controlled sources, and the brands getting cited have structured, consistent data across websites, listings, reviews, and social. And in the post-recommendation verification loop, people check those same channels and make purchase decisions based on what they find.
Brands winning consistently across all three moments aren't running separate strategies for each channel. They've built one foundation that works in all three places.
That foundation is what Yext delivers: a Knowledge Graph that structures and syncs brand data across every channel customers search, verify, and decide from.
Other vendors address parts of the visibility challenge. But Yext built the infrastructure that powers it all, invests in research to understand how the landscape is shifting, and turns those findings into value that brands can rely on. As the research shows, that investment is a measurable competitive advantage in every channel that matters today — not just a hedge against what AI might do next.
See where your brand stands in search
Customers are already searching, asking AI, verifying, and deciding, often in the same session. The brands they choose are the ones whose data is ready at every step of that journey.
Find out how Yext can build that foundation for your brand.
*Sources: Yext Research — The Data Synchronization Effect (Feb 2026) | AI Citation Behavior Across Models (Q4 2025) | Yext Consumer Search Survey (Mar 2026)
