When ChatGPT recommends a plumber or Gemini suggests a salon, reviews are doing most of the heavy lifting. But not in the way you might think.
The old model was simple: more stars equals better ranking. Google's algorithm weighted review ratings, and businesses fought to maintain 4.5+ averages. That model still matters for traditional search. But AI recommendations work differently.
Important
AI doesn't just count stars. It reads the actual text of your reviews and makes judgments about your business.
What AI actually sees in your reviews
Large language models don't just count stars. They read. Every review is text data, and the AI is parsing it for meaning, sentiment, and credibility signals.
A review that says "John was on time, explained the repair clearly, and the price was fair" tells the AI something specific. It associates your business with punctuality, communication, and transparency. When someone asks "Who's a reliable plumber that explains what they're doing?", that review makes you more likely to surface.
Contrast that with "Great service 5 stars." The AI learns almost nothing. It's a positive signal, but it's thin. It doesn't differentiate you from anyone else.
"A thousand generic 5-star reviews are worth less than 500 detailed ones that mention specific services and employee names."
This is why review quality matters as much as quantity.
The velocity problem
AI systems have a recency bias. They know that businesses change over time. A company with 2,000 reviews from 2019 and silence since then looks abandoned. A company with 500 reviews, but 50 of them from the last month, looks alive.
Review velocity is the rate at which you're collecting new reviews. For AI recommendations, this matters more than total count. The AI is trying to answer the question "Who's good right now?", and old reviews don't answer that question.
Pro Tip
Businesses with lower total review counts but higher velocity consistently outrank competitors with more reviews overall.
We've seen this pattern repeatedly: the AI interprets fresh, consistent review flow as a signal that the business is actively serving customers and maintaining quality.
The response signal
Here's something most businesses miss: AI systems read your responses too.
When you respond to a review, that response becomes part of your digital footprint. A thoughtful response to a negative review shows the AI that you handle problems professionally. A template response copied across dozens of reviews looks like you're not actually paying attention.
Worse, businesses that don't respond at all send a signal that they're either overwhelmed or don't care. Neither interpretation helps you get recommended.
"Respond to everything, but vary your responses. Reference specific details from the review. This creates a body of text that tells the AI you're engaged and customer-focused."
Platform diversity and citation weight
Google reviews matter most, but they're not the only signal. AI systems aggregate data from Yelp, Facebook, BBB, industry directories, and anywhere else your business is mentioned.
Having reviews across multiple platforms does two things. First, it creates redundancy. If your business has consistent positive signals across five platforms, that's more credible than great reviews on one platform alone. Second, it feeds different data sources that AI systems pull from.
Important
If you have 4.8 stars on Google but 3.2 on Yelp, that discrepancy is a red flag. AI systems notice when signals don't align.
The sentiment layer
Star ratings are crude. A 4-star review could be mildly positive or secretly negative. AI systems go deeper by analyzing actual sentiment.
Sentiment analysis looks at the language in reviews to determine how customers actually feel. "The job was fine, I guess" registers differently than "The job exceeded my expectations." Both might be 4 stars, but the sentiment signals are very different.
This means that how your customers write about you matters. Businesses that train customers to share specific positive details get better sentiment signals than businesses that just ask for ratings.
Pro Tip
"Tell us what stood out" prompts better language than "Please rate us."
Building a review profile that wins AI recommendations
The strategy isn't complicated, but it requires consistency:
Capture reviews at the moment of satisfaction. The best reviews come immediately after positive experiences, when customers are most likely to write something specific and enthusiastic. NFC badges, QR codes, and follow-up texts within 30 minutes of service all work.
Train your team to ask. The businesses with the best review velocity aren't lucky. They've built review requests into their service process. Every technician, every visit, every happy customer is an opportunity.
Respond to everything. Not with templates. With actual responses that reference what the customer said. This takes time, but it builds a response corpus that AI systems read.
Fix the gaps. If you're weak on Yelp or Facebook, focus there. Platform diversity matters. You want consistent signals everywhere.
Watch your velocity, not just your count. Monthly review trends matter more than lifetime totals. If you're collecting fewer reviews than last month, figure out why.
Reviews aren't just social proof anymore. They're the primary dataset that AI systems use to decide who gets recommended. The businesses that understand this have a massive advantage over the ones still optimizing for star averages.
Further Reading
- Local Consumer Review Survey 2025 — BrightLocal's research shows 98% of consumers read online reviews for local businesses
- Google Review Policies — Official guidelines on what's allowed in review collection
- ChatGPT Local Search Data Sources — Local Falcon's analysis of where ChatGPT pulls business data
- 40 Online Review Statistics — Key data points on review impact
Dylan Allen is the CEO of Cheers, the GEO platform for local service businesses.