From reviews to recommendations: Why Google reviews determine visibility in the AI world

When AI becomes a search engine, Google reviews take center stage

Regardless of whether potential customers are looking for the best insurance or mortgage advice, they are increasingly asking their questions directly to a large language model (LLM) such as ChatGPT, Google Gemini or Perplexity — instead of Googling them.

What is often forgotten: In addition to current data from the Internet, the answers from these tools are also based on huge amounts of data from freely available sources. This includes Google Reviews.

LLMs are trained on a wide range of publicly available data — including customer reviews. OpenAI, GPT-4 Technical Report

In other words, public reviews have a strong influence on whether and how positively a brand is presented by AI.

SEO was yesterday, the era of Generative Engine Optimization (GEO) is beginning

LLMs are trained with billions of texts — including news articles, forum posts, websites and even online reviews such as Google Reviews. It is true that there are currently many theories about how LLMs can be influenced. However, it is clear that Google reviews play a central role in how LLMs rate a company due to their relevance.

Other reasons for their relevance in LLMs: Google reviews are publicly available, verified and maintained by Google and are easy to read by machine — ideal conditions for being incorporated into the training and decision-making logic of large language models.
‍

A mistake with long-term consequences

But mistakes can often be corrected through clever corporate communication or overlaid with positive actions.

However, with the shift towards AI systems, the long-term impact of individual negative customer testimonials is changing — they can enroll themselves more deeply in digital recommendation mechanisms than ever before.

Once the data has been incorporated into a model, it can influence the results for months or even longer — regardless of subsequent corrections. Stanford Human-Centered AI Report, 2024

• Individual negative reviews are highlighted disproportionately, as LLMs compress and weight content more heavily.
• Old entries don't disappear — they become part of the long-term memory of AI models.
• Companies lose control over when and how the brand appears in AI responses.

This is both a challenge and an opportunity — depending on how actively a company cultivates its reputation.

Risks & opportunities for companies

risks

• Negative customer feedback is less balanced by positive reviews and has a negative impact on the image in the long term.
• An inactive or empty review profile often appears irrelevant and unprofessional.
• Few ratings = distorted perception — and this is exactly what the AI stores.

possibilities

• With an active, genuine evaluation culture, you not only strengthen trust, but also the company's visibility in the AI search.
• Smart touchpoints (e.g. after consulting or signing a contract) give you fresh voices exactly where they count.
• If you get a positive rating today, you'll be recommended by AI tomorrow — automatically.
  

With little effort: making companies fit for the future of AI

AI is still in its infancy. Anyone who already understands the mechanisms today and uses them to their advantage can gain a major competitive advantage.

‍

Ask satisfied customers for a review

‍Actively ask satisfied customers for their opinion and ask for Google reviews — there are tools that automate the process and simplify it for customers.

96% of consumers are willing to write a review to a company Brightlocal 2025

‍

Monitor reviews in a targeted manner

‍Monitor Google reviews regularly — including trends, content, and response speed.

Professionally cushion negative reviews

‍React promptly and transparently — good answers have a de-escalating effect and strengthen the digital profile.

Mini checklist: Review strategy in the age of AI

• Do you have a system for recording & evaluating Google reviews?
• Are you specifically activating positive feedback after relevant touchpoints?
• Are there clear processes for dealing with negative reviews?
• When it comes to reputation measures, do you also think of their effect on AI answers?

Long story short: Whoever is rated today will be recommended tomorrow

Online reputation is no longer just relevant for people — it has long been part of the database from which AIs shape their worldview.

Google reviews are therefore becoming a central factor of Generative Engine Optimization (GEO) for every company. Sectors such as banks and insurance companies are particularly affected, as trust is their most important asset and must therefore be strategically maintained.

➡ How visible is the company in the new world of AI search?
➡ How does AI interpret and present the brand?
➡ And what can you do today to be perceived positively tomorrow?

Anyone who is now actively looking after their digital reputation will be rewarded by AI — with relevance, visibility and trust.