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

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 is that the answers provided by these tools are based not only on current data from the internet but also on huge amounts of data from freely accessible sources. This also 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 age of Generative Engine Optimization (GEO) begins

LLMs are trained with billions of texts - including news articles, forum posts, websites and online ratings such as Google Reviews.
There are currently many theories about how LLMs can be influenced. However, one thing is clear: due to their relevance, Google reviews play a central role in how LLMs evaluate a company.

Other reasons for their relevance in LLMs: Google reviews are publicly accessible, verified and maintained by Google as well as easily machine-readable - ideal conditions for being incorporated into the training and decision-making logic of large language models.

A mistake with long-term consequences

However, mistakes could often be corrected through clever corporate communication or overlaid with positive actions.

With the shift towards AI systems, however, the long-term impact of individual negative customer reviews is changing - they can become more deeply embedded 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 disproportionately emphasized as LLMs compress and weight content more heavily.
• Old entries do not 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 maintains its reputation.

Risks & opportunities for companies

Risks

• Negative customer reviews are less likely to be offset by positive reviews and have a negative impact on your image in the long term.
• An inactive or empty review profile often appears irrelevant and unprofessional.
• Few reviews = distorted perception - and this is exactly what AI stores.

Opportunities

• An active, genuine review culture not only strengthens trust, but also the visibility of the company in AI searches.
• Smart touchpoints (e.g. after consulting or concluding a contract) allow you to gain fresh votes exactly where they count.
• Those who are rated positively today will be recommended by AI tomorrow - automatically.
  

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

AI is still in its infancy. Those who already understand the mechanisms and use them to their advantage can gain a major competitive advantage.

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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

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Monitor reviews in a targeted manner

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

Professionally cushion negative reviews

Respond promptly and transparently - good responses 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?
• Do you specifically activate positive feedback after relevant touchpoints?
• Are there clear processes for dealing with negative reviews?
• Do you also consider the effect of reputation measures on AI responses?

Long story short: Those who are 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 form their world view.
Google ratings are therefore becoming a central factor in Generative Engine Optimization (GEO) for every company. Industries such as banks and insurance companies are particularly affected, as trust is their most important asset and must therefore be strategically cultivated.
➡ How visible is the company in the new world of AI search?
➡ How does the AI interpret and present the brand?
➡ And what can you do today to be perceived positively tomorrow?

Those who actively take care of their digital reputation now will be rewarded by AI - with relevance, visibility and trust.