Google AI Overviews displays your negative reviews without users searching for them: how to protect your brand

A prospect types "ERP software for industrial SMEs" into Google. They're not looking for reviews. They don't mention any brand names. Yet in the AI Overview box that appears at the top of the page, your company shows up with a mention of a negative review from 2022. The prospect won't look any further.

This scenario is no longer hypothetical. Since Google's massive rollout of AI Overviews in 2024, B2B companies are discovering a concerning phenomenon: their negative reviews automatically surface in contexts where no one was looking for them. Google's generative system aggregates, synthesizes, and exposes reputational information without the user having formulated a query like "reviews" or "problems."

This article analyzes the mechanism behind this phenomenon, measures its real impact on B2B companies, and details concrete strategies to regain control of your reputation in the era of generative search.

How AI Overviews Surface Your Negative Reviews Without Explicit Queries

AI Overviews function fundamentally differently from traditional search. Google no longer simply lists links corresponding to keywords. Its generative system analyzes the intent behind the query and builds a synthetic response by drawing from multiple sources.

The Contextual Aggregation Mechanism

When a user searches for a product or service category, the algorithm identifies market players, their characteristics, and what the web says about them. This aggregation automatically includes reputation signals: Google Business reviews, mentions on specialized forums, comments on third-party platforms, LinkedIn discussions.

The problem: negative reviews often possess characteristics that make them particularly "quotable" by LLMs:

• Specificity of details: a dissatisfied customer precisely describes their problem, creating content rich in entities and context
• Strong emotional language: sharp formulations are more easily extracted as "facts" by models
• Thematic recurrence: if multiple sources mention the same problem, AI considers it validated information

Queries That Trigger Involuntary Exposure

At AISOS, we observe that certain categories of searches systematically generate unsolicited reputational mentions:

• Comparative searches: "best B2B CRM France" or "alternative to [competitor]"
• Industry searches: "accounting software publisher SME"
• Geolocated searches: "digital transformation consulting firm Lyon"
• Decision-making searches: "how to choose a logistics provider"

In each of these cases, the user isn't asking for reviews. But the AI Overview, seeking to provide a complete answer, spontaneously includes reputation elements to "help" with the decision.

Measured Impact on B2B Companies

The unsolicited exposure of negative reviews in AI Overviews has quantifiable consequences on the B2B buying journey.

User Behavior Data

According to a Gartner study published in March 2025, 67% of B2B buyers consider information displayed in generative boxes more reliable than traditional organic results. This trust amplifies the impact of a negative mention: it's no longer perceived as an isolated review, but as an objective synthesis from Google.

Journey data also shows that 78% of users don't scroll past the AI Overview when it seems to answer their question. A negative review displayed in this box therefore doesn't get a "second chance": the prospect probably won't see the dozens of positive reviews present lower in the results.

Concrete Case: B2B SaaS Publisher

A management software publisher for industrial mid-market companies saw a 23% drop in incoming demo requests over one quarter. Analysis revealed that for the query "ERP software industrial SME," the AI Overview mentioned a security incident from 18 months ago, which had been resolved and publicly documented. The information appeared in a context where the user wasn't looking for incident history.

The estimated cost: EUR 340,000 in commercial pipeline not generated during the period.

Why Traditional E-Reputation Strategies No Longer Work

Most B2B companies manage their online reputation with approaches designed for traditional SEO. These methods show their limitations when facing generative systems.

The Problem with Positive Review Volume

The classic strategy consists of "drowning" negative reviews under a volume of positive reviews. In traditional SEO, this approach works: recent and numerous results rise, old ones fall.

AI Overviews don't operate on this logic. The system doesn't rank reviews by date or volume: it extracts information it deems relevant to the query. A negative review from 2021 mentioning a specific problem can be cited if the model believes this information helps answer the user's question.

The Ineffectiveness of Right to be Forgotten

Some companies try to have negative reviews removed via right to be forgotten procedures or platform reports. Even when these approaches succeed, they don't solve the fundamental problem: LLMs have been trained on web snapshots that include this content. The information may have disappeared from the original source while remaining in the model's "memory."

The Limitation of Review Responses

Responding professionally to negative reviews remains a best practice. But AI Overviews rarely cite company responses. The synthetic format favors the customer's assertion ("Customer service doesn't respond") over the company's response ("We have since implemented 24/7 support").

GEO Strategies to Regain Control of Your Reputation

Brand protection in the AI Overview era requires a specific approach, oriented toward Generative Engine Optimization (GEO). Here are the actionable levers.

Create Content That Addresses Objections Before They're Raised

AI Overviews build their responses by aggregating sources. If your own content explicitly addresses friction points mentioned in negative reviews, the system can cite it as a counterpoint.

Concrete actions:

• Publish detailed FAQ pages addressing historical problems and their resolutions
• Create case studies that demonstrate resolution of identified weak points
• Publicly document your product improvements with precise dates

Example: if a negative review mentions slow support response times, create a page "Our Support Commitment: 2-hour Average Response Time in 2025" with verifiable data.

Structure Data for LLM Citations

Language models more easily extract explicitly structured information. Optimize your content for citation:

• Direct statements: "[Company Name] guarantees 99.9% SLA since January 2024" rather than vague formulations
• Dated numerical data: LLMs favor precise and temporally situated information
• Explicit named entities: clearly mention your brand name associated with positive attributes

Develop an Ecosystem of Positive Third-Party Mentions

AI Overviews cross-reference sources. Information present only on your site carries less weight than information corroborated by third parties.

Reputational networking strategy:

• Obtain mentions in B2B trade media in your sector
• Participate in rankings and comparisons where your strengths are documented
• Encourage customer testimonials on LinkedIn with specific details
• Publish customer cases on third-party platforms (Capterra, G2, Trustpilot B2B)

The goal: create a critical mass of structured positive mentions that LLMs can cite as a priority.

Monitor AI Overviews for Your Strategic Queries

You can't fix what you don't measure. Implement systematic monitoring:

• Identify the 20-30 strategic queries for your business (industry, comparative, decision-making)
• Test weekly what the AI Overview displays for each
• Document cited sources and extracted information
• Identify patterns: which negative sources recur? On which formulations?

AISOS audits reveal that 60% of B2B companies have never checked what AI Overviews say about them on their main business queries.

90-Day Action Plan to Protect Your Brand

Here's an operational roadmap to regain control of your reputation in generative results.

Days 1-30: Audit and Diagnosis

• List all relevant industry, comparative, and decision-making queries
• Test each query in Google with AI Overview activated
• Map negative mentions: source, date, exact content, frequency of appearance
• Identify pages from your site that are cited (or absent) from AI Overviews

Days 31-60: Corrective Content Production

• Create a response page for each major objection identified
• Update existing pages with recent numerical data
• Publish 2-3 case studies demonstrating resolution of historical problems
• Structure content with explicit statements and named entities

Days 61-90: Amplification and Networking

• Solicit structured customer testimonials on third-party platforms
• Pitch articles in 2-3 specialized B2B media outlets
• Update profiles on comparison sites with recent data
• Install monthly AI Overview monitoring

What AI Overviews Permanently Change for B2B Reputation

The phenomenon described in this article is not a temporary anomaly. It reflects a profound transformation in how B2B buyers access information.

AI Overviews, like responses from Perplexity, ChatGPT or Gemini, construct a synthetic reality of your company from everything that exists on the web. This synthesis doesn't distinguish between a 2021 review and a 2025 certification. It doesn't weight an isolated comment against 200 positive reviews. It extracts what seems relevant to the question asked.

For B2B companies, this means reputation becomes an asset built proactively, not an indicator monitored passively. Every page published, every mention obtained, every structured data point becomes a potential element of the response prospects will see tomorrow.

Leaders who integrate this reality into their digital strategy today will gain a decisive advantage. Others will discover, like the SaaS publisher mentioned above, that their commercial pipeline depends on algorithms they never sought to understand.

The question is no longer whether AI Overviews will mention your company. It's deciding what they'll say about it.