How Brands Should Handle Negative Mentions in Google and Bing AI Answers

AI-generated answers in Google and Bing have changed how brand reputation is formed in search results. Users increasingly receive direct summaries instead of visiting websites, which means a negative mention can shape perception before any interaction with the brand. For businesses, this creates a new reputational risk that requires a different approach from traditional search optimisation or review management.

How AI systems generate brand-related answers

AI answers in Google Search and Bing Chat are built on large language models that combine indexed web content, trusted publishers, structured data, and historical context. When a user searches for a brand, the system does not rely on rankings alone, but on patterns found across multiple sources that mention the company in a similar way.

This means AI does not distinguish clearly between “main” and “secondary” sources. An old forum thread, an outdated news article, or a critical blog post can be used alongside authoritative media if it appears relevant to the query intent. The system focuses on consensus and repetition rather than freshness alone.

As a result, brands lose some control over first impressions. Even if a negative page no longer ranks highly, its content may still be used to generate AI answers if it fits the semantic context of brand-related questions.

Why sentiment matters more than rankings

In AI-generated answers, sentiment weighting plays a larger role than classic position-based visibility. If negative descriptions appear frequently across different sources, the model may summarise them as a defining trait of the brand.

This explains why companies sometimes see criticism in AI answers despite strong SEO performance. The model is not rewarding optimisation efforts but attempting to reflect what it interprets as the prevailing narrative.

For marketers, this requires a shift in focus: managing how a brand is described across the web, not just where it ranks, becomes a core reputation task.

Why negative mentions surface without traffic impact

One of the most confusing aspects for businesses is seeing negative statements in AI answers even when those pages generate no measurable traffic. This happens because AI systems extract information independently of click behaviour.

The presence of a negative mention in a crawlable, indexable source is enough for it to be used. Traffic, engagement metrics, or conversion data are not prerequisites for inclusion in AI-generated summaries.

Additionally, AI systems often value explanatory or opinion-based content, even if it comes from low-traffic sources, because such content provides clear language patterns that are easy to summarise.

The role of historical content and persistence

Older content plays a disproportionate role in AI answers. If a negative article or discussion has existed for years without counterbalancing material, it may be treated as a stable reference point.

This persistence explains why rebranding or product improvements do not automatically change AI output. Without new, authoritative signals, the system continues to rely on what it already “knows”.

Brands must therefore actively replace outdated narratives with newer, well-documented information published on credible domains.

Practical ways to influence AI-generated brand narratives

There is no direct way to edit AI answers, but there are proven methods to influence the data they are built on. The most effective approach combines structured content, authoritative distribution, and clear authorship signals.

Publishing detailed brand explanations, issue responses, and factual clarifications on trusted websites helps AI systems identify reliable reference points. Content should be explicit, neutral in tone, and supported by verifiable details.

Equally important is third-party validation. Mentions in industry media, expert commentary, and analytical articles carry more weight than self-published statements alone.

Common mistakes when trying to remove negative mentions

A frequent mistake is attempting to suppress criticism through mass content production. Low-quality articles created only to counter negative phrases often reinforce the problem rather than solve it.

Another error is focusing solely on takedown requests. While removal may work in limited cases, AI systems usually retain learned patterns even after a page disappears.

The most effective strategy is long-term narrative correction: replacing vague or emotional criticism with consistent, fact-based explanations that become the new dominant signal across reputable sources.