The SEO landscape is undergoing one of its most volatile transformations in history. With the integration of Google AI Overviews (formerly known as the Search Generative Experience, or SGE), the mechanics of search visibility have fundamentally shifted. For years, B2B software companies and SaaS brands have relied on a reliable playbook: publishing self-serving “best [category] software” listicles, ranking their own product as number one, and using those pages to capture high-intent organic traffic.
However, recent data reveals that this exact strategy is now backfiring in spectacular fashion. Instead of boosting brand authority, these biased listicles are actively feeding competitor visibility. According to a groundbreaking analysis conducted by SEO expert Lily Ray, Google AI Overviews regularly cite these self-promotional lists as sources of information, but they fail to recommend the brand that wrote them. In fact, in 69% of analyzed cases, Google’s AI bypassed the hosting brand entirely to recommend their competitors instead.
This dynamic introduces a harsh new reality for search engine marketers: in the era of generative search, a citation is no longer synonymous with a recommendation. In fact, publishing biased comparison content might be the very thing that helps your closest competitors win the AI search wars.
The Data Behind the AI Overview Disconnect
To understand how Google’s AI models treat self-promotional content, Lily Ray conducted a comprehensive, multi-month analysis of B2B search behavior. Using Ahrefs Brand Radar, Ray tracked 100 high-value B2B search queries based on the formula “best [category] software” across three distinct checkpoints: April 15, May 15, and June 8.
The findings paint a stark picture of how Google’s algorithms parse and distribute value from these pages:
- Of the 100 search queries monitored, 80 prompts successfully triggered a Google AI Overview.
- Within those AI-generated answers, self-promotional listicles—pages written by a brand that ranks its own software at the top—were cited a total of 323 times.
- In 224 of those instances, Google pulled data directly from the brand’s page but chose not to recommend that brand to the user.
- This translates to a massive 69% disconnect, where Google used the brand’s content as a source of truth while steering potential buyers toward competitors.
This discrepancy demonstrates that Google’s AI is highly capable of extracting structured information from a page while completely ignoring the author’s self-serving intent. The generative engine treats the listicle as a directory of options, filters out the inherent bias of the self-ranking publisher, and serves up the alternative products listed in the text to searchers looking for advice.
Case Studies: Feeding the Competition
To illustrate how this phenomenon plays out in live search results, Ray highlighted several instances across popular B2B software categories. The most prominent example occurred within the learning management system (LMS) niche.
When searching for the query “best LMS for selling courses,” Google’s AI Overview cited an in-depth article published by Oasis LMS. However, the AI Overview did not recommend Oasis LMS to the user. Instead, the generative answer recommended Kajabi, Thinkific, LearnWorlds, and Teachable—which are the exact competitor brands that Oasis LMS had mentioned and analyzed within its own article.
This pattern was not an isolated incident. Ray documented similar algorithmic behavior across a diverse range of software categories, including:
- Help desk software
- Task management tools
- Online survey builders
- Customer Relationship Management (CRM) platforms
- Search Engine Optimization (SEO) software
In each case, B2B brands spent valuable resources creating comprehensive, comparison-focused content, only for Google to use that content as free training data or reference material to promote the market leaders in their niche.
Why Google AI Overviews Recommends Competitors
To understand why this happens, it is necessary to look under the hood of how Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) function in search engine environments. Google’s AI Overviews do not simply copy and paste search results; they synthesize information from across the web to provide a consensus-based answer.
The Problem of Weak Brand Signals
When Google’s AI processes a query like “best CRM software,” it looks for consensus. If a relatively unknown CRM brand writes an article ranking itself as number one, Google’s algorithms compare that claim against the rest of the web. If the broader internet—including forums, news outlets, and independent review platforms—does not back up that claim, the AI recognizes the self-ranking as biased or low-authority.
As a result, Google uses the article to identify which competitors are worth talking about, but excludes the authoring brand because its external brand signals (such as third-party reviews, backlink profiles, and general search volume) do not support a recommendation.
The Power of Established Market Leaders
Ray’s research confirmed that brands with established market presence continue to dominate AI Overview recommendations. The companies that regularly appeared in the generative summaries were those that already led their respective categories, possessed robust backlink profiles, and received frequent mentions across independent third-party sources.
Because these legacy brands have strong trust signals, the AI views them as safe, authoritative recommendations, leaving smaller or more biased publishers to serve merely as the “citations” that validate the competitors’ superiority.
The Collateral Damage: Falling Organic Visibility
The issues surrounding self-promotional listicles extend far beyond AI Overview citations. Brands that have heavily relied on these formats are seeing a dramatic collapse in their standard organic search visibility.
According to Ray’s tracking, a noticeable downward trend began around January 20 across dozens of domains that heavily utilized self-promotional content. Many of these websites had scaled up aggressive Search Engine Optimization and Generative Engine Optimization (GEO) playbooks. These strategies often relied on:
- Large-scale deployment of AI-generated comparison and review articles.
- Creating dozens of “best [niche] software” pages designed to rank their own product first.
- Using repetitive, highly templated comparison pages designed to capture long-tail query volume.
This aggressive scaling proved highly vulnerable to Google’s quality updates. The organic declines accelerated dramatically during Google’s May 2026 core update. Many SaaS and B2B websites that built their organic traffic on self-ranked lists experienced devastating traffic losses. This aligns with earlier findings indicating that some SaaS and B2B brands lost 30% to 50% of their visibility after Google targeted self-promotional, low-utility listicles.
The Rise of Third-Party and UGC Alternatives
As Google suppresses self-serving brand content, it is shifting its trust to alternative sources. Ray’s analysis revealed that Google is relying heavily on independent third-party publishers and user-generated content (UGC) to populate its AI Overviews for “best” queries.
In particular, Reddit has seen a massive surge in citations within AI Overviews. Because Reddit threads feature real discussions from actual users, Google’s algorithms view them as less biased than a company-owned blog post. Other domains that have captured the lion’s share of “best of” citations include trusted mainstream publishers like Forbes and video-rich platforms like YouTube.
For B2B brands, this shift highlights a critical truth: you cannot control your narrative solely through your own domain. If you want Google’s AI to recommend your product, you must win the conversation on the platforms that Google trusts.
The Legal and Regulatory Risks of Self-Ranking
Beyond the loss of organic traffic and the unintended promotion of competitors, there are growing legal risks associated with self-serving listicles. Creating biased content that masquerades as independent consumer advice has increasingly caught the attention of regulatory bodies like the Federal Trade Commission (FTC).
Under the FTC’s Consumer Review Rule, companies face potential legal liability when they publish content that misleads consumers about its independence. Specifically, legal risks arise when:
- Company-controlled content is presented to the public as an independent, unbiased review.
- Product ratings or rankings are published without being based on real, verifiable testing.
- Material financial relationships—such as a brand owning the website that publishes the “best of” list—are not clearly and conspicuously disclosed to the reader.
As industry experts have noted, these low-quality listicles create significant legal risk for brands that fail to differentiate between honest marketing and deceptive review practices. As search engines and regulators both crack down on these tactics, the self-promotional listicle is rapidly transitioning from an SEO asset to a major operational liability.
How to Rebuild Your B2B Search Strategy
If your brand has relied on self-serving comparison pages and “best of” listicles, it is time to pivot your search strategy. To succeed in an organic search landscape governed by AI Overviews and helpful content algorithms, consider implementing the following adjustments:
1. Focus on Genuine Digital PR and Brand Mentions
Because Google relies on external consensus to make recommendations, your off-page brand presence is more important than ever. Invest in digital PR campaigns that secure natural, un-sponsored mentions on high-authority industry publications, respected tech blogs, and mainstream news outlets. The more independent sources that talk about your product, the more likely Google’s AI is to recommend you.
2. Engage with User-Generated Platforms
With Reddit and online forums dominating AI search citations, monitoring and participating in these communities is essential. Ensure your brand has a natural, helpful presence on Reddit, Quora, and niche-specific forums. Do not spam these platforms; instead, focus on answering user questions honestly and encouraging satisfied customers to share their positive experiences organically.
3. Pivot to High-Transparency Product Comparisons
If you publish comparison content on your own website, move away from biased, self-congratulatory lists. Instead, create highly objective, feature-by-feature comparisons that acknowledge both your strengths and your weaknesses. Clearly disclose your ownership of the site, and avoid claiming you are the undisputed “best” choice for every single use case. Google’s algorithms—and human buyers—highly value transparency.
4. Diversify Your Content Formats
Do not rely solely on text-based blog posts to capture search traffic. Diversify your content marketing into video (YouTube), podcasts, and downloadable templates or tools. YouTube videos are highly visible in both standard search results and AI Overviews, providing an excellent alternative channel for capturing high-intent search traffic.
Conclusion
The era of gaming search rankings through biased, self-promotional listicles is coming to an end. Lily Ray’s research serves as a stark warning to B2B and SaaS brands: trying to force your way to the top of the recommendation engine using self-serving content will likely end up driving traffic straight to your competitors.
To survive and thrive in this new search landscape, marketers must shift their focus from superficial content creation to authentic brand-building. By prioritizing transparency, securing genuine third-party validation, and respecting the guidelines set by both search engines and regulators, you can position your brand to win the recommendations that matter most in the age of AI.