How AI Decides Which Brands to Recommend

When someone asks ChatGPT "what's the best CRM for a small sales team?" it doesn't return ten ranked links. It synthesises an answer. It draws on everything it has ever processed about CRM software — forum posts, review aggregations, comparison articles, expert commentary, Reddit threads, product documentation, industry reports — and constructs a response that reflects what the internet collectively believes.

The brands that appear in that response are not necessarily the ones with the best websites. They're the ones the internet has formed the clearest, most positive, most consistent picture of. That process is fundamentally different from how Google rankings work. And it requires a fundamentally different strategy to influence.

The Entity Model: What AI Builds About Your Brand

AI systems represent brands as entities — structured collections of attributes that describe what the brand is, what it does, who it serves, and how authoritative it is. This entity model is built from every reference to your brand that the AI system has encountered.

Think of it as a profile constructed from the outside in. Your own website contributes something. But so does every Reddit thread, every review, every journalist mention, every comparison blog, every LinkedIn post that references you, every forum discussion about your category. The entity model is the aggregate of all of those signals weighted by source authority.

A brand with high entity authority has a clear, consistent, widely-corroborated profile. AI systems can confidently include it in answers because there is strong consensus about its identity, category, and credibility. A brand with low entity authority has an ambiguous, thin, or inconsistent profile. AI treats that uncertainty by either describing the brand vaguely or skipping it in favour of more clearly defined alternatives.

The Four Inputs That Shape AI Brand Recommendations

1. Training data breadth and quality

The foundation of AI brand knowledge is training data — the vast corpus of text processed during model training. For your brand, this means every piece of web content that mentioned you before the model's knowledge cutoff: your own content, but also third-party content about you.

Breadth matters. A brand that appears in 500 distinct sources — across different platforms, topic areas, and author types — has broader training data coverage than a brand that appears in 50 sources, even if those 50 sources include very high-authority sites. Coverage signals that the brand is widely known and discussed.

Quality matters too. A mention in a respected industry publication, an academic paper, or a government source carries more weight than a mention in a low-authority blog. AI models learn which sources to trust and weight mentions accordingly.

2. Brand search volume as a proxy for awareness

Brand search volume — how often people search for your brand by name — is one of the strongest predictors of AI citation. Research shows a correlation coefficient of 0.334 between brand search volume and AI mentions. This is higher than the correlation with domain authority or content volume.

Why? Because AI models were trained on data that reflects human behaviour, including what gets linked to, discussed, and referenced. Brands that are heavily searched are naturally more discussed across the web, which means they appear in more training data contexts. The search volume signal and the AI citation signal are both reflections of the same underlying reality: people know about this brand.

Brands that have invested in awareness — through TV, events, PR, partnership campaigns — consistently outperform brands of equivalent quality that have focused only on performance marketing. Brand investment has a direct return in AI visibility.

3. Third-party citation patterns

The sources AI trusts most are not brand websites. They are the places people go to form independent opinions. Reddit, Trustpilot, G2, Capterra, industry comparison sites, news publications, and expert roundups carry disproportionate weight in entity model formation.

This is why a brand can rank on page one of Google and still be absent from AI recommendations. Google ranks pages. AI evaluates entity authority. The signals are partially overlapping but distinct. A brand with 20 quality backlinks and a fast website can rank well for SEO while having low entity authority in AI systems because it has few independent third-party endorsements.

Platform-specific citation patterns vary considerably. Gemini cites sources in 30.4% of responses and draws heavily from structured web content. ChatGPT cites at 28.6% and shows 87% correlation with Bing's top 10 results in real-time mode. Claude cites at just 8.7%, relying more on brand authority than specific source citation. Google AI Overviews cite at 3.1% — the lowest of any platform. Each requires a different emphasis.

4. Entity consistency across surfaces

AI models form entity models by reconciling information from multiple sources. When sources disagree — different descriptions of what you do, different claims about your size or history, different categorisations of your brand — AI confidence drops.

Brands that present consistently across their website, LinkedIn, Crunchbase, Wikipedia, press coverage, review platforms, and directory listings are easier for AI to model accurately. Consistency is a trust signal. It tells the model: this information is reliable.

A practical audit: search for your brand name in ChatGPT, Claude, and Gemini. Read how they describe you. Then compare that to how you describe yourself. If there are discrepancies — outdated information, wrong category descriptions, missing attributes — you have an entity accuracy problem to fix. The fix is to update your owned sources and build third-party presence that reinforces the correct description.

How AI Visibility Differs from SEO — And Why It Matters

The easiest way to understand the difference is to look at what each system is trying to do. Google Search is a retrieval system. It retrieves the most relevant pages for a query. The signals it uses — keywords, backlinks, page speed, Core Web Vitals — are optimised to surface the best page for a given query.

AI recommendation systems are synthesis systems. They don't retrieve a page. They construct an answer using everything they know. The signals that matter are about brand authority and entity clarity, not page-level ranking factors.

Dimension	SEO	AI Visibility
Unit of optimisation	Web page	Brand entity
Primary signal	Backlinks, keywords, technical factors	Third-party citations, brand search volume, entity consistency
Output	Ranked list of pages	Synthesised answer with brand mentions
Key metric	Position 1-10	Share of memory across platforms
Competition	The page ranked above you	The brand recommended instead of you
User outcome	Click to website	Recommendation accepted, may never visit your site

The practical implication: companies that have invested heavily in SEO have built a strong page-level foundation. But they haven't necessarily built a strong entity model. The additional work required — building third-party presence, standardising brand descriptions, improving citation patterns — is different from SEO work. It requires a different set of tactics and a different measurement approach.

What This Means for Your Brand Strategy

If you want to appear when AI recommends brands in your category, the investment is in entity authority. That means three things in practice.

Third-party presence. Systematically build your presence on the platforms AI trusts: Reddit, review sites, industry publications, comparison articles. Not through promotion, but through genuine contribution, earned coverage, and review accumulation. Every independent mention of your brand, in a credible context, strengthens your entity model.

Entity consistency. Audit how your brand is described across all surfaces and standardise it. Your name, your category description, your key differentiators — these should read consistently whether someone finds you on LinkedIn, Trustpilot, G2, your own site, or a news article. Inconsistency creates uncertainty in AI models. Consistency creates confidence.

Brand awareness investment. Brand search volume predicts AI citation better than almost any other measurable factor. That means brand awareness campaigns — PR, events, partnerships, broad media — have a direct return in AI visibility. The awareness you build today translates into training data signal in future model cycles.

The brands winning in AI search in 2026 are not necessarily the ones with the best SEO. They're the ones with the strongest entity models — built through real-world presence, genuine third-party endorsement, and consistent brand communication across every surface where AI might find information about them.

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