E-E-A-T, or: How Brands Get Cited in AI Answers
E-E-A-T is Google's shorthand for content quality: Experience, Expertise, Authoritativeness and Trust. It was originally used by humans evaluating web pages. Now, AI answer engines use it to decide which sources to cite. In other words: It has become the key to making your brand visible to AI.
Here's what each signal looks like through an AI citation lens, and how you can act on it.
Experience
Models reward content that is clearly first-hand and specific. This means original data, real case studies, time-specific examples and figures that you have created yourself. What AI has no use for are generic pages that merely repeat what everyone else is saying. These pages offer models nothing to quote or credit you for.
Do: Publish things that only you could write. Share your benchmarks, results and methods.
Expertise
Who is behind the content matters. Models tend to prioritise content with a named author who has real credentials and in-depth knowledge of a specific topic. In other words, if two sources are being considered, a model is more likely to quote the one with a visible, credible author than an anonymous marketing text.
Do: Include the names, roles and credentials of the authors of your more substantial content. Also, specialise in a few topics rather than touching on many topics superficially.
Authoritativeness
Authority is what other websites say about you – and it's the signal that brands pay least attention to. There are various ways to improve your authority: through earned media; by maintaining a presence on credible review and rating platforms in your industry; and by having well-maintained entries on Wikipedia and Wikidata. Models cross-check content for accuracy, so having your claims corroborated by independent sources is what turns them into citable facts.
Do: Treat your online presence (Wikipedia, Wikidata, industry directories, and review platforms) as essential infrastructure.
Trust
For a model, trust is largely about consistency and accuracy. When sources contradict each other (for example, if the founding year is given as an incorrect date or the product description differs), the model fills the gap and often does so incorrectly. This is not hypothetical: in our benchmark of 43 brands, 92% of DACH brands had at least one factual inaccuracy in AI-generated responses about them. The most common (and commercially significant) error is service misattribution, whereby the model incorrectly describes products or services that a company does not actually offer. This mostly happens when the source material is inconsistent and out of date.
Do: Make sure your core facts are identical everywhere a model might look. Fix any contradictions before creating new content.
Where to start
When trying to increase their brand visibility, most companies jump straight to producing more content. A smarter approach is actually the opposite: first, ensure that AI systems can clearly identify your brand as an entity – a distinct company with consistent facts, such as what it does, where it operates, and the services it offers (authoritativeness and trust). Then, and only then, you can start adding first-hand insights from named experts.
See what AI says about you
Our free Quick Check tool shows you how the major AI engines currently describe your brand, including the specific things they get wrong. It helps you identify which of the four signals to fix first.
