AI systems “trust” information they can find, understand and connect to a user’s question with reasonable confidence.

That does not mean ChatGPT, Google AI Mode or another AI platform gives every website a permanent trust score. Different systems use different models, indexes and retrieval methods. The sources selected can also change according to the query, location, available data and level of risk involved.

Still, the pattern is clear. AI search systems favour information that is:

  • Relevant to the exact question
  • Accessible to their crawlers
  • Clear enough to interpret
  • Supported by evidence
  • Connected to an identifiable source
  • Consistent with other reliable information
  • Current enough for the subject
  • Useful beyond what is already widely available

For businesses, the lesson is simple: being visible in AI search is less about finding a hidden optimisation trick and more about becoming a source worth retrieving.

AI trust has two different meanings

Discussions about AI trust often mix up two separate questions.

The first is: Can people trust an AI system?

The second is: Which information does an AI system rely on when producing an answer?

Research into trustworthy AI concentrates mainly on the first question. IBM identifies principles including accountability, explainability, fairness, privacy, reliability, transparency, security and safety. Deloitte’s Trustworthy AI framework covers similar ground, including whether systems are private, explainable, impartial, accountable and robust.

These principles concern the design, governance and use of AI. They help organisations build systems that deserve an appropriate level of human confidence.

The distinction matters because trust should not mean blind acceptance. A 2024 literature review published in Computers in Human Behavior: Artificial Humans describes the need for calibrated trust. People may reject useful AI because they distrust it, but they may also rely on it without questioning the risk of an incorrect answer.

Trustworthy AI should help users understand what a system can do, where its limits sit and when human judgement is still required.

When marketers ask what AI systems trust, however, they are usually asking something more practical: “What makes my website more likely to be used or cited in an AI-generated answer?”

What information do AI systems use?

An AI answer can draw on several information sources, depending on the product and question.

Training data

Large language models learn patterns from extensive collections of text, code and other material during training. This gives them broad knowledge, but it does not guarantee that every answer reflects the latest information.

Training data can also contain errors, conflicting claims and outdated material. This is one reason a fluent answer should not automatically be treated as a verified answer.

Live search and retrieval

Search-connected AI systems can retrieve information from the web when responding to a query. This process is often called grounding or retrieval-augmented generation.

Google explains that its generative search features use core Search systems to retrieve relevant, current pages. AI Overviews and AI Mode may also use query fan-out, where the system runs several related searches to explore different parts of a question before constructing its response.

This means one detailed page can become relevant to several connected searches, even when it does not repeat every possible keyword variation. Our guide to Google AI Mode and its impact on search explores how this changes the discovery journey.

Structured and first-party data

AI-powered search experiences may also use structured sources such as product feeds, business profiles, maps, databases and knowledge graphs.

For a local company, an accurate Google Business Profile may be more useful than another general blog post. For an ecommerce business, complete product data can help a system understand price, availability, specifications and delivery information.

Structured data can provide additional context, but it must agree with the visible page. It cannot rescue weak, misleading or inaccessible content.

The user’s context

The source that best answers a question can depend on what the user actually needs.

A search for “best running shoes” is broad. “Best waterproof running shoes for wide feet under £120” gives an AI system a much more specific task. The most useful source for the second query may be a detailed comparison or first-hand test rather than the largest footwear website.

Relevance is therefore contextual. A recognised brand can still lose visibility to a smaller specialist source when that source provides the clearer and more useful answer.

The signals that make content easier to trust

No public checklist can guarantee an AI citation. Google and OpenAI both state that inclusion or top placement cannot be guaranteed. Their published guidance does, however, reveal the foundations that matter.

1. Direct relevance

AI systems need to connect a page with the user’s intent.

A strong page answers its main question early, then covers the supporting questions a reader is likely to ask next. Descriptive headings, concise definitions and concrete examples make those connections easier to identify.

This does not mean writing awkwardly for machines. Google’s current guidance says there is no need to rewrite content in a special style for generative AI or create a separate page for every long-tail variation.

2. Original information

A page that only paraphrases existing search results gives an AI system little reason to select it over the original sources.

Useful original material can include:

  • First-party research
  • Survey findings
  • Expert commentary
  • Original photographs
  • Product testing
  • Detailed methodology
  • Anonymised performance data
  • Templates, calculators or checklists
  • A defensible point of view based on real experience

Google’s guidance for generative AI search describes this as valuable, non-commodity content. It specifically contrasts first-hand insight with pages that merely restate information already available elsewhere.

3. Clear evidence and sourcing

Claims become more useful when readers can inspect where they came from.

Link to the original report rather than a blog that mentions it. Name the organisation responsible for the research. Include the publication date, sample size and relevant limitations when using survey data.

A citation does not make a claim true by itself. It makes the claim traceable, which gives both readers and retrieval systems more context for evaluating it.

4. Identifiable expertise

Readers should be able to tell who created the content and why that person is qualified to discuss the subject.

Useful signals include:

  • A named author
  • A relevant biography
  • A transparent editorial or review process
  • An informative About page
  • First-hand evidence
  • Clear ownership and contact details

Google recommends assessing content through “Who, How and Why”. Its people-first content guidance also explains that trust is the most important part of E-E-A-T, while experience, expertise and authority contribute to it.

E-E-A-T is not one standalone ranking factor. It is a framework for understanding the qualities that multiple systems and signals try to identify.

5. Accuracy and consistency

A page should not contradict itself, misrepresent its sources or disagree with established facts without strong evidence.

Consistency also matters across a brand’s wider presence. Business names, service details, addresses, prices and product specifications should agree across the website, profiles and feeds.

When information changes, update the substance of the page. Changing a publication date without improving the content does not make it fresh.

6. Technical accessibility

An AI system cannot retrieve a page it cannot access.

For Google’s AI features, a page must be indexed and eligible to appear in Search with a snippet. Google says standard SEO foundations still apply, including crawlability, internal linking, textual content and a good page experience. There is no separate AI schema or special markup required.

ChatGPT Search has its own access requirement. OpenAI states that publishers should allow OAI-SearchBot and its published IP addresses if they want their content to be available for inclusion.

Check that:

  • Important pages are not blocked in robots.txt
  • Canonical tags point to the intended URLs
  • Key information appears as readable text
  • Internal links lead crawlers to important content
  • JavaScript does not prevent content from rendering
  • Structured data matches the visible page
  • Snippet controls reflect your preferred level of exposure

Our article on building better AI practices for SEO covers how these foundations fit into a wider search strategy.

7. Appropriate freshness

Freshness matters when the answer can change. It matters less when the subject is stable.

An AI system answering a question about current tax rules, product availability or platform features needs recent information. A page explaining a settled mathematical principle does not need a monthly rewrite.

Show when a page was published and when it was meaningfully updated. Remove outdated examples, check external links and state the period covered by any data.

8. Independent recognition

Links, citations and genuine mentions can help establish that a source is recognised beyond its own website.

That does not justify manufacturing references or placing the brand in irrelevant discussions. Google’s guidance explicitly warns against pursuing inauthentic mentions as an AI visibility tactic.

Authority is built through work worth referencing: useful research, expert contributions, original tools, strong reporting and consistent subject knowledge.

What AI systems do not automatically trust

Certain tactics may make content look optimised without making it more reliable.

AI systems do not automatically trust content because it:

  • Is long
  • Repeats the target keyword
  • Contains an FAQ section
  • Uses AI-generated wording
  • Includes large amounts of schema
  • Has been divided into tiny “AI-friendly” chunks
  • Appears on a recently registered domain
  • Calls itself authoritative
  • Uses an llms.txt file

Google says there is no ideal page length, no special AI markup and no requirement to split content into small chunks. It also says llms.txt does not improve visibility in Google Search.

FAQs and structured data can still help readers and clarify a page. They are useful formats, not proof of quality.

How to make your website a source AI systems can use

Start with the questions for which your business has something real to contribute.

Then:

  1. Answer each core question near the top of the relevant page.
  2. Add evidence, examples or first-hand knowledge competitors cannot reproduce.
  3. Identify the author and explain the basis of their expertise.
  4. Cite original, credible sources for factual claims.
  5. Keep changeable information accurate and dated.
  6. Connect supporting pages through descriptive internal links.
  7. Make important content crawlable, indexable and available as text.
  8. Maintain consistent business and product information across platforms.
  9. Track traffic and conversions from AI-driven discovery where reporting allows.
  10. Review outputs manually rather than assuming an AI mention is accurate.

The goal is not to convince a machine that every page deserves attention. It is to publish material that makes your business the logical source for a specific question.

That is slower than an optimisation trick. It is also far harder for competitors to copy.

FAQs

Do AI systems have a trust score for websites?

There is no universal public AI trust score. Search and answer systems use different models, indexes and signals to retrieve information. A source may be suitable for one query but irrelevant to another.

E-E-A-T is not a single ranking factor. It describes qualities such as experience, expertise, authority and trust that Google’s systems may identify through a mixture of signals. Clear authorship, evidence and first-hand knowledge can support those qualities.

Schema can help search engines interpret certain details and qualify pages for relevant search features, but it does not prove that the content is correct. The markup should always match what users can see on the page.

Yes. Google does not exclude content simply because AI helped create it. The content still needs to be accurate, useful, original enough to add value and compliant with search policies. Producing large numbers of low-value pages can breach scaled content abuse policies.

The site must be accessible to OAI-SearchBot and should not block OpenAI’s published crawler IP addresses. Inclusion and ranking are not guaranteed, so publishers should also focus on relevant, reliable and well-sourced content.

Trustworthy AI is AI designed and governed to be reliable, safe, transparent, accountable, privacy-conscious and fair within its intended context. IBM’s overview of trustworthy AI and the NIST AI Risk Management Framework provide broader explanations of these principles.

Want your business to become a clearer, more credible source across traditional and AI-powered search? Speak to Sierra Six Media, an experienced SEO agency in London, about building sustainable visibility around the questions your customers are asking.

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