How to rank in AI: what matters most?

How to rank in AI (Artificial Intelligence) is no longer a niche question. Every time Google answers a question with an AI-generated summary, some source was chosen to appear there, and others were left out.

A survey published in September 2026 set out to understand why, asking 131 SEO experts what actually drives that choice. The result helps separate real tactics from industry guesswork.

This article translates that data into practical priorities for anyone working with SEO strategies, and with digital marketing strategies more broadly, who has to decide where to invest time first.

How does optimization for AI work in search results?

Optimization for AI in search results is, essentially, a more demanding version of the SEO you already know. Google confirms that it uses the same index and the same ranking systems from traditional search to feed AI-generated summaries, which means there is no secret parallel path to appearing there.

Caption: how to rank in AI depends on content access, matching the question and the reputation of the source cited

That continuity changes how to think about LLM SEO (Large Language Model). Instead of treating AI SEO as a new discipline, it makes more sense to understand which long-standing signals now weigh more, and which became less relevant, inside the same system.

One of those signals is grounding, when the AI system checks the claims in the generated text against the set of pages it retrieved from the web for that query. An exclusive data point coming from an unknown source tends to be ignored in that check. The same data point, from a trusted source, tends to be cited.

That mechanism also explains fan-out, when the system breaks the original question into several subqueries before assembling the final answer. Covering the topic in depth, and not just the literal question, raises the odds of appearing in one of those subqueries. The article on query fan-out and optimization for AI details how that breakdown works in practice.

While it adjusts ranking signals, Google is also testing new formats for its relationship with content producers. A recent pilot explores ways of paying publishers whose material feeds AI answers, a move the article on the pilot that pays for content in AI explains in detail.

Which factors weigh most for ranking in artificial intelligence?

The factors that weigh most for ranking in artificial intelligence, according to the “Google AI Ranking Factors” survey (Cyrus Shepard, Zyppy Signal, 09/16/2026), are Google's access to the content and the match between question and answer. The study surveyed 131 SEO experts, on a scale from -3 to +3, measuring each factor's impact on brand citation.

One important distinction before the table. The same authors published, a week earlier, another survey called “2026 Google Ranking Factors Expert Survey”, on general organic ranking factors, with no AI focus.

They are two distinct studies, with numbers that should not be mixed. The data below belongs exclusively to the September 16 survey, focused on AI answers.

Here is how the 13 factors assessed sit on the survey's scale:

Table: Impact of each factor on a brand's citation in Google's AI answers, according to the Google AI Ranking Factors survey (Zyppy Signal, 09/16/2026), with 131 SEO experts.

Two points stand out in that list. The first is the distance between the factors at the top, technical and tied to access and content matching, and the factor at the bottom.

The second is that bottom itself. Structured data weighs little, and the llms.txt file barely registers, at just +0.05 on the scale.

That result does not invalidate schema markup, but it repositions its priority. It is worth keeping structured data as a technical best practice, without treating it as a shortcut to ranking in AI. The real weight sits in content, authority and access factors, not in the markup layer.

Why does brand weigh more than one-off AI SEO tactics?

Brand weighs more than one-off AI SEO tactics because the model's parametric memory, what it already knows about an entity before even searching the web, works as a prior trust filter. A name the model already recognizes starts ahead when it decides who to cite.

That pattern is one of the most cited findings among the experts surveyed by Zyppy Signal. According to the consensus recorded in the study, brand and trust weigh more than AI-specific tactical optimizations.

A brand's consolidated presence in the model's memory tends to be worth more than one-off adjustments made just to appear in a single answer.

The practical reason lies in the grounding mechanism described in the previous section. When the AI system checks the text's claims against the pages retrieved from the web, an exclusive data point from an unknown source tends to be discarded for lack of external corroboration.

The same data point, attributed to a source with established reputation and brand mentions and authority, passes that filter more easily.

That logic changes where to invest effort. Brand mentions and authority built over time, in third-party publications, interviews, partnerships and press coverage, feed that model memory directly. Isolated technical tweaks, made without that reputational backing, tend to produce limited results.

This hierarchy does not mean technical execution stops mattering, because it remains a prerequisite for access, as the highest-weighted factor in the table above shows. It means that on its own it does not sustain recurring citation without reputation behind it.

How does E-E-A-T influence ranking in artificial intelligence?

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) influences ranking in artificial intelligence because it gathers the signals the Zyppy Signal survey identified as most decisive: source reputation, corroboration across the web and first-hand experience, with trust named by Google's own documentation as the most important aspect of the set.

It is worth separating the four elements of E-E-A-T to apply each one. Experience is the first-hand involvement AI cannot fabricate on its own, such as a real case study or data collected by the team itself. Expertise is technical command of the topic, demonstrated by whoever signs the content.

Authoritativeness is external recognition of that source on the subject, built through mentions, citations and third-party coverage. Trustworthiness is the sum of all that, plus site transparency, with real authorship, a clear editorial policy and visible contact information.

Content strategies focused on E-E-A-T tend to reinforce exactly factors 7, 8 and 9 in the table above, the ones covering exclusive information, corroboration and source reputation. It is no coincidence that those three factors rank among the heaviest in the survey, right behind the purely technical factors of access and matching.

One concrete path to gaining authority on a topic is organizing content into clusters, instead of publishing standalone articles with no connection between them. That reinforces recognition of expertise around a specific subject, which feeds the authoritativeness factor of E-E-A-T directly.

How do you structure direct answers to appear in AI responses?

Direct answers appear more often in AI responses when the content delivers the information in the first sentence of the section, with no introduction or wind-up before the point. AI answer systems extract self-contained passages from a page, not the whole page, so each section has to make sense on its own.

That requirement connects directly to the “structure that makes content extraction easier” factor, at +1.69 in the survey, and to the “prominence of the answer on the page” factor, at +1.65. Both measure essentially the same thing from different angles: how easy it is for an automated system to isolate the right answer inside the text.

In practice, well-built direct answers follow three simple rules. The first is to open with the answer, not with context. The second is to keep one idea per paragraph, without splicing two pieces of information into the same sentence. The third is to avoid a paragraph that depends on the previous one to make sense.

That logic is the basis of the AEO (Answer Engine Optimization) format, optimization aimed specifically at answer engines. The article on what AEO is and how to apply it details the steps for turning an ordinary page into one structured for that kind of extraction.

Beyond paragraph format, three tactics have a proven effect on the chance of citation: citing named sources, including a direct expert quote and bringing in a verifiable statistic with a date.

The three together deliver more than any one alone, and the gain tends to be larger for pages that are not yet at the top of traditional search.

How do you build a GEO and AEO checklist to guide your SEO strategies?

Building a GEO (Generative Engine Optimization) and AEO checklist to guide your SEO strategies means translating the survey's 13 factors into concrete actions, in the priority order the data itself suggests.

Much of that overlaps with content strategies focused on E-E-A-T, already detailed in the previous section. The list below follows that order, from the heaviest factor to the lightest.

Here is a practical routine organized around the highest-impact factors:

1. Make sure the page is indexable, crawlable and snippet-eligible, removing technical access blocks.
2. Answer the main question at the very start of the page and of each section, with no introduction before the answer.
3. Strengthen the brand's presence outside your own site, with mentions, partnerships and third-party coverage.
4. Bring data, examples and exclusive information only your organization can offer.
5. Cover the topic deeply enough to appear in the subqueries generated by fan-out, not only in the literal question.
6. Keep consistency between organic position and editorial presence around the topic.
7. Produce content with first-hand experience, such as case studies and proprietary data.
8. Seek external corroboration, with other trusted sources reinforcing the same information.
9. Invest in the reputation of the publication and of the authors who sign the content.
10. Structure the text into direct answers, lists and tables that are easy to extract.
11. Visually highlight the main answer in each section, instead of burying it in long text.
12. Keep basic structured data as a technical best practice, without treating it as a priority.
13. Leave the llms.txt file in the background, since its measured impact is close to zero.

That checklist works as a prioritization map for digital marketing strategies that involve several fronts at once, from content to technical authority. It avoids the common mistake of treating every factor as equally urgent.

Much of this checklist assumes the content is good enough to carry exclusive facts and first-hand experience, which matters even more when production uses artificial intelligence. The article on how to create content with AI without destroying your SEO shows where the line sits between gaining speed and losing editorial quality.

Which metrics should you track to measure visibility in AI?

The most relevant metrics for measuring visibility in AI include AI Share, generative visibility and the effect of both on the site's organic traffic. AI Share measures how often a brand is cited in AI answers, and generative visibility extends that measurement across several platforms at once.

Unlike traditional ranking, these metrics still have no single standardized dashboard across tools. That is why many teams combine reports from specialized platforms with organic traffic tracking directly in GA4 (Google Analytics 4).

Tracking SEO and AI visibility side by side helps identify spikes or drops in organic traffic that coincide with changes in how the brand appears cited.

One market figure helps size the stakes. According to a Semrush study, visitors who reach a site from AI citations convert up to 4.4 times more than average search traffic.

That conversion gap reinforces why tracking these metrics matters, even before a consolidated measurement standard exists for them.

For anyone already following organic performance closely, the natural next step is cross-referencing those AI signals with behavior observed in the traditional organic traffic funnel. The article on how to read the SEO report in GA4 shows where to look for those variations inside the tool itself.

Frequently asked questions about how to rank in AI

Where do you start to rank in AI?

Where you start to rank in AI depends on where the brand stands today. Anyone with a weak technical foundation, with indexing or crawling problems, should resolve that first, because the access factor leads the survey by a wide margin over all the others.

Anyone with the technical foundation already sorted gets more return from investing in reputation and third-party coverage than in fine-tuning markup or an llms.txt file. The survey data makes that plain: brand and trust factors weigh more than any isolated optimization tactic.

What the two scenarios have in common is that SEO and AI visibility do not replace the content strategy that already works for traditional search. They demand the same foundation, applied with more rigor in depth, authorship and external corroboration.

If your team already has that diagnosis and wants a structured plan to apply it, mkt4edu has a dedicated SEO for LLM and AI practice to support that prioritization. You can also talk to the team and work out where your institution should begin.