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Tag: AI Search

  • How to Track AI Traffic on Your Website (and What the Numbers Can’t Tell You)

    How to Track AI Traffic on Your Website (and What the Numbers Can’t Tell You)

    The question we are asked most about AI search has changed. A year ago it was “is AI reading my site?” Now it is “how do I know, and how do I track it?” Here is how to track AI traffic in the tools you already use, the order to set them up in, and the three things the numbers still cannot tell you.

    The AI traffic question has moved from “if” to “how”

    Clients have been asking us some version of this for about a year, and the wording has shifted from a yes-or-no question to a measurement one: where do I look, what counts, and is any of it worth my time? That is a healthy shift, because it means AI search has become a channel people want in the monthly report, next to organic, paid and referral.

    This post is the practical companion to our plain-English guide to AEO, which explains why being chosen by an AI tool is a different job from ranking in a search engine. It sits alongside our piece on how AI crawlers read your website, which covers the bots. Here we are interested in the person on the other end of the AI tool, and whether you can see them arrive.

    First, an honest word about proportion

    For almost every website we look at, AI-assistant traffic is still a small slice of the whole. If you run ads, or have a strong organic or social presence, sessions that begin in ChatGPT or Perplexity will sit low in your channel report, and some owners are rightly unbothered by them. The clients who care are the ones looking for a new channel, or who have watched organic soften and want to know where the readers went. For them, small and growing is exactly the pattern worth tracking early.

    What AI traffic looks like on a small B2B site. In the twelve months to September 2026, 111 of the 1,389 sessions on contentifai.agency arrived from an AI assistant: about one in twelve, or nine a month. ChatGPT sent the most, then Perplexity and Gemini, with a handful from Claude. Around half were engaged sessions. Nobody would build a forecast on nine visits a month, and we don’t. We watch the share.

    The word “sessions” is doing careful work in that box, because most of what AI companies do to a website involves no person at all. Cloudflare’s analysis of AI bot activity across its network found that training accounted for nearly 80% of AI crawling by July 2025, with search indexing at about 17% and user-triggered requests at roughly 3% (Cloudflare, 2025). The traffic you can see in analytics is that last, thin category: a person asked a question, the tool fetched or cited your page, and the person clicked. Everything else is machines reading, and that belongs to the crawler conversation rather than this one.

    How to track AI traffic: four places to look, in order

    Back in 2025 our answer to “can this be tracked?” was yes, with some work, and a prediction that the big platforms would bring it in-house once they had settled the data streams and the privacy questions. They have done exactly that. The setup below uses tools most B2B sites already have, starting with the one that needs no configuration at all.

    1. Google Analytics 4: the AI Assistant channel

    Since 13 May 2026, GA4 has sorted visits from recognised AI tools into their own channel. Google’s release notes describe a new AI Assistant channel in the default channel group reports, with the medium set to “ai-assistant” automatically (Google, 2026). Open Reports, then Acquisition, then Traffic acquisition, and it sits alongside Organic Search and Referral. Our own site started showing it in June.

    There are three things worth knowing before you rely on it. It applies from that date forward only, so your history is not reclassified. It depends on the AI tool passing a referrer, which the mobile apps often strip, so some visits still land in Direct. And smaller or newer tools may not be on Google’s list yet. For anything before May, or for a tool Google has missed, filter the session source for chatgpt.com, perplexity.ai, gemini.google.com, claude.ai and copilot.microsoft.com. A saved exploration takes ten minutes and gives you the back history the new channel lacks.

    2. Search Console: the generative AI performance report

    Clicks from Google’s AI Overviews and AI Mode arrive in GA4 looking like ordinary Google search visits, so analytics cannot tell you how often Google’s own AI used your pages. Search Console now can, up to a point. The generative AI performance report shows how many times links to your site appeared in AI Overviews and AI Mode, by page, country, device and date, and Google rolled it out to every property worldwide on 31 August 2026 (Google, 2026). You will find it under Performance.

    Read the limits before you read the numbers. The report gives impressions only, with no queries, no clicks and no position. An impression means a link to your page was shown inside an AI answer; it says nothing about whether anyone read it or acted on it. Treat it as a presence measure, useful for seeing which pages Google’s AI draws on, and leave it there.

    3. Server or CDN logs: which bots, and why they came

    Analytics counts people, whereas logs count requests, and the logs are where you see which AI companies are reading your site and why. The major providers publish separate user agents for training crawlers, search-index crawlers and fetches made for a live user, and a CDN such as Cloudflare groups them for you. This is the view that answers “is AI reading my site?” rather than “is AI sending me anyone?”, and a quarterly look is enough. Our AI crawlers post covers the named bots.

    4. Ask people: the attribution you control

    Every method above depends on a referrer surviving the journey, and many will not. The one source you control is the person. Add “an AI assistant (ChatGPT, Gemini, Perplexity or similar)” as an option to every “how did you hear about us?” field you have: the contact form, the discovery call notes, the onboarding questionnaire. Ask at the first opportunity rather than waiting until someone has become a paying client, because the enquiry that never converts still tells you the channel is working. Over a year this plain question gives you a cleaner picture than any dashboard.

    What the numbers can’t tell you

    Here is the part the vendors selling “AI visibility scores” tend to skip. With the four sources running, you will know that AI tools read your site and roughly how many people they send. You will still be missing three things, and no tool on the market reliably fills the gap.

    What the person asked. Search Console’s AI report holds back the queries, and GA4 never had them. You can see that someone came from ChatGPT; you cannot see whether they asked “best content agencies in the North East” or “is schema markup worth it”, which are very different visits.

    What the tool said about you. An impression or a referral tells you a link was shown. It does not tell you whether the answer was accurate, whether you were the recommendation or the fourth name in a list of six, or whether the tool used your page to corroborate a fact in an answer about something unrelated to your services. Being a credible reference is good for you, but it is not the same as being chosen.

    What happened next. The jump from “read an AI answer” to “became a client” is the one every marketer wants to measure, and it is still invisible. Appearing in AI search is not the same as being recommended by AI search, and being recommended is not the same as being bought from.

    This is why we do not hand clients an ROI formula for AI search. There is not an honest one yet. What we can say is simpler: if your buyers are asking AI tools questions in your category, you will want to be in the answers, and the sensible measure for now is whether your share of that channel is growing month on month.

    Want this measured for you each month?

    Measure is our monthly AI visibility and web performance report: we review your Google Analytics, Search Console and presence in AI answers, explain what changed in plain English, and give you three prioritised recommendations. From £200 a month on a 28-day rolling agreement.

    If you are doing it yourself: the four mistakes to avoid

    Plenty of businesses will want to run this in-house, and that is a perfectly good choice when someone has the time to look at the numbers each month. If that is you, the setup above will take an afternoon. The harder part is reading the results without fooling yourself, and over the past year we have watched the same four mistakes make AI traffic look bigger, or smaller, than it is.

    • Counting crawler hits as visitors. Server logs record every request a bot makes, so a single training crawl can look like hundreds of “AI visits” if it is added to the analytics total.
    • Reading Search Console AI impressions as clicks. The report contains no clicks, so an impression count is a presence figure and nothing more.
    • Waiting until the channel is big enough to matter before tracking it. Leave it until then and you will have no baseline to compare against when it does matter.
    • Relying on GA4 alone. The referrer is the most fragile link in the chain, and a form field would have caught what analytics dropped.

    Avoid those four and the numbers you collect will be modest but trustworthy, which is the right footing for a channel still finding its size.

    What to put in your own monthly report

    Once the tracking is in place, the question becomes what to look at each month. Our advice is to keep it to five lines, whether that report is something you write for yourself or something you ask an agency to produce for you.

    1. Share of sessions from the AI Assistant channel, and whether it is rising or falling.
    2. Engaged-session rate for that channel against organic, because a small channel that engages well is worth more than its size suggests.
    3. AI impressions by page in Search Console, to see which content Google’s AI is drawing on.
    4. The count of “AI assistant” answers on your forms, your one source that does not depend on a referrer.
    5. The pages AI tools are citing, which tells you what to write more of.

    That last line is where measurement connects back to the work. Tracking is the easy part now that Google has built most of it. What earns the citations is still the content: specific, useful, written so a machine can quote it, on a site the crawlers can read. Measurement tells you whether that work is paying off, and it cannot do the work for you.

    A moving target, and a conversation if you want one

    What you have just read is the state of AI traffic tracking as we see it in autumn 2026: four sources, three blind spots, and a share worth watching rather than a number worth forecasting. The tools are changing quickly, and by the time you read this some of the detail may have moved again, so treat the method as the durable part and the menu paths as the bit to check.

    If you would like help with any of it, whether that is writing the content that earns the citations, measuring what the AI tools are doing with your site each month, or a complimentary AI readiness check to see where you stand today, get in touch and we will talk it through.

    Frequently asked questions about tracking AI traffic

    The questions owners and marketing managers ask us most about measuring AI search.

    How do I see AI traffic in Google Analytics 4?

    Open Reports, then Acquisition, then Traffic acquisition, and look for the AI Assistant channel. GA4 has assigned it automatically since 13 May 2026 for recognised tools such as ChatGPT, Gemini and Claude. For earlier data, filter the session source for chatgpt.com, perplexity.ai, gemini.google.com and claude.ai.

    Does Search Console show AI Overviews and AI Mode traffic?

    It shows part of the picture. The generative AI performance report, available to all sites since 31 August 2026, shows how often links to your pages appeared in AI Overviews and AI Mode, by page, country, device and date. It does not show queries, clicks or position.

    Why does my AI traffic look small?

    For most sites it is small. The large majority of AI activity on a website is crawling for training or indexing rather than people visiting, and sessions that begin in an AI tool are usually a small share of the total, growing from a low base. Track the share and its direction rather than the raw number.

    Can I tell what people asked the AI before visiting my site?

    Neither GA4 nor Search Console passes the question through. The closest substitutes are long, question-shaped queries in your standard Search Console report, the questions prospects raise on calls, and a “how did you hear about us?” field with an AI option.

    Is there an ROI formula for AI search traffic?

    There is no honest one yet. You can measure sessions, engagement and self-reported attribution, but not what the tool said about you or what the person did after reading it. The practical measure for now is whether your share of the channel is growing and whether the people it sends engage and enquire.

    What is the difference between an AI crawler visit and AI traffic?

    A crawler visit is a machine fetching your pages to train a model or build an index; it shows in server logs and never in analytics. AI traffic is a person who asked an AI tool a question and clicked through to your site; it shows in GA4 as a session with an AI referrer. The first vastly outnumbers the second.

  • Famous Isn’t Found: Why Brand Size Doesn’t Decide AI Visibility

    Famous Isn’t Found: Why Brand Size Doesn’t Decide AI Visibility

    Big brands do not automatically win AI search. The models deciding which businesses get mentioned reward being talked about, useful and specific, not being widely recognised. Here is why that gap exists, what it means for a focused B2B SMB, and how to show up in AI search before anyone knows your name.

    Being known and being found are different jobs…

    There is an assumption underneath most marketing plans: the bigger and better known the brand, the more visible it must be everywhere, including in AI answers. It feels safe, yet it is an assumption neveretheless, and an often mistaken one. Reassuringly for the SMBs of the world, the gap it hides is where smaller specialists win.

    Being famous means people know your name and attach it to the thing you do. That attachment is real brand power, and it takes years of spend and repetition to build. Now watch what happens the moment someone does not know your name. They ask ChatGPT, Perplexity or Google’s AI for a specialist in your field, or the best way to solve the problem you solve, and the answer is assembled from whatever the model trusts on that subject. If you are not in that answer, your fame never gets a chance to work, because the conversation ends before your name comes up.

    That unbranded moment now sits inside most buying journeys. Forrester’s Buyers’ Journey research found that 94% of business buyers now use generative AI during their buying process (Forrester, 2026). The category-level question, asked by someone who has never heard of you, is where AI search does its filtering, and it is exactly where name recognition counts for least. Discovery has to come first. Fame is what you build after people can find you.

    What the research shows: size does not buy citations

    Kantar’s Chosen by AI research plotted brand size against share of model, the percentage of category queries in which a brand appears across AI engines, and the two did not line up. In its UK airline analysis, some of the biggest domestic names combined large brand sizes with small shares of model, while carriers far smaller in the UK market held several times their share of AI mentions (Kantar, 2026). The report’s own summary is blunt: “LLMs don’t care if you’re famous.” What they weigh is whether a brand is being talked about, useful, and matched to the specific need in the question.

    Read that the right way round if you run a B2B SMB. The systems assembling answers are not tallying advertising budgets or headcount. They are looking for evidence of specific, checkable usefulness across the sources they trust. That is a contest a focused specialist can enter, and in a narrow field, win. We would make this argument from our own brand and website audit findings alone (and we do further down). Of course, it helps that someone with a far larger dataset has measured it. 

    The gap on our own website: known by name, invisible by category

    We have lived a small version of this ourselves. When we ran an AI readiness check on our own website, the AI tools knew us by name. Ask about Contentifai directly and the answers came back accurate and reasonably complete. Ask the category question instead, the one a buyer who has never heard of us would ask, and we were (largely) absent. Known by name, invisible by category.

    That is the famous-isn’t-found gap at its smallest scale, and finding it in our own results is why we take it seriously. It also revealed where the work is. Our site passed its technical checks; what the category answers drew on was third-party material we did not yet appear in. Which points to the two halves of the fix: content specific enough to be the best answer on a subject, and a footprint beyond your own site that corroborates it. Our guides to E-E-A-T and how AI crawlers read your website cover the trust and readability layers underneath both.

    Why the specialist is well placed

    A household-name generalist covers your subject in passing. You cover it in depth, every working day. AI systems choosing sources for a narrow question reward the second of those, because depth produces the specific, checkable, repeatedly corroborated material that makes a safe citation.

    There is a timing advantage too. AI models are still settling their view of who the reliable sources are for narrower B2B subjects, and that view is being formed now, from the content and third-party evidence available. The brand that shows real depth early, in a subject that is properly its own, earns a head start in that picture, and larger competitors arriving later with broader, shallower material have to work to overturn it. Nothing about AI retrieval is permanent, but a head start compounds. Waiting for the space to mature hands it to someone else.

    Across the AI readiness checks we run, the repeating pattern is: smaller, tightly focused businesses were not out-cited on their specific subject, while bigger, broader competitors show up thinly everywhere.

    How to show up in AI search when nobody knows your name

    The practical work follows from the mechanism, and none of it requires a big brand budget.

    Claim a narrow subject and go deep. Publish useful, specific material on the thing you know best, built on first-hand work rather than aggregation. Our guide to writing content specific enough to be quoted covers what that looks like sentence by sentence.

    Make the machine’s job easy. Server-readable pages, clean heading structure, schema markup, answers stated before elaboration. This is the entry ticket, and most B2B sites have gaps in it.

    Build the trust file. Named authors, real credentials, consistent business facts, honest sourcing. These are the signals both Google’s raters and AI systems lean on.

    Get talked about beyond your own site. A presence that exists only on your own domain is a presence the models can largely miss, because answers are assembled from a wide, scattered pool of sources, from brand sites to reviews, directories, video and third-party content (Kantar, 2026). Reviews, press, directories and named case studies all count.

    Measure the stranger’s question, not your own name. Track the category queries a buyer who has never heard of you would ask, across more than one AI tool, and check who appears. A useful starting set is three questions: 

    1. “recommend a [your category] for a [your client type] in [your region]” 
    2. “who are the best [your category] for [the problem you solve]” 
    3. “what should I look for in a [your category]” 

    Brand-name queries flatter you; category queries tell you the truth.

    Find out where you stand on the questions strangers ask

    Our complimentary AI Readiness Check tests your visibility on category-level questions across ChatGPT, Perplexity and Google’s AI, alongside the technical and trust factors underneath, and sends back one prioritised report.

    Fame is the prize, not the entry ticket

    None of this argues that brand building is wasted. Name recognition remains the most valuable asset in marketing, and the businesses that hold it earned it. The argument is about order. In AI search, you have to be discoverable before anyone knows your name, and the work that makes you discoverable, real depth, clear structure, earned third-party evidence, is the same work that builds a name over time. Good content is good content plain and simple. The machines have simply started paying attention to the businesses that were already doing it properly.

    Where this sits in the bigger picture

    This is one piece of the wider discipline of being chosen by machines and then by people, which our plain-English guide to AEO covers end to end. Two companion pieces take the argument further: why no single source or platform gets you chosen by AI, and how to write content specific enough to be quoted. 

    If you would rather start with evidence about your own brand, request a complimentary AI Readiness Check and we will show you how you show up on the questions that matter. To track it every month, our AI visibility and web performance report checks how ChatGPT and Google’s AI Overviews describe you, from £200 a month.

    Frequently asked questions about brand size and AI visibility

    The questions B2B owners and marketers ask most about this gap.

    Does brand size matter in AI search?

    Far less than most marketers assume. Kantar’s research plotting brand size against share of AI mentions found the two do not line up: well-known brands can hold small shares of AI visibility while smaller, more specific brands hold large ones. What counts is being talked about, useful and matched to the question.

    Do big brands automatically appear more in AI answers?

    No. Large brands appear often for their own names, but category-level answers are assembled from whichever sources the model trusts on that subject. A broad brand covered thinly across many subjects can lose those answers to a smaller brand with real depth in one.

    Can a small business show up in AI search ahead of bigger competitors?

    Yes, on the subjects it owns. AI systems reward specific, checkable, corroborated depth, which a focused specialist can build faster than a generalist. The realistic goal is not out-citing a household name everywhere; it is being the cited source for your narrow field.

    Why isn’t my company showing up on ChatGPT?

    Usually one of three gaps: the machine cannot read your site properly, your content is not specific enough to be the best answer to any question, or there is too little third-party material corroborating you. Testing category-level questions across several AI tools shows which gap you have.

    Does domain authority decide AI visibility?

    Not on its own. Traditional authority metrics still influence what gets crawled and ranked, but AI citation leans on specificity, trust signals and third-party corroboration as much as on link profiles. It is possible to be visible in AI answers without a big domain, and invisible with one.

    What is share of model?

    Share of model is the percentage of category-relevant queries in which a brand appears across AI-generated answers, an AI-search counterpart to share of search. It measures presence in the answers buyers see, rather than clicks or rankings.

    How do I show up in AI search?

    Go deep on a subject you can own outright, make your site readable to AI crawlers, name your authors and keep your facts consistent, build third-party mentions beyond your own domain, and test category-level questions across several AI tools to track progress.

    Is AI SEO different for small businesses?

    The mechanics are the same; the opportunity is different. Small specialists can win narrow subjects outright, which is where AI citation is decided, while enterprise programmes spread effort across broad terms. Depth on owned subjects is the small-business edge.

  • E-E-A-T Explained: Why Google’s Quality Framework Now Drives AI Citations

    E-E-A-T Explained: Why Google’s Quality Framework Now Drives AI Citations

    A plain-English guide to Google’s foundational quality framework, what each pillar means, why third-party validation matters, and how you can apply E-E-A-T to earn both SEO and AEO visibility.

    E-E-A-T stands for Experience, Expertise, Authoritativeness and Trust. Google built the framework to help its human reviewers judge whether a page deserves to be believed, and the same signals now shape which brands AI tools cite. Here is what each part means, where B2B websites most often fall short, and how you can improve trust signals for your brand.

    What is E-E-A-T and where does it come from?

    E-E-A-T, sometimes written EEAT, stands for Experience, Expertise, Authoritativeness and Trust. It is the quality framework at the centre of Google’s Search Quality Rater Guidelines, the document given to the thousands of human reviewers Google employs to judge whether its search results surface content people can rely on.

    There are two aspects of E-E-A-T that often surprise marketers. The first is that E-E-A-T is not an algorithm, and it is not a score your website receives. Google’s announcement of the framework’s current form is plain on this point: the guidelines are used by search raters to evaluate the performance of its ranking systems, and they do not directly influence ranking (Google, 2022). Rater judgements teach Google’s systems what quality looks like. This means that you cannot bolt E-E-A-T onto a finished page or post; you need to build the qualities it describes from the outset.

    The second surprise is the timing. Google worked with three letters, E-A-T, from 2014, then added the extra E for Experience in December 2022 (Google, 2022), recognising that first-hand involvement with a subject is its own kind of quality, separate from formal credentials. Within a year, AI-generated answers had begun to change how people search, and a framework written for human reviewers found a second audience: machines making the same judgement, at far greater speed.

    The four pillars of E-E-A-T, in plain English

    Each part of the framework asks a different question about the person and organisation behind a page. Taken together they read less like a checklist and more like a character reference.

    Experience: have you actually done the thing?

    Experience asks whether the content creator has first-hand involvement with the subject: a review written by someone who has used the product, a guide written by a practitioner who has run the process, a case study drawn from delivered work. For a B2B firm this is the most readily available pillar to evidence, yet many brands struggle to translate their rich experience into thoughtful and compelling content. The specific detail of real client work is exactly what generic, aggregated content cannot fake.

    Expertise: do you know the subject deeply?

    Expertise is depth of knowledge, whether it comes from qualifications, years of practice, or both. On the page, it shows up as precision. A specialist writes with specifics, edge cases and honest limitations; a content mill writes in confident generalities. Raters are asked to look for the former, and the systems trained on their judgements reward the same thing.

    Authoritativeness: do others treat you as a source?

    Authority is reputational, and most of it lives off your own website. Who links to you, cites you, mentions you, or invites you to write and speak? A brand can claim expertise for itself, but authority has to be conferred by others, which is why it is the slowest pillar to build and the hardest to fake.

    Trust: the pillar the other three serve

    The guidelines do not treat the four as equals. Trust is the foundation and the current Search Quality Rater Guidelines describes it as the most important member of the E-E-A-T family (Google, 2025). An experienced, expert, authoritative page that misleads its reader still fails. Trust covers accuracy, transparency about who you are and how to reach you, honest sourcing, and a secure site. The other three exist to support it.

    Why AI citations run on the same trust signals

    An AI system assembling an answer faces the same problem a quality rater does. Asked to recommend an agency, explain a regulation or compare suppliers, it has to decide which sources to believe, and it cannot interview anyone. It reads what your site states, checks it against what the rest of the web says about you, and weighs whether you are safe to quote. This is E-E-A-T as a machine process.

    The research on generative engines points the same way. The first major study of AI-search visibility, from researchers at Princeton and Georgia Tech, found that adding citations, quotations and statistics lifted a source’s visibility in AI-generated answers by up to 40% (Aggarwal et al., 2024). The qualities being rewarded there, verifiable claims and identifiable sourcing, are trust signals under another name.

    There is a mechanical layer underneath: none of these signals count if the machine never receives them, which is what our guide to how AI crawlers read your website covers. And signals on your own site are only half the picture, as we found when we ran an AI readiness check on our own website: we passed every crawlability test and still did not appear for category-level questions, because those answers draw on third-party sources too.

    Third-party signals: what the rest of the web says about you

    Here is the part of the framework that can derail a B2B marketing plan: a brand’s own statements about itself are the weakest form of evidence it can offer. Raters are told to look beyond the site to independent sources, and AI systems do the same by construction, because they build answers from many places at once. External corroboration carries more weight than another page of self-description: a Companies House record that matches your stated facts, team LinkedIn profiles that match your author bylines, press mentions, client reviews, and case studies with named clients.

    Across the AI readiness checks we have run on B2B websites, the most consistent E-E-A-T gap is anonymous authorship: articles published with no named human behind them, on sites where the team page and the blog do not connect.

    See your E-E-A-T gaps in one report

    Our AI Readiness Check, complimentary until 27 November, reviews authorship, entity consistency, schema, crawler access and what AI tools currently say about your brand, and sends back a single prioritised report.

    How to improve E-E-A-T on a B2B website

    The practical work is unglamorous, which is a large part of why it works. Name your authors, and give each one a page linking their credentials, background and profiles. Write from delivered work: specific engagements, first-party findings, and truthful numbers. Keep your entity facts identical everywhere they appear, from your site footer to your directory listings. Earn third-party validation through reviews, press and named client case studies. State the facts in a form machines can read outright, which is where structured data comes in; our short guide to Schema Markup for B2B Websites covers the five core types, including the Person and Organisation markup that connects your authors to your brand.

    Remember, E-E-A-T is not a technique to retrofit onto thin content, and treating it as one misses what the framework is designed for. It describes what genuinely useful work by a real, accountable business looks like from the outside. Good content is good content; Google wrote down what that has always meant, and the machines learned to read it.

    Content designed for human raters, read by machines

    E-E-A-T began as instructions for human reviewers and has ended up as the closest public description of how machine systems decide who to believe. The work it asks for, named authors, first-hand substance, consistent facts, earned validation, is the same work that makes content worth reading in the first place. For the wider picture of appearing in AI answers, start with our plain-English guide to AEO. 

    Request a complimentary AI Readiness Check to see how your website and content measure up. To build first-hand substance month by month, see monthly blog content, written from your own expertise.

    Frequently asked questions about E-E-A-T

    The questions marketers ask most, phrased the way they search for them.

    What does E-E-A-T stand for?

    Experience, Expertise, Authoritativeness and Trust. It is the quality framework in Google’s Search Quality Rater Guidelines, used by human reviewers to judge whether content, and the people and businesses behind it, can be relied on. Trust is treated as the foundation that the other three support.

    Is E-E-A-T a Google ranking factor?

    Not directly. Google states that the rater guidelines do not influence ranking. Raters use the framework to score search results, and those scores teach Google’s ranking systems what quality looks like. The signals associated with E-E-A-T, such as clear authorship, accuracy and reputation, are what the systems learn to reward.

    What is the difference between E-A-T and E-E-A-T?

    Experience. Google used E-A-T (Expertise, Authoritativeness, Trust) from 2014 and added the first E in December 2022, to recognise first-hand involvement with a subject as its own quality signal, distinct from formal expertise. A practitioner who has genuinely done the work scores on Experience even without academic credentials.

    How do I improve E-E-A-T on my website?

    Name your authors and publish author pages with real credentials. Write from first-hand work rather than aggregation. Keep your business facts consistent across your site, directories and profiles. Earn third-party validation such as reviews, press mentions and named case studies. Then add Organisation and Person schema so machines can read those facts directly.

    Does E-E-A-T apply to B2B websites?

    Yes, and it tends to favour them. B2B specialists usually have genuine experience and expertise to show; the common failure is presentation, publishing anonymous content and hiding the team, rather than any shortage of substance. Sites covering finance and other high-stakes subjects are held to a higher standard still.

    How does E-E-A-T relate to AI search?

    AI tools choosing sources for an answer face the same judgement Google’s raters make: which pages, and which businesses, can be believed. Research on generative engines found that content with citations and verifiable claims earns markedly more AI visibility, which makes E-E-A-T the closest thing to a published blueprint for being cited.

    Why does Trust matter most in E-E-A-T?

    Google’s guidelines rank trust above the other three pillars because they exist to support it. An expert, experienced, authoritative page that misleads its readers still fails the framework. Accuracy, transparency about who is behind the site, and honest sourcing decide whether the rest counts at all.

  • What Is AEO? A Plain-English Guide for B2B Brands That Want to Be Found by AI

    What Is AEO? A Plain-English Guide for B2B Brands That Want to Be Found by AI

    Your buyers now ask ChatGPT, Perplexity and Google’s AI features before they open your website. Answer engine optimisation (AEO) is how you make sure those answers mention you. Here is what the term means, how it differs from SEO, and why smaller specialist firms have a genuine edge.

    The question your buyers no longer type into Google

    Picture a marketing director shortlisting agencies, software or consultants. Five years ago, that search started with a keyword and ten blue links. Now it starts with a question typed into an AI assistant, and the answer comes back as a paragraph naming three or four options. Either your brand is in that paragraph, or you were never in the running.

    This is not a fringe behaviour. Forrester’s Buyers’ Journey Survey of nearly 18,000 business buyers found that 94% used AI during their most recent purchase, and twice as many buyers named generative AI or conversational search as a more meaningful source of information than any other, ahead of vendor websites, product experts and sales teams (Forrester, 2026).

    We wrote about the early stage of this shift in our guide to visibility in the age of zero-click search. What has changed since is the scale, and the vocabulary. Marketers now face a cluster of new acronyms, AEO and GEO chief among them. This article explains what they mean, what to do about them, and why the news is better for specialist B2B firms than the headlines suggest.

    AEO meaning: answer engine optimisation in plain English

    Answer engine optimisation (AEO) is the practice of earning a place in the direct answers that AI tools give to a question. When someone asks ChatGPT to recommend providers, or Google’s AI Overview summarises a topic, AEO is the work of making your content one of the sources those answers are built from, and your brand one of the names they mention.

    Generative engine optimisation (GEO) is a close sibling. Where AEO focuses on appearing in answers, GEO concerns how generative AI models read, remember and represent your brand across everything they have learned. In practice the two overlap so heavily that most B2B firms can treat them as one discipline.

    Search engine optimisation (SEO), the practice both terms grew out of, has not gone anywhere. Search engines still matter, and much of what makes a site good for Google makes it good for AI. The difference sits in what each system does with your content. A search engine ranks pages and lets the reader choose between them. An answer engine selects a small number of sources and composes the answer itself. Your content used to compete for a click. Now it competes for a citation.

    Where the jargon stops

    The labels matter less than the behaviour behind them. Whether a given tool counts as an answer engine, a generative engine or a search feature, the question for your business is the same: when a buyer asks about your category, does the machine know you, trust you and quote you? From here, we will use AEO as the umbrella term.

    Key takeaway: SEO gets you ranked, while AEO and GEO get you quoted right where prospective buyers are considering your services.

    The two new jobs your content now has

    Content used to have one discovery job: win a click from a list of results. That job is shrinking. In its place are two new ones, and they have to be done in order.

    Job one: be chosen by the machine

    Before an AI answer can mention you, its systems have to find your content, read it without obstruction, understand what your business is and what it does, and then judge it credible enough to cite to the reader. That calls for pages a machine can parse, facts a machine can verify, consistent details about your business across the web, and content that answers real questions directly rather than circling them. 

    We explain the technical side of this in our companion article: How AI Crawlers Read Your Website.

    Job two: be chosen by the human

    A reader who arrives from an AI answer is not a cold visitor. They have already seen a summary of the topic and a shortlist of names. They arrive comparing, and they arrive informed. Content that merely restates what the AI told them gets a polite close of the tab. Content that adds judgement, experience and a point of view is what turns a citation into a client. Of course, buyers doing this research still verify what AI tells them; the machine opens the door, and your writing still has to walk them through it.

    Most B2B content was built for neither job. It was built for the old one: winning a click from organic search.

    AEO vs SEO: what carries over and what changes

    If you have invested in doing SEO properly, none of that spend is wasted. Clear site structure, topical depth, E-E-A-T signals (experience, expertise, authoritativeness, and trustworthiness), schema markup and genuine expertise count for as much in AI answers as they did in rankings, and often more. Our guide to B2B website structure covers the foundations, including structuring your content to guide buyers through their decision journey.

    Building on SEO and E-E-A-T foundations, the following three things have changed with the rise of AEO and GEO:

    First, questions replace keywords as the unit of planning. Prompts typed into AI tools run far longer than typed searches, and they are phrased as questions. Content planned around the questions buyers ask, with a direct answer near the top of each section, is the format answer engines favour.

    Second, your presence beyond your own website carries more weight. AI systems build category answers from directories, round-ups, reviews and independent coverage as much as from brand websites. Being described accurately in third-party sources is now part of the content job.

    Third, evidence works harder than eloquence. Researchers at Princeton and Georgia Tech tested what makes content more likely to be used in generative answers, and found that adding clear statistics and citing credible sources lifted a source’s visibility by up to 40% (Aggarwal et al., 2024). The machines reward precisely what good editors have always asked for: specific claims, named sources, and a point.

    Which brings us to the observation we keep returning to at Contentifai: good content is good content. There is no trick to game here, and the firms hunting for one have missed the point. Thoughtful, carefully written work that leaves the reader knowing more than they did before is what humans value and what machines now measure for. 

    AEO matters, but it is a way of making good content legible to a second kind of reader. It is never a substitute for the content itself.

    AI answers are not assembled by brand size. They are assembled from sources that describe a category clearly, which means a ten-person specialist firm that is specific, consistent and well evidenced can appear in answers ahead of a well-known household name for relevant queries.

    We know because we ran the test on ourselves. When we put our own website through our AI readiness check, ChatGPT and Google’s AI described Contentifai accurately by name: our positioning, our founder, our sectors, our location. But category-level questions, the “recommend me an agency” kind, did not surface us at all, because those answers are drawn from third-party lists and directories we had not yet appeared in. 

    Our own readiness check verdict: AI knows us by name, but not yet by category. Most B2B firms will find the same pattern, and it is fixable.

    We published the full findings in our self-audit case study.

    The stakes are not theoretical for firms like ours, or like yours. Semrush surveyed more than 600 B2B professionals and found that 66% regularly use AI to research products, vendors or suppliers, with agencies and service providers the most-researched category of all, at 51% (Semrush, 2026).

    Find out what AI currently says about your brand

    Our AI Readiness Check, complimentary until 27 November, shows you what AI tools can and cannot see on your website, what they say about your brand today, and what to fix first. Many firms will discover the same pattern we did, and how to improve their visibility in generative AI answers.

    How to start with AEO: three steps we recommend

    1. Check that the machine can read your website. Crawler access, page rendering and schema markup decide whether AI systems receive your content in the first place. Our guide to how AI crawlers read your website walks through the checks, three of which you can run yourself today.
    2. Answer real questions directly. Plan content around the questions buyers actually ask, put the answer near the top, then elaborate and go into depth. A well-written FAQ section with matching schema markup is a useful addition as well.
    3. Build your third-party footprint. Directories, industry round-ups, reviews and independent coverage are the raw material of category answers. Consistent, accurate descriptions of your business across those sources teach the machines who you are.

    Different AI engines draw on different sources, so a fix that moves your visibility in one may do little in another. That is not a reason to hold back. It is a reason to treat AEO as steady, cumulative work rather than a single technical fix.

    Frequently asked questions about AEO

    Short answers to the questions we hear most, from clients and from our search data research.

    What does AEO stand for?

    AEO stands for answer engine optimisation: the practice of making your content and brand appear in the direct answers given by AI tools such as ChatGPT, Perplexity and Google’s AI Overviews, rather than only in ranked lists of links.

    What is the difference between AEO and SEO?

    SEO earns your pages a position in a list of search results that the reader chooses from. AEO earns your content a place inside the answer itself. The foundations overlap heavily; the difference is that AEO competes for citation and mention rather than for the click.

    What is GEO in marketing?

    GEO stands for generative engine optimisation. It covers how generative AI models read, remember and represent your brand in their training and their answers. In day-to-day B2B practice, GEO and AEO overlap enough to be treated as one discipline.

    Does AEO replace SEO?

    No. AI systems lean on many of the same signals search engines use, so strong SEO is the foundation AEO builds on. A site that abandoned SEO would weaken both. What changes is the goal: from ranking well to being quoted.

    What is zero-click search?

    A zero-click search is one where the person gets their answer on the results page itself, from a snippet or an AI summary, without visiting any website. It is the behaviour AEO responds to, and our zero-click guide covers it in depth.

    How do I get cited by AI tools?

    Make your content machine-readable (crawlable, well structured, marked up with schema), answer specific questions directly, include verifiable facts with named sources, and build accurate mentions of your brand across the third-party sources AI draws on.

    How do I optimise my content for ChatGPT?

    Allow OpenAI’s search crawler in your robots.txt file, keep your content in server-rendered HTML, lead each section with a direct answer, and keep your business details consistent everywhere they appear online. Write content that aligns with real questions your buyers are asking.

    Is AEO worth it for small B2B businesses?

    Yes, and often more so than for large ones. AI answers favour clear, specific, well-evidenced sources over sheer brand size, so a focused specialist can appear in category answers that larger competitors miss.

    How long does AEO take to show results?

    Expect months rather than weeks. Technical fixes such as crawler access and schema can register quickly, but building the third-party footprint that feeds category answers is cumulative work, much as building search authority was.

    What does an AI readiness check cover?

    Ours covers crawler access, page rendering, schema markup, content structure, brand entity consistency, and live tests of what AI tools currently say about your brand, delivered as a one-page report with priorities. It is complimentary to request until 27 November.

    Be contēnt with being the answer

    Your buyers now ask machines first and humans second, so your content has to be chosen twice. The firms that win will not be the ones chasing tricks. They will be the ones whose work is clear enough for a machine to quote and good enough for a human to trust. 

    If you would like to know where you stand today, request a complimentary AI Readiness Check and we will show you. When you are ready to act on it, our content and AI visibility services start with content optimisation from £220.

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