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Tag: answer engine optimisation

  • 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.

    Table of Contents

    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 free AI Readiness Check 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 free to request.

    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 free AI Readiness Check and we will show you.

  • From Demo to Live Rails: The Agentic Half-Year

    From Demo to Live Rails: The Agentic Half-Year

    Every year in finance has its buzzword. The first half of 2026 had something rarer: a buzzword that started writing cheques. For two years, agentic AI lived in conference demos and proof-of-concept decks. This year it went to work.

    The clearest signal came in March, when Santander and Mastercard completed Europe’s first live end-to-end payment executed by an AI agent, the first agentic payment carried out within a regulated banking framework using Mastercard Agent Pay. This was a controlled pilot, not a commercial rollout, and both companies said as much. But it was no mere demo either: the payment ran on Santander’s live infrastructure, under real conditions, set in motion by software acting for a customer.

    By June, the whole industry had caught up. Money20/20 Europe in Amsterdam built its agenda around four themes: AI and the agentic age, the rebundling of financial services, stablecoin infrastructure, and faster-moving regulation. Three of those describe how money moves. One describes who now decides where it goes. That fourth shift is the one we think finance will still be talking about in December, because it changes something every business quietly depends on: how customers find you in the first place.

    This article was originally published in FinanceX Magazine Issue #24 (July 2026) exploring key themes in European finance and fintech during the year to date. We’re grateful to share our insights and commentary around how AI is becoming increasingly embedded in financial infrastructure and consumer and banking practices throughout Europe. The article is republished here with permission.

    Table of Contents

    When the Buyer Is an Algorithm, Who Are You Talking To?

    Here is the part that should make any marketing director look up. For as long as commerce has existed, selling has meant persuading a person. We have built whole disciplines around human attention: the headline that stops the scroll, the case study that earns the click, the logo that feels trustworthy at a glance.

    Agentic commerce quietly removes the person from that moment. When an AI agent compares the options and completes the purchase, it does not pause on a clever tagline or warm to a polished brand. It reads, ranks and recommends based on what it can actually parse and verify. And the scale is not small. McKinsey research suggests AI agents could mediate between $3 trillion and $5 trillion of global consumer commerce by 2030.

    So the question changes. It is no longer only “how do we persuade the buyer?” but “how do we get recommended by the thing the buyer now trusts to choose?” The customer relationship does not disappear. It goes quiet. The brand has to show up earlier, further upstream, inside the answer the agent gives before a human is even in the room.

    The Firms Building to Be Found

    The firms getting ahead of this have noticed that visibility now has two audiences: people, and the machines acting for them. They are building for both.

    The marketing world has even named the discipline. Writing for the World Economic Forum in January, Stagwell’s Mark Penn argued that search engine optimisation has given way to answer engine optimisation. The shorthand is AEO, and the principle behind it is plain: if an AI cannot read your content clearly, it cannot recommend you with confidence.

    In practice this looks less like a campaign and more like housekeeping. Clean descriptions. Clear specifications. Claims a machine can check. Even Mastercard, building the payment side of all this, was careful to describe its agents as “visible, governed participants” in the flow. Visible is the word that keeps recurring.

    For a B2B firm the parallel is direct, and slightly uncomfortable. When a prospect asks an AI “who are the best advisers for a problem like ours?”, your firm is either named in that answer or absent from the shortlist. There is no second page to drift onto. The agent offers a handful of options, and the criteria for making the cut are increasingly the clarity, structure and credibility of what you have already published.

    Becoming the Answer: What This Means for Every B2B Brand

    At Contentifai we spend our days helping B2B firms with a version of exactly this problem, and the first six months of 2026 have sharpened it. The lesson we take from the agentic half-year is not that marketing is finished. It is that visibility is being re-platformed.

    For most of the past decade, being found meant ranking on a results page a person would scroll. Increasingly, it means being the answer a machine returns when nobody is scrolling at all. That is a different job. It rewards clarity over cleverness and substance over noise, and it favours content credible enough for an AI to repeat with confidence.

    For smaller firms, this is quietly good news. An agent does not care how big your marketing budget is. It cares whether your expertise is legible, specific and trustworthy. The businesses that write clearly about what they do, and prove it, will get recommended. The ones hiding behind vague claims will slip out of the conversation without ever knowing why.

    That is the work we care about at Contentifai: helping firms become the answer, not the result no one reaches. If your business depends on being chosen, it is worth a conversation; contact us today to discuss your content goals.

  • What We Found When We Ran Our AI-Readiness Check On Our Own Website

    What We Found When We Ran Our AI-Readiness Check On Our Own Website

    We run AI-readiness checks for clients every week. Here is what happened when we pointed one at our own website: what we found and fixed plus the things that surprised us.

    Why would we run an AI-readiness check on our own website? We spend a lot of our time telling clients to look at how AI sees their website, so it was only fair to point the same check at ourselves. We ran it against contentifai.agency to answer one question: when someone asks ChatGPT, Google’s AI Overview or Perplexity about what we do, what comes back, what is already working, and where are the specific areas we can improve.

    What follows is the full result, the parts we were pleased with and the parts we have since corrected.

    Table of Contents

    What an AI-visibility check reveals about a brand

    Known by name, but invisible by category: This is the split an AI-visibility check tends to expose. AI describes a brand accurately when you name it, yet leaves it out when a buyer asks the broader category-related questions. We were no exception.

    Ask AI about Contentifai directly and the answer is accurate and fair. It describes our positioning, our founder, our sectors and where we are based, and it cites our own pages alongside reputable press and business records. Google’s AI Overview even built an unprompted, favourable table comparing us with fellow agencies.

    But, ask the generic buying question instead, something like: “recommend UK B2B content marketing agencies” and we are nowhere to be seen. Ask our exact niche, “agencies that pair human writers with AI for B2B content”, and ChatGPT puts us at the top, while Google’s AI Overview still leaves us out.

    That difference between the two answers is the point of the whole exercise. Being known by name is not the same as being found by category, and when a buyer asks the broad question to help them find information and answers to their challenges, the answer gets assembled from lists and sources we were not yet part of.

    The technical foundations AI search rewards

    What our check found is already working: We went in expecting a list of structural problems, and were pleased to find the foundations in good shape.

    Our site is fully server-rendered and open to AI crawlers. Nothing meaningful is hidden behind scripts a machine cannot read, our robots file lets the AI crawlers in, and a current sitemap is declared, so the systems reading us get the real page rather than a blank one.

    Our brand entity is accurate across the tools we tested. The descriptions matched who we are, which tells us the foundations, our own pages and the third-party mentions, are pulling in the same direction.

    Our structured data was largely in place too: named organisation and business details, a real named author on our articles with their own author page and dates, and a full set of linked social profiles. In plain terms, the machines could tell who wrote what and who we are, without having to guess.

    And there was plenty for AI to draw on. A detailed About page, a founder biography, a white paper, case studies and a steady blog gave the tools real, expertise-led material to quote, which is a large part of why the branded answers came back so well.

    Having strong foundations really matters and we focus on strengthening the website fundamentals of our clients. With solid foundations, you can build a brand with confidence and knowing that the website and other digital structures won’t cause issues as the brand grows (slowing site speed, broken links, pages not loading correctly, etc.).

    Brand Survival in the Age of AI: Read The Whitepaper

    We set our thinking down in a white paper, Brand Survival in the Age of AI, because the question underneath this check is bigger than any single audit. As more of the web is written by machines, the brands that hold their ground will be the ones that keep a person in the loop: for the people making the content, and for the people reading it. 

    This is what we mean by content written by humans, for humans. Being legible to machines is how you get found, and a human deciding whether to trust you is still how you get chosen. 

    Download the white paper to learn more about how humans, AI, and brand values matter.

    Common AEO fixes: copy, schema and llms.txt

    What the check found, and what we fixed: Every website carries a list of small things to put right. Here is ours, grouped by the kind of fix each one needed.

    The first group was our own copy. We keep a house list of words we prefer not to use, the tired marketing terms we strip out of client work as a matter of routine, and a few of them had settled onto our homepage and About page over time. We had let our own house get untidy while keeping everyone else’s in order, so we cleared them out. We also found a “team and culture” link that led nowhere, a placeholder that had never been finished.

    The second group was the plumbing. A handful of small structured-data faults had crept in: a malformed link to our X profile, an address that did not match across pages, and two competing blocks disagreeing over who the author was. None of these matter on their own, but together they are the kind of noise that makes a machine a little less certain about you. The one we were least comfortable with was our llms.txt file returning a 404. For an agency that talks about being readable to AI, not having the very file built for that purpose was a fair thing to be caught on, and we have added one.

    The third group was housekeeping. A long tail of thin tag pages had built up over the years, adding little and blurring the picture of what we cover, so we started trimming them back.

    None of this was unusual. It is what a thorough check turns up on almost any site: small, specific items that are quick to put right once you can see them.

    Summary of AI readiness quick-fixes we applied

    1. Our own banned words on the homepage and About page
    2. A placeholder link that led nowhere
    3. A set of small schema faults
    4. A missing llms.txt file
    5. A long tail of thin tag pages

    The answer engine optimisation work that comes next

    What we are doing next: The fixes above were the work to do straight away. The list that follows is the slower, more deliberate programme.

    Those fixes were low effort, low risk and well worth doing without delay, and most are already done. The more interesting list is the slower one, the decisions rather than the tidy-ups.

    The big one is the category problem. To show up when AI answers the generic question, we need to increase our activity with third-party publications and directories, which AI answers are built from. It is not an afternoon’s work, but it is the lever that actually moves us from known by name to found by category.

    Alongside that, we are verifying and standardising our core business profiles and listings so our location signals are clean and consistent everywhere they appear, and we are publishing our own answer-engine pillar pages so our content joins the unbranded results rather than only the branded ones.

    These are the same two lists we hand every client: the work you action now, and the programme you commit to. Knowing which is which is half the value of the check.

    Order of AI readiness fixes we actioned

    1. Cross-publish in directories and listings that AI draws from
    2. Standardise professional business profile listings
    3. Publish brand-relevant and contextually rich answer-engine pillar pages
    4. Build backlinks and PR around context and pillar pages

    How we run an AI visibility check

    A proper AI-visibility check reads a site the way both a person and an AI would, across several engines and signals, to find what is working and what is holding it back.

    When we run a check, we put the same questions to ChatGPT, Google’s AI Overview and Perplexity, then read what each one says about the brand both by name and by category. From there we look under the bonnet at items including: 

    • How the site renders to a crawler, 
    • Whether the structured data is clean and consistent, 
    • Whether the files AI readers look for are in place (an llms.txt, for one), 
    • Who the author signals point to, and 
    • Which third-party sources the AI is leaning on. 

    A person reads the site the way a buyer would, and AI helps us test it at the scale and from the angle at which machines read the site.

    The value is not in spotting a single missing tag. It is in reading all of those signals together and working out which gaps actually change whether a brand gets named, and how to prioritise the findings into actionable tasks. That is the difference between a quick look and a set of findings you can act on, and it is the part that takes practice. We run these checks often enough to know what matters on a given site and what can safely be left for now.

    As the web fills with machine-written pages, being legible to the machines reading them matters more every month. 74% of newly created web pages contain AI-generated content, and a brand the machines cannot read clearly is a brand that quietly drops out of the answer. And those answers are now mainstream: around one in five Google searches returned an AI summary in 2025 and users are far less likely to click through to a website when one appears. That kind of absence is easy to miss until the enquiries thin out, which is why it pays to have a check done properly rather than guessed at.

    What an AI readiness check shows you

    An AI readiness check, in miniature: Here is why we ran this on ourselves, and what a check like this tends to reveal.

    We ran our own method on our own site because it is the clearest way to show what these checks involve and what they bring to light (plus we found issues worth fixing). The pattern we found is the one we see most often: a brand that AI already knows and describes well, with a real opportunity to be present when buyers ask the wider question. Most of the fixes are small, a few are strategic, and all of them are easier to act on once they are written down in front of you.

    Learn more and request your AI readiness check today

    If you would like the same picture of your own site, visit our free AI Readiness Check page to learn more and complete the short request form today.

    Be contēnt with your cōntent.

    Frequently asked questions

    A few of the questions we are most often asked about AI readiness checks and AI visibility.

    What is an AI readiness check?

    An AI readiness check looks at how AI tools such as ChatGPT, Google’s AI Overview and Perplexity see your website: whether they can read it, describe your brand accurately, and include you when buyers ask. It finds the gaps and the easiest fixes, usually in under thirty minutes.

    Why doesn’t my business show up when AI recommends companies in my field?

    Usually because AI builds those generic recommendations from third-party round-ups and directories rather than from your own site. If your brand is not listed in the sources AI quotes, it can describe you accurately by name yet still leave you out of the category answer.

    How can I check whether AI mentions my business?

    Ask an AI tool about your business by name and see whether the answer is accurate, then ask the generic question a buyer would type and see whether you appear at all. The gap between those two answers shows you how much work is in front of you.

    What is an llms.txt file, and do I need one?

    An llms.txt file is a plain-text file at the root of your domain that gives AI tools a clear summary of your site. Adoption is still early and no major AI platform commits to reading it yet, but it is quick to add and signals that your site is built with AI readers in mind.

    Is appearing in Google’s AI Overview different from appearing in ChatGPT?

    Yes. The two pull from different sources and update at different speeds, so a brand can sit at the top of ChatGPT’s answer while Google’s AI Overview leaves it out. Being cited across both takes consistent entity signals and a presence in the sources each one draws on.

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