What is Answer Engine Optimisation (AEO)? What It Needs to Work
Software vendors and marketing agencies are selling answer engine optimisation (AEO) as a separate discipline, with its own tools, retainers, and urgency. But the tactics it prescribes—schema, answer blocks, extractable formatting—only matter if everything else is already up to speed.
AEO has become part of my daily work as a fractional SEO strategist. But the teams asking me about it almost always have more pressing issues to address. They want to know how to get cited in AI Overviews before they've confirmed Google is even crawling the pages in question.
In this guide, I’ll help you understand what answer engine optimisation is, what the evidence shows earns AI citations, which pages and queries are worth the effort, and which tactics you should ignore for now.
What is answer engine optimisation?
Answer engine optimisation is the practice of structuring and strengthening content so it can be extracted, trusted, and cited by AI engines. The most common targets include Google AI Overviews and AI Mode, ChatGPT, Perplexity, Gemini, and Claude.
In traditional search, you compete for a ranked position by targeting a specific search query. The first goal is to get users clicking on your result. In an AI answer, the reader may never click through at all—visibility means being named or quoted inside the answer itself, not ranked below it. That single shift is what the AEO market is built around.
But it isn't a new mechanism. Just like traditional search engines, AI systems select from sources they can already reach, parse, and trust.
How AEO relates to GEO and AI search optimisation
AEO, generative engine optimisation (GEO), artifical intelligence optimisation (AIO), and language learning model optimisation (LLMO) all describe overlapping work. This is commonplace in the marketing industry—when a shiny new idea hits the mainstream, every marketer and their dog rushes to come up with a confusing new acronym for it.
In practice, the terms get used interchangeably to mean the same thing: making content more likely to be retrieved, quoted, and cited by generative AI systems.
The difference between AEO and SEO is more obvious, so for the sake of this guide, I’ll be using AEO to refer to all AI-based search marketing acronyms mentioned above.
How do answer engines choose which sources to cite?
Generative AI answers work through retrieval-augmented generation.
The system retrieves a set of candidate pages from an existing index and then generates an answer grounded in what it retrieves.
According to Google's official guidance, its generative search features rely on the same core Search ranking systems and retrieve from the same Search index that powers ordinary results—there's no separate AI index to get into.
Before generating an answer, the model runs what’s called a query fan-out.
It generates a set of concurrent related queries to gather more than the original question asked for. A search for "best fractional SEO consultant" might fan out into queries about pricing, agency comparisons, and what a fractional engagement involves—pulling in pages that never directly targeted the original phrase.
Three conditions decide whether a page is even in contention to be cited:
Indexed and eligible: The search engine has to index the page and treat it as eligible for a snippet before any generative surface can use it.
Crawlable: Crawlers can't retrieve what they can't reach, however well it's written. This is where technical SEO foundations either hold or fail.
Retrieved for the right query: Fan-out pulls pages in for questions adjacent to the one they target, rewarding depth across a topic rather than a single exact-match page.
Everything the AEO market sells operates after this point.
Why answer engine optimisation depends on your SEO foundations
According to Ofcom's Online Nation report, Google Search handles three billion UK searches per month, and around 30% of those searches now show an AI Overview. 53% of UK adults say they see these summaries often.
A site built around the differences between AEO and SEO ends up with schema on pages that don't rank and can’t be crawled, answer blocks on pages with no authority behind them, and no reason for an answer engine to prefer it over an established source.
Authority is still the gate
Topical depth across connected pages makes a source one that the AI system has encountered repeatedly in credible contexts. A single well-formatted page doesn't produce that, however cleanly it answers the question.
Lower-ranked sources gain the most from optimisation, but that only matters if you're in the retrieval set to begin with. Build the SEO topic clusters and the pillar page architecture that put you there first. Crawlability, page speed, clear structure, and named authorship serve both systems equally.
All of these factors are foundational SEO best practices, which is why treating AEO as a separate challenge is the wrong approach.
What actually earns AI citations
A lot of AEO advice comes from companies selling software. It’s in their best interests to convince you it’s your key to online marketing success.
But peer-reviewed GEO research benchmarking nine optimisation methods against 10,000 real queries gives a clearer picture:
Adding citations, quotations from credible sources, and statistics produced visibility increases of over 40%.
Keyword stuffing produced little to no improvement and performed worse than doing nothing on a live generative engine.
Lower-ranked sources gained the most: one method lifted fifth-ranked source visibility by 115.1%, while top-ranked source visibility fell by around 30%.
The things that earn citations are the things that make content genuinely worth citing: evidence, verifiable sourcing, and first-hand experience. Formatting helps a system find them. It doesn't create them.
| Common AEO tactic | What the evidence shows | Works without SEO foundations? |
|---|---|---|
| Adding original statistics and data | Visibility increased by over 40% in controlled testing | No—needs an authoritative page to attach to |
| Quoting credible sources | Among the strongest performing methods tested | No—citation strength depends on existing trust |
| Citing primary sources | Comparable lift to quotations, strongest for lower-ranked pages | Partially—helps most once you're already in the retrieval set |
| Answer-first section openings | Not tested directly, but aligns with how systems extract passages | Only alongside real topical depth |
| FAQ and schema markup | Not among the methods tested, and no evidence of a citation benefit | No—improves rich results, not AI answer inclusion |
| Keyword-led rewriting for AI | Performed worse than doing nothing | No—risks scaled content abuse issues |
First-hand experience and original data are the one input a model can't generate for itself. A model can approximate everything else on this list. It can't approximate that. Bring in subject matter experts when your own team lacks the depth to supply it.
Off-site mentions do work that your own pages can't—they put you in the retrieval set for queries where nothing you've published ranks. Improving brand visibility in AI search doesn’t just happen on your own website.
Which pages can be optimised for answer engines?
Exposure to AI answers isn't equal across your site. Treating every page the same spreads your effort across pages that face no AI competition at all. Prioritising the work by exposure prevents you from wasting SEO strategy budget on pages with nothing at stake.
According to Pew Research Center's analysis of 68,879 searches, just 8% of one- or two-word searches produced an AI summary, rising to 53% for searches of 10 or more words. Searches beginning with a question word produced one 60% of the time.
Work through your own pages in this order:
Pull your query data: Start from queries already generating impressions, not from a keyword tool. Separate short and navigational queries from long, question-shaped ones.
Identify the exposed pages: The pages ranking for question-led and conversational queries are the ones competing with an AI answer. Prioritise those.
Check the foundations before the formatting: For each priority page, confirm it's indexed, crawlable, and supported by real depth on the topic.
Restructure and add evidence: Only once the above holds. Lead each section with a direct answer, and add the sourcing and original data that earns citations.
While this tells what can get cited, it doesn’t tell you what should get cited. And that’s the real question. Wasting time and resources on pages that don’t return any commercial interest is a common pitfall you need to avoid.
Which AI citations are actually worth earning?
A citation is only worth what the query behind it is worth. Being named as the source for a definition puts your brand in front of someone who now has their answer and no reason to go further. Being named in the answer to "which tools should I look at" puts you on a shortlist instead.
Why the easiest citations are the least valuable
Volume lives where the value doesn't. Definitional and how-to queries trigger AI answers most reliably, because they're long, question-shaped, and have a settled answer the model is confident summarising. They're also the queries where a complete answer ends the search.
Comparison and evaluation queries behave differently. The answer to "best X for Y" is a list of named options, and a reader working through that list has a reason to keep going—to check pricing, read a case study, or make contact. This is the logic that makes product-led SEO effective: putting content where the buying decision actually happens.
Those comparison and evaluation queries are also where models lean hardest on independent corroboration, because recommending a vendor carries more risk than defining a term.
The table below sets out what a citation is worth by query type.
| Query type | What a citation gets you | Worth prioritising? |
|---|---|---|
| Definitional ("what is X") | Brand exposure to someone whose question is now answered, with little reason to click through | Low priority |
| How-to and troubleshooting | Credibility with someone solving a problem, who may return later, but a weak near-term commercial signal | Low to medium |
| Comparison and alternatives ("X vs Y", "alternatives to X") | Presence at the point a shortlist forms | High |
| Category and recommendation ("best X for Y") | Inclusion in the set of options considered at all | Highest |
| Pricing and evaluation | Contact with a buyer already past the research stage | High, and usually the least contested |
Effort and value run in opposite directions across these query types. A site working top-down through its blog spends most of its time at the bottom of the buying list.
Answer engine optimisation tactics you can safely ignore
Google published official documentation about this in May 2026, updated in July, and it addresses a good deal of what the AEO market sells on top of the guidance already covered above.
Avoid these non-essential tactics when you’re building your AEO strategy:
llms.txt and similar files: Google Search doesn't use them, and creating one neither helps nor harms visibility.
Chunking content for machines: There's no requirement to break pages into smaller pieces for AI to understand them, and no ideal page length to target.
Rewriting content for AI systems: Models handle synonyms and intent, so writing separate content for every phrasing variation risks running into scaled content abuse policies.
Special schema markup: Generative AI search doesn't require any schema.org markup. Structured data remains useful for rich results eligibility, not as a route into AI answers.
None of this makes the underlying work optional. The techniques selling under the AEO name are the ones organic search already requires—sourcing, structure, and crawlability—repriced and repackaged for a newer anxiety.
How do you measure answer engine optimisation performance?
Google Search Console now reports generative AI performance directly, as first-party data pulled straight from Google's own systems. Everything covering ChatGPT, Perplexity, and Claude comes from third-party estimations—no external tool has access to those systems' internals.
For a marketing leader reporting to a board, this is the practical difference: one set of numbers is verifiable, the other is directional. Google has already cautioned against tools that claim to report internal metrics they can't actually access, so treat any dashboard promising exact AI citation counts with the same scepticism.
The best AI visibility tracking tools don’t make false promises.
Fix the foundations before you buy an AEO strategy or platform
Answer engines and search engines are drawing on the same underlying signals: whether your site is reachable, authoritative, and worth quoting.
Before you waste months adding schema and answer blocks to pages that can’t be retrieved, pay for a software subscription that reports on visibility you have no mechanism to earn, fix the SEO foundations that make your website citable.
I help B2B and SaaS teams build the architecture and strategy that makes AEO worth the effort. If your team is being sold AEO work that’s not returning results, book a consultation with me for a free diagnosis.
Frequently asked questions about answer engine optimisation
Does llms.txt help with AI search?
No. llms.txt is a proposed standard for listing your content in a format AI crawlers can read, but no major answer engine has confirmed it uses the file for retrieval or citation. Adoption claims mostly come from vendors rather than from the platforms themselves. Treat it as a cheap experiment if you want one, but don't let it displace work on pages that answer engines can already reach.
Do I need an AEO tool?
An AEO tool is worth paying for once you have pages that answer engines could plausibly retrieve and a reason to track movement across them. Before that, the subscription is a waste of money. Start with the query data you already own in Search Console. Add a tool when you need to see what's happening outside Google rather than inside it.
How do I get leadership buy-in for answer engine optimisation?
Leadership buy-in for answer engine optimisation comes easiest when you present it as a continuation of organic search rather than a new line item competing for its own budget. Present the pages already exposed to AI answers, the reporting you can stand behind, and what the same work does for ordinary rankings.
How do I know if an AI citation drove a sale?
Direct attribution for an AI citation is mostly unavailable because generative answers don't pass the referral data that would let you trace a click back to a specific mention. Watch branded search movement, direct traffic increases, and what prospects mention on discovery calls as proxies instead.
