We can all sense that something has changed in how buyers conduct their research. But most B2B marketers have not caught up with it yet.
A CFO opens Copilot and types: "Which accounting platforms offer AI-powered forecasting?" A marketing director asks ChatGPT: "What are the best agencies for B2B lead generation?" A Head of IT asks Claude: "What project management software works best for a team of fifty?"
None of them went to Google first. And when the AI answered, it named specific brands. Yours may not have been one of them.
This is the problem that Answer Engine Optimisation (AEO) exists to solve.
What AEO actually is
Answer Engine Optimisation is the practice of structuring your content, your brand presence, and your technical foundations so that AI-powered platforms cite and recommend you when buyers ask questions relevant to your business.
Just as SEO emerged to help brands get found in search engines, AEO has emerged to help brands get found in AI systems. It does not replace SEO. It extends it for an era where the answer, not the link, is the product.
When ChatGPT or Perplexity generates a response to a buyer question, it is not serving a list of links. It is synthesising an answer from sources it considers credible and relevant. Our job, as B2B marketers, is to be one of those sources.
Why this matters right now
Gartner projected that traditional search volume would drop 25% in 2026 as users shift to AI assistants. ChatGPT alone has over 800 million weekly active users. Around 60% of Google searches now end without a single click to a website.
The discovery layer is moving. Buyers are increasingly getting their answers inside the AI response itself, without ever visiting a vendor’s site.
That matters for two reasons beyond the obvious traffic one.
First, the intent behind AI queries is really high. When someone asks an AI for a recommendation, they are past the browsing phase. They want an answer they can act on. AI-referred traffic converts at higher rates than organic search precisely because the AI has already filtered and, implicitly, endorsed.
Second, buyers trust what AI tells them. Probably too much if you've ever had n argument with an LLM (I certainly have!). Research from Capgemini found that 73% of consumers globally trust content created by generative AI. When an AI says “I’d recommend Brand X for this use case”, that carries weight. It lands like expert advice, not an advert.
The brands that build AEO presence now will be the defaults AI recommends for years. Think of SEO in 2008. The companies that invested early still dominate today. The same compounding effect is available in AEO, but only for those who move while most of their competitors are not paying attention.
How AI answer engines decide what to cite
Before you can optimise for AI, you need to understand how it works. It is meaningfully different from traditional search.
Large language models like ChatGPT are trained on vast amounts of web data. What they know about your brand comes from that training: your content, mentions in publications, reviews, directory listings, third-party coverage. When a user asks a question, the model synthesises from everything it has absorbed, weighting sources it considers authoritative.
Retrieval-based systems like Perplexity work differently. They pull real-time information from the web when generating answers, making current content and domain authority more directly relevant.
Google’s AI Overviews blend both approaches, drawing on traditional search signals alongside AI synthesis.
The practical implication is that no single fix works across all platforms (oh, if only it was that easy). A robust AEO strategy has to account for all three models. But the underlying principles are consistent: AI rewards clarity, consistency, and credibility.
The five things AEO actually optimises
Content structure. AI systems parse content differently from humans. They break pages into individual passages and evaluate each one independently. Clear headings, direct answers at the top of each section, factual statements with specific numbers, and Q&A formatting all increase the likelihood of being cited. A page that states “our platform processes two million transactions per day with 99.9% uptime” is far more citable than one that says “we offer industry-leading reliability.” Specific beats vague, always.
Entity recognition. AI needs to understand what your brand is, which category it sits in, and how it relates to other things in its world. This means consistent naming across every platform you appear on, proper schema markup on your website, and presence on the platforms that define entities in AI systems: your Google Knowledge Graph entry, industry directories, authoritative databases. If AI cannot confidently place your brand in a category, it will not confidently recommend you.
Source authority. LLMs weight sources by perceived credibility. Coverage in respected industry publications, thought leadership on high-authority sites, mentions from recognised experts: these all increase the probability that AI treats your content as worth citing. What others say about you matters at least as much as what you say about yourself. Often more. This is why I think PR will make a comeback.
Factual consistency. AI cross-references information across sources. If your founding date, revenue figure, or product description varies between your website, your LinkedIn, your press mentions, and your directory listings, AI loses confidence in citing any of them. Inconsistency reads as unreliability. Fixing it is unglamorous work. It matters enormously. For us B2B marketers, those 'fact books' and 'core scripts' will be coming back in vogue.
Semantic alignment. AI categorises content using semantic relationships. Using the terminology, frameworks, and concepts your industry actually uses, and doing so naturally within authoritative content, strengthens the connection between your brand and the queries you want to own. Write for the buyer’s language, not your internal vocabulary.
How to get started
Step one: audit what AI currently says about you.
Open ChatGPT, Perplexity, and Claude. Ask the questions your buyers actually ask. "What are the best platforms for [your category]?" "Which [your service type] providers work with [your target industry]?" "Tell me about [your brand name]."
Note where you appear. Note how accurately you are described. Note which competitors appear instead of you. This is your baseline. Do it across at least fifteen to twenty prompts that represent your real buyer questions. The gaps you find become your content and authority priorities.
Step two: map your target queries.
Build a list of twenty to thirty questions your ideal customers are likely to ask an AI assistant. Include category queries ("best X software for Y"), comparison queries ("X versus Y versus Z"), and recommendation queries ("which X should I use for this use case"). This is your AEO query universe: the questions you need to own.
Step three: restructure your existing content.
You do not necessarily need to create new content. You need to make what you have more legible to AI systems. Start with your most important pages. Lead each section with a direct answer. Add FAQ sections that use the exact language from your target query list. Replace vague claims with specific, citable statements. Use clear heading hierarchies. Make every section able to stand alone as a passage.
Step four: build your authority footprint.
Identify where AI systems go to assess credibility in your category. Industry publications. Analyst reports. Review platforms. Expert directories. Community platforms that AI crawls: LinkedIn, Reddit, relevant industry forums. Pursue presence on those consistently. Not volume. Consistency and quality. One well-placed byline in a credible industry publication does more for AEO than ten posts on your own blog.
Step five: fix your entity consistency.
Audit every place your brand appears online. Your website, your Google Business Profile, your LinkedIn company page, your directory listings, your press mentions. Make sure your brand name, description, category, and key facts are identical everywhere. This is the kind of work that nobody wants to do but everybody benefits from.
Step six: measure and iterate.
Start tracking how your AI citation rate changes over time. Run your target query list monthly across the main platforms and record where you appear. Track whether AI referral traffic is showing up in your analytics. This will not be perfect attribution. It does not need to be. You are looking for directional signals: more citations, more accurate descriptions, more queries where you feature.
What good AEO looks like in practice
A page that states "our platform processes two million transactions per day with 99.9% uptime" is far more citable than one that says "we offer industry-leading reliability."
A FAQ section that asks "which B2B marketing platforms are best for companies with under fifty employees?" and answers it directly is far more useful to an AI system than a generic features page.
A founder with a consistent, expert-level presence in trade publications is far more likely to have their brand cited than one who only publishes on their own site.
These are not complicated ideas. But most B2B brands are not doing them systematically, yet! Which is the opportunity!
The honest caveat
AEO is not a one-time project. AI models update continuously. What works today may need adjusting in six months. The platforms themselves are evolving. Perplexity’s citation logic is not identical to ChatGPT’s, which is not identical to Google’s AI Overviews.
As marketers, we must build the habit. The brands that treat AEO as an ongoing discipline rather than a box to tick are the ones that will compound advantage over time.
Most companies have not even started yet. That window will not stay open indefinitely.
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