Search behaviour is changing faster than most marketing teams are adapting. Users no longer just type queries into Google and scan ten blue links. They ask questions to AI tools and expect a direct, sourced answer. For B2B brands and growth-focused businesses, this shift represents both a risk and a significant opportunity. Answer Engine Optimisation (AEO) is the discipline that addresses it directly.
Traditional SEO is about ranking a page in a list of results. AEO is about being the source that answer engines cite when they synthesise a response. These are related goals but they require distinct strategies.
In traditional SEO, success means appearing at position one or two for a target keyword. In AEO, success means being quoted, paraphrased, or linked by ChatGPT, Perplexity.ai, Google AI Overviews, or similar tools when a user asks a question relevant to your expertise.
The key implication: AI answer engines reward content that is authoritative, clearly structured, and easy to extract. Keyword density matters far less than answer clarity.
Understanding how each platform works shapes how you optimise for it.
Google AI Overviews draw primarily from pages that already rank well in traditional Google Search. Strong traditional SEO is therefore still the baseline. AI Overviews tend to favour pages with clear definitions, numbered steps, and FAQ-style content. Structured data (particularly FAQPage and HowTo schema) increases your visibility here.
Perplexity.ai actively crawls the web in real time and surfaces citations from a broad range of sources. It favours content that directly answers specific questions, uses clear headings, and comes from domains with established topical authority. Perplexity is particularly responsive to recent, well-cited content.
ChatGPT (in browsing mode) retrieves live web content and prioritises pages that are unambiguous, well-structured, and from credible sources. In its base model (without browsing), it relies on training data, which means older, well-established content from reputable domains holds significant weight.
The common thread across all three: content that is clear, direct, and authoritative performs best.
Google's E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness) was developed for human quality raters but its signals map directly onto what AI engines look for. For a full breakdown, refer to Google's Search Quality Evaluator Guidelines.
Experience means demonstrating first-hand knowledge. Case studies, original data, and practitioner-led content signal experience in a way that generalist summaries never can.
Expertise is communicated through author credentials, depth of analysis, and the use of appropriate technical language. Author bio pages that link to published work and social profiles strengthen this signal.
Authoritativeness is built through backlinks from credible domains, citations from industry publications, and consistent publishing in a defined topical area.
Trustworthiness is reflected in HTTPS, clear privacy policies, accurate contact information, and transparent editorial processes.
When AI engines evaluate whether to cite a source, they are applying a version of these same filters. Sites that invest in E-E-A-T signals earn AI citations more reliably than those that do not.
Structured data is the bridge between your content and AI answer engines. When you mark up content with schema.org vocabulary, you are providing a machine-readable layer that AI systems can parse without ambiguity.
The most valuable schema types for AEO are:
Implement schema via JSON-LD and validate through Google's Rich Results Test. For more on implementation, Search Engine Journal's AEO coverage provides practical examples.
Featured snippets and AI answers share the same content preferences. Both favour:
A practical framework: write the direct answer in 40-60 words at the top of each section, then support it with detail. This gives AI engines an immediately extractable response while giving human readers the depth they need.
Certain formats consistently outperform others in AI citation rate:
Long-form content that covers a topic comprehensively in a single page performs better than the same coverage spread across multiple thin pages. AI engines prefer depth in one place.
Most analytics platforms do not yet natively track AI referral traffic reliably, but there are practical approaches. Direct and dark social traffic has grown as a proportion of inbound visits partly because AI tools do not pass referral parameters consistently.
Set up branded search tracking in Google Search Console to monitor increases in branded query volume, which often reflects growing AI-driven awareness. Use Perplexity.ai and ChatGPT manually to query topics where you want to appear: are you cited? If not, identify which competitors are and analyse the structural and authority differences in their content.
For a systematic approach to monitoring and improving your AEO performance, Viaduct Generation's Optimisation service tracks visibility across both traditional and AI-driven search channels.
AEO is still an emerging discipline. Most B2B brands have not adapted their content strategies to reflect it. That is an advantage for those who act now. The sites that are already visible in AI answers are accruing a compounding authority advantage that will be increasingly difficult to close later. The question is not whether AEO matters; it is whether your content is ready for it. If you want to audit your current position, Viaduct Generation's contact page is the starting point.
