There's a version of this article that opens with a dramatic stat about how many searches now go to AI engines instead of Google. I'm not going to do that, partly because those numbers are changing faster than anyone can verify them, and partly because the more important point isn't the volume.

The important point is this: when your potential customers type "best AI marketing tools" or "marketing attribution software" into ChatGPT or Perplexity, they get a response that names three or four vendors. If your brand isn't one of them, you don't exist in that interaction. And traditional SEO does almost nothing to change that.

Answer engine optimisation is the discipline of making sure your brand gets cited. Here's how it works.

What is answer engine optimisation, and how is it different from SEO?

Answer engine optimisation (AEO) is the practice of improving the likelihood that AI-powered search tools — ChatGPT, Perplexity, Google AI Overviews, Claude, Gemini — cite your brand, content, or product in their responses.

SEO and AEO share some foundations but diverge significantly on what actually drives results.

The critical difference: SEO is largely about your own site. AEO is largely about what others say about you. AI engines don't primarily read your homepage to decide whether to cite you. They read the G2 profiles, the industry blogs, the comparison articles, and the Wikipedia pages that were part of their training data or that they retrieve in real time.

This is why a brand can have excellent SEO and near-zero AEO visibility. Strong on-site content earns Google rankings. It doesn't automatically earn AI citations.

How do AI engines decide what to cite?

Understanding this is the foundation of any AEO strategy. AI engines use a combination of two mechanisms.

Training data. Large language models are trained on enormous amounts of web content. Brands and products that appeared frequently in credible, authoritative sources before the model's training cutoff are embedded in the model's weights. This is why a brand like HubSpot gets cited constantly — not because it's necessarily the best product for every use case, but because it's been written about extensively for over a decade across thousands of credible sources.

Live retrieval (RAG). Newer AI systems — Perplexity in particular, and increasingly ChatGPT with browsing — retrieve current web content to supplement their training data. This makes live authority signals more important: if a credible source just published an article naming your brand, that can influence citations relatively quickly.

Both mechanisms reward the same thing: presence across third-party sources that AI engines trust. That means review platforms, comparison blogs, industry directories, analyst reports, Wikipedia, and media coverage.

Your own blog content matters for SEO. For AEO, it's the external footprint that moves the needle.

What are the eight signals that actually affect AEO visibility?

These are the specific levers. Not all of them have equal weight, and the weighting varies by query type, but these are the areas worth working through systematically.

1. Review platform profiles

G2, Capterra, TrustRadius, and Software Advice are among the most-cited sources in AI responses to software queries. A complete profile on each, with a healthy volume of reviews (50+), with accurate category tagging, is one of the highest-leverage AEO actions available. It's also underestimated because it feels basic. Most AI citations for "best [category] tools" trace back directly to these platforms.

2. Third-party listicle coverage

Industry blogs and comparison sites that publish "best tools for X" articles get cited heavily. These aren't necessarily the highest-authority domains by traditional SEO standards — they're sites that happen to publish the format AI engines are trained to retrieve for tool-comparison queries. Getting your brand listed on the right ten sites matters more than a hundred generic backlinks.

3. Structured FAQ and schema markup

FAQPage JSON-LD schema signals to AI engines that a page contains structured answers to specific questions. Pages with this markup are more likely to have their content extracted and cited in AI responses. Every piece of content you publish should include a structured FAQ section with schema markup.

4. Wikipedia presence

Wikipedia remains one of the highest-trust training data sources for language models. A Wikipedia article for your brand — supported by reliable references — directly increases citation likelihood for branded queries. Products with narrower feature sets than yours will outrank you in AI responses if they have a Wikipedia article and you don't. This is fixable, and it's low-effort relative to its impact.

5. Google Knowledge Panel

Claiming your Google Knowledge Panel establishes a verified brand entity that AI engines cross-reference. It's a fifteen-minute action that contributes to branded recall in AI responses. If your brand appears in 60% of AI responses to branded queries instead of 100%, an unclaimed Knowledge Panel is part of the reason.

6. Consistent category language across external sources

If you call your product an "AI marketing operating system" and your G2 profile, your Wikipedia article, your Capterra listing, and the comparison blogs covering your category use different terminology, AI engines have trouble confidently associating your brand with that category. Consistent category language across all external sources is how you train AI models to place your brand correctly.

7. Media coverage with named references

Coverage in credible industry outlets — TechRadar, TechCrunch, VentureBeat, CMSWire, Marketing Week — contributes directly to AI training data and live retrieval. A product review in TechRadar is worth more for AEO than fifty generic blog mentions. Target the outlets that appear most frequently in AI citations for your category.

8. Internal content structured for AEO extraction

Your own content contributes to AEO when it's structured for extraction. Clear definitions in the first 200 words. Explicit comparison tables. FAQ sections with schema markup. Short, direct answers to specific questions. AI engines retrieve content in structured chunks — the more your content is formatted to be extracted cleanly, the more likely it is to be cited.

What does an AEO audit actually involve?

An AEO audit is a systematic measurement of where your brand currently stands in AI engine responses. Here's what to cover.

Step 1: Define your query universe. List the 10–20 queries your ICP most commonly uses to discover products like yours. Include category queries ("best marketing automation software"), job-to-be-done queries ("how to track marketing attribution"), and direct comparison queries ("HubSpot alternatives").

Step 2: Run each query across multiple AI engines. ChatGPT, Perplexity, Google AI Overviews, and Claude are the priority. Note: which brands are cited in each response? Which external URLs are referenced? Does your brand appear? If so, in what context? If not, which competitors are appearing instead?

Step 3: Identify your citation gap. For each query where you don't appear, identify which sources are being cited. Those sources are your target list for off-site authority building.

Step 4: Audit your AEO signals. Work through the eight signals above. Score each one: complete, partial, or missing. Prioritise the ones most likely to affect the specific queries where you have the largest gap.

Step 5: Track changes over time. AEO isn't a one-time fix. Re-run your query set every four to six weeks and measure whether citation rates are improving. Correlate changes in external authority signals with changes in citation behaviour.

DOJO's AEO audit workflow runs this continuously, across a defined query set, and surfaces ranked recommendations rather than a static spreadsheet. For teams without a dedicated SEO resource, that's the practical path. For a manual starting point, the AI SEO tools guide covers the current tool landscape.

How long does AEO take to show results?

Honestly, faster than traditional SEO in some cases, slower in others. It depends on which signals you're working on.

Technical and profile-based changes (claiming your Knowledge Panel, completing your G2 profile, adding FAQ schema) can influence AI responses within a few weeks, especially in AI engines that use live retrieval. These are quick wins.

Third-party content (getting listed on listicle sites, earning media coverage, building a Wikipedia article) takes longer to get done but can show up in citations relatively quickly once the content is indexed.

Training data influence is the longest play. Getting deeply embedded in the sources AI models are trained on takes time and sustained external authority building. The brands with the highest AEO visibility today built that authority over years. The brands starting now are building for the next wave of model training.

Start with the quick wins. Build toward the long-term signals simultaneously.

What's the relationship between SEO and AEO — do you have to choose?

You don't have to choose, and you shouldn't. The most effective approach runs them in parallel, because they share foundational inputs: high-quality content, external authority, structured markup, clean site architecture.

The difference is in where the weight goes. A purely SEO-focused strategy prioritises keyword targeting, backlink volume, and technical health. A strategy that also covers AEO adds schema markup on every piece, FAQ sections built for extraction, review platform profiles, Wikipedia, and targeted third-party listicle coverage.

The incremental effort for AEO, once you have a solid SEO foundation, is lower than most teams expect. The gap is mostly in awareness — knowing which specific external sources matter, and building a systematic process for improving your presence on them.

For challenger brands that are just building their organic presence, running SEO and AEO in parallel from the start is significantly more efficient than running SEO first and retrofitting AEO later.

Further reading.

• What are the best AI SEO tools in 2026 — and are any of them built for AEO too?

• What is an AI marketing operating system — and does your team actually need one?

• What is agentic AI marketing — and why does it make traditional automation obsolete?

Run your AEO audit in minutes. See exactly where your brand appears in AI engine responses, which queries are returning your competitors, and what to do about it. Start free trial →