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AI discoverability is how well a business shows up when customers ask AI assistants for recommendations, comparisons, or answers instead of (or alongside) typing queries into a search engine. The industry has a few names for the practice of improving it: AEO (Answer Engine Optimisation), GEO (Generative Engine Optimisation), and LLMO (LLM Optimisation) all refer to the same discipline. Onsomble talks about AI discoverability because it names what actually matters: whether customers can discover you when they ask an AI a question and get an answer, not a list of links.

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Why this matters now

Customer discovery journeys have shifted. When someone wants to find a plumber, compare insurance providers, or choose a product, they increasingly ask an AI assistant instead of running a search. ChatGPT, Claude, Gemini, and Perplexity are now real entry points to a buying decision. Two consequences for businesses:
  1. Your presence in AI assistant answers is now part of how customers decide. If the assistant doesn’t mention you, the decision happens without you.
  2. Who gets mentioned, and how, has material business impact. Prominence, accuracy, and framing in an AI answer matter in the same way that ranking in a Google result mattered before.
AI discoverability is the discipline that grew up around that reality.

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How AI discoverability differs from SEO

The two share some DNA: both are about being visible to the systems customers use to find you. But the practical mechanics diverge quickly. The biggest mental shift: with SEO, you wanted customers to click through to your site. With AI discoverability, increasingly, customers never leave the conversation with the assistant. Your job is to make sure the assistant’s answer is one that represents you well and makes the customer want to engage with you, whether that’s through a workflow in the same conversation or by picking up the phone.

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What improving it actually involves

In practice, AI discoverability is a loop:
1

Measure

Run realistic customer questions through the major AI models. Capture what they said. Turn that into numbers (visibility, share of voice, sentiment) that compare you against your competitors.
2

Interpret

The numbers are only useful if you can explain what they mean. Where are you strong? Where are you weak? What specifically is being said, and why?
3

Act

Translate insights into changes: content you publish, accuracy you correct, positioning you sharpen, third-party signal you build.
4

Re-measure

Rerun scans on a cadence. Watch the metrics. Did the changes move the needle?
This isn’t a one-off audit. AI discoverability is an ongoing practice. Models update, competitors do the same work, and the questions customers ask keep changing.

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What discoverable content looks like

A handful of patterns consistently produce better AI representation:
  • Direct answers to specific questions. Content structured as “What does X do?” / “Answer: X does…” is more extractable than the same information buried in marketing prose.
  • Clear positioning. Specificity about who the business is for and what it’s best at shows up in how the AI describes it.
  • Factual accuracy on key details. Pricing, service area, hours, product lineup. AI models can and do reproduce these verbatim when they’re easy to extract.
  • Consistency across the web. If your website, your directory listings, and your press all describe you the same way, that consistency reinforces the signal models pick up on.
  • Structured formats where they make sense. FAQs, comparison pages, and explicit Q&A formats work disproportionately well.
None of this conflicts with SEO; in most cases it’s complementary. But the framing is different: you’re writing for a model that will synthesise and relay, not a reader who will scan and click.

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Where the field is going

A few tentative observations about the direction of the discipline:
  • The tooling is still young. Most established SEO tools are still figuring out what AI discoverability looks like in their products. Purpose-built platforms like Onsomble will lead until the incumbents catch up.
  • Interactivity is the next layer. Being discovered is the first wave. The next wave is businesses being able to do things through AI assistants: answer questions, take bookings, provide quotes.
  • The metrics will mature. Right now, the metrics borrow a lot from SEO metaphors. As the discipline settles, more AI-native measures will emerge.

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Onsomble’s take

Onsomble is built specifically for AI discoverability. Scanning, measuring, and recommending: the measure-interpret-act loop made explicit. See:

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