You already know AI visibility matters. The harder question is what to actually do about it on Monday morning. This is the owned-side playbook: the work on your own properties that you fully control. For the off-site half, see the guide on earning AI citations.
Start with the prompts that matter
You cannot optimise for everything, so do not try. Write down the real questions buyers ask AI in your category, then group them: category questions, comparison questions, and use-case questions. That list is your target. Everything below points at it.
Structure pages so models can actually read them
Retrieval favours pages that are easy to extract from. Vague intros and walls of text get passed over for cleaner sources. So write for extraction:
Use clear H2s phrased as the question, then answer it in the first sentence underneath.
Lead with the claim, then support it. Self-contained statements are what models lift into answers.
Keep paragraphs short and scannable. Dense blocks bury the point.
Add an FAQ section and mark it up with FAQPage schema, plus Schema.org structured data so machines can parse your content.
Consider an llms.txt file, an emerging convention that points AI crawlers to the content you want them to read.
Build the content AI reaches for
Some formats earn their way into AI answers far more often than others. Prioritise them:
Honest comparison pages, including you against the alternatives buyers actually weigh you against.
Use-case pages that mirror how buyers phrase their needs, not how you label your features.
Clear, definitive answers to the category questions on your list.
Strengthen the signals around your brand
Owned content gets you part of the way. But models weight independent mentions more heavily than anything you publish about yourself, which is why what AI models already know about you comes largely from third parties. Earning those citations is its own playbook, covered in the guide to getting cited by AI. Do not skip it: owned and earned work together.
Measure, then iterate
AI optimisation is a loop, not a launch. Models update constantly, so treat your changes as experiments:
Baseline where you appear across platforms before you change anything.
Change one thing at a time so you can attribute movement.
Re-measure after a week or two and keep what works.
Without that loop you are optimising in the dark. Trackbase gives you the baseline and the day-by-day movement, so the playbook above becomes a measurable cycle instead of a guess. None of this is a quick win. All of it compounds.
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