How to increase your AI visibility
Increasing AI visibility is a loop, not a checklist: measure which buyer questions each engine answers without you, identify the domains that win them, close the cheapest gaps first, then verify that your Share of AI Answer actually moved. Everything else in AEO is commentary on those four steps — and any advice that cannot be checked against your own tracked prompt set is a guess, however confidently it is delivered.
Last updated 2026-07-28
Why start with measurement rather than with fixes?
Because without a baseline you cannot tell a successful change from ordinary variance, and grounded AI answers vary enough run to run that this is not a theoretical concern. The same prompt genuinely can cite you on Tuesday and not on Wednesday with nothing having changed on the web.
A week of daily runs before you touch anything costs a few cents a day and converts every subsequent decision from an opinion into a test. It also frequently changes what you were going to do: most teams discover their losses are concentrated in two or three prompts and one or two domains, rather than spread evenly the way a generic playbook assumes.
How does the gap analysis prioritise the work for you?
The domains repeatedly cited on prompts you lose are a ranked to-do list, because each one is a place an engine already trusts for your exact question. You do not have to guess which surfaces matter in your category — your own results name them, in frequency order.
Presence on those domains is usually far cheaper than trying to outrank anyone. A completed G2 or Capterra profile, one genuinely useful Reddit answer, or an accurate entry in a category roundup can appear in citations within days, at essentially zero cost. Compare that with commissioning a page and waiting a quarter to see whether it ranks. This is why answer-source mix belongs in your four core metrics rather than being an afterthought.
What should you do first, second and last?
Sequence by payback time, not by ambition. The work that moves the number fastest is almost always the least interesting work, which is why most plans get this backwards.
| Do it | Work | Typical payback | Cost |
|---|---|---|---|
| First | Claim and complete directory and marketplace listings; fix inconsistent brand naming; unblock crawlers on key pages | Days | Hours of someone’s time |
| First | Add a plain one-sentence description, current pricing, and comparison tables to your own site | Days to weeks | Low |
| Second | Publish direct-answer comparison content for the specific prompts you lose | Weeks | Medium |
| Second | Get accurate entries into the roundups and comparison posts already cited in your category | Weeks | Medium — outreach, not payment |
| Last | Community participation, video, and earned coverage | Months | Ongoing effort |
| Never | Prompt-stuffing, paid listicle placements, community spam | None | Wasted |
How should effort be split across engines?
Do the shared work first, because it moves every engine at once, then use your per-engine scores to decide where targeted effort pays. A large ChatGPT-versus-Gemini gap usually points at classic search visibility; a Perplexity gap more often points at content freshness.
- ChatGPT — heaviest reach, so weight it most. Prioritise the directories and comparison articles it cites. See how to track your brand in ChatGPT.
- Gemini — closest to classic SEO. Improving pages Google already ranks for your category questions moves it fastest, and moves it first.
- Perplexity — fastest feedback loop. New content appears in its citations quickest, which makes it your canary for whether a change is working at all.
- Claude — strong for technical and B2B categories. Documentation quality and precise, factual product descriptions matter more here than anywhere else.
- Grok — recency-driven. Launches and community buzz move it quickly; skip it entirely if your buyers are not on X.
What can you not control?
What the model says. Engines synthesize from many sources, weight them in ways nobody outside the labs can inspect, change behaviour without notice, and produce different answers to the same question on consecutive days. Anyone promising guaranteed AI recommendations is describing something that does not exist.
What that means practically is that AEO is odds management with an unusually good feedback loop, not control. You can make yourself present, current, quotable and consistently named in the places engines look; you cannot make them look. The honest version of this discipline is to run the loop, keep what demonstrably moved your number, and be sceptical of anyone — including us — who claims more than that.
Step by step
- 1Fix your prompt set: 10–15 core buyer questions plus a longer extended list, covering discovery, comparison, alternatives and brand checks. Hold it constant — it is the yardstick.
- 2Run it daily across the engines that matter to you, with web grounding on, and record your Share of AI Answer baseline before changing anything.
- 3Sort your losses by who wins them: directories mean claim your listing; comparison posts mean publish or pitch a better one; communities mean participate honestly; video means make the video.
- 4Sequence by payback time — listings and profile fixes first (hours), content second (weeks), PR and community last (months).
- 5Change one thing at a time and re-read the trend weekly. Keep what moved the number; stop what did not.
Frequently asked questions
Should I optimize for all five engines at once?
Do the shared work first — listings, comparison content, a liftable site — because it moves every engine. Then use your per-engine scores to target: a large ChatGPT/Gemini gap usually points at classic SEO, while a Perplexity gap points at content freshness.
How much does it cost to run this loop?
The measurement costs a few cents a day in API usage for a core prompt set across five engines, plus whatever tooling you use — $29 once for AI Visibility Tracker, or a monthly subscription for a hosted platform. The fixes themselves are mostly time rather than money: claiming listings, correcting descriptions and writing genuinely useful answers.
What if my competitors are much bigger?
Size helps them in ungrounded answers, where models over-favour brands with a large training-data footprint, but grounded answers are decided by what retrieval surfaces for a specific question. That is a far more contestable surface: an accurate directory listing and one genuinely useful comparison page can put a small brand into answers alongside incumbents.