How AI search decides what to show
AI search surfaces retrieve pages, then synthesize an answer from them. That two-step process rewards pages that are easy to retrieve for a question and easy to quote once retrieved: a direct answer, stated early, in language close to how the question is asked.
It also rewards familiarity. Engines lean on sources they already cite for a topic, so part of the work happens off your own site, in the places those answers draw from.
The on-page changes worth making
Step 1
Lead with the answer
The first two or three sentences under the title should resolve the query on their own. Context and nuance come after, not before.
Step 2
Structure around real questions
Use the phrasing buyers actually type as section headings. Engines match questions to passages, and a heading in question form is the strongest match signal a page can give.
Step 3
Make passages self-contained
A definition, a step list, or a comparison should make sense when lifted out alone. If a paragraph needs the one above it to parse, it will not be quoted.
Step 4
Add the machine-readable layer
Structured data for the page type, an llms.txt inventory, and clean crawl access for AI agents. Low effort, done once, and it removes silent blockers.
Step 5
Prefer specifics over positioning
Concrete numbers, named methods, and dated facts get cited; adjectives do not. Every vague sentence is a passage an engine will skip.
The off-site half of the job
Ask the engines your buyers' questions and note which domains they cite. Those citation lists are the influence map for your topic: comparison sites, community threads, and industry references that answers keep drawing on.
Being described accurately in the places engines already read often moves answers more than another page on your own domain. Prioritize the cited domains where your brand is absent or described wrongly.
How to tell whether any of it worked
Generated answers vary between draws, so checking once before and once after a change proves nothing. Measure mention rate across repeated samples on a fixed question set, and treat a shift as real only when it clears the normal wobble.
That before-and-after discipline is the whole reason to baseline before you start editing. Changes are cheap; knowing which change mattered is the expensive part.