Search stopped being a list of links and became an answer. That single change breaks the assumption underneath a decade of SEO tactics: that ranking produces a click. Today you can rank, be summarized, and never be visited — which means the strategy has to optimize for something other than position.
Two jobs that used to be one
Traditional SEO earned a position in a list. AI SEO — sometimes called answer engine or generative engine optimization — earns a citation inside a generated answer. They overlap heavily but they are no longer the same job, and the second one is where the growth is.
- ›Classic search — position determines clicks, and the click is the outcome you optimize.
- ›AI answers — being cited determines whether you exist at all, and the citation itself carries brand value even without a click.
- ›The overlap — crawlable, well-structured, factually specific pages still feed both. Nothing here asks you to abandon fundamentals.
What actually earns a citation
Language models assembling an answer pull toward sources that are unambiguous, specific, and easy to attribute. That has practical consequences for how a page is written, and it rewards a different style than link-chasing content did.
- 01Answer the question in the first two sentences. Burying the answer under an introduction hands the citation to whoever did not.
- 02Be specific enough to be quotable — concrete numbers, named steps, real constraints. Hedged generic prose gets averaged away.
- 03Structure with honest headings that match how people actually phrase the question.
- 04State your scope plainly: who this applies to, where, and when. Models favor sources that qualify themselves.
- 05Keep facts current and dated. Stale specifics are worse than no specifics.
Five tactics that still carry weight
Most of the AI SEO advice in circulation is fundamentals restated with new vocabulary. These five genuinely changed in emphasis rather than just in name.
- 01Topical depth over keyword breadth — a cluster that covers a subject completely gets cited more than scattered pages chasing separate terms.
- 02Entity clarity — make it unambiguous what your business is, where it operates, and what it does, in structured data and in plain prose.
- 03First-hand information — original numbers, real processes, and things only you could know are the hardest inputs for a model to synthesize from elsewhere.
- 04Machine-readable context — schema markup, clean HTML, and a current llms.txt so crawlers get facts rather than inference.
- 05Reputation surface — reviews, profiles, and third-party mentions, since answers about a business assemble from more than that business's own site.
A sequence that fits a small team
AI SEO does not require a bigger content operation. It rewards a more concentrated one — depth on a narrow subject beats breadth across many, and that favors small teams that pick a lane.
- 01Pick one subject you can credibly own, narrower than feels comfortable.
- 02Publish the definitive page on it, then the supporting pages that answer every adjacent question.
- 03Fix the machine-readable layer once — schema, llms.txt, clean markup.
- 04Query the major AI assistants monthly with your customers' real questions and log whether you are cited.
- 05Expand only after you are consistently the cited source on the first subject.