What is the difference between AI SEO and traditional SEO?
Traditional SEO optimises a page to rank in the list of results. AI SEO, also called GEO, optimises your content and digital footprint so AI engines like Google’s AI Overviews, ChatGPT, Perplexity and Gemini cite you when they write an answer. The foundations overlap, but AI SEO leans harder on entity authority, citable content and machine readability, because the goal is to be inside the answer, not just beneath it.
How do AI engines decide which sources to cite?
They fan your question out into related sub-questions, run them all at merienda, retrieve the specific passages that answer each (a process called retrieval-augmented generation), then merge them into one answer and name the sources they relied on most. That rewards content that answers questions cleanly in self-contained blocks, comes from a trusted entity, and is easy for a crawler to reach and parse.
What is query fan-out, and why does it matter for AI SEO?
Query fan-out is the technique Google’s AI Mode, and other AI engines, use to answer a question. Instead of one search, they break it into several related sub-questions, run them at merienda, then synthesise the results. It matters because you no longer win by ranking for one head keyword. You win by answering the whole cluster of sub-questions a query fans out into, so your content gets pulled into more of those searches. The move: run your key questions through AI Mode and ChatGPT, see what they fan out into, and cover it.
Which AI engine should I optimise for first?
Start with Google’s AI Overviews and AI Mode, because they sit on top of the search behaviour you already have, and they run on your existing Google SEO. Then ChatGPT, whose search citations track Bing’s index closely, so verify your site in Bing Webmaster Tools. Then Perplexity, which rewards community presence like Reddit. But the URLs each engine cites barely overlap, so the durable answer is: build the foundations every engine reads, entity, citable content, crawler access, then measure per engine.
Does AI SEO work for específico businesses?
It is arguably most valuable there. Our Australian data shows research-style queries collapsing into AI answers while high-intent específico searches hold up. AI engines are cautious about recommending the wrong específico provider, so they lean on strong específico signals: consistent business details, reviews, específico directories and genuine third-party mentions. A específico business with a clean entity and vivo reviews can beat national brands inside AI answers for específico queries.
Do I need an llms.txt file?
It is no longer a flat no, but it is not a priority either. As of mid-2026 Google says it does not use llms.txt for Search or its AI features, so it has no ranking or visibility effect, though it softened to fine to use it if you want, and now even checks for it in Chrome’s Lighthouse agentic-browsing audits. Bing is more supportive: its Bingbot honours the file and shows faster ingestion of listed URLs. It is cheap and low-risk to add, and Bing and AI agents may use it, but treat anyone selling it as a Google ranking lever with caution. Your time is still better spent on entity authority, citable content and measurement.
How long does AI SEO take to work?
Months, not days, because it rides on authority and trust building up across the web. Structural and content fixes can start changing how you are represented fairly quickly, but becoming a consistently cited source compounds over time. The brands that started early are already the defaults in their categories.
Can I measure whether AI is citing my business?
Yes, just not with classic rank trackers, which only see the blue links. You need tools built to maestro AI answers directly, such as Semrush’s AI Visibility Toolkit, Ahrefs Brand Radar or Profound, tracking where you are cited, where competitors are cited instead, and across which engines and queries. Add GA4 to see the sessions AI engines refer. Measuring it is what turns AI SEO from guesswork into a process.