AI search vs Google SEO: what's actually different.

Every week someone messages us asking the same question: "is AI search just SEO with a new label?"

The short answer is no. The longer answer is more useful: about 60% of the work overlaps, 30% is new and AI-specific, and 10% is now obsolete. If you can tell which is which, you save yourself months of work doing redundant things.

The fundamental difference

Traditional SEO is a ranking game. Google shows ten blue links. Your job is to be one of them — ideally one of the top three, which take ~60% of clicks combined.

AI search is a citation game. ChatGPT, Claude, Gemini and Perplexity don't show ten blue links. They synthesise an answer and mention two to five sources inside it. Your job is to be quoted in the answer — and the criteria for "quoteable" are different from the criteria for "rankable".

The overlap (60%)

Most of the foundational work serves both Google and AI engines:

• Technical hygiene. Fast pages, mobile-friendly, crawlable, no broken links, sitemap.xml, robots.txt configured correctly. Required for both.
• Page structure. Clean H1/H2/H3 hierarchy, descriptive titles, useful meta descriptions, internal linking. Both Google and AI engines parse this structure to understand topic.
• Content quality. Original, useful, demonstrably written by humans (or carefully edited if AI-assisted). Both engines penalise thin, duplicate, or obviously-templated content.
• Authority signals. Backlinks from credible sources, mentions on industry sites, consistent profile across Google Business, LinkedIn, Wikipedia, niche directories. Both engines weight these heavily.
• Image and media optimisation. Alt text, descriptive filenames, captions. Helps both Google Images and AI multimodal models understand visual content.

If you're already doing serious SEO work, you've done 60% of AI search optimisation by default. The mistake is assuming you've done all of it.

What's new (the 30%)

Schema markup at much higher resolution

Google has rewarded schema for years. AI engines depend on it. The bar moves from "have Organization schema" to "have detailed Organization, FAQPage, Product/Service, BreadcrumbList and (where real) Review schema across your site". More on this here.

Q&A-shaped content

AI engines quote content that maps cleanly to user queries. A blog post titled "Best invoicing software 2026" ranks fine on Google but is hard for an LLM to quote. The same content rewritten as "What's the best invoicing software for UK sole traders?" with a direct answer in the opening paragraph — that gets cited.

llms.txt and llms-full.txt

An emerging convention. A plain-text file at yoursite.com/llms.txt that summarises what your site is about in a format LLMs can read directly. Few businesses have one. It's a real edge while that's still true.

Authority across multiple search ecosystems

Traditional SEO is Google-centric. AI search optimisation requires presence and accuracy across multiple indexes — Bing (for ChatGPT and Perplexity), Google (for Gemini and AI Overviews), and Claude's independent crawl. The work isn't drastically more, but you can no longer ignore Bing.

Conversational keyword targeting

Old keyword research: "invoicing software" (700 searches/month).
New keyword research: "what's the easiest invoicing tool for a freelancer in the UK who hates spreadsheets" (50 searches/month, 10x conversion rate, far easier to rank for).

What's obsolete (the 10%)

• Exact-match keyword stuffing. LLMs see right through it. They also see right through it on Google now, but specifically: writing pages that repeat your target keyword 30 times in the body doesn't help AI search at all.
• Position-tracking obsession. Knowing exactly where you rank for keyword X on Google still matters but matters less. AI engines don't have a "position 1" — you're either cited or you aren't.
• Click-through-rate optimisation. AI engines often synthesise answers without sending the user to your site at all. CTR-led tactics (clickbait title tags, etc.) become less useful.
• Low-effort link-building campaigns. Buying low-quality backlinks worked occasionally on Google. AI engines are far less tolerant of obvious link patterns and will quietly skip you.

Side-by-side

Signal	Traditional SEO	AI search
Page speed	Important	Important
Backlinks	Critical	Critical
Schema markup	Useful (rich results)	Essential
Keyword targeting	Short-tail + long-tail	Conversational only
Title tag length	Critical (60 chars)	Less important
Meta description	Important for CTR	Used as citation context
FAQ blocks	Nice to have	One of the highest-quoted formats
llms.txt	Ignored by Google	Read directly by LLMs
Position tracking	Core metric	Less relevant — citation Y/N is the metric
Bing presence	Optional	Required (ChatGPT, Perplexity)

How to do both without doubling your workload

The honest answer: you don't need a separate AI search programme. You need to extend your existing SEO programme with three things:

1. Comprehensive schema markup across every page that describes a product, service, location, FAQ or breadcrumb path.
2. A Q&A-shaped content layer (blog posts, FAQ pages, comparison pages) targeting conversational queries.
3. An llms.txt file at your domain root.

That's it. The rest of your existing SEO work continues to pay dividends in both systems.