Traditional SEO helps people discover your website through search engines. AI SEO extends that work to search experiences that generate answers, including Google AI Overviews, AI Mode and ChatGPT search.
The distinction matters, but it is often overstated. Businesses do not need to abandon SEO and start again. They need to understand where customers search, how their expertise is represented and whether that visibility supports useful outcomes.
What is traditional SEO?
Search engine optimisation improves how a website can be found, understood and used through organic search. It connects technical implementation, content, site structure and the information that helps people assess a business’s credibility.
The work includes making important pages accessible to crawlers, using descriptive titles, connecting related content and answering the questions behind a search. It also means making the website useful after someone arrives.
Traditional SEO has never been only about inserting keywords or chasing a number-one ranking. A relevant visit that leads to an enquiry can matter more than a large volume of poorly matched traffic.
What is AI SEO?
“AI SEO” has two common meanings. One is using AI tools to support SEO tasks, such as reviewing a content inventory or preparing a research brief. The other is improving visibility in AI-powered search and answer experiences. This article focuses on the second meaning.
You may also see GEO, meaning generative engine optimisation, and AEO, meaning answer engine optimisation. These terms describe overlapping areas of work rather than one universal technical standard.
Google’s guide to generative AI search places this work within SEO. Its AI search features draw on its search index and ranking systems. Other platforms have their own policies and product behaviour, so Google’s guidance should not be treated as a specification for every AI service.
Traditional SEO vs AI SEO: a practical comparison
| Area | Traditional SEO | AI SEO |
|---|---|---|
| Main focus | Discovery through organic search results and useful website visits. | Discovery, references and links within AI-generated search answers. |
| Customer journey | People explore results, snippets, maps, products and other search features. | People may receive an answer, ask follow-up questions and visit supporting sources. |
| Content | Useful, relevant pages that address the audience’s needs. | The same useful pages, with clear explanations and evidence worth referencing. |
| Technical work | Crawling, indexing, internal links, performance and appropriate metadata. | Those foundations, plus platform-specific access and discovery checks. |
| Measurement | Search visibility, organic visits, enquiries and sales. | AI visibility reports where available, sampled citations, identifiable referrals and business outcomes. |
This is a comparison of emphasis, not a clean division. Search engines use AI, conventional search results can answer questions directly, and AI search experiences still link to websites. A sensible plan accounts for that overlap.
What changes when an answer appears before the click?
Imagine a business owner researching a WordPress redevelopment. They might search for “enterprise WordPress development” and compare service pages. They might also ask an AI service: “What should we check before moving a large publishing website to WordPress?”
Both journeys need credible information. The second makes it particularly useful to explain requirements, limitations, trade-offs and how decisions are made. A vague page claiming to deliver “innovative solutions” gives a potential customer very little to assess.
Our recommendation is to turn the questions you already answer in discovery calls into useful website content. Explain who a service suits, what affects its scope and when a different approach would be more appropriate. This helps people make decisions; it does not guarantee an AI citation.
What stays the same?
Google Search Essentials continues to emphasise helpful content, descriptive language and discoverable links. Those remain useful priorities when reviewing a site for AI search.
- Accessible information. Important explanations should be easy to find and read, rather than buried in images or disconnected pages.
- Clear purpose. Give each page a role and address the questions relevant to it.
- Evidence. Use genuine examples, attributable sources and specific experience where you have them. Do not invent results or testimonials.
- Accurate details. Keep information about what you do, where you work and how to contact you current.
- A useful next step. Make it straightforward to enquire, compare an option or find more detailed information.
AI search discovery is different from AI training
Crawler access needs a precise brief. OpenAI’s crawler documentation, for example, distinguishes OAI-SearchBot, which supports search, from GPTBot, which crawls content that may be used for model training. Their controls are independent.
“Block AI” therefore leaves important questions unanswered. Decide which uses you want to permit, then check the relevant platform’s instructions against your robots.txt, hosting and firewall rules. Keep private content protected rather than weakening access controls to chase visibility.
Do you need special AI files, schema or a new writing style?
Google says its search systems do not require llms.txt files or special schema markup for AI visibility. Its guidance also rejects the idea that every page needs to be divided into tiny fragments or rewritten around every possible prompt variation.
Clear headings, concise explanations and useful comparisons are good editorial choices when they help the audience. They should not become rigid “AI formatting rules” imposed on every page. Appropriate structured data can support your wider SEO implementation, but it is not a citation switch.
Be sceptical of claims that one plugin, file or content template guarantees inclusion. A platform still decides what it retrieves and displays.
How should you measure AI SEO?
A brand mention, a linked citation, a visit and a qualified enquiry are different events. Counting them as interchangeable produces misleading reports. Keep business outcomes at the centre, then add visibility evidence with clear limits.
- Google AI visibility. Google’s Generative AI performance reports in Search Console provide dedicated visibility data, including impressions and pages appearing in AI features. Review these alongside broader search performance.
- Identifiable AI referrals. Review visits from AI services where analytics can identify the source. Connect them with recorded enquiries or sales where possible.
- Sampled answers. Check a defined set of relevant questions over time. Record the platform, date and context. Treat the findings as observations rather than a complete market share measurement.
- Commercial outcomes. Review lead quality and completed actions. Analytics cannot attribute every earlier interaction, especially when someone reads an answer without visiting your website.
One screenshot of an AI answer is a weak basis for a strategy. A useful measurement plan is repeatable, explicit about its limitations and connected to decisions you can act on.
Where should your business start?
Begin with your main services and the questions customers ask before buying. Check whether existing pages explain them clearly, whether the information can be accessed and whether the claims have support.
Then agree a short list of improvements: repair technical barriers, strengthen a priority service page, publish a useful comparison or improve enquiry tracking. Review the evidence before expanding the work.
At Codex Collective, our SEO & AI Search service connects these priorities with content and implementation. Where reporting needs attention, Analytics & Tracking helps establish the evidence needed to decide what to improve next.
The aim is to make your business easier to discover, understand and choose wherever customers search. To review how your website supports that, discuss your project with us.
Platform guidance checked on 25 October 2026. Features, access controls and reporting can change. Check the official documentation linked above before implementing platform-specific changes.
