The short version: traditional SEO is about getting a page to rank so someone clicks it. AI SEO is about getting your content quoted, cited or recommended inside an answer that a machine writes on your behalf. The technical groundwork is largely the same (crawlable pages, clean structure, real authority), but the target has moved from position one to being the source the model trusts. Most of the SEO work you have already paid for still counts. What changes is how you format content, how you measure success, and how much of your traffic you should realistically expect to keep.

What Traditional SEO Was Built to Do

Traditional SEO assumed a fairly predictable chain of events. Someone types a query, sees ten blue links, and clicks one. Your job was to be one of those links, ideally near the top.

That produced a well-known playbook: keyword research, on-page optimisation, internal linking, page speed, backlinks from decent sites, and a Google Business Profile if you serve a local area. It worked because rankings and clicks were tightly connected. Move from position five to position two and your traffic went up in a way you could see in Search Console within a few weeks.

What AI SEO is Optimising for Instead

AI search does not rank your page against nine others and let the user decide. It reads a set of sources, pulls out the parts it considers factual and relevant, and writes a single answer. You are either in that answer or you are invisible for that query.

That is why the practice picked up new names, generative engine optimisation (GEO) and answer engine optimisation (AEO), depending on who you ask. The mechanics are less about persuading a person to click and more about making your content easy for a language model to extract, verify and attribute. Clear question-and-answer structure, specific numbers, dates, named products, and a definition stated plainly near the top of a section all help.

Some businesses handle this in-house; others work with AI SEO service providers who offer white-label delivery covering areas such as Reddit engagement, AI-platform monitoring and content production. Either way, the same underlying principles apply.

It also spreads across more surfaces than Google. ChatGPT, Perplexity, Gemini and Google’s own AI Mode all pull from the web in different ways, and being cited in one does not guarantee being cited in another.

The Numbers Behind the Shift

This is not a theoretical debate any more, and the data is worth knowing before you decide how much to change.

• Google AI Overviews were triggering on roughly 49% of tracked queries, according to BrightEdge data published in 2024, a significant jump on the year before.
• Ahrefs, analysing around 300,000 keywords of Search Console data, measured a 58% drop in click-through rate for the top-ranked page when an AI Overview appears above it.
• Pew Research found people clicked an organic result roughly half as often when an AI summary was present, falling from about 15% of visits to 8%.
• Seer’s analysis suggested brands cited inside an AI Overview still pick up meaningful clicks, while uncited brands on the same queries lose the bulk of them.

Exposure is very uneven by sector. Informational and research-heavy niches (B2B tech, education, healthcare, insurance) see AI Overviews on the large majority of their queries. Transactional e-commerce and “buy this now” searches have been far less affected so far. If you sell a physical product or a local service, check your own Search Console before assuming the sky is falling.

What Actually Changes in Your Day-to-day Work

Three things, in our experience of auditing SME sites through this transition.

First, content shape. A 2,000 word page that takes six paragraphs to answer the question it is titled after will lose to a 900 word page that answers it in the first two sentences and then explains why. Front-load the answer, keep sections self-contained, and make sure each heading matches a question a real person asks.

Second, measurement. Impressions and average position tell you less than they used to. You need to be tracking whether your brand appears in AI answers for your key queries, and watching referral traffic from AI tools separately in your analytics. Expect fewer clicks per impression and judge pages on leads and enquiries rather than raw sessions.

Third, consistency of information. AI systems cross-check what they find. If your opening hours, service list, prices or company name are different on your site, your Google Business Profile and the directories you are listed on, you become a less reliable source to cite. Structured data and accurate listings matter more now than when they were mainly about rich snippets.

What Has Not Changed at All

A model can only cite what it can crawl, parse and trust. That means indexation, site speed, mobile rendering, sensible internal linking and genuine mentions from other credible sites are still the foundation. Nobody has found a shortcut around that.

Be sceptical of anyone selling a quick technical fix. The llms.txt file, for example, has been widely promoted as a way to feed AI crawlers, but Google has not confirmed it uses the file, so treat it as an experiment rather than a requirement.

Where to Put Your Effort as a Small Business

Do not rebuild your strategy from scratch. Pick your ten most commercially important queries, search each one in Google and in ChatGPT, and look at who gets named. That tells you far more than a generic AI SEO vs traditional SEO checklist ever will.

Then work on the pages closest to those answers. Sharpen the opening, add the specifics a model can quote, fix your structured data, and make sure your business details match everywhere they appear. That is the same discipline good SEO always required, applied to a search results page that now answers before it links.