AI SEO
 "Zoran Artmagic")
Reviewed by Zoran Artmagic ·
AI SEO, also called KI SEO in German, is an umbrella term for the combination of artificial intelligence and search engine optimisation. It is used in two meanings that should be kept apart: first, optimising for search systems that work with AI, i.e. Google with its AI Overviews and AI assistants such as ChatGPT, Claude, Gemini, Copilot and Perplexity. Second, using AI as a tool in SEO work, for example for research, copy or technical analysis. This entry classifies both meanings and shows where AI genuinely helps in store SEO and where it does harm.
The terms around AI and search
Many abbreviations have emerged around the topic, and they often mean the same thing:
Term | Meaning | More |
AI SEO, KI SEO | umbrella term for SEO in connection with AI, in both meanings | this entry |
GEO, Generative Engine Optimization | optimising to be named in the answers of AI assistants | |
AEO, Answer Engine Optimization | older term for the same goal, originally focused on voice assistants and direct answers | |
LLMO, Large Language Model Optimization | optimising for language models, usually synonymous with GEO | |
ChatGPT SEO | ChatGPT’s particularities: crawlers, index, product feeds | |
AI content SEO | how AI-written texts rank and serve as sources |
Google itself considers optimising for its AI features part of normal search engine optimisation. How Google justifies this and where this view has gaps is shown in the article on Google’s guide to AI search.
AI in search isn’t new
Google has used AI to understand search queries for years: RankBrain since 2015, BERT since 2019, later MUM. These systems enabled Google to grasp the meaning of a question instead of just matching words. What is new is that AI now formulates the answer itself, in Google’s AI Overviews and AI Mode as well as in ChatGPT and other assistants. This changes the question for merchants: being in first place is no longer enough. What also matters is whether your store appears as a source or recommendation in the AI answer.
Meaning 1: optimising for AI search
The fundamentals remain the same: a page that can’t be crawled, loads slowly or is thin in content won’t be used by AI systems either. Building on this, the emphasis shifts:
Questions instead of keywords: people describe their situation to AI assistants. Content that fully answers specific questions is more likely to be cited.
Topics instead of individual terms: AI systems break a question down into many sub-questions. Whoever covers a topic comprehensively becomes visible. More on this under semantic SEO.
Unambiguous data: structured data, complete product information and clear company information help AI systems assign information correctly.
Trust and mentions: what other websites say about a store feeds into the selection. More on this under E-E-A-T.
Access for AI crawlers: the robots.txt decides which AI providers may read a store.
Agents that buy: with protocols such as UCP, AI assistants turn from recommenders into buyers. More on this in the article on UCP and agentic commerce.
This is described in detail in the entry on Generative Engine Optimization, and ChatGPT’s particularities in the entry on ChatGPT SEO.
Meaning 2: AI as a tool in SEO work
Language models can speed up many SEO tasks considerably. But not every task is equally suitable. The line runs where expertise, experience and responsibility are required.
Task | Suitability | What matters |
keyword ideas and grouping of search terms | good | check search volume and competition with real data, don’t let it estimate. More on this under keyword research |
drafts for meta titles and descriptions | good | generate from product data for large ranges, check samples |
alt texts for product images | good | the image must be described correctly, not just the product name |
outlines and briefings for guides | good | build in customer questions and your own experience |
analysis of data from Search Console and analytics | good | cross-check results with the raw data |
structured data and small scripts | good | validate every result technically |
product descriptions in large numbers | with caution | based on real product data, with checks, not as interchangeable standard text |
expert guides and buying advice | with caution | AI for the draft, experts for content, experience and review |
mass-produced pages without added value | unsuitable | violates Google’s policies on scaled content |
fake reviews or testimonials | unsuitable | deceives customers and is legally vulnerable |
The risks
Wrong information: language models phrase errors convincingly too. Technical data, dimensions, standards and legal statements must be checked.
Interchangeable texts: anyone using the same tools with the same instructions as the competition gets very similar texts. That doesn’t set a store apart and comes close to the problem of manufacturer texts; more on this under duplicate content.
Quantity over quality: Google treats content produced mainly in large quantities for search engines as spam, regardless of whether a person or an AI wrote it.
Lack of experience: what an AI can’t provide is what Google means by experience: real experience with products and customers. That is exactly what makes good content distinguishable.
Images: AI-generated product images must remain recognisable as such. Google Merchant Center requires that the corresponding labels in the image data are preserved. More on images under Google Images.
Google doesn’t assess content by how it was created, but by whether it is helpful. AI is allowed; arbitrary mass-produced texts are not. How to revise AI texts so that they rank is described in the entry on AI content SEO.
AI in Shopware and Shopify
Both systems now come with their own AI features. Shopware offers, depending on the plan, AI assistants that suggest product descriptions, extract properties from texts or summarise reviews. Shopify offers similar features with Shopify Magic and the Sidekick assistant, such as suggestions for product descriptions and SEO information.
These features save time on routine work. For the pages that generate revenue, however, the same rule applies as for any AI tool: draft by the AI, content, experience and approval by people. More on the systems under Shopware SEO and Shopify SEO.
A proven workflow
Data first: collect real product data, search terms, customer questions and experience.
AI for the draft: outline, raw text, variants for titles and descriptions.
People for substance: check facts, add experience, ensure tone and brand.
Check technically: validate structured data, set internal links, avoid duplicates.
Measure the effect: watch rankings, clicks and mentions in AI answers.
Frequently asked questions
What is AI SEO?
An umbrella term for search engine optimisation in connection with AI: on the one hand optimising for AI search systems, on the other using AI in SEO work.
Is AI SEO the same as GEO?
Partly. GEO only refers to optimising for the answers of AI assistants. AI SEO is also used for AI as a tool.
Does Google penalise AI texts?
No, not because of the AI. Google assesses whether content is helpful. Mass-produced content without added value counts as spam, regardless of how it was created.
Can AI replace SEO work?
It can speed up many steps, but not expertise, experience and responsibility for the result. Especially for stores, knowledge of products, customers and technology is decisive.
Which AI tools are suitable for SEO?
General language models such as ChatGPT, Claude or Gemini for texts and analysis, the AI features in Shopware and Shopify for routine work, and specialised SEO tools with real search data for research and monitoring.
How arboro makes stores visible in AI search is described on the page GEO for online stores. How often a store is named in AI answers is measured by arboro.PULSE; where it stands today is shown by the AI Readiness Check.