The difference between AIO and SEO

The goal for SEO has been largely the same since it first became a proffesion: get eyes on your site. Although the methods to achieve this over the years has changed, the outcome has largely always been a measurable increase in traffic to your website which (hopefully) convert to sales or ad revenue. But wait a minute… what was that? Oh, it looks as if AIO has entered the chat.

With the new developments search engines have made through AI, such as overviews, the focus is shifting. It is not all about getting your site to appear in SERPs, it’s about getting it to appear at all in ai responses. Either you exist or you dont, and the information AI has on you and your brand can significantly impact the way new traffic comes through.

Optimizing content for Large Language Models (LLMs) differs from traditional SEO in several key ways. While there is overlap, such as the importance of high-quality content and structured data, LLM optimization focuses more on how AI-driven models process and generate responses, rather than ranking on search engine results pages (SERPs). Here’s a breakdown of the differences: 

AIO (Artificial Intelligence Optimization) is the process of optimizing your online content to manipulate the information offered through AI tools. As automation starts to dominate online search more and more it’s imperative that you know what AI knows about your brand and how to improve this. It is not dissimilar to what a traditional SEO does, and very well might be the next evolution of an SEOs career. In this post we will look at the key differences between SEO and AIO and how you could be adopting AIO in your current SEO strategy. 

1. Search Intent vs. AI Answer Generation 

To start, the underlying purpose of SEO and AIO is fundamentally different. SEO is a series of processes and audits which seek to understand human-led search intent and engineer branded content to appear based on this. It focuses on how to rank on search engines and will primarily focus on getting traffic by understanding user behaviours and delivering on exciting and interesting content.  

AIO on the other hand does not need use marketing techniques like that. The primary focus of AIO is to make sure content is readable, understandable and citable by large LLMs so that it can be cited or referenced in AI generated responses. An AIO may not guarantee a ‘rank’ bit more a heightened understanding of how AI perceives and regurgitates information concerning a particular brand.  

• Traditional SEO: Focuses on ranking in search engines (Google, Bing) based on keywords and search intent. The goal is to attract clicks to your website. 

• LLM Optimization: Aims to ensure your content is correctly processed by AI so that it can be cited or referenced in AI-generated responses. There’s no guarantee of a “rank”; instead, it’s about making your content AI-friendly. 

2. Content Structure and Formatting 

Content structure and formatting also differs between the two expertise. Traditional SEOs will focus on clear headings, using hierarchical structures like H tags to denote where information lies. It wants to appease the human reader and search engine at the same time, and is trying to make accommodations for both parties to appreciate the content.  
 
AIO does not need to focus on how a human may process or enjoy this information. Instead, the information might be more formulaic, in plain text and presented without the risk of misinterpretation. If you were to present a piece of content which can be perfectly understood by AI and SEO would certainly cringe at the blandness and unrankability of it! 

• Traditional SEO: Uses structured headings (H1, H2, etc.), bullet points, and concise paragraphs for readability and search engine crawling. 

• LLM Optimization: Focuses on plain-text clarity, logical structure, and concise, well-explained answers so that LLMs can directly pull useful information without misinterpretation. 

Here is a quick example: 

• SEO Approach: “The best running shoes of 2024 have been reviewed and ranked by experts based on durability, comfort, and support.” 

• LLM Optimization Approach: “The best running shoes of 2024 include [Brand A] for durability, [Brand B] for comfort, and [Brand C] for support.” 

This is because LLMs favour structured, factual answers over salesy or vague content. 

3. Link Building vs. AI Citations 

It is widely believed that backlinks from authoritative online sources to a website improves authority. As such, an SEO may focus on getting cited by news outlets or other reputable areas to signify to a search engine that the content is trustworthy.  This can be supplemented with a content strategy that improves the semantic association.  
 
On the other hand, AIO is focused on getting cited in AI responses. Often links will be hallucinated by ai based on what it thinks could be true about a brand. It is an AIO’s job to understand why AI may be making up these links and how to get them to direct to real pages on your cite.  

• Traditional SEO: Focuses on backlinks from authoritative sources to improve ranking. 

• LLM Optimization: Focuses on getting cited in AI responses, which means ensuring your content is trustworthy, well-referenced, and authoritative. 

How to get cited? 

• Publish research-backed, data-driven content. 

• Get mentions on high-authority sites, forums, and social media discussions. 

• Use clear authorship (real names, company associations). 

Why? LLMs look for reliable sources rather than just backlink numbers. 

4. Crawling & Indexing vs. AI Model Training 

It is commonly understood that Googlebot crawls and indexes web pages, ranking them based on a variety of on-page and off-page factors. SEO professionals often focus on optimizing meta descriptions, sitemaps, and page speed to ensure better ranking in search engine results. 

In contrast, AI models train on web content, evaluating elements such as authority, structure, and clarity to determine how they present information. This means LLM optimization isn’t about the dynamic, real-time browsing experience of search engines, but instead about ensuring that data is accessible and formatted correctly. 

For example, while SEO focuses on optimizing meta descriptions and speeding up websites, LLMs prioritize making content easy for AI models to process, using structured data, Markdown, and ensuring readability in plain text. 

Why? Because AI models don’t “browse” your site like humans or Googlebot; they train on snapshots of the content, so accessibility is key. 

• Traditional SEO: Googlebot crawls and indexes web pages, ranking them based on on-page and off-page factors. 

• LLM Optimization: AI models train on web content and use various factors (authority, structure, clarity) to determine how they present information. 

Example: 

• SEO focuses on meta descriptions, sitemaps, and page speed. 

• LLMs focus on data accessibility (e.g., using Markdown, structured data, and making sure content is readable in plain text). 

Why? AI models don’t browse your site dynamically like humans or Googlebots; they train on snapshots of information. 

5. Search Results vs. Direct Answers 

In traditional SEO, search engines return multiple results, and users typically click on links to find what they need. 

With LLM optimization, however, AI directly provides answers or summarizes content, eliminating the need for users to visit websites. 

To maintain visibility, focus on including brand mentions in ways that AI can easily recognize (e.g., “According to [Your Brand], the best way to…”). Also, optimize your content for authoritative citations within AI-generated summaries. 

Why? As AI-powered search becomes more prevalent, it could lower click-through rates, so ensuring your brand is directly mentioned in AI responses is crucial. 

• Traditional SEO: Search engines return multiple results, and users click on links. 

• LLM Optimization: AI directly provides answers or summarizes content without requiring users to visit websites. 

How to stay visible? 

• Include brand mentions in AI-friendly ways (e.g., “According to [Your Brand], the best way to…”). 

• Optimize for authoritative citations in AI-generated summaries. 

Why? AI-powered search may reduce click-through rates, so ensuring your brand is directly mentioned in AI answers is key. 

6. Content Freshness vs. AI Model Updates 

In traditional SEO, Google often prioritizes fresh content for certain queries, like news or trending topics. 

For LLM optimization, AI models are trained on periodic data snapshots, meaning updates may take longer to be reflected. 

To stay ahead, focus on publishing timeless, evergreen content that remains relevant even between AI model updates. Also, keep structured data and citations up to date so that AI can retrain with the latest information. 

Why? AI models don’t update in real-time like Google search results, so ensuring your content is always relevant and fresh is key. 

• Traditional SEO: Google prioritizes fresh content for certain queries (e.g., news, trends). 

• LLM Optimization: AI models are trained on periodic data snapshots, so updates might take longer to be reflected. 

What to do? 

• Publish timeless, evergreen content that remains relevant between AI model updates. 

• Keep structured data and citations fresh so AI retrains with the most recent information. 

Why? AI models don’t update in real-time like Google search results. 

Final Takeaway: SEO vs. LLM Optimization 

Factor	Traditional SEO	LLM Optimization
Goal	Rank higher on search engines	Get included & cited in AI responses
Content Focus	Keywords, backlinks, structured data	Clarity, factual accuracy, semantic understanding
Search Behavior	Users click on results	AI provides direct answers
Technical Setup	Meta tags, sitemaps, page speed	Plain text, AI-readable formatting
Updates	Frequent updates for freshness	AI models retrain periodically

Traditional SEO still matters, but optimizing for AI involves creating clearer, fact-driven, and well-structured content that LLMs can reference effectively.