What does an AI search engine look at?
What does an AI search engine look at?
What exactly does an AI search engine look at when assessing and displaying content? AI search engines are changing the way information is found and presented. Instead of a list of blue links, as with traditional search engines, AI search engines often offer direct answers, contextual summaries and personalised results. This has major consequences for SEO and content marketing.
We'd be happy to walk you through how an AI search engine works, which factors are important for indexing and how you can optimise your content to become more visible.
What is an AI search engine and how does it differ from traditional search engines?
An AI search engine uses artificial intelligence to understand search queries and display relevant results. Where traditional search engines such as Google mainly work on the basis of keywords and backlinks, AI search engines use natural language processing (NLP), machine learning and knowledge graphs to interpret context and generate content.
Differences between traditional and AI search engines
| Feature | Traditional search engine (e.g. Google) | AI search engine (e.g. Google SGE, Perplexity, ChatGPT Search) |
| Search method | Keywords and backlinks | Context and semantic relationships |
| Results | List of links to websites | Direct answers and summaries |
| Interaction | One-way traffic (type and click) | Conversation-driven and personalised |
| Data sources | Web pages and structured data | Websites, documents, databases and real-time interactions |
Thanks to this approach, an AI search engine better understands the intent behind a search query and can generate answers without users having to click through various web pages. This changes how companies need to optimise their content.
How does an AI search engine interpret content?
AI search engines use natural language processing (NLP) and machine learning to analyse texts and make connections. This means they look beyond keywords alone and also pay attention to context, meaning and reliability.
Three key pillars for AI-driven search results:
- Semantic search – AI looks at the meaning behind words and not just at exact search terms. For example: a query such as “best restaurants in Amsterdam” is linked to reviews, location data and menus, not just to pages containing that exact phrase.
- Knowledge graphs – AI search engines build a network of knowledge based on information from various sources, such as Wikipedia, news websites and academic articles.
- Context and intent – AI tries to understand why someone is searching for something and adapts the answer accordingly. A user searching for “best running shoes”, for example, gets different results from someone searching for “best running shoes for marathon runners”.
Factors that determine how an AI search engine assesses your content
To become more visible in AI search engines, it's important to understand which factors they look at when assessing content.
1. Relevance: Does AI understand your content well?
AI search engines look for content that directly answers search queries. This means you should:
- Give clear and complete answers to questions.
- Structure information logically with subheadings and bullet points.
- Use natural language, the way a person would write it.
2. Authority: How reliable is your content?
AI search engines assess whether a source is reliable and knowledgeable. They do this by looking at:
- Source citation – Refer to reliable external sources.
- Author expertise – AI looks for information about who wrote the content and their background.
- E-E-A-T principles (Experience, Expertise, Authoritativeness, Trustworthiness) – Google and other AI systems use these principles to assess quality.
3. Structure: How easily can AI process your content?
AI search engines prefer content that is easy to scan and interpret. This means:
- Using structured data (Schema Markup) so that AI understands what your content is about.
- Clear headings and subheadings to improve readability.
- Using FAQ sections, because AI often takes answers from these sections.
4. User signals: How valuable do visitors find your content?
AI search engines analyse how users interact with your content:
- Time on page – How long does someone stay on your page?
- Interaction – Are links and buttons clicked?
- Engagement – How often is your content shared or liked?
How do you optimise content for an AI search engine?
Want your content to score better in AI search engines? These are the key steps:
Write answer-focused content
- Use question-and-answer structures in your texts.
- Add an FAQ section with frequently asked questions in your industry.
Use structured data
- Implement Schema Markup to help AI search engines understand your content better.
- Use structured lists and tables where possible.
Increase your authority
- Publish content on reliable and relevant websites.
- Collect reviews and mentions from other sources that AI considers valuable.
The future of AI search engines: What does this mean for SEO?
AI search engines are becoming increasingly sophisticated, and that has an impact on SEO strategies. Want to be prepared for these changes? Then get in touch for a BE Digital scan. Together we'll look at where opportunities remain for your company to optimise for AI search engines. Make sure your content is valuable, structured and reliable. AI search engines will keep evolving, but companies that respond to this smartly will maintain strong online visibility.