New Developments in SEO Due to the Rise of AI and LLMs

AI and large language models (LLMs) such as ChatGPT, Google Gemini, and Claude are fundamentally changing how people search for information online. Whereas SEO for years mainly focused on blue links in Google, the attention is now shifting to AI-generated answers, conversations, and tasks that are solved directly within the interface of these tools.

For marketers, SEO specialists, and WordPress teams, this means: new developments in SEO due to the rise of AI and LLMs affect not only your rankings but your entire content strategy and digital visibility.

In this article, we walk through step by step:

• how AI tools are used for information and advice
• the difference between classic search results and AI answers
• why rankings alone are no longer sufficient
• what Generative Engine Optimization (GEO) is as a complement to SEO
• how your content is used as a source for AI models
• practical examples of changing search behavior

Main section

How AI Tools Are Used for Information and Advice

Users increasingly go directly to AI tools instead of a classic search engine. You can see this in three clear patterns:

1. From Search Query to Conversation

Instead of typing "best CRM software 2024" into Google, users enter in ChatGPT or Gemini:

• "I am a SaaS startup with 10 employees, which CRM suits us and why?"
• "Create a comparison table of three CRM systems with price, features, and integrations with WordPress."

The AI immediately provides a structured answer, often with explanations, tables, and follow-up question options. The user does not have to open and compare 5–10 pages themselves.

2. From Discrete Information to Concrete Advice

AI tools are also used for context-specific advice, for example:

• "Write a content plan for a B2B SaaS company targeting HR managers in the Netherlands."
• "Explain how I can structure my WordPress blog for topical authority on email marketing."

These are questions that used to be answered through multiple searches, blogs, and whitepapers. Now a single summarizing answer comes from an AI model.

3. From Informational Query to Task Execution

LLMs are also deployed to perform tasks directly:

• generate an outline for a pillar article
• propose internal link structure for a content cluster
• rewrite meta descriptions based on existing WordPress content

The search engine thus becomes not only a source of information but a workspace. This has a direct impact on how and where your content remains visible.

Difference Between Classic Search Results and AI Answers

The core of the new developments in SEO due to the rise of AI and LLMs lies in how answers are presented.

Classic Search Results (SERPs)

• You see a list of links, snippets, and sometimes rich results.
• The user chooses which sources to open.
• Click-through to your site is necessary to read your content.
• SEO focuses on positions, CTR, and on-page optimization.

AI Answers (Generative Engines)

• The user receives one compiled answer, often without clicking through directly.
• The AI combines information from many different sources.
• Source attribution is limited, sometimes with a few links, sometimes none at all.
• The user remains in the AI interface and asks follow-up questions.

Important: in AI answers, your content is no longer visible one-to-one. You become part of a summary. This requires a different way of thinking about visibility.

Why Rankings Alone Are No Longer Enough

Rankings remain important but are no longer the only relevant signal. There are three reasons:

1. Fewer Clicks, More "Zero-Click" Answers

If an AI tool already answers the question, there is no need to click through. Even if you still rank well in Google, traffic can decline because users get their answers sooner.

2. Visibility Shifts to Multiple "Layers"

You need to consider:

• classic SEO (visible in search results)
• AI discoverability (being included in AI answers)
• on-platform visibility (for example, within WordPress via internal links and content clusters)

A page that ranks well but is not clear and structured enough to be used by AI models loses value over time.

3. Authority Is Interpreted More Broadly

AI models look not only at one page but at patterns across your entire domain and beyond. Consistent expertise on a topic (topical authority) becomes more important than a single optimized landing page.

Introduction to GEO: Generative Engine Optimization

Generative Engine Optimization (GEO) is an extension of classic SEO. While SEO focuses on search engines, GEO focuses on generative engines: systems that generate answers based on existing content, such as ChatGPT, Gemini, and Claude.

What Is GEO in Practice?

GEO revolves around the question: "How do I ensure my content is usable and citable for AI models?" This means, among other things:

• Clear structure: headings, subheadings, lists, and clear definitions make it easier for models to extract information.
• Explicit context: mention target audience, use cases, limitations, and examples within the text itself.
• Depth over superficiality: generic content is more quickly replaced by AI summaries; in-depth, specific content remains necessary as a source.
• Consistency within a content cluster: multiple articles that together cover a topic fully increase the chance that your domain is seen as an authority.

GEO does not replace SEO but adds an extra layer: you optimize not only for the search results page but also for the "invisible" selection processes of AI models.

How Content Is Used as a Source for AI Models

AI models are trained on large amounts of text from the public web, supplemented with other sources. Additionally, some systems use live data via search engines or their own indexes.

For your content strategy, this means:

• Public content (blogs, documentation, knowledge bases) can serve as training or reference material.
• Specialized content (niche topics, in-depth guides) carries relatively more weight because there are fewer alternatives.
• Consistent terminology helps models better recognize your brand, products, and solutions.

If your site is a clear, structured source on a specific theme, the chance increases that AI systems incorporate your insights into their answers, even if your brand name is not always explicitly mentioned.

Changing Search Behavior: What Are We Seeing?

The new developments in SEO due to the rise of AI and LLMs are especially visible in how questions are asked:

• More context: "for a B2B SaaS in the HR sector" instead of just "B2B SaaS."
• More steps in one question: "Explain, give examples, and create a content calendar" in one prompt.
• More conversation: follow-up questions like "make it shorter," "adapt this for the Netherlands," "turn it into a WordPress briefing."

This means content must not only answer a question but also support the logical next steps. Think of:

• checklists
• step-by-step guides
• examples that are easy to reuse in prompts

Practical examples

Practical Examples of Changing Search Behavior

Example 1: SEO Agency with WordPress Focus

A potential client used to search for:

• "seo agency amsterdam"

Now you see prompts like:

• "What questions should I ask an SEO agency experienced with WordPress and B2B SaaS?"
• "Create a shortlist of criteria to choose a good SEO agency for a WordPress site."

An agency that has a well-structured article about "questions to ask an SEO agency," including a checklist and examples, has a better chance of serving as a source for AI answers than a generic service page.

Example 2: Content Strategy for a SaaS Product

A marketer asks an LLM:

• "Design a content cluster for a SaaS solution for time tracking, targeting Dutch SMEs."

If your site already has a clear cluster around:

• time tracking for SMEs
• comparisons with Excel and other tools
• implementation combined with WordPress and client portals

then the AI can use your content as a basis for the proposed structure. You see this reflected in:

• topics that match your articles
• terminology similar to your wording
• use cases you have described extensively before

Example 3: Incorrect Assumptions by AI and How Content Corrects Them

AI models can provide outdated or incomplete information, for example about a recent feature in your SaaS product. If you have an up-to-date, clear changelog and documentation, you increase the chance that:

• the AI finds the correct information during live web consultation
• users still end up on your site via source attribution or follow-up questions

This calls for a content strategy where documentation, knowledge base, and blog together provide a consistent picture of your product and positioning.

Best Practices for SEO, AI/LLMs, and GEO

To respond to the new developments in SEO due to the rise of AI and LLMs, you can apply the following best practices:

• Build content clusters around clear themes instead of isolated articles. This helps both search engines and AI models recognize your expertise.
• Use structured composition with clear h2/h3 headings, definitions, step plans, and summaries.
• Write explicitly for your target audience: mention sector, role, company size, and context within the text itself.
• Update your content regularly so AI systems find current information during live consultation.
• Integrate SEO and GEO into your WordPress publishing workflow: consider both search results and usability for AI answers with every publication.

Common Mistakes in the New SEO Reality

We see some recurring mistakes in organizations starting with AI and LLMs:

• Focusing only on keywords and rankings without paying attention to structure, depth, and topical authority.
• Using AI for quick, generic content without adding own insights, data, or examples. This content adds little value as a source for AI models.
• No coherence between articles: isolated blogs without a clear internal linking strategy or content cluster.
• No governance around AI content: no review process, no version control, no alignment with brand voice and terminology.
• Expecting AI to take over all the work instead of seeing AI as part of a broader content engine with human editing and strategy.

Conclusion

The rise of AI and LLMs is transforming SEO from a purely search engine-driven discipline into a broader strategy for digital visibility. New developments in SEO due to the rise of AI and LLMs affect search behavior, content format, distribution, and metrics.

For professional teams, this means:

• SEO remains important but must be complemented with GEO: being visible in generative answers.
• Content must not only be findable but also usable as a source for AI models.
• A structured, well-managed content workflow (from briefing to WordPress publication) becomes crucial to consistently publish and update.

Those who invest now in clear content structure, topical authority, and a thoughtful AI content workflow build an advantage in a landscape where search engines and generative engines increasingly merge.