By exploring how modern AI search evaluates user intent, SEO expert Paul Gordon explains a decision-making model that goes far beyond traditional ranking systems.
Search engines powered by artificial intelligence no longer rely solely on keywords and backlinks to decide what content to show users. Instead, they aim to understand how people think, feel, and make decisions. In his work on Layered Contextual Fans, Paul Gordon introduces a framework that explains how AI systems evaluate user intent across multiple stages before deciding what to recommend. This approach highlights why visibility in AI-driven search is increasingly about context rather than simple optimisation.
At the heart of the Layered Contextual Fan model is the idea that AI expands its understanding of a user’s situation before narrowing down to a final recommendation. Early in the process, the system considers emotional and informational signals. Only later does it focus on practical actions such as purchasing or contacting a business. This widening and narrowing effect explains why some websites rank well but are still ignored by AI-generated answers.
Why Traditional SEO Models Are Not Enough
Conventional SEO strategies often assume that users follow a direct path from search query to solution. A keyword is entered, a relevant page appears, and a conversion takes place. However, AI-powered search systems evaluate a much broader decision journey. They assess emotional intent, trust signals, and situational relevance long before determining whether a result deserves to be recommended.
This means a page can be technically optimised and still fail to appear in AI-driven results. If content does not address the user’s underlying concerns or emotional context, it may never reach the final recommendation stage. AI search is designed to identify not just relevance, but suitability, based on a deeper understanding of human decision-making.
The Six Layers of the Contextual Fan
Paul Gordon’s model breaks the decision process into six layers. Each layer represents a different stage in how users move from an initial feeling to a final action. AI systems assess content against these layers to determine whether it aligns with the user’s needs.
Emotional Trigger
The process begins with emotion. Before users know exactly what they want, they often feel uncertainty, stress, curiosity, or urgency. AI systems detect emotional cues in language patterns and contextual signals. Content that acknowledges these emotions and offers reassurance has a higher chance of being considered early in the decision process.
Reassurance and Discovery
Once the emotional trigger is recognised, users seek understanding rather than immediate solutions. They want to explore options and learn without pressure. AI evaluates whether content explains concepts clearly, answers basic questions, and provides a sense of guidance. Educational and supportive content performs strongly at this stage.
Trust and Credibility Checks
As users gain confidence that a solution exists, trust becomes critical. AI looks for indicators of reliability such as consistent messaging, social proof, expertise, and evidence of real-world experience. At this layer, the system is deciding which sources are credible enough to move forward in the recommendation process.
Control and Options
Users then want to feel in control. They compare alternatives, evaluate pros and cons, and look for transparency. AI assesses how well content explains choices, limitations, and processes. Businesses that clearly outline options without exaggeration are more likely to progress through this layer.
Practical Commitment
At this stage, users focus on practical details. Pricing, availability, timelines, and ease of engagement become important. AI evaluates whether the content provides clear answers and reduces friction. A lack of clarity here can cause a business to drop out of consideration, even if earlier layers were strong.
Conversion Readiness
The final layer is action-oriented. Users are ready to book, buy, or make contact. AI ensures that previous expectations align with the final step. Clear calls to action, consistent messaging, and a smooth transition from information to action determine which option is ultimately recommended.
AI Search Is About Eligibility, Not Just Rankings
One of the key insights from Paul Gordon’s framework is that AI search is not simply ranking pages in order. Instead, it is deciding which options are eligible to be recommended. A website may appear high in traditional search results but still be excluded from AI-generated responses if it fails to meet emotional or trust-based criteria.
This shift changes how success in search should be measured. Visibility is no longer just about ranking positions, but about whether content satisfies the full decision journey. AI rewards depth, clarity, and alignment with user intent across all stages.
Practical Implications for SEO and Content Strategy
The Layered Contextual Fan model suggests a more holistic approach to SEO and content creation. Businesses should focus on addressing emotional concerns, building trust, and providing transparent information at every stage of the user journey.
Content should be designed to support users from initial uncertainty through to final action, rather than targeting isolated keywords. By aligning content with how people actually make decisions, brands increase their chances of being selected and recommended by AI-powered search systems.
Ultimately, this approach moves SEO beyond optimisation tactics and towards genuine relevance. In an AI-driven search environment, understanding context is no longer optional. It is essential for long-term visibility and trust.
