🎟️ WTSFest Portland is taking place on May 7th!
🎟️ WTSFest Portland is taking place on May 7th!
Author: Aimee Jurenka
Last updated: 11/05/2026
For years, SEO taught us to chase rankings. Pick a keyword. Build a page. Earn links. Climb the SERP.
That model is not gone, but it is no longer enough.
AI-driven search has changed the game. The question is no longer just “Where do I rank?”, iIt is now “What am I recognized for?”.
Because AI systems do not think in rankings the way traditional search engines do. They think in relationships. They look for patterns, repeated associations, semantic alignment, and structural clarity. They try to understand who you are, what you do, who you serve, and how all of those things connect.
If AI cannot confidently make those connections, your brand may be left out of the answer entirely.
That is the shift.
And that is why I believe we need to move from ranking strategies to recognition strategies.
It’s about what you’re recognized for.
Traditional search engines are built to retrieve documents. They index pages, match keywords, and rank results using relevance and authority signals.
LLMs and AI answer engines work differently. They break content into chunks. They convert meaning into embeddings. They retrieve information based on semantic similarity, not just exact keyword matches. Then they synthesize that information into a response.
That means your brand has to be more than present. It has to be understandable, and it has to be connected to the right concepts in a way AI can actually retrieve.
AI systems do not think in rankings. They think in relationships.
That means your brand needs:
In traditional SEO, you could sometimes get away with vague positioning, broad language, or pages that ranked mostly because they had enough authority behind them.
In AI search, vague brands disappear.
If your homepage says one thing, your services page says another, your blog articles talk about ten disconnected topics, and your internal linking is all over the place, AI has a much harder time understanding what you should be retrieved for.
This is where topic entity building comes in.
Topic entity building is the process of creating semantic architecture that helps AI understand and retrieve your brand.
It connects:
The goal is to make it easier for AI systems to answer three core questions:
In short: we are designing for connection.
This is not about pumping out endless new content just to look busy. It’s about creating a structure that makes your expertise legible.
A topic entity is a clear, specific concept you want AI to associate with your brand.
That concept might be:
A few examples:
A strong topic entity is specific and recognizable.
A weak one is vague and generic.
For example:
Why?
Because if it could describe anything or anyone, it will not help AI recognize you.
This is one of my favorite examples because it makes the concept feel real.
When you ask an AI answer engine what Women in Tech SEO is, it understands WTS is an organization and community.
How can it do that?
Because the Women in Tech SEO site repeatedly connects the brand to the same ecosystem of concepts:
That repeated relationship-building teaches AI what the brand is.
This is the whole point.
AI does not magically “know” a brand because the brand exists. It knows because the relationships are visible, consistent, and reinforced.
This framework is intentionally practical.
Start with your brand and money pages. Usually that means:
Brand is much more important in AI Search, and utilizing your “about” page is one of the best ways to strengthen that.
Ask yourself:
This is where you clean up outdated copy, mixed messages, and vague language.
Once your core pages are identified, define the concepts you want associated with them.
Keep them:
Don’t say things like this:
Say things like this:
Map these out somewhere simple. A spreadsheet is fine.
The point is to make your entity strategy visible and reusable so it can guide future updates, internal links, and content organization.
Turn each topic entity into a collection or hub page.
This page becomes the central destination for that topic. That means:
This does not have to be a giant content hub that needs six weeks of dev time and a project manager to get approved. A blog category page can do the job.
That is one of the easiest wins here. For many teams, turning a blog category page into a more intentional collection page is the fastest path to building entity structure without the heavy lifting.
This is where a lot of teams miss an opportunity.
You probably already have content. It is just scattered. So instead of starting from zero, reorganize what exists under the right topic entity hubs.
That can include:
Try to keep one post tied to one primary entity whenever possible.
Why?
Because clean boundaries help AI better understand what a piece of content is supporting. If one post is trying to reinforce three different concepts, the signal gets muddy. We are not trying to create chaos with “sort of related” tagging. We are trying to create clear topical lanes.
Internal linking still matters. But here, it matters a little differently.
The goal is not just to pass authority around. The goal is to reinforce relationships.
That means:
You are teaching AI that these pieces belong together. Every internal link is part of the semantic map.
This is where people usually want a magical dashboard and a single perfect metric.
That doesn’t exist right now, but there are useful ways to measure movement.
One of the most useful signals available right now is the Bing AI Performance Report, especially grounding queries.
Grounding queries give you visibility into whether the language AI is using to retrieve you aligns with the topic entities you are trying to build.
If your target entity is “AI visibility for local brands,” and your grounding queries start reflecting that language or close conceptual variations, that is a strong signal your entity structure is getting picked up.
The second method is manual prompt testing across models like ChatGPT, Gemini, Claude, and Perplexity.
A simple version of that process looks like this:
This matters because single-prompt screenshots are basically chaos disguised as certainty. A one-off result is not an insight.
A repeatable testing set gives you directional data. Not the perfect truth. Directional data. That’s enough to tell whether your visibility is moving in the right direction.
You might like to check out more about my own AI visibility measurement process, Signals Over Noise, and I’ve also built an AI mention rate tool to make it easier for you to try this out for yourself.
One of the most useful parts of this approach is that it can work without a massive content rebuild.
For one of our clients, instead of launching a giant content sprint, we focused on one category tied to one topic entity. We reorganized existing content, strengthened the cluster, and measured the impact, using WAIKAY’s Knowledge Score, over roughly eight weeks.
The results showed improvement across most of the measured models:
The important takeaway was not just the uplift. It was how the uplift happened.
It demonstrates that you do not need to rebuild the site from scratch to see movement. You can reorganize what already exists, build stronger entity alignment around it, and improve AI visibility from there.
That is the part people need to hear.
Because too many teams assume AI visibility means throwing away everything they built for traditional SEO.
It does not.
A lot of that existing content still has value. It just may need stronger semantic structure, better organization, and clearer ties to the concepts you actually want to own.
The biggest insights here are simple:
When you:
…you make it easier for AI systems to recognize you, retrieve you, and include you.
That is the shift from ranking to recognition.
You don’t need to roll this out across your whole site tomorrow.
Pick one entity.
Build one hub.
Reorganize a handful of supporting posts.
Measure what changes.
It’s fine to start small, you just need to get started.
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