The Entity Era of SEO: Building Trust in an Age of AI Overviews

For years, the SEO playbook was pretty straightforward: identify keywords, create content, and earn links to climb the rankings. That still matters, but search is starting to reward something else even more: clear signals that help Google understand who you are and why you’re credible.

With the rise of AI Overviews and the increasing prevalence of zero-click answers, users are beginning to interact with the web differently. Instead of scanning lists of blue links, many people now ask AI assistants like ChatGPT, Gemini, Perplexity, or Google’s experimental features for direct answers and recommendations. These platforms are increasingly behaving less like traditional search engines and more like answer engines.

Visibility now takes more than keyword optimization. It depends on how clearly Google can identify your business as a real entity, then confirm the details it finds across your site and trusted sources.

We still don’t have a full look under the hood of AI-driven search experiences, but the pattern is clear: consistent business information, credible citations, and structured data reduce uncertainty and make it easier for search systems to accurately reference your brand.

Let’s break down what we can say with confidence today, plus where we think entity-driven SEO is headed as AI Overviews continue to expand.

Understanding Trust in AI: E-E-A-T and the Risk of Misinformation

To understand how to optimize for AI assisted search (also referred to as Generative Engine Optimization or GEO), it helps to consider Google’s longstanding E-E-A-T principles: Experience, Expertise, Authoritativeness, and Trustworthiness.

AI-driven answers come with a recurring challenge: they can sometimes present incorrect details with confidence, even when those details aren’t supported by reliable sources. These errors are often referred to as “hallucinations.” 

These hallucinations pose problems for your brand. For example, if an AI tool recommends a business with an outdated phone number or an old address, user trust erodes quickly.

Search engines attempt to avoid this by relying on:

• Data in their existing index
• Known trusted data sources
• Structured information
• Patterns of consistency across the web

While AI chatbots and LLMs don’t verify business data in real time, inconsistent or contradictory information can still make it harder for both search engines and AI driven platforms to interpret your business.

This is where NAP Consistency (Name, Address, Phone Number) becomes critical. Businesses with clean, unified information across the web provide both search engines and AI systems with clearer, more trustworthy signals of identity.

Where AI and Search Engines Get Their Business Data

One common misconception is that AI models directly pull data from business listing aggregators like Data Axle or Localeze. While search engines like Google, Apple, and Bing use these aggregators, LLMs typically don’t draw from them as directly.

Here is what we know:

• Search engines, which power many AI Overviews, draw heavily from major data aggregators.
• Aggregators feed platforms including Apple Maps, Siri, Alexa, and GPS systems.
• AI tools sometimes rely on search engine results, not raw business databases, to generate answers.
• When AI Overviews rely on Google’s index, inconsistencies at the aggregator level can affect visibility.

While AI models don’t directly check Data Axle or Localeze in the background, the systems that supply data to AI Overviews do rely on those sources. Inconsistencies spread when your listings are inaccurate at the aggregator level, and those inconsistencies may influence what an AI system presents through the search engines it references.

A Note on Early Evidence: Schema and Citations May Influence AI Responses

Research on AI Overviews and entity driven rankings is extremely new. However,  we’ve been running our own early tests at Aztek, and what we’re seeing so far suggests:

Businesses with stronger structured data and consistent citations appear more frequently in ChatGPT’s local business recommendations than those without, even when reviews or reputation metrics are similar.

This doesn’t prove cause and effect. It could be simple correlation or an indirect signal, and it may be specific to one model. Even so, the direction lines up with what we already know about how entity-based SEO works.

Structured Data: Communicating Context in a Machine-Readable Way

If NAP consistency establishes trust, Schema Markup establishes clarity. Schema allows you to explicitly define:

• Who your business is
• What you offer
• Where you operate
• Which online profiles represent your brand

To a human, a phone number is obviously a phone number. To an AI system or crawler, it is only a string of characters unless structured data defines its meaning.

Schema won’t guarantee you show up in AI Overviews, but it can help by:

• Improve machine understanding
• Help search engines build stronger connections between entities
• Reduce ambiguity, which is one of the primary causes of AI uncertainty

How Aztek Uses Schema to Establish AI Understanding

At Aztek, we use a system that both strengthens Schema implementations and submits business data, at the location level, directly to major data aggregators online. This combined approach helps reinforce entity clarity in both structured data and citation networks.

On top of that, Aztek also emphasizes Schema strategies that go beyond basic markup by helping brands build a richer knowledge graph. This includes:

• Defining the business as an Organization or LocalBusiness
• Linking relevant People such as owners or experts
• Providing detailed Service and AreaServed information
• Connecting verified SameAs profiles across the web
• Nesting related entities to paint a complete picture of your business

The clearer you spell out who you are and what you do, AI-driven results have less room to misinterpret where you belong.

How AI May Select Businesses in the Future: A Hypothetical Scenario

Imagine a user asks: "What is the best plumber near me for tankless water heaters?"

Here’s a simplified example of how this can play out:

1. The query is processed. The AI identifies that the user needs a specific service from a local provider.
2. The model references search engine data. Many AI tools rely on search results, business listings, or trusted directories.
3. Businesses with unclear or conflicting information may be deprioritized. Outdated addresses, missing service descriptions, or mismatched phone numbers reduce confidence.
4. Entities with clear information may have an advantage. A business with consistent citations and Schema describing tankless water heater installation is easier for a system to understand.
5. A recommendation is generated. Even if two businesses have similar reviews, the one with cleaner entity signals may earn the mention.

This is just an example, but it mirrors what we’ve seen in traditional SEO and lines up with the early patterns we’re noticing in newer AI-driven search experiences.

Adapting Your Strategy for an Entity Driven Future

SEO is shifting from a keyword centered model to an entity centered model that rewards clarity, consistency, and trust.

We recommend take the following steps to prepare for this shift:

1. Audit Your Digital Footprint

Confirm that your business information is consistent across Google, Bing, Apple Maps, and major directories.

2. Strengthen Your Citation Network

If your sources are outdated or inaccurate, those wrong details spread quickly. Listing management tools make it easier to keep your information consistent across the platforms that tend to get referenced the most.

3. Implement Detailed Schema

Use Organization or LocalBusiness markup that includes:

• Services
• Service areas
• People
• Reviews
• Social profiles
• Operating hours

4. Treat Entity Building as Continuous Work

AI systems increasingly rely on clear identity signals. Clean, consistent data is now a competitive advantage.

5. Leverage Aztek’s Enhanced Schema and Aggregator Submission System

When you combine strong schema with consistent, location-specific distribution to major data aggregators, you reduce gaps in your business data and make it easier for search systems to verify your details wherever they show up.

Strengthen Your Entity Signals

AI Overviews are pushing more people to get answers without clicking, which means Google has to be confident it understands who you are, what you do, and where you operate. The brands that win are the ones with clear, consistent signals across their website and the sources Google relies on to confirm business details.

If you want help tightening those signals, Aztek can review your website and structured data to find gaps and fix them. Reach out and we’ll map out the most practical next steps to improve how search systems represent your business.