Ryan Goloversic
🟩 Published by Ryan Goloversic • March 26, 2026
How Does Generative Engine Optimization (GEO) Work in AI Search?
If you’re trying to understand where SEO is going, this blog will give you a needed fresh perspective:
We are moving from ranking pages to training machines.
Generative Engine Optimization is a new concept that builds on the SEO foundation. One consideration is that optimization is reactive; it relies on current data. In the age of AI, we must create new training data. We must be responsive. We need to act as navigators, guiding customers from problem to solution with our original ideas derived from real experience. This is more of an art than a science.
The other side of the optimization coin is the art of orientation. Below is a breakdown of generative engine orientation (GEO), how it works, and how it changes the way you approach search.
Watch: What Is Generative Engine Orientation and How Does It Work?
We’re going to talk about generative engine optimization, or as we’ve coined it, generative engine orientation.
This pertains to search without clicks, answer engine optimization, and AI search visibility in general.
Many are interested in AEO, AIO, GEO. While optimization is good, it’s not enough. It implies hacking or gaming a machine that doesn’t want to be hacked. It wants to help, just like the searcher who is trying to solve a problem.
We’ve seen this trend across new mediums. When the internet first started, people gravitated toward authentic, real people over advertisers. The same happened with social media. Humans respond to misaligned incentives by ignoring them over time. We always hunt for what is real.
Google is hunting for information gain, and so are LLMs. This practice is a first-principles approach to help you survive algorithm shifts. Stop optimizing. Start orienting the machine.
The core focus of this video is to help you understand the customer journey and the machine journey.
Why Is SEO Shifting From Mindshare to Machine Share in AI Search?
Previously in marketing, the focus was market share and mindshare. That’s the premise behind authority marketing.
I’ve experienced this firsthand. I spent 8 years producing helpful content that trained entire generations of kiteboarders who learned the sport through our marketing programs. I was solving problems as they arose. Customers were frustrated with the lack of honest reviews. They were hungry for culture and insider knowledge. They wanted to be part of a tribe.
So we built one. A tribe of riders turned marketers.
We covered the full spectrum. Teaching tricks, sharing travel destinations, publishing deep investigative gear reviews based on real testing with the sales team, and producing behind-the-scenes content that humanized the brand. We thought in years, not quarters. We aimed to help, not sell.
By the end, the phone rang daily. The inbox was full. Live chat pinged constantly with customers asking better questions because of the content. Even better, they felt like they were talking to a celebrity when myself or someone from the sales team responded.
We built trust and real relationships long before they reached the bottom of the funnel.
As AI emerged, I noticed Google shifting toward AI-driven search experiences over traditional results. At the same time, it became clear that people are leaning into AI the same way they did with the early internet, early YouTube, and early social media.
AI is now the trusted guide in the messy middle.
That leads to a simple conclusion. We must train the AI to guide our customers. If we do that well, the AI will recommend us when the customer is ready to buy. And make no mistake, Google is now an AI system.
Introducing Machine Share
We are moving toward what we’ve coined machine share.
The reframe is this. AI is now influence over your customer.
Google does not exist to sell. It exists to help.
We are already seeing transactional intent queries shift into AI-generated results and answer panels.
So the strategy changes. You don’t just optimize for rankings. You orient the machine.
And the best way to orient the machine is to orient your customer.
One of our team leaders, Megan, has a background in PR. We lean into that. We interview customers, their customers, and product designers to uncover real-world insight. We dig up the analog dirt so we can leave digital receipts.
This is our version of PR for AI. We are helping the system understand who you are, what you’ve done, and why you matter.
How Does the Customer Journey Work in Modern SEO and AI Search?
I was doing this years ago with MACkite. We pulled customers out of what Google calls the messy middle.
If you’re unfamiliar with how this works, think of it like an ecosystem.
There’s local SEO, the map pack, and customers who are ready to buy. That’s the bottom of the funnel.
But real marketing happens before that. You need to bring in people who are not aware of you yet. Then you need to guide them through the problems you uniquely solve. That’s the messy middle.
We use information architecture to structure the website as a resource that Google and AI can parse, but we keep it human-centric. Think nested pillars within nested pillars. Resources, guides, deep dives. Everything is designed to guide a decision from high-level understanding down to edge cases and weird branches.
We cover the professional layer, new information from interviews, real experience from the team, and the questions normal people actually ask.
We cast a net so wide, deep, and original that the system has no choice but to recognize you as the authority and guide the right customers to you.
The human side of this is decision architecture. We orient the searcher’s decisions so they can make the right one, even when it doesn’t benefit us.
Why? Because that’s how you build trust with both people and the machine evaluating you through a kind of invisible trust score.
You earn trust over time through actions, not words.
This is why pure optimization is a mistake. You can’t fool Google, and you can’t fool an AI that learns from your behavior and remembers patterns over time.
This is why we developed the Navigator Framework to help guide customers by tapping into the art of selling. I was heavily influenced by SPIN Selling by Neil Rackham and No Thanks, I’m Just Looking by Harry Friedman. Both helped me understand sales psychology.
I now apply the reverse logic of probing to answer engine research. Instead of traditional keyword research, we focus on probing questions, but aimed at how people will probe the machine. This helps us target queries and prompts that align with our core headers.
From there, we design the decision architecture on each page in alignment with the database-like structure of the website. The information architecture. The semantic hierarchy.
In simple terms, it’s the flow of thought. Headers and structure built for a real customer journey from top to bottom. That’s how we build out a site.
I’ll break down the difference between the customer journey and the machine journey later in this article.
Why Is Information No Longer a Competitive Advantage in AI Search?
The way this ties back in with AI is that we live in the post-information era.
Everything is getting amalgamated, flattened through AI. All of that information is out there.
Information is now a commodity.
So new information, new unique ideas, what we like to say is analog sweat in digital receipts.
These things are like water in a charred desert of the AI landscape.
When you can provide new ideas from your experience, from your sales team, from you testing the products, new things that nobody’s talking about, this is how you train the machine.
What Is a Mesocluster in SEO and How Does It Train AI Models?
You do this through the use of your website and something we’ve coined as mesoclusters.
Now, a mesocluster is an extension of a topical cluster.
I chose the word mesocluster because it’s easy to think about it like training cycles from bodybuilding.
What’s happening is Google indexes your website, but they’re also indexing your social media.
So we practice building our topical cluster on the website.
Then we take our headers, our unique ideas, our frames, and we seed them on LinkedIn, on social media, in thought leaders’ comments.
The whole idea is to bridge new and original ideas not only to the humans who are having conversations, but back to the machine that is effectively training off all of us and hunting for the delta, the gap between what’s known and what’s unknown.
How Do the Customer Journey and Machine Journey Connect in GEO?
So I’m going to show a quick visualization here, and we’re just going to talk about the classic customer journey and how it relates to the machine journey.
On the human side, you have to think customers are always asking questions.
This is done through search queries. It could be a prompt within an LLM.
You have to start thinking about your content and your website and all of your marketing across all platforms.
How are you answering their questions?
How can you uniquely solve their problems?
How Does Google AI Actually Evaluate Content and Entities?
On the machine side, when somebody prompts or types in a query in Google, it’s running through topical clusters.
LLMs train off Google. Google is landing on your website. It’s following your interlinks.
It wants to look for topical depth and new ideas. It looks at your digital body language. What you post on social media, how people engage with you everywhere. It’s looking to see if you have real EEAT. It’s looking to see if you’re coherent overtime.
Not only is it measuring your new ideas, it’s measuring you as an entity.
Person, place, thing, idea.
This is just marketing jargon for when the machine has indexed you as something more than just a string of letters.
You carry weight. You might be an authority.
You’re known within their system and how they understand you.
How Should You Structure Content for AI Search and Answer Engines?
When a person asks an LLM or Google a question, generally in layman terms, definitely not the technical terms of your industry, you want to address those questions in layman terms.
In the very strange ways that they’re asking.
Then you want to think about your website being built like a database so the machine can index you, cite you, crawl your topical depth, crawl your social media, and then pull from the knowledge graph.
If you are the best answer, the most original, and the best fit for that customer, this is where Google takes all of that and what they would call the messy middle.
The machine is trying to validate you.
How Does Google Measure Trust and Authority in AI Search Results?
It’s doing an originality check.
It’s looking for proof of E-E-A-T.
That means:
- videos
- you using the product
- new ideas
- new concepts you’ve introduced
- or just what other people are not saying
You also have to think about being helpful.
Google only exists to help people.
So try doing comparisons, trade-offs, real evaluation.
You should be helping your customers even when it hurts you.
Because then the machine will trust you.
What Is Digital Body Language and How Does It Affect SEO?
Measuring trust is a very nebulous thing, but Google has been moving towards this forever.
With AI being almost human-like, they very much can measure your trust via coherence over time across your entire digital body language.
Think about how customers engage with you in reviews.
How you respond to people on your social media.
Are you coherent?
Are your ideas original, or are you parroting what other people are saying?
Do you see surface level, or do you see chains of causality?
Ultimately, Google is looking for real experts.
If information is a commodity, that means experience is now the most expensive letter in the acronym for E-E-A-T.
How Does AI Influence Buying Decisions Without Clicks?
Coming back to attribution and closing the sale, this is where we can look at synthesis versus the sale.
When you are original and you have actually guided the machine, oriented the machine, oriented the customer, this is when it synthesizes your answer and you influence them with your ideas.
This plays back to the concept of mindshare and machine share.
How Should You Approach Sales and Psychology in AI Search?
I’ve always thought about mindshare in my marketing.
How are we influencing the market?
Are we acting like an enterprise sales rep?
Giving people better questions, better answers, empowering them.
So by the time they reach the sales staff, they are educated to ask the best possible questions.
So that that sales team can give them a sale that wears well.
I’m a huge fan of Harry Freedman, Neil Rackham.
I love the frameworks of SPIN selling.
I love the idea of probing people and guiding them toward the best fit.
Why Is Attribution Harder in AI Search and Answer Engines?
The machine synthesizes your answer, and then this comes back to the offline sale.
When it comes to attribution, it’s messy.
They might see something in an AI panel, something in an LLM, something that they researched.
Then they might call you a week later.
This might be through the map pack.
They might walk into your store.
What Should You Do to Win in Generative Engine Optimization (GEO)?
In the era of AI, where information is a commodity, you need to start focusing on helping your customers.
Build your website to help.
Build it like a database to help the AI, but also think about the user side.
Build it in a way that they can navigate through their problems.
And you are the answer.
What Is the End Goal of GEO and AI Search Strategy?
The main focus here is you need to start thinking about the generative engine orientation journey map and how the machine relates back to the customer.
The end goal here is mindshare and machine share.
Are you influencing the conversation?
Ryan Goloversic
Author of the Character Profile and the Laws of GEO, the tactical survival guide for the post-SEO era.
Ryan is an authority architect for the AI-driven search era. After a decade spent pioneering the content systems that turned MACkite into a global retail powerhouse, he synthesized the Character Profile Framework, a technical model designed to bridge the gap between business intent and algorithmic recommendation.
Drawing on the concept of expansionary business, Ryan’s work at Rygo Labs focuses on eliminating the “Trust Tax” of extractive marketing.
As a competitive athlete on the GKA World Tour, he brings the same physics-based discipline to digital ecosystems: if the alignment is off, the system collapses.
He doesn’t build for clicks. He builds for Predictive Continuity, ensuring that when Google and AI agents search for an authority to recommend, your entity is the only logical choice.
He’s also a globally recognized kiteboarder and Airush team rider. His marketing career began in video, where he built the largest retail-based SEO and YouTube program in watersports with MACkite. When he’s not leading GEO campaigns or consulting teams, you can find him in the gym or competing on the KPLxGKA world tour.