GEO Playbook: How to Optimize & Measure AI Visibility

AI search already feels like a major shift. For more than two decades, search marketing was built around a relatively stable idea: people search, results appear, and brands compete to be found. AI is starting to change that journey.

Consumers can ask ChatGPT to compare products, use Gemini to research a purchase, or get an AI-generated answer without working through a page of search results. And this behavior is already showing up in the data. Adobe found that 38% of U.S. consumers had used generative AI for online shopping, with product research and recommendations among the most common use cases. During the 2025 holiday season, traffic from generative AI tools to U.S. retail sites grew 693% year over year.For brands and agencies, the implication is hard to ignore: being discoverable increasingly means being visible not only in search results, but also in AI-generated answers.

With a shift this significant, it might seem like the industry should already know how to respond. But the reality is: AI search is already here. The playbook for winning in it isn’t.

GEO, AEO, LLMO, AI SEO, AIO, and AI Search Optimization are still used to describe overlapping practices. Measurement frameworks vary across platforms. Ownership can sit anywhere from SEO and content to PR and brand. And the industry is still learning which optimization practices consistently translate into greater AI visibility. That gap explains a lot about where Generative Engine Optimization (GEO) is today.

How Mature Is GEO Today?

ChatGPT was released publicly in late 2022. In November 2023, researchers introduced Generative Engine Optimization (GEO) as a formal framework for improving visibility within generative engine responses. In other words, businesses are trying to build strategies around a discipline that is only a few years old.

But GEO hasn't had the luxury of developing slowly. AI search is already changing how people discover and evaluate information, pushing brands to respond while the discipline itself is still taking shape. Interest and investment are growing, specialized tools and services are emerging, and more teams are beginning to experiment with AI visibility.

That's what makes the current stage of GEO interesting: adoption and category maturity are moving at different speeds.

As the market has developed, the questions businesses ask have evolved with it. Each reflects a different stage of maturity: first understanding the new visibility problem, then finding a way to measure it, and now figuring out how to turn that measurement into action.

You can see that progression in three questions:

“What is GEO?” → “Do we show up?” → “What do we do about it?”

First: “What is GEO?”

The earliest challenge was understanding what had changed. For years, search visibility revolved around a familiar question: Where do we rank?

AI search introduced a different kind of visibility. When someone asks ChatGPT to recommend a product or Gemini to compare several options, the AI can mention certain brands, recommend some over others, draw from different sources, or leave a brand out altogether. So brands had a new question to think about: Are we part of the answer? That was the first shift GEO had to make sense of.

Then: “Do we show up?”

Once brands understood that AI-generated answers had become another place where discovery happens, understanding the concept wasn't enough. They wanted to know where they stood. Does ChatGPT mention us? How often? Which competitors appear instead? What does Gemini say about our brand? Which sources are being cited?

That demand led to a new layer of measurement. What started as manually testing prompts could now be tracked more systematically across AI engines, topics, competitors, mentions, and citations.

In our recent research across 240+ brands and more than 37,000 AI responses, we explored this measurement problem in more detail and identified six metrics that can help build a more complete picture of AI search performance.

Read: [AI Search Performance Metrics: What Actually Measures Success]

With a clearer way to measure AI visibility, brands can move beyond simply asking “Do we show up?” They can start asking a more useful set of questions: Where are we strong? Where are we falling behind? And what might be shaping that difference?

Now: “What do we do about it?”

Once AI visibility becomes measurable, a new problem appears: knowing where you stand is not the same as knowing what to do next.

A brand may know that it appears less often than competitors, that certain topics perform poorly, or that AI engines rely heavily on third-party sources when talking about its category. But those numbers don't explain the response on their own. Teams now have to interpret what is behind them.

Is the gap related to search discoverability? Is important information missing from the website? Are competitors better represented across authoritative third-party sources? Is there a content gap around the questions customers are actually asking? And once a possible cause is identified, another question follows: who should act on it?

The answer may involve SEO, content, PR, brand, or web teams, sometimes several at once. This is what makes GEO different from simply adding another performance metric to a dashboard. The data has to translate into decisions across functions.

This is also where measurement starts becoming more valuable. Consistent GEO data gives organizations a baseline from which they can build benchmarks, define KPIs, assign responsibilities, and eventually evaluate whether their actions are actually improving AI visibility.

So the question is no longer simply: “Are we visible?” It becomes: “What is influencing our visibility, what can we change, who should act, and did it work?”

That shift marks an important stage in GEO's development: from making AI visibility observable to making it operational.

What Comes Next for GEO

Businesses aren't waiting for GEO to be fully figured out. That creates an important tension for the category: Investment is growing before the operating model is fully established.

For GEO to mature, the market will need more than greater awareness or more experimentation. Organizations need the structure that allows an emerging practice to become something they can manage consistently. That means more consistent measurement. More useful benchmarks. Clearer KPIs. Better-defined responsibilities. Repeatable workflows. And, eventually, enough accumulated learning to understand which actions actually improve performance.

These pieces build on one another. Better measurement makes benchmarking possible. Benchmarks give teams a reference point for setting goals. Goals create clearer accountability. And clearer accountability makes it easier to build workflows around who needs to act, when, and why.

This is how GEO can move from experimentation toward broader adoption. And one of the major enablers of that transition will be tooling.

The Role of Tools in GEO Adoption

The role of GEO tools can be easy to underestimate. At first glance, they solve a relatively simple problem: instead of manually asking ChatGPT a set of questions, a platform can monitor AI visibility more systematically. But the bigger contribution isn't simply automation. It's consistency.

A marketer can manually ask ChatGPT 20 questions and learn something useful. An agency can run a prompt test for a client and identify interesting gaps. But it's difficult to build a quarterly benchmark, assign a KPI, or evaluate progress from a collection of one-off checks.

For that, teams need to measure the same things consistently across relevant prompts, topics, competitors, AI engines, and time. That's where tools become important to GEO's development. By turning individual observations into consistent data, tools give organizations a baseline. And once a baseline exists, those numbers can start gaining business context.

The progression is straightforward:

Data creates a baseline. A baseline makes comparison possible. Comparison helps establish benchmarks. Benchmarks can become KPIs. KPIs give teams something to own and improve.

And once teams have something measurable to work against, they can begin building workflows around it. This becomes especially important because GEO is cross-functional.

An SEO team may need to investigate discoverability or technical issues. Content teams may respond to information gaps. PR may need to understand which third-party sources are shaping AI answers. Brand teams may care about how consistently the company is represented. The actions are different, but consistent GEO measurement gives those teams a shared view of the outcome they are trying to influence.

That is why the role of tools goes beyond putting AI visibility on a dashboard. They can help create the measurement layer around which organizations begin establishing benchmarks, KPIs, ownership, actions, and feedback loops.

Over time, that can change how GEO is adopted. For agencies, GEO can become a more repeatable client service rather than a one-off analysis. For brands, AI visibility can become part of regular search, content, PR, and digital planning rather than an isolated experiment. And as more organizations measure and learn from GEO consistently, the market itself can begin developing clearer benchmarks and a better understanding of what effective GEO looks like.

No GEO platform can guarantee what ChatGPT, Gemini, or another AI engine will say. But tools don't need to control the answer to contribute to GEO adoption.

Their role is to give organizations a consistent way to measure what is happening, decide where to act, and learn whether those actions made a difference.

That is how an emerging practice starts becoming an operational capability. GEO began with a simple question: Are we showing up in AI answers? The next stage is much more practical: How do we improve, who needs to act, and how do we know if it's working?

The first stage of GEO was understanding the shift. The next was learning how to measure it. Now the challenge is turning those measurements into benchmarks, decisions, and repeatable workflows.

AI search is already here. Now the industry is building the playbook for how to manage visibility within it.

Frequently Asked Questions (FAQ)

What is GEO?

Generative Engine Optimization (GEO) is the practice of improving how brands and content appear in AI-generated answers across platforms like ChatGPT, Gemini, and Perplexity.

How is GEO different from SEO?

SEO focuses on visibility in traditional search results, while GEO focuses on how brands are mentioned, cited, and represented in AI-generated answers.

How can brands measure GEO performance?

Brands can track metrics such as AI mentions, citations, share of voice, rankings, and competitor visibility across AI engines over time.