Tracking AI Visibility
Why is the topic of “tracking AI visibility” gaining momentum continuously? More people now ask ChatGPT, Gemini, or Perplexity a question instead of typing it into Google. Even classic searches increasingly start with an AI-generated summary. That means a brand’s visibility increasingly lives inside AI answers, not just in rankings and clicks. This shift is often called Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO). It can be measured without guesswork: run a representative set of prompts repeatedly, record the answers, and look at the pattern. This post covers why it’s worth tracking AI visibility now, how the method works, and where specialized AI visibility tools fit in. This is the first post in our AI Visibility series.
The shift in search behavior
Even people who still type a query into Google usually see an AI-generated answer today before the first blue link appears. Search engines have moved AI summaries to the top of the results page, and the effect on click behavior is measurable. According to an analysis by SparkToro based on Similarweb clickstream data, around 68 percent of Google searches in the US ended without a click to any external website in early 2026.
This shift is real, but it doesn’t mean your website has stopped mattering. There are now two audiences visiting it instead of one. People still arrive through search, social media, and direct traffic. At the same time, large language models are scanning the site, summarizing it, and feeding that summary into, say, a ChatGPT reply or an AI Overview. Because this second audience never shows up in typical SEO data or traffic reports, it’s worth measuring deliberately. A meaningful share of a brand’s current visibility likely already runs through it.
That’s exactly why you should track your AI visibility and give it its own place next to your existing SEO tracking within your Marketing Measurement Strategy, not as a replacement for it.
Why a single prompt won’t tell you anything
The natural first move is to open ChatGPT or another tool, ask a question about your brand or category, and see what comes back. That’s a reasonable starting point, but the reason it only tells part of the story is structural, not a data gap.
When an LLM answers a question that benefits from current information, it typically pulls in live web results through Retrieval-Augmented Generation (RAG) instead of relying purely on what it learned during training. That live connection is also why answers shift: which sources come back depends on what’s freshly indexed and ranked at that moment, and how well a model can make sense of those sources depends heavily on how clearly structured the underlying data is. The same question asked twice can therefore produce two different answers, with different brands mentioned, different sources cited, and a different tone.
One practical detail: if you’re testing manually, always start a new chat session for each prompt. Follow-up questions inside the same conversation let the model reference its own earlier answers, which skews the result. If you’re testing through an API instead, set the temperature parameter to 0 to minimize the model’s generative variance (though live retrieval and model-side updates will still cause some fluctuation).
Recording one response and treating it as "how visible we are" is like asking one person their opinion and reporting it as market sentiment.
How to actually track AI visibility
The measurement principle is straightforward once you treat it as what it is: a sampling exercise, similar to brand health tracking, except here the model is the one being surveyed:
- Define representative prompts. Write a set of prompts that mirrors how your buyers actually ask questions, not just brand-name searches.
- Run each prompt repeatedly, in a fresh session every time. A single response isn’t a pattern, and continuing the same chat session lets earlier answers bias the next one. Submit the same prompt multiple times in new sessions until the results stabilize.
- Query more than one model. Depending on the market, ChatGPT, Gemini, Perplexity, Claude & Co. all carry a meaningful share of AI search sessions. Tracking AI visibility with only one model gives you a distorted picture.
- Aggregate before you conclude anything. Look at the average across responses: Share of Voice in AI answers (how often the brand appears at all), Brand Citation Rate (how often it’s cited as a source), the ranking within recommendations, and the sentiment surrounding the brand. Here’s a list of AI search KPIs.
- Repeat on a schedule. Visibility in AI answers shifts as models update and competitors publish new content, so a one-off check has a short shelf life.
You can run this process manually at first, with a spreadsheet and a handful of prompts. We recommend automating it once the prompt set and query volume grow.
Manual tracking or a dedicated tool?
If you want to track AI visibility, you can start manually right away, following the steps above. In some cases, that’s even a good way to get an initial feel for your visibility. But given the time and data it requires, a manual approach isn’t sustainable long term. Eventually you’ll need a more automated solution. One option is to automate the process with a set of scripts that query LLMs directly, store the answers, process them, and display the data in a custom-built report. This is a custom solution we’ve already built for some of our clients.
Given the growing demand in this space, a number of AI visibility tools have emerged over the past few years. These tools answer the AI visibility question with minimal time and data requirements on your end. You provide your brand name, industry, website, and ideally a list of relevant topics. The tool then drafts a set of prompts, sends them to several LLMs, and shows the resulting visibility, ranking, mentions, and sentiment in a dashboard, typically querying at least three different models. These suggested prompts are a starting point. The prompts that actually matter are the ones your buyers would realistically ask, and defining those well is a topic in itself, one we’ll return to later in this series.
All three approaches follow the same measurement principle, but they differ in time investment, cost, customizability, and transparency.
| Manual tracking | AI visibility tools | Custom-built solution | |
|---|---|---|---|
| Effort | High, especially at scale | Low once set up | Low once set up |
| Consistency | Depends on discipline and available time | Automated, repeatable schedule | Automated, repeatable schedule |
| Depth | Full control over prompt wording and context | Depends on the tool’s prompt logic | Full control, tailored to your brand and topics |
| Cost | Internal (time only) | Subscription cost | Project-based investment |
| Best suited for | A first, focused check on a handful of core topics | Ongoing monitoring across many prompts and competitors | Companies with specific requirements a standard tool doesn’t cover |
The takeaway
AI has become a genuine research and decision channel, sitting alongside search rather than replacing it outright. If you can track AI visibility and fold those numbers into your existing visibility and awareness measurement, you get the fuller picture and stay a step ahead as AI-driven search keeps growing. In the next post in this series, we’ll look at how AI visibility tools actually work under the hood, and how to choose the right one for your specific use case.
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