The realities of tracking brand visibility in AI search in 2026

With AI search adoption growing in 2025 and 2026, many organisations have watched their traditional organic search traffic decline as ‘zero-click searches’ offer users answers to their queries directly in the search experience.

For boards and the C-suite, the reality that AI-powered search is intercepting website traffic has created a significant challenge: how do we justify digital performance when users no longer need to click through to a website to get all of their answers? This shift has left many marketers struggling to explain why traditional metrics are falling, even when brand authority is high.

In response, new paid AI analytics tools have emerged, offering to report how present a brand is in AI-generated answers, as well as suggest SEO and generative engine optimisation (GEO) tactics to improve prominence - but reporting AI visibility isn’t as straightforward as it may sound.

This article discusses considerations for, and challenges of, measuring AI search/GEO performance that businesses should be aware of as of April 2026.

Note: For the purposes of this article, mentions of ‘generative engine optimisation’ or ‘GEO’ can be interchanged with ‘answer engine optimisation’ or ‘AEO’.

Key takeaways

Success in AI search is measured by visibility and sentiment of your brand in AI summaries rather than traditional rankings.
Research shows AI provides different answers for the same prompts over time, meaning marketers should measure performance using aggregate data instead, not a once-off snapshot.
Since AI platforms don’t share real prompt data (as at April 2026), organisations must build measurement baselines using synthetic prompts that mirror the natural language customers may use along their purchase journey.

New metrics for a zero-click search world

To measure presence in AI search, we need new metrics that track whether your brand is mentioned, and whether your website or other owned channels are cited (i.e. linked/referenced as a source).

GEO metrics you can track using paid tools

Visibility rate (also known as presence rate) - The percentage of AI-generated answers that mention a brand or product
Citation rate (also known as owned citations) - The percentage of AI responses mentioning your brand that include a clickable link from your website as a source/reference
Share of voice - A brand’s mentions in AI search compared to competitors, often considering which brands appear first in results
Sentiment - When a brand is mentioned in an AI-generated answer, is it written about positively, negatively or with neutral sentiment?

So how can we measure these?

Top GEO and AEO analytics tools in 2026

To measure AI search performance, a handful of new paid GEO/AEO tools have entered the market, such as Profound, Scrunch and AthenaHQ. Meanwhile, traditional SEO tools like Ahrefs and SEMrush are improving their own trackers. All are paid tools.

For information on selecting a tool, Search Engine Land published a guide on How to choose the best AI visibility tool in April 2025.

A table comparing AI search visibility tools, created by Search Engine Land in April 2025.

Source: Search Engine Land

How do AI search analytics tools work?

Nearly all GEO analytics tools on the market function similarly. They search your target prompt in your selected LLMs at recurring intervals, record the LLM’s response to those queries in their system for ongoing trend reporting, and then analyse and report on your GEO metrics vs competitors over time.

This is a form of probabilistic reporting. It suggests that if a human were to search these prompts, they would see a similar result.

Is there any free AI search data available?

In February 2026, Google Search Console’s counterpart, Bing Webmaster Tools, added an ‘AI Performance’ section to its dashboard, showing the total citations a website received in Copilot. Data is fairly basic and does not include any competitor information.

Google has been receiving mounting pressure to add similar information for AI Mode and AI Overviews to Search Console. As of April 2026, Search Console’s ‘Search Appearance’ report, which shows top-performing queries, pages, countries, etc. is powered by aggregate data from traditional search, AI Mode and AI Overviews - but users aren't given the ability to filter between these channels.

Why can’t we just use existing SEO metrics?

We can’t rely on click data as our primary metric in AI search because many people don’t click through to a website. Instead, they read the result AI has given them, and either leave or continue searching. If we don’t track visibility before a click occurs, then we’re missing out on a key performance marker.

GEO metrics don’t replace SEO metrics

These new metrics complement those we’ve used traditionally to measure SEO success, like keyword rankings and click data, to tell a more complete story about your search visibility.

Building your baseline with synthetic prompts

In traditional SEO reporting, we track how we rank in organic search for a set of target keywords we selected based on search data and keyword research.

AI search visibility tracking functions similarly, based on a set of target prompts (phrases, questions or sentences we want to show up for) - only we don’t have access to prompt datasets from which to identify targets. This makes defining our baseline prompts challenging.

Why can’t we access prompt data?

Defining a measurement baseline is difficult because the data gap in AI search is twofold. First, major platforms like OpenAI, Anthropic and Google have not yet released public datasets showing what people are searching for day to day. There is no confirmation of when – or if – they will ever provide this data.

Second, even if this data was released, AI search is becoming increasingly hyper-personalised. Users are moving away from simple 2-5 word keywords and are instead providing deep context - including their age, location, specific pain points, and even past conversation history - to get a better result. This means that almost every prompt is now a unique, one-of-a-kind event, making it impossible to identify a single ‘winning keyword’ to track in the traditional sense.

Design infographic: Keyword (Short, Basic) vs Prompt (Longer, Context-rich)
- get design support

“Health insurance cover Melbourne” vs “I’m 32, married and live in Melbourne. We need private health insurance that covers dental and physio, but I don’t want podiatry or optometry. What are the best-rated options for couples that have good flexibility?”

Alt text: An infographic comparing SEO keywords vs AI search prompts for the Australian health insurance market. The left shows a traditional search keyword, Health insurance cover Melbourne. The right shows a conversational AI prompt from a 32 year old looking for dental and physio cover for a couple, illustrating the shift from keywords to contextual intent.

How might we generate target prompts without real search data?

Until AI platforms release real prompt data, we must make up target prompts. These are known as synthetic prompts.

Measuring your AI visibility against synthetic prompts may sound abstract, but there are ways to ensure the prompts you choose are relevant to your business and robust enough to track across the customer journey.

Below are five options for creating an initial list of GEO target prompts. These approaches give you a head start when setting up prompt tracking. They allow you to review and edit prompts – a faster and easier process than manually creating prompts from scratch.

1. Built-in tool suggestions

Many AI analytics tools will suggest prompts for you based on the information you provide about your business:

Brand name (and alternative names)
Owned URLs
Competitors
Personas
Key topics/categories and other information.

The more these tools know about your brand, products/services and customers, the better they can make recommendations, so be sure to provide detailed context.

2. LLM-generated journey prompts

(Securely) feed your customer data and competitors into your preferred LLM and ask it to generate prompts that span across awareness, consideration, evaluation/decision, etc. Your brief to the LLM should include generating prompts that cover:

Problem-based prompts
Negative-constraint prompts (people looking for a solution that does not have a specific downside)
Price-based prompts (if applicable)
Comparison prompts (product/service vs product/service, feature vs feature and brand vs brand, alternatives to {product})
Integration prompts (if applicable, e.g. which CRMs play nicely with Slack without a custom API?)

3. Mining industry forums

Don’t guess what people are asking; look where they are actually confused. Platforms like Reddit, Quora and niche industry forms are goldmines for generating natural language prompts.

Try this workflow:

Find a list of 10-20 active forum threads in your niche
Paste the URLs into an LLM and ask it to summarise the common pain points and questions people mention in the comments
Ask the LLM to then generate follow-up or fan-out queries* based on your findings, which you can consider for prompts.

*When people send a prompt to an AI search, LLMs take that prompt and divide it into multiple sub-searches, known as fan-out queries, to generate a more complete answer. Tracking your visibility against these can help you determine if your content is well-rounded enough.

4. Mining Google Search Console for long-tail searches

While Google Search Console (GSC) doesn’t yet offer a dedicated AI prompt report, your existing search data is a great resource for identifying prompts. GSC’s Search Performance report captures queries that trigger AI Overviews and AI Mode (as well as traditional organic search results), providing a direct window into how your users are already seeing and engaging with your brand.

Luminary’s Analytics Specialist, Sarah Crooke, suggests using GSC’s data export feature to export the raw data into BigQuery and to get around GSC’s 1000-row export limit. By filtering for queries made up of nine or more words, you can isolate the specific, natural-language questions your audience is actively using.

5. Converting SEO keywords into prompts

Many GEO/AEO tools also allow you to import your existing SEO keyword list. Once uploaded, their in-built AI will suggest a variation of each keyword in the style of a prompt (though, in my experience, the suggested prompts are thin and lacklustre).

Workflow for obtaining a baseline visibility rate

Below is a process flow for setting up your AI search analytics tool to define a baseline visibility rate and trend over time.

Design needed - Add a visual that shows a four-step linear flow:

Input brand data, personas and competitors into tool
Generate synthetic prompts
Tool tracks brand’s visibility for target prompts across dozens of runs
Brand obtains a probabilistic visibility rate and citation rate

Alt text: A four-step process for setting up a GEO/AEO analytics tool and measuring AI search visibility. The workflow includes importing brand and competitor data, generating synthetic prompts, tracking those prompts across dozens of AI runs, and calculating the final probabilistic visibility and citation rate.

Overcoming volatility and bias in reporting

AI search results are heavily personalised

Not only are people’s prompts highly personalised, but so are the results/summaries AI generates for them. LLMs tailor results based on users’ search history, any information they’ve fed into the tool about themselves, and any search preferences they’ve added, such as requesting more brevity.

AI search results are highly volatile

Volatility in search results refers to how often results change. A January 2026 report by SparkToro showed AI is significantly more unpredictable than traditional search. The study found that there is less than a 1% chance that ChatGPT or Google’s AI will provide the exact same list of brand recommendations twice when asked the same prompt 100 times. Even more striking, the odds of brands appearing in the same order were as low as 1 in 1000.

A screenshot from a January 2026 SparkToro report showing the very low odds that ChatGPT, Claude and Google AI will show the same list of brands in two or more AI-generated responses.

Source: sparktoro.com

This suggests that conducting a one-off ‘snapshot’ of AI search performance is almost meaningless. For reporting to be trustworthy, tools must run the same prompt dozens of times to calculate a stable visibility rate.

Sentiment analysis can be skewed by jargon and sarcasm

Language is nuanced, and AI might flag a response ‘neutral’ when it actually contains a subtle, negative comparison.

Don’t take the automated sentiment scores at face value. Periodically spot-check the raw responses to ensure AI’s interpretation aligns with your brand’s reality.

LLMs often have bias toward US content

Because AI models are trained heavily on US information, they often default to US-based recommendations for general prompts, e.g. “Top reviewed health insurance”. Marketers in Australia should consider including localised prompts, e.g. “What are top reviewed health insurance companies in Australia?”

In summary

Measuring GEO performance in 2026 requires a mindset shift: we are no longer tracking ‘positions’; we are tracking probability and associations.

Because AI generates a fresh response every time – shaped by a user’s unique history – you’ll rarely see the same answer twice. This means your GEO measurement strategy can't rely on a single snapshot; it must be built on aggregate data collected over time.

By using a mix of paid analytics tools, synthetic prompts mapped to the customer journey, and tracking visibility rate trends over time, organisations can better understand their true digital footprint in AI search.

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The realities of tracking brand visibility in AI search in 2026

Key takeaways

New metrics for a zero-click search world

GEO metrics you can track using paid tools