Learning how to track Google AI Mode traffic is the newest technical challenge for modern search marketers. On June 3, 2026, Google introduced Search Generative AI performance reports in Search Console, offering dedicated views of impressions within generative AI features like AI Overviews and AI Mode. At last, there’s official reporting showing how often these features involve your pages. However, the standard report does not include the queries behind the AI activity, and it is UI only. Neither the Search Analytics API nor the BigQuery bulk export exposes the generative AI data natively, meaning there is no official plug-and-play way to track Google AI Mode traffic at the query level.
Except, the queries turn up anyway. Fragments of real AI conversations, replies like “yes go on,” follow-ups, and whole pasted prompts started appearing as ordinary queries in the performance report. Anastasia Kourou posted about these strange strings on LinkedIn, and Google’s John Mueller replied, confirming this data had always been available in the performance report and pointing to Google’s documentation.
Below are the most practical ways to track Google AI Mode traffic by pulling these hidden AI queries into the open, plus a free tool powered by a custom-trained machine learning model that does it for you.
Why You Need to Track Google AI Mode Traffic
When you track Google AI Mode traffic, you gain invaluable insights into how users converse with search engines. Once the leak was public, SEOs started building ways to pull those queries out. The early wave, back in June 2025, was word count regex, surfacing long conversational queries by length alone. The approaches have gotten much more specific since.
5 Methods to Track Google AI Mode Traffic
Method 1: Glenn Gabe’s Full Inventory in Excel
The performance report UI caps any table at 1,000 rows, so on a big site, most of your query data stays hidden. Glenn Gabe’s answer to help track Google AI Mode traffic is to skip the UI entirely. He pulls the complete query set through the Search Analytics API using Analytics Edge in Excel, then has Claude organize the list and flag likely AI Mode queries. If you already live in Excel, this puts the full inventory inside the workflow you know.
Method 2: Jean-Christophe Chouinard’s Custom Regex
Jean-Christophe Chouinard went after the same problem from inside the report. He published a regex on LinkedIn that flags conversational strings directly in the performance report’s Query filter, using the Custom regex option. He built it by comparing prompts people commonly type into LLMs against strings rarely seen in GSC before AI Overviews.
Paste his long alternation regex into the filter, and you get an instant look, free, inside the report you already have open to successfully track Google AI Mode traffic. Separately, his January 12, 2026 case study showed AI Mode clicks are tracked in Search Console, but the queries behind them are almost exclusively anonymized.
Method 3: Amin Foroutan’s Advanced GSC Visualizer
Amin Foroutan’s Advanced GSC Visualizer is a free Chrome extension that bolts advanced charting, annotations, and an AI assistant onto your Search Console data, with one-click API access. While there is no specific AI filter in it, it drastically speeds up your workflow when you attempt to track Google AI Mode traffic.
Method 4: Use Specialized Python MCP Servers
My Search Console MCP has the conversation detector built in as a tool called genai_conversation_queries, and my BigQuery MCP runs the same detector against the bulk export, which is where the anonymized pool of queries lives. Both run locally, so your data moves only between you and Google. What they do well is automation and scale with query-level labels on every row. It requires some configuration, but it is a powerhouse way to track Google AI Mode traffic. Just as you must scrub your financial data before you sell a high-ticket dropshipping store, you must scrub your query data to get a real look at your AI traffic.
Using Machine Learning to Track Google AI Mode Traffic
Pattern lists and per-run sorting cannot reliably catch edge cases like rank tracker probes, agent harness prompts, or “my location is” probes. To accurately track Google AI Mode traffic at scale, you need a machine learning model.
The detector tool uses FacebookAI’s xlm-roberta-base model, pre-trained on 100 languages and fine-tuned on labeled Search Console conversation exhaust plus synthetic rows across 8 languages.
How the AI Classifier Works to Track Google AI Mode Traffic
- Step 1: Drop in Search Console or BigQuery query exports as a CSV, and several files will stack into one run.
- Step 2: The model will classify your queries into 1 of 7 buckets.
- Step 3: If you add a Search Console generative AI report, the tool will extract page data and map this against your queries. You will see an “AI page” column in the final export.
This tool allows you to easily track Google AI Mode traffic without advanced coding knowledge. Everything goes to the model, gets classified in memory, and none of this is stored. Each query comes back with a bucket and a confidence score.
Limitations When You Track Google AI Mode Traffic
The tool comfortably handles up to 100,000 unique queries per run in the browser. However, the main limitation applies to every method: Google anonymizes rare queries. Conversation strings are rare, and a massive part of the pool never shows, so always treat the output results as an undercount when you try to track Google AI Mode traffic.
If you want to properly optimize your site and accurately track Google AI Mode traffic, use these methods to lift the veil on exactly how AI search users are finding your content.
