Google Search Console has never been better at collecting data. It just hasn’t gotten much better at helping us interpret it.

Open almost any property, and you’ll find thousands of queries, landing pages, and performance metrics. That’s great until you’re trying to answer a deceptively simple question: What should I do with this?

For years, the answer has been to export the data into Excel or Google Sheets, build a few pivot tables, apply some filters, and start digging for patterns. It’s effective, but it’s also slow. More often than not, you’re hunting for insights you don’t even know exist.

That’s where AI fits into the workflow. It can accelerate the part that takes the longest: finding meaningful patterns hidden across thousands of rows of search data.

Think of Google Search Console as the source of truth and AI — whether you prefer ChatGPT or Claude — as the analyst sitting beside you. GSC tells you what happened. Your AI tool of choice can help you figure out why it happened, uncover opportunities you might overlook, and organize messy data into something you can actually act on.

A quick note on regex

Every example below starts in the same place in Google Search Console: Performance → Queries → + Add Filter → Query → Custom (regex).

From there, you’ll enter a regular expression to filter your query data.

The good news is you don’t have to memorize regex syntax anymore. Instead, let ChatGPT write it for you. You can prompt:

Create a regex for Google Search Console that matches queries beginning with question words.

ChatGPT will return something similar to (?i)^(who|what|why|how|can|does|will|should)\b

Need something more specific? Just describe the pattern you’re looking for.

For example:

• Create a regex for Google Search Console that matches queries containing five or more words.
• Create a regex for Google Search Console that identifies comparison searches.
• Create a regex for Google Search Console that finds branded queries containing product names.

The better you describe the pattern, the better the regex.

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Here are seven ways to combine GSC with AI to spend less time sifting through data and more time making decisions.

1. Stop looking at queries and start looking at intent

Most GSC analysis still happens at the keyword level. The problem? Users don’t search by keyword. They search with intent.

Instead of reviewing thousands of individual queries, use regex to isolate investigation-focused queries before exporting.

Use the regex: (?i)^(best|top|vs|review|reviews|compare|comparison)

Next, export your query data and ask Claude or ChatGPT to classify search intent with the prompt:

• “Categorize these queries into informational, navigational, investigation, transactional, and local intent. Return a CSV with classifications and confidence scores.”

Maybe informational traffic is growing while commercial investigation queries are declining. Maybe transactional queries have strong rankings but weak click-through rates. Maybe a group of comparison-related queries is driving impressions but lacks dedicated content.

Those insights are difficult to spot one keyword at a time. Intent segmentation makes them obvious.

2. Discover questions your audience is already asking

Question-based keyword research isn’t new. What is new is how quickly AI can help identify themes across hundreds of question-oriented searches.

Use the regex: (?i)^(who|what|where|when|why|how|can|does|should|will)\b

Export the results. Then ask Claude or ChatGPT:

• “Group these questions into common themes and identify unanswered topics.”

Instead of reviewing hundreds of individual questions, you’ll start seeing broader patterns, from pricing concerns to product comparisons, implementation challenges, and industry-specific use cases.

This quickly becomes more than a content exercise. These themes can influence FAQ development, support resources, sales enablement, and AI Overview optimization.

The best opportunities often aren’t hidden in individual queries. They’re hidden in clusters of related questions.

3. Find queries most likely to trigger AI Overviews

While Google doesn’t provide a filter for “queries likely to trigger AI Overviews,” you can create your own approximation.

Start by isolating common informational and comparison patterns with regex: (?i)^(what is|how to|best|vs|difference between|guide to)

Export the matching queries and ask Claude or ChatGPT:

• “Review these queries and group them by the content format needed to answer them effectively.”

The resulting themes often include definitions, tutorials, comparisons, or expert recommendations.

With this process, you’re identifying where your content may need to shift from ranking for keywords to becoming the best source for answering questions. Increasingly, those aren’t always the same thing.

4. Track emerging trends

Traditional keyword research tends to be reactive. By the time a trend becomes obvious in your keyword tools, your competitors are already targeting it.

Google Search Console is a great resource for identifying those shifts early. You just have to know how to find them.

Instead of searching for individual keywords, use ChatGPT to build regex around broader concepts.

You’ll need to craft a prompt specific to changes in your industry. For example:

• “Create a Google Search Console regex to identify searches related to AI agents, copilots, assistants, automation, and autonomous workflows.”

The result: (?i)(ai agent|agentic|copilot|assistant|automation)

This same approach works for new technologies, product categories, competitors, industry buzzwords, or changing customer concerns.

Once you’ve filtered and exported the data, let your AI analyst do the heavy lifting.

Try a prompt like:

• “Review these queries and identify emerging themes, new terminology, and shifts in search behavior. Highlight which topics appear to be gaining traction, recommend whether they deserve a new content asset or an update to an existing page, and identify any patterns that could influence our content strategy.”

Rather than simply confirming that a trend exists, AI can help determine whether it’s meaningful enough to act on and what your next move should be.

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5. Surface conversion intent hiding in informational traffic

One of the most overlooked opportunities in Search Console is identifying bottom-of-funnel signals in queries that appear informational at first glance.

Ask ChatGPT:

• “Create a regex for searches that indicate evaluation, comparison, pricing, alternatives, migration, implementation, or vendor selection intent.”

Example output: (?i)(cost|pricing|price|vs|alternative|compare|implementation|migration)

Apply that regex to your query report and export the filtered data.

Then ask Claude or ChatGPT to analyze the results:

• “Review these Google Search Console queries and identify recurring buying signals. Group them into themes (e.g., pricing, comparisons, implementation, vendor evaluation), recommend which existing pages should better address this intent, and identify opportunities to improve content through stronger CTAs, internal links, comparison tables, FAQs, or supporting resources.”

You may discover that pages built for top-of-funnel education are already attracting visitors who are evaluating solutions. Instead of creating new content, the better opportunity may be to refine what already exists, making it easier for users to take the next step without disrupting the informational experience.

Sometimes the biggest content opportunity isn’t publishing another page. It’s recognizing the conversion intent that’s already finding its way to the ones you have.

6. Find audience-specific opportunities

One of my favorite ways to uncover new content opportunities is by filtering queries for specific industries, audiences, or customer segments. It’s a quick way to see whether your content is resonating with the audiences you intended to reach or revealing opportunities you hadn’t considered.

Start by asking ChatGPT to create a regex based on the audience segments that matter most to your business.

Example prompt:

• “Create a Google Search Console regex that identifies queries related to healthcare, manufacturing, retail, education, financial services, government, and nonprofit organizations.”

Example output: (?i)(healthcare|hospital|medical|manufacturing|factory|retail|education|school|financial|bank|government|public sector|nonprofit)

Apply that filter in Google Search Console and export the results.

Then ask Claude or ChatGPT:

• “Analyze these queries and group them by audience segment. Identify which industries show the strongest search demand, what recurring questions or pain points each audience has, and recommend opportunities for new content, landing pages, case studies, or internal linking that would better serve those audiences.”

Maybe healthcare-related searches consistently focus on compliance, while manufacturing queries revolve around implementation. Maybe retailers are searching for entirely different use cases than financial services organizations.

7. Uncover ‘striking distance’ opportunities at scale

Every SEO knows the classic recommendation: “Look at keywords ranking in positions 5-15 to identify opportunities within striking distance.”

The challenge, again, is doing this at scale. A report with hundreds of queries where your site is within striking distance of a top-ranking position can quickly become overwhelming.

Take any of the regex patterns above a step further. Use the same filters based on your needs and goals, then filter your data to positions 5-15 before exporting the queries.

Then ask your AI analyst:

• “Identify recurring themes across these queries and recommend page-level optimizations rather than keyword-level optimizations.”

Rather than recommending tweaks to individual keywords, AI often surfaces larger opportunities. You may identify missing subtopics or incomplete comparison content. Maybe you have relevant content, but weak internal linking or missing use cases.

The result is often fewer optimizations, but significantly more impactful ones.

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Turn Google Search Console data into decisions

As SEOs, we don’t have a problem with a lack of data. We have a prioritization problem.

Google Search Console has always been one of the richest sources of insight into how people discover your business. The challenge has long been turning thousands of rows into something actionable.

That’s where AI fits into the workflow. It helps uncover patterns, organize information, and surface opportunities you might have otherwise missed. It’s not an SEO strategist or a replacement for experience and critical thinking.

The real advantage isn’t writing better regex or exporting cleaner spreadsheets. It’s spending less time searching for insights and more time acting on them.

Because data doesn’t improve SEO. Better decisions do.