2025 marked a turning point for organic search. The rise of Google’s AI Overviews has led to a noticeable drop in traditional organic clicks. It didn’t just change how content is sourced in search but how search engines decide what content to pull or cite.
Google’s AI Overviews now appear on 21% of keywords according to recent Ahrefs data, with informational searches driving nearly all of them. And when they appear, traditional organic clicks don’t just decline, they often disappear.
Not because your rankings dropped, but because users are getting their answers directly from AI-generated summaries without ever visiting your site.
Visibility is no longer about ranking; it’s about making sure you get cited in AI Overviews.
Yet most content teams have no systematic way to identify what AI Overviews are surfacing versus what their content actually covers.
In a recent post, Chris Long revealed how he used ChatGPT to analyze this gap, prompting an agent to search Google, pull the AI Overview, review his blog post, then identify what was missing.
We decided to pressure-test this content gap analysis approach and build something more scalable to help optimize for AI Overviews.
Table of contents
Why manual AI Overview gap analysis doesn’t scale
Chris Long recently demonstrated a clever workaround:
- Prompt ChatGPT to search Google
- Pull the AI Overview
- Compare it to an existing blog post
- Identify what’s missing
The premise is simple and powerful.
We decided to pressure-test the approach and build something more scalable by prompting ChatGPT to:
- Search for “what is A/B testing;”
- Analyze the AI Overview;
- Review CXL’s A/B testing guide.
We then asked it to identify content gaps using this prompt:
| 1. Go to https://google.com and search for “what is A/B testing.” 2. Analyze the complete Google AI Overview shown for this query. 3. Read the blog post at https://cxl.com/blog/ab-testing-guide/ 4. Identify content gaps between the AI Overview and the blog. 5. Find content gaps between information included in the AI Overview that’s not present on our blog. |
Three minutes later, we had a solid analysis showing exactly what information appeared in Google’s AI Overview but was missing from our post.
The output was useful, but the process wasn’t.
Here’s the problem: You can’t run this at scale. Every query means re-prompting ChatGPT or queuing up a massive batch that takes forever to process. If you’ve got 50 high-priority keywords to analyze, you’re either spending hours re-prompting or waiting on one enormous request that might fail halfway through.
You don’t build a sustainable SEO system by manually running ChatGPT prompts for every blog post.
What’s needed isn’t more prompts. It’s a systematic approach that activates automatically when AI Overviews appear, analyzes gaps in real-time, and delivers actionable insights that answer one question:
What information did AI need to complete this answer, and where did it have to source it from competitors?
That’s a fundamentally different problem than rankings, validation, or keyword optimization.
It’s about information loss.
Building an AI-powered gap analysis system
One insight carried over from our analysis of ChatGPT’s hidden search queries: AI tooling is now fast enough to build systems, not just run experiments.
So we built a Chrome extension that performs compression audits automatically. No coding experience required, just ChatGPT and about 20 minutes.
Here’s how it works:
- The extension detects when a Google AI Overview appears on a search results page.
- When triggered, it prompts you to add your blog URL.
- The system:
- Extracts the AI Overview content
- Analyzes your article
- Identifies information AI had to source elsewhere
- Flags structural and conceptual gaps
This isn’t about replacing your content strategy. It’s about creating a feedback loop that tells you exactly where your content falls short of what AI is surfacing before you lose traffic to competitors who figured it out first.
The technical implementation is straightforward because ChatGPT can now write functional Chrome extensions from a single prompt. We’re past the era of needing developers for every automation project. If you can describe what you want clearly, you can build it.
The 20-Minute Build: ChatGPT → Chrome → API
Here’s the complete process for building your own AI Overview gap analysis extension.
Step 1: Prompt ChatGPT to generate the extension code.
Use this exact prompt:
| “Build a Chrome extension. The extension should activate whenever a Google AI Overview appears on a search results page. When triggered, it should prompt the user to add a blog article URL. After the URL is added, the extension should analyze the content shown in the AI Overview and compare it with the blog article. It should then perform a gap analysis, highlighting what information appears in the AI Overview but is missing from the blog, and suggest what to add to improve the chances of being referenced. The extension should be powered by the ChatGPT API on the backend, have a clean and intuitive UI, and be easy to use. At the end, provide a downloadable ZIP file that can be uploaded directly as a Chrome extension.” |
ChatGPT will generate the complete code and package it as a downloadable ZIP file. No manual coding required.
Step 2: Install the extension in Chrome.
Navigate to chrome://extensions and enable “Developer mode” in the top-right corner. Click “Load unpacked” and upload the unzipped extension folder.
If you encounter errors, copy the error message back into ChatGPT and ask it to fix the issue. Download the updated folder and repeat the process. We hit two bugs during our build—both resolved by pasting error messages back into ChatGPT.
Step 3: Configure your OpenAI API key.
Go to platform.openai.com/api-keys and create a secret key specifically for this extension. Confirm that billing is configured so API calls don’t fail mid-analysis.
Open the extension from your Chrome toolbar and paste in your API key and you’re done with setup.
Step 4: Test it in the wild.
Search for any keyword where you have existing content. When the AI Overview appears, your extension activates automatically. Add your blog URL and wait 30 seconds for the gap analysis.
We tested it with “What is A/B testing?” against CXL’s A/B testing guide. The extension pulled the AI Overview, analyzed our content, and delivered specific gaps in under 30 seconds.
What the gap analysis actually reveals
For our A/B testing guide, the extension identified:
- Statistical concepts mentioned in the AI Overview but missing from our post;
- Specific examples Google’s AI surfaced from competitor content;
- Structural elements that made other sources easier for AI to parse;
- Technical depth differences between what AI cited and what we covered.
This is actionable intelligence, and the competitive advantage here is speed.
Most content teams won’t systematically analyze AI Overviews until it’s standard practice (probably 12-18 months from now). By then, the teams that built systems like this will have already closed hundreds of content gaps and captured the AI citation advantage.
Failure modes and limitations
This approach isn’t perfect. Here’s what needs improvement:
The extension is experimental. You’ll hit bugs. We encountered issues with certain page structures where the AI Overview didn’t parse correctly. Some blog URLs returned errors because of authentication walls or unusual site architectures. These are solvable problems, but they require iteration.
API costs add up. Every analysis triggers an OpenAI API call. If you’re running this across hundreds of keywords weekly, budget for API expenses. It’s not expensive compared to lost traffic, but it’s not free either.
The analysis is only as good as your prompt engineering. The default prompt works well for straightforward content comparisons. But if you’re analyzing technical content, B2B comparisons, or niche topics, you’ll need to refine the prompt to extract meaningful insights.
Context windows have limits. Very long blog posts might exceed the API’s context window, forcing you to analyze sections separately rather than the entire article at once.
None of these are dealbreakers. They’re just realities of building systems rather than running one-off analyses.
Citation or obscurity: Stress-test your content’s depth
AI Overviews aren’t a feature you can ignore until they affect your traffic. They’re already affecting your traffic.
The competitive edge goes to teams that systematically analyze what AI surfaces and close those gaps faster than everyone else. That means building systems into workflows rather than waiting for traffic to drop and then running manual audits.
Get started with your build now or download the experimental Chrome extension and test it yourself. But expect bugs, iterate on the prompts, and refine the analysis for your specific content type.
The goal isn’t to deploy a perfect get cited in AI Overviews optimization system on day one. It’s to create a feedback loop that tells you where your content falls short before your traffic numbers do.
Want to go deeper?
→ Read our complete guide to AI-powered content strategy
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