The no-BS checklist to win Google’s AI Overviews in 2025
Make your website AI-ready
Legacy SEO tactics won’t carry over.
Rankings without representation in the Overview box mean less surface area, less authority, and less buyer trust.
Most websites still treat AI-driven search like a curiosity. But visibility is already collapsing for content that doesn’t meet Google’s new interpretation of “helpful.”
The format has changed. The game has changed. And so must the workflow.
This month, I’m testing a rebuilt SEO approach with a client of mine— one built for how AI Overviews crawl, select, and display content.
Below is the checklist I’m using.
1. Content that feeds AI Overviews
Most AI Overviews don’t credit the best-written content. They credit the clearest.
That means structure, scannability, and directness now carry more weight than brand voice or longform polish.
The fastest way to get excluded is to bury the answer in paragraph four, or to publish content that reads like it was outsourced to a low-tier freelancer and bulked up for word count.
So, before trying to “optimize for AI,” most brands need to answer a more basic question: are we shipping anything worth extracting?
Start by flagging “commodity content”.
These are posts written to hit a keyword quota, not to solve a real problem.
You’ll recognize them by vague intros, walls of copy, padded conclusions, and sections that all sound the same.
AI Overviews skip these because they have nothing new to say and no clear way to convey it.
Audit for structure
Google’s AI crawlers prioritize clarity. And that starts with formatting.
Use H1 for the core problem or question, then clean H2s to break down the solution.
Stick to one question per section. Strip intros down to 3-4 lines max, and put your clearest answer within the first 100 words.
Treat each blog like a self-contained knowledge card.
TL;DRs aren’t optional anymore
Add summary boxes up top, especially on pages targeting common queries or categories with rich snippet competition.
Make the TL;DR skimmable. Use bolding, bullets, or an “Answer first, context later” layout.
Google looks for extractable chunks that can be used as is. Example: one paragraph, three bullet points, or a callout box styled like an executive summary.
Depth over volume
AI Overview visibility rewards depth, not length. Remove redundant fluff.
Expand weak sections that deserve better treatment.
Don’t be afraid to kill content that’s fundamentally thin. Better to consolidate three average posts into one standout than leave them to rot in the index. Thin pages will dilute the perceived authority of your entire domain.
✅ What good structure looks like:
Clear H1: “How to Improve Time-to-Hire Without More Recruiters”
H2s: “Common Bottlenecks,” “Process Improvements,” “Metrics to Track”
Intro: Two-line setup, then jump to the answer.
TL;DR Box: High up. Direct answer. CTA optional.
❌ What gets ignored:
8-paragraph intros about “why hiring is hard”
H2s like “Introduction,” “Conclusion,” or “Let’s dive in”
Posts written for the SERP, not the buyer
AI Overviews will increasingly behave like editorial filters.
Your job isn’t to impress an algorithm. It’s to make it easy for the algorithm to extract and trust your answer.
Structure, not volume, is what feeds the machine.
Also check out: Why most SEO playbooks are outdated (and how to build one that works in 2025)
2. Technical hygiene that powers visibility
No matter how strong your content is, AI Overviews won’t feature pages they can’t crawl, index, or trust structurally.
Visibility starts with infrastructure.
That means your site needs to be technically sound, optimized not just for Googlebot, but for Google’s real-time extraction layer.
This is a living layer of SEO that now determines whether your answer qualifies to be included in an Overview.
Check your crawlability, not just your index status
Start with the basics: robots.txt and sitemap.xml.
Are they blocking anything critical, like blog archives, product pages, or tag clusters?
Use Google’s URL Inspection Tool and run routine tests with Ahrefs Site Audit or Screaming Frog to make sure priority pages are being crawled regularly.
Flag any 4xx/5xx errors or redirect chains immediately.
A broken sitemap or an overzealous disallow rule can silently pull your top content out of the search ecosystem, and you won’t always notice it in rankings.
Mobile-first means performance-first
Core Web Vitals are not optional.
AI Overviews have a bias toward fast, clean, mobile-friendly pages. Focus on:
LCP (Largest Contentful Paint): Aim for under 2.5s
CLS (Cumulative Layout Shift): Below 0.1 for stability
INP (Interaction to Next Paint): A new, critical signal as of March 2024
Use PageSpeed Insights to evaluate page-level performance. If key templates are underperforming, loop in dev immediately. Don’t leave it to chance or a quarterly backlog review.
Schema markup signals credibility
Structured data gives AI Overviews context.
Use schema types like Article, FAQPage, and HowTo on blog content and guides.
Make sure your JSON-LD includes author, headline, dateModified, and publisher fields.
For comparison or feature pages, add Product or Review schema where relevant.
More importantly, validate your schema weekly using Google’s Rich Results Test. AI Overviews lean heavily on structured content for safe extraction.
This isn’t a dev-only job. It’s shared across SEO, content ops, and growth teams.
Who owns crawl health?
Who checks the schema across new templates?
Who loops in dev when Core Web Vitals drop below the threshold?
Map it. Document it. Treat it like you would any other growth ops function.
3. Visual content that earns the edge
AI Overviews are visually rich snippets that favor structured, multi-format content, especially when answers involve steps, comparisons, or conceptual explanations.
That means the days of publishing text-only blogs or dumping stock images at the top are over.
The best-performing AI snippets often pull from pages with visuals that explain the idea better than a paragraph could, such as annotated screenshots, step-by-step diagrams, and clean infographics.
Not filler banners or recycled Canva templates.
Invest in content that shows
Use tools like napkin.ai to turn your frameworks or step-by-step advice into custom visuals.
Replace wordy explanations with labeled flowcharts, decision trees, or “before vs. after” side-by-sides.
These assets increase the chance of being pulled into Google’s visual modules inside AI Overviews.
When showing product functionality or UI, skip the generic screen captures. Use real, clean screenshots with annotations that highlight the key action or insight. Treat every image like a standalone insight card.
Image hygiene isn’t optional
Google can’t understand what your image is unless you tell it. Every embedded image should follow these rules:
Descriptive, hyphenated file name (
resume-scoring-algorithm.png, notscreenshot123.png)Alt text that clearly describes what the image shows
Concise caption if it needs added context (especially for mobile readers)
Don’t upload 2MB JPEGs.
Compress for web. Use WebP when possible.
Fast-loading images keep you in the eligibility range for AI snippets that rely on quick rendering.
4. Conversion tracking post-AIO click
What happens after someone clicks on AIOs matters more than ever.
And most teams have no idea what that post-click journey looks like.
Because AIO clicks don’t always follow standard attribution rules.
Many show up in GA4 as “Direct” or “Other.” CRM platforms struggle to track source/medium cleanly unless the UTM structure is airtight.
And even when tracking works, most teams don’t connect those visits to assisted conversions or the downstream pipeline.
Here’s what to fix first:
Set up scroll depth, CTA clicks, and video views as tracked events
Build segments in GA4 for “Direct” visitors who land on pages with AIO potential
Use custom dimensions to flag high-AIO-opportunity pages
Match these sessions with CRM leads using first-party cookies or user ID stitching
Track micro-conversions like guide downloads or chatbot engagement
🧪 Quick diagnostic prompt
Ask your team this: “How many leads or MQLs last month started on a page that ranks in an AI Overview?”
If no one can answer, you have a tracking problem.
Also check out: How to build a growth flywheel that doesn’t require a $50k/month budget
5. Workflow shifts to operationalize it all
AI-readiness isn’t something you check post-publish. It needs to live inside your content brief, your dev cycles, and your SEO governance.
Otherwise, you’ll always be patching things too late.
Start with your content process
Bake an AI-readiness checklist into every content deliverable. That means clear structure, schema setup, compressed visuals, and clean metadata, before anything goes live.
Hold your writers and editors accountable for formatting clarity, not just voice and flow.
Review crawl behavior monthly
Use log file monitoring tools like JetOctopus, Screaming Frog Log File Analyzer, or your hosting provider’s raw logs.
Track which URLs Googlebot hits most, how often, and what it skips. You can’t optimize for AI Overviews if your most valuable content barely gets crawled.
AIOs are nothing but a quality filter. You’re competing with the best-explained answer, in the clearest format, from the most trusted source.
Most teams won’t adapt. They’ll keep chasing keywords, counting traffic, and wondering where their pipeline went.
But you can build for what search looks like now.
This month, I’m testing this workflow live with a client. I’ll share what works, what doesn’t, and what’s worth scaling.
📍 Want a detailed Generative Engine Optimization audit?
I’m booking a few strategic consults this month for teams that are ready to fix visibility at the source.
I'm curious about Google’s real-time extraction layer. You mention that this is a living layer of SEO that now determines whether your answer qualifies to be included in an Overview. How so? Any source?