Google‘s AI search optimization guide: what to do next

Google released its first official guide to optimizing for AI search on May 15, 2026. On-site SEO is still the foundation, according to the guide. 

And several long-standing best practices are retired: llms.txt files, content chunking, AI-specific rewrites, and special schema.

But the big question that Google’s guide doesn’t address: how your brand is described across the sources AI systems pull from, and whether those descriptions match the story you want to tell. 

Below, learn what the guide validates, the off-site layer it leaves out, and what do to next. 

What Google’s guide validates

Optimizing for Google’s generative AI features is still SEO, the guide states, because those features run on the same ranking and quality systems as traditional search engines. If a page can’t rank for a regular query, retrieval-augmented generation won’t put it inside an AI-generated answer.

What that means in practice:

• Technical accessibility: If your robots.txt blocks the relevant AI crawlers, your content isn’t in the retrieval set
• Content quality and E-E-A-T signals: The guide leans on experience, expertise, authority, and trust, the same signals that drive organic rankings
• Originality and a distinct point of view: Google explicitly favors content with first-hand experience over content that summarizes what’s already on the web
• Structure over chunking: Multi-topic pages work fine if the structure is clear. You don’t need to pre-fragment content for AI systems.

To go deeper on these tactics, check out our guide to optimizing for AI search.

The unmentioned off-site layer

Google’s guide focuses almost entirely on your own website, but AI systems pull from a much bigger ecosystem: review platforms, comparison sites, Reddit threads, analyst coverage, industry publications, podcast transcripts, and anywhere else your brand is mentioned or compared.

A Semrush survey of 1,000 US consumers found that 43% have discovered a brand through AI. When asked what makes a brand stand out in those answers, only 20% pointed to being first-mentioned. The bigger factor by far was how clearly and accurately the brand was described.

Semrush’s AI Visibility Study shows the same pattern in citation data:

• Across five major industries, AI models cite community-edited sources and review platforms far more often than corporate marketing content
• Wikipedia gets referenced more than once per ChatGPT response in the digital technology category
• Reddit drives 120%+ citation frequency in technology and consumer electronics
• Even Microsoft’s corporate blog generates fewer AI citations than Reddit threads about Microsoft products

The mechanics differ by platform: Google AI Mode and AI Overviews retrieve from Google’s index, while ChatGPT, Perplexity, and Gemini draw on training data plus their own retrieval. Across all of them, the brands recommended are the ones described favorably and often across multiple independent sources.

This is where AI search visibility splits from traditional SEO. You can rank a single page with strong on-site work. Getting recommended in an AI answer could take multiple third-party sources saying the same favorable things about you. 

Google’s guide doesn’t tell you how to build that. 

For the framework, our guide to building brand visibility in AI search covers how visibility, awareness, and perception fit together.

How to act on Google’s AI optimization guide 

1. Audit how AI systems describe you today

Start with a baseline. In under 30 minutes with a free Semrush account, you can:

1. Check whether you’re appearing in AI answers: Open Domain Overview, enter your domain, and scroll to the AI Search section. Two metrics matter: Mentions (how often AI answers reference your brand across the Semrush prompt database) and Cited Pages (which URLs AI is pulling from). If Mentions is near zero relative to your organic traffic, you have visibility work to do. 
2. Verify AI crawlers can access your site: Run Site Audit on your domain and check the AI Search Health score in the overview. A low score usually means your robots.txt or meta tags are blocking ChatGPT-User, OAI-SearchBot, or Google-Extended. Fix crawler access before content work. Because AI systems can’t cite pages they can’t reach.
3. Compare your visibility to category competitors: Enter your domain in the AI Brand Visibility Tool and scroll to Competitor Visibility Comparison. The tool scores each brand on a 0-100 AI Visibility scale. If category leaders score 3-5 times higher than you, they’re shaping how the category gets described in AI before buyers see your name.
4. Test prompts in AI platforms directly: Pick 3-5 comparison and recommendation prompts your buyers would actually ask (“best X for Y,” “X vs. competitor”), then run each in ChatGPT, Gemini, and Google AI Mode. For each response, log: whether you’re mentioned, how prominently, which competitors appear, and what features AI associates with each brand.

2. Improve your on-site SEO

Crawler access, content quality, extraction-friendly structure, and consistent entity information remain a crucial foundation for both traditional SEO improvements and visibility in AI systems. 

1. Ensure OpenAI crawler access: Make sure your robots.txt doesn’t block ChatGPT-User, OAI-SearchBot, Google-Extended, or other AI crawlers. Then run Site Audit and resolve any indexability issues (broken links, redirect chains, duplicate content) that surface.
2. Add E-E-A-T signals to priority pages: Pages competing for AI citations need author bios with credentials, citations to primary sources, original data or screenshots, and a visible “last updated” date. Generic content without these signals tends to get skipped over for citation.
3. Restructure for clean extraction: Each H2 should open with a direct, one-sentence answer to its heading. Multi-topic pages work fine, per Google, but only when the structure makes sub-topics easy to lift. Use lists, tables, and definition-first paragraphs to showcase the facts you want cited.
4. Lock entity consistency across the web: Use the same product name, description, and positioning everywhere: your site, third-party listings, review platforms, app stores, and partner sites. AI systems weigh consistency heavily when picking which brand description to use.

3. Build presence on third-party sources AI systems trust

This is where most teams are thinnest, and where the gap from competitor brands shows up fastest.

Build presence in the following places: 

• Review platforms (G2, Capterra, Trustpilot, niche category sites): Claim and complete your profiles on G2, Capterra, and Trustpilot at minimum. Add product descriptions, feature lists, current pricing, and screenshots. Set up review-request flows for current customers, targeting at least the median review count of your top category competitor.
• Comparison and category content: Pitch inclusion in the “best X tools” and “X vs Y” articles AI systems already pull from. Use Backlink Gap to find publications linking to your competitors but not to you, then sort by Authority Score. That’s your outreach shortlist.
• Community presence: Find the communities where your buyers spend time (Reddit, niche forums, Stack Overflow, industry Discord servers) and contribute: answer questions in your domain, share original analysis, and add to discussions about your category. Don’t pay for placements or coordinate fake testimonials. Google’s guide explicitly retired “seeking inauthentic mentions” as a tactic, and AI systems on every platform weight community content precisely because it reads like real user experience.
• Industry analyst recognition: Pursue inclusion in G2 Grid, Forrester Wave, Gartner Magic Quadrant, and any niche category reports. The briefing motion is slow, but reports get cited by AI systems for as long as they’re indexed. Start with the analysts whose reports already appear in your category’s top AI citations.
• Earned media and contributed content: Pitch original data, customer stories, and expert commentary to the publications AI systems cite in your category. A bylined piece or feature in a high-citation publication gets pulled into AI descriptions of your brand for months.

Brand Monitoring tracks mentions across reviews, news, blogs, and forums in one dashboard, and surfaces unlinked mentions that a backlink tool would miss. Set up alerts for new mentions (positive and negative) so you can respond fast and feed the data back into your perception tracking.

4. Track perception, not just presence

Mentions aren’t enough if the descriptions are wrong. Use this weekly perception workflow to stay ahead of changes: 

1. Pull the Perception report: Open the AI Visibility Toolkit, go to Brand Performance, and review the Perception report. Check Share of Voice, sentiment by platform, and Key Sentiment Drivers for the week.
2. Flag inaccurate descriptions: Log outdated features, deprecated product names, missing differentiators, and comparisons that frame competitors more favorably. Capture the prompt, the platform, and the AI response for each one.
3. Trace each flag to its source: Use the Perception report’s Key Sentiment Drivers and the Visibility Overview’s Cited Pages to identify which third-party sources are feeding the inaccurate description. Typically, this means updating one or two high-authority external sources rather than your own site.
4. Publish corrective content: Brief analysts on what’s changed, update comparison content with current data, push press for product updates, and use platform-specific moderation paths (G2 review responses, Wikipedia edits where appropriate). Re-check the Perception report in 4-6 weeks for movement.

For teams operating across multiple brands, product lines, or markets, Semrush Enterprise AIO tracks AI mentions and perception across that scope from one place.

What’s next: the agentic layer

Google’s guide offered a short “Explore agentic experiences” section, which names Universal Commerce Protocol (UCP) and WebMCP as emerging standards. Both define how AI agents discover, evaluate, and act on websites programmatically.

The section is brief, but its presence in foundational documentation signals where Google expects AI search to head. The shift from AI answers to AI agents is already underway: deep research features, browsing agents, and emerging agentic commerce tools are taking actions on behalf of users in flows that bypass traditional SERPs entirely.

Our analysis of agentic search covers the four dimensions agents test as they take on more delegation: brand discovery, clarity, authority, and trust. The work above puts you in position for all four. As agents become more autonomous, each one starts to matter more.

Google didn’t write a guide for this

AI search visibility used to be an SEO problem. 

Now it splits into two: the on-site work is solved, and the off-site work is where the recommendation decision sits. Cleaner robots.txt, stronger E-E-A-T, better extraction structure: those are prerequisites Google’s guide codifies well. 

The off-site work is harder and slower. Google can’t write a guide for it because the work depends on independent sources Google doesn’t control. 

Start before your competitors do.