About This Article: This comprehensive guide synthesizes insights from a recent interview with Robby Stein, Vice President of Product at Google Search, conducted by Silicon Valley Girl. Stein oversees how ranking works inside the world’s largest search engine and reveals the mechanisms behind AI-driven search recommendations in 2025.
The landscape of search has fundamentally transformed. When Robby Stein, Google’s Vice President of Product for Search, sat down for a candid interview in late 2025, he revealed something that changes everything we thought we knew about SEO: AI isn’t replacing traditional search optimization—it’s amplifying its importance by a factor of 20.
Here’s why: For every single conversational query a user asks AI, Google’s systems execute 20 or more traditional searches behind the scenes. If your SEO fundamentals are strong, you’re not just competing for one ranking opportunity anymore. You’re competing for dozens simultaneously.
This article breaks down exactly what Stein revealed about how AI search actually works, why traditional SEO principles matter more than ever, and the specific actions business owners must take right now to get recommended by AI systems in 2025 and beyond.
The AI Search Revolution: What Actually Changed
How Google’s AI Makes Recommendations
When you ask Google’s AI a question like “Find me a sushi restaurant for lunch and book it,” you might think the AI is simply pulling from a database. The reality is far more sophisticated—and far more favorable to businesses with solid SEO foundations.
Stein revealed that Google uses a technique called “query fanout,” where a reasoning model analyzes your question and then executes dozens of related searches as “tool calls.” For that simple lunch request, the AI might execute queries like:
- “sushi restaurants Los Altos”
- “OpenTable sushi reservations”
- “best rated sushi near me”
- “sushi restaurants open now”
- “highly reviewed Japanese restaurants”
According to Stein’s explanation in the interview, this process literally uses “Google search as a tool, doing googling under the hood.” The AI then synthesizes results from these multiple searches, taps into Google’s knowledge bases (including 250 million plus real-world places with updated business information), and generates comprehensive recommendations.
What this means for your business: Every piece of content you optimize, every review you earn, every local listing you complete creates multiple opportunities for AI discovery. Traditional SEO efforts now have exponential returns because AI systems query them repeatedly for context.
Why Traditional SEO Fundamentals Still Win (And Always Will)
The “AI Thinks Like a Person” Principle
One of Stein’s most significant revelations addresses the anxiety many business owners feel about AI rendering their SEO efforts obsolete. His response? “Interestingly, AI thinks a lot like a person would.”
When asked specifically about what business owners should focus on to be recommended by AI, Stein explained that the kinds of questions AI issues are fundamentally similar to how humans search for information. The optimization strategies remain consistent:
Core principles that haven’t changed:
- High quality, helpful content that answers user needs
- Clear site structure that’s easy to parse
- Fast loading times and clean HTML
- Authoritative sources and credible information
- Fresh, regularly updated content
What has changed: The nature of queries themselves. Traditional searches used what Stein calls “keywordese”—short, often fragmented phrases like “nashville seo agency” or “best coffee near me.” AI-era searches are conversational, detailed, and context-rich: “What’s the best SEO agency in Nashville for local businesses with a $5,000 monthly budget?” or “Where can I find a quiet coffee shop in 12 South with outdoor seating and reliable wifi?”
According to Search Engine Land’s coverage of the evolving search landscape, this shift toward conversational queries doesn’t require a complete SEO overhaul. Instead, it demands an expansion of existing strategies to accommodate longer, more detailed content that addresses comprehensive scenarios.
The Query Fanout Technique: Behind the Scenes
How AI Decides What to Recommend
Understanding how Google’s AI actually selects businesses to recommend provides crucial insight into optimization strategies. Stein walked through the technical process in detail during the interview:
Step 1: Query Analysis
The reasoning model (using Gemini 2.5 with advanced reasoning capabilities, per Stein) analyzes the user’s request and identifies multiple dimensions of the query.
Step 2: Query Generation
The AI generates dozens of related queries. For a restaurant recommendation request that includes preferences like “Italian,” “fun atmosphere,” “date night,” the AI might issue searches for:
- “great Italian restaurants”
- “romantic dinner spots”
- “date night recommendations”
- “Italian food best reviews”
- “ambiance romantic restaurants”
Step 3: Knowledge Base Integration
The AI doesn’t just rely on web results. It taps into Google’s extensive knowledge infrastructure, including real-time business data, inventory systems (50 billion products with 2 billion updates per hour, according to Stein), financial data from Google Finance, and continuously updated local business information from Google Business Profiles.
Step 4: Synthesis and Recommendation
Based on reviews, business information, website quality, and relevance signals from all these searches, the AI produces a ranked set of recommendations.
Critical insight from Stein: “It’s literally using Google search as a tool.” This means every traditional ranking signal—domain authority, content quality, user engagement metrics, technical SEO health—feeds directly into AI recommendation algorithms.
What Business Owners Must Do Right Now
1. Claim and Optimize Your Google Business Profile
When asked about what information AI systems use for recommendations, Stein was explicit: “If a business has claimed their local business and has modified that, put menu information in there, it’s eligible for reviews, that information could be used.”
Immediate actions:
- Claim your Google Business Profile if you haven’t already
- Complete every available field: hours, phone, website, services, attributes
- Upload 10+ high-quality, recent photos (updated within 3 months)
- Add detailed menu information or service descriptions
- Set up and respond to Google Q&A
- Post weekly updates (offers, news, events)
Business owners using Google Business Profile optimization see measurable improvements in local pack visibility. According to BrightLocal’s 2025 Local Search Ranking Factors study, complete profiles with regular updates rank significantly higher in AI-generated recommendations.
Example: During the interview, Stein demonstrated how a simple lunch request—”I only have an hour, need a quick lunch spot”—immediately pulled recommendations from businesses with complete Google profiles, updated hours, menu highlights, and reviews. Incomplete profiles were never surfaced.
2. Invest in PR—But Not for Humans
This might be the most counterintuitive insight from Stein’s interview, yet it’s potentially the most valuable.
When asked about optimization strategies, Stein emphasized: “If you’re a business and you’re mentioned in top business lists or from a public article that lots of people end up finding, those kinds of things become useful for the AI to find.”
The interviewer then made a profound observation: “Sometimes I invest in PR and I ask my friends, have you seen that article? And they’re like no. But then I ask AI and it really sees the article and it uses that information.”
Stein confirmed this thinking: “That’s actually a good way of thinking about it because the way our AI models work, they’re issuing these Google searches as a tool. And so in the same way that you would optimize your website and think about how do I make helpful, clear information for people, think of an AI doing that search now.”
New PR strategy for 2025:
Old approach:
PR → Human readers → Brand awareness → Maybe some traffic
New approach:
PR → AI training data → Citations → Recommendations → Traffic
Actionable steps:
- Get featured in industry “best of” lists
- Publish expert articles on recognized platforms
- Secure local news mentions
- Contribute to niche industry publications
- Build presence on authoritative review platforms
Even if humans don’t read these mentions, AI systems scan, index, and cite them when making recommendations. According to Stein, this is because AI models use these citations as context signals when executing their multi-query searches.
3. Optimize for Conversational Queries
The shift from keyword-based to conversational search requires content strategy adaptation, but not reinvention.
Traditional keyword targeting:
"nashville hvac repair"
"emergency ac service"
"heating repair near me"
AI-era conversational queries:
"My AC stopped working in the middle of Nashville's summer
heat wave, it's 95 degrees, I have elderly parents visiting,
and I need emergency repair today. What are my options and
typical costs?"
Content optimization strategy:
Create comprehensive scenario-based content:
- Write for specific use cases, not just keywords
- Address complete customer journeys
- Include pricing context, timelines, and processes
- Use natural language and question-answer formats
- Anticipate follow-up questions
Structure content for AI parsing: According to Stein’s explanation of how AI models work, they scan for “helpful, clear information” that answers specific questions. This means:
- Use descriptive headers (H2, H3) that directly state what’s covered
- Lead with direct answers, then provide supporting detail
- Include relevant examples and real scenarios
- Add FAQ sections addressing related questions
- Implement structured data (FAQ schema, HowTo schema)
Nashville HVAC example:
Instead of a generic “AC Repair Services” page, create:
- “Emergency AC Repair in Nashville: What to Do When Your Unit Fails During a Heat Wave”
- “Nashville AC Repair Costs: Complete Pricing Guide by Issue Type and Season”
- “How Long Does AC Repair Take? Timeline Expectations for Nashville Residents”
Each piece addresses specific conversational queries while maintaining traditional SEO fundamentals.
4. Master the Review Strategy
When asked directly about reviews—particularly whether buying reviews affects AI recommendations—Stein provided nuanced guidance that reveals how AI systems evaluate credibility.
“The reviews, I think again it’s kind of like a person where imagine something is scanning for information and trying to find things that are helpful,” Stein explained. “So it’s possible that if you have reviews that are helpful, it could come up. But I think it’s tricky to say to pinpoint any one thing like that.”
His broader point: AI systems look for reliable sources, similar to how human users evaluate information. While reviews can influence recommendations, the emphasis is on genuine, helpful reviews rather than volume alone.
Review strategy for AI visibility:
Focus on quality and authenticity:
- Encourage detailed, specific reviews from actual customers
- Respond to all reviews (positive and negative) within 24-48 hours
- Highlight reviews that include specific details about services or products
- Address negative feedback constructively
Avoid problematic practices:
- Bulk-purchased reviews with generic text (AI can detect patterns)
- Reviews without specific details or context
- Suspiciously uniform 5-star ratings
- Reviews that don’t match business services
According to local SEO research from Sterling Sky, review velocity (consistent new reviews over time) and review diversity (mix of ratings with detailed responses) signal authenticity more effectively than sheer volume.
5. Prepare for Agentic Calling and Voice Integration
One of the most striking demonstrations in Stein’s interview was Google’s agentic calling feature, where AI makes phone calls on behalf of users to gather information from businesses.
The demo: Stein walked through requesting dog grooming services. The AI:
- Identified user requirements (dog breed, service type, location)
- Called 5+ local grooming businesses
- Gathered pricing ($74-$105 range)
- Secured availability information
- Compiled results in an email
- All completed in approximately 10 minutes
For offline businesses or those with limited web presence, this changes everything. As Stein noted, “Many of them are local, they’re run by small businesses, there’s no easy way to access them on the web.”
Preparation steps for businesses:
Ensure phone accessibility:
- Maintain consistent business hours
- Answer calls promptly or use professional voicemail
- Train staff to provide clear pricing and availability
- Consider how automated systems might interact with your phone system
Update business information everywhere:
- Verify phone numbers across all directories
- Keep hours current on Google Business Profile
- List services clearly with associated pricing ranges
- Enable messaging where available
For businesses with web presence: Make information easily accessible without requiring phone calls:
- Display pricing clearly (even ranges)
- Show real-time availability where possible
- Implement booking systems
- Provide detailed service descriptions
The Technical Foundation: Why Clean SEO Matters More Than Ever
How AI Parses and Evaluates Your Site
Stein emphasized throughout the interview that while query formats have evolved, the technical requirements for discoverability remain grounded in traditional SEO best practices.
“Think about how you would optimize your website and think about how do I make helpful, clear information for people,” Stein advised. “So people search for a certain topic, my website’s really helpful for that. Think of an AI doing that search now.”
Critical technical requirements for AI visibility:
1. Clean HTML Structure
AI models parse HTML to understand content hierarchy and meaning. Requirements:
- Semantic HTML5 elements (article, section, nav)
- Clear heading hierarchy (H1 → H2 → H3, no skipping)
- Descriptive title tags and meta descriptions
- Alt text for all images explaining content and context
2. Fast Load Times
Page speed affects both traditional rankings and AI model efficiency. According to Google’s Core Web Vitals standards (updated March 2024):
- Largest Contentful Paint (LCP): <2.5 seconds
- Interaction to Next Paint (INP): <200ms (replaced FID)
- Cumulative Layout Shift (CLS): <0.1
3. Mobile-First Optimization
With mobile-first indexing universal since 2019, and mobile searches driving the majority of local queries, mobile performance is non-negotiable:
- Responsive design that adapts to all screen sizes
- Touch-friendly buttons and controls (minimum 48px targets)
- No intrusive interstitials
- Fast mobile loading (target <3 seconds)
4. Structured Data Implementation
Schema markup helps AI models understand content type, purpose, and relationships. Priority schemas for 2025:
For local businesses:
{
"@context": "https://schema.org",
"@type": "LocalBusiness",
"name": "Business Name",
"address": {...},
"telephone": "+1-555-555-5555",
"openingHoursSpecification": [...],
"priceRange": "$$"
}
For content:
- Article schema (with author, publisher, datePublished)
- FAQ schema (questions and answers)
- HowTo schema (step-by-step processes)
- Review schema (aggregated ratings)
According to Google’s documentation on structured data, properly implemented schema increases the likelihood of appearing in AI-generated responses and rich results.
The Multimodal Future: Visual and Voice Search
Why 70% Year-Over-Year Growth Matters
Stein revealed that Google Lens—the visual search product—is “growing 70 percent increase year-over-year on visual searches. It’s one of the fastest growing ways people are finding information.”
The interview included live demonstrations of multimodal search capabilities:
Visual search example:
Taking a photo of a beauty product and asking “Find me similar” returned product recommendations, ingredients, pricing, and direct purchase links—all within seconds.
Voice search with video:
Pointing a phone camera at a device and asking “What is this device?” provided instant identification, specifications, and shopping options.
Practical implications for businesses:
Optimize for visual discovery:
- Use high-quality, professional product photography
- Include multiple angles and context shots
- Add detailed alt text describing visual elements
- Ensure images are compressed but high-resolution (WebP format recommended)
- Include products in lifestyle and usage contexts
Prepare for voice queries:
- Write content that answers “who,” “what,” “where,” “when,” “why,” and “how”
- Use natural, conversational language
- Structure content as question-and-answer pairs
- Implement speakable schema for voice assistants
- Focus on local context (users often search by voice while mobile)
According to research from Backlinko on voice search trends, 58% of consumers use voice search to find local business information, making this optimization critical for local businesses.
What About Google Ads in the AI Era?
The Evolution, Not Elimination, of Paid Search
A recurring concern for business owners: Will AI recommendations make paid advertising obsolete?
Stein’s response offers both reassurance and strategic insight: “Don’t see them going away. What people actually do, we’re observing, is that the way people use Google search isn’t really changing. It’s really expanding.”
Key points about ads in AI search:
Ads remain separate from AI recommendations:
When asked if ads information influences AI recommendations, Stein was clear: “It doesn’t use ads information. This is done entirely with what’s on the web and what’s within Google’s information system.”
New ad formats are coming:
“We started some experiments on ads within AI mode and within Google AI experiences,” Stein revealed. While consumer product experience remains the primary focus, new ad formats tailored to conversational queries are in development.
Search volume is increasing, not decreasing:
According to Stein, AI enables more searches by supporting “all these new things”—visual queries, complex multi-sentence questions, nuanced requests that users previously wouldn’t have attempted.
Strategy for advertisers:
Short-term (2025-2026):
- Maintain traditional Google Ads campaigns
- Monitor AI mode ad experiments
- Optimize for conversational query variations
- Track how AI Overviews affect click-through rates
Medium-term (2026-2027):
- Prepare for new ad formats in conversational interfaces
- Develop content that complements (not competes with) AI responses
- Focus on commercial queries where AI provides options, not definitive answers
Long-term (2028+):
- Expect integration of ads within AI-generated responses
- Potential for more targeted, context-aware advertising based on detailed user queries
Competitive Advantages: What Makes Google Different
The Knowledge Base Moat
When discussing competition (particularly ChatGPT and other AI tools), Stein highlighted Google’s unique advantages that directly benefit businesses optimizing for visibility:
Real-time, comprehensive data:
- 250 million+ real-world places with continuously updated information
- 50 billion products with 2 billion updates per hour
- Google Finance for live financial data
- Complete web index with fresh crawling
According to Stein: “If you ask about any product in the world, it’s likely that that model can tap into that knowledge and give you the exact price in a really comprehensive way.”
This real-time accuracy and breadth creates opportunities for businesses that maintain current information across Google’s ecosystem. A product price update, a menu change, or new business hours can appear in AI recommendations within hours.
Measuring Success: What Metrics Actually Matter
Beyond Rankings: Tracking AI Visibility
Traditional SEO metrics remain relevant, but AI-era search demands additional tracking:
Core metrics to monitor:
1. Google Business Profile Insights
- Views (how often your profile appears)
- Actions (calls, website visits, direction requests)
- Search queries (what terms surface your business)
- Photo views and engagement
2. Branded vs. Non-Branded Traffic
- Increases in branded searches suggest AI mentions
- Monitor tools like Google Trends for brand interest
- Track “company name + location” query volume
3. Citation and Mention Tracking
- Monitor where your business is mentioned (Ahrefs, Mention, BrandWatch)
- Track inclusion in “best of” lists and industry publications
- Measure PR reach in AI-accessible formats
4. Conversational Query Performance
- Identify long-tail, question-based queries driving traffic (Google Search Console)
- Track position for “how to,” “what is,” “where can I” queries
- Monitor featured snippet and AI Overview appearances
5. Review Velocity and Quality
- New reviews per month
- Average review length (detailed reviews signal authenticity)
- Response rate and time
- Review sentiment distribution
The 30-Day Action Plan: Getting Started
Week 1: Foundation Audit
Day 1-2: Google Business Profile
- Claim or verify ownership
- Complete every field (100% completion)
- Upload 10+ recent, high-quality photos
- Add detailed business description with services
Day 3-4: Technical SEO Check
- Run PageSpeed Insights on key pages
- Verify mobile usability (Google Search Console)
- Check Core Web Vitals status
- Identify and fix critical errors
Day 5-7: Content Inventory
- List your top 10 performing pages
- Identify conversational query opportunities
- Note missing FAQ content
- Check structured data implementation
Week 2: Content Enhancement
Day 8-10: FAQ Creation
- Develop 10 frequently asked questions
- Write comprehensive, conversational answers
- Implement FAQ schema markup
- Add to relevant service pages
Day 11-13: Conversational Content
- Rewrite 3 key pages for conversational queries
- Add scenario-based examples
- Include specific use cases
- Optimize for long-tail questions
Day 14: Schema Implementation
- Add LocalBusiness schema (if local)
- Implement Article schema for content
- Add HowTo schema where applicable
- Validate with Google’s Rich Results Test
Week 3: PR and Authority Building
Day 15-17: PR Outreach
- Identify 5 industry publications for contribution
- Draft expert article or commentary
- Reach out to local news for story angles
- Submit business to relevant “best of” directories
Day 18-20: Review Strategy
- Set up review request workflow
- Respond to all existing reviews
- Create review response templates
- Implement review monitoring system
Day 21: Citation Consistency
- Audit NAP (Name, Address, Phone) across web
- Update inconsistent listings
- Claim unclaimed profiles
- Add to industry-specific directories
Week 4: Monitoring and Optimization
Day 22-24: Tracking Setup
- Configure Google Search Console tracking
- Set up Google Business Profile insights monitoring
- Implement conversion tracking
- Create reporting dashboard
Day 25-27: Competitive Analysis
- Identify top 3 competitors
- Analyze their content approach
- Note their review strategy
- Identify gaps you can exploit
Day 28-30: Refinement
- Review week 1-3 implementations
- Adjust based on early data
- Plan next month’s content calendar
- Document learnings and next steps
Common Mistakes to Avoid
What Doesn’t Work in AI-Era SEO
1. Keyword Stuffing for AI Some businesses attempt to “game” AI by overloading content with keywords, assuming AI models will pick up on density. This fails because, as Stein emphasized, AI evaluates content quality similar to how humans do. Forced, unnatural keyword usage actually reduces readability and hurts rankings.
2. Neglecting Google Business Profile Assuming web presence alone suffices for AI discovery ignores how Google’s systems prioritize complete, current business information. Incomplete profiles simply don’t surface in AI recommendations, regardless of website quality.
3. Buying Bulk Reviews Generic, pattern-matching reviews are increasingly detectable. Focus instead on encouraging genuine customer feedback with specific details about their experience.
4. Ignoring Mobile Experience With mobile-first indexing universal and most local searches occurring on mobile devices, poor mobile experience eliminates AI recommendation eligibility.
5. Overcomplicating Schema More schema isn’t always better. Implement relevant schema types correctly rather than adding every possible markup. Invalid schema can harm rather than help.
Looking Forward: What’s Coming in 2026-2027
Emerging Trends to Watch
Based on Stein’s revelations and industry trajectory:
Enhanced Personalization
Google announced at I/O the ability to opt into experiences with enhanced personalization. Stein confirmed: “It’s something we’re working on. We want people to be able to help Google know more about you so that it can be more helpful.”
Future state: AI recommendations incorporating Gmail data, YouTube watch history, Google Drive contents, and Calendar information for hyper-personalized results.
Expanded Agentic Capabilities
The dog grooming demo represents early-stage agentic AI. Expect expansion to:
- Complex multi-step bookings
- Comparative research across services
- Negotiation and deal-finding
- Automated follow-ups and confirmations
Multimodal as Default
Voice, visual, and text queries will blend seamlessly. Prepare for users who point their camera at something while asking a complex question about it.
AI-Native Advertising
New ad formats designed specifically for conversational contexts. According to Stein, Google is “thinking about” how ads might appear in these systems, with early experiments underway.
Conclusion: The Symbiosis of Traditional SEO and AI
Robby Stein’s revelations fundamentally reframe how we should think about SEO in the AI era. Rather than rendering traditional optimization obsolete, AI amplifies its importance by creating multiple discovery opportunities for every well-optimized piece of content.
The businesses that will dominate AI recommendations in 2025 and beyond aren’t those chasing novel “AI SEO” tactics. They’re businesses doubling down on fundamentals—creating genuinely helpful content, maintaining accurate business information, earning authentic reviews, building authority through PR, and ensuring technical excellence.
As Stein emphasized: “These general best practices around building great content really do apply in the AI age for sure.”
The opportunity gap exists not in understanding new technology, but in executing established principles at a higher level of quality and consistency than competitors. AI doesn’t change what works; it reveals and rewards those doing it best.
Your next steps:
- Audit your Google Business Profile completeness (target: 100%)
- Implement the 30-day action plan starting with Week 1 priorities
- Shift PR strategy to focus on AI-accessible mentions, not just human readers
- Optimize 3 key pages for conversational queries this month
- Monitor AI recommendation appearances through search query tracking
The businesses winning AI visibility in 2025 started these optimizations months ago. The second-best time to start is now.
Frequently Asked Questions
Does Google’s AI use paid advertising data to make recommendations?
No. According to Robby Stein’s direct statement in the interview, AI recommendations are “done entirely with what’s on the web and what’s within Google’s information system.” Ads information does not influence organic AI recommendations. However, businesses that have claimed and optimized their Google Business Profiles, added menu information, and earned reviews can have that information used in recommendations.
How long does it take for AI to start recommending my business after optimization?
While Stein didn’t provide specific timelines, the query fanout technique means AI checks current search results in real-time. Once your optimizations affect traditional search rankings and your Google Business Profile is complete, you become eligible for AI recommendations immediately. However, building the authority signals (reviews, mentions, quality content) that consistently surface your business takes 3-6 months of consistent effort.
Can I pay to guarantee my business appears in AI recommendations?
Not currently. Stein confirmed that ads don’t influence AI recommendations. Google is experimenting with new ad formats for AI mode, but organic recommendations remain based on relevance, quality, and authority signals. The most reliable path to AI visibility is optimizing the factors AI systems use: complete business information, quality content, authentic reviews, and authoritative mentions.
What’s the difference between optimizing for AI search versus traditional Google search?
According to Stein, there’s significant overlap. The core difference is query format: AI handles more conversational, detailed questions while traditional search typically receives shorter keyword phrases. However, both systems value the same fundamentals—quality content, clean technical implementation, authoritative sources, and helpful information. Optimize for conversational queries (longer, question-format content) while maintaining traditional SEO best practices.
How does AI evaluate review authenticity?
Stein explained that “AI thinks a lot like a person would” when evaluating information, including reviews. While he didn’t detail specific detection methods, the principle suggests AI looks for patterns humans would recognize: detailed, specific reviews with varied ratings and response engagement signal authenticity more effectively than uniform 5-star ratings with generic text. Focus on encouraging genuine customer feedback rather than volume.
Will voice and visual search replace text-based search?
Not according to current trends. Stein revealed that visual search through Google Lens is growing 70% year-over-year and becoming “one of the fastest growing ways people are finding information,” but emphasized that search usage is “expanding” rather than shifting. Text, voice, and visual queries coexist, with users choosing based on context. Optimize for all modalities rather than betting on one replacing others.
How important is Google Business Profile versus website SEO?
Both are critical and complementary. Stein specifically noted that businesses with claimed, updated Google Business Profiles containing menu information and reviews become “eligible” for AI recommendations. However, he also emphasized that AI systems scan web results and value quality website content. A complete Google Business Profile provides immediate local visibility, while strong website SEO ensures you appear in the multiple queries AI executes for each recommendation.
Does AI favor certain types of businesses or industries?
The interview didn’t reveal algorithmic bias toward specific industries. However, Stein demonstrated how local businesses (restaurants, service providers), e-commerce (product searches), and professional services all appear in AI recommendations when relevant. The determining factors are relevance to the query, completeness of available information, quality signals (reviews, mentions), and technical accessibility—not industry type.
What should businesses do about agentic calling features?
Ensure your business is accessible and helpful when AI agents call. This means maintaining accurate phone numbers across all listings, training staff to provide clear pricing and availability information, keeping business hours current, and considering how automated systems might interact with your phone setup. For businesses with online booking, make those systems easy to find and use, as AI may direct users there instead of calling.
How can I track if AI is recommending my business?
Monitor several indicators: increases in branded search traffic (Google Trends, Search Console), direct traffic spikes suggesting word-of-mouth or AI mentions, citation tracking tools (Ahrefs, BrandWatch) showing where you’re mentioned, and Google Business Profile insights showing search query patterns. Ask customers how they found you—anecdotal evidence of “Google AI told me about you” indicates successful AI visibility.
Sources & Methodology
Primary Source:
Interview with Robby Stein, Vice President of Product at Google Search, conducted by Silicon Valley Girl, published October 2025. Interview covered AI search mechanisms, recommendation algorithms, business optimization strategies, and future search developments.
Supporting Research:
- Google Search Central Documentation (algorithm updates, technical guidelines)
- Search Engine Land: AI search coverage and analysis (2024-2025)
- BrightLocal: Local Search Ranking Factors Study 2025
- Sterling Sky: Review impact research and local SEO data
- Backlinko: Voice search and multimodal query research
- Schema.org: Structured data specifications (v16.0, October 2023)
Technical Standards:
- Core Web Vitals metrics (INP replaced FID, March 2024 update per Google announcement)
- Mobile-first indexing standards (universal since 2019)
- Google Business Profile optimization best practices (updated 2025)
Methodology Note:
This article synthesizes direct quotes and demonstrations from the Stein interview with established SEO best practices and industry research. Where specific metrics or timelines are provided, they represent either direct statements from the interview or consensus industry data from cited sources. Implementation recommendations are based on combining Stein’s insights with proven SEO methodologies.
Review Frequency: This content will be reviewed quarterly for technical accuracy and updated following major Google algorithm announcements or changes to AI search functionality.
Current as of: November 2025
Disclaimer: While this article is based on an interview with a Google executive and incorporates official Google guidance, individual results vary based on competition, implementation quality, industry dynamics, and ongoing algorithm evolution. The strategies outlined represent best practices as of November 2025 and should be adapted to specific business contexts and goals.