The digital shopping landscape just experienced a seismic shift. Google has rolled out a suite of AI-powered shopping features across Search and the Gemini app that fundamentally changes how consumers discover, compare, and purchase products online. For businesses investing in digital marketing, e-commerce optimization, and search visibility, these updates represent both a challenge and an unprecedented opportunity.
At ALM Corp, we’ve helped generate over $7 billion in sales and more than 1 million leads for our clients by staying ahead of digital marketing trends. As Google continues to integrate artificial intelligence into the shopping journey, understanding and adapting to these changes isn’t optional—it’s essential for survival in the increasingly competitive digital marketplace.
Let’s dive deep into what these AI shopping tools mean for your business and how you can position yourself to thrive in this new era of AI-driven commerce.
The Four Game-Changing AI Shopping Features
Google’s latest rollout introduces four distinct AI-powered capabilities that are currently available to users in the United States, with plans for broader expansion on the horizon.
AI Mode Shopping: Conversational Commerce Takes Center Stage
The first major update transforms how people search for products. AI Mode in Search now combines Gemini’s language models with Google’s Shopping Graph—a massive database containing over 50 billion product listings that updates 2 billion entries every hour.
What makes this revolutionary is the shift from keyword-based searching to natural, conversational queries. Instead of typing “men’s running shoes size 10 blue,” shoppers can now describe exactly what they’re looking for in plain language: “I need comfortable running shoes for marathon training that won’t cause blisters, preferably in blue or gray, under $150.”
According to Vidhya Srinivasan, Vice President and General Manager of Ads & Commerce at Google, shoppers using AI Mode are asking questions that are 23 times longer than traditional searches. This isn’t just a minor shift—it’s a complete reimagining of search behavior.
The AI responds contextually based on query type. Visual shopping questions generate shoppable image grids, while product comparison queries produce detailed side-by-side tables highlighting key factors like ingredients, texture, dimensions, and real reviewer insights. This means consumers can make more informed decisions without leaving Google’s ecosystem.
Gemini App Shopping: From Inspiration to Purchase in One Conversation
The Gemini app has evolved beyond being a simple chatbot into a full-fledged shopping assistant. Users can now brainstorm gift ideas, explore products within specific budgets, and browse real listings—all within a single conversational interface.
Imagine asking Gemini, “I need gift ideas for my tech-savvy dad who loves photography, budget around $200.” The app doesn’t just provide generic suggestions; it pulls actual products from the Shopping Graph, displays comparison tables, shows current prices, and indicates where you can buy each item.
This feature is currently available to U.S.-based Gemini app users aged 18 and older with consumer accounts, but the implications are universal: the line between discovery and transaction is disappearing.
Agentic Checkout: AI That Shops While You Sleep
Perhaps the most futuristic feature is agentic checkout—a system that actively monitors prices and can even complete purchases on your behalf with your permission.
Here’s how it works: You find a product you want but it’s slightly above your budget. Instead of manually checking prices every day, you set up tracking rules specifying exactly what you want and your price threshold. When the price drops within your budget, Google sends you an alert. If the merchant is eligible and you’ve granted permission, Google can complete the purchase on the merchant’s site using Google Pay after you confirm the purchase and shipping details.
This feature is rolling out initially with eligible merchants including Wayfair, Chewy, Quince, and select Shopify merchants, with more partnerships in development. The system is built on Google’s Shopping Graph and payments infrastructure to ensure accuracy and security.
Think about what this means: Google isn’t just helping people find products anymore—it’s becoming an active participant in the transaction itself.
AI-Powered Local Business Calling: Bridging the Digital-Physical Divide
The fourth feature might be the most surprising: Google will now call local businesses on your behalf to check product availability and pricing.
When searching for products “near me,” users may see a “Let Google Call” option. After answering a few questions about what they’re looking for and their budget, Google’s system—powered by Duplex technology enhanced with Gemini models—calls nearby stores, asks relevant questions, and sends a summary by email or text.
This feature is starting with a limited set of shopping categories, but Google has been careful to design it responsibly. Srinivasan noted, “We are trying to be mindful of how merchants experience features in terms of making sure we don’t call too often, having a very clear idea about exactly what to ask and things of that nature.”
Why These Changes Matter for Your Business
If you’re a retailer, brand, or business with any e-commerce component, these updates fundamentally change where and how your customers make purchase decisions. Let’s break down the specific implications.
The Shift from Clicks to Conversions Within Google’s Ecosystem
Traditionally, Google Search served as the starting point of a customer journey. Someone would search for a product, click through to your website, browse, and hopefully convert. You controlled the experience once they landed on your site.
With conversational shopping in AI Mode, that dynamic is shifting. Shoppers can now conduct extensive research, compare products, read reviews, and make decisions without ever leaving Google’s interface. They’re spending more time inside Google’s product grids and comparison tables before—or instead of—visiting your website.
This isn’t necessarily negative, but it does require a strategic shift. Your visibility within Google’s Shopping Graph becomes more critical than ever. Clean, comprehensive product feeds with accurate specifications, competitive pricing data, high-quality images, and authentic reviews are no longer nice-to-haves—they’re essential for appearing in AI-generated responses.
Product Data Quality Is the New SEO
At ALM Corp, we’ve always emphasized that data-driven strategies deliver results. In this new landscape, your product data quality becomes a ranking factor as important as traditional SEO signals.
Google’s AI models pull from the Shopping Graph to answer queries, which means your product information needs to be:
- Complete: Every specification, dimension, color option, and feature documented
- Accurate: Exact match between your listings and actual products
- Current: Real-time updates for inventory, pricing, and availability
- Rich: High-quality images from multiple angles, detailed descriptions, and authentic customer reviews
- Structured: Proper use of schema markup and product feeds that Google can easily parse
Think of product data optimization as the technical SEO of e-commerce. Just as we’ve helped clients improve their organic visibility through comprehensive SEO strategies, optimizing your product data ensures visibility in AI-powered shopping experiences.
The Importance of Google Pay and Seamless Checkout Experiences
Agentic checkout introduces a new dimension to e-commerce strategy. For the feature to work, merchants need to support guest checkout and Google Pay integration.
If your checkout process is cumbersome, requires account creation, or doesn’t support streamlined payment methods, you’re potentially excluding yourself from this automated purchasing channel. The businesses that will benefit most are those with frictionless checkout experiences.
This aligns with a broader principle we advocate at ALM Corp: conversion rate optimization isn’t just about your website design—it’s about reducing friction at every touchpoint. In a world where Google can complete purchases on behalf of consumers, ensuring your systems integrate smoothly with these AI agents becomes a competitive advantage.
Local Businesses: Optimize Your Google Business Profile Immediately
The AI-powered calling feature has particular implications for brick-and-mortar retailers and local businesses. When Google’s AI calls to check inventory and pricing, it’s relying on the information available in your Google Business Profile and the quality of your staff’s responses.
To capitalize on this feature:
- Complete your Google Business Profile with accurate hours, contact information, product categories, and attributes
- Maintain real-time inventory accuracy if you use Google’s inventory management features
- Train staff to handle AI-powered calls professionally and provide consistent information
- Update pricing regularly to ensure alignment between your physical store and online presence
- Encourage and respond to reviews since Google may reference your reputation when recommending stores to call
This feature effectively extends Google’s reach into the offline shopping experience, creating a bridge between digital discovery and physical transactions. Local businesses that optimize for this will see increased foot traffic and conversions.
Strategic Recommendations: How to Thrive in the AI Shopping Era
Based on our experience driving measurable results for thousands of clients and our analysis of these Google updates, here are actionable strategies to position your business for success.
Audit and Enhance Your Product Feed
Conduct a comprehensive audit of your product data across all channels where you list products: Google Merchant Center, your e-commerce platform, marketplaces, and anywhere else your products appear.
Look for:
- Incomplete or missing fields
- Inconsistent naming conventions
- Outdated pricing or inventory information
- Low-quality images
- Generic or duplicate descriptions
- Missing schema markup
Then create a regular maintenance schedule. Remember, Google updates 2 billion listings every hour. Your product data should be equally dynamic, with automated feeds that sync changes in real-time.
Invest in Conversational Content and Long-Tail Optimization
Since AI Mode queries are 23 times longer than traditional searches, your content strategy needs to evolve. Start creating content that answers complex, conversational questions about your products.
Instead of just optimizing for “wireless headphones,” create comprehensive content addressing queries like:
- “Best wireless headphones for video editing that won’t cause ear fatigue during 8-hour work sessions”
- “Wireless headphones under $200 with active noise cancellation for commuting on trains”
- “Comfortable wireless headphones for people who wear glasses that don’t create pressure points”
This type of long-tail, intent-focused content aligns with how people are now searching in AI Mode. It also demonstrates expertise and addresses specific customer pain points, which AI models reward when generating recommendations.
Prioritize Review Generation and Management
Reviews have always been important, but in an AI-driven shopping experience, they become even more critical. Google’s AI references reviewer insights when generating product comparisons and recommendations.
Implement systematic review generation programs:
- Follow up with customers post-purchase requesting honest reviews
- Make the review process as frictionless as possible
- Respond professionally to all reviews, both positive and negative
- Showcase reviews prominently on product pages with schema markup
Quality reviews don’t just influence AI recommendations—they also build trust and provide the authentic social proof that converts browsers into buyers.
Optimize for Google Pay and Streamline Checkout
If you’re an e-commerce business, evaluate your checkout process with fresh eyes:
- Does it support Google Pay?
- Can customers check out as guests?
- How many steps are required from cart to confirmation?
- Are there unnecessary form fields or decision points?
Reducing checkout friction benefits all customers, but it’s essential for compatibility with agentic checkout. Consider this an opportunity to audit your entire conversion funnel and eliminate barriers.
Embrace Multi-Channel Integration
The boundaries between channels are dissolving. A customer might start their journey in the Gemini app, compare products in AI Mode on Search, receive a price alert later, and complete a purchase through agentic checkout—all without visiting your website directly.
Your marketing strategy needs to recognize this reality. Instead of treating channels as silos, create an integrated approach where product data, messaging, pricing, and inventory are consistent everywhere your business appears.
This is where ALM Corp’s integrated digital marketing strategies deliver amplified results. We ensure all your digital efforts work in concert, creating a cohesive presence across every touchpoint where customers interact with your brand.
Monitor Performance with AI-Aware Analytics
Traditional analytics focused on website traffic, bounce rates, and conversion funnels. In an AI-mediated shopping environment, you need to track different metrics:
- Impressions and clicks within Google Shopping surfaces
- Product visibility in AI-generated comparisons
- Price competitiveness relative to AI-surfaced alternatives
- Review quality and quantity compared to competitors
- Google Merchant Center performance metrics
- Agentic checkout conversion rates (when available)
Set up dashboards that monitor your presence within Google’s ecosystem, not just traffic to your website. This requires a shift in mindset: visibility and influence within AI-powered platforms is as valuable as owned-channel traffic.
The Bigger Picture: AI Is Transforming the Entire Customer Journey
While Google’s shopping updates are significant, they’re part of a larger trend: artificial intelligence is fundamentally restructuring how consumers discover, evaluate, and purchase products across every category.
Other platforms are making similar moves. Amazon is integrating AI shopping assistants, social media platforms are experimenting with conversational commerce, and standalone AI applications are emerging as shopping destinations in their own right.
The businesses that will thrive in this environment are those that recognize AI isn’t just another marketing channel—it’s a fundamental shift in how commerce operates. Success requires:
- Adaptability: The willingness to evolve strategies as AI capabilities advance
- Data excellence: Commitment to maintaining high-quality, comprehensive product and business data
- Customer-centricity: Focus on meeting customer needs regardless of which interface they use
- Integrated thinking: Recognition that touchpoints are interconnected and should be optimized holistically
- Continuous learning: Staying informed about AI developments and adjusting accordingly
At ALM Corp, these principles guide our approach to digital marketing. We don’t just chase trends—we help clients build resilient, adaptable strategies that deliver results regardless of how technology evolves.
Looking Ahead: What’s Next for AI Shopping?
Google has indicated these features are just the beginning. Expect merchant participation in agentic checkout to expand beyond the initial partners as more retailers support necessary integrations. Local calling will likely extend to more product categories over time. And while these features currently focus on U.S. shoppers, international availability is inevitable.
Beyond Google, watch for AI shopping features to proliferate across platforms. The underlying principle—using AI to reduce friction, provide personalized recommendations, and streamline transactions—will become standard across e-commerce.
For forward-thinking businesses, now is the time to position yourself advantageously. The companies that optimize their product data, embrace new transaction models, and integrate AI touchpoints into their customer journey will capture disproportionate value as these technologies mature.
Partner with Experts Who Understand the AI Revolution
Navigating these changes requires expertise, strategic thinking, and the ability to execute across multiple disciplines—SEO, e-commerce optimization, paid media, analytics, and technology implementation.
At ALM Corp, we’ve built our reputation on delivering integrated strategies and amplified results for our clients. Our data-driven, results-obsessed approach means we don’t just help you understand changes like Google’s AI shopping tools—we help you capitalize on them.
Whether you need to optimize your product feeds, restructure your content strategy for conversational search, implement conversion rate optimization for agentic checkout compatibility, or develop a comprehensive approach to AI-era commerce, our expert team provides passionate support and transparent partnership every step of the way.
The digital landscape never stands still, and neither do we. We embrace innovation and cutting-edge technologies to keep you ahead of the curve—not just responding to changes, but anticipating and preparing for what’s next.
Ready to thrive in the age of AI shopping? Let’s discuss how ALM Corp’s comprehensive digital marketing solutions can position your business for success in this transforming landscape. Contact our team to start the conversation.
Frequently Asked Questions About Google’s AI Shopping Tools
What are Google's new AI shopping tools?
Google’s AI shopping tools are a suite of artificial intelligence-powered features that transform how consumers search for, compare, and purchase products online. Launched in 2024, these tools include AI Mode Shopping (conversational product search), Gemini App Shopping (AI-powered shopping assistant), Agentic Checkout (automated price tracking and purchasing), and AI-Powered Local Business Calling (automated inventory checks with local stores). These features are currently available to users in the United States and utilize Google’s Shopping Graph—a database containing over 50 billion product listings that updates 2 billion entries every hour—combined with advanced Gemini language models to provide personalized, conversational shopping experiences.
When did Google launch these AI shopping features?
Google began rolling out these AI shopping features in late 2024, with initial availability limited to users in the United States. The rollout is happening in phases, with AI Mode Shopping and Gemini App Shopping available first, followed by gradual expansion of Agentic Checkout to eligible merchants including Wayfair, Chewy, Quince, and select Shopify stores. The AI-Powered Local Business Calling feature launched in a limited set of shopping categories with plans to expand to more product types over time. International availability and broader merchant eligibility timelines have not yet been officially announced by Google.
Which countries have access to Google's AI shopping tools?
As of the initial launch, Google’s AI shopping tools are exclusively available in the United States. This includes AI Mode Shopping in Google Search, shopping features in the Gemini app (for users 18 years and older with consumer accounts), Agentic Checkout with participating merchants, and AI-Powered Local Business Calling. Google has not provided specific timelines for international expansion, but given the company’s global reach and the competitive nature of e-commerce technology, it’s anticipated that these features will gradually roll out to additional markets, likely starting with English-speaking countries and major e-commerce markets such as the United Kingdom, Canada, Australia, and European Union nations.
How does Google's AI shopping differ from traditional Google Shopping?
Traditional Google Shopping relies on keyword-based searches where users input specific product terms and browse through product listings based on relevance, price, and other filters. Google’s AI shopping tools fundamentally differ by enabling conversational, natural language queries that are approximately 23 times longer than traditional searches. Instead of typing “blue running shoes size 10,” users can describe their exact needs: “comfortable marathon training shoes that prevent blisters, preferably blue or gray, under $150.” The AI then generates contextual responses including shoppable image grids, detailed comparison tables with specifications and reviewer insights, and personalized recommendations. Additionally, AI shopping introduces autonomous features like automated price monitoring and purchasing (Agentic Checkout) and proactive inventory checking through AI-powered phone calls to local businesses—capabilities that don’t exist in traditional Google Shopping.
What is Google's Shopping Graph?
Google’s Shopping Graph is a comprehensive, dynamic database that powers the AI shopping experience. It contains over 50 billion product listings from retailers worldwide, with approximately 2 billion listings updated every hour to reflect real-time changes in pricing, inventory availability, product specifications, and new product additions. The Shopping Graph aggregates data from multiple sources including merchant feeds, product catalogs, verified seller information, customer reviews, and product images. It uses machine learning to understand product relationships, identify duplicate listings, verify product authenticity, and maintain data accuracy. When users interact with AI Mode Shopping or Gemini App Shopping, the AI models query the Shopping Graph to provide current, accurate product information, enabling features like real-time price comparisons, inventory availability checks, and personalized product recommendations based on specific user requirements.
What is AI Mode Shopping and how does it work?
AI Mode Shopping is an advanced search experience within Google Search that combines Gemini language models with Google’s Shopping Graph to handle conversational shopping queries. When enabled, AI Mode allows users to describe products using natural language rather than relying on traditional keywords and filters. The system interprets the user’s intent, preferences, and requirements, then searches the Shopping Graph’s 50+ billion product listings to generate contextual responses. For visual product queries, AI Mode displays shoppable image grids where users can browse products visually. For product comparison requests, it generates detailed side-by-side comparison tables highlighting key factors like specifications, ingredients, texture, dimensions, pricing, and authentic reviewer insights. The AI understands context and can handle follow-up questions, refine searches based on additional criteria, and provide increasingly personalized recommendations as the conversation progresses.
How long are typical AI Mode shopping queries compared to regular searches?
According to Vidhya Srinivasan, Google’s Vice President and General Manager of Ads & Commerce, shoppers using AI Mode are asking questions that are approximately 23 times longer than traditional search queries. While a traditional search might consist of 2-4 words like “wireless headphones,” an AI Mode query might be: “I need wireless headphones under $200 with active noise cancellation for my daily train commute, comfortable enough to wear for 2 hours, and compatible with both my iPhone and laptop.” This dramatic increase in query length reflects a fundamental shift in search behavior—users are providing detailed context, specific requirements, budget constraints, use cases, and personal preferences because they trust the AI to understand and process complex, conversational requests rather than simple keyword matches.
Can AI Mode Shopping show me product comparisons?
Yes, product comparison is one of AI Mode Shopping’s core capabilities. When you ask comparative questions like “compare Sony WH-1000XM5 vs Bose QuietComfort 45 headphones” or “which is better for sensitive skin between CeraVe and Cetaphil moisturizers,” AI Mode generates detailed side-by-side comparison tables. These tables go beyond basic specifications to include nuanced factors such as ingredient differences, texture descriptions, user experience insights from verified reviews, performance in specific scenarios, durability considerations, warranty information, and price comparisons across retailers. The AI synthesizes information from multiple sources including manufacturer specifications, professional reviews, customer feedback, and verified purchase data to provide comprehensive comparisons that help users make informed decisions. You can also ask follow-up questions to dive deeper into specific comparison criteria, and the AI will maintain context throughout the conversation.
What types of products can I search for using AI Mode?
AI Mode Shopping supports searches across virtually all product categories available in Google’s Shopping Graph, which encompasses consumer goods, electronics, fashion and apparel, beauty and personal care, home and garden, sports and outdoor equipment, toys and games, books and media, automotive parts and accessories, pet supplies, health and wellness products, office supplies, and much more. The system is particularly effective for complex products where detailed specifications matter, such as electronics and technical equipment; subjective products where personal preferences are important, like clothing, cosmetics, and home decor; and products where use case context is critical, such as sports equipment, baby products, and specialized tools. However, AI Mode may have limitations with highly regulated products, prescription items, age-restricted goods, or products requiring professional consultation, where Google maintains additional safety and compliance protocols.
Does AI Mode Shopping work on mobile devices?
Yes, AI Mode Shopping is accessible on mobile devices through the Google Search app and mobile web browsers. The experience is optimized for mobile interfaces with touch-friendly product grids, swipeable image galleries, and responsive comparison tables that adapt to smaller screens. Mobile users can type queries using their keyboard or use voice input to ask shopping questions conversationally. The mobile experience maintains all the core functionality of desktop AI Mode, including natural language queries, contextual product recommendations, detailed comparisons, and integration with the Shopping Graph. Mobile AI Mode also leverages device-specific features such as camera integration for visual searches, location services for finding products nearby, and seamless integration with Google Pay for streamlined mobile checkout. Given that the majority of product searches now originate on mobile devices, Google has prioritized ensuring the AI shopping experience is fully functional and user-friendly on smartphones and tablets.
What shopping features are available in the Gemini app?
The Gemini app now includes comprehensive shopping capabilities powered by Google’s Shopping Graph, transforming it from a conversational AI assistant into a full-featured shopping companion. Users can brainstorm gift ideas by describing the recipient and receiving personalized product suggestions, explore products within specific budget ranges with real pricing and availability, conduct product research by asking detailed questions about specifications and comparisons, view actual product listings with images and prices directly in the chat interface, access comparison tables that evaluate multiple products across key criteria, get buying advice including where to purchase and which retailers offer the best value, and receive personalized recommendations based on stated preferences and requirements. The shopping experience is fully conversational—users can ask follow-up questions, refine searches, add new criteria, and explore alternatives naturally through conversation. All product information is real-time and sourced from the Shopping Graph’s 50+ billion listings.
Who can use Gemini app shopping features?
Gemini app shopping features are currently available to users who meet the following eligibility criteria: they must be located in the United States, have a consumer Google account (not a workspace or educational account), be 18 years of age or older, and have the Gemini app installed on their device. The features work on both Android and iOS versions of the Gemini app. Business or enterprise Google Workspace accounts may have different feature availability depending on their organization’s settings and Google’s enterprise policies. The age restriction to 18+ is consistent with e-commerce regulations and Google’s policies around shopping and financial transactions. Users outside the United States currently do not have access to shopping features in Gemini, though this is expected to change as Google expands availability internationally.
Can Gemini help me find gifts within a specific budget?
Yes, gift finding within budget constraints is one of Gemini’s most powerful shopping applications. You can provide detailed context such as: “I need gift ideas for my tech-savvy dad who loves photography, budget around $200” or “Birthday gift for my 10-year-old niece who likes science experiments and outdoor activities, $50 maximum.” Gemini processes this information and searches the Shopping Graph for relevant products within your specified price range. It presents actual products with current prices, brief descriptions, and buying options. You can then refine the search by asking follow-up questions like “show me options under $150” or “which of these would be best for a beginner?” The AI maintains budget awareness throughout the conversation and can explain why certain products fit your criteria, compare options within your price range, and suggest alternatives if initial recommendations don’t quite match your needs. This makes Gemini particularly valuable for gift shopping where you have clear budget constraints but need creative, personalized ideas.
How does Gemini app shopping differ from searching on Google?
While both Gemini app shopping and Google Search (including AI Mode) leverage the Shopping Graph and AI capabilities, they offer distinct experiences designed for different use cases. Gemini app shopping is conversational and exploratory—it’s ideal for brainstorming, open-ended product discovery, getting advice, and having extended discussions about what you need. The conversation persists over multiple interactions, with Gemini remembering context from earlier in the chat. Google Search (including AI Mode) is query-focused and results-oriented—it’s optimized for finding specific products quickly, comparing known items, and moving efficiently from search to purchase. Google Search integrates more tightly with merchant websites, checkout flows, and Google Pay. Gemini is better when you’re in the research phase and unsure exactly what you want, while Google Search is better when you have clearer intent and are closer to making a purchase decision. Both complement each other in the overall shopping journey.
Can I complete purchases directly in the Gemini app?
Currently, the Gemini app provides shopping discovery, research, and recommendation features, but the actual purchase transaction typically occurs through external pathways. When you find products you’re interested in through Gemini, the app provides links to retailers where you can complete the purchase, either through their websites or apps. Gemini can show you buying options including which stores carry the product, current pricing across retailers, and whether items are in stock. However, the checkout and payment process happens on the merchant’s platform rather than within Gemini itself. This is different from Agentic Checkout (available in Google Search), which can complete purchases on your behalf with permission. As Google continues developing these features, future versions may offer more integrated purchasing capabilities within the Gemini app ecosystem, but the current functionality focuses on the pre-purchase research and discovery phases of shopping.
What is Agentic Checkout and how does it work?
Agentic Checkout is an autonomous shopping feature where Google’s AI actively monitors product prices on your behalf and can complete purchases when conditions you’ve specified are met. The process works as follows: First, you identify a product you want but that’s currently outside your budget or you want to wait for a price drop. You then set up tracking rules specifying the exact product, your target price or budget threshold, and your preferences. Google’s AI continuously monitors the product across the Shopping Graph, tracking price changes in real-time. When the price drops to meet your criteria, Google sends you an alert notification. If the merchant is eligible for agentic checkout and you’ve previously granted permission, Google can then complete the purchase on the merchant’s website using Google Pay, after you confirm the final purchase details and shipping information. The entire process is built on Google’s Shopping Graph for accurate product tracking and their secure payments infrastructure to protect your financial information. You maintain control throughout—the AI can’t make purchases without your explicit confirmation.
Which stores and merchants support Agentic Checkout?
Agentic Checkout initially launched with a select group of participating merchants and is gradually expanding. The initial partners include Wayfair (furniture and home goods), Chewy (pet supplies), Quince (premium essentials and apparel), and select merchants using the Shopify platform. For merchants to support agentic checkout, they must meet specific technical requirements including support for guest checkout (allowing purchases without mandatory account creation), integration with Google Pay for streamlined payment processing, compatibility with Google’s API infrastructure for automated purchase completion, and real-time inventory and pricing feeds that sync with the Shopping Graph. Google has indicated plans to expand merchant participation beyond this initial group as more retailers implement the necessary technical integrations and as the feature proves successful. The expansion will likely prioritize larger e-commerce platforms, popular retailers with strong online presence, and merchants in high-demand product categories.
Is Agentic Checkout safe and secure?
Yes, Agentic Checkout is designed with multiple layers of security and user control. Google built the feature on their established payments infrastructure that already processes billions of secure transactions through Google Pay. Your payment information is encrypted and tokenized, meaning merchants never receive your actual credit card numbers—they receive secure, one-time-use tokens instead. The AI cannot make purchases without your explicit confirmation—you must review and approve each transaction, including verifying the final price, shipping address, and order details before the purchase is completed. You have complete control over which merchants can use agentic checkout on your behalf, and you can revoke permissions at any time. The price monitoring utilizes Google’s Shopping Graph, which continuously validates product information and pricing accuracy across billions of listings. Additionally, standard consumer protections apply—purchases made through agentic checkout are subject to the same return policies, fraud protections, and customer service support as manual purchases. Google also maintains transaction records for your review and tracks purchase activity to identify any unusual patterns.
How do I set up price tracking with Agentic Checkout?
Setting up price tracking with Agentic Checkout involves several straightforward steps within Google Search or AI Mode. First, search for the specific product you’re interested in using Google Search. When you view product details, look for the price tracking or “Track price” option (interface may vary as the feature rolls out). Click to set up tracking, then specify your preferences including your target price or maximum budget, whether you want alerts for any price drop or only when it reaches your threshold, and how you want to be notified (email, mobile notification, or both). You can also specify additional criteria such as specific sellers you prefer, color or size options, and whether you’ll accept refurbished or used alternatives. Once configured, Google’s AI begins monitoring the product across all retailers in the Shopping Graph. If you want to enable automated purchasing (not just price alerts), you’ll need to grant agentic checkout permission, verify your Google Pay payment method is current, and confirm your default shipping address. You can manage all your tracked products, modify tracking parameters, and review or cancel tracking at any time through your Google account settings.
Can Agentic Checkout work for products that are currently out of stock?
Yes, Agentic Checkout’s price tracking capabilities extend to out-of-stock products, though with some important considerations. You can set up tracking for products that are temporarily unavailable, and Google’s AI will monitor both price changes and inventory status across multiple retailers in the Shopping Graph. When the product comes back in stock and meets your price criteria, you’ll receive an alert notification. This is particularly valuable for high-demand products that frequently sell out, seasonal items that may be temporarily unavailable, newly released products with limited initial availability, or products where you’re willing to wait for both restocking and a price drop. However, agentic checkout cannot complete automated purchases for out-of-stock items—the product must be available for immediate purchase when your criteria are met. For discontinued products or items that may never return to stock, the tracking will persist for a period but eventually expire if the product remains permanently unavailable. You’ll receive status updates about long-term unavailability so you can decide whether to continue tracking or explore alternative products.
How does Google's AI calling feature for local businesses work?
Google’s AI-Powered Local Business Calling feature uses advanced conversational AI technology (Duplex enhanced with Gemini models) to contact local businesses on your behalf to check product availability, pricing, and other relevant information. The process begins when you search for products “near me” in Google Search and see a “Let Google Call” option for relevant results. After clicking this option, you answer a few brief questions about what you’re looking for, your budget range, specific requirements (size, color, features), and when you need the product. Google’s AI then analyzes nearby businesses that likely carry the product, prioritizes them based on distance, ratings, and likelihood of having what you need, and places phone calls during business hours. During the call, the AI asks store representatives specific questions about availability, pricing, variations, and any other details relevant to your query. The AI is programmed to be polite, professional, and efficient in its conversations. After completing the calls, Google compiles the information into a summary delivered to you via email or text message, showing which stores have the product, pricing at each location, and any additional relevant details the store provided.
What types of products can Google's AI call local businesses about?
The AI-Powered Local Business Calling feature launched with a limited set of shopping categories and is expanding over time. Initially, the feature focuses on product categories where local availability varies significantly and where phone inquiries are common, including electronics and accessories (checking if specific models are in stock), home improvement and hardware items, automotive parts and accessories, sporting goods and equipment, pet supplies, musical instruments, appliances (checking availability of specific models), specialty items not typically shown in online inventory systems, and products where specification details matter and may require clarification. The feature is designed for products sold in brick-and-mortar retail stores with phone support during business hours. It’s not currently used for services, appointments, reservations, or highly specialized professional consultations. As Google refines the technology and gathers feedback from merchants and users, additional product categories will be added. The system also learns which types of inquiries are most successful and appropriate for automated calling.
How do local businesses receive calls from Google's AI?
When Google’s AI calling feature contacts a local business, the call appears as an incoming phone call during the business’s operating hours. The AI identifies itself at the beginning of the call, explaining that it’s calling on behalf of a customer who is looking for specific product information. For example, the AI might say: “Hello, I’m calling from Google on behalf of a customer who is looking for a product. I’m an automated assistant. The customer is looking for [product description]. Do you have this item in stock?” The AI is programmed to be respectful, patient, and professional, and to handle various conversation flows including confirmation that the store carries the product category, specific product availability questions, pricing inquiries, questions about alternatives if the exact item isn’t available, and thanking the store representative before ending the call. Google has implemented safeguards to minimize disruption, including limiting call frequency to individual businesses (preventing multiple automated calls in short periods), calling only during posted business hours, ending calls efficiently once information is gathered, and maintaining records of calls for quality assurance and merchant feedback.
Can businesses opt out of receiving AI calls from Google?
Yes, Google recognizes that not all businesses may want to participate in the AI calling feature and provides mechanisms for merchants to opt out or manage how they’re included. Businesses can adjust their participation through their Google Business Profile settings, where they can indicate preferences for automated calls, specify optimal times for receiving inquiries, or opt out entirely. Google also monitors merchant feedback and will reduce or eliminate calls to businesses that consistently indicate they cannot accommodate automated inquiries or that experience operational challenges with the feature. Vidhya Srinivasan specifically noted that Google is “trying to be mindful of how merchants experience features in terms of making sure we don’t call too often, having a very clear idea about exactly what to ask and things of that nature.” If a business repeatedly indicates during calls that they cannot help with automated inquiries, Google’s system learns to deprioritize or exclude that business from future automated calling. Businesses concerned about the feature should review their Google Business Profile settings or contact Google Business support to manage their preferences.
Is the AI calling feature available in all locations?
No, the AI-Powered Local Business Calling feature is currently available only in the United States and only for certain product categories. The limited initial rollout allows Google to test the technology, gather feedback from both users and merchants, refine the conversational AI to handle various store interactions more effectively, and ensure the feature provides value without creating operational challenges for businesses. Geographic expansion will depend on several factors including language support (the feature needs to communicate fluently in local languages), regional business communication norms and preferences, regulatory compliance (some regions have specific regulations around automated calling), and merchant ecosystem readiness and acceptance. As Google refines the technology and expands language capabilities, the feature will likely become available in other English-speaking markets first, followed by major international markets. Businesses and consumers in regions without current access can expect gradual rollout over the coming months and years as the technology matures.
How should e-commerce businesses prepare for Google's AI shopping tools?
E-commerce businesses should take multiple strategic actions to prepare for and capitalize on Google’s AI shopping tools. First, conduct a comprehensive audit of your product data quality, ensuring all product feeds contain complete, accurate specifications, high-resolution images from multiple angles, detailed descriptions that answer common questions, real-time pricing and inventory information, and proper schema markup for Google to parse effectively. Second, optimize your Google Merchant Center account by verifying all products are approved, maintaining 100% data accuracy, implementing automated feed updates for real-time synchronization, and monitoring performance metrics specific to AI shopping surfaces. Third, invest in review generation by systematically requesting reviews from satisfied customers, responding professionally to all reviews (positive and negative), addressing reviewer concerns to demonstrate customer service quality, and showcasing reviews prominently on product pages with proper markup. Fourth, ensure technical compatibility by supporting Google Pay for streamlined checkout, enabling guest checkout without mandatory account creation, optimizing for mobile shopping experiences, and implementing structured data markup across all product pages. Fifth, develop conversational content by creating detailed product guides that answer complex questions, developing comparison content for your products versus alternatives, addressing long-tail, specific use case queries, and building FAQ sections that AI models can reference. Finally, monitor your presence in AI shopping experiences through Google Search Console and Merchant Center analytics, track how products appear in AI-generated responses, analyze which products receive visibility and conversions, and continuously refine based on performance data.
Do Google's AI shopping tools mean less traffic to e-commerce websites?
Google’s AI shopping tools do represent a shift in where customers spend time during their shopping journey, and businesses may see changes in traditional traffic patterns. Customers can now conduct extensive research, compare products, view specifications, and read reviews without leaving Google’s ecosystem, which means some interactions that previously required visiting your website may now happen within Google’s AI interfaces. However, this doesn’t necessarily mean less business—it means evolving how you capture value. High-quality product data ensures visibility in AI-generated responses, increasing brand awareness even if clicks are deferred. Customers who do reach your website from AI shopping tools are often further along the purchase journey with higher intent and conversion likelihood. Agentic checkout can actually facilitate more transactions by removing friction, even if customers never manually visit your site. The key is understanding that customer journeys are changing, not disappearing. Smart businesses will optimize for visibility and conversion wherever customers make decisions—whether on their owned website, within Google’s AI experiences, in the Gemini app, or through agentic checkout flows. The businesses that will struggle are those clinging to outdated metrics like raw traffic instead of focusing on actual conversions and revenue. By maintaining strong product data, competitive pricing, excellent reviews, and seamless checkout experiences, you can capture sales regardless of which interface customers use.
How important is product data quality for AI shopping visibility?
Product data quality is absolutely critical for visibility in Google’s AI shopping experiences—it’s comparable to how foundational SEO is for traditional search visibility. Google’s AI models rely on the Shopping Graph to answer queries and generate recommendations. If your product data is incomplete, inaccurate, inconsistent, or poorly structured, the AI cannot confidently recommend your products or include them in comparisons. Specifically, high-quality product data means complete specifications including every relevant attribute (dimensions, materials, compatibility, etc.), accurate and current pricing that matches your actual selling price, real-time inventory status so AI doesn’t recommend out-of-stock items, high-resolution images from multiple angles showing product details clearly, detailed descriptions that address common questions and use cases, proper categorization and attributes so products appear for relevant queries, structured data markup that helps Google parse and understand product information, and authentic reviews with sufficient quantity and detail for AI to reference. Think of product data as your product’s resume when applying for visibility in AI-generated responses. The more comprehensive and accurate your data, the more confidently AI can recommend your products. According to Google, 2 billion listings in the Shopping Graph are updated every hour—this real-time nature means your product data needs to be equally dynamic, with automated feeds that reflect changes immediately. Businesses that invest in product data quality will see disproportionate visibility in AI shopping experiences compared to competitors with poor data hygiene.
Should local businesses optimize their Google Business Profile for AI calling?
Yes, local businesses should absolutely optimize their Google Business Profile (GBP) to benefit from the AI calling feature and broader AI shopping trends. A well-optimized GBP increases the likelihood that Google’s AI will contact your store when relevant queries occur, ensures the AI has accurate information about your business, and improves overall visibility in local search results. Specific optimization steps include completing every field in your Google Business Profile (hours, categories, attributes, descriptions, phone number), maintaining accurate and current inventory information if you use Google’s inventory features, uploading high-quality photos of your store, products, and team, consistently responding to reviews to demonstrate active management and customer service, adding specific product categories and attributes that match what customers search for, ensuring your phone number is correct and answered during business hours, training staff to handle AI-powered calls professionally and provide consistent information, keeping hours updated including special hours for holidays, and monitoring insights to understand when and how customers find your business. Additionally, consider posting regular updates about new products, special availability, or limited-time offerings. The AI calling feature essentially extends Google’s reach into offline shopping, creating a bridge between online search and in-store purchases. Businesses with comprehensive, accurate GBPs will receive higher-quality referrals from AI calling and will be prioritized by Google’s system when determining which stores to contact for customer inquiries.
How will Google's AI shopping affect digital advertising and PPC campaigns?
Google’s AI shopping tools will significantly impact digital advertising strategies, though they represent evolution rather than elimination of existing approaches. Paid advertising remains relevant but needs adaptation for the AI era. Shopping ads and Product Listing Ads (PLAs) will still appear in AI shopping experiences, so maintaining high-quality product feeds and competitive bidding strategies remains essential. However, the context in which ads appear is changing—they may show alongside AI-generated product comparisons, within conversational shopping flows in Gemini, or as alternatives to organic AI recommendations. Businesses should focus on product data quality even more than before, since ads compete with rich organic AI results that pull from the Shopping Graph. Brand awareness campaigns become more valuable because even if customers don’t click your ads immediately, familiarity influences which products AI recommends and which options customers trust in AI-generated comparisons. Attribution modeling will need updating, as customer journeys now include touchpoints like Gemini conversations, AI Mode explorations, and agentic checkout flows that traditional attribution may not capture. Advertisers should test ad formats specifically designed for AI experiences as Google develops them, invest in review generation since reviews influence AI recommendations and ad credibility, maintain competitive pricing since AI makes price comparisons more transparent and immediate, and focus on total conversion value rather than just traffic metrics, as customers may convert through various AI-mediated pathways. Long-term, advertising in an AI-driven shopping environment will reward businesses with genuinely competitive offerings, strong reputations, and excellent product data rather than those simply willing to pay the most for clicks.
What metrics should businesses track for AI shopping performance?
Businesses need to expand beyond traditional e-commerce metrics to effectively measure performance in AI shopping environments. Key metrics include Shopping Graph visibility (how often your products appear in AI-generated responses, measured through impression data in Google Merchant Center), AI-surfaced click-through rates (clicks from AI Mode, Gemini app, or other AI shopping interfaces to your product pages), product comparison inclusion rate (how frequently your products appear in AI-generated comparison tables), agentic checkout conversion rate (for participating merchants, tracking completed purchases through automated checkout), price competitiveness score (how your pricing compares to alternatives AI might recommend), review quality and quantity metrics (average rating, total review count, recency of reviews, and review velocity), product data quality score (completeness, accuracy, and feed health metrics from Google Merchant Center), local inventory accuracy (for businesses using AI calling, tracking whether reported availability matches actual inventory), cross-channel customer journey analysis (understanding how customers move between AI interfaces, organic search, your website, and conversion), cost per acquisition from AI channels (if you’re running paid campaigns in AI shopping experiences), and most importantly, total revenue and conversion value regardless of specific touchpoint attribution. Set up enhanced e-commerce tracking and UTM parameters that identify traffic from AI shopping sources. Create dashboards that separate AI-driven metrics from traditional e-commerce KPIs so you can understand performance in both contexts. Regularly analyze which products perform well in AI shopping versus traditional channels to identify optimization opportunities and inform inventory, pricing, and marketing strategies.
What structured data markup is important for AI shopping?
Proper structured data markup is essential for Google’s AI to understand, categorize, and recommend your products effectively. The most critical schema types include Product schema (schema.org/Product) with comprehensive properties such as name, description, image URLs, brand, SKU/GTIN, offers (including price, currency, availability, and seller), aggregateRating, and review information. Implement Offer schema (schema.org/Offer) for each product variant with specific pricing, availability status (InStock, OutOfStock, PreOrder, etc.), valid pricing dates, shipping details, and seller information. Use Review and AggregateRating schema (schema.org/Review and schema.org/AggregateRating) to provide detailed review data including reviewer name, rating value, review body, and review date. For products with variants, implement ProductGroup or hasVariant properties to show relationships between different versions. Include BreadcrumbList schema to show product categorization hierarchy. For local businesses, implement LocalBusiness schema with product inventory if applicable. Use Organization schema for brand information and credibility. Implement FAQPage schema for product questions and answers that AI can reference. Ensure all schema is validated using Google’s Rich Results Test and Structured Data Testing Tool, follows the latest schema.org specifications, includes all required and recommended properties, is kept updated when product information changes, and is properly nested (for example, reviews nested within product schema). JSON-LD format is generally preferred over microdata or RDFa for cleaner implementation. Remember that structured data isn’t just for traditional search—it’s increasingly how AI systems understand and categorize content for conversational responses.
How do I integrate Google Pay to support Agentic Checkout?
Integrating Google Pay to support Agentic Checkout involves both technical implementation and business setup. First, create a Google Pay business account through the Google Pay Business Console, providing necessary business verification information, tax details, and bank account information for settlements. Second, implement the Google Pay API on your e-commerce platform, which for many popular platforms like Shopify, WooCommerce, Magento, and BigCommerce can be done through available plugins or extensions. For custom implementations, follow Google’s official integration guide at developers.google.com/pay/api. Third, configure your checkout flow to support guest checkout without requiring account creation, as Agentic Checkout needs to complete purchases for users who may not have accounts on your site. Implement tokenized payment processing where Google Pay provides secure payment tokens rather than raw card data. Fourth, ensure your API infrastructure can accept programmatic purchase requests from Google’s Agentic Checkout system, including automated cart creation, address validation, order submission, and confirmation. Fifth, implement real-time inventory validation to prevent agentic checkout from completing purchases for out-of-stock items. Sixth, set up order management systems to handle automated orders appropriately, ensuring they integrate with your fulfillment workflows. Finally, test thoroughly in sandbox environments before going live, verify that automated purchases process correctly, ensure proper error handling for edge cases, and monitor automated transactions closely after launch. Google provides technical documentation, sandbox testing environments, and support for merchants implementing these integrations. If you work with an e-commerce platform provider or development agency, consult them about best practices for your specific technical stack.
What feed requirements does Google have for AI shopping features?
Google’s product feed requirements for AI shopping are comprehensive and stringent, as the AI relies on accurate, complete data to generate helpful recommendations. Essential feed requirements include unique product identifiers (id, GTIN/UPC/EAN when applicable, brand, and MPN), comprehensive titles that describe the product clearly without promotional text, detailed descriptions (minimum 160 characters recommended, covering key features, materials, and uses), high-quality images (minimum 800×800 pixels, maximum 16MB, clear white or simple background, no promotional overlays), accurate pricing with currency specification and no misleading discounts, real-time availability status (in stock, out of stock, preorder, backorder), product categories using Google’s product taxonomy, shipping information (weight, dimensions, shipping costs), and condition (new, refurbished, used). Recommended but highly valuable attributes include color, size, gender, age group, material, pattern, detailed product specifications, custom labels for internal categorization, product highlights (key features in bullet format), and product details (additional specifications). For specific categories, additional required attributes exist—for example, apparel requires size, color, and gender; electronics may require technical specifications; and consumables require expiration dates. Feed format can be XML, CSV, or TSV with proper encoding (UTF-8 recommended). Feed updates should occur at minimum daily, though hourly updates are better for price and inventory changes. Google Merchant Center provides feed specifications, validation tools, and error reports to help ensure compliance. Feeds must also comply with Google’s shopping ads policies regarding prohibited content, editorial standards, and misrepresentation. The higher quality and more comprehensive your product feed, the better your products will perform in AI shopping experiences where the AI needs rich data to understand and recommend products effectively.
How often should I update my product feed for AI shopping?
Product feed update frequency is critical for AI shopping success, as Google’s Shopping Graph updates 2 billion listings every hour and AI recommendations rely on current, accurate data. At minimum, update your product feed daily to ensure basic accuracy, but more frequent updates provide competitive advantages. For price changes, implement real-time or hourly updates because AI shopping features like Agentic Checkout monitor prices continuously and customers receive immediate alerts when prices drop. For inventory status, update as close to real-time as possible (ideally every 15-60 minutes) to prevent AI from recommending out-of-stock products, which creates poor customer experiences and wastes merchant calls for local businesses. For new product additions, add them to your feed immediately upon availability so AI can recommend them as soon as they’re in stock. For product information changes (specifications, images, descriptions), update within 24 hours to maintain data accuracy. For seasonal or promotional changes, update before promotions begin and immediately after they end. Most modern e-commerce platforms and feed management tools support automated, scheduled feed generation and submission. Implement incremental updates where possible, sending only changed data rather than full feeds each time, which reduces processing time and speeds up the reflection of changes in Google’s systems. Monitor Google Merchant Center for feed errors or warnings and address them immediately, as feed errors can result in products being excluded from AI shopping features. Consider implementing API-based content updates for critical changes (like inventory) rather than relying solely on scheduled feed submissions, as APIs can provide true real-time synchronization. The businesses that maintain the most current, accurate product data will have significant visibility advantages in AI shopping experiences.
What data does Google collect through AI shopping features?
Google collects various types of data through AI shopping features to provide personalized experiences, improve recommendations, and optimize the shopping ecosystem. Data collected includes search queries and conversational inputs (the questions you ask in AI Mode or Gemini about products), product interaction data (which products you view, compare, click on, or show interest in), shopping preferences and patterns (categories you browse, price ranges you target, brands you prefer), tracking and alert settings (products you’re monitoring and price thresholds you’ve set), purchase history through agentic checkout (products bought, prices paid, purchase frequency), location data when searching for local products or using “near me” queries, device and browser information for optimizing the experience across platforms, feedback and engagement signals (which recommendations you find helpful, which you dismiss), and aggregated and anonymized data about how users interact with AI shopping features overall. Google’s Privacy Policy governs how this data is collected, used, and protected. Data is used to personalize recommendations, improve AI model accuracy, optimize Shopping Graph data quality, detect fraud and abuse, provide customer support, and develop new features. Users can control many privacy settings through their Google Account, including pausing shopping activity, deleting shopping history, managing ad personalization settings, and controlling location tracking. For Agentic Checkout, payment information is handled through Google Pay’s secure infrastructure, and merchants receive tokenized payment data rather than actual card numbers. Users concerned about privacy should review their Google Account privacy settings and adjust preferences according to their comfort level.
Is my payment information secure with Agentic Checkout?
Yes, payment information security is a core design principle of Agentic Checkout, which is built on Google Pay’s established, secure infrastructure that already processes billions of transactions. Multiple security layers protect your information including encryption in transit and at rest using industry-standard protocols, tokenization where merchants receive secure, single-use tokens instead of your actual card numbers, PCI DSS compliance with payment card industry security standards, two-factor authentication options for high-value purchases or sensitive account changes, fraud detection systems that monitor for unusual purchasing patterns, user confirmation requirements (the AI cannot complete purchases without your explicit approval of final details), secure communication channels between Google’s systems and merchant platforms, regular security audits and vulnerability testing, and limited data retention where transaction details are stored only as long as necessary for legitimate business purposes. Additionally, standard consumer protections apply to purchases made through Agentic Checkout—you can dispute charges, request refunds, and report fraudulent transactions just as with manual purchases. Your payment instruments (cards, bank accounts) linked to Google Pay are stored securely in your Google Account with encrypted protection. You can review transaction history, manage payment methods, and revoke agentic checkout permissions at any time through your Google Account settings. Google’s security and privacy teams continually monitor for threats and update protections as new security challenges emerge. While no system is 100% immune to security risks, Google Pay and Agentic Checkout employ industry-leading security practices to minimize vulnerabilities.
Can I delete my AI shopping history and data?
Yes, Google provides multiple options for managing and deleting your AI shopping activity and data. Through your Google Account settings, you can access My Activity where all your interactions with Google services are logged, including AI shopping queries, product views, comparisons, and conversations. From there, you can delete individual activity items by clicking the X or “Delete” option next to specific entries, delete activity from specific date ranges by selecting custom date filters and bulk deleting, delete all shopping and assistant activity by filtering for those activity types and removing everything, or set up auto-delete which automatically removes activity older than a specified period (3, 18, or 36 months). You can also pause shopping activity collection entirely, which stops Google from saving future shopping searches and interactions, though this may reduce personalization quality. For Agentic Checkout specifically, you can view your purchase history and transaction details, remove individual transaction records (though some information may be retained for legal and accounting purposes), and revoke agentic checkout permissions to prevent future automated purchases. For Gemini app conversations, you can delete individual chats or your entire conversation history. Deleting data from your account doesn’t necessarily delete all copies immediately—Google may retain some data for specific periods to comply with legal obligations, resolve disputes, enforce agreements, and maintain system security. Deleted shopping activity should no longer influence future personalized recommendations. To comprehensively manage your data, review settings in your Google Account under “Data & privacy” where you can control activity settings, review what data is collected, and make deletion or export requests under data portability provisions.
Does Google's AI calling feature record phone conversations with businesses?
Google’s AI calling feature (using Duplex technology enhanced with Gemini models) does record calls between the AI and business representatives, and both Google and legal requirements govern how these recordings are handled. In most jurisdictions, automated calling systems must disclose that calls may be recorded, and Google’s AI explicitly identifies itself at the beginning of calls as an automated assistant calling on behalf of a customer. The AI states something like “I’m calling from Google on behalf of a customer. I’m an automated assistant” to ensure transparency. Call recordings serve several purposes including quality assurance to verify the AI accurately understood and conveyed information, training and improvement of the AI models to handle conversations more effectively, resolving disputes if there’s disagreement about what was discussed during the call, and compliance with regulations governing automated calling systems. Recordings are stored securely with access limited to authorized personnel and systems. Businesses receiving calls from Google’s AI can request information about the calling system, report issues or concerns through Google Business support, or opt out of receiving automated calls through their Google Business Profile settings. Customers on whose behalf calls are made receive summaries of what stores said, but typically don’t receive actual audio recordings—they get structured information extracted from the conversations. Recording practices must comply with federal and state laws governing telephone recording, which vary by jurisdiction but generally require one-party or all-party consent depending on location. Google’s implementation is designed to meet legal requirements in jurisdictions where the feature operates.
What are Google's policies on AI-generated shopping recommendations?
Google maintains policies and guidelines governing how AI-generated shopping recommendations function to ensure fairness, accuracy, transparency, and user trust. Key policy principles include no guaranteed placement (merchants cannot pay specifically for inclusion in AI-generated recommendations beyond standard advertising programs), relevance and helpfulness as primary ranking factors where recommendations prioritize meeting user needs rather than commercial interests, transparency about ads and sponsored content (clearly distinguishing paid placements from organic AI recommendations), accuracy requirements where product information must be current and correctly represented, no manipulation or gaming of AI recommendations through deceptive practices, spam, or misleading data, diversity and competition where recommendations include options from various merchants rather than favoring specific retailers, privacy protection with recommendations based on shopping context and preferences without inappropriate use of sensitive personal information, compliance with shopping policies including restrictions on prohibited products, dangerous items, and misleading claims, and appeals and corrections processes where merchants can report inaccurate information or request review of exclusions. Google’s AI is designed to surface genuinely helpful recommendations based on the user’s query, context, and needs. While the specific algorithm is proprietary, Google has stated that factors influencing AI recommendations include product data quality and completeness, price competitiveness and value, customer reviews and ratings, merchant reliability and trustworthiness, product availability and shipping speed, relevance to the user’s specific query and context, and shopping graph signals about product popularity and trends. Merchants cannot directly pay to boost organic AI recommendations, maintaining separation between ads and algorithmic results similar to traditional Google Search principles.
Will Google's AI shopping features expand to other countries?
While Google has not announced specific timelines, international expansion of AI shopping features is highly likely given Google’s global reach and the competitive nature of e-commerce technology. Several factors will influence expansion timing and prioritization including language support and localization (AI models need to understand regional languages, dialects, and shopping terminology fluently), merchant ecosystem readiness (sufficient participating retailers in target markets with quality product data), regulatory compliance (ensuring features meet local e-commerce, privacy, consumer protection, and automated communication regulations), payment infrastructure integration (Google Pay availability and adoption in the target market), and competitive pressure from local or regional e-commerce platforms offering similar AI capabilities. Most analysts expect initial international rollout to English-speaking markets such as the United Kingdom, Canada, Australia, New Zealand, and Ireland where language barriers are minimal and Google Pay is established. This would be followed by major European markets (Germany, France, Spain, Italy) and developed Asian markets (Japan, South Korea, Singapore) where e-commerce is advanced and Google has strong presence. Emerging markets with rapidly growing e-commerce sectors (India, Brazil, Southeast Asian nations, Mexico) would likely follow as infrastructure and merchant participation reach sufficient scale. Businesses operating internationally should monitor Google’s announcements, prepare product data infrastructure that can support multiple markets, and stay ready to implement AI shopping optimizations in new regions as they become available.
What new AI shopping features might Google add in the future?
Based on current trends in AI technology, e-commerce evolution, and Google’s demonstrated capabilities, several future AI shopping features are plausible. Visual search integration where users can photograph products or upload images and receive AI-powered identification, purchasing options, and alternatives is likely, building on Google Lens technology. Predictive shopping where AI anticipates needs based on patterns (suggesting reorders before you run out, recommending seasonal products at relevant times) could provide proactive value. Voice commerce integration enabling completely hands-free shopping through voice commands to Google Assistant enhanced with Gemini capabilities would leverage existing voice infrastructure. Augmented reality try-before-you-buy where AI helps visualize products in your space or on your person before purchasing could reduce return rates and improve confidence. Sustainability and ethics filtering where AI helps users find products meeting specific environmental, labor, or ethical criteria could address growing consumer concerns. Multi-modal shopping combining text, voice, images, and video in fluid conversational experiences would create richer interactions. Cross-platform continuity where shopping conversations and context seamlessly transfer between devices, apps, and interfaces (starting research on phone, continuing on desktop, completing on smart display) could remove friction. Social shopping integration where AI facilitates group decision-making, gift registries, and shared wishlists could tap into social commerce trends. Advanced personalization using AI to understand individual preferences, priorities, and context more deeply could deliver increasingly relevant recommendations. Subscription and service bundling where AI recommends and manages recurring purchases, service combinations, and subscription optimizations could maximize customer value and convenience. As Google continues developing Gemini and integrating AI across services, expect increasingly sophisticated, contextual, and helpful shopping features.
How will AI shopping affect smaller businesses and independent retailers?
AI shopping presents both opportunities and challenges for smaller businesses and independent retailers. Challenges include resource constraints for maintaining comprehensive product data with limited technical staff, visibility competition against larger retailers with more extensive catalogs and reviews, price pressure as AI makes price comparison more transparent and immediate, technical requirements to support integrations like Google Pay and agentic checkout, and data quality imperatives that require investment in product information management. However, significant opportunities exist including niche specialization where AI can surface specialized products from small retailers for very specific queries that mass retailers don’t serve well, local advantage through AI calling features that can drive foot traffic to local stores with unique inventory, authentic differentiation where genuine customer service, unique products, and authentic brand stories can stand out in AI recommendations, personalization at scale where AI helps small businesses provide personalized experiences previously only available to large companies with extensive tech infrastructure, and reduced advertising costs if organic AI recommendations surface their products based on genuine fit rather than paid placement. To succeed, small businesses should focus on product data excellence ensuring complete, accurate information even with smaller catalogs, reputation management actively generating and managing reviews to build credibility, local optimization maintaining comprehensive Google Business Profiles and responsive communication, niche expertise positioning themselves as specialists in specific categories where they have genuine advantages, community engagement building authentic customer relationships that generate word-of-mouth and reviews, and integration where technically feasible partnering with platforms like Shopify that support Google shopping features with lower technical barriers. AI shopping could actually level some playing fields by surfacing the best match for queries regardless of retailer size, if smaller businesses maintain data quality and genuine product-market fit.
