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The landscape of digital retail is undergoing a massive paradigm shift. As consumer behavior transitions from rigid keyword-based queries to fluid, conversational interactions with artificial intelligence, search engines must adapt how they categorize and retrieve product data. To help brands navigate this evolution, Google has unveiled a suite of next-generation tools within its Merchant Center platform designed specifically for the era of AI-driven commerce. Announced at the annual Google Marketing Live 2026 event, these updates aim to bridge the gap between structured retailer inventories and the unstructured, highly context-aware queries handled by Gemini and other AI search surfaces. By introducing AI Performance Insights and Conversational Attributes, Google is giving e-commerce businesses the tools they need to maintain visibility, measure search equity, and optimize their product feeds for conversational search experiences. The Evolution of E-Commerce Product Discovery For over two decades, search engine optimization for e-commerce relied on a highly predictable formula: matching product titles, metadata, and backend tags to specific keywords typed into a search box. If a shopper wanted “men’s waterproof trail running shoes size 11,” a retailer simply had to ensure those exact keywords populated their Merchant Center feed and product landing pages. Today, the advent of generative AI and large language models (LLMs) has changed the rules of discovery. Instead of hunting with strict keyword phrases, consumers are increasingly asking complex, conversational questions. They might ask, “I’m planning a hiking trip to the Pacific Northwest next month and need durable, water-resistant trail shoes that won’t slip on muddy terrain—what do you recommend?” To surface the correct product in response to such a highly nuanced prompt, an AI system needs more than static specs. It requires contextual depth, natural descriptions, and structured data that mirrors real human conversation. Google’s latest updates directly address this need, transforming how product catalogs are structured, processed, and analyzed. Understanding AI Performance Insights One of the biggest challenges for modern digital marketers is measuring performance inside AI-driven search environments. Traditional ranking tools struggle to track visibility within dynamic, highly personalized generative summaries like Google’s AI Mode or Gemini-powered recommendations. To solve this transparency issue, Google is launching AI Performance Insights. This brand-new reporting dashboard inside Merchant Center is built to help retailers quantify their organic and paid footprint across Google’s various AI-enabled surfaces. Key Features of AI Performance Insights AI Surface Tracking: Merchants can see how often their products are surfaced in AI-driven search responses, including Google’s conversational shopping flows, Gemini, and AI-powered maps. Share of Voice (SoV) Benchmarking: The tool measures a brand’s share of voice against similar competitors in the space. This allows retailers to see who is winning the organic recommendation game for key conversational categories. Performance Attribution: By tracking CTR (click-through rate) and conversion signals coming directly from AI recommendations, brands can determine which product attributes are successfully triggering conversational placements. This reporting tool will first roll out in Australia, Canada, India, New Zealand, and the United States in the coming months, with broader global expansion expected shortly after. The Power of Conversational Attributes While AI Performance Insights helps retailers measure their visibility, Conversational Attributes is the tool designed to actively improve it. This new product data capability enables retailers to enhance their listings using natural, conversational language directly within Google Merchant Center. Rather than relying solely on rigid manufacturer-provided specifications, merchants can now add conversational product attributes and narrative descriptions. Google’s AI systems use this enriched, structured data to map products to highly specific, long-tail user queries. How Conversational Attributes Work in Practice Consider a traditional retailer listing a premium winter jacket. Historically, the feed might contain details like: Brand: MountainGear Color: Black Material: Polyester/Gore-Tex Insulation: Down While accurate, this description does not align with how a user would naturally query an AI assistant. Through the new Conversational Attributes portal, the retailer can input conversational descriptors, such as: “Perfect for freezing temperatures down to sub-zero climates.” “Lightweight feel, ideal for urban commuting or heavy mountain hiking.” “Designed with an adjustable hood that fits over ski helmets.” When Google’s AI processes a user query asking for “a warm jacket that isn’t too bulky for walking to work in freezing weather,” the system can instantly match the conversational attributes to the user’s intent. This semantic matching capability ensures high-intent shoppers connect with the products that meet their specific lifestyles, reducing bounce rates and boosting conversions. Unlike AI Performance Insights, which is launching regionally first, Conversational Attributes is rolling out globally, allowing merchants worldwide to begin optimizing their product feeds immediately. Integration of Ask Advisor in Merchant Center In addition to the new insights and attribute fields, Google is integrating Ask Advisor directly into the Merchant Center ecosystem. As part of a larger initiative to launch Ask Advisor across Ads, Analytics and Merchant Center, this conversational AI assistant acts as an on-demand consultant for e-commerce managers. Rather than manually digging through spreadsheets or complex performance menus, merchants can query Ask Advisor using natural language. For example, a retailer can ask, “Why did my impressions drop for my footwear inventory last week?” or “Which conversational attributes should I add to my outdoor gear collection to improve my AI share of voice?” Ask Advisor then analyzes the account’s data, offering immediate diagnostic insights and tailored optimization strategies. Why E-Commerce SEOs and Retailers Must Adapt Now As shopping experiences become increasingly agentic, feed optimization is rapidly becoming the next frontier of search engine optimization. Here is why prioritizing these new features is critical for retailers: 1. Early-Adopter Advantage Just as early adopters of schema markup and rich snippets gained a competitive edge in traditional search results, retailers who embrace conversational attributes early will capture a larger share of voice in Gemini and AI-driven recommendations. As competitor benchmarks populate inside AI Performance Insights, brands that fail to adapt run the risk of watching their competitors monopolize conversational real estate. 2. Adapting to Agentic Shopping Patterns Modern consumers expect AI to act as a personal shopping assistant. If a shopper asks Gemini to plan a

Most businesses sound completely interchangeable online, and the rapid rise of AI-driven search engines is making this reality impossible to ignore. When ChatGPT, Google Gemini, Google’s AI Overviews, or Perplexity summarize what your business does, they do not invent their summaries out of thin air. Instead, they build their understanding directly from your website, directory profiles, customer reviews, and digital footprint. If your public copy reads like a generic template, the AI’s summary of your brand will read exactly the same way. This reality is shifting the landscape of search engine optimization. AI search visibility is no longer just a technical problem to be solved with schema markup and crawl budgets; it is a fundamental positioning problem. The businesses that stand out in this new era are not necessarily those with the deepest pockets or the most aggressive keyword-stuffing tactics. Instead, they are the organizations that clearly articulate exactly who they serve, what they do differently, and why the customer should care. Everything else—from standard SEO and PPC to structured schema and programmatic optimization—is simply an amplifier for that underlying brand message. Why businesses default to tactics instead of positioning The ancient military strategist Sun Tzu famously observed: “Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat.” While Sun Tzu was analyzing physical warfare, his words perfectly describe the modern digital marketing landscape. Far too often, business owners and marketing directors sit in meetings asking their agencies to “do something about the SEO” or “increase organic traffic,” while their homepages still claim they “deliver exceptional results with great customer service.” When search algorithms shift, traffic dips, or a business realizes that AI is changing how people research and buy things, the instinct is to act immediately. This reaction usually manifests as tactical overactivity: tweaking keywords, launching new ad campaigns, rewriting title tags, or publishing more generic posts on social media. We stay busy because activity feels like progress. This bias toward immediate action is deeply hardwired into human psychology. In his groundbreaking work, “Thinking, Fast and Slow,” psychologist Daniel Kahneman mapped out two primary cognitive systems that dictate human decision-making: System 1: Fast, automatic, emotional, and subconscious. It operates on heuristics and patterns to save cognitive energy. System 2: Slow, deliberate, analytical, and logical. It requires significant mental effort and focus. Kahneman’s research revealed that System 1 runs our lives roughly 95% of the time. We are pattern-matching, reflex-driven creatures. When faced with the uncertainty of a shifting search landscape, we rarely engage the slow, uncomfortable thinking of System 2. Instead, we reactively grab the nearest tactical lever. Psychologists call this specific reflex “action bias”—the subconscious urge to act in the face of uncertainty, even when standing still or thinking deeply would yield better results. Consider a professional soccer goalkeeper during a penalty kick. Statistical analyses of penalty kicks show that goalkeepers have the highest probability of saving a shot if they remain standing in the middle of the goal. Yet, they dive to the left or right 93.7% of the time. Why? Because diving feels active, responsible, and engaged. Standing still and watching the ball sail past looks like a lack of effort—even when staying put was the statistically superior play. In digital marketing, business owners perform the equivalent of unnecessary dives every day. They adjust their Google Ads budgets weekly because waiting for statistical significance is stressful. They add tertiary services to their homepages because they fear missing out on a single lead. They jump onto new social media platforms because they assume their slow organic growth is a platform problem rather than a positioning problem. Meanwhile, their underlying business positioning remains completely undifferentiated. Tactics are stacked on top of a generic foundation, resulting in highly active, highly expensive, and ultimately unsuccessful marketing campaigns. AI removes the hiding places for generic marketing For decades, businesses could survive with mediocre positioning. The traditional digital ecosystem was highly reliant on user patience and the path of least resistance. Humans are inherently prone to cognitive laziness; we naturally conserve mental energy. If a business was simply visible at the top of Google Search—even with a bland, generic value proposition—it could buy its way to success through sheer ad spend or local proximity. AI search is the great equalizer that is rapidly washing away these hiding spots. The transition from traditional search engines to AI-driven answer engines is exposing generic marketing. To succeed in SEO, PPC, and AI engine optimization, brands must realize that why AI still runs on search and SEO still runs the show is fundamentally about the quality and clarity of the information being crawled. The winners in the AI era are those who completed the difficult strategic positioning work before trying to optimize their technical footprint. These forward-thinking businesses figured out exactly what they stand for, who they serve, and what makes them unique. They learned how to articulate their value clearly and concisely to an audience that is increasingly fatigued by choice and looking for the easiest, most reliable answer. When AI summarizes you, what does it say? To understand how AI perceives your business, perform a simple test. Open ChatGPT, Claude, or Google Gemini and input the following prompt: “Recommend an IT support firm in [Your City]” or “Who is the best business accountant in [Your City]?” Read the output carefully. In most cases, the AI’s summary will be remarkably bland, listing companies that offer “reliable support, experienced teams, and customer-focused services.” Now, audit the websites of those recommended businesses. You will find a sea of marketing wallpaper. They all claim to be “passionate, experienced, client-focused experts delivering exceptional results through tailored solutions backed by decades of collective experience.” This kind of copy acts as visual and cognitive static. It covers every surface, blends into the background, and communicates nothing of substance. While these businesses may win clients due to physical proximity or price cuts, they do not win on brand equity. AI magnifying this problem because

The modern digital advertising landscape demands both agility and precision. As search engines evolve and machine learning becomes the backbone of modern campaign management, marketers are continuously looking for ways to streamline their workflows and maximize their return on ad spend (ROAS). Managing campaigns across multiple platforms, however, has historically introduced significant operational friction. To address these challenges, Microsoft has rolled out a suite of major advertising updates designed to simplify cross-platform campaign management, elevate AI-driven bidding capabilities, and provide deeper reporting insights. These updates emphasize automation, ease of import, and data transparency, helping businesses of all sizes get the most out of their ad budgets. From a centralized Import Center to advanced cross-account portfolio bidding, these changes reflect Microsoft’s commitment to building a more integrated, efficient, and intelligent advertising ecosystem. Simplifying Cross-Platform Workflows with the New Import Center For most digital marketers, Google Ads and Meta Ads serve as the primary pillars of their paid media strategies. Extending those campaigns to Microsoft Advertising—which accesses valuable audiences across Bing, Yahoo, AOL, and various partner networks—has often required manual recreation or clunky, repetitive import processes. To eliminate this friction, Microsoft has introduced a centralized Import Center. This new hub is designed to serve as a single dashboard where advertisers can manage, monitor, and optimize imports from both Google Ads and Meta Ads. Key Features of the Import Center The updated Import Center is not just a portal; it is an active management system that gives advertisers greater control over how their imported campaigns behave. Within the new hub, advertisers can: Search and Filter Imports: Easily locate specific import schedules, platforms, or historical runs, which is particularly beneficial for large agencies managing dozens of accounts. Edit or Pause Imports: Adjust schedules, change import settings, or pause automated syncs directly from the dashboard without needing to recreate the import from scratch. Access Imported Campaigns: Navigate directly to the newly imported campaigns to make immediate structural or creative adjustments. View Troubleshooting Guidance: Receive explicit diagnostics when elements of an import do not map correctly (such as mismatched bid strategies, regional targeting differences, or ad extension formatting errors). Get Post-Import Performance Recommendations: Access automated suggestions immediately after imports complete, helping to align the imported settings with Microsoft’s specific network dynamics. By transforming import tasks from a passive, background background utility into an interactive command center, Microsoft reduces the manual labor associated with multi-channel expansion. Advertisers can scale their reach across the Microsoft Search and Audience Networks with fewer errors and higher consistency. Advanced AI Bidding: Cross-Account Portfolio Bidding Automated bidding strategies rely heavily on data density. For machine learning models to accurately predict conversion probability and set the optimal bid for every search query, they need to process a steady stream of conversion signals. For advertisers running highly segmented accounts or managing multiple brands, data siloing has historically hindered bidding efficiency. To solve this problem, Microsoft has expanded its AI-powered bidding suite by introducing cross-account portfolio bidding for Search and Shopping campaigns. How Cross-Account Portfolio Bidding Works Portfolio bidding allows advertisers to group multiple campaigns together under a single bid strategy. The bidding engine then dynamically shifts budget and adjusts bids across those campaigns to achieve a collective target, such as a target cost-per-acquisition (CPA) or target ROAS. With cross-account portfolio bidding, this capability is scaled across multiple accounts within a single Manager Account. This change offers several distinct advantages: Aggregated Learning Signals: By pooling performance signals from multiple accounts, Microsoft’s AI-powered bidding algorithms can learn at a much faster rate. This is especially helpful for lower-volume accounts that would otherwise struggle to exit the “learning phase.” Optimal Budget Allocation: The system can shift focus and budget dynamically to the accounts and campaigns that are performing best at any given moment, maximizing the efficiency of the overall budget. Streamlined Management: Instead of managing dozens of individual bid strategies across separate accounts, search marketers can set a single portfolio goal and let the AI manage the adjustments. New Bid Strategy Reporting Metrics With greater automation comes a natural demand for greater transparency. Marketers need to know exactly how automated bidding systems are pacing and whether they are hitting their targets. To facilitate this, Microsoft has introduced several new reporting metrics directly into the user interface: Avg. Target ROAS: Displays the weighted average of your target return on ad spend over a selected period, accounting for any adjustments made to the target during that time. Avg. Target CPA: Shows the average cost-per-acquisition target the system was optimizing for, helping to identify how bid targets fluctuated in response to market conditions. Avg. Target Impression Share: Offers clarity on the visibility levels the automated system aimed to secure, helping to diagnose fluctuations in impression share. These metrics make it easier to diagnose performance variations and evaluate how factors like conversion delays—the time it takes for a user to convert after clicking an ad—impact the algorithm’s real-time adjustments. Granular Analysis with Improved Reporting and Custom Columns To successfully optimize modern digital campaigns, advertisers must be able to view and analyze data on their own terms. Standard reporting dashboards often fall short when businesses utilize complex conversion funnels or unique key performance indicators (KPIs). Microsoft is addressing this by expanding the flexibility of its reporting suite, specifically targeting its custom column capabilities. Custom columns allow advertisers to build formulas and segment metrics directly within the Microsoft Advertising interface, eliminating the need to constantly export data to external spreadsheets or business intelligence tools. Enhanced Custom Column Features With this latest roll-out, advertisers gain a much deeper level of granularity inside their reporting dashboards: Full Access to Conversion Metrics: Advertisers can now use all available conversion metrics within their custom columns. This includes specialized calculations that combine conversion volume, value, and rates to yield business-specific metrics. Goal-Name Segmentation: You can now segment reporting data by specific conversion goal names. For instance, if you track “Newsletter Sign-ups,” “Form Fills,” and “Purchases” as different conversion types, you can isolate these metrics into separate custom

Understanding the Paradigm Shift in Local Search Optimizing for visibility in Google Ask Maps starts with recognizing how local search is changing. For years, local SEO revolved around a familiar pattern: a user typed a query, Google returned a local three-pack, and the user scrolled through a list of businesses to make their own decision. Google Ask Maps fundamentally alters this dynamic. Instead of presenting a long, unfiltered list of service providers or storefronts, Ask Maps interprets the searcher’s intent, narrows the competitive field down to a handful of options, and actively explains why specific businesses are a good match. This shift represents a migration from discovery to curation. For local business owners and digital marketers, the implications are profound. Visibility in Ask Maps is no longer just about pushing your business to the top of a traditional search engine results page (SERP). It is about how your business is understood, categorized, and positioned by Google’s conversational AI. When the search experience becomes recommendation-driven, the standard local playbook must evolve. Rather than treating Ask Maps as an isolated marketing channel, businesses must focus on building a cohesive digital footprint. The goal is to make your business easier for Google to comprehend, simpler to match to real-world customer scenarios, and highly trusted. While the core fundamentals of local search still matter, the way these signals are synthesized is changing. Visibility in Ask Maps Is a Filtering Problem First The most immediate difference when using Ask Maps is the highly restricted set of results shown to the user. In standard Google Maps searches, a user can scroll past the top three listings, looking at dozens of alternatives, reading reviews, and manually filtering by distance, rating, or hours. Ask Maps changes this by performing the comparison process on behalf of the user before displaying any results. During initial testing and rollouts, Ask Maps typically displays only three to eight businesses per query. The platform acts as a digital gatekeeper, narrowing down the market, interpreting the specific nuances of the user’s prompt, and presenting a highly curated subset of options. Crucially, the system accompanies these options with a brief written explanation of why each business has been selected. This mechanics-level shift redefines the meaning of organic visibility. Simply ranking near the top of a category list is no longer the ultimate prize. Instead, your business must qualify for a very tight group of recommended entities. To do this, you must satisfy two distinct processes within the Ask Maps engine: Eligibility: Google determines which businesses meet the baseline criteria for the geographic area and service category. Confidence: The AI evaluates which of those eligible businesses it can confidently recommend and justify to the searcher. Because the engine must explain its recommendations, it prioritizes businesses that provide the most explicit, unstructured proof of their expertise and suitability. For a deeper analysis of how this environment operates, read about how Google Ask Maps is moving from listings to recommendations. Ask Maps Needs Enough Information to Explain Your Business Ask Maps does not just index businesses; it characterizes them. When answering user queries, the AI describes service providers using qualitative attributes such as responsiveness, specialized experience, transparency, or suitability for specific, high-stress situations. As searches become more complex or closely tied to immediate customer pain points, these narrative justifications become the core of the response. This shift places a new requirement on local optimization. It is no longer enough for Google to simply know your business name, address, phone number, and primary category. The AI system requires enough context to answer a highly practical real-world question: Under what specific circumstances should this business be recommended? To help the AI answer this question, your online presence must clearly articulate: The exact types of projects and service calls your business handles. The situational challenges your team is equipped to solve (e.g., emergency repairs, historic home preservation, eco-friendly installations). The common questions, risks, and objections your customers typically raise. Your specific methodology for handling those situations. If your digital footprint lacks this contextual detail, the AI has to make assumptions. In conversational search, a lack of clear information leads to a lack of recommendation confidence. If Google cannot explain why your business is the ideal choice for a specific user prompt, it will simply bypass you in favor of a competitor that provides clearer evidence. Google Business Profile Becomes the Identity Layer The Google Business Profile (GBP) remains the foundational layer of local search, but its role has shifted from a static directory card to a dynamic identity layer. For initial, broad queries, Ask Maps relies heavily on the core data structured within your GBP. This includes your business description, services menu, reviews, visual assets, and operational attributes. Many businesses treat their GBP as a set-it-and-forget-it asset, updating it only when business hours change. To stand out in Ask Maps, your profile must convey a highly specific, situational identity. A generalist profile that lists broad categories like “Plumber” or “HVAC Contractor” does not give the AI enough material to generate a convincing recommendation justification. Instead, businesses must optimize their GBP to reinforce specific operational contexts. This includes: Detailed Services: Breaking down broad categories into specific offerings (e.g., changing “leak repair” to “trenchless sewer pipe repair” or “emergency slab leak detection”). Contextual Updates: Regularly posting updates that highlight specific challenges solved for local customers. Situational Attributes: Leveraging all applicable business attributes, such as emergency service hours, response times, or specialized equipment certifications. This level of detail helps Google’s AI match your business to highly specific conversational queries. When your profile contains rich, precise information, you reduce the engine’s reliance on inference. For more on how search engines process local business profiles, read about how Google defines your entity. Reviews Help Shape How Your Business Is Positioned While customer reviews have always been a critical ranking factor in local search, Ask Maps uses review content in a much more structured, semantic way. The language used by your customers in their reviews directly

The organic search landscape is undergoing its most volatile shift in a decade. As generative AI and automated overviews transform how users interact with traditional search engine results pages (SERPs), publishers and technical SEOs are searching for stable, high-yield traffic sources. For many, Google Discover has emerged as the ultimate channel for rapid audience acquisition and explosive traffic spikes. Yet, as critical as Discover is to modern digital publishing, many of its underlying mechanisms remain shrouded in mystery. One of the most significant—yet least understood—developments in this space is the rollout of Google Discover publisher profiles and follow features. Introduced to give users more direct control over their content feeds, these profiles represent a structural evolution in how Google aggregates, categorizes, and serves content from both websites and social media platforms. Because official Google documentation offers minimal guidance on how these profiles work, digital marketers and publishers have been left to decode the system on their own. This comprehensive guide details how Google Discover publisher profiles function today, how they connect to the Knowledge Graph and social ecosystems, and how you can optimize your brand’s presence to capture this highly coveted visibility. The Strategic Evolution of Google Discover In September 2025, Google executed a major update to its Discover platform, fundamentally changing how users interact with content creators and news outlets. By introducing publisher follows and dedicated profile pages, Google moved Discover away from being a purely algorithmic, passive feed and closer to a curated, user-controlled content ecosystem. You can read more about this transition in the official Google Discover updates announcement. This update did not happen in a vacuum. It was rolled out alongside preferred sources in Google Search, an initiative designed to give users direct influence over the domains they see most frequently in their search results. To understand more about how this system operates under the hood, explore the mechanics of preferred sources and subscription spotlighting. For publishers, these changes offer a dual benefit. First, they provide a centralized landing page within the Google ecosystem that aggregates their web articles and social media updates. Second, they offer a direct mechanism for brand affinity: when a user clicks “Follow,” the publisher’s content is prioritized in that user’s personalized feed, establishing a reliable baseline of organic traffic that bypasses standard algorithmic volatility. What Is a Google Discover Publisher Profile? At its core, a Discover publisher profile is an automatically generated or curated entity landing page hosted by Google. It acts as a digital hub, consolidating a brand’s footprint across the web and social media. When a user interacts with a Discover card and navigates to the publisher’s profile, they are presented with a unified view of the brand’s output. A standard, non-customized Discover publisher profile typically contains several key elements, which Google pulls programmatically from various data sources: Brand Name and Follow Button: The official name of the entity, accompanied by a prominent “Follow on Google” CTA that allows users to subscribe to future updates. Profile Photo or Logo: This visual identifier is primarily sourced from Google’s Knowledge Graph. If no Knowledge Graph entry exists for the brand, Google will often default to the profile photo used on the brand’s connected YouTube channel. Total Followers: This metric displays the aggregated follower count across the brand’s connected social media channels. It is important to note that this number represents external social media reach, not the internal number of followers the brand has accumulated directly on Google Discover. Social Profile Links: Interactive icons linking to the publisher’s official social media accounts. Currently, Google Discover supports integration with YouTube, TikTok, Instagram, Facebook, X (formerly Twitter), and LinkedIn. About Section: A concise editorial description of the brand. In most instances, Google extracts this text directly from Wikipedia or another highly trusted source tied to the entity’s Knowledge Graph entry. If those are unavailable, it may pull from the site’s primary About Us page. Latest Posts: A feed of recent content, which programmatically blends traditional web articles with social media posts from the brand’s linked social channels. A prime example of a standard, highly enriched profile is the Liverpool FC publisher profile, which cleanly aggregates the club’s massive digital footprint into a singular, cohesive Google experience. The Rise of Editable and Premium Publisher Profiles For the first few months following the September 2025 rollout, publisher profiles were entirely static and algorithmically generated. Publishers had no direct control over how their logos looked, which social links were displayed, or what content was prioritized. However, the paradigm shifted in early 2026. Industry observers and technical SEOs began noticing highly customized, premium-looking profile layouts in the wild. This discovery, highlighted in public discussions like Andell Dam’s profile layout thread, revealed that Google was testing direct publisher controls. It was subsequently confirmed that Google had quietly launched a limited beta program, granting select publishers direct administrative access to their profile pages. To learn more about this rollout, read the analysis of how Google gave 54 publishers control over their Discover profiles. For an example of what these administrative privileges look like in practice, you can view the Fox News publisher profile. Advanced Features in Editable Profiles Publishers accepted into this exclusive testing group gain access to customization features that dramatically improve user engagement and referral traffic: Customized Banner Images: Instead of a plain white background, premium profiles can feature a bold, horizontal brand banner at the top of the page, matching the aesthetic of traditional social media profiles. Pinned Posts: Publishers can manually select high-performing or evergreen articles and social posts, pinning them as Discover cards at the very top of their profile feed to maximize visibility and CTR. Custom External Links: Unlike standard profiles that only link to articles and pre-defined social channels, editable profiles allow publishers to add arbitrary external links. For example, Fox Weather used this feature to link directly to their mobile application and live broadcast stream—high-value properties that traditionally struggle to gain direct organic search visibility. Two Distinct Models: Web Publishers vs.

The pace of modern digital business has accelerated to a point where speed is no longer just a competitive advantage—it is a baseline requirement for survival. With the rapid expansion of artificial intelligence, the constant emergence of new communication channels, and shifting consumer expectations, companies must continuously execute and iterate on their digital strategies. If a brand cannot launch campaigns, landing pages, or digital products quickly, its competitors certainly will. Yet, despite the clear demand for agility, most organizations find themselves bogged down by systemic delays. According to insights from the recent Storyblok Global Speed-to-Market Benchmark Report, there is a massive gap between intention and execution in modern go-to-market (GTM) workflows. The report reveals that only 22.5% of teams say they consistently deliver at the pace the market demands. This means more than three-quarters of organizations are struggling to keep up, leaving revenue, market share, and customer engagement on the table. When digital initiatives stall, the blame is often placed on poor communication or a lack of project management. However, the data points to a deeper, more structural issue: technology limitations. Legacy content management systems (CMS), fragmented toolchains, and inefficient development workflows are quietly sabotaging speed-to-market across industries. Understanding these hurdles and finding ways to resolve them is crucial for any business aiming to scale and maintain relevance. The Bottlenecks Sabotaging GTM Velocity To solve the speed-to-market puzzle, organizations must first look closely at where their digital operations are breaking down. The Global Speed-to-Market Benchmark survey gathered insights from hundreds of GTM professionals to pinpoint exactly where friction occurs. The findings highlight four key bottlenecks that consistently drag down delivery timelines, all of which point directly back to technical dependencies and outdated infrastructure. 1. The Approval Process: A Cycle of Endless Revisions The single biggest hurdle to fast execution is the approval and review process, cited by more than 50% of the teams surveyed. Far from a quick, final sanity check, the sign-off phase has become a prolonged drag on progress. More than half of all GTM teams must go through three or more rounds of content revisions before a campaign can go live. For nearly one in five teams (approximately 20%), that number escalates to five or more rounds of back-and-forth edits. This endless review cycle rarely stems from a pursuit of creative perfection. Instead, it is usually a byproduct of fragmented software stacks and disjointed workflows. When feedback is scattered across multiple channels—such as email threads, Slack messages, PDF markups, and project management boards—there is no single, reliable source of truth. Stakeholders lose track of which draft is current, ownership of final approvals becomes muddy, and deadlines slip by unnoticed. This process friction is inherently tied to technology. In fact, only 50% of teams feel their current CMS even somewhat supports speedy go-to-market execution. When content creation tools are completely disconnected from the actual layout and design tools, reviews become abstract, leading to fear-based hesitation and endless revision cycles. To fix this, forward-thinking organizations are transitioning toward modern visual collaboration tools and headless CMS solutions. By decoupling content management from the underlying presentation layer, a headless CMS provides a single structured repository where marketers, developers, legal compliance teams, and designers can collaborate. When this infrastructure is equipped with visual editing and in-context commenting, stakeholders can see exactly how the content will appear on the live site and leave precise feedback directly in the platform. This eliminates version confusion and speeds up approval workflows. 2. Overreliance on Developers: The Ticket Queue Bottleneck In many traditional setups, marketing teams are entirely dependent on engineering resources to launch or update digital experiences. The survey highlights just how severe this dependency is: 38% of marketing and digital teams require developer support for most or even all of their campaigns. This constant need for technical intervention creates a dual burden on the organization: Marketing Teams Lose Autonomy: Marketers cannot launch landing pages, tweak copy, or test alternative layouts without submitting a ticket and waiting for developer availability. This prevents them from reacting quickly to sudden market shifts or cultural trends. Developers Lose Engineering Focus: Instead of building core product features, improving platform performance, or working on strategic software engineering initiatives, technical talent is pulled away to handle minor content updates. The benchmark report found that more than a third of developers spend between 25% and 50% of their working hours supporting GTM campaigns. Furthermore, 42% of respondents state that their current technology platform makes this support far more complex than it needs to be. This dynamic creates frustration on both sides. Developers feel bogged down by repetitive tasks, while marketers feel slowed by technical gatekeeping. The solution is not to merge these distinct roles, but to adopt a digital architecture that allows each department to operate independently within their areas of expertise. This is where modern component-based design and headless CMS architectures shine. Developers build reusable, structurally sound content blocks and layout components once. Marketers can then use these visual building blocks to design, edit, and publish new pages on their own, without writing a single line of code. This gives marketers full creative autonomy while freeing up developers to focus on high-impact software engineering. 3. Compounding Tech Limitations: The Hidden Operational Drag While process and personnel issues are highly visible, underlying technical limitations act as a quiet, persistent tax on productivity. Nearly one-third of GTM teams point to tech limitations as a major root cause of slow digital delivery. When asked about the specific technical challenges they face, respondents identified three main issues: Complex Deployment Processes (39%): Launching new content or updates involves convoluted pipelines, long build times, or high-risk server deployments that require constant oversight. Tool Integration Problems (25%): Systems do not talk to one another seamlessly. Data and content must be manually copied and pasted between the CMS, localization tools, personalization engines, and analytics platforms. Fragmented or Outdated Legacy Systems (14%): Monolithic, legacy software suites that have been customized over years become brittle and difficult to update, leaving teams

The search engine optimization landscape is undergoing one of its most disruptive periods in history. For years, structured data—commonly referred to as schema markup—was championed as the ultimate bridge between human-readable web content and machine-readable databases. SEO professionals spent countless hours writing, testing, and deploying JSON-LD code to earn coveted rich snippets, enhance click-through rates (CTR), and ensure search engines understood their content’s context. However, two major developments have sent shockwaves through the search marketing community, forcing a critical re-evaluation of structured data’s actual value. First, Google systematically stripped away FAQ and How-To rich results from the search engine results pages (SERPs). Second, groundbreaking research from Ahrefs has challenged a foundational belief: that schema markup is a critical driver for gaining citations in modern, AI-powered search engines. As search engines transition from classic keyword retrieval systems into complex, AI-driven answer engines, SEOs must adapt. Here is an in-depth exploration of why these changes occurred, what the latest data reveals, and how you should pivot your optimization strategy to remain visible in both traditional and generative search landscapes. The Evolution and Sudden Fall of Google’s FAQ Rich Results To understand why schema’s value is being questioned, we must first look at how Google handled visual real estate on the SERPs over the past decade. Schema markup was introduced in 2011 by a collaboration between Google, Bing, Yahoo!, and Yandex under the Schema.org initiative. The goal was simple: create a shared XML/JSON-LD vocabulary to help search engines understand what web pages are actually about. For years, implementing schema yielded highly visible rewards. The most popular of these was FAQ schema. By adding a few lines of code to a webpage, publishers could display dropdown question-and-answer accordions directly beneath their organic search listings. This provided several distinct advantages: Increased SERP Footprint: A single listing with FAQ schema could occupy twice the vertical space of a standard listing, pushing competitors further down the screen. Improved Click-Through Rates (CTR): Interactive elements naturally draw the human eye, leading to higher engagement. Pre-empting User Intent: Answering common queries directly on the search page established immediate authority. However, this led to widespread manipulation. SEOs began adding irrelevant FAQ schema to almost every page to monopolize SERP real estate. In response, Google gradually rolled back the feature. The final blow came when Google officially announced it would restrict FAQ rich results to highly authoritative, well-known health and government websites, effectively rendering the markup useless for the vast majority of commercial and informational publishers. This move signaled a broader shift in Google’s strategy. The search giant was no longer interested in giving away valuable SERP real estate for free to webmasters who simply optimized their structured code. Instead, Google began preparing its interface for a clean, streamlined look designed to accommodate its own AI-generated answers. The Ahrefs Study: Deconstructing Schema’s Value in AI Search With traditional rich results fading, many SEO professionals shifted their narrative. They argued that even if schema no longer generated visual snippets on standard Google search pages, it remained vital for “AI SEO.” The theory was that Large Language Models (LLMs) and conversational search engines (like Perplexity AI, Google’s AI Overviews, and ChatGPT) relied heavily on structured data to crawl, parse, and cite sources. To test this hypothesis, the data science team at Ahrefs conducted a comprehensive study analyzing the relationship between schema markup and citations in generative search engines. The findings challenged the long-held assumption that structured data is a prerequisite for AI visibility. Key Finding 1: AI Search Engines Skip the Schema The research revealed that generative search engines do not rely on JSON-LD or microdata to understand content and generate citations. Instead, these advanced engines process the raw, unstructured HTML and natural language of a page. Because LLMs are trained on massive datasets of human language, they are incredibly proficient at understanding context, relationships, and entities directly from standard text without needing a structured code translation. Key Finding 2: High Citation Rates Occur Without Schema Ahrefs analyzed a vast pool of queries that triggered AI search summaries. They discovered that a significant portion of the web pages cited by AI search engines did not have specialized schema implemented. Pages with simple, clean semantic HTML (such as standard paragraph tags, bulleted lists, and clear table formats) were cited just as frequently—and in some cases, more frequently—than pages heavily optimized with complex schema packages. Key Finding 3: Crawl Efficiency vs. Contextual Understanding While search engines do use structured data to verify specific factual details (such as price, availability, or event dates), their retrieval-augmented generation (RAG) pipelines do not rely on schema to synthesize answers. The RAG systems pull directly from the visible text on the page to build their conversational responses. If your content is buried inside structured data but isn’t clear, readable, or valuable on the front end of the page, the AI crawler is highly likely to ignore it. Why LLMs and AI Search Engines Don’t Need Schema Anymore To comprehend why schema’s role is shrinking, we have to look at the underlying technology powering modern AI search. Early search engines were syntactic; they matched keywords on a page to keywords in a search query. Schema was a crutch that helped these basic algorithms understand that “Apple” referred to the technology company and not the fruit. Today’s search systems are semantic. They utilize vector embeddings and natural language processing (NLP) to understand concepts, user intent, and real-world entities. Here is why modern AI search systems can bypass schema entirely: 1. Advanced Semantic Understanding Modern LLMs process text by converting words into mathematical vectors in a multi-dimensional space. This allows them to understand synonyms, tone, context, and structural relationships naturally. An AI doesn’t need a JSON-LD tag telling it “this is an author” when it can read the sentence “Written by Jane Doe, a certified financial analyst with ten years of experience” and extract that entity relationship instantly. 2. The RAG (Retrieval-Augmented Generation) Workflow When you ask an AI search engine a question,

The landscape of digital advertising is undergoing a profound shift. At the annual Google Marketing Live event, Google unveiled a major leap forward in how marketers interact with its suite of advertising, measurement, and commerce tools. The headline announcement of the event is the launch of Ask Advisor, a brand-new, Gemini-powered AI companion designed to act as a unified intelligence layer across Google Ads, Google Analytics, Google Merchant Center, and the broader Google Marketing Platform. Historically, digital marketers, agency professionals, and e-commerce business owners have had to jump back and forth between isolated dashboards to launch, optimize, and analyze their digital campaigns. Ask Advisor aims to dismantle these silos. By introducing an agentic AI assistant capable of pulling data and executing tasks across multiple systems, Google is attempting to turn complex multi-platform workflows into a single, conversational stream. What is Google Ask Advisor? Ask Advisor is not just another chatbot; it is designed to function as an active, collaborative partner. Powered by Google’s advanced Gemini AI models, Ask Advisor operates as a central bridge that connects insights, workflows, and automated optimizations across Google’s core marketing ecosystem. Instead of forcing marketers to extract data from Google Analytics, cross-reference it with Google Merchant Center feed health, and manually adjust bids or copy within Google Ads, Ask Advisor acts as the singular interface to handle these cross-platform tasks. The primary value proposition of Ask Advisor is its capability to streamline complex workflows. According to Google, the tool allows users to build and launch campaigns, run detailed performance reviews, and surface actionable optimization opportunities without ever having to leave their current workspace. This frictionless integration marks a significant evolutionary step from passive AI assistance to proactive, agent-based task execution. How the Gemini-Powered Integration Works To understand the power of Ask Advisor, it helps to examine how it operates beneath the surface. Google’s marketing ecosystem has traditionally relied on distinct products that require manual integration, such as linking Google Ads to Google Analytics accounts or syncing Merchant Center product feeds. While these integrations allow data to flow between products, managing them still requires a high level of technical expertise and manual oversight. Ask Advisor changes this paradigm by using Gemini to build a shared cognitive layer across these tools. When a marketer inputs a prompt, Ask Advisor accesses campaign performance metrics, audience behaviors, and product inventory details simultaneously. Here is a breakdown of how it synthesizes information from different sources: Google Merchant Center: Ask Advisor monitors inventory status, product attributes, pricing competitiveness, and approval statuses to ensure marketing decisions align with actual stock levels. Google Ads: The tool reviews active campaign budgets, bids, creative assets, targeting parameters, and historical performance metrics. Google Analytics: By pulling user behavior data, conversion paths, and post-click engagement metrics, the AI can connect creative efforts with actual business outcomes. Google Marketing Platform: For enterprise advertisers, Ask Advisor can scale these insights across larger programmatic, search, and creative management systems. This deep integration enables the tool to explain performance fluctuations and suggest immediate remedies. If conversions drop for a specific product category, for instance, Ask Advisor can analyze whether the issue stems from an out-of-stock product in Merchant Center, a tracking error in Google Analytics, or a creative fatigue problem within Google Ads, offering a clear solution on the spot. Practical Use Cases: Bringing Ask Advisor to Life The theoretical benefits of unified AI are compelling, but how does Ask Advisor actually function in a day-to-day workflow? Consider a few practical scenarios where this technology can save hours of manual labor: 1. Cross-Platform Campaign Generation Imagine a digital marketer managing an e-commerce store who wants to launch a new promotional push. Under the traditional workflow, this would require checking product feed health in Merchant Center, creating campaigns and ad groups in Google Ads, setting up tracking parameters, and eventually monitoring the results in Google Analytics. With Ask Advisor, the marketer can simply input a high-level command: “Find new customers for my hair care products.” The AI-driven assistant automatically accesses the hair care product category in the linked Merchant Center account, pulls the appropriate product images and details, suggests target audience segments based on Google Analytics historical data, and draft a tailored Google Ads campaign. Once the marketer reviews and approves the plan, the campaign is ready to go live. 2. Simplifying Complex Reporting and Diagnostics Answering a simple question like, “Why did my conversion rate drop last week?” has historically required building custom reports, segmenting traffic by source, and reviewing product landing pages. Ask Advisor changes this by allowing marketers to ask these complex questions directly. The tool will scan data points from both Google Ads and Google Analytics to identify the exact cause—such as a sudden drop in mobile traffic or a slow-loading landing page—and deliver a clear, written explanation alongside recommended fixes. 3. Real-Time Ad Creative Optimization Creative assets are the driving force behind modern campaign performance. Ask Advisor can cross-reference ad copy performance in Google Ads with user behavior data in Google Analytics to pinpoint which messaging converts best. It can then offer suggestions to replace underperforming assets or use Gemini to automatically generate new variations that align with highly successful landing page elements. The Shift Toward Agentic Workflows in Digital Advertising The introduction of Ask Advisor represents a defining moment in Google’s advertising strategy: the transition to “agentic” AI. While early AI tools in digital marketing were largely analytical or generative—designed to write headlines or predict budget spending—agentic AI is designed to take action. It shifts the role of the marketer from a direct operator to a director or strategist who guides and oversees automated tasks. By positioning Gemini as the connective tissue of its entire ad stack, Google is redefining how campaigns are structured and managed. This shift could democratize advanced advertising strategies, allowing small business owners who lack deep analytical backgrounds to run highly optimized, cross-platform campaigns. At the same time, it frees up enterprise media buyers and agency executives from tedious data aggregation,

Stop Treating AI Visibility As One Problem. It’s Actually Three, On Three Different Layers The organic search landscape is undergoing its most significant transformation since the invention of the search engine. For decades, search engine optimization (SEO) was a relatively straightforward game of indexing, keywords, backlinks, and search engine results page (SERP) rankings. Today, that paradigm is fracturing. With the rise of generative search engines, conversational agents, and answer engines like ChatGPT, Perplexity, Gemini, and Claude, the goal is no longer just to rank blue links—it is to be cited, recommended, and surfaced within artificial intelligence-generated answers. When a marketing executive realizes their brand has suddenly vanished from ChatGPT’s recommendations or is completely absent from a Perplexity citations list, panic usually sets in. The knee-jerk reaction is almost always the same: “We need more content. We need to write more blog posts, target more keywords, and build more backlinks.” But in the age of Generative Engine Optimization (GEO), this legacy approach is fundamentally flawed. When your brand disappears from conversational AI systems, the fix is rarely “more content.” Instead, the key to recovery lies in diagnosing which specific layer of the AI architecture has broken down. AI visibility is not a singular, monolithic problem. It is actually three distinct problems occurring on three entirely different technical layers. To fix your visibility, you must first understand where the pipeline has ruptured. Layer 1: The Ingestion and Access Layer (The Pipeline) Before an artificial intelligence model can synthesize information about your brand, it must first be able to access and digest your data. This is the Ingestion and Access Layer, and it serves as the foundational pipeline for all AI visibility. If this layer breaks down, your brand simply does not exist to the AI, regardless of how high-quality your content is or how strong your domain authority remains on traditional Google Search. The Double-Edged Sword of Robots.txt In the early days of generative AI, many publishers and brands rushed to block AI crawlers like GPTBot, PerplexityBot, and ClaudeBot using their robots.txt files. The motivation was understandable: protect intellectual property, prevent scraping without compensation, and preserve traditional web traffic. However, blocking these user-agents has had a massive, often unintended side effect. If an AI engine’s crawler is blocked from your site, the real-time search components of those engines cannot access your latest product updates, pricing, or authoritative resources. You have effectively locked the door to the very systems you want to be discovered by. Paywalls, Gatekeepers, and Login Screens AI models cannot bypass authentication screens, paywalls, or complex JavaScript rendering pipelines easily. If your most valuable, authoritative content is hidden behind a heavy registration wall or a paywall, LLM crawlers will bypass it. While gating content is a viable lead-generation strategy, it acts as a total visibility barrier for conversational AI. Brands must strike a careful balance between gated lead magnets and open-web documents that AI engines can easily ingest. Structured Data and Schema Markup In traditional SEO, schema markup helps search engines display rich snippets. In the context of AI search, structured data acts as an explicit roadmap. Large Language Models (LLMs) are highly adept at processing structured data formats like JSON-LD. When you provide clean, validated schema for products, organizations, reviews, and FAQs, you make it incredibly easy for the ingestion layer of an AI engine to parse, categorize, and store your business information accurately. Without this structure, the crawler is forced to rely on unstructured HTML, which increases the likelihood of extraction errors or outright omission. Layer 2: The Foundational Model Layer (The Parametric Brain) Even if an AI crawler can access your site, that does not mean the underlying model “knows” who you are when it is offline. This brings us to the second layer: the Foundational Model Layer. This is the model’s parametric memory—the core brain of the LLM that is built during its massive, resource-intensive training phases. When a user asks ChatGPT a question without web search enabled, the model relies entirely on its pre-trained weights to formulate an answer. If your brand is not embedded deep within those weights, you do not exist in the model’s fundamental understanding of the world. Optimizing for this layer is entirely different from optimizing for a live web crawler. The Power of Entity-Based SEO To be recognized at the foundational level, your brand must transition from being a collection of keywords to becoming a verified “entity” in the digital ecosystem. AI models are trained on massive datasets like Common Crawl, Wikipedia, Wikidata, and major academic and journalistic databases. If your brand does not have a presence in these high-authority, foundational datasets, it lacks a node in the LLM’s knowledge graph. To build entity authority, brands must focus on consistency across the web. Your company name, address, key executives, core offerings, and industry classifications must be identical across all authoritative directories, public registries, and media mentions. This consistency allows the model during its training phase to connect the dots and establish your brand as a trusted, distinct entity within its vector space. The Vector Space and Semantic Proximity When models are trained, words, concepts, and entities are converted into high-dimensional vectors. Entities that are frequently mentioned together in high-quality training data are placed closer together in this mathematical vector space. If your brand is consistently mentioned alongside industry leaders, best-in-class solutions, and authoritative industry whitepapers, the model learns that your brand is semantically close to those top-tier concepts. When a user asks the model to “list the top enterprise security tools,” the model pulls from this semantic proximity. If you have not built that association in the foundational training data, you will be left out of the offline response. Layer 3: The Retrieval and Contextual Layer (The Live RAG Process) The third layer is where real-time magic happens. Because LLMs have training cutoffs and are prone to hallucinations, modern AI search engines utilize a architecture known as Retrieval-Augmented Generation (RAG). When a user inputs a query into Perplexity or

Google Search Universal Cart, expands UCP and AP2 The landscape of online shopping is undergoing its most significant transformation since the invention of the digital shopping cart. Google is shifting from a platform where consumers search for products to a fully integrated transaction layer where AI agents execute purchases on behalf of users. At the center of this paradigm shift are three groundbreaking developments: the introduction of the Google Universal Cart, the global expansion of the Universal Commerce Protocol (UCP), and the debut of the Agent Payments Protocol (AP2). Announced by Vidhya Srinivasan, Vice President and General Manager of Ads and Commerce at Google, these features represent a major leap forward into the era of “agentic commerce.” Along with these product updates, Google revealed that its foundational Shopping Graph has grown to contain over 60 billion product listings—a massive jump from the 50 billion listings reported earlier this year. This vast pool of structured data serves as the engine powering Google’s new AI-driven shopping experiences. The Evolution of Google’s Shopping Graph To understand the power behind the Universal Cart and these new commerce protocols, it is essential to look at the sheer scale of the Google Shopping Graph. Moving from 50 billion to 60 billion product listings in just a matter of months is a testament to Google’s aggressive data aggregation strategy. The Shopping Graph is not just a static directory of products. It is a dynamic, real-time dataset that tracks product availability, pricing fluctuations, merchant reviews, shipping times, video demonstrations, and product compatibility. By linking this massive data repository with advanced generative AI models, Google is transforming how consumers interact with search results, turning informational searches directly into transactional opportunities. Introducing Google Universal Cart: One-Click Checkout Across the Web For years, the biggest point of friction in digital commerce has been cart abandonment. Consumers frequently research products across multiple retail sites, adding items to different carts, only to walk away when faced with the tedious process of entering shipping information, payment details, and loyalty numbers on half a dozen separate websites. The Google Universal Cart directly addresses this issue. With this feature, users can add products from multiple, completely distinct retailers into a single, unified Google Universal Cart and check out with a single click using Google Wallet. Instead of forcing users to navigate away to individual merchant websites, the Universal Cart keeps the consumer inside the Google ecosystem. This cross-platform cart maintains a persistent list of chosen items as users move seamlessly between different Google surfaces. The Universal Cart will be accessible across: Google Search: Add items directly from the search results page or Google Shopping tab. Gemini: Add items during natural language conversations with Google’s AI assistant. YouTube: Add products mentioned in video reviews or creator content directly to the cart. Gmail: Interact with promotional emails and add items to the cart without leaving the inbox. This cross-functional integration ensures that no matter where a consumer discovers a product within Google’s suite of services, they can instantly secure it in their unified cart. How the Universal Cart Optimizes Your Purchases Google’s Universal Cart is not just a passive aggregator; it is an intelligent shopping assistant. The cart actively compares prices, checks real-time inventory across participating merchants, and determines which retailer offers the best deal, shipping speed, or overall value for your specific location. Furthermore, Google’s AI features allow the Universal Cart to anticipate consumer needs and resolve logistical issues before they occur. A prime example provided by Google involves building a custom PC: Imagine you are building your first custom computer and add various components—such as a motherboard, RAM, a processor, and a power supply—from several different retailers to your Google Universal Cart. Before you click buy, the Universal Cart’s built-in intelligence will proactively scan the items, flag any product incompatibilities (such as RAM that is incompatible with the motherboard), and suggest functional alternatives. Additionally, because the cart is built on the secure foundation of Google Wallet, it automatically understands your specific credit card payment perks, merchant loyalty memberships, and active promotional offers. It calculates these variables in real time to maximize your cash back, points, or discounts without requiring you to manually enter promo codes or look up credit card terms. Supported Merchants and Ecosystem Partners To ensure widespread adoption from day one, Google has partnered with some of the largest retailers and e-commerce platforms in the world. Initial launch partners supporting the Universal Cart include: Nike Sephora Target Ulta Beauty Walmart Wayfair Shopify merchants (including popular brands like Fenty and Steve Madden) By including Shopify, Google is ensuring that independent merchants and mid-sized direct-to-consumer (DTC) brands can leverage this unified checkout technology alongside retail giants, leveling the playing field for businesses of all sizes. Expanding the Universal Commerce Protocol (UCP) To facilitate the seamless flow of transactional data between merchants and Google’s AI systems, Google is expanding its Universal Commerce Protocol (UCP). UCP is the standardized framework that allows retailers to communicate product data, inventory status, and transactional capabilities with Google in real time. Google has announced plans to expand UCP internationally. The protocol will roll out to merchants and consumers in Canada and Australia in the coming months, with a subsequent expansion planned for the United Kingdom later in the year. Beyond geographical expansion, Google is also taking UCP into new industry verticals. While the protocol originally focused primarily on physical retail products, it is now being adapted to support: YouTube Integration: Deeper shoppable video experiences for content creators and brands. Hotel Bookings: Standardizing the process of finding, selecting, and instantly booking hotel rooms directly from Google Search and Maps. Local Food Delivery: Streamlining the process of ordering from local restaurants through unified search and payment protocols. This expansion signals Google’s ambition to become the primary interface for almost all local, digital, and service-based transactions online. Agent Payments Protocol (AP2): Secure Transactions for the AI Era As artificial intelligence evolves from passive search assistants to active autonomous agents, a major technical and security