Tag: AI

  • Behind the Scenes at Google I/O 2026: Unveiling Hidden Innovations

    Behind the Scenes at Google I/O 2026: Unveiling Hidden Innovations

    Attending Google I/O 2026 for the first time felt like stepping into a realm of boundless energy and optimism, almost as thrilling as witnessing a crowning ceremony.

    The initiatives launched last year have transformed into robust pillars of growth. Ask Maps, for instance, has become the blueprint for introducing Ask YouTube. Gemini 3.5 Flash fuels Antigravity, akin to Claude Code but under Google’s banner, and Googlers are already harnessing it to construct the exciting features shown on stage.

    The pace of innovation was breathtaking, everything rolled out swiftly and assuredly.

    Every announcement seemed to cater to a diverse audience.

    • Gemini Omni was likened to Nano Banana but designed for video content (see this strange proof).
    • Smart glasses are making a much-discussed return.
    • There are video game-like experiences that can be instantly prompted and played.
    • The capability for Workspace to bring documents to life with mere conversations.
    • A feature allowing the transformation of Google Maps images into surreal dreams seems more like a solution waiting for a problem, perhaps for Hollywood studios looking to bypass on-location shoots?
    • I even have Gemma on my phone, enabling in-flight conversations with a smaller model. (Thanks to American Airlines’ free Wi-Fi, I’m all set.)

    And yet, the most intriguing element remains to be addressed.

    Gemini and Search: Converging Evolution

    Gemini is beginning to resemble Search, while Search is adopting features of Gemini.

    Both platforms now include features that satisfy similar needs: keeping tabs on the web and alerting users when something of interest arises.

    In Search, these are known as information agents. In Gemini, they go by Spark or Daily Brief. The connection is unmistakable.

    ```json
{
  "alt": "Large tech presentation with speaker on stage, introducing 'Ask YouTube', to a full audience.",
  "caption": "Innovation takes the stage as 'Ask YouTube' is unveiled in front of an excited crowd, promising new features available this summer.",
  "description": "A large tech event features a speaker presenting 'Ask YouTube' on a massive screen, announcing its availability in the U.S. this summer. The venue is packed with attendees capturing the moment on their devices. Stage lighting and modern design elements underscore the futuristic theme of the event. This announcement is part of a wider tech conference, drawing a diverse crowd eager for new advancements. Keywords: tech presentation, Ask YouTube, innovation, audience, event."
}
```

    I asked a product manager about their approach to long-term feature management and overlapping utilities. Their response was simple: “Right now, it’s all about velocity.”

    Shipping fast is the mantra shared by three other product managers, all behind key I/O features initiated and deployed within this whirlwind year, 2026. It’s astounding.

    The product manager elaborated, “Velocity is achieved through reduced managerial overhead.”

    This implies jumping on board quickly and figuring out the finer details later.

    Once You See It, You Can’t Unsee It

    Armed with this understanding, the rest of the day wore a new perspective. The demos were impressive, yet I pondered: what’s the next step with these innovations?

    Though I now have Gemma on my phone, one developer couldn’t provide a tangible day-to-day use case. I witnessed AI Mode’s monitoring prowess by prompting it to “keep me updated.” Despite seeing the connection of components, my questions about managing these alerts as they age went unanswered, indicating it’s still an early-stage demo.

    Many features appear not to address their second-order effects thoroughly. It seems engineers are using these systems at a command line level rather than considering user interfaces.

    A notable point is my current inability to delete old Gemini chats in a web browser, a functionality available in the Mac app.

    ```json
{
  "alt": "Presentation slide detailing shipping timeline for models from 2024 to 2026.",
  "caption": "Unveiling the future: A detailed roadmap showcasing the ambitious shipping timeline for upcoming models.",
  "description": "This image shows a presentation slide with a timeline titled 'Shipping at relentless pace,' outlining shipping schedules for various models from 2024 to 2026. The timeline is divided into columns for each year, highlighting Frontier and Open Models in distinct colors. Audience members are visible, indicating a live presentation setting. Keywords: shipping timeline, presentation slide, future models, live event."
}
```

    Universal Cart Sparks Discussions

    A frequently mentioned feature during I/O was Universal Cart, Google’s new cross-platform shopping protocol.

    My opinion? If you’re Google, it’s an exciting development because, upon adoption, it further solidifies their control over the complete shopping experience. Conversely, for others, this development might be a cause for concern.

    Despite these concerns, the group I conversed with didn’t seem troubled, feeling distanced from the growing anti-AI sentiment in the U.S.

    Speaking with an SEO expert at a major ecommerce brand implementing Universal Cart, they related the velocity comment to their own implementation experience, describing it as feeling rushed.

    The AI Content Guidelines Controversy

    The emphasis on speed helps explain the controversies surrounding Google’s AI content guidelines.

    Just four days before I/O, Google’s Search quality team advised publishers to “write for humans, not AI.” Shortly thereafter, the AI agent team demonstrated capabilities where Google’s own agents browse, interpret, transact, and create web content.

    As Google shifts towards AI handling more tasks, the advice given to publishers starts to sound less sincere.

    Impact on the Web Ecosystem

    I don’t wish to undermine the engineers’ efforts. I communicated my respect for their work directly to them. Building products for search and clients myself, I can relate to frequent criticisms over compliments.

    ```json
{
  "alt": "Screenshot of a menu interface on a digital platform labeled Gemini with options for chat, search, images, and videos.",
  "caption": "Navigate effortlessly with Gemini's sleek menu interface, offering quick access to chats, search options, new image and video features, and more.",
  "description": "This image showcases the menu interface of a digital platform called Gemini. The menu includes options such as New Chat, Search Chats, and new features for Images and Videos. Below, recent items are listed including 'Gemini Spark: Availability and Features' along with options to pin or rename items. The design features a dark theme with white text and icons, providing a modern and user-friendly experience."
}
```

    Still, the potential downside of overlapping features, difficulty in managing or reconciling data could lead to significant technical challenges later. The current AI strategy appears to be: prioritize feature utilization first, reconcile later.

    Nevertheless, I admire Google’s rapid progress and look forward to future developments. Leveraging substantial resources, they can experiment comprehensively to identify successes.

    Regrettably, my enlightening conversation with the product manager was abruptly concluded as we were asked to vacate the premises.

    Spotting the Bright Spots

    Google reports unprecedented high search query volumes. They are enhancing authentication and provenance through SynthID’s expansion into Search and Chrome, welcoming new partners like OpenAI, and integrating C2PA content credential verification.

    These are indeed significant accomplishments.

    However, the relentless pace might lead to unforeseen challenges. My hope is that the quest for speed doesn’t further destabilize the already-fragile web ecosystem.

    In conclusion, it’s undeniably an exhilarating era for search technology.

    Dig deeper.


    Inspired by this post on Search Engine Land.


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  • Unlocking AI Search: Making Your Brand Truly Machine-Readable

    Unlocking AI Search: Making Your Brand Truly Machine-Readable

    As I delved into audits across Prince Edward Island, one issue stood out: businesses with significant expertise weren’t visible to AI systems because their knowledge wasn’t rendered into a machine-readable format.

    Despite their leadership in biotech, manufacturing, and other sectors, critical business information was often trapped in PDFs, behind forms, or muddled in vague marketing copy. It was also disconnected from structured data systems that AI engines need for verification.

    We’re living in a world where 88% of companies are integrating AI. Yet, McKinsey notes that 86% of leaders admit to being unprepared for its daily integration.

    Many brands mistakenly equate AI visibility with being featured in a Gemini summary or a ChatGPT result, without solidifying the structured digital groundwork needed for ongoing visibility.

    AI Visibility: The Basics Before the Buzz

    If you’re only focusing on large language model (LLM) responses, you’re lagging. LLM visibility reflects authority—it doesn’t build it.

    According to a study by Responsive, 22% of B2B buyers now use generative AI for vendor research. Traditional search use is expected to drop by 50% by 2028 as AI solutions become the go-to answer engines, as Gartner predicts.

    Now, discovery happens through synthesizing answers rather than listing URLs. Until you’re part of the Knowledge Graph as a verified entity, your brand’s visibility will be inconsistent.

    The Insights from 19 Case Studies: Expertise Powers AI Search

    AI systems value concrete, structured data over descriptive text. Brands chasing fleeting AI mentions without anchoring their data won’t achieve lasting visibility, but those establishing structured data relationships will always be recognized.

    ```json
{
  "alt": "The CapmatchOne logo with a gradient circle and bold text.",
  "caption": "Discover innovation with the CapmatchOne logo, featuring sleek typography and a modern gradient circle.",
  "description": "The CapmatchOne logo features bold, modern typography coupled with a gradient circle, symbolizing connection and innovation. The sleek design conveys a sense of progress and creativity. This image can be used for branding or promotional purposes, appealing to audiences interested in innovative solutions and forward-thinking designs."
}
```

    Thus, SEO has evolved from simply creating content to becoming an information architect. As the case studies reveal, expertise remains a key signal that AI systems can interpret.

    Case No.EntityIndustryThe discoveryThe SME solution
    1BioVectraBiotechTechnical authority trapped in PDFsEncoded cGMP data into facts
    2Wyman’sFood manufacturingSustainability was a narrativeStructured supply chain schema
    3Murphy Hospitality GroupHospitalityInvisible venue specificationsConstructed event logic
    4InvescoFinTechOpaque compliance dataBuilt regulatory ground truth
    5Sekisui DiagnosticsMedTechInnovation lacked readabilityEngineered diagnostic logic triples

    Why SEOs Must Focus on Education

    The main obstacle to AI readiness is the gap in education. We must evolve into information architects, comprehending our clients’ business logic deeply.

    SEOs as Subject Matter Experts

    Understanding is foundational. For instance, auditing a biotech firm requires a grasp of compliance as keen as their lead scientist’s.

    AI relies on structured context for accurate answers. Vague marketing language feeds insufficient responses.

    Clients Must Prepare Their Data

    Data quality and governance activation equate to maximizing AI-driven value. SEOs must educate clients on digital presence impacting AI brand perception.

    Focus on True AI Authority

    Appearing in a ChatGPT reply isn’t the goal; becoming an authoritative node in the Knowledge Graph is. It ensures visibility across AI platforms like Gemini and Claude.

    AI advancements will persist rapidly. SEOs and clients not prioritizing structured data will be left behind in AI discovery systems.


    Inspired by this post on Search Engine Land.


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  • Mastering Multi-Channel Marketing: Stop Juggling, Start Thriving

    Mastering Multi-Channel Marketing: Stop Juggling, Start Thriving

    Every Monday, I dive into my role as a paid media manager knowing the chaos that awaits. From Google Ads to TikTok and Reddit, my task is to pull the data from each platform, put it into a comprehensible spreadsheet, and report to my boss by 10 a.m. Amidst all this, I try to decipher what worked last week and why. It’s a frenetic start to the week, to say the least.

    Remembering when managing multi-channel campaigns meant juggling just Google Ads and a Facebook campaign feels almost nostalgic now. Today, it’s a tangled web of 12 channels, each with their peculiarities in terms of attribution logic and campaign structures. The disarray is real and mostly ignored, to the detriment of performance marketers like me.

    I realize that this Monday morning ritual is less about campaign management and more about tedious chores like data entry and reformatting. Managing campaigns across numerous networks involves reopening platforms repeatedly just to align disparate data points.

    ```json
{
  "alt": "A woman in an office surrounded by four computer screens showing marketing analytics.",
  "caption": "Navigating the complexities of digital marketing metrics, a woman finds herself amid a sea of analytics data.",
  "description": "In an office setting, a woman sits at a desk surrounded by four large monitors displaying various marketing analytics figures. The screens show data such as ROAS, CPA, CTR, and CPL, highlighting campaign performances. Her expression suggests concentration or concern as she navigates complex digital marketing metrics. This image captures the intensity and focus required in data analysis and decision-making in a modern business environment."
}
```

    The prevailing problem isn’t just the time I lose, but the lag it introduces to my operations. When my performance data is scattered across various platforms, delays in identifying key insights can lead to wasted budgets. The inconsistency in strategies across channels further exacerbates the issue.

    I’ve come to understand that relying on native dashboards from Google, Meta, and others won’t rescue us from this inefficiency. These platforms prefer keeping us tethered to their interfaces, contributing to the fragmentation. But a paradigm shift is on the horizon: AI-native management tools that promise seamless cross-platform synchronization without the need for multiple dashboards.

    The change is happening right now, reimagining how campaigns are managed with AI. It means planning campaigns with simple briefs and automatically syncing creative adjustments across all channels. This reorientation is not just an incremental improvement but a transformational leap that alleviates the operational burdens we’ve carried for too long.

    ```json
{
  "alt": "Woman in office using a large monitor displaying an analytics dashboard with performance metrics.",
  "caption": "In a sleek, modern office space, a woman engages with a dynamic analytics dashboard, tracking performance metrics on her wide display.",
  "description": "A woman in a contemporary office setting is focused on an ultra-wide monitor displaying a detailed performance analytics dashboard. The screen showcases key metrics such as ROAS, CPA, conversions, and reach, alongside a visual funnel diagram, under a 'Unified Portfolio Dashboard' by adplus. Her workspace includes a keyboard, notebook, and a coffee mug, suggesting a productive environment. This image embodies themes of data analysis, modern technology, and professional settings."
}
```

    For agencies like mine, AI brings another boon: automated and branded client reports that compile multi-network performance data without the Sunday-night grind.

    What actions can we take this week? First, I’ll track where my hours truly go throughout a week — seeing is believing when it comes to confronting administrative bloat. Second, standardizing naming conventions across accounts is surprisingly effective in smoothing out cross-platform wrinkles. Third, I’ll delve into evaluating current AI-native tools, as I suspect many teams are operating on outdated assumptions about their capabilities.

    Achieving an operational edge in paid media transcends budget size. It’s about faster data-action cycles, unified cross-network performance views, and liberating our teams from the laborious chains of manual processing. This operational edge could mean the difference between thriving and merely surviving in a competitive landscape.


    Inspired by this post on Search Engine Land.


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  • Is AI as Popular as It Seems? Insights from New Data

    Is AI as Popular as It Seems? Insights from New Data

    AI core
    Recently, I’ve been exploring the fascinating divergence in AI adoption between professional circles and general consumers. According to Datos and SparkToro’s latest data, this trend is becoming increasingly apparent.

    It was intriguing to see how AI usage is starting to plateau among consumers while remaining on the rise in professional environments. Tools like Claude, ChatGPT, and Gemini are seemingly more popular in the B2B landscape.

    Why we care. As I delve deeper into AI’s impact, it’s becoming clear that a universal AI strategy won’t work for everyone. It’s essential to identify whether my audience aligns with these broader trends or if their AI engagement habits are entirely different.

    ChatGPT desktop growth slowed. From Fishkin’s analysis, it appears that ChatGPT’s usage in the U.S. has stagnated over recent months while Claude and Gemini continue their growth trajectories. It seems that professionals are continually finding value in these tools.

    ```json
{
  "alt": "Bar chart illustrating AI usage by businesses with varying audience ranks.",
  "caption": "Exploring AI's prominence in business: A chart highlights how AI usage differs among B2B professionals, possibly influencing LinkedIn activity.",
  "description": "This image displays a bar chart from a presentation titled 'Rand’s Theory: Maybe AI use is huge with businesses, not consumers.' The chart shows percentages of US B2B professionals who have searched for AI solutions. The bars represent 'Your Audience' and 'US Average' with notable differences in usage across platforms. A red annotation suggests the data may explain LinkedIn's lower engagement in pro-AI search activities. Keywords: AI usage, B2B professionals, LinkedIn, search activity."
}
```

    At its zenith, 37% of U.S. desktop users engaged with OpenAI or ChatGPT back in September 2025. This number dipped slightly to 34% by March, a trend mirrored, albeit with higher numbers, in the EU and U.K.

    Claude gained with professionals. I noticed Claude is particularly gaining traction among professional users. Fishkin’s data suggests a significant rise in usage among business professionals, resonating with the notion that AI adoption is stronger in B2B contexts.

    The analysis even revealed that Claude’s use among B2B professionals was 373% higher than the U.S. average, reinforcing the tool’s growing popularity in business circles.

    ```json
{
  "alt": "Bar chart showing the AI usage trends among generic US consumers, comparing your audience to US average with various platforms.",
  "caption": "Exploring the AI Landscape: A bar chart reveals how generic US consumers engage with AI across different platforms, highlighting your audience's preferences versus the national average.",
  "description": "This image features a bar chart detailing AI usage among generic US consumers, with a breakdown by platform. The chart compares your audience's engagement level to the US average, highlighting various platforms ranked by usage. The data is visually represented in bars, with colors indicating different audience metrics. The chart is designed for insights into AI usage patterns, offering a visual representation of consumer interactions with technology. This can serve as a crucial resource for understanding market trends and audience behavior in AI technology adoption."
}
```

    Consumer audiences look different. Interestingly, when it comes to the retail-shopping consumer audience, ChatGPT isn’t as prevalent, being 15% less likely to be used compared to the typical American consumer. For this group, Claude isn’t even in the top four AI tools.

    This might explain why AI seems so prevalent in professional networks like LinkedIn, while its visibility is not as pronounced among general consumers.

    The research. You can view Rand Fishkin’s detailed insights on LinkedIn by watching his video here.

    View embedded content


    Inspired by this post on Search Engine Land.


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  • Revamped Google Asset Studio Now Boosted by Gemini AI Tools

    Revamped Google Asset Studio Now Boosted by Gemini AI Tools

    Today, I’m excited to share that Google is making significant enhancements to Asset Studio, aimed at helping advertisers like us generate creative assets more efficiently by leveraging the power of Gemini. This was announced at Google Marketing Live 2026.

    Driving the news. Asset Studio will now feature AI-driven creation capabilities across text, images, and videos, allowing us to use natural language prompts to guide the process.

    Google assures us that the platform is capable of understanding:

    • Marketing briefs
    • Brand guidelines
    • Website content
    • Campaign goals

    By doing so, it generates creative assets that span different themes and formats, tailored to our needs.

    Additionally, Google is integrating the Gemini Omni, their multimodal model, into Asset Studio. This enhances our workflows, especially in video creation.

    With 1-Click Creative Testing, we can quickly identify top-performing assets in terms of campaign objectives. This means more efficient testing and better results for us.

    How it works. By applying Gemini models, Asset Studio interprets our marketing briefs, guidelines, and objectives. Using natural language prompts, we can generate and perfect our assets, whether they’re text, image, or video. Plus, Gemini Omni ensures our video workflows are seamless.

    The aim is clear: centralize creative production and minimize the challenges we face when building campaigns across platforms like Google and YouTube.

    Why we care. Creative production bottlenecks are a major issue for us advertisers. Google’s updates show that integrating generative AI into our workflows makes creative production much more streamlined.

    For those of us managing cross-platform campaigns, the ability to swiftly generate and test creative assets is a game-changer.

    What to watch. As we automate more of our creative processes, it’s important to compare the performance of AI-generated assets against those from traditional workflows. We might need to rethink approval processes and brand safety in light of AI’s growing role.

    Availability. We can expect the new Asset Studio features to become globally available in English this summer, opening up new possibilities for our advertising strategies.

    Dig deeper. There are more updates from Google Marketing Live 2026 that are worth exploring for additional insights and tools that could benefit our campaigns. For example:


    Inspired by this post on Search Engine Land.


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  • Experience Google’s New Meridian Integration in Analytics 360

    Experience Google’s New Meridian Integration in Analytics 360

    Today, I’m excited to share that Google is making Analytics 360 even more powerful by integrating the Meridian marketing mix modeling platform. They’ve also introduced a new predictive conversion metric that promises to enhance media mix decisions for advertisers.

    I learned about these updates during the Google Marketing Live 2026 event, where Google unveiled several enhancements aimed at expanding measurement capabilities. The integration of Meridian, Google’s open-source marketing mix modeling tool, directly into Analytics 360 is a significant step forward.

    Driving the news. With this integration, I’m able to unify first-party and cross-channel data, measure incremental performance, forecast campaign outcomes, and optimize media mix investments with greater ease.

    Moreover, Google is rolling out Qualified Future Conversions (QFCs), a predictive reporting metric powered by Gemini. QFCs link current ad activity to future sales signals like branded search behavior, providing insights that were previously harder to visualize.

    ```json
{
  "alt": "Dashboard for 2026 year plan showing budget recommendations and performance metrics for various ad channels.",
  "caption": "Plan your 2026 marketing budget with optimized strategies and predicted outcomes based on detailed analytics.",
  "description": "This dashboard image presents a 2026 year plan with detailed recommendations for ad budget allocation across multiple channels, including Google Ads, Meta Ads, and TikTok Ads. It features a graph showing optimized vs. projected revenue, a table with ad costs and incremental revenue, and a sidebar for creating customized plans. The interface is user-friendly, designed for efficient budget management, and provides insights into optimizing marketing efforts. Keywords: marketing, budget, analytics, ad channels, optimization."
}
```

    How it works. From my perspective, Meridian combines first-party data, media signals, and cross-channel performance metrics in Analytics 360. This helps to model incremental impact while Qualified Future Conversions use Gemini’s predictive signals to understand potential future purchasing behaviors.

    In the long run, Google aims to integrate QFC insights into Meridian for more accurate predictive modeling. This is part of their broader effort to simplify measurement and refine ROI forecasting in today’s complex media landscape.

    Why we care. As I’ve observed, measurement and attribution are becoming increasingly challenging with evolving customer journeys and the emphasis on privacy. These latest updates highlight Google’s commitment to helping advertisers like us better understand and plan for long-term performance.

    ```json
{
  "alt": "Screenshot of a digital campaign dashboard with graphs and data tables.",
  "caption": "Explore insights with this campaign dashboard, showcasing conversion trends and detailed metrics for strategic decisions.",
  "description": "This image displays a comprehensive campaign dashboard interface. It features a graph depicting conversion trends over time, with blue and red lines representing different metrics. Below the graph, detailed data tables list campaigns, conversion goals, bid strategies, and performance numbers. The left sidebar shows navigation options like campaigns, ad groups, and tools. This setup is designed for managing and analyzing marketing campaigns effectively, providing insights for performance optimization."
}
```

    The combination of Meridian and QFCs can empower marketers to make better budgeting decisions by accurately linking current campaign activity to future outcomes. It’s a tool we should all keep an eye on.

    What to watch. Predictive measurement is becoming crucial. I’m looking forward to testing whether Meridian and QFCs can offer more actionable forecasting compared to existing solutions.

    Availability. I found out that Meridian integrations are rolling out globally in Google Analytics 360, supporting all languages. QFCs are in a restricted global pilot phase, with wider beta access anticipated later this year.

    Dig deeper. If you’re interested, there’s more news from Google Marketing Live 2026, including tests of new conversational ad formats and AI-powered tools in the Merchant Center, as well as expansions across various Google services.


    Inspired by this post on Search Engine Land.


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  • Unlock New Google Deals: AI-Powered Offers and Seamless Checkout

    Unlock New Google Deals: AI-Powered Offers and Seamless Checkout

    I’m excited to share that Google is enhancing its Direct Offers with AI-generated bundles, native checkout features, and enticing travel deals. This announcement, made at Google Marketing Live 2026, marks a significant upgrade for the platform.

    Driving the news. Google aims to make promotional offers more visible within AI-powered Search experiences.

    Brands will soon have the ability to upload a variety of promotional types:

    – Discounts

    – Giveaways

    – Local coupons

    – Product bundles

    Google’s Gemini will assist in creating personalized offers that align with search intent. This means tailored promotions based on user queries and browsing habits.

    How it works. Advertisers can upload eligible promotions and campaign guardrails through Google Ads. Gemini will then curate relevant offers like bundles and discounts that resonate with the shopper’s search and browsing behavior.

    Additionally, Google is introducing native checkout support for merchants using the Universal Commerce Protocol (UCP), enabling users to complete purchases directly within AI-driven shopping experiences.

    Travel partners such as Booking and Expedia will soon showcase travel offers directly within AI-assisted trip planning features, enhancing the overall travel booking experience.

    Why we care. This integration is transforming promotions into an integral part of conversational shopping, steering away from conventional deal extensions and static offers.

    Advertisers now need to optimize their promotions to fit within AI-powered discovery and recommendation systems.

    The introduction of native checkout options is expected to streamline the transition from product discovery to conversion, potentially boosting sales.

    What to watch. It’s worth observing how Google’s shift towards AI-assisted promotional commerce influences conversion rates and consumer shopping patterns.

    Availability. Currently, Direct Offers is available as a pilot for advertisers in the U.S.

    Dig deeper. Stay informed with more updates from Google Marketing Live 2026:

    Google tests new conversational ad formats in AI Mode and Search

    – Google launches AI Performance Insights and Conversational Attributes in Merchant Center

    Google brings Meridian marketing mix modeling into Analytics 360

    Google expands Demand Gen with YouTube creator tools

    Google upgrades Asset Studio with Gemini-powered creative generation and video tools

    Google expands Universal Commerce Protocol and launches new agentic shopping tools

    Google launches Ask Advisor across Ads, Analytics and Merchant Center


    Inspired by this post on Search Engine Land.


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  • Discover Google Chrome Lighthouse’s New AI Scan Feature

    Discover Google Chrome Lighthouse’s New AI Scan Feature

    I’ve recently discovered that Google has introduced a new feature in Chrome Lighthouse to check for llms.txt files. Though Google mentions that llms.txt isn’t necessary for AI search visibility, Lighthouse has started flagging sites based on their presence.

    Google’s latest Lighthouse audits, under the “Agentic Browsing” category, now focus on a site’s usability for machine interaction. I find this interesting as it aligns with Google’s push towards better machine readability.

    The new audits are part of Chrome’s evolving “Agentic Browsing” features, which analyze if sites are prepared for automated interaction. This concept came soon after Google issued guidance on AI search optimization, debunking the necessity of llms.txt files in their new guide on generative AI features.

    What Lighthouse Evaluates Now. Lighthouse’s Agentic Browsing tests focus on how well my site is built for machine interactions, incorporating various deterministic audits as per Google’s documentation. These checks include:

    – WebMCP integration.

    – Accessibility tree integrity.

    – Layout stability through CLS.

    – Presence of an llms.txt file.

    These audits help ensure that there’s a machine-readable summary at the site’s domain root. Google explains that without llms.txt, agents might take longer to understand a site’s main structure.

    The impact of these audits doesn’t translate into a traditional Lighthouse score but into a fractional pass ratio related to agentic readiness signals.

    The Tension. Interestingly, while these audits don’t directly affect SEO rankings, their mention in Google’s readiness checks could make SEOs reconsider their stance on llms.txt files.

    Agentic Engine Optimization. Google’s approach aligns with insights shared by Addy Osmani from Google Cloud AI about Agentic Engine Optimization. Osmani emphasizes creating web content that is semantically structured, token-efficient, and easy for AI to process.

    SEO vs. llms.txt. According to Google, creating llms.txt or similar files isn’t necessary for AI search success, as outlined in the guide on Mythbusting generative AI search. The AI systems can discover, crawl, and index a variety of file types encountered on the internet.

    John Mueller from Google responded to concerns about the role of llms.txt in a discussion with Lily Ray on Bluesky, stating that the use of these files is more for functionality and not directly linked to search engine optimization.

    Google’s Take on AI Agents. Besides llms.txt, Google’s Lighthouse guidelines place strong emphasis on accessibility and interface stability. The insight I gained is that AI agents heavily rely on the accessibility tree as their core data model, focusing on integrity and proper layout.

    Ultimately, while Google indicates llms.txt isn’t needed for search, including such files might be beneficial for adapting to Google’s evolving tools that prioritize machine readability.

    Further Exploration.

    Meet llms.txt, a proposed standard for AI website content crawling

    llms.txt isn’t robots.txt: It’s a treasure map for AI

    Does llms.txt matter? We tracked 10 sites to find out


    Inspired by this post on Search Engine Land.


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  • Build Personalized Apps in Google Search with Agentic AI

    Build Personalized Apps in Google Search with Agentic AI

    Have you ever wanted to customize your Google Search experience? Now you can build your own apps right within Google Search.

    I discovered this amazing feature powered by Google Antigravity and Gemini 3.5, which lets me set up a search feature that delivers exactly the kind of information I need, formatted just how I like it, and sourced from where I trust.

    During this year’s Google I/O, Liz Reid, head of Google Search, unveiled this innovation. She mentioned, “Search can build the ideal response, in the right format for your question – completely on the fly. You’ll get custom generative UI, including visual tools and simulations, tailored to your needs.”

    Exciting Examples

    Imagine creating custom layouts for understanding astrophysics or how your wristwatch works. Google assembles interactive visuals, tables, and real-time simulations to suit your learning style.

    I’ve also been able to manage ongoing tasks like wedding planning or home moves with customized dashboards that act as helpful companions throughout the process.

    Let’s not forget fitness! I asked Google Search to build me a custom fitness tracker. It taps into live data like weather and reviews to keep me on track, making my health goals more achievable.

    Visualizing the Experience

    These custom search experiences, including generative UI examples, will become widely available this summer. I’m particularly excited as they roll out first to Google AI Pro and Ultra subscribers in the U.S.

    Why This Matters

    It’s groundbreaking to have the ability to code mini apps within Google Search, answering questions in ways that are uniquely mine. It’s a level of personalization I’m thrilled about, achievable only through such advanced generative-AI tools.


    Inspired by this post on Search Engine Land.


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  • Explore Google Search’s New Power with Gemini 3.5 Flash

    Explore Google Search’s New Power with Gemini 3.5 Flash

    Today, I’m excited to share that Google has announced the launch of its latest AI model, Gemini 3.5 Flash. This powerful update is now the default engine for Google’s AI Mode, transforming how we experience search every day.

    At the recent Google I/O, I learned about Gemini 3.5 Flash directly from Google’s head of Search, Liz Reid. She described this model as Google’s “newest Flash model delivering sustained frontier performance for agents and coding.” It’s thrilling to know that this technology is now impacting users worldwide.

    What really excites me is that 3.5 Flash doesn’t just enhance AI Mode in Google Search; it also powers the Gemini app for everyone, regardless of whether they are paid users or not. It’s great to see Google making such advancements widely accessible.

    Developers, you’re in for a treat! 3.5 Flash is now integrated into Google Antigravity, Gemini API for Google AI Studio, Android Studio, and more. For those in enterprise, it’s now part of the Enterprise Agent Platform and Gemini Enterprise.

    Koray Kavukcuoglu, CTO of Google DeepMind and Chief AI Architect, shared that Gemini 3.5 Flash rivals the intelligence of large flagship models while providing the speed we expect from the Flash series. It outshines previous models, making remarkable strides in agentic and coding performance benchmarks. I’m truly impressed by its capabilities in multimodal understanding too.

    Why should I care? Well, with Gemini 3.5, Google Search’s AI Mode is smarter and more efficient than ever. I’m eager to explore how AI Mode’s responses evolve, especially for the queries that matter most to my site.

    The rapid changes in search technology mean it’s crucial to stay informed and adaptable. This update reaffirms the importance of keeping pace with Google’s innovations.


    Inspired by this post on Search Engine Land.


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