Search marketing is still as powerful as ever. Google recently surpassed $100 billion in ad revenue in a single quarter, with more than half coming from search. But search alone can no longer deliver the same results most businesses expect.
As Google Ads Coach Jyll Saskin Gales showed at SMX Next, real performance now comes from going beyond traditional search and using it to strengthen a broader PPC strategy.
The challenge with traditional Search Marketing
As search marketers, we’re great at reaching people who are actively searching for what we sell. But we often miss people who fit our ideal audience and aren’t searching yet.
The real opportunity sits at the intersection of intent and audience fit.
Take the search [vacation packages]. That query could come from a family with young kids, a honeymooning couple, or a group of retirees. The keyword is the same, but each audience needs a different message and a different offer.
Understanding targeting capabilities in Google Ads
There are two main types of targeting:
Content targeting shows ads in specific places.
Audience targeting shows ads to specific types of people.
For example, targeting [flights to Paris] is content targeting. Targeting people who are “in-market for trips to Paris” is audience targeting. Google builds in-market audiences by analyzing behavior across multiple signals, including searches, browsing activity, and location.
The three types of content targeting
Keyword targeting: Reach people when they search on Google, including through dynamic ad groups and Performance Max.
Topic targeting: Show ads alongside content related to specific topics in display and video campaigns.
Placement targeting: Put ads on specific websites, apps, YouTube channels, or videos where your ideal customers already spend time.
The four types of audience targeting
Google’s data: Prebuilt segments include detailed demographics (such as parents of toddlers vs. teens), affinity segments (interests like vegetarianism), in-market segments (people actively researching purchases), and life events (graduating or retiring). Any advertiser can use these across most campaign types.
Your data: Target website visitors, app users, people who engaged with your Google content (YouTube viewers or search clickers), and customer lists through Customer Match. Note that remarketing is restricted for sensitive interest categories.
Custom segments: Turn content targeting into audience targeting by building segments based on what people search for, their interests, and the websites or apps they use. These go by different names depending on campaign type—“custom segments” in most campaigns and “custom search terms” in video campaigns.
Automated targeting: This includes optimized targeting (finding people similar to your converters), audience expansion in video campaigns, audience signals and search themes in Performance Max, and lookalike segments that model new users from your seed lists.
Building your targeting strategy
To build a modern targeting strategy, you need to answer two questions:
How can I sell my offer with Google Ads?
How can I reach a specific kind of person with Google Ads?
For example, to reach Google Ads practitioners for lead gen software, you could build custom segments that target people who use the Google Ads app, visit industry sites like searchengineland.com, or search for Google Ads–specific terms such as “Performance Max” or “Smart Bidding.”
You can also layer in content targeting, like YouTube placements on industry educator channels and topic targeting around search marketing.
Strategies for sensitive interest categories
If you work in a restricted category such as legal or healthcare and can’t use custom segments or remarketing, use non-linear targeting. Ignore the offer and focus on the audience. Choose any Google data audience with potential overlap, even if it’s imperfect, and let your creative do the filtering.
Use industry-specific jargon, abbreviations, and imagery that only your target audience will recognize and value. Everyone else will scroll past.
Remember: High CPCs aren’t the enemy
Low-quality traffic is the real problem. You’re better off paying $10 per click with a 10% conversion rate than $1 per click with a 0.02% conversion rate.
When evaluating targeting strategies, focus on conversion rate and cost per acquisition, not just cost per click.
Search alone can’t deliver the results you’re used to
By expanding beyond traditional search keywords and using content and audience targeting, you can reach the right people and keep driving strong results.
Watch: How to build a modern targeting strategy like a pro + Live Q&A
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Natural language is quickly becoming the default way people interact with online tools. Instead of typing a few keywords, users now ask full questions, give detailed instructions, and are starting to expect clear, conversational answers. So, how can you make sure your content provides the answer to their question? Or better yet, how can you make it possible for them to interact with your website in a similar way? That’s where Microsoft’s NLWeb comes in.
Meet NLWeb, Microsoft’s new open project
NLWeb, short for Natural Language Web, is an open project recently launched by Microsoft. The aim of this project is to bring conversational interfaces directly to websites, rather than users having to use an external chatbot that’s in control of what’s shown. Instead of relying on traditional navigation or search bars, NLWeb is designed to allow users to ask questions and explore content in a more personal, conversational way.
At its core, NLWeb connects website content to AI-powered tools. It enables AI to understand what a website is about, what information it contains, and how that information should be interpreted for the purpose of returning personalized results. With this project, Microsoft is moving toward a more interoperable, standards-based, and open web that allows everyone to prepare their website for the future of search.
This project was initiated and realized by R.V. Guha, CVP and Technical Fellow at Microsoft. Guha is one of the creators of widely used web standards such as RSS and Schema.org.
How NLWeb works
NLWeb works by combining structured data, standardized APIs and AI models capable of understanding natural language. Every NLWeb instance acts as a Model Context Protocol (MCP) server, which makes your content discoverable for all the agents operating in the MCP ecosystem. This makes it easy for these agents to find your website.
Using structured data, website owners then present their content in a machine-readable way. AI applications can then consume this data and answer user questions accurately by matching them to the most relevant information. The result is a conversational experience powered by existing content, either directly on a website or through using an online search tool. A conversational interface for both human users and AI agents collecting information.
An important thing to note is that NLWeb is an open project. It’s not a closed ecosystem, meaning that Microsoft wants to make it accessible to everyone. The idea is to make it easy for any website owner to create an intelligent, natural language experience for their site, while also preparing their content to interact with and be discovered by other online agents, such as AI tools and search engines.
How does natural language work?
Natural language simply refers to the way we speak and write. This means using full sentences that allow room for intent, context and nuance. More than keywords or short commands, natural language reflects how people think and what they are looking for exactly.
To give you an example: a focus keyphrase might be running shoes trail. But using natural language, the request would look more like this: What are the best running shoes for trail running in wet conditions?
Natural language in AI tools
Modern AI tools are designed to understand this kind of input. The large language models behind these tools can analyze intent and context to generate responses that fulfill the given request. This is why conversational interfaces feel more intuitive than traditional search or forms.
Tools like AI chat assistants, voice search, and even traditional search engines rely heavily on natural language understanding and users have quickly adapted to it.
The current state of search
The way people find information online is changing fast. A change that is heavily influenced by the use of AI-powered tools. We now expect personalized answers instead of a list of results to sort through ourselves. AI chatbots also give us the option to follow up on our original search query, which turns search into a conversation instead of a series of clicks.
Research from McKinsey & Company shows that AI adoption and natural language interfaces are becoming mainstream, with 50% of consumers already using AI-driven tools for information discovery. The majority even say it’s the top digital source they use to make buying decisions. As these habits continue to grow, websites that aren’t optimized for natural language risk becoming invisible in AI-generated answers.
Why this is interesting for you
The shift to natural language isn’t just a technical trend. As discussed above, it directly impacts your online visibility and competitive position.
If users ask an AI system for information, only a handful of sources will be referenced in the response. This is because, like search engines, AI platforms also need to be able to read the information on your website. Being one of those sources can be the difference between being discovered or being overlooked.
NLWeb collaborates with Yoast
With NLWeb, you are communicating your website’s content clearly and in a standardized way. That means your brand, products, or expertise can appear in AI-powered answers instead of your competitors. To help as many website owners as possible benefit from this shift, Yoast is collaborating with NLWeb.
The best part? If you’re a user of any of our Yoast plans designed for WordPress, you’re well ahead here. Yoast’s integration with NLWeb will roll out in phases, starting with functionality that helps our users using WordPress express their content in ways AI systems can interpret accurately, without any additional setup required. So sit tight and let us help you prepare your website for the new world of search!
NLWeb aims to make your content understandable not just for people, but for the AI systems that are increasingly relevant to your website’s discovery.
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Google’s been quietly upgrading Search Console and Analytics with AI. No fanfare. Just better data filtering. They sit quietly inside platforms you already use, like Search Console and Google Analytics, and they change how data is surfaced, filtered, and interpreted.
These updates don’t power AI Overviews or conversational search. They work behind the scenes in platforms you already use. Google is using AI to reduce manual analysis, surface issues faster, and help marketers understand complex datasets without exporting everything to spreadsheets.
Indexing patterns and performance trends are easier to spot, even if the underlying work still requires human judgment. Google’s automating the diagnostics. You still handle the strategy.
Key Takeaways
Google’s embedding AI into Search Console and Analytics 4 to cut down on manual data analysis. The AI handles filtering and pattern detection—you still make the decisions.
AI-powered features focus on filtering, pattern detection, and prioritization rather than execution.
Google Search Console AI helps surface performance insights faster.
Google Analytics 4 uses AI for anomaly detection, predictive metrics, and guided analysis.
Predictive metrics in GA4 (like churn probability) give you directional guidance, not guarantees. Use them to build hypotheses, not to replace analysis.
Why Google Is Embedding AI in SEO Tools
Google’s SEO tools have always produced more data than most teams can realistically analyze. As sites grow, so do performance reports and behavioral metrics. AI helps Google address that scale problem.
The main shift is from reactive analysis to proactive surfacing of insights. Instead of expecting marketers to manually filter reports, compare date ranges, and segment data, Google is using AI to highlight patterns and outliers automatically.
Search Console now groups issues more intelligently, with clearer prioritization, and more context around what matters. Analytics delivers automated insights, anomaly detection, and predictive metrics.
The most practical benefit is time savings. AI-powered filtering lets you type what you want to see instead of clicking through multiple dropdowns. You can ask for specific trends, segments, or anomalies and let the system do the slicing for you. That alone removes a lot of friction from daily SEO work.
Your SEO expertise still matters. AI just handles the mechanical steps that used to slow you down. Google’s goal is to help marketers spend less time finding the signal and more time deciding what to do with it. For teams managing complex sites, this automation is table stakes.
If you want to understand how AI fits into broader SEO workflows, check out our guide on AI SEO.
AI Features in Google Search Console
Google Search Console has gradually introduced AI-assisted functionality that focuses on diagnostics and data interpretation rather than automation.
As a start, Search Console’s performance reporting benefits from smarter analysis. The platform highlights notable changes in clicks, impressions, and rankings without requiring manual comparison. This helps teams catch traffic drops or unexpected gains earlier, before they become larger problems.
Conversational-style filtering saves even more time. Instead of manually applying multiple filters, marketers can describe what they want to see, and Search Console narrows the data automatically. This reduces the time spent digging through reports just to answer basic questions.
Here’s how it works in practice: Instead of clicking Performance > Filters > Query > Contains > ‘product name’ > Apply, you type ‘show me queries for product pages with declining CTR.’ The AI interprets your request, applies the right filters, and shows you the data. That’s the time savings—going from five clicks to one typed question.
Note: Conversational filtering is rolling out gradually and may not be available in all Search Console accounts yet.”
AI won’t fix your indexing issues or update your site. It finds problems faster so you can fix them yourself. The value comes from speed and clarity, not automation. For SEO teams, this shortens the path between detection and action without removing human oversight.
AI Features in Google Analytics 4
This is partly because GA4 handles more complex event-based data and cross-device behavior.
Analytics Advisor is the most visible AI feature. Currently in Beta and not available for everyone yet, It automatically flags unusual patterns, such as sudden traffic spikes, drops, or changes in engagement. These insights appear without manual configuration and are designed to draw attention to potential issues or opportunities.
To access Analytics Advisor, click the lightbulb icon in the top right corner of any GA4 property. The insights refresh daily and highlight metrics that deviate from your baseline. You might see ‘Pageviews from organic search increased 47% compared to last week’ with a link to explore the affected pages. That’s faster than manually comparing week-over-week reports.
Predictive metrics add another layer. Examples include purchase probability, churn probability, and revenue prediction for eligible properties. These metrics help teams forecast outcomes based on historical behavior rather than relying purely on past performance.
Predictive metrics require at least 1,000 positive and 1,000 negative examples of the target event over 28 days. If your site doesn’t meet that threshold, you won’t see predictions for purchase probability or churn. This makes the feature more useful for high-traffic e-commerce sites than small content publishers.
Another important use of AI in GA4 is automated anomaly detection. The platform monitors metrics continuously and alerts users when behavior deviates from expected patterns. This can surface tracking issues, campaign impacts, or site problems more quickly than manual review.
GA4’s AI points you toward what matters. You still handle the investigation. Teams still need to validate data quality, understand context, and decide how insights should influence strategy.
Other Google Tools Getting Smarter With AI
Beyond Search Console and GA4, other Google tools now have AI-supported features. Several other Google tools marketers use regularly now rely on machine learning to guide decisions and reduce manual work.
Google Analytics 4’s predictive metrics extend beyond reporting. They influence how audiences are built and activated, especially when connected to Google Ads. This allows marketers to target users based on likely future behavior rather than past actions alone.
Google Ads leans on machine learning to suggest budget shifts, adjust bids automatically, and test creative variations. You can accept or reject these suggestions, the control stays with you. These systems focus on optimization suggestions rather than forced changes, leaving final control with advertisers.
Here’s what matters: diagnostic AI explains what’s happening now. Predictive AI estimates what comes next. Diagnostic AI explains what is happening now and why. Predictive AI estimates what might happen next. Both influence how marketers act, but they serve different purposes. Understanding which type of insight a tool provides helps teams decide how much weight to give its recommendations.
This changes your daily workflow. Instead of checking reports manually and looking for problems, you respond to flagged issues. Instead of building audience segments from scratch, you refine AI-generated segments. The shift is from ‘find the problem’ to ‘validate the finding.’ That’s faster, but it requires trust in the system’s baseline accuracy.
Should You Trust AI to Support Your Reporting?
Google’s using AI to decide what you see first in your reports. That raises control questions. These tools influence what you see first, what gets flagged, and what feels urgent.
Trust the insights. Verify the recommendations. AI supports reporting by prioritizing information, not by defining truth. Understanding its role helps teams use it effectively without losing oversight.
Is AI Taking Too Much Control?
One concern is that AI-driven data points could push marketers into autopilot mode. When tools highlight issues automatically, it’s tempting to assume they reflect the full picture.
AI helps you see more. It surfaces technical problems and data anomalies that teams often miss because they’re buried in reports or obscured by volume. AI helps surface data anomalies that teams might miss due to scale or limited time. It reduces the chance that important issues stay hidden in reports.
Don’t follow every data point blindly. AI recommendations are based on models and thresholds that may not reflect business context. Treat insights as starting points, not final answers. Validation still matters.
Who Really Gets the Advantage?
People assume big brands with more data get better AI insights. Not true. Everyone has access to the same tools.
The advantage goes to teams that actually use the insights. A local contractor who spots a data anomaly flagged by Search Console and acts on it outranks a national franchise that ignores the same alert.
AI lowers the barrier to analysis, but it doesn’t guarantee better outcomes. Interpretation and execution still determine results.
FAQs
Does AI in GA4 replace manual analysis?
No. AI highlights anomalies and predictions, but analysts still need to validate findings and decide how to act.
Are predictive metrics in GA4 always accurate?
Predictive metrics are estimates based on historical data. They provide directional guidance, not certainty.
Conclusion
AI makes Google’s SEO tools more efficient. It doesn’t replace the need for strategy. You still need to validate insights, understand your business context, and decide how to act on recommendations. The teams winning with these tools treat AI as an assistant, not an autopilot.
They use automated insights to find problems faster, then apply their own expertise to fix them. That combination (AI-powered detection plus human strategy) is what drives results. Start by exploring the AI features already available in your Search Console and GA4 accounts. Check what Analytics Advisor has flagged. Look at how Search Console groups your indexing issues.
See if the insights align with what you’re already tracking manually. Then decide where automation saves you real time.
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The January 2026 SEO Update by Yoast is part of our monthly webinar series covering the latest developments in search and AI. In each session, we review the most important news from the past month and explore what it means for your search strategy. Hosted by Carolyn Shelby and Alex Moss, this month’s update looks at key industry shifts and practical takeaways for staying competitive. Below is a recap of the topics discussed and what they mean for your strategy.
Here’s the recap video on YouTube
Watch the full recap on YouTube to hear Carolyn and Alex dive deeper into these topics, answer audience questions, and provide additional examples of how these changes could affect your work.
SEO and AI news from January 2026
SEO is shifting from rankings to selection
Microsoft’s recent guide on AEO (Agentic Engine Optimization) and GEO (Generative Engine Optimization) highlights a major change: the goal isn’t just to rank, but to be chosen by AI and users. Tools like Gemini and ChatGPT don’t just match keywords; they evaluate brand authority, structured data, and real-world mentions. If your content isn’t clear, well-organized, or trustworthy, AI may overlook it, even if it performs well in traditional search. To stay competitive, focus on structured data, fast-loading pages, and strong brand signals.
Agentic commerce is on the rise
Google’s Universal Commerce Protocol (UCP) is an open-source framework designed to help AI handle purchases. This means AI won’t just recommend products, but could also buy them for users. For businesses, optimizing for AI “selection” is now as important as ranking. If you sell products, prioritize product schema, fast load times, and a strong brand presence to ensure AI picks you.
Google’s core updates continue to reshape publishing
The December 2025 core update hit news publishers hard, particularly those relying on prediction-based content (like “2026 Oscar predictions”). Google is favoring original, authoritative reporting over speculative or AI-generated content. If you’re in publishing, EEAT (Experience, Expertise, Authoritativeness, Trustworthiness) remains critical.
YouTube is a growing force in AI search
Gemini is now pulling YouTube videos into its responses, even for non-video queries. If you’re not repurposing content for YouTube, you’re missing an opportunity. Optimize video titles, descriptions, and transcripts so AI can find and cite your work.
New tools are changing how we work
Anthropic’s Claude CoWork can organize files and automate tasks, while open-source tools like Moltbot (formerly Clawdbot) let you run AI agents locally. These tools aren’t just novelties, but signs of how quickly AI is integrating into workflows. For SEO, staying adaptable and testing new tools will be key.
Yoast is helping AI work for everyone
Yoast is building on Microsoft’s NLWeb framework to help AI systems better understand web content. The goal is to ensure small publishers and businesses aren’t left behind as AI-driven discovery grows. If you’re using WordPress, Yoast SEO’s existing tools—like schema markup and readability checks—already support this effort. We’ve also added Gemini and Perplexity to our AI Brand Insights tool, so you can track how AI models perceive your brand.
What to focus on in 2026
Structure your content so AI can parse it easily (schema markup helps)
Build brand authority across channels—social media, PR, email, and YouTube all send signals AI notices
Understand agentic commerce if you sell products. Fast, well-structured pages will help AI “select” you
Avoid AI-generated slop. AI can help draft content, but human insight and expertise are irreplaceable
Sign up for the next SEO Update by Yoast
The next SEO Update by Yoast is on February 24, 2026, at 4 PM CET (10 AM EST). Sign up to join the live discussion or get the recording. Don’t miss it!
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We are excited to announce an update to our Offer schema within Yoast SEO for Shopify. This update introduces a more robust way to communicate pricing to search engines, specifically introducing sale price strikethroughs.
What’s new?
Previously, communicating a “sale” was often limited to showing a single price. With this update, we’ve refined how our schema handles the Offer object. You can now clearly define:
The original price: The “base” price before any discounts.
The sale price: The current active price the customer pays.
Why this matters
When search engines understand the relationship between your original and sale prices, they can better represent your deals in search results. This update is designed to help trigger those eye-catching strikethrough price treatments in Google Shopping and organic snippets, improving your click-through rate by visually highlighting the value you’re offering.
Organic search results for ‘cable knit hat’ showing how the structured data appears on Google.
How to use it
The schema automatically bridges the gap between your product data and the structured data output. Simply ensure your product’s “Regular Price” and “Sale Price” are populated, and our updated schema handles the rest. For more information about the structured data included with all our products, check out our structured data feature page.
Get started
If you are a Yoast SEO for Shopify customer, you can access your product schema by opening a product in the Yoast product editor in your Shopify store. If you are not a customer and want to learn more, you can start a 14 day free trial of Yoast SEO for Shopify from the Shopify App Store.
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The open web is the part of the internet built on open standards that anyone can use. This concept creates a democratic digital space where people can build on each other’s work without restrictions, just like how WordPress.org is built. For website owners, understanding and leveraging the open web is increasingly crucial. Especially with the rise of AI-powered systems and the general direction that online search is taking. So, let’s explore what the open web is and what it means for your website.
What is the open web?
The open web refers to the part of the internet built on open, shared standards that are available to everyone. It’s powered by technologies like HTTP, HTML, RSS, and Schema.org, which make it easy for websites and online systems to interact with each other. But it is more than just technical protocols. It also includes open‑source code, public APIs, and the free flow of data and content across sites, services, and devices. Creating a democratic digital space where people can build on each other’s work without heavy restrictions.
Because these standards are not owned or patented, the open web remains largely decentralized. This allows content to be accessed, understood, and reused across devices and platforms. This not only encourages innovation but also ensures that information is discoverable without being locked behind proprietary ecosystems.
The benefits of an open web
The open web is built on publicly available protocols that enable access, collaboration, and innovation at a global scale.
The most important benefits include:
Collaboration and innovation: Open protocols enable developers to build on each other’s work without proprietary restrictions.
Accessibility: Users and AI agents alike can access and interact with web content regardless of device, platform, or underlying technology.
Democratization: No single company controls access to information, giving publishers greater autonomy.
Inclusion: The open web creates a more level playing field, where everyone gets a chance to participate in the digital economy.
The open web vs the deep web
To give you a better idea of what the open web is, it helps to know about the “deep web” and closed or “walled garden” platforms. The deep web covers content not indexed by search engines, while closed systems or walled gardens restrict access and keep data siloed.
On the open web, anyone can access information freely. A good example of that is Wikipedia. Accessible to anyone looking for information on a topic and anyone who wants to contribute to its content. Closed-off platforms, like proprietary apps or social media ecosystems, create places where content is only available if you pay or use a specific service. Well-known examples of this are social media platforms such as Facebook and Instagram. Another example is a news website that requires a paid subscription to get access.
In essence, the open web keeps information discoverable, accessible, and interoperable – instead of locked inside a handful of platforms.
AI and the open web
The popularity of AI-powered search makes open web principles more important than ever. Decentralized and accessible information allows AI tools to interact with content directly and use it freely to generate an answer for a user.
“We believe the future of AI is grounded in the open web.”
Ramanathan Guha, CVP and Technical Fellow at Microsoft.
Microsoft’s open project NLWeb is a prime example. It provides a standardized layer that enables AI agents to discover, understand, and interact with websites efficiently, without needing separate integrations for every platform.
What this means for website owners
For website owners, including small business owners, embracing the open web means making your content freely available in ways that AI can interpret. By using structured data standards like Schema.org, your website becomes discoverable to AI tools. Increasing your reach and ensuring that your content remains part of the future of search.
Yoast and Microsoft: collaborating towards a more open web
Yoast is proud to collaborate with NLWeb, a Microsoft project that makes your content easier to understand for AI agents without extra effort from website owners. Allowing your content to remain discoverable, reach a wider audience with and show up in AI-powered search results.
The open web strives towards an accessible web where content is available for everyone. A web where it doesn’t matter how big your website or marketing budget is. Giving everyone the chance to be found and represented in AI-powered search. NLWeb helps turn this vision into reality by connecting today’s open web with tomorrow’s AI-driven search ecosystem
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Search today looks very different from what it did even a few years ago. Users are no longer browsing through SERPs to make up their own minds; instead, they are asking AI tools for conclusions, summaries, and recommendations. This shift changes how visibility is earned, how trust is formed, and how brands are evaluated during discovery. In AI-driven search, large language models interpret information, decide what matters, and present a narrative on behalf of the user.
Search has evolved; users now rely on AI for conclusions instead of traditional SERPs
Conversational AI serves as a new discovery layer, users expect quick answers and insights
Brands must navigate varied interpretations of their presence across different LLMs
Yoast AI Brand Insights helps track brand mentions and identify gaps in AI visibility across models
Understanding LLM brand visibility is crucial for modern brand strategy and perception
The rise of conversational AI as a discovery layer
“Assistant engines and wider LLMs are the new gatekeepers between our content and the person discovering that content – our potential new audience.” — Alex Moss
Search is no longer confined to typing queries into a search engine and scanning a list of links. Today’s discovery journey frequently begins with a conversation, whether that’s a typed question in a chatbot, a voice prompt to an AI assistant, or an embedded AI feature inside a platform people use every day.
This shift has made conversational AI a new layer of discovery, where users expect direct answers, recommendations, and curated insights that help them make decisions and build brand perception more quickly and confidently.
Discovery is happening everywhere
Users are now encountering AI-powered discovery across a range of interfaces:
AI chat interfaces
Tools like ChatGPT allow users to ask open-ended questions and follow up in a conversational manner. These interfaces interpret intent and tailor responses in a way that feels natural, making them a go-to for exploratory search.
Platforms such as Perplexity synthesize information from multiple sources and often cite them. They act as research helpers, offering concise summaries or explanations to complex queries.
Embedded AI experiences
AI is increasingly built directly into search and discovery environments that people already use. Examples include AI-assisted summaries within search results, such as Google’s AI Overviews, as well as AI features embedded in browsers, operating systems, and apps. In these moments, users may not even think of themselves as “using AI,” yet AI is already influencing what information is surfaced first and how it is interpreted.
This broad distribution of AI discovery surfaces means users now expect accessibility of information regardless of where they are, whether in a chat, an app, or embedded in the places they work, shop, and explore online.
How people are using AI in their day-to-day discovery
Users interact with conversational AI for a wide range of purposes beyond traditional search. These models increasingly guide decisions, comparisons, and exploration, often earlier in the journey than classic search engines.
Here are some prominent ways people use LLMs today:
Product comparisons
ChatGPT gives a detailed brand comparison
Rather than visiting multiple sites and aggregating reviews, there are 54% users who ask AI to compare products or services directly, for example, “How does Brand A compare to Brand B?” and “What are the pros and cons of X vs Y?” AI synthesizes information into a concise summary that often feels more efficient than browsing search results.
“Best tools for…” queries
Result by ChatGPT for “best crm software for smbs.”
Did you know 47% of consumers have used AI to help make a purchase decision?
AI users frequently ask for ranked suggestions or curated lists such as “best SEO tools for small businesses” or “top content optimization software.” These queries serve as discovery moments, where brands can be suggested alongside context and reasoning.
Trust and validation checks
Many users prompt AI models to validate decisions or confirm perceptions, for example, “Is Brand X reputable?” or “What do people say about Service Y?” AI responses blend sentiment, context, and summarization into one narrative, affecting how trust is formed.
In a study by Yext, it was found that 42% users employ AI for early-stage exploration, such as brainstorming topics, gathering potential search intents, or understanding broad categories before narrowing down specifics. AI user archetypes range from creators who use AI for ideation to explorers seeking deeper discovery.
Local discovery and service search
ChatGPT recommendations for “best cheesecake places in Lucknow, India.”
AI is also used for local searches. For example, many users turn to AI tools to research local products or services, such as finding nearby businesses, comparing local options, or understanding community reputations. In a recent AI usage study by Yext, 68% of consumers reported using tools like ChatGPT to research local products or services, even as trust in AI for local information remains lower than traditional search.
In each of these moments, conversational AI doesn’t just surface brands; it frames them by summarizing strengths, weaknesses, use cases, and comparisons in a single response. These narratives become part of how users interpret relevance, trust, and fit far earlier in the decision-making process than in traditional search.
Not all LLMs interpret brands the same way
As conversational AI becomes a discovery layer, one assumption often sneaks in quietly: if your brand shows up well in one AI model, it must be showing up everywhere. In reality, that’s rarely the case. Large language models interpret, retrieve, and present brand information differently, which means relying on a single AI platform can give a very incomplete picture of your brand’s visibility.
To understand why, it helps to look at how some of the most widely used models approach answers and brand mentions.
How ChatGPT interprets brands
ChatGPT is often used as a general-purpose assistant. People turn to it for explanations, comparisons, brainstorming, and decision support. When it mentions brands, it tends to focus on contextual understanding rather than explicit sourcing. Brand mentions are frequently woven into explanations, recommendations, or summaries, sometimes without clear attribution.
From a visibility perspective, this means brands may appear:
As examples in broader explanations
As recommendations in “best tools” or comparison-style prompts
As part of a narrative rather than a cited source
The challenge is that brand mentions can feel correct and authoritative, while still being outdated, incomplete, or inconsistent, depending on how the prompt is phrased.
How Gemini interprets brands
Gemini is deeply connected to Google’s ecosystem, which influences how it understands and surfaces brand information. It leans more heavily on entities, structured data, and authoritative sources, and its outputs often reflect signals familiar to traditional SEO teams.
For brands, this means:
Visibility is closely tied to how well the brand is understood as an entity
Clear, consistent information across the web plays a bigger role
Mentions often align more closely with established sources
Gemini can feel more predictable in some cases, but that predictability depends on strong foundational signals and accurate brand representation across trusted platforms.
How Perplexity interprets brands
Perplexity positions itself as an answer engine rather than a general assistant. It emphasizes citations and source-backed responses, which makes it popular for research and comparison queries. When brands appear in Perplexity answers, they are often tied directly to cited articles, reviews, or documentation.
This creates a different visibility dynamic:
Brands may be surfaced only if they are referenced in cited sources
Freshness and topical relevance matter more
Competitors with stronger editorial or PR coverage may appear more often
Here, brand presence is tightly coupled with external content and how frequently that content is used as a reference.
How these models differ at a glance
AI Model
How brands are surfaced
What influences the visibility
ChatGPT
Contextual mentions within explanations and recommendations
Once you see how differently large language models interpret brands, one thing becomes clear: looking at just one AI model gives you an incomplete picture. AI-driven discovery does not produce a single, consistent version of your brand. It produces multiple interpretations, shaped by the model, its data sources, and users’ interactions with it.
Therefore, tracking across your brand across multiple LLM models is essential because:
Brand visibility is fragmented by default
Across different LLMs, the same brand can show up in very different ways:
Correctly represented in one model, where information is accurate and well-contextualized
Completely missing in another, even for relevant queries
Partially outdated or misrepresented in a third, depending on the sources being used
This fragmentation happens because each model processes and prioritizes information differently. Without visibility across models, it’s easy to assume your brand is ‘covered’ when, in reality, it may only be visible in one corner of the AI ecosystem.
Different audiences use different AI tools
AI usage is not concentrated in a single platform. People choose tools based on intent:
Some use conversational assistants for exploration and ideation
Others rely on citation-led answer engines for research
Many encounter AI passively through search or embedded experiences
If your brand appears in only one environment, you are effectively visible only to a subset of your audience. This mirrors challenges SEO teams already recognize from traditional search, where performance varies by device, location, and search feature. The difference is that with AI, these variations are less obvious and more challenging to track without dedicated insights.
Blind spots create real business risks
Limited visibility across LLMs doesn’t just affect awareness; it also impairs learning. Over time, it can lead to:
Inconsistent brand narratives, where AI tools describe your brand differently depending on where users ask
Missed demand, especially for comparison or “best tools for” queries
Competitors are being recommended instead, simply because they are more visible or better understood by a specific model
These outcomes are rarely intentional, but they can quietly influence brand perception and decision-making long before users reach your website.
So all these points point to one thing: a broader, multi-model view helps build a more complete understanding of brand visibility.
The challenge: LLM visibility is hard to measure
As brands start paying attention to how they appear in AI-generated content, a new problem becomes obvious: LLM visibility doesn’t behave like traditional search visibility. The signals are fragmented, opaque, and constantly changing, which makes tracking and understanding brand presence across AI models far more complex than tracking rankings or traffic.
Below are some key challenges brand marketers might face when trying to understand how their brand appears to large language models.
1. Lack of visibility across AI platforms
Different LLMs, such as ChatGPT, Gemini, and Perplexity, rely on various data sources, retrieval methods, and citation logic. As a result, the same brand may be mentioned prominently in one model, inconsistently in another, or not at all elsewhere.
Without a unified view, it’s difficult to answer basic questions like where your brand shows up, which AI tools mention it, and where the gaps are. This fragmentation makes it easy to overestimate visibility based on a single platform.
2. No clear insight into how AI describes your brand
AI models often mention brands as part of explanations, comparisons, or recommendations, but traditional analytics tools don’t capture how those brands are described. Teams lack visibility into tone, context, sentiment, or whether mentions are positive, neutral, or misleading.
This makes it hard to understand whether AI is reinforcing your intended brand positioning or subtly reshaping it in ways you can’t see.
3. No structured way to measure change over time
AI-generated answers are inherently dynamic. Small changes in prompts, updates to models, or shifts in underlying data can all influence how brands appear. Without consistent, longitudinal tracking, it’s nearly impossible to tell whether visibility is improving, declining, or simply fluctuating.
One-off checks may offer snapshots, but they don’t reveal trends or patterns that matter for long-term strategy.
4. Limited ability to benchmark against competitors
Seeing your brand mentioned in AI answers is a start, but it doesn’t tell you the whole story. The real question is what’s happening around it: which competitors appear more often, how they’re described, and who AI recommends when users are ready to decide.
Without comparative insights, teams struggle to understand whether AI visibility represents a competitive advantage or a missed opportunity.
5. Missing attribution and source clarity
Some AI models summarize or paraphrase information without clearly attributing sources. When brands are mentioned, it’s not always obvious which pages, articles, or properties influenced the response.
This lack of source visibility makes it difficult to connect AI mentions back to specific content efforts, PR coverage, or SEO work, leaving teams guessing what is actually driving brand representation.
6. Existing tools weren’t built for AI visibility
Traditional SEO and analytics platforms are designed around clicks, impressions, and rankings. They don’t capture AI-powered mentions, sentiment, or visibility trends because AI platforms don’t expose those signals in a structured way.
As a result, teams are left without reliable reporting for one of the fastest-growing discovery channels.
Together, these challenges point to a clear gap: brands need a new way to understand visibility that reflects how AI models surface and interpret information. This is where tools explicitly designed for AI-driven discovery, such as Yoast AI Brand Insights, come into play.
How does Yoast AI Brand Insights help?
It won’t be wrong to say that the AI-driven brand discovery can be fragmented and opaque; therefore, leading us to our next practical question: how do brand marketing teams actually make sense of it?
Traditional SEO tools weren’t built to answer that, which is where Yoast AI Brand Insights comes in. It’s designed to help users understand how brands appear in AI-generated answers and is available as part of Yoast SEO AI+.
Rather than focusing on rankings or clicks, Yoast AI Brand Insights focuses on visibility and interpretation across large language models.
Track brand mentions across multiple AI models
One of the biggest gaps in AI visibility is fragmentation. Brands may appear in one AI model but not in another, without any obvious signal to explain why. Yoast AI Brand Insights addresses this by tracking brand mentions across multiple AI platforms, including ChatGPT, Gemini, and Perplexity.
This gives teams a clearer view of where their brand appears, rather than relying on isolated checks or assumptions based on a single model.
Identify gaps, inconsistencies, and opportunities
AI-generated answers don’t just mention brands; they frame them. Yoast AI Brand Insights helps surface patterns in how a brand is described, making it easier to spot:
Where mentions are missing altogether
Where descriptions feel outdated or incomplete
Where competitors appear more frequently or more favorably
These insights turn AI visibility into something teams can actually act on, rather than a black box.
Shared insights for SEO, PR, and content teams
AI-driven discovery sits at the intersection of SEO, content, and brand communication. One of the strengths of Yoast AI Brand Insights is that it provides a shared view of AI visibility that multiple teams can use. SEO teams can connect AI mentions back to site signals, content teams can understand how messaging is interpreted, and PR or brand teams can see how external coverage influences AI narratives.
Instead of working in silos, teams get a common reference point for how the brand appears across AI-driven search experiences.
A natural extension of Yoast’s SEO philosophy
Yoast AI Brand Insights builds on principles Yoast has long emphasized: clarity, consistency, and understanding how search systems interpret content. As AI becomes part of how people discover brands, those same principles now apply beyond traditional search results and into AI-generated answers.
In that sense, Yoast AI Brand Insights isn’t about chasing AI trends. It’s about giving teams a more straightforward way to understand how their brand is represented, where discovery is increasingly happening.
From rankings to representation in AI-driven search
AI-driven discovery is no longer an edge case. It’s becoming a regular part of how people explore options, validate decisions, and form opinions about brands. As large language models continue to evolve, the question for brands is not whether they appear in AI-generated answers, but whether they understand how they appear, where they appear, and what story is being told on their behalf. Gaining visibility into that layer is quickly becoming a foundational part of modern brand and search strategy.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-29 09:42:292026-01-29 09:42:29Why does having insights across multiple LLMs matter for brand visibility?
Discord has moved far beyond its gaming roots. Today, it’s becoming a direct access channel for brands that care about real engagement and meaningful digital PR outcomes.
This isn’t a Discord 101 guide. Most marketers already understand what the platform is and how servers work. Most marketers don’t know how to use Discord for engagement and PR, even as email pitches fail and social algorithms tank reach.
Discord matters now because it removes friction. Brands get real-time access to fans, creators, journalists, and niche communities without algorithmic interference. Over 200 million people use Discord monthly, and brands from Shopify to The New York Times now run active servers. Conversations happen in the open, persist over time, and create context that traditional channels struggle to replicate.
Brands can show up consistently in spaces people actually want to join. That changes how relationships form and stories emerge.
In this article, we’ll break down how marketers and PR teams can use Discord to drive engagement, support press outreach, host event-style campaigns, and turn community activity into earned media.
Key Takeaways
Discord works best as a relationship channel, not a broadcast platform. Engagement comes from participation, not posting frequency.
PR teams can use Discord to build trust and shared context before any formal outreach happens.
Features like roles, private channels, and stages support controlled access for media and creators.
Event-driven engagement inside Discord often creates moments journalists and creators want to reference.
Earned media from Discord grows out of visible conversation, not promotional messaging.
Most brands fail on Discord by broadcasting instead of conversing. The platform rewards brands that facilitate discussion, respond quickly, and give members real access to decision-makers.
Why Discord Is More Than Just a Community Platform
Discord gets grouped with other community tools, but that undersells what it actually does.
Discord is an owned communication layer. Members opt in. Conversations persist. There’s no feed to fight and no algorithm deciding who sees what. Engagement teams tired of declining social reach find that valuable.
The platform has also expanded into professional and brand-led use cases. B2B companies, SaaS platforms, media brands, and creator-led businesses now use Discord to host product discussions, feedback loops, and industry conversations. These servers often function as always-on focus groups where insight flows both directions.
Shopify hosts channels for developers and partners. Notion uses Discord for product feedback and feature requests. These aren’t gaming communities—they’re professional spaces where brands get direct access to customers, partners, and media without paying for ads or fighting algorithms.
For PR teams, Discord introduces something email can’t replicate: visible context. Journalists and creators don’t just receive a message. They see how a brand responds to questions, explains decisions, and engages with its community over time.
A tech journalist following a SaaS brand’s Discord sees how they handle bug reports, communicate delays, and support users. That context makes it easier to cover the company fairly when news breaks. Email alone can’t build that kind of ongoing visibility.
That ongoing presence builds familiarity before coverage is ever discussed. Discord blends access, continuity, and transparency into a single environment, which sets the foundation for both engagement and digital PR.
Core Features That Make Discord Ideal for Engagement and PR
Discord’s strength is ongoing conversation, not one-way distribution. That distinction changes how engagement and digital PR teams plan campaigns.
Chat channels stick around. Conversations don’t disappear after a day or get buried by new posts. Conversations don’t disappear after a day or get buried by new posts. A strong AMA thread, product debate, or media Q&A can remain active and searchable for weeks, giving journalists and creators extended context without repeated outreach.
Roles and access control make Discord viable for PR use cases. Teams can create press-only channels, creator lounges, or embargoed spaces tied to launches. Access feels intentional rather than promotional, which increases participation and trust.
Here’s how that works in practice: You can create a #press-only channel where journalists see embargoed announcements, background context, and Q&A access before public launches. A #creators channel might include early product access, collaboration opportunities, and direct messaging with your team. Fans see neither of these spaces—they get their own channels focused on community discussion and support. That segmentation makes Discord feel exclusive and valuable to each group.
Events, stages, and AMAs introduce timed engagement bursts. Moderated formats work well for leadership conversations, briefings, and launches. These events concentrate attention while still allowing real interaction.
Stages support up to 1,000 listeners with interactive Q&A. That’s enough for most brand events without requiring webinar software or event platforms. The recording stays in the channel afterward, so people who missed the live session can still participate in the discussion.
Integrations extend Discord’s usefulness. Feedback tools, shared resource hubs, and workflow automations connect Discord activity to broader marketing and PR efforts. Instead of living in a silo, Discord becomes part of day-to-day operations.
The key advantage is flexibility. Discord lets teams design micro-environments around how people actually communicate.
Using Discord to Build Journalist and Creator Relationships
Most PR teams still rely on cold email, despite falling response rates. Journalists and creators increasingly prefer communication that feels conversational and contextual rather than transactional.
Discord makes non-pitch engagement possible. Skip the ask. Invite journalists and creators into private or semi-private channels first. These spaces offer early context, background discussion, or access to subject-matter experts without pressure.
Buffer runs a Discord server where journalists can ask the CEO or product team questions directly. No PR gatekeepers. No scheduling calls. Just post a question in the #media channel and get a response within hours. That accessibility makes Buffer easier to cover than competitors who require formal interview requests and two-week lead times.
Direct access to decision-makers changes expectations. Journalists can ask follow-up questions, clarify details, or observe how a brand thinks before deciding whether a story fits. Creators can explore ideas collaboratively rather than responding to a single brief.
Here’s a simple journalist outreach flow:
Create a private #press channel with embargoed access
Invite 10-15 journalists who cover your industry (not thousands)
Share early context on product launches, company updates, or industry insights
Let them ask follow-up questions async
When a story fits, the relationship already exists
Over time, transparency and responsiveness in chat build trust faster than long email threads. When a pitch does make sense, the relationship already exists.
This approach works particularly well for tech, SaaS, and creator-driven industries where speed, access, and nuance influence coverage decisions.
This approach doesn’t work for every brand. Mass consumer brands or highly regulated industries might struggle with open-channel discussions. But for companies selling to creators, developers, or digital professionals, Discord shortens the relationship-building cycle from months to weeks.
Event-Based Engagement: How to Use Discord for Launches, AMAs, and More
Smart brands treat Discord like a live venue, not a static community.
Product launches often include countdown channels, staged reveals, and post-drop discussion. Leadership teams host AMAs. Engineers, designers, and product managers run Q&A sessions that surface both feedback and insight.
Good events take prep work.. Clear goals, advance question collection, and active moderation improve outcomes and keep discussions focused.
Effective Discord events typically include:
Before the event:
Announce 3-5 days early with clear agenda
Create dedicated event channel
Collect questions in advance via Google Form or channel thread
Assign at least 2 moderators
Test Stage or voice channel setup
During the event:
Pin the event agenda
Start with 3-5 pre-submitted questions to build momentum
Let mods filter and prioritize live questions
Keep responses under 3 minutes each
Screenshot strong quotes for later use
After the event:
Post a recap with key quotes, decisions, or takeaways
Thank participants by name
Share recap as blog post or social content
Leave the channel open for continued discussion
Most effective Discord events run 45-60 minutes. Longer sessions lose energy. Shorter sessions feel rushed. Plan for 10-12 questions max, with flexibility for strong follow-ups.
Events focused on audience value beat pure announcements every time. These moments also create reusable assets. Quotes, insights, and screenshots often become blog content, social posts, or supporting material for PR outreach.
Driving Earned Media Through Discord Engagement
Growing your Discord server matters less than what happens inside it.
Active communities generate stories organically. Journalists reference AMA insights. Industry newsletters cite ongoing discussions. Blogs quote real community sentiment.
Community-driven narratives often outperform traditional press releases because they show participation rather than positioning. Readers trust stories that reflect real dialogue.
A transparent Q&A or high-energy discussion thread can become the foundation for coverage. Discord surfaces narratives that feel timely, authentic, and grounded in lived interaction.
To maximize earned media potential from Discord:
Make conversations screenshot-friendly. Clear usernames, well-formatted responses, and threaded discussions make it easier for journalists to reference your server.
Highlight notable members. When industry experts or recognizable creators participate in your Discord, that increases media appeal.
Track quotable moments. Assign someone to screenshot strong quotes, insights, or exchanges during active discussions. These become PR assets.
Pitch the conversation, not just the product. Send journalists a link to an active discussion thread, not a press release. Let them see the community energy firsthand.
Common Mistakes When Using Discord for PR and Engagement
The biggest mistake? Treating Discord like a broadcast channel.
Post links without conversation and your server dies.. Members expect response and interaction, not scheduled promotion.
Another issue is weak moderation. Servers without clear purpose or active moderators lose focus fast, which discourages journalists and creators from participating.
PR teams also create friction when they treat creators or journalists like captive audiences. Discord works because participation is voluntary and collaborative.
Guide discussion. Share insider context. Show up consistently. Respect the community’s time.
“Mistake #1: Broadcasting Without Responding Posting ‘Check out our new blog post!’ and disappearing doesn’t work. People expect you to discuss the post, answer questions, or explain why it matters. If you’re not ready to engage, don’t post.
Mistake #2: No Clear Server Purpose Servers that try to be everything—community hub, support forum, news feed, social network—confuse members. Pick 2-3 core functions and build around those. Zapier’s Discord focuses on automation discussion and customer success. That’s it.
Mistake #3: Treating Journalists Like Fans Journalists don’t want hype. They want context, access, and honesty. A press channel filled with marketing language gets ignored. Background information, data, and direct responses get used.
Mistake #4: Inconsistent Presence Posting daily for two weeks, then ghosting for a month, breaks trust. If you can’t maintain active engagement, don’t launch a server. Better to have no Discord than an abandoned one.
Mistake #5: Over-Moderation or Under-Moderation Too many rules kill discussion. No rules create chaos. Find the balance: clear guidelines, active mods who participate (not just police), and flexibility for organic conversation.”
Tools, Bots, and Setups to Maximize PR ROI
The right setup makes Discord manageable for small teams and scalable for larger ones.
Roles segment audiences cleanly. Press, creators, and fans shouldn’t share the same access paths. Clear onboarding channels explain where to engage and what matters.
Bots support efficiency:
Event scheduling and reminders
Moderation and automation
Engagement and activity tracking
Larger teams often use ticket-style workflows to route media requests or creator inquiries without cluttering channels.
The goal is structure without rigidity. Discord should feel organized, not over-engineered.
For Events:Sesh – Schedules events with automatic reminders. Members RSVP directly in Discord, and the bot pings them 15 minutes before start time.
For Moderation:MEE6 – Auto-moderates spam, assigns roles based on activity, and sends custom welcome messages to new members. Free tier handles most small-to-mid sized servers.
For Analytics:Statbot – Tracks message volume, active members, peak engagement times, and channel-level activity. Shows which conversations generate the most participation—useful for PR teams measuring impact.
For Workflow:Zapier’s Discord integration – Connects Discord to Google Sheets, Notion, or your CRM. Auto-post media inquiries to a tracking sheet or notify your team in Slack when someone joins your press channel.
For Ticketing:Ticket Tool – Creates private support threads for media requests, creator pitches, or partnership inquiries. Keeps channels clean while routing requests to the right team member.
FAQs
How do you engage a Discord community?
Run regular events like AMAs, Q&As, or feedback sessions. Assign roles that give members status and access (not just colors). Recognize active contributors publicly. Create channels for member-led discussions, not just brand announcements. Give people reasons to return daily, like ongoing conversations or exclusive content drops.
How can you increase Discord engagement?
You can run a small server (under 500 members) with one dedicated person spending 30-60 minutes daily. Larger servers need at least 2-3 moderators to handle different time zones and maintain a consistent presence. Consider community volunteers once your server reaches 1,000+ active members.
What’s the minimum team size needed to run a Discord server effectively?
What’s the minimum team size needed to run a Discord server effectively?
Expect 3-6 months before Discord activity generates measurable earned media. Relationships take time. The first month focuses on setup and onboarding. Months 2-3 build conversation patterns. Months 4-6 typically produce quotable moments and media references. Results accelerate after you establish a consistent presence and trust.
How long does it take to see PR results from Discord?
Expect 3-6 months before Discord activity generates measurable earned media. Relationships take time. The first month focuses on setup and onboarding. Months 2-3 build conversation patterns. Months 4-6 typically produce quotable moments and media references. Results accelerate after you establish consistent presence and trust.
Conclusion
Discord rewards dialogue over distribution. That makes it a natural fit for engagement and digital PR teams focused on relationships rather than reach.
Brands that use Discord well create space for trust, transparency, and real participation. Those signals translate into earned media, stronger creator relationships, and long-term community value that social platforms can’t replicate.
Start small. Launch a focused server with clear purpose, maybe a press channel and a creator lounge. Host one monthly AMA or live event. See what surfaces organically before scaling up. The platform rewards consistent, genuine engagement more than polished campaigns.
Discord won’t replace your email list or social media presence. But for building the kind of relationships that lead to coverage, partnerships, and authentic advocacy, it’s one of the most effective channels available right now.
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Microsoft has been promising to give data on the performance of websites mentioned in AI results within Bing and Copilot since February 2023 and then again in April 2023. But then decided to let us down and only lump the data together with web queries, not giving us a clear view of how our sites perform within Bing’s AI experiences.
Now Bing is reportedly testing showing a new report within Bing Webmaster Tools named AI Performance report.
AI Performance report. This report is currently in a super limited beta – Microsoft has not announced anything about this publicly. But a source told us this report shows citation data from both Microsoft Copilot and partners. It shows the number of citations and the number of cited pages by day.
You can see how many times Copilot cited your website and across how many pages. It does not show you how many people clicked from those citations on Copilot to your site.
It does also let you see the data listed by “grounding queries” and “pages.” Grounding queries is likely not the full query entered into the search box on Copilot but how Bing interprets that query. Plus, it will show you the “intent” behind the query, whether it is a navigational, informational, or other form of query.
The report also shows you the specific pages cited by Copilot.
ETA. Again, Microsoft has not announced this report yet but some are seeing it go live within Bing Webmaster Tools under the Search Performance report named “AI Performance.” I do not know when you or I will gain access to the report.
Why we care. It is great to see more AI performance reporting coming from Bing Webmaster Tools, but I really do wish for click data. Every publisher, content creator, and site owner wants to know how the click-through rate from AI experiences compares to web search.
It just feels like all the search engines are deliberately hiding this data from us.
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Google will now jump you directly into AI Mode when you do a follow-up question from AI Overviews within Google Search. This makes the “transition to a conversation even more seamless,” Robby Stein, VP of Product, Google Search wrote.
Plus, Google AI Overviews are powered by Gemini 3 by default, globally.
AI Overviews jumping to AI Mode. We covered when Google was officially testing this back in December and also before Google confirmed the test in October 2025. The ask a follow-up question within the Google Search AI Overviews will jump you into a conversation directly in AI Mode.
Google said this is about “making the transition to a conversation even more seamless,” within Google Search.
Why is Google doing this? Google said that during its testing, it “found that people prefer an experience that flows naturally into a conversation – and that asking follow-up questions while keeping the context from AI Overviews makes Search more helpful.”
Here is how it works:
When you click on “Show more,” Google will overlay AI Mode directly over the search results. You can to click the X at the top right of the screen to go back to the search results. And all the sources are removed from this view, so much for sending more traffic to publishers and content creators…
Note, this is live on mobile only right now.
Gemini powering AI Overviews. Google also said that it is rolling out Gemini 3 as the default model for AI Overviews globally. Robby Stein said, “we’re making Gemini 3 the new default model for AI Overviews globally, so you get a best-in-class AI response right on the search results page, for questions where it’s helpful.”
This is different from his previous announcement about a week ago, where Gemini 3 Pro would power AI Overviews for complex queries for English globally for Google AI Pro & Ultra subs.
Now, Gemini 3 is the default model uses for AI Overviews globally.
Why we care. While Gemini 3 may provide better quality responses for AI Overviews, the bigger news is that Google officially rolling out that follow up questions go to AI Mode from Google Search’s AI Overviews.
This is a big deal, because this will likely result in even fewer clicks from Google Search to publishers and instead will drive more searchers into AI Mode.
AI Overviews show up at the top of the search results for many queries. It is hard enough to get clicks from those citation cards now, and it will be even harder as this new follow-up experience rolls out. Google is actively pushing those searchers from Search into AI Mode and not to your website.
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