Posts

Is SEO Dead in 2026?

/0 Comments/in /by

Is traditional SEO is dead? Not exactly. But definitely evolving. Google still controlled a whopping 89% of all U.S. web traffic in 2025. It’s still a search powerhouse, no doubt, but it isn’t the only show in town anymore.  

SEO as we know it is no more. The way people find information is changing dramatically.

Google’s rolling out 12-plus algorithm changes per day. At the same time, platforms like TikTok, Amazon, and generative AI tools like ChatGPT and Claude are becoming major players in the search game. 

Let’s face it. Traditional SEO tactics aren’t always the best option.  

To succeed, you must adapt.  

In 2026, it’s less about search engine optimization and more about search everywhere optimization.  

Let’s dig into the data for a pulse check on SEO in 2026. 

Key Takeaways

  • SEO isn’t dead, but traditional tactics alone won’t cut it. To stay visible, your strategy must account for AI Overviews, zero-click searches, and shifting user behavior across platforms.
  • AI Overviews and SERP features now dominate page one. If your content isn’t cited or structured for AI, you risk being invisible—no matter your ranking.
  • Brand signals like search volume, authority, and trust matter for AI visbility. Google favors entities, not just pages. Build real-world credibility if you want to rank.
  • Optimize for LLMs and SEO at the same time. Clear formatting, concise answers, and fact-rich content help you rank and get quoted in generative results.
  • Search is no longer just on Google. Users discover content through social media, marketplaces, and AI engines—your optimization strategy needs to reach beyond traditional search.

Is SEO Dead?

Google doesn’t share its search volume data. However, approximations place it in the tens of billions, somewhere over 15 billion per day. 

This shows that SEO still holds weight, but AI and LLM searches are growing. Currently, these platforms account for about 6% of global search volume, which doesn’t seem like much. But when you consider that the number is about triple what it was a year ago, it makes marketers start to take notice.

According to SmartInsights, the top 3 positions carry double-digit click-through rates, but these drop drastically for positions lower down the page. Just look at the chart below: 

A graphic showing Google CTR growth for featured snippets.

This drastic drop highlights how Google’s been steadily moving toward a “zero-click” search experience.  

Does this mean AI Overviews are surely going to kill SEO? Well, no, but they’re definitely shaking things up. In fact, Google’s been moving toward its “answer engine” model and its new AI mode for a while now. 

Features like featured snippets and answer boxes already provide concise information directly on the search results page, reducing the need for users to click through to websites.

This trend is driven by the rise of “zero-click content”—content that’s so comprehensive and informative that it satisfies user intent right on the search engine results page (SERP).

Essentially, users can find their answers without visiting a website.

AI Overviews take the zero-click approach to a whole new level, providing even more content directly in the search results.

A graphic saying "What are the top trends in digital marketing."

So, how do we come to grips with both truths—that zero-click search directly results in less engagement with SEO results and that organic search is still a significant driver of traffic?

A common concern for marketers is that emerging AI engines, like ChatGPT, will kill the industry as we know it. But consider this: AI search engines still rely on Google and other algorithm-driven engines for information.

Instead of assuming SEO is dead, we should consider how SEO works today in conjunction with these trends.

 
The Face of the New SEO Campaign

To understand what success looks like in the new world of search, let’s look at a successful campaign of one of our NP Digital clients, RefiJet. 

RefiJet has quickly become a leader in the motorcycle and auto loan refinancing space over the last decade. But to grow further, they needed to differentiate themselves from competitors and grow their digital footprint, all while AI search was changing the very way the game is played. Their company also faced macroeconomic challenges as high interest rates pushed many borrowers to the sidelines.

Our strategy for them blended new AI search principles with traditional SEO best practices. We focused on traditional technical SEO aspects such as crawlability, site speed, and structured data optimization. These moves boosted RefiJet’s inclusion in AI overviews.

Next, we launched on-page optimization tactics. These were aimed at catching traditional long-tail, high-intent search queries. We also leveraged retrieval-augmented generation (RAG) to showcase RefiJet’s authority in its space and boost citations across the web.

Stats showing the result of NP Digital's campaign with RefiJet.

This blended approach helped RefiJet achieve some pretty eye-catching results:

  • Their SERP features increased 30,800% (that’s not a typo) since May 2024
  • Their rankings in the highly coveted 1-3 slots in Google increased 522% year-over-year
  • Traffic from LLMs is up 2012% and site-wide page views from LLMs are up 7144% year over year.
  • Most importantly, RefiJet’s funded loans from organic search and LLMs are up 178% year over year.

So, no. Traditional SEO is not dead. The “new” strategy just takes a modern, blended approach to modern search problems.   

SEO Isn’t Dying (It’s Just Changing)

So, is SEO dead? At this point, I think you know my answer. 

That would be a resounding no. 

SEO isn’t going anywhere. However, for brands to find success with SEO strategies, there are specific things to keep in mind when developing campaigns. 

We know Google functions more as a discovery engine but here is what else you need to know to dominate SERPs. 

AI Is Taking Up A Larger Portion of the SERPs

If you’ve searched for anything on Google lately, you’ve probably seen it. That big, AI-generated box right at the top—pushing organic results further down the page.

Google’s AI Overviews are live, and they’re eating up prime SERP real estate. For certain keywords, especially broad informational ones, they dominate. And if your content doesn’t get cited in those summaries? You might not even show above the fold.

But it’s not just AI Overviews. Google has been quietly expanding other SERP features too, like interactive knowledge panels, visual product listings, “Discussions and Forums,” and even its experimental AI mode inside Search Labs. The days of ten blue links are long gone.

An example AI overview.
An example AI overview.

This shift doesn’t mean SEO is over. It means we have to rethink how we optimize. Your content still needs to be the best answer, but now it also needs to be the kind of content Google’s AI is willing to quote.

If you haven’t yet, start digging into how AI Overviews work. See which types of pages Google is pulling from. Understand the patterns.

SEO isn’t dying. But the way we earn visibility is shifting. Fast.

Technical Fundamentals Still Matter

Google’s focus isn’t backlinks, keyword density, or a specific SEO metric. Instead, the focus is on a seamless and enjoyable user experience. 

What metrics does Google use to gauge user experience?  

Using a clear navigation structure is a good place to start. If you want people to spend a lot of time on your site, you need to understand how users navigate. This includes using a clear URL structure, enabling breadcrumbs, and linking internally. 

Core Web Vitals—a set of standardized metrics Google uses to measure real-world page performance—is another good launchpad. These include: 

  • Largest Contentful Paint (LCP): The time from when a user starts loading a page until the largest image or text block is visible in the viewport. Goal: 2.5 seconds or less.  
  • Interaction to Next Paint (INP): The time between a user action, like a click or key press, and the next time it takes for the page to respond. Goal: 200 milliseconds or less.  
  • Cumulative Layout Shift (CLS): How much a webpage’s layout unexpectedly shifts during loading. Goal: A CLS score of less than 0.1.  

Other important user experience metrics include dwell time, time spent on page, bounce rate, and exit rate. You can find these metrics in Google Analytics. 

So, how can you improve user experience? There are a few steps you can take that will positively impact the metrics mentioned above: 

  • Improve site speed: The faster your site loads, the better experience the user will have (We saw the impact this can have in our RefiJet example). You can gauge site speed with tools like PageSpeed Insights and Pingdom.  
Google PageSpeed Insights.
  • Optimize for mobile: You can’t afford to not optimize for mobile, as it accounts for more than 50% of web traffic. Tools like PageSpeed Insights can give you the information you need to start, like eliminating render-blocking resources or reducing unused code. You will also want to consider a responsive design if you’re not already using one. 
Errors found in Google PageSpeed Insights.

Social Search Is Taking A Larger Share

Google remains a powerful tool, but, as we’ve established, it’s no longer the sole player in search and discovery. 

Platforms like TikTok, Reddit, and even voice search engines—such as Alexa and Siri—are reshaping SEO. The question is: Are you reshaping your strategies to match them? 

When Google is deciding what to rank and where to rank it, it looks past its own dataset toward other spots online, like the platforms mentioned above.  

The SproutSocial interface.

Source: https://sproutsocial.com/insights/social-media-search/

All these platforms have one thing in common: They cater to users who prefer quick, conversational, or visual content. 

So, what does optimizing your content strategy to leverage these platforms look like in practice? Each app has its own wrinkles you need to consider to maximize your performance across channels:

  • Reddit: Participate in relevant subreddits and provide value without overtly promoting. 
  • YouTube: Create a combination of long-form and short-form videos, targeting different users on the platform. 
  • Voice search: Focus on conversational keywords and provide clear answers to common questions. 

You may be asking, why don’t users just use those platforms to find what they need? 

They do, but before you say “SEO does not matter,” remember while Google is a search engine, it can provide results from other platforms, making them relevant.  We’ve seen this recently with Reddit results surging to the top of the SERPs, and showing up in a whopping 97.5 percent of Google search queries for product reviews.

As  younger audiences use social media or videos more for discovery, Google will continue to update and adapt to meet user needs. And since Google pulls from so many different spaces (not just social), it still offers more reliable results on topics people want to find. 

Take Reddit, for example. It shows up in a whopping 97.5 percent of Google search queries for product reviews. 

Google Loves Brands

As your brand grows, you’ll find your rankings climb because Google takes authority, trustworthiness, and relevance into account. Typically, well-established brands have a higher authority and level of trustworthiness. Branded search volume is the number of searches for keywords containing your brand name on a search engine. This is one of the metrics for tracking growth because it reflects user’s interest and awareness of your brand.  

Let’s consider what happens when you type “men’s running shoes” into Google’s search bar. Here is an example of what you might get: 

A list of results for men's running shoes.

Brands, brands, and more brands. 

If you search my name, Google assumes you want to look at my website, businesses, and information about me or my social accounts. 

Results of a Google search for Neil Patel.

Often, Google assumes that people searching for these terms already know what they want (and likely plan to make a purchase). This is especially the case if a customer is searching for an already well-established brand.  

So, how do you establish your brand?  

Aligning with the E-E-A-T framework is a good start. When your brand exudes Expertise, Experience, Authoritativeness, and Trustworthiness, Google will notice (and so will users).  

To build those signals:

  • Create content that shows off your real-world experience and authority. Think in-depth tutorials or original research. Customer stories help, too.
  • Earn mentions and backlinks from reputable sources. Digital PR matters here.
  • Foster community. Social proof, like reviews, forum engagement, or user-generated content, tells Google and users that your brand is alive and active.

While some argue SEO is dead, building brand authority proves otherwise. In the age of AI and zero-click searches, it’s your ticket to higher rankings and increased visibility. 

To be clear, E-E-A-T doesn’t only help for branded terms. Be sure to optimize for branded and non-branded terms to get in front of the most users. 

Intent Is More Important Than Ever

Google is getting better at understanding intent, and users expect results that feel tailored to what they actually want, not just what they typed. If someone searches “best running shoes,” are they looking to buy now, compare options, or read reviews? If your page doesn’t match that intent, it’s not going to rank or convert.

It’s not just about categories like “informational” or “transactional” anymore, either. Google’s updates and AI enhancements have made search more personalized. Things like location and device type all influence which results appear and in what format.

That means one-size-fits-all content just won’t cut it. You need to build pages that solve specific problems for specific searchers, and make it obvious within the first few seconds that your content delivers the answer.

Look at the top results for your target terms. What kind of experience is Google rewarding? Long guides? Product roundups? Local directories?

When you align with intent, you’re not just improving your SEO, you’re giving users what they came for. And that’s how you win in the long run.

Create Content That’s Friendly For LLMs and SEO (there’s crossover)

Your niche is where your product or services fit in the market. What do you offer, and LLM tools like ChatGPT and Perplexity are changing how people search. Users have the ability to ask a question and get an instant summary. That means your content has to be referenced in this zero-click section of the search.

This is where LLMO (large language model optimization) comes in. It overlaps with SEO in a lot of ways. Clear structure and concise answers are good for both. But there are key differences.

LLMs don’t care about keyword density; they care about relevance and clarity. They’re more likely to pull from well-organized content (read as “easy to parse”) and rich in facts. Formatting matters. Use short copy blocks and bulleted lists to increase readability, and, on the technical side, clean HTML and schema markup help machines understand your content even more.

When it comes to backlinks, they still matter for SEO, but LLMs are more influenced by how well your content explains a topic.

If you want to future-proof your content, think about both: ranking high in search and being the source that LLMs pull from when people skip the SERPs entirely. For example below, well-known and reviewed medical sources pop up for this medical question.

A medical LLM report.

Smart content creators are already optimizing for both worlds. Don’t get left behind.

User-Generated Content/Original Content Matters

Have you noticed that when you search on Google, your results are different than those Google is focusing on rewarding content that actually shows experience.

That’s why original content, especially from real users, is more valuable than ever. Some good examples of this type of content are:

  • Customer reviews
  • Community Q&As
  • Case studies
  • Proprietary research
  • Photos from your team.

Here’s an example of a successful UGC campaign from GoPro:

A UGC campaign example from GoPro

Source: https://www.sevenatoms.com/blog/ugc-marketing-examples

These types of content act as trust signals that feed directly into Google’s E-E-A-T framework (experience, expertise, authoritativeness, and trustworthiness).

If you haven’t already, get familiar with E-E-A-T. It’s the lens Google uses to figure out if your content deserves to rank. And in a world where LLMs are regurgitating the same surface-level info, showing firsthand knowledge is how you stand out.

User-generated content helps with that. So does publishing original insights—whether that’s internal data, lessons learned, or your unique take on industry trends. This is the kind of material Google can’t find anywhere else. It’s also what LLMs prefer to cite when pulling answers.

If you’re just rephrasing what’s already out there, you’re invisible. But if you create something worth referencing, both humans and machines will take notice.

Start building a content library that’s not just SEO-optimized, but undeniably your brand.

Focus Metrics Are Changing

Clicks and rankings used to be the gold standard in SEO. Not anymore.

Today, traditional SEO numbers like clicks and rankings only tell part of the story. With AI Overviews and zero-click searches taking over the SERPs, it’s possible to “rank” without getting any traffic. In this new environment, the way we measure success needs to evolve.

Instead of obsessing over position one, look at visibility across AI and SERP features. Are you showing up in AI summaries? In featured snippets? In the “People Also Ask” box? These touchpoints matter more now because they shape user behavior before a click even happens.

Engagement metrics are shifting, too. Scroll depth, dwell time, and interaction with on-page elements can reveal more about content quality than bounce rate ever did. The same goes for branded search volume and return visits—both strong signs that your content is resonating.

Search Everywhere Optimization Has Taken Center Stage

Search engines no longer corner the market on search. Non-search platforms, like social media and generative AI engines, are increasingly being used for search and discovery, disrupting traditional SEO norms. 

This is what search everywhere optimization is all about.  

You can no longer assume that users are only using search engines to find services and products that they need. They’re also using marketplaces (e.g., Amazon, Walmart), social media (e.g., TikTok, Pinterest), and generative AI (e.g., ChatGPT).  

This means you need to expand your search optimization efforts, well, everywhere! Here’s how: 

  • Social media: Platforms like TikTok and Instagram prioritize engaging, visual content. Optimize by using trending hashtags, creating shareable posts, and collaborating with influencers. Forums like Reddit are also highly cited in LLM results.
  • Generative AI engines: Tools like ChatGPT are shaping search behavior by delivering conversational and context-aware responses. Businesses should focus on producing concise, relevant, and authoritative content to rank within these engines. 
  • Marketplaces: Amazon and similar sites act as search engines for product discovery. Ensuring optimized product titles, descriptions, and reviews is crucial. 

We’ve seen this trend of the expanded search surface for a while now, but in 2026, your audience can be found across more platforms than ever. That’s why finding where your audience hangs out, and spending time to see how they’re interacting within the community, is such an important part of modern marketing strategy.

The traditional direct path of top-of-funnel to mid-funnel to bottom-of-funnel doesn’t play anymore. Your audience can convert from virtually anywhere in today’s market. Understand where they are, understand how they’re interacting, and understand the nuances of marketing on each platform, and you’ll be good to go.

FAQs

 

Is local SEO dead?

Not even close. Local SEO remains essential for businesses that rely on local customers. In fact, features like the local pack, Google Business Profiles, and map results are highly influential, especially on mobile. What’s changing is how users find you. Optimize for reviews and local content to stay competitive.

How long will SEO exist?

SEO is here to stay, but it continues to evolve. As long as people use search engines, social platforms, and AI tools to discover information, SEO will exist, even if tactics evolve. 

Conclusion

SEO isn’t dead; it’s adapting to how people search today.

With AI reshaping the SERPs and user behavior shifting fast, what worked five years ago won’t cut it now. But the fundamentals still matter: create useful content, match search intent, and build trust with your audience.

If you’re unsure where to start, look at your content strategy. Are you prioritizing originality and structure? That’s what both Google and LLMs are rewarding.

Now’s also the time to rethink how you measure success. Traffic is great—but brand signals like engagement and trust are carrying more weight, and will only grow in importance in the future.

Want more tactical advice? Check out our guides on search engine trends and how to improve your SEO rankings.

Modern platforms like AI didn’t kill SEO; they just made it smarter. And we all need to follow suit.

Read more at Read More

What is NLWeb (Natural Language Web)?

/0 Comments/in , /by

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. 

Read more: Yoast collaborates with Microsoft to help AI understand Open Web »

The post What is NLWeb (Natural Language Web)? appeared first on Yoast.

Read more at Read More

AI-Powered Functionality in Google’s SEO Tools

/0 Comments/in /by

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.

AI-Powered configuration in Google Analytics.

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.

An example of Search Console grouping with AI.

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.

An AI query workflow.

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.

Analytics Advisor in GA4.

Source

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 in GA4.

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. 

Read more at Read More

Recap: The January 2026 SEO Update by Yoast

/0 Comments/in , /by

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!

The post Recap: The January 2026 SEO Update by Yoast appeared first on Yoast.

Read more at Read More

What is the open web?

/0 Comments/in , /by

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 

Read on: Yoast collaborates with Microsoft to help AI understand Open Web »

The post What is the open web? appeared first on Yoast.

Read more at Read More

Why does having insights across multiple LLMs matter for brand visibility?

/0 Comments/in , /by

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.

Table of contents

Key takeaways

  • 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.

Also read: What is search intent and why is it important for SEO?

Answer engines

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.

Also read: Why is summarizing essential for modern content?

Idea generation and research exploration

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

local search results on chatgpt
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 Prompt phrasing, training data, general relevance
Gemini Entity-driven, aligned with authoritative sources Structured data, brand consistency, trusted signals
Perplexity Citation-based mentions tied to sources Content coverage, freshness, external references

Why brands need insights across multiple LLMs?

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.

Must read: When AI gets your brand wrong: Real examples and how to fix 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.

The post Why does having insights across multiple LLMs matter for brand visibility? appeared first on Yoast.

Read more at Read More

Bing Webmaster Tools testing new AI Performance report

/0 Comments/in , /by

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.

Read more at Read More

Google AI Overviews follow up questions jump you directly to AI Mode

/0 Comments/in , /by

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.

Read more at Read More

Yahoo debuts Scout, an AI search and companion experience

/0 Comments/in , /by

Yahoo today launched the first version of its AI-powered answer engine, Yahoo Scout. Scout is available at scout.yahoo.com and is embedded across Yahoo’s network, including Yahoo News, Finance, Mail, and Search. Think of it as a Yahoo-branded AI companion designed to guide users directly within Yahoo’s properties.

What is Yahoo Scout. Yahoo Scout is Yahoo’s take on an AI search engine and companion, similar to Google’s AI Mode or OpenAI’s ChatGPT, but with a distinct Yahoo flair. The goal is to give Scout a real personality — fun, engaging, and easy for people of all ages to use and understand, Yahoo told me.

  • When you first visit Yahoo Scout, you’re greeted by a playful homepage with a search box, a catchy slogan, and an animated icon that makes the experience feel friendly and inviting.
  • Below the search box, Yahoo offers suggested searches, with filters for topics like news, finance, sports, shopping, and travel.
  • On the left, Scout shows your past queries, making it easy to jump back in where you left off.

Here’s a screenshot of the homepage. This one features a cowboy hat, but other versions include a crystal ball, a gold medal, a walking cartoon brain, and more.

Yahoo Scout’s advantage. The Yahoo Search team gave me early access to Yahoo Scout. While the interface feels familiar if you’ve used competing tools, the Yahoo-specific elements clearly set it apart.

Yahoo’s advantage over many AI search competitors is its massive, built-in audience across Mail, News, Finance, and Search. It has more than 500 million user profiles and deep data on queries, usage, intent, and behavior. Yahoo also maintains over one billion knowledge-graph entities and tracks 18 trillion consumer events and signals across its properties. Together, this gives Yahoo the ability to deliver more personal AI-driven search experiences and more accurately categorize queries.

Yahoo is the second largest email company and third largest search engine, the company told me.

Yahoo Scout can pull rich content from across Yahoo directly into its responses. This includes features like Yahoo Finance widgets, detailed financial data, tables and citations, weather, news, and more.

  • “Search is fundamentally changing, and our team has been inspired to use our decades of experience and extremely rare assets to create something uniquely useful for Yahoo’s hundreds of millions of monthly users. This beta launch is just the starting point. From search to our industry-leading verticals, Yahoo Scout will help our users accomplish their goals online faster and better than ever before,” said Jim Lanzone, CEO of Yahoo.

Sending traffic to you, the publisher. Scout is closely tied to Yahoo’s original mission: being a trusted guide to the internet, Lanzone said. From the ground up, Yahoo built Scout to honor the open web by driving traffic downstream to content creators.

Yahoo Scout responses use large, wide blue highlights across the text. When you hover over them, you can click through to the original source.

Each response also includes a “featured source” that’s easy to spot and select. Scout further emphasizes content with tables and imagery while surfacing relevant news articles and sources throughout its answers.

Early AI search engines did little to send traffic back to the sources behind their answers, Lanzone said. Yahoo wanted to set an example for how to do this the right way. There isn’t enough revenue for every publisher to rely on licensing deals with AI companies, and historically, the model that worked best was simple: send traffic back to the original sources.

Here’s an example of how Yahoo Scout links to its sources:

When you hover over the blue highlights, the source appears, and you can click through to visit it. The purple “Read more” featured-source section also aims to drive traffic downstream.

CTR expectations. I asked Yahoo about the expected click-through rate from Scout to publishers. They said they don’t know yet. Yahoo plans to learn from real-world usage once Scout goes public and iterate to improve downstream clicks. This is Scout’s first release, and real user data should be telling.

They expect queries in Yahoo Scout to be longer than in Yahoo Search, with lighter ad loads and a much higher click-through rate than the industry average.

Yahoo also told me it plans to give publishers access to impression and click data in the future, possibly through a Yahoo Webmaster Tools–style product. Crawling and indexing would remain separate, since that layer is still powered by Microsoft Bing.

Yahoo Scout in every Yahoo property. You’ll be able to access Yahoo Scout across all Yahoo properties.

  • Yahoo Mail will summarize emails with AI and extract actionable items, such as adding events to your calendar.
  • Yahoo Search will add AI summaries powered by Scout.
  • Yahoo News will surface key article highlights and include the daily digest audio summary.
  • Yahoo Finance will introduce a new Analyze button powered by Scout.

Examples of Yahoo Scout in action. Here are a few examples of Yahoo Scout. It’s not perfect, but for a six-month project, I’m impressed.

I asked Scout for help explaining how SEO works, and it delivered a solid response. SEO is complex, and not everyone will agree with every detail, but the answer was thoughtful and useful. There are citations throughout the summary:

I then asked it to share sources for finding content on the topic as a follow-up. There were clear missed opportunities to link out more, which I pointed out to Yahoo, and they agreed.

I asked Yahoo Scout how to navigate to the sources it mentioned, and at that point, it did provide links:

Here’s a screenshot of another citation that appears when you hover your mouse cursor over it.

Here are some other searches I tried:

  • Entertainment: Scout incorporates news articles, with larger graphics in clickable card formats.
  • Finance: Yahoo brings in Yahoo Finance. I was unable to generate stock charts, although in a demo I was given, I was shown that live. So maybe it was being worked on during my tests:
  • Weather: I was testing this Sunday morning, as the big snow storm was touching down in New York:

I was able to get a Yahoo Weather chart:

With tips on how to stay warm:

  • Sports: The Super Bowl is coming up, and I was hoping to get some predictions:

As a lifelong Jets fan, I asked whether the team has any chance of winning the Super Bowl in the next 10 years. The answer wasn’t encouraging, but I was happy to see a chart embedded directly in the response.

  • Shopping: And then Yahoo gave me some advice on how to dress during this weather:

Ads and commissions. Yahoo Scout will show ads at the bottom of some responses. It will also monetize commerce-related queries through affiliate commissions, a common web revenue model.

  • Yahoo told me the ads are still powered by Microsoft Advertising, but Yahoo controls how those ads appear within these interfaces.
  • These ads will be charged on a CPC basis, not an impression basis, as some other AI engines announced.

Here is a screenshot of a Progressive Insurance ad for questions about car insurance.

Here is a screenshot of product results that are labeled, “Yahoo may earn commission from these links.”

How Yahoo Scout came about. For about three years now, Yahoo has been hinting about making a return to the search game. In 2009, Yahoo made a deal with Microsoft to have Microsoft power Yahoo Search and that was the end of Yahoo building its own search technology. Literally, Yahoo has outsources Search since then and has not done its own search technology until now, with Yahoo Scout.

That is until now. About six month ago, Yahoo acquired Eric Feng’s company to lead up consumer search at Yahoo. Eric Feng is known for co-founding an online video platform startup called Mojiti, which was acquired by Hulu in 2007, in which Eric became the founding CTO and head of product at Hulu. But before that, he worked at Microsoft in the Research labs, working on solving problems with Search.

“Yahoo’s deep knowledge base, 30 years in the making, allows us to deliver guidance that our users can trust and easily understand, and will become even more personalized over the coming months,” said Eric Feng, Senior Vice President and General Manager of Yahoo Research Group, the creators of Yahoo Scout. “Yahoo Scout now powers a new generation of intelligence experiences across Yahoo, seamlessly integrated into the products people use every day.”

Jim Lanzone, the CEO of Yahoo, who in his own right has a long history in search, as the CEO of Ask.com for many years, told me that Eric Feng has been instrumental in building out Yahoo Scout in the past 6 months. And there is so much more to come, this is just the first public release and you can expect many more interations and improvements to Yahoo Scout in the near future.

Anthropic. Yahoo Scout is not built on its own LLM, Yahoo partnered with Anthropic to use Claude as Yahoo Scout’s primary foundational AI model. Anthropic is one of the top artificial intelligence companies in the market. It has arguably the best AI for coders and coding frameworks named Claude. Anthropic was founded in 2021 by former members of OpenAI, including siblings Daniela Amodei and Dario Amodei, who serve as president and CEO, respectively. In September 2023, Amazon announced an investment of up to $4 billion. Google committed $2 billion the next month. As of November 2025, Anthropic has an estimated value of $350 billion.

While the foundational AI models use Anthropic, Yahoo has customized it and incorporates Yahoo’s proprietary data to make it unique and useful. Doing these searches on Anthropic will not give you anywhere close to the same experience as you would get on Yahoo Scout.

“When you’re serving hundreds of millions of users, you need AI that can do more than retrieve information – it has to reason, synthesize, and explain. Yahoo is building toward a more personalized, trustworthy kind of search, and Claude’s ability to deliver that quality of guidance at scale is at the heart of Yahoo Scout,” said Ami Vora, Head of Product at Anthropic.

Microsoft Bing. Plus, Microsoft Bing data is also incorporated into Yahoo Scout. The underlining search index is from Bing, but the responses, ranking, and experience is all Yahoo. “Yahoo Scout also builds on Yahoo’s long-standing relationship with Microsoft by leveraging Microsoft Bing’s grounding API. By combining this API with Yahoo’s trusted data and content ecosystem, Yahoo Scout ensures that answers are informed by authoritative sources from across the open web, Yahoo wrote.

Plus, Yahoo is also joining Microsoft’s Publisher Content Marketplace pilot. Microsoft’s Publisher Content Marketplace can help support revenue for publishers, the company said. Yahoo wrote this is, “reflecting a shared commitment to expanding publisher reach, connecting original work with new audiences, and supporting sustainable revenue opportunities for publishers.”

Hallucinations. I asked about hallucinations and Yahoo told me they put in a lot of guardrails to prevent hallucinations as much as possible. The Yahoo entity graph, the news content, and other Yahoo-specific data are used to ground the responses so that communications should be minimal and less than some other AI engines. In fact, they believe the hallucination rate would be “very low” compared to other AI engines.

Agents. Many AI engines are releasing agentic experiences, AI agents, to complete tasks for you. Google, OpenAI and Microsoft are investing big time into this.

Yahoo Scout has added some elements of this including inside of Yahoo Mail to add calendar events, smart compose features and more. Yahoo promises a lot more to come on this front.

Why we care. It’s an exciting time for search. For someone like me who has spent more than 20 years in search, it’s nice to see Yahoo step back into the space. Watching industry veterans like Jim Lanzone, Eric Feng, and Brian Provost take on search with AI is making it fun again, and I’m excited to see what Yahoo does next.

Availability. The Yahoo Scout answer engine is available today in beta for U.S. users at Scout.Yahoo.com and in the Yahoo Search app on iOS and Android. For more about Yahoo Scout, see this help document.

Read more at Read More

Is your account ready for Google AI Max? A pre-test checklist

/0 Comments/in , /by
Is your account ready for Google AI Max? A pre-test checklist

AI Max is Google’s latest foray into semi-keywordless targeting. 

While you need keywords for the system to have a starting place, Google uses signals beyond keywords in deciding how to show ads to searchers.

In accounts with a strong history of broad match success, AI Max can be highly effective at finding new conversions. 

If accounts are not well-optimized or have not been successful with broad match, AI Max can be a huge money pit.

To clear up a rumor before we get into the data: you do not have to use AI Max to have ads appear in AI Overviews. 

Broad match keywords can show ads in AI Overviews regardless of your AI Max usage. 

We’re looking at AI Max as a conversion expansion option, not just an option to show in AI Overviews.

This article examines the review steps you should take before you decide to test AI Max.

What to check before enabling AI Max

Accurate conversion tracking 

Your conversion tracking must be accurate, deduplicated, and focused on business outcomes. AI Max optimizes toward what you have defined as success. 

If you aren’t tracking all your conversions, or if your conversions are inflated, AI Max will be working from inaccurate data and making poor decisions.

Automated bidding with a conversion-focused strategy 

Broad match only works well when you have a bid strategy that is focused on conversions, such as:

Our experiments with AI Max have shown that it is much more predictable with one of the target options (Target CPA or Target ROAS) than with the max bid options (Maximize conversion value or Maximize conversions). 

Since the Max conversion options are meant to get you the most possible, regardless of the CPA or ROAS, they will often continue to spend your budget when the next set of conversions could have exceptionally high CPAs or very low ROAS.

If you use AI Max with one of the max bid options, pay close attention to your budget and the AI Max data.

Conversion volume

Technically, you can enable AI Max without any conversions for a campaign. 

However, with under 30 conversions per month, AI Max has been highly erratic. 

At over 100 conversions per month, it has done well more often than not, assuming you have had success with broad match in the past. 

In general, you will want to test AI Max in campaigns that have at least 30 conversions per month.

If you are going to test AI Max, starting with non-brand campaigns that have a high conversion volume will usually give you a better introduction to AI Max’s possibilities for your account.

No impression share lost due to budget

If you’re already losing impressions due to your budget, your handpicked keywords will receive even less budget if you enable AI Max. 

The goal is to spend as much as you can on your top keywords, and then have AI Max experiment with the budget we can’t spend. 

If you are already losing impressions due to your budget, then enabling AI Max usually results in poorer performance.

Have proven broad match success

AI Max will treat all of your keywords as broad match, and then expand even further than your broad match keywords. 

If you haven’t successfully used broad match, then enabling AI Max will be a waste of money.

You should first ensure that broad match can work for you, which might require reorganizing ad groups, testing new ads, and optimizing your landing pages. 

Only after you have consistently seen good results with broad match should you try AI Max.

Dig deeper: How to tell if Google’s AI Max for search is actually working

Should you use URL expansion? 

When you enable AI Max, you can expand URLs to other pages on your website. 

This means that Google can pick any page of your website to use as a landing page when AI Max triggers an ad.

Google allows you to exclude URLs. Most sites should exclude:

  • Help files and support pages.
  • Pages not built for conversions.
  • Pages that do not have conversion tracking enabled.
  • FAQs.
  • Blogs.
  • Old landing page tests that are still live.
  • Old website designs that are still live.
Google Ads - Add URL exclusions

A few people have found success with using AI Max with blogs and support pages. However, these seem to be exceptions more often than the standard result.

AI Max has struggled when there are many geographic landing pages. 

We’ve seen accounts that target different geographies by campaign, and each campaign has its own set of landing pages. 

AI Max has routinely mismatched the campaign’s geographic target with landing pages intended for other geographies. 

For example, your California campaigns are sending all of their traffic to landing pages dedicated to Texas traffic.

If you want to use AI Max URL expansion, and you have landing pages dedicated to various geographies, you will need to exclude all the landing pages that are irrelevant to the geography of your campaign.

For companies that create dedicated landing pages for each campaign or ad group, I have yet to see an example of AI Max finding better landing pages.

In every example, AI Max’s URL expansion has needed to be turned off. Eventually, this option might work for advertisers, but I have yet to see that happen.

You can review the URLs that Google is using and exclude them. If you turn on URL expansion, you will want to regularly review these URLs.

Dig deeper: AI Max in action: What early case studies and a new analysis script reveal

Get the newsletter search marketers rely on.

See terms.

Should you try automatically created assets? 

My great hope for AI Max is the automatically created assets. 

I wish I could enable this only for extensions. AI Max can help you scale messaging tremendously. 

It can go through all of your ad groups and automatically create sitelinks and callouts at the ad group level. 

This level of customization is one that many advertisers never have time to fully explore.

We had a client who enabled this feature, and suddenly, all their sitelinks linked to pages that were irrelevant to the keywords. 

We’ve seen other clients use this feature, and their callouts improved dramatically. 

Google still has a ways to go in how they auto-create assets, but this is a feature I have high hopes for.

Unfortunately, you can’t enable this feature for only ad assets (extensions). If you enable automatically created assets, Google will create additional RSA assets for you.

These assets can cause customer confusion by:

  • Making promises your brand doesn’t meet.
  • Using messaging that isn’t compliant with the law for regulated industries or doesn’t follow your brand guidelines.

You can write guidelines for how you want your ads to appear and rules on what shouldn’t be used. 

If you’re going to have Google automatically create assets, you’ll want to add guidance on how the ads should be created.

Note that term exclusions and text guidelines (Google’s official names for these features) don’t appear to be enabled in all accounts right now and may still be rolling out to advertisers.

Overall, Google’s auto-generated RSA assets have a poor track record, and if you enable them, you will want to regularly review what Google is creating on your behalf.

How to test AI Max

Since Google has a history of matching broad match keywords to other brands and generic keywords, AI Max has been very inconsistent with brand keywords.

I’d suggest starting with your top non-brand keywords to test AI Max. 

For most brands, there are more conversions to be had in non-brand expansion than in finding more people who are already searching for your brand.

AI Max can be enabled at the campaign or ad group level. 

One of the best ways to run a limited test with AI Max is to enable it only in a few ad groups that have a lot of conversion data and a successful history with broad match.

In the interface, enabling AI Max for only a few ad groups is painfully slow. 

You have to enable AI Max at the campaign level, then go into every ad group and turn it off where you don’t want it enabled.

The Google Ads Editor lets you turn AI Max on or off at the ad group level.

Google Ads - Settings for AI Max

If you want to test AI Max in only a few ad groups, then use the editor for your initial setup.

Dig deeper: When to trust Google Ads AI and when you shouldn’t

Is your account ready to test AI Max?

Google has long sought a keywordless targeting option for search. Its first step was Performance Max. 

AI Max is another foray to introduce advertisers to the idea that they don’t have to choose every keyword in their search campaigns.

Like all Google Ads products, they usually perform poorly when first launched. 

After a few years of data gathering, refinement, and additional advertiser controls, these products often prove successful.

AI Max is still a new product. Some accounts have had success with it. Others have only found failure. 

As with everything Google Ads-related, you should test it before widely adopting it.

The best way to test AI Max is to find non-brand ad groups with high conversion volume and a successful history with broad match keywords. 

Perform limited tests in these ad groups. If you find success, you can expand the ad groups that use AI Max.

During your tests, you must review your search terms, URLs, and auto-created assets. 

If you are not going to add these tasks to your workflow, then you are not ready to test every AI Max feature.

AI Max’s potential is enormous. However, it isn’t a good solution for everyone. Its ad writing can be quite poor. 

The new search terms can be hit-or-miss. You must babysit it. However, the time savings and potential are undeniable.

While I believe there is a bright future for AI Max, you must first ensure you complete all steps to verify that your account is ready to test it. 

If you follow the steps outlined in this article, you’ll know if you’re ready to test AI Max and see what these new campaign options can do for you.

Read more at Read More