On May 20th, 2026, the next major release of WordPress came out: WordPress 7.0. While previous releases focused on improving the block editor, this release takes it to a new level. It pushes the platform into the next phase of its roadmap with smarter workflows and a more app-like experience. So, let’s dive into what’s new and what features are interesting for you.
WordPress 7.0 introduces a refreshed admin interface. One thing that’s been changed is the new way to transition between pages in your backend. When navigating to another page, this now looks a lot smoother than before, thanks to the CSS View Transitions API. The new update also comes with a new addition to the menu bar at the top, called the Command Palette shortcut. When you click on this icon (or use the shortcut ⌘K or Ctrl+K), you get easy access to the command palette that allows you to navigate your backend or perform other actions from that bar.
The Command Palette in the menu at the top.
Although it’s a seemingly small thing, another cool thing to mention is the new color palette. As you can see in the screenshot above, the default color scheme has changed. The palette previously known as ‘Modern’ is now the new default, better aligning the admin with the visual direction of the block and site editor. If you preferred the old look, don’t worry, it’s still available under your profile preferences, now listed as ‘Fresh’.
Overall, these improvements and others give a fresh look and feel to the backend of your website. With the intent of making WordPress feel less like a traditional CMS and more like a modern web app.
Revisions are now more visual
Whenever you need to check or restore an earlier version of a page, the revisions in WordPress help you do so. These give you an idea of what has been changed on your page and when. Now, WordPress 7.0 makes this even easier with visual revisions instead of the raw text shown until now.
An example of the visual revisions in WordPress 7.0
The revisions feature can be found in the same spot as before, and now, when you click it, it takes you to a preview of your page, where you can use the slider at the top to view earlier versions. The slider also shows you the date and time of the change. When looking at an earlier version of the page, additions are shown in green, changed sections in yellow, and deleted sections in red. Allowing you to locate the changes made right away.
As before, this allows you to quickly restore previous versions of a page, find the source of layout issues and review updates. This visualization of the revisions makes it easier to do so, as you won’t have to dive into the text to figure out what changed. You’ll notice it right away when sliding between revisions.
New blocks in the block editor
As expected, the block editor has also gotten some new additions with the release of WordPress 7.0. For starters, the new Breadcrumbs block lets you add breadcrumbs to your pages, improving navigation on your site. When added, it automatically adds the correct breadcrumb path to the top of your page, but it also gives you options to customize it. The other new block in this release is the Icon block. This allows you to add icons to your pages from a directory of icons added to the backend.
Current selection of icons you can use in the Icon block.
There are also some improvements to existing blocks, such as the Grid Block and Cover block. The Grid block used to have an Auto/Manual toggle, but this has now been replaced by several options to help you set the responsiveness of the block and columns shown. The Cover Block now includes the option to use embedded videos as the background, so you can display videos from platforms like YouTube there. These new blocks and improvements continue to further reduce the need for plugins and custom work to achieve the desired design.
Better responsive design controls
Designing for mobile just got a little bit easier. This latest version of WordPress introduces viewport-based controls, allowing you to show or hide blocks depending on the user’s screen size. Simply go to the block, click ‘Show’ in the toolbar and select which devices should show the block (desktop, tablet, or mobile). This will automatically hide it on the devices that you don’t select. This allows you to fine-tune your design for different devices and build responsive designs without using custom CSS. A big win for anyone building sites without relying heavily on code.
Smarter pattern editing
Patterns and templates now come with different editing modes to make changes without accidentally messing up the design. When selecting a pattern, the List View will show you all the text and image elements in that pattern. This allows you to focus on the content-focused elements and change those where needed. However, when you click ‘Edit pattern’, it will also show you the remaining elements (design elements such as spacers), so you can still adjust those. This helps users focus on content optimization, while still giving the option to make changes to the design or layout if needed.
A list view showing the content and image elements in a pattern, with a button to edit the pattern further.
This new approach makes it a bit easier to customize patterns to fit specific use cases across your website.
Connect to AI tools of your choice
WordPress 7.0 doesn’t come with any AI-powered tools, but it is laying some groundwork. It comes with a Connectors section below Settings in your WordPress backend. Here you can connect to external integrations, including AI providers or agents. This allows you to connect to Claude, Gemini, OpenAI, and more. You can search the directory if the integration you’re looking for isn’t listed right away.
The Connectors section in your WordPress settings
This gives you one central place to maintain any integrations that your website or plugins need to connect to by API keys or other credentials. In addition, this gives developers a future-proof ecosystem and standardized framework to work with.
A new list filter for plugins
WordPress 7.0 adds a filter that allows plugins to register custom tabs on the Plugins screen. This enables grouping plugins under a custom tab with a proper label. For example, thanks to this feature we were able to add a dedicated “Yoast” tab on the Plugins screen. This groups all Yoast plugins on that website in one view, making it easier for site admins to check versions, manage activation, and keep the overview of their Yoast suite.
Final thoughts
As always, these are just a few highlights. New blocks, smarter workflows, a modern admin and AI foundations. There’s a lot more we haven’t discussed here. For example, performance was not ignored in this release. Particularly, client-side media processing (faster uploads, less server strain), continued improvements to block rendering, and responsiveness. These changes help WordPress scale better, especially for media-heavy sites. It’s also worth noting that WordPress 7.0 raises the minimum PHP version to 7.4.
Still to come: real-time collaboration
Originally, the real-time collaboration feature was going to be shipped in this release. But a short while back it was decided to postpone the release of this feature to ensure the stability of this release. This feature will probably be part of a future release.
But for now, we can get going with the new features in WordPress highlighted above! So, go update to the latest version or dive into more details in the release post on WordPress.org.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-21 08:07:592026-05-21 08:07:59WordPress 7.0 is out: the 7 highlights of this release
As part of the WTS Global Week celebrations, join Yoast and Women in Tech SEO for a special online coffee chat celebrating two incredible community milestones: 7 years of WTS and 16 years of Yoast.
SEO has always been more than algorithms, rankings, and updates; it’s built through people sharing ideas, supporting one another, and learning together. In this relaxed and inspiring session, Carolyn Shelby, Samah Nasr, and Areej AbuAli will reflect on the power of community in shaping careers, building confidence, and helping the SEO industry grow into a more collaborative and inclusive space.
Have you ever wondered where SEO professionals really learn beyond courses and documentation? Or how people find mentors, supportive communities, and opportunities to grow in the industry? Maybe you’re just starting out and trying to figure out which resources are actually worth your time.
Together, we’ll talk about how community creates learning opportunities, opens doors for newcomers, and provides the support people need to grow in SEO. Expect practical tips, career insights, honest experiences, and advice for those looking to deepen their involvement in the industry and connect with others in the space.
The session will include a 30-minute community chat followed by a live Q&A with attendees, giving everyone the chance to join the conversation and share their perspectives.
Bring your coffee or tea, questions, and stories; we’d love for you to be part of it.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-19 10:22:012026-05-19 10:22:01Yoast x WTS Global: SEO is built in community
You prompt ChatGPT with something, and suddenly your brand name shows up in the response. Sounds like a win, right? But before you share the screenshot with your team, there’s one important question to ask: Is your brand being cited or mentioned?
As AI search and LLM-driven discovery continue to grow, understanding the difference between AI brand mentions and AI citations is becoming increasingly important for SEO and brand visibility. In this article, we’ll break down what AI brand mentions are, how they work, and how they differ from citations.
Since we know you’re excited to celebrate your AI visibility win, let’s get straight into it.
AI brand mentions occur when an AI tool references your brand in responses, while citations support the information with sources
Understanding the difference between mentions and citations is crucial for SEO and brand visibility
To improve AI mentions, create clear, structured, and extractable content that addresses user queries directly
Brands need to build authority through trusted mentions across various platforms to enhance visibility and acceptance by AI systems
Both mentions and citations are crucial; mentions help AI identify your relevance, while citations reinforce your credibility
What is an AI brand mention?
An AI brand mention happens when an AI tool references your brand name inside a generated response, recommendation, comparison, or summary. The brand mentions can be either linked (also known as explicit mention) or unlinked (also known as implicit mention).
Here’s an example of ChatGPT’s response to, “What are some of the best WordPress SEO plugins?”
ChatGPT mentions Yoast SEO explicitly and implicitly
AI can mention brands in different conversational contexts depending on the user’s query and intent. Here are some of the most common ways AI-generated responses include brand mentions:
Direct recommendations
This happens when AI directly suggests a brand, product, or service as a possible solution to the user’s query. For instance, these mentions typically appear in recommendation-style prompts where users are actively seeking options or tools.
Comparisons
AI may mention brands while comparing products, services, features, pricing, or use cases. In such cases, the brand becomes part of a broader evaluation or decision-making discussion.
Examples within answers
Sometimes, AI uses brands as examples to explain concepts, trends, workflows, or industry practices. These mentions help provide context and make the explanation easier for users to understand.
Contextual references
Brands can also naturally appear in broader discussions about a topic or industry. These mentions are less promotional and more about establishing topical relevance within the conversation.
How do LLMs decide what to mention?
Large language models don’t “choose” brands the way a human would. They generate responses based on patterns, probabilities, and signals they’ve learned over time. When a brand shows up in an AI answer, it’s usually because multiple underlying factors align.
LLMs learn from vast datasets that show how often certain brands appear alongside specific topics.
When people repeatedly discuss a brand in connection with a particular use case, the model develops a strong association. Over time, this increases the likelihood that the brand will appear in responses to similar queries.
But it’s not just frequency. Context matters just as much.
What topics is the brand linked to?
What problems does it appear to solve?
What other terms show up around it?
Brands that appear across multiple contexts build deeper, more flexible associations. Those with limited or inconsistent mentions struggle to surface.
2. Retrieval-Augmented Generation (RAG)
Many modern AI systems extend beyond their training data using Retrieval-Augmented Generation (RAG). This is where things get more dynamic, and where many brands either gain visibility or disappear entirely.
At a basic level, here’s what changes:
Without RAG, the model answers using only what it learned during training
With RAG, the system first retrieves relevant information from external or live sources, then passes both the user query and the retrieved content into the model
The model then combines this new information with its existing knowledge to generate a more accurate, up-to-date response.
Descriptive diagram of RAG and training data by Amazon AWS
When a user submits a query, the retrieval system acts as a gatekeeper. It scans indexed sources, such as web pages, documentation, articles, and forums, to find content that best matches the query.
3. Context and semantic understanding
LLMs don’t rely on exact keyword matches. They interpret intent. When someone asks a question, the model maps it to broader concepts and then surfaces brands that fit those meanings.
For example, a query about “tools for remote teams” might connect to:
Collaboration
Async work
Team communication
Workflow management
LLMs are more likely to surface brands that consistently associate themselves with these ideas, even if users don’t use the exact phrase. This is where entity clarity becomes critical. If your brand is described differently across sources, the model struggles to understand what you actually do.
Overall, it’s not just about what you say, but how your content connects to related topics. Therefore, linking your brand to relevant concepts, use cases, and terminology helps AI systems understand when your brand is relevant. This is where it helps to semantically link entities to your content, so those relationships are clearer and easier for models to pick up.
4. Authority and cross-source validation
LLMs don’t rely on a single source. They validate information by comparing patterns across multiple sources and weighing the trustworthiness of those sources. When a claim appears consistently across many independent platforms, the model is more confident in including it. If it shows up in only a few places, that confidence drops.
AI systems combine semantic understanding with retrieval signals to assess which sources to trust. This typically includes:
Source credibility: Well-known publications, academic content, government sites, and recognized organizations are prioritized
Citation patterns: Sources that are frequently referenced by others are treated as more authoritative
Recency: More recent information is often weighted higher, especially for fast-changing topics
Transparency: Content with clear authorship, dates, and references is considered more reliable
Authority in AI is about being consistently referenced across credible, independent sources. This is why PR, earned media, and third-party mentions play a bigger role in AI visibility than they traditionally did in SEO.
5. Relevance to the query
Before anything else, the model evaluates fit. Even highly authoritative or frequently mentioned brands won’t appear unless they clearly match the user’s intent, such as the use case, audience, or problem being solved.
In simple terms, if your brand isn’t a strong answer to the query, it won’t be included.
When surfacing a brand in answers, AI models may include nuances like:
Beginner vs advanced users
Budget vs premium solutions
Niche vs general use cases
Modern AI systems have shifted from traditional keyword matching to query understanding. They use Natural Language Processing (NLP) to understand the “why” behind the text strings. If explained technically, gen AI converts text queries (prompts) into vectors that allow it to find semantic similarity and return relevant answers.
6. Sentiment and human feedback (RLHF)
LLMs don’t rely solely on training data or web sources. They are continuously improved through human feedback, a process known as Reinforcement Learning from Human Feedback (RLHF).
In this process, human evaluators review model responses and guide them based on whether the answers are:
Helpful
Accurate
Safe
Trustworthy
How does this affect brand mentions? If a brand is consistently associated with negative sentiment, the model may learn to avoid or deprioritize it. On the other hand, brands that appear in neutral or positive contexts across sources are more likely to be included.
In this way, RLHF acts as a layer that refines raw data signals, aligning brand mentions more closely with quality, trust, and user expectations.
Tips to get more mentions
Getting your brand mentioned in AI answers isn’t a completely new discipline. It closely overlaps with what many now call LLM SEO. If you’ve already been working on visibility, authority, and content quality, you’re on the right track.
Here are a few practical ways to improve your chances of being mentioned:
Publish definitive, extractable resources
Create content that is easy for AI systems to understand and reuse. This means clear definitions, structured explanations, and direct answers rather than long, vague introductions.
For example, a well-structured guide that clearly defines “what is customer data management” with concise sections is far more likely to be picked up than a generic blog post that buries the answer halfway through.
Address evaluative queries
AI assistants often respond to questions like “best tools for X” or “which platform should I choose?” If your content directly addresses these comparisons, you increase your chances of being included.
Like a comparison page, for example, Yoast vs. Rank Math, that explains when your product is better suited than alternatives, it gives the model a clear context to recommend you.
Strengthen authority signals
Mentions across trusted, independent sources significantly improve your visibility. This includes being featured in industry publications, contributing expert insights, or earning mentions in reviews and comparisons.
For example, a brand cited in multiple reputable blogs and reports is more likely to be surfaced than one that only publishes content on its own website.
Keep cornerstone pages current
Freshness plays a key role, especially for topics that evolve quickly. Regularly updating the content of your key pages signals that your information is reliable and up to date. For example, a “best tools” page updated every few months with current data is more likely to be retrieved than one that hasn’t been touched in years.
Broaden entity clarity
Your brand should be consistently described across your website and external platforms. This helps AI systems clearly understand what you do and when to mention you. For example, if your product is always positioned as “project management software for remote teams,” that repeated clarity strengthens your association with that use case.
AI brand mentions vs AI citations
Before sharing the comparison, let me give you a brief overview of citations. AI citations are references that AI systems and search engines include to support the answers they generate.
Citations usually point to a specific source, such as a webpage, report, or article, and credit the source of the information. In many cases, a response can include both a brand mention and a citation at the same time.
ChatGPT’s response mentions brands and cites resources to back its answer
Next, let’s see how they are different.
Aspect
AI brand mention
AI citation
Definition
Your brand name appears within the AI-generated response
AI attributes information to your content, often with a link or reference
Format
Mentioned naturally in text, no link required
URL, footnote, or inline source reference
What it signals
Brand awareness and category relevance
Authority, credibility, and trustworthiness
Impact
Builds mindshare and keeps you in the consideration set
Acts as proof of expertise and can drive traffic
Traffic potential
Indirect, through increased brand recall
Direct, via clickable or attributed sources
Frequency
More common across most AI responses
Less common and more competitive
Where it appears
Across most LLMs, even without live web access
More common in systems with retrieval or web access
How to optimize
PR, earned media, third-party mentions, community presence
Create citation-worthy content, structured data, original research
Mentions get you in the conversation. Citations make you the source.
Mentions make the AI familiar with your brand. Citations make the AI willing to vouch for it.
In short, the most effective strategy is to optimize for both.
Do citations still matter?
Yes, citations still matter, but they are no longer a standalone strategy.
AI systems still use citations as supporting signals to validate information, confirm credibility, and discover trustworthy sources. When multiple reputable websites reference the same brand or source, it reinforces trust and helps AI systems verify the information’s reliability.
While both mentions and citations matter, mentions currently carry more weight for relevance and AI visibility. Citations still help reinforce authority and trust, but mentions give AI systems richer contextual signals about where a brand fits, how often it appears in conversations, and why it matters within a topic.
How to achieve citations and mentions both?
Brands that consistently appear in relevant conversations while publishing credible content are more likely to earn both mentions and citations. Here are some easy strategies that you can follow:
Create mention-worthy content
The easiest way to earn both mentions and citations is to publish content people naturally want to reference. This includes thought leadership, original research, unique insights, industry commentary, and practical resources that add real value. When your content contributes something new to the conversation, it becomes easier for journalists, creators, communities, and AI systems to pick it up.
Focus on contextual brand mentions
AI systems pay attention to how and where your brand is discussed. Mentions across community discussions, industry blogs, PR coverage, podcasts, forums, and trend-based conversations help reinforce your relevance within a topic. The goal is not just visibility, but also appearing consistently in meaningful, context-rich discussions.
Build credibility for citations
If you want more citations, credibility becomes essential. AI systems are more likely to reference content that demonstrates strong expertise and trustworthiness. This is where principles like E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) become important.
AI brand mentions vs. citations: FAQs
While mentions help AI systems recognize and associate your brand with specific topics, citations strengthen trust and authority by validating your content as a reliable source.
The reality is that both work together. Brands that consistently appear in relevant conversations while publishing credible, high-quality content are far more likely to strengthen their AI visibility over time.
Here are some common questions around AI brand mentions and citations:
Are citations and backlinks the same?
Not exactly. Backlinks are traditional SEO links that point from one website to another, mainly to help search engines understand authority and ranking signals. AI citations, on the other hand, are references AI systems use to support or validate the answers they generate. While citations can include links, their primary role is attribution and trust rather than passing ranking value. For a deeper understanding, read AI citations vs backlinks.
If a brand is mentioned, will it be cited too?
Not always. A brand can be mentioned in an AI response without being directly cited as a source. This usually happens because AI systems often recognize brands through repeated contextual mentions across the web, even when they are not using that brand’s content as the primary supporting source for the answer.
Why should businesses focus on both mentions and citations from AI?
Mentions and citations support different aspects of AI visibility. Mentions help AI systems understand where your brand fits within a topic, while citations reinforce authority and trust.
How to track both mentions and citations for my brand?
Tracking AI visibility manually across platforms can quickly become difficult. Tools like Yoast SEO AI+ help brands monitor how they appear across AI-driven search experiences. With AI Brand Insights, you can track mentions, citations, and overall brand presence across AI platforms to better understand where your visibility is growing and where opportunities exist to improve your AI brand visibility using Yoast AI Brand Insights.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-19 06:48:132026-05-19 06:48:13What are AI brand mentions? And how are they different from citations?
If you’ve ever opened a new post and immediately closed it again because you had no idea what to write, this one’s for you.
Yoast AI Content Planner is now available for Yoast SEO Premium users. Open a new post in your WordPress editor and you’ll find five relevant post ideas waiting for you, built from your existing site content. Pick one and Yoast builds out a structured draft, ready to write into.
What does it do?
Yoast AI Content Planner scans your existing site content, spots the gaps that matter, and gives you five relevant post ideas, right inside the WordPress editor. Pick the one that feels right and Yoast turns it into a structured starter draft, complete with a title, an outline, a focus keyphrase, a meta description, and content notes for each section.
You go from blank page to ready-to-write in minutes.
What do you get?
Here’s what Yoast builds for you once you choose an idea:
Site-specific post ideas. The suggestions come from your existing content and site structure, so they’re relevant to what you’ve already built, not generic topics that could apply to anyone.
A structured starter draft. Your chosen idea becomes a full draft framework: title, H2 outline, focus keyphrase, meta description, and content notes for each section. The structure is already there. You just fill it in.
A focus keyphrase suggestion. Yoast suggests a keyphrase for you, giving your post a strong SEO foundation from the very first step, without requiring you to research one yourself. A focus keyphrase is simply the main word or phrase you want your post to be found for in search.
Idea regeneration. If the first set of five ideas doesn’t feel right, you can generate a fresh set with one additional spark per session.
A couple of things worth knowing
Yoast AI Content Planner lives inside the WordPress post editor. You access it from any new empty post. There’s nothing new to install, no separate login, and no additional setup required.
The Content Planner feature appears when you create a new post.
For the feature to work well, your site needs to have enough published content for Yoast to build a meaningful picture of what you already cover. If there isn’t quite enough yet, you’ll see a low-confidence warning rather than suggestions.
How to get it
Yoast AI Content Planner is available now for Yoast SEO Premium users. Open a new post in your WordPress editor to get started.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-12 08:47:172026-05-12 08:47:17New: Yoast AI Content Planner turns a blank post into a structured draft
Each month, we host an SEO update covering the latest in search and AI. During this month’s edition, our SEO experts Carolyn Shelby and Alex Moss, cover everything from the latest advances in Agentic AI to Google’s spam and core updates and why simply publishing more content is no longer enough and in many cases actively works against you. Read this recap for the highlights or watch the April 2026 SEO Update by Yoast to delve into the latest news.
Watch the full recap on YouTube to dive deeper into these topics, hear some examples and hear the answer to audience questions.
SEO and AI news from April 2026
Google introduces new AI agent signals and infrastructure
Google added a new Google-agent user agent, signaling more explicit support for AI-driven crawling and interaction. At the same time, proposals like WebMCP aim to standardize how AI agents interact with websites, while Google leadership suggests search is evolving into an AI agent manager.
Why it matters: The web is being restructured around agent access, not just human browsing.
Actionable takeaway:
Ensure your content is accessible and understandable for both traditional crawlers and emerging AI agents.
Google continues expanding AI capabilities and efficiency
Google introduced TurboQuant, a new approach to AI model compression that significantly improves efficiency. At the same time, Google is expanding task-based features in AI Mode and refining how users interact with AI-driven search experiences.
Why it matters: As AI becomes faster and more integrated, user expectations and search behavior will continue to shift.
Actionable takeaway:
Focus on making content easy to extract and act on within AI-driven workflows.
Structured data and documentation evolve for AI-first search
Why it matters: Search engines are adapting their systems to better interpret and label AI-generated content.
Actionable takeaway:
Use structured data and clear linking practices to improve how your content is interpreted and displayed.
Core updates, spam policies, and enforcement continue to tighten
Google completed its March 2026 spam update and core update, while also introducing updates to spam policies addressing tactics like back button hijacking and improving spam reporting tools.
Why it matters: Enforcement is becoming more granular, targeting both technical manipulation and low-value content.
Actionable takeaway:
Review your site for outdated or risky tactics and ensure a strong focus on quality and user experience.
Platforms and tools expand AI-driven workflows
Elementor launched Angie, an agentic AI for WordPress, while Cloudflare introduced EmDash as a WordPress alternative and continued work on agent readiness standards.
Anthropic released Claude Design and previewed Mythos, while OpenAI tested an AdsBot and introduced a ChatGPT ad manager interface.
Why it matters: AI is increasingly embedded directly into content creation, workflows, and monetization systems.
Actionable takeaway:
Evaluate how AI tools fit into your content and operational workflows, not just your marketing strategy.
Authority, trust, and content quality remain central
Why it matters: As AI systems synthesize answers, they rely more heavily on trusted, differentiated sources.
Actionable takeaway:
Invest in original, high-quality content and consistent brand signals across channels.
Measurement and reporting begin shifting toward AI visibility
Bing previewed AI Citation Share, and new dashboards are emerging that map how AI systems ground answers in source content. A temporary Google Search Console glitch also highlighted how dependent SEOs still are on traditional metrics.
Why it matters: Visibility is moving beyond rankings into citation, inclusion, and influence within AI-generated responses.
Actionable takeaway:
Start paying attention to how your content appears in AI systems, not just where it ranks.
Also in the news…
Several additional developments are worth watching:
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-29 11:42:422026-04-29 11:42:42The April 2026 SEO Update by Yoast recap
Search is changing fast – make sure you’re not falling behind.
Sign up for the next SEO Update by Yoast and get expert-led clarity on what’s happening in SEO right now and what it means for your strategy.
Join Carolyn Shelby and Alex Moss as they unpack the most important SEO news, algorithm shifts, and industry developments – so you can focus on what actually moves the needle.
Who should sign up?
This update is ideal if you:
Want expert insight into recent SEO changes and trends
Need help refining or validating your SEO strategy
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-28 11:33:402026-04-28 11:33:40The SEO Update by Yoast – May 2026
From today, your AI tools, dashboards, and automated workflows can now talk directly to Yoast SEO, thanks to the new Abilities API, built to work hand in hand with WordPress 6.9 .As WordPress evolves, we evolve with it, and the release of the Yoast SEO Abilities API is an extension of these new capabilities.
What does that mean in plain English?
If you use AI assistants, automated workflows, or custom dashboards, they can now automatically find and read your Yoast content scores, without anyone needing to build a custom connection or dig through documentation. It just works.
What can these tools see?
Once connected, any compatible tool can instantly pull the following from your most recent posts:
SEO scores and focus keyphrases
Readability scores
Inclusive language scores
What can you do with this?
Here are a few examples of what’s now possible:
Ask an AI assistant “How is my SEO health looking this week?” and get a real answer based on your actual posts
Set up a fully autonomous AI workflow, where agents can flag trends in your recent posts.
Pull your content scores into an external dashboard or reporting tool, with no manual exports needed
In short, Yoast SEO is ready to plug straight into your workflow, whatever that looks like. As WordPress continues to open up new capabilities, you can expect Yoast to be right there alongside it.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-28 08:29:152026-04-28 08:29:15New: Yoast SEO Content Analyses scores can now chat with “AI” through new API
In our Rethinking SEO in the age of AI article, we briefly explored how AI might move beyond simple prompt-and-response interactions. One emerging direction is agentic AI. Systems that can take action, not just generate answers. While this space is still evolving, we’re already seeing early signs of tools that can identify gaps, suggest improvements, and adapt to changing trends with minimal input. If these capabilities continue to develop, they could reshape how we think about maintaining continuous discoverability in SEO.
Agentic AI for SEO represents a shift from traditional visibility and ranking to being trusted and understood by AI systems
The web’s structure remains stable, but interaction through AI agents changes how content is accessed and consumed
SEO must evolve to focus on being structured, reliable, and adaptable for AI interpretation
Challenges include data quality, integration complexity, and balancing automation with human judgment
The future of discoverability in an agent-driven web emphasizes collaboration between AI and human insight, expanding SEO’s role beyond just ranking
Understanding the coexistence of web and AI agents
Before understanding agentic SEO, let’s first look at the role of AI in shaping the web. Is it staying the same, or quietly changing?
For a long time, the web has been more than just a collection of pages. It has functioned as an interconnected graph of entities. Websites representing people, businesses, ideas, and concepts, all linked together through content, context, and trust. This structure, often referred to as the open web, has remained relatively stable for decades. Humans created content, users discovered it through search or links, and meaning was formed through exploration.
What seems to be shifting now is not the structure itself, but how that web is accessed and consumed.
Earlier, discovery was largely a direct interaction between humans and websites. You searched, clicked, read, compared, and formed your own conclusions. Today, AI systems are increasingly stepping into that journey. They sit between the user and the web, interpreting, summarizing, and sometimes even deciding which information to surface.
This is where the idea of AI agents begins to emerge. Not just as tools that generate responses, but as systems that can navigate the web, retrieve information, and potentially act on it. Early examples, such as experiments in natural language interfaces like NLWeb, hint at a web that can be interacted with more conversationally, without losing its openness and interconnectedness.
Some refer to this shift as the beginning of an “agentic web.” But it’s important to see it less as a complete transformation and more as a layer forming on top of the existing web. The open web still exists, content is still created by people, and links still matter. What’s evolving is how that content is discovered, interpreted, and used.
And that shift in interaction is where things start to get interesting for SEO.
If AI agents are starting to reshape how people interact with the web, it naturally raises a follow-up question: where does that leave SEO?
For years, SEO has largely been about helping users find your content. You optimized for rankings, improved visibility on search engines, and relied on users to click, read, and navigate. But if AI agents begin to mediate that journey, not just retrieving information but interpreting and acting on it, then SEO may need to expand its role.
Not necessarily replace what exists, but build on top of it.
From ranking pages to being selected by systems
In a more agent-driven environment, discoverability may no longer depend solely on where you rank, but also on whether your content is selected, trusted, and used by AI systems.
That introduces a subtle but important shift:
It’s not just about being visible
It’s about being understandable, reliable, and usable by machines
AI agents don’t browse the web the way humans do. They:
Parse structured and unstructured data
Look for clear signals of authority and accuracy
Combine information from multiple sources before presenting it
So instead of optimizing only for clicks, SEO may also involve optimizing for inclusion in AI-generated responses and workflows.
What stays, what evolves, what gets added
Let’s ground this a bit. Traditional SEO doesn’t disappear. Many of its fundamentals still apply, but their role may shift.
This has created a split from a completely open web into two – the ‘human’ web and the ‘agentic’ web… SEOs will have to consider both sides of the web and how to serve both.
That framing makes the shift clearer.
Your content still needs to rank. But it also needs to work at a second layer of the web, where AI systems interpret, select, and sometimes act on information before a human ever sees it.
So now, your content needs to be:
Understood without ambiguity
Trusted enough to be referenced
Structured well enough to be reused
In that sense, SEO doesn’t disappear in an agentic web. It stretches.
From helping users find information…
to helping systems choose it.
Role of agentic AI in SEO
If the web is gradually being experienced through both humans and AI agents, then it’s worth asking what role these agents might begin to play in SEO itself. Not as a replacement for SEO teams, but as a new layer within how SEO work gets done.
What we’re starting to see is a shift from SEO as a set of periodic tasks to something more continuous, assisted, and adaptive. Some early tools already hint at this. They don’t just analyze data, they suggest actions. In some cases, they even implement changes. If this direction continues, agentic AI could become less of a tool you use and more of a system you collaborate with.
Let’s break down where this role might start to take shape.
How agentic AI may reshape SEO workflows
Shift
Traditional SEO approach (how it typically works today)
With agentic AI (emerging direction)
Audits → Always-on optimization
SEO teams run audits at set intervals (monthly, quarterly) using tools such as site crawlers.
Issues such as broken links, missing metadata, or slow pages are identified and then manually fixed over time.
Improvements often depend on when the audit is conducted.
Systems continuously monitor site performance, flag issues as they arise, and may suggest or implement fixes in real time.
Optimization becomes ongoing rather than dependent on manually scheduled audits.
Reacting → Anticipating
Actions are usually triggered by visible changes.
For example, a drop in rankings leads to an investigation, or an algorithm update prompts content revisions.
SEO is often a response to what has already happened.
AI systems analyze patterns in search behavior and performance data to detect early signals.
This could mean identifying emerging topics, shifting intent, or declining engagement before it significantly impacts performance.
Manual execution → Guided systems
Tasks such as keyword research, clustering, content optimization, and internal linking are performed manually or with tools.
SEO specialists interpret the data and execute changes step by step.
AI assists with these tasks by identifying keyword opportunities, grouping topics, suggesting optimizations, and even applying specific changes.
SEOs shift toward guiding strategy, reviewing outputs, and setting priorities.
Static content → Adaptive content
Content is created, published, and revisited occasionally.
Updates are often triggered by performance drops, outdated information, or scheduled content refresh cycles.
Content evolves more dynamically.
Systems can recommend updates based on performance, refine sections for clarity, or restructure content to better match user intent and AI consumption patterns.
Generic UX → Contextual journeys
Most users experience the same content and navigation structure.
Personalization is limited or rule-based, such as basic recommendations or segmented landing pages.
Experiences become more contextual.
Content, navigation, and recommendations can adapt based on user behavior, intent, or journey stage, creating more relevant and engaging interactions.
A quick example: structuring content for machines, not just humans
If agentic systems rely on structured, connected, and machine-readable content, then this isn’t entirely new territory for SEO.
In many ways, we’ve already been moving in this direction through structured data and schema. What’s changing is how important and foundational it may become.
For example, features like schema aggregation in Yoast SEO bring together different pieces of structured data across a site and connect them into a more unified graph. Instead of treating pages as isolated units, they help search engines better understand how entities, content types, and relationships fit together.
This might seem like a technical detail, but it reflects a broader shift.
If AI agents are parsing, combining, and interpreting content across multiple sources, then clarity and connection at the data level become more important. Not just for visibility in search results, but for how content is understood and reused.
So while agentic AI may feel like a new layer, some of the foundational work, like structuring content, defining entities, and building semantic relationships, is already part of modern SEO. It just becomes more critical in this context.
So, where does this leave SEO teams?
If there’s one pattern across all of this, it’s not replacement, but redistribution.
Agentic AI may take on:
Repetitive tasks
Data-heavy analysis
Continuous monitoring
Which leaves humans to focus more on brand-building aspects like:
Strategy and positioning
Editorial judgment and brand voice
Deciding what should be done, not just what can be done
In that sense, agentic AI doesn’t redefine SEO overnight. But it does start to reshape how it’s practiced.
Understanding the risks and challenges of agentic AI for SEO
So far, agentic AI might sound like a natural evolution of SEO. But, as with most shifts in technology, it may also come with trade-offs.
Not because the technology is inherently problematic, but because it introduces new dependencies, new layers of complexity, and new decisions for SEO teams to navigate. In that sense, adopting agentic AI isn’t just about adding a new capability. It may also involve rethinking how much control to delegate and where human judgment continues to play a critical role.
Here are some of the challenges that could emerge as this space evolves:
1. High technical and integration complexity
Agentic systems are unlikely to operate in isolation. They may need to connect with your CMS, analytics tools, and multiple data sources.
This could introduce challenges such as:
Managing integrations across platforms
Ensuring consistent and reliable data flow
Defining clear workflows across systems
For many teams, this might not be plug-and-play. It could require time, experimentation, and coordination across different roles.
2. Data quality and dependency
Agentic AI may be heavily dependent on the quality of data it receives. If the data is:
Outdated
Incomplete
Poorly structured
Then the outputs could reflect those gaps.
At scale, even small inconsistencies might influence multiple recommendations or decisions. Which is why maintaining clean, reliable data sources may become even more important in an agent-driven setup.
3. Risk amplification and the need for governance
One of the strengths of agentic AI is speed. But that same speed might also amplify unintended outcomes.
Without clear guardrails:
Content updates could introduce inaccuracies
Technical changes might lead to issues like broken links or indexing errors
Best practices may not always be consistently followed
This is where governance frameworks and approval checkpoints may become essential, not to slow things down, but to keep them aligned.
4. Hallucinations and accuracy considerations
AI systems can sometimes generate outputs that sound plausible but aren’t entirely accurate.
In an SEO context, this might look like:
Misinterpreted data
Inaccurate keyword insights
Fabricated or blended information
The challenge is that these outputs can be difficult to spot at a glance. This suggests that validation and source-checking may remain an ongoing part of the workflow.
5. Limited understanding of nuance
SEO often goes beyond data and structure. It includes tone, context, and intent. Agentic systems may not always fully capture:
Brand voice and positioning
Legal or compliance nuances
Subtle differences in user intent
This could result in outputs that are technically sound, but not always contextually aligned. Human input may still play a key role here.
6. Balancing automation with human judgment
A broader question that may arise is how much to automate.
Too much automation might: Reduce control over strategy or brand
Too little might: Limit efficiency and scalability
Most teams may find themselves balancing the two. Using agentic AI to extend their capabilities, while still guiding direction and decision-making.
7. High initial investment and learning curve
While agentic systems may offer long-term efficiency, getting started could take time. This might involve:
Learning how the systems work
Setting up workflows and integrations
Aligning outputs with business goals
There’s also a level of uncertainty here. The technology is still evolving, and so are the tools built around it. Which means costs, capabilities, and best practices may continue to shift.
For many teams, adoption may not be immediate. It could happen gradually, through testing, iteration, and figuring out what actually works in practice.
8. Zero-click experiences and shifting traffic patterns
As AI systems become more involved in surfacing information, zero-click experiences may become more common.
Users might:
Get answers directly within AI interfaces
Interact without visiting the original source
This doesn’t necessarily reduce the importance of SEO, but it may shift how success is measured. Visibility and influence could become just as relevant as traffic.
What discoverability might look like in an agent-driven web?
Agentic AI may open up new possibilities for how SEO is done. But alongside that, it may also introduce new considerations.
It could require:
Stronger data foundations
Clear governance and review processes
A thoughtful balance between automation and human input
In many ways, the goal may not be full automation. It may be a better collaboration.
Even if agents take on more execution, the responsibility for direction, accuracy, and trust is likely to remain human. And maybe that’s the more interesting shift here. Not whether AI agents will “take over” SEO, but how they might reshape what good SEO looks like.
If discoverability is no longer just about ranking, but also about being selected, interpreted, and reused by systems, then the role of SEO starts to expand. It becomes less about optimizing for a single interface and more about preparing content to exist across multiple layers of the web.
How do we design content that works for both humans and machines?
We don’t have all the answers yet. And maybe that’s okay.
Because this isn’t a fixed destination. It’s something that’s still taking shape.
And as it does, SEO may continue to evolve alongside it. Not disappearing, not being replaced, but adapting to a web that is becoming more dynamic, more layered, and a little less predictable.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-28 07:00:042026-04-28 07:00:04Ensuring continuous discoverability with agentic AI for SEO
For a long time, we defined SEO success by rankings and traffic. If you reached the top of the search results and brought people to your site, you did your job. That approach worked when discovery was linear, and search engines were the primary gatekeepers. But modern search behavior does not stop at discovery. Users want clarity, reassurance, and confidence before they make decisions. With so many options to choose from, users want to understand what a product does, how it compares to alternatives, and whether it fits their needs.
There is a shift in SEO, one that pushes closer to product thinking and long-term value creation. Search engines reward content and experiences that help users make informed decisions, not just pages that match keywords. That means SEO can no longer exist solely in the acquisition channel. SEO must support the entire journey, from first touch to post-purchase experience.
SEO now focuses on user clarity and informed decision-making rather than just rankings and traffic.
Businesses should adopt an approach that integrates product understanding and user intent into keyword research.
Technical SEO remains crucial; a well-structured site improves visibility for both users and AI systems.
Product content, including descriptions and FAQs, serves as a powerful SEO asset that should be optimized.
Schema markup is essential for AI systems to accurately interpret product information, enhancing visibility and recommendations.
Technical SEO has always been product thinking
Technical SEO has always mattered, and it’s been tied to product quality, or at least product page quality. Site speed, internal linking, structured content, and clear navigation all shape how users experience a product online.
A fast, well-structured site helps users and AI platforms better understand your products. That means better visibility in search engines and AI recommendations alike. Good SEO looks at the system as a whole, prioritizes changes based on impact, and focuses on removing friction, which are the same principles that guide good product decisions.
Think like a product marketer, not just an SEO
Ranking for keywords does not automatically mean you are reaching the right audience or communicating the right value. Product marketers spend time understanding who the product is for, what problem it solves, and why someone should choose it over alternatives. SEO benefits enormously from that same approach.
Keyword research is not just a targeting exercise. It reveals how people describe their problems, what they care about, and what information they need before making a decision. Applying those insights to product descriptions, category pages, and supporting content pulls SEO closer to real user intent.
This is how SEO moves beyond traffic and starts contributing to the full customer journey: awareness, consideration, conversion, and, just as importantly, retention.
Your product is your most underrated SEO asset
Many SEO strategies still treat content as something separate from the product. Blogs live in one place while product pages are left to focus purely on conversion.
But products are content. Product names, descriptions, specifications, FAQs, reviews, and even post-purchase information all reflect the real information users are looking for. This content often holds far more SEO value than a generic blog post. Still, most brands do not optimize it with the same level of care.
When product pages are clear, well-structured, and written in the language customers actually use, they become powerful discovery assets.
AI is changing how products are discovered and bought
ChatGPT now supports direct purchases through integrations with platforms like Shopify, using OpenAI’s Agentic Commerce Protocol. That means users can discover and buy products directly within an AI conversation without ever visiting a product page on a website.
For businesses, this changes what visibility looks like. SEO is no longer just about ranking in search results. SEO is about making sure your products are understandable, trustworthy, and accessible to AI systems that act as intermediaries.
And the scope of that is broader than it first appears. Google’s Universal Commerce Protocol (UCP) extends AI-mediated commerce well beyond the checkout, covering the full lifecycle from product discovery through to order management, post-purchase support, and loyalty. That means the journey SEO needs to support has grown significantly. It is not just about being found and bought; it is about being the kind of brand an AI agent would confidently recommend, follow up with, and return to. Read more about ACP and UCP and what they mean for SEOs.
Why schema matters more than ever
If AI systems are going to recommend and sell products, they need structured information to rely on. Schema provides that structure. It tells search engines and AI platforms what a product is, how much it costs, whether it is available, how it is reviewed, and how it fits into a broader catalog.
Without structured data, products become harder for machines to interpret and surface. With it, they become eligible for richer visibility across search engines, LLMs, and emerging shopping experiences.
This goes beyond the basics. Pricing, availability, reviews, FAQs, shipping details, and even compatibility information all contribute to how well an AI agent can evaluate and surface your products. Third-party reviews on platforms like Trustpilot also play a role. Agents use external signals to validate brand credibility before making a recommendation. If that structured data is incomplete or inconsistent, your products risk being entirely invisible to agent-mediated discovery.
Conclusion
The rules of SEO have not been torn up but extended. Product thinking, structured data, clear content, and technical rigor have always mattered. What has changed is the audience you are optimizing for. Alongside the human visitor, you now have AI agents evaluating, recommending, and, in some cases, completing purchases on a user’s behalf. The businesses that will thrive are those that make their products easy to understand, easy to trust, and easy to surface, whether a person or a machine is doing the searching.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-23 13:28:162026-04-23 13:28:16Why your product is your most important SEO asset
Most SEO strategies are built with one goal: getting people through the door. That usually means driving traffic to the website, ranking for high-volume keywords, and bringing in new users. But what happens after someone signs up or makes a purchase? That part of the funnel often gets ignored. SEO doesn’t stop at acquisition. It can and should be used to support retention, improve onboarding or post-purchase experience, and make your product or offering easier to understand. So let’s break down the opportunity in post-conversion content, why it matters for SEO, and how to identify and optimize it effectively.
A lot of SEO strategies overlook post-conversion content, even though this type of content is great for an improved user experience.
Post-conversion content can include help docs, knowledge bases or product guides serving as long-tail SEO assets.
Engaged users generate positive signals, aiding in SEO through branded searches and reduced churn.
Identify post-conversion content by analyzing support tickets, customer interactions, and internal search queries.
Creating valuable guides and linking related content boosts retention and makes SEO efforts more effective.
Most brands stop too early
SEO strategies (understandably) love to focus on the top of the funnel: traffic, rankings, and new users. However, conversion isn’t the finish line. After someone signs up or makes a purchase, they’re still searching. They’re still learning, and they’re still deciding if they want to stick with you.
This is where SEO can step in to support:
Onboarding flows or post-purchase journeys
Help docs
Community content
Knowledge bases
All of these are searchable, indexable, and incredibly useful. Not just for users, but for long-term organic growth.
The opportunity in post-purchase content
Once someone starts using your product or receives their purchase, they often turn to Google (or your internal search) for answers about setup, usage, sizing, care, troubleshooting, or returns, depending on your business and industry. This is where content such as help centers, knowledge bases, product explainers, FAQs, or how-to guides comes into play. If they’re structured well, optimized for real user queries, and regularly updated, they become long-tail SEO machines.
Another overlooked asset is community forums or customer reviews/Q&A sections. Real user questions and real answers lead to long-tail keywords and user-generated content that basically maintains itself.
SEO benefits of retaining users and reducing churn
Retention isn’t just a product or support goal, but an SEO goal too. Engaged users generate more branded searches, click through internal content more often, share links, leave reviews, and make repeat purchases, creating positive engagement signals.
Reducing churn means people stay in your ecosystem longer, giving your website content more opportunities to show up, get linked, and build authority.
How to identify high-value post-conversion content
This part isn’t guesswork; you already have the answers. The key is to tap into the real questions and friction points your users experience after they convert. Here’s how to do it:
1. Support tickets
Look at the most common questions that indicate that something is not working or that users don’t understand something. If the same issue keeps popping up, that’s a signal you need better documentation or that your current documentation is not easy to find.
How to use it: Turn top support issues into searchable help documents, step-by-step tutorials, or even short videos embedded in your knowledge base or product pages.
2. Customer interactions
Your customer-facing teams hear things you won’t get from tickets. They will understand why certain products, features, or steps in the buying journey cause confusion.
How to use it: Create content that supports onboarding or post-purchase usage, expands on underused products, features, or clarifies key steps in getting value from what was purchased. Pull direct language from how customers describe problems and try to use it to your advantage. They’ll likely use the same language to search for a solution.
Your internal site or knowledge base search is one of the best indicators of intent. What users search for after logging in or visiting your site tells you exactly what they are struggling with.
How to use it: Identify top queries that return poor results or no results. Create or improve content that answers those questions. Optimize titles, headers, and metadata so the right article appears first.
4. Feature usage or product engagement data
Low usage doesn’t always mean low interest; it might mean unclear setup, poor discoverability, or hidden value.
How to use it: Look at features or products with low adoption but high impact. Interview users who use them and reverse-engineer what made it work for them. Then build content that guides others to the same outcome.
Types of high-value content to create
Feature walkthroughs or product usage guides: clear, step-by-step guides and how-tos with screenshots or GIFs.
Setup checklists: especially for more complex products
Integration or compatibility guides
Advanced use case tutorials
Other explainers and tactful guides for common mistakes
These pieces not only improve user experience but also target long-tail search queries, reduce support load, and strengthen retention.
Below are examples of great post-conversion content:
Microsoft combines training hubs, such as the Educator Center, with help content and community resources to support users throughout their post-purchase journey.This example comes from Nike’s website, which mainly focuses on product care and styling tips to help customers use and maintain their products.
Internal linking strategies that keep users engaged
Post-conversion content shouldn’t live in isolation. It should be linked, surfaced, and reused across your entire ecosystem.
Ways to keep users moving:
Link between related help documents
Add “next steps” CTAs to knowledge base articles
Include product education content in lifecycle emails
Use breadcrumbs, related content widgets and in-context links
Done right, this turns your post-conversion content into an internal SEO web that improves engagement and makes users more confident in using your products.
Why supporting existing users is good SEO and good business
If your SEO strategy only focuses on acquisition, you’re leaving money (and traffic) on the table. Post-conversion content helps users get more value from your products, reduces friction, and builds long-term loyalty, all while creating indexable, intent-driven pages that search engines can surface at key moments.
Want to take action? Start by auditing your post-conversion content. Map out the key moments after signup or purchase, and ensure users receive support at each step. Surface help docs, feature guides, and tutorials where they are needed most and connect them with clear, intentional internal links.
SEO isn’t just about discovery. It’s about usability. It’s about confidence. It’s about making sure your users stay, not just show up. If you want to build long-term, defensible growth, that’s where you should be focusing.
Bring your coffee or tea, questions, and stories; we’d love for you to be part of it.
