Nearly 90% of businesses are worried about losing organic visibility as AI transforms how people find information, according to a new survey by Ann Smarty.
Why we care. The shift from search results to AI-generated answers seems to be happening faster than many expected, threatening the foundation of how companies are found online and drive sales. AI is changing the customer journey and forcing an SEO evolution.
By the numbers. Most prefer to keep the “SEO” label – with “SEO for AI” (49%) and “GEO” (41%) emerging as leading terms for this new discipline.
87.8% of businesses said they’re worried about their online findability in the AI era.
85.7% are already investing or plan to invest in AI/LLM optimization.
61.2% plan to increase their SEO budgets due to AI.
Brand over clicks. Three in four businesses (75.5%) said their top priority is brand visibility in AI-generated answers – even when there’s no link back to their site.
Just 14.3% prioritize being cited as a source (which could drive traffic).
A small group said they need both.
Top concerns. “Not being able to get my business found online” ranked as the biggest fear, followed by the total loss of organic search and loss of traffic attribution.
About the survey. Smarty surveyed 300+ in-house marketers and business owners, mostly from medium and enterprise companies, with nearly half representing ecommerce brands.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-24 17:10:572025-10-24 17:10:5790% of businesses fear losing SEO visibility as AI reshapes search
It’s easy to fall into doom and gloom that AI is replacing content marketers. It’s really replacing outdated workflows, though.
Over 90 percent of large marketing teams now use AI to generate content. They’re moving faster, publishing more, and rethinking production from the ground up. But speed alone won’t make content perform.
Audiences tune out shallow, generic material. Human creativity still drives differentiation. Strategy, originality, and clear brand perspective separate useful content from noise.
The teams that win combine AI’s efficiency with human insight. That requires knowing where automation fits and where it doesn’t. If you haven’t defined how to use AI for content creation inside your workflow, now’s the time.
This piece explores what effective AI vs human content looks like today and how to build it without losing your edge.
Key Takeaways
Most companies have already integrated AI into their content workflows, but don’t fall in the trap of treating them as shortcuts rather than systems.
Content that earns visibility today is structured, specific, and backed by human perspective, not just keyword targeting.
Strategic AI use supports ideation, formatting, optimization, and repurposing, but quality control stays human.
Personalization, brand voice, and original data continue to drive trust and engagement.
Success comes from balancing scale with clarity. The best content performs because it’s relevant, not frequent.
Managing The AI Flood
AI-generated content has reshaped digital publishing. Brands produce more blog posts, email copy, and landing pages than ever. But volume brings saturation and diminishing returns.
Not all AI content is low quality, but much of it reads identically. Teams optimize for speed without strategy. The result? More output, less substance.
Content that still works doesn’t feel mass-produced. It stands out by doing one or more of these things:
Offers a clear point of view or original framework
Goes deeper than surface-level summaries
Reflects genuine understanding of the audience
Adds context, nuance, or experience AI can’t fake
Search engines adapt to this shift. Platforms like Google and Perplexity look at content with structure, specificity, and trust signals over keyword stuffing or volume. AI tools are more likely to cite content that demonstrates expertise and clarity.
The opportunity isn’t to publish more. Build better systems for quality and relevance at scale. Winning teams won’t lean on AI to fill gaps, but reinforce strengths.
Human guidance makes the difference. Without it, content becomes another drop in the flood.
Rebuilding The Content Workflow
AI accelerates content production. It also forces teams to rethink how work gets done.
Instead of replacing content professionals, AI shifts where their time and value go. Manual tasks like keyword clustering, formatting, or metadata writing now run through automation. What remains critical is work AI can’t do well: aligning content to business goals, telling compelling stories, and capturing audience nuance.
How does this work in practice? Writers, strategists, and editors move upstream. They spend more time setting direction, defining tone, and curating inputs. Downstream, AI helps turn those inputs into faster iterations, formatted assets, and scalable deliverables.
This shift creates a more responsive content engine. One that reaches insight faster. One that makes room for testing and repurposing without burning out your team.
The result? More consistent output, more flexibility, fewer bottlenecks.
To get there, rebuild the workflow around what your team does best, not just what AI does quickly.
The sections below break down how to apply this shift at each stage, from ideation to optimization, so you can create a system that scales without sacrificing value.
Ideation
Strong content starts with strong ideas. That’s still a human job.
AI makes the early stages faster. Instead of starting from scratch, marketers use AI to scan top-performing content, surface related questions, and generate keyword clusters in seconds. Tools like ChatGPT, Ubersuggest, and BuzzSumo help teams quickly identify gaps, trends, and angles worth exploring.
But ideation is only useful when it’s aligned with strategy. AI should support the process, not drive it. You need that human point of view as a starting point.
Real-Time Performance Feedback
AI doubles as a smart editor.
Tools like Clearscope, MarketMuse, and Surfer SEO give real-time scoring on keyword coverage, topic depth, readability, and search intent. You can spot weak sections, catch missing subtopics, and verify your draft aligns with how people actually search.
Instead of waiting for performance to drop before making updates, fix issues before content even publishes. That means fewer rewrites and better outcomes from day one.
Brand Voice Support
One of the biggest risks with AI content? Sounding like everyone else. Brand voice systems help.
Feed AI tools with examples of your tone, preferred phrases, and messaging guardrails to guide outputs toward consistent brand reflection. Prompt libraries, templates, and style frameworks give AI clearer direction and reduce heavy editing later.
But it’s not set-and-forget. Someone still needs to review and fine-tune. AI can help scale your voice, but it won’t define it for you.
Content Repurposing
Most content teams don’t need more ideas. They need more mileage from content they already have.
AI makes breaking down webinars, blog posts, or whitepapers into new formats easier. With the right content repurposing plan, turn a single piece into multiple social posts, email sequences, video scripts, or short-form summaries in minutes.
This approach saves time and extends the reach of your core ideas. The key is setting rules around tone and structure so AI keeps output aligned with your original intent.
Graphics
Visual content used to slow down many content workflows. Not anymore.
AI-powered design tools like Canva, Midjourney, and Runway help marketers produce branded graphics, thumbnails, and motion assets much faster. Instead of waiting days for design resources, teams create visuals in parallel with written content without sacrificing quality.
This means faster turnarounds on social content, better visual support for blog posts, and more consistency across formats. As with writing, human review remains necessary, but AI handles much of the heavy lifting.
SEO Formatting
Formatting for SEO used to eat up hours, particularly at scale. AI tools now handle much of that backend work.
From writing meta descriptions and alt text to adding schema markup and internal links, automation streamlines the technical side of publishing. Tools like SEO.ai and Surfer can also suggest keyword tweaks and intent matches based on real-time SERP data.
This doesn’t replace SEO strategy, but it cuts down the grunt work. Teams can focus more on aligning content with search intent, not just checking boxes.
The New Age of AI-Optimized Content: What Does It Look Like?
The rise of AI hasn’t lowered the bar for content quality. It’s raised it.
With machine-generated content flooding every channel, visibility now depends on value, not volume. Search engines and users reward content that brings clarity, trust, and depth.
Your content strategy needs to shift focus. Specificity, structure, and perspective matter more than keyword counts and content frequency.
AI-optimized content that performs well today typically checks a few key boxes:
Built around real expertise, often supported by proprietary data or firsthand experience
Clearly structured, using headings, bullets, and schema markup to improve readability and search parsing
Leads with utility, helping readers solve problems, take action, or understand something faster
Reflects your brand’s voice and positioning, not a generic blend of scraped internet copy
Human content professionals have leverage here. AI can get a draft to 70 percent, but that last 30 percent (the part that connects, converts, or earns backlinks) still requires human input.
One of the most overlooked opportunities right now? Simply tightening your structure. Clear formatting helps search engines surface your content and makes it easier for generative tools like ChatGPT and Perplexity to cite and summarize it correctly.
AI can help get content out the door faster. But if you want that content to show up, earn trust, and drive results, human oversight isn’t optional. It’s the differentiator.
Multimedia Integration
A well-placed visual can do more than dress up a page. It boosts visibility, extends engagement, and increases the odds of being cited by generative search engines.
Search engines also reward content that blends formats. Multimedia helps break up long blocks of text, reinforces key takeaways, and signals structure that AI engines can easily parse.
To make it work, start planning visuals alongside your copy, not after the fact. That upfront alignment leads to stronger storytelling and assets that actually support performance, not just polish the page.
AI’s Impact on Content Distribution
Content doesn’t drive results if no one sees it. That’s always been true. What’s changed is how distribution works and who you’re optimizing for.
Today, your audience includes both people and machines. The rise of generative search and large language models (LLMs) means your content isn’t just being read by humans. It’s being crawled, summarized, and cited by AI systems that prioritize structure, metadata, and clarity.
To stay visible, your distribution strategy needs to reflect that.
Start with metadata. Schema markup, structured tags, and optimized alt text all help AI tools understand and surface your content across search, snippets, and summaries. This isn’t just a technical checkbox. It’s the infrastructure that supports discoverability.
Then think about format. Repurpose long-form assets into LinkedIn posts, email sequences, YouTube Shorts, or Reddit threads. Tailor messaging by platform. Adjust tone for different audiences. A one-size-fits-all approach wastes reach.
Finally, use automation to your advantage. Tools like Buffer, Zapier, and Hootsuite can help schedule, adapt, and push updates across multiple channels at once. That frees your team from repetitive tasks and ensures consistency wherever your audience finds you.
Distribution used to be about checking the promotion box. Now it’s a system with humans on one end and AI on the other.
Done well, distribution doesn’t just get more eyes on your content. It makes sure the right people and the right algorithms see it in the right place, at the right time.
Staying Ahead of the Content Curve
Predictability used to be a strength in content planning. But with AI constantly changing how content is created, distributed, and discovered, agility matters just as much.
Keeping your edge means paying attention to two things: where AI is going, and how your audience is reacting right now.
Start by tracking signals. Tools like Exploding Topics, Glimpse, and SparkToro help identify early trends and shifts in search behavior before they hit the mainstream. Combined with real-time performance data from platforms like GA4 or social analytics, you can spot what’s resonating and what’s falling flat while there’s still time to act.
Adaptability is key. A/B testing thumbnails, headlines, or messaging lets you make micro-adjustments without overhauling your entire campaign. And monitoring where and how AI engines cite your content can highlight gaps worth closing or opportunities to double down on.
Future-proofing doesn’t mean locking in a rigid plan. It means building a system that can flex with your audience and the algorithms that serve them.
FAQs
Can AI-generated content rank in search engines?
Yes, but only if it’s high quality. Google doesn’t penalize AI content specifically. What matters is whether the content provides value, demonstrates expertise, and meets user intent. AI-assisted content that’s edited and enhanced by humans typically performs better than purely AI-generated material.
How do I balance AI vs human-generated content in my strategy?
Use AI for tasks like ideation, outlining, formatting, and repurposing. Keep humans involved in strategy, editing, brand voice, and final review. A good rule: AI can get you to 70 percent, but humans should handle the final 30 percent that makes content distinctive and valuable.
What are the risks of using too much AI in content creation?
Over-reliance on AI leads to generic, samey content that doesn’t stand out. Other risks include factual errors, lack of brand voice, and content that sounds robotic. Users and search engines increasingly favor content with clear human expertise and originality.
How is human vs AI content different in terms of engagement?
Human-created or human-edited content typically generates higher engagement because it includes personal experiences, emotional resonance, and authentic storytelling. AI content often lacks nuance and personality, which can reduce trust and engagement rates.
Conclusion
The shift to AI-assisted content isn’t slowing down. But speed and automation aren’t enough to drive results on their own. The real differentiator is how well your system blends efficiency with insight.
Human-led strategy still drives the most meaningful outcomes, whether that’s developing a content plan built around real audience data or shaping assets to align with how search and generative engines work today.
If you haven’t revisited your content approach recently, now’s the time. You can start by refining your SEO content strategy or building smarter processes around AI content optimization.
In a space full of content, only the most useful, intentional, and well-structured will rise to the top.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-23 19:00:002025-10-23 19:00:00AI vs Content Marketers: The New Content Marketing Formula
What Is AI Optimization (And Why You Should Care)?
AI optimization is the process of making your website accessible and understandable to AI-powered search tools. Like ChatGPT, Claude, Gemini, Perplexity, Google AI Overview, and Bing Copilot.
Some call it “AI search optimization.” Others “AI content optimization.”
Terminologies vary, but they’re all about the same thing:
Make your site easy for large language models (LLMs) to find, understand, and reference in their answers.
It’s not a brand-new strategy. It’s built on the core SEO principles.
Only now, you’re optimizing for tools that pull, summarize, and use your information — not just rank.
But why is AI optimization so important now?
AI tools are expected to drive more traffic than traditional search engines by 2028.
And here’s the kicker:
This traffic pool is only getting bigger.
Over 700 million people use ChatGPT every week. Millions more use Perplexity, Gemini, and other AI platforms.
Google’s AI Mode already has more than 100 million monthly active users. And that’s just in the US and India.
As it rolls out globally, adoption will only grow.
AI search optimization helps you be visible to these users.
It ensures your site appears in AI-powered search results, increasing your chances of getting referral traffic and finding new customers.
How AI Search Works
LLMs find relevant content across the web based on users’ prompts, then combines it into one comprehensive answer with source links.
There are three broad steps:
1. Understanding Your Prompt
First, AI interprets what you’re asking.
Some platforms (and specific models) may even expand or tweak your query for better results.
For instance, if I search “best sneakers,” ChatGPT’s o3 model searches for more specific phrases like “best running shoes 2025.”
2. Retrieval
Next, the AI platform searches for information in real time.
Different platforms use different sources (Google’s index, Bing, curated databases, etc.). But they all work the same way.
They gather relevant content from across the web for your expanded query.
3. Synthesis
Finally, AI decides which sources to include.
How?
The exact criteria aren’t public. But these factors seem to matter the most:
Authority: Recognized brands (entities it knows) and established experts
Structure: Clear, scannable content with direct answers
Context: Content that covers topics semantically (related concepts, not just keyword matches)
The most relevant sources get cited. The rest get ignored.
Which means ranking well isn’t enough. Your content also needs to be properly structured for AI systems.
I Analyzed 10 Queries Across Multiple AI Search Platforms: Here’s What I Found
Before we move forward to discuss how to optimize for AI search, I wanted to understand three things:
Do different AI platforms cite different types of content?
Which domains consistently appear across platforms?
Does multi-platform presence actually matter for AI visibility?
So I ran a simple experiment.
I searched 10 queries across ChatGPT 5, Claude Sonnet 4, Perplexity (Sonar model), Gemini 2.5 Flash, and Google’s AI Mode — a mix of commercial, informational, local, and trending topics.
And I found some interesting insights.
How Each Platform Chooses Sources
Platforms
Citation Behavior
ChatGPT
Acts like a community aggregator. Mixes Reddit discussions with Wikipedia and review sites.
Claude
Prefers recent, authoritative sources. Zero Reddit citations. Focuses on 2024-2025 content
Perplexity
Most diverse. Balances buying guides, YouTube reviews, and some Reddit.
Gemini
Relies mostly on training data. And since there’s no option to turn on web search, you can’t get it to cite sources for most of your queries.
Google AI Mode
Pulls from beyond top search results. 50% of citations weren’t on page one of Google.
The “Citation Core” Effect
Certain domains have achieved what we call the “citation core” status.
Citation core (n.): A small group of sites and brands that every major AI search tool trusts, cites, and uses as default sources.
Wikipedia showed up 16 times. Mayo Clinic owned health queries. RTINGS controlled electronics reviews.
These sites have become AI’s default sources.
What This Means for Brand Sites
One pattern jumped out: Official brand websites were underrepresented.
In my test, they made up around ~10% of all citations.
But that doesn’t mean your site doesn’t matter for informational or educational queries.
It means most sites aren’t yet AI-friendly. And that’s the opportunity.
When your site is structured, detailed, and optimized, it becomes one of the few brand-owned sources AI can actually cite for product specs, features, case studies, and stats. Information third-party sites can’t provide.
Think of it like this: Your website gives you the authoritative base layer. Off-site presence just amplifies it.
These findings aren’t surprising. But they reinforce what we’ve suspected all along.
In fact, a lot of what we do here at Backlinko aligns with these patterns:
Google’s guideline says good SEO is good AI optimization.
Their official guidelines mostly rehash standard SEO practices, with a few AI-specific points. Like using preview controls and ensuring structured data matches visible content.
But the foundation to make your site AI search-ready starts with three teams working in sync:
Developers: They make your site technically accessible to AI crawlers
SEOs: They structure content so AI can extract and understand it
Content teams: They create information worth extracting
Most companies treat these as separate projects.
That’s a mistake.
Leigh McKenzie, Head of SEO at Backlinko, explains why:
“Ranking in Google doesn’t guarantee you’ll show up in AI tools. SEO is still table stakes. But generative engines don’t just lift the top results. They scan at a semantic level, fan queries out into dozens of variants, and stitch together answers from multiple sources.”
You’ll need a coordinated effort to execute.
Let’s look at exactly what each team needs to do for effective AI search optimization.
Note: Most traditional SEO practices work for AI optimization too.
I’m not covering the basics here, like using sitemaps and including metadata. You should already be doing those.
Instead, I’m focusing on factors that specifically impact AI search visibility. These are insights based on my own experience, analyzing what’s working across different sites, and comparing notes with other SEOs.
Want the complete list?
I’ve created an AI Search Engine Optimization Checklist that covers everything — the well-known tactics, the experimental ones, and the “can’t hurt to try” optimizations that might give you an edge.
Developer Tasks
Understanding how to optimize for AI search starts with your developers. Because they control whether AI can actually access and understand your content.
No access means no citations.
Here’s what they need to check:
1. Make Your Site Accessible to AI Crawlers
AI crawlers need permission to access your site through your robots.txt file.
If you block them, your content won’t appear in AI search results.
Here are the main AI crawlers:
GPTBot (OpenAI/ChatGPT)
Google-Extended (Google’s AI Overview)
Claude-Web (Anthropic/Claude)
PerplexityBot (Perplexity)
To check if you’re blocking them, go to yoursite.com/robots.txt.
Look for any lines that say “Disallow” next to these crawler names.
If you find them blocked (or want to make sure they’re allowed), add these lines to your robots.txt:
Your developers handled the technical requirements. AI can now access your site.
But access doesn’t guarantee visibility in AI results.
Your SEO team controls how AI discovers, understands, and prioritizes your content.
Here’s what they need to control in your AI SEO strategy:
7. Structure Pages for Fragment-Friendly Indexing
AI pulls specific fragments from your pages — sentences and paragraphs it can use in responses.
Your page structure affects how easily AI can extract these fragments.
Start with a clean heading hierarchy.
Proper H2s and H3s help AI (and your readers) understand where one idea ends and another begins.
Go a step further by breaking big topics into unique subsections.
Instead of one giant guide to “healthy recipes,” create separate sections for “healthy breakfast recipes,” “healthy lunch recipes,” and “healthy dinner recipes.”
That way, you match the variations people actually search for.
Pro tip: Don’t bury your best insights in long paragraphs.
Use callouts (like this one)
Add short lists and bullets
Drop quick tables for comparisons
That’s how you turn raw text into structured fragments AI can actually use.
When your content is structured this way, every section becomes a potential answer.
8. Build Topic Clusters That Signal Full Coverage
Internal linking creates topical connections across your site.
When you link related pages together, you’re building topic clusters that show comprehensive coverage.
This is standard SEO practice that also helps AI discovery.
Create pillar pages for your main topics. These are comprehensive guides that link out to all related content.
For “project management,” your pillar would link to:
Task automation guide
Team collaboration tools
Workflow optimization
Resource planning
Each supporting page links back to the pillar and to other relevant pages in the cluster.
This helps both users and AI understand page relationships.
The cluster structure accomplishes two things:
First, it improves crawl efficiency. AI finds your hub and immediately discovers all related content through the links.
Second, it demonstrates topical depth. Organized clusters show comprehensive coverage better than scattered pages.
This structural approach helps organize your site architecture to showcase expertise through strategic internal linking.
9. Add Schema Markup to Label Your Content
When AI crawls your page, it sees text.
But it doesn’t know (without natural language processing) if that text is a recipe, a review, or a how-to guide.
Schema explicitly labels each element of the page.
It makes data more structured and easier to understand.
There are several types of schema markups.
I’ve found the FAQ schema particularly effective for AI search visibility.
Here’s how it looks:
json{
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "What is churn rate?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Churn rate is the percentage of customers who cancel during a specific period."
}
}]
}
This markup tells AI exactly where to find questions and answers on your page.
The Q&A format matches how AI structures many of its responses, making your content easy to process.
Depending on the content management system (CMS) you’re using, you can add schema using plugins, add-ons, or manually.
For instance, WordPress has several good plugins.
After implementation, you can test it at validator.schema.org to ensure it’s working properly.
Note: Schema is just one type of metadata. Others include title tags, meta descriptions, and Open Graph tags.
Keeping them accurate and consistent may help AI platforms interpret your content correctly.
You can check your metadata using browser dev tools or SEO extensions, like SEO META in 1 CLICK.
10. Add Detailed Content to Category and Product Pages
Most category pages are just product grids. That’s a missed opportunity for AI search optimization.
The same goes for individual product pages with just specs and a buy button.
These pages get tons of commercial searches.
But they lack substantial content.
So, AI has limited information to work with when answering product queries.
You want to add buyer-focused information directly on these pages, like this:
They can cover:
Feature comparison tables
Common questions with clear answers
Use cases and industry applications
Technical specifications that matter
For product pages, go beyond basic descriptions.
Include materials, dimensions, compatibility, warranties, reviews — whatever matters to your buyers.
For example, GlassesUSA.com has several details on its product pages than just product specifications.
They include information that AI can use when answering specific questions.
Similarly, for category pages, add content that helps buyers choose.
What’s the difference between options? What should they consider? Which product fits which need?
Eyewear retailer Frames Direct does this well.
It has detailed content at the end of its category pages.
The key is putting this information directly on the page. Not hiding it behind tabs or “read more” buttons.
When someone asks AI about products in your category, you want substance it can quote. Not just a grid of images it can’t interpret.
11. Track Where AI Mentions Your Brand (and Where It Doesn’t)
You need to know where AI is mentioning your brand and where it isn’t.
Because if competitors appear in AI results and you don’t, they’re capturing the traffic you should be getting.
You can try checking this manually.
Run your target queries (e.g., “nutrition tracking app 2025”) across different AI platforms.
Scan the answers. And see if your brand shows up.
But that’s slow. And you’ll only catch a small slice of what’s happening.
It tracks how often your brand appears in AI-generated answers across various platforms like ChatGPT, Google AI Mode, and Google AI Overview. (In the “Visibility Overview” report.)
You can see exactly which topics and prompts your brand appears for.
And which prompts your competitors appear for, but you don’t. (In the “Competitor Research” report.)
For instance, if you find that AI cites competitors for “Cats and Feline Care” but skips your brand, that’s a clear signal to create or optimize a page targeting that exact query.
You also get strategic recommendations. So you can spot gaps, fix weak content, and double down where you’re already winning. (In the “Brand Performance” reports.)
With a tool like AI SEO Toolkit, you’re not guessing about your AI search visibility.
You’re improving based on real AI visibility data.
12. Optimize for Natural Language Prompts, Not Just Keywords
But they ask AI, “What’s the warmest jacket for Chicago winters under $300?”
Your content needs to match these natural language patterns.
Start by identifying how people actually phrase questions in your industry.
Use the AI SEO Toolkit to find high-value prompts in your industry.
Go to the “Narrative Drivers” report.
And scroll down to the “All Questions” section to see which prompts mention your brand and where competitors appear instead.
Document these prompt patterns.
Share them with your content team to create pages that answer these specific questions — not just target the base keyword.
The goal isn’t abandoning keywords.
It’s expanding from “winter jacket” pages to content that answers “warmest jacket for Chicago winters under $300.”
Content Tasks
Your site is technically ready. Your SEO is taken care of.
Now your content team needs to create valuable information and build presence across the web.
Here’s how to optimize content for AI search:
13. Publish Original Content with Data, Examples, and Insights
Generic blog posts restating common knowledge rarely perform well in AI search results.
But content with fresh angles and concrete examples does.
At Backlinko, we focus on publishing content that provides unique value through examples, original research, and exclusive insights.
Like this article:
And even if we’re talking about a common topic (e.g., organic traffic), we add fresh examples.
So how do you make your content stand out?
Run small studies or polls to produce original data. Even simple numbers can set your content apart.
Use screenshots, case studies, and workflows from real projects.
Back up your points with stats and cite credible sources.
Add expert quotes to strengthen authority.
Test tools or strategies yourself, and share the actual results.
AI systems look for concrete details they can pull into answers.
The more unique evidence, examples, and voices you add, the better.
14. Embed Your Brand Name in Context-Inclusive Copy
Context-inclusive copy means writing sentences that make sense on their own.
Each line should carry enough detail that an AI system understands it without needing the surrounding text.
But take that a step further.
Don’t just make your sentences self-contained.
Embed your brand name inside them so when AI reuses a fragment, your company is part of the answer.
Instead of: “Our tool helped increase conversions by 25%”
Write: “[Product] helped [client] increase checkout completions by 25%”
The second version keeps your brand attached to the insight when AI extracts it.
So how do you do this in practice?
With data: Tie your brand name directly to research findings or surveys you publish
With comparisons: Mention your brand alongside alternatives, so it’s always part of the conversation
With tutorials: Show steps using your product or service in real workflows
With results: Attach your brand name to case studies and examples
Here’s an example from Semrush, using their brand name vs. “we”:
The goal is simple:
Every quotable fragment should carry both context and your brand name.
That way, when AI pulls it into an answer, your company is mentioned too.
15. Create Pages for Every Use Case, Feature, and Integration
Specific pages are more likely to appear in AI responses than generic ones.
So, don’t bundle all features on one page.
Create dedicated pages for each major feature, use case, and integration.
Here’s an example of JustCall doing it right with unique pages for each of its main features and use cases:
The strategy is simple: match how people actually search.
For instance, someone looking for “Slack integration” wants a page about that specific integration. Not a features page where Slack is item #12 in a list.
Structure these pages to answer real questions, like:
What problem does this solve?
Who typically uses it?
How does it actually work?
What specific outcomes can they expect?
Get granular with your targeting. Instead of broad topics, focus on specific scenarios.
For example:
→ Ecommerce sites can create pages for each product application
→ Service businesses can detail each service variation
→ Publishers can target specific reader scenarios
The depth of coverage signals expertise while giving AI exact matches for detailed queries.
This specificity is what makes AI content optimization work. You’re creating exactly what AI systems need to cite
16. Expand Your Reach Through Non-Owned Channels
AI engines lean heavily on third-party sources. Which means your brand needs to show up in places you don’t fully control.
This goes beyond your on-site efforts.
But it’s still part of the bigger AI visibility play. And your content team can drive it by publishing externally and fueling PR.
Take this example: when I search “best duffel bags for men 2025” in Claude, it references an Outdoor Gear Lab roundup of top bags.
If you sell duffels, you’d want to be in that article.
There are two ways to expand your presence on non-owned channels.
One is publishing on other sites yourself — guest posts, bylined articles, or original research placed on authority blogs and industry outlets.
These extend your reach, position you as an expert, and increase your AI search visibility.
You’ll find guest post opportunities in several well-known sites. Like Fast Company here, which has an authority score of 67.
The other way to build visibility is getting featured by others.
Think reviews, roundups, and product comparisons that highlight your solution.
This usually involves working closely with your PR team.
But the content team fuels those opportunities with the data, case studies, and assets that make the pitch worth covering.
Either way, the goal of this AI content strategy is the same: substantive coverage.
A one-line mention usually isn’t enough. You need full features, detailed reviews, or exclusive insights that stand out.
Because the more credible coverage you earn (whether you wrote it or someone else did), the more evidence AI has to pull into its answers.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-22 14:05:192025-10-22 14:05:19AI Optimization: How to Rank in AI Search (+ Checklist)
The message from this month’s SEO Update is clear: AI and data accuracy are reshaping how we plan, optimize, and measure SEO. This is not just a slate of updates, but a signal to rethink impressions, content creation, and tooling so you stay effective. Chris Scott, Yoast’s Senior Marketing Manager, hosted the session. Alex Moss and Carolyn Shelby shared deep dives on AI trends, Google updates, and Yoast product news.
Data and rankings in flux
A key shift centers on data. Google removed the num=100 parameter, which changed how much ranking data shows up per page in Google Search Console. The result isn’t a sudden performance drop; it’s a correction. Impressions can look lower because the data is being cleaned up, and that matters more than the raw numbers. Paid search data stays solid, since ads rely on precise counting for financial reasons.
AI content and media: use it, don’t rely on it
Sora 2 can generate short videos from text prompts, providing handy visuals to accompany blog posts. Use AI visuals to complement your core messaging, not to replace it. In e-commerce, Walmart, WooCommerce, and Shopify are testing AI-enabled shopping features. Don’t rush a full switch before major buying events.
Local SEO and engines beyond Google
Bing’s Business Manager now has a refreshed UI focused on local listings, signaling a push into local search. Diversifying beyond Google can reveal new AI-powered opportunities. It’s about testing where AI-driven search and shopping perform best, not moving budgets blindly.
AI mode and how people behave
Research into AI-dominant sessions shows a distinct pattern: users linger 50 to 80 seconds on AI-generated text, and clicks tend to be transactional. Intent patterns shift, too. Now, comparisons lead to review sites, decisive purchases land on product pages, and local tasks point to maps and assets.
Meta descriptions and AI generation
Google tested AI-generated descriptions for threads lacking meta content, but meta descriptions aren’t obsolete. Best practice is to lean on Yoast’s default meta templates (like %excerpt%) as a reliable fallback. Write with an inverted pyramid in mind, which puts key information first, so AI can extract it cleanly. Keep a fallback description in Yoast SEO so automation stays under your control.
AI in everyday workflows
ChatGPT updates push toward more human-to-human interactions, and tools like Slack can summarize threads and search discussions by meaning, not just keywords. Growth in AI usage feels steadier now; some younger users opt for other AI tools.
Insights from Microsoft and Google
The core rules haven’t changed: concise, unique, value-packed content wins. Shorter, focused writing works best for AI synthesis; trim fluff and sharpen clarity. The message is simple because clarity beats complexity, especially as AI becomes more central to how content is consumed.
Yoast product updates to watch
The Yoast SEO AI+ bundle adds AI Brand Insights to track mentions and citations in AI outputs, and pronoun support has been added to schema markup for inclusivity. If you’re tracking AI relevance beyond traditional signals, this bundle can be a smart addition.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-22 13:11:592025-10-22 13:11:59A recap of the October 2025 SEO Update by Yoast
Yelp just unveiled its 2025 Fall Product Release, a sweeping AI-driven update that turns the local discovery platform into a more conversational, visual, and intelligent experience.
Driving the news: Yelp’s rollout includes over 35 new AI-powered features, headlined by:
Yelp Assistant, an upgraded chatbot that instantly answers customer questions about restaurants, shops, or attractions—citing reviews and photos.
Menu Vision, which lets users scan menus to see photos, reviews, and dish details in real time.
Yelp Host and Yelp Receptionist, AI-powered call solutions that handle reservations, collect leads, and answer questions with natural, customizable voices.
Natural language and voice search, allowing users to search conversationally (“best vegan sushi near me”) for smarter, more relevant results.
Popular Offerings, which highlights a business’s most-mentioned services, products, or experiences.
Why we care. Yelp’s new AI tools make it easier to capture and convert high-intent customers at the moment of discovery. With features like Yelp Assistant, AI-powered call handling, and natural language search, businesses can respond instantly, stay visible in smarter search results, and never miss a lead. The update turns Yelp from a review site into an always-on customer engagement platform—giving advertisers more efficient ways to connect, communicate, and close.
What’s next. Yelp plans to make its AI assistant the primary interface for discovery and transactions in 2026, merging instant answers, booking, and customer messaging into one seamless experience.
The bottom line. Yelp’s latest AI release gives brands smarter tools to engage customers in real time—turning everyday search and service interactions into instant connections.
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OpenAI announced the launch of its first web browser, which they named ChatGPT Atlas. Atlas is currently available on Mac only right now and has all the features you would expect from an AI browser. But the most surprising part is that its built-in search features seem to be powered by Google and not Microsoft Bing, its early partner and one of its largest investors.
How to download Atlas. If you are on a Mac, you can download ChatGPT Atlas at chatgpt.com/atlas. From there, the web browser will download to your computer, you double click on the installer and then drag the application to your application folder.
What Atlas does. It is a web browser, first and foremost. You can go directly to web pages and browse them, but as you do that, there is ChatGPT available on the sidebar, like other AI powered web browsers. You can ask ChatGPT questions, you can have it re-write your content in Gmail and other tabs, offers personalization and memory, plus it will help you complete tasks, code and even shop using agentic features.
Search in Atlas. The interesting thing is that when you search in ChatGPT Atlas, it gives you a ChatGPT like response but also adds search vertical tabs to the top, like you have in other search engines. Like web, images, videos, news and more. Then when you go to those tabs, there is a link at the top of each set of search results to Google.
Here are screenshots:
More details. ChatGPT Atlas is launching worldwide on macOS today to Free, Plus, Pro, and Go users. Atlas is also available in beta for Business, and if enabled by their plan administrator, for Enterprise and Edu users. Experiences for Windows, iOS, and Android are coming soon.
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Noticed your traffic dropping even though your rankings look stable? You’re not alone.
AI tools like ChatGPT, Perplexity, and Google’s AI Overviews are now answering the same questions that used to send people to your site.
If your brand isn’t showing up in those AI-generated responses, you may be losing visibility. And the tough part? You won’t be able to measure that lost visibility with traditional analytics tools.
That’s where AI visibility tools come in. They tell you when your brand shows up in AI answers, which platforms mention you, and how often your content gets cited. In short, they track your presence across large language models (LLMs) and AI search engines so you know if your LLM seeding efforts are paying off.
The good news is a handful of tools are already helping brands track their AI visibility. Some existing platforms have added AI tracking to their SEO suites. Others focus exclusively on LLM citations.
Each gives a different way to see (and improve) your AI presence. Let’s look at some of the best AI visibility tools available right now.
Key Takeaways
AI visibility tools track brand mentions and content citations across LLM platforms like ChatGPT, Perplexity, Claude, and Google’s AI Overviews.
Think of these tools as the AI-era version of SEO tools. They give you hard data on whether your optimization tactics are actually working.
Most platforms are still adapting alongside AI search behavior, so look for tools that update often.
The right tool for you depends on your budget, whether you want standalone tracking or built-in SEO features, and how technical your team is.
Combining AI visibility metrics with traditional analytics gives you the complete picture of content performance across all channels.
Why Are AI Visibility Tools Important?
The way people search is fundamentally changing. Gartner predicts traditional search engine volume will drop 25 percent by 2026 due to AI platform and virtual agent usage.
People are getting answers directly from AI platforms instead of clicking through to websites. That makes knowing where your brand appears in AI responses as vital as tracking your Google rankings.
AI visibility tools solve a measurement problem. They monitor which LLM platforms cite your content and your brand mentions in AI Overviews. They measure changes over time so you can evaluate whether your LLM SEO efforts are actually working.
Think of these tools as Google Analytics for AI search. Without this data, you’re guessing about what resonates with AI platforms. With it, you see exactly what content drives citations and what gets ignored. These tools reveal patterns in what content formats, topics, and structures earn the most AI citations.
Traditional SEO metrics like page views, rankings, and backlinks still matter. But they tell only part of the story.
Don’t ignore the growing segment of your audience interacting with your content through AI platforms. They might not visit your site, but their interactions still influence visibility and authority.
Combining standard analytics with AI visibility data shows the complete picture of your content’s reach and what’s actually driving results across channels.
Top 5 AI Visibility Tools on The Market
The LLM visibility tool market is growing fast. New platforms launch regularly with different features, tracking methods, and pricing structures.
After comparing what’s out there, these five AI visibility tools stand out. They range from budget-friendly all-in-one platforms to enterprise-focused citation intelligence.
Ubersuggest
Ubersuggest has added new AI Visibility features to its SEO toolkit. The big win? You can now monitor AI citations and see how they connect to your traditional search performance, all from one dashboard.
Ubersuggest AI Visibility makes it easy to add AI visibility tracking into your marketing program. Key metrics the tool tracks include:
Brand Visibility: How often your brand gets mentioned across AI-generated answers in a given period.
Industry Rank: Your average position compared to other brands in your space.
Top Prompts: The main questions people are asking in AI platforms relevant to your industry, and how your brand appears in those answers.
Competitor Visibility: How your brand’s presence in AI visibility trends compared to competitors over time.
Along with the easy-to-navigate interface, Ubersuggest’s pricing is a major advantage. Most enterprise tools charge per project or lock you into long-term contracts. Ubersuggest takes a different route with flat monthly pricing and unlimited project tracking. That means an agency managing 20 clients pays the same as someone tracking just two sites.
You also get full access to all the traditional SEO features Ubersuggest is known for, so you don’t have to pay for two separate platforms to see the full picture.
Because Ubersuggest is built on years of SEO infrastructure, its data is consistent and reliable. And some teams might not be comfortable with other new visibility tools, many of which launched in the past year and are still working out bugs in their tracking.
Profound is a new platform specifically designed for enterprise brands that need detailed intelligence about how AI platforms discuss them. This goes beyond counting citations.
The system analyzes the context around every brand mention, including:
Sentiment: Whether AI platforms position you positively or negatively.
Competitive mentions: Which competitors get mentioned alongside your brand.
Authority: Topic clusters where you’re seen as an authority versus areas where others dominate.
Profound is built for customization. Its team builds dashboards tailored to your industry, integrates with your existing systems, and creates reporting formats that match your organization’s workflow.
Need specialized tracking for regulated industries? They configure it. Competitive intelligence can also be scaled across hundreds of queries, and alert systems can let you know if your brand suddenly drops from an AI response.
The tradeoff? Price. Annual contract costs typically start high and scale based on how many brands you track, query volume, and customization needs. This isn’t built for small businesses.
With that said, Profound’s depth and customization justify the cost for brands where AI visibility directly impacts market position and revenue.
Semrush added AI visibility tracking to its existing SEO suite. Already using the platform? The new features integrate smoothly into your workflow.
The tool monitors citations across major AI platforms and provides visibility scoring that works like domain authority, providing a single number showing how your AI presence compares to competitors over time.
The real benefit of Semrush’s functionality is that it connects AI visibility data with everything else it already tracks. You can see which pages earn both backlinks and AI citations. You can see whether content that ranks in traditional search also appears in AI responses. That integrated view helps you understand what’s working across all your marketing channels.
For teams trying to consolidate tools, this setup is efficient. You get traditional SEO and AI visibility data in one report, no platform-switching required.
The tradeoff is its agency pricing. Semrush limits how many projects you can track per account tier. Adding clients means upgrading plans or buying additional accounts. Managing 30-plus brands? Costs climb fast compared to platforms with unlimited project tracking.
Overall, this may be a smart add-on if you are already onboarded onto Semrush. But it might not be the most affordable option for smaller teams or tighter budgets.
Ahrefs made its name with backlink analysis and competitive research before becoming one of the most popular SEO tools around. Its move into AI visibility adds another layer to an already powerful platform.
This new functionality tracks citations across AI platforms and lets you filter by specific engines, monitor changes over time, and compare your visibility to competitors. Standard stuff.
Ahrefs stands out by connecting link data with AI citations. Its backlink index is one of the largest available and updates frequently. The platform shows correlations between your link profile and AI visibility, revealing which linked pages get cited most often in AI responses.
That connection offers real insight. Content earning quality backlinks tends to appear more in AI citations. Understanding that relationship helps you identify what makes content citation-worthy and apply those patterns to other pieces. Combine that with Ahrefs’ broader SEO features, and you get a well-rounded picture of your brand visibility online.
The major caveat, though, is the pricing, which follows a similar structure as Semrush. Plans limit tracked projects, so costs increase as you scale. Five clients work fine. Fifty clients get expensive.
For teams that prioritize link building alongside AI visibility, Ahrefs handles both well. Just know you’ll pay premium prices.
ScrunchAI is a newer offering that focuses exclusively on AI visibility. Already using other tools for standard optimization and just need LLM citation tracking? Scrunch’s specialized approach might fit.
The platform monitors brand appearances across ChatGPT, Claude, Perplexity, Google’s AI Overviews, Bing AI, and emerging AI search engines. Real-time tracking alerts you to citation frequency changes, new platforms surfacing your content, or sudden visibility drops.
Where ScrunchAI stands out is that it tracks both citation quality and frequency. It can tell whether AI platforms position your brand as a primary resource, secondary resource, and if any misinformation shows up alongside your name.
ScrunchAI also provides recommendations based on your data. Certain content structures get cited more often? It suggests creating similar pieces. Missing from responses where competitors appear? It flags those gaps with specific topic ideas.
Another interesting feature is query simulation. You can run industry-specific prompts to see if your brand appears and compare results across different AI engines. That gives you a clear picture of where you’re strong and where to focus your next optimization push.
In terms of pricing, Scrunch lands in the middle of our list. Monthly plans scale based on query volume and update frequency rather than limiting projects. That makes costs predictable for agencies.
The tradeoff is betting on a newer company. Established platforms have proven track records. ScrunchAI is still building its reputation, though early users report solid performance and responsive support.
Choosing the Right AI Visibility Tool for You
Selecting an AI visibility tool requires matching capabilities with your specific constraints and goals.
Start with three core questions: What’s your budget? How technical is your team? Do you need standalone AI tracking or an integrated SEO platform? Here are some key focus areas:
Budget determines realistic options. Tools like Ubersuggest provide AI visibility alongside comprehensive SEO features at accessible prices for small businesses and agencies. Enterprise platforms like Profound deliver granular intelligence but require substantial financial commitment that only makes sense at scale.
Technical capabilities matter. Some platforms assume comfort with data analysis and provide extensive export, API, and customization options. Others prioritize simplicity with clear dashboards and straightforward recommendations. Match the tool’s complexity to your team’s skills and bandwidth.
Consider your existing technology stack. Already investing in Ubersuggest, Semrush, or Ahrefs for SEO? Their AI visibility features extend current workflows. You avoid learning new interfaces and keep data centralized. If you’re starting from scratch or want laser focus on AI tracking, a specialized platform like ScrunchAI might be the better fit.
Consider your scaling needs. Requirements differ dramatically between tracking five websites versus managing 50 client accounts. Some tools charge per project or impose account limits, creating expensive scaling challenges. Others offer unlimited projects under single subscriptions, simplifying budgeting as you grow.
Data reliability should influence decisions. Newer tools might offer attractive features but lack infrastructure for consistent metrics. Established platforms benefit from years of data collection and algorithm refinement. Request demos, compare results across tools, and check user reviews before committing.
Finally, assess how tools adapt to AI search changes. AI search is changing at lightning speed, and the tools that don’t update will quickly fall behind. The best platforms have active roadmaps, regular feature updates, and expanding coverage across emerging AI engines.
FAQs
What is the best AI tool for increasing visibility?
The best AI visibility tool depends on your budget and needs. Ubersuggest offers strong value for small businesses and agencies, combining AI citation tracking with full SEO capabilities at accessible pricing. Enterprise brands might prefer Profound’s deeper analytics. Test several options to find which interface and features match your workflow best.
Are AI visibility tools better than traditional marketing methods?
AI visibility tools complement traditional marketing rather than replacing it. You still need solid content strategy, SEO fundamentals, and audience understanding. Think of them as an extension of your analytics, not a replacement. Use them alongside traditional metrics for a complete view of performance across all channels where audiences find information.
How do AI visibility tools integrate with existing SEO strategies?
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AI visibility tools track LLM citations the same way traditional tools monitor search rankings. Platforms like Ubersuggest, Semrush, and Ahrefs combine both metrics in unified dashboards. This lets you optimize content for standard search results and AI citations simultaneously, creating strategies that cover all the ways people discover information today.
Conclusion
AI search isn’t slowing down. Platforms that answer questions before users ever click a link are expanding fast. Tracking your presence in AI-generated responses is essential now.
The tools covered here provide visibility into how AI platforms cite your content and mention your brand. Some integrate AI tracking into broader SEO platforms. Others focus exclusively on LLM citations. Your choice depends on budget, needs, and existing systems.
Start measuring your AI visibility now. The brands paying attention today will outperform the ones waiting to catch up later.
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Google is starting to roll out its new Text Guidelines feature in Google Ads, a tool first announced at the Think Retail event five weeks ago that gives advertisers more control over AI-generated ad copy.
Driving the news. The feature, now appearing in some accounts, lets marketers set campaign-level text parameters — guiding Google’s AI to stay within brand tone, language preferences, and compliance requirements when generating text assets.
Why we care. As Google Ads leans deeper into AI-powered creative, advertisers have been asking for stronger brand safety and message consistency controls. Text Guidelines offer a way to fine-tune AI output without sacrificing automation or performance.
How it works:
Found at the campaign level, Text Guidelines apply only when text customization is turned on.
Advertisers can define rules to steer AI-generated text assets toward specific brand or legal standards.
Designed to support “brand-safe creative” and improve asset quality.
The bottom line. Text Guidelines give brands a new lever to shape how Google’s AI writes for them — tightening control without slowing down automation.
First seen. This rollout was spotted by PPC Speacialist Arpan Banerjee
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Anchor text, which is also known as link text, is the visible, clickable text of a hyperlink. It usually appears in a different color and is often underlined. Good anchor text tells readers what to expect when they click and gives search engines valuable context about the linked page. Getting your anchor text right helps users navigate your content more easily, improves your internal link structure, and provides search engines with clues about your page relationships, which can positively influence your SEO.
Anchor text enhances user navigation and provides context for search engines, improving SEO outcomes.
Good anchor text clearly describes the linked content and avoids misleading or over-optimized phrases.
Different types of anchor text exist, each with specific use cases; mix them for variety and clarity.
Yoast SEO offers tools to analyze competing links and improve anchor text for better search engine ranking.
To enhance anchor text, ensure it matches the linked content, flows naturally, and clearly signals clickable links.
What does an anchor text look like?
Anchor text is the part of a link that describes the linked page. It guides both readers and search engines toward relevant information. For example, if we link to our post about keyword research tools, the phrase “keyword research tools” is the anchor text.
In HTML, it looks like this:
<a href="https://yoast.com/keyword-research-tools/">keyword research tools</a>
The first part is the URL, while the second, the visible text, is the anchor text. Ideally, the words you choose should naturally describe the content on the linked page.
Why are link/anchor texts important?
Links are vital for SEO. They show how your pages connect and help search engines understand your site structure. The anchor text in those links provides extra context.
When Google crawls your site, it uses link text as a clue to what each linked page is about. If multiple links all use the same focus keyphrase, Google might not know which page should rank highest for that topic, leading to competition between your own pages.
That’s why thoughtful, descriptive anchor text matters. It helps search engines interpret your site and helps readers decide whether a link is worth clicking. Over-optimized or misleading link text can confuse both.
Tip: Avoid using your main focus keyphrase in multiple anchor texts within one post, as it can create competing links. Your linking should always feel natural and avoid over-optimization.
An example of internal links with good anchor texts
Different kinds of anchor text
Anchor text applies to both internal and external links. External sites can link to your content in various ways, and each type sends a different signal to search engines:
Branded links: Use your brand name as anchor text (e.g., Yoast)
If Yoast SEO detects that one of your links contains your focus keyphrase or a synonym of it, then Premium users get a warning. The reason? You don’t want multiple pages trying to rank for the same phrase.
For example, say your focus keyphrase is potato chips. If you link to another page using that exact phrase, Yoast SEO will flag it as a competing link. You’ll see a notification in your SEO analysis, so you can adjust it before publishing. If you have Yoast SEO Premium or Yoast SEO for Shopify, the check will also look for the synonyms of your keyphrase.
The competing links check in Yoast SEO helps you improve your linking
How to improve your anchor link texts
If Yoast SEO alerts you about competing links, or if you simply want to improve the quality of your link text, here are some best practices to follow.
1. Create a natural flow
Your writing should feel effortless. If a link feels awkward or forced into a sentence, it probably doesn’t belong there. Always prioritize readability, as a smooth flow improves both engagement and SEO. For more advice on writing content that feels natural while still ranking well, read our SEO copywriting guide.
2. Match the link text to the linked content
Readers should immediately understand what to expect when they click on a link. For example, a link that says meta description should lead to a post explaining what a meta description is and how to optimize it. Clear, logical linking builds trust and helps users navigate your content with ease.
3. Don’t trick your readers
Never mislead readers with inaccurate or confusing link text. If your link text says, “potato chips,” it shouldn’t lead to a page about cars. Consistent and honest linking keeps readers engaged and signals quality to search engines.
4. Make it clear that the link is clickable
Use visual cues such as color contrast or underlining, so it’s easy to tell when text is a link. This not only improves usability but also helps people using assistive technology to navigate your content. To see more on writing accessible, well-structured posts, visit our blogging guide.
5. Bonus tip: put your entire keyphrase in quotes
When using long tail keyphrases, you might see a warning about links that include parts of your focus keyphrase. To avoid this, put your full keyphrase in quotes, for example, “learning how to knit.” This tells Yoast SEO to look for the entire phrase rather than matching individual words.
If you’d like to learn more about writing effective link text and improving your content for SEO, take our SEO copywriting course, which is included with Yoast SEO Premium.
Go Premium and get free access to our SEO courses!
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But internal links work best when you write good anchor text for them. Each link should serve a clear purpose and guide readers naturally to related topics. Avoid adding unnecessary or irrelevant links just for the sake of having more connections.
Thoughtful internal linking improves the user experience and helps search engines understand your site’s structure, which is essential for strong SEO performance.
This is anchor text
Anchor text remains a small but powerful element of SEO. It helps users decide whether to click, gives search engines valuable context, and supports a logical site structure.
Keep your anchor text relevant, natural, and transparent and avoid manipulative or over-optimized linking practices. Search engines are now smarter than ever at spotting unnatural links, especially in the era of AI and semantic understanding.
So stay genuine, link with intent, and use Yoast SEO to guide you along the way.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-20 12:00:002025-10-20 12:00:00What is anchor text, and how can you improve your link texts?
Your content has 15 seconds. That’s it. In those precious moments, your reader’s brain makes a critical decision: scan or abandon. The statistics are sobering. Users read only 20-28% of webpage content, spending an average of 15 seconds on a page before deciding whether to stay or leave. Yet many content creators still write as if their audience will consume every carefully crafted sentence from start to finish.
Key takeaways
Readers scan content in 15 seconds, favoring scannable formats like bullet points for quick comprehension.
Research shows that effective scannable content enhances cognitive processing and engages readers better.
Key factors like motivation, task type, and focus determine how deeply someone will read your content.
Mobile usage has reshaped reading habits, increasing demand for short, structured, and scannable content.
To create scannable content, writers should respect cognitive patterns and optimize content structure with clear visuals.
The reality? Your readers aren’t reading; they are scanning, which is why scannable content becomes important. This isn’t a failure of modern attention spans or a sign that people don’t value quality content. It’s neuroscience in action. The human brain has evolved sophisticated pattern recognition systems that help us quickly identify relevant information while filtering out the noise. And do you know what the most potent triggers for this system are? The humble bullet point.
When readers encounter well-structured bullet points in your blog piece, their brains release small hits of dopamine, the same neurotransmitter associated with completing tasks and achieving goals. This is a biological reward system that makes scannable content easier to process and pleasurable to consume.
Understanding the cognitive psychology behind how people process information isn’t just academic curiosity. It’s also the key to creating content that converts, engages, and serves your audience’s actual reading behaviors. Tools like Yoast’s AI Summarize feature recognize this reality, helping content creators quickly identify and restructure their essential points into the scannable formats readers crave.
The scanning habits of our brain
The myth of linear reading
If you believe your readers start at the top of your content and methodically work their way through each paragraph, you’re operating under a dangerous misconception. Eye-tracking research from the Nielsen Norman Group reveals that people don’t read online content, they scan it in predictable patterns.
F-shape scanning pattern: It is one of the most common reading patterns, where readers scan horizontally across the top, make a second horizontal scan partway down, then scan vertically down the left side.
Layer cake pattern: This includes scanning headings and subheadings.
Spotted pattern: Jumping to specific words or phrases that catch attention.
F-shape reading pattern of the brain
This isn’t laziness, it’s cognitive efficiency at its best. Our brains are wired to seek the path of least resistance when processing information. In a world where we’re bombarded with more content than we could ever consume, scanning helps us quickly identify what deserves our full attention.
Cognitive load theory explains why this happens. Our working memory can only hold about 5 to 9 pieces of information at once. When content is presented in dense paragraphs, our brains work harder to extract meaning, creating mental fatigue that leads to abandonment.
Factors that determine reading depth
Not all scanning is created equal. Four key factors determine whether someone will scan briefly or dive deeper into your content:
Level of motivation: When readers desperately need specific information, like troubleshooting a technical problem, they’ll invest more cognitive resources in careful reading. But for general browsing, they’ll skim for signals of value.
Type of task: Fact-finding missions (like researching product features) create different reading behaviors than exploratory browsing. Task-oriented readers scan for specific data points, while browsers scan for interesting concepts.
Level of focus: A reader juggling multiple browser tabs while checking their phone will scan differently than someone in a quiet environment dedicated to learning. Multitasking reduces the cognitive resources available for deep processing.
Personal characteristics: Some people are naturally deep readers who prefer narrative content, while others are chronic scanners who gravitate toward lists and summaries. Age, education, and cultural background all influence these preferences.
The impact of mobile evolution on content consumption
Smartphone usage hasn’t just changed where we consume content, it’s rewired how we process information. The average smartphone user checks their device 96 times daily, creating a constant state of partial attention that makes scanning the dominant reading mode.
Mobile screens compress information into narrow columns, overwhelming traditional paragraph structures. This physical constraint has trained our brains to prefer “thumb-friendly” content architecture: short paragraphs, frequent subheadings, and plenty of white space.
The impact transcends mobile devices. Desktop readers now expect the same scannable formats they’ve grown accustomed to on their phones. Content that doesn’t accommodate these evolved reading behaviors feels dated and inaccessible.
The psychology behind bullet points
Understanding why bullet points work so effectively requires a quick look at how your brain processes information. When you encounter a wall of text, your mind has to work overtime to extract the key points, organize the information, and remember what matters. Bullet points do this heavy lifting for you, turning complex information into digestible chunks that your brain can process with minimal effort.
1. The mental burden relief of cognitive load reduction
Bullet points aren’t just visually appealing, but also easy to scan. They’re cognitive performance enhancers. When information is presented in bullet format, our working memory can process it more efficiently because each point operates as a discrete unit.
Research in cognitive psychology shows that structured information reduces the mental effort required for comprehension. This creates what researchers call “cognitive ease”, a state where information feels more trustworthy and credible simply because it’s easier to process.
The famous 7±2 rule (also known as Miller’s Law) explains why bullet points work so well. Our working memory can comfortably hold 5-9 items at once. Well-crafted bullet lists respect this limitation by chunking information into digestible pieces that our brains can easily manipulate and remember.
When content flows smoothly through our mental processing systems, we unconsciously associate that ease with quality and authority. This is why bullet points improve comprehension and credibility.
2. Pattern recognition and predictability
Human brains are pattern-recognition machines, constantly seeking familiar structures that help us predict what will happen next. Bullet points, through their predictable format, provide precisely this kind of psychological comfort.
Visual hierarchy serves as a roadmap for our attention. When readers see a bullet list, they instantly understand the structure: each point will present a discrete piece of information, all points are roughly equivalent in importance, and the data can be consumed in any order.
Gestalt principles explain why this works so well. Our brains use proximity (related items grouped), similarity (consistent formatting signals related content), and continuation (visual flow guides attention) to organize information efficiently. Bullet points leverage all three principles simultaneously.
This predictability reduces cognitive anxiety. Readers don’t need to invest mental energy figuring out how information is organized, they can focus entirely on processing the content.
3. The psychology of completion
Perhaps the most fascinating aspect of bullet point psychology is how it triggers our brain’s reward system. Each bullet point creates a micro-task that can be “completed” simply by reading. This completion triggers a small dopamine release; the same neurotransmitter associated with crossing items off a to-do list.
The Zeigarnik effect demonstrates why this matters. Our brains create psychological tension around incomplete tasks, making them more memorable than completed ones. Bullet points cleverly exploit this by creating multiple small completion opportunities within a single piece of content.
This neurological reward system explains why people find lists inherently satisfying. We’re not just consuming information; we’re experiencing a series of small accomplishments that make reading feel productive and rewarding.
4. Visual breathing room
White space isn’t space; it’s cognitive breathing room. Dense paragraphs create visual clutter that triggers stress responses in our brains, making content feel overwhelming before we even begin reading.
Bullet points introduce strategic white space that gives our visual processing system room to operate. This breathing room prevents cognitive overload and makes content more approachable and manageable.
Eye movement research shows that readers’ gaze patterns follow predictable paths through well-spaced content. White space guides attention naturally, creating a visual rhythm that supports comprehension rather than fighting against it.
The science of information processing
Working memory and executive function
Working memory is the temporary storage system where we manipulate information while processing it. Unlike long-term memory, which has virtually unlimited capacity, working memory can only handle a few items simultaneously.
Bullet points support working memory by presenting information in pre-chunked units. Instead of extracting key points from dense paragraphs, a task that requires executive function resources, readers can directly process the distilled information.
Research comparing narrative versus expository text comprehension shows structured formats consistently outperform traditional paragraphs for information retention and comprehension speed. The brain’s executive functions can focus on understanding content rather than organizing it.
This is particularly important for complex or technical information. When cognitive resources are allocated efficiently, readers can engage with more sophisticated concepts without experiencing mental fatigue.
The discrete thought advantage
Each bullet point functions as a self-contained information unit, allowing for what cognitive scientists call “discrete processing.” Unlike paragraphs, where ideas build upon each other sequentially, bullet points can be processed independently.
This creates a “mental reset” opportunity between points. Readers can fully process one concept before moving to the next, preventing cognitive overload when multiple ideas compete for working memory space.
The difference is like comparing building a tower (paragraphs) versus collecting individual blocks (bullet points). Building requires awareness of the entire structure, while collecting allows focus on each piece.
Speed vs. comprehension
Critics often argue that scannable content sacrifices depth for speed, but research suggests a more nuanced reality. Studies show that bullet formats can improve comprehension for certain types of information while dramatically increasing processing speed.
The key matches the format of the content type. Bullet points excel for factual information, feature lists, and step-by-step processes. They’re less effective for narrative content, complex arguments, and emotional storytelling.
In research studies, retention rates for structured information consistently outperform unstructured text. The sweet spot appears to be content that balances scanning speed with information density, exactly what effective bullet points achieve.
This is where AI-powered tools like Yoast’s AI Summarize feature become invaluable. They can analyze dense content and identify the key points that would benefit from bullet formatting, helping writers optimize speed and comprehension without sacrificing essential nuances.
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Bullet points are not isolated components; they’re part of a broader ecosystem of scannable elements that work together to create user-friendly content. An effective scannable design incorporates multiple layers of visual hierarchy.
Headings and subheadings serve as navigation anchors, allowing readers to identify relevant sections quickly. They’re the highway signs of content, helping people find their destination without reading every word.
Numbers and statistics act as attention magnets, drawing the eye with their specificity and authority. Our brains are wired to notice numerical information, making stats powerful tools for engagement.
Bold text and formatting provide visual cues that guide attention to key concepts. Strategic emphasis helps readers identify the most important information without overwhelming the overall design.
White space ties everything together, preventing visual overcrowding and giving each element room to breathe. The silence between notes makes music coherent.
Choosing from Lists and other formats
Different content types call for different scannable formats. Understanding when to use each format prevents the monotony of bullet point overuse while optimizing for specific communication goals.
Bullet points: They excel for features, benefits, and key takeaways where order doesn’t matter. They’re perfect for highlighting multiple advantages or listing unranked options.
Numbered lists: These lists work best for processes, rankings, and sequential information. They provide clear progression and help readers track their position within the content.
Tables: Ideal for comparisons and data-heavy content. They allow readers to scan vertically and horizontally, facilitating quick comparisons across multiple variables.
Paragraphs: An essential storytelling instrument, context-building, and complex arguments requiring narrative development. The key is using them strategically rather than defaulting to them automatically.
The mobile-first psychology
Mobile usage hasn’t just changed screen sizes, it’s fundamentally altered how we consume content. Thumb-scrolling creates different engagement patterns than mouse-based navigation, favoring content that works with natural thumb movements.
The “thumb-friendly” hierarchy prioritizes easily tappable elements and accommodates one-handed usage. This means shorter sections, more frequent headings, and content designed for vertical scrolling rather than horizontal scanning.
Responsive design psychology goes beyond technical implementation. It requires understanding how reading behaviors change across devices and optimizing content structure for each context.
Implementing psychology-driven content
Knowing the science behind scannable content is one thing—putting it into practice is another. The good news? You don’t need a psychology degree to create content that respects how your readers’ brains work. With a few strategic adjustments to your writing process, you can transform dense, intimidating content into clear, engaging material that people actually read and act on. Here’s how to make the psychology work for you.
The content creator’s checklist
Pre-writing considerations: Analyze your audience’s attention constraints and reading context. Are they researching solutions under pressure, browsing casually, or seeking deep understanding? This determines your optimal scannable structure.
During writing: Identify natural breaking points during writing where concepts shift or new ideas emerge. These transition moments are perfect for bullet points, subheadings, or formatting changes supporting scanning behaviors.
Post-writing optimization: Simulate scanning behavior by reading only headings, first sentences, and formatted elements. Does the content still make sense and provide value? If not, restructure to serve better scanning readers.
Tools and techniques
Readability analyzers: They provide objective metrics for content accessibility, but understanding their psychological basis helps interpret results more meaningfully. High readability scores often correlate with scannable structure.
Heat mapping tools: One of the most potent tools for revealing reader attention patterns, showing where scannable elements succeed or fail. This data helps optimize formatting for real usage rather than theoretical best practices.
User testing methodologies: A one of the kind testing methods that is used for content structures and can also include card sorting exercises, first impression tests, and task-based evaluations. They reveal how well your formatting serves actual reader goals.
Respecting your reader’s brain
Understanding the psychology of scannable content isn’t about manipulating readers, but about respecting how their brains process information. Everyone wins when we create content that works with cognitive patterns rather than against them.
Readers get information they can consume efficiently without sacrificing comprehension. Content creators build trust and engagement by serving their audience’s genuine needs rather than forcing outdated consumption models.
The competitive advantage goes to those recognizing that effective content serves the reader’s brain, not the creator’s ego. Attention is the scarcest resource, so content that respects cognitive limitations while delivering genuine value will consistently outperform material that ignores psychological realities.
Ready to implement these insights with Yoast SEO? Start by auditing your existing content through a psychological lens. Look for opportunities to break up dense paragraphs, add scannable elements, and create the visual breathing room that modern readers crave. Your audience’s brains and content performance will thank you.
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http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-17 09:31:502025-10-17 09:31:50The psychology of scannable content and bullet points
