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LLM optimization in 2026: Tracking, visibility, and what’s next for AI discovery

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LLM optimization in 2026: Tracking, visibility, and what’s next for AI discovery

Marketing, technology, and business leaders today are asking an important question: how do you optimize for large language models (LLMs) like ChatGPT, Gemini, and Claude? 

LLM optimization is taking shape as a new discipline focused on how brands surface in AI-generated results and what can be measured today. 

For decision makers, the challenge is separating signal from noise – identifying the technologies worth tracking and the efforts that lead to tangible outcomes.

The discussion comes down to two core areas – and the timeline and work required to act on them:

  • Tracking and monitoring your brand’s presence in LLMs.
  • Improving visibility and performance within them.

Tracking: The foundation of LLM optimization

Just as SEO evolved through better tracking and measurement, LLM optimization will only mature once visibility becomes measurable. 

We’re still in a pre-Semrush/Moz/Ahrefs era for LLMs. 

Tracking is the foundation of identifying what truly works and building strategies that drive brand growth. 

Without it, everyone is shooting in the dark, hoping great content alone will deliver results.

The core challenges are threefold:

  • LLMs don’t publish query frequency or “search volume” equivalents.
  • Their responses vary subtly (or not so subtly) even for identical queries, due to probabilistic decoding and prompt context.
  • They depend on hidden contextual features (user history, session state, embeddings) that are opaque to external observers.

Why LLM queries are different

Traditional search behavior is repetitive – millions of identical phrases drive stable volume metrics. LLM interactions are conversational and variable. 

People rephrase questions in different ways, often within a single session. That makes pattern recognition harder with small datasets but feasible at scale. 

These structural differences explain why LLM visibility demands a different measurement model.

This variability requires a different tracking approach than traditional SEO or marketing analytics.

The leading method uses a polling-based model inspired by election forecasting.

The polling-based model for measuring visibility

A representative sample of 250–500 high-intent queries is defined for your brand or category, functioning as your population proxy. 

These queries are run daily or weekly to capture repeated samples from the underlying distribution of LLM responses.

Competitive mentions and citations metrics

Tracking tools record when your brand and competitors appear as citations (linked sources) or mentions (text references), enabling share of voice calculations across all competitors. 

Over time, aggregate sampling produces statistically stable estimates of your brand visibility within LLM-generated content.

Early tools providing this capability include:

  • Profound.
  • Conductor.
  • OpenForge.
Early tools for LLM visibility tracking

Consistent sampling at scale transforms apparent randomness into interpretable signals. 

Over time, aggregate sampling provides a stable estimate of your brand’s visibility in LLM-generated responses – much like how political polls deliver reliable forecasts despite individual variations.

Building a multi-faceted tracking framework

While share of voice paints a picture of your presence in the LLM landscape, it doesn’t tell the complete story. 

Just as keyword rankings show visibility but not clicks, LLM presence doesn’t automatically translate to user engagement. 

Brands need to understand how people interact with their content to build a compelling business case.

Because no single tool captures the entire picture, the best current approach layers multiple tracking signals:

  • Share of voice (SOV) tracking: Measure how often your brand appears as mentions and citations across a consistent set of high-value queries. This provides a benchmark to track over time and compare against competitors.
  • Referral tracking in GA4: Set up custom dimensions to identify traffic originating from LLMs. While attribution remains limited today, this data helps detect when direct referrals are increasing and signals growing LLM influence.
  • Branded homepage traffic in Google Search Console: Many users discover brands through LLM responses, then search directly in Google to validate or learn more. This two-step discovery pattern is critical to monitor. When branded homepage traffic increases alongside rising LLM presence, it signals a strong causal connection between LLM visibility and user behavior. This metric captures the downstream impact of your LLM optimization efforts.

Nobody has complete visibility into LLM impact on their business today, but these methods cover all the bases you can currently measure.

Be wary of any vendor or consultant promising complete visibility. That simply isn’t possible yet.

Understanding these limitations is just as important as implementing the tracking itself.

Because no perfect models exist yet, treat current tracking data as directional – useful for decisions, but not definitive.

Why mentions matter more than citations

Dig deeper: In GEO, brand mentions do what links alone can’t

Estimating LLM ‘search volume’

Measuring LLM impact is one thing. Identifying which queries and topics matter most is another.

Compared to SEO or PPC, marketers have far less visibility. While no direct search volume exists, new tools and methods are beginning to close the gap.

The key shift is moving from tracking individual queries – which vary widely – to analyzing broader themes and topics. 

The real question becomes: which areas is your site missing, and where should your content strategy focus?

To approximate relative volume, consider three approaches:

Correlate with SEO search volume

Start with your top-performing SEO keywords. 

If a keyword drives organic traffic and has commercial intent, similar questions are likely being asked within LLMs. Use this as your baseline.

Layer in industry adoption of AI

Estimate what percentage of your target audience uses LLMs for research or purchasing decisions:

  • High AI-adoption industries: Assume 20-25% of users leverage LLMs for decision-making.
  • Slower-moving industries: Start with 5-10%.

Apply these percentages to your existing SEO keyword volume. For example, a keyword with 25,000 monthly searches could translate to 1,250-6,250 LLM-based queries in your category.

Using emerging inferential tools

New platforms are beginning to track query data through API-level monitoring and machine learning models. 

Accuracy isn’t perfect yet, but these tools are improving quickly. Expect major advancements in inferential LLM query modeling within the next year or two.

Get the newsletter search marketers rely on.

See terms.

Optimizing for LLM visibility

The technologies that help companies identify what to improve are evolving quickly. 

While still imperfect, they’re beginning to form a framework that parallels early SEO development, where better tracking and data gradually turned intuition into science.

Optimization breaks down into two main questions:

  • What content should you create or update, and should you focus on quality content, entities, schema, FAQs, or something else?
  • How should you align these insights with broader brand and SEO strategies?

Identify what content to create or update

One of the most effective ways to assess your current position is to take a representative sample of high-intent queries that people might ask an LLM and see how your brand shows up relative to competitors. This is where the Share of Voice tracking tools we discussed earlier become invaluable.

These same tools can help answer your optimization questions:

  • Track who is being cited or mentioned for each query, revealing competitive positioning.
  • Identify which queries your competitors appear for that you don’t, highlighting content gaps.
  • Show which of your own queries you appear for and which specific assets are being cited, pinpointing what’s working.

From this data, several key insights emerge:

  • Thematic visibility gaps: By analyzing trends across many queries, you can identify where your brand underperforms in LLM responses. This paints a clear picture of areas needing attention. For example, you’re strong in SEO but not in PPC content. 
  • Third-party resource mapping: These tools also reveal which external resources LLMs reference most frequently. This helps you build a list of high-value third-party sites that contribute to visibility, guiding outreach or brand mention strategies. 
  • Blind spot identification: When cross-referenced with SEO performance, these insights highlight blind spots; topics or sources where your brand’s credibility and representation could improve.

Understand the overlap between SEO and LLM optimization

LLMs may be reshaping discovery, but SEO remains the foundation of digital visibility.

Across five competitive categories, brands ranking on Google’s first page appeared in ChatGPT answers 62% of the time – a clear but incomplete overlap between search and AI results.

That correlation isn’t accidental. 

Many retrieval-augmented generation (RAG) systems pull data from search results and expand it with additional context. 

The more often your content appears in those results, the more likely it is to be cited by LLMs.

Brands with the strongest share of voice in LLM responses are typically those that invested in SEO first. 

Strong technical health, structured data, and authority signals remain the bedrock for AI visibility.

What this means for marketers:

  • Don’t over-focus on LLMs at the expense of SEO. AI systems still rely on clean, crawlable content and strong E-E-A-T signals.
  • Keep growing organic visibility through high-authority backlinks and consistent, high-quality content.
  • Use LLM tracking as a complementary lens to understand new research behaviors, not a replacement for SEO fundamentals.

Redefine on-page and off-page strategies for LLMs

Just as SEO has both on-page and off-page elements, LLM optimization follows the same logic – but with different tactics and priorities.

Off-page: The new link building

Most industries show a consistent pattern in the types of resources LLMs cite:

  • Wikipedia is a frequent reference point, making a verified presence there valuable.
  • Reddit often appears as a trusted source of user discussion.
  • Review websites and “best-of” guides are commonly used to inform LLM outputs.

Citation patterns across ChatGPT, Gemini, Perplexity, and Google’s AI Overviews show consistent trends, though each engine favors different sources.

This means that traditional link acquisition strategies, guest posts, PR placements, or brand mentions in review content will likely evolve. 

Instead of chasing links anywhere, brands should increasingly target:

  • Pages already being cited by LLMs in their category.
  • Reviews or guides that evaluate their product category.
  • Articles where branded mentions reinforce entity associations.

The core principle holds: brands gain the most visibility by appearing in sources LLMs already trust – and identifying those sources requires consistent tracking.

On-page: What your own content reveals

The same technologies that analyze third-party mentions can also reveal which first-party assets, content on your own website, are being cited by LLMs. 

This provides valuable insight into what type of content performs well in your space.

For example, these tools can identify:

  • What types of competitor content are being cited (case studies, FAQs, research articles, etc.).
  • Where your competitors show up but you don’t.
  • Which of your own pages exist but are not being cited.

From there, three key opportunities emerge:

  • Missing content: Competitors are cited because they cover topics you haven’t addressed. This represents a content gap to fill.
  • Underperforming content: You have relevant content, but it isn’t being referenced. Optimization – improving structure, clarity, or authority – may be needed.
  • Content enhancement opportunities: Some pages only require inserting specific Q&A sections or adding better-formatted information rather than full rewrites.

Leverage emerging technologies to turn insights into action

The next major evolution in LLM optimization will likely come from tools that connect insight to action.

Early solutions already use vector embeddings of your website content to compare it against LLM queries and responses. This allows you to:

  • Detect where your coverage is weak.
  • See how well your content semantically aligns with real LLM answers.
  • Identify where small adjustments could yield large visibility gains.

Current tools mostly generate outlines or recommendations.

The next frontier is automation – systems that turn data into actionable content aligned with business goals.

Timeline and expected results

While comprehensive LLM visibility typically builds over 6-12 months, early results can emerge faster than traditional SEO. 

The advantage: LLMs can incorporate new content within days rather than waiting months for Google’s crawl and ranking cycles. 

However, the fundamentals remain unchanged.

Quality content creation, securing third-party mentions, and building authority still require sustained effort and resources. 

Think of LLM optimization as having a faster feedback loop than SEO, but requiring the same strategic commitment to content excellence and relationship building that has always driven digital visibility.

From SEO foundations to LLM visibility

LLM traffic remains small compared to traditional search, but it’s growing fast.

A major shift in resources would be premature, but ignoring LLMs would be shortsighted. 

The smartest path is balance: maintain focus on SEO while layering in LLM strategies that address new ranking mechanisms.

Like early SEO, LLM optimization is still imperfect and experimental – but full of opportunity. 

Brands that begin tracking citations, analyzing third-party mentions, and aligning SEO with LLM visibility now will gain a measurable advantage as these systems mature.

In short:

  • Identify the third-party sources most often cited in your niche and analyze patterns across AI engines.
  • Map competitor visibility for key LLM queries using tracking tools.
  • Audit which of your own pages are cited (or not) – high Google rankings don’t guarantee LLM inclusion.
  • Continue strong SEO practices while expanding into LLM tracking – the two work best as complementary layers.

Approach LLM optimization as both research and brand-building.

Don’t abandon proven SEO fundamentals. Rather, extend them to how AI systems discover, interpret, and cite information.

Read more at Read More

How to balance speed and credibility in AI-assisted content creation

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How to balance speed and credibility in AI-assisted content creation

AI tools can help teams move faster than ever – but speed alone isn’t a strategy.

As more marketers rely on LLMs to help create and optimize content, credibility becomes the true differentiator. 

And as AI systems decide which information to trust, quality signals like accuracy, expertise, and authority matter more than ever.

It’s not just what you write but how you structure it. AI-driven search rewards clear answers, strong organization, and content it can easily interpret.

This article highlights key strategies for smarter AI workflows – from governance and training to editorial oversight – so your content remains accurate, authoritative, and unmistakably human.

Create an AI usage policy

More than half of marketers are using AI for creative endeavors like content creation, IAB reports.

Still, AI policies are not always the norm. 

Your organization will benefit from clear boundaries and expectations. Creating policies for AI use ensures consistency and accountability.

Only 7% of companies using genAI in marketing have a full-blown governance framework, according to SAS.

However, 63% invest in creating policies that govern how generative AI is used across the organization. 

Source- “Marketers and GenAI- Diving Into the Shallow End,” SAS
Source- “Marketers and GenAI- Diving Into the Shallow End,” SAS

Even a simple, one-page policy can prevent major mistakes and unify efforts across teams that may be doing things differently.

As Cathy McPhillips, chief growth officer at the Marketing Artificial Intelligence Institute, puts it: 

  • “If one team uses ChatGPT while others work with Jasper or Writer, for instance, governance decisions can become very fragmented and challenging to manage. You’d need to keep track of who’s using which tools, what data they’re inputting, and what guidance they’ll need to follow to protect your brand’s intellectual property.” 

So drafting an internal policy sets expectations for AI use in the organization (or at least the creative teams).

When creating a policy, consider the following guidelines: 

  • What the review process for AI-created content looks like. 
  • When and how to disclose AI involvement in content creation. 
  • How to protect proprietary information (not uploading confidential or client information into AI tools).
  • Which AI tools are approved for use, and how to request access to new ones.
  • How to log or report problems.

Logically, the policy will evolve as the technology and regulations change. 

Keep content anchored in people-first principles

It can be easy to fall into the trap of believing AI-generated content is good because it reads well. 

LLMs are great at predicting the next best sentence and making it sound convincing. 

But reviewing each sentence, paragraph, and the overall structure with a critical eye is absolutely necessary.

Think: Would an expert say it like that? Would you normally write like that? Does it offer the depth of human experience that it should?

“People-first content,” as Google puts it, is really just thinking about the end user and whether what you are putting into the world is adding value. 

Any LLM can create mediocre content, and any marketer can publish it. And that’s the problem. 

People-first content aligns with Google’s E-E-A-T framework, which outlines the characteristics of high-quality, trustworthy content.

E-E-A-T isn’t a novel idea, but it’s increasingly relevant in a world where AI systems need to determine if your content is good enough to be included in search.

According to evidence in U.S. v. Google LLC, we see quality remains central to ranking:

  • “RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data: [redacted]% of 70 days of search logs plus scores generated by human raters and used by Google to measure the quality of organic search results.” 
Source: U.S. v. Google LLC court documentation
Source: U.S. v. Google LLC court documentation

It suggests that the same quality factors reflected in E-E-A-T likely influence how AI systems assess which pages are trustworthy enough to ground their answers.

So what does E-E-A-T look like practically when working with AI content? You can:

  • Review Google’s list of questions related to quality content: Keep these in mind before and after content creation.
  • Demonstrate firsthand experience through personal insights, examples, and practical guidance: Weave these insights into AI output to add a human touch.
  • Use reliable sources and data to substantiate claims: If you’re using LLMs for research, fact-check in real time to ensure the best sources. 
  • Insert authoritative quotes either from internal stakeholders or external subject matter experts: Quoting internal folks builds brand credibility while external sources lend authority to the piece.
  • Create detailed author bios: Include:
    • Relevant qualifications, certifications, awards, and experience.
    • Links to social media, academic papers (if relevant), or other authoritative works.
  • Add schema markup to articles to clarify the content further: Schema can clarify content in a way that AI-powered search can better understand.
  • Become the go-to resource on the topic: Create a depth and breadth of material on the website that’s organized in a search-friendly, user-friendly manner. You can learn more in my article on organizing content for AI search.
Source: Creating helpful, reliable, people-first content,” Google Search Central
Source: Creating helpful, reliable, people-first content,” Google Search Central

Dig deeper: Writing people-first content: A process and template

Train the LLM 

LLMs are trained on vast amounts of data – but they’re not trained on your data. 

Put in the work to train the LLM, and you can get better results and more efficient workflows. 

Here are some ideas.

Maintain a living style guide

If you already have a corporate style guide, great – you can use that to train the model. If not, create a simple one-pager that covers things like:

  • Audience personas.
  • Voice traits that matter.
  • Reading level, if applicable.
  • The do’s and don’ts of phrases and language to use. 
  • Formatting rules such as SEO-friendly headers, sentence length, paragraph length, bulleted list guidelines, etc. 

You can refresh this as needed and use it to further train the model over time. 

Build a prompt kit  

Put together a packet of instructions that prompts the LLM. Here are some ideas to start with: 

  • The style guide
    • This covers everything from the audience personas to the voice style and formatting.
    • If you’re training a custom GPT, you don’t need to do this every time, but it may need tweaking over time. 
  • A content brief template
    • This can be an editable document that’s filled in for each content project and includes things like:
      • The goal of the content.
      • The specific audience.
      • The style of the content (news, listicle, feature article, how-to).
      • The role (who the LLM is writing as).
      • The desired action or outcome.
  • Content examples
    • Upload a handful of the best content examples you have to train the LLM. This can be past articles, marketing materials, transcripts from videos, and more. 
    • If you create a custom GPT, you’ll do this at the outset, but additional examples of content may be uploaded, depending on the topic. 
  • Sources
    • Train the model on the preferred third-party sources of information you want it to pull from, in addition to its own research. 
    • For example, if you want it to source certain publications in your industry, compile a list and upload it to the prompt.  
    • As an additional layer, prompt the model to automatically include any third-party sources after every paragraph to make fact-checking easier on the fly.
  • SEO prompts
    • Consider building SEO into the structure of the content from the outset.  
    • Early observations of Google’s AI Mode suggest that clearly structured, well-sourced content is more likely to be referenced in AI-generated results.

With that in mind, you can put together a prompt checklist that includes:

  • Crafting a direct answer in the first one to two sentences, then expanding with context.
  • Covering the main question, but also potential subquestions (“fan-out” queries) that the system may generate (for example, questions related to comparisons, pros/cons, alternatives, etc.).
  • Chunking content into many subsections, with each subsection answering a potential fan-out query to completion.
  • Being an expert source of information in each individual section of the page, meaning it’s a passage that can stand on its own.
  • Provide clear citations and semantic richness (synonyms, related entities) throughout. 

Dig deeper: Advanced AI prompt engineering strategies for SEO

Create custom GPTs or explore RAG 

A custom GPT is a personalized version of ChatGPT that’s trained on your materials so it can better create in your brand voice and follow brand rules. 

It mostly remembers tone and format, but that doesn’t guarantee the accuracy of output beyond what’s uploaded.

Some companies are exploring RAG (retrieval-augmented generation) to further train LLMs on the company’s own knowledge base. 

RAG connects an LLM to a private knowledge base, retrieving relevant documents at query time so the model can ground its responses in approved information.

While custom GPTs are easy, no-code setups, RAG implementation is more technical – but there are companies/technologies out there that can make it easier to implement. 

That’s why GPTs tend to work best for small or medium-scale projects or for non-technical teams focused on maintaining brand consistency.

Create a custom GPT in ChatGPT
Create a custom GPT in ChatGPT

RAG, on the other hand, is an option for enterprise-level content generation in industries where accuracy is critical and information changes frequently.

Run an automated self-review

Create parameters so the model can self-assess the content before further editorial review. You can create a checklist of things to prompt it.

For example:

  • “Is the advice helpful, original, people-first?” (Perhaps using Google’s list of questions from its helpful content guidance.) 
  • “Is the tone and voice completely aligned with the style guide?” 

Have an established editing process 

Even the best AI workflow still depends on trained editors and fact-checkers. This human layer of quality assurance protects accuracy, tone, and credibility.

Editorial training

About 33% of content writers and 24% of marketing managers added AI skills to their LinkedIn profiles in 2024.

Writers and editors need to continue to upskill in the coming year, and, according to the Microsoft 2025 annual Work Trend Index, AI skilling is the top priority.  

Microsoft 2025 Annual Work Trend Index
Source: 2025 Microsoft Work Trend Index Annual Report

Professional training creates baseline knowledge so your team gets up to speed faster and can confidently handle outputs consistently.

This includes training on how to effectively use LLMs and how to best create and edit AI content.

In addition, training content teams on SEO helps them build best practices into prompts and drafts.

Editorial procedures

Ground your AI-assisted content creation in editorial best practices to ensure the highest quality. 

This might include:

  • Identifying the parts of the content creation workflow that are best suited for LLM assistance.
  • Conducting an editorial meeting to sign off on topics and outlines. 
  • Drafting the content.
  • Performing the structural edit for clarity and flow, then copyediting for grammar and punctuation.
  • Getting sign-off from stakeholders.  
AI editorial process
AI editorial process

The AI editing checklist

Build a checklist to use during the review process for quality assurance. Here are some ideas to get you started:

  • Every claim, statistic, quote, or date is accompanied by a citation for fact-checking accuracy.
  • All facts are traceable to credible, approved sources.
  • Outdated statistics (more than two years) are replaced with fresh insights. 
  • Draft meets the style guide’s voice guidelines and tone definitions. 
  • Content adds valuable, expert insights rather than being vague or generic.
  • For thought leadership, ensure the author’s perspective is woven throughout.
  • Draft is run through the AI detector, aiming for a conservative percentage of 5% or less AI. 
  • Draft aligns with brand values and meets internal publication standards.
  • Final draft includes explicit disclosure of AI involvement when required (client-facing/regulatory).

Grounding AI content in trust and intent

AI is transforming how we create, but it doesn’t change why we create.

Every policy, workflow, and prompt should ultimately support one mission: to deliver accurate, helpful, and human-centered content that strengthens your brand’s authority and improves your visibility in search. 

Dig deeper: An AI-assisted content process that outperforms human-only copy

Read more at Read More

Structured data with schema for search and AI

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Structured data helps search engines, Large Language Models (LLMs), AI assistants, and other tools understand your website. Using Schema.org and JSON-LD, you make your content clearer and easier to use across platforms. This guide explains what structured data is, why it matters today, and how you can set it up the right way.

Table of contents

Key takeaways

  • Structured data helps search engines and AI better understand your website, enhancing visibility and eligibility for rich results.
  • Using Schema.org and JSON-LD improves content clarity and connects different pieces of information graphically.
  • Implementing structured data today prepares your content for future technologies and AI applications.
  • Yoast SEO simplifies structured data implementation by automatically generating schema for various content types.
  • Focus on key elements like business details and products to maximize the impact of your structured data.

What is structured data?

Structured data is a way to tell computers exactly what’s on your web page. Using a standard set of tags from Schema.org, you can identify important details, like whether a page is about a product, a review, an article, an event, or something else.

This structured format helps search engines, AI assistants, LLMs, and other tools understand your content quickly and accurately. As a result, your site may qualify for special features in search results and can be recognized more easily by digital assistants or new AI applications.

Structured data is written in code, with JSON-LD being the most common format. Adding it to your pages gives your content a better chance to be found and understood, both now and as new technologies develop.

Read more: Schema, and why you need Yoast SEO to do it right »

A simple example of structured data

Below is a simple example of structured data using Schema.org in JSON-LD format. This is a basic schema for a product with review properties. This code tells search engines that the page is a product (Product). It provides the name and description of the product, pricing information, the URL, plus product ratings and reviews. This allows search engines to understand your products and present your content in search results.

<!DOCTYPE html>
<html lang="en">
<head>
    <title>Product Title</title>
    <meta name="description" content="Brief description of the product">
    <script type="application/ld+json">
    {
      "@context": "https://schema.org",
      "@type": "Product",
      "name": "Sample Product",
      "image": "https://www.example.com/product-image.jpg",
      "description": "Product description",
      "brand": {
        "@type": "Brand",
        "name": "Brand Name"
      },
      "sku": "12345",
      "offers": {
        "@type": "Offer",
        "url": "https://www.example.com/product-page",
        "priceCurrency": "USD",
        "price": "99.99",
        "availability": "https://schema.org/InStock"
      },
      "aggregateRating": {
        "@type": "AggregateRating",
        "ratingValue": "4.5",
        "reviewCount": "11"
      },
      "review": [{
        "@type": "Review",
        "reviewRating": {
          "@type": "Rating",
          "ratingValue": "4",
          "bestRating": "5"
        },
        "author": {
          "@type": "Person",
          "name": "Jane Smith"
        },
        "reviewBody": "Review text goes here"
      }]
    }
    </script>
</head>
<body>
    <!-- Your webpage content goes here -->
</body>
</html>

Why do you need structured data?

Structured data gives computers a clear map of what’s on your website. It spells out details about your products, reviews, events, and much more in a format that’s easy for search engines and other systems to process.

This clarity leads to better visibility in search, including features like star ratings, images, or additional links. But the impact reaches further now. Structured data also helps AI assistants, voice search tools, and new web platforms like chatbots powered by Large Language Models understand and represent your content with greater accuracy.

New standards, such as NLWeb (Natural Language Web) and MCP (Model Context Protocol), are emerging to help different systems share and interpret web content consistently. Adding structured data today not only gives your site an advantage in search but also prepares it for a future where your content will flow across more platforms and digital experiences.

The effort you put into structured data now sets up your content to be found, used, and displayed in many places where people search and explore online.

Is structured data important for SEO?

Structured data plays a key role in how your website appears in search results. It helps search engines understand and present your content with extra features, such as review stars, images, and additional links. These enhanced listings can catch attention and drive more clicks to your site.

While using structured data doesn’t directly increase your rankings, it does make your site eligible for these rich results. That alone can set you apart from competitors. As search engines evolve and adopt new standards, well-structured data ensures your content stays visible and accessible in the latest search features.

For SEO, structured data is about making your site stand out, improving user experience, and giving your content the best shot at being discovered, both now and as search technology changes.

Structured data can lead to rich results

By describing your site for search engines, you allow them to do exciting things with your content. Schema.org and its support are constantly developing, improving, and expanding. As structured data forms the basis for many new developments in the SEO world, there will be more shortly. Below is an overview of the rich search results available; examples are in Google’s Search Gallery.

Structured data type Example use/description
Article News, blog, or sports article
Breadcrumb Navigation showing page position
Carousel Gallery/list from one site (with Recipe, Course, Movie, Restaurant)
Course list Lists of educational courses
Dataset Large datasets (Google Dataset Search)
Discussion forum User-generated forum content
Education Q&A Education flashcard Q&As
Employer aggregate rating Ratings about employers in job search results
Event Concerts, festivals, and other events
FAQ Frequently asked questions pages
Image metadata Image creator, credit, and license details
Job posting Listings for job openings
Local business Business details: hours, directions, ratings
Math solver Structured data for math problems
Movie Lists of movies, movie details
Organization About your company: name, logo, contact, etc.
Practice problem Education practice problems for students
Product Product listings with price, reviews, and more
Profile page Info on a single person or organization
Q&A Pages with a single question and answers
Recipe Cooking recipes, steps, and ingredients
Review snippet Short review/rating summaries
Software app Ratings and details on apps or software
Speakable Content for text-to-speech on Google Assistant
Subscription and paywalled content Mark articles/content behind a paywall
Vacation rental Details about vacation property listings
Video Video info, segments, and live content

The rich results formerly known as rich snippets

You might have heard the term “rich snippets” before. Google now calls these enhancements “rich results.” Rich results are improved search listings that use structured data to show extra information, like images, reviews, product details, or FAQs, directly in search.

For example, a product page marked up with structured data can show its price, whether it’s in stock, and customer ratings right below the search listing, even before someone clicks. Here’s what that might look like:

Some listings offer extra information, like star ratings or product details

With rich results, users see helpful details up front—such as a product’s price, star ratings, or stock status. This can make your listing stand out and attract more clicks.

Keep in mind, valid structured data increases your chances of getting rich results, but display is controlled by Google’s systems and is never guaranteed.

Keep reading: Rich snippets everywhere »

Mobile rich results

Tasty, right?

Results like this often appear more prominently on mobile devices. Search listings with structured data can display key information, like product prices, ratings, recipes, or booking options, in a mobile-friendly format. Carousels, images, and quick actions are designed for tapping and swiping with your finger.

For example, searching for a recipe on your phone might bring up a swipeable carousel showing photos, cooking times, and ratings for each dish. Product searches can highlight prices, availability, and reviews right in the results, helping users make decisions faster.

Many people now use mobile search as their default search method. Well-implemented structured data not only improves your visibility on mobile but can also make your content easier for users to explore and act on from their phones. To stay visible and competitive, regularly check your markup and make sure it works smoothly on mobile devices.

Knowledge Graph Panel

A knowledge panel

The Knowledge Graph Panel shows key facts about businesses, organizations, or people beside search results on desktop and at the top on mobile. It can include your logo, business description, location, contact details, and social profiles.

Using structured data, especially Organization, LocalBusiness, or Person markup with current details, helps Google recognize and display your entity accurately. Include recommended fields like your official name, logo, social links (using sameAs), and contact info.

Entity verification is becoming more important. Claim your Knowledge Panel through Google, and make sure your information is consistent across your website, social media, and trusted directories. Major search engines and AI assistants use this entity data for results, summaries, and answers, not just in search but also in AI-powered interfaces and smart devices.

While Google decides who appears in the Knowledge Panel and what details are shown, reliable structured data, verified identity, and a clear online presence give you the best chance of being featured.

Different kinds of structured data

Schema.org includes many types of structured data. You don’t need to use them all, just focus on what matches your site’s content. For example:

  • If you sell products, use product schema
  • For restaurant or local business sites, use local business schema
  • Recipe sites should add recipe schema

Before adding structured data, decide which parts of your site you want to highlight. Check Google’s or other search engines’ documentation to see which types are supported and what details they require. This helps ensure you are using the markup that will actually make your content stand out in search and other platforms.

How Yoast SEO helps with structured data

Yoast SEO automatically adds structured data to your site using smart defaults, making it easier for search engines and platforms to understand your content. The plugin supports a wide range of content types, like articles, products, local businesses, and FAQs, without the need for manual schema coding.

With Yoast SEO, you can:

  • With a few clicks, set the right content type for each page (such as ContactPage, Product, or Article)
  • Use built-in WordPress blocks for FAQs and How-tos, which generate valid schema automatically
  • Link related entities across your site, such as authors, brands, and organizations, to help search engines see the big picture
  • Adjust schema details per page or post through the plugin’s settings

Yoast SEO also offers an extensible structured data platform. Developers can build on top of Yoast’s schema framework, add custom schema types, or connect other plugins. This helps advanced users or larger sites tailor their structured data for specific content, integrations, or new standards.

Yoast keeps pace with updates to structured data guidelines, so your markup stays aligned with what Google and other platforms support. This makes it easier to earn rich results and other search enhancements.

Yoast SEO helps you fine-tune your schema structured data settings per page

Which structured data types matter most?

When adding structured data, focus first on the types that have the biggest impact on visibility and features in Google Search. These forms of schema are widely supported, trigger rich results, and apply to most kinds of sites:

Most important structured data types

  • Article: For news sites, blogs, and sports publishers. Adding Article schema can enable rich results like Top Stories, article carousels, and visual enhancements
  • Product: Essential for ecommerce. Product schema helps show price, stock status, ratings, and reviews right in search. This type is key for online stores and retailers
  • Event: For concerts, webinars, exhibitions, or any scheduled events. Event schema can display dates, times, and locations directly in search results, making it easier for people to find and attend
  • Recipe: This is for food blogs and cooking sites. The recipe schema supports images, cooking times, ratings, and step-by-step instructions as rich results, giving your recipes extra prominence in search
  • FAQPage: For any page with frequently asked questions. This markup can expand your search listing with Q&A drop-downs, helping users get answers fast
  • QAPage: For online communities, forums, or support sites. QAPage schema helps surface full question-and-answer threads in search
  • ReviewSnippet: This markup is for feedback on products, books, businesses, or services. It can display star ratings and short excerpts, adding trust signals to your listings
  • LocalBusiness is vital for local shops, restaurants, and service providers. It supplies address, hours, and contact info, supporting your visibility in the map pack and Knowledge Panel
  • Organization: Use this to describe your brand or company with a logo, contact details, and social profiles. Organization schema feeds into Google’s Knowledge Panel and builds your online presence
  • Video: Mark up video content to enable video previews, structured timestamps (key moments), and improved video visibility
  • Breadcrumb: This feature shows your site’s structure within Google’s results, making navigation easier and your site look more reputable

Other valuable or sector-specific types:

  • Course: Highlight educational course listings and details for training providers or schools
  • JobPosting: Share open roles in job boards or company careers pages, making jobs discoverable in Google’s job search features
  • SoftwareApp: For software and app details, including ratings and download links
  • Movie: Used for movies and film listings, supporting carousels in entertainment searches and extra movie details
  • Dataset: Makes large sets of research or open data discoverable in Google Dataset Search
  • DiscussionForum: Surfaces user-generated threads in dedicated “Forums” search features
  • ProfilePage: Used for pages focused on an individual (author profiles, biographies) or organization
  • EmployerAggregateRating: Displays company ratings and reviews in job search results
  • PracticeProblem: For educational sites offering practice questions or test prep
  • VacationRental: Displays vacation property listings and details in travel results

Special or supporting types:

  • Person: This helps Google recognize and understand individual people for entity and Knowledge Panel purposes (it does not create a direct rich result)
  • Book: Can improve book search features, usually through review or product snippets
  • Speakable: Reserved for news sites and voice assistant features; limited support
  • Image metadata, Math Solver, Subscription/Paywalled content: Niche markups that help Google properly display, credit, or flag special content
  • Carousel: Used in combination with other types (like Recipe or Movie) to display a list or gallery format in results

When choosing which schema to add, always select types that match your site’s actual content. Refer to Google’s Search Gallery for the latest guidance and requirements for each type.

Adding the right structured data makes your pages eligible for rich results, enhances your visibility, and prepares your content for the next generation of search features and AI-powered platforms.

Read on: Local business listings with Schema.org and JSON-LD »

Structured data for voice assistants

Voice search remains important, with a significant share of online queries now coming from voice-enabled devices. Structured data helps content be understood and, in some cases, selected as an answer for voice results.

The Speakable schema (for marking up sections meant to be read aloud by voice assistants) is still officially supported, but adoption is mostly limited to news content. Google and other assistants also use a broader mix of signals, like content clarity, authority, E-E-A-T, and traditional structured data, to power their spoken answers.

If you publish news or regularly answer concise, fact-based questions, consider using Speakable markup. For other content types, focus on structured data and well-organized, user-focused pages to improve your chances of being chosen by voice assistants. Voice search and voice assistants continue to draw on featured snippets, clear Q&A, and trusted sources.

Google Search Console

If you need to check how your structured data is performing in Google, check your Search Console. Find the structured data insights under the Enhancement tab and you’ll see all the pages that have structured data, plus an overview of pages that give errors, if any. Read our Beginner’s guide for Search Console for more info.

The technical details

Structured data uses Schema.org’s hierarchy. This vocabulary starts with broad types like Thing and narrows down to specific ones, such as Product, Movie, or LocalBusiness. Every type has its own properties, and more specific types inherit from their ancestors. For example, a Movie is a type of CreativeWork, which is a type of Thing.

When adding structured data, select the most specific type that fits your content. For a movie, this means using the Movie schema. For a local company, choose the type of business that best matches your offering under LocalBusiness.

Properties

Every Schema.org type includes a range of properties. While you can add many details, focus on the properties that Google or other search engines require or recommend for rich results. For example, a LocalBusiness should include your name, address, phone number, and, if possible, details such as opening hours, geo-coordinates, website, and reviews. You’ll find our Local SEO plugin (available in Yoast SEO Premium) very helpful if you need help with your local business markup.

Here are two examples of structures:

Movie hierarchy

  • Thing
  • CreativeWork
    • Movie
    • Properties: name, description, director, actor, image, genre, duration

Local business hierarchy

  • Thing
  • Organization/Place
    • LocalBusiness
    • Properties: name, address, phone, email, openingHours, geo, review, logo

The more complete and accurate your markup, the greater your chances of being displayed with enhanced features like Knowledge Panels or map results. For details on recommended properties, always check Google’s up-to-date structured data documentation.

In the local business example, you’ll see that Google lists several required properties, like your business’s NAP (Name and Phone) details. There are also recommended properties, like URLs, geo-coordinates, opening hours, etc. Try to fill out as many of these as possible because search engines will only give you the whole presentation you want.

Structured data should be a graph

When you add structured data to your site, you’re not just identifying individual items, but you’re building a data graph. A graph in this context is a web of connections between all the different elements on your site, such as articles, authors, organizations, products, and events. Each entity is linked to others with clear relationships. For instance, an article can be marked as written by a certain author, published by your organization, and referencing a specific product. These connections help search engines and AI systems see the bigger picture of how everything on your site fits together.

Creating a fully connected data graph removes ambiguity. It allows search engines to understand exactly who created content, what brand a product belongs to, or where and when an event takes place, rather than making assumptions based on scattered information. This detailed understanding increases the chances that your site will qualify for rich results, Knowledge Panels, and other enhanced features in search. As your website grows, a well-connected graph also makes it easier to add new content or expand into new areas, since everything slots into place in a way that search engines can quickly process and understand.

Yoast SEO builds a graph

With Yoast SEO, many of the key connections are generated automatically, giving your site a solid foundation. Still, understanding the importance of building a connected data graph helps you make better decisions when structuring your own content or customizing advanced schema. A thoughtful, well-linked graph sets your site up for today’s search features, while making it more adaptable for the future.

Your schema should be a well-formed graph for easier understanding by search engines and AI

Beyond search: AI, assistants, and interoperability

Structured data isn’t just about search results. It’s a map that helps AI assistants, knowledge graphs, and cross‑platform apps understand your content. It’s not just about showing a richer listing; it’s about enabling reliable AI interpretation and reuse across contexts.

Today, the primary payoff is still better search experiences. Tomorrow, AI systems and interoperable platforms will rely on clean, well‑defined data to summarize, reason about, and reuse your content. That shift makes data quality more important than ever.

Practical steps for today

Keep your structured data clean with a few simple habits. Use the same names for people, organizations, and products every time they appear across your site. Connect related information so search engines can see the links. For example, tie each article to its author or a product to its brand. Fill in all the key details for your main schema types and make sure nothing is missing. After making changes or adding new content, run your markup through a validation tool. If you add any custom fields or special schema, write down what they do so others can follow along later. Doing quick checks now and then keeps your data accurate and ready for both search engines and AI.

Interoperability, MCP, and the role of structured data

More and more, AI systems and search tools are looking for websites that are easy to understand, not just for people but also for machines. The Model Context Protocol (MCP) is gaining ground as a way for language models like Google Gemini and ChatGPT to use the structured data already present on your website. MCP draws on formats like Schema.org and JSON-LD to help AI match up the connections between things such as products, authors, and organizations.

Another project, the Natural Language Web (NLWeb), an open project developed by Microsoft, aims to make web content easier for AI to use in conversation and summaries. NLWeb builds on concepts like MCP, but hasn’t become a standard yet. For now, most progress and adoption are happening with MCP, and large language models are focusing their efforts on this area.

Using Schema.org and JSON-LD to keep your structured data clean (no duplicate entities), complete (all indexable content included), and connected (relationships preserved) will prepare you for search engines and new AI-driven features appearing across the web.

Schema.org and JSON-LD: the foundation you can trust

Schema.org and JSON-LD remain the foundation for structured data on the web. They enable today’s rich results in search and form the basis for how AI systems will interpret web content in the future. JSON-LD should be your default format for new markup, allowing you to build structured data graphs that are clean, accurate, and easy to maintain. Focus on accuracy in your markup rather than unnecessary complexity.

To future-proof your data, prioritize stable identifiers such as @id and use clear types to reduce ambiguity. Maintain strong connections between related entities across your pages. If you develop custom extensions to your structured data, document them thoroughly so both your team and automated tools can understand their purpose.

Design your schema so that components can be added or removed without disrupting the entire graph. Make a habit of running validations and audits after you change your site’s structure or content.

Finally, stay current by following guidance and news from official sources, including updates about standards such as NLWeb and MCP, to ensure your site remains compatible with both current search features and new interoperability initiatives.

What do you need to describe for search engines?

To get the most value from structured data, focus first on the most important elements of your site. Describe the details that matter most for users and for search, such as your business information, your main products or services, reviews, events, or original articles. These core pieces of information are what search engines look for to understand your site and display enhanced results.

Rather than trying to mark up everything, start with the essentials that best match your content. As your experience grows, you can build on this foundation by adding more detail and creating links between related entities. Accurate, well-prioritized markup is both easier to maintain and more effective in helping your site stand out in search results and across new AI-driven features.

How to implement structured data

We’d like to remind you that Yoast SEO comes with an excellent structured data implementation. It’ll automatically handle most sites’ most pressing structured data needs. Of course, as mentioned below, you can extend our structured data framework as your needs become bigger.

Do the Yoast SEO configuration and get your site’s structured data set up in a few clicks! The configuration is available for all Yoast SEO users to help you get your plugin configured correctly. It’s quick, it’s easy, and doing it will pay off. Plus, if you’re using the new block editor in WordPress you can also add structured data to your FAQ pages and how-to articles using our structured data content blocks.

Thanks to JSON-LD, there’s nothing scary about adding the data to your pages anymore. This JavaScript-based data format makes it much easier to add structured data since it forms a block of code and is no longer embedded in the HTML of your page. This makes it easier to write and maintain, plus both humans and machines better understand it. If you need help implementing JSON-LD structured data, you can enroll in our free Structured Data for Beginners course, our Understanding Structured Data course, or read Google’s introduction to structured data.

Structured data with JSON-LD

JSON-LD is the recommended way to add structured data to your site. All major search engines, including Google and Bing, now fully support this format. JSON-LD is easy to implement and maintain, as it keeps your structured data separate from the main HTML.

Yoast SEO automatically creates a structured data graph for every page, connecting key elements like articles, authors, products, and organizations. This approach helps search engines and AI systems understand your site’s structure. Our developer resources include detailed Schema documentation and example graphs, making it straightforward to extend or customize your markup as your site grows.

Tools for working with structured data

Yoast SEO automatically handles much of the structured data in the background. You could extend our Schema framework, of course — see the next chapter –, but if adding code by hand seems scary, you could try some of the tools listed below. If you need help with how to proceed, ask your web developer for help. They will fix this for you in a couple of minutes.

The Yoast SEO Schema structured data framework

Implementing structured data has always been challenging. Also, the results of most of those implementations often needed improvement. At Yoast, we set out to enhance the Schema output for millions of sites. For this, we built a Schema framework, which can be adapted and extended by anyone. We combined all those loose bits and pieces of structured data that appear on many sites, improved these, and put them in a graph. By interconnecting all these bits, we offer search engines all your connections on a silver platter.

See this video for more background on the schema graph.

Of course, there’s a lot more to it. We can also extend Yoast SEO output by adding specific Schema pieces, like how-tos or FAQs. We built structured data content blocks for use in the WordPress block editor. We’ve also enabled other WordPress plugins to integrate with our structured data framework, like Easy Digital Downloads, The Events Calendar, Seriously Simple Podcasting, and WP Recipe Maker, with more to come. Together, these help you remove barriers for search engines and users, as it has always been challenging to work with structured data.

Expanding your structured data implementation

A structured and focused approach is key to successful Schema.org markup on your website. Start by understanding Schema.org and how structured data can influence your site’s presence in search and beyond. Resources like Yoast’s developer portal offer useful insights into building flexible and future-proof markup.

Always use JSON-LD as recommended by Google, Bing, and Yoast. This format is easy to maintain and works well with modern websites. To maximize your implementation, use tools and frameworks that allow you to add, customize, and connect Schema.org data efficiently. Yoast SEO’s structured data framework, for example, enables seamless schema integration and extensibility across your site.

Validate your structured data regularly with tools like the Rich Results Test or Schema Markup Validator and monitor Google Search Console’s Enhancements reports for live feedback. Reviewing your markup helps you fix issues early and spot opportunities for richer results as search guidelines change. Periodically revisiting your strategy keeps your markup accurate and effective as new types and standards emerge.

Read up

By following the guidelines and adopting a comprehensive approach, you can successfully get structured data on your pages and enhance the effectiveness of your schema.org markup implementation for a robust SEO performance. Read the Yoast SEO Schema documentation to learn how Yoast SEO works with structured data, how you can extend it via an API, and how you can integrate it into your work.

Several WordPress plugins already integrate their structured data into the Yoast SEO graph

Keep on reading: Open-source software, open Schema protocol! »

Conclusions about structured data

Structured data has become an essential part of building a visible, findable, and adaptable website. Using Schema.org and JSON-LD not only helps search engines understand your content but also sets your site up for better performance in new AI-driven features, rich results, and across platforms.

Start by focusing on the most important parts of your site, like business information, products, articles, or events, and grow your structured data as your needs evolve. Connected, well-maintained markup now prepares your site for search, AI, and whatever comes next in digital content.

Explore our documentation and training resources to learn more about best practices, advanced integrations, or how Yoast SEO can simplify structured data. Investing the time in good markup today will help your content stand out wherever people (or algorithms) find it.

Read more: How to check the performance of your rich results in Google Search Console »

The post Structured data with schema for search and AI appeared first on Yoast.

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Build Brand Awareness: Strategies to Boost Visibility

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If your target audience doesn’t know you exist, they won’t buy from you. Simple as that.

That’s why you need to build brand awareness the right way. Not just through paid ads or ranking for keywords. Real brand awareness is how people remember you, talk about you, and choose you when they’re ready to buy. 

Here’s something most marketers miss: AI tools like ChatGPT and Google’s AI Overviews are now major discovery channels. These platforms cite recognizable brands more than unknown ones. If your brand isn’t mentioned across the web, you’re invisible in AI search results too. 

This guide focuses on organic growth. We’ll cover consistent messaging, smart partnerships, and making the most of platforms you already use. If you want to show up, stand out, and stick in people’s minds, here’s how to do it.

Key Takeaways

  • Brand awareness drives visibility in both traditional search and AI-powered searches
  • Consistent branding across platforms builds familiarity faster than sporadic campaigns. 
  • Thought leadership and strategic partnerships amplify reach without ad spend. 
  • You can build strong brand awareness organically with a focused, persistent plan.

Why Brand Awareness Matters More Now Than Ever

Familiarity breeds trust. The more people recognize your brand through brand mentions, the more likely they are to choose you over competitors.

Studies back this up. According to Invesp, 59% of customers prefer to buy from brands familiar to them. The more people recognize your brand, the more likely they are to choose you over competitors. Familiar brands feel safer. That trust shows up in clicks, conversions, and customer loyalty.

But there’s a new wrinkle: AI visibility.

Platforms like ChatGPT, Perplexity, and Google’s AI Overviews pull from recognizable brands when generating responses. If your brand isn’t mentioned in high-quality content, forum discussions, or authoritative sources, AI tools skip over you. That means potential customers never see your name.

Take a look at a Google AI Overview result for “best project management tools.” You’ll see names like Asana, Monday.com, and Trello cited repeatedly. Those brands didn’t get there by accident. They earned consistent mentions through strong branding, thought leadership, and organic content.

AI overviews for "Best project management tools."

Brand awareness also builds equity. The more recognizable you are, the easier it becomes to launch new products and charge preferred prices. Recognition compounds over time.

Elements of a Brand Awareness Strategy

Before you jump into tactics, you need a foundation. Brand awareness doesn’t happen from random acts of marketing, but a formal strategy.

Start with a clearly defined brand identity. That means locking in your tone of voice, visual style, core values, and key messaging. These elements should carry through your website, social profiles, email campaigns, and any other channel you use. Ideally, put this together in a guide that your team can reference when needed.

Next, understand your audience. You can’t build awareness if you don’t know who you’re targeting. Create detailed buyer personas and perform customer journey mapping so you know what platforms they use, what content they consume, and what problems they’re trying to solve.

You also need a clear content distribution plan. Will you focus on LinkedIn and YouTube? Or prioritize SEO and email marketing? The best strategies start narrow and expand once you’ve mastered one or two channels.

Organic Strategies to Increase Brand Awareness

Here’s where we get tactical. These strategies don’t require ad budgets, but they do require consistency.

Refine and Define Your Brand Identity

Let’s get into a little more detail about brand identities. After all, if you can’t clearly describe your brand’s personality, your audience won’t be able to either.

A real identity goes beyond logos and color palettes. It’s about consistent voice, values, and visuals across every touchpoint. Look at Slack: their playful tone and clean design are instantly recognizable whether you see a billboard or a tweet.

A Slack billboard.

Buffer does this exceptionally well. Check out their homepage and Instagram side by side. The fonts, colors, photography style, and tone are completely aligned. That consistency makes the brand easier to recognize and harder to forget.

The Buffer website.
Buffer's Instagram.

This is what you’re aiming for. Unified branding builds memory and trust.

Here’s your action plan:

  • Document your brand guidelines (tone, colors, fonts, logo usage)
  • Train your team on how to apply those guidelines
  • Audit your current channels to spot inconsistencies
  • Fix the gaps before launching new campaigns

Optimize Profiles on Search Engines and Social

Your digital storefronts often make the first impression, not your website.

Google Business Profiles, LinkedIn, Facebook, Instagram, and even TikTok bios are discovery points. If those profiles are incomplete or outdated, you’re wasting opportunities to build awareness.

Take this optimized Google Business Profile for a local coffee shop. They’ve included high-quality photos, accurate hours, keywords in the business description, customer reviews, and direct links to their website and menu. This kind of completeness signals credibility to both users and search algorithms.

The Google Business profile for the Black Pearl Coffee shop.

The same logic applies to social platforms. A half-finished LinkedIn profile or an Instagram bio with no link hurts your brand more than it helps. Fill out every field. Use keywords naturally. Link to your site.

Pro tip: Claim your brand name on every major platform, even if you’re not active there yet. You don’t want someone else grabbing your handle or creating confusion.

Consider Influencer/Other Brand Partnerships

You don’t need to go viral to reach more people. You can start by tapping into someone else’s audience.

Influencer marketing and strategic brand collaborations amplify your visibility organically. But follower count isn’t everything. Look for:

  • Alignment in audience demographics and values
  • Authentic content that matches your brand tone
  • A track record of real engagement, not just vanity metrics

Gymshark is a perfect example. They partnered with micro-influencers who created TikTok workout videos while wearing their gear. The content looked native to the platform and felt genuine because it was. That authenticity drove massive brand awareness without traditional advertising.

Influencers that partner with Gymshark on TikTok.

Another route: collaborate with complementary brands. If you sell coffee, partner with a local bakery for a co-branded event. Cross-promote on social. Share each other’s audiences. Both brands win.

Find Engagement Opportunities With Your Audience

Conversations spark memory. The more your audience interacts with you, the more likely they are to remember you.

Engagement doesn’t have to be complicated. It can be as simple as replying to comments on Instagram or as involved as hosting live Q&A sessions on LinkedIn. Spotify Wrapped is a masterclass here. Users eagerly share their personalized results every year, generating millions of organic impressions.

Spotify Wrapped

Duolingo takes a different approach with humor. Their social team replies to comments with witty, on-brand responses that often get more engagement than the original post. That two-way interaction builds presence faster than broadcasting alone.

A social media interaction with Duolingo.

Here are practical ways to boost engagement:

  • Respond to every comment on your posts (yes, every one)
  • Ask questions in your captions to spark replies
  • Run polls and surveys to gather feedback
  • Host AMAs (Ask Me Anything) on Reddit or Instagram Live
  • Create shareable content that encourages tagging and reposting

 The more people interact with your brand, the more familiar you become.

Use A/B Testing

Guessing what resonates with your audience is a waste of time. Test it.

A/B testing helps you figure out what messaging, visuals, and formats drive the most engagement. More engagement means more brand recognition.

Start simple. Test two email subject lines to see which gets more opens. Try two different Instagram captions to see which gets more comments. Experiment with video thumbnails on YouTube.

Tools like Google Optimize, Optimizely, or even native platform analytics can help you run these tests. The insights you gain will help you refine your brand messaging over time.

Practice an Omnichannel Strategy

Your audience isn’t glued to one platform. They move between email, social media, search engines, podcasts, and even voice assistants.

Omnichannel marketing means showing up across all of them with consistency. Not copy-pasting the same content everywhere, but adapting your core message to fit each channel’s format and audience expectations.

Canva nails this. Their email campaigns, LinkedIn posts, and TikTok videos all maintain the same visual identity and helpful tone. The messaging shifts slightly to match each platform, but the brand feels cohesive.

An email from Canva.
Canva's Linkedin Page.
Canva's Instagram page.

That cohesion makes the brand easier to remember and trust. People see you everywhere, and repetition builds familiarity.

Here’s how to execute an omnichannel strategy:

  • Identify the three to five platforms your audience uses most
  • Develop content formats that work on each (blog posts, videos, infographics, podcasts)
  • Use scheduling tools to maintain a consistent presence
  • Track performance to see where you’re gaining traction

 You don’t need to be everywhere. Just be consistent where you want to show up.

Provide Value (Without Asking For Something Back)

Not every piece of content needs a CTA or a sales pitch.

Free value builds goodwill and gives people a reason to remember you. Think templates, tutorials, calculators, and guides. No gates. No hard pitch. Just useful content.

HubSpot mastered this years ago. Their free CRM, blog templates, and educational resources turned them into a go-to source for marketers. People associate HubSpot with helpfulness, not just software.

Reports from HubSpot.

You can do the same on a smaller scale:

  • Publish how-to guides that solve real problems
  • Create free tools or templates your audience can download
  • Share behind-the-scenes insights into your processes
  • Offer free consultations or audits (if it fits your business model)

When you consistently give without asking, people remember. And when they’re ready to buy, you’re top of mind.

Build Out A Thought Leadership Plan

Thought leadership isn’t about ego. It’s strategic positioning.

People trust brands that demonstrate expertise. That trust leads to mentions, shares, backlinks, and citations in AI tools. All of these feed into organic brand awareness.

Effective thought leadership formats include:

  • Guest posts on authoritative industry blogs
  • Original research or data studies published on your site
  • Speaking opportunities at conferences or webinars
  • Contributions to expert roundups and interviews
  • Regular insights shared on LinkedIn or Twitter

The key is consistency. One viral post won’t make you a thought leader. Publishing valuable insights month after month will.

And here’s the bonus: thought leadership directly impacts E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), which Google uses to evaluate content quality. The more you establish your expertise, the better your content performs in search and AI results.

Generate Social Proof

People trust people more than they trust brands.

That’s why social proof (testimonials, reviews, user-generated content) is one of the most effective ways to build credibility and awareness.

Feature happy customers in your marketing. Encourage product photos and reviews. Highlight tweets or Instagram posts tagging your brand. Showcase case studies that demonstrate real results.

This example from Glossier does it perfectly. They regularly feature customer photos and testimonials across their social channels and website. Real people using real products. That authenticity drives trust and recognition.

Social proof from Glossier.

Here’s how to generate social proof:

  • Ask satisfied customers for testimonials and reviews
  • Create a branded hashtag and encourage customers to use it
  • Run contests that incentivize user-generated content
  • Feature customer stories in your email campaigns and blog posts
  • Display review ratings prominently on your website

The more your customers talk about you, the more awareness you build.

How To Measure Brand Awareness Strategy Success

Not everything that matters can be measured, but a lot of it can.

Here are the key signals that your brand awareness strategy is working:

  • Search traffic for branded keywords: Track how many people search for your brand name or variations in Google Search Console. Rising branded searches indicate growing awareness.
  • Brand mentions: Use tools like Brand24, Mention, or Google Alerts to monitor how often your brand gets mentioned across the web and social media. More mentions mean more visibility.
  • Social engagement: Look beyond follower counts. Are people commenting, sharing, and tagging your brand? High engagement signals strong awareness.
  • Direct traffic: Check your analytics for direct traffic (people typing your URL directly into their browser). This suggests they already know who you are.
  • Survey responses: Run simple brand awareness surveys asking, “Have you heard of [Your Brand]?” Track the percentage over time.
  • AI visibility: Search for industry-related queries in ChatGPT or Google’s AI Overviews. Does your brand get mentioned? This is becoming increasingly important for brand mentions and overall visibility. Dedicated tools like Profound also specifically focus on AI visibility.

Here’s a snapshot of brand tracking in Mention:

How brand mentions are tracked in Mention.

Review these metrics monthly. Trends matter more than one-off spikes. A consistent upward trajectory means your strategy is working.

FAQs

How to build brand awareness?

Start with a clear brand identity and consistent messaging. Optimize your profiles across search and social platforms. Publish valuable content regularly. Engage with your audience. Partner with influencers or complementary brands. Focus on providing value without always asking for something in return.

Why build brand awareness?

Because people buy from brands they recognize and trust. Brand awareness drives customer loyalty, makes new product launches easier, and increases your visibility in both traditional search and AI-powered tools. Without awareness, you’re invisible to potential customers.

How long does it take to build brand awareness?

Typically, three to six months to see initial traction, but long-term brand awareness builds over years. Consistency matters more than speed. Stick with your strategy, measure your progress, and refine based on what’s working.

<h2>Conclusion</h2>

Conclusion

Brand awareness isn’t a vanity metric. It’s the foundation of every sale you’ll make tomorrow.

If people don’t remember you, they can’t choose you. That’s why consistent branding, smart engagement, and value-driven content matter so much. These strategies don’t require massive budgets. They require focus and persistence.

Start with one or two tactics from this guide. Master those before expanding. Track your metrics to see what’s working. Improve your visibility step by step.

Want help building a brand people actually remember? NP Digital can help you develop a full-funnel strategy that drives awareness and growth.

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Google Search Console adds Query groups

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Screenshot of Google Search Console

Google added Query groups to the Search Console Insights report. Query groups groups similar search queries together so you can quickly see the main topics your audience searches for.

What Google said. Google wrote, “We are excited to announce Query groups, a powerful Search Console Insights feature that groups similar search queries.”

“Query groups solve this problem by grouping similar queries. Instead of a long, cluttered list of individual queries, you will now see lists of queries representing the main groups that interest your audience. The groups are computed using AI; they may evolve and change over time. They are designed for providing a better high level perspective of your queries and don’t affect ranking,” Google added.

What it looks like. Here is a sample screenshot of this new Query groups report:

You can see that Google is lumping together “search engine optimization, seo optimization, seo website, seo optimierung, search engine optimization (seo), search …” into the “seo” query group in the second line. This shows the site overall is getting 9% fewer clicks on SEO related queries than it did previously.

Availability. Google said query groups will be rolling out gradually over the coming weeks. It is a new card in the Search Console Insights report. Plus, query groups are available only to properties that have a large volume of queries, as the need to group queries is less relevant for sites with fewer queries.

Why we care. Many SEOs have been grouping these queries into these clusters manually or through their own tools. Now, Google will do it for you, making it easier for more novie SEOs and beginner SEOs to understand.

More details will be posted in this help document soon.

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The agentic web is here: Why NLWeb makes schema your greatest SEO asset

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The agentic web is here: Why NLWeb makes schema your greatest SEO asset

The web’s purpose is shifting. Once a link graph – a network of pages for users and crawlers to navigate – it’s rapidly becoming a queryable knowledge graph. 

For technical SEOs, that means the goal has evolved from optimizing for clicks to optimizing for visibility and even direct machine interaction.

Enter NLWeb – Microsoft’s open-source bridge to the agentic web

At the forefront of this evolution is NLWeb (Natural Language Web), an open-source project developed by Microsoft. 

NLWeb simplifies the creation of natural language interfaces for any website, allowing publishers to transform existing sites into AI-powered applications where users and intelligent agents can query content conversationally – much like interacting with an AI assistant.

Developers suggest NLWeb could play a role similar to HTML in the emerging agentic web. 

Its open-source, standards-based design makes it technology-agnostic, ensuring compatibility across vendors and large language models (LLMs). 

This positions NLWeb as a foundational framework for long-term digital visibility.

Schema.org is your knowledge API: Why data quality is the NLWeb foundation

NLWeb proves that structured data isn’t just an SEO best practice for rich results – it’s the foundation of AI readiness. 

Its architecture is designed to convert a site’s existing structured data into a semantic, actionable interface for AI systems. 

In the age of NLWeb, a website is no longer just a destination. It’s a source of information that AI agents can query programmatically.

The NLWeb data pipeline

The technical requirements confirm that a high-quality schema.org implementation is the primary key to entry.

Data ingestion and format

The NLWeb toolkit begins by crawling the site and extracting the schema markup. 

The schema.org JSON-LD format is the preferred and most effective input for the system. 

This means the protocol consumes every detail, relationship, and property defined in your schema, from product types to organization entities. 

For any data not in JSON-LD, such as RSS feeds, NLWeb is engineered to convert it into schema.org types for effective use.

Semantic storage

Once collected, this structured data is stored in a vector database. This element is critical because it moves the interaction beyond traditional keyword matching. 

Vector databases represent text as mathematical vectors, allowing the AI to search based on semantic similarity and meaning. 

For example, the system can understand that a query using the term “structured data” is conceptually the same as content marked up with “schema markup.” 

This capacity for conceptual understanding is absolutely essential for enabling authentic conversational functionality.

Protocol connectivity

The final layer is the connectivity provided by the Model Context Protocol (MCP). 

Every NLWeb instance operates as an MCP server, an emerging standard for packaging and consistently exchanging data between various AI systems and agents. 

MCP is currently the most promising path forward for ensuring interoperability in the highly fragmented AI ecosystem.

The ultimate test of schema quality

Since NLWeb relies entirely on crawling and extracting schema markup, the precision, completeness, and interconnectedness of your site’s content knowledge graph determine success.

The key challenge for SEO teams is addressing technical debt. 

Custom, in-house solutions to manage AI ingestion are often high-cost, slow to adopt, and create systems that are difficult to scale or incompatible with future standards like MCP. 

NLWeb addresses the protocol’s complexity, but it cannot fix faulty data. 

If your structured data is poorly maintained, inaccurate, or missing critical entity relationships, the resulting vector database will store flawed semantic information. 

This leads inevitably to suboptimal outputs, potentially resulting in inaccurate conversational responses or “hallucinations” by the AI interface.

Robust, entity-first schema optimization is no longer just a way to win a rich result; it is the fundamental barrier to entry for the agentic web. 

By leveraging the structured data you already have, NLWeb allows you to unlock new value without starting from scratch, thereby future-proofing your digital strategy.

NLWeb vs. llms.txt: Protocol for action vs. static guidance

The need for AI crawlers to process web content efficiently has led to multiple proposed standards. 

A comparison between NLWeb and the proposed llms.txt file illustrates a clear divergence between dynamic interaction and passive guidance.

The llms.txt file is a proposed static standard designed to improve the efficiency of AI crawlers by:

  • Providing a curated, prioritized list of a website’s most important content – typically formatted in markdown.
  • Attempting to solve the legitimate technical problems of complex, JavaScript-loaded websites and the inherent limitations of an LLM’s context window.

In sharp contrast, NLWeb is a dynamic protocol that establishes a conversational API endpoint. 

Its purpose is not just to point to content, but to actively receive natural language queries, process the site’s knowledge graph, and return structured JSON responses using schema.org. 

NLWeb fundamentally changes the relationship from “AI reads the site” to “AI queries the site.”

Attribute NLWeb llms.txt
Primary goal Enables dynamic, conversational interaction and structured data output Improves crawler efficiency and guides static content ingestion
Operational model API/Protocol (active endpoint) Static Text File (passive guidance)
Data format used Schema.org JSON-LD Markdown
Adoption status Open project; connectors available for major LLMs, including Gemini, OpenAI, and Anthropic Proposed standard; not adopted by Google, OpenAI, or other major LLMs
Strategic advantage Unlocks existing schema investment for transactional AI uses, future-proofing content Reduces computational cost for LLM training/crawling

The market’s preference for dynamic utility is clear. Despite addressing a real technical challenge for crawlers, llms.txt has failed to gain traction so far. 

NLWeb’s functional superiority stems from its ability to enable richer, transactional AI interactions.

It allows AI agents to dynamically reason about and execute complex data queries using structured schema output.

The strategic imperative: Mandating a high-quality schema audit

While NLWeb is still an emerging open standard, its value is clear. 

It maximizes the utility and discoverability of specialized content that often sits deep in archives or databases. 

This value is realized through operational efficiency and stronger brand authority, rather than immediate traffic metrics.

Several organizations are already exploring how NLWeb could let users ask complex questions and receive intelligent answers that synthesize information from multiple resources – something traditional search struggles to deliver. 

The ROI comes from reducing user friction and reinforcing the brand as an authoritative, queryable knowledge source.

For website owners and digital marketing professionals, the path forward is undeniable: mandate an entity-first schema audit. 

Because NLWeb depends on schema markup, technical SEO teams must prioritize auditing existing JSON-LD for integrity, completeness, and interconnectedness. 

Minimalist schema is no longer enough – optimization must be entity-first.

Publishers should ensure their schema accurately reflects the relationships among all entities, products, services, locations, and personnel to provide the context necessary for precise semantic querying. 

The transition to the agentic web is already underway, and NLWeb offers the most viable open-source path to long-term visibility and utility. 

It’s a strategic necessity to ensure your organization can communicate effectively as AI agents and LLMs begin integrating conversational protocols for third-party content interaction.

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90% of businesses fear losing SEO visibility as AI reshapes search

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AI search evolution

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.

Yes, but. While AI search is booming, multiple studies suggest that ChatGPT and LLM referrals convert worse than Google Search – and AI systems won’t have parity with organic search within the next year.

The survey. SEO for AI (GEO) Statistics: 90% of Businesses Are Worried About the Future of SEO and Organic Findability Due to AI / LLMs

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AI vs Content Marketers: The New Content Marketing Formula

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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.

A graphic showing usage of AI-generated content by bloggers/

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.

A graphic showing AI-assisted content ideation.

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.

A graphic showing how real-time performance feedback works with AI.

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.

A graphic showing how to build brand voice systems for AI output.

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.

A graphic showing how content repurposing works at scale with AI support.

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.

AI tools that can help with multimedia production.

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.

A graphic showing how to automatically format SEO and metadata using AI.

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.

A graphic showing how to improve AI visibility for content.

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
A graphic showing how to structure content for AI visibility.

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.

A graphic showing how to properly integrate smart multimedia into AI-generated content.

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.

A graphic explaining how to write to human and machine audiences.

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.

An example of how to make real-time adjustments from engagement signals with AI content.

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.

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AI Optimization: How to Rank in AI Search (+ Checklist)

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When a potential user asks ChatGPT, Google AI, or Perplexity for recommendations, does your brand appear in the answer?

Not just cited — actually mentioned in the response?

That’s a crucial distinction.

Brands that AI systems mention with context and positive sentiment attract the most intent-driven traffic.

Semrush research shows that visitors who find a brand in an AI answer are 4.4 times more valuable than those from traditional search.

They’re pre-qualified. They’ve seen AI endorse your solution.

And unlike SEO, AI doesn’t care about website authority.

Most sources cited in AI responses don’t even rank in Google’s top 20.

Ranking positions of LLM – Cited search results

So if you follow best practices, your startup can earn favorable mentions over more established competitors.

Meritocracy.

How do you make that happen?

Read on to learn:

  • How AI search works
  • How the Backlinko team approaches AI SEO
  • Best practices to make your site AI-ready

Let’s start with the basics of AI optimization.

Grab our free AI Search Optimization Checklist and follow the exact steps we use to get cited across ChatGPT, Google, and more.

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.

Traditional Search vs. AI Search

But why is AI optimization so important now?

AI tools are expected to drive more traffic than traditional search engines by 2028.

Google and LLM Unique Visitor Growth Projection (Moderate Case)

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:

How AI Search Works

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.”

ChatGPT – O3 model – Best sneakers

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.

The Queries I Tested

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.

Citation Core

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.

AI Citation Breakdown

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:

  • Creating citable content
  • Doing A/B tests to optimize our site
  • Earning third-party validation.
  • Building authority across platforms (Did you check our YouTube channel?)

How to Optimize Your Website for AI Search

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 there’s much more to it than that.

Getting cited in AI answers is a team sport.

PR, product, support, and customer success all play a role. (Check out AI Search Strategy: The Seen & Trusted Brand Framework where we discuss this.)

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

How to optimize your website for AI search

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.

Robots.txt blocking AI crawlers

If you find them blocked (or want to make sure they’re allowed), add these lines to your robots.txt:

code icon
User-agent: GPTBot
Allow: /
User-agent: Google-Extended
Allow: /
User-agent: Claude-Web
Allow: /
User-agent: PerplexityBot
Allow: /

This tells AI crawlers they can access your entire site.

2. Whitelist AI Bots in Your Firewall

Cloudflare, Sucuri, and other web application firewalls (WAFs) sometimes block legitimate AI crawlers as “suspicious bots.”

For instance, Cloudflare now blocks AI bots from accessing its clients’ websites by default.

You have to turn off this security feature to ensure AI bots can crawl your site.

Cloudflare – Security bots

Check your firewalls and security tools.

See if they’re blocking requests from AI user agents or giving 403 errors. And address that issue.

Remember: no access, no citations.

3. Use Semantic HTML So AI Knows What’s Important

AI needs to understand what’s important on your page.

Clean HTML helps. Messy code doesn’t.

Use proper HTML tags for your content:

  • <h1> for your main title
  • <h2> and <h3> for subheadings
  • <article> for blog posts
  • <main> for primary content
  • <aside> for sidebars

Don’t dump everything in generic <div> tags.

Non-sematic & Sematic HTML

Also, avoid hiding important content behind JavaScript.

AI crawlers can’t execute JavaScript.

If your main content only appears after JavaScript runs, AI will miss it entirely.

Like in this example of Airbnb:

JavaScript

4. Add Visible Dates for Freshness Signals

AI systems need to know when your content was published or last updated.

This is especially important for time-sensitive topics like news, prices, or trends.

Without visible dates, AI might assume your content is outdated and skip it entirely.

So, display your dates prominently on every page.

Use a consistent format like “Published: June 15, 2024” or “Last Updated: August 15, 2025.”

Get Rich Slowly – A values driven life

Also, add schema markup for dates in your code:

code icon
json"datePublished": "2024-01-15",
"dateModified": "2024-12-15"

This gives AI a machine-readable version it can’t misinterpret.

5. Remove Barriers to Content Access

Pop-ups and overlays can prevent AI from reading your content.

For instance, the crawler might grab your newsletter signup text instead of your actual article.

So you want to avoid:

  • Full-screen pop-ups on page load
  • Content that requires clicking “Read More” to expand
  • Infinite scroll that hides content

If you must use popups, delay them by at least 30 seconds.

Or better, use exit-intent popups, which appear when the visitors are about to leave.

Make sure your main content is immediately visible in the HTML.

Think of it this way: AI crawlers are impatient visitors who won’t interact with your page.

Give them what they came for immediately, or they’ll leave empty-handed.

6. Optimize Your Server Response Time

Your server response time is how long it takes your server to start sending data after receiving a request.

If it’s slow, your pages take longer to load.

And AI crawlers may move on before they ever see your content.

You can use Google’s PageSpeed Insights tool to check your server response time.

PageSpeed Insights – Server responded slowly

It also shows your page’s overall performance and speed.

PageSpeed Insights – Pages overall performance & speed

If you score below 50, your site likely has serious speed issues.

How to improve it?

  • Enable caching. This stores copies of your pages so your server doesn’t rebuild them for every visitor.
  • Compress your images before uploading them. Large images are the most common cause of slow pages.
  • Use a content delivery network (CDN). This serves your content from servers physically closer to your visitors, reducing load time.

The faster your server responds, the more likely AI crawlers are to reach and index your content.

Further reading: Page Speed and SEO: Best Practices

SEO Tasks

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.

H1

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.

Topic Cluster

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.

Content Pillar Page

Use descriptive anchor text throughout.

“Project management automation guide” provides context. “Click here” doesn’t.

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.

Markup Types

There are several types of schema markups.

I’ve found the FAQ schema particularly effective for AI search visibility.

Here’s how it looks:

code icon
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.

WordPress – Schema Markup

After implementation, you can test it at validator.schema.org to ensure it’s working properly.

Schema Markup Validator – Testing WordPress plugins

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.

Ecommerce Comparison – Basic

You want to add buyer-focused information directly on these pages, like this:

Ecommerce Comparison – More detailed

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.

GlassesUSA – Information that AI can use

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.

Frames Direct – Detailed content at the end of category

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.

Gemini – Nutrition tracking app 2025

But that’s slow. And you’ll only catch a small slice of what’s happening.

Alternatively, you can use Semrush’s AI SEO Toolkit.

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.)

Semrush – AI SEO – Petlibro – Visibility Overview

You can see exactly which topics and prompts your brand appears for.

Semrush – AI SEO – Petlibro – Your Performing Topics – Prompts

And which prompts your competitors appear for, but you don’t. (In the “Competitor Research” report.)

Competitor Research – Petlibro – Missing Topics

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.)

Semrush – AI SEO – Petlibro – AI Strategic Opportunities

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

Your keyword strategy needs to evolve for AI search.

People still search Google for “winter jacket.”

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.

Semrush – AI SEO – Petlibro – Breakdown by Question

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:

Backlinko – ChatGPT Using Google Search

And even if we’re talking about a common topic (e.g., organic traffic), we add fresh examples.

Backlinko – Organic Traffic – Builds Authotity

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.

Reviews

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”:

Semrush Blog – Using 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:

JustCall – Products & Solutions menu

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.

Claude – Outdoor Gear Lab result

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.

Fast Company – Guest post opportunities

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.

Further reading: Journalist Outreach: 9 Steps to Earn High-Authority Links

Don’t Overlook Community Platforms

AI systems also pull information from community platforms.

In fact, Reddit and Quora are two of the most referenced sources in ChatGPT and Google AI Mode.

Semrush – AI Visibility Index – Top 10 sources

This is where your content team should collaborate with social media or community teams to control conversations. How?

Answer questions thoroughly. Share genuine insights. Mention your product only when it’s genuinely relevant to the discussion.

Reddit thread – Lead gen forms

Over time, these contributions will become part of the dataset AI references.

When someone asks about solutions in your space, your helpful answers may influence AI’s response.

Your Next Move in AI Search Optimization

Don’t try to tackle everything at once. Start simple.

First, run your site through Semrush’s AI SEO Toolkit.

It shows where your brand already appears in AI search results and where you’re missing opportunities.

Those missing prompts are your biggest opportunities.

Then, grab our free AI Search Optimization Checklist.

It breaks down the technical, SEO, and content steps you need to follow, so you’re not guessing your AI search optimization.

Next up, check out the 6 AI SEO tools we love using.

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A recap of the October 2025 SEO Update by Yoast

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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.

Next actions and a quick invitation

For more news, you can join the next SEO Update by Yoast on November 24. The transcript, video, and news items are all available on the SEO Update by Yoast October Edition webinar page. For more information and options to watch future webinars, you can also visit the main Yoast webinars listings.

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