A Comprehensive Guide to AI Search Content Optimization

Creating content that thrives in an AI-driven search landscape requires a strategic blend of SEO optimization fundamentals and new approaches tailored to how modern search engines (and AI assistants) operate. As generative AI transforms how people find information, marketers and content creators must evolve their tactics to ensure their content remains visible and valuable. In this guide, we’ll explore exactly what it takes to craft the perfect piece of content for AI search, covering everything from audience intent to technical structure and ongoing optimization.

Before we dive in, it’s worth pointing out that content optimization is just one piece of the AI search puzzle. Your brand’s overall online presence, how often you’re cited, where you’re mentioned, and the sentiment around those mentions also play a growing role in how AI systems evaluate and surface your content. Distribution, third-party signals, and entity-level authority all contribute to how your brand is understood in an AI-powered ecosystem.

We’ll explore that broader landscape in a future post on AI visibility strategy and off-page signals.

For now, this guide is focused on what you can control directly: creating content that’s structured, optimized, and AI-ready from the inside out.

 Okay, now let’s dive in...

Why Optimizing for AI Search is Important

A Semrush study projects that visitors from AI search will overtake those from traditional search by 2028, reflecting a major shift in how people find content online. This trend isn’t happening in isolation; user behavior is already changing. For instance, ChatGPT’s weekly active user count exploded 8× from late 2023 to April 2025 (now over 800 million), signaling massive adoption of AI tools for search-like queries. 

Google has introduced AI-driven results in the form of AI Overviews and an AI Mode search interface that summarizes answers at the top of results, reducing reliance on the classic “10 blue links”. In fact, recent data shows nearly 60% of Google searches result in no clicks because the answer is provided on the results page. People are also increasingly turning to non-traditional platforms, about 40% of Gen Z users prefer searching on TikTok or Instagram over Google. All of this underscores that visibility now hinges on appearing in AI-generated answers and across diverse search platforms, not just traditional search rankings.

Consider the SEO software tool industry. I recently had a conversation with a major SEO tool provider who was demoing their new AI SEO tools for me. During this conversation, I noted that their old tools were likely to become obsolete in the near future, to which they all uncomfortably nodded in agreement. This is where we all are.

Also consider BCM! Some of the highest-performing organic search keywords for the BCM website now trigger AIO results in Google, providing us with less click data to measure success.

Why the AI Shift Matters and What We are Doing About It

We’re entering a new phase of search behavior where we as marketers must pivot. Users are no longer just Googling. Many are now (and everyone, soon) turning to AI tools like ChatGPT or Perplexity instead, or engaging with AI-generated summaries at the top of Google’s results pages instead of scrolling down to traditional "blue links." In both cases, the net effect is clear: fewer direct clicks to your website from search engines.

But the story doesn’t end there. The users who do reach your site, often after encountering your content in an AI Result are arriving more qualified and more engaged. According to Semrush, traffic referred from AI-powered results converts at 4.4× the rate of traditional search traffic. Google backs this up, noting that users who click through from AI Overviews tend to stay longer and engage more deeply.

So, what now? If traditional search is being nullified, and high-value traffic is increasingly filtered through AI, then the strategic play is clear: you need to position your content where AI can find and feature it, early in the user’s discovery journey.

That’s what the rest of this article is about. What follows is a blueprint for creating content that’s not only helpful and human-friendly but also built to win in AI-powered search results.

How To Make Your Content "AI-Ready"

Now that we’ve examined why AI is changing the search landscape, the next question is: What does it actually take to create content that AI systems can find, understand, feature and source/backlink?

It turns out that a lot of the fundamentals still matter: clear structure, helpful information, and a good user experience, but there are also new opportunities to optimize for AI-driven search readiness. The following sections (or “chunks” - more on that later) break down the key areas where you can optimize your content to be more visible and valuable in AI-powered results. Think of them as levers you can pull to increase your chances of being cited, surfaced, or summarized by AI tools like Google’s AI Overviews, ChatGPT, Claude, Perplexity, etc..

Let’s walk through them one by one.

Know Your Audience

Any successful content strategy starts with a deep understanding of your audience - this hasn’t changed in the era of AI search. User intent and relevance are paramount. Google’s guidance for AI-driven experiences echoes a familiar principle: focus on people first. Rather than trying to second-guess “what Google’s AI wants,” craft unique, high-quality content that directly addresses your audience’s needs and questions. AI search models are explicitly built to satisfy user queries, so content that fulfills the searcher’s intent will naturally be favored. In practical terms, this means you should research the motivations and pain points behind your target queries. Are users looking for a quick fact, an in-depth tutorial, a comparison of options, or something else? Tailor your content’s scope and tone accordingly.

Google’s own guidance for AI search echoes what BCM and other marketers have always known: put people first.

"Google's automated ranking systems are designed to present helpful, reliable information that's primarily created to benefit people, not to gain search engine rankings, in the top Search results."

That means understanding the context, motivations, and pain points behind the search. Is the user looking for a quick stat, a detailed how-to, or a trusted product recommendation? Your tone, depth, and structure should reflect that. For example, a student researching a career path may need a very different answer than a hiring manager searching for the same topic, and Google's AI (and others) increasingly personalize results to reflect that.

This trend isn’t isolated to organic search. We're seeing it in paid social, programmatic, and AI-optimized ad platforms as well, where media performance is now driven less by third-party targeting and more by deep, real-time audience insights. It’s no longer about chasing the algorithm; it’s about feeding it what it needs.

That’s why BCM invests heavily in audience planning tools — not just to inform paid media strategies, but to power smarter content creation. Whether we're building a campaign or writing a page, audience understanding is the performance multiplier and ALWAYS the first step.

In short: if relevance is the new currency of AI search, then knowing your audience is how you earn it. Align your content with real intent, and you'll be better positioned to win in any AI-driven discovery environment.

Know What Your Audience Wants

Understanding your audience is one thing; knowing exactly what information they’re seeking is another. In the age of AI search, this goes beyond traditional keyword research into the realm of topic and question research.

AI-driven search queries tend to be more conversational and specific than the terse keywords of old. Users ask natural-language questions like they’re talking to an expert. To meet this need, conduct research to discover the common questions and subtopics within your niche. Tools like People Also Ask boxes, community forums, and AI query and prompt research can reveal what real questions your content should answer.

Once you identify these questions, shape your content to address them directly. This is sometimes called Answer Engine Optimization (AEO) - optimizing your content so that AI “answer engines” can easily extract and present your answers. For example, if users often ask an AI, “How do I choose the right running shoes for flat feet?”, you should have a section explicitly answering that, rather than burying the advice in a generic paragraph. Prompt shaping is useful here: phrase key points as Q&A or in statements that read like succinct answers. Not only does this help human readers scan, but it also aligns with how AI models parse content. In fact, Google’s AI search systems often retrieve specific passages, not entire pages, so wording matters at the snippet level. Studies have shown that AI prompts are far more conversational than traditional search keywords, meaning content creators must anticipate and answer these natural-language prompts in their writing. Writesonic reports that traditional Google searches average 4.2 words, whereas AI prompts, such as those used with ChatGPT, average 23 words. This indicates a significant move toward more context-rich and nuanced user inputs. By doing thorough topic research and shaping your content around the actual questions people ask, you increase the likelihood that your content will be the one AI chooses to quote or cite in response.

Content Structure for AI Readability 

When it comes to structuring content for AI readability, think clean, organized, and easy to read. AI search engines “scan pages like speed readers,” looking for structural clues to quickly understand your content. Clear HTML formatting isn’t just a nice-to-have - it can make or break whether an AI includes your information. Here are some key structural elements to get right:

• Use Descriptive Headings:

  Every page should have one clear <h1> that states the main topic, followed by logical <h2>s for major sections and <h3>s for subsections. These act as signposts for AI, outlining the main topics and subtopics at a glance. For example, use an H2 like “Benefits of Organic Skincare” rather than a clever but vague heading. Direct, keyword-rich headings help AI (and users) navigate your content hierarchy.
  
  

• Incorporate Lists and Tables:

  Breaking information into bullet points or numbered steps makes it digestible. AI systems appreciate simple lists that distill key points. If you have data or comparisons, consider using table-structured data in table form, which can be easily parsed and is often featured prominently in search results. For instance, a table comparing product specs or pricing could get pulled into an AI answer summary. The goal is modular clarity: each list or table should deliver a coherent nugget of information that stands on its own.
  
  

• Keep Text in Plain HTML:

  Avoid burying important text inside interactive elements that require clicks or scripts (accordions, tabs, slideshows). Content that only loads after user action or via heavy JavaScript is effectively invisible to AI crawlers. Google most of the time does a good job at navigating content through JavaScript, but the other AI crawlers do not.  Ensure your key points and keywords appear in the initial HTML source. A quick test: view your page source (Ctrl+U) and check that all critical info is present as text. If not, move it out of those hidden JavaScript widgets.
  
  

• Use Formatting for Emphasis:

  Utilize bold text or call-out boxes for must-know facts, and use anchor links or jump lists for long content so users (and AI) can skip to relevant sections. A well-structured page might include a brief intro summary, a table of contents (for long guides), and clearly separated sections for each subtopic. Such a structure not only aids human readers but also makes it easier for AI to identify relevant chunks (more on chunking below).

In short, build your content like an outline for both humans and machines. Clear headings, well-organized lists, and immediately visible text help AI quickly grasp what your page offers. By structuring content in a logical, standards-compliant way, you set the stage for better AI-driven visibility.

Content “Chunking”: What is Content Chunking and why is it important to AI search?

In the AI era, it’s not just whole pages that rank - portions of pages can rank, too. This is where content chunking comes in. Google’s advanced indexing can evaluate and surface specific passages or “chunks” of a page independently. In practical terms, an AI-powered search might pull a single paragraph or a few sentences from deep within your article if that snippet directly answers a user’s query. To capitalize on this, you should write in modular, self-contained chunks that address distinct subtopics or questions. 

This is the new content best practice and takes no additional effort. It's simply about the structure of your written content.

How do you do this? Start by breaking down complex topics into smaller sections, each with a clear focus. For example, instead of one long article about “Digital Marketing 101,” create sections like “What Is Digital Marketing?”, “Key Channels in Digital Marketing”, “Measuring Digital Marketing Success”, etc., each under its own heading. Within those sections, ensure that each paragraph or group of paragraphs sticks to one specific idea and could stand alone. Google’s passage indexing will reward this by potentially ranking that single passage for related queries.

It also helps to phrase some headings or sentences as questions and answers. This aligns with “micro-answer optimization,” which is about providing succinct, direct responses to common queries within your content. For instance, you might have an H3 asking “How often should I update my website’s content?” followed by a concise answer. This Q&A format is gold for AI - it’s highly likely to be extracted as a direct answer snippet. Even if you’re not using a Q&A format, be explicit and specific in key sentences. Avoid requiring too much context from previous paragraphs; each chunk should make sense on its own.

The benefit of content chunking is twofold: it caters to skimming readers and it caters to AI systems that use passage-level retrieval. By writing focused, answer-driven content modules, you increase the odds that some part of your page will hit the bullseye for a user’s detailed query and be elevated to an AI answer or featured snippet. Think of each chunk as an island of insight that could be discovered independently. In summary, don’t bury the answer 800 words deep in a sea of text - break it out, label it clearly, and you’ll be far more discoverable in the AI search world.

Take note, this page is broken out into chunks as described above.  See what I did there?  ;-)

E-E-A-T: How Do You Demonstrate EEAT in Content for AI Search?

Quality and credibility of content matter more than ever in AI search results. Google’s emphasis on E-E-A-T,  which now stands for Experience, Expertise, Authoritativeness, and Trustworthiness, carries into the AI realm. In 2022, Google even updated its guidelines to add “Experience” to the E-A-T criteria, reinforcing the need for content creators to demonstrate first-hand or life experience on a topic. But what does all this mean for crafting content?

Firstly, expertise: ensure that your content is factually accurate, comprehensive, and reflects deep knowledge. If you have credentials or years of experience in the subject, say so (an author byline or brief bio can help establish that credibility). AI systems often favor sources that are known authorities—either big names or sites that consistently produce high-quality, cited work.

Next, authoritativeness: build your content on sound research and reference reputable sources where appropriate. For example, linking to credible statistics or official guidelines not only helps users but also signals to algorithms that your content is well-grounded. It’s akin to providing references in an academic paper. Authoritative content tends to attract natural backlinks and mentions, which further boosts your overall site authority - a virtuous cycle that benefits AI visibility too.

Then, trustworthiness: this covers everything from the accuracy of your claims to the transparency of your content. Avoid clickbait or exaggerated promises that could undermine trust. Instead, be honest and clear. If you’re writing about a health or financial topic (YMYL - “Your Money or Your Life” areas), citing medical professionals or financial experts, and including disclaimers where necessary, can enhance trust. Also, maintain a professional site design (no spammy ads or malicious scripts) as site quality can affect perceived trust.

Lastly, the new experience element means highlighting personal experience or firsthand knowledge where relevant. For instance, an article about “best project management tools” can stand out if the author notes that they have 10 years of project management experience and have tried all the major tools. Anecdotes or case studies can demonstrate that experiential depth.

Google’s own guidelines reiterate that its systems reward content that demonstrates expertise and trustworthiness. In fact, content that Google’s AI surfaces often comes from sites that have built a strong E-E-A-T profile over time. So, as you create content, infuse it with expert insight, back it with authority, and polish it for trust (proper grammar, updated information, secure site). Over the long term, focusing on E-E-A-T not only improves your human audience’s perception but also directly influences how AI and search engines rank and feature your pages. Quality content isn’t just the same old mantra here, but a strategic asset. The shift to AI-driven results rewards pages demonstrating expertise and trustworthiness. Make sure yours is one of them.

We know that our clients' most engaging content related to topics that people are interested in is the top performer. This isn’t rocket science, though it can take a series of attempts to find the right formula.

Using Media (Images, Video, and Visuals) to Support AI Search

Incorporating media, especially images, video, charts, and diagrams, has always been a powerful way to enhance user experience, but in the AI search era, it can also directly influence how your content is understood, summarized, and surfaced by AI systems.

Here’s why media matters for AI search:

• AI uses visual signals to enhance results. In Google’s SGE and AI Overviews, we’re already seeing images pulled from web content alongside answer summaries. These images often come from well-optimized pages where the media includes descriptive alt text, schema, and clear contextual placement.
• Media increases on-page engagement, which can improve content's overall engagement performance. Videos or graphics that hold attention help reduce bounce rates and increase dwell time, both positive behavioral signals.
• AI can cite images and videos. For example, a helpful infographic or explainer video could be the asset chosen for an AI answer box, especially for “how-to” or visual search queries.

Best Practices for AI-Optimized Media

• Always use descriptive file names and alt text. Don’t upload “IMG_01234.jpg”, instead, use seo-content-structure-diagram.jpg. Your alt text should be natural and descriptive, e.g., “Diagram showing ideal content structure with headings and chunks.” This helps AI understand what the image represents.
• Add schema markup for media. Use ImageObject schema for images and VideoObject for videos. This makes it easier for AI systems to parse the file, description, and even licensing info, which may influence whether it gets surfaced in an AI result.
• Embed video with supporting context. Don’t just drop a YouTube embed with no explanation. Include a short written summary of what the video contains. AI will often crawl the surrounding text and may use that to evaluate the video’s relevance.
• Include original media where possible. Stock images are fine, but unique visuals (e.g., charts, annotated screenshots, or product walkthroughs) are more likely to be perceived as valuable and may help with visibility in AI results.
• Use captions and transcripts. These add context and accessibility, and they help AI understand the media content. For video, a transcript can serve double duty as both a user resource and indexable content.
• Avoid performance trade-offs. Optimize file size to avoid hurting page speed. Use modern formats like WebP for images and lazy loading where appropriate.
• AI Generated Media. If you're using AI to generate visuals (charts, diagrams, illustrations), be transparent and label them appropriately. You can even mark them up with AI-generated content indicators if relevant, which Google has stated won’t hurt rankings as long as the media is helpful and accurate.

(Yes, ChatGPT created the above infographic and some of the other images on this post. ;-))

Natural Language and Semantic Richness

To resonate with AI-driven search, your content should be written in natural, conversational language - but also packed with semantically rich terms related to your topic. Why? AI models have become extremely good at understanding context and intent. They favor content that reads like it was written for humans, not search engines. Gone are the days of awkwardly repeating a keyword 20 times; instead, focus on covering the topic in a clear, flowing manner using a variety of relevant terms and phrases.

Write how you speak. This means using a conversational tone appropriate to your audience. If your audience is marketers and business owners, you might say, “Let’s consider an example…” or “You might be wondering…”. This kind of tone can make your content more accessible and engaging, and AI models trained on vast swaths of human text tend to retrieve content that matches the conversational style of a user’s query. In fact, Google explicitly recommends using natural language and avoiding an over-optimized, keyword-stuffed approach - their AI systems prefer “conversational, informative content that addresses user needs”. So instead of writing: “best running shoes flat feet,” you’d write: “What are the best running shoes for someone with flat feet? Based on podiatrist advice, here’s what to look for…”.

Use semantic variation. Think of all the related terms and subtopics associated with your main topic and incorporate them naturally. This not only provides comprehensive coverage (good for your human readers) but also helps AI understand the breadth of your content’s relevance. For example, an article about electric cars might naturally mention charging stations, range anxiety, battery life, EV tax credits, Tesla, etc. These related terms signal that your content has depth. AI engines build knowledge graphs of entities and concepts. By including relevant entities (with context) in your content, you increase the chances of being seen as an authoritative source on the topic cluster.

Also, answer implied questions within your content. Use the question-and-answer format where possible, as discussed in chunking. If your section heading is a question, answer it directly in the first sentence of that section, then elaborate. For instance: “Q: What’s the difference between a credit score and a credit report? A: A credit score is a numerical rating, while a credit report is a detailed history…”. This style caters to the way people ask questions and the way AI will likely extract answers.

Avoid jargon overload unless your audience expects it, and when you use industry terminology, consider providing a brief definition. AI can sometimes simplify or explain terms if the user asks, and if your content already includes the explanation, it’s more likely to be chosen. For example, stating “CMS (Content Management System)” the first time you mention CMS ensures that even if an AI is summarizing, the meaning is clear to everyone.

Lastly, maintain good grammar and coherence. AI models like content that is easy to parse. Shorter sentences and active voice can help with clarity, but vary your sentence structure enough to keep it interesting. The goal is a clear, conversational style that mirrors how a helpful expert would explain the topic to a curious reader. If you find yourself writing something that feels unnatural just to include a keyword, pause and rephrase it. It’s better to write something that a user would upvote as an excellent answer on a forum - that’s the kind of content AI loves to serve. As one actionable tip: optimize for passages, not just pages. Each paragraph should convey a complete thought clearly. This way, whether a user skims or an AI plucks out a single passage, the message is intact and understandable. Write for humans first, and the AI will follow.

Content Freshness

In fast-moving sectors, today’s helpful answer can become tomorrow’s outdated information. Freshness matters - both to users and to search engines (AI included). Generative AI tools typically draw from their indexes or training data which may be at some point in the past but, they use current data provided by search engine results (ChatGPT uses Bing search and this is a whole other topic), so keeping your content refreshed improves the likelihood that your information is current in the AI’s “mind”. From an SEO perspective, regularly updating and republishing content can provide a ranking boost or at least sustain your visibility over time. More importantly, it signals to algorithms that your content is being maintained and remains relevant.

So, how do you keep content fresh? One approach is to establish a periodic review cycle for your important pages or posts. Every so often (e.g., quarterly or annually, depending on the topic), revisit the content to check for any facts, figures, or recommendations that need an update. Did new research emerge? Are there new tools or techniques in your industry that warrant mention? By integrating the latest insights, you ensure your content stays authoritative. Even minor updates like adding recent statistics or examples can help. For instance, if you have a piece about “Social Media Marketing Trends,” updating it with data from 2025 and republishing can make it far more attractive to searchers (and AI) looking for the newest info.

Beyond updates, consider the timestamp. Google’s AI summaries often show the publication date of cited articles. A very old date might imply stale info, whereas a recent date could suggest more current relevance. If you substantially update an article, you may choose to indicate as such (e.g., “Updated June 2025”) and even adjust the publish date if appropriate. This isn’t about gaming the system; it’s about legitimately keeping content up-to-date and informing readers that it’s refreshed. Some sites have found success with republishing old content after significant updates, effectively giving it new life and improved rankings.

Also, capitalize on content freshness for trending queries. AI search, like traditional search, will prioritize timely information for queries that demand it (think breaking news, latest guidelines, etc.). If your niche has any element of news or rapid development, be sure you’re producing content that covers those new developments quickly. That way, your site becomes known as a current source. AI models may explicitly favor “newer” sources for questions on evolving topics (for example, an AI might preface an answer with “As of 2025…” which implies it looked for the latest info).

Finally, freshness ties back to user trust. A user is more likely to trust an article that’s been updated recently, assuming the topic isn’t timeless. It gives the impression that the author or business is active and attentive. In terms of internal process, maintain an editorial calendar not just for new content, but for updating existing high-value content. Track which pieces bring in steady traffic and ensure they don’t fall behind competitors’ content in quality or accuracy over time.

In summary, don’t set and forget your content. Treat it as an asset that requires periodic investment. Your goal is to consistently offer the most relevant, accurate answers. Doing so will pay dividends as AI and traditional search algorithms alike reward that freshness with sustained visibility. The perfect piece of content for AI search is one that’s not just well-crafted once, but well-maintained over its lifespan.

The Technical Side of AI Content Optimization

All the great content in the world won’t help if search engines and LLM crawlers, and bots can’t properly crawl, index, or interpret it. The technical underpinnings of your content, site architecture, crawlability, and indexation remain mission-critical in the AI search era. Ensure your site meets Google’s technical requirements so content can be discovered and understood.

Start with crawlability: Can search engine bots access your pages easily? This means having a logical site architecture with clean navigation and internal links (more on internal linking later). Important pages should not be buried several levels deep or orphaned with no links pointing to them. Check your robots.txt and meta tags to be sure you’re not unintentionally blocking important content. Google specifically advises to allow Googlebot to crawl and to serve content with a 200 OK status code. Similarly, ensure that your pages aren’t behind login walls or paywalls that prevent AI from training on or retrieving them - if it’s private to users, it’s invisible to AI.

Next, consider indexability: Even if crawled, will your content be indexed and eligible to appear in results? Do not use a noindex tag on pages you want to rank (and conversely, use it if there are pages you want excluded). Keep an eye on your index coverage in Google Search Console to catch any pages that are discovered but not indexed due to issues. Also, site speed and rendering can affect indexation: heavy client-side rendering (JavaScript) might mean Google’s crawler can’t see your content immediately. Large Language Models primarily train on the raw HTML of pages, not on content that only appears after scripts run. Therefore, prefer server-side rendering or static HTML for crucial content. As noted earlier, important text hidden behind interactive elements or requiring user action won’t be seen by AI bots in a timely manner.

Additionally, maintain a solid site architecture. Use a coherent URL structure and organize content into categories or silos that make sense. For example, a recipe site might have /recipes/desserts/chocolate-cake - this hierarchy gives context. A well-structured site helps search algorithms discern relationships between pieces of content, which can enhance contextual relevance. A flat, interlinked structure with relevant cross-links also ensures no page is too far from the homepage.

Don’t forget basic technical SEO hygiene: unique title tags and meta descriptions for each page, proper use of canonical tags to avoid duplicate content issues, an updated XML sitemap to feed search engines your URL list, and using HTTPS for security. While these might seem old-school, they still form the foundation for how content is discovered and ranked. Google’s John Mueller advises that meeting technical requirements “covers you for search generally, including AI formats”. In other words, if your content is technically sound for traditional SEO, you’re off to a solid start for AI SEO as well. Just be mindful of the additional point: AI systems prioritize speed and clarity in crawling, so lean towards simplicity - lightweight, accessible HTML over complex scripts.

Schema Markup: How Can Schema Markup Help AI Understand Your Content?

Yes, I keep talking about it and am not going to stop! 

Structured data, aka “schema markup,” is your secret weapon for speaking directly to search engines and AI in their own language. By adding schema markup to your pages, you provide machine-readable context about your content, which can enhance how your material is understood and presented. Google affirms that structured data helps its systems better interpret your content and can make your pages eligible for special rich results.

For AI search, schema can be especially powerful. Why? Because AI summaries and answers often draw on specific facts or snippets from pages, schema markup explicitly highlights those facts. For example, implementing FAQ schema around common questions on your page might increase the likelihood that Google’s AI will draw directly from those Q&A pairs when formulating an answer. Similarly, the HowTo schema can outline step-by-step instructions, the product schema can feed detailed specs or ratings, etc. In essence, schema markup provides a structured framework that AI can easily parse.

To leverage this, identify what type of structured data fits your content. Some popular ones include: FAQPage (with questions and answers), HowTo, Article (with author and date info), Product (with price, availability), Recipe (ingredients, cooking time), BlogPosting, and Organization (for your business details). Mark up elements that align with what your audience might ask. For instance, if you have a travel article with a list of top restaurants, adding Restaurant schema for each entry (with attributes like cuisine type, rating, price range) could help an AI that’s compiling a travel guide answer. Always ensure that any content you mark up with schema is also visible on the page (don’t add schema for information that isn’t actually shown to users).

Simple Blog Schema Example from the Beeby Clark Meyler blog:

Implementing schema is not overly difficult - often it’s a JSON-LD snippet you can add to your page template. There are many plugins and schema generators, and Google’s Structured Data Testing Tool to help validate it. The payoff can be significant. Pages with schema are more likely to appear as rich results (like those expandable FAQs in search results, star ratings, knowledge panel info, etc.), which in turn signals to users (and AI) that your content is authoritative. Moreover, in Google’s new SGE (Search Generative Experience), we’ve seen AI answers accompanied by citations and even images pulled from pages; having structured data increases the chance your content is chosen for those enhancements. In fact, structured data clarity “increases the chances of appearing in rich snippets and enhanced search features”.

Some of the most common Schema types. See schema.org for more info and Schema types.

One caveat: follow schema guidelines carefully. Don’t markup content that doesn’t match or try to spam with irrelevant schema, as Google could penalize or ignore it. Focus on the accuracy and relevance of markup. Done right, schema markup is like adding signposts for AI: it explicitly labels the most important pieces of your content, making it easier for AI to grab the right info to answer a query. In a world where AI might be synthesizing answers from multiple sources, you want to make your source as easy as possible to mine. Schema helps you do just that.

Page Speed and Performance are Important for AI Crawling

Page experience has long been an SEO factor, and it’s just as crucial for AI search. Users who click an AI-generated result expect a fast, seamless experience. After all, the AI just gave them a concise answer, so if they choose to “learn more” on your site, don’t disappoint them with a sluggish or cluttered page. Google has made it clear that even the best content will be disappointing if the page itself is slow, confusing, or hard to use. So, performance and UX optimizations are an integral part of “perfect content” for AI search.

Believe me, we understand the ongoing struggle of optimizing website performance. Achieving perfect scores can be challenging, often due to factors beyond your control, like CMS and hosting. However, there's always room for improvement in areas you can influence!

Start with load speed. Optimize images, leverage browser caching, use a fast hosting solution, and minimize render-blocking resources. Google’s Core Web Vitals (like Largest Contentful Paint and Cumulative Layout Shift) are metrics worth monitoring - they rolled these out as ranking factors in 2021 to emphasize the importance of speed and stability. A faster site not only pleases users but also helps with crawling (Googlebot has a budget; a quick-to-load site means it can crawl more pages) and possibly with how AI evaluates quality. If two pages have similar info and one loads significantly faster, it’s reasonable to think the faster one offers a better user experience and might be preferred.

Beyond speed, focus on page clarity and usability. Make sure that when a visitor lands from an AI summary, they can easily find the deeper content promised. If your page is “cluttered, difficult to navigate, or makes it hard to find the main information,” users will bounce - and that negative signal can hurt you. Ensure your content is front and center, not buried under huge banners or interrupted by aggressive pop-ups. Use a clean design with readable font sizes and sufficient contrast. Mobile-friendliness is non-negotiable as well; more than half of searches are mobile, and AI results appear on mobile too.

Consider user engagement signals. While traditional ranking was heavily about relevance and backlinks, AI search could place more weight on engagement metrics - if users consistently click a result and then quickly hit back (because the page was too slow or unhelpful), that content might be deemed inferior. Google’s AI Mode specifically notes the importance of “dwell time” and user interaction as indicators of content quality. So optimizing for a positive user experience (fast load, easy navigation, helpful layout) indirectly boosts your AI search performance by keeping those visitors engaged once they arrive.

Google Page Speed Insights

In summary, treat page speed and UX as signals of content quality. A snappy, well-structured page is more likely to satisfy users and thus satisfy the AI that sent them there. Use tools like Google PageSpeed Insights or Lighthouse to audit your pages. Trim the fat (excess scripts, huge videos loading automatically, etc.), and test on real devices. Remember, an AI search result might just pull a snippet from you, but if the user clicks through, that’s your chance to shine with a great experience. Don’t squander it with a slow or messy page. Fast, user-friendly pages signal quality to both users and AI, reinforcing that your site is a good destination for future searchers.

BCM runs weekly audits tracking your site’s overall performance, and data on a page-by-page level is also available. We can assist in interpreting this data if needed.

LLMS.txt For Guiding the AI Bots and Helping Them Understand

As AI assistants like ChatGPT, Claude, and Perplexity increasingly reference live web content to answer user queries, a new question has emerged: How can I help these large language models (LLMs) better understand and represent my content?

Enter the llms.txt file — a proposed new standard designed to work alongside, not replace, things like robots.txt. But where robots.txt tells bots what not to crawl, llms.txt helps LLMs discover what’s important about your site and where to find it.

What Is llms.txt?

llms.txt is a simple, human-readable Markdown file placed at the root of your domain (e.g. yourdomain.com/llms.txt). It’s intended to give LLMs a high-level guide to your content — pointing them to important documentation, use cases, product pages, or examples that represent your site accurately.

It’s not about blocking or training permissions like robots.txt. Instead, it’s a curated summary of your most useful, trustworthy resources, optimized for AI tools to reference in real time during inference.

Why It Matters in the AI Search Era

Language models are constantly looking for context. When your site has a clear llms.txt, you’re proactively telling AI systems, “Here’s what matters here — and here’s where to find it.” That can improve how (and whether) your brand is represented when users ask a question that touches your space.

While adoption is still early, OpenAI, Anthropic, and other major AI developers have signaled interest in supporting this standard. Think of it less as an SEO tactic and more as AI discoverability insurance — particularly if your site contains technical documentation, educational resources, or product explainers that often get referenced.

Should You Create One?

If you publish content that could benefit from more accurate AI citations, yes, creating an llms.txt is a smart, lightweight move. 

You can include things like:

• Links to your best product pages, blog posts, or help articles
  
  
• A short description of your business or platform
  
  
• Any additional guidance you want LLMs to keep in mind
  
  

Here’s a simple example:

# BCM Digital

> BCM helps brands grow through intelligent media and content strategies.

## Key Resources

- [SEO Services](https://example.com/seo)

- [AI Content Optimization Guide](https://example.com/ai-guide)

- [About Us](https://example.com/about)

As of now, there's no required syntax, but clarity, relevance, and good linking are key. This file won’t affect your Google rankings, but it might just help LLMs tell your story more accurately. It may take some time before this standard is more widely utilized, but it is also time to stay ahead of the game.  There will be more on this to come from BCM.

Internal Linking and Content Hubs

No page is an island - especially in the context of AI understanding. To bolster your content’s authority and visibility, build a network of internal links and content hubs around your topic areas. Internal linking has always been an SEO best practice for distributing link equity and guiding users, but it’s even more meaningful now as AI algorithms attempt to grasp your site’s overall expertise on a subject.

A content hub (or “topic cluster”) strategy can be highly effective. This means creating a central, comprehensive piece of content (often called a pillar page) on a broad topic, and then having multiple supporting articles that dive into subtopics, all interlinked. For example, you might have a pillar page on “Email Marketing Best Practices,” with satellite pages on “How to Write a Great Subject Line,” “Email Segmentation Strategies,” “A/B Testing Your Emails,” etc. Each of those subtopic pages links back to the pillar and to each other, where relevant, forming a hub of interrelated knowledge.

Why does this matter for AI? Because AI search will gauge not just the relevance of a single page, but the depth of coverage a source has on a topic. By clustering your content and linking it, you signal that your site has breadth and depth - a strong topical authority. Google’s AI mode is said to evaluate content across multiple formats and pages, rewarding a “strong topical presence”. In practice, when an AI answers a complex query, it might combine information from different pages of your site (since LLMs can retrieve from multiple sources). If your own pages reinforce each other, you increase the chance that the AI pulls entirely from your ecosystem of content, citing perhaps your pillar page as the main reference.

To implement this, be intentional with internal links. Within your content, whenever a related concept is mentioned that you have a page for, link to it with descriptive anchor text. For instance, in an article about social media marketing, if you mention “SEO ROI” and you have an SEO hub, link the text “SEO strategy” to that page. These contextual links help AI understand the relationships between content pieces. They also help users navigate and spend more time on your site (which, as noted, is a positive engagement signal).

Additionally, ensure your navigation and taxonomy support these hubs. Use categories, tags, or menus to group related content. A well-structured site architecture where, say, all “AI Marketing” content is under an /ai-marketing/ section or at least cross-linked, creates a silo of expertise. Some SEO experts mention the concept of “entity hubs” where all content about a specific entity (topic) is interlinked to create a authoritative resource hub - this can be powerful for AI which builds knowledge contextually.

Another tip: create summary or roundup pages that link out to multiple resources. For example, a page like “Ultimate Guide to Content Marketing” which briefly covers subtopics and links to detailed posts on each. This not only serves as a great user resource, but in AI terms, it positions that page as a central node of information. If an AI is looking for an authoritative source to cite, a page that clearly organizes a wealth of info (with links to deeper content) might be seen as very useful.

Remember to regularly audit internal links as you add new content. Go back to older posts and link to your new ones where relevant, and vice versa. This keeps your content web tight and up-to-date. The outcome of strong internal linking and hubs is a clustered authority: you’re not just answering one question in isolation, you’re answering the entire set of user questions around a topic across your site. That makes you the kind of source AI search would be confident in highlighting. As a bonus, internal linking boosts traditional SEO by improving crawl paths and distributing link equity, so it’s a true win-win.

Monitoring AI Search Performance and Optimization

After crafting and structuring your content, the job isn’t done. The “perfect” piece of content is one that’s continually monitored and optimized for performance. The perfect piece of content is one that’s constantly evaluated and refined. But here’s the challenge: the signals we used to rely on — impressions, clicks, sessions — will not tell the full story anymore. With AI Result Snippets (AIRS) and LLMs like ChatGPT, Claude, and Perplexity providing answers directly in-platform, a growing share of brand exposure happens without a click.

So, how do we measure the business impact of content in the new AI-driven search Landscape?

It used to be simple:
User sees SERP –> clicks result –> lands on site –> converts.
We could assign a value directly to that path.

Now, it’s murkier:
User sees brand or excerpt in AIRS or LLM  –>remembers  –>converts days later, or never clicks at all.

This new behavior compresses the funnel and decouples exposure from visits. But just because it’s harder to measure doesn’t mean it has no impact; in fact, many of your most valuable users may now first encounter your brand in a clickless environment.

What to Track Instead

You’ll still want to monitor:

• Engagement metrics (time on page, conversions, bounce rate) from AI-referred traffic
• Search Console trends, even if AI Overview data hasn’t rolled out yet
• Emerging tools that aim to detect when your content is cited in LLMs or AIRS

But also consider:

• Brand lift studies to measure whether AI-based exposure is increasing unaided brand recall
• Custom experiments or matched-market tests to isolate the impact of content appearing in AIRS or LLMs
• Direct traffic surges following known AIRS exposure (e.g., a spike in branded search after Perplexity answers feature your domain)

We may need to start treating LLM exposure more like an impression channel — closer to display or video in attribution modeling — where the value is not always direct but contributes to downstream actions.

This will take a data science mindset and increased focus on testing, stitching together multi-touch behaviors, evaluating modeled conversions, and testing hypotheses about exposure without clicks. As tools evolve, so will attribution models, but marketers can’t wait for perfect visibility. We need to experiment with what we can and use directional data to keep improving.

Lastly, stay educated and agile. The AI search landscape is evolving rapidly. Today’s best practice might shift as AI models get updated or user habits change. Follow industry news (Google’s Search Central blog, Search Engine Land, etc.) for any new guidelines on AI search optimization. The key is a mindset of continuous improvement. The beauty (and challenge) of digital content is that you can always optimize further. And don’t downplay the small optimizations. The aggregation of the 1% improvements is usually what we’re working on and will make a difference over time.

Set up a routine: maybe a monthly or quarterly review of your top content pieces for AI performance. Look at the data, read the latest advice, and make an optimization plan. Over time, this iterative approach will inch your content closer and closer to “perfect” for AI search purposes. As Google’s guidance suggests, focusing too much on just raw clicks is shortsighted - instead, evaluate the overall value of the visits and engagement you’re getting. Optimize for the outcomes (leads, sales, subscriber growth, etc.), and that will inherently align you with what the algorithms want: user satisfaction.

Tools for Tracking AI Search Visibility Over Time?

One of the biggest challenges with optimizing for AI search isn’t just getting visibility, but measuring it. And historical data? There isn't much, but it's building.

Unlike traditional organic search, where rank tracking has matured over 20+ years, AI-powered results (like SGE, Copilot, and Perplexity) are dynamic, personalized, and rarely expose who gets cited — and when.

The problem is pretty simple if you think about it.  Ask ChatGPT or Google AIO the same question twice, and you will get two different answers.  Sometimes with sourced citations, sometimes not. Sometimes with hallucinations and sometimes 100% fact. 

Measuring that directly is impossible, but there are methods for proxying the available data.

Several platforms are starting to offer probabilistic historical tracking for AI search visibility, including:

• SEMrush: Basic AI Overview tracking by keyword

• seoClarity: Daily visibility in SGE with limited historical granularity

• Authoritas: Deeper tracking of GenAI results via API

• BrightEdge: Enterprise-grade AI SERP monitoring

While none offer perfect visibility (yet), using these tools in combination with manual logs and AI Overviews snapshots can help you identify patterns in which types of content tend to get cited over time and the probability of your content getting cited. BCM is having many conversations with these vendors and others to ensure we're using the best tools for tracking.  We may even build it ourselves.

It’s 2025. Your Competitors Are Also Reading This.

Your competitors are reading this article (or one just like it). They’re running the same prompts through ChatGPT, trying to reverse-engineer AI SEO. They’re attending webinars on AI content optimization, listening to the same podcasts, and bookmarking the same case studies.

Some of them are executing.

Now Is the Time to Move from Reading to Leading

AI-optimized content isn’t a trend. It’s the new operating system for digital marketing. But creating more content with AI isn't the primary strategy - creating better, optimized, purpose-built content is.

If you’re waiting to “see how this all plays out,” you’re handing your advantage to someone else. And you won’t know it until your rankings, traffic, and authority start fading without a clear reason.

So here’s what you do next:

• Structure your content for AI agents — Clear, scannable sections(Chunks), concise answers, and schema markup (like FAQ schema-see below) matter more than ever.
• Focus on information quality and specificity — AI search favors clear topical depth, not vague generalist copy.
• Claim your citations—If you want to appear in the answer, not just under it, your content needs to be reference-worthy, not just keyword-optimized.

TL;DR

2025 isn’t the year to watch — it’s the year to act.
Because the brands that will dominate AI Search in 2026 are laying the groundwork right now.

Frequently Asked Questions (Or another chance to say, “See what I did there?”)

Do I need to rewrite all my existing content for AI search?
No, but you may want to consider it for your most important pages. Start by identifying your highest-value pages and optimizing them with better structure, semantic clarity, schema markup, and updated information. Then build a review/update cycle.

How can I tell if my content is appearing in AI Overviews or cited by AI tools?
Currently, Google Search Console does not report AI Overview impressions, but it’s expected soon. You can track click referrers in Google Analytics from those websites. Use tools like Semrush, Otterly, or Profound, and others that are developing tools to understand AI presence through algorithmic modeling and data proxies.

Will using llms.txt block my site from showing up in AI search results?
No — llms.txt controls training, not visibility. To manage visibility in search results, use robots.txt and meta tags. The purpose of the llms.txt file is to point LLM bots to important documentation, use cases, product pages, or examples that represent your site accurately

Is structured data really necessary if my content is already well-written?
For your best chance at success, yes.  Schema markup increases your chances of being featured in rich results, cited in AI responses, and understood faster by machines. Think of it as metadata that supports your great writing.

What’s the easiest thing I can do today to make my content more AI-friendly?
“Easy” is relative, but for the content team, add clear, question-based subheadings and directly answer each question in the first sentence of the section. Also, check for semantic variety, page speed issues, and missing alt text.  For the development team, ensure that schema markup can be easily added to your site’s pages and that the content team is able to update the markup.

How often should I update content for freshness signals?
It depends on the topic. Evergreen content can be refreshed yearly. Time-sensitive or competitive content (like “2025 marketing trends”) should be reviewed quarterly or sooner. Etc.

What kind of media works best in AI search results?
Original visuals with alt text and relevant schema are ideal. Infographics, annotated screenshots, and short explainer videos often perform well in AI Overviews and featured snippets.

How do I balance writing for humans with optimizing for AI?
The goals of human-centered writing and AI optimization are increasingly aligned, especially if you're following E-E-A-T principles (Experience, Expertise, Authoritativeness, and Trustworthiness).

Focus first on the user’s needs. AI systems are designed to serve users - so content that satisfies intent, demonstrates credibility, and adds unique value is the same content most likely to be surfaced in AI-powered results.

In short: write for people. Structure and support it so AI can understand it. That’s the balance and the opportunity.

Does AI optimization replace traditional SEO?
No. Think of it as an evolution. Traditional SEO practices like technical hygiene, internal linking, and keyword research still matter — you’re just adding a layer of AI-readiness to them.

Can BCM help with content optimization for AI search?
Yes — we’re actively guiding clients through this transition, from content audits and schema implementation to AI-driven content strategies and performance monitoring, to content development programs.

Final Thoughts on AI Search Content Readiness

The rise of AI in search is not the end of SEO or content marketing - it’s an evolution. The fundamentals of creating helpful, user-focused, well-structured content remain as important as ever. What’s changed is how that content is being discovered and consumed. By implementing the strategies above - from understanding user intent and structuring content for AI readability, to ensuring technical accessibility, demonstrating E-E-A-T, and continuously refining - you’ll position your content to excel in both classic search and the new AI-driven search experiences.

Remember, Google’s core goal (in addition to making money and keeping shareholders happy) is still to help people find outstanding, original content that adds unique value. If you keep that mission at the heart of your content creation process, you’re already on the right path. The rest is about fine-tuning for the medium: speaking the language of AI without losing the human touch. The perfect piece of content for AI search is, ultimately, just the perfect piece of content for your audience, period - delivered in a way that today’s technology can easily digest and disseminate. Stay user-centric, stay adaptable, and your content can thrive no matter how search evolves. This shift necessitates that content creators and SEOs adapt their strategies to align with AI-driven search, but those who do will find new opportunities to shine. 

As always, you know where to find me!

Some of the sources researched and quoted in this article:

• Google Search Central - Succeeding in AI Search (Google’s AI Experiences)
• Semrush - Impact of AI Search on SEO Traffic
• Search Engine Land - Why Your Content Is Invisible to AI Search Engines
• Search Engine Land - Chunks, Passages and Micro-Answers in Google AI Mode
• Search Engine Land - Search Everywhere Optimization
• Search Engine Land - 12 Actionable Tips for Google AI Mode

P.S.

We used a lot of acronyms here, though probably not all of the new ones being thought up by creative SEO's everywhere. There is no industry that has as many acronyms as the marketing industry! If you're looking for an explanation:

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