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Tag: Content Optimization

Unlock AI Search Success: Master Content Optimization
As someone who’s been working with brand content for a while, I’ve gathered quite a bit of material that could use a refresh to improve our presence in AI-generated search results. In this context, let’s call this AEO—Answer Engine Optimization—to encapsulate our strategy going forward.

Recently, I’ve been fielding a lot of questions from brand marketers eager to enhance their AEO. To them, the suggestion of revising old content has often been an illuminating solution.

This insight opens up several important follow-up questions that I’d like to delve into now.

How do you reformat content for better AEO performance?

When it comes to content reformatting, I follow these core principles: topical breadth and depth, chunk-level retrieval, and answer synthesis.
- Topical breadth and depth.
- Chunk-level retrieval.
- Answer synthesis.
Let me break down what these mean in practical terms.

Optimize for topical breadth and depth

I organize my site using a hub-and-spoke model. This involves creating a hub page for each main category or keyword theme, which serves as a comprehensive introduction and links to detailed spoke pages.

Each spoke page tackles one specific aspect in detail, which helps in addressing various user questions and broadens the overall topical landscape for our content.

By linking related spoke pages to each other and back to the hub, I reinforce content connections, providing AI systems with clearer signals about topic relationships.

Optimize for chunk-level retrieval

I focus on making each content chunk comprehensible on its own, without relying on the entire page for context. This involves crafting sections that are semantically tight, with each focused on a single idea.

Keep each passage tightly centered on one concept — Our Family Wizard does an excellent job of this

Optimize for answer synthesis

I start answers with a clear, concise sentence, then elaborate using well-structured summaries like “Summary” or “Key takeaways.” A plain, factual style works best.

Here’s an example of effective formatting from Baseten, which places a TL;DR summary at the beginning of a post discussing AI inference:

Dig deeper: How to keep your content fresh in the age of AI

How will humans react to that formatting?

My experience so far has been that AI readability, focused on clarity, actually appeals to human readers who appreciate content they can understand quickly.

AI systems resonate with content that:
- Names rather than infers answers.
- Has sections with clear intent.
- Allows easy extraction of key points without rewriting.
In some cases, it requires being more explicit than traditional SEO practices, like defining terms upfront, summarizing sections, and providing conclusions early on.

The challenge for me is balancing clarity with nuance, especially since AI-produced content can sometimes oversimplify intricate details.

When optimizing, I focus on:
- Explaining initially, then expanding.
- Identifying insights, then substantiating them.
- Presenting the answer before adding any complexities.
This strategy makes the content appealing for both AI and human audiences.

Although, I’ve noticed that AI-generated content sometimes feels too generic, especially when it lacks personal perspectives and insights not readily available online.

I keep an eye out for AI content characteristics like the “dreaded em dash” and aim to remove them when refining my content.

Dig deeper: Refreshing content: How to update old content to drive new traffic

How do you prioritize which content to revise?

In AEO, I find the focus shifts from sheer traffic metrics to answer value.

I begin by identifying content that:
- Displays clear expertise or proprietary insight.
- Addresses repeated questions but doesn’t state answers clearly.
- Is already used internally for explanation or training purposes.
Another vital factor: if content indirectly highlights one of our core services, it becomes a priority for revision.

Content types like reports and evergreen guides often top my list for prioritization due to their structured nature, ideal for AEO adjustments.

My simple AEO prioritization test involves:
- Checking if an AI model can quote or summarize the page accurately.
- Determining if the page’s answer is clear within a few seconds.
- Ensuring key takeaways are explicitly labeled.
If the answers are ‘no’ and the content is crucial for business growth, it’s likely a strong candidate for reformatting.

Dig deeper: How to use AI to refresh old blog content

How do you approach metadata when revising content for AEO?

While SEO uses metadata as ranking levers, in AEO, these elements act as context anchors.

Let’s dive into some key elements.

Title tags

For AEO, title tags should describe the page’s main answer or purpose in addition to the topic.

A title like “Session replay software” might become “Session replay: what it is, when to use it, and when not to use it.” Clearer signals aid AI citation decisions.

Headings (H1-H3)

Rather than generic headers, I align them with specific questions or assertions suited for user inquiries.
- What is compliance monitoring?
- Why does compliance monitoring matter for {x} industry?
- Issues from lacking compliance monitoring
- When to invest in compliance monitoring?
If answering these takes more than a few sentences, it likely needs refinement for clear, direct responses.

Meta descriptions

In AEO, meta descriptions serve as a compressed intent signal rather than appearing directly in search results.

They should clarify:
- The target audience of the content.
- The problem it addresses.
- Its framing context.
Viewed through the AEO lens, they function as concise briefing notes for both users and AI systems.

Dig deeper: Meta tags for SEO: What you need to know

What changes—and what doesn’t—in the shift to AEO

While SEO and AEO often align, understanding where they diverge helps optimize for AI search visibility.

I’m not suggesting a drastic shift in strategy, but recognizing that AI engages with content differently from traditional algorithms is crucial for repurposing valuable content.

Inspired by this post on Search Engine Land.
March 2, 2026
Mastering Fresh Content: Stand Out in an AI-Driven World

I’ve come to realize that AI has dramatically simplified the publishing process, but it also means standing out amidst the noise is increasingly challenging. The good news is, by focusing on clarity, intent alignment, and a few strategic SEO adjustments, we can make significant progress.

As AI breaks down the barriers to production, the web is getting flooded with content that is polished, optimized, but often lacks distinctiveness. When everything seems competent, you and I must strive harder to differentiate our voices.

Though AI has transformed how content is churned out, the core of what users seek—intent—remains unchanged. They sift through headlines and descriptions, rewarding clarity and effectiveness. This is why foundational elements matter even more now.

I find that keeping content fresh isn’t about being novel for novelty’s sake. It’s about diving back into what makes content truly unique: distinct messaging, structured delivery, and a deep grasp of our audience’s needs.

The Real Problem with AI Content

The crux of the issue with AI-generated content isn’t its factualness—it’s its sameness. AI draws from vast pools of existing content, often reproducing unremarkable tropes and conclusions. Individually, they seem fine; collectively, they’re indistinguishable.

This homogeneity is why so much content today feels the same. Even when relevant, it seldom provides a unique reading experience.

Both users and search engines are responding in kind. In a sea of similar content, differentiation becomes key. At this juncture, originality, specificity, and intent alignment have taken on heightened importance.

Ironically enough, AI has increased the value of originality. As automated content inundates the web, signals like clarity, usefulness, and intent alignment become beacons of high-quality content.

Many teams falter here, competing with AI by focusing on quantity over quality. Freshness isn’t about novelty; it’s about crafting content that feels distinctly human and undeniably helpful.

Fresh, Unique Content is Still Built on Classic SEO Principles

Ever since content creation tools evolved, what’s been constant is how people interact with search engines. Users still show up with an issue to solve, skimming through results to pick what seems most relevant.

Despite the rise of AI, this behavior endures.

Page titles, headings, and meta descriptions serve as that crucial first contact with the user. They function almost like ad copy, contrary to assumptions that these elements are becoming obsolete.

Classic SEO principles—clear search intent alignment, descriptive language, organized structure—continue to underpin fresh content.

Although these aren’t groundbreaking ideas, their importance has surged. A tweak in clarity doesn’t just help search engines index a page; it helps users find answers to their questions.

Small SEO Changes Can Lead to a Strong Impact

A recent experiment on my website examined whether more descriptive titles could boost clicks without altering the underlying content. We tested the hypothesis by aligning page titles more closely with search intent and user needs.

The result? A greater alignment led to a substantial increase in click-through rates, proving that small changes can powerfully impact visibility and engagement.

Strategies for Keeping Content Fresh in an AI-Saturated World

Remaining fresh in the AI era isn’t about jumping on every new tool but requires intentionality in creating, positioning, and maintaining content.

1. Treat Intent as Strategy

The essence of SEO has always been search intent, not keyword stuffing. Before crafting content, ask what problem the searcher is trying to address and what a good answer would look like in their context.

2. Use Page Titles and Headlines as Tools

In a crowded SERP, an effective title is crucial to catch a user’s attention and make them click.

3. Refresh Before You Create

Oft-overlooked is the power of improving existing content. You don’t need to produce new content incessantly when updates can achieve better results.

4. Lean into Specificity and Constraints

While AI excels at general advice, human-guided content shines through specificity and context, offering expert insights and breaking down misconceptions.

5. Use AI as an Accelerator

AI should accelerate tasks that don’t require judgment. Editorial responsibilities still lie with us, ensuring content aligns with our goals.

6. Measure Freshness by Behavior

It’s not the volume of content but engagement metrics like time on page and scroll depth that define freshness.

7. Accept that ‘Traditional’ Doesn’t Mean Outdated

Mainstays like clarity, structure, and relevance have only gained importance in our AI-driven landscape.

Why Fresh Content Actually Wins

While AI has revolutionized content speed and accessibility, truly effective content remains appealing and relevant, aligning with users’ search intent and preferences.

Inspired by this post on Search Engine Land.

February 24, 2026
Boost SEO: Mastering Content Tools for Google’s Initial Retrieval
I often find myself over-crediting Google’s understanding of my web pages. It’s easy to imagine Google as an AI wizard that fully comprehends nuances, expertise, and quality. Yet, during the DOJ antitrust trial, I learned something intriguing.

Google’s VP of Search, Pandu Nayak, testified about a first-stage retrieval system that relies heavily on word matching, rather than any magical AI trick. The foundation is based on older information retrieval techniques, like inverted indexes and postings lists. Okapi BM25, a well-known lexical retrieval algorithm, was cited as a crucial link in Google’s system evolution.

After this initial stage, which is all about word matching, Google employs advanced AI models like BERT on a smaller set of content. These content tools are key to optimizing documents for this stage, yet many use them incorrectly, despite their real value.

In this exploration, I’ll dive into the mechanics of first-stage retrieval, its significance, what content tools actually reveal, and how to effectively use these tools to get noticed by Google without obsessing over perfect scores.

How first-stage retrieval works and why content tools map to it

Understanding BM25 is essential. This retrieval function, crucial to Google’s first-stage system, prioritizes topicality by scanning vast amounts of data quickly, narrowing candidates for further processing.

And for me, as a content creator, certain details stood out.
- Term frequency with saturation: At some point, repeating keywords has diminishing returns.
- Inverse document frequency: Less common terms score higher, so specificity is rewarded.
- Document length normalization: Longer documents can be penalized, as density matters.
- The zero-score cliff: Not mentioning a term means zero visibility for related queries.
So, effectively using these tools means identifying gaps in my content and ensuring relevant terms appear. Tools like Surfer SEO and Clearscope guide me in avoiding the zero-score pitfall, offering significant value.

AI enhancements like RankEmbed can assist, but counting on them to fill vocabulary gaps is a gamble. I focus on ensuring my core content is strong at the first retrieval stage.

What the research on content tools actually shows

Research shows a weak-positive correlation between content tool scores and rankings, with studies yielding a 0.10 to 0.32 range. While meaningful, these findings are often derived from studies conducted by vendors using their own tools.

The real test remains: do these tools help a new page climb in rankings? The consistent finding is their efficacy in positioning content for retrieval, not securing high rankings against competitors.

Why not skip these tools altogether?

It’s a mistake to write off these tools, especially since expert writers, myself included, often use overly technical language that audiences may not search for or understand, a classic example of the “curse of knowledge.”

A real-world example is Clearscope helping Algolia align their language with their audience’s searches, ultimately lifting their content’s page ranking significantly.

By showing me what vocabulary is used by successful pages, content tools reduce hours of analysis to minutes, whether I’m a frequent publisher or a solo blogger.

What about AI-powered retrieval?

Dense vector embeddings power AI retrieval but supplement rather than replace word matching due to computational limits. Hybrid systems combining traditional and AI search techniques consistently perform best.

The takeaway for me is clear: AI matters, but traditional retrieval carries significant weight and serves as the foundation of effective content scoring tools.

How to actually use content scoring tools

Common advice tells me to get high scores with tools like Surfer SEO or Clearscope. However, I focus on using them wisely to target the zero-score terms and refine competitor analysis.

Running these tools during research, not during writing, ensures I remain focused on quality and audience relevance rather than just scoring high numbers.

A note on entities

Google’s Knowledge Graph processes the relationships between entities more deeply than most tools measure. Recognizing the gap between flat keyword lists and Google’s more complex understanding helps me focus on providing detailed context.

Retrieval before ranking

Content tools effectively decode retrieval stage vocabulary, a less sensational, but fundamentally honest function. They help me pass the first stage of Google’s pipeline, setting the stage for engaging with more advanced ranking factors later on.

Inspired by this post on Search Engine Land.
February 23, 2026
ChatGPT Prefers Early Content: 44% of Citations from Opening Sections

I recently stumbled upon a fascinating study that shows how ChatGPT pulls most of its references from the beginning sections of content. It’s clear from this research that the AI favors straightforward definitions, a balanced tone, and densely packed entities.
According to Kevin Indig, a Growth Advisor who analyzed 1.2 million AI responses and 18,012 citations, ChatGPT has a strong preference for using citations from the top of the content. This was a revelation for me and definitely something to keep in mind when writing.
Why we care. The traditional search landscape often rewards depth and gradual payoffs. However, AI is changing that game by favoring clear entities and direct answers right at the start. If I don’t make sure my key information is front and center, it’s less likely to be cited by AI.
By the numbers. In examining various datasets, Indig’s team found a “ski ramp” pattern—44.2% of citations originate from the first 30% of content, 31.1% from the middle, and only 24.7% come from the final third, with a noticeable drop towards the end.
Breaking it down even further, I learned that at a paragraph level, AI citations largely come from the middle sentences (53%), with 24.5% from the first sentence and 22.5% from the last.
The big takeaway. This really drives home the importance of front-loading critical insights at the article level. Within paragraphs, focusing on clarity and meaningful content rather than trying to hook readers with a dramatic first sentence seems to be more effective.
Why this happens. Large language models like ChatGPT are trained on various styles of writing that prioritize a “bottom line up front” approach. It seems these models use the early sections as a framework for interpreting the rest of the data.
Efficiency and context establishment remain key priorities for these models, even though they can process large sets of data.
What gets cited. Indig noted five key traits of content frequently cited by ChatGPT: definitive language, a Q&A structure, entity richness, balanced sentiment, and business-grade clarity. Learning this has been incredibly insightful for how I craft my content.
Indig’s team looked at a massive volume of data, identifying the traits of highly cited content by analyzing 18,012 verified citations from ChatGPT responses. The study focused on where and why the AI pulls content, using advanced techniques to match responses to source sentences.
Bottom line. It seems the narrative approach of crafting an “ultimate guide” might not be the best for AI retrieval. Instead, a more structured, briefing-style format appears to be more successful.
This study convinced me that writers now face what Indig calls a “clarity tax.” We need to present definitions, entities, and conclusions upfront rather than saving them for the conclusion.
The report. For those interested, you can delve deeper into these findings in The science of how AI pays attention.

Inspired by this post on Search Engine Land.

February 18, 2026
Unlock AEO Success with Content Siloing: Boost Authority & Crawlability

Do you want to take your Answer Engine Optimization (AEO) to the next level? Content siloing might just be the strategy you need. It’s a tactic that has transformed how I approach structuring topics to enhance authority and improve crawlability. Let’s delve into what content siloing is and how you can successfully implement it to boost AI citations.

Think of content siloing as creating a tightly knit topic network within your website, where each piece of content supports and strengthens the others. By organizing related content into isolated ‘silos,’ you not only streamline user navigation but also make it easier for search engines to index and understand the relevance of your content. This improved visibility can lead to better ranking in AI-powered search results.

Implementing content siloing involves a strategic approach to linking content. Begin by identifying your core topics and create subtopics that branch off these main areas. Each article within a silo should link to related content, reinforcing the overall theme and strengthening your site’s authority on the subject matter. This method ensures that your website becomes a trusted source of information in the eyes of both users and search algorithms.

Inspired by this post on HiGoodie Blog.

February 12, 2026
Enhance Marketing Success with Profound’s Knowledge Bases

As someone deeply involved in marketing, I know how crucial it is to have access to accurate and comprehensive company information. That’s why when our marketing team uses Profound to upload Knowledge Bases, it gives us a single source of truth for company-specific data.

This capability empowers us, as agents, to provide the right context about your brand every time we execute a marketing action on your behalf. This streamlined approach ensures consistency and accuracy in representing your brand.

Inspired by this post on Try Profound Blog.

February 12, 2026
Google AI Mode: Why Content Placement Isn’t Key

I recently came across an intriguing study by SALT.agency, focused on Google’s AI Mode and its citation practices. Contrary to popular belief, this analysis shows that AI Mode doesn’t have a preference for content placed “above the fold.”

After sifting through over 2,300 URLs cited by AI Mode, researchers discovered no link between a text’s vertical position on a page and its likelihood of being cited by Google.

Pixel depth is irrelevant. The study revealed that AI Mode pulls text from all over a page, even from content located thousands of pixels down.

Page layout vs. content visibility. While different layouts like large hero images or narrative formats might push text deeper down the page, this doesn’t impact whether it gets cited.

Subheadings make a difference. One key pattern identified was AI Mode’s tendency to highlight a subheading and the subsequent sentence. This suggests Google’s heading structures are crucial for content navigation.

Google’s approach. The assumption is that AI Mode employs fragment indexing technology, breaking pages into sections and pulling the most relevant fragment, irrespective of its position.

Dan Taylor, a partner at SALT.agency, confirms that there’s no secret formula for appearing in AI Mode citations. The focus should always be on crafting well-structured, authoritative content that meets customer needs.

Our takeaway. This study challenges the notion that specific AI-focused templates or rigid structures enhance content visibility in AI Mode. The real work lies in creating meaningful, structured content.

Research background. SALT scrutinized 2,318 URLs in AI Mode responses. The vertical pixel position of each cited fragment was meticulously recorded using a Chrome bookmarklet and a 1920×1080 viewport.

The study. Research: Does Structuring Your Content Improve the Chances of AI Mode Surfacing?

Inspired by this post on Search Engine Land.

February 9, 2026
Unlocking AI Visibility: Why Ranking Content Falls Short

I’ve been contemplating how even when content ranks well on search engines, it can still falter when it comes to AI retrieval. These AI systems assess pages very differently, based not just on their rank, but also on how information is extracted, embedded, and structured.

There’s an intriguing disconnect between traditional ranking and being successfully parsed by AI. A webpage can comply with excellent SEO guidelines and still miss the mark with AI-generated responses and citations.

In many situations, content quality isn’t the issue. It’s about whether the information can be reliably extracted after being segmented and embedded by AI systems.

This challenge is becoming increasingly common as search engines view pages as complete entities, but AI systems dive into the raw HTML to extract meaning from fragments rather than entire pages.

Crucial insights can get lost if they’re not appropriately structured or if they rely too heavily on visual rendering or inference.

This leads to a divergence between what’s visible in search and what’s accessible via AI, where content might exist in an index but lacks substantial meaning for AI retrieval.

The visibility gap is something I’ve been grappling with: Understanding the difference between ranking versus retrieval is key.

As search winds its processes around rankings, AI systems engage with fragments operated within a different representation of similar information. It’s here the visibility gap takes shape.

A page might rank high, but if its embedded content is incomplete or poorly organized, then the AI retrieval process becomes unreliable.

Treat retrieval as an entirely unique visibility factor. It doesn’t override SEO, but increasingly defines whether content can be effectively surfaced, summarized, or cited when AI filters come into play.
Dig deeper: What is GEO (generative engine optimization)?

Another structural issue arises when content never even becomes accessible to AI. Many AI crawlers only parse raw HTML without executing JavaScript or client-side rendering. This creates blind spots, especially for JavaScript-heavy sites where the core content may appear in Google’s index but remains invisible to AI.

Testing if your content appears in initial HTML is quite straightforward. Simply inspect the HTML response at fetch time rather than the version rendered in a browser.

Running requests with AI user agents like “GPTBot” reveals if your site returns blank HTML even if it appears fully populated to users, highlighting its absence in initial responses.

Tools like Screaming Frog can validate this at scale. Disabling JavaScript rendering can reveal what AI systems see—if your essential content only displays with JavaScript, it can be indexed by Google’s search but not by AI retrieval systems.

Keep in mind that even with content returned, excessive code and scripts can hinder extraction by AI systems. Cleaner HTML results in more reliable embeddings, enhancing AI visibility.

To tackle this, deliver fully rendered HTML when AI systems fetch your content. Pre-rendering can often fix these retrieval issues, ensuring content is present in initial responses.

Delivery can be managed effectively at the edge layer, providing AI crawlers with complete pages instantly. Human users receive a dynamic version while AI sees what it needs to extract meaning.

If pre-rendering isn’t viable, focus on ensuring primary content is accessible in a clean initial HTML response, even without script execution.

Columns laden with excessive markup can interfere with proper extraction, diminishing the content’s value.

The next structural failure to consider is when content is optimized for keywords rather than the entities AI seeks. Traditional SEO applies keyword relevance, but AI retrieves based on entity relationships.

Without clear definition, entity signals can weaken, causing pages to underperform in retrieval even if they rank well for queries.

AI evaluates sections independently once extracted, making the consistency of header tags essential to maintaining coherence.

Ensuring sections have a single, defined purpose allows for better embedding when isolated from larger context.

Finally, conflicting signals or metadata can dilute the semantics retrieved by AI, creating noise and ambiguity.

SEO doesn’t have to mean choosing between ranking and retrieval anymore. Both must be prioritized to succeed in today’s landscape.

Inspired by this post on Search Engine Land.

February 6, 2026
How AI Search Shapes SEO Visibility in Higher Education

I recently delved into fascinating research that sheds light on how higher education data informs SEO visibility and AI search. This exploration reveals what truly enhances visibility in this AI-driven era.

Contrary to some beliefs, AI search hasn’t rendered SEO obsolete. Now, the challenge is to excel both in ranking and in earning those vital AI citations.

Every time I Google something these days, there’s a significant chance an AI Overview will appear before any organic results or ads, framing my query, shortlisting sources, and shaping which brands I consider.

According to Ahrefs, AI Overviews now feature for about 21% of keywords. This means that while search rankings remain crucial, AI summaries increasingly dictate early brand consideration.

I’ve noticed that brands aren’t losing visibility just because they slip from the third to the seventh position on search engines. They’re often losing because they’re not even mentioned in AI answers.

Research conducted by Search Influence and UPCEA, where I serve as CEO, reveals insights into AI-assisted search usage and organizational adaptation in the higher education space.

Key Takeaways

AI citations are emerging as a trust signal: Being cited by AI can enhance credibility and secure early user consideration before direct source comparison occurs.

AI visibility is collective: AI pulls from various sources like YouTube, LinkedIn, and beyond—your URL isn’t everything.

Established brands need to adapt: Even well-known brands can be overlooked if their content doesn’t align with how users ask questions.

Most organizations recognize AI’s importance but lack action plans: Awareness exists, but execution is hindered by a lack of ownership and processes.

Content structure determines inclusion: Content that is structured for easy retrieval and decision-making often gets cited over long narratives.

To grasp the evolving search landscape, we need to examine both user behavior and organizational responses.

The study “AI Search in Higher Education: How Prospects Search in 2025” surveyed prospective adult learners and revealed significant patterns in online discovery using AI tools.

The findings show increased AI-assisted discovery and shifts in trust signals. Meanwhile, a UPCEA member institution poll uncovers gaps in AI strategy adoption.

The question isn’t whether AI search will impact your field; it’s whether your brand will be cited, overlooked, or represented by competitors.

Inspired by this post on Search Engine Land.

February 4, 2026
LinkedIn Learns to Thrive Amid AI-Powered Search Challenges

Have you heard the news about LinkedIn’s recent experiences with AI-powered search? It turns out that Google’s AI Overviews have significantly impacted our non-brand B2B awareness traffic, cutting it by up to 60% in some areas, even while rankings remained steady. This shift compels us to rethink our discovery strategies fundamentally.

I’ve noticed we’re transitioning from the traditional ‘search, click, website’ model to a more dynamic approach: ‘Be seen, be mentioned, be considered, be chosen.’ This new paradigm reflects a deeper understanding of modern digital visibility.

By the numbers. Early in 2024, our B2B organic growth team started researching Google’s Search Generative Experience (SGE). By the time SGE evolved into AI Overviews in 2025, the impact was undeniable. Our non-brand, awareness-driven traffic took a hit of up to 60% across specific B2B topics.

Yes, but. Many of the insights we’re gathering are reiterations of established SEO and AEO best practices. I’ve learned that LinkedIn’s guidance emphasizes strong headings, clear information hierarchy, improved semantic structure, and accessibility. It also stresses publishing authoritative, fresh content by experts and moving quickly to gain an early advantage.

Why we care. These strategies should be familiar to anyone versed in technical SEO and content-quality fundamentals. LinkedIn’s article may not present new tactics, but it highlights the relevance of modern SEO/AEO and AI-driven visibility.

Dig deeper. If you’re curious about optimizing for AI search, explore these 12 proven LLM visibility tactics.

Measurement is broken. A significant challenge we face is the ‘dark’ funnel—the difficulty of quantifying how visibility in LLM answers affects our bottom line when discovery occurs without a click.

LinkedIn has seen triple-digit growth in LLM-driven traffic to its B2B marketing websites. However, while we can track conversions from these visits, many websites are also experiencing similar growth. Although it’s an emerging channel, LLM-driven traffic still represents a small portion of overall traffic.

What LinkedIn is doing. To tackle these challenges, we’ve formed an AI Search Taskforce that spans SEO, PR, editorial, product marketing, and more. We’re correcting misinformation in AI responses, publishing new content optimized for AI visibility, and testing social content for AI discovery strength.

Is it working? It’s exciting to see our efforts yielding results. Our early tests are showing a meaningful increase in visibility and citations, particularly from our owned content. According to one external datapoint from Semrush, our structural advantage in AI search is significant, with Google AI Mode citing LinkedIn in 15% of responses.

Incomplete story. While LinkedIn’s developments are noteworthy, some details remain unclear. We’re still waiting on specifics like the exact topics behind the traffic decline, how much click-through rates have softened, sample sizes, and timeframes. These details could provide clarity on the broader industry impact.

Bottom line. I believe LinkedIn’s insights affirm that visibility is the new currency in digital marketing. However, there’s still much to prove if our playbook truly differentiates us from basic SEO practices.

Curious to learn more? Check out LinkedIn’s detailed article on our adaptation strategies: How LinkedIn Marketing Is Adapting to AI-Led Discovery

Inspired by this post on Search Engine Land.

February 3, 2026

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