Tag: SEO Strategy

Unleashing Content Power in the AI Era: Beyond SEO Traffic

For over a decade, the content formula was clear-cut: choose a keyword, craft an article, publish, promote, rank, and convert. But now, that system is failing.

In today’s world, content marketing is in transformation. AI delivers direct answers to search queries within the results page. With large language models processing information faster than we can distribute it, a new content approach is essential.

While the cost of content creation plummets, the challenge of standing out becomes steeper. Here’s a method for thriving in a market where visibility is far from guaranteed.

The decline of informational SEO

Informational SEO was once a beacon for growth. The idea was simple: produce enough articles, get traffic, and grow. But that traffic was always just a proxy for real progress.

Now, AI tools deliver instant summaries, reducing the need for users to click through. If your strategy revolves around responding to common queries, you’re up against highly trained AI, rendering traditional informational SEO strategies ineffective.

Content needs a new purpose, evolving beyond customer support and sales to creating genuine brand notoriety.

Dig deeper: The dark SEO funnel: Why traffic no longer proves SEO success

All content marketing is advertising

SEO’s evolution into a competition for boardroom-worthy metrics has diluted its effectiveness. It’s time to reset focus.

Content serves two purposes: as a business in itself or as a strategy to boost another business. For most, content acts as advertising—building brand recall, as proven by advertising science, hinges on fame, feeling, and fluency.

Dig deeper: Fame engineering: The key to generative engine optimization

From pull to push content

Gone are the days when we could rely on attracting users through search alone. AI now answers questions instantly, reducing the effectiveness of content designed only to draw in search engine traffic. It’s time to pivot towards pushing content to audiences directly through media, partnerships, and events.

In this overcrowded media landscape, it’s not about access—it’s about strategy and targeting.

Dig deeper: Why your content strategy needs to move beyond SEO to drive demand

The scarcity of being found

Kevin Kelly’s insight in “The Inevitable” reveals a crucial shift: visibility is now a scarce commodity. As content production skyrockets, curation and distribution become the keys to visibility, shifting the value from creation to distribution.

With finite human attention, being found is a matter of scarcity economics. Today, it’s not just about creating content but making sure it’s uniquely visible.

Dig deeper:

Powerful messaging in an age of abundance

Rory Sutherland’s concept of impactful messaging emphasizes the need for distinct, memorable signals in marketing. When everything is efficient, inefficiency and peculiarity become powerful signals. Just as lavish wedding invitations signal importance through their very wastefulness, marketing must adopt similar strategies to stand out.

In a world awash with competent yet forgettable content, distinct efforts stand out and make a lasting impression.

Dig deeper: Revisiting ‘useful content’ in the age of AI-dominated search

Fame as a strategic objective

Paul Feldwick’s principles of fame—interest, reach, distinctiveness, and voluntary public engagement—shape how we approach content marketing now. Creating unique and engaging content that stands out is essential for becoming memorable and broadening reach.

It’s not enough to produce content; it must be distinctive, distributed effectively, and encourage engagement.

Operationalizing fame in search marketing

To thrive in the AI era’s content landscape, marketers must adopt a new mindset. Focus on five steps: differentiate infrastructure from fame-building initiatives, invest in originality, prioritize distribution before creation, establish distinctive brand assets, and measure your growth in fame, not just traffic.

Understanding that fame, not content volume, catalyzes growth is vital. By crafting memorable and distributed content, we can achieve genuine recall in our audience’s minds.

Dig deeper: Why creator-led content marketing is the new standard in search

The return of creativity

Automation takes the mundane out of our hands, empowering us to create outstanding content. Successful content strategies will pivot from producing large volumes to making each piece count, driving creative impact. As information proliferates, brands must strive not only to be visible but also to be remembered.

In the AI age, the brands that will shine are those that master the art of being found, focusing on creative impact rather than mere existence.

Inspired by this post on Search Engine Land.

March 3, 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
Mastering AAO: The Future of SEO is Here
Incomplete terminology often results in an incomplete strategy. To bridge this gap, I’m here to offer a clearer framework for optimizing when AI systems both recommend and act.

Search engine optimization (SEO) – be found. Answer engine optimization (AEO) – be the answer. AI engine optimization (AIEO) – be the recommendation. Lastly, assistive agent optimization (AAO) – be chosen when there’s no human in the loop. These are four distinct stages, each absorbing the one before it.

The constant term across the latter two stages is “assistive.” It highlights the purpose: what the system provides the user. The shift happens when “engine” becomes “agent,” marking our industry’s move from systems that recommend to those that act.

For me, this naming debate distracts us from the real work. The SEO industry has splintered across multiple terms that essentially describe the same discipline. Each term has its advocates, and while debating these labels, we aren’t progressing with the actual work.

So, let’s cut to the chase: I’ll lay out why AAO is an effective solution so we can all get back to focusing on our jobs.

Every competing acronym offers partial coverage, none captures it all

Every AI system making recommendations or autonomous decisions—be it Google, Bing, ChatGPT, Perplexity, or Copilot—relies on three components: large language models, knowledge graphs, and traditional search. I refer to these as the algorithmic trinity.

The balance of these elements differs by platform, but the trinity itself remains universal. Even those at Google I’ve conversed with agree on this architectural structure.

SEO has always described the engine’s purpose, which I’ve appreciated. Let’s examine how the competing acronyms align against these three components.
- GEO describes the mechanism over intent. It involves the LLM layer, includes search as necessary, but overlooks the knowledge graph entirely. This technology-specific term lacks longevity when the technology advances.
- Entity SEO covers the knowledge graph layer but only acknowledges search as a delivery mechanism and LLMs secondarily. It fails the glossary test, often confusing non-specialists.
- LLM optimization candidly reveals its scope but neglects the knowledge graph and search components entirely.
- AI SEO tacks the term “AI” onto the traditional term, making it accessible to outsiders but lacking durability. As we move to 2026, users are more likely researching rather than searching.
All these terms are incomplete, and it naturally follows that incomplete terminology leads to incomplete strategy. Practitioners tend to optimize only for the part their acronym emphasizes, neglecting others.

Assistive agent optimization (AAO) evolves cleanly from answer engine optimization and encompasses everything required for crafting a comprehensive strategy:
- “Assistive” clearly defines the purpose for the entire algorithmic trinity.
- “Agent” identifies the actor deploying all three components to reach a decision.
- “Optimization” captures what we do.
It’s a stable three-legged stool, ensuring consistency, much like sitting on a stool with evenly matched legs—one that doesn’t wobble.

Explore further: SEO, GEO, or ASO? What to call the new era of brand visibility in AI [Research]

The glossary test shows AAO isn’t flawless, but it’s our best option

Generative engine optimization, entity SEO, and LLM optimization all require niche understanding, failing the glossary test.

Although “assistive” in AAO isn’t instantly recognizable, “agent” is now a part of popular vocabulary. We see every tech company promoting agents, and “optimization” is self-explanatory. Two out of three terms land smoothly, and the third is easily understood.

If you can propose a more fitting term that perfectly covers the algorithmic trinity and passes the glossary test, I’m open to it. After all, what matters is the discipline, not the terminology.

Importantly, AAO describes a role: optimizing so the assistive agent favors your brand. Roles endure beyond technologies. The right term will endure for years, independent of prevailing model architectures or retrieval methods.

What changes when you adopt the AAO framework

Your brand identity becomes foundational rather than optional. When an agent reviews hotel options, supplier choices, or consultant recommendations, it doesn’t thumb through pages seeking the best title tag. Instead, it assesses the brand: its essence, service, audience, reliability, and confidence in those facts.

This trust originates from the entity home—the page you own that roots everything the algorithmic trinity knows about your brand—and extends through all corroborating sources. If your brand isn’t clearly understood, the agent will select one that is.

The funnel resides within the agent now. The well-trodden acquisition funnel (awareness, consideration, decision) used to bounce users around, with search engines acting as traffic sources. Now, under AAO, this entire journey takes place within AI, without users encountering a list of options. The agent becomes aware of, evaluates, and decides on your brand before presenting the result. Your mission is thus to ensure your brand is the answer when the agent processes its funnel internally.

You might think, “We’re not there yet.” Yes, that’s true for most, but the funnel is already within the assistive engine. With platforms like ChatGPT, Perplexity, Google AI Mode driving users to the perfect click—the pinnacle in AI zeroing in on a single user solution—most tend to accept what’s presented. What’s presently lacking is the agent making the purchase decision.

The web index is no longer the sole source of truth it once was. For two decades, it dominated, but that monopoly is crumbling:
- Proprietary datasets feed agents directly, evolving search into what I term ambient research, where in-app pushes surface brand suggestions without a query.
- Agents and engines utilize APIs, booking systems, and internal databases that don’t intersect traditional web indices. The index will persist as an essential anchor, but it’s no longer the sole gatekeeper. It’s time we strategize with that understanding.
The push layer is also resurfacing. For years, we depended on search engines to understand our content—rendering JavaScript, deciphering complex pages—and they responded. This passive approach will continue, but proactive methods are gaining ground.

IndexNow, nurtured by Fabrice Canel at Bing, along with MCP and whatever Google deploys next, all facilitate one key function: enabling us to push structured data to action-oriented systems instead of waiting for them to retrieve it. It’s reminiscent of the 1990s, with proactive URL submissions and active ecosystem feeding.

Google’s absence from IndexNow isn’t due to the concept’s flaws—it’s quite ingenious—but perhaps because it wasn’t Google’s brainchild, sparking aspirations for a proprietary adaptation.

We must also consider that JavaScript rendering was Google’s generous favor, not an industry standard. Many AI agent bots don’t process JavaScript, so content reliant on client-side rendering may never be seen by an increasing number of agents.

(This all aligns with the 10-gate DSCRI-ARGDW pipeline, which I’ll detail in the next series segment.)

Further reading: The origins of SEO and what they mean for GEO and AIO

Your SEO skills remain relevant; the focus shifts from engines to agents.

You don’t need to perfect each intermediary step before embracing AAO, as AAO encompasses AIEO, AIEO encompasses AEO, and AEO encompasses SEO—the skills stack remains, only the focus shifts: aim to be chosen by the agent, recommended during research, and mentioned during inquiries.

The compounding advantage discussed in “Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it” applies here as well. Our top performers secured 59.5% of all citability by February, rising from 30.9% in December—a notable 293% increase in concentration over two months.

Those adopting this perspective will consistently build pipeline confidence while others remain entangled in debates over acronyms, further widening the gap over time.

The discipline now has a name, the agents are already operational, the push layer is in play, and the era of complacency has ended.

The initial two articles explored the “what” and the “why.” Next week, I’ll delve into the “how.” I plan to unveil the 10-gate pipeline I’ve been referring to: DSCRI-ARGDW, a crucial conduit between your content and a conversion by an AI engine.
- Discovered: The bot becomes aware of your existence.
- Selected: The bot deems your data worthy of retrieval.
- Crawled: The bot captures your content.
- Rendered: The bot transcribes what it retrieves into a readable form.
- Indexed: Content is committed to the algorithm’s system memory.
- Annotated: The content undergoes classification across various dimensions.
- Recruited: The algorithm leverages your content.
- Grounded: The content’s credibility is confirmed against multiple sources.
- Displayed: The content is showcased to the user.
- Won: The moment of triumph – the engine secures the perfect click.
Inspired by this post on Search Engine Land.
February 24, 2026
Mastering Vibe-Coding: SEO Tools Without Losing LLM Control
I interact with LLMs daily, both at work and in my personal projects. For many of us in tech, leveraging these language models has become second nature.

It’s well-known that folks in the tech sector, like me, engage with LLMs at twice the rate of the general population. In my case, LLM usage often exceeds a full day each week.

Even as regular users, we sometimes find ourselves frustrated when an LLM doesn’t quite deliver the responses we expect. Here’s how I effectively communicate with LLMs during vibe coding sessions. These insights are just as valuable when navigating extended interactions with an LLM UI like ChatGPT.

Choosing My Vibe-Coding Environment

Vibe coding is the art of co-creating software with AI. I lay out my vision, the AI generates code, and together we refine it to match my intent. However, the process isn’t always smooth sailing.

$```json { "alt": "Table showing SERP API providers with highlighted SerpApi using page token for AI overviews.", "caption": "Explore the future of AI overviews with dedicated SERP APIs like SerpApi, designed for efficiency and reliability.", "description": "This image illustrates the concept of reverse-engineering Google AI overviews using SERP APIs. It features a table of SERP providers, highlighting the 'SerpApi' which employs a 'page_token' for fetching AI overviews. This professional method offers a reliable solution for managing proxy rotation and JavaScript execution. Keywords: SERP API, Google AI, page token, SerpApi." } ```$

The first step in my workflow involves choosing a coding environment. This space serves as a hub for interacting with the LLM, drafting, and executing code. I’m partial to Cursor, having started on their free Hobby plan, but I’ve since upgraded to the Pro+ account due to my extensive usage.

For those interested, here are some environment options:
- Cursor: Widely used by vibe coders for its customizable interface.
- Windsurf: An alternative that executes terminal commands independently.
- Google Antigravity: A unique option favoring agent-driven development.
In my examples, I’ll be using Cursor, but the principles are applicable across platforms. Even if you’re simply delving deep into LLM conversations, the same guidelines apply.

Why Prompting Alone Isn’t Enough

You might ask why we’d even need a tutorial for vibe coding. It’s true—the basic idea is simple: specify an outcome, and the LLM delivers. However, once the complexity increases, especially when dealing with multifile systems or tools, context management becomes crucial.

The context window is a pivotal concept. It’s the memory scope LLMs use to handle input/output data, a window defined by token limits. For example, GPT-5.2 allows a 400,000-token window, while Gemini 3 Pro goes up to 1 million. Understanding this helps in avoiding token overflow, which can diminish retrieval accuracy.

Expert commentator Matt Pocock explains the nuances of context windows well—view his YouTube video for more insight. For now, keep in mind that effective planning minimizes verbosity and assumes clear window management.
- One team, one dream. Divide projects into manageable phases, clearing LLM memory regularly between tasks.
- Do your own research. While you don’t need exhaustive detail, grasp general methods and potential build paths.
- Trust but verify during troubleshooting. Get clarifications from the LLM and cross-check details externally.
$```json { "alt": "Screen showing code and notes for AI model selection and logging.", "caption": "Diving into AI model selection, this screen showcases notes on using GPT models and detailed instructions on creating a logging system with W&B Weave for data analysis.", "description": "This image captures a computer screen displaying code and notes related to AI model selection and logging. Key points include instructions on choosing GPT models such as gpt-4-turbo, recommendations for reasoning models, and guidance on setting up W&B Weave logging with the 'src/weave_logger.py' file. The image is useful for those interested in AI, programming, and data analysis, offering insights into structured query analysis and project initialization." } ```$

Explore Further: How Vibe Coding Transform Search Marketing Workflows

Tutorial: Creating an AI Overview Question Extraction System

To produce high-ranking content in AI Overviews, address the questions they respond to. This tutorial guides you in developing a tool to extract such questions, not just to provide a use case but also to demonstrate effective system development via vibe coding. It’s not a guaranteed path to AI prominence but offers strategic insights.

Step 1: Planning

Before diving into Cursor or any other tool, identify your goals and necessary resources. Although it’s early days, using generative AI for initial brainstorming can be beneficial. I often start by articulating my end goal in a sentence or two, alongside requisite steps, in AI tools like Gemini or ChatGPT. Missteps here are okay—this stage is about outlining thoughts, not finalizing builds.

For instance, I could outline:
```
I’m an SEO, aiming to leverage Google's AI Overviews to inspire our authors' content. We need to extract implicit questions addressed by AI Overviews. Proposed steps include:

1 – Choose a keyword target.
2 – Run a search and collect the AI Overview.
3 – Deploy an LLM to derive underlying questions from the AI Overview.
4 – Preserve questions in an accessible format.
```
With a clear direction, select your preferred LLM. While I’m partial to Gemini for chats, modern models with robust reasoning will suffice. Initiate a session, state your intent to build an AI Overview extractor, and share your planning prompt.

Step 2: Laying the Foundation

Cursor offers diverse models which I find advantageous. For this task, start in Plan mode, allowing for structured discussions and informed decision-making.

Kick off discussions with our defined project prompt.

Making modifications is crucial, so carefully review the LLM’s plan to ensure alignment with your vision. Address any disparities through collaborative discussions with the model.

Consider seeking insights into possible project failure points and implement preventive measures accordingly. For efficiency, I tend to request models to generate outline files for improved context window management, validating internal consistency before proceeding.

Step 3: The Build

With the foundation laid, shift to Agent mode using your selected model—in my case, Gemini 3 Pro—to execute the building phase. Keep an eye out for required approvals during script execution to ensure a smooth process.

Once script development is complete, proceed with library installations via the provided requirements.txt file. For organized dependency management, setting up a virtual environment is recommended.

Running your first script execution often surfaces unforeseen challenges. Tackle these by leveraging comprehensive diagnostic feedback, ensuring issues are resolved before moving forward.

Troubleshooting and Improvements

My initial run revealed a lack of expected AI Overview detection—a misstep rectified through close inspection of terminal outputs, model adjustments, and informed re-execution.

Embrace troubleshooting as a key growth component in the vibe coding journey, enhancing reliability and performance as you fine-tune system components.

Dive Deeper: Inspiring Examples of Responsible Vibe Coding for SEO

Logging and Output Management

Employ Weave for maintaining organized records of query inputs and LLM outputs. This robust tool aids in both immediate log assessment and long-term query-trace reference.

Use the analyze_query trace to monitor pivotal data points, fostering awareness of the direct connection between query intentions and AI Overview content insights.

Structure Over Vibes: A Strategic Approach

Across my years of vibe coding, I’ve learned structure creates reliability—increasing complexity demands methodical workflows, ensuring sustainable success. Remember to keep the vibes in your collaborations strong, united by a shared purpose and approach.

Inspired by this post on Search Engine Land.
February 20, 2026
Unlock Media Exposure with this Proven Press Release Strategy
Are you looking to amplify the reach of your next press release? Employ this innovative framework to transform your announcements into exceptional successes for your clients.

I had given up on press releases years ago, convinced they had lost their impact. But a conversation with a trusted friend and mentor totally shifted my viewpoint.

She revealed that while the days of organic features from merely publishing a press release were over, great results were still attainable. Her secret? She effectively pitched relevant journalists, using the press release’s key points as leverage once it went live.

Skeptically, I gave her strategy a shot. The results were incredible, leading to multiple organic features for my client.

My immediate thought was, “If such a minor tweak yielded these results, imagine the possibilities with a full-fledged strategy.”

This method I’m about to share is the culmination of a year packed with trials and enhancements to amplify the efficacy of my press releases.

Although it demands more research, planning, and execution, the pay-off is exponential and undoubtedly justifies the additional effort.

Research Phase

You’ll start with what your client wants to communicate to the world. Here’s how to proceed:
- Identify related topics like economic impact, related technology, legislation, and key industry players.
- Locate media coverage in the past quarter on these topics in outlets where you’d like your client featured.
  - Compile a list with links to each article, its main points, the journalist’s contact information, and links to related social media posts they’ve shared.
- Organize the list by how closely it aligns with your client’s message.
Planning Phase

Draft your client’s press release, using opportunities to cite articles from your compiled list with relevant links.

Ensure each citation is relevant and adds value to your message. Aim for three to five citations to maintain focus.

Simultaneously, create personalized pitches to the journalists whose articles you’re citing, ensuring they align with their beat and previous coverage.

Briefly mention their past work — a short, recognizable quote suffices. Include links to current social media discussions showcasing interest in the topic. Conclude with your press release link and a specific call to action.

Avoid trying to win favor through citations. Instead, illustrate the link between your client’s message and their prior coverage, making it easier for journalists to revisit the topic from a fresh angle.

Execution Phase

Initially, interact with journalists on your list via social media for several days. Comment on recent posts, especially those covering your target topics. This starts building name recognition and rapport.

Once your press release is published, promptly send your pitches to the three to five journalists you cited, including the live release link. (I recommend linking to the most credible syndication rather than the wire service version.)

Subsequently, approach other pertinent journalists, customizing each pitch with relevant points from their past articles that align with your client’s message.

Track all earned organic features. While some may emerge from the press release publication itself, more commonly, they result from direct pitches, opening new doors for visibility.

Review each new feature for references to other articles from your compiled list. Then approach the original article’s journalist, referencing the new piece that relates to or enhances their work.

The Psychology Behind Why This Works

This strategy taps into two potent psychological principles:
- Everyone likes to see their work acknowledged, validating their viewpoint in the process.
- Building on a previously covered topic is less labor-intensive than starting from zero, appealing to journalists’ needs to streamline their work.
This framework will elevate your next press release, garnering more media coverage, increasing client satisfaction, and achieving impactful results with minimal effort — truly shining as a professional.

Inspired by this post on Search Engine Land.
February 20, 2026
Master AI Search: Embrace Inclusion Over Top Positions

I’ve been thinking a lot about the key performance indicators (KPIs) for AI search, and it’s time to shift our focus a bit.

Lately, I’ve noticed many SEO experts on platforms like LinkedIn and during conferences discussing the idea of “ranking No. 1 on ChatGPT,” equating it to securing the top spot on Google.

On Google, being first is often like striking gold.

Moving from the second to the first position on Google can supercharge your traffic and conversions, sometimes by 100%-300%.

However, this isn’t necessarily true with AI-generated responses, primarily because these responses are subject to constant change.

Our research indicates that AI users evaluate an average of 3.7 businesses before making a choice.

Thus, appearing first in ChatGPT’s results isn’t as crucial as it is in Google’s search results.

Given this scenario, our AI strategy should prioritize “being part of the consideration set” over being the first mention and focus on what AI communicates about us.

In the past months, my team has devoted over 100 hours observing how people use ChatGPT and Google’s AI Mode for finding services.

What became clear quickly is that user behavior on AI search platforms is distinctively different from that on Google, beyond just the use of natural language versus keyword searches.

Surprisingly, about 75% of observed sessions still involved keyword searching.

A significant difference is that AI search results prompt users to consider more businesses than traditional organic search results.

Comparing multiple options is more straightforward within a chat interface than through clicking multiple search result links.

Explore further: Adapting to AI-centric search behavior

In both Google’s AI Mode and ChatGPT, users typically consider 3.7 businesses from the results shown.

This significantly affects the importance of being the top result and elevates the value of other positions, as 75% of users also review businesses listed from positions 2 to 8.

$```json { "alt": "Google search results for 'Fractional CMO,' showing articles and discussions about fractional chief marketing officers.", "caption": "Curious about fractional CMOs? Discover insights and opinions on this unique role in the marketing world through these Google search results.", "description": "The image displays Google search results for 'Fractional CMO,' highlighting various articles from websites like Chief Outsiders, CMOx, and discussions on Reddit. Fractional CMOs are senior marketing executives working on a part-time or contract basis, offering strategic direction. The search results also include a 'People also ask' section with common questions about fractional CMOs. Keywords: Fractional CMO, marketing, search results, Google." } ```$

Ultimately, what drives conversions isn’t solely your position in that list.

These aren’t traditional rankings; they’re more akin to recommendations which might change in order or format, underscoring AI’s probabilistic nature.

AI chat interfaces allow users to scan and assess more options feasibly than Google search results do.

If a user is evaluating fractional CMO options, it’s more work through Google Search than ChatGPT.

In Google’s results for “fractional CMO,” only two appear above the fold, each requiring click-through to view their full details.

$```json { "alt": "Text discussing benefits of hiring a fractional CMO for franchise growth, listing six fractional CMO firms.", "caption": "Discover how hiring a fractional CMO can drive your franchise's growth with strategic marketing leadership, and explore top firms offering these services.", "description": "The image contains text about hiring a fractional Chief Marketing Officer (CMO) for a home care company starting to franchise. It explains the benefits of hiring a fractional CMO, including strategic marketing planning, brand development, and lead generation. It lists six fractional CMO firms: Fractional CMO, Chief Outsiders, Magnetude Consulting, GoFractional, Authentic (Fractional Leadership), and Chameleon Collective, detailing each firm's offerings. This guide helps in understanding how fractional CMOs can enhance your franchise's growth strategy without long-term commitments." } ```$

Contrast that with ChatGPT, where the model offers eight options with concise descriptions.

This convenience makes it easier to make informed choices.

We need to ensure that what the model says about us aligns with our message.

Many marketers prioritize rankings and traffic but overlook messaging and positioning.

Our study shows approximately 60% of users finalize their decisions based solely on AI responses without further exploring the business’s website or using Google.

To enhance conversion, we must deliver the correct message and ensure the AI conveys it accurately.

For instance, even if Dr. Lanciano is the best in glaucoma care, if the AI promotes Ravi D. Goel and Bannett Eye Centers, users might lean towards them if that suits their needs.

This reaffirms that appearing last doesn’t negate conversion opportunities if the AI message resonates well, unlike traditional search.

Visibility alone doesn’t bring in revenue; conversions do, and these happen when prospects perceive your solution as a fit.

Explore further: Measuring AI search visibility impact

We’re still approaching AI search through the SEO lens where top positions generate the most traffic, but this isn’t the case in AI-driven searches.

AI interactions involve evaluating multiple options with each query changing response dynamics considerably.

Thriving in AI search means being part of the consideration set and being described appealingly.

It’s vital to appear on the list but more critical how you are presented since that’s what influences decisions.

In essence, SEOs need to act like copywriters and salespeople to drive meaningful results.

Explore further: Is SEO a brand or performance channel? It’s both now

Inspired by this post on Search Engine Land.

February 18, 2026
How to Achieve Consistent AI Brand Visibility

AI outputs can be wildly inconsistent, and Rand Fishkin recently spotlighted this issue. His research revealed that AI tools produce varied brand recommendations, which highlights the need for a deeper understanding beyond ranking positions.
After reading his work, I realized the solution is rooted in something I’ve been developing for years – building consistent visibility through confidence and corroboration.
Fishkin’s data showed that AI systems are confidence engines. They draw results based on confidence levels, which explains the inconsistency in output. It’s a problem when there’s low confidence, but once AI systems are confident, they provide consistent recommendations.
The journey to AI confidence involves several stages, and understanding this process can fundamentally change how brands approach AI visibility.
Take the entity home as an example. It’s the foundation of AI interpretation of your brand. Confidence also builds when third-party data aligns with your own narrative. Brands that manage this well don’t just appear in AI recommendations; they dominate them.
There’s a method behind all this that I’ve formalized and even filed for patenting. It’s a complex system of strategies but starts with ensuring that your brand’s digital footprint aligns perfectly with high-authority sources.
Fishkin’s work confirms the importance of AI visibility, a subject I’ve been tracking and developing solutions for over the last decade. It bridges a significant gap in understanding how brands can leverage AI for long-term authority and presence.

Inspired by this post on Search Engine Land.

February 17, 2026

Stop Chasing SEO Rankings: Build a Robust Visibility System

I’ve learned that SEO is evolving beyond just marketing and into the realm of organizational design. It’s about structuring, validating, and aligning information across the business to enhance visibility.

When our information becomes fragmented, visibility suffers, leading to more than just rank instability; it threatens our brand’s interpretation and mentions.

For those of us leading SEO, the choice is clear: remain as channel optimizers or become architects of systems that define brand understanding and citation. With AI systems now assembling information at scale, this transformation isn’t happening in isolation.

The visibility shift beyond rankings

As we look to the future, LLMs will shape organic search alongside traditional algorithms. Simply chasing rankings isn’t enough. We need to optimize for interpretation, citation, and synthesis across AI systems.

While our click rates may vary, the real transformation means treating visibility as an interpretation issue rather than just positioning. AI systems rely on cohesive data, narratives, and mentions. Discrepancies lead to inconsistent output.

Now, collaboration can’t be casual. LLMs demand clarity and consistency in the information they process. When our messages and data are fragmented, so too is our visibility.

This is a leadership challenge. Our visibility shouldn’t exist in silos but as a system that manages information creation, validation, and distribution across our organization.

If we want structural visibility, we must build the system to support it.

```json
{
"alt": "Flowchart of the visibility supply chain with gates for strategic relevance, SEO, content quality, UX, and distribution.",
"caption": "Discover the visibility supply chain! Navigate through strategic relevance, SEO fundamentals, content value, user engagement, and more to achieve maximum online visibility.",
"description": "This image illustrates the visibility supply chain, presenting a series of gates aimed at optimizing content visibility. Starting with raw content, it proceeds through Gate 1 (Strategic Relevance), Gate 2 (Technical SEO Fundamentals), Gate 3 (Content Quality & Value), Gate 4 (UX & Engagement), and Gate 5 (Distribution & Amplification), ultimately achieving Maximum Visibility. Each stage addresses key questions like alignment with business goals, crawlability, uniqueness, user interaction, and audience reach, presenting a structured approach to enhancing digital content visibility."
}
```

Building the visibility supply chain

I’ve realized that collaboration needs to be ingrained in the supply chain. It shouldn’t rely on the relationship between the SEO manager and PR manager.

For a seamless transition from a marketing silo to an operational framework, we must treat content as a product needing precise refinement before entering the broader ecosystem.

Enter visibility gates—nonnegotiable checkpoints filtering brand data for AI consumption.

Implementing visibility gates

Think of our content moving through a pipeline. At each joint, a gate refines the output:

The technical gate (parsing)
- Does the product page template use valid schema.org markup (product, FAQ, review)? The goal here is to ensure that the data is structured for seamless AI consumption.
The brand signal gate (clustering)
- Does our PR align with core entities? Are we using consistent terminology to help LLMs cluster our brand? This phase aims to prevent linguistic drift.
The accessibility/readability gate (chunking)
- Is the content ready for RAG systems? Here, we focus on delivering high-information-density prose suitable for AI retrieval.
The authority and de-duplication gate (governance)
- Does this asset induce “knowledge cannibalization” or unnecessary noise? This step filters out conflicting information to ensure a single source of truth.
The localization gate (verification)
- Is entity information consistent globally? This ensures alignment in cross-referencing data to build trust in models.

While gates guard the ecosystem entry points, accountability is key to ensure changes translate into actions.

Embedding visibility into cross-functional OKRs

Alignment without visible results can’t sustain change. Sophisticated infrastructure will fail without cross-functional influence.

To advance beyond mere collaboration, visibility must be embedded into our organizational performance strategy.

We need shared visibility OKRs, not just SEO-specific goals.

When stakeholders are incentivized with visibility KPIs, SEO becomes more than just a team job. It becomes a vital business priority.

For product teams: “Achieve 100% schema validation and <100ms time-to-first-byte for top-tier entity pages.”
For PR and communications: “Increase ‘brand-as-a-source’ citations in LLM responses by 15%.”
For content teams: “Ensure 90% of new assets meet ‘high information density’ for RAG retrieval.”

This collective focus aligns our organization with modern search engine mechanisms.

Measuring visibility across the organization

While gates assess the quality entering our ecosystem, a unified dashboard measures output, enhancing transparency.

If PR teams understand which mentions drive AI citations, they’ll focus on authoritative publications rather than any media outlet.

We need to transition from rank reporting to assessing entity health and Share of Model (SoM). This dashboard becomes our brand’s single truth source.

While systems and incentives are important, they need active management.

```json
{
"alt": "Venn diagram titled 'Hacker vs. Convincer' highlighting elements of technical feasibility and organizational buy-in.",
"caption": "Exploring the balance between technical feasibility and organizational buy-in, this Venn diagram reveals the sweet spot for maximum visibility in project management.",
"description": "This Venn diagram titled 'Hacker vs. Convincer' illustrates the intersection between technical feasibility and organizational buy-in. On the left, technical aspects such as code optimization and site speed are highlighted, while on the right, elements like stakeholder management and ROI demonstration are key. The center emphasizes 'Maximum visibility' as the area where technical excellence meets strategic alignment, critical for successful project management. Keywords: Venn diagram, technical feasibility, organizational buy-in, project management."
}
```

Dig deeper: Why most SEO failures are organizational, not technical

Join us at Semrush for tools to enhance your brand’s visibility.

Hiring for AI-era visibility

Building a visibility system isn’t enough; we need a workforce matching this model. We should hire beyond generalists, focusing on two pillars: the hacker and the convincer.

Feature	The hacker (technical architect)	The convincer (visibility advocate)
Core mission	Ensuring the brand is discoverable by machines.	Ensuring the brand is supported by humans.
Primary domain	RAG architecture, schema, and LLM testing.	Cross-departmental OKRs, C-suite buy-in, and PR alignment.
Success metric	Share of model (SoM) and information density.	Resource allocation and budget growth.
The gate focus	Technical, accessibility, and authority gates.	Brand signal and localization gates.

The hacker: The engine room

The technical visionary, constantly pushing boundaries. They optimize beyond just search bars, ensuring our brand’s discoverability by AI.

This individual bridges technical insights with business results, ensuring the hacker’s ideas are realized in practice.

Dig deeper: Why governance maturity is a competitive advantage for SEO

Leading the transition in the first 90 days

As we adapt to operational SEO, consider these first steps:

Set the vision: Define what visibility-first looks like for your business.
Take stock of talent: Do you have the hackers and convincers? Evaluate for skills and mindset.
Audit the gaps: Identify and address communication breakdowns between SEO and PR, or SEO and product teams.
Shift the KPIs: Focus on authority, impressions, sentiment share, and revenue, not just rankings.
Be radically transparent: Share data in real time, without siloed thinking.

During the first 90 days, focus on:

Days 1-30 (Audit): Map your brand’s entity footprint to identify conflicts.
Days 31-60 (Infrastructure): Integrate visibility gates into your CMS or project management tools like Jira or Asana.
Days 61-90 (Incentives): Link 10% of PR and product teams’ bonuses to entity integrity or citation growth in AI.

The SEO leader as a systems architect

As AI advances, an effective SEO leader transitions to becoming a systems architect, crafting the infrastructure for both machine and human brand interactions.

This journey is complex, demanding upfront changes to ingrained processes and transparent communication.

The future goes beyond keywords to optimizing how information flows through the digital ecosystem, embracing this transition will build a resilient organization visible by default.

Dig deeper: AI governance in SEO: Balancing automation and oversight

Inspired by this post on Search Engine Land.

February 13, 2026

Boost Your News Visibility in Google’s Social-First Era

We’re stepping into an era where the visibility of web content is spreading across a multitude of search and social platforms. Google has always been a force to reckon with, but it’s no longer the only player in the search experience. Video-based social media platforms like TikTok and community sites such as Reddit are carving out spaces as go-to search engines for their dedicated audiences.

This evolving landscape is reshaping how we consume news content. Google’s news SERP is adapting to the era of personalized query responses afforded by LLMs and the influence of social media platforms. To keep up, Google has introduced AI-powered SERP features like AI Overviews and AI Mode. These features prioritize content that is “helpful, reliable, and people-first,” drawing heavily from social media platforms.

As search and social media intertwine more closely than ever before, we need to embrace a new strategy. This involves creating newsroom teams comprising social media experts, SEO specialists, and AI enthusiasts working together towards a unified content visibility goal.

When I optimize news content for social platforms, I also consider the potential performance of these posts on the Google SERP. I’ll delve into optimizing specific SERP features, but first, let’s explore making news content friendly for social platforms.

First, let me offer some sanity tips. It’s tempting to optimize content for every social media platform, but I find it more effective to focus on one or two where my audience is active and my growth opportunities are highest. By reviewing analytics and conducting audience surveys, I can identify the platforms where my audience consumes news content.

Optimize News Content for Social Media Platforms

I begin by considering how my content might appear on different platforms. Here’s my breakdown of which content types work best on each platform and how they might appear on Google:

YouTube

Creating YouTube video content involves following video SEO best practices. With guidance from this comprehensive YouTube SEO guide, I create a successful video strategy by ensuring my video titles align with the content.

Google prioritizes YouTube’s search ranking through relevance, engagement, and quality. I make sure my metadata accurately reflects my video content to ensure it stands out as relevant in a search.

One trend I’ve noted is that older event content on YouTube continues to rank well on Google, even after related articles have faded. Similarly, explainer videos show longevity on the SERP.

Facebook

Facebook, though perhaps not as trendy as it once was, still reaches a diverse audience. This platform excels with community-based content and entertainment news that incites conversation.

Even though Facebook’s dedicated news tab was removed, its posts are becoming more visible on Google’s SERP, which might make it worth reconsidering from a search perspective.

X

Since Elon Musk’s takeover, X’s audience has shifted more to the political right, while its role as a hub for breaking news, live updates, and political content remains strong. Sports content also performs well here, especially in the U.S.

Instagram

For Instagram, focusing on visually-driven stories, such as celebrity fashion and health topics, is key. The platform also performs well for sports highlights, often appearing in Google’s dedicated publisher carousel or “What people are saying.”

Reddit

Reddit’s unique user base requires a specific strategy to engage niche communities outside other platforms. Whether the content is about tech trends, health, or sports, it’s crucial to understand Reddit’s audience and adhere to its guidelines.

TikTok

The predominantly young, diverse user base on TikTok gravitates towards visual, conversational, and opinion-based content. Short-form videos that are authentic and engaging perform best.

Pinterest

Pinterest might be old-school, but it’s growing with Gen Z, making it ideal for lifestyle content. When I create on Pinterest, I focus on fashion, DIY, and motivational content, using high-quality visuals and a more relaxed posting schedule.

Social Content Opportunities by Google SERP Feature

Understanding how social content appears in different SERP features helps me maximize visibility. For instance, Top Stories capture breaking news while the “What people are saying” feature emphasizes emotionally engaging user-driven content.

Threat or Opportunity?

Instead of viewing social media content on Google’s SERPs as competition, we can leverage it as an opportunity to increase visibility. Our focus should be on integrating social-forward strategies to expand brand engagement and not solely relying on traditional SEO tactics.

Inspired by this post on Search Engine Land.

February 13, 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