Have you ever wondered how search engines and AI perceive your brand? It all starts with the entity home, a pivotal page that shapes your digital identity. Let me tell you why it’s more important than you might think.
In my experience, this isn’t just about filling out the ‘About Us’ page on your website. It’s about creating a rich narrative that algorithms can trust. This single page acts as an anchor for how bots, algorithms, and even people view and validate your brand. I’ve seen firsthand how optimizing this page increased conversion rates by 6% for those who landed there.
For years, many in SEO, myself included, overlooked this. The focus was always on rankings and traffic, often neglecting the foundational elements of how a brand’s identity is communicated online. But the landscape has changed, and so must we.
What the Entity Home Isn’t
Let’s clear up some misconceptions before delving deeper.
Not a Ranking Trick
Improving the entity home isn’t some quick fix that will skyrocket your page views overnight. It’s about cultivating long-term trust and credibility.
Not Just Schema
Sure, schema is helpful for visibility in search, but it cannot replace substance. I’ve learned that the claims and evidence presented on your site are far more important.
Not Always the About Page
While it’s common, the About page isn’t always your entity home. In my case, I had to identify the URL that best showcased my brand’s identity and provided stable, long-term information.
Not Enough Without Corroboration
Declaring your claims on one page won’t cut it if they’re not backed by credible third-party sources. I’ve realized that evidence and corroboration create a trust bridge algorithms rely upon.
Three Audiences, One Anchor
The entity home serves three critical audiences, and I’ve noticed that many brands neglect two of them.
Bots map your digital footprint. Algorithms resolve identities from your entity home. People use it to verify your credibility before converting. Each element requires a slightly different approach, one that I’m continually fine-tuning.
The Entity Home is Just One Page and That Isn’t Enough
Your entity home lays the groundwork, but it doesn’t tell your whole story. I’ve learned to extend my brand narrative across other pages on my site.
I’ve structured pages to express who I am, what I do, and align them with supporting, independent sources. This multi-layered approach has been pivotal in how AI and search engines understand my brand.
Shifting my focus to assistive and agent-driven interactions has been a challenge, but it’s clear this is where the future lies. The change is happening faster than anticipated, and I’m adapting my strategy accordingly.
Building for Machines and Humans Simultaneously
At first glance, it seems building for machines might detract from the human element, but I’ve found the opposite to be true. Structured clarity satisfies both algorithms and human readers. There’s a mutual benefit in crafting content that speaks to both audiences effectively.
Getting the Entity Home Right Requires Definition, Proof, and Corroboration
Defining the core URL of my brand’s identity has been a meticulous process. I’ve ensured it contains explicit claims supported by robust third-party evidence.
This isn’t a sprint; it’s an ongoing education for algorithms. I reinforce my claims through continuous corroboration, ensuring that my brand stands on stable, trustworthy ground.
Recently, I’ve noticed something exciting happening on Bing. Now, when I use Bing Webmaster Tools, I can click a query to view its cited pages or select a page to see its grounding queries. It feels like a new level of connectivity where multiple queries and pages are seamlessly linked together.
Microsoft has introduced query-to-page mapping within its AI Performance report on Bing Webmaster Tools. I find this feature incredibly helpful because it lets me directly connect AI-generated queries to cited URLs. This makes my SEO strategies more precise.
Why it matters to us. Before this update, Bing’s dashboard presented queries and pages separately, which limited our optimization efforts. Now, I can align specific AI-triggering queries with the exact pages they reference, focusing my updates on real AI-driven demand rather than guesswork.
Here’s the scoop. The Grounding Query–Page Mapping feature is a game-changer in the AI Performance dashboard:
With a click on a grounding query, I can see which pages are cited.
I can also click a page to find out which grounding queries are driving its citations.
The mapping system is many-to-many, meaning one query can be linked to multiple pages and vice versa.
Catch up with Bing. Back in February, Microsoft launched the AI Performance report in Bing Webmaster Tools, marking its initial GEO-focused dashboard. This tool keeps track of where and how often my content gets cited in AI answers across platforms like Bing, Copilot, and more.
It tracks the grounding queries, cited URLs, and visibility trends over time, providing an insightful view into citation visibility.
The buzz. According to Microsoft, this update came about due to “strong positive customer feedback and numerous requests,” and I can see why it’s so well-received.
I’ve noticed that AI is drastically changing the landscape for marketing agencies, and it’s a pressure felt from both sides. Though we welcomed AI as a tool to enhance efficiency, it seems to be impacting our margins in unexpected ways.
In 2024, 44% of digital marketing agencies, including mine, identified AI as a potential threat. By 2025, this concern had increased to 53%, as highlighted in SparkToro’s survey of agency owners worldwide.
The real kicker? We aren’t just passive observers in the AI disruption; we’re actually participants. We’ve adopted AI to streamline tasks and reduce costs, attempting to boost our profitability. Meanwhile, our clients are following suit, using AI to cut budgets or opt to handle tasks internally.
This dual pressure has created a challenging environment for agencies like mine.
The Promise That Became a Problem
When advanced AI tools such as ChatGPT and Claude emerged, I initially saw them as opportunities. They offered ways to automate tedious tasks, ostensibly improving our efficiency and competitiveness.
Our equation appeared simple: automate more tasks with AI, reduce manpower, and profit from the savings. However, clients performed the same calculations and reached a different conclusion: why pay an agency when AI can produce satisfactory content, analyze campaigns, or generate ads on their own?
This shift prompted unwelcome questions about the value we provide.
Some services we once charged premium prices for are now being completed in-house or through automation tools. Al Sefati, CEO of Clarity Digital Agency, has frequently discussed the hurdles that boutique agencies face in this AI-driven market.
Earlier this year, I faced clients who “put marketing on pause,” despite good performance metrics. One manufacturing client even walked away from a contract due to tariff uncertainties. In tightening budget scenarios, where AI renders some marketing services commoditized, agencies like ours become easy targets for budget cuts.
The Margin Trap Nobody Talks About
We began using AI to do more with fewer team members, expecting to see higher profits. But our clients expect these savings to benefit them, not enhance our bottom line.
This has led to an unpleasant trend of shrinking retainers. SparkToro’s research indicates that sales cycles are becoming longer, with more agencies reporting delays in closing deals that extend from 7-8 weeks to over 12 weeks.
The reason? Potential clients are evaluating, “If AI makes this cheaper and faster, shouldn’t our rates be reduced as well?”
Even as efficiency through AI increases, client expectations haven’t decreased—they’ve grown. Agencies are now expected to demonstrate tangible results, link investments directly to revenue, and offer genuine ROI.
This presents a dilemma: adopt AI and risk downgrading our perceived service value, or resist AI changes and fall behind more adaptable competitors.
The Junior Talent Crisis Nobody’s Preparing For
One concerning insight from the report suggests that 66% of agency owners are worried about dwindling career opportunities for junior staff. Historically, agencies have relied on entry-level employees to perform routine tasks such as keyword research, content optimization, and campaign setup.
While not glamorous, these tasks are crucial stepping stones for junior marketers to develop skills and progress to strategy and client leadership roles. However, AI is rapidly taking over these process-oriented tasks.
This shift raises a vital question: how will we cultivate new talent if there’s no foundational work for them to learn from?
What AI Can’t Replace Yet
Despite the disruptions, some agencies are successfully navigating these changes. Larger agencies report healthier sales and stronger pipelines than smaller firms. This is partly due to their ability to weather economic changes and a focus on strategic offerings that AI cannot easily replicate.
Those of us thriving have stopped competing solely on execution. We now offer something AI can’t easily mimic: strategic insights, market experience, and storytelling that aligns with business outcomes.
“Clients desire teams that truly understand their industry,” notes Sefati.
Agencies that succeed are often those with deep expertise in specific verticals like B2B SaaS, financial services, healthcare, and ecommerce. This specialization allows us to maintain our value by offering nuanced insights and strategic thinking that AI struggles to deliver.
The Uncomfortable Truth About Commoditization
In the past, simply having the technical skills to launch campaigns gave agencies a competitive edge. But as AI and martech tools advance, more brands develop internal capabilities that rival what agencies offer.
This shift is reflected in data from SparkToro’s research, with only 14% of agencies claiming a “very healthy” pipeline, while the majority experience average or below-average pipelines.
Smaller agencies, especially those with 1-10 people, are feeling this pressure acutely. They often lack sales staff, forcing founders to juggle sales and client delivery roles, making it harder to compete when budgets shrink.
How Your Agency Can Escape the Squeeze
It’s crucial to focus on what AI can’t replicate and make strategic adjustments as client expectations rise and margins narrow.
Be Honest About What AI Has Commoditized
Embrace AI rather than shying away from it. Acknowledge what AI has commoditized and concentrate on areas it can’t;
If your agency still relies on AI-performed services such as basic content creation or standard reporting, it’s time to pivot. Focus on strategic, creative, or nuanced tasks that distinguish your agency from AI applications.
Lead with AI, Don’t Hide from It
Change the narrative around AI and lead with it in client discussions. Highlight the unique value add your agency provides beyond AI capabilities.
For instance, emphasize how only your team can fully understand a client’s market dynamics or interpret data insights contextually to improve strategic initiatives.
Rethink Pricing Models
Updating pricing strategies is essential. Outcome-based fees and performance partnerships could better align your agency’s incentives with client success, leveraging the efficiencies AI brings.
Rebuild the Talent Pipeline
Address the diminishing opportunities for junior staff by involving them in high-level strategic work alongside seasoned specialists. This approach will prepare the future frontline of agency talent as their role expands beyond AI-executed tasks.
The Old Agency Model Isn’t Coming Back
Over 64% of agencies are optimistic about revenue growth in the coming year, but this hinges on whether they innovate or wait for an outdated model to return—it won’t.
The squeeze is a lasting reality. The key to thriving is to reimagine what agencies offer and how we deliver it—making our roles indispensable, not replaceable.
Will your agency evolve to leverage AI’s capabilities and become irreplaceable, or will it be swept aside as clients discover they can handle tasks independently?
As someone deeply invested in SEO, I’ve often pondered: Could AI eventually render SEO obsolete? This question has sparked considerable debate as AI capabilities continue to expand.
While AI can streamline technical tasks, there’s a consensus that it won’t entirely replace the need for human expertise in SEO. Early studies affirm that human input remains vital.
AI efficiently handles structured data tasks, yet it falls short without meticulous data oversight and expert human guidance.
The advent of AI signifies a shift in workflow dynamics, raising the bar on execution and focusing human expertise on more strategic areas.
AI’s potential to reduce reliance on semi-technical expertise is notable, especially in well-structured domains like coding. However, crafting AI-driven solutions without human refinement often proves inadequate.
The challenge for generative AI lies in its machine-like processing. Only those with technical know-how can truly harness its potential for tasks like generating functional product descriptions or scalable alt text.
AI’s effectiveness is directly linked to the quality of human instructions. Expertise in creating carefully structured prompts is indispensable.
Despite the aid AI offers, its reliance on structured data and human oversight underscores why SEO isn’t fading anytime soon.
A closer look at AI’s progression reveals the persisting need for human intervention, especially as the web’s uncurated nature challenges AI’s data processing capabilities.
While AI tools are growing more sophisticated, they still depend on human expertise to function seamlessly within comprehensive SEO strategies.
The complexity of implementing full SEO automation highlights the irreplaceable value of human judgment in managing intricate data environments.
As AI tools evolve, they serve as companions to SEO, boosting efficiency but not substituting the strategic insight SEO professionals bring to the table.
For SEO to truly become obsolete, AI must autonomously manage tasks reliably and efficiently, a feat still eluding current technology.
Society’s adoption of AI faces barriers; perceptions of AI as a threat slow its integration despite its potential to enhance SEO practices.
As AI becomes normalized, its role within SEO will likely evolve, but the human touch remains essential in delivering creative and impactful results.
I’ve recently discovered Perplexity’s innovative Comet browser for iOS, which defaults to Google Search. It makes perfect sense, given that mobile users typically focus on navigating, finding local results, and completing transactions. As Perplexity CEO Aravind Srinivas points out, “Google does a much better job … than anyone else … including Perplexity.”
Comet for iOS. This browser integrates Perplexity’s AI assistant directly, providing a seamless experience. It cleverly merges AI-generated answers with standard search outcomes, so for numerous queries, you won’t miss the familiar results page.
While browsing, I can query using my voice, which is incredibly convenient. The assistant’s capabilities include summarizing entire pages, answering questions, and even drafting emails on my behalf.
One feature I find particularly useful is Deep Research, which generates cited summaries and prepares materials tailored for serious inquiry.
What Comet does. The assistant can take action on my behalf. Among other things, it excels at summarizing articles and sharing outputs, researching people or topics across tabs, and assisting with bookings or filling out forms. It’s like having a digital personal assistant ready at all times.
What Perplexity is saying.
“The search experience in Comet iOS provides traditional search result pages for fast, local, and high-intent queries that are more common on mobile. Meanwhile, the Comet Assistant easily allows for more advanced knowledge and intelligence powered by the Perplexity answer engine. The intention is for users to have the smoothest browsing experience possible for the real use cases of iOS.”
Why we care. As search continues to evolve towards hybrid models, optimizing for both traditional Google results and AI-generated responses becomes crucial. This shift underscores Google’s stronghold in commercial and local search, while driving the competition into the AI domain.
When I think about how much AI search has evolved, I’m amazed by how it’s deeply rooted in years-old patents. These historical blueprints are the architects of AEO, GEO, and our modern SEO strategies.
It’s fascinating to me that whenever a new large language model (LLM) is released or Google makes an AI update, the SEO community seems to panic. We tend to overlook that the features we’re scrambling to optimize for were often designed in the patent offices a decade ago. Our focus on the present and future blinds us to the wisdom of the past.
If we want to stay ahead in 2026, we need to shift from being futurists to becoming archaeologists.
To truly serve our clients, a balanced research framework is essential. By revisiting foundational patents, we can grasp the core rules, while also keeping an eye on how current AI developments breathe life into those regulations.
There’s a myth that understanding AI search requires being a prompt engineer or diving into every research paper from OpenAI. In reality, many of the algorithms powering today’s innovations were penned in mathematical language over a decade ago.
I deeply respect Bill Slawski, the late, great SEO archaeologist, who spent over 20 years unearthing insights from dry, technical patent filings to forecast the present we are experiencing now.
Looking back, his method of analyzing history certainly proved its relevance.
The SEO algorithms aren’t mysterious; they’re mathematical. Many features introduced today are based on blueprints filed between 2007 and 2016. To succeed, it’s vital to dive into historical documentation.
Understanding strategy versus mechanics is crucial. We need to categorize our learning as either strategic or mechanical. The transition from ‘strings to things’, or entities, required verification to distinguish real from fabricated.
It’s crucial to separate AEO from GEO, as they demand distinctive content architectures and fulfill different objectives. AEO targets direct answers, while GEO requires synthesis and demonstrates the interplay between concepts.
It’s easy to neglect basic SEO fundamentals amidst the influx of AI developments. The essentials, like technical SEO, remain pivotal.
The persistence of technical debt exposes how the tolerance for neglecting foundational SEO tasks has vanished.
The technical backend of our websites, whether using traditional CMS or modern headless architectures, requires careful attention to succeed in AEO and GEO.
To become a proactive SEO architect rather than a reactive time traveler, we must integrate verified facts and trusted source connections into our strategic framework.
Google’s foray into AI Overviews is rapidly transforming the landscape of shopping queries. I’ve discovered that these AI Overviews now appear in 14% of all shopping searches—an impressive leap from just 2.1% in November 2025. This data comes from a comprehensive analysis by Visibility Labs.
Ecommerce brands, which previously seemed shielded from the impact of AI-driven click reductions in search results, are now beginning to feel the heat. This change signifies a shift they can no longer ignore.
Why This Matters to Us. As AI Overviews extend their reach across product searches, the risk for ecommerce brands is escalating. The chance of losing visibility and clicks prior to customers engaging with standard organic or Shopping listings is becoming a real concern.
The Analysis. The Visibility Labs study specifically analyzed product-intent keywords associated with results that included a Shopping box, irrespective of whether they were paid or organic. This included terms like “weighted blanket,” “mushroom coffee,” “protein powder,” and “blue T-shirts.”
Out of this extensive research, involving 20,900,323 shopping-related keywords, 2,919,229 keywords featured an AI Overview. This equates to a 14.0% penetration rate.
Expert Opinion. Jeff Oxford, the founder and CEO of Visibility Labs, emphasizes:
“Focusing on AI SEO is no longer a luxury; it’s becoming a necessity. Ecommerce sites must look beyond traditional SEO tactics and start weaving AI SEO best practices into their search optimization strategies.”
Most content out there tends to be too generic, making it less effective in AI search. I’ve discovered that using customer personas allows me to pinpoint real problems and step into the search space much earlier.
Whenever buyers pose a question, my goal is to deliver a clear answer. That’s essentially the “They Ask, You Answer” (TAYA) framework, which thrives even in AI-driven discovery.
Though it sounds straightforward, I’ve seen many teams struggle to anchor their approach. This typically results in generic questions that lead to generic content.
This is problematic since AI is transforming search behavior, shifting from simple queries to in-depth, context-rich questions. The difference lies in the questions we choose to answer, and that’s where customer personas shine.
The Problem with Generic Questions
Chances are, both I and my competitors have tackled these generic questions already or could do so quite easily.
The trap of generic questions occurs when marketing teams, including mine at times, begin brainstorming content ideas with broad topics like:
What is CRM software?
What is marketing automation?
What is warehouse management?
While reasonable, these questions are not what real buyers ask. Real buyers ask questions based on their specific situations, such as:
“What CRM should a 10-person sales team use?”
“Why are leads slipping through the cracks in our marketing?”
“Why is our warehouse picking speed so slow?”
This distinction is subtle but crucial. The second set of questions integrates a person and a problem, transforming the quality of the content I produce.
Why This Matters More in AI-Driven Discovery
With AI, buyers are asking detailed, context-rich questions, such as:
“I run a 15-person marketing team, and we’re struggling to track leads properly. What should we do?”
The AI provides explanations, outlines solutions, and suggests vendors, essentially giving the buyer a consultation. My content’s job is to explain why a specific persona faces a specific issue, framing how it should be perceived.
This positions me into the conversation earlier, increasing the likelihood of staying top of mind as the user’s understanding evolves.
Imagine this scenario, using myself as the subject:
Marcus.
50 years old.
Meeting old friends in Birmingham, UK.
Looking for things to do for the day.
I might start with a broad question:
“I’m looking for some things to do with friends in Birmingham on the weekend. I’m 50, and I have some old friends visiting for a day. We’ll enjoy some beers, but need activities too.”
The answers might include bars, food, and activity bars. An F1 gaming arcade could be suggested, sparking my interest since I enjoy games but not cars, which prompts my follow-up question:
“Ah, we all like games. What gaming arcades could you recommend?”
The responses might highlight a pinball arcade in Digbeth.
“Pinball Factory in Digbeth sounds fun. What else is there to do around there, food- and drinks-wise?”
This kind of dialogue allows me to refine my day’s plan perfectly for my friends.
Being part of the conversation from the start helps shape the dialogue and boosts the chance of being included in the final decision.
Personas Make TAYA Far More Precise
With personas, I think like my customers, identifying the questions they might ask long before they reach my offerings.
When I define a customer segment, I delve into that persona, understanding their problems and goals to think like them, which helps in crafting content that answers their early-stage questions.
Instead of creating content for a vague audience, I focus on real people, addressing specific needs like, “The best day out in Birmingham for a group of 50-year-old gamers.”
This small shift often leads to valuable content, positioning me within meaningful conversations rather than competing on crowded commercial queries.
A Simple Way to Uncover Better Questions
No need for a complex persona framework. Often, a simple three-question exercise reveals the problems buyers seek to solve.
For each persona, I ask:
What are they responsible for? Examples include sales targets, marketing leads, or warehouse operations.
What problems complicate that responsibility? Issues like missed targets or inefficient operations might arise.
What might they search for when facing these problems?
Now, the questions I generate differ greatly from generic ones:
Instead of saying: “What is CRM software?”
I see questions like:
“Why are leads slipping through the cracks in our CRM?”
“What CRM should a small sales team use?”
“Why is our warehouse picking speed so slow?”
These questions reflect real situations, providing the most substantial content opportunities.
‘They Ask, You Answer’ Works Better with Personas
TAYA covers five key areas: cost, problems, comparisons, reviews, and best-of. These topics offer structure, but approached generically, they mirror what everyone else is doing.
Generic questions like:
“How much does CRM software cost?”
“What problems do warehouse systems have?”
“HubSpot vs. Salesforce”
“Best CRM systems”
“Salesforce review”
Can be transformed into more targeted questions:
“What does CRM cost for a 10-person sales team?”
“Why do my warehouse managers struggle with picking accuracy?”
“HubSpot vs. Salesforce for a small B2B marketing team”
“Best CRM for growing sales teams”
“Is Salesforce suitable for a mid-size sales organization?”
Although the topic remains the same, the approach is tailored to the buyer’s reality. This makes the content more useful and aligns with AI interactions.
Targeted questions might include:
“We’re a small marketing team struggling to track leads properly. What CRM should we use?”
If my content already answers these persona-centered questions, it increases the chance of my explanations becoming part of their conversation.
In short, personas enhance TAYA by transitioning from broad topics to specific questions associated with real problems, improving the content and aligning better with buyers’ needs.
Start with the Problem, Not the Product
A common misstep in content marketing is leading with the product. Buyers, however, start with a problem.
By using personas, I anchor content in the buyer’s perspective rather than my own, ensuring the focus is on the customer.
This change can mean the difference between influence and mere existence of my content.
Where You Enter the Conversation Matters
“They Ask, You Answer” is an effective framework when the questions I address are of high quality.
Personas help in turning vague topics into precise problems, resulting in content that resonates with buyers and AI systems while earning their trust.
Ever wondered what exactly Answer Engine Optimization (AEO) is? In this guide, I’ll walk you through how AEO works and share tips on getting your brand featured in AI-driven shopping responses on platforms like ChatGPT and Google.
By understanding AEO, you can enhance your brand’s presence when prospective customers ask questions related to your industry online. This guide aims to simplify the concept and provide actionable insights to get your brand noticed more efficiently across myriad digital touchpoints.
As I delve into the world of AI-driven search, it’s clear that advice around AI is becoming way too simple. What really sets you apart are knowledge graphs, expert entities, and how you influence trusted datasets.
Recently, I came across a Harvard Business Review article that resonates with the shifts we’re noticing in SEO. AI Overviews and Google’s AI-enhanced search features are not only creating what’s known as a zero-click environment but they’re also redefining user journeys and behaviors.
User journeys that were once multi-touch are now compressed into a single, synthesized answer. The metaphor of the “Search” monolith crumbling visually captures this transformation.
In this dramatic shift, brands like mine lose many traditional touchpoints, requiring a change in marketing strategy. HBR brilliantly highlights how algorithms are reshaping first impressions. However, while pointing in the right direction, the article’s tactical advice feels too generic and superficial.
Much of the advice sounds strategic yet lacks deep operational insight. This gap is crucial for sustainable visibility and long-term success.
The challenge is deeper than what appears as simple advice to navigate at an executive level. Real structural change is essential to adapt to the evolving search landscape.
The Problem with Flock Tactics
The HBR article brings forward schema, authorship signals, and branded concepts but these suggestions risk becoming “flock tactics.” They spread because they’re easy to grasp, yet they lose their edge once widely adopted.
Schema
Schema is highly debated in LLM and AI optimization. Although Microsoft Bing uses schema for its LLMs, Google’s models have a more complex relationship with third-party LLMs.
Incorporating schema in AI and SEO activities is useful, but presenting it as a fundamental tactic neglects its diminishing returns when everyone implements it.
Another oversight is the importance of external knowledge systems such as Wikidata. LLMs often rely on these authorities more than on any single website.
There’s a significant gap in understanding how models process structured versus unstructured data signals.
E-E-A-T — Shallow Authorship Signals
Using real experts’ credentials aligns with E-E-A-T but often becomes superficial, focusing on bios and headshots without actually strengthening expertise.
There’s a profound difference between mere display of bios and nurturing an expert entity recognized in academia or industry.
Only genuine expertise creates the signals that AI models trust.
Vanity Concepts
Creating branded concepts like “The Acme Index” sounds appealing but is difficult to successfully execute. External adoption is key for them to gain traction.
These concepts must be embraced by reputable sources, which is a hurdle many brands fail to overcome.
The Structural Blind Spots
Beyond tactics, there are deeper structural issues in perceiving AI solely as an external shift rather than an opportunity to innovate internally.
Internalizing AI Infrastructure
The potential to integrate AI deeply into operations, through AI assistants or domain-specific agents, is often overlooked.
In controlled environments, fundamentals like site architecture and data structures remain crucial for success, even if they need to be reimagined for AI.
