Tag: Content Strategy

Google Ask Maps SEO: Earn Visibility Through Trust

I see Google Ask Maps changing local visibility in a meaningful way. Instead of showing people a long list of nearby businesses and leaving them to sort through everything, Ask Maps narrows the options, interprets the searcher’s intent, and explains why certain businesses look like a strong fit.

That changes how I think about local SEO. Visibility is no longer only about ranking somewhere near the top of a long results list. It is increasingly about whether Google understands a business well enough to recommend it with confidence.

I would not treat Ask Maps as a separate optimization channel or a brand-new tactic to chase. I would focus on making the business easier for Google to understand, easier to match to real customer situations, and easier to trust. The foundations of local SEO still matter, but the way those signals work together matters even more.

Visibility in Ask Maps starts with filtering

One of the first things I notice about Ask Maps is how small the result set can be. In testing, it often showed around three to eight businesses, depending on the query. That feels very different from traditional Google Maps, where people can scroll through dozens of options and compare them on their own.

With Ask Maps, much of that comparison happens earlier. Google filters the market first, interprets what the person is really asking for, and then presents a smaller group of businesses with an explanation of why each one fits.

That means I have to think beyond the question of whether a business ranks. I also have to ask whether Google has enough confidence to include that business in a short recommendation set and explain why it belongs there.

I think of this as a two-step problem. First, Google decides which businesses are eligible for the query. Then, it decides which eligible businesses it can confidently recommend.

Ask Maps needs enough detail to explain the business

Ask Maps does more than list businesses. It interprets and describes them. Even for simple searches, I often see businesses framed around qualities such as responsiveness, experience, specialization, professionalism, or the kinds of situations they seem best suited for.

That creates a different optimization challenge. It is not enough for Google to know that a business exists or that it offers a basic service. Google needs enough information to answer a more practical question: when should this business be recommended?

To support that, I want Google to understand the types of jobs the business handles, the situations it commonly deals with, the concerns customers usually have, and how the business approaches those situations.

If that information is vague, scattered, or inconsistent, Ask Maps has less to work with. When Google cannot clearly explain why a business fits a specific situation, I would expect that business to be less likely to appear as a recommendation.

Google Business Profile becomes the identity layer

For me, the Google Business Profile sits at the foundation of this whole process. In earlier-stage queries, Ask Maps appears to rely heavily on profile data, including business descriptions, services, reviews, ratings, hours, and operational details.

Many businesses still treat their profile like a basic listing to fill out and keep current. That is necessary, but I do not think it is enough for an environment where Google is trying to describe and recommend businesses. The profile needs to communicate a clear, specific identity.

A generic profile might say that a business offers plumbing, HVAC, electrical work, or another broad service. A stronger profile clarifies the kinds of problems it handles, the situations it is built for, and the details that make it useful to specific customers.

For example, I would use the profile to reinforce details such as emergency availability, response times, specific repair or installation types, experience with older homes, complex systems, or common customer problems the business solves.

That level of specificity gives Google more direct evidence. Instead of forcing the system to infer what the business is known for, I want the profile to make that identity clear.

Reviews shape positioning, not just credibility

Reviews have always mattered in local search, but I see them playing a more structured role in Ask Maps. Review language can show up in the way Google describes a business, especially around themes like responsiveness, honesty, communication, professionalism, and quality of work.

That tells me reviews are doing more than supporting credibility. They are helping define how the business is positioned.

I would still pay attention to rating, volume, and recency. But I would also look closely at what customers actually say. The language inside reviews can give Google useful context about what the business does, how it works, and what customers value about the experience.

A vague review such as “great service” signals satisfaction, but it does not explain much. A detailed review that mentions a same-day response, a drain backup, clear communication about options, and a repair-focused solution gives Google several stronger signals about the business.

Over time, those patterns accumulate. In that sense, I view reviews as one of the main ways Google learns what a local business is known for.

Website content matters more when decisions get harder

I also see website content becoming more important as queries become more complex. For basic service searches, the Google Business Profile and reviews may carry a lot of the weight. But when the search involves higher cost, uncertainty, or trust, Google appears to look for deeper supporting evidence.

That is where the website can help. Many service pages explain what a business offers and why it is qualified. That still matters, but it does not always match how people search when they are trying to make a difficult decision.

In more situational searches, people are not just looking for a service. They are trying to understand a problem, compare options, reduce risk, and decide what to do next.

That is why I would build content around the customer’s situation, not just around the service name. Stronger pages explain what leads to the problem, how to recognize it, what options are available, how to think through the decision, and what outcomes to expect.

For example, a furnace repair page can go beyond a basic list of services. It can cover common symptoms, when repair makes sense, when replacement might be worth considering, and how a homeowner can evaluate the decision. That kind of content lines up more closely with the prompts Ask Maps is trying to interpret.

I also see a strong fit for jobs-to-be-done pages. Instead of organizing every page around a service category, I would create pages around the situation the customer is trying to solve and the decision they are working through.

Trust signals matter more as risk increases

As searches move from simple service needs into decision-making, trust becomes more important. When people mention cost, honesty, uncertainty, or fear of making the wrong choice, Ask Maps tends to highlight qualities such as transparency, fairness, careful workmanship, and clear communication.

That makes sense to me because it reflects how people actually think in those moments. When someone faces an expensive repair or an unexpected issue, they are not only asking who can do the work. They are asking who they can trust to handle it correctly.

I would support that trust with evidence across the business’s online presence. Reviews can show that customers felt respected and informed. Website content can explain the process. Examples of completed work can show experience. Clear “what to expect” sections can reduce uncertainty.

The higher the perceived risk, the more supporting evidence matters. I want Google to see a consistent pattern that the business explains options clearly, avoids unnecessary pressure, handles similar situations, and leaves customers confident in the outcome.

Detailed customer reviews do more than boost ratings. They give Google Ask Maps the context it needs to understand, position and confidently recommend a local business.

External signals should reinforce the same story

For more complex or trust-heavy queries, Ask Maps may look beyond the Google Business Profile, reviews, and website. Third-party platforms, directories, and other public sources can help reinforce how Google understands a business.

I do not take that to mean every external mention is equally important. I take it to mean consistency matters. If a business is described one way on its website, another way in reviews, and differently across directories or social platforms, the overall picture becomes harder to interpret.

When those signals align, they strengthen each other. Business descriptions, services, customer experiences, types of work handled, and overall positioning should tell the same story wherever they appear.

From a practical standpoint, I would not try to appear on every possible platform. I would make sure the important sources are accurate, credible, and consistent.

I would optimize for evidence, not just keywords

As local search decisions become more specific and higher risk, Google needs deeper signals from business profiles, reviews, and website content to recommend the right provider.

Taken together, these patterns push me to think differently about optimization. Traditional local SEO often starts with keywords and rankings. Those still matter, but they do not fully explain what Ask Maps is doing.

I find it more useful to think in terms of evidence. For a business to be recommended, Google needs enough information to understand what it does, what types of jobs it handles, what situations it fits, how customers experience it, and whether it can be trusted in higher-stakes decisions.

Each source contributes something different. The Google Business Profile establishes the baseline identity. Reviews add real-world context. Website content provides depth and explanation. External sources help confirm the same picture.

Individually, none of those elements tells the whole story. Together, they create a clearer and more consistent understanding of the business. That is where the shift from ranking to recommendation becomes most obvious: keywords can support relevance, but evidence supports recommendation.

My practical framework for Ask Maps visibility

When I evaluate a business for Ask Maps visibility, I would look at five areas: identity, relevance, trust, context, and consistency.

Google Ask Maps rewards more than keyword relevance. This visual shows why reviews, service details, trust signals, and real proof help local businesses get recommended.

Identity asks whether Google can clearly understand what the business does and where it operates. Relevance asks whether the business can be matched to specific services and situations. Trust asks whether there is enough proof that customers feel confident choosing it.

Context asks whether the content reflects the decisions customers are actually trying to make. Consistency asks whether different sources reinforce the same understanding of the business.

I do not see this as a checklist to complete once. I see it as a practical way to evaluate how clearly and consistently a business is represented across the sources Ask Maps appears to use.

What I would avoid

With any new search feature, it is easy to overcorrect. I would avoid treating Ask Maps as an isolated channel that needs thin content, unnatural profile language, generic service-page duplication, or review language that feels forced.

Those tactics may create more content, but they do not necessarily create more useful evidence. The better approach is to align more closely with how customers actually search, evaluate options, and make decisions.

A practical local SEO framework shows how businesses can earn visibility in Google Ask Maps by clarifying identity, proving relevance, building trust, adding context, and staying consistent online.

When the business presence reflects real customer needs clearly and consistently, it naturally creates the kinds of signals Ask Maps seems to rely on.

What I still do not know about Ask Maps

I would treat all of this as directional, not definitive. Ask Maps is still being tested and refined, and the system is not fully documented.

The result structure can vary by query and test environment. The feature’s usability is also still changing. In many cases, users may still need to click into a Google Business Profile to call, book, or engage, rather than acting directly from the Ask Maps response.

Measurement is another open issue. Right now, I do not see a clean way to isolate Ask Maps visibility or performance inside standard reporting tools. That makes it difficult to attribute calls, traffic, or conversions directly to this experience.

I also would not assume the same signal weighting applies to every query. Google Business Profile data, reviews, website content, and external sources may all matter, but their relative importance likely changes based on the search intent and the complexity of the decision.

The real shift is from ranking to recommendation

I see Ask Maps as a version of local search where retrieval, evaluation, and decision support are moving closer together. Instead of making users search, compare, research, and decide across several steps, Google is trying to guide more of that process inside one experience.

That changes the meaning of visibility. In Ask Maps, it is not enough for a business to simply appear. The business needs to be understood well enough for Google to explain why it fits the situation and trusted enough to be recommended.

For businesses and SEOs, I would not respond by chasing a narrow trick. I would build a clearer, more complete, and more consistent representation of the business across the sources that shape Google’s understanding.

The businesses most likely to benefit are the ones that are easiest to interpret, easiest to trust, and easiest to match to real-world customer needs.

Inspired by this post on Search Engine Land.

July 3, 2026
6 Claude Content Audit Workflows I Reuse for Better SEO
I see existing content as a goldmine, but only when I have a practical way to improve it. The hard part is usually finding the time, and that is where Claude has made a large, messy job feel much more manageable for me.

I do not start by building a giant content audit system. I start with one article, run one focused audit, refine the output, and then turn the prompt into a reusable Claude skill. Over time, those one-off audits become a working library I can improve every time I use it.

I use Claude to uncover topical gaps, flag outdated information, check brand voice, and evaluate whether a page is easy for AI systems to retrieve and cite. The real value comes from iteration: each time I improve a skill, the next audit becomes faster and more useful.

Here are six content audit workflows I would build in Claude. The first four work at the page level, so I can start with a single article before moving into larger library-wide analysis.

Page-level audits

When I am not ready to build a full workflow, I start with page-level audits. These audits only require one article, which means I do not need a content inventory, a data export, or a complicated setup. After each session, I ask Claude to turn the process into a reusable skill for future page-level reviews.

1. Brand voice consistency

I use a brand voice consistency audit when a content library has drifted over time. Voice can shift because of new writers, changing services, product updates, or evolving positioning. This audit helps me spot where a page no longer sounds aligned with the brand.

If I do not have detailed brand guidelines with strong examples, I let Claude extract the voice guide from high-quality content. That usually works better than relying on vague phrases like “conversational but authoritative” or “educational, not too formal.”

I pick three to five articles that represent the brand at its best. If possible, I download them as markdown files and ask Claude to describe how the voice works in concrete terms.
- How the articles usually open, such as whether they begin with a direct claim, a counterintuitive statement, or a specific scenario.
- How sentences and paragraphs are built, including average length, range, rhythm, and how paragraphs tend to close.
- Three to five personality dimensions framed as “We say X, but not Y,” with do and don’t examples.
- Words and phrases the brand tends to use, and words or phrases it should avoid.
- Specific constructions, phrases, and conventions the brand never uses.
Instead of accepting a vague voice description, I want Claude to return concrete observations. For example, it might say that articles open with a direct claim rather than a scene-setting paragraph, sentences average 15 to 20 words and rarely exceed 30, and transitions are functional, such as “here’s why that matters,” rather than formulaic, such as “furthermore.”

I also want example pairs, such as: “We’d say ‘the data shows three things,’ not ‘there are multiple factors to consider.’” The goal is not to create a voice guide for writers. The goal is to create one an LLM can understand and apply consistently.

Once I like the output, I ask Claude to save it as a skill and evaluate an article against it. If Claude flags issues I disagree with, I update the skill until the feedback becomes useful and repeatable.

I can then use that skill to find voice inconsistencies in older content, check new drafts for alignment, and even generate more on-brand first drafts. I still edit the output, but the starting point is much stronger.

Dig deeper: How to train Claude to sound like your brand

2. Coverage comparison

When I need to improve content performance, I use a coverage comparison to find topical gaps. This helps me understand what competing pages cover that my article misses.

I use the Claude in Chrome extension to have Claude review the top three to five ranking pages for my target keyword. Then I ask Claude to compare those pages against my content and highlight the most important gaps.
- What competitors are doing well.
- What my article already does well.
- Where I can improve the piece without bloating it.
If I want the output in a table, I ask Claude to format it that way. If I want a downloadable DOCX for review or handoff, I ask for that instead.

When Claude recommends additions I would never publish, I make a note of those exclusions before packaging the workflow into a skill. That way, the skill gets closer to my editorial standards each time I refine it.

3. Freshness audit

Old content adds up quickly, and it is hard to prioritize refreshes while I am also producing new material. A freshness audit skill helps me identify what needs attention without rereading every older article from scratch.

I give Claude an older article and ask it to flag anything time-sensitive: statistics tied to a specific year, named tools or platforms, references to “current” or “recent” trends, and claims that depend on a market, regulatory, or product context that may have changed. I am not asking Claude to rewrite the article yet. I am asking it to build an issue list I can act on.

If my company has launched new products, removed old services, changed positioning, or updated terminology, I include that context in the input. That helps Claude flag what should be added, removed, or revised.

Dig deeper: How to turn Claude Code into your SEO command center

4. AEO and AI retrievability

I use an AEO and AI retrievability audit to understand whether a page is likely to be surfaced in AI-generated answers. Tools such as ChatGPT, Perplexity, and Google AI Overviews tend to favor content that answers questions directly. If an article buries the answer under too much preamble, or structures key information in a way that is hard to extract, it becomes less useful for those systems.

I give Claude the article and the target query, then ask it to evaluate several retrieval signals.
- Whether the article answers the main question directly and early.
- Whether key statements are specific enough for an LLM to quote or cite.
- Where an FAQ-style section would improve clarity.
- Whether the page includes authority signals, such as primary research, first-person experience, outbound citations, or specific examples.
Once I save this as a skill, it becomes an extra editor focused specifically on AI visibility and answer retrieval.

Library-level audits

Once I am ready to move beyond individual pages, I use library-level audits. These require performance data, a content inventory, a connector, or a manual export.

5. Performance triage

When I think about a traditional content audit, performance triage is usually what comes to mind. It helps me analyze a content library and identify the pages that deserve attention first.

Before I begin, I make sure Claude has access to the right data through a connector such as BigQuery or the Semrush API. If that is not available, I export the data I normally use for large-scale audits, such as traffic, clicks, engagement metrics, conversions, rankings, and related performance signals.

I ask Claude to prioritize pages that have suffered meaningful performance drops in the past six to 12 months, pages with high impressions but consistently low click-through rates, and pages that have been live long enough to rank but never gained traction.

I also define what a meaningful performance drop looks like for the site I am analyzing, because traffic patterns vary by industry, audience, and page type. Then I ask Claude for a prioritized list of what is worth investigating and why. From there, I use the page-level audits above to diagnose the problem.

If I have run this analysis before, I give Claude the previous output. That helps the skill learn the kind of prioritization and reasoning I expect.

Dig deeper: How to build a Claude Code-powered second brain for agency work

6. Topical gap analysis

I treat entities as a major part of AEO and semantic search. A topical gap analysis helps me see whether my content library has enough coverage to build authority around the entities tied to my brand.

The core question I ask is simple: what is my content library not covering that it should?

To start, I create a list of target entities. For example, at my agency, I want to be known for SEO and AEO. If I have a clear list of services or products, I can use that instead of a formal entity list.

Using Cowork or Code, I ask Claude to analyze my sitemap and compare it to those target entities. If I have a Screaming Frog export with URLs, page titles, and meta descriptions, I use that as input for a more accurate analysis.

Then I ask Claude to identify topic clusters that are missing or underrepresented based on the target entities, services, or products. If I want prioritization, I can use the Semrush MCP so Claude can check search volume for potential keywords.

Not every gap is worth filling. I filter the results against audience needs, business relevance, and editorial standards. Then I feed those decisions back into Claude so the skill produces better recommendations next time. The final list can go directly into my content creation workflow or be handed off to a content team.

I do not try to audit everything at once

I have seen content audits stall because the scope feels too large, not because the team lacks data. My preferred approach is to pick one audit and one article, run the workflow, save the skill, and use it again on the next piece.

For me, iteration is part of the value. I enjoy taking one Claude skill, improving it, and then chaining it with other skills to uncover more content opportunities. Starting small is what makes the system easier to keep using.

Inspired by this post on Search Engine Land.
July 3, 2026
AI Search Content Structure: Boost Brand Discovery

I structure content for AI search by making every page clear, credible, and easy for answer engines to understand. That means I do not rely on keywords alone. I combine strong SEO fundamentals with topical authority, earned media, and answer-first formatting so AI systems can recognize what my brand knows, where it is trusted, and why it should be surfaced in relevant responses.

When I think about AI visibility, I focus on discovery from the start. I want my content to answer real questions directly, connect related topics naturally, and support each claim with signals that build confidence. This approach helps improve how my brand appears across AI search experiences, traditional search results, and emerging discovery platforms.

For me, the goal is simple: create content that is useful for people and understandable for machines. By organizing information around intent, authority, and clarity, I make it easier for AI tools to cite, summarize, and recommend my brand when users are looking for trusted answers.

Inspired by this post on HiGoodie Blog.

July 3, 2026
AI Search Trust Is Falling: What Marketers Must Fix

A year ago, I saw 82% of consumers say AI-powered search was more helpful than traditional search. By 2026, that number had fallen to 54%, a 28-point drop in sentiment in just 12 months.

That does not mean people are abandoning AI search. In fact, 70% of consumers say they are using AI tools for search more than they did last year. The tension is clear: adoption is rising, but trust is slipping.

That is the core issue I believe search marketers need to solve in 2026. It is no longer enough to appear in AI answers. I need my brand, and the brands I work with, to be visible, accurate, credible, and trusted when AI systems surface information.

To understand the shift, Fractl partnered with Search Engine Land to expand our 2025 research. We surveyed 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are changing in the AI search era. Disclosure: I am the co-founder of Fractl.

Here is what I believe the data means for 2026 search strategy.

Consumers are using AI more, but trusting it less

AI search adoption is no longer the main story. Seventy percent of consumers report increased use of AI tools for search over the past year, while only 3% say their use has decreased. The bigger question is whether people trust what those tools return.

One surprising finding is that baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically embrace AI while older users lag behind. What I see instead is a more complicated market where trust has to be earned across every generation.

In 2025, only 3% of consumers said AI was less helpful than traditional search. By 2026, that skeptic group had grown to 17%, nearly six times larger than the year before. Even among the 54% who still find AI helpful, enthusiasm is softer: 37% say it is only somewhat more helpful, while 17% say it is much more helpful.

I think hallucinations and low-quality AI content are changing how people evaluate the entire channel. Consumers may use AI because it is convenient, but convenience does not automatically create confidence.

AI content volume has become a brand trust risk

In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%. For me, that makes AI content scale a reputational issue, not just an operational decision.

If I publish AI-assisted content at scale without disclosure, strong editorial standards, or obvious quality signals, I am asking my audience to trust a process they are increasingly skeptical of. That is a risk more brands need to take seriously.

Gen Z is especially strict. Fifty-four percent of Gen Z consumers say heavy AI use in a brand’s marketing would decrease their trust, compared with 32% of baby boomers and 33% of Gen X. Women are also more likely than men to penalize brands for heavy AI use, 44% vs. 34%.

That matters because Gen Z is often the audience most likely to engage deeply, share content, shape online conversations, and influence long-term organic visibility. If that audience matters to a brand, AI-generated filler is not a harmless shortcut.

Disclosure is now a consumer expectation

Across every major content format, more than 80% of consumers want AI-generated content labeled. Video leads at 91%, followed by images at 90%, audio at 87%, and written content at 84%. More than half of respondents strongly agree with labeling in every category.

I do not read that as a mild preference. I read it as a near-universal expectation. The brands that treat AI disclosure as optional are creating a gap between how they operate and what their audiences want.

Consumers still believe AI will shape the future of search. Sixty-four percent agree that AI will replace traditional search engines within five years, nearly unchanged from 66% in 2025. The channel is not going away. But being present in AI results and being trusted in AI results are now two different challenges.

Google still leads on trust, especially for buying decisions

When consumers are making purchase decisions, 39% turn to Google first. Reddit follows at 15%, AI tools at 14%, and review sites and friends or family each at 11%. The trust people have built with Google has not automatically transferred to AI tools.

Platform preference also changes by query type. Google dominates five of six major search categories. It is the first stop for local businesses, product research, travel planning, and health questions. YouTube overtakes Google for how-to content, while ChatGPT is now the second-most-used destination for health questions and ranks strongly for product research, travel planning, and how-to content.

That tells me there is no single AI search platform to optimize for. I need to map content strategy to actual user behavior: where people search, what they are trying to decide, and which platforms influence confidence at each stage.

Before making a purchase decision, the average consumer checks 2.4 platforms. Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2. This behavior is consistent enough that I now think of search optimization as a multi-platform visibility strategy, not a rankings-only discipline.

A brand that appears in Google results but nowhere else can lose to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has strong third-party review content. Visibility now has to travel with the buyer.

AI is changing marketing operations quickly

AI now touches 53% of marketing work on average, up from 38% in 2025. In practical terms, the equivalent of one full workday per week has shifted to AI-assisted workflows in just 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it is involved in three-quarters or more.

For SEO and content teams, this means competitors are moving faster. But speed alone is becoming commoditized. Accuracy, original insight, expert judgment, and brand credibility are much harder to copy.

Marketers are also feeling pressure to adopt AI. Fifty-five percent of marketing roles report a 7-out-of-10 level of pressure to use it. SEO and analytics teams feel that pressure most, while PR is not far behind. As AI makes generic content easier to produce, the advantage shifts toward what AI cannot automate well: judgment, relationships, trust, and reputation.

The quality tradeoff is real. Only 26% of marketers say AI made their work both faster and better. Nearly half say it made their work faster but more generic, and 7% report an outright quality decline.

That is where I see a major competitive opening. If other teams are scaling generic AI content while I invest in original data, expert quotes, third-party validation, and earned brand mentions, I am building assets that are more visible, credible, and retrievable across search engines, social platforms, and LLMs.

AI governance is still too weak

About three in four organizations conduct human editorial review before publishing AI-generated content. Sixty-two percent check for brand voice, 54% check facts, and 42% conduct legal or compliance review. Only 27% evaluate content for bias.

That means nearly half of AI-generated content may enter the market without fact-checking, legal review, or plagiarism checks. Too many teams are still relying on surface-level review: Does it sound right? Is the tone appropriate? Are there typos?

In a year when consumers are already prepared to distrust generic AI content, I see governance as one of the cheapest gaps to close and one of the most expensive to ignore.

The disclosure gap is just as serious. Heavy, generic AI use is now a brand-trust liability, yet only 20% of organizations always disclose AI use to their audiences. Compare that with the 84% average consumer demand for labeling written content, and the disconnect is obvious.

The takeaway is not to abandon AI. It is to stop treating governance as optional. Every AI workflow needs accuracy checks, transparency standards, bias review, and human accountability before content reaches an audience.

AI hallucinations are already a brand problem

A year ago, about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved to 24%. At the same time, 27% of brands have already been misrepresented in AI-generated responses, and 14% say an AI inaccuracy has affected a customer relationship, sale, or PR situation.

More brands have been misrepresented by AI than have a formal monitoring process. That should concern every search and communications team.

If AI is summarizing my category, comparing my product, or explaining my brand incorrectly, that is not only an SEO issue. It is a reputation risk, a revenue risk, and a PR issue waiting to escalate.

When AI misrepresents a brand, I believe fixing the source matters more than arguing with the output. That can mean reaching out to publishers for updates, correcting owned profiles, improving brand pages, and publishing clear correction content tied to the entity.

Organic traffic is under pressure, not in freefall

Half of the marketers surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI. That is meaningful, but it is not the whole story.

The larger shift is not simply from Google to ChatGPT. It is from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across platforms, communities, assistants, and review environments.

The same marketers reporting organic losses are often finding visibility elsewhere. Fifty-seven percent report growth from social platforms such as TikTok, Reddit, and YouTube. Forty percent see growth from AI assistants such as ChatGPT, Gemini, and Perplexity. Thirty-one percent see growth in direct or branded traffic, while only 10% report no visibility growth anywhere.

That is why I think 2026 brand visibility depends on brand mentions and entity authority across the web, not just individual page rankings in Google.

Marketers are prioritizing the easiest tactics

Many teams are moving in the right general direction: community building, earned authority, owned audiences, expert content, and traffic diversification. The most prioritized strategies include building brand presence on social platforms at 59%, GEO and AEO optimization at 54%, and creating authoritative expert content at 44%.

Half of surveyed marketers say organic traffic has fallen since AI Overviews arrived, but the data points to pressure rather than collapse, with 30% reporting no change.

But the least prioritized strategy is original research and data, at only 15%. I see that as a strategic inversion.

Original, proprietary research is one of the hardest content assets for AI to replicate or commoditize. It earns citations, attracts links, builds topical authority, and gives journalists, communities, search engines, and AI systems something distinctive to reference.

In GEO, the same pattern appears. Many marketers are using content-led tactics that AI can easily replicate. Long-tail FAQs can help with AI Overviews, and schema can support structure, but neither one builds credibility by itself.

As organic search pressure grows, marketers are finding brand visibility gains across social platforms, AI assistants, direct traffic and Google AI features, according to Fractl and Search Engine Land.

The stronger moat is entity authority: proprietary data, expert perspectives, topical depth, and third-party validation. These are the assets that make a brand worth citing.

GEO measurement is lagging behind execution

Only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results. That is understandable for a newer channel, but GEO is becoming too important to manage casually.

Marketers are leaning into practical GEO tactics, with FAQ optimization leading the pack, while entity authority, original research and citations trail behind.

I believe visibility tracking, citation monitoring, branded search lift, and AI-assisted conversion analysis all need more attention. Teams that can prove GEO ROI will be able to defend and grow investment while others are still guessing.

The main barrier to deeper AI integration is not leadership buy-in. Only 2% cite that as the obstacle. The top barrier is team training and skill gaps at 26%, followed by tool fragmentation at 20%, budget constraints at 19%, unclear ROI at 12%, and legal or compliance concerns at 12%.

For search teams, that means AI literacy, prompt strategy, content quality control, and GEO measurement skills may be more valuable right now than adding another tool to the stack.

Most marketers see early signs their GEO strategy is working, but only 12% report measurable results, highlighting a major gap in AI search measurement.

What I would do for a 2026 search strategy

First, I would audit the brand’s AI footprint. I would query the brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews, then document what is accurate, what is missing, and what is wrong. Waiting until an AI error becomes a PR issue is too late.

Second, I would invest in entity authority and original research. AI cannot invent legitimate proprietary survey data, named expert perspectives, verified brand facts, or original market analysis. Those assets become more valuable as AI systems get better at rewarding genuine authority.

Third, I would distribute visibility across multiple platforms. Google organic remains necessary, but it is no longer sufficient. A brand needs a consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media.

Fourth, I would build AI content governance, not just AI content workflows. Consumer demand for AI disclosure ranges from 84% to 91% across formats, while only 20% of brands always disclose. That gap is a reputational liability and may become a legal and regulatory one.

Fifth, I would close the GEO measurement gap. If I can connect AI search mentions to traffic, lead quality, and revenue, I can prove ROI at a time when most teams cannot. That creates a budget and strategy advantage that compounds.

Finally, I would double down on what AI cannot easily replicate: proprietary data, named experts, human-verified claims, transparent sourcing, and a consistent high-quality brand voice. In 2026, the brands that treat quality as a strategic differentiator are the ones most likely to be surfaced, cited, and trusted.

Methodology

Fractl and Search Engine Land surveyed 1,008 U.S. consumers and 150 marketers in Q2 2026. The consumer sample was nationally representative across age, gender, and region. The marketer sample included companies ranging from fewer than 10 employees to more than 5,000 and covered roles in SEO, content, social, analytics, paid media, PR, and marketing leadership.

Where noted, findings are compared year over year against the same questions asked in Fractl’s 2025 consumer study conducted with Search Engine Land.

Inspired by this post on Search Engine Land.

July 3, 2026
Delegation Search: Why AI Now Shapes Decisions

I used to think of search as retrieval. I would open tabs, compare sources, read reviews, cross-check details, and then make the decision myself.

Now I see search becoming something different: delegation.

More users are realizing they do not need to compare 15 pages or jump between Google, Maps, reviews, forums, and videos before they act. They can ask AI to do much of that work for them.

In many ways, this is the closest most people have come to having a personal assistant. For a long time, delegation was a luxury. It usually meant having someone else research options, summarize information, and make recommendations. In practice, that kind of help was mostly available to people with money or support teams around them.

Now that capability is much more widely available. I believe that changes search behavior at a fundamental level. Users increasingly want synthesis instead of retrieval, recommendations instead of endless exploration, and reduced effort instead of exhaustive research. They want help evaluating options and making decisions.

This is a real behavioral shift. Where people once might have phoned a friend, they now ask an LLM.

Why I believe users are delegating more decisions

At the heart of this move from search to delegation is basic human psychology. Our brains are wired for cognitive ease. We naturally gravitate toward behaviors that reduce effort, simplify decisions, and save time.

AI tools fit that pattern perfectly. They remove friction from the decision-making process by helping users open fewer tabs, make fewer comparisons, carry less cognitive load, and reach outcomes faster.

I also see users becoming more comfortable with answers that are good enough and delivered quickly, rather than perfect answers that require a lot of effort to uncover.

For years, search behavior was built around gathering as much information as possible before making a decision. AI has changed that value exchange. Users do not always need every possible answer. They need confidence that the answer in front of them is sufficient.

Reflect Digital’s SearchPulse research found that up to 61% of AI users say they use these tools because of their speed and ease. Disclosure: I am Reflect Digital’s founder and CEO.

As technology has become part of everyday life, our expectations around convenience have evolved with it. We are already conditioned to optimize more of our lives than ever before, and AI is becoming another mechanism for doing exactly that.

Dig deeper: The delegation boundary: How AI decides which brands win

Why delegation in search will not look the same for everyone

One of the biggest mistakes I think businesses can make right now is assuming this shift to delegation is happening evenly across all audiences and all search journeys. It is not.

AI search adoption varies significantly depending on factors such as household income, profession, and digital confidence.

People also delegate differently depending on the task they are trying to complete. Vacation planning is a useful example. Building an itinerary is an ideal delegation task because it traditionally requires maps, travel sites, timing decisions, logistics, and constant comparison.

Now, a user can ask AI something like: "Plan me a five-day itinerary around Tuscany with wine tasting, scenic towns, and minimal driving." That is decision outsourcing in action.

But choosing the vacation itself may still involve more exploration. A person may still want to browse destinations, look at imagery, watch videos, or validate ideas independently before narrowing the options.

The key point is that delegation is contextual. I believe businesses need to understand where delegation naturally fits within their audience’s decision-making process.

How I identify delegation opportunities in an audience

The important thing to understand is that delegation is rarely universal across an entire customer journey. AI adoption is not binary. People delegate specific types of decisions at specific moments.

I look for delegation opportunities in moments where users experience high cognitive load, too many variables, time pressure, repetitive comparison, decision fatigue, or information overload.

These are the moments where delegation becomes appealing. To understand what that means for a specific audience, I ask where they get overwhelmed, where they compare too many options, where they are trying to save time, and where they repeatedly ask for reassurance or recommendations.

I also look for the parts of the journey that feel effort-heavy rather than emotionally enjoyable. The more effort a task requires, the more likely delegation becomes.

Then I compare those answers with the areas where users may still want exploration, such as inspiration, entertainment, identity expression, aspirational browsing, and emotionally led decisions.

For example, a user may delegate the work of building a travel itinerary but still enjoy exploring vacation destinations on their own.

That distinction matters. The businesses that win in this new search environment will understand not only what their audience is searching for, but also what they are trying to offload.

Dig deeper: Why your brand isn’t making the AI recommendation set

What delegation behavior looks like in practice

Once I start looking for delegation-driven decisions, they become surprisingly easy to spot. They often appear when users ask AI to narrow down options, recommend the best fit, validate a choice, summarize information, compare alternatives, or reduce effort.

That means searches start to sound more like: "What’s best for me?" "What would you recommend?" "Compare these options." "Give me the top three." Or, "Summarize this for me."

Traditional search behavior, by contrast, is more exploration-heavy. It involves deeper comparisons, source checking, manual research, and detailed information gathering.

Most users will move between these two modes depending on what they are searching for and why. But I do not think businesses should rely only on internal assumptions or gut instinct to understand where those delegation moments exist.

Gut instinct only goes so far. To understand this shift properly, I believe businesses need to speak directly with their audience and combine behavioral observation with research such as surveys, customer interviews, roundtables, usability testing, journey analysis, search behavior analysis, and AI prompt analysis.

The goal is to understand where users experience friction, feel overwhelmed, seek reassurance, want recommendations, and feel comfortable outsourcing decision-making.

The real competitive advantage comes from understanding what your audience no longer wants to do themselves.

Dig deeper: Brand depth determines what AI systems recommend

What delegation search means for content strategy

This is where the shift becomes commercially important. I believe businesses now need both search-support content and decision-support content because both behaviors still exist.

Search-support content is designed for exploration. It is usually comprehensive, detailed, comparison-driven, educational, and deeply indexable. It helps users who still want to research extensively and validate decisions themselves.

Decision-support content serves a different purpose. It needs to be synthesized, recommendation-oriented, clearly structured, trust-heavy, and outcome-led.

This kind of content helps both users and AI systems quickly understand what a business offers, who it is for, when it is appropriate, and why it should be trusted.

For example, a traditional search-support page might compare every CRM platform feature in detail. A decision-support page might clearly explain the best CRM for a 50-person B2B sales team with limited implementation resources.

One page supports exploration. The other reduces decision-making effort.

Websites increasingly need to support two parallel journeys: humans who are exploring and humans who are delegating. Put another way, they need to support journeys for both people and AI agents.

How I audit content for delegation behavior

If delegation is becoming part of an audience’s decision-making process, the next question is simple: does the content support it?

I usually start by auditing existing content through two lenses: exploration support and decision support.

First, I ask whether the content helps someone explore. This is traditional search-support behavior. It includes detailed explanations, comparisons, educational depth, broad keyword coverage, manual research support, and multiple options without strong direction.

That type of content helps users gather information and evaluate independently.

Then I ask whether the content helps someone decide. Decision-support content reduces effort by offering clear recommendations, summarized takeaways, structured comparisons, strong trust signals, direct answers, contextual guidance, and outcome-focused language.

One of the easiest ways I spot gaps is by asking: "If an AI system landed on this page, would it clearly understand what we recommend, who this is for, and why it matters?"

Many businesses currently have a lot of exploration content but very little decision-support content. That creates a gap. Delegation is no longer only about being discoverable. It is about being usable within a decision-making process.

Dig deeper: From searching to delegating: Adapting to AI-first search behavior

The risk of misunderstanding this shift

Some businesses are already making the mistake of abandoning traditional search behavior too early. I think that is a serious error because traditional search is not disappearing.

At the same time, delegation behavior cannot be ignored. Different audiences, moments, and decision types now require different search experiences.

The businesses that succeed will not be the ones chasing every AI trend. They will be the ones that deeply understand when users want exploration, when users want delegation, and how to support both effectively.

That matters because users increasingly seek help evaluating options and making decisions.

The brands that succeed in the future of search will be those that truly understand their audience and let that knowledge guide their strategy.

Inspired by this post on Search Engine Land.

July 3, 2026
AI and SEO Explained: What Marketers Need to Know Now
If it feels like the whole internet woke up and decided every sentence needed to start with “AI,” I get it. I feel that fatigue too.

As marketers, we are getting hit every day with LinkedIn hot takes, rushed prompt hacks, and promises that ChatGPT will either 10x our productivity or replace us completely.

And right in the middle of all of that is the digital marketer trying to figure out whether AI is just another buzzword cycle or the start of a major rewrite of how we handle content, SEO, PPC, reporting, and almost everything else.

So I want to break it down in plain English.

Think of this as my AI starting guide for marketers who are tired of needing someone younger to translate every new acronym, the same way many of us once had to help our parents get online or open an AOL chat window.

Defining AI and LLMs, and why they matter

I am not asking “what is AI” just to chase keyword density. I want to start with a shared definition, because a lot of these terms get used interchangeably, and not always correctly.

At its core, artificial intelligence refers to machines performing tasks that usually require human intelligence, such as understanding language, recognizing patterns, making decisions, and generating content.

The kind of AI getting the most attention right now is generative AI: models that can create text, images, code, video, and other outputs based on patterns learned from huge datasets.

Tools like ChatGPT, Gemini, and Claude do not “think” the way people do. They predict the next most likely word, phrase, or response based on what they have been trained on.

That matters because AI is not a magic shortcut to instant wealth, overnight automation, or effortless headcount reduction. I see it more as large-scale data aggregation and pattern recognition.

Large language models, or LLMs, are not creating net-new truth from nothing. They process massive amounts of existing information and produce answers based on patterns, probabilities, and what looks like internet consensus.

For content creators and marketers, that is a major shift. I am no longer thinking only about optimizing for a traditional search engine click. I also have to think about whether machines can understand, summarize, cite, and reuse my content.

The biggest implication is the rise of zero-click search. AI systems can answer users directly through experiences like Google AI Overviews or ChatGPT responses, often without sending that user to the original website.

That changes SEO from a pure traffic game into an authority, visibility, and data-ingestion game.

That is why I think marketers need to understand what AI does well, what it struggles with, and where it actually belongs in a broader marketing strategy.

AI jargon I think marketers need to know

Before going deeper, I want to separate a few terms that often get mashed together: AI, machine learning, NLP, generative AI, LLMs, and AI agents. They are related, but they are not the same thing.

Understanding the difference helps me make better decisions about which tools to use, where to trust them, and where human judgment still matters most.

Artificial intelligence (AI)

Artificial intelligence is the broad umbrella term for machines performing tasks that usually require human intelligence. That includes problem-solving, learning, speech recognition, language understanding, and decision-making.

In marketing and search, AI has become a catch-all phrase. But in practice, most of the tools I use fall into more specific categories.

Example of AI: Siri and Google Assistant use AI to interpret voice commands and respond in context.

Machine learning (ML)

Machine learning is a subset of AI. Instead of giving a system explicit instructions for every possible situation, we feed it data so it can identify patterns and make predictions.

In marketing, machine learning powers ad targeting, customer segmentation, recommendations, predictive analytics, and plenty of optimization systems we already rely on.

Example of machine learning: Netflix uses machine learning to recommend shows based on viewing history.

Natural language processing (NLP)

Natural language processing helps machines understand, interpret, and generate human language.

NLP is why ChatGPT can carry on a conversation and why Google can understand that “cheap running shoes” and “affordable sneakers” are closely related searches.

Example of natural language processing: Google Translate uses NLP to understand and convert language in real time.

Generative AI

When people casually say “AI,” they often mean generative AI, which is a branch of artificial intelligence that creates content instead of only analyzing existing data.

Generative AI models are trained on massive datasets to learn patterns in language, images, audio, code, or video. Then they use those patterns to produce something new.

But I always remind myself that these systems are still predicting likely outputs. They are not thinking, reasoning, or understanding the world like a person.

That is also why generative AI can go off track. When a model confidently makes something up, we call it a hallucination.

Some of the most infamous hallucination examples include AI answers suggesting people eat small rocks or use glue to keep cheese on pizza. Funny in hindsight, but a serious reminder that fact-checking is not optional.
- ChatGPT can draft articles, emails, and outlines.
- Midjourney and DALL·E can create images.
- Claude can help write and refine code.
- Sora can generate video from prompts.
Large language models (LLMs)

Large language models are a specialized type of generative AI trained on huge amounts of text, including books, websites, code, and other online sources, to generate human-like responses.

I think of LLMs as the engine behind many chatbot experiences. They are the part that interprets what I type and produces a response.

When I use an LLM effectively, I do not treat it like a replacement for my brain. I give it context, examples, constraints, and direction. It can help refine a draft, suggest wording, or organize messy thoughts, but I still own the strategy and final judgment.

In short, LLMs react to input. They do not act independently unless they are connected to tools and workflows that let them take action.
- GPT models from OpenAI, used in ChatGPT.
- Claude models from Anthropic.
- LLaMA models from Meta.
AI agents

AI agents go beyond responding to prompts. They can work through multi-step tasks, use tools, navigate websites, fill out forms, call APIs, analyze files, and complete workflows with less hand-holding.

They are still powered by LLMs under the hood, but the key difference is that they have goals, tools, and a degree of autonomy.

That is why AI agents feel more consequential for marketers. They are not just talking; they are beginning to do the work.
- ChatGPT can search the web, analyze files, and review code.
- Google Gemini in Workspace can summarize email threads and suggest replies.
- Microsoft Copilot can assist across Microsoft 365 workflows.
How I see AI affecting marketing today

Once the terminology is clearer, the marketing impact becomes easier to see. AI is changing how people search, how content is produced, how visibility is measured, and how stakeholders talk about growth.

People have been saying SEO is dying for years. I do not think SEO is dead, but I do think “SEO is changing” undersells the size of the shift.

We are in the middle of a major industry pivot, and AI is at the center of it.

Organic traffic is being cannibalized

AI Overviews are Google’s automated summaries that appear at the top of some search results, often pulling from multiple sources.

I think of them like Featured Snippets turned up several notches. They do not simply quote one source and send the click back. They blend sources, rewrite information in Google’s voice, and may push attribution lower on the page.

For broad informational queries, that means the first thing a user sees may be Google’s answer instead of my blue link. The likely result is a lower click-through rate and fewer visits to publisher and brand websites.

Before AI Overviews, informational queries were often useful for introducing a brand early in the research journey. Now, more of that attention and trust can stay with Google.

Claim: AI Overviews only appear for fluffy queries, so my traffic is safe.

Reality: Google is testing and expanding AI Overviews across more serious query types, including YMYL, product, and B2B searches.

What I would do next: Stop chasing every possible click, measure visibility and influence alongside conversions, and build enough topical authority that my brand becomes a source AI systems can confidently cite.

Content creation is exploding, and so is the noise

Generative AI has removed one of content marketing’s biggest bottlenecks: production time. Work that used to take a team a month can now be drafted by one marketer in a week.

That is not automatically bad. The problem is that when everyone can publish “good enough” content quickly, the internet gets louder and less useful.

Claim: More content means more traffic.

Reality: That was already questionable before AI. Now, search systems are increasingly tuned to reduce the visibility of generic, low-value, quickly produced content.

Google’s Helpful Content updates, Bing’s spam improvements, and social platform feed changes all point in the same direction: thin content is easier to produce, but it is also easier to ignore.

What I would do next: Focus on authority-driven content such as case studies, original data, expert analysis, and proprietary insights. I would publish less, promote more, and use AI for research, outlining, repurposing, and refreshing instead of simply flooding the web.

Search results are becoming deeply personalized

Traditional SEO has dealt with personalization for years through local results, logged-in history, and device context. LLM-powered tools like ChatGPT, Perplexity, and Gemini take that much further.

The same question can produce different answers depending on the user, their prompt, their past interactions, available data, and the model being used.

For example, if someone asks, “What is the outlook for Tesla?” a financial analyst may get an answer focused on stock performance and filings, while a new driver may see information about models, battery life, and charging infrastructure.

A Semrush Enterprise AI source analysis view tracks how often domains are cited in ChatGPT results, revealing shifting visibility trends across LinkedIn, Wikipedia, Semrush and industry publishers.

Claim: I will just optimize for the top answer in ChatGPT the way I optimize for position one in Google.

Reality: The idea of one universal top answer is breaking down. Personalization makes it harder to define, track, and reverse-engineer a single ranking position.

What I would do next: Track visibility across search engines and LLMs, build a recognizable brand entity, invest in multiple content formats, use structured data, and create clear, citable answers that machines can understand.

Attribution is breaking

When Google, Bing, Perplexity, or another AI-driven platform answers a question directly, users may never visit the website that influenced the answer. Even when they do visit, their journey may start in an AI tool, move through another search, and only later reach the site.

That breaks the clean channel → click → conversion model marketers have relied on for years.

Claim: I will measure traffic from LLMs directly in analytics.

Reality: That assumes users are clicking through from AI answers. In many cases, they are not.

A Semrush-style AI sentiment dashboard visualizes how brands appear across AI search, with mention trends, sentiment mix, and a competitive leaderboard.

What I would do next: Move beyond last-click attribution, pay more attention to assisted conversions, and track broader demand signals such as direct traffic, branded search volume, brand mentions, sentiment, and “How did you hear about us?” responses.

I would also budget for influence that is hard to perfectly track, including podcasts, PR, thought leadership, community visibility, and media coverage.

Clients and bosses expect magic

Because AI hype is everywhere, stakeholders often expect it to make everything faster, cheaper, and better without understanding the risks, learning curve, or human oversight involved.

Claim: We can replace our SEO or content team with AI tools and get the same results.

Reality: AI can accelerate tasks, but it does not replace strategy, judgment, subject-matter expertise, or a real understanding of customer needs.

What I would do next: Set expectations early. AI can make some work faster and cheaper, but it is not a push-button strategy. I would show stakeholders the hidden work behind good AI output, including prompt refinement, editing, fact-checking, compliance, and final review.

The best use of AI is not to remove human thinking. It is to free up more human time for the strategic work that actually moves the business forward.

Search is evolving

I am not interested in getting stuck in a debate over Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), or any other acronym. The important point is simpler: search today is not what it was yesterday.

Organic visibility is no longer only about ranking in Google. Search now includes AI answers, YouTube, Reddit, newsletters, communities, social platforms, and every place people go to discover, compare, and validate information.

If I am only thinking about the traditional search bar, I am already behind. The better path is to build authority, create content worth citing, understand how AI systems interpret information, and measure visibility across the full discovery journey.

AI is not the end of SEO. It is a major shift in how search works, how content is consumed, and how brands earn trust. The marketers who adapt will be the ones who separate useful strategy from the noise.

Inspired by this post on Search Engine Land.
July 3, 2026
How Google AI Prefers Competitors in ‘Best’ Listicles

Recently, I’ve been delving into an intriguing study by Lily Ray, which reveals some unexpected findings about Google’s AI Overviews. Apparently, these Overviews frequently reference brands’ own listicles but tend to recommend their competitors.
The study highlighted that Google AI Overviews cited these self-promotional listicles in a whopping 69% of B2B software-related queries. Yet, they favored rival brands in their recommendations. This got me thinking about the strategies brands employ to influence AI search outcomes.
Detailed Findings. I discovered that the analysis was quite comprehensive. Ray reviewed 100 B2B queries spanning categories like “best [category] software.” She gathered data across three specific periods: April 15, May 15, and June 8.
The study found that out of 80 queries that triggered an AI Overview, self-serving listicles were referenced 323 times, yet in 224 instances, Google didn’t actually recommend those brands. This mismatch intrigued me.
Analysis of Recommendations. While examining specific cases, it became evident that Google sometimes cited a brand’s listicle but opted to recommend more renowned competitors instead. For instance, in the search for “best LMS for selling courses,” Oasis LMS was mentioned, yet Kajabi and others were pushed forward as the preferred options.
This pattern wasn’t just isolated to LMS software; it appeared in multiple domains like help desk tools, task management, and more. It made me ponder over the dominance of stronger brands in recommendations.
Observing Organic Declines. An interesting trend noted was a drop in organic visibility for websites heavily leaning on self-promotional listicles. I noticed beginnings of these declines back in January and observed further drops post-Google’s May 2026 core update.
Interestingly, these sites also seemed to have expanded into AI-generated content and other “best” pages prominently featuring their own brands.
Rise of Third-party Citations. Ray’s analysis also showed an upsurge in Google comprising third-party content for “best” queries. Platforms like Reddit, Forbes, and YouTube gained traction in citations.
Understanding Impact. I believe it’s crucial to realize that merely having your content cited doesn’t equate to a recommendation. This situation offers competitors the chance to snag attention and, ultimately, valuable visibility.
Keeping Up with Changes. Previously, Search Engine Land shared insights on how some SaaS and B2B businesses witnessed visibility losses after banking on self-ranked “best” lists. The risks are significant when company-driven content doesn’t transparently disclose material relationships as mandated by the FTC’s Consumer Review Rule.
About Ray’s Data. To reach her conclusions, Ray employed Ahrefs Brand Radar to examine numerous AI Overview responses. Her analysis spanned 100 B2B software queries, focusing on citations versus actual recommendations.
The full report is available on Ray’s Substack, titled Why Calling Yourself the Best Could Be Helping Your Competitors Win in AI Search.

Inspired by this post on Search Engine Land.

June 18, 2026
Mastering AI Search: Building Machine-Friendly Content
For a long time, “ultimate guides” were my go-to for SEO dominance. They were carefully crafted to meet Google’s algorithm standards for high-value content.

Incorporating the “skyscraper technique” further solidified the idea that length equates to depth.

Yet, as the web evolved, so did search intent. Users’ desire for quick answers and AI’s rise diminished the importance of lengthy content. Google’s system now frowns upon content that offers zero informational gain.

So, what are my next steps?

Extractability is the new content challenge, affecting every stage from briefing to publication.

AI platforms like Gemini limit approximately 380 words for query grounding, making it crucial for me to adapt.

The extraction data reveals:
- Pages under 5,000 characters: 66% AI extraction rate.
- Pages over 20,000 characters: 12% AI extraction rate.
The once high-traffic “ultimate guides” now stand in the way of effective AI visibility.

What steps into this void is a new, challenging form of content—where every sentence must pull its own weight by clearly stating entities, relationships, conditions, or citable claims.

Dig deeper: How to write for AI search: A playbook for machine-readable content

The “padlock principle” is now my guide, turning search from keyword chasing to addressing specific problems for specific people. My content became more like solutions than broad categories.

For instance, a car insurance page now targets new drivers under 25, declined by standard insurers, turning from general to particular needs.

Breaking from tradition, each content piece now aims to solve a defined user problem. With AI’s impact on SEO, I’ve embraced strategic shifts to make my content more credible and logically structured.

Here are the three strategic rewrites I apply for effective problem-first positioning:

Replace categorical identity with problem identity
- Before: “We are an insurance provider.”
- After: “We solve the underwriting problem for first-time drivers under 25 who are declined by standard insurers.”
Rewrite titles as outcomes, not labels
- Before: “Car Insurance | BrandName”
- After: “Car insurance for new drivers under 25 declined by most providers”
Lean into constraints rather than suppressing them

Recognizing target limitations adds credibility to my service offerings, contrasting the generalized advice typically available for free.

The content landscape has radically shifted from information archives to pieces serving individual, extraction-friendly sentences. My approach leverages structured, meaning-rich content that AI systems can confidently source.

Building an LLM-friendly foundation involves familiarizing myself with semantic triples, because AI judges content with a retrieval efficiency that applies across various format types.

So, whether I’m crafting a blog or a product description, explicit headings signal relevance, boosting my content’s retrieval likelihood by 17.54%.

Adopting the citation-bait formula, I begin each paragraph with a direct declarative opening, followed by trimmed-down contextualization and structured evidence—ensuring the content is both extractable and engaging.

In pursuing content harmony between machine readability and human interest, I capitalize on the AI inverted pyramid approach. By positioning narrative transitions after structured answers, I balance AI efficiency with engaging storytelling.

Every part of my content creation—from heading formulation to section structuring—serves a dual purpose: making content AI-retrievable while nurturing human trust and engagement. I constantly refine this synergy, ensuring each piece of content wholly aligns with emerging AI standards.

Ultimately, I strive for a content strategy that doesn’t yet exist, one that will meet evolving needs by balancing the semantic precision AI demands with the rich narratives only human creativity can offer.

Inspired by this post on Search Engine Land.
June 17, 2026
Unlocking AI Search Power: Next-Question Intent Explained
I realized that many web pages effectively address initial search queries, but often fall short when it comes to guiding the user toward their final decision. This is where the concept of next-question intent becomes crucial. It’s a tool that not only aids users but also aligns with AI systems for enhanced content utility and visibility.
In the world of GEO, much of the discussion revolves around how AI systems discover, extract, and suggest content. While these aspects are essential, I’ve learned that what truly determines visibility is the substantive content these systems find once they’ve reached my pages.
Next-question intent isn’t just about answering the initial query. It’s about whether my page provides enough depth for the user to take their next step, be it selecting a product or making a decision.
Often, a user’s first search is just a starting point. Key decisions hinge on follow-up questions and considerations that must be addressed.
By crafting content that anticipates these subsequent inquiries, I equip AI systems with rich materials to synthesize, compare, and recommend.
From Results to Narratives: Traditional Search vs. AI Search
Traditional search was once about offering a suite of links for users to peruse and decipher. Now, AI search focuses on delivering synthesized responses, pulling information from multiple sources.
This shift emphasizes the need for my content to provide comprehensive information that can help build AI-generated answers. Next-question intent is vital here.
While search intent asks what the user wants to do, next-question intent goes further. It asks what the user will need to know next to trust, compare, or decide.
In this AI-driven environment, content must support a complete answer pathway, far beyond the initial query.
Be the brand AI recommends.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
See your AI visibility
The First Query is Often Only the Doorway
The initial search often serves as just the beginning, an entry point. True decision-making occurs through follow-ups and specific concerns that arise thereafter.
Take the query “best CRM software for small business” as an example. It opens the door, but the true selection journey starts with follow-up questions.
- Which platform is easiest for a two-person team?
- Which integrates best with QuickBooks?
- Which one works for a business without a formal sales department?
- Which one is best for a local service company rather than a software startup?
- Which one won’t frustrate owners or interns with tech complexity?
These aren’t ancillary. They define the decision-making path.
Otherwise well-structured content may falter if it fails to engage at this level, leaving AI systems with less context to assemble an answer, thereby reducing visibility.
Next-Question Intent is Not Just a Writing Exercise
As I’ve delved into content creation, it’s clear that next-question intent goes beyond simply writing better content—it ensures my pages support the next steps in a user’s decision-making process.
Practically speaking, it means crafting answer-ready content that addresses initial user needs, foresees additional decision layers, and provides concrete, verifiable information.
Visibility in AI search isn’t just about where I rank. It’s about citations and whether my brand becomes a trusted source in context-rich settings.
To achieve this, my content must offer enough substance for systems to understand what my brand does, whom it serves, when it’s useful, why it’s trustworthy, and how it fares against alternatives.
Where Good Content Goes Thin
While I often find that brands have content that’s accurate and keyword-optimized, it still might not suffice in the AI search environment.
AI systems require clarity and context to determine what I offer, who benefits from it, when it’s applicable, and why claims are valid.
This depth is where many pages fall short.
- A service claim like “customized marketing strategies” begs the question: customized how?
- A product claim like “safe for families” prompts: safe for which family members?
- A software claim like “built for small businesses” asks: which type of business?
General claims offer little for people and even less for AI systems to utilize. Specific, structured, evidence-backed content serves a far better purpose.

Inspired by this post on Search Engine Land.
June 16, 2026
Unlocking the Secrets of Google Discover Headline Formats
I recently delved into a fascinating study on Google Discover headline formats, looking at a staggering 3.4 million articles. The results were eye-opening and showed that a simple headline rewrite often doesn’t yield the expected lift.

You might have come across these bold statements before:
- Quote-led headlines outperform plain declarative ones by nearly 29%.
- Question headlines underperform both, sometimes by 24%.
- Format drives the result: Rewrite a statement as a quote, or add that magic word, and you should expect a real lift.
To put these claims to the test, I examined 1,674,518 English articles and 1,690,295 French articles from the 1492.vision Discover corpus. That’s quite a hefty sample size!

What I found was a deeper flaw than just numbers. It turns out that all three claims treat headline format as a leverage point for visibility. However, the data clearly shows that the impact of a headline’s format mainly reflects the publisher’s audience and the specific Discover surface used.

One striking analysis was Simpson’s paradox. An anomaly that, once noticed, appeared across the entire dataset.

Here’s what we’re really measuring:

Rather than clicks from Discover, our metric is hits per article: how often an article appears across the 1492.vision fleet. This serves as a proxy for visibility.

The dataset was limited to editorial articles, excluding platforms like YouTube because they have different headline norms. We’ll dive back into these at the end, as they bring more clarity than anything else.

Why is volume important? The crux of the argument depends on slicing this vast dataset by publisher, Discover surface, topic, and language while still keeping enough data in each segment for valid insights. This is where the real difference between numbers and insights, and between a genuine format effect and a statistical illusion, lies.

Here’s a sneak peek: when you pool all publishers together, a clear gradient appears with quote-led headlines leading the pack and statements trailing.

The frequently cited +29% is actually a conservative estimate for editorial pieces: quote-led headlines achieve a +37% lift in English and +48% in French. Even questions don’t lag behind as much as expected since they outperform statements to some extent (+7% EN, +16% FR).

Though claim 1 appears understated and claim 2 misguided at the aggregate level, these are the observations on which most headline advice leans. Let’s delve further to understand what the data is really revealing.

Let’s shift to the hidden aspects, starting with publishers. The raw comparison isn’t effectively between quotes and statements. It’s more about one set of publishers versus another because the publishers employing quotes often differ from those who don’t.

Some media, like celebrity-focused outlets, regional newspapers, and sites attuned to trending topics, gravitate towards quotes, and naturally earn more Discover hits compared to entities that prefer factual presentations.

This is a prime example of Simpson’s paradox: a strong trend at the aggregate level that fades or reverses when segmented into groups.

To focus on the format itself, publishers must each be their own baseline: comparing quotes with statements within the same publishing entities while controlling for audience and topic diversity.

So, the question is, how does each format fare on its own? Let me walk you through the rest of this journey as we unpack these layers.

Inspired by this post on Search Engine Land.
June 15, 2026