Tag: AI Search

  • How AI Is Reshaping Search Demand Across 1M Keywords

    How AI Is Reshaping Search Demand Across 1M Keywords

    I do not see search demand disappearing. I see it moving. In this analysis, 29% of high-volume search demand is declining, while nearly the same amount is growing somewhere else. Overall demand is essentially flat because people are redistributing how and where they search instead of abandoning search altogether.

    That changes how I think about SEO strategy. I would not start by panicking over shrinking keywords. I would start by identifying which queries are losing volume, which ones are gaining momentum, and where a brand can build enough authority to appear in both traditional search results and AI-generated answers.

    This study looks at where search demand is shifting, which industries are seeing the sharpest changes, and what those patterns mean for SEO teams trying to adapt to AI-driven discovery.

    In 2024, Gartner predicted that traditional search engine volume would fall 25% by 2026 as consumers shifted to AI chatbots and virtual agents. Fractl and Search Engine Land set out to test that prediction. (Disclosure: I’m the co-founder of Fractl.)

    I worked from Semrush data covering 1,010,848 high-volume keywords, each with at least 10,000 monthly searches, across 379 brands in eight verticals. I also looked at survey responses from 1,004 U.S. consumers to better understand how AI is changing the way people search.

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    The analysis measured which keywords gained or lost search volume over the past year, how those shifts differed by industry, and how consumer behavior is evolving as AI tools become part of everyday discovery.

    The 29% search decline is real, but it depends on the vertical

    Across more than 1 million high-volume keywords, I found that 29% of search volume is in measurable decline. That is 4 percentage points above Gartner’s forecast. In a dataset representing 35.4 billion monthly searches, that difference represents a meaningful amount of search activity.

    The impact is not evenly distributed. FinTech showed the largest decline at -37.7%, while Lifestyle saw the smallest decline at -15.2%. Only three of the eight verticals, Insurance, SaaS, and Lifestyle, came in below Gartner’s 25% threshold. FinTech, HealthTech, and Wellness were well above it.

    The pattern makes sense when I look at how information-heavy each category is. When a chatbot can answer the question completely, such as summarizing drug interactions, explaining deductibles, or giving a quick overview of a fund, search volume is more likely to fall. When people need to compare prices, complete a transaction, or navigate to a specific site, search demand tends to hold up better.

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    That is why transactional verticals such as SaaS, Lifestyle, Insurance, and Travel are growing or staying close to flat. Information-heavy verticals such as HealthTech, FinTech, and Wellness are seeing the largest declines.

    Before reacting to broad claims about AI-driven search decline, I would benchmark these findings against the specific vertical in question. HealthTech and FinTech teams should expect more exposure than the overall 29% decline suggests. SaaS and Lifestyle teams have more reason to challenge the idea that search demand is simply collapsing.

    Search demand is being redistributed

    The headline number gets attention, but the offset is just as important. Demand did not vanish. It moved to a different set of words, and those are the terms I would want to understand first.

    Among the high-volume keywords tracked, 40.7% are in measurable decline, meaning they lost more than 15% of their volume over the past year. Within that group, the average decline is -41%, and 112,378 keywords lost more than 40% of their volume. For brands that depend on those terms, the impact is significant.

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    At the same time, 20.1% of keywords are growing by more than 15%. When I add up the volume on both sides, the decline and growth almost cancel each other out.

    The 285,489 declining keywords represent roughly 10.29 billion monthly searches. The 140,835 growing keywords represent roughly 10.31 billion monthly searches. Across the entire dataset, the net change is +16.8 million searches per month.

    Fewer keywords are growing than declining, but the growing keywords carry more volume each. That is why the totals balance out. In practical terms, I see demand relocating more than shrinking.

    The vertical-level growth-to-decline ratios show where that new demand is landing. Lifestyle leads at 2.6x, with 40% of keywords growing versus 15% declining. SaaS follows closely at 2.5x, with 48% growing versus 19% declining. HealthTech sits at the other end with an inverted ratio of 0.4x, making it the most disrupted vertical in the set.

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    The first audit I would run is simple: pull the tracked keyword set, filter it by year-over-year volume change, and see which side of the ledger the portfolio sits on.

    Non-branded queries are the most vulnerable

    I see non-branded queries as the easiest ones for AI chatbots to replace. When a search term does not include a brand name, the user is not necessarily trying to reach a specific site or source. The full exchange can happen inside the chat window.

    Across the dataset, 90% of all tracked search volume is non-branded. HealthTech, at 99.6%, and Wellness, at 98.5%, are the most exposed. Insurance, at 73.8%, and SaaS, at 82.0%, are less exposed, and both are growing overall. SaaS volume is up 48% year over year, while Lifestyle is up 40%.

    If I wanted to identify the content most at risk, I would start with keyword patterns. They offer one of the clearest signals in the study.

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    The reason SaaS and Lifestyle can be heavily touched by AI and still grow comes down to what happens after the AI answer. If AI recommends a project management platform or a couch, many people still search for the specific brand, retailer, review, or product page before buying. The AI answer creates a downstream search.

    HealthTech and FinTech often behave differently. A drug-interaction question or a “what is a deductible” query can be answered completely inside the chat window. There may be no next step that sends the user back to Google.

    If a category produces complete AI answers with no natural next click, I would treat AI visibility as a core strategy, not an SEO side project. In those cases, showing up in the answer may be the entire opportunity.

    70% of consumers use AI more, but only 17% use search less

    The keyword data shows what is happening in the index. The survey data shows what is happening in the minds of the people doing the searching.

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    Search behavior is spreading across more platforms. Many people are adding AI to their routines without giving up Google.

    Social platforms are also acting like search engines in a way they did not a few years ago. YouTube leads at 68%, followed by Reddit at 57%, Instagram at 42%, Facebook at 40%, and TikTok at 33%.

    If I had not already prioritized YouTube and Reddit, I would move them up the list. Both rank ahead of TikTok, Instagram, and Facebook as search destinations, and both can also surface in Google results, which gives visibility there a compounding effect.

    What has actually moved from Google to AI

    More than a third of respondents, 35%, say they have not replaced traditional search with AI for anything yet. Among those who have, how-to guides and tutorials have taken the biggest hit.

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    For purchase research, 47% of consumers start with a traditional search engine, tied with online retailers at 47%. Only 13% start with an AI chatbot, and shoppers check an average of three online sources before making a purchase.

    The number I would bring to a strategy meeting is this: nearly one in five consumers, 18%, have bought something based on an AI recommendation without checking it against a separate search.

    That creates a different kind of buyer journey. In that path, the brand may never receive a search-driven touchpoint. To be considered, the brand has to be one of the names the chatbot returns.

    Gen Z and millennials are 2.5x more likely than baby boomers to buy based on an unverified AI recommendation, at 20% versus 7%. Across all consumers, 59% say they are likely to visit a brand’s website after an AI chatbot mentions or recommends it.

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    That is the emerging conversion funnel I am watching closely. Brand mentions in AI answers are starting to function like rankings. Visits to a brand’s website after an AI mention are starting to look like the new click-throughs.

    Trust is still mixed. In the survey, 33% of consumers trust AI and traditional search equally, 46% trust search more, and 20% trust AI more.

    More than half of consumers, 56%, are at least somewhat skeptical of AI product recommendations. I read that as a sign that people are willing to let AI filter and shortlist options, but many still want to verify before they buy.

    The 5-year outlook: Google is not going away, but the shift matters

    When asked whether Google will still be their primary search tool in five years, 52% of consumers say yes, including 17% who say definitely and 35% who say probably. Another 27% are unsure, while 20% say probably or definitely not.

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    The top reasons people prefer AI over traditional search are better summaries across sources, at 21%; faster and more direct answers, at 20%; and the ability to ask conversational follow-up questions, at 19%. More personalized results and avoiding website click-throughs were much lower, at 6% and 4%.

    When asked what would bring them back to traditional search, the top answer was AI giving unreliable answers, at 35%. That means much of this shift depends on whether AI maintains trust as adoption scales. More accurate search results followed at 29%, then a preference for multiple source links at 22%, and privacy concerns at 20%.

    The 20% who expect to leave Google are not the majority, but I would not dismiss them. A strategy does not need to be rebuilt entirely around them today, but brands do need to appear where those users are already moving.

    What this means for content and SEO strategy

    I see Gartner’s 25% prediction as a useful directional warning. The real shift may be steeper, but calling it only a decline misses the more important story. Total search volume is basically flat. What has changed is which searches carry the demand.

    AI visibility is not just a threat to manage. I see it as a distribution channel. With 59% of consumers saying they are likely to visit a brand’s website after an AI mention, GEO has become a meaningful part of brand discovery.

    Earned media, credible third-party coverage, and strong entity signals all help brands appear in chatbot answers. That is why digital PR and GEO are becoming more closely connected.

    Search is moving, not disappearing.

    The brands that lose will be the ones still optimizing mainly for queries that AI now answers better. The brands that win will be the ones building enough authority to become the answer, whether that answer appears in Google or inside a chatbot.

    Methodology

    This study combined two data sources to test Gartner’s 2024 prediction that traditional search engine volume would fall 25% by 2026.

    Fractl and Search Engine Land analyzed Semrush search volume data for 1,010,848 high-volume keywords with 10,000 or more monthly searches each, covering 379 brands across eight verticals: FinTech, HealthTech, Wellness, Travel, Education, Insurance, SaaS, and Lifestyle. The dataset represented 35.4 billion in aggregate monthly search volume. Keyword-level year-over-year volume change was measured as of April 2026 and classified as declining, meaning more than 15% loss; stable, meaning within 15%; or growing, meaning more than 15% gain. Query pattern groupings, including “What is X,” “Best X for Y,” “X vs. Y,” and “How to X,” were applied at the keyword level.

    Fractl and Search Engine Land also surveyed 1,004 U.S. consumers about their search habits, AI tool adoption, and purchase research behavior. The sample was 52% women, 46% men, and 1% nonbinary, with 49% millennials, 26% Gen X, 16% Gen Z, and 9% boomers. The median respondent age was 41, with a range of 18 to 82.


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  • Submit Your SMX Next Pitch and Share Bold Search Ideas

    Submit Your SMX Next Pitch and Share Bold Search Ideas

    SMX Next returns online Nov. 18, and I’m excited to help shape a program focused on today’s complex search landscape and the tactics that will define success in 2027 and beyond.

    Search marketing isn’t just changing. From my perspective, it has become an entirely new kind of challenge, and that is exactly why fresh voices and practical expertise matter so much right now.

    In SEO, I’m seeing the field shift toward AI Overviews, search everywhere optimization, and the rise of autonomous AI agents that browse on behalf of users. Trustworthiness, digital authority, and precise alignment with user intent are no longer nice-to-have ideas. They are becoming essential.

    On the PPC side, generative AI and deep automation are creating new levels of personalization. At the same time, they are raising urgent questions for marketers: How do we keep strategic control, protect data privacy, and avoid wasted spend?

    If you’re an enthusiastic search marketer with a passion for sharing what you know, I hope you’ll consider submitting a session pitch for SMX Next. I’m looking for subject matter experts who can share insights, strategies, and tactics that help SEO and PPC marketers thrive in 2027.

    Whether you’ve been speaking for years or you’re a practitioner ready to share something new you’ve developed, I want to hear from you. I’m especially interested in new speakers with diverse points of view and real-world experience.

    The deadline for SMX Next pitches is Aug. 7.

    When I review session proposals, I’m looking for ideas that feel original, specific, and useful. Advanced, forward-thinking topics or unique frameworks that aren’t already common at other search events will stand out.

    I also want to see actionability. Be clear about what attendees will be able to do better, faster, or differently after your session.

    Bring the data whenever you can. A case study, concrete example, or tested approach makes your pitch stronger, especially when you explain how the lesson can scale across different types of organizations.

    Keep the scope focused. A 30-minute session works best when it goes deep on a narrow or specialized topic instead of trying to cover too much at once.

    Most importantly, give attendees something tangible to take with them. I’m looking for sessions that leave people with a clear action plan, framework, or process they can put to work right away.

    Visit this page for more details on how to submit a session idea, or go directly to this page to create your profile and submit your pitch.

    If you have questions, feel free to contact me directly at kathy.bushman@semrush.com. I’m looking forward to reading your proposals!


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  • How I Turn Proprietary Data Into AI Citations

    How I Turn Proprietary Data Into AI Citations

    Why proprietary data is your most defensible AI citation asset - featured-image

    When I want a page to feel genuinely original, I start with original numbers. They are still one of the most reliable ways to make content stand apart, especially when those numbers come from the business itself instead of a one-off study created just to fill a content calendar.

    The old approach was to pay a PR or research firm for a loosely related survey, like a car insurance FinTech commissioning road-trip research to earn a mention in Yahoo. I see that play as increasingly outdated. Almost every product now creates data worth publishing, and extracting that data is easier than it has ever been.

    I do not need a full research department to compete here. The bar for standing out is lower than many teams assume.

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    First-party data: The strongest correlation of originality

    On-Page.ai’s recent information gain study scored 150 top-3 Google pages across 50 keywords and 10 verticals. The study looked at how much each page added beyond the rest of its ranking cohort, grading contribution from 0 to 100 by meaning rather than wording.

    The median page scored 52. More importantly, original data correlated with that score more strongly than any other page-level trait, including content length.

    Pages with at most 1 unique figure averaged an information gain score of 40.2. Pages with 15 or more unique figures averaged 62.1, and the score increased steadily at every step in between.

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    The good news is that the bar is not especially high. The study found that top organic results usually include only 4 unique data points on average. If I publish a page with more than 4 real original claims, figures, or answers, I create another lever for earning visibility in increasingly competitive organic search.

    The analysis also found that almost every search leaves adjacent questions unanswered. On-Page used synthetic reader questions, meaning plausible related questions generated for the study, and found room for new pages to answer those questions more completely. That immediately reminds me of query fan-out.

    I saw a similar pattern in an analysis of ChatGPT citations.

    “A single evergreen page covering 10+ query intents is worth more in AI citation reach than 10 single-intent pages. The ROI of comprehensive content is front-loaded: one well-built page compounds citation reach over time. The long tail exists, but the top 5% of pages capture a disproportionate share of ongoing citation activity.” – The science of how AI picks its sources

    That is why I believe high-intent prompts should be monitored across the full buyer journey. I would map them across the five stages from Reasoning Lift: Problem, Exploration, Comparison, Validation, and Selection. I would also use more accurate AI prompt tracking to understand where those questions emerge, then answer them with the kind of knowledge only the brand can provide.

    My main takeaway is simple: most pages are only middling on originality, genuinely original pages are still a minority, and scoring high enough to stand out is achievable without an extraordinary lift.

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    The limitation is just as important. This study focuses on classic search visibility and rankings, which makes sense because the SEO concept of information gain comes from Google patent language. It does not analyze AI citations or mentions, and it does not appear to include AI Mode or AI Overviews.

    Caveat: Being the primary source may not win the citation

    This is the part of proprietary data advice I think gets skipped too often. Everyone says to publish original research. Far fewer people test whether AI rewards the brand that created the number or the page that presents it in the clearest, most extractable way.

    More data analysis is still coming, but based on analyses completed at Growth Memo over the last year, I already see two patterns worth paying attention to.

    • The entity types that predict ChatGPT citations the most are DATE and NUMBER (from The science of what AI actually rewards). Highly cited pages tend to be dense with specific entities, such as a particular methodology, a precise statistic, or a named comparison. Even when another source picks up my proprietary findings and gets cited instead, those external third-party authority signals can still build over time.
    • Entity-richness and balanced sentiment matter (from The science of how AI pays attention). Generic advice is vague and risky. Specific entities are grounded and verifiable. Proprietary data can produce, verify, validate, and create entity-rich content at the same time. I can explain why a feature saves a certain percentage of dollars, how many hours clients save, or how performance compares with previous vendors. When I add balanced sentiment to the analysis and explanation, I get a stronger tactic from the same asset.

    If the hypothesis holds that first-party data is crucial in the era of AI search, then publishing proprietary data is necessary, but it is not enough. LLM extraction structure, along with the sites AI search engines already trust for a topic, helps decide who actually earns the citation, even when the brand owns the data.

    That is the frustrating part: an aggregator can repackage my benchmark into a cleaner, answer-ready page and collect the citation my research earned.

    • Who wins: Brands that already have proprietary product, usage, or pricing data and also structure that data for extraction while continuing to build organic brand authority. This connects directly to How to build an AI SEO strategy that outlasts tactics.
    • Who loses: Brands publishing opinion content that any tool can replicate, brands ignoring off-site authority, and primary sources that bury their own numbers inside narrative instead of surfacing them clearly.

    I do not yet know whether some verticals reward data content more than others. The science series found that citation signals vary sharply by vertical, so I would be surprised by a uniform payoff. Still, I would not claim a pattern without data.

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    How to structure data for extraction

    Owning the data gets me into the visibility race. How I structure that data may decide whether I win the citation.

    In an analysis of 18,012 verified ChatGPT citations, we found a ski-ramp distribution: 44.2% of all citations came from the first 30% of a page. The middle 30-70% earned 31.1%, and content buried deep in a long post was roughly 2.5x less likely to be cited.

    The follow-up analysis across 7 verticals made the target even clearer. The 10-20% band of a page is where AI reads hardest in every vertical, while the first 10% is usually navigation and intro filler that AI skips. The bottom 10% of any page earns only 2.4-4.4% of citations regardless of vertical.

    When I apply that to a data study, the structure becomes straightforward.

    • I lead with the headline statistic. My strongest number belongs in the first 30% of the page, ideally right after the title block where the 10-20% band begins. I want the number, the comparison, and the implication visible quickly.
    • I define the metric immediately. I include one sentence explaining what the number measures and which population it covers. An undefined statistic is harder to extract with confidence.
    • I box the methodology. I make the sample size, time window, and collection method easy to find in a short labeled block. Attribution confidence is part of what makes a number citable.
    • I front-load every secondary finding. I rank findings by strength, with the strongest first. A 20-paragraph narrative buildup may help human suspense, but it can cost machine citations.
    • I skip the suspense close. AI reads more like a busy editor than a patient student. The payoff-at-the-end structure that worked for ultimate guides often works against extraction.

    This post first appeared on the author’s website and is republished here with permission.


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  • Remembering Bruce Clay: SEO Pioneer’s Final Lessons

    Remembering Bruce Clay: SEO Pioneer’s Final Lessons

    My heart sank when I learned that Bruce Clay had passed away. I knew he had been in the hospital, but my mind went straight to the two long conversations we had last fall: one simply to catch up, and one for what would become a deeply meaningful podcast interview.

    I first reached out to Bruce nearly 25 years ago. I had emailed him cold to ask whether I could republish some of his industry writing about ethics. He said yes. Somehow, the article I cited unintentionally ranked No. 2 on Google for “Bruce Clay” for years. I joked with him about that more than once, and he always seemed both amused and slightly annoyed, probably because I had done it with his own content and his own blessing.

    A few years later, I worked with Bruce and many other search professionals on the board of the Search Engine Marketing Professionals Organization, better known as SEMPO. It was a business nonprofit built to support and legitimize the then-new search industry. We promoted best practices, helped make the business case for search, and later became involved in U.S. Internet policy work in the early 2010s.

    SEMPO brought together board members from around the world, and in a very literal way, it took some of us around the world. That work is where I really got to know Bruce. Later, we would run into each other at conferences, sometimes even on the same panels. We were doing serious work, but we also had a great time doing it. The organization lasted about 15 years, and if I remember correctly, Bruce was one of its founding members around 2000 or 2001.

    One memory of Bruce has stayed with me vividly. A group of us from the SEMPO board were walking back to our hotel on the east side of Midtown Manhattan after dinner. A snowstorm had just begun, one that would leave several feet of snow by the next day. The usual roar of traffic had been softened by the weather and the empty streets. It was eerie, but almost joyously quiet. The city that never sleeps seemed to be taking a nap under a blanket of snow.

    Then something happened that I had never seen before, and have never seen since.

    As snow poured silently into the streets, a massive lightning strike hit just a few blocks away, over Bruce’s shoulder. I do not know whether he saw it directly. It felt like an explosion. We stood there for several minutes trying to understand the contrast: a shattering bolt of lightning between skyscrapers, in the middle of a torrent of snowflakes, with not a drop of rain.

    None of us knew what to call it. I believe Bruce called it “thunder snow,” and the name stuck. In that moment, his naming streak continued.

    Bruce was, and remains, the real deal in search. His legacy was never only about coining a term. He pushed the field forward, taught others generously, and stayed deeply connected to the people he cared about. Like many of the earliest professionals in search, he helped shape practices that still feel foundational today. Through his writing, interviews, books, tools, and hundreds of industry events, he became one of the people the industry looked to for clarity. For many who remember the beginning, and for many who still followed him closely, Bruce was the GOAT.

    I always felt that Bruce approached search intellectually. I do not think he saw it only as a job. It was exciting, unfinished, and new. Very few people get to help invent an entirely new discipline, and Bruce understood what that meant. He also recognized that AI is one of those moments now, and he approached it with the same curiosity, energy, and insight he brought to early search. Many people in the industry may only now be realizing that Bruce pioneered things they do every day. They feel obvious now, but they were not obvious then. Even the basics had to be debated and established.

    He was not only passionate about search. He was passionate and generous toward the people in search. If you cared about the work, you were part of his tribe. That was true for thousands of people in the industry, myself included.

    With Bruce, I could get deep into the weeds of the trade and still talk broadly about where everything was headed. He was an engineer with an MBA, and that combination came through in his leadership, expertise, and authority. He understood the work from top to bottom, and then back to the top again.

    He was also genuinely kind. He had friends around the world. In our last conversations, I sensed that he was content with his life and accomplishments, and that he felt blessed by the path life had given him. He had nothing left to prove.

    In the podcast interview, Bruce was as sharp and insightful as ever. He offered some of the most sensible thinking I have heard about where search is going in the world of LLMs. He was still innovating, just as he had been when search first began taking shape nearly 30 years ago.

    Because search is so closely tied to language, I have been especially interested in how we think about, and what we call, this “new” thing. Bruce’s perspective helped crystallize my own research. Over the last year, I have watched much of the industry move toward the same conclusion he shared in our discussion.

    If you are one of the many thousands of people who talked shop with Bruce over the years, I think you will recognize him in the ideas that follow. You may even relive some of your own conversations with him.

    As I reviewed the podcast transcript, I realized we had recorded hours of conversation beyond search, including cars and all kinds of other subjects. At the end of our first conversation, he said goodbye with great love and care. That was Bruce. Those words land differently with me now, and they always will.

    Rest in peace, Bruce. I miss you already.

    What Bruce taught me in our final industry conversation

    When I asked Bruce to talk about how he got started in the 1990s, he took us back to 1996. He had been working in corporate roles and wanted to become a consultant. His background was in math, programming, mainframes, PCs, networking, and optimization. When the Internet began moving into the mainstream, he saw something that matched both sides of his skill set: marketing and technical work.

    He started studying search engines because that was where the opportunity was. He experimented with what they wanted, adjusted web pages, and watched rankings appear. Then people began calling him and paying him. What he thought might become a one-person consulting business grew quickly into something global, with offices and work across Japan, Australia, Asia, Europe, India, and beyond. Bruce told me he never would have predicted it would take off the way it did.

    I reminded him how small the field was in those days. There were literally only tens of people doing this early on. Bruce was one of the first to build a legitimate service for businesses that needed to rank for their own brand names and for broader generic terms, while other corners of the field were still experimenting with black-hat tactics.

    Bruce pointed out that this was three years before Google. Search was a wild west. There were more than 20 major search engines, and many of them were taking data from one another. At the first SEO conference he remembered attending, all of the leading people in the field sat together at one round table in a bar. He joked that if a natural disaster had happened there, the whole industry might have disappeared.

    We talked about Danny Sullivan, Search Engine Watch, Search Engine Strategies, and the early vocabulary of the industry. Bruce had long been credited with helping coin the term “SEO,” though he was careful to say that no one can know who said something first. What he did know was that only a handful of people were in the room when the term started to take hold.

    At the time, other terms were in play, including “search engine positioning” and “ranking.” Bruce believed “optimization” won because it sounded technical, valuable, and precise. It was like fine-tuning a race engine. People could see themselves building a profession around it. Once the industry attached itself to that word, the term spread quickly around the world.

    That led us into the newer terms now being proposed around AI, including AIO, GEO, and AEO. I have been writing about how many of these terms still depend on the word “optimization.” Bruce’s view was clear: search engine optimization was never limited to organic blue links. It was about optimizing for anything a search engine produces that can drive business and traffic.

    In Bruce’s view, if AI appears inside search and influences discovery, citations, visibility, or traffic, then it belongs under SEO. GEO and AIO were not separate disciplines to him. They were extensions, just like link building or on-page optimization. He warned that many new terms are marketing labels more than practical new fields. If the work required to appear in AI results is still mentions, links, schema, authority, content structure, and rankings, then the work is still SEO.

    That point stayed with me. Bruce said that if someone claims you no longer need SEO and only need AI optimization, you should watch closely, because either they are going to do SEO under a different name or they do not understand what they are doing. He believed ranking in AI was possible, but the method was deeper and more complex than traditional SEO. To him, it was still SEO, just several levels more advanced.

    We also discussed whether AI feels like search did in the late 1990s. Bruce believed it does in important ways. AI depends heavily on search engines because search engines have spent decades fighting spam and building trust signals. AI systems do not yet have that same history, so they rely on what search engines have already learned to filter, evaluate, and rank.

    Bruce also believed AI could still be gamed at the content level. If enough pages repeat a false idea, an AI system may begin to treat it as true. He had already seen examples of people trying to influence AI answers by placing their names into “best SEO” lists across enough sources. To him, this was a sign that AI would need its own version of the spam fight search engines have been having for decades.

    One of the most important parts of our conversation was Bruce’s explanation of Google AI Mode and how it changes the way SEOs should think about structure. He described how a query can produce an overview, followed by sections and subsections that allow users to drill into narrower parts of a topic. When a user clicks into a section, the supporting sites can change to match that specific subtopic.

    That means content cannot simply be built around one broad keyword anymore. Bruce believed pages need to be structured so each section can stand on its own as an expert answer. A page should support a topic, but every H2-level section may need its own clarity, completeness, and internal logic. In his view, this raises the importance of siloing across a site and within a page.

    I framed this as a shift from keyword-led thinking to context-led thinking. Bruce agreed and connected it to entities, fan-outs, references, and cross-links. Keywords helped build the industry, but he believed the future depends on understanding entities in context. If content cannot answer the question clearly, it fails the core purpose of AI-assisted search.

    Bruce described the long-term target as something like the Star Trek computer: no matter what question someone asks, the system provides the answer. We are not there yet, but that is the direction. For websites, he believed the future architecture is question-centered, highly usable, structured into sub-silos, and able to answer and refer within a page while also fanning out to supporting pages.

    That naturally led us to content. Bruce said that for years SEO treated content like a stepchild, but now content is a peer. If SEO teams and content teams do not share the same goal, they will keep writing the way they did 20 years ago and fail in the AI search environment. He was already being hired to train content teams, even though he did not consider himself a “content guy” in the traditional sense.

    He believed the industry still suffers because SEO and content do not cross-pollinate enough. Content marketers may not attend SEO conferences, and SEOs may not spend enough time learning how content teams actually work. That separation matters more now because the structure of a page, the expertise of each section, and the way a topic is divided all affect visibility in AI-driven search experiences.

    Bruce’s advice was direct: stop spreading one keyword across a page and calling that optimization. Instead, build each section as if it were a standalone expert answer. If the sections belong to the same theme, they should support one another, but each needs to carry its own weight. In his words, the hierarchy is no longer only the page. The hierarchy is also the section of the page.

    When I asked Bruce about AI-generated content, he made an important distinction. AI is a tool, not a solution. He did not believe businesses should simply generate content, read it once, and publish it. Detection tools are inconsistent, and search engines may not reliably identify every AI-generated page. But that does not make low-effort AI content a good strategy.

    Bruce believed AI is strongest as a research assistant. His own Pre-Writer product was built around that idea: gather deep research and give a human writer a stronger starting point. The writer still finishes the work, adds style, voice, judgment, compliance, and business understanding. For Bruce, reducing a four- or five-hour writing project to two hours was a win. Replacing the writer entirely was not.

    He was especially clear that writers are artists. AI does not know a business the way its people do, and it does not bring the same finesse or judgment. The future, in Bruce’s view, requires writers, SEOs, and AI workflows to be integrated around shared goals. Without that maturity, teams will keep producing pages that look like they were built for search 10 years ago, and those pages will be ignored.

    We ended by talking about tools. Bruce reminded me that in the beginning, he wrote tools because none existed. He built one of the first page analyzers, including what he once called a keyword density analyzer. He later received a patent related to that kind of technology. His tools were never meant to replace large platforms like Semrush, Ahrefs, or Surfer. They were meant to extend them by analyzing things those platforms did not.

    Bruce pointed people to seotools.com and described the tools as inexpensive power tools, not products designed for the masses. Some users did not understand them at first, but came back later when they saw the value. He was still building, still solving problems, and still thinking about what the industry needed next.

    Near the end, Bruce mentioned a newer tool designed to show traffic loss through Search Console data over time, helping site owners see whether they had fallen off a cliff or declined gradually. It struck me as classic Bruce: while others complained that something should exist, he was building it.

    I thanked him for the conversation, and he answered with warmth: he was glad I had him on, and he loved talking with me. I hear those words differently now. I am grateful we had that final conversation, and I am grateful for everything Bruce gave to search, to this industry, and to the people inside it.

    Listen to the full episode

    Listen on Podbean

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  • GraphRAG SEO: Why Entity-First Retrieval Matters

    GraphRAG SEO: Why Entity-First Retrieval Matters

    Making a brand machine-readable and improving its odds of being selected for AI-generated answers are important, but I see them as only part of the larger shift. Under the surface, a retrieval layer is changing how AI systems identify entities, connect facts, and decide which brands deserve to be cited.

    That layer is GraphRAG. Once I understand how it works, “optimize for AI” stops feeling like a vague instruction and starts looking like a practical SEO strategy.

    What is GraphRAG, actually?

    GraphRAG extends traditional retrieval-augmented generation (RAG) by adding a knowledge graph. That graph helps AI understand entities and the relationships between them, instead of treating content as disconnected text fragments.

    Microsoft Research introduced GraphRAG in 2024, and a broader ecosystem has formed around it since then. Instead of pulling from a flat sea of text chunks, GraphRAG builds a map.

    In that map, nodes are the entities: a company, product, person, certification, location, or concept. Edges are the relationships between those entities, such as “offers,” “is certified by,” “authored,” or “operates in.”

    I think of it as a system of things and the lines connecting them. When a model works from a map instead of a pile of scraps, it does not have to guess its way toward an answer. It can follow the relationships.

    If the map says Entity A holds Certification B in Region C, the system can follow that path with confidence instead of inferring the connection and hoping it is right. That is why graph-based retrieval can produce more complete, better-grounded answers to complex questions with fewer hallucinations.

    Microsoft described this failure mode in its GraphRAG patent, “Knowledge Graph Extraction” (US20250131289A1). The patent calls out a recall problem in naive RAG: a less prominent entity can disappear inside chunk embeddings, which means the system may retrieve nothing useful.

    It also describes one of the fixes: entity resolution. When duplicate spellings or variations of the same thing are merged, the system can treat them as one entity instead of scattering their authority across several weak signals. That is one of the core building blocks behind graph-based retrieval.

    Dig deeper: What patents reveal about the foundations of AI search

    Why strong content still gets passed over

    Traditional RAG works by chopping content into fixed chunks, turning each chunk into a vector, and storing those vectors in a database. When I ask a question, the system retrieves the closest chunks in vector space and passes them to a language model to generate an answer.

    That can work for simple questions like “What is the capital of France?” It struggles with the questions that usually matter most in business: the multi-step questions.

    If I ask a system to find a provider that offers a specific service, holds a specific certification, and operates in a specific region, naive RAG may stitch together an answer from scraps that merely sound related. It does not truly understand how the facts connect, so it guesses across the gaps.

    When a system has to guess, the safer move is often to leave a brand out rather than risk saying something inaccurate about it. That is the part I think many SEO teams need to sit with.

    This explains a common frustration: “Our content is strong, but AI systems still do not cite us.” The issue may not be content quality. GraphRAG consistently outperforms naive RAG on complex, multi-hop questions where vector search falls apart. That is where the visibility leak often starts.

    In many cases, the machine could not reliably tell what the brand is, how its facts fit together, or whether it could trust those relationships enough to cite the brand by name.

    The three problems GraphRAG is built to fix

    I see GraphRAG lining up with three SEO problems that show up again and again: disambiguation, attribution, and relationships.

    Disambiguation matters when the same entity appears under different names and gets counted as several weaker signals instead of one strong one. If “the firm,” “the agency,” and the actual brand name never resolve to a single entity, authority gets split.

    Attribution matters when the fact survives but the credit disappears. When content is blended into an AI answer, the brand behind the original insight can easily vanish.

    Relationships matter when the connections that give expertise meaning stay buried in prose instead of being declared in a way a machine can read.

    If I have ever watched AI repeat something a company wrote without naming it, or credit a competitor for a specialty the company actually owns, I have seen all three problems in action.

    What ties them together is simple: this is not only a content problem. It is an identity problem.

    Same sentence, more machine-readable context

    I want to make the idea of an entity concrete, because it can become abstract quickly. I will use one real-world example and one fictional example.

    Start with Wayne Gretzky. Search his name in almost any AI client and I expect to see a confident summary: facts, former teams, records, and related links. That confidence is not luck. It is what a well-established entity looks like. His identity is nailed down and agreed upon across the web, so the system does not have to guess who he is.

    Now imagine the opposite. Picture a goaltending coach in Moncton. I will call her Marie Tremblay. Her About page says: “Our head coach, Marie ‘Lefty’ Tremblay, has run elite goaltending camps across the Maritimes for 20 years.”

    That is a good sentence. A parent understands it immediately. I would not rewrite it into robotic prose just to satisfy a machine. Optimizing for AI does not mean abandoning human voice.

    The better move is to keep the sentence and add context around it. I need to make explicit what a human reader infers automatically.

    That means clarifying that “Lefty” and “Marie Tremblay” are the same person. It means connecting Marie to the academy, to goaltending as a discipline, and to the Maritimes as the region she serves. It also means making “20 years” and “elite” verifiable claims rather than loose adjectives.

    A human gets all of that from one sentence. A machine may not. My job is to close the gap between what the reader understands and what the system can verify, so Marie becomes as legible to AI retrieval systems as a famous entity like The Great One already is.

    Why a flat triple is no longer enough

    Knowledge graphs are built on triples: subject, predicate, object. “Acme offers consulting” is clean and useful, but it is flat. A bare triple cannot easily carry the high-stakes details that matter, such as whether the relationship is true, where it applies, who says so, and what evidence supports it.

    The standards community is working on that gap. The W3C is extending the model with Resource Description Framework (RDF)-star, which allows site owners to make statements about statements. In practice, that means source, date, confidence, and other metadata can attach directly to a relationship instead of floating around as a disconnected claim. It is moving through the RDF 1.2 standardization process, with the RDF 1.2 Primer serving as a plain-English introduction.

    Microsoft’s GraphRAG patent points in a similar direction. It pulls claims into a subject-action-object structure and weights relationships by how often they appear, instead of treating every stated link as equally reliable.

    The practical lesson is clear to me: the future is not just saying two things are related. It is saying they are related and showing the proof in a form a machine can verify. A richer triple beats a flatter page.

    The publishing layer is starting to respond

    I am also watching the publishing layer, because that is where the shift is becoming visible outside the models themselves.

    On June 1, the new open standard EntityMap launched a 33-day public consultation ahead of its July 1 launch. It was started by Fred Laurent, CTO of InLinks and Waikay, with backing from Dixon Jones. For anyone following entity SEO and the move from “strings to things,” those names matter.

    The concept is deliberately familiar. Where sitemap.xml tells search engines which pages exist, an entitymap.json file tells AI systems what an organization knows: which entities it covers, how they relate, and where the evidence lives.

    EntityMap aims at the same three problems: disambiguation, attribution, and relationships. It also builds in the richer-triple idea by allowing declared relationships to carry receipts, including a source URL, publisher, and timestamp.

    I would treat it as a signal, not a mandate. EntityMap is a proposal in consultation, not a requirement. No major engine has committed to reading files like these, so I would not turn it into another box-checking exercise yet. The important point is that credible people are building entity-first publishing standards, and that direction is worth watching.

    The honest state of GraphRAG

    I do not think GraphRAG belongs in hype territory, because two realities keep it grounded.

    First, GraphRAG is expensive. Building the map requires a language model to extract entities and relationships, and that is the costly part. By Microsoft’s own estimate, graph extraction accounts for roughly 75% of indexing costs. That LLM cost is one reason web-scale, real-time graph retrieval has not taken over everything overnight.

    Second, the cost curve is bending. Recent research is attacking the infrastructure problem directly, including TurboQuant, a vector compression method from Google Research and NYU, presented at ICLR 2026. It reduces the memory footprint of vectors these systems traverse while preserving quality well enough to make the economics more interesting.

    That does not mean every engine is running GraphRAG across the open web today. It means the economics are improving, which helps explain why entity-first standards are emerging now. I am cautious about anything framed as inevitable, but this shift makes practical sense.

    Structured data still matters. Schema.org markup, a clean Knowledge Panel, consistent NAP, and strong entity signals are not going away. Entity-first work extends that discipline. It does not replace it.

    My entity-first action plan

    Here is how I would make this practical without betting everything on one standard.

    Inventory entities, not just keywords. I would go beyond the search terms that historically brought traffic and list the things the brand genuinely knows about: products, services, people, methods, concepts, locations, and credentials. That becomes an entity map, whether or not it ever gets published as a formal file.

    Disambiguate, then connect to the graph. I would claim and confirm the brand’s Wikidata entity and Google Knowledge Panel where possible. I would standardize naming, resolve variants, and keep sameAs links consistent across structured data. This is how “Lefty” and “Marie Tremblay” become one clear identity instead of two weak signals.

    Make relationships explicit. I would use Schema.org types and properties such as Organization, Person, Product, knowsAbout, sameAs, and author so expertise is declared rather than implied. I would also mirror those relationships in internal linking.

    Attach evidence to every claim. I would connect important facts to verifiable sources: named authors, first-party data, citations, documentation, and dated references. Graph-based systems increasingly need proof behind a relationship, not just the assertion.

    Front-load defining facts. Retrieval still works through narrow windows, so I would place the clearest, most verifiable statement of what the brand is and what it does near the top of important pages.

    Watch the publishing layer without overcommitting. I would read the EntityMap spec, follow how it develops, and decide later whether an entity index belongs in the stack. Schema.org work should continue either way.

    Tie the entity map to revenue. I would map entity coverage to the queries and answer surfaces that influence leads, sales, margin, and retention. That helps leadership see entity work as revenue protection, not an academic exercise.

    Measure what AI systems can recognize

    Rankings and clicks still matter, but they describe the old search-page model. I would add metrics that show whether AI systems can recognize, trust, and cite the brand.

    AI citation share measures how often the brand is named or cited in AI answers compared with competitors. I would track it monthly with an AI visibility tool.

    Entity recognition asks whether priority entities have confirmed Knowledge Panels, Wikidata entries, and consistent identity signals. It is simple, but foundational.

    Relationship completeness looks at how many priority entities have explicit, marked-up relationships and consistent sameAs links.

    Attribution rate tracks how many core claims are backed by linked, verifiable evidence.

    Answer-equity proxies include branded-query lift, assisted conversions from AI referrals, and lead stability as raw click volume softens. These business signals help show whether authority is compounding even when CTR is harder to read.

    Where graph-based retrieval is heading

    I expect graph-based retrieval to keep moving toward multimodal graphs, where text connects to images, audio, video, and structured data. I also expect more streaming and incremental indexing for live data, plus domain-specific ontologies for areas like medicine, finance, and law.

    The move from strings to things is gaining momentum. The brands that stay visible will not simply be the ones publishing the most content. They will be the ones machines can understand without guessing, with clear entities, explicit relationships, and claims backed by evidence.

    I do not need to wait for a new standard to launch before preparing. I can make a brand more legible now to systems that do not just read pages, but read what the brand knows. In the answer economy, I see the real battleground as identity, not just content.


    Inspired by this post on Search Engine Land.


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  • Fabrice Canel Leaves Microsoft Bing After Iconic Run

    Fabrice Canel Leaves Microsoft Bing After Iconic Run

    After nearly 30 years at Microsoft, I am seeing one of Bing’s most influential search leaders close a remarkable chapter. Fabrice Canel announced that he is retiring from Microsoft, writing on LinkedIn, “I am retiring from Microsoft, effective today July 1st.” He also reflected, “Today marks nearly 30 years with Microsoft. Thirty years…”

    When I think about Fabrice Canel’s impact, I think first about the foundation of Microsoft Bing Search. He was responsible for indexing at Bing, including crawling, URL discovery, content selection, and content processing. Those areas are core to how search engines understand the web, and Fabrice helped shape them at massive scale.

    He was also the person behind the IndexNow initiative, and he played a major role in creating and powering Bing Webmaster Tools. For anyone working in SEO, publishing, or technical search, those contributions matter because they helped make discovery, indexing, and webmaster communication faster and more practical.

    I have watched Fabrice contribute far beyond product work. He has spoken at countless industry events, including SMX, and has written extensively about how search works, how sites can perform better in Bing, and how search is evolving with generative AI. He helped run one of the world’s most important search engines, while also giving the SEO community tools, education, and direct insight.

    In his retirement message, Fabrice addressed fellow Microsoftees, engineers, attorneys, marketers, webmasters, publishers, SEO champions, product leaders, journalists, people across search and AI, and even friends at Google. His note was warm, personal, and full of gratitude for the people who shaped his Microsoft journey.

    He described his three decades at Microsoft as a wonderful adventure, from solving real business problems with IndexNow to helping webmasters and publishers thrive in the constantly changing world of SEO and AI. He thanked colleagues, partners, publishers, and the people he trained and mentored, saying they are ready to carry the mission forward.

    Fabrice also shared that, after many conversations with family and friends, he decided to take advantage of Microsoft’s Voluntary Retirement Program. His message ended with the same sense of warmth and storybook style that many in the industry have come to associate with him: gratitude for Microsoft, confidence in the Bing team’s future, and a final wish that everyone stay curious, keep innovating, and make content easier to find.

    Why do I care so much about this? Because Fabrice has been a true friend to the search industry. His work will live on through the products, systems, and initiatives he helped create, and his willingness to share knowledge has made a lasting difference for SEOs, publishers, developers, and search professionals.

    I know Fabrice has trained a team to continue the work, and I believe Bing remains in good hands. Still, I would be lying if I said I am not sad to see him retire. It has been an honor to work with him and learn from him over the years, and his legacy at Microsoft Bing will be felt for a long time.


    Inspired by this post on Search Engine Land.


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  • Google AI Mode Recipe Links Give Publishers a Boost

    Google AI Mode Recipe Links Give Publishers a Boost

    I’m seeing Google make recipe results in AI Mode more publisher friendly with a new visual treatment that gives recipe creators more visibility. For some recipe responses, Google is now showing details such as the creator name, recipe ratings, and the number of ingredients.

    What is new. Google’s Robby Stein said AI Mode now includes “prominent links at the top of responses with useful details and images,” including creator names, ratings, and ingredient counts. From my view, the key shift is that Google is trying to make recipe sources easier to recognize and visit directly from AI Mode.

    I also noticed that Google has been testing top stories carousels in AI Overviews, although that feature does not appear to be live yet.

    What it looks like. The new treatment places recipe links, images, and useful recipe details more prominently in the AI Mode experience, giving users a clearer path from the AI-generated response back to the original recipe page.

    Previously. Back in March, Robby Stein announced earlier changes to recipe results in AI Mode. At the time, he said Google had heard feedback and was making updates to better connect people with recipe creators across the web.

    Image

    I see this latest update as part of Google’s effort to address concerns around AI recipe slop and to make original recipe content more visible when people search for cooking ideas through AI-powered results.

    Why I care. Recipe bloggers, and content creators more broadly, have been frustrated that Google’s AI experiences often send less traffic than traditional search results. This update suggests Google is trying to encourage more searchers to click through from AI Mode to the publishers and creators behind the recipes.

    If Google continues adding more clickable link units into AI search experiences, I think it could help ease some of the tension between publishers and Google. The bigger question is whether these changes will drive enough meaningful traffic back to recipe sites and other content creators.


    Inspired by this post on Search Engine Land.


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  • How AI Search Is Redefining Global SEO Ownership Now

    How AI Search Is Redefining Global SEO Ownership Now

    Global SEO data hub

    Earlier this year, I made the case that the core fundamentals of international SEO still matter. I still believe that. Hreflang, localization, technical excellence, and market-specific content remain essential because search engines and LLMs still need to discover, understand, and connect content with the right audiences.

    What has changed is the environment those fundamentals now operate in.

    For decades, I watched multinational organizations treat markets as mostly separate digital ecosystems. Content created in one market usually stayed there, and governance focused on managing websites, content, and technical implementation across different regions.

    Today, those boundaries are much harder to see.

    AI systems can translate content, synthesize information from multiple sources, and increasingly sit between organizations and their customers. Information that once lived inside one market can now shape visibility, recommendations, and customer experiences across many regions.

    As those boundaries blur, I see the governance challenge expanding. International SEO is no longer only about managing websites across countries. It now requires organizations to manage the knowledge, expertise, and information that search engines and AI systems use to represent them globally.

    Why I believe the governance model must change

    Historically, many website and localization decisions were built around operational efficiency. Headquarters created content, technology platforms, and standards for global distribution, while local markets adapted those assets for their own audiences.

    That model worked because scale often outweighed the limitations of localization. Consistency improved, costs came down, and organizations could deploy content and technology across dozens of markets far more efficiently than local teams could manage independently.

    The challenge now is that AI systems are changing what gets rewarded.

    Scale and standardization still matter, but search engines and AI systems increasingly look for signals of expertise, relevance, and geographic specificity. Content that reflects local regulations, market conditions, customer expectations, and industry practices often provides context that translation alone cannot reproduce.

    At the same time, AI systems can magnify inconsistency. Contradictory product information, conflicting entity definitions, inaccurate regulatory guidance, and fragmented technical implementations can create confusion across search engines, answer engines, and AI-powered experiences.

    That is why I do not think organizations can optimize only for efficiency or only for localization anymore. They need governance models that protect global consistency while giving local markets room to contribute the expertise and context that increasingly drive visibility and trust.

    Hreflang solved routing, not understanding

    In my previous hreflang article, I argued that hreflang still belongs in an international search strategy, even in the age of AI. I stand by that view.

    But hreflang does not decide which market perspective should be prioritized when AI systems synthesize information from multiple sources. It also does not determine which content demonstrates the strongest expertise when AI-generated answers are produced.

    As search moves from retrieval toward synthesis, I believe organizations need to think beyond routing users to the right page. They also need to govern the knowledge that powers those answers.

    What I would centralize

    My simplest rule is this: if an activity creates enterprise risk when it is handled inconsistently, it should usually be governed centrally.

    Technical SEO standards are a clear example. Search engines and AI systems do not evaluate websites one market at a time. They evaluate the broader ecosystem of signals an organization provides. CMS governance, structured data standards, entity definitions, AI crawler policies, measurement frameworks, and technical infrastructure all benefit from consistency.

    Many international organizations have already faced a version of this problem.

    Years ago, before hreflang existed, many global companies used IP detection to route users to the market website they believed was most appropriate. The problem was that Google primarily crawled from U.S.-based IP addresses. When Google tried to access French or Japanese content, it was often redirected to the U.S. site instead.

    Individual markets could not solve that on their own because the routing rules affected every market at once. The solution required global governance with local input.

    I see AI crawler management creating a very similar challenge today.

    Organizations now have to decide which AI systems can access their content and whether those systems can reach the market-specific information they are meant to understand. For companies still relying on geographic routing, market gateways, or IP detection, the governance issue should feel familiar even if the technology is new.

    The platforms have changed, but the lesson has not. Some decisions are too interconnected to manage market by market.

    What I would localize

    If technical infrastructure benefits from consistency, content benefits from expertise.

    For years, multinational organizations followed a simple model: create content in the primary market, then translate, adapt, and distribute it globally. That approach delivered real efficiencies. It helped organizations scale content production, maintain brand consistency, and support dozens of markets with shared resources and common technology platforms.

    Traditional search engines could lean on signals like hreflang and country targeting to understand regional relevance. AI systems increasingly evaluate the content itself. When multiple markets publish nearly identical versions of the same information, language models may treat them as variations of one source rather than distinct expressions of expertise.

    To stand on its own, content increasingly needs market-specific signals such as local regulations, terminology, customer expectations, industry practices, and other forms of geographic specificity.

    That is why I believe content ownership, audience research, local authority building, regulatory content, and market expertise should usually stay close to the market. The goal is not localization for its own sake. The goal is to make sure expertise comes from the people closest to the customer and that the content reflects the realities of the market it serves.

    The strongest multinational organizations will still use global content frameworks, shared resources, and common technology platforms because those efficiencies remain valuable. The hard part is preserving those efficiencies while giving local markets enough space to contribute expertise that is visible, differentiated, and meaningful.

    For years, organizations balanced scale against localization. Increasingly, I think they are balancing scale against representation. The markets that remain visible in AI-driven search experiences will often be the ones that contribute enough unique expertise to stand on their own, rather than simply echo the dominant market version.

    What I think needs shared ownership

    Governance ultimately comes down to accountability. Whether responsibility sits with a Chief Digital Officer, CMO, enterprise search team, or AI governance group matters less than whether ownership is clear. As search becomes more connected to marketing, technology, product, legal, and AI initiatives, organizations need clear decision rights, escalation paths, and accountability.

    The companies that succeed will not necessarily be the ones with the largest SEO teams or the most advanced AI tools. I expect the winners to be the organizations that know exactly how knowledge is created, governed, validated, and represented across markets.

    My practical rule for determining ownership

    For me, the distinction comes down to risk and expertise.

    Responsibilities that create enterprise-wide consequences when implemented inconsistently generally belong closer to headquarters. Activities that depend on local customer knowledge, regulations, language, or market conditions are usually best managed in-market.

    Many of the most important decisions need both perspectives, which means they are best handled through shared governance.

    10 governance decisions I would review with every global SEO team

    The exact structure will vary by organization, but I would encourage most multinational companies to evaluate ownership across these areas.

    Typically centralized

    1. Technical SEO standards

    I would centralize these standards to ensure consistency in crawling, indexing, structured data, and technical implementation across markets.

    2. CMS and infrastructure governance

    I would govern this centrally to prevent fragmentation while maintaining a common technology foundation.

    3. Entity definitions and taxonomies

    I would keep these consistent so products, services, brands, and organizational relationships are represented clearly across markets.

    4. AI crawler and bot governance

    I would establish consistent policies for crawler access, monitoring, verification, geographic routing, and exception management. Governance should usually sit with headquarters, while markets should still be able to request business-specific exceptions.

    5. Measurement and reporting frameworks

    I would centralize reporting definitions so markets are evaluated with comparable success metrics.

    Typically localized

    6. Market-specific content

    I would keep creation and validation close to local teams so content reflects customer needs, regulations, terminology, market conditions, and the geographic signals that help AI systems recognize local relevance. Global content frameworks can still support that work where appropriate.

    7. Audience and search behavior research

    I would manage this in-market to capture differences in language, intent, customer expectations, and emerging market trends.

    8. Local authority building

    I would localize this work because market-specific expertise, trust, partnerships, citations, and visibility cannot be fully manufactured from headquarters.

    Typically shared

    9. Product and knowledge management

    I would treat this as shared ownership because it needs global consistency as well as local validation, market expertise, and regulatory accuracy. Headquarters should define the framework, while markets validate that products, services, and policies reflect local realities.

    10. AI visibility and representation

    I would also make this shared. Headquarters should establish monitoring and escalation processes, while local teams validate market-specific accuracy and identify emerging issues in how products, services, and brands are represented across AI systems.

    The new global SEO mandate

    I do not think the objective is to centralize everything or localize everything. The real mandate is to place ownership where decisions can be managed most effectively, so the organization can balance consistency with expertise.


    Inspired by this post on Search Engine Land.


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  • My New SEO Stack: Tools I Use for Faster AI Search Wins

    My New SEO Stack: Tools I Use for Faster AI Search Wins

    New SEO stack old toolset

    I see generative AI and automation creating both excitement and anxiety across the SEO industry. With 87% of Americans reading AI summaries, I believe any SEO team that is not adapting its toolset is already starting to fall behind.

    When I move away from rigid enterprise tools and toward agile, AI-driven workflows, I can work faster, spot new search signals earlier, and show clients or internal stakeholders that I understand where search is heading.

    In this guide, I’ll walk through what the old SEO stack looked like, what I now add to it, and how I combine both approaches without abandoning the fundamentals that still matter.

    Here’s what an old SEO stack looks like

    I still believe traditional SEO practices matter because generative AI search experiences continue to depend on core search ranking systems, quality systems, and the broader signals search engines have used for years.

    That said, the classic SEO stack was built for a simpler search environment. It usually centered on rank tracking, keyword research, and technical site audits.

    Rank trackers

    For a long time, I treated keyword rankings as the heartbeat of an SEO campaign. I would add target keywords, monitor SERP positions, and expect higher rankings to translate into more search traffic. But rankings have become far more fragmented.

    Now I need to pay attention to AI Overviews, local packs, shopping carousels, and many other search features that can change the value of a ranking completely.

    A third-place local pack ranking, for example, may drive two or three times more traffic than a number one ranking in an AI Overview. That makes old-school rank tracking useful, but incomplete.

    Keyword tools

    Keyword tools still help me understand what people search for, how competitive a topic might be, and which queries match specific user intent. In the past, that information often felt close to a crystal ball.

    I would choose keywords based on difficulty, search volume, intent, and other factors. The better the data, the easier it was to shape a campaign around the right opportunities.

    The problem is that search volume has always looked backward. A keyword may have shown 10,000 monthly searches last month, but that does not mean it will perform the same way this month. Demand can rise, fall, or shift quickly.

    Today, the bigger issue is opportunity loss. A keyword that generated tens of thousands of clicks in 2022 may now be answered directly inside an AI Overview. Even when search volume has not dropped, zero-click behavior can reduce the traffic I can realistically capture.

    Site audit tools

    I still rely on site audit tools because crawlers still crawl websites, interpret content, and surface technical issues. I need to know whether search engines can access, understand, and navigate the pages I care about.

    Audit tools help me find broken links, redirect problems, missing metadata, slow pages, thin content, and other technical issues that can hold a site back.

    But I do not expect crawl audits alone to tell me whether my content will appear in AI-driven search experiences. Technical health is necessary, but it is no longer the full picture.

    Signals such as brand mentions can influence whether a site is included in LLM outputs from tools like ChatGPT, Claude, and Gemini. Many older site audit tools were not built to track those signals.

    That is why I still keep parts of the old stack, but I now add tools and workflows that help me understand AI visibility, brand presence, and faster data-driven decision-making.

    Here’s what a new SEO stack looks like

    If I am optimizing only for Google’s traditional results, I am missing where search behavior is moving. Between the first and second half of 2025, LLM referral traffic grew by 80%. Conversion rates reached 18%, even though LLM referrals still represented 2% or less of total traffic in the dataset.

    That tells me the channel is still small, but meaningful. Now is the time to build a stack that helps me understand, measure, and improve performance across AI-driven discovery.

    LLMs

    I want my site to appear in LLM responses, but I also use LLMs to strengthen my SEO process. These tools can support analysis, content review, competitor research, metadata refinement, and structured data work.

    For example, I can connect ChatGPT with Google Search Console to automate SEO analysis, use Claude to refine copy and conduct content audits, or use Gemini to generate schema markup and compare competitor pages against my own.

    I use the LLM that best fits the task, but I keep human oversight in place. These tools help me improve speed and performance; they do not replace judgment, strategy, or editorial review.

    The biggest shift is speed. Large datasets that once took hours, days, or weeks to review can now be explored in minutes when I use LLMs carefully and integrate them into a repeatable workflow.

    APIs

    The old workflow often meant logging into dashboards, exporting CSV files, and cleaning everything in Excel. I still do that when needed, but APIs let me pull data directly from platforms like Google Search Console and Google Analytics.

    APIs can sound intimidating, but LLMs make the learning curve easier. I can use them to help with authentication, JSON parsing, and the basic structure of repeatable data workflows.

    Once I can connect to APIs, I can stop waiting on manual exports and start building faster reporting, monitoring, and analysis systems around the data I already use.

    Lightweight scripts

    Python scripts are now within reach for many SEOs, especially with tools like Claude Code and similar coding support inside ChatGPT or Gemini. I do not need to be a full-time developer to automate repetitive SEO work.

    I can create scripts that pull top pages from Google Search Console, compare title tags against character limits, flag 30-day performance changes, or generate a clean CSV output for review.

    Instead of waiting for a vendor to add the exact feature I need, I can build a small script that removes a bottleneck. A hundred-line script can replace hours of manual work without requiring another SaaS license.

    I also like that scripts make the logic visible. If I hand the workflow to another teammate, they can inspect what the script does and understand how the output was created.

    Notebooks and local workflows

    SEO teams usually have data scattered across shared folders, Google Sheets, Notion docs, monthly CSV dumps, and long-running audit trackers. I have seen how quickly that fragmentation slows decisions down.

    Notebooks and local workflows help me turn scattered files into a working system. A script can pull the data, an API can surface the signal, and an LLM can help interpret the results before the output lands in a notebook or spreadsheet.

    The value is consistency. I get cleaner data formats, shared access, and documented logic instead of rebuilding the same process every time someone needs a report or audit update.

    As search optimization becomes more connected to generative AI, I need workflows that scale. Local workflows help me keep data consistent while giving the team a faster way to act on what we find.

    Creating hybrid workflows that mix old and new SEO stacks

    I do not think the old SEO stack is obsolete. I also do not think the new tools replace everything. The strongest approach is a hybrid workflow that keeps proven SEO fundamentals while adding AI, APIs, scripts, and notebooks where they create real leverage.

    Tool + custom script + AI layer

    To build a practical hybrid workflow, I would start with a familiar audit tool such as Screaming Frog, then run a Python script that joins the crawl data with Google Search Console data.

    From there, I could flag pages with high impressions and low clicks, send those pages to an LLM for title and intent analysis, place the output into a notebook or spreadsheet for editors, and turn approved recommendations into change logs.

    Work like this used to take weeks, so many teams pushed it aside. At enterprise scale, the amount of data could easily become overwhelming. With a hybrid SEO stack, I can complete larger projects in a fraction of the time.

    For me, the goal is not to chase every new tool. The goal is to build a more agile SEO stack that can handle today’s massive datasets, identify AI search signals, and help teams move faster without losing the core SEO basics.


    Inspired by this post on Search Engine Land.


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  • Inside Zero Click New York 2026: AI Marketing Takeaways

    Inside Zero Click New York 2026: AI Marketing Takeaways

    On June 11, 2026, I saw more than 1,000 marketing leaders come together in New York for Zero Click New York, Profound’s largest AI Marketing summit to date.

    What stood out to me was the range of leaders and brands shaping the conversation. Speakers from Coca-Cola, LinkedIn, Delta Air Lines, U.S. Bank, and CVS Health shared how they are rethinking marketing strategy, team design, and measurement as AI changes the way audiences discover and trust information.

    I also found the research sessions especially important. The summit explored Claude’s citation mechanics, ChatGPT’s emerging ads business, and the data behind the kinds of content AI systems are most likely to trust. Together, these conversations made Zero Click New York 2026 feel like a clear marker for where AI Marketing is heading next.


    Inspired by this post on Try Profound Blog.


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