USA Today Co. is using AI-assisted shell files to publish breaking sports coverage faster. The strategy is designed to capture search traffic before Google’s AI Overviews summarize the news.
The publisher tested the approach during the 2026 Winter Olympics and is now using it for coverage of the 2026 FIFA World Cup, Digiday reported.
USA Today pre-writes breaking stories. The USA Today network, which includes the flagship site and more than 200 local publications, creates automated shell files for likely breaking news events. AI pulls subheads, photos, and links from the publisher’s archive. Editors turn that material into ready-to-publish files, allowing reporters to add new details, update the headline, and publish quickly.
“We’re trying not to be as reliant on SEO strategy. Pre-writes are huge,” Alicia DelGallo, USA Today Sports editorial director, told Digiday.
The search window is shrinking. Publishers have long pre-written stories to move faster in Google Search. AI Overviews have increased the pressure.
DelGallo said USA Today wants to publish while search interest is still rising, before Google has enough information to generate an AI Overview.
Barry Adams, founder of Polemic Digital, told Digiday he has seen AI Overviews appear for news events within about four hours and no later than half a day, though he said there is no firm data yet.
Olympics coverage drove 116 million views. USA Today Co. said its national and local network generated 116 million page views from Winter Olympics coverage between Jan. 1 and Feb. 28. The flagship USA Today site drew 91 million page views, up 82% from the 2022 Winter Olympics.
DelGallo said the shell-file system helped the publisher move quickly on breaking Olympics coverage, including Lindsey Vonn’s crash.
Why we care. AI Overviews can compress breaking news into answers within hours. Publishing first improves your chances of capturing search demand before Google answers the query itself.
World Cup gets the playbook. USA Today Co. is now using the system for World Cup coverage, with five shell files ready each day. The publisher is also investing in original reporting. It has reporters in all 16 host cities and a dedicated World Cup hub.
DelGallo said the newsroom wants stories that don’t read like generic search content. That means stronger byline authority, more on-the-ground reporting, and angles readers can’t find elsewhere.
Traffic may still fall short. USA Today Co. has 40 million monthly unique visitors to its sports content and expects a World Cup traffic boost, especially with the U.S. co-hosting the tournament. DelGallo said USA Today still expects “massive audience” spikes from the World Cup. But she said AI Overviews have likely lowered the traffic ceiling compared with a year ago.
Google AI Overviews cited self-promotional “best” listicles while excluding the brands behind them from recommendations in 69% of cases, according to a new analysis of B2B software queries by Lily Ray.
Brands have used self-serving listicles to influence AI search results, but Ray found Google often cited those pages while recommending competitors instead.
By the numbers. Ray analyzed 100 B2B “best [category] software” queries in Google AI Overviews across three dates: April 15, May 15, and June 8.
Of the 80 prompts that triggered an AI Overview, self-promotional listicles were cited 323 times.
In 224 cases, Google cited a brand’s own page but didn’t recommend that brand.
Competitors get recommended. Ray documented several cases where Google cited a brand’s “best” listicle while recommending better-known competitors.
For “best LMS for selling courses,” Google cited Oasis LMS but did not recommend it. Instead, it recommended Kajabi, Thinkific, LearnWorlds, and Teachable — all of which are named in the Oasis LMS article.
Similar patterns appeared in queries for help desk, task management, survey, CRM, and SEO software.
Stronger brands still appeared. Brands that already led their categories, were widely mentioned by third-party sources, and had stronger link profiles were more likely to appear in AI Overview recommendations, according to Ray.
The data showed a consistent split between citations and recommendations. A brand’s page could appear as a source while competitors received the recommendation.
Organic visibility fell. Ray also reported organic search declines for many sites that relied heavily on self-promotional listicles.
The declines began around Jan. 20, across dozens of sites she analyzed. Many also scaled other SEO- and GEO-focused content formats, including AI-generated articles, comparison pages, and large volumes of “best” pages ranking their own brand first.
Those declines continued and accelerated during Google’s May 2026 core update, according to Ray.
Review sites gained citations. Ray found Google relied heavily on third-party and user-generated-content sites for “best” queries, with Reddit citations increasing sharply in recent months.
Forbes, Reddit, and YouTube were among the most-cited domains in AI Overview responses containing “best.”
Why we care. A citation is not a recommendation. Your content can appear in an AI answer while helping competitors capture the visibility that matters most.
Search Engine Land also reported that the tactic may create legal risk under the FTC’s Consumer Review Rule when company-controlled content is presented as independent reviews, reviews are not based on real use, or material relationships are not clearly disclosed.
About the data. Using Ahrefs Brand Radar, Ray collected AI Overview answer text and cited sources for 100 B2B “best [category] software” queries at three checkpoints between April and June. The analysis measured two outcomes: whether a self-promotional listicle was cited and whether the brand behind it was recommended.
Adobe announced a new solution to help businesses ensure their brands are visible, trusted, and chosen across AI surfaces.
Called Adobe Brand Visibility, the product is part of Adobe CX Enterprise, an agentic AI system designed to simplify customer lifecycle management — from acquisition and prospect engagement to conversion and long-term loyalty.
AI traffic is exploding. The use of LLMs to identify and research products and services marks a significant shift for both marketers and consumers. Alongside the announcement, Adobe released data showing substantial growth in LLM usage. AI traffic to U.S. retail sites surged 1,324% between October 2024 and May 2026. In the travel sector, AI traffic increased 2,215% over the same period.
“We used to get back the same thing (a SERP page with links on it). Now, the answers appear to be random, but they aren’t at scale. But companies don’t have tools to do it,” Loni Stark, vice president of strategy and product, Adobe, told MarTech.
Measuring brand visibility in AI search. Adobe Brand Visibility is Adobe’s first generative engine optimization (GEO) product since its acquisition of Semrush in May. It combines Adobe LLM Optimizer with Semrush’s AI Optimization tool.
Adobe Brand Visibility draws on nearly 300 million real-world AI search prompts, which Adobe says is the largest global database of its kind, helping teams identify which prompts they’re winning or losing.
Combined with Adobe’s first-party signals from owned channels, the platform gives marketers a view of how their brands appear across ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. Metrics include mention frequency, audience reach, competitive share of voice, and content gaps. AI agents then surface prioritized recommendations, enabling teams to deploy updates quickly and measure their impact directly in the platform.
Competitive intelligence. Adobe Brand Visibility includes competitive brand comparison tools that let marketers benchmark against competitors, identify where their brands are cited, track brand mentions, and analyze historical trends.
The platform also includes SEO intelligence, reflecting the continued importance of SEO fundamentals in AI search visibility. Powered by Semrush data spanning 28.5 billion keywords and 43 trillion backlinks collected over 17 years, it shows where existing search authority should be generating AI citations and where content investments can close gaps across both channels.
There’s still much to learn about how LLMs work and how brands can improve visibility, but Star is confident Adobe is well positioned to lead in this space.
“Adobe had owned data. Semrush had data and trends. We don’t have all of the answers, but we have the best data,” Stark said.
Ask ChatGPT or Gemini to “review my on-page SEO,” and you’ll get a perfectly reasonable answer.
Reasonable. Generic. Boring. Uninspired. And almost identical to the answer your competitors get when they ask the same question.
That’s the problem with AI out of the box. It’s a generalist. It knows a little about everything and nothing about you — your business, your customers, your market, or the way you do SEO. The questions are loosely framed and inevitably come back with general answers.
The good news is that’s also the opportunity. The same tools that produce generic answers can become specialist assistants that encode your knowledge, process, and standards. No code required.
Building one is simpler than most people think. With tools like GPTs, Gems, and Claude Projects, you can package your SEO process into a reusable assistant that helps identify opportunities, automate repetitive tasks, and apply your expertise consistently.
Why generic AI gives generic answers
You don’t need a computer science degree here, but a basic understanding of how AI works helps explain the benefits of this approach.
Large language models are prediction engines. They’ve been trained on a huge slice of the internet and human knowledge, and when you ask a question, they predict the most plausible response based on everything they’ve seen.
In other words, by default, you get something close to the internet’s average opinion on a topic.
For SEO, the internet’s average opinion is … fine. It’s the same advice repeated across a million articles. Check your title tags. Improve your content. Build some links. Blah.
What the model doesn’t know is anything about your specific situation:
Your business, services, and commercial priorities.
Your marketplace and competitors.
Your customers and the problems they’re trying to solve.
Your way of working — the checklists, thresholds, and judgment calls you’ve refined over years.
The output is only as contextual as the input. Give it nothing, and you get the average. Give it your knowledge, and you get something far more useful.
It’s a computing problem as old as computing itself: garbage in, garbage out (GIGO).
There are a few ways to add that missing context, in increasing order of effort:
Better prompts: Include context in your question: who you are, what the business does, who the customer is, and what good looks like. This works, but you end up pasting the same 500-word preamble into every chat. It’s tedious and easy to skip when you’re busy, which impacts the quality of the output.
Custom instructions and knowledge files: Most AI platforms now let you save a set of standing instructions and upload reference documents. The AI reads these every time, so you set the context once and it persists.
Simple AI apps: Package those instructions and documents into a named, reusable tool with a specific job. This is where GPTs and Gems come in.
Actual software: Use AI coding tools to build real scripts and applications when you need automation beyond a chat interface.
The great thing is that the jump from a “big prompt” to a “simple app” is smaller than it sounds.
The skill is the same: clearly describing the job, the process, and the standards. If you can write a good brief for a junior team member, or a standard operating procedure (SOP), you can absolutely build one of these.
This is an important point because most people assume this is far more complicated than it is, and that assumption is holding them back. You don’t need to be a developer to do this.
The development of custom tools is no longer a heavily technical job. It’s becoming more of a creative endeavor enabled by these new AI tools and the simple, descriptive way of building apps.
If you can document your process, you can build an AI app.
The platforms: GPTs, Gems, Claude, and Replit
A quick tour of the main options for building simple AI apps:
GPTs (ChatGPT): Custom versions of ChatGPT with their own instructions, knowledge files, and capabilities. They’re shareable via the GPT Store, which is handy if you want to publish a tool for clients or your audience.
Gems (Gemini): Google’s equivalent. Custom versions of Gemini with instructions and knowledge files, with the obvious appeal for SEOs living in the Google ecosystem alongside Search Console, Analytics, Drive, and Sheets.
Claude Projects (Claude): Anthropic’s take. Project-level instructions and knowledge with a large context window, so it can hold a lot of your documentation in mind at once. My personal favorite at the moment.
Replit: A browser-based platform where you describe an app in plain English, and AI builds and deploys actual working software. Use this when a chat interface isn’t enough and you want a real tool with a real interface processing real data.
Claude Code: An agentic coding tool from Anthropic where you delegate coding tasks in plain language, and it writes, runs, and fixes the code. It’s brilliant for building scripts that crunch large exports — say, processing a 100,000-row Search Console export that would choke a chat window.
For most SEO and marketing professionals dealing with day-to-day optimization work, the sweet spot is the first tier: GPTs, Gems, or Claude Projects. They take minutes to build, require no code, and capture 80% of the value.
I’ll use Gemini Gems for the worked example below, as it’s the closest to home for those of us who live in Google’s world. The principles transfer directly to GPTs and Claude, and if you want to build something a little more advanced, have a play with Replit.
Google Gemini interface (on the Gems page)
Why not use existing SEO tools?
Standard SEO tools are brilliant at what they do — crawling, rank tracking, and link data. I use them every day. But they share a weakness: They’re generic by design, while your business is totally unique (or at least it should be). They have to work for every business in every industry, so they can’t know what matters to you. Everyone sees the same scores, the same recommendations, and the same “issues,” many of which don’t matter for your situation.
The tools are also largely focused on analysis and opportunity. The kinds of tools you can build with AI are more focused on the actual work.
Vanilla AI has the same problem from a different direction. Hugely capable, zero context.
The strength of building your own simple AI tools is personalization:
Your business: The AI knows your services, priorities, and commercial goals.
Your marketplace: It understands your competitors, customers, and niche.
Your knowledge: It applies your process — the way you’ve learned to do this work over the years — rather than the internet’s average.
That last point is the big one. After 30 years of doing this, my honest take is that the value isn’t the AI. The value is the knowledge and process you encode into it. Your experience is what matters — the AI is just your superpower.
What should you automate?
A simple rule: Automate repetitive tasks. Good candidates are tasks that are:
Repetitive: You do them the same way, over and over.
Process-driven: You could write the steps down for a junior team member to follow.
Data-heavy: They involve staring at exports and spotting patterns — exactly what machines are good at and humans get bored with and subsequently do poorly.
Reviewing Search Console data ticks all three boxes. So do first-pass on-page reviews, log file triage, internal link analysis, and monthly reporting prep.
What you don’t automate is judgment: strategy, prioritization against business goals, and the final call on what actually ships. The AI does the legwork and surfaces the candidates. You decide.
Example: Search Console quick-wins Gem
Let’s build a simple tool to help you mine Google Search Console for content ideas and easy wins.
I wrote “How to unlock easy wins in Google Search Console” two years ago, covering the creaky old human way of doing it. Let’s automate it to free up time for the really valuable creative work.
Note: This is a purposely simple example that’s ideal for AI and automation because the task is repetitive and the data is free.
Step 1: Define the job
Write one sentence describing what the tool does:
“Review Google Search Console performance data and identify prioritized quick-win opportunities, with specific recommended actions for each.”
Simple enough.
Step 2: Document your process
This is the important bit, and it’s where you have to think about the process.
What do you actually do here? What process do you follow? What easy wins and opportunities are you looking for?
Striking-distance keywords: Queries ranking just off page one (or just off the top positions) with meaningful impressions. Small improvements here can have an outsized impact.
High impressions, low CTR: You’re visible but not winning the click — usually a title and meta description problem, or a SERP feature is eating your lunch.
Declining queries and pages: Anything trending down versus the previous period that deserves attention before it becomes a problem.
Query-page mismatches: Queries landing on the wrong page, or multiple pages competing for the same query.
Unexpected queries: Things you rank for accidentally that hint at content opportunities.
For each of these, also note the thresholds and judgment calls. What counts as “meaningful impressions” — 100? 500? What CTR is “low” for position 3 versus position 8?
This is your experience being made explicit, possibly for the first time.
Step 3: Write the Gem instructions
Now open Gemini, create a new Gem, and translate that process into instructions. A solid structure is:
Role: Who the Gem is.
Task: What it does with the data it’s given.
Process: The steps, checks, and thresholds — your documented process from Step 2.
Output: The exact format you want back.
Guardrails: What it should never do.
Here’s an abridged example to adapt:
Role: You are an experienced SEO analyst. You are methodical, skeptical, and prioritize commercial impact over vanity metrics.
Task: I will provide an export of Google Search Console performance data (queries and/or pages, with clicks, impressions, CTR, and position). Review it and identify quick-win opportunities.
Process: Check for, in priority order:
Striking-distance queries — average position 5–15 with 100+ impressions.
High-impression, low-CTR queries — flag where CTR is significantly below what you’d expect for that position.
Pages or queries declining versus the comparison period.
Multiple pages ranking for the same query.
Output: A prioritized table with opportunity, query/page, current metrics, recommended action, and expected impact (high/medium/low). Maximum 15 rows. Quality over quantity.
Below the table, provide a short plain-English summary of the three actions I should take first.
Guardrails: Only use the data provided. Never invent queries, pages, or metrics. If the data is insufficient to assess something, say so. Ask clarifying questions if the export format is unclear.
That guardrails section matters more than people realize. “Only use the data provided” is your main defense against the AI confidently inventing things.
Gems can reference uploaded knowledge files. This is where you fine-tune things and add depth without bloating the instructions.
Examples include:
Your on-page optimization checklist (for when the Gem recommends title or content changes).
Your title and meta description guidelines, so suggested rewrites follow your standards.
A short brand and business context document — who the client is, what they sell, and which products or services are commercial priorities.
This lets the Gem prioritize opportunities that matter, not just opportunities that exist. That’s especially important when reviewing Search Console data, as most sites show up for a wide range of searches that aren’t aligned with the client’s core goals.
Step 5: Save it
It really is that simple. Hit save, and you’ve created an AI app.
Step 6: Feed it data and test
Export your performance data from Search Console (Performance report > Export, or via the API or Sheets if you want more rows), then start a chat with your Gem and upload the file.
Browse to the Performance report and click Export in the upper-right corner. In this example, I use Google Sheets to keep everything in the Google ecosystem.
Then upload the file and ask for the output you want.
The Gem’s output — a prioritized quick-wins table for a real site
If at first you don’t succeed: The first output here wasn’t terribly useful for this site.
The recommendations didn’t align with the client’s goals. I had to revisit my third knowledge file regarding the business’s commercial goals and priorities.
After refining that document and running the analysis again, the suggestions became much more useful.
Step 7: Iterate like you would with a junior team member
The first version will get things wrong. That’s expected, and it’s actually the useful part.
A bad recommendation is a way to identify what could be improved. Whatever your answer is, that’s a rule that was missing from the instructions. Add it, and the Gem gets a little closer to working the way you do.
Treat it like a new team member. Review its work, correct it, and update the brief. After a few rounds, you’ll have something that delivers a genuinely useful first pass in seconds — and a documented process that’s valuable in its own right.
A note of caution
Some honesty before you let this loose on client work:
AI gets things wrong: Confidently. Always verify recommendations against the actual data before acting, and never let AI output go straight to a client without review.
Mind the data: GSC exports are business data. Check the privacy and data settings on whatever platform you use, especially when client information is involved, and make sure your approach aligns with any agreements you have in place.
It’s a first pass, not a final answer: The tool surfaces candidates. You supply the judgment. The moment you stop checking is the moment you make a mistake.
More simple SEO tools to build
Once you’ve built one, the pattern repeats. Same recipe — role, task, process, output, guardrails, and knowledge files — different job.
Any manual task you do repeatedly is a good candidate for this type of tooling. Examples include:
Keyword research assistant. Feed it seed terms and keyword exports. It clusters by intent and maps keywords to your site structure using your intent categories and customer personas.
On-page optimization reviewer. Paste a URL’s content and target query. It reviews the page against your checklist and suggests improvements in your preferred style.
Technical SEO triage. Feed it crawl exports. It prioritizes issues based on actual impact for your site rather than default tool severity scores.
Link opportunity finder. Feed it competitor backlink exports. It identifies realistic, relevant prospects based on your criteria and drafts outreach angles.
Content strategist. Load it with your personas and content strategy frameworks. It generates briefs and ideas anchored to real customer problems rather than generic topics.
Analytics insight reviewer. Feed it GA4 exports. It summarizes what changed, why it might have changed, and what’s worth investigating in plain English.
Search Console opportunity finder. The example we just built, easily extended into variants for content decay, cannibalization, or indexing reviews.
Each of these is an afternoon’s work.
The constraint isn’t technical. It’s whether you’ve documented your process clearly enough to hand it over. If not, this is a good opportunity to systemize your business and accelerate the work with a simple app.
For the past two years, the SEO industry has been asking Google for two things: more visibility into AI traffic and more control over how content appears in AI experiences.
They announced new controls that allow site owners to opt out of AI-powered experiences (AI Overviews, AI Mode, etc.) and introduced new AI reporting within Google Search Console. (Note that both of these are in early beta and are not yet available for everyone.)
On paper, this is a victory for things moving in the right direction for publishers.
Instead, the conversation immediately split into camps. Some focused on the new reporting. Others focused on the new controls and began debating whether to opt out of AI altogether.
What caught my attention wasn’t the announcement itself. It was how quickly the conversation shifted from gaining visibility to voluntarily giving it up.
Before we go any further, let’s clear up what Google actually announced.
The new controls do not turn off AI Overviews, stop people from using AI Mode, or slow AI adoption. Users are still going to search and ask questions, and increasingly do so through AI-powered experiences.
Google introduced a way for publishers to have more control over whether their content can be surfaced in those experiences. (Was this the plan all along, or was it exclusively because of the UK Competition and Markets Authority demanding it?)
Screenshot courtesy of Google’s announcement
That’s an important distinction because many people are treating this as a decision about AI itself. It isn’t.
AI Mode doesn’t disappear because a publisher opts out.
AI Overviews don’t disappear when a website decides not to participate.
The user experience remains largely unchanged. The only thing that changes is which brands are eligible to appear.
If Expedia opted out tomorrow, people wouldn’t stop planning vacations. If NerdWallet opted out tomorrow (like I did their stock), people wouldn’t stop researching credit cards. Google would simply surface someone else in its place.
This isn’t a decision about whether AI succeeds or fails. It’s a decision about whether your brand is present when customers choose to use it.
Why AI opt-out sounds good but is actually a trap
I understand the appeal. Publishers are worried about losing more clicks, frustrated by changing search behavior, and concerned about how AI systems use their content.
Those concerns are beyond valid.
Where I disagree is with the assumption that opting out changes user behavior.
It doesn’t.
Users aren’t deciding whether to use AI based on your participation. They’re deciding whether AI helps them get answers faster. For a growing number of searches, it does.
That’s why opting out of AI inclusion and opting users out of AI experiences are two different things.
A publisher can choose not to participate. Users can still use AI Mode. Google can still answer the question. The only thing that changes is which brands are eligible to appear.
That’s the trap.
The practical outcome isn’t less AI. It’s more visibility for your competitors. They gain citations, exposure, and the opportunity to become the trusted answer, while your brand becomes less visible.
If the concern is that AI is changing how customers discover information, disappearing from AI-powered experiences feels like a pretty dumb move.
The challenge isn’t finding ways to be less visible. It’s finding ways to remain visible as search behavior continues to evolve.
Google finally gives us AI data… and SEOs still complain.
The other part of Google’s announcement that received less attention was the reporting.
For years, the industry has been asking for more visibility into AI-driven search experiences. We wanted better attribution, better reporting, and a clearer understanding of how users interact with AI-powered search.
Now Google is beginning to provide some of that visibility, and almost immediately the conversation shifted to why it isn’t enough. Note that many of these screenshots are illustrative and are even from industry friends and well-respected search practitioners in our space. No shade intended to any one individual, simply wanting to illustrate the movement.
Maybe that’s true. The data isn’t perfect. The reporting doesn’t answer every question. I’d love more visibility into citations, AI Mode interactions, and better any sort of attribution modeling.
I especially agree with Dan’s post above, but waiting for perfect data has never been a winning strategy.
SEO has always operated with imperfect data. We’ve spent years making decisions based on estimated search volume, incomplete attribution, and reporting limitations. Some of the biggest wins in my career came from acting on directional signals rather than perfect certainty.
The same applies here.
The mistake is treating every reporting enhancement as either perfect or useless. We’re getting more visibility than we had six months ago, and we’ll likely have more six months from now.
My reporting approach: SEO+ reporting
Part of the reason this debate exists is that many teams are still measuring success through a traditional SEO lens.
Traditional reporting focuses on clicks, rankings (ewww), traffic, and conversions. Those metrics still matter, and I don’t see them disappearing anytime soon. The problem is that they’re no longer telling the entire story.
Users are discovering brands across more surfaces than ever before, especially outside of the Google ecosystem. Traditional organic search still matters, but so do AI Overviews, AI Mode, ChatGPT, Perplexity, Bing Copilot, Reddit, YouTube, and a growing list of ecosystems users rely on in their purchasing journey.
That’s why I’ve started thinking about reporting as “SEO+” rather than just SEO. (Yeah, I’m lazy and used the streaming naming convention “+” because… yeah, lazy.)
The goal isn’t to abandon traditional metrics. The goal is to expand what we’re measuring. Alongside traffic and conversions, I want to understand where brands are being cited, how often they’re being mentioned, how many unique URLs are being cited, whether branded search demand is increasing, how AI platforms reference them, and whether visibility is expanding even when attribution remains borked.
This is where I think many organizations are making the same mistake they made with content years ago.
With one of my clients, a lot of our content influences revenue months before a customer converts. Looking only at last-click reporting dramatically understates the impact. That’s why I started reporting on “content assists” as a key metric in their reporting. AI visibility is creating a similar challenge. A customer might first encounter your brand through an AI Overview, revisit you through traditional search, and ultimately convert through a completely different channel (probably a paid channel… ‘cause everyone loves ROAS).
The influence is real even when the attribution path is messy.
That’s why I’m less interested in measuring traffic alone and more interested in measuring discoverability. The brands that consistently appear across search, AI, and recommendation platforms are building familiarity long before a conversion occurs.
The wrong question
Most of the discussion around Google’s announcement has centered on a single question:
Should I opt out of AI?
I think that’s the wrong question.
The better question is whether you can afford to be absent from the places where customers increasingly discover information, products, and brands.
Users aren’t waiting for the SEO industry to decide whether AI is good or bad. They’re already using it.
That’s why I view Google’s announcement less as an AI opt-out feature and more as a strategic decision point. Opting out doesn’t remove AI from the equation. It simply increases the likelihood that someone else becomes the answer instead.
Some brands will use it.
Their competitors are hoping they do
Will you lean into change, or will you be another person complaining that Google owes them free clicks?
This post first appeared on the author’s website and is republished here with permission.
ChatGPT’s product recommendations changed 80.2% when search was enabled, according to a study of 20,000 responses by Jeff Oxford, founder and CEO of Visibility Labs.
Oxford tested 1,000 product-recommendation prompts 10 times each with ChatGPT search enabled and 10 times with search disabled.
Only 19.8% of products recommended without search also appeared in recommendations generated with search enabled.
Search changed top picks. Even products ChatGPT recommended most often without search rarely carried over. Among products that appeared in 100% of search-disabled responses, only 15.8% also appeared when search was enabled.
Oxford expected the most consistently recommended products to remain common when search was turned on. Instead, that group had the lowest overlap.
Source mentions tracked visibility. The study also examined whether products mentioned in ChatGPT’s cited sources appeared more often in its recommendations. Oxford reported a 0.4 Pearson correlation between cited-source mentions and recommendation frequency.
The study measured recommendation frequency with a “Visibility Score,” defined as the percentage of runs in which a product appeared for a given prompt. Products mentioned more often in cited sources tended to have higher Visibility Scores.
The analysis didn’t establish that cited-source mentions caused products to be recommended.
Search narrowed recommendations. ChatGPT responses with search enabled contained an average of 5.2 products, compared with 6.2 when search was disabled.
Across 10 runs of each prompt, ChatGPT returned an average of 19 unique products per prompt with search enabled and 21.8 with search disabled.
Why we care. Search changed which products ChatGPT recommended, including products it named every time when web access was disabled. The findings suggest products appearing in cited sources may receive greater visibility when search is enabled, though the study does not determine whether cited-source visibility matters more than broader web visibility.
About the data. Oxford analyzed 1,000 product-recommendation prompts, running each 10 times with search enabled and 10 times with search disabled. Product names were standardized so naming variations counted as the same product. Because the study was observational, it did not establish a causal relationship between cited-source mentions and recommendation frequency.
AI referrals to U.S. travel sites nearly tripled in May. AI visitors spent more time on site and bounced less than visitors from traditional sources, according to Adobe.
By the numbers. Traffic from AI sources to U.S. travel sites grew 194% year over year in May 2026, Adobe said. It was up 2,215% since October 2024, when Adobe began tracking AI traffic.
AI-assisted travel planning has expanded beyond early research. Travelers use large language models to compare destinations, evaluate hotel amenities, build itineraries, find promotions, and book trips.
AI visitors showed stronger engagement. AI-referred travel visitors still converted 28% less than non-AI traffic. That gap has actually narrowed nearly 70% since October 2024, Adobe said.
Engagement metrics were stronger. AI-referred travelers were 21% more engaged than non-AI visitors, spent 70% longer per visit, and had bounce rates 41% lower.
Adobe said those engagement patterns suggest more purposeful, high-intent behavior, though AI-referred travel visitors still converted at lower rates.
Travel pages and AI readability. Adobe also measured how readable travel websites are to large language models. Its AI Content Visibility Checker scores how much page content AI systems can read.
Hotels and car rentals led the travel sector. Hotel homepages scored 63% readability, while car rental homepages scored 59%. Product pages scored higher: 73% for hotels and 71% for car rentals.
Even so, more than one-third of content on some leading travel pages remained unreadable to AI systems, Adobe said.
Where travel sites scored best. Hotels led across several page types, including destination guides, activities, search results, customer service, and promotions pages.
Car rentals led FAQ pages. Cruises led blog and news content. Airlines trailed the leading travel sectors across every page type Adobe measured.
The pattern favored pages with rich, structured information. Property details, amenities, vehicle descriptions, and core offerings gave AI systems more content to parse.
Retail’s conversion advantage. AI referrals to U.S. retail sites also hit a record high in May, rising 138% year over year and 1,324% since October 2024.
Retail AI traffic has progressed further on conversion. Adobe said AI-referred retail visitors converted 54% better than non-AI traffic, reversing last year’s pattern, when AI conversion rates were nearly half as high.
Cosmetics and electronics led retail readability, aided by ingredient lists, tutorials, product specifications, how-to guides, and customer service content. Grocery and furniture lagged.
Why we care. Adobe’s data suggests AI referrals are becoming more commercially valuable, especially in retail, while its visibility scores indicate many sites still leave significant content inaccessible to AI systems. If key content is blocked, buried, or poorly structured, you may lose visibility before a traveler or shopper reaches your site.
About the data. Adobe’s findings were based on more than 8 million visits to U.S. travel sites, more than 1 trillion visits to U.S. retail sites, and more than 100 million SKUs. The company also surveyed more than 5,000 U.S. consumers in March about how they use AI for shopping and travel planning.
AI search is changing at a pace none of us has experienced before in marketing.
The presentations I saw at Zero Click NY highlighted both how much AI search has changed over the past six months and the characteristics that may become lasting features of the landscape.
Of all the points covered, these seven stood out as the most important.
From the rise of the marketing engineer, to the differences between Claude and ChatGPT results, to Claude’s meteoric rise among businesses over the past 12 months, here are the most impactful takeaways I left with.
1. Every AI relies on different content
ChatGPT and Claude share only about 8% of their citations, per Profound data. Put differently, 92% of what ChatGPT cites wouldn’t be cited by Claude for the same query. A brand can own visibility in one engine and be virtually invisible in the other.
On top of that, they don’t just cite different websites. They prefer different kinds of content.
ChatGPT indexes heavily on community content: Reddit, Quora, and forums make up roughly 16% of its citations.
Claude sits at less than 1%. Claude, by contrast, loves listicles (36% of citations vs. ChatGPT’s ~20%) and opinion content (13.2% vs. 7.2%).
The relationship to traditional search splits the same way. About 64% of the websites Claude cites also appear in Google’s top 50 for the same query. For ChatGPT, it’s only 37%.
In other words, “just do the SEO work you’ve been doing” might work for Claude visibility, but likely won’t for ChatGPT.
Takeaway: It’s critical to communicate to stakeholders that “AI visibility” will inevitably vary by LLM, and you’ll have to prioritize them depending on whom you’re trying to reach (more on that later).
Track visibility by engine because the work that wins in one might do almost nothing in another. UGC and community seeding move ChatGPT, while listicles and traditional rankings move the needle on Claude.
2. Claude is quietly winning B2B — so sequence your optimization by audience
If you’ve seen the generative AI traffic-share charts, Claude looks like a rounding error.
But web traffic is the wrong chart. Roughly 85% of Anthropic’s revenue comes from enterprise and API usage that never shows up in consumer traffic data.
The right chart comes from Ramp’s AI Index, which tracks corporate card spend across tens of thousands of businesses.
A year ago, single digits of those businesses were paying for Anthropic. Today, it’s 34.4% — ahead of OpenAI at 32.3%. For the first time, more businesses pay Anthropic than OpenAI.
I came away from this presentation asking myself: If business users are increasingly living in Claude while consumers live in ChatGPT, shouldn’t your optimization priorities focus on where your audience is?
Should B2B brands prioritize Claude visibility first? Should B2C brands prioritize ChatGPT first?
Almost nobody is doing this because people aren’t really thinking about who uses ChatGPT, Gemini, or Claude. That will likely change.
3. ChatGPT ads are here, and this is what we’re seeing
The moment is here: Your competitors are buying visibility through ChatGPT ads. ChatGPT ads are live and self-serve, sitting directly inside the chat product.
The same two weeks brought GPT 5.5, citation chips turning into clickable hyperlinks (referral traffic jumped roughly 60% overnight, with homepage referral share leaping from roughly 3.5% to 24%), and Google moving AI Mode into its main search box.
None of that was an accident. The hyperlinks are the click-tracking rails an ads business needs. The analysis of more than 100,000 ad placements surfaced three things everyone should internalize.
ChatGPT Ads match on topic
Ads match on topic similarity, not intent. Only 14% of real user prompts carry commercial intent, but 20% of prompts trigger ads — a math problem can serve an ad.
The embedding analysis found that ad titles and descriptions are the single biggest drivers of which conversations you show up in. Your title and description are now targeting parameters, not just creative.
Paying for ads
“Pay-to-play” is here. About one in five ad placements appears against a mention of a direct competitor, and the brand mentioned organically shows up as the advertiser only about 8% of the time.
Someone else is twice as likely to be the advertiser on your organic mention as you are.
Startup CRM Adia is already placing ads against prompts where Salesforce appears, and Salesforce is playing defense, showing paid placements 40% of the time, even when it’s already mentioned organically.
Ad inventory is scarce and expensive
ChatGPT shows roughly one ad per conversation, the median conversation is three turns, only 30% of eligible users see ads at all, and CPMs/CPCs are running around four times Meta’s.
Expect that to change in predictable ways: more ad slots per answer, ads deeper into conversations, and follow-up suggestions engineered to create more turns, which means more inventory.
The lesson: Organic AEO and paid defense are now the same job. If you’re tracking your brand’s organic citations but not who’s advertising against them, you’re seeing half the board.
4. Claude is the most directly optimizable AI right now
When Claude searches the web, it pulls from Brave. Not “influenced by” Brave. According to the talk I saw, it pulls directly from it.
In Profound’s latest testing, 79.2% of Claude’s citations came directly from Brave’s top 10 results for the equivalent search.
There’s no meaningful reshuffling or reranking. No other model trusts its search provider to anything like this.
That makes Claude the most directly optimizable model in AI search: a visible index, a checkable ranking, and (as we’ll see next) predictable retrieval behavior.
If takeaway 2 convinced you that Claude matters for B2B, this is the playbook: Figure out where you rank on Brave for your key prompts and treat that as your Claude visibility roadmap.
A window this transparent doesn’t stay open. Optimize for it while it exists.
5. Claude only performs web searches a third of the time
There’s a catch, and it’s a big one. ChatGPT triggers web search on roughly 95% of prompts. Claude searches only about a third of the time — likely because every search costs money (Brave’s public API pricing runs around $5 per thousand searches), so Claude has a real financial incentive to answer from its weights.
You can only optimize Claude when it actually retrieves.
The good news is that its search behavior is predictable. Recency-framed prompts (“best X in 2026”) trigger search about 81% of the time.
Ranking-oriented prompts (“top 10…”) trigger it 67% of the time, location-dependent prompts 55%, and comparisons 51%.
Definitional and procedural prompts — “what is a CRM?” and “how do I…” — mostly don’t trigger search at all, which makes them nearly worthless optimization targets for Claude.
The lesson: Before you invest in Claude visibility for a prompt category, test whether Claude actually searches for it.
Recency, rankings, locations, and comparisons are the surface areas where Brave rankings translate into Claude citations.
Everything else is answered from memory you can’t touch.
6. Query fan-out: A raffle on one stage, near-deterministic on another
Two speakers described the same mechanism in almost opposite terms, and the tension between them is instructive.
Query fan-out is the set of synthetic queries an AI engine runs in the background to gather content before generating an answer.
Mike King of iPullRank framed it as a raffle: You can’t see or control the fan-out, so the job is to maximize your raffle tickets — more surface area across owned, earned, and shared properties, and, crucially, the right content formats.
Even if you rank for a fanned-out query, the wrong format makes you ineligible.
His research points to new measures of what wins retrieval — content-to-query cosine similarity and information gain both correlate strongly with AI search performance.
Josh Blyskal of Profound’s data tells a different story for Claude specifically: Its fan-outs are near-deterministic.
The same prompt produces the same fan-out string about 65% of the time, and 94% of Claude’s fan-outs are stamped with the current year (ChatGPT does this only 17% of the time).
ChatGPT’s fan-outs churn constantly. Claude barely moves. Both views may be right — for different engines.
Where fan-outs are stable, as in Claude, you can read them and build content targeted directly at them. The year-stamping behavior alone argues for putting the current year in your titles.
Where fan-outs are volatile, as in ChatGPT, King’s raffle logic applies: Buy more tickets through formats and surface area.
One mechanism, two strategies, chosen per engine. Which, again, may require you to prioritize one over the other.
7. The marketing engineer is here, and agents are the new workforce
It would be easy to dismiss “marketing engineer” as a vendor-manufactured job title. The hiring market says otherwise.
Google has hired its first marketing engineer. Figma posted the role at a $295,000 base salary. RBC and Autodesk have made hires.
It became a breakout search term on Google, and Google’s own AI marketing lead called marketing engineers “the hire for 2026.”
Who is the ideal candidate to become a marketing engineer? Is this a role where you start with an engineer and teach them marketing, or vice versa?
The emerging consensus profile is a marketer first — someone with channel experience and taste — who builds and maintains AI systems, reports to the head of marketing, and unblocks the rest of the team. A marketer who ships systems end to end.
The underlying logic is that most marketing work decomposes into pipelines: extract data, transform it, and load it somewhere useful. Agents can now run those pipelines on a loop.
Monitoring competitor pricing and auto-generating sales battle cards.
Watching landing pages and AEO presence on a schedule and staging A/B tests.
Pulling objection themes out of 800 sales calls and drafting content to address each one.
Tasks that used to be “we’ll get to it someday” projects become an afternoon of agent building. The constraint stops being headcount and becomes creativity.
If your team doesn’t have someone in this role yet, there’s a good chance it will eventually.
There still is no clear playbook for AI search. When that playbook does emerge, however, the first step may be to prioritize one LLM over another based on who you want to find you.
And in many cases, that “who” is going to be an agent. At the same time, we’ll have agents assisting us in the work we’re doing, and the demand for people who can engineer these systems will continue to grow.
A year ago, 82% of consumers said AI-powered search was more helpful than traditional search. By 2026, that number had dropped to 54%, a 28-point decline in sentiment over 12 months.
Consumers aren’t giving up on AI search, though. Seventy percent say they’re using AI tools for search more than they did last year.
How should search marketers adapt their GEO strategies? Where are we going wrong as we bring AI deeper into our workflows?
To find out, Fractl partnered with Search Engine Land to expand our 2025 research, surveying 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are evolving in the age of AI. (Disclosure: I’m the co-founder of Fractl.)
Here’s what the data means for your 2026 search strategy.
Consumers are using AI more and trusting it less
1. Usage is saturated. The growth story is over.
Seventy percent of consumers report increased use of AI tools for search over the past year. Just 3% say it’s decreased.
Surprisingly, baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically love AI and older generations are lagging behind. In reality, early adopters are signaling that while usage may be rising, trust still has to be earned.
That matters because the remaining competitive battle isn’t about adoption. It’s about trust, quality, and which brands consumers find credible when AI surfaces answers.
2. The trust erosion is faster than anyone projected.
In 2025, the AI skeptic camp (consumers who found AI less helpful than traditional search) represented just 3% of respondents. In 2026, that segment grew to 17%, nearly six times larger than the year before.
The 54% who still find AI helpful are mostly hedging: 37% say it’s “somewhat more helpful,” compared with 17% who say it’s “much more helpful.” Enthusiasm has declined rapidly as hallucinations have become a more widely recognized challenge.
3. AI content volume is now a brand trust liability
In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%.
For search marketers, the implication is significant. Scaling content output with AI is no longer a neutral operational decision.
Consumers are paying attention, and a substantial portion of your audience has an opinion about it. Publishing without disclosure, or publishing at scale without clear quality signals, is now a reputational variable.
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%).
The audience most likely to engage deeply with your brand online, share your content, and drive long-term organic visibility is also the audience with the lowest tolerance for AI-generated filler. Quality isn’t optional if Gen Z matters to your brand.
5. Disclosure is no longer a nice-to-have. It’s a near-universal consumer expectation.
Across every 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%. The percentage of respondents who strongly agree exceeds 50% in every category.
This isn’t a soft preference. It’s close to a mandate, and as we’ll cover in Part 2, most brands are nowhere near meeting it.
6. Consumers believe AI will dominate search. They just don’t love what it currently delivers.
Sixty-four percent of consumers agree AI will replace traditional search engines within five years, essentially unchanged from 66% in 2025. The belief that AI will eventually dominate search remains intact, even as satisfaction scores decline.
What this tells search marketers is that the channel isn’t going away. But being present in AI results and being trusted in AI results are increasingly separate challenges. Optimize for both.
Google still leads on trust, but the map is getting more complex
7. For purchase-intent queries, Google leads AI roughly 3-to-1
When consumers are making purchase decisions, 39% turn to Google first. Reddit comes in second at 15%, just ahead of AI tools at 14%. Review sites and friends and family each come in at 11%.
The trust consumers have built in Google hasn’t automatically extended to AI.
8. Platform preference varies by query type. Optimize accordingly.
Google dominates five of six major search categories. For local businesses (74%), product research (58%), travel planning (57%), and health questions (55%), it’s the default first stop. However, YouTube overtakes Google for how-to content at 50%.
ChatGPT has become the second-most-used destination for health questions at 26%. It also ranks second or third for product research (19%), travel planning (18%), and how-to content (17%).
There’s no single AI search platform to optimize for. Each query category has its own preferred platform. Map your content strategy to where your audience actually goes for each topic.
9. Consumers use 2.4 platforms before making a purchase decision
Before making a purchase decision, the average consumer checks 2.4 platforms, and that behavior is consistent across generations: Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2.
Google remains the default authority for product recommendations, while Reddit and AI tools reinforce confidence.
In 2026, search optimization is no longer limited to page rankings. It’s built around cohesive content strategies that strengthen your entity authority while helping people learn, engage, and convert across multiple platforms.
A brand that appears in Google results but nowhere else loses to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has review content on third-party sites.
How AI is changing marketing operations (and where the gaps are)
10. AI integration in marketing teams has crossed the majority threshold
AI now touches 53% of marketing work on average, up from 38% in 2025. The equivalent of one full workday per week has shifted to AI-assisted workflows in 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it’s involved in three-quarters or more.
For SEO and content teams, this means your competitors are producing at a higher velocity. Volume advantages are increasingly commoditized. Accuracy, original insight, and brand credibility aren’t.
11. SEO and analytics teams are under the highest AI adoption pressure
We’re in an operational pressure cooker: 55% of marketing roles report a 7:10 level of pressure to adopt AI. SEO and analytics roles feel the greatest pressure, but PR sits at 5.8. As AI commoditizes generic content, the advantage shifts to what AI can’t automate: human judgment, relationships, and trust.
12. We’re buying production speed at the cost of quality
Only 26% say AI made their work faster and better. Nearly half admit it made their work faster, but more generic. Seven percent report an outright decline in quality.
This is where your competitive advantage lives. If your peers are scaling AI slop while your team invests in original data, expert quotes, and earned brand mentions, you’re building assets that make your brand more visible, credible, and retrievable across search engines, social platforms, and LLMs.
How you apply AI to your workflows will separate the brands that scale entity authority and brand visibility from those that scale slop and fade into a sea of sameness.
13. Nearly half of AI-generated content doesn’t go through governance processes
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 a legal or compliance review. Only 27% evaluate content for bias.
Nearly half of AI-generated content is entering the market without fact-checking, legal review, or plagiarism checks. Instead, most marketers are focusing on subjective, surface-level editorial review: Does it sound right? Is the tone appropriate? Are there typos?
In a year when consumers are already primed to distrust AI slop, your brand’s AI governance process is one of the cheapest gaps to close and one of the most expensive to ignore.
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, and the compliance gap is significant.
For search marketers producing content at scale with AI, this is an emerging trust and brand risk, not just an ethical concern. The takeaway isn’t to abandon AI. It’s to stop treating governance as optional. Every AI workflow needs clear checks for accuracy, transparency, and human review before content reaches your audience.
14. AI hallucinations about your brand are already a PR problem, and most teams don’t have a process to catch them
A year ago, only about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved, reaching 24%.
Meanwhile, 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 people. If AI is summarizing your category, comparing your product, or explaining your brand incorrectly, that’s not just an SEO issue. It’s a reputation risk, a revenue risk, and a PR issue waiting to become a headline.
When AI misrepresents your brand, fixing the source matters more than disputing the output. Reach out to the publisher for an update, update owned profiles, and publish a correction page tied to your brand.
Where visibility is being won and lost
15. Organic traffic is under pressure, not in freefall
So yes, 50% of the marketers we surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI.
This is a prime example of traffic diversification. The real shift isn’t from Google to ChatGPT. It’s from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across multiple platforms and communities.
The same marketers reporting organic losses are often finding new ground elsewhere:
57% report visibility growth from social platforms (TikTok, Reddit, and YouTube).
40% see growth from AI assistants (ChatGPT, Gemini, and Perplexity), early evidence that GEO investment is generating returns.
31% see growth in direct or branded traffic.
Only 10% report no visibility growth anywhere.
Your 2026 brand visibility strategy now depends on how effectively you build brand mentions and entity authority across platforms, not just on individual page rankings in Google.
16. Most teams are doubling down on the easiest tactics
Which strategies are marketers prioritizing to hedge against AI’s impact?
The good news is that teams are moving toward the right categories: community building, earned authority, owned audiences, expert content, and traffic diversification.
The most prioritized strategies for maintaining visibility in the AI era include building brand presence on social platforms (59%), GEO/AEO optimization (54%), and creating authoritative expert content (44%).
The least prioritized strategy is investing in original research and data, at 15%.
That’s a strategic inversion. Original, proprietary research is one of the hardest content assets for AI to replicate, synthesize, or commoditize. It generates citations, earns links, and builds topical authority in ways that FAQ pages and generic thought leadership can’t.
Teams investing here are building durable moats. Others are investing in areas where AI makes competition easier.
17. In GEO, the popular tactic is also the least defensible
When we drilled into the specific GEO tactics marketers were using, most were content-led and easily replicated by AI systems. Long-tail FAQs matter for AI Overviews, but they’re easy to replicate. Schema helps, but it doesn’t build credibility.
Entity authority creates the strongest moat: proprietary data, expert perspectives, topical authority, and third-party validation. These brands create the source material that journalists, communities, search engines, and AI systems rely on.
18. GEO measurement is lagging execution by a wide margin
It’s no surprise that only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results.
While that’s normal for a new channel, GEO is becoming a serious function. Visibility tracking, citation monitoring, and branded search lift need more attention. Building measurement infrastructure for AI search visibility is a competitive advantage. Teams that can prove GEO ROI can defend and grow investment.
19. The main obstacle to AI adoption isn’t budget or buy-in. It’s skills.
The top barrier to deeper AI integration in marketing is team training and skill gaps (26%). Tool fragmentation comes second at 20%, followed by budget constraints (19%), unclear ROI (12%), and legal and compliance concerns (12%).
Leadership buy-in stands at just 2%, indicating that executive support is largely in place. The gap is execution capability. For search marketing teams specifically, investing in AI literacy, prompt strategy, content quality control, and GEO measurement skills is more valuable right now than adding new tools.
What this means for your 2026 search strategy
The data across both consumers and marketers tells a coherent story. Users are adopting AI search faster than they’re developing trust in it. Marketers are deploying AI faster than they’re governing it. For search professionals, both gaps create specific, actionable opportunities.
Audit your brand’s AI footprint before someone else does
Brands have already been misrepresented in AI responses. Query your brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews. Document what’s accurate, what’s missing, and what’s wrong. Build a monitoring cadence before you’re in damage-control mode.
Invest in entity authority and original research
AI can’t generate proprietary survey data, original research, named expert perspectives, or verified brand facts. Marketers prioritizing original research are building assets that will become even more valuable as AI systems get better at rewarding genuine authority over generic content.
Distribute your visibility across multiple platforms
Consumers are checking 2.4 platforms before buying, and they’re doing it consistently across every generation. Google organic is necessary, but it isn’t sufficient.
Your brand needs a coherent, consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media. If a consumer asks ChatGPT about your category and you’re not mentioned, or you’re mentioned inaccurately, you’ve lost that decision before they ever reach a search results page.
Build AI content governance, not just AI content workflows
Consumer demand for AI disclosure ranges from 84% to 91% across formats. Only 20% of brands always disclose. This disconnect is a reputational liability and, increasingly, a legal and regulatory one. Establish disclosure policies, fact-checking checkpoints, bias reviews, and hallucination escalation processes as operating standards.
Close the GEO measurement gap
If you can build attribution frameworks that connect AI-assisted search mentions to traffic, lead quality, and revenue, you’ll be able to prove ROI at a time when most teams can’t. That’s a budget and strategy advantage that compounds.
Double down on what AI can’t replicate
Proprietary data. Named experts. Human-verified claims. Transparent sourcing. Consistent brand voice at high quality. The brands that treat quality as a strategic differentiator in 2026 are the ones whose names will come up when consumers and AI systems go looking for answers.
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 spanned 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.
“Ultimate guides” were the undisputed heavyweight champions of SEO. They were built specifically to align with how Google’s algorithm measured content value.
But the web moved on. Search intent shifted toward fast answers, AI saturation destroyed length as a credibility signal, and Google’s systems began penalizing the one thing ultimate guides were engineered to produce: zero information gain.
So, what now?
The new content constraint is extractability, and it changes every structural decision downstream, from brief to publication.
Your content has a word limit: the grounding budget
AI engines like Gemini allocate approximately 380 words per webpage for query grounding, regardless of the article’s total length. It’s a retrieval constraint you have to adapt to.
The extraction data is precise:
Pages under 5,000 characters: 66% AI extraction rate.
Pages over 20,000 characters: 12% AI extraction rate.
Generative systems now answer many queries without requiring a click. The traffic those pages once captured no longer exists to be captured. The 4,000-word ultimate guide content marketing approach actively destroys generative search visibility.
What replaces the informational library is something structurally different and considerably more demanding to produce. Every sentence must earn its place by naming an entity, stating a relationship, preserving a condition, or making a citable claim.
Traditional keyword targeting asked one question: “What are people searching for?”
Problem-first positioning asks a harder one: “What situation has produced this search, and what does a genuinely useful answer look like inside that situation?”
That’s where the padlock principle becomes useful. Your business is a lock that opens for multiple combinations, each representing a distinct problem for a distinct person.
For example, a car insurance provider targeting “car insurance” is a category. The same provider building separate pages for “an 18-year-old new driver declined by standard insurers” and “a courier using a vehicle for commercial work” is a solution.
The distinction sounds philosophical until you realize it affects every downstream structural decision. Andrew Holland is right: AI killed low-grade informational SEO. Here’s some tactical advice to shift your content approach.
3 tactical rewrites for 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
Acknowledging that your solution works for teams of 100 or more but not for solo operators signals to a retrieval system that your content can be cited with confidence. Generic advice is the content AI already generates for free.
Constraint-aware, condition-specific guidance is what AI cannot replicate and therefore must source.
This logic collapses one of the most entrenched distinctions in digital marketing. The traditional separation between informational content and commercial landing pages was always somewhat artificial, but AI retrieval has made it structurally unsustainable.
What replaces the previous distinction is a fundamentally different content architecture: Every page is a document that knows exactly who it is for, states the problem it solves in the first sentence, and earns its keep by delivering a resolution specific enough to be cited but human enough to convert.
Marketers should start injecting problem-positioned, AI-readable answers directly into commercial pages rather than blogs. Low-grade information recaps like the “best tools for X” roundup and the “how-to” guide that adds nothing to existing knowledge have been absorbed by generative systems that now answer those queries without a click.
Every sentence must be self-contained and able to survive alone. AI retrieval systems do not read your article the way a human does: sequentially, with accumulating context.
Instead, an LLM will lift sentences in a “send this to someone without context” type of way by extracting passages and evaluating sentences as independent semantic units.
If a sentence requires its neighbors to make sense, it cannot be extracted and evaluated as an independent semantic unit (i.e., it’s neither easily understood nor useful for a machine).
The three failure patterns and their fixes:
Failure
Example
Fix
Unresolved pronoun
“It also includes unlimited storage.”
“The Dropbox Business Standard Plan includes 5TB of encrypted cloud storage.”
Stripped condition
“The price has dropped significantly.”
“The Asana Enterprise Plan costs $24.99 per user per month, down from $30.49 in Q1 2024.”
Vague claim
“Our platform makes team management easier.”
“The Asana Enterprise Plan streamlines cross-functional project tracking for teams of over 100 people.”
If you want to write LLM-friendly content, no matter what content format you are creating, here’s my advice: look into semantic triples.
Because AI systems evaluate content using identical retrieval infrastructure regardless of page type, the semantic triples (subject, predicate, object, conditions preserved) apply equally to blog articles, product descriptions, and pricing pages.
Here’s a concrete application of semantic triples: Make your heading more explicit. Explicit headings placed directly above their corresponding paragraphs add mathematical relevance (i.e., they improve cosine similarity scores), which means that an AI is 17.54% more likely to select that passage if it has a good headline.
The citation bait formula
How do you keep content fresh in the age of AI?
First, accept that you’re optimizing paragraphs, not pages.
The citation-bait formula defines how to structure the paragraph blocks that sentences belong to.
Step 1: Direct declarative opening (40 to 60 words)
No preamble. No “in this section we will explore.” The answer first, always. This block is what generative systems extract.
Step 2: Context (one to two sentences maximum)
Expand without burying. Every additional sentence beyond two reduces the density of what came before.
Step 3: Structured evidence
A table, a numbered list, or a comparison. Something extractable in its own right, independent of the surrounding prose.
Step 4: Self-contained heading
The H2 or H3 that follows must name the topic, intent, and scope of what just appeared. Not “Key takeaways.” Not “Overview.”
The heading must make complete sense when read entirely out of context, because in generative retrieval, it frequently will be.
Managing the tension between AI-readable structure and human persuasion is difficult. Like Shrek’s onion analogy, LLM-friendly content has more layers than most people realize. You don’t have to choose between the two. You have to layer them.
The AI inverted pyramid places machine-readable answer blocks at the opening of each section. Human storytelling — the anecdote, the constraint, the actual number/stat/finding — belongs immediately after, connected by a natural transition that moves the reader from optimized structure and into earned narrative.
Jessica Foster identified Dove’s “Real Beauty Stories” as a great example of this type of copywriting. Dove opens with structured how-tos that satisfy intent-driven retrieval, then anchors those tutorials to the lived experiences of real customers.
The machine gets a citable answer at the top of the block. The human gets a reason to believe it in the body. Neither layer compromises the other because they occupy different positions in the document.
Casey Nifong has a great audit workflow for existing content:
Identify the main question each section answers.
Find the clearest direct answer buried in the paragraphs and move it to the top.
Strip conversational lead-ins that delay the core answer.
Run both the isolation test and the disambiguation test on every mid-page sentence.
Leave stories, examples, and brand voice intact below the answer block, connected by natural transitions.
The missing angle: Your workflow doesn’t exist yet
You now know good content no longer looks like a 4,000-word-long ultimate guide. Now it’s time to figure out what workflow produces said new good content.
Most articles on Search Engine Land describe the destination, not the road. That’s because you’re responsible for the journey. You need to build your editorial checklist, prompt structure (if you’re using LLMs to restructure existing content), and grounding budget calculation.
Go beyond theory and build an editorial system that consistently produces LLM-friendly content without sacrificing the human specificity no model can replicate.
Meta launched AI Mode in Facebook Search. AI Mode gives users AI-generated answers based on public content from Facebook Groups, Reels, and other Meta apps.
Instead of showing a standard list of search results, AI Mode uses Meta AI to answer questions directly within Facebook. Meta said responses are grounded in what people are publicly saying across its apps, including real experiences and recommendations.
AI answers in search. AI Mode supports both broad discovery and specific questions. Users can search or explore their Feed and receive responses from Meta AI within Facebook. This gives Facebook a new way to surface public social content.
Groups and Reels could become part of how Meta answers questions about products, places, hobbies, and everyday advice.
Source selection is unclear. Meta said the feature delivers “real answers from real people.” But it didn’t explain how AI Mode selects which public posts, Groups, or Reels appear in responses. It also didn’t say whether brands, creators, or publishers will be able to see when their content is used.
Why we care. Facebook search is moving toward an AI answer experience built on public social content. That could change how people discover recommendations, local information, and brand-related conversations across Meta’s apps.
A familiar name. Obviously, Meta’s new AI Mode feature shares its name with Google’s AI Mode. Meta gets no points for creativity.
What Meta is saying. AI Mode is powered by Meta AI and Muse Spark. Meta didn’t explain how Muse Spark influences search ranking, source selection, or answer generation.
The search update was part of a broader Facebook AI rollout that also included new creative tools for photos, videos, profile pictures, and Stories.
The idea that AI is killing advertising misses the bigger shift. As AI expands across search, assistants, productivity tools, and transactions, advertising is moving with it.
Ad density may be changing within AI experiences, but advertising opportunities are expanding across a growing number of surfaces.
At the same time, paid and organic are becoming harder to separate. The same AI systems increasingly power ad campaigns, search experiences, and brand visibility across Google’s ecosystem.
That changes how brands should think about visibility.
Paid and organic are no longer separate channels competing for the same click. They are increasingly different ways of influencing the same AI systems, which means the signals shaping organic visibility may also affect paid performance.
The old model: Paid and organic on one finite SERP
Google’s SERP was a finite surface: 10 organic blue links, a few ad slots, and a knowledge panel on the right. The user landed, scanned, and clicked.
Paid and organic teams operated on separate budgets, separate tools, and separate quarterly reports, and rarely talked to each other because manual Google Ads kept the paid specialist busy full time. Titles, descriptions, bids, and campaign structure were all chosen by hand and required constant attention, which is why the organic team had no part in any of it.
DSA changed that for me. It read my organic pages to decide which ads to run, who to show them to, when, at what bid, and what title to use. I controlled the descriptions. The engine decided everything else, and it did it better than I would’ve done manually because it was reading the same signals the organic side was already optimizing for.
When someone at Google in Singapore explained how PMax worked, I thought, “That’s exactly what I was doing.”
PMax took the DSA logic and extended it across every Google surface simultaneously: Search, YouTube, Gmail, Display, Maps, and Shopping, all in one campaign, with the engine making every placement decision from your assets and audience signals.
AI Max brought the same intelligence into Search campaigns, specifically, with Gemini underneath instead of rules. PMax and AI Max run on the same Gemini brain: one focused on Search, the other spread across every surface, applying the same funnel logic to different contexts with different signal layers on top.
And if Gemini’s understanding of your brand is thin, it fills those decisions with whatever it thinks will work, which isn’t necessarily your brand narrative, and you have no direct way to override it. You train it, or you lose control of your own ads.
The new model: Gemini sits inside every surface, and it carries ads with it
Gemini now sits inside every layer of the Google ecosystem:
Discovery (Search, Maps, YouTube, Lens, News, Discover, and Shopping), productivity (Gmail, Docs, Drive, Photos, and Calendar).
Distribution (Android, Chrome, Google Play, Pixel, Wear OS, Google TV, and Nest).
Transaction (Google Pay, Wallet, Flights, Hotels, and Travel).
Assistive surfaces themselves (AI Mode, AI Overviews, Assistant, NotebookLM, and the Gemini app).
That’s how many connected consumers spend most of their workday, and most of those surfaces either carry ads now or have the infrastructure to start carrying them.
Microsoft Advertising sits inside Copilot across Bing, Edge, Windows Consumer, Office Consumer, Teams Free, and GitHub.
OpenAI Ads launched in February for logged-in users on Free and Go tiers in the U.S., placing ads below ChatGPT responses and clearly labeling them as sponsored. By May, OpenAI had opened a self-serve Ads Manager and was expanding internationally.
The ads layer travels with the engine, the engine is everywhere, and ads therefore have the potential to be everywhere. Most brands still treat paid as a separate channel run by a separate team on a separate dashboard, which is a search-era inheritance that was never ideal but now needs to be dropped.
Performance Max already runs the auction across YouTube, Display, Search, Discover, Gmail, and Maps as one campaign type. Search is one surface among many, and the “ads are dying in AI search” narrative is measuring the wrong thing. It sees ad slots compress inside the assistive interface while ignoring that the surface base has multiplied by an order of magnitude.
Ad density follows the delegation the user has made to the machine
The dominant narrative in 2026 is that ads are dying because AI is replacing search, and ads inside AI are a problem nobody has fully solved yet. That’s partially correct: Ad density per session drops as AI takes more control, and nobody – including Google – has yet figured out how to insert ads into the AI response itself without killing the experience that makes the AI valuable in the first place.
But this is the part the analysis gets wrong: This doesn’t add up to fewer ads overall.
Search ads are Google’s goose with the golden egg, and the goose may be slowing down — though nobody outside Google actually knows, because Google doesn’t break out search ad revenue from YouTube, Display, and the rest. That ambiguity is doing a lot of work.
What we do know is that total ad revenue has kept growing even as AI has taken over more of the search experience, which proves the flock is already working.
Kodak invented the digital camera and then buried it to protect film-processing revenue, and we know how that ended. Google appears to be doing what Kodak didn’t: building the replacement while the original is still profitable.
Every surface Gemini sits inside is a new bird in the flock, each laying a smaller egg that grows over time, and when Google finally cracks ads inside the AI response itself, that’s one more goose. The surface base has expanded faster than density has dropped, and the ad-density problem in Search and AI is temporary.
The more the user delegates decisions to the machine, the less room the machine has to surface a paid option. Search keeps the user in charge, so the engine surfaces ads the user might pick. Assistive narrows the options, so a sponsored slot still has a chance. Agentic executes the decision, so the ad has nobody to persuade. Ad density follows that delegation, mode by mode, with AI deciding which brands win at each mode.
Ad density follows the delegation the user makes to the machine.
Google is running two moves at once, and it seems most people have noticed only the first one. Gemini is taking over the recommendation, targeting, and auction logic on surfaces that have carried ads for years. And Google is adding ads to surfaces where they were previously absent, with AI Overviews now eligible for ads above, below, and within the answer, and AI Mode testing conversational ad formats.
The first move is AI taking over the existing ad business. The second is the ad business expanding into surfaces it never occupied. The net effect is more AI-driven ads across more of the stack than ever before.
The freemium system still works, but the ad is becoming part of the surface
The monetization model that works at consumer internet scale is simple: pay with money, or pay with attention.
YouTube is Google’s clearest example — and proof that it works: free with ads, paid without, and the vast majority of users have always chosen ads.
Gmail draws the same line: Where the user pays directly, Google doesn’t insert ads. Where the user pays with attention, Google monetizes it.
I learned about freemium the hard way. When our children’s media company, Boowa & Kwala, survived the dot-com crash, we added a paid tier that removed the ads. Out of a million unique visitors a month, a few hundred paid. Almost nobody chose to pay.
The freemium contract — free access in exchange for ads — is the deal they actively prefer, and the numbers prove it. And for ad-driven businesses, pure volume makes the money. In Big Tech, Google has the clear advantage.
ChatGPT is already running ads on free tiers.
Gemini is ad-free without login, but that’s a launch state, not a permanent model.
Perplexity is blocking users instead of monetizing them, which is a different bet on the same problem — and a bet with a limited runway.
Every AI surface is in the process of landing on the same answer because there is no other answer.
What changes is how the ads appear. The classic SERP ad was clearly labeled and set off in a colored panel. The Gemini recommendation that surfaces a product inside a Gmail context, the Copilot suggestion that names a vendor inside a Word document, and the agent that picks a supplier on the user’s behalf are something else entirely.
The ad becomes ambient. It dissolves into the surface, and what advertising looks like becomes harder to identify as advertising. Gemini reads context and intent with enough precision that an ad placed in a meeting summary can feel useful rather than disruptive, which is a risk profile Google’s rules-based systems could never have accepted.
At Boowa & Kwala, when we scaled free ad-supported views from 100 million to 1 billion, revenue multiplied by roughly two, and costs rose by around 20%. Surface (a.k.a. pageviews) multiplied tenfold, revenue doubled, costs grew by a fifth, and we went from profitable to significantly more profitable.
The aim was never to push revenue up at the same rate as surface expansion. It was to keep expanding the surface, knowing the incremental delivery cost was negligible.
Google’s ratios at planetary scale differ from ours, but the structural shape almost certainly doesn’t: surface expansion plus near-zero incremental cost equals profit growth, regardless of whether revenue per surface keeps pace.
Cohort, intent, and profit drive both paid and organic
PMax, AI Max, AI Overviews, AI Mode — Gemini is driving all of them. The AI optimizing your paid campaigns is the same AI evaluating your organic content, reading the same user, in the same moment, with the same intent.
The engine reads three signals:
Cohort.
Intent.
Profit.
In paid, you declare all three explicitly when you structure your campaigns. In organic, the engine infers all three from behavior: clicks, dwell time, and return-to-search serve as proxies for the profit signal that is missing there. Google denied using behavioral signals for years. Its own court case documentation told a different story.
Which means the organic discipline the whole series has been building — the funnel query pathway, the entity home, and the corroboration stack — has always been pointing at one thing: engineer the page so precisely for the right cohort that the behavioral signal does the same job as a correctly structured PMax campaign. The user lands, stays, converts, and doesn’t go back and research the same thing again. Google reads that behavior and infers your profit tier.
My bet, and I want to be clear it’s a bet rather than a documented fact, is that Gemini can’t serve a paid ad in real time without grounding against current search results because the ad has to match the organic context it’s appearing in.
If it doesn’t ground, the ad is inconsistent with what the user sees organically, which breaks the experience and loses the click. So the grounding process for paid is the same process as for organic: same knowledge graph, same search index, same LLM.
That means training Gemini on your brand through organic improves your paid performance through the same mechanism. One training investment, two outputs. I’ll be proven right on this eventually, and this article is the timestamp.
The same AI runs your organic and your paid. Train it once, win twice.
You can’t directly target Gemini in AI surfaces. You can only train it.
Across AI-driven placements, Gemini decides everything: where to show your ad, what to show, how to show it, who to show it to, when, and at what bid. The advertiser feeds it information and sets the parameters, but Gemini makes every decision that matters.
What you’re buying when you spend on Google Ads in 2026 is the right to feed a recommendation system that analyzes your brand on its own terms. The explicit signals you declare in paid — cohort, intent, and profit — are a real advantage over organic, where the engine has to infer all three from behavior.
But your ability to dominate through pure campaign structure is vastly reduced when Gemini doesn’t understand or trust your brand. The control has shifted: you guide it through signal clarity, not through the settings dashboard, and that guidance works best when your organic foundation is solid.
Use paid to find the combinations that work, build organic pages around them
In a correctly structured PMax or AI Max campaign, you declare cohort, intent, and profit margin explicitly: this audience, this goal, this margin, in the same campaign. You don’t mix a luxury hotel and a budget guesthouse in the same ad group because the cohort is different, the profit margin is different, and handing the engine a mixed signal makes it spend your budget resolving a contradiction you created.
Organic doesn’t let you declare profit directly. The engine infers it from who landed, who stayed, who converted, and who never came back to search for the same thing. That behavioral signal is the only proxy it has for the profit tier, and it’s a thin signal compared to the explicit declaration you make in paid.
The smartest move for any brand running both is to treat them as a single loop. Run paid to find which cohort-intent-profit combinations actually convert. Build the organic pages around those combinations, designed so precisely for the right cohort that the behavior on the page sends the engine the same signal the paid campaign explicitly declared.
The paid side becomes cheaper because organic pages provide the behavioral confirmation the engine needs. The organic side gets stronger because the paid data tells you exactly which pages to build and for whom, and then feeds the engine the same signal the paid campaign declared explicitly, for free.
Most travel sites serve the same page template to a budget traveler looking for a €30 guesthouse in Bangkok and a wealthy traveler looking for a €3,000 suite at the Peninsula. Same layout, same fields, same photo grid, same review format.
The engine has to infer which cohort the page serves mostly from behavior because the differentiation of the pages is limited. Build the page for the person rather than the query, and you hand the engine the cohort signal it’s currently having to guess. That’s not a UX decision. That’s your profit margin declaration to an engine that can’t see your margins any other way.
And you win on all three fronts simultaneously. A page built precisely for the right person converts better because it works better for the human.
Better conversion behavior sends cleaner implicit signals to the engine, which improves your organic ranking for that cohort. And cleaner organic signals reduce your paid CPC because the engine has less to guess about. Better pages, more organic, cheaper paid – the same work produces all three.
When Gemini isn’t convinced about you, you pay on both sides simultaneously
The three revenue taxes — the doubt tax, the ghost tax, and the invisibility tax — operate on the organic side. Because the engine powering your organic results is the same one powering your paid placements, you pay all three on both sides simultaneously.
The doubt tax: When the engine hedges on basic facts about you organically, it rewrites your paid creative to soften the same claims.
The ghost tax: When the engine prefers competitors in organic comparisons, your paid creative gets passed over even when your bid is competitive.
The invisibility tax: When the engine doesn’t surface you organically, it doesn’t show your ad either. You’re not in the running.
Paid surfaces carry two additional taxes that don’t exist on the organic side, and one discount you earn when you get it right.
The taxes and discounts in AI-driven paid search include:
The mistrust tax: What you pay when the engine’s confidence in your brand is low. A CPC premium because Quality Score penalizes low entity trust, and message distortion because the Gemini Filter rewrites your creative away from your intended positioning. You can’t turn the filter off. The practical answer isn’t constraining it. It’s improving the entity confidence that the engine reads when deciding how to filter.
The intent tax: This is self-inflicted. Build an ad group with mixed intent, and you hand the engine a contradiction. Gemini will spend your money figuring out a mess you made. Each ad group should align on cohort, intent, and profit margin — any mix across those three, and Gemini is billing you to resolve the confusion.
The confidence discount: This is the blade cutting the other way. Every properly defined ad group is secretly doing two jobs: it buys you an efficient placement today, and it teaches the engine which cohort you serve tomorrow. When the engine trusts you, it stops second-guessing your ads, your CPC drops, and your creative lands cleaner. That’s worth more than any bid adjustment you make.
Google has a structural advantage that Microsoft and OpenAI can’t match
Google has all the cards: the model, the surfaces, and the ads platform, all owned and tuned together in absolute harmony. Microsoft has the surfaces but lacks the LLM to drive them at the same level.
OpenAI has the model and launched a real ads business in February 2026, but lacks the surfaces – no Gmail, no YouTube, no Maps, no Play – and without surfaces, an ads business can’t compound at scale. Only Google has all three working as one system.
Paid and organic are now inseparable. The goose is fading, but Google can afford to let it. They know it rises like a phoenix, and in the meantime, they’ve got the biggest gaggle.
As AI tool usage has become more common, I’ve seen impressive examples of people building tools to automate complex processes that once required significant manual effort. I’ve also seen teams adopt AI simply because it’s available, often with little practical benefit.
My approach is to focus on AI applications that save time and solve real problems.
Recently, I needed to align the SEO architecture for more than a dozen websites across three separate businesses, eight regional domains, and multiple languages, including three English dialects, Italian, Japanese, Spanish, Thai, French, and Korean.
Historically, mapping thousands of URLs to create cohesive hreflang XML sitemaps would have required specialized software or days of spreadsheet work. Instead, I used Google Gemini to build a custom Python script that handled the heavy lifting.
Here’s how the project evolved from an initial prompt into a highly customized automation tool, and what it taught me about using AI for technical SEO.
Where AI delivers the most value
I use AI primarily for practical, time-saving tasks, including:
Generating regex patterns when I need a quick solution without researching syntax from scratch.
Creating complex spreadsheet formulas for reporting workflows that rely on manual data exports.
Accelerating research and planning for projects that require competitive analysis across multiple business lines.
Building custom automation tools for recurring SEO and data-processing tasks.
The hreflang project discussed here falls into that final category.
The challenge was clear: map thousands of URLs across more than a dozen multilingual websites into accurate hreflang XML sitemaps.
Rather than tackling the project manually, I used Google Gemini to help build a custom Python solution.
Here’s how the process unfolded.
Phase 1: Asking for an approach, not just a script
A common pitfall when using generative AI for coding is asking it to sprint before it knows the route. If you simply type, “Write a Python script to create an hreflang sitemap,” you’ll get a generic, fragile piece of code that breaks the moment it encounters real-world data.
Instead, I started by asking for an approach. I explained the scenario: multiple regional domains, organic growth over several years resulting in mismatched URL slugs, translated subfolders, and appended revision years.
Gemini suggested a multi-step, data-driven approach:
Crawl the websites to collect live URLs and their metadata.
Use Python in Google Colab to process the raw data.
Run an exact match cluster first to group identical slugs.
Use an advanced semantic AI model (such as SentenceTransformers) to fuzzy match translated pages based on their titles and normalized URLs.
Phase 2: Crawling and data collection
Following the strategy, I used a crawler to spider all the regional websites. The goal was to generate a unified comma-separated values (CSV) file containing the live URLs, status codes, title tags, and H1s. Screaming Frog worked perfectly for this application.
A critical point: Your AI output is only as good as your crawl data (remember the old saying, “garbage in, garbage out”).
An AI script will fail to map an obvious “exact match” if the target URL is a 404 or a 301 redirect in your source data. You must filter your CSV to include only indexable content before feeding it to the script.
Google Colab provides a free, cloud-based Jupyter notebook environment where you can write, paste, and execute Python code without worrying about local installations or environment variables. You can access it through Google Drive. I found the free version had enough capacity to handle this project.
I uploaded the CSV to Colab, and Gemini provided the initial Python script. The script used a domain-mapping routine to assign language codes, clean the URLs, and generate an XML tree. The initial output was far from perfect.
Phase 4: The iteration (where the real work happens)
If you expect AI to deliver a flawless, edge-case-proof script on the first try, you’ll be disappointed. You’ve probably heard the comparison of AI tools to interns, meaning you need to check their work. That’s very true.
The real value of AI lies in the iteration. As we ran the script, we encountered several unmatched URLs, leaving pages orphaned rather than grouping them with their international counterparts.
Here’s how I iteratively trained the AI to handle the nuances of human-managed websites.
The directory flattening problem
The U.S. site had recently reorganized its blog into topical folders, while the Mexican and Italian sites hadn’t yet been reorganized.
I prompted Gemini with these specific mismatched examples. It responded by adding a URL flattener function to the script, which stripped the topical folders behind the scenes so the translated slugs could align cleanly.
The aggressive semantic trap
To prevent the AI from mixing up different topics, we implemented concept traps. Initially, they were too strict. A UK article about the manufacturing sector wouldn’t match an Italian article because the U.S. title was slightly more generic.
I instructed Gemini to loosen the traps for generic industries while keeping them strictly enforced for critical acronyms (such as “SEO” versus “SEM”). This gave the AI the breathing room it needed to match creative translations.
The translated slug epiphany
The biggest breakthrough came while auditing the Mexican blog orphans. For example, the Spanish URL /detras-de-escenas-historias... is a direct translation of the English /behind-the-scenes-stories... I pointed this out to Gemini.
Instead of forcing me to hard-code hundreds of manual matches, Gemini updated the script to create a “Combined Semantic Signature.” It dynamically translated core operational phrases in the slugs, effectively bridging the language gap for the semantic matching model and connecting dozens of orphaned pages almost instantly.
The project reinforced a simple lesson: AI works best when it’s treated as a collaborator rather than a shortcut.
Be the strategist, let AI be the coder: Don’t just demand a final product. Discuss the architecture, edge cases, and logic first. Treat AI like a junior developer that needs clear architectural direction.
Provide concrete examples: When a script fails, don’t just say, “It’s broken.” For this project, I provided either exact URLs that failed and the URLs they should have matched with, or groups of URLs with mismatches. AI needs concrete patterns to fix its logic.
Embrace the iterative loop: Expect to run the code, identify anomalies, and feed them back into the prompt. Each iteration makes the tool significantly smarter.
Leverage Google Colab: You don’t need to be a Python expert to use Python for SEO. Colab bridges the technical gap, allowing you to run complex data science libraries directly in your browser.
By the end of the project, we had a robust, highly customized Python script that could process a massive CSV and generate a cross-referenced hreflang XML sitemap in minutes.
AI isn’t going to replace technical SEOs anytime soon. However, SEOs who know how to collaborate with AI to build custom, scalable, and useful tools will have a significant advantage.
Much of the GEO conversation focuses on how AI systems discover, extract, cite, and recommend content. That work matters. But visibility also depends on what the content contains once it’s found.
Next-question intent is a way to test whether a page provides enough information to support the user’s next decision, not just the initial query.
The first search is often only the starting point. Real decisions happen in the follow-up questions, comparisons, constraints, and objections that come next.
Content that helps answer those questions gives AI systems more useful material to summarize, compare, cite, and recommend.
From results to narratives: Traditional search vs. AI search
Traditional search was built around a results page: a ranked set of links users could scan, compare, and interpret for themselves. AI search is increasingly built around a synthesized answer drawn from multiple sources.
That changes what content must do. A page can rank, index, and appear technically sound, yet still fail to provide the information needed to support an AI-generated answer. That’s where next-question intent matters.
Search intent asks, “What is this user trying to do?”
Next-question intent asks, “What will the user need to know next before they can trust, compare, choose, buy, book, or move on?”
That question is becoming increasingly important because AI systems don’t simply match queries to pages. They assemble answers, comparisons, qualifications, and recommendations.
In that environment, content must support the full answer path, not just the first query.
A user’s first search is often broad, incomplete, or simply exploratory. It signals a direction. Real value appears in what comes next: the follow-up, the objection, the comparison, the constraint, the “practical anxiety,” the “Yes, but what about my very specific situation?” moment.
As the simplest example, someone searches “best CRM software for small business.” The first query becomes a doorway. But the actual buying process begins with the 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 make an owner, office manager, or intern quietly resent tech?
These queries aren’t add-on or side questions. They’re the actual decision path.
Otherwise competent content fails at this stage. It answers the query, but doesn’t help complete the conversation. A page can define the category, mention benefits, include a few keywords, and still omit information buyers need to make decisions.
In traditional search, the user might click a few results and assemble context manually. In AI search, the system will assemble it for them. If your content lacks that useful context, it gives the system less to work with and may appear less visible.
Next-question intent is not just a writing exercise
The risk with any new content framework is that it becomes a fresh label for familiar advice. Next-question intent should do more than remind you to “write better content.” It should help you test whether a page contains enough context to support the next step in a user’s decision.
In practical terms, next-question intent means asking whether the content is answer-ready.
Answer-ready content addresses the user’s initial need, anticipates the next layer of decision-making, and provides specific, verifiable, and contextual information to support a synthesized answer.
This distinction matters because AI search visibility isn’t exclusively about rankings. It’s also about citations, mentions, recommendations, and whether a brand is recognized as a trusted answer in a given context.
Those outcomes require something more than volume. They depend on whether the brand’s content provides the system with enough substance to understand what the brand does, who it serves, when it’s useful, why it’s trustworthy, and how it compares to alternatives.
Where good content goes thin
Most brands have decent content that’s accurate, readable, and optimized around a keyword. There may even be an FAQ section, like a useful but decorative basket of afterthoughts.
In AI search, decent may not be enough.
AI systems need extractable clarity, but they also need context. They must understand what something is, who it’s for, when it’s useful (and when it’s not), what evidence supports the claim, and what the user should consider next.
This level of context is where many pages go thin.
As an example, a service page says, “We offer customized marketing strategies.” But what does customized mean?
A real strategy?
A lightly personalized template?
A monthly call where everyone nods at a dashboard no one has time to interpret?
The product page says “safe for families.” Safe for whom?
Babies?
Pets?
People with health issues?
A software page says, “built for small businesses.” What business?
A solo bookkeeper?
A nonprofit?
A 40-person heating and cooling company?
A founder doing payroll late at night after working all day?
Broad claims offer humans little to trust and AI systems little to use. Specific, structured, evidence-backed content offers something better.
A next-question audit looks beyond keyword coverage and asks whether a page contains the information needed to support the next step in the user’s journey.
For every important page, you should ask:
What’s the primary question this page answers?
What would a serious buyer, reader, or researcher ask next?
What objection would stop them from acting?
What comparisons would help them understand the category?
What proof would make this answer trustworthy?
What detail would make this financially, technically, locally, or personally relevant?
Where are we applying broad language because we haven’t done the harder thinking?
The best inputs for the audit often come from inside the business, not from keyword tools alone. Customer reviews, comparison queries, demo questions, sales calls, support tickets, chat logs, internal site search, and objection patterns can all reveal the questions real people ask when making decisions.
That information is often closer to the buyer’s actual path than a neat spreadsheet of keywords.
Examples of next-question content across industries
For a local service business, next-question content might involve service areas, prices, appointment windows, insurance, reviews, emergency availability, or what happens after someone books.
B2B software may invest in next-question content that involves integrations, user roles, implementation times, costs for switching, security, support, or whether a lower-tier plan is useful.
For higher-trust categories like medical, financial, and legal, next-question content involves scope, credentials, risk, regulation, evidence, or when to speak with a qualified professional.
The point isn’t to stuff pages with every possible question. It’s to build content around how people actually decide.
AI search rewards content that completes the answer
Next-question intent helps you avoid one of the least useful responses to AI search: publishing more content because visibility feels uncertain. The better move is more specific, decision-ready content.
If your page says, “I/we help small businesses grow,” explain which small businesses, what kind of growth, what constraints, what proof, what trade-offs, and what alternatives.
For example:
“We help local service businesses without in-house marketing teams improve search visibility and generate more qualified appointment requests by clarifying their website content, answering the questions clients actually ask, and building pages that support both traditional and AI-generated search. This is best for businesses looking for durable visibility rather than a quick paid-ad spike.”
In that same line of thought, if a page says “We’re eco-friendly,” explain the materials, sources, use cases, certifications, limitations, disposal issues, and even circumstances where that claim doesn’t apply.
If a page says “This is AI-powered,” explain what that AI tool actually does, what it automates, what remains human-led, what data it uses, and where users will still need judgment.
This isn’t writing for bots. It’s writing for real people whose decisions are increasingly being mediated by AI-generated answers. The goal is to make your expertise, relevance, and trustworthiness easier to understand and use.
Traditional SEO asked whether a page could rank. AI search asks whether a page can contribute to the answer.
Any page can be indexed, optimized, and technically sound, yet still fail if it lacks substance. It might answer the initial query, but ignore the information users need to make a decision.
The opportunity isn’t to chase every new acronym or rebrand every content plan as a new discipline. It’s to build answer-ready content.
That means clearer definitions, stronger examples, honest comparisons, better proof, more precise positioning, and direct answers to the questions customers ask every day.
In traditional search, visibility belonged to the page that best matched the query. In AI search, it increasingly belongs to the content that helps people move forward.
AI Overviews and Google AI Mode now dominate conversations across the SEO community. One trend already stands out: Search is evolving from an information retrieval tool to a recommendation tool.
For travel brands, this changes the rules of online discovery. The challenge is no longer just helping search engines understand your website. It’s helping AI systems understand when your business should be recommended.
How AI search has changed travel planning
Many users now spend substantial time every week interacting with large language models (LLMs). With LLMs, they can organize conversations by project and create folders for upcoming trips. They can also build on previous chats that already recognize their interests, travel preferences, and demographic profiles.
This marks a departure from the traditional search process. Historically, travel planning started with Google searches for topics like:
“Hotels in Porto”
“Things to do in Rome”
“Best restaurants in Barcelona”
Today, this process is far more conversational.
Rather than typing a series of disconnected searches, a traveler might create a new folder called “Summer 2026” in ChatGPT and start with a broad question that gradually evolves into a complete itinerary. For example:
“Where should I stay in Porto for a quiet weekend within walking distance of the historic center?”
“Which area of Rome is best for families traveling with young children?”
What follows is an ongoing conversation that might expand into restaurant recommendations, attractions, accommodation options, transportation advice, and day-by-day planning.
When travelers ask AI assistants these questions, they aren’t looking for a list of websites. Instead, they’re looking for a recommendation.
How AI Overviews impact the travel search experience
AI Overviews synthesize information from multiple sources and present users with curated recommendations rather than a collection of links. As a result, trust, consistency, and contextual understanding become critical visibility factors.
A hotel may influence a traveler’s decision through an AI-generated response without leading to an immediate website visit. The traveler’s next action may be a branded search, a visit to a travel review site, or a booking through an online travel agency (OTA).
To earn recommendations from AI models, your brand first needs to be clearly defined. AI must have confidence in who you are, what you offer, who you serve, and when your brand is relevant.
To do this, choose one primary category and one clear position for your brand. Invest in digital PR and earn mentions beyond your own website. Aim to be included in travel articles that cover topics relevant to your category.
Most importantly, ensure your business information is accurate, consistent, and easy to interpret across your website, Google Business Profile, TripAdvisor, OTA listings, and social media platforms.
Zero click doesn’t mean zero impact
The way we measure search performance is changing. Traditional SEO metrics still matter. However, travel marketers should start expanding how they measure visibility.
One of the biggest mistakes is assuming that fewer clicks mean less visibility.
A traveler may discover your property through an AI-generated response, search for it later, visit a TripAdvisor profile, or book through another channel.
This is why branded search growth is becoming a valuable signal of AI visibility. Travel marketers should also monitor AI mentions, citations, and assisted conversions.
Assisted conversions reveal the channels and touchpoints that influence a booking, even if they aren’t the final source of the conversion. You can monitor these conversions in Google Analytics 4 by navigating to Advertising > Attribution > Conversion Paths and Attribution Reports.
Why TripAdvisor and OTA listings provide semantic context for AI recommendations
TripAdvisor has become much more than a review platform. OTAs have become more than booking platforms.
When a user asks for recommendations, AI systems rarely rely on a single source. Instead, they build understanding by combining information from multiple platforms.
Your website is only one part of the ecosystem.
AI systems build confidence in recommendations by validating information across sources. What others say about your brand in reviews, travel guides, media mentions, OTA listings, or local citations increasingly matters. In many ways, this is simply online reputation at scale.
This additional context helps AI models determine when a property is relevant for specific traveler needs, such as:
Family-friendly.
Popular with business travelers.
Located in a walkable area.
Known for exceptional dining.
Better suited to luxury or budget travelers.
How to differentiate your travel brand
A family-friendly hotel should consistently highlight family rooms, kids’ activities, children’s pools, and family-focused reviews. A romantic hotel should reinforce signals like couples’ stays, intimate atmospheres, spa experiences, and special-occasion packages.
Likewise, a business hotel should emphasize meeting rooms, workspaces, fast Wi-Fi, and proximity to business districts. A restaurant known for exceptional dining should earn reviews, media mentions, and third-party recommendations that consistently reference its food, chef, or culinary experience.
Many businesses naturally fit into more than one category. However, the clearer your primary positioning is, the easier it becomes for generative search engines to identify when your brand is relevant and should earn a recommendation.
The same principle applies to destinations. Generative search engines rely on signals across review platforms, travel guides, local listings, and publisher content when recommending where travelers should stay, visit, or explore.
3 practical ways to strengthen entity signals across platforms
As AI systems become more reliant on entities rather than individual webpages, travel businesses need to focus on creating a clear and consistent digital footprint.
1. Use structured data to clarify business attributes
Structured data helps search engines and AI models interpret key business information. For travel brands, this type of data includes accommodation types, amenities, locations, and other business details.
Highlight the attributes that differentiate your property. That might include family-friendly facilities, wellness experiences, exceptional dining, pet-friendly accommodation, or proximity to major attractions.
The clearer and more structured your information is, the easier it becomes for AI-powered experiences to surface your business in relevant recommendations.
2. Eliminate entity ambiguity across platforms
Review what third-party sources say about your brand across the web. Look for conflicting information. AI search experiences pull information from multiple sources, and inconsistencies can reduce confidence in your brand.
Imagine a hotel with different phone numbers, outdated descriptions, inconsistent categories, or conflicting amenity information across various platforms. This is exactly the kind of ambiguity AI systems struggle with.
Maintain consistent information across your website, Google Business Profile, TripAdvisor listings, and OTA profiles to reduce ambiguity and strengthen confidence in your business data.
3. Prioritize operational business information
Start by auditing your existing customer reviews.
What did they enjoy most about their stay?
What made their experience memorable?
What would they improve?
Constructive feedback is a fast and easy way to identify what truly differentiates your brand from competitors. Details like amenities, accessibility features, opening hours, parking, and pet policies help AI systems answer specific travel queries.
Google Business Profile is another critical source of operational information. The categories, attributes, amenities, and opening hours listed on your profile help AI models answer travel queries with greater confidence.
If you need to provide additional context, Google Business Profile allows you to publish posts linking back to relevant content on your website. Regularly publishing Google Business Profile posts can help drive engagement, profile visits, and customer interactions while keeping your listing updated with fresh content about your products, services, events, and offers.
Generative search is more democratic than traditional search. AI models recommend businesses, not websites. Visibility is no longer shaped exclusively by what happens on your website. It’s shaped by the broader digital footprint your brand has built across the web.
For travel brands, that means thinking beyond rankings and clicks. Reviews, OTA listings, travel guides, media mentions, and business profiles all contribute to how AI systems understand your brand and when they recommend it.
It’s time to be creative, experiment, and build partnerships with complementary businesses. Most importantly, it’s time to build the signals AI systems trust.
Trust in AI search is declining, consumers are validating information across more platforms, and AI visibility is increasingly tied to brand authority rather than traditional SEO metrics.
Those are among the key findings from new research by Fractl and Search Engine Land, presented by Fractl cofounder Kelsey Libert at SMX Advanced. The study offers a detailed look at how consumers evaluate AI-generated answers, which signals influence AI recommendations, and where brands are falling short on governance and disclosure.
After the conference, I spoke with Libert to dig deeper into the data. Our conversation covered the growing trust gap in AI search, the role of earned media and entity authority in AI visibility, and why many organizations are still unprepared for the operational challenges AI introduces.
The honeymoon is over
The headline finding from Libert’s research is hard to ignore. In 2025, 82% of consumers found AI search more helpful than traditional search results. By 2026, that figure had dropped to 54%, a decline of 28 percentage points in a single year. The skeptic camp grew sixfold over the same period.
I asked Libert what she thinks is driving that erosion.
“Hallucinations. Initially, AI was a frictionless instant-answer machine that felt superior to Google’s crowded SERPs. As people lost trust in AI answers and had to put in more effort to validate them, that instant gratification disappeared, and the helpfulness score dropped dramatically,” Libert said.
She isn’t entirely pessimistic about where things are headed, though.
“AI is on an exponential improvement scale, so I expect this number to restabilize over the next year as people learn how to refine their prompts and engineers improve the technology,” Libert said.
That restabilization may come sooner than expected. Libert pointed to a June 5 CNN report covering Anthropic’s warnings that AI may soon be capable of improving itself without human intervention. Whether that accelerates the recovery of consumer trust or deepens concern about AI reliability remains to be seen.
In the meantime, consumers are hedging. The research found that buyers now check an average of 2.4 platforms before validating a purchase, a pattern consistent across Gen Z, millennials, and boomers.
Google still leads AI tools three to one for trusted product recommendations, commanding 39% of consumer trust versus 14% for AI tools. Reddit, at 15%, sits between them.
For SEOs worried about the erosion of organic traffic, Libert’s framing offers a more nuanced picture than the typical doom narrative. About 50% of marketers report traffic declines since AI Overviews launched, and 61% directly blame AI tools.
At the same time, 57% see traffic growth from social platforms, including TikTok, Reddit, and YouTube, and 40% see growth from AI assistants such as ChatGPT and Perplexity.
The channel map Libert presented at SMX is worth understanding.
Google remains dominant at 84.8 billion visits, serving primarily as an intent-capture engine.
YouTube and Instagram/TikTok together handle brand discovery.
ChatGPT and Gemini are used primarily for research and learning.
Facebook and Reddit serve human-validation functions.
Search isn’t disappearing. It’s fragmenting, and brands that optimize for only one of these channels are leaving significant visibility on the table.
The GEO hierarchy: Table stakes, high risk, and the moat
Libert’s research categorized generative engine optimization tactics into three tiers, and the distinctions matter for how marketers should allocate effort.
The most commonly used tactic is FAQ optimization, employed by 49% of marketers. Libert calls this high risk, and the reason is straightforward.
“The high-risk category is based on how easily AI can replicate that content, and general industry FAQs are typically pretty easy for AI and your competitors to produce.”
FAQ strategies can be strengthened by layering in proprietary data, subject matter expertise, and unique insights, but on their own, they offer little defensibility.
Table-stakes tactics include building brand mentions (43%), establishing topical authority (36%), and implementing structured data (30%). These are necessary but not sufficient.
The moat, as Libert describes it, consists of original data and proprietary studies (35%) and digital PR (24%). These are harder for AI to replicate, which is precisely what makes them valuable.
“LLMs and Google need fresh content to pull into timely and common RAG queries. Beyond that, data journalism and digital PR efforts help increase your brand’s entity authority by helping you earn brand mentions across influential sources across the web,” Libert explained.
The signal strength behind this approach is backed by Ahrefs research analyzing roughly 75,000 brands across ChatGPT, AI Mode, and AI Overviews.
Branded web mentions and YouTube impressions showed the strongest correlations with AI visibility, ranging from 0.50 to 0.74 on the Spearman scale.
Backlink count and ad spend fell in the weakest tier, below 0.30. The practical implication, as Libert put it, is that “AI systems reward brand presence and mentions more than traditional SEO scale metrics.”
I asked Libert what she would do first if building brand mentions from scratch. Her answer was tactical and specific:
Use Semrush or Ahrefs to identify high-authority, niche-relevant publishers that have covered competitors, build relationships with those journalists, and pitch concepts that fill gaps in their coverage using proprietary data or subject matter expertise.
Use SparkToro to identify the publishers, YouTube channels, and podcasts a target audience actually consumes, then prioritize earned placements in those venues.
Use Reddit to study what performs well in relevant subreddits and contribute substantive commentary or content, not promotional noise.
“Earned media always comes back to one principle. Focus on creating fresh, educational, actionable, valuable, and newsworthy industry insights, and repurpose that content across the channels of influence for your target market,” LIbert said.
One of the most striking data points in Libert’s presentation involves the gap between what consumers expect and what brands actually do.
Between 84% and 91% of consumers say they want AI labeling across all content formats, including text, video, audio, and images. Only 20% of organizations say they always disclose their AI use. One-third never disclose at all.
Libert doesn’t think consumers are simply opposed to AI in marketing. The concern is more specific.
“Consumers aren’t wary about AI use in marketing. They’re concerned when brands use it for their entire marketing workflow with zero checks and balances.”
She cited the research finding that nearly half of marketers admit to not fact-checking their AI-produced content and that 48% say AI makes their work faster but more average.
Dove came up as one of the few brands actively positioning against what Libert calls “AI-dominant marketing slop,” though she acknowledges the field is still mostly treating disclosure as a compliance checkbox rather than a brand signal.
There is also a deeper question about where AI assistance ends and human creativity begins, one the industry hasn’t settled.
“There’s some debate on when it needs to be disclosed, since plenty of people use it more as an assistant in the creative process vs. having it do everything end-to-end,” Libert said. “In that case, who truly owns the creativity, and what level of disclosure is required? That’s still up for debate.”
The inverted pyramid problem
The slide that drew the most reaction in the SMX room showed how marketers are allocating their human review time for AI-generated content. Editorial review claimed 72% of attention. Voice and tone review claimed 62%. Fact-checking fell to 54%, plagiarism and legal review to 42% each, and bias evaluation to just 27%.
Libert called this an “all of the above” problem, spanning training gaps, workflow fragmentation, and leadership priorities.
“When employees can’t keep up with the basic learning process to execute their workflow effectively with AI, how can you expect them to also focus on the checks and balances of AI’s output without the proper support from leadership to take time to develop and refine entirely new workflows?” she said.
“We’re focusing on the surface-level review of AI’s output because that’s all people have the capacity and proper support for. That data point is a huge leadership SOS.”
Her core argument here aligns with the most pointed line in her presentation: fix the infrastructure before you scale the output. AI won’t kill credibility. Untrained oversight will.
What smaller brands can actually do
The closing argument of Libert’s SMX talk, that AI rewards brand equity rather than creating it, raises a fair concern for newer entrants who lack years of accumulated authority. I pushed her on this.
Her answer was more optimistic than the headline suggests. AI Overviews aren’t simply surfacing the top search results. They are surfacing brands that have built genuine authority around niche topics.
“Younger brands can still compete by focusing on building out their entity authority around the long tail, where most conversions actually live,” she said.
She also sees an opening created by the same corporate inertia that slows large-brand adaptation.
“Plenty of larger brands are stuck in more corporate bureaucracy and are slower to adapt to the changes and opportunities of using AI to scale insightful data analysis and thought leadership content. There’s actually plenty of room for smaller brands to compete, now more than ever.”
The caveat?
“It’s a matter of slowing down and learning how to use AI effectively, not scaling AI slop.”
The 2026 AI search playbook that Libert presented at SMX distills to four imperatives:
Monitor brand representation across all influential platforms.
Build entity authority through original research and subject matter expertise.
Triangulate visibility across search, video, social proof, and trusted media.
Govern AI use with formal disclosure and review processes rather than ad hoc workarounds.
None of this is particularly complicated in concept. The difficulty is organizational.
The brands that treat credibility as infrastructure rather than aesthetics are most likely to be cited, recommended, and trusted as AI search continues to mature.
Claude may be more directly tied to Brave Search rankings than other AI answer engines, according to information Jonathan Clark shared on LinkedIn from a Zero Click by Profound session.
Clark, managing partner at Moving Traffic Media, said the session’s key takeaway was that Claude “doesn’t re-rank search results” and instead appears to use Brave’s top 10 results directly in its answers.
Claude searched less often. Claude used web search in 36.6% of prompts, compared with about 90% for ChatGPT, according to Clark.
Claude was most likely to search when prompts signaled freshness, rankings, location, or comparison intent. Recency-focused prompts such as “best XYZ” triggered search 81% of the time, while ranking-focused prompts triggered search 67% of the time.
Location-focused prompts triggered search 55% of the time, while comparison prompts such as “X vs. Y” triggered search 51% of the time.
Brave rankings carried weight. Claude’s citations overlapped with ChatGPT’s in only 8% of cases when responding to the same prompts, according to Clark.
Claude’s results had much higher overlap with Google rankings, at 64%. This suggests that Google SEO efforts may carry over more readily to Claude than strategies focused specifically on improving visibility in ChatGPT, according to Clark.
The finding also increased the importance of Brave rank tracking. Clark said Claude uses Brave, and that ranking well in Brave gives us “something we can monitor and correlate to data.”
Some prompts stayed in memory. Prompts such as “how does,” “what is,” and “steps to” were less likely to trigger Claude to search the web. When Claude doesn’t search, it can’t cite web pages. Claude searched most often for prompts containing terms such as “best,” “top,” “near me,” and comparison-style queries, according to Clark.
Years showed up often. Clark also noted two patterns that could make Claude easier to test:
Claude’s query fan-outs were nearly deterministic, producing the same fan-out 65% of the time across users.
The fan-outs often included years.
That means page titles with current-year signals may have an advantage in Claude-triggered searches, especially for ranking and freshness-driven prompts.
Why we care. Claude visibility appears to depend more heavily on ranking in the search results Claude uses. Clark’s takeaway was that Claude may be one of the most optimizable AI answer engines today because its search behavior appears more consistent and more closely tied to observable search rankings.
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