For a long time, SEOreporting revolved around dashboards. When a meeting was on your schedule, you’d spend your day preparing by exporting data from Google Search Console, cleaning it in spreadsheets, and layering charts into Data Studio.
Now, AI coding agents are changing that workflow. Instead of the manual work that would previously take hours, you can use tools like Claude Code to surface customized data with polished visuals in just minutes.
Here’s how to turn Google Search Console data into custom reports and speed up your reporting workflow.
What Claude Code can do with GSC data
Claude Code isn’t the same as using Claude in a browser tab. The standard Claude.ai interface works like a regular chatbot. Claude Code, on the other hand, is Anthropic’s terminal-based AI coding assistant.
It still feels conversational, but instead of living in a browser tab, it can interact directly with files, folders, spreadsheets, and scripts on your machine. It can read exported GSC CSV files, process large datasets locally, generate charts and summaries, analyze trends across pages and queries, and ultimately create structured deliverables from raw data.
Claude Code isn’t simply generating text responses like a chatbot. Instead, it’s creating a local reporting environment that behaves like a lightweight software project.
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There’s a learning curve
Before you can start building beautiful, custom reports, you’ll need to set up Claude Code. If you’re not an engineer or developer, this process can feel overwhelming at first. There is a learning curve, but don’t give up.
Setup is actually the most time-intensive piece of the process, but it’s a one-time process. Depending on your technical experience, the initial setup may take a couple of hours.
The “reports in minutes” concept really applies after the environment is configured. Once you’re past the initial setup and Claude is connected to GSC, you can run any custom SEO report you want in a matter of minutes.
If you’re in an enterprise environment, this setup process can go faster with a little help from the tech team. If you’re an agency or an SEO consultant, you can always lean on the expertise of in-house developers or engineers or an outside contractor.
Getting started
If you don’t already have one, create an account atClaude.ai. You can sign up with Google, email/password, or enterprise SSO.
Most SEOs using Claude Code for reporting have a paid plan or use Anthropic API access. But you can use a free plan at the time of writing.
Install Node.js
Claude Code runs locally on your machine, so you’ll first need Node.js installed. You can also use it on a Chromebook by activating the Linux subsystem.
For the purposes of this tutorial, I used a Mac.
Next, download the current LTS (Long-Term Support) version. Once installed, you’ll have access to npm, which is used to install Claude Code.
To verify the installation, open Terminal (Mac/Linux) or PowerShell (Windows) and run:
node -v npm -v
If both commands return version numbers, you’re ready to continue.
Install Claude Code
Next, install Claude Code globally:
npm install -g @anthropic-ai/claude-code
Once the installation finishes, start Claude Code by running:
claude
The CLI will walk you through authentication and connect to your Anthropic account. After that, Claude Code can work directly with local project folders containing exported SEO data, scripts, spreadsheets, and reporting templates.
At this point, you’ll be able to interact with Claude Code in the terminal using commands much like you would with an AI chatbot.
To kick off the workflow, I gave Claude a prompt:
“I have a marketing meeting coming up, and I want to show our performance from Google Search Console.”
One benefit is that Claude now becomes an onboarding assistant. Claude will ask a handful of clarifying questions to get started. For example, during the setup process, Claude asked:
Whether to use a service account or OAuth credentials to access the Google Search Console API.
Which reporting views or marketing priorities mattered most.
Where the reporting project should live locally on the machine.
Which Google Search Console property to connect to.
Claude also asked where the reporting project should live locally.
(As an aside, we prefer to store it inside a dedicated code directory rather than a standard Documents folder because development projects can sometimes run into file permission or syncing issues when stored inside cloud-synced folders like Documents or Desktop.)
Next, I established how the visuals will be built before connecting to GSC.
We like using Observable Framework, an open-source framework for building data apps, dashboards, and reports.
You don’t necessarily need to follow this exact structure; Claude Code is highly customizable, and you’ll settle into what works for you.
And remember: if you’re unsure about any next steps, you can just ask Claude, and it will help guide the setup.
Connecting to GSC
Before Claude Code can start generating reports from live GSC data, you’ll need to connect it to the Search Console API.
This is another technical part of the process, but the good news is that Claude can walk you through much of the setup interactively.
To establish the connection, you’ll need to create a Google Cloud Project (GCP) and configure API credentials.
That setup process typically includes:
Creating a Google Cloud project.
Enabling the Search Console API.
Generating OAuth credentials or API secrets.
Adding those credentials to a local environment file.
In larger organizations, your IT or development team may already manage this infrastructure.
If not, you can still configure it yourself using a standard Google account or Google Workspace account.
Generating reports
Once you’ve finished connecting to GSC, congratulations! You made it through the hardest part. Once setup is complete, your reporting process changes entirely.
You can now focus on the reporting views you want to create, such as:
“Show me the top 10 landing pages that gained traffic this month.”
“Create a chart of declining nonbrand queries over the last 90 days.”
“Compare CTR trends by device type.”
“Show me the top-performing pages from New York last month.”
Claude is now like an on-demand reporting assistant. You simply open the project folder, launch Claude Code, and ask for the charts you need.
In addition, you can be more dynamic in your meetings.
Instead of building a rigid dashboard ahead of time and hoping stakeholders ask predictable questions, you can generate new views dynamically as questions come up.
That means you can walk into a meeting, ask Claude for a completely new chart or segmentation, and generate it in minutes rather than rebuilding an entire dashboard manually.
Now let’s look at some reports you might quickly run before your next meeting.
Here’s an example of a custom SEO performance dashboard generated from Google Search Console data.
While some of these metrics are available inside GSC, building your own report gives you much more flexibility in how trends, comparisons, and supporting metrics are visualized together.
You could also generate a bar chart with YoY rankings, or a heat map of rankings for keywords by month. Both examples are below.
What we like to include in our reporting is a combination of scorecards, time-series charts, year-over-year bar chart comparisons, and heat maps that break down the key drivers behind a metric.
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Claude Code completely transforms SEO reporting
SEO reporting has always been a push and pull between speed and flexibility.
Dashboards are fast once they are built, but they are often rigid. Custom analysis is powerful but historically has been time-intensive.
Claude Code changes everything.
Now you can interact with your GSC data more dynamically, explore new questions as they arise, and create reporting views that would have previously taken hours to build manually.
Once the initial setup is complete, reporting becomes far more adaptable to the needs of you and your stakeholders.
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SEO is a white-collar job. So does that mean our jobs will be eliminated, too? The answer isn’t as obvious as you might think.
Yes, the world is changing. But if you’ve been doing SEO for a while, you should be used to that by now.
SEOs have always been forced to wear strange combinations of hats: part technical analyst, part content strategist, part UX researcher, part marketer, and part analyst.
I don’t think AI will make SEO expertise obsolete. But it will make shallow SEO obsolete.
The people who thrive will be the ones who understand search behavior, business outcomes, technical systems, content strategy, analytics, and how to turn all of that into better decisions.
The old version of SEO stopped working years ago
I’ve been doing SEO since before there was a word for “SEO.” Every few years, there’s a viral article declaring that “SEO is dead.” One of the first to catch fire was a 2005 article by Jeremy Schoemaker, repeating something he’d heard from Jason Calacanis.
We know the reality. SEO never died. But over the years, it’s changed a lot.
Look at this screenshot of a Google search for [flowers] in 2007 versus the same search in 2026.
Google’s “flowers” SERP in 2007, when a No. 1 organic ranking controlled most of the visible page.
Google’s “flowers” SERP in 2026, where organic listings compete with ads, shopping results, local packs, AI features, and other search elements.
This example is near and dear to my heart because I wrote that title tag in 2007. I was fortunate enough to lead SEO at 1-800-Flowers at a time when a No. 1 organic ranking meant significant traffic and revenue.
Twenty years later, their team has maintained the No. 1 organic ranking. However, today it’s so buried on the SERP that I wonder whether it gets any clicks at all.
This phenomenon isn’t limited to searches for “flowers.” Search for any competitive head term these days, and chances are you’ll see the organic result buried.
Is SEO “dead”? That really depends on your definition of “SEO.”
If your definition is “getting to the top of Google organic search” by spending your whole day writing title tags, then yeah, SEO is pretty much dead. It has been for a long time.
If your definition of SEO is understanding that people are looking for your goods and services, understanding their needs, answering their questions, and meeting them wherever they go to find information, then your journey as an SEO expert — or whatever you eventually decide to call yourself — is only beginning.
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Why true SEO experts are uniquely positioned to thrive
There’s one phenomenon I’ve noticed with AI, not just in SEO, but across every industry. You might have noticed it too.
On social media, you’ll see a lot of AI-generated videos. The vast majority are silly “look what I can do with AI” videos. You see them, maybe press “Like,” and then forget about them. But the ones with staying power are made by people who understand filmmaking: pacing, framing, lighting, composition, camera movement, editing, sound design, and how to build toward an emotional payoff.
In other words, even though everyone can generate videos with AI now, the differentiator is no longer how “cool” the visuals are. It’s how skillfully creators use AI as a tool to achieve their vision.
There’s an analogous situation happening with SEO and AI. I’ve noticed a lot of people typing simplistic prompts and, like Neo in “The Matrix,” declaring, “I know SEO.”
What these folks don’t realize is that SEO is a lot more than title tags, and it was never just about reverse-engineering search engines. It was always about reverse-engineering the human brain, drawing on knowledge and experience across keyword lists, user behavior, content strategy, technical systems, analytics, persuasion, UX, and business outcomes.
When others are typing simplistic prompts into their LLMs, SEO experts will be having deep conversations with their LLMs, teaching them, challenging them, and finding ways to get the best out of them. Those who excel in this new world won’t be the ones who have all the answers. They’ll be the ones who have the right questions.
While it’s still early, and I’m convinced we haven’t even scratched the surface of ways to use LLMs in SEO, here are just a few ways I’ve been using AI in my SEO work to make it more efficient and effective than ever.
1. Performing SEO basics with unprecedented efficiency and effectiveness
I’m generally not a fan of AI-generated long-form writing. You end up with generic, inauthentic slop that, in the words of Shakespeare, is “full of sound and fury, signifying nothing.”
I predict that a year from now, most people will be able to spot the clear signs of AI-generated copy: not just obvious tells like excessive use of em dashes and repetitive phrasing (“That’s not X … it’s Y!”), but a lack of authentic personality and stories.
Metadata is one of the places where I don’t mind AI assistance because its job isn’t to invent original thought. It’s to compress the page’s value, intent, and positioning into the right format for the right surface.
The big mistake I see people making with AI-generated metadata is that their prompts are far too generic: “Write a title tag for this page.”
A seasoned SEO knows the goal isn’t to create a “pretty title tag.” It’s to create the most effective title tag possible for human, search engine, and AI discovery. It takes into account various search intents, brand positioning, competitor gaps, conversion drivers, and practical space limitations.
AI opens up new opportunities that weren’t practical before. Not many people know that ideally, your title tag, Open Graph tag, and Twitter card should be distinct from one another because they’ll be shown to different audiences on Google, Facebook, and X. And it took me a few tries to remind AI that title tag length isn’t based on character count, but on pixel width.
Those “in the know” will start using AI to generate everything: title tags, meta description tags, OG tags, Twitter cards, and the right structured data.
Someone without SEO experience will write generic prompts and wonder why their perfectly polished title tags aren’t doing anything for them a year from now.
2. Turning SEO recommendations into dev-ready tickets
One “edge” I’ve had throughout my career is the ability to translate vague marketing goals into precise technical requirements developers can actually execute.
But as technology has become more complex, I found myself hitting my own limits. I understood the principles of coding, but had a hard time articulating exactly what I needed developers to do. Googling hardly ever helped because I’d just find high-level articles written by consultants, some of whom clearly didn’t understand it either.
A practical example is modern React or single-page app architecture, where a page may look complete to users while key SEO content is assembled after load from JavaScript rather than appearing as crawlable HTML.
In the past, I might’ve written a vague recommendation like “we need more crawlable content on this page,” forcing my poor developer to figure out what that means.
With AI, I can turn that into a real implementation ticket: grounding the LLM in the site’s tech stack, translating the SEO need into concepts like server-side rendering, hydration, DOM content, and crawlable links, and adding examples, test cases, edge cases, and acceptance criteria.
The point isn’t to become a React engineer. It’s to communicate SEO requirements in a way that developers can execute without forcing them to think too much about it. Trust me, your developer will thank you.
3. Mining GSC, GA4, and Semrush or Ahrefs data for actual user needs
The holy grail of SEO has always been to read your users’ minds and create content that meets their needs. Anyone who’s spent a lot of time with SEO data knows that there are enormous amounts of insights locked within this data. The first problem is unlocking them. The second problem is getting them into a format that will get people to pay attention.
In the past, I would literally lock myself in a room with a giant spreadsheet open on my screen. I’d go through search terms one by one, categorizing and clustering them, and, if I was lucky, end up with a handful of insights days later.
I might start with a list of 30,000 keywords and get through maybe a few hundred before getting completely exhausted. And when I’d present my insights, along with my giant pivot table, to stakeholders, they’d nod their heads, and then everyone would forget about them.
LLMs are changing the game. You can simply upload data from GSC, GA4, and Semrush and Ahrefs, along with your own business and market insights, and then simply ask your LLM questions.
Here are just a few recent examples of analyses I’ve done for my clients. These would once have taken days or weeks. Now I can get to a strong first pass in minutes.
Analyze our GSC keyword data and organize the keywords into topical clusters. Which topics do we clearly have a “right to own” in Google’s eyes?
Review our top competitors and uncover keywords within this topical neighborhood that they rank for but we don’t. What kind of content do we need to “break in”?
Surface GSC queries that get lots of impressions but few clicks. What improvements can we make to our titles, snippets, or positioning to drive more clicks?
Examine organic landing pages that attract a lot of traffic but fail to convert. What is the search intent behind the keywords driving traffic to these pages, and how can we improve conversion?
Find keywords where we’re in “striking distance” of stronger rankings. What additional content do we need to create or adjust to push us to the top?
Analyze the queries people type into our on-site search. What are examples of searches they might perform on Google or prompts they might use in LLMs when looking for this information?
There are literally an endless number of questions you can ask. I didn’t present these as sample prompts because they’re thought starters. While you’ll probably get a decent answer, the real value from AI comes only when you:
Dig deep into specific concepts, pages, and keywords.
4. Prototyping page layouts, content modules, and more
Something else I’ve found LLMs can do really well is generate a solid wireframe of a page or page module that you can pass on to your web designer and developer. But this is another area where the quality of the output depends almost entirely on the quality of your prompt and the context you provide the LLM.
Most people will simply type “design me a web page,” perhaps with a few “wish list” items they’d like to see. AI may produce something that looks “complete” on the surface, perhaps a hero section, a list of benefits, some FAQs, and a call to action (CTA). But when executed, it’ll feel lifeless, generic, and disconnected from the actual business problem.
The better approach is to ground the LLM with as much background information as possible. This doesn’t need to include every SEO report, but rather the ones that provide the highest-quality signals, such as the ones we discussed above: topic clusters, competitor gaps, conversion data, and on-site search data. Add other useful information like sales objections, customer reviews, your brand’s unique value propositions, and a clear explanation of what the page needs to accomplish.
With proper context, AI can help lay out something that transcends a generic landing page. For example, it can propose a strong hero section with suggested wording, recommendations for CTAs, section order, comparison tables, proof blocks, FAQs based on real questions, trust elements, and paths for different stages of intent.
Remember that it works in reverse, too. Upload a screenshot of an existing page, either yours or your competitor’s, tell the LLM what your goals are for the page, and ask it to critique the page.
AI can also open up other SEO opportunities that have previously been roadblocks.
Want to do A/B testing? Tell the LLM the hypothesis you want to test, and have it come up with variants for you.
Want to prototype a simple interactive tool? Provide your requirements, provide the underlying data, and see what your LLM can do.
In some cases, it can go beyond a static mockup and produce a working prototype that a developer can evaluate, harden, and turn into production code.
Your edge as an SEO is knowing what information to feed the model, what problems the page actually needs to solve, and which ideas are strategically useful versus just AI-generated decoration.
The one thing that I haven’t seen AI do very well yet is generate professional-quality design and production-quality code. But everything up to that point is at your fingertips now.
5. Making analytics useful again
As I’m sure it was for many of you, July 1, 2024, was a dark day for me. That’s when Google shut down Universal Analytics and forced us all onto GA4.
Since it was called Urchin, I’d all but mastered UA. Then one day, all of my reports and dashboards were simply gone. And I had no interest in spending another decade on a learning curve just to recreate reports that they’d once given me by default.
But with the arrival of LLMs, you can simply ask the LLM to walk you through building whatever report you want.
The first report I had to re-create was the on-site search report, one that’s inexplicably missing from GA4. I wrote my own prompt to walk me through creating this, but for the purposes of this article, I had ChatGPT write the prompt:
Act as a senior GA4 analytics consultant.
I want to rebuild a useful onsite search report in GA4/Looker Studio. GA4 does not provide the same dedicated Site Search report that Universal Analytics had, but I can use the `view_search_results` event, the `search_term` parameter, and any custom parameters needed.
Create a practical, implementation-ready plan that covers:
1. How to confirm onsite search tracking is working.
2. Recommended event name and parameters, including which should be registered as custom dimensions.
3. How to track searches when the site does not use URL query parameters.
4. The most useful report sections, including:
- total searches
- unique searchers
- top search terms
- zero-result searches
- refined or repeated searches
- searches followed by exits
- searches followed by conversions
- searches by page, device, and user type
5. Step-by-step instructions for building the report in GA4 Explore and Looker Studio.
6. A QA checklist to make sure the data is accurate.
Keep the answer concise, practical, and usable by both a marketer and a developer.
The key to writing these prompts, or prompts that generate prompts, is including the phrase “step by step.” One of the nice things about AI is that it doesn’t judge.
Take as long as you need, ask it to break the setup down into steps as granular as you like, and feel free to ask “dumb” questions. It’ll oblige enthusiastically.
You can imagine what this opens up. One of the classic issues with SEO analytics is that all too often, they’re merely vanity metrics.
Conversions, clicks, impressions, and rankings may look impressive at first, but eventually the dreaded “so what” question will arise. Who really cares if you see impressions and rankings growing like wildfire if your revenue isn’t increasing?
This is where you want to ask your AI to help you tie data to business performance.
Which unbranded keywords are actually driving revenue?
Which are leading to soft conversion goals like email signup, account creation, or pricing page visits?
Which search queries bring in engaged visitors who come back later through brand search, direct traffic, or email?
Again, the sky’s the limit. You can build a report or dashboard to answer just about any question your stakeholders have, provided you’re collecting the right data, and if you’re not, AI can help you create tickets for your web developer to collect that data.
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The work is changing. The need for expertise isn’t.
Like I said, this is only scratching the surface of how AI can help transform the work we do as SEOs.
But let’s get to the question everyone is really asking: Is your job safe?
I don’t have a crystal ball. But one thing is pretty clear to me. Not every SEO job will survive unchanged. Big companies will likely cut roles. Teams will likely get smaller. A lot of tactical work that used to require specialists may be done faster, cheaper, or “good enough” by someone using AI.
If your value is limited to tasks that AI can perform on command, there may be challenges ahead.
But if your value is understanding customers, interpreting search behavior, connecting data to business outcomes, translating strategy into execution, and helping companies become more findable, useful, and trusted, then AI isn’t the end of your career. It may be the best leverage you’ve ever had.
And there’s another reason I’m optimistic. The same AI disruption hitting SEO is hitting every other white-collar profession, too. If large companies do lay off significant numbers of talented people, many of those people aren’t just going to disappear from the economy.
Some will start businesses. Some will finally pursue ideas they’ve had in their heads for years. Some will use AI to build prototypes, launch products, test markets, and create companies in ways that would have required far more capital and staff just a few years ago.
That should give us hope.
Many of the great companies we know today started with little more than a few people, an idea, and the willingness to figure things out as they went. Steve Jobs and Steve Wozniak, Bill Gates and Paul Allen, Mark Zuckerberg, Jeff Bezos, Larry Page and Sergey Brin, Michael Dell, and many others did not begin with massive corporations behind them. They began with ideas, persistence, and the tools available to them at the time.
If they were able to accomplish what they did with their tools, imagine what a new generation of entrepreneurs will be able to do with AI.
Maybe you’ll be one of those entrepreneurs. Or maybe your role will be helping one of them turn their ideas into businesses people can actually discover, understand, trust, and choose.
Either way, the products, services, brands, and businesses built with AI will still need to be found. They will still need to explain why they matter. They will still need to earn attention, authority, and trust.
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Large language models (LLMs) excel at synthesizing enormous amounts of information into personalized responses to plain-language prompts. These responses draw on massive training datasets and are often enhanced with internet searches. The fastest way to influence what LLMs say about your brand is to influence the content they retrieve through those searches.
At Evertune Research, we use the Evertune AI marketing platform to track hundreds of brands across 250 categories across every major LLM. This gives us clear insight into which content AI models cite most often, especially when users ask for brand or product recommendations across industries.
For this analysis, we reviewed the 6,000 most-cited URLs per model across ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overview, and Perplexity for March and April. We found that these models share a key behavior: they heavily cite listicles.
Half of LLMs’ most-cited URLs are listicles
Of the roughly 25,000 unique URLs we reviewed, half were listicles. Across nearly 400 million citations from all models, 63% pointed to listicles.
They’re tightly focused on a single topic, like “best laptops for gamers,” which makes them highly relevant to user prompts.
Their structured format also makes the content easy for models to parse and reproduce.
For brand-related queries, listicles do much of the work for LLMs by comparing products head-to-head on features, price, materials, and more—a format ChatGPT now features prominently in its shopping widget.
Listicles were pervasive across every model we reviewed. They accounted for 40–65% of the most-cited URLs, with Copilot at the low end and Gemini at the high end.
The vast majority of listicles in our analysis featured ranked lists, such as “Top 5 CRM Tools.” Depending on the model, these made up 71% to 86% of listicles. Unranked lists, such as “7 Ways to Save on Groceries,” were a distant second. Institutional rankings (e.g., data-heavy lists like U.S. News & World Report’s Best Colleges rankings) accounted for just 1.4% to 4.7% of listicles.
Corporate, earned media, and affiliate domains were the top sources of listicles in our analysis. It’s worth noting, however, that individual pages may contain affiliate content even when the broader domain does not.
For example, Forbes.com is an earned media domain, but it includes affiliate segments such as Forbes Advisor and Forbes Vetted. It ranked among the top three sources on every model for listicles in our URL dataset.
A word of warning before making listicles the foundation of a GEO strategy: Google has already signaled its intent to crack down on promotional listicles. Simply ranking your own brand No. 1 alongside competitors could also run afoul of a Federal Trade Commission rule that “prohibits a business from misrepresenting that a website or entity it controls provides independent reviews or opinions about a category of products or services that includes its own products or services,” among other prohibitions.
URLs that thrive on multiple models
We reviewed the 6,000 most-cited URLs across six LLMs, which in theory produced a pool of 36,000 URLs. In practice, the dataset contained about 25,000 unique URLs, since many appeared among the most-cited results across multiple models.
Among the models, the three Google Gemini-powered models — Gemini, AI Mode, and AI Overviews — showed the highest overlap. More than half of Google AI Mode’s most-cited URLs also appeared among Google AI Overviews’ most-cited URLs. Gemini likewise shared a large portion of its top-cited URLs with both Google AI Mode and Google AI Overview.
The remaining models also shared the most URLs with Google AI Mode and Google AI Overviews, though the overlap was much smaller. Perplexity shared more than 20% of its URLs with both models, while ChatGPT shared more than 15% with each.
Given the thousands of URLs models cite on any topic, that still represents meaningful overlap. Copilot, by contrast, shared just 4% to 6% of its URLs with any other model.
The URLs that models cite most deviate for many reasons, including model training, sites’ crawl permissions and other factors. Traditional SEO that moves content higher in search results, no matter if the search is by a bot or a person, also plays a role, especially for Google AI Mode and Google AI Overview.
Page components of heavily cited URLs
Our review of the roughly 25,000 URLs heavily cited by LLMs found that these pages typically ranged from 1,000 to 2,000 words, averaged 18 words per sentence, linked frequently, and used structured headings (H2s and H3s) throughout.
Copilot favored the most concise content, typically citing pages with 964 words and 24 paragraphs. Gemini skewed more verbose, typically citing pages with 1,977 words and 53 paragraphs.
Although there’s no cookie-cutter formula for success in AI visibility, we found that the most-cited pages typically included the following components:
GEO takeaways
Each LLM has its own preferences and quirks, and a strong GEO strategy accounts for them. But our analysis of more than 25,000 URLs suggests that some GEO best practices can improve brand visibility and sentiment across models.
All LLMs cite large volumes of highly structured, hyper-specific content, which listicles exemplify. Avoid spammy, self-promotional listicles that Google penalizes, but otherwise aim to create and appear in lists where relevant.
Traditional SEO supports GEO. Pages that perform well in human search results also tend to perform well in bot-driven searches. This is especially true for Gemini-based models.
Pay attention to the page structures most often cited by the model you want to target. Copilot tends to favor brevity, while Gemini responds better to more expansive content. In general, keep pages under 2,000 words, use frequent links, apply strong structure, and include images and lists when relevant.
An AI visibility report tracks how often your brand is cited across AI-generated responses. Think of it as a companion to your SEO reporting, not a replacement for it.
Your tracked prompt set is the foundation of every number Writesonic shows you. If you don’t understand what those prompts cover, you’ll misread your data.
Portfolios organize your tracked URLs by content type. Get this set up early and keep it updated as new content goes live.
Citation data is inherently noisy. A single-period dip rarely means anything. A sustained two-to-three-month trend does.
The Action Center is where the quick wins live. Use it to find pages with citation visibility gaps and start closing them.
Here’s something that should keep marketers up at night: your buyers are researching purchases in ChatGPT and Perplexity, and most brands have no idea whether they’re showing up in those answers.
That gap is exactly what an AI visibility report is built to close. It tells you how often your brand gets cited in AI-generated responses, which pages are driving those citations, and where competitors are outperforming you in the moments that matter most.
Writesonic has one of the more practical toolsets for building this kind of reporting. But I want to make one thing clear: I’m not trying to do a review of the platform. This is a working guide for content teams that need to get this reporting off the ground and want to understand what the data actually means before they put it in front of a client or a leadership team.
Why AI Visibility Reporting Matters for Marketing Teams
Buyers don’t just Google things anymore. A growing portion of them open ChatGPT, type a question, and act on whatever comes back. Salesforce research found that 41 percent of consumers used AI tools as part of their research process in 2024. That number has only grown since.
If your brand isn’t being cited in those responses, you’re losing potential customers.
AI visibility reporting helps you understand not just if you appear, but which topics you’re being cited for, how that’s changing over time, and who’s beating you in the answers your buyers are reading.
Where this fits in your stack matters, too. AI visibility reporting isn’t a replacement for organic search analytics or conversion data, but an added signal. This tells you whether AI systems find your content credible enough to surface. Teams that treat it as a complement to their larger organic strategy get more out of it than those trying to use it standalone.
The two questions it should help you answer: Are we showing up where buyers are actually looking? And if not, what do we fix first?
Understanding Your Prompt Set Before You Report on Anything
Every number in Writesonic traces back to your tracked prompt set. These are the specific questions the platform monitors across ChatGPT, Perplexity, Gemini, and other AI tools to see whether your content gets cited in the response.
Get this wrong, and everything downstream looks worse than it is.
The platform assigns default topic labels to clusters of prompts. Those labels are usually broad. A marketing blog running this kind of reporting might see their prompt topics labeled “content marketing” and “digital marketing.” Both are accurate but they are closely related terms that cover a huge swathe of subtopics. Due to the lack of specificity, you may encounter issues building and reporting on AI visibility if you only rely on the pre-populated topic list.
Here’s what works better: export the full prompt list, drop it into an AI tool, and ask it to summarize the underlying themes, intent types, and audience categories. That same marketing agency’s list of 100 prompts might actually break into much more specific themes, like Organic & search visibility, Paid media & SEM, and Email & conversion.
The screenshot above is a portion of Claude’s output when I asked it to perform this exercise. As you can see, there’s a lot more information here to guide our content reporting (and creation). Not only do we have a clearer idea of the GEO content pillars we’re tracking against, but also the audience and intent for each category.
This type of output influences how you read everything else. If you find that your prompt set skews heavily toward one audience, your citation numbers for content aimed at a different audience will look artificially low. You can’t treat this as losing ground. You’re just being measured against prompts that page was never written for.
The practical rule: only report on content that genuinely aligns with your tracked prompt themes. Flagging low citation share on a page that serves a completely different audience creates confusion in client reports. Know your prompt set first, then interpret your data.
To pull the list, navigate to the Prompts section and use the export option. Fifteen minutes of AI-assisted theme analysis is worth doing before you touch anything else.
Setting Up Portfolios to Track Your Content Over Time
Portfolios are folders. They allow you to organize the URLs you’re tracking by content type so you can report on categories rather than hunting down individual pages every time you pull a report.
Create them early and keep them simple. At minimum, you want separate portfolios for blog posts, core website pages, and comprehensive guides. If your client has distinct product lines or service areas, break those out too.
The part that really matters is the workflow. As soon as a new piece of content goes live, add the URL to its portfolio. Teams that skip this step spend far too much time during reporting cycles searching for pages that should have been tracked from day one. Make it part of the implementation process: publish, review, then add to portfolio.
One thing worth knowing: portfolios aren’t limited to your own content. You can add competitor URLs and track their citation performance in the same view. That’s useful when you need to show a client exactly where a competitor is outpacing them on a specific topic, without having to cross-reference separate reports mid-meeting.
How to Report on a Single Piece of Content
The path is: Overview > Citations > Content Performance. Set your date range and filter by URL slug.
You’ll mainly want to look at Citation Count or Citing Answers, which are how many times that page was cited across all tracked prompts in the selected period.
If you look at Citation Share, the number may appear small. That’s because this view measures a single page’s citation contribution across your entire prompt set, not just the prompts that are relevant to what the page covers. A tightly focused blog post will naturally have limited citation surface area relative to the full prompt universe you’re tracking.
Second, pay attention to the prompts the page is and more importantly, is not being cited for. You can see the full prompt set by clicking on the number in the ‘Answers citing your content’ tab. In this case, I clicked on the 100.
You’ll then be taken to the All Prompts & Answers view, where you can see which prompts and platforms are surfacing your content and which ones are not.
If a page is ranking well for some prompts but missing others that closely match its content, those gaps are actionable. Adding a structured FAQ section or a more direct answer to a specific question can sometimes close them — and that’s something Writesonic can help you generate.
Third, be careful with month-over-month comparisons. A single dip is not a signal. LLM citation patterns shift constantly as models update and competitive content changes. Before treating a decrease as a problem, remove the comparison period and look at a three-to-four-month trend line instead. A trough followed by recovery is a very different story than a genuine sustained decline.
When you do see a real downward trend, don’t touch the content first. Cross-reference with your SEO data and generative engine optimization metrics. Often, the issue is external, like a model update, and editing the content won’t fix it.
Reporting Content Categories with Portfolios
Another useful feature inside Writesonic is the ability to report on content performance at the portfolio level, not just the page level.
To access it, navigate to Overview > Page Tracker > Portfolios. If you’ve organized portfolios by content type, topic cluster, service area, or funnel stage, this view gives you a meaningful way to evaluate how a group of pages is collectively performing in AI-generated answers.
This matters because page-level reporting only tells you so much. When you’re managing a content program at scale, you need to be able to say, “our informational content about hotel amenities is being cited regularly” or “our location-based pages are getting picked up but not driving brand mentions.” Portfolios let you have that conversation at the category level, which is how most content strategies are built and how most stakeholders think about performance.
Two metrics worth understanding here are citation share and visibility contribution.
Citation share tells you what percentage of all AI answers cite at least one page from that portfolio. Think of it as reach for that content category. A 1.6% citation share, like the example above, means those pages appeared in roughly 660 out of 40,000 tracked answers. Reported at the portfolio level, this becomes a concrete benchmark you can share: how often AI tools are drawing from this type of content, and how that’s trending over time.
Visibility contribution is a layer deeper. It measures the percentage of your brand’s total AI visibility that comes from pages in that portfolio being cited alongside a brand mention. It tells you which content categories are driving brand recognition in AI answers, not just traffic or citations. A portfolio with strong visibility contribution means your content and your brand name are appearing together in AI responses, which is the outcome you’re optimizing for.
Together, these two metrics help you go beyond vanity reporting and start answering the questions clients and stakeholders actually care about: Is this content working? Are people seeing our brand name? Which content categories should we double down on, and which need attention?
If a portfolio has solid citation share but low visibility contribution, AI tools are referencing those pages frequently but not associating them with your brand. That’s a signal to look at how clearly your brand is represented within the content itself. If a portfolio is underperforming on both, that’s a prioritization conversation. And if a portfolio is driving strong numbers on both, that’s proof-of-concept worth scaling.
Understanding Volatility: What’s Signal and What’s Noise?
LLM citation data is noisy by nature. This isn’t a Writesonic-specific problem. It’s how these models work. AI citation drift, where sources shift in and out of responses as models retrain, re-rank sources, or adjust sampling, has been documented across platforms. Research from SISTRIX shows citation sources can change significantly week over week, even when the underlying content is untouched.
One data point tells you almost nothing. The question is always whether you’re looking at a trend or a snapshot.
For example, look at the graph above. This shows the number of citations a page has over a two-month span. As you can see, there are several peaks and valleys, even within the span of a few days. However, if you were to draw a trend line, the result would be relatively flat and even increase a bit towards the end of the second month.
That’s why it’s important to remember that a one-period decrease is not a call to action. A consistent downward pattern over two to three months is worth digging into. Before you touch any content, pull SEO performance and AI Overview impression data for the same window. If organic traffic is stable and AI Overview appearances are flat, the Writesonic dip is most likely a model or sampling artifact.
This is worth saying explicitly to leadership and clients. AI visibility reporting is newer and messier than traditional SEO reporting. Setting that expectation upfront builds credibility. Trying to explain unexpected volatility after the fact does the opposite.
What Writesonic Can’t Tell You
Transparency on limitations makes reporting more credible, not less.
As mentioned earlier, Writesonic tracks a defined prompt set, not every AI query relevant to your category. Your citation numbers reflect performance within that sample. That distinction matters when someone asks why results look lower than expected. The tracked set may simply not cover the full range of queries where your content performs well.
Other things to be aware of include:
Prompt volume isn’t search volume. AI platforms don’t publish query data the way Google does. Estimating how many times people search specific prompts in platforms like ChatGPT requires multiple data sources, a scoring methodology, and sampled user data. That means LLM prompt volume should always be taken with a grain of salt, no matter what AI visibility platform you’re using.
Citation change versus buyer behavior. A drop in citations might reflect a model update or a competitor adding a stronger page. It doesn’t necessarily mean fewer buyers are encountering your brand. Separating those two things requires additional data sources like conversion tracking, qualitative research, or broader competitive analysis.
Competitive visibility outside the tracked set. You can see how competitors are performing within your prompt set. You can’t see how they’re performing in AI queries you aren’t tracking at all.
For each gap, the fix is the same: layer in additional signals. Use organic performance, GEO and AEO analysis alongside broader competitive research to paint the full picture. Writesonic works best as one input among several, not as a standalone source of truth.
Using Quick Wins to Improve AI Visibility Now
The Action Center is where the most immediately actionable reporting lives. Navigate to Action Center > Boost Content Visibility > Refresh existing content for AI visibility to find existing pages where competitors are being cited more often than you for the same prompts.
These are your quick wins. The pages themselves usually aren’t the problem; they’re just missing specific structural elements that AI models tend to pull from. Common recommendations from the platform include FAQ sections, comparison tables, and explicit key takeaway sections. These signal to large language models (LLMs) that a page directly answers a specific question and improves your chances of being cited.
Writesonic will generate draft versions of those elements for you. Use them as a starting point, not a final output. Editorial judgment still applies. Not every recommendation fits every page. A conversion-focused product page probably shouldn’t get a sprawling FAQ section that complicates the user journey, even if the data suggests it would improve citation share.
This module is particularly useful at campaign kick-off. Teams can surface concrete page improvements in the first few weeks while the broader strategy is still being developed, giving clients something tangible early.
New Content Opportunities in the Action Center
Beyond refreshing existing pages, the Action Center also identifies topics where competitors are earning citations, and you have no content covering them at all.
Navigate to Action Center > Boost Content Visibility > Create content inspired by competitors winning in AI citations for this view. The recommendations here are about where to create new pages or blog posts, not about tweaking what you have. If a competitor is consistently cited on a topic that aligns with your tracked prompt themes and your site has nothing on it, that’s a real gap in your AI visibility coverage, and a direct input for your content calendar.
Review this section at least quarterly alongside your standard keyword research. The two often point in the same direction.
FAQs
What KPIs matter for executive AI visibility reporting?
Lead with citation share trend direction over a rolling 90-day period, not raw citation counts. Raw numbers require too much context without supporting data. Showing category-level performance for priority topics, plus specific wins and gaps, lands better in executive reporting than a single number that needs a two-paragraph explanation.
How do you create reports showing brand visibility in AI platforms?
Use Writesonic’s Content Performance and Page Tracker views to pull citation data by URL and topic. Present directional trends and be explicit about what your prompt set covers.
How do you report AI search visibility to leadership?
Frame AI visibility as one signal alongside organic search, not a standalone metric. Show specific wins (pages gaining citation share) alongside gaps, and tie recommendations directly to business priorities. Explain volatility upfront so a single-period dip doesn’t derail an entire reporting session.
Where can you find AI visibility reports with sentiment analysis?
Writesonic includes sentiment indicators alongside citation data. You can dig deeper into how your brand is being discussed on LLMs by navigating to Overview, then the Sentiment dashboard under Brand Visibility.
Conclusion
Most teams that struggle with AI visibility reporting don’t have a data problem. They have an interpretation problem. The numbers look strange, the volatility is hard to explain, and it’s difficult to know what to act on.
Writesonic helps with that, but only if you come in with the right expectations. Know what your prompt set covers. Organize your portfolios from the start. Read citation data as a directional trend, not a precise scorecard. Use the Action Center to find the generative engine optimization improvements most likely to move the needle quickly. Teams that build these habits now will be ahead of the curve as AI-driven search grows and the tools mature.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-15 19:00:002026-05-15 19:00:00How to Create an AI Visibility Report with Writesonic
As people increasingly gravitate to generative AI experiences and find information in new ways,
we’re publishing a new resource to help website owners, SEOs, and developers understand how to
optimize their content for appearance in generative AI features in Search, and in turn Google
Search overall.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-15 00:00:002026-05-15 00:00:00A new resource for optimizing for generative AI in Google Search
Keyword volume is one signal, not the full story. It tells you that demand exists, but not where it lives, how it’s being answered, or whether your brand is part of the conversation.
The Ubersuggest keyword tool and Answer the Public now pull data from Google, Bing, YouTube, TikTok, Instagram, and Amazon, giving you a multi-platform view of where your audience is actually searching.
AI tools like ChatGPT and Gemini generate answers, not link lists. Ubersuggest’s AI Search Visibility feature tracks whether your brand appears in those answers and how your visibility compares to competitors.
Ubersuggest’s global keyword data lets you identify regions where demand already exists for your product or service, so you can prioritize expansion instead of guessing.
The highest-value content opportunities sit at the intersection of strong multi-platform demand and low brand visibility. Knowing where that gap is tells you exactly where to focus.
Search is no longer a single-channel game. For a long time, SEO meant one thing: get found on Google. But Google’s own SVP Prabhakar Raghavan noted that roughly 40 percent of young people now turn to TikTok and Instagram for searches instead of Google, a number that’s only likely to grow over time.
Add ChatGPT, Gemini, YouTube, and other rising channels on top of that, and the picture becomes clear: keyword volume alone can’t tell you where demand actually lives, how it’s being answered, or whether your brand is part of the conversation.
The good news is that Ubersuggest is a great tool to help you adapt to this shift. I’ll cover here how Ubersuggest keyword ideas data actually surfaces, and how to layer multiple signals into a strategy built for the way search works today.
What Keyword Data Actually Tells You (And What It Doesn’t)
Keyword research is still the foundation of any solid content strategy. Search volume tells you how much interest exists around a topic. Keyword difficulty helps you gauge how competitive that space is. Search intent tells you what kind of content actually fits the query. All of that is genuinely useful, and none of it is going away.
But traditional keyword data was built for a world where Google was the only game in town. That world doesn’t exist anymore.
A user today might search “best email marketing tool” on Google, watch comparison videos on YouTube, follow threads on Reddit, scroll TikTok for creator recommendations, and then ask ChatGPT for a final opinion before choosing a product. Each of those touchpoints is a moment of demand. Most keyword research tools only capture one of them.
The practical result: you can have a well-optimized piece ranking on page one for a target keyword and still be invisible to a significant chunk of your audience. That’s not a traffic problem you can fix by adjusting your meta tags.
Two questions worth asking before you build any content plan:
Where does demand for this topic actually live across platforms?
Is my brand showing up when people ask AI tools about this subject?
Ubersuggest addresses both. Here’s how each capability works.
How the Ubersuggest Keyword Tool and Answer the Public Surface Multi-Platform Demand
If you used Answer the Public a few years ago, it was a visualization tool that pulled suggestions from Google Autocomplete. Useful, but limited to one platform.
That’s no longer what it is. Answer the Public (now integrated with the Ubersuggest keyword generator) pulls keyword and hashtag data from Google, Bing, Amazon, YouTube, TikTok, and Instagram. That’s a meaningful shift. You’re not just seeing what people type into a search bar anymore. You’re seeing what they watch, hashtag, and shop for across the platforms where they actually spend their time.
Here’s what that looks like in practice. Enter a broad keyword like “marketing” and select a platform.
Switch to Instagram and you’ll see the hashtags your audience is actively using around that topic. Switch to TikTok and you get a keyword wheel showing what creators and users are searching within the app.
You can also compare how results shift over time, which tells you whether interest in a topic is growing or fading on a specific platform. That matters for content planning. A keyword might have modest Google search volume but strong TikTok traction, which is a signal that short-form video would outperform a blog post for that topic. You’d never see that from Google data alone.
For content teams, this changes the planning conversation. Rather than asking “what should we write?” you start asking “what format and platform does this topic actually call for?” That’s a more useful question, and it leads to content that actually reaches people where they’re searching. For a closer look at using the two tools together, see how to use Answer the Public with Ubersuggest.
The AI Search Layer: What Ubersuggest’s AI Visibility Data Shows You
Multi-platform keyword data covers where demand lives across traditional and social search. AI Search Visibility covers something different: whether your brand shows up when AI tools answer questions in your category.
The distinction matters more than it might seem. When someone asks ChatGPT “what’s the best CRM for a small sales team?” they don’t get ten blue links to evaluate. They get a generated answer. Your brand is either mentioned in that answer or it isn’t. There’s no page-two for AI responses.
This is the core challenge of AI search: it’s not about ranking, it’s about being cited. And right now, most brands have no systematic way to know whether they’re being cited at all.
Ubersuggest’s AI Search Visibility feature is built to solve that. It runs repeated queries across AI platforms, aggregates the results, and gives you a clear, data-backed picture of how often your brand appears in AI-generated responses for your most important topics. One AI response is a data point. Hundreds of responses is a pattern.
The feature surfaces four key metrics:
Brand Visibility %: How often your brand is mentioned across aggregated AI responses for relevant prompts.
Industry Rank: Where you sit relative to competitors in your space.
Top Prompts table: The specific questions and prompts where your brand does and doesn’t appear in AI answers.
Competitor Visibility trend chart: How competitors’ AI presence is changing over time.
A note on variability: AI responses are inherently inconsistent. Ask the same question twice and you may get a different answer, different brand mentions, or a different level of detail. That’s normal, and it’s exactly why aggregating data across hundreds of repeated queries gives a more reliable read than spot-checking a single response on a given day.
One of the most actionable outputs from this feature is the Top Prompts table. It tells you which specific AI search prompts are driving brand visibility in your category, and which prompts your competitors are dominating without you. Those gaps are your content brief.
Ubersuggest’s AI visibility features are built to cut through that noise, aggregating responses at scale so your visibility score reflects a real pattern rather than a single snapshot. This is the piece of Ubersuggest keyword research that most marketers haven’t built into their workflow yet. The window to get ahead of competitors here is still open, but it won’t be for long.
Going Global: Using Ubersuggest Data Across Markets
Expanding into new markets is one of the highest-leverage growth moves a brand can make, and one of the most expensive to get wrong. NP Digital now operates in 19 countries, and that growth wasn’t built on guesswork. It came from identifying where demand already existed and going after the regions with the clearest signal first.
Ubersuggest’s global keyword data makes that analysis accessible without a research team. Type any keyword into the Ubersuggest keyword tool, run a search, and filter by country. You’ll see where search volume for your topic is concentrated across global markets.
The insight here is about prioritization. You don’t need to tackle every market at once. You need to find the markets where demand already exists for what you offer, because those are the ones where content and campaigns can work with the grain of existing intent rather than trying to create it from scratch.
Layer in the city-level targeting from AI Search Visibility and you get a second useful data point: not just where people are searching, but where your brand is (or isn’t) showing up in localized AI responses. A market might have strong keyword volume and competitors with high AI visibility, or it might have strong volume and very little AI presence from anyone, which is a wide-open opportunity. That combination turns global expansion strategy from a gut call into a data-backed decision.
For most brands, the low-hanging fruit is closer than it looks. Start by running your core keywords through the global filter and see which regions surface demand you’re currently not serving.
How to Put It All Together
The data points covered above aren’t meant to live in separate tabs. Here’s how to run them as a single workflow.
Step one: map where demand lives.
Use the Ubersuggest keyword tool and Answer the Public to build a multi-platform picture of your topic. Pull keyword volume from Google and Bing, but don’t stop there. Check TikTok and Instagram data for hashtag and creator trends. Check YouTube for video search volume. Check Amazon if your category has a commerce angle. You’re mapping where your audience is actively searching, not just where you’ve historically published.
Step two: audit your AI search presence.
For the topics where you’ve found strong demand, run them through AI Search Visibility. Which prompts is your brand appearing for? Which ones are competitors owning? The Top Prompts table will show you both. If your competitors are consistently cited for a topic your brand should own, that’s a content and PR gap. If nobody in your space is showing up consistently, that’s a first-mover opportunity.
Step three: close the gaps.
The highest-value content opportunities sit where demand is real and brand visibility is low. Those are the topics to build content around, earn citations for, and develop PR relationships that put your brand in front of journalists and creators who influence what AI models learn over time. Publishing more isn’t the goal. Publishing the right content, on the right platforms, on the topics where you’re currently invisible, is.
This framework is repeatable. Run it quarterly as your AI search visibility data evolves and as platform demand shifts. The brands that build this into their routine workflow will compound their advantage over time. For a broader foundation on getting the most out of the platform, the Ubersuggest guide is the right place to start.
FAQs
How accurate is Ubersuggest?
Ubersuggest pulls from multiple sources, including Google’s keyword planner data, to provide search volume estimates. Like any keyword tool, these are estimates rather than exact figures. For most strategy decisions, they’re directionally reliable. For AI Search Visibility, reliability is stronger because the tool aggregates data across hundreds of repeated AI queries rather than relying on a single response, which smooths out the inherent variability of AI-generated answers.
How does Ubersuggest work?
Ubersuggest combines keyword research, site audit tools, competitive analysis, and AI visibility tracking in one platform. For traditional keyword data, it pulls from search engine databases to surface volume, difficulty scores, and related terms. For AI visibility, it runs repeated queries across tools like ChatGPT and Gemini, aggregates the results, and shows how often your brand appears in those AI-generated responses compared to competitors.
How do I use Ubersuggest for keyword research?
Head to app.neilpatel.com, enter a keyword, and review the volume, keyword difficulty score, and related term suggestions. From there, you can filter by country for global demand data, use the Content Ideas tab to see which topics are already performing well in your space, or switch over to Answer the Public to pull platform-specific data from TikTok, Instagram, YouTube, and Amazon alongside traditional search engines.
Conclusion
To do marketing well in today’s world, you need to optimize for multiple platforms and regions.
SEO is no longer just a “Google” game. You must optimize for YouTube, Instagram, TikTok, ChatGPT, and all the other platforms your users use.
On top of that, you should look to expand globally.
Now, it’s too hard to tackle every country, but go after the low-hanging fruit first. What other countries have demand for your products and services? Those are the countries worth considering to move into next.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-13 19:00:002026-05-13 19:00:00Ubersuggest Keyword Ideas: What the Data Actually Tells You
Google is bringing Gemini into Google Ads dashboards, aiming to make data analysis more interactive, visual and accessible.
What’s happening. Google Ads is rolling out a new Dashboards feature that lets advertisers explore performance data using charts, graphs and tables, powered by Gemini.
Users can customise views simply by typing prompts, with the dashboard updating in real time based on their queries.
Why we care. Data analysis in Google Ads has traditionally required manual setup and navigation across reports.
This update shifts that workflow toward a more conversational model, where advertisers ask questions and get instant visual answers.
Zoom in. Dashboards will display key metrics like impressions, clicks, video views and cost, alongside visual breakdowns of performance across devices, audiences and campaign types.
The goal is to give advertisers a clearer, faster way to understand what’s happening in their accounts.
What to watch. How widely advertisers adopt prompt-based reporting, and whether this reduces reliance on custom-built reports and external analytics tools.
Google Discover has publisher profile pages. They live at profile.google.com/cp/ and appear when someone taps a publisher’s name on a Discover card. These pages aren’t new. They launched in August 2025 with the Follow button rollout, and by November 2025 Google’s documentation referred to them as “source overviews.”
For most of the 47,000+ publishers we monitored, the pages are auto-generated: a name, follower count, social links pulled from the Knowledge Graph, recent posts, and a footer label that reads “Profile generated by Google.”
Since March 2026, though, something changed for a small subset of publishers. A group gained access to enhanced profiles: custom banner images, a configurable links shelf, and the ability to pin posts (labeled “Pinned” in the publisher interface, formerly “Featured Posts”).
They also gained control over the order of their social links, website, and content tabs — something standard profiles don’t allow. On standard profiles, social links are sorted algorithmically by follower count, with the website listed last. On claimed profiles, the publisher decides.
The “Profile generated by Google” label also disappeared entirely, replaced by nothing — a quiet signal that the profile had been claimed.
There’s no public documentation explaining how to get access. No Search Console toggle. No application form. Google appears to have hand-selected participants for what is effectively an invitation-only pilot program.
We identified 54 publishers in this cohort. All are U.S.-based. All publish in English. And what they have — and haven’t — done with the feature over two months of monitoring reveals patterns every publisher should watch before the program scales.
How we found the 54
Our Profile Features Monitor tracks 46,926 publishers across seven languages: English, French, German, Italian, Spanish, Dutch, and Portuguese. To isolate the enhanced cohort, we filtered for publishers that showed persistent enhanced-profile signals across multiple snapshots: active links, full banner headers, or both.
The result: 54 domains with stable access to the enhanced profile surface. The composition of that group offers clues about Google’s intentions:
Tier
Publishers
Examples
National
15
WSJ, Fox News, NY Post, Newsweek, Inquirer
Regional Paper
13
Boston Globe, SFGate, CT Insider, Times Union
Local TV
14
KTLA, PIX11, MyFox8, WSMV, Atlanta News First
Lifestyle Brand
6
Delish, The Dodo, Country Living, House Beautiful
Specialty
6
Pew Research, The Athletic, Gothamist, Civil Beat
The skew toward local news and community publishers is striking and aligns with Google’s public emphasis on supporting local journalism. Nearly half the cohort — 27 of 54 publishers — consists of regional newspapers and local TV stations. National brands are included too, but they’re not the majority.
The two-tier profile system
Under the hood, Google operates two distinct profile architectures. Understanding the difference matters because this isn’t just a cosmetic upgrade. It’s a structural split.
Standard profile (99.9% of publishers):
Auto-generated from public sources.
“Profile generated by Google” label visible.
No publisher control over content or layout.
Claimed profile (the 54 publishers):
No generation label.
Publisher can configure the banner, links shelf, and pinned post.
Publisher controls the order of social links, website, and content tabs (standard profiles sort them by follower count).
This isn’t Search Console verification, structured data markup, or any existing publisher tool. It’s a separate, invitation-only system.
What the 54 publishers actually did
This is where it gets interesting. Access to a feature and its effective use are different. Here’s what the data shows across each configurable surface.
Forty-one of the 54 publishers uploaded a banner image. The remaining 13 have the capability — a “prepared” state — but haven’t used it yet.
What stands out is the production quality. There are no amateur banners in the cohort. Every uploaded image reflects clear professional design investment.
Five distinct visual archetypes emerged:
Brand-pattern: No photography, just the wordmark or abstract identity repeated as a tile. Pure prestige.
Tier predicts archetype. National publishers cluster around brand-pattern banners. Local outlets lean into civic identity and city imagery. Lifestyle brands showcase their content directly.
One anomaly: The Athletic uploaded a solid black square — 656×656 pixels. Whether that reflects deliberate minimalism aligned with The Athletic’s dark UI or simply a broken upload is unclear. It’s the only non-image banner in the cohort.
The format split is revealing: 71% used square banners — likely Google’s recommended ratio — while 29% used wide landscape formats. None used portrait layouts. Based on CDN serving patterns, the minimum recommended resolution appears to be 512 pixels on the longest side.
Publishers that chose wide formats made deliberate design decisions: SecretNYC uses a manifesto-style collage, the New York Post uses a headline grid, and Barron’s uses a geometric pattern. Square appears to be the default safe option.
Links: local TV dominates, nationals ignore it
Thirty-three of the 54 publishers enabled the links feature. Of those, 31 added at least one link, for a total of 65 configured links across the cohort.
The content is overwhelmingly focused on on-site navigation: 85% of links point to the publisher’s own sections, weather pages, live streams, or app downloads. This functions more like a mini site navigation layer than a promotional surface.
The tier gap is enormous:
Local TV: 31 links across 14 hosts (average 2.2 per publisher). Fox affiliates consistently shelve: Watch Live, Weather, Local News, Sub-region, Contact.
National: 9 links across 15 hosts (average 0.6 per publisher). Most nationals didn’t bother.
Three outliers worth noting:
PIX11 published “How to make PIX11 a preferred source on Google,” meta-promoting Discover follows from within the Discover profile itself.
Gothamist funneled donations through `pledge.wnyc.org` with a purpose-specific utm_campaign=discover-profile tag.
Fox Nation placed a direct subscription conversion link (“Subscribe to Fox Nation”) on what most publishers treat as a navigational surface.
Pinned posts (formerly Featured Posts): capability granted, rarely used
Fifty-two of the 54 publishers enabled the Pinned feature. Only 13 currently use it with an active pinned post.
Lifestyle brands were the strongest adopters: five of six had the feature active. Among national publishers, only 2 of 15 used it. The capability exists across nearly the entire cohort. Adoption does not.
About text: Wikipedia out, self-branding in
On standard profiles, the “About” section is auto-generated by Google, usually sourced from Wikipedia. On claimed profiles, publishers write their own.
Within the cohort, 38 of 54 use a custom-written description, while only 16 retain a Wikipedia-sourced version — a surprisingly low number for publishers of this size and prominence.
The tone splits cleanly by publisher tier.
Local TV stations lean promotional (“Your trusted source for breaking news, accurate weather forecasts and local sports across Greensboro…” ).
National and digital-native publishers stay more factual (“Gothamist is a website about New York City news, arts, events and food, brought to you by New York Public Radio”).
One publisher takes a mission-driven approach: Delish — “you don’t have to know how to cook, you just have to love to eat!”
The implication for publishers preparing for this feature: once you claim the profile, you take control of the About section. It becomes your pitch on a Google-owned page.
Notably, the most visible publishers in the cohort chose factual descriptions over promotional copy.
UTM tracking: the blind spot
Only three of the 65 configured links include analytics parameters. Gothamist tagged its donation link with utm_campaign=discover-profile, making it the only publisher in the cohort treating the profile as a measurable acquisition channel.
The Philadelphia Inquirer instrumented two links, but one reused an Instagram bio campaign tag (mktg_acq_ig_organic_bio_offer), meaning Discover traffic from that link will be misattributed to Instagram in analytics.
The other 62 links have no tracking at all. In practice, 95% of the cohort has no way to measure whether profile links generate traffic.
Social platform priorities
On claimed profiles, publishers control the display order of social links and content tabs. Standard profiles don’t: Google sorts links algorithmically by follower count and places the website last. That means the ordering we observe on claimed profiles reflects deliberate editorial choices, not algorithmic defaults:
Local TV stations list Facebook first: 86% (12 of 14). Zero list X/Twitter first.
National publishers spread their bets: Facebook 33%, Instagram 20%, X 20%, YouTube 13%.
Concrete examples: Newsweek places YouTube first and Articles second. Delish leads with Website, followed by Instagram. These are active editorial decisions about which audience channel matters most.
The local TV finding is particularly notable. Despite news media’s historical reliance on X/Twitter, not a single local station in this cohort places it as their primary social link.
Sister-site coordination
For media groups with multiple properties in the cohort, setup patterns reveal whether profile management is centralized or handled locally:
Hearst Connecticut, which has five papers in the cohort, shows near-identical configuration across all profiles. The links structure is the same, including a shared Hearst checkout funnel with publication-specific site IDs. The setup points to a centralized digital team managing profile operations across the group. Even so, each masthead still uses distinct banner art.
Dow Jones, across The Wall Street Journal and jp.wsj.com, uses shared banner artwork: the same wordmark tile, confirmed through perceptual hashing. That points to brand coordination at the asset level.
Everyone else Everyone else — including Fox affiliates, Dotdash Meredith properties, and the Fox News group — shows completely different setups across properties, even within owned-and-operated chains. Profile management appears to be handled locally rather than centrally.
The rollout is still active
Comparing snapshots #9 and #12 — taken 19 days apart — confirms this isn’t a frozen experiment. During that window, four publishers added banners (jp.wsj.com, New York Post, SecretNYC, and Everyday Health), one activated Links for the first time (New York Post), and jp.wsj.com (The Wall Street Journal’s Japanese edition) entered the cohort entirely.
No publishers lost features. The program is still expanding within the cohort, and new participants continue to appear.
The adoption paradox
We scored each publisher on a composite 0–6 scale, assigning one point for each of the following:
Banner uploaded
Links feature active
Featured Posts active
At least one configured link
Four or more social platforms listed
Any UTM tracking present
Nobody scored 6. The distribution:
Score
Publishers
%
2
22
41%
3
10
19%
4
14
26%
5
8
15%
6
0
0%
National publishers with the largest audiences are the least engaged with the configurable surface, with a mean score of 2.93. Most uploaded a banner and stopped there.
Local TV stations — despite having the smallest Discover footprints — are the most engaged, with a mean score of 3.57. Lifestyle brands score highest overall at 3.83, yet their Discover visibility trajectory is the flattest in the cohort.
And here’s the critical finding: feature adoption shows no correlation with visibility trajectory.
Across the cohort, the 180-day late/early capture ratio ranges from 0.23x for Prevention — down 77% — to 4.27x for NewsNation — up 327%. Variance is massive within every tier.
KTLA scores high on adoption, with seven links, a full banner, and active profile engagement, and grew 3.69x. But Delish also scores high and declined to 0.90x. MyFox8 configured five links and fell to 0.52x.
Publishers that fully utilized the configurable surface show no better visibility trajectory than those who used it minimally.
This feature gives publishers a controlled surface for branding and navigation, not a ranking lever. It’s a profile page, not an algorithm input.
What this means for publishers
The program is U.S.-only and invitation-only for now. Across the six other language markets we monitor — French, German, Italian, Spanish, Dutch, and Portuguese — we found zero enhanced profile deployments: not a single banner or configured link outside the English-language cohort.
But the underlying infrastructure is already in place. All 47,000+ publishers we track already have profile pages with follower counts, social links, and content feeds. The enhanced features sit on top of that existing architecture. Google isn’t rebuilding the system. It’s selectively unlocking capabilities within it.
If — or when — Google scales this, here’s how publishers should prepare:
Audit your structured data now. Profile social links are pulled from your sameAs/JSON-LD markup. Errors there will carry over to your profile. Verify what Google will display before you’re given control.
Design a banner. Use a square format (1:1 ratio) with a minimum resolution of 512px, and treat it as a professional brand asset. The 54 publishers in this cohort set a clear standard: there were no amateur images. Think about which archetype fits your brand: a wordmark tile for prestige brands, local landmarks for regional publishers, or content-driven imagery for vertical and lifestyle outlets.
Plan your link strategy. The data suggests that section navigation and utility content — weather, live streams, and similar recurring destinations — drive the most engagement. Local TV stations treating the profile as a mini site navigation layer are the clearest power users. Decide now which five to seven links represent your most valuable entry points.
Instrument from day one. Almost nobody in the current cohort tracks profile link performance. Adding a dedicated UTM campaign parameter — utm_campaign=discover-profile, for example — would put you ahead of 95% of the pilot group on attribution alone.
If you’re a media group, decide your operating model. Should profile management be centralized or handled newsroom by newsroom? The cohort shows both models. Hearst Connecticut runs one coordinated setup across five papers, while Fox affiliates manage profiles independently at the station level. The important part is that the choice is deliberate — not something decided accidentally when individual newsrooms start receiving invitations.
Methodology
Data comes from the 1492.vision Profile Features Monitor, which tracks roughly 47,000 publishers across seven languages through recurring snapshots of profile metadata. The 54-publisher cohort was identified through persistent enhanced-feature signals observed across multiple snapshots between March and May 2026.
Visibility trajectories are based on proprietary capture data. All findings are descriptive only: the cohort reflects Google’s selection criteria, not a random sample, and this dataset does not support causal claims about feature impact.
The full analysis — including the complete 10-phase timeline, banner image gallery, snapshot-by-snapshot evolution, and tier-by-tier breakdowns — is available at 1492.vision/research/discover-publisher-profiles-en.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/05/google-discover-publishers-aeLVnD.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-12 17:20:512026-05-12 17:20:51Google quietly gave 54 publishers control over their Discover profiles. Here’s what they did with it.
Google has confirmed a bug with the Discover report within Google Search Console. Google had a data “logging” error that caused a decrease in clicks and impressions for the Discover report between the dates of May 7, 2026 until May 8, 2026.
Google said this is just a “data logging only” and your positioning in Google Discover was not impacted.
The issue. Google again said a data logging issue caused reporting issues with the Discover report between May 7, 2026, and May 8, 2026.
This may have resulted in a “decrease in clicks and impressions in the Discover performance report,” Google posted.
Why we care. There were a number of publishers noticing a drop in clicks and impressions based on this report, keep in mind, if you do also, it is likely related to this reporting bug.
Annotate your reporting and update your stakeholders that May 7 – May 8 data for Discover was broken and should be disregarded.
Won is the gate that pays for everything: the click happens, the recommendation lands, and the agent transacts.
Won has changed beyond recognition in the last 24 months. It used to mean a click on a search result, the human doing the picking, the brand competing for attention against nine blue links. It still means that, sometimes.
It can also mean an assistive engine naming your brand to a user who accepts the recommendation. Or it can mean an Agent transacting on the user’s behalf.
All of this is about delegation: how much we delegate to machines, and when. Delegation in the context of search and AI is far from new. We’ve delegated “finding the books to the librarian” since AltaVista.
What’s new is that the boundary of what and where we delegate is now flexible: the user can hand more of the journey to the engine than ever before, and the brand that wants to win has to be ready for every option on the delegation spectrum.
What hasn’t changed: The point of search
Underneath the three mechanisms sits the same commercial truth that’s been the point of search since Sergey Brin first articulated it: get the user to the best solution to their problem as efficiently as possible.
AI hasn’t changed the point of search. AI assistive engines and agents simply get the user to the best solution to their problem significantly more efficiently than search: a 15-minute purchase journey through ChatGPT that would have taken a week through traditional Search. In essence, AI removes an enormous amount of friction inherent in search.
The delegation boundary is the line between what the user does for themselves and what they hand to the engine. The further the user pushes it toward the engine, the less work the user does, and the faster won arrives. The further the user holds it back, the longer won takes.
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From problem to purchase in 15 minutes with ChatGPT
I’m a double bass player, and I have a huge bass amp in my living room. I also have a guitar my father gave me when I was 18, which I’ve never played much, because I became a professional double bass player in the 1990s, so the guitar has lived as a keepsake for most of its life.
A few months back, I got an offer to play a solo gig at the jazz club opposite my flat. I had a guitar, but no guitar amp, and I didn’t want to buy a guitar amp for one gig. I figured I could just use the bass amp.
Here’s my conversation with ChatGPT:
Me: Can I play guitar through my bass amp? Will I break it?
ChatGPT: No, you won’t break it. But it’ll sound terrible.
Me: How do I make it sound good?
ChatGPT: Three pedals: reverb, compression, equalization.
Me: Which ones?
ChatGPT: Boss, JHS, TC Electronic.
Me: Price?
ChatGPT: Around $250 for the set.
Me: Cheaper. I’m more of a singer than guitarist.
ChatGPT: I can get you there for $125.
Me: I need delivery by Friday, guaranteed. Saturday gig.
ChatGPT: Thomann in Europe, Sweetwater in the US. Both will hit Friday.
Me: Europe.
ChatGPT:[Thomann link]
Me: Clicks. Buys.
The Google team specifically asked me to make this point in the keynote, and I want to land it here too because they care about it for a reason most marketers haven’t worked out yet.
The engine made decisions for me all the way down the funnel. It decided whether the question even had an answer, which pedals were worth shortlisting at which price tier, and who could meet a Friday deadline.
My delegation boundary sat at the perfect click. ChatGPT owned the entire research-and-recommendation funnel. I owned the buy button, and only because Thomann doesn’t yet have an agential checkout. If they had, the agent would have transacted while I was making coffee.
The point isn’t that the funnel compressed from a week to fifteen minutes. The point is what happened inside the compression: the engine made dozens of small decisions on my behalf, each one quietly closing off options that might otherwise have stayed open.
A different engine might have surfaced different brands, recommended a different price tier, or picked a different supplier. The brand that wins isn’t the one the user chose. It’s the one that survived every one of those upstream decisions the engine made before the user ever saw a recommendation.
Two things put my boundary that far to the right.
Emotional weight: The pedals just needed to be good enough, not perfect.
Domain expertise: I’m not a guitarist. I have no opinions about boutique pedal brands. The comparison work a serious guitarist would relish is friction I’d happily have someone else do.
A working professional would have approached the same purchase entirely differently. A studio musician whose tone is their living would have gone into the shop, plugged the pedals in, and decided in the room. A geeky enthusiast would have spent the week on Reverb forums comparing JHS to Strymon to Walrus, because for them, the research is the fun, the comparing is the point.
The point here is that you have the same purchase made by three buyer personas, each with a completely different position on the delegation boundary, and Thomann needs to be ready to win all three.
I’ve bought over €2,000 of equipment from Thomann since.
The single-mode assumption is dead, three modes coexist now
For two decades, “optimize for search” was the whole of the job: get in the top 10 and win the click at each stage of the funnel. Exhausting work, and it’s no longer enough.
That single approach has been replaced with three modes running in parallel. Search hasn’t gone anywhere. It just has assistive and agential sitting alongside as alternatives for all or part of the journey for each use.
Search tolerates a fuzzy or unclear brand because the human will do the sorting.
Assistive tolerates less of it, because the AI is recommending you to the user, and the AI’s credibility is on the line every time it uses your name.
Agent tolerates none of it, because the agent transacts without asking, and a fuzzy brand is exactly the kind of risk the agent will quietly route around, and the user will never know.
Won looks different in each mode.
In search, won is the click, and then the user finishes the journey on your site, and you need to deal with friction (objections, questions, and clarifications).
In assistive, won is the AI engine naming you and the user accepting the name without further verification, and so most of the friction has already been dealt with.
In agent, won is the transaction completed without anyone consulting the user, friction reduced to zero from the user’s point of view.
The pedal-buying journey sat at the assistive end, and could have been at the agential end. The professional studio musician sits at the search end. The geeky enthusiast sits at the search end too, because for him, the friction is the entertainment.
The delegation boundary moves with every purchase, person, and culture
A user might delegate a coffee order to an agent without thinking, ask an assistive engine for advice on a kitchen renovation but make the final supplier call themselves, then spend an hour happily window-shopping for a bracelet that’s “just what I always wanted.” Same person, same week, three positions on the boundary, and all three engines doing useful work.
The diagram shows that one person sets the delegation boundary at completely different positions depending on the purchase. A wedding venue lives at the far left of search mode, because the decision is emotional and irreversible, and you wouldn’t want AI to make that journey for you.
A few notches to the right, still in search mode, you’ve got someone who’s a sock collector and is window-shopping the listings because the choosing is the point. Further right, still in search mode but inching toward assistive, you’ve got the strategic business contract where only the human holds the strategic context that the AI doesn’t have access to.
Cross over into assistive mode, and you’ve got the holiday I’m planning, where I’m asking the AI for advice but cross-checking through search to challenge the results before I commit. A notch further right, still inside assistive mode, you’ve got the kitchen renovation where I’m taking the AI’s advice on what’s possible, but I’m picking the supplier myself, because the supplier relationship is one I’m going to live with for years.
Cross again into agent mode, and you’ve got the holiday I already know: the agent books the cheapest familiar hotel without consulting me, because I’ve stayed there before and it’ll be fine.
Further right, the pragmatic sock purchase where I just need the right pair in the right size by Tuesday, and the brand doesn’t matter a great deal. And right at the far end, the taxi from A to B, where I genuinely don’t think about it, the agent picks the ride, the agent pays, and I get to my destination with zero fuss or thought.
Same person, eight different positions on the boundary. The variability is the whole AAO game: you have to have a strategy that will win the click at every stage across the Delegation Boundary. The boundary tracks risk, human preference, emotional weight, reversibility, and a half-dozen other things.
You have to wrap that into your strategy on top of the cascading confidence, the 10 gates, the entity home, the push-layer entry modes, the framing gap, and the funnel flip this series has been piling up since February 2026.
7 factors tell you where the delegation boundary sits in your category
Score yourself against seven broad factors (and add your own, if you like):
Emotional weight: The more the purchase touches identity, family, or values, the harder to delegate.
Domain expertise required: The more specialized the decision, the more users either delegate fully (because they know they don’t know) or refuse to delegate (because they think they do).
Price relative to income: A $2 coffee delegates easily, a $20,000 car doesn’t.
Purchase frequency: Habitual purchases delegate readily, one-offs need scrutiny.
Regulatory context: Financial, medical, and legal categories carry compliance constraints.
Cultural context: Trust in agents varies by market and demographic.
Evaluate your category and you get a credible read of where your audience sits, how much friction they’re willing to hand to the engine, and where you need to focus your work. You’ve also done something less obvious, but even more important than scoring: you’ve grouped your audience by behavior at the decision moment rather than by demographic or category labels.
For two decades, we built paid and organic strategies based on how humans see the world: by category, by demographic, by geography. In Google Ads, five-star hotels in Bali went in one ad group, hostels in Bali in another, and five-star hotels in Thailand in a third, because that’s how a marketer thinks.
With AI, the engines stopped thinking that way. For Performance Max and AI Max, you absolutely must group by intent to get performance. Five-star hotels in Bali sit in the same cohort as five-star hotels in Thailand, because the intent (luxury accommodation) holds the cohort, and the geography doesn’t.
Gemini thinks in intent cohorts, not human categories, and the same lesson applies across Organic, ChatGPT, Claude, and other LLMs.
What matters here: AI groups by intent, AI builds the cohorts, and the brand optimizing against the old human structure is competing in categories the engines have left behind.
For years, Google representatives have told us that SEO (or assistive agent optimization, the newer discipline that extends SEO to cover assistive engines and agents) is all about intent, and the point they perhaps didn’t make clear enough is: that’s because it’s how AI thinks.
The user delegates, the engine commits
Here’s the point most brands haven’t seen yet: the user and the engine work in tandem, and your strategy needs to account for that.
Nothing happens without the user’s mandate. The agent doesn’t take over because the assistive engine has decided it’s time. The user pushes the boundary by handing the engine a mandate, and the engine acts within that mandate. The delegation boundary isn’t drifting toward the engine on its own: it is being intentionally moved by the user on a case-by-case basis.
Every engine has been delegated to, and delegation forces commitment. We hand search the job of finding the 10 best links for our query, and Search has to commit to which 10. We hand the assistive engine the job of recommending one brand from a comparison set, and it has to commit to which one. We hand the agent the job of executing the transaction, and it has to commit to which transaction.
Different layers of delegation, different consequences when the commitment is wrong, but the act of commitment is universal across the delegation spectrum, because every engine has been handed a job that ends in an answer (even if that answer is giving us a choice of 10).
The algorithms learn at three levels, and they learn differently across the three engines (search, assistive, and agential):
Individual level: What this specific user has accepted, overridden, ignored, repeated. The narrowest scale, fully personalized. Heaviest in Agent mode (where the agent acts on this specific user’s behalf), significant in Assistive mode, negligible in Search.
Cohort level: What users with this combination of intent signals have accepted on average across thousands of comparable journeys. Wider than the individual, narrower than everyone. Decisive in Assistive mode (the engine needs to know “what works for users like this one”), important in Agent mode as a fallback when individual signal is sparse, lighter in Search.
Global level: What the algorithmic trinity has encoded about the brand from the world’s record. Not user behavior but encoded knowledge, stored in three places simultaneously: the LLM as parameters baked into model weights during training, the search engine as the indexed corpus and ranking signals, and the knowledge graph as entity nodes, relationships, and attributes. Three storage layers, three update cadences, three signal types, all encoding the same fundamental thing: what the AI has come to believe about the brand from everything brands publish and everything others say about them.
Three concentric circles. The user at the center. The cohort around the user. The global world around the cohort. The first two layers are about behavior: what users do with you.
The third layer is about knowledge: what the algorithmic trinity has encoded about you across LLM parameters, search index, and knowledge graph.
The brand has direct leverage at the third layer (everything you publish, everything written about you, everything that becomes part of the corpus the trinity reads from) and indirect leverage at the first two (through the experiences that shape what users do).
Important: The third level is the one that compounds the longest. Every won event Thomann has earned across every customer in every cohort over the last 28 years isn’t just training the cohort signal: it’s feeding the global priors that every future model trained on widely available data will inherit.
Brand confidence work done today (and every “today” past and present) compounds into future models on substrates the brand never directly trains. That’s the deeper structural reason systematic effort compounds in the AI era. Train the substrate, not just the signal.
Why the cohort signal isn’t destiny, and where your opportunity lies
If the engines weighted what they had already learned as the only voices that mattered, no challenger brand could ever break in.
The dominant brands would dominate forever, the individual histories, the cohort patterns, and the global priors would lock in, and AAO would be a closed game where only the incumbents win.
Luckily, that isn’t the reality. What the engine has already learned is heavy, but it isn’t absolute. The brand that establishes a stronger claim than the brands the engine recommends and advocates for can break in, and the layers the brand can move are cohort and global. (The individual layer can be influenced through other communications channels, but is effectively closed in the AI engines because they are increasingly walled gardens.)
The route in is to claim, frame, and prove. Make your claim, frame the claim, and prove it with evidence across enough sources that the engine has the corroboration to move you up.
Underneath that work sits the understandability, credibility, and deliverability process:
Understandability built through clear knowledge of who you are and what you do.
Credibility built through N-E-E-A-T-T.
Deliverability built through topical ownership.
Get all three. Once your confidence clears the threshold:
The cohort signal that currently favors your competitor stops being the only voice the engine hears.
The global layer starts absorbing your contribution alongside the incumbents’.
Your claim becomes framed and proved in a way the engine can resolve and prioritize.
That’s the only way an outsider gets in. It’s also the route every dominant brand took to become dominant in the first place.
What wins at won is confidence
When push comes to shove, what decides the outcome at won is the confidence the algorithm has in your brand at the moment of decision.
Confidence at the moment of:
Ranking in search.
Recommending in assistive.
Action in agent.
The mode, channel, and cohort change, but the question doesn’t.
Content and context are table stakes today. Content is what you’ve published, where, in what structure: every brand doing digital marketing seriously has been doing it for years, since Bill Gates wrote “Content is king” in 1996.
Context is the match between your content and the user’s intent at the moment they’re asking. Both are table stakes, so neither differentiates at the recruitment, grounding, display, and won gates.
Confidence is what’s left, and confidence is what we can measure at the bottom of the funnel. Three things, you measure at the bottom of the funnel brand results (the due diligence rabbit hole):
You measure the accuracy of the results about your brand.
You measure the positive sentiment in the results about your brand.
You measure the consistency of the results about your brand across all of the engines.
How right, how positive, how consistent: that is your measurement of confidence across the AI spectrum.
This is exactly where Thomann won my pedal purchase. ChatGPT was confident enough in Thomann’s information to commit to a Friday delivery on Thomann’s behalf, on a Tuesday, with my Saturday gig riding on it.
Hundreds of European suppliers could, in theory, have gotten those pedals to me by Friday. ChatGPT picked Thomann because they had published the stock data, shipping times, warehouse logistics, country-by-country delivery commitments, returns policy, price, and credibility signals, in the kind of structured, accurate, consistent detail that makes an algorithm willing to put its name behind a recommendation.
Confidence work laid down years before the moment ChatGPT needed it was compounded into a single won event in 15 minutes flat.
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Who’s making the decision at won — and when
Map your intent cohorts. Score each one against the seven factors. Work out which mode each cohort will use to make which decision. Then train the seven AI employees (Google, ChatGPT, Perplexity, Claude, Copilot, Siri, and Alexa) to represent you with positive sentiment, accurate facts, and consistent narrative across every one of those moments, because they’re already working 24/7, they’re already talking to your customers, and the only question left is whether they’re recommending you or your competitor.
Untrained employees cost you money every day they’re untrained. Trained employees generate revenue every day they’re trained. Won is the moment you’ve trained them for, or the moment they’ve handed to your competition.
The next question is how you measure all of this, and it turns out my answer rewrites how brands should think about measuring AI-era search-assistive-agential success entirely. That’s the next article in the series.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/05/Search-according-to-Sergey-Brin-UVHCj5.png?fit=960%2C540&ssl=1540960Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-05-12 15:00:002026-05-12 15:00:00The delegation boundary: How AI decides which brands win
