Google announced its latest and greatest AI model, Gemini 3.5 Flash today at Google I/O. Google’s head of Search, Liz Reid, said Gemini 3.5 Flash is Google’s “newest Flash model delivering sustained frontier performance for agents and coding.” She added that is now being used to power AI Mode globally.
Gemini 3.5 Flash. Not only is Gemini 3.5 Flash powering AI Mode in Google Search, but it is also powering the Gemini app, for all users, not just paid users.
For developers, 3.5 Flash is now live in Google Antigravity, Gemini API for Google AI Studio and Android Studio and for enterprise users for Enterprise Agent Platform and Gemini Enterprise.
Koray Kavukcuoglu, CTO of Google DeepMind and Chief AI Architect, said:
“Gemini 3.5 Flash delivers intelligence that rivals large flagship models on multiple dimensions, at the speeds you have come to expect from the Flash series.”
“It’s our strongest agentic and coding model yet, outperforming Gemini 3.1 Pro on challenging coding and agentic benchmarks like Terminal-Bench 2.1 (76.2%), GDPval-AA (1656 Elo) and MCP Atlas (83.6%), and leading in multimodal understanding (84.2% on CharXiv Reasoning).”
“When looking at output tokens per second, it is 4 times faster than other frontier models. Landing in the top-right quadrant of the Artificial Analysis index, 3.5 Flash delivers frontier-level intelligence at exceptional speed — proving you no longer have to trade quality for latency.”
Why we care. Gemini 3.5 is already powering Google Search’s AI Mode and is likely soon to power AI Overviews. It is a step up from the previous AI model and will continue to get smarter and more useful.
It is important for you to see how the AI Mode responses differ from the previous model for the queries and prompts that matter to your site.
Search is changing rapidly and you need to stay on top of these changes.
Google also announced some new agentic commerce features today in Google Search including Universal Cart, expanding Universal Commerce Protocol and Agent Payments Protocol (AP2).
Plus, Google’s Shopping Graph now contains 60 billion product listings, which is up from 50 billion from earlier this year, announced Vidhya Srinivasan, VP/GM Ads & Commerce.
Universal Cart. Google announced what it is calling the Universal Cart, where you can put products and items from multiple retailers into one single Google Universal Cart and check out on all those items with your Google Wallet with the click of a button.
As you are on Google Search, you can add items directly to your Google Universal Cart without having to go to a specific retailer’s website. This will work across Google Search, Gemini, YouTube and Gmail, so just keep throwing items in your cart – across Google interface and retailer and the cart will maintain your list.
Here is a screenshot of Universal Cart showing multiple retailers:
Google will find the best prices and deals, including which retailer has it in stock and let you check out with your preferred retailer.
Plus, Google said Universal Cart will “anticipate your needs and help solve problems before they.” Google’s example:
“Say you’re building your first custom PC and add a few parts from several retailers to your cart. Your cart will proactively flag any product incompatibilities and suggest alternatives. Since the cart was built on Google Wallet, it understands your payment method perks, loyalty information and merchant offers to help you choose. This lets you quickly find opportunities for hidden savings or points without having to remember them yourself.”
Merchants. Google listed a number of merchants that support this, including Nike, Sephora, Target, Ulta Beauty, Walmart, Wayfair, and Shopify merchants such as Fenty and Steve Madden.
Availability. This is available in Google Search and the Gemini app in the U.S. starting this summer and with YouTube and Gmail later on.
UCP and AP2. Google expanded the Universal Commerce Protocol on Google to Canada and Australia in the coming months and in the U.K. later on. UCP will also be coming to YouTube and more Google verticals including hotel booking and local food delivery.
Agent Payments Protocol (AP2) helps agents make payments for you, securely and with accountability, Google said. “Just tell your agent the specific brands and products you want and how much it can spend, and the agent only makes the purchase when your criteria are met,” Google explained.
Google will launch AP2 to Google products in the coming months, starting with Gemini Spark.
The question I get asked most in 2026 is: How do we measure this?
How do we measure whether our brand is showing up in ChatGPT?
How do we measure whether Perplexity is recommending us?
How do we measure whether the work we did last quarter on grounding for AI Mode moved the needle?
Nobody has solved this.
Anyone selling you a clean dashboard for tracking presence in grounding, visibility in display, or action at won across search, assistive, and agent simultaneously is selling you a snapshot view that amounts to a bad best guess.
The standard advice is “track these queries that we think people might ask,” or “track these queries that are a best-guess adaptation of search keywords.”
That advice is unhelpful because prebuilt keyword lists pick queries that are easy to track, map to existing marketing efforts, or would be ideal if the audience were predictable.
The visibility question is right. The precise-number answer it expects is wrong.
The measurement question, as the industry currently frames it, uses the wrong reference discipline. Brands still hunting for the perfect AI-era visibility KPI are hunting for something that doesn’t exist and never will.
The right answer is a methodology that takes its discipline from how economists measure systems too complex and opaque to measure precisely. My methodology is the Funnel Query Pathway, and it does more than measurement. It’s one operational artifact that does three jobs simultaneously: strategy, measurement, and analysis.
Marketers want a number on a dashboard, tracking week over week, tied to a specific query on a specific engine for any user, the way search delivered for 20 years. Search could deliver that number because the surface was finite, the rankings were stable, the click was measurable, and the journey was observable. Assistive and agential surfaces deliver none of that.
We’re operating in a new environment now, and that environment forces us to ask different questions, measure different signals, and act on different proof.
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Why AI visibility is a macro measurement problem
I studied economics and statistical analysis at Liverpool John Moores University, which is why the shape of this measurement problem looks familiar. The same shape shows up whenever a discipline that worked at one scale tries to operate at a scale where its instruments stop applying.
Microeconomics versus macroeconomics is the canonical case. The corner shop measures inventory precisely, the central bank can’t measure inflation precisely, and both disciplines are correct at their scales. Neither discipline’s instruments work in the other’s environment. The discipline I’m proposing isn’t macroeconomics applied to brands. It’s the macro instinct applied to AI-era brand measurement.
AI surfaces are macro for the same three structural reasons macroeconomics had to develop its own discipline.
The first is opacity. The system’s internal state isn’t observable, the way central banks can’t observe every transaction and modern LLMs can’t expose why they decided what they decided.
I call this brand-user-algorithm (BUA) opacity. The user can’t see the alternatives the algorithm rejected, the brand can’t see the journey within the walled garden, and the algorithm can’t fully introspect on why it decided what it did.
The second reason is personalization, the AI-era equivalent of heterogeneous agents: Each user gets a different answer because the engine factors in different context.
The third is the explosion of possibilities, and the explosion isn’t just across the seven engines. The surfaces now include apps (Copilot in Word, ChatGPT inside Slack, Perplexity in Comet), operating systems (Copilot baked into Windows, Apple Intelligence in macOS and iOS), and hardware (Lenovo Copilot+ laptops with a dedicated Copilot key, Samsung Galaxy AI on the phone, and Meta Ray-Bans on your face).
Ambient research becomes a major entry mode. The AI surfaces a recommendation unprompted because it understands the context.
That’s where the funnel query pathway lives. Importantly, it isn’t an evolution of keyword mapping or a pimped-up intent-based methodology. Because it looks at the macro level, it’s a fundamentally different beast.
The unit of measurement is a cohort
Most practitioners running keyword campaigns think they’re grouping queries by intent, but more often than not, they’re grouping by category, which isn’t the same thing as intent. A typical Google Ads campaign would place every Phuket hotel query into one ad group, with the implicit logic that “Phuket hotels” is a logical intent group. It isn’t.
“Phuket hotels” defines the destination. The buyer behind “5-star hotels in Phuket” and the buyer behind “cheap hotels in Phuket” share a destination and have almost nothing else in common: different budgets, decision criteria, conversion paths, and downstream behavior. Grouping them produces an ad group whose performance averages across two cohorts that should never have been combined.
Categories group things. Cohorts group people.
Intent is about people, not things. Google engineers tell me this is the most common mistake they see in AI Max and Performance Max campaigns because the algorithm routing a prospect doesn’t ask, “What category is this query in?” It asks, “What cohort does this user belong to, with what intent?”
The intersection of cohort and intent defines the node
A cohort is a group of people who’ll behave in a similar way given a specific stimulus. XL men, luxury travelers, and parents shopping for kids. Each is a cohort, defined by some durable identity that persists across time and context. The XL man is still an XL man when he’s buying winter coats in November, a vacation in July, and a wedding ring in March.
An intent is the situational vector that crosses through the cohort at a moment in time. Buying a shirt, booking a hotel for next month, and kitting out a child for summer. Each is an intent, and each one spans many cohorts. Buying a shirt pulls in XL men, S men, women, and parents shopping for kids, all walking different paths to different brands at different price points.
Every cohort carries many intents across a lifetime, and the same intent spans many cohorts across the market. The intersection of cohort and intent is what defines a node in the Funnel Query Pathway tree. XL men buying a shirt in winter is a node. Luxury travelers booking a hotel for next month is a node. Parents shopping for kids’ shorts for summer is a node.
Importantly, cohort alone doesn’t work because XL men buying pajamas behave differently from XL men buying office shirts or holidays. Intent alone won’t track because luxury travelers booking Bali behave differently from budget travelers booking Bali. The intersection is where behavioral coherence lives, and behavioral coherence is what makes the node trackable in the opaque AI surfaces we’re working with.
The query qualifies for tracking when both cohort and intent are legible in it
The test for whether a query belongs in a funnel query pathway tree is whether both cohort and intent are legible in the query itself. “Men’s red shirt from Uniqlo” surfaces a man shopping for clothes (the cohort) and buying a red shirt at the buying moment (the intent), with the brand named as the commercial destination. Both axes are legible.
“Hotels in Bali” surfaces an intent but hides the cohort (luxury, business, budget, honeymoon, family, backpacker), which is why it can’t function as a node. The people submitting it will behave nothing alike as they work their way down the funnel. Narrow it to “cheap hotels in Bali,” and the budget cohort emerges alongside the intent, and the query qualifies for the funnel query pathway.
The test is behavioral coherence, not specificity. If both axes are clear, it’s a node. If not, narrow it until they are, and you’ll discover the cohort and intent that together make sense to your business.
Build the funnel query pathway from the conversion moment upward
The funnel query pathway doesn’t track what users actually type. It tracks what the cohort would ask given the intent. Every query in the tree is a theoretical representative of cohort behavior at the buying moment, not an empirical record of individual users.
This is the macro discipline in practice. We don’t research search volume for these queries because they aren’t necessarily queries anyone has typed. We construct them by reasoning forward from cohort plus intent, building the ideal pathway a representative member of the cohort would walk.
The “would” carries the entire methodology, and the moment you slip into thinking about what users “actually” type, you’ve collapsed back into the micro instinct the methodology was designed to escape.
Once a query passes the test, it’s your starting point. The funnel query pathway (branching tree) builds upward from there. This mirrors the funnel flip at the query level. AI-era acquisition starts at the conversion moment and projects upward because the algorithm forward-calculates the conversion path from intent, not from awareness.
Start with the ideal branded BOFU query for one cohort with one intent, then project upward through the evaluation questions that cohort would ask, then upward again through the awareness questions that would come even earlier.
Example: Building one funnel query pathway tree from a single Uniqlo query
Take Uniqlo as the brand and “men shopping for clothes” as the cohort. The intent is the situational vector that defines the buying moment, and different intents inside the same cohort produce different trees: men buying a shirt, men buying winter outerwear, and men buying gym kit. Each is a node.
Start with one. For example, pick the intent of buying a red shirt, which I do often. The branded bottom-of-funnel query that fits the cohort-intent intersection is “men’s red shirt from Uniqlo.” That’s the conversion node.
Five to 10 variations of similarly shaped queries fit the same intersection and don’t need to be tracked individually: “men’s Uniqlo Oxford shirt,” “Uniqlo men’s smart shirt,” “men’s red dress shirt Uniqlo,” and “Uniqlo men’s casual red shirt.” Each is the same cohort with the same intent landing on the same brand. Pick the one that’s most useful for your business. Build upward.
Next, find the middle-of-funnel branches that would land at your ideal BOFU query. In our example, “men’s red shirt from Uniqlo,” we’re looking for the evaluation queries the same man would ask the engine before arriving at the branded buying moment. The cohort is still men shopping for clothes, the intent is still buying a red shirt, and the brand isn’t named yet because the cohort is still considering options:
“Best red shirt for men”
“Red shirt for office work”
“Where to buy a quality red Oxford shirt”
“Which red shirt looks best with chinos”
“Affordable men’s red shirts that don’t fade”
“Red shirts for men under €50”
“Best affordable clothing brands for men”
“Minimalist menswear brands with color ranges”
“Where to buy quality basics for men online”
“Best affordable men’s shirt brands”
Ten branches, all the same cohort, all the same intent, all logically routing to “men’s red shirt Uniqlo” as the ideal BOFU commercial query for the brand.
Top-of-funnel branches that would land at each of those middle-of-funnel queries are the broader awareness questions the same man would ask even earlier, before narrowing to specific shirt types or brands.
For “best red shirt for men”:
“Can men wear red shirts to work”
“How to add color to a man’s wardrobe”
“Shirt color rules for office wear”
“How many shirts should a man own”
“Which shirt colors suit men with what skin tone”
“What color clothing would make me stand out in a crowd”
That’s one 60-query funnel query pathway. I could’ve included 120 or more. That’s a choice, as we’ll see. As a rule of thumb, 60 is a reasonable number from a budget-versus-insights perspective. The point of the macro approach is that it doesn’t need you to go granular to measure.
The important thing here is that the 60 queries all route to one branded buying moment for one cohort with one intent. Do it again with another intent inside the same cohort (men buying winter outerwear, men buying office trousers), then another cohort (women shopping for clothes, with the intent of buying pajamas, branded BOFU “women’s pajamas Uniqlo”).
The tracking surface is a forest of trees, accumulated as the methodology runs.
AI routing uses the same math as Google Ads bidding
I discovered this while running keynotes and workshops for Google Marketing Live in Asia Pacific this month, in conversations with senior Google engineers about how Gemini routes recommendations.
The math Gemini runs to decide which answer to surface next is the same math Google Ads has been running to decide which ad to serve next: forward-calculate the probability that this cohort, with this intent, lands at a conversion, and pick the path most likely to get them there.
Every practitioner who’s bid on a campaign in the last 15 years has been working with that probability calculation. For me, this is the most useful framing the funnel query pathway can inherit, because it explains why the cohort-with-intent unit aligns with the engine’s internal logic.
The engine isn’t tracking categories or queries in isolation. It’s running a funnel pathway probability calculation on cohort plus intent. Every node you populate teaches the engine which path is the fastest way to get this user to the best solution to their problem.
Ads includes profit margin. Organic doesn’t.
The operational formula in Ads is cohort x intent x conversion rate x profit margin. Google holds all four because the advertiser provides Google with the commercial information needed to optimize bidding. The auction maximizes expected profit because Google has the inputs to calculate it.
The operational formula in organic is cohort + intent + conversion rate. Profit margin drops out because the engine doesn’t have the commercial information. The engine doesn’t know your gross margin on a red shirt versus your gross margin on pajamas, and it doesn’t optimize for your bottom line. It optimizes for user satisfaction, which is its own proxy for engine-level commercial outcome, but not for yours.
The principle holds across both surfaces: cohort + intent + conversion rate is the unit AI algorithms work with best. What differs is the precision of the conversion estimate. In organic, the conversion is inferred from behavioral patterns. In Ads, it’s measured from data provided by the advertiser.
Interestingly, the macro discipline operates in organic where micro precision isn’t available. Micro precision operates in Ads where it is. Luckily, the funnel query pathway tree works on both. Populate it once, and use it for organic content, Ads campaign structure, and analytical insights across both.
Build the funnel query pathway from the conversion moment upward
One terminological clarification in the 15-gate model I’ve built. The AI engine pipeline runs 10 binary gates:
Discovered, selected, crawled, rendered, and indexed (DSCRI), which are handled by the bot, invisible to the algorithm.
Annotated, recruited, grounded, displayed, and won (ARGDW), which are handled by the algorithm, invisible to the bot.
Our framework extends another five gates after being won: onboarded, performed, integrated, devoted, and codified (OPIDC), which are handled by post-transaction operations that serve people, invisible to both bot and algorithm.
Fifteen gates total, each a binary checkpoint where the brand either survives or doesn’t.
Nobody inside the system sees the whole chain. Only the brand does. Won itself has three flavors depending on surface:
The imperfect click in traditional search.
The perfect click in assistive engines.
The agentic click in assistive agents.
The funnel sits on the display gate. The user’s journey from question to purchase moves through three phases at display — awareness, consideration, and decision. Phases are continuous human positions. Gates are binary machine checkpoints.
The funnel query pathway tracks the queries the user submits across those three phases, with the branded buying-moment query landing at the decision phase that triggers won. Gates and phases aren’t synonyms, and conflating them breaks the methodology.
Step 1: Start at the bottom of the funnel
Identify the queries your ideal customer profile (ICP) would ideally submit using your brand name at the moment they’re ready to buy. The emphasis is on “ideally.”
Keyword research asks what people actually type. The funnel query pathway asks what the cohort with this intent would ideally ask the engine just before they purchase from you, with your brand name in the query. Branded, bottom-of-funnel, intent-confirmed, cohort-coherent.
Calibrate the specificity to the cohort definition. “Men’s red shirt from Uniqlo” fits the broad cohort of men shopping for clothes. “Men’s extra-large red shirt from Uniqlo” fits a sizing sub-cohort that behaves differently because size availability constrains the consideration set. Either is fine. Pick the cohort level where you want to operate, then operate consistently upward within the branches of your tree.
Generic keyword research won’t surface these queries because keyword tools optimize for volume, and cohort-with-intent queries are usually low volume by design. You have to know your cohort well enough to write them down yourself. If you can’t write five, your ICP work needs more depth before this methodology will produce results that are actually useful to your business.
Step 2: Project the pathway upwards
Each bottom-of-funnel query branches into multiple middle-of-funnel queries (the evaluation questions the same cohort would ask before arriving at the buying moment), each of which branches into multiple top-of-funnel queries (the awareness questions that would come even earlier).
Build out gradually, one bottom-of-funnel query at a time. The funnel flip operates at the query level: Generation starts at the conversion query and projects upward, rather than starting at top-of-funnel awareness and hoping the buyer arrives at conversion.
Granularity is cohorts x intents. Tracking is a budget call.
The question of how many trees to build has one answer: as many as the team can populate. The question of how many trees to track has one answer: as many as give you statistically meaningful data.
The starting unit is one cohort with one intent. Men shopping for clothes, with the intent of buying a red shirt. That’s one tree, around 60 queries.
Add intents inside the same cohort (XL men buying winter outerwear, office trousers, and gym kit). Add cohorts (XL women, parents). Cohorts times intents gives the tree count. The numbers scale with the budget:
Cohorts
Intents per cohort
Trees
Approx. queries
1
1
1
60
3
5
15
900
5
10
50
3,000
10
10
100
6,000
What changes with resolution is the precision of the diagnosis. Track three trees, and you have a low-resolution read on three cohort-with-intent intersections. Track 100, and you have a high-resolution read on most of your buying landscape. Both are defensible macro reads because macro is about defining your methodology and scope to reliably read direction and rate of change, rather than specific values.
This methodology means you can start small and build out. Start tracking three Funnel Query Pathways for your most profitable ICP this month, then add another next month. Group them, and you can compare like with like starting today using a macro approach that scales and survives over time.
Populate the tree, and you teach the engine the conversion path
The shaping mechanism is what makes the funnel query pathway more than a measurement methodology. The engine routes recommendations by predicting what comes next for the cohort with the intent.
When the brand feeds the AI with content that builds logically structured funnel query pathways and answers each node, the engine learns the chain:
Which awareness questions belong to this cohort.
Which evaluation questions follow them.
Which branded buying-moment query is the conversion answer.
For obvious pathways (red shirts), the algorithms already have the pathways ingrained, but for less popular pathways, the engine has no opinion, and you have every opportunity to shape its perception.
Since the engine is an active participant in the funnel alongside the user, it can form a predictive map, and the path it surfaces for any prospect in the cohort is the path the brand trained.
Shaping isn’t a side effect. It’s the compounding mechanism, and it means the brand stops competing for individual query rankings and starts engineering the inference paths the engine forward-calculates from. The competitor optimizing query by query is optimizing against a model the engine has already moved past.
The deeper move: Mapping the funnel query pathway into every webpage
The methodology can sit beside the website as a tracking document, and that works, but the deeper move is mapping the funnel query pathway into your strategy, both on-site and off-site.
Every node in every tree corresponds to a query the engine surfaces for the cohort. Every query needs a passage that answers it. Every page names the cohort it’s serving. Every passage names the intent that might bring the cohort there and clearly outlines the next step in the cohort’s conversion path.
Top-of-funnel pages route toward the evaluation pages.
Middle-of-funnel pages route toward the branded buying-moment pages.
Bottom-of-funnel pages close the conversion.
If you can align the content across your brand’s digital footprint to the forward-calculation logic the engine is already running — cohort, intent, awareness layer, evaluation layer, conversion layer — then when the engine forward-calculates the next step for any user in the cohort, the brand’s site is one of the few places that has the complete chain laid out, and the probability calculation tilts in your favor.
Build all the funnel query pathways for your ICP, and you’re teaching the machine exactly what the path looks like for every cohort-intent intersection you serve, while encouraging it to bring the subset of its users who are your ideal audience right to your door.
One framework for strategy, measurement, and analysis
The funnel query pathway does three jobs simultaneously: strategy, measurement, and analysis.
Strategy: You populate every node of the tree with content that proves the answer at that phase of the buying journey: awareness content at the top, evaluation content in the middle, and the branded conversion moment at the bottom. Stop running content generation as a calendar against a keyword list, and start engineering paths that represent your ICP’s buying journey.
Measurement: You run the same funnel query pathways across the three modes (search, assistive, and agent) and the engines (Google, ChatGPT, Perplexity, Claude, Copilot, Siri, Alexa, etc.). You can’t track every surface those engines appear on (Copilot in Word, ChatGPT in Slack, Apple Intelligence in iOS, and Copilot+ on a Lenovo laptop are all closed contexts that don’t let you rank-track). But every surface runs the same underlying engine, so your tracking extrapolates to every surface each engine sits inside.
Analysis: You can use the pattern of where the brand surfaces and where it doesn’t across the funnel query pathway, by mode and by engine, as the macro view you can rely on for a like-for-like comparison over time.
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What you actually get from the funnel query pathway
Here’s what you actually get from running the funnel query pathway: a quarter-after-quarter read of whether AI is recommending your brand to the right people at the right moment.
You see direction, momentum, and a record of what’s working. You build, you measure, you analyze, and you adjust. Then you do it again next quarter. The brands that start this discipline now will be the ones AI knows by name in three years.
Pick one cohort, the most strategically important if you have several. Pick one intent inside that cohort. Write five to 10 branded bottom-of-funnel queries that cohort-with-intent would ideally submit at the buying moment (“men’s red shirt from Uniqlo” in our example).
Pick one and map upward: five to 15 middle-of-funnel queries that would land at it, then three to 10 top-of-funnel queries that would land at each of those. You now have one tree, somewhere between 50 and 200 queries.
Run strategy, measurement, and analysis on the funnel query pathway branches.
Strategy: Do you have pages and passages that address each of the nodes? Fill the gaps.
Measurement: Run the tree across engines and document where the brand surfaces.
Analysis: Where are the gaps clustered, which node is weakest, and which engines are recruiting most consistently?
Build out the content that fills the gaps in your ICP funnel query pathways, and track that set of queries monthly. You’ll see results, and you’ll be able to measure them.
AI-era optimization is about defining your methodology, picking your ICP and tracking, and building and strategizing with a macro mindset, which is the subject of the next article in this series.
Part 13, “The delegation boundary: How AI decides which brands win,” mapped how delegation moves between user and engine across search, assistive, and agent modes.
Up next: The micro-macro shift, the paradigm framework that names the structural change in measurement, analysis, and strategy that the AI era requires.
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 Solutions2026-05-19 15:00:002026-05-19 15:00:00The funnel query pathway: A framework for measuring AI visibility
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.
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 Solutions2026-05-19 13:00:002026-05-19 13:00:00How AI may increase the value of SEO expertise
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.
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.
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OpenAI is making a clearer push into e-commerce advertising by letting retailers generate ads directly from their product catalogues inside ChatGPT.
What’s happening. Retailers can now connect product feeds to ChatGPT, allowing the platform to automatically create ads using product names, images and attributes, instead of building campaigns manually.
The ads themselves don’t change for users. They still appear beneath responses and are clearly labelled as sponsored.
Why we care. Running ads at scale has been a major barrier for e-commerce brands in ChatGPT.
This update removes that friction, especially for retailers with large inventories, by turning product catalogues into ready-to-run ad campaigns.
Zoom in. Brands set rules for which products to include, then let the system generate ads automatically.
It mirrors how shopping campaigns work on platforms like Google, where structured feeds power both organic and paid visibility.
What’s new. Previously, product data could inform ChatGPT’s answers, but it couldn’t be used for advertising.
Now, that same data powers both, effectively linking organic presence with paid campaigns.
Between the lines. This signals a shift in how OpenAI plans to monetise shopping.
Rather than taking a cut of transactions, it’s moving toward capturing ad budgets already spent on platforms like Amazon and Meta.
What they’re saying. Industry analyst Debra Aho Williamson called feed-based automation “table stakes,” noting that ChatGPT’s edge lies in serving ads based on conversational intent rather than traditional signals.
Ad tech partners like StackAdapt say the setup integrates easily with existing feeds, lowering adoption barriers.
Cost-per-action models are also reportedly in development, pointing to a deeper push into performance advertising.
What to watch. Expect more retailers to test ChatGPT as a performance channel as setup becomes easier. The bigger question is whether conversational intent can drive conversions as effectively as traditional search or marketplace signals.
Wikipedia was once widely considered an unreliable source. Today, however, it’s often treated as a credible reference point because of its extensive citations and collaborative editing process.
It’s also one of the primary sources AI search systems rely on. Alongside Reddit, Wikipedia heavily influences the information surfaced by ChatGPT and Google.
The downside to this is that Wikipedia isn’t always foolproof. Negative or outdated information often persists on certain pages for months or even years. That information is then funneled back into AI search systems and relayed to users.
This creates a feedback loop where outdated or negative narratives can gain long-term visibility and credibility across AI search platforms.
So, how does one navigate the scenario when negative information ends up on Wikipedia?
How content ends up on Wikipedia
One of the main criteria of getting information on Wikipedia is verifiability. Media outlets and Wikipedia users verified by the platform itself are often the main providers of content.
For instance, respected third-party outlets such as news organizations and scientific journals are often the main sources. This leads to these outlets serving as gatekeepers of sorts.
It also means that verifiability is sometimes prioritized on Wikipedia over pure accuracy of content. Unfortunately, media outlets don’t always achieve 100% accuracy in their reporting.
Another issue is that Wikipedia’s editors are often decentralized volunteers. This means that content uploaded to the platform is often based on general consensus.
The result is that there’s no central authority on Wikipedia that can quickly “fix” disputed content.
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Why does negative and outdated information stick?
Wikipedia openly acknowledges that controversies surround the platform. It even maintains a page documenting those disputes over the years.
Negative or outdated information can persist for several reasons. In many cases, it also originates from a single high-profile news story or legal issue that continues to be cited long after the situation changes.
Citations
Wikipedia citations have extreme permanence. Once information is essentially backed by a “reputable” and verified source, removal from the platform becomes extremely difficult. Even information that has long since been disproven can remain on Wikipedia if it comes from a proper source.
The echo chamber effect
The web is a highly influential sphere. Wikipedia serves as both the influencer and the influenced in terms of absorbing and spewing information. Negative claims often circulate and reinforce themselves through Wikipedia — and this is only becoming more prominent with AI search platforms.
Risk aversion
Simply put, Wikipedia’s editors don’t want to be viewed as biased. This means they often avoid removing content from verified sources.
Differing news coverage
Negative stories often receive more coverage than positive ones. Corrections also tend to attract far less attention than the original reports, creating an imbalance in the sources Wikipedia relies on.
Wikipedia’s role in AI search
Wikipedia has become a major source for generative AI platforms, giving its content an added layer of credibility in AI-generated answers.
ChatGPT and Google AI Overviews frequently condense information from Wikipedia and other sources, such as Reddit and news outlets, into simplified narratives. As a result, outdated controversies or disputed claims can quickly spread to large audiences.
The issue is compounded by changing user behavior. Many users now rely on AI-generated summaries instead of clicking through to verify information themselves. Some estimates suggest roughly 40% don’t fact-check AI search results.
That means when AI systems surface negative Wikipedia content, it can shape perception almost instantly.
How Wikipedia and AI disrupted a social media company
My online reputation management company recently helped repair the image of a prominent marketing company. (For the sake of privacy, we’ll refer to them as Organization Z.)
Organization Z faced plagiarism claims nearly a decade ago. These claims were eventually cleared and dismissed, with any hint of wrongdoing squashed. However, the claims appeared on Organization Z’s Wikipedia page, where they were labeled a “controversy.”
Making matters worse was that far more attention was paid on Wikipedia to the apparent “controversy” than to the fact that Organization Z’s name was eventually cleared.
AI search engines then began to pull this information directly from Wikipedia. When users searched for the brand online, they encountered terms such as “controversy” and “plagiarism” despite all claims having been dismissed.
The controversy continued resurfacing online years after the claims had been dismissed.
How to navigate negative content on Wikipedia
Before diving into solutions, it’s important to understand what doesn’t work. Editing your own Wikipedia page creates a conflict of interest, and Wikipedia edits are closely monitored. You also can’t remove content without a strong policy-based justification, as the platform has strict standards around sourcing and removals.
With that in mind, here is a practical, step-by-step framework many ORM specialists recommend for addressing negative or outdated Wikipedia content.
1. Perform an audit
Identify the claims circulating on Wikipedia, along with the sources used. Outline any outdated references or integrity gaps.
Determine whether the information on the page is still relevant and whether the coverage is fair and balanced.
2. Compare Wikipedia to current coverage
Compare the Wikipedia page with how the brand, person, or issue is currently represented online. In this context, it’s the same step you would take while performing an AI narrative audit.
Identify whether important context is missing, outdated, or overemphasized. The goal is to spot gaps between reality and the narrative Wikipedia presents.
3. Address the citations
Now that you’ve identified mismatches and analyzed the sources Wikipedia is using, you can begin to address those citations. You’re not altering Wikipedia itself. You’re altering what Wikipedia cites.
Aim to publish factual, positive content that reflects the current reality. Prioritize third-party mentions on reputable media outlets or in academic journals.
4. Strengthen positive, balanced coverage
Build your brand image online with a specific focus on highlighting achievements and industry recognition. Make it clear that you’re a reputable voice in your industry, and Wikipedia will soon reflect that.
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AI search raises the stakes
Wikipedia remains a powerful source of information, but its reliance on citations and consensus can allow outdated or negative narratives to persist.
That becomes more consequential when AI search engines amplify those narratives in generated answers.
While brands can’t directly control what appears on Wikipedia, they can influence the sources that shape it. The key is to strengthen accurate, balanced coverage across reputable outlets and regularly audit how your brand appears online.
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Google is set to automatically link Google Ads accounts with associated YouTube channels — according to communications sent to multiple advertisers — tightening the connection between video engagement and ad performance.
What’s happening. Advertisers have received notices that, from June 10, 2026, Google Ads accounts that aren’t already linked to a YouTube channel will be automatically connected.
The update removes the need for manual linking and ensures advertisers can access video engagement data and targeting features by default.
Why we care. Linking a YouTube channel unlocks deeper insights and more advanced targeting options — something many advertisers either overlook or delay setting up.
By automating the process, Google is effectively making video data a standard part of campaign optimisation.
Zoom in. Once linked, advertisers can access organic video metrics, including view counts, directly within Google Ads.
They can also build audience segments based on how users interact with their YouTube content — from video views to channel engagement.
What else. The integration allows advertisers to track “earned actions,” such as subscriptions or additional views driven by ads, and use those engagements as conversion signals.
That creates a clearer picture of how video campaigns influence user behaviour beyond just clicks.
What to watch. How advertisers adapt their measurement strategies once organic and paid video data are combined, and whether this leads to broader use of engagement-based conversion tracking in campaigns.
Bottom line. Google is making YouTube data harder to ignore — turning automatic linking into a default step for better targeting, measurement and performance.
First spotted. Several advertiser reported getting the comms from Google, including Founder of JXT Group, Menachem Ani, founder of PPC News Feed Hana Kobzová, and PPC Specialist Arpan Banerjee.
