I see ChatGPT’s high-reasoning mode acting like a very different search surface for brand visibility. In a Semrush analysis with Kevin Indig, ChatGPT cited different domains than it did in minimal reasoning mode and ran nearly five times as many web searches before answering.
By the numbers, the shift is hard to ignore. Only 25.6% of cited domains overlapped between minimal and high reasoning for the same prompts. That means nearly three in four sources changed when ChatGPT moved from Instant-style answers to Thinking-style answers.
I also noticed that Thinking mode used more sources overall. Citation rates rose from 50% in minimal reasoning to 68% in high reasoning. When ChatGPT did cite sources, it used more of them too, increasing from 2.6 to 4.5 citations per response. Across the test set, high reasoning ran 1,130 web searches, compared with 245 for minimal reasoning.
Reddit lost ground in high-reasoning answers. Reddit’s citation share dropped from 15% to 7% when high reasoning was turned on. User-generated content and review sites also declined, falling from 14.3% to 6%.
At the same time, I saw more weight shift toward institutional and official sources. Government and academic sources rose from 1.9% to 8.8%, while official documentation and support pages grew from 12.4% to 17.5%.
Comparison prompts drove the most search activity. At the comparison stage, high reasoning averaged 24 sub-queries per prompt, compared with 5.5 for minimal reasoning. Average citations also peaked there, reaching 9.8 per high-reasoning response versus 5.8 for minimal reasoning.
For example, I would expect a CRM comparison to trigger separate searches for pricing, integrations, security, support pages, and documentation before ChatGPT forms its final answer.
Early citations also appeared to last longer. High reasoning was more likely to carry a brand from early research into later buying questions. In four of the 20 journeys tested, a brand cited at the problem stage still appeared at the selection stage. Minimal reasoning showed no full-journey persistence, meaning no brand cited at the Problem stage survived through to the Selection stage of the same journey.
I also found the domain reuse pattern important. High reasoning reused the same domains more often within a single answer, with the same domain appearing multiple times in 51 of 100 high-reasoning responses. Minimal reasoning did this in 26 of 100 responses.
Finance saw the biggest citation jump. The lift varied by category, but finance had the largest increase, with citation rates rising 28 percentage points in high reasoning. Health and lifestyle rose 24 points, while B2B SaaS gained 16 points.
Consumer tech barely moved, rising only 4 points. Even though high reasoning ran more sub-queries for consumer tech prompts than for any other category, it often landed on the same brands and sources as minimal reasoning.
Why I care about this: content can appear in fast ChatGPT answers but disappear when users ask more complex questions. Visibility depends on whether my pages, documentation, and third-party references can surface across the smaller searches ChatGPT runs before it answers.
About the data: Semrush and Indig tested 100 prompts across 20 buyer journeys in B2B SaaS, finance, consumer tech, and health and lifestyle. Each prompt ran once in minimal reasoning and once in high reasoning. The analysis tracked citation rate, cited sources, and fan-out queries.
Making a brand machine-readable and improving its odds of being selected for AI-generated answers are important, but I see them as only part of the larger shift. Under the surface, a retrieval layer is changing how AI systems identify entities, connect facts, and decide which brands deserve to be cited.
That layer is GraphRAG. Once I understand how it works, “optimize for AI” stops feeling like a vague instruction and starts looking like a practical SEO strategy.
What is GraphRAG, actually?
GraphRAG extends traditional retrieval-augmented generation (RAG) by adding a knowledge graph. That graph helps AI understand entities and the relationships between them, instead of treating content as disconnected text fragments.
Microsoft Research introduced GraphRAG in 2024, and a broader ecosystem has formed around it since then. Instead of pulling from a flat sea of text chunks, GraphRAG builds a map.
In that map, nodes are the entities: a company, product, person, certification, location, or concept. Edges are the relationships between those entities, such as “offers,” “is certified by,” “authored,” or “operates in.”
I think of it as a system of things and the lines connecting them. When a model works from a map instead of a pile of scraps, it does not have to guess its way toward an answer. It can follow the relationships.
If the map says Entity A holds Certification B in Region C, the system can follow that path with confidence instead of inferring the connection and hoping it is right. That is why graph-based retrieval can produce more complete, better-grounded answers to complex questions with fewer hallucinations.
Microsoft described this failure mode in its GraphRAG patent, “Knowledge Graph Extraction” (US20250131289A1). The patent calls out a recall problem in naive RAG: a less prominent entity can disappear inside chunk embeddings, which means the system may retrieve nothing useful.
It also describes one of the fixes: entity resolution. When duplicate spellings or variations of the same thing are merged, the system can treat them as one entity instead of scattering their authority across several weak signals. That is one of the core building blocks behind graph-based retrieval.
Traditional RAG works by chopping content into fixed chunks, turning each chunk into a vector, and storing those vectors in a database. When I ask a question, the system retrieves the closest chunks in vector space and passes them to a language model to generate an answer.
That can work for simple questions like “What is the capital of France?” It struggles with the questions that usually matter most in business: the multi-step questions.
If I ask a system to find a provider that offers a specific service, holds a specific certification, and operates in a specific region, naive RAG may stitch together an answer from scraps that merely sound related. It does not truly understand how the facts connect, so it guesses across the gaps.
When a system has to guess, the safer move is often to leave a brand out rather than risk saying something inaccurate about it. That is the part I think many SEO teams need to sit with.
This explains a common frustration: “Our content is strong, but AI systems still do not cite us.” The issue may not be content quality. GraphRAG consistently outperforms naive RAG on complex, multi-hop questions where vector search falls apart. That is where the visibility leak often starts.
In many cases, the machine could not reliably tell what the brand is, how its facts fit together, or whether it could trust those relationships enough to cite the brand by name.
The three problems GraphRAG is built to fix
I see GraphRAG lining up with three SEO problems that show up again and again: disambiguation, attribution, and relationships.
Disambiguation matters when the same entity appears under different names and gets counted as several weaker signals instead of one strong one. If “the firm,” “the agency,” and the actual brand name never resolve to a single entity, authority gets split.
Attribution matters when the fact survives but the credit disappears. When content is blended into an AI answer, the brand behind the original insight can easily vanish.
Relationships matter when the connections that give expertise meaning stay buried in prose instead of being declared in a way a machine can read.
If I have ever watched AI repeat something a company wrote without naming it, or credit a competitor for a specialty the company actually owns, I have seen all three problems in action.
What ties them together is simple: this is not only a content problem. It is an identity problem.
Same sentence, more machine-readable context
I want to make the idea of an entity concrete, because it can become abstract quickly. I will use one real-world example and one fictional example.
Start with Wayne Gretzky. Search his name in almost any AI client and I expect to see a confident summary: facts, former teams, records, and related links. That confidence is not luck. It is what a well-established entity looks like. His identity is nailed down and agreed upon across the web, so the system does not have to guess who he is.
Now imagine the opposite. Picture a goaltending coach in Moncton. I will call her Marie Tremblay. Her About page says: “Our head coach, Marie ‘Lefty’ Tremblay, has run elite goaltending camps across the Maritimes for 20 years.”
That is a good sentence. A parent understands it immediately. I would not rewrite it into robotic prose just to satisfy a machine. Optimizing for AI does not mean abandoning human voice.
The better move is to keep the sentence and add context around it. I need to make explicit what a human reader infers automatically.
That means clarifying that “Lefty” and “Marie Tremblay” are the same person. It means connecting Marie to the academy, to goaltending as a discipline, and to the Maritimes as the region she serves. It also means making “20 years” and “elite” verifiable claims rather than loose adjectives.
A human gets all of that from one sentence. A machine may not. My job is to close the gap between what the reader understands and what the system can verify, so Marie becomes as legible to AI retrieval systems as a famous entity like The Great One already is.
Why a flat triple is no longer enough
Knowledge graphs are built on triples: subject, predicate, object. “Acme offers consulting” is clean and useful, but it is flat. A bare triple cannot easily carry the high-stakes details that matter, such as whether the relationship is true, where it applies, who says so, and what evidence supports it.
The standards community is working on that gap. The W3C is extending the model with Resource Description Framework (RDF)-star, which allows site owners to make statements about statements. In practice, that means source, date, confidence, and other metadata can attach directly to a relationship instead of floating around as a disconnected claim. It is moving through the RDF 1.2 standardization process, with the RDF 1.2 Primer serving as a plain-English introduction.
Microsoft’s GraphRAG patent points in a similar direction. It pulls claims into a subject-action-object structure and weights relationships by how often they appear, instead of treating every stated link as equally reliable.
The practical lesson is clear to me: the future is not just saying two things are related. It is saying they are related and showing the proof in a form a machine can verify. A richer triple beats a flatter page.
The publishing layer is starting to respond
I am also watching the publishing layer, because that is where the shift is becoming visible outside the models themselves.
On June 1, the new open standard EntityMap launched a 33-day public consultation ahead of its July 1 launch. It was started by Fred Laurent, CTO of InLinks and Waikay, with backing from Dixon Jones. For anyone following entity SEO and the move from “strings to things,” those names matter.
The concept is deliberately familiar. Where sitemap.xml tells search engines which pages exist, an entitymap.json file tells AI systems what an organization knows: which entities it covers, how they relate, and where the evidence lives.
EntityMap aims at the same three problems: disambiguation, attribution, and relationships. It also builds in the richer-triple idea by allowing declared relationships to carry receipts, including a source URL, publisher, and timestamp.
I would treat it as a signal, not a mandate. EntityMap is a proposal in consultation, not a requirement. No major engine has committed to reading files like these, so I would not turn it into another box-checking exercise yet. The important point is that credible people are building entity-first publishing standards, and that direction is worth watching.
The honest state of GraphRAG
I do not think GraphRAG belongs in hype territory, because two realities keep it grounded.
First, GraphRAG is expensive. Building the map requires a language model to extract entities and relationships, and that is the costly part. By Microsoft’s own estimate, graph extraction accounts for roughly 75% of indexing costs. That LLM cost is one reason web-scale, real-time graph retrieval has not taken over everything overnight.
Second, the cost curve is bending. Recent research is attacking the infrastructure problem directly, including TurboQuant, a vector compression method from Google Research and NYU, presented at ICLR 2026. It reduces the memory footprint of vectors these systems traverse while preserving quality well enough to make the economics more interesting.
That does not mean every engine is running GraphRAG across the open web today. It means the economics are improving, which helps explain why entity-first standards are emerging now. I am cautious about anything framed as inevitable, but this shift makes practical sense.
Structured data still matters. Schema.org markup, a clean Knowledge Panel, consistent NAP, and strong entity signals are not going away. Entity-first work extends that discipline. It does not replace it.
My entity-first action plan
Here is how I would make this practical without betting everything on one standard.
Inventory entities, not just keywords. I would go beyond the search terms that historically brought traffic and list the things the brand genuinely knows about: products, services, people, methods, concepts, locations, and credentials. That becomes an entity map, whether or not it ever gets published as a formal file.
Disambiguate, then connect to the graph. I would claim and confirm the brand’s Wikidata entity and Google Knowledge Panel where possible. I would standardize naming, resolve variants, and keep sameAs links consistent across structured data. This is how “Lefty” and “Marie Tremblay” become one clear identity instead of two weak signals.
Make relationships explicit. I would use Schema.org types and properties such as Organization, Person, Product, knowsAbout, sameAs, and author so expertise is declared rather than implied. I would also mirror those relationships in internal linking.
Attach evidence to every claim. I would connect important facts to verifiable sources: named authors, first-party data, citations, documentation, and dated references. Graph-based systems increasingly need proof behind a relationship, not just the assertion.
Front-load defining facts. Retrieval still works through narrow windows, so I would place the clearest, most verifiable statement of what the brand is and what it does near the top of important pages.
Watch the publishing layer without overcommitting. I would read the EntityMap spec, follow how it develops, and decide later whether an entity index belongs in the stack. Schema.org work should continue either way.
Tie the entity map to revenue. I would map entity coverage to the queries and answer surfaces that influence leads, sales, margin, and retention. That helps leadership see entity work as revenue protection, not an academic exercise.
Measure what AI systems can recognize
Rankings and clicks still matter, but they describe the old search-page model. I would add metrics that show whether AI systems can recognize, trust, and cite the brand.
AI citation share measures how often the brand is named or cited in AI answers compared with competitors. I would track it monthly with an AI visibility tool.
Entity recognition asks whether priority entities have confirmed Knowledge Panels, Wikidata entries, and consistent identity signals. It is simple, but foundational.
Relationship completeness looks at how many priority entities have explicit, marked-up relationships and consistent sameAs links.
Attribution rate tracks how many core claims are backed by linked, verifiable evidence.
Answer-equity proxies include branded-query lift, assisted conversions from AI referrals, and lead stability as raw click volume softens. These business signals help show whether authority is compounding even when CTR is harder to read.
Where graph-based retrieval is heading
I expect graph-based retrieval to keep moving toward multimodal graphs, where text connects to images, audio, video, and structured data. I also expect more streaming and incremental indexing for live data, plus domain-specific ontologies for areas like medicine, finance, and law.
The move from strings to things is gaining momentum. The brands that stay visible will not simply be the ones publishing the most content. They will be the ones machines can understand without guessing, with clear entities, explicit relationships, and claims backed by evidence.
I do not need to wait for a new standard to launch before preparing. I can make a brand more legible now to systems that do not just read pages, but read what the brand knows. In the answer economy, I see the real battleground as identity, not just content.
I’m seeing a major shift in how people plan trips: 40% of travelers now use AI to research, compare, and organize their travel decisions.
That changes how I think about travel content. It is no longer enough to write only for traditional search results. I also need to make content clear, useful, and easy for AI systems and large language models to understand, summarize, and cite.
In this guide, I focus on practical travel AI optimization strategies, including stronger FAQs, schema markup, topical authority, and a content strategy built around the questions real travelers ask.
My goal is simple: create travel content that answers intent directly, builds trust, and gives AI platforms the structured context they need to reference my brand when travelers are planning their next trip.
Earlier this year, I made the case that the core fundamentals of international SEO still matter. I still believe that. Hreflang, localization, technical excellence, and market-specific content remain essential because search engines and LLMs still need to discover, understand, and connect content with the right audiences.
What has changed is the environment those fundamentals now operate in.
For decades, I watched multinational organizations treat markets as mostly separate digital ecosystems. Content created in one market usually stayed there, and governance focused on managing websites, content, and technical implementation across different regions.
Today, those boundaries are much harder to see.
AI systems can translate content, synthesize information from multiple sources, and increasingly sit between organizations and their customers. Information that once lived inside one market can now shape visibility, recommendations, and customer experiences across many regions.
As those boundaries blur, I see the governance challenge expanding. International SEO is no longer only about managing websites across countries. It now requires organizations to manage the knowledge, expertise, and information that search engines and AI systems use to represent them globally.
Why I believe the governance model must change
Historically, many website and localization decisions were built around operational efficiency. Headquarters created content, technology platforms, and standards for global distribution, while local markets adapted those assets for their own audiences.
That model worked because scale often outweighed the limitations of localization. Consistency improved, costs came down, and organizations could deploy content and technology across dozens of markets far more efficiently than local teams could manage independently.
The challenge now is that AI systems are changing what gets rewarded.
Scale and standardization still matter, but search engines and AI systems increasingly look for signals of expertise, relevance, and geographic specificity. Content that reflects local regulations, market conditions, customer expectations, and industry practices often provides context that translation alone cannot reproduce.
At the same time, AI systems can magnify inconsistency. Contradictory product information, conflicting entity definitions, inaccurate regulatory guidance, and fragmented technical implementations can create confusion across search engines, answer engines, and AI-powered experiences.
That is why I do not think organizations can optimize only for efficiency or only for localization anymore. They need governance models that protect global consistency while giving local markets room to contribute the expertise and context that increasingly drive visibility and trust.
Hreflang solved routing, not understanding
In my previous hreflang article, I argued that hreflang still belongs in an international search strategy, even in the age of AI. I stand by that view.
But hreflang does not decide which market perspective should be prioritized when AI systems synthesize information from multiple sources. It also does not determine which content demonstrates the strongest expertise when AI-generated answers are produced.
As search moves from retrieval toward synthesis, I believe organizations need to think beyond routing users to the right page. They also need to govern the knowledge that powers those answers.
What I would centralize
My simplest rule is this: if an activity creates enterprise risk when it is handled inconsistently, it should usually be governed centrally.
Technical SEO standards are a clear example. Search engines and AI systems do not evaluate websites one market at a time. They evaluate the broader ecosystem of signals an organization provides. CMS governance, structured data standards, entity definitions, AI crawler policies, measurement frameworks, and technical infrastructure all benefit from consistency.
Many international organizations have already faced a version of this problem.
Years ago, before hreflang existed, many global companies used IP detection to route users to the market website they believed was most appropriate. The problem was that Google primarily crawled from U.S.-based IP addresses. When Google tried to access French or Japanese content, it was often redirected to the U.S. site instead.
Individual markets could not solve that on their own because the routing rules affected every market at once. The solution required global governance with local input.
I see AI crawler management creating a very similar challenge today.
Organizations now have to decide which AI systems can access their content and whether those systems can reach the market-specific information they are meant to understand. For companies still relying on geographic routing, market gateways, or IP detection, the governance issue should feel familiar even if the technology is new.
The platforms have changed, but the lesson has not. Some decisions are too interconnected to manage market by market.
What I would localize
If technical infrastructure benefits from consistency, content benefits from expertise.
For years, multinational organizations followed a simple model: create content in the primary market, then translate, adapt, and distribute it globally. That approach delivered real efficiencies. It helped organizations scale content production, maintain brand consistency, and support dozens of markets with shared resources and common technology platforms.
Traditional search engines could lean on signals like hreflang and country targeting to understand regional relevance. AI systems increasingly evaluate the content itself. When multiple markets publish nearly identical versions of the same information, language models may treat them as variations of one source rather than distinct expressions of expertise.
To stand on its own, content increasingly needs market-specific signals such as local regulations, terminology, customer expectations, industry practices, and other forms of geographic specificity.
That is why I believe content ownership, audience research, local authority building, regulatory content, and market expertise should usually stay close to the market. The goal is not localization for its own sake. The goal is to make sure expertise comes from the people closest to the customer and that the content reflects the realities of the market it serves.
The strongest multinational organizations will still use global content frameworks, shared resources, and common technology platforms because those efficiencies remain valuable. The hard part is preserving those efficiencies while giving local markets enough space to contribute expertise that is visible, differentiated, and meaningful.
For years, organizations balanced scale against localization. Increasingly, I think they are balancing scale against representation. The markets that remain visible in AI-driven search experiences will often be the ones that contribute enough unique expertise to stand on their own, rather than simply echo the dominant market version.
What I think needs shared ownership
Governance ultimately comes down to accountability. Whether responsibility sits with a Chief Digital Officer, CMO, enterprise search team, or AI governance group matters less than whether ownership is clear. As search becomes more connected to marketing, technology, product, legal, and AI initiatives, organizations need clear decision rights, escalation paths, and accountability.
The companies that succeed will not necessarily be the ones with the largest SEO teams or the most advanced AI tools. I expect the winners to be the organizations that know exactly how knowledge is created, governed, validated, and represented across markets.
My practical rule for determining ownership
For me, the distinction comes down to risk and expertise.
Responsibilities that create enterprise-wide consequences when implemented inconsistently generally belong closer to headquarters. Activities that depend on local customer knowledge, regulations, language, or market conditions are usually best managed in-market.
Many of the most important decisions need both perspectives, which means they are best handled through shared governance.
10 governance decisions I would review with every global SEO team
The exact structure will vary by organization, but I would encourage most multinational companies to evaluate ownership across these areas.
Typically centralized
1. Technical SEO standards
I would centralize these standards to ensure consistency in crawling, indexing, structured data, and technical implementation across markets.
2. CMS and infrastructure governance
I would govern this centrally to prevent fragmentation while maintaining a common technology foundation.
3. Entity definitions and taxonomies
I would keep these consistent so products, services, brands, and organizational relationships are represented clearly across markets.
4. AI crawler and bot governance
I would establish consistent policies for crawler access, monitoring, verification, geographic routing, and exception management. Governance should usually sit with headquarters, while markets should still be able to request business-specific exceptions.
5. Measurement and reporting frameworks
I would centralize reporting definitions so markets are evaluated with comparable success metrics.
Typically localized
6. Market-specific content
I would keep creation and validation close to local teams so content reflects customer needs, regulations, terminology, market conditions, and the geographic signals that help AI systems recognize local relevance. Global content frameworks can still support that work where appropriate.
7. Audience and search behavior research
I would manage this in-market to capture differences in language, intent, customer expectations, and emerging market trends.
8. Local authority building
I would localize this work because market-specific expertise, trust, partnerships, citations, and visibility cannot be fully manufactured from headquarters.
Typically shared
9. Product and knowledge management
I would treat this as shared ownership because it needs global consistency as well as local validation, market expertise, and regulatory accuracy. Headquarters should define the framework, while markets validate that products, services, and policies reflect local realities.
10. AI visibility and representation
I would also make this shared. Headquarters should establish monitoring and escalation processes, while local teams validate market-specific accuracy and identify emerging issues in how products, services, and brands are represented across AI systems.
The new global SEO mandate
I do not think the objective is to centralize everything or localize everything. The real mandate is to place ownership where decisions can be managed most effectively, so the organization can balance consistency with expertise.
I see generative AI and automation creating both excitement and anxiety across the SEO industry. With 87% of Americans reading AI summaries, I believe any SEO team that is not adapting its toolset is already starting to fall behind.
When I move away from rigid enterprise tools and toward agile, AI-driven workflows, I can work faster, spot new search signals earlier, and show clients or internal stakeholders that I understand where search is heading.
In this guide, I’ll walk through what the old SEO stack looked like, what I now add to it, and how I combine both approaches without abandoning the fundamentals that still matter.
Here’s what an old SEO stack looks like
I still believe traditional SEO practices matter because generative AI search experiences continue to depend on core search ranking systems, quality systems, and the broader signals search engines have used for years.
That said, the classic SEO stack was built for a simpler search environment. It usually centered on rank tracking, keyword research, and technical site audits.
Rank trackers
For a long time, I treated keyword rankings as the heartbeat of an SEO campaign. I would add target keywords, monitor SERP positions, and expect higher rankings to translate into more search traffic. But rankings have become far more fragmented.
Now I need to pay attention to AI Overviews, local packs, shopping carousels, and many other search features that can change the value of a ranking completely.
A third-place local pack ranking, for example, may drive two or three times more traffic than a number one ranking in an AI Overview. That makes old-school rank tracking useful, but incomplete.
Keyword tools
Keyword tools still help me understand what people search for, how competitive a topic might be, and which queries match specific user intent. In the past, that information often felt close to a crystal ball.
I would choose keywords based on difficulty, search volume, intent, and other factors. The better the data, the easier it was to shape a campaign around the right opportunities.
The problem is that search volume has always looked backward. A keyword may have shown 10,000 monthly searches last month, but that does not mean it will perform the same way this month. Demand can rise, fall, or shift quickly.
Today, the bigger issue is opportunity loss. A keyword that generated tens of thousands of clicks in 2022 may now be answered directly inside an AI Overview. Even when search volume has not dropped, zero-click behavior can reduce the traffic I can realistically capture.
Site audit tools
I still rely on site audit tools because crawlers still crawl websites, interpret content, and surface technical issues. I need to know whether search engines can access, understand, and navigate the pages I care about.
Audit tools help me find broken links, redirect problems, missing metadata, slow pages, thin content, and other technical issues that can hold a site back.
But I do not expect crawl audits alone to tell me whether my content will appear in AI-driven search experiences. Technical health is necessary, but it is no longer the full picture.
Signals such as brand mentions can influence whether a site is included in LLM outputs from tools like ChatGPT, Claude, and Gemini. Many older site audit tools were not built to track those signals.
That is why I still keep parts of the old stack, but I now add tools and workflows that help me understand AI visibility, brand presence, and faster data-driven decision-making.
Here’s what a new SEO stack looks like
If I am optimizing only for Google’s traditional results, I am missing where search behavior is moving. Between the first and second half of 2025, LLM referral traffic grew by 80%. Conversion rates reached 18%, even though LLM referrals still represented 2% or less of total traffic in the dataset.
That tells me the channel is still small, but meaningful. Now is the time to build a stack that helps me understand, measure, and improve performance across AI-driven discovery.
LLMs
I want my site to appear in LLM responses, but I also use LLMs to strengthen my SEO process. These tools can support analysis, content review, competitor research, metadata refinement, and structured data work.
For example, I can connect ChatGPT with Google Search Console to automate SEO analysis, use Claude to refine copy and conduct content audits, or use Gemini to generate schema markup and compare competitor pages against my own.
I use the LLM that best fits the task, but I keep human oversight in place. These tools help me improve speed and performance; they do not replace judgment, strategy, or editorial review.
The biggest shift is speed. Large datasets that once took hours, days, or weeks to review can now be explored in minutes when I use LLMs carefully and integrate them into a repeatable workflow.
APIs
The old workflow often meant logging into dashboards, exporting CSV files, and cleaning everything in Excel. I still do that when needed, but APIs let me pull data directly from platforms like Google Search Console and Google Analytics.
APIs can sound intimidating, but LLMs make the learning curve easier. I can use them to help with authentication, JSON parsing, and the basic structure of repeatable data workflows.
Once I can connect to APIs, I can stop waiting on manual exports and start building faster reporting, monitoring, and analysis systems around the data I already use.
Lightweight scripts
Python scripts are now within reach for many SEOs, especially with tools like Claude Code and similar coding support inside ChatGPT or Gemini. I do not need to be a full-time developer to automate repetitive SEO work.
I can create scripts that pull top pages from Google Search Console, compare title tags against character limits, flag 30-day performance changes, or generate a clean CSV output for review.
Instead of waiting for a vendor to add the exact feature I need, I can build a small script that removes a bottleneck. A hundred-line script can replace hours of manual work without requiring another SaaS license.
I also like that scripts make the logic visible. If I hand the workflow to another teammate, they can inspect what the script does and understand how the output was created.
Notebooks and local workflows
SEO teams usually have data scattered across shared folders, Google Sheets, Notion docs, monthly CSV dumps, and long-running audit trackers. I have seen how quickly that fragmentation slows decisions down.
Notebooks and local workflows help me turn scattered files into a working system. A script can pull the data, an API can surface the signal, and an LLM can help interpret the results before the output lands in a notebook or spreadsheet.
The value is consistency. I get cleaner data formats, shared access, and documented logic instead of rebuilding the same process every time someone needs a report or audit update.
As search optimization becomes more connected to generative AI, I need workflows that scale. Local workflows help me keep data consistent while giving the team a faster way to act on what we find.
Creating hybrid workflows that mix old and new SEO stacks
I do not think the old SEO stack is obsolete. I also do not think the new tools replace everything. The strongest approach is a hybrid workflow that keeps proven SEO fundamentals while adding AI, APIs, scripts, and notebooks where they create real leverage.
Tool + custom script + AI layer
To build a practical hybrid workflow, I would start with a familiar audit tool such as Screaming Frog, then run a Python script that joins the crawl data with Google Search Console data.
From there, I could flag pages with high impressions and low clicks, send those pages to an LLM for title and intent analysis, place the output into a notebook or spreadsheet for editors, and turn approved recommendations into change logs.
Work like this used to take weeks, so many teams pushed it aside. At enterprise scale, the amount of data could easily become overwhelming. With a hybrid SEO stack, I can complete larger projects in a fraction of the time.
For me, the goal is not to chase every new tool. The goal is to build a more agile SEO stack that can handle today’s massive datasets, identify AI search signals, and help teams move faster without losing the core SEO basics.
LLMs have changed how people search and how Google responds. The SERP has not been limited to 10 blue links for a long time, but traditional search has usually centered on one core intent: the thing someone is trying to find.
Now, AI Overviews can create a full answer directly in the SERP. They do more than respond to the original query. They also bring in related terms, contextual refinements, and supporting information that help searchers make better decisions.
That is why I pay close attention to Google query expansion. When I understand how Google connects related searches, I can find visibility opportunities that competitors may miss.
What is Google query expansion?
I think of Google query expansion as Google broadening a searcher’s query so it can return more accurate results, especially for long-tail searches that might otherwise produce weak or limited results.
This can happen through synonyms. For example, Google may connect “budget” with “affordable” when the intent is similar.
It can also happen through intent expansion. Google may understand what my audience means even when they do not type the exact words I expected.
Related topic expansion matters too. Google can use similar searches and connected topics to surface content that supports the searcher’s broader need.
I do not use this as an excuse to stuff keywords into a page. Instead, I use query expansion as a research signal. When I see related searches that make sense, I can add useful supporting information and help my content rank for a wider range of relevant queries.
Here is a simple example. If I have an article about backyard chicken care and someone searches “What’s the average lifespan of a chicken?”, my page might appear even if I never used the word “lifespan.”
In that case, Google has decided the article is semantically relevant. Once I know Google has made that connection, I can add a helpful section about chicken lifespan. That gives the page a stronger chance to rank for the term and attract more traffic.
It can also improve the odds that my content appears in relevant AI Overviews.
The difference between Google query expansion and query fan-outs
Google query expansion and query fan-outs are related, but I do not treat them as the same thing.
Query expansion is part of traditional search. Google broadens a query with synonyms, related terms, and intent signals before results are generated. Because of that, my content can rank for searches I did not directly target.
Query fan-outs are part of AI Mode. They break a query into multiple related subqueries while the AI response is being generated. Because of that, my content can be retrieved as a source for an AI-generated answer.
So why does traditional query expansion still matter in a search world shaped by LLMs and AI Overviews?
Because the same semantic relationships that help Google expand a query can also influence which content AI systems retrieve during query fan-outs.
How I find query expansion opportunities
The first place I look is Google Search Console. It is one of the clearest ways to confirm whether query expansion is already happening for my site and my content.
My workflow is straightforward. I go to Performance > Search results, filter by a specific page, pull the full query list, and sort by impressions.
From there, I look for queries I never intentionally targeted. I pay attention to synonyms with meaningful impressions, question-based searches that may be especially useful for AI visibility, and broader keywords that are not currently addressed on the page.
I do not assume every discovered query deserves a content update. Sometimes a page appears for terms that are not truly relevant. When that happens, I audit the page and make sure the content is not drifting into unrelated topics that fail to match the promise of the SERP result.
How I plan better content with query expansion
Once I understand which expanded queries Google is connecting to my content, I use that data to strengthen the page instead of chasing isolated keywords.
I write for topic coverage
For a long time, strong SEO has been less about exact keywords and more about semantic relevance. I try to build coverage around subtopics, related questions, and adjacent ideas because that gives Google more context than a page built around one keyword alone.
I answer questions adjacent to the main topic
For example, if I am working on content for a company that sells chicken feed, I would not only explain the feed itself. I would also consider why the right balance matters and how the right feed can support chicken health.
I can find those adjacent questions by reviewing query expansion data in Google Search Console, checking tools like Ahrefs, and studying the SERP to see what supporting information Google is already surfacing for the topic.
I use expansion data to find content gaps
If Google Search Console shows that Google is pulling my page for a query I have not planned for, and that query is genuinely relevant, I treat it as a signal that the page may need more complete coverage.
Sometimes query expansion data includes odd or unrelated searches. I ignore those. But when I find adjacent queries that clearly strengthen the topic, I add them to the page in a useful and natural way.
I also revisit content regularly, usually at least once a quarter. New queries can appear, while others fade away. Since I am already keeping content fresh for the SERP, query expansion gives me another practical way to make each topic stronger.
How I use query expansion to improve AI Overviews visibility
AI Overviews often pull from ranking pages on a topic to build a more complete answer. Those answers can include semantic connections and supporting subtopics, not just the exact phrase someone searched.
That is why I cross-reference my query expansion data with the main keyword in the SERP. If an AI Overview includes supporting topics that are relevant to my page, I consider adding those topics to the content.
For example, I followed this process for a blog post titled “Tandem vs. Spread Axles in Trucking.” After filtering by impressions, I found that the page appeared for “tandem truck meaning,” even though that exact phrase was not specifically included in the content.
The page ranked first, but it was not included in the AI Overview for that specific query. That told me there was an opportunity.
Because the page already ranked well, I could use the expanded query and the supporting information in the SERP to create a section that better addressed both the query expansion term and the query fan-out patterns behind the AI Overview.
That is the value of this process. Query expansions can reveal supporting topics that strengthen traditional search visibility and improve the chances of being included in AI-driven results.
How query expansion helps my SEO strategy evolve
I use query expansion as a practical way to identify supporting topics and expand content coverage across search experiences.
As clicks become harder to earn, I want my content to appear across more relevant search moments. Broader visibility can strengthen brand awareness, support AI visibility, and keep my content in front of the people most likely to need it.
With Projects in Profound, I can turn my AEO data into a clear, ranked list of opportunities instead of another report I have to interpret from scratch.
Each opportunity is broken into practical tasks, with an agent ready to help do the work. That makes it easier for me to move from insight to execution without getting stuck in endless analysis.
For me, Projects is about spending less time deciding what to do next and more time acting on the opportunities that can improve visibility, performance, and momentum.
I see existing content as a goldmine, but only when I have a practical way to improve it. The hard part is usually finding the time, and that is where Claude has made a large, messy job feel much more manageable for me.
I do not start by building a giant content audit system. I start with one article, run one focused audit, refine the output, and then turn the prompt into a reusable Claude skill. Over time, those one-off audits become a working library I can improve every time I use it.
I use Claude to uncover topical gaps, flag outdated information, check brand voice, and evaluate whether a page is easy for AI systems to retrieve and cite. The real value comes from iteration: each time I improve a skill, the next audit becomes faster and more useful.
Here are six content audit workflows I would build in Claude. The first four work at the page level, so I can start with a single article before moving into larger library-wide analysis.
Page-level audits
When I am not ready to build a full workflow, I start with page-level audits. These audits only require one article, which means I do not need a content inventory, a data export, or a complicated setup. After each session, I ask Claude to turn the process into a reusable skill for future page-level reviews.
1. Brand voice consistency
I use a brand voice consistency audit when a content library has drifted over time. Voice can shift because of new writers, changing services, product updates, or evolving positioning. This audit helps me spot where a page no longer sounds aligned with the brand.
If I do not have detailed brand guidelines with strong examples, I let Claude extract the voice guide from high-quality content. That usually works better than relying on vague phrases like “conversational but authoritative” or “educational, not too formal.”
I pick three to five articles that represent the brand at its best. If possible, I download them as markdown files and ask Claude to describe how the voice works in concrete terms.
How the articles usually open, such as whether they begin with a direct claim, a counterintuitive statement, or a specific scenario.
How sentences and paragraphs are built, including average length, range, rhythm, and how paragraphs tend to close.
Three to five personality dimensions framed as “We say X, but not Y,” with do and don’t examples.
Words and phrases the brand tends to use, and words or phrases it should avoid.
Specific constructions, phrases, and conventions the brand never uses.
Instead of accepting a vague voice description, I want Claude to return concrete observations. For example, it might say that articles open with a direct claim rather than a scene-setting paragraph, sentences average 15 to 20 words and rarely exceed 30, and transitions are functional, such as “here’s why that matters,” rather than formulaic, such as “furthermore.”
I also want example pairs, such as: “We’d say ‘the data shows three things,’ not ‘there are multiple factors to consider.’” The goal is not to create a voice guide for writers. The goal is to create one an LLM can understand and apply consistently.
Once I like the output, I ask Claude to save it as a skill and evaluate an article against it. If Claude flags issues I disagree with, I update the skill until the feedback becomes useful and repeatable.
I can then use that skill to find voice inconsistencies in older content, check new drafts for alignment, and even generate more on-brand first drafts. I still edit the output, but the starting point is much stronger.
When I need to improve content performance, I use a coverage comparison to find topical gaps. This helps me understand what competing pages cover that my article misses.
I use the Claude in Chrome extension to have Claude review the top three to five ranking pages for my target keyword. Then I ask Claude to compare those pages against my content and highlight the most important gaps.
What competitors are doing well.
What my article already does well.
Where I can improve the piece without bloating it.
If I want the output in a table, I ask Claude to format it that way. If I want a downloadable DOCX for review or handoff, I ask for that instead.
When Claude recommends additions I would never publish, I make a note of those exclusions before packaging the workflow into a skill. That way, the skill gets closer to my editorial standards each time I refine it.
3. Freshness audit
Old content adds up quickly, and it is hard to prioritize refreshes while I am also producing new material. A freshness audit skill helps me identify what needs attention without rereading every older article from scratch.
I give Claude an older article and ask it to flag anything time-sensitive: statistics tied to a specific year, named tools or platforms, references to “current” or “recent” trends, and claims that depend on a market, regulatory, or product context that may have changed. I am not asking Claude to rewrite the article yet. I am asking it to build an issue list I can act on.
If my company has launched new products, removed old services, changed positioning, or updated terminology, I include that context in the input. That helps Claude flag what should be added, removed, or revised.
I use an AEO and AI retrievability audit to understand whether a page is likely to be surfaced in AI-generated answers. Tools such as ChatGPT, Perplexity, and Google AI Overviews tend to favor content that answers questions directly. If an article buries the answer under too much preamble, or structures key information in a way that is hard to extract, it becomes less useful for those systems.
I give Claude the article and the target query, then ask it to evaluate several retrieval signals.
Whether the article answers the main question directly and early.
Whether key statements are specific enough for an LLM to quote or cite.
Where an FAQ-style section would improve clarity.
Whether the page includes authority signals, such as primary research, first-person experience, outbound citations, or specific examples.
Once I save this as a skill, it becomes an extra editor focused specifically on AI visibility and answer retrieval.
Library-level audits
Once I am ready to move beyond individual pages, I use library-level audits. These require performance data, a content inventory, a connector, or a manual export.
5. Performance triage
When I think about a traditional content audit, performance triage is usually what comes to mind. It helps me analyze a content library and identify the pages that deserve attention first.
Before I begin, I make sure Claude has access to the right data through a connector such as BigQuery or the Semrush API. If that is not available, I export the data I normally use for large-scale audits, such as traffic, clicks, engagement metrics, conversions, rankings, and related performance signals.
I ask Claude to prioritize pages that have suffered meaningful performance drops in the past six to 12 months, pages with high impressions but consistently low click-through rates, and pages that have been live long enough to rank but never gained traction.
I also define what a meaningful performance drop looks like for the site I am analyzing, because traffic patterns vary by industry, audience, and page type. Then I ask Claude for a prioritized list of what is worth investigating and why. From there, I use the page-level audits above to diagnose the problem.
If I have run this analysis before, I give Claude the previous output. That helps the skill learn the kind of prioritization and reasoning I expect.
I treat entities as a major part of AEO and semantic search. A topical gap analysis helps me see whether my content library has enough coverage to build authority around the entities tied to my brand.
The core question I ask is simple: what is my content library not covering that it should?
To start, I create a list of target entities. For example, at my agency, I want to be known for SEO and AEO. If I have a clear list of services or products, I can use that instead of a formal entity list.
Using Cowork or Code, I ask Claude to analyze my sitemap and compare it to those target entities. If I have a Screaming Frog export with URLs, page titles, and meta descriptions, I use that as input for a more accurate analysis.
Then I ask Claude to identify topic clusters that are missing or underrepresented based on the target entities, services, or products. If I want prioritization, I can use the Semrush MCP so Claude can check search volume for potential keywords.
Not every gap is worth filling. I filter the results against audience needs, business relevance, and editorial standards. Then I feed those decisions back into Claude so the skill produces better recommendations next time. The final list can go directly into my content creation workflow or be handed off to a content team.
I do not try to audit everything at once
I have seen content audits stall because the scope feels too large, not because the team lacks data. My preferred approach is to pick one audit and one article, run the workflow, save the skill, and use it again on the next piece.
For me, iteration is part of the value. I enjoy taking one Claude skill, improving it, and then chaining it with other skills to uncover more content opportunities. Starting small is what makes the system easier to keep using.
I started this series with a simple observation: AI systems do not always give the same answer to the same question. My argument was that this inconsistency is not just randomness. It is confidence loss across a pipeline we can measure, diagnose, and improve.
As I worked through the AI engine pipeline gate by gate, I eventually reached the won gate. That is where three kinds of clicks appear: the imperfect click of search, the perfect click of recommendations, and the agentic click of agents.
That is also where I realized this conversation could not stay inside marketing. When an agent makes the purchase, it becomes a client I have to satisfy directly.
The funnel now runs through machines that connect directly to the business itself. SEO therefore becomes part of something larger: assistive agent optimization, and ultimately AI-era business engineering.
To understand why, I need to connect the pieces. The framework explains why AI systems make the decisions they make and what shapes those decisions. When I apply those principles across the business, the goal becomes clear: organize the company so search engines, AI assistants, agents, and people can find it, understand it, recommend it, and buy from it.
Everything Builds On SEO
The process sits above the familiar disciplines I already work with: SEO, content, PR, paid media, and digital marketing. It helps me prioritize the actions that most affect recommendations and visibility.
Here is the part every SEO should value: assistive agent optimization is built on SEO. It does not replace it.
I think of it like a Russian doll. SEO sits at the center. It draws from the open web, the same crawled and indexed foundation search has always used.
At that core are two parts of the algorithmic trinity: the search engine, which indexes and ranks information, and the knowledge graph, which stores entities and the relationships between them.
The next layer is assistive engine optimization. It adds the third component: the large language model. The LLM provides reasoning, grounding, and conversation.
Instead of returning only a list of links, it evaluates corroborating evidence and answers the user directly. This layer builds on traditional SEO with entity corroboration, machine-readable proof, and signals that help AI systems understand what content actually means.
The outer layer is the agent. It introduces what the layers below it never had: direct access to business systems through protocols such as MCP. An agent can check inventory, compare prices, and complete transactions without visiting a page or clicking through a search result. This is where AI stops recommending and starts acting.
Each layer depends on the one beneath it. The stronger the SEO foundation, the more effectively I can build everything above it. That makes SEO more central to digital marketing, and to the business itself, than it has ever been.
If I understand how machines read the web, I hold the foundation every other AI-facing initiative depends on.
The Funnel Has Not Changed, But The Build Direction Has
The acquisition funnel has not fundamentally changed since marketers first drew it in the 1800s. Awareness still sits at the top, consideration in the middle, and decision at the bottom. The customer still moves downward while the brand tries to catch them. What has changed is where I have to stand to catch them.
Traditional marketing stood in front of people in the real world, on billboards, shelves, and stages. Digital marketing did the same online through SEO, paid search, social media, and content. AI-era marketing extends that logic again.
Now I have to stand where I always stood and also inside the AI engines. Those engines put brands in front of buyers, present the best solution, and increasingly make the purchase.
The modern buyer mixes all three modes in a single purchase, so I have to be present in all of them. The client still travels from the top of the funnel down, but the engines learn from the bottom up. That is how I need to build for them.
Marketers draw the funnel top-down because that is the customer path. But businesses have always had a reason to read it the other way. Winning the result for your own name is the cheapest and highest-converting move because it reaches the warmest traffic: people already at the door.
I have made that case since 2012, when I started working on brand SERPs. Your name is the one search result you can most completely own, yet the industry ignored it for years.
Comparison and consideration queries come next because they sit near the purchase, where buyers are most likely to convert. Awareness is the last thing I build, because those people often do not yet know what they want or what the solution might be.
The engines make this flip unavoidable. Search engines let users move between sites on the way down the funnel, so top-down building could still work. Assistive engines pull the funnel inside themselves. Now I build from the bottom up because that is how the machine learns who to trust.
Agents push this even further. The funnel goes dark, and the choice often goes with it. Each step takes more of the journey out of my hands, and each rewards the same brand: the one built from the bottom up.
The Agentic Spectrum Decides How Much Must Change
Two ideas tell me how much of a business has to change. The first is the delegation boundary. The second is the agentic spectrum.
The delegation boundary is the micro view. It tracks how much of one buyer journey, from searching to comparing to choosing to buying, a person hands to a machine.
The agentic spectrum is the macro view. It asks what share of the clientele has gone agentic and how quickly that share is growing.
The micro view tells me how to win one buyer in the moment. The macro view tells me how much of the business has to change to keep winning buyers over time. This is the number I would start measuring first.
Here is why it reorganizes the business, not just the marketing. When the agent makes the purchase, it becomes a client I have to satisfy directly, even as it acts for the person behind it. It answers to one priority: keeping its own user happy.
That means the sale turns on confidence. Can the machine trust the business to meet the need and keep its client satisfied?
That confidence has to clear a much higher bar than search or assistive engines required. It runs across the full funnel. If I earn it across the stack, I become the brand the agent buys from.
Preparing for that is what AI-era business engineering means. Pricing, qualification, product data, checkout, service, and retention all need to be built so an agent can transact as cleanly as a person can.
The agent navigates the whole funnel on its own. I have to convince it at every stage, from awareness to the final yes, while getting almost no visibility into the journey. What I do get is granular measurement at negotiation and transaction stages. The agent tells me what it wants, and I either satisfy it or I do not.
That is why I need to build the business to work cleanly with agents and people alike, from the top of the funnel to the moment the deal is struck.
Translating what a company does for humans into something machines can read and act on used to feel optional. Ignoring search engines and assistive engines was never wise, but many companies survived it. In the age of agents, ignoring the engines hands a growing share of the clientele to competitors.
Your Untrained Salesforce Is Already Selling
Every business now has a salesforce it never hired: Google, ChatGPT, Perplexity, Claude, Copilot, Siri, Alexa, and many more. The number keeps growing as major tech platforms add AI answers inside social media, video, search, operating systems, and workflow tools.
The apps people already use now embed assistants that recommend tools, vendors, and products. A buyer does not need to open a separate AI engine for this to happen.
Those engines reach prospects in explicit, implicit, and ambient ways. However they appear, the outcome is the same: they work around the clock, speak to prospects in rooms I will never see, and decide whether to recommend me or a competitor.
The default state of that salesforce is untrained. If someone asks about my category, it answers with the brands it happens to understand, and that may not be mine. It may hedge on basic facts, confuse the brand with a namesake, cite proof that does not exist, recommend the wrong use case, or name a competitor at the exact moment the user was looking for me.
The cost is real, but it often never appears on a dashboard. I cannot watch the AI research the brand, evaluate it, recommend it, or talk a buyer out of choosing it. It all happens inside the machine. That is why I pay attention to three taxes: invisibility, ghost, and doubt.
AI engines recommend the solution they are most confident in, and that is not always the best solution. It is often the one they understand best. The recommendation depends on what they grasp and how confident they are in it.
So if my solution is truly the best, I have to train them. I have to educate them and brief them. They answer to the user, and my client is their client. They retain that client by surfacing the strongest solution they can see.
The practical question is simple: have I made it unmistakably clear that I am the best answer to the specific problems I solve, for the ICP I serve?
Three Taxes Quietly Cost Recommendations
I pay a tax at every stage of the funnel for as long as this AI salesforce is not working explicitly in my favor.
Someone types the brand name directly into an engine, and instead of a clean answer, it hedges with phrases such as “claims to be,” “reportedly serves,” or “says on its website.” Worse, it may start offering alternatives.
Search engines usually do that only when a competitor pays heavily to appear on the brand SERP. Otherwise, the brand owns its own name.
AI can raise the alternative on its own, purely because it is uncertain. That is why brand SERP and AI résumé protection are no longer optional.
That hedge and nudge are the doubt tax. I pay it when the engine lacks enough independent corroboration to commit. It sits at the understandability layer, and the cost is every prospect who came looking for the brand by name and left with doubt.
The ghost tax appears when a prospect asks the engine to compare the category and name the best options. The engine lists several brands, but mine is missing. It knows I exist, yet it does not surface me because its confidence in my credibility is too low.
The invisibility tax appears at the top of the funnel. Someone asks a question I am well qualified to answer, and I am nowhere in the response because the engine never identified me as belonging in that conversation. I never see it because the conversation ends without me.
I need to track these taxes across every engine and every layer, and I should not use only my own account. It is biased toward me. The right approach is proper tracking, neutral testing, and better questions.
The funnel query pathway is the best way to read this over time and across the web. What I am measuring is leakage at each layer. Because the system is opaque, I read the macro trend rather than overreacting to one response.
Then I build from the bottom up and clear the taxes in revenue order.
I clear the doubt tax first because it affects the warmest traffic.
I clear the ghost tax next because it affects buyers comparing close options.
I clear the invisibility tax last because it sits furthest from the purchase.
That is the funnel flip again. AI engines have turned the old top-down playbook upside down.
The Algorithmic Trinity Is Where The Work Lands
I train the AI salesforce in three places, and I need to be present in all three for that training to hold.
Large language models do the reasoning at the moment of the query. This is the intelligence layer: ChatGPT, Claude, and Gemini.
Search engines index and rank fresh content. This is the information layer: Google and Bing.
Knowledge graphs store entities and verified relationships. This is the verification layer: Google’s Knowledge Graph, Wikidata, and Bing’s entity graph.
Those three layers are the algorithmic trinity.
I may be aiming at dozens of platforms and surfaces where this salesforce appears, but there are only a few machines at the root. At mass-market scale, the practical LLM list narrows quickly to ChatGPT and Gemini. There are two major web indexes, Google and Bing, and two major knowledge graph owners, Google and Bing again.
Everything I train reaches back to the same small set of underlying systems. The corroboration work I do for one engine often strengthens the foundation for all of them.
That is why the effort compounds. The knowledge graph confirms the entities the LLM reasons about. The search engine surfaces the fresh content the LLM grounds on. The AI salesforce becomes fully trained when all three converge on the same answer about the brand.
That convergence is where I win: independent systems reaching the same conclusion about who I am, what I do, who I serve, and why I am credible. When I give them that picture in detail, they can hold it with confidence.
At that point, the trinity can surface the brand at the bottom of the funnel, recommend it over competitors in the middle, and advocate for it at the top across search engines, assistive engines, and agents.
The results vary because each platform mixes technologies differently, but the direction starts to favor the trained brand.
Google owns all three layers and remains the dominant force across search and assistive engines, so it remains the main target.
I am not suggesting that I ignore smaller players such as Claude or DuckDuckGo. They matter to the audiences that use them. But for most brands, users, and SEOs, the major public engines are still the key to commercial success.
A tight digital footprint, cleaned up and optimized on-site and off-site, feeds the trinity. At mass-market scale, that means Gemini and ChatGPT, Google’s and Bing’s knowledge graphs, and Google’s and Bing’s search indexes.
The useful side effect is that this strategy also helps with smaller players.
Third-Party Proof Is What AI Believes
Knowing where the work is ingested is only half the job. I also need to know which evidence the AI salesforce believes. Not all evidence carries the same weight, and the gap between weak and strong proof is often the differentiator.
The weakest evidence is what a brand publishes about itself, in its own voice, on its own properties: homepage copy, about pages, and product descriptions. I call this first-party evidence. It is a claim and a baseline, but it proves little on its own because the engines know who wrote it.
If I surface a client outcome, case study, or customer review on my own off-site channel, I move up to second-party evidence. The substance is no longer entirely my assertion, even though I still control the publish button.
Then there is evidence I had no hand in publishing: clients and partners describing their own experiences, an independent journalist’s article, an analyst report, or coverage controlled entirely outside my reach. That is third-party evidence, and it is the strongest proof the salesforce can read because I could not directly shape it.
It is also the category many brands lack because it requires real-world activity, not just publishing. First-party claims, second-party corroborates, and third-party proves. Without proof, nothing stands.
Three Levels Of Effort Create Different Outcomes
Most brands sit at the bottom without consciously choosing to. The minimum-effort brand keeps a website, runs some content marketing, responds to occasional mentions, and otherwise lets the ecosystem do what it does. It appears in machine-readable form but does not shape that form.
Because minimum effort is treated as normal, many companies land here and never recognize it as a decision. Their AI salesforce is barely trained.
The next level appears when a brand notices specific problems and fixes them: an incorrect fact in an AI Overview, a competitor outranking it for a query, or a structured data gap. Those fixes help, and the brand becomes better positioned.
But the work is still symptom-driven. It patches what breaks loudly without building the discipline that prevents the next break. The salesforce is partially trained, but problems are driving the strategy.
The systematic brand runs an operational discipline against the pipeline every week: entity home maintenance, evidence harvested from service teams, machine-readable proof, distribution across publication tiers, and continuous monitoring of the brand SERP and AI résumé.
Most companies are not organized to make that happen naturally. But if I can harvest, codify, and distribute the evidence created by business operations, I can train the AI salesforce to work in my favor around the clock.
I would start from the entity home. I would organize the brand SERP and the AI résumé, then optimize the digital footprint wherever the brand appears. That is understandability, and it is the most important first move.
With the core entity locked, I can build credibility on top of it through engagement, reviews, client feedback, PR, and evidence that the business is genuinely good at what it does.
Deliverability follows because work on the brand SERP and AI résumé already strengthens credibility and reach. Then I can spread the same discipline across every entity the company owns: products, services, and people.
For each entity, I need the right content, presence where the audience is looking, a path down the funnel, and a clear connection back to the entity home. I need to walk the walk and apply the mirror principle.
The Salesforce Is Already Working
In 2026 and beyond, the AI salesforce operates inside the supply chain as well as the sales funnel. AI sits at the gates that decide whether to include a brand in what it knows, whether to deploy it in an answer, and whether to reselect it after every transaction.
Every outcome customers experience feeds back into the system for the next prospect who has never heard of the brand. That is the convergence this series has been pointing toward. The salesforce is selling 24 hours a day, for the brand or for a competitor. The difference is how well it has been trained.
This is why I see the discipline as AI-era business engineering, not just AI-era marketing. It is not a content tactic. It is a reorganization of how the business operates so pricing, qualification, product presentation, sales, retention, and customer success all create machine-readable evidence as a byproduct of doing the job.
SEOs Are In The Best Seat In The Room
When I speak with entrepreneurs and CEOs, I use nine questions to show where the company stands.
Tech, bottom to top: Is our entity home locked down so engines have one source of truth about who we are? Is our structured data complete enough for them to verify what we claim? Are we discoverable across every engine when topical questions appear?
Marketing, bottom to top: What does our brand SERP look like today, and what does the AI résumé say when engines are asked about us directly? Where is our third-party corroboration weakest, and what are we doing about it this quarter? Which topical territory do we own in the engines, and which territory do we want but not yet hold?
Branding, bottom to top: Does our brand story match what AI is currently saying about us, and where is the gap? Are our client outcomes being engineered into machine-readable evidence, or are they dying in CRMs and quarterly retrospectives? Are we placing proof now for the categories we want to own in three years?
All of those questions run from the bottom up, which is ironic because marketers usually work the funnel from the top down. The customer is the one moving from top to bottom, looking for a solution.
So I take a step back and read the funnel from the bottom up. Everyone is building the same thing: understandability, credibility, and deliverability. They are just approaching it from different ends.
The business builds from the foundation up: know who you are, know who you serve, become credible, then reach the right people.
The marketer wants the maximum audience and starts with reach, then works down to who the brand is and why it should be trusted.
AI starts at the bottom. Who are you? Are you credible? Only then will it put the brand in front of more people.
The SEO is the person who can see that it is all the same system. I understand that I must work from the foundation up, the way the machine does, and then meet the customer coming down from the top.
I should build for the customer, but work upward toward them. That has always been the stronger approach, and AI engines have now made it obvious.
The business now has two kinds of clients: the human and the agent. I need to speak to both. The agent is emulating a person and reflecting the world’s view of the brand, so pleasing the agent and pleasing the human are closely connected.
That is what makes SEO impossible to sideline. I am well positioned to tell the business and the marketers what must change to satisfy the agent without losing the human.
Whether agents represent 5% of the business today or nearly all of it, the agentic share will grow year after year. That means I have to step out of the SEO corner and look at the wider business. I am in a rare position to see business, marketing, and machines at the same time.
The audience used to be only human. Now it includes machines, too, and I am the one who can speak to both.
This is the 19th and final piece in my AI authority series, and it has been a long journey. My thanks to Danny Goodwin, Angel Niñofranco, and the Search Engine Land team for their immense support throughout.
When I started, the framework was a complete idea, but I had not fully worked through all the details. Week by week, I worked through each of the 15 gates, and every one turned out to be more intricate, more in-depth, and more thought-provoking than I expected.
What I have finished is a practical framework for SEO, marketing, and business in the AI age, one that search professionals, marketers, and business leaders can apply to real business problems.
Series Index
Parts 1 through 18 built this framework step by step: cascading confidence, assistive agent optimization, the AI engine pipeline, infrastructure gates, competitive gates, the entity home, the push layer, annotation, topical ownership, the funnel flip, the framing gap, pipeline repair, the delegation boundary, funnel query pathways, macro measurement, customer-success proof, AI opinion formation, and the collapse of paid and organic visibility across AI surfaces.
The best and worst part of the web, in my view, is that I can share an opinion freely even when that opinion is not technically accurate.
But I keep wondering what happens when that freedom comes with real accountability, not only for what I say online, but also for whether the words came from me or from AI.
A recent report makes that question feel a lot less theoretical. A German court held Google accountable for AI Overview content, treating those AI-generated summaries as Google’s own content and rejecting the idea that users alone were responsible for fact-checking the results.
I want to unpack what that could mean for businesses, SEOs, and individuals who are leaning harder on AI every day.
The ‘disclaimer’ defense is cracking
For the last few years, I have seen nearly every AI platform rely on some version of the same warning: AI can make mistakes, so users should verify important information.
Most of us accepted that as the price of using these tools.
But the German court essentially said that a warning about possible errors does not automatically erase responsibility when those errors cause harm. If a system creates new claims that were never in the source material, those claims are no longer just someone else’s words. They become the platform’s words.
Why? Because the conversation moves away from whether AI is useful and toward who owns the consequences when AI gets something wrong.
What this means for businesses
I see many companies rapidly adopting AI across content creation, customer service, product descriptions, reporting, legal reviews, hiring, and internal communications. In many cases, they are blindly trusting the output because the efficiency gains are so tempting.
Most of the conversation still centers on speed and cost. Can we create content faster? Can we answer support tickets more cheaply? Can we automate this process?
Those are fair questions. I ask them too.
But this ruling adds a more important question: Who is responsible when the output is wrong?
What happens if an AI-generated support response gives a customer inaccurate guidance? What happens if an AI-written article damages a competitor’s reputation? What happens if an AI-generated report includes fabricated information that influences a business decision?
The more we position AI as a trusted source of information, the harder it becomes to argue that we should not be accountable for what it says.
The situation is kinda funny…
The irony is that most AI vendors already know this.
That is why nearly every platform includes warnings, disclaimers, and usage policies.
At the same time, those same companies market AI as smarter, faster, more capable, and increasingly reliable.
I do not think you can tell users to trust the answer while also arguing that nobody should trust the answer.
At some point, those positions collide. We are already starting to see Google’s solution: an option to opt out of AI.
Germany may simply be one of the first courts willing to force Google, or any other LLM business, to take clearer responsibility for the systems it puts in front of users.
What SEOs should be paying attention to
Ironically, I think this ruling could end up benefiting everyone.
Right now, the debate is focused on whether AI companies should be responsible for the content their systems generate. But I can see accountability expanding well beyond AI.
The internet has spent decades creating distance between actions and consequences. Anonymous accounts, fake profiles, throwaway emails, and now AI-generated content all make it easier for people to say things without owning them.
That is why I find this ruling so interesting.
It is not just about Google. It is about the idea that “I did not write it” may no longer be enough.
The image below shows a real email that Russell and Nina Westbrook received. A real person sat behind a keyboard and sent a message hoping they would die in a car crash.
That is not free speech. It is hate speech.
The internet, especially now that AI is layered into it, needs more confidence that content is accurate and that the people and companies creating it can be held accountable.
I do not believe we get to claim the productivity gains when AI is right and then blame the algorithm when it is wrong.
This post first appeared on the author’s website and is republished here with permission.
