Microsoft Advertising is expanding its Performance Max reporting with publisher-level conversion and spend data — giving advertisers more visibility into where results are actually coming from
What’s happening. According to Microsoft Ads Product liaison Navah Hopkins, the PMax Website Publisher URL report now includes conversion and spend metrics, moving beyond basic placement visibility into actionable performance data.
This gives advertisers clearer insight into which placements are driving real outcomes — not just impressions or clicks.
Why we care. This update gives advertisers visibility into which placements are actually driving conversions and spend — not just impressions. That means better optimisation decisions, from scaling winning inventory to cutting wasted spend. It also makes it easier to trust and justify Performance Max performance with concrete data, rather than relying on aggregated reporting.
How advertisers can use it. The update opens up several practical use cases. High-performing placements can now inform Audience Ads strategies, such as building remarketing campaigns or impression-based audiences from winning inventory.
At the same time, advertisers can identify poor-fit placements and exclude them using account-level URL exclusion lists, helping protect brand safety and improve efficiency.
Between the lines. This is another step toward making automated campaigns more transparent. Rather than replacing control entirely, platforms are starting to give advertisers clearer signals on what’s working — and where to act.
What to watch:
Whether this level of transparency expands further across PMax reporting
How advertisers balance automation with manual optimisation
If similar reporting features roll out across other platforms
Google is enforcing a hard cutoff for older API versions, meaning advertisers and developers who don’t upgrade risk losing access to critical campaign management tools.
What’s happening. Google Ads API v20 will officially sunset on June 10, 2026. From that date onward, all requests to v20 will fail, requiring migration to a newer version to maintain uninterrupted API access.
Why we care. If you rely on the Google Ads API and don’t upgrade in time, automated workflows — including reporting, bidding and campaign management — could suddenly stop working. This could lead to data gaps, performance issues and operational disruption. Migrating early ensures continuity and avoids last-minute fixes that can impact campaign performance.
What to do. Google is urging users to upgrade as soon as possible and provides resources like release notes and upgrade guides to support the transition. Developers can also use the Google Cloud Console to review recent API activity, including which methods and versions their projects are calling.
Between the lines. API sunsets are routine, but the impact can be significant for advertisers relying on custom scripts, tools or third-party platforms. Missing the deadline could disrupt reporting, bidding or campaign automation workflows.
I’ve built 10+ SEO agent skills in 34 days. Six worked on the first try. The other four taught me everything I’m about to show you about the folder structure most LinkedIn posts about AI SEO skills gloss over.
What makes these agents reliable isn’t better prompts. It’s the architecture behind them. Here’s how to build an agent from scratch, test it, fix it, and ship it with confidence.
Why most AI SEO skills fail
Here’s what a typical “AI SEO prompt” looks like on LinkedIn:
You are an SEO expert. Analyze the following website and provide a comprehensive audit with recommendations.
That’s it. One prompt. Maybe some formatting instructions. The person posts a screenshot of the output, gets 500 likes, and moves on. The output looks professional. It reads well. It’s also 40% wrong.
I know because I tried this exact approach. Early in the build, I pointed an agent at a website and said, “find SEO issues.” It came back with 20 findings. Eight didn’t exist. The agent had never visited some of the URLs it was reporting on.
Three problems kill single-prompt skills:
No tools: The agent has no way to actually check the website. It’s working from training data and guessing. When you ask, “Does this site have canonical tags?” the agent imagines what the site probably looks like rather than fetching the HTML and parsing it.
No verification: Nobody checks if the output is true. The agent says, “missing meta descriptions on 15 pages.” Which 15? Are those pages even indexed? Are they noindexed on purpose? No one asks. No one verifies.
No memory: Run the same skill twice, you get different output. Different structure. Different severity labels. Sometimes different findings entirely. There’s no consistency because there’s no template, no schema, no record of past runs.
If your skill is a prompt in a single file, you don’t have a skill. You have a coin flip.
Every agent in our system has a workspace. Think of it like a new hire’s desk, stocked with everything they need. Here’s what the workspace looks like for the agent that crawls websites and maps their architecture:
agent-workspace/
AGENTS.md instructions, rules, output format
SOUL.md personality, principles, quality bar
scripts/
crawl_site.js tool the agent calls to crawl
parse_sitemap.sh tool to read XML sitemaps
references/
criteria.md what counts as an issue vs noise
gotchas.md known false positives to watch for
memory/
runs.log past execution history
templates/
output.md expected output structure
Six components. One prompt file would cover maybe 20% of this.
AGENTS.md is the instruction manual
I wrote thousands of words of methodology into AGENTS.md. Instead of “crawl the site,” I laid out the steps: “Start with the sitemap. If no sitemap exists, check /sitemap.xml, /sitemap_index.xml, and robots.txt for sitemap references.
Respect crawl-delay. Use a browser user-agent string, never a bare request. If you get 403s, note the pattern and try with different headers before reporting it as a block.”
Scripts are the agent’s tools
The agent calls node crawl_site.js –url to analyze website data. It doesn’t write curl commands from scratch every time. That’s the difference between giving someone a toolbox and telling them to forge their own wrench.
References are the judgment calls
This contains criteria for what counts as an issue. Known false positives to watch for. Edge cases that took me 20 years to learn. The agent reads these when it encounters something ambiguous.
Memory is institutional knowledge
Here I keep a log of past runs:
What it found last time.
How long the crawl took.
What broke.
The next execution benefits from the last.
Templates enforce consistency
This is where I get specific about the output I want: “Use this exact structure. These exact fields. This severity scale.” Output templates are the difference between getting the same quality in run 14 as you did in run 1.
Walkthrough: Building the crawler from scratch
Let me show you exactly how I built the crawler. It maps a site’s architecture, discovers every page, and reports what it finds.
Version 1: The naive approach
I provided the instruction: “Crawl this website and list all pages.”
The agent wrote its own HTTP requests, used bare curl, and got blocked by the first site it touched. Every modern CDN blocks requests without a browser user-agent string, so it was dead on arrival.
Version 2: Added a script
I built crawl_site.js using Playwright. This version used a headless browser and a real user-agent. The agent calls the script instead of writing its own requests.
This worked on small sites, but it crashed on anything over 200 pages. Because there was no rate limiting and no resume capability, it hammered servers until they blocked us.
Version 3: Introducing rate limiting and resume
I added throttling with a two requests per second default and never every two seconds for CDN-protected sites. The agent reads robots.txt and adjusts its speed without asking permission. I also added checkpoint files so a crashed crawl can resume from where it stopped.
This worked on most sites, but it failed on sites that require JavaScript rendering.
Version 4: JavaSript rendering
This time, I added a browser rendering mode. The agent detects whether a site is a single-page app (React, Next.js, Angular) and automatically switches to full browser rendering.
It also compares rendered HTML against source HTML, and I found real issues this way: Sites where the source HTML was an empty shell but the rendered page was full of content. Google might or might not render it properly. Now we check both.
This version worked on everything, but the output was inconsistent between runs.
Version 5: Time for templates and memory
For this version, I added templates/output.md with exact fields: URL count, sitemap coverage, blocked paths, response code distribution, render mode used, and issues found. This way every run produces the same structure.
I also added memory/runs.log. The agent appends a summary after every execution. Next time it runs, it reads the log and can compare results, like “Last crawl found 485 pages. This crawl found 487. Two new pages added.”
Version 5 is what we run today. Five iterations in one day of building.
THE CRAWLER'S EVOLUTION
v1: Raw curl → blocked everywhere
v2: Playwright script → crashed on large sites
v3: Rate limiting → couldn't handle JS sites
v4: Browser rendering → inconsistent output
v5: Templates + memory → stable, consistent, reliable
Time: 1 day. Lesson: the first version never works.
The pattern is always the same: Start small, hit a wall, fix the wall, hit the next wall.
Five versions in one day doesn’t mean five failures. It means five lessons that are now permanently encoded. I’ve rebuilt delivery systems four times over 20 years. The process doesn’t change. You start with what’s elegant, then reality hits, and you end up with what works.
Tip: Don’t try to build the perfect skill on the first attempt. Build the simplest thing that could possibly work. Run it on real data and watch it fail. The failures tell you exactly what to add next. Every version of our crawler was a direct response to a specific failure. Not a feature we imagined. A problem we hit.
This is the most important architectural decision I made.
When you write “use curl to fetch the sitemap” in your instructions, the agent generates a curl command from scratch every time. Sometimes it adds the right headers. Sometimes it doesn’t. Sometimes it follows redirects. Sometimes it forgets.
When you give the agent a script called parse_sitemap.sh, it calls the script. The script always has the right headers, always follows redirects, and always handles edge cases. The agent’s judgment goes into WHEN to call the tool and WHAT to do with the results. The tool handles HOW.
Our agents have tools for everything:
crawl_site.js: Playwright-based crawler with rate limiting, resume, and rendering
parse_sitemap.sh: Fetches and parses XML sitemaps, counts URLs, detects nested indexes
check_status.sh: Tests HTTP response codes with proper user-agent strings
extract_links.sh: Pulls internal and external links from page HTML
The agent decides which tools to use and what parameters to set. The crawler chooses its own crawl speed based on what it encounters. It reads robots.txt and adjusts. It has judgment within guardrails.
Think of it this way: You give a new hire a CRM, not instructions on how to build a database. The tools are the CRM. The instructions are the process for using them.
Progressive disclosure: Don’t dump everything at once
Here’s a mistake I made early: I put everything in AGENTS.md. Every rule. Every edge case. Every gotcha. Thousands of words.
The agent got confused. It had too much context and it started prioritizing obscure edge cases over common tasks. It would spend time checking for hash routing issues on a WordPress blog.
The fix: progressive disclosure.
Core rules that affect the 80% case go in AGENTS.md. This is what the agent needs to know for every single run.
Edge cases go in references/gotchas.md. The agent reads this file when it encounters something ambiguous. Not before every task. Only when it needs it.
Criteria for severity scoring go in references/criteria.md. The agent checks this when it finds an issue and needs to decide how bad it is. Not upfront.
This is the same way a skilled employee operates. They know the core process by heart. They check the handbook when something weird comes up. They don’t re-read the entire handbook before answering every email.
If your agent output is inconsistent but your instructions are detailed, the problem is usually too much context. Agents, like new hires, perform better with clear priorities and a reference shelf than with a 50-page manual they have to digest before every task.
The 10 gotchas: Failure modes that will burn you
Every one of these lessons cost me hours. They’re now encoded in our agents’ references/gotchas.md files so they can’t happen again.
Agents hallucinate data they can’t verify
I asked the research agent to find law firms and count their attorneys. It made every number up. It had never visited any of their websites.
Only ask agents to produce data they can actually fetch and verify. Separate what they know (training data) from what they can prove (fetched data).
Knowledge doesn’t transfer between agents
This fix I figured out on day one (use a browser user-agent string to avoid CDN blocks) had to be re-taught to every new agent. Day 34, a brand new agent hit the exact same problem.
Agents don’t share memories. Encode shared lessons in a common gotchas file that multiple agents can reference.
Output format drifts between runs
The same prompt can result in different field names: “note” vs. “assessment.” “lead_score” vs. “qualification_rating.” If you run it twice, get two different schemas.
The fix: Create strict output templates with exact field names. Not “write a report.” “Use this exact template with these exact fields.”
Agents confidently report issues that don’t exist
The first three audits delivered false positives with total confidence.
The fix wasn’t a better prompt. It was a better boss. A dedicated reviewer agent whose only job is to verify everyone else’s work. The same reason code review exists for human developers.
Bare HTTP requests get blocked everywhere
Every modern CDN blocks requests without a browser user-agent string. The crawler learned this on audit number two when an entire site returned 403s.
All it required was a one-line fix, and now it’s in the gotchas file. Every new agent reads it on day one.
Don’t guess URL paths
Agents love to construct URLs they think should exist: /about-us, /blog, /contact. Half the time, those URLs 404.
My rule is: Fetch the homepage first, read the navigation, follow real links. Never guess.
‘Done’ vs. ‘in review’ matters
Agents marked tasks as “done” when posting their findings. Wrong. “Done” means approved. “In review” means waiting for human verification.
This small distinction has a huge impact on workflow clarity when you have 10 agents posting work simultaneously.
Categories must be hyper-specific
“Fintech” is useless for prospecting because it’s too broad. “PI law firms in Houston” works. Every company in a category should directly compete with every other company.
My first attempt at sales categories was “Personal finance & fintech.” A crypto exchange doesn’t compete with a budgeting app. Lesson learned in 20 minutes.
Never ask an LLM to compile data
Unless you want fabricated results. I asked an agent to summarize findings from five separate reports into one document. It invented findings that weren’t in any of the source reports.
Always build data compilations programmatically. Script it. Never prompt it.
Agents will try things you never planned
The research agent tried to call an API we never set up. It assumed we had access because it knew the API existed.
The fix: Be explicit about what tools are available. If a script doesn’t exist in the scripts folder, the agent can’t use it. Boundaries prevent creative failures.
Build the reviewer first
This is counterintuitive. When you’re excited about building, you want to build the workers. The crawler. The analyzers. The fun parts.
Build the reviewer first. Without a review layer, you have no way to measure quality. You ship the first audit and it looks great. But 40% of the findings are wrong. You don’t know that until a client or a colleague spots it.
Our review agent reads every finding from every specialist agent. It checks:
Does the evidence support the claim?
Is the severity appropriate for the actual impact?
Are there duplicates across different specialists?
Did the agent check what it says it checked?
That single agent was the biggest quality improvement I made. Bigger than any prompt tweak. Bigger than any new tool.
The human approval rate across 270 internal linking recommendations: 99.6%. That number exists because a reviewer verifies every single one.
I’ve seen the same pattern with human SEO teams for 20 years. The teams that produce great work aren’t the ones with the best analysts. They’re the ones with the best review process. The analysis is table stakes. The review is the product.
BUILD ORDER (WHAT I LEARNED THE HARD WAY)
What I did first: Build workers → Ship output → Discover quality problems → Build reviewer
What I should have done: Build reviewer → Build workers → Ship reviewed output → Iterate both
The reviewer defines quality. Build it first. Everything else gets measured against it.
Tip: If you’re building multiple agents, the reviewer should be the first agent you build. Define what “good output” looks like before you build the thing that produces output. Otherwise, you’re shipping hallucinations with formatting. I learned this across three audits that were embarrassing in hindsight.
The validation standard (Our unfair advantage)
The reviewer catches technical errors. But there’s a higher bar than “technically correct.”
We have a real SEO agency with real clients and a team with 50 years of combined experience. Every agent finding gets validated against one question: “Would we stake our reputation on this?”
Would we actually send this to a client, put our name on the report, and tell the developer to build it?
Below are four tests we use for every finding:
The Google engineer test: If this client’s cousin works at Google, would they read this finding and nod? Would they say, “Yes, this is a real issue, this makes sense”? If the answer is no, it doesn’t ship.
The developer test: Can a developer reproduce this without asking a single follow-up question? “Fix your canonicals” fails. “Change CANONICAL_BASE_URL from http to https in your production .env” passes.
The agency reputation test: Would we defend this finding in a client meeting? If I’d be embarrassed explaining it to a technical CMO, it gets cut.
The implementation test: Is this specific enough to actually fix? Not “improve your page speed” but “your hero video is 3.4MB, which is 72% of total page weight. Serve a compressed version to mobile. Here’s the file.”
This is our unfair advantage. We’re not building agents in a vacuum. Most people building AI SEO tools have never run a real audit. They don’t know what “good” looks like. We do. We’ve been delivering it for 20 years with real clients. That’s why our approval rate is 99.6%.
Sandbox testing: Train on planted bugs
You don’t train an agent on real client sites. You build a test environment where you KNOW the answers. We built two sandbox websites with SEO issues we planted on purpose:
A WordPress-style site with 27+ planted issues: missing canonicals, redirect chains, orphan pages, duplicate content, broken schema markup.
A Node.js site simulating React/Next.js/Angular patterns with ~90 planted issues: empty SPA shells, hash routing, stale cached pages, hydration mismatches, cloaking.
The training loop:
Run agent against sandbox.
Compare agent’s findings to known issues.
Agent missed something? Fix the instructions.
Agent reported a false positive? Add it to gotchas.md.
Re-run. Compare again.
Only when it passes the sandbox consistently does it touch real data.
Think of it like a driving test course. Every accident on real roads becomes a new obstacle on the course. New drivers face every known challenge before they hit the highway.
The sandbox is a living test suite. Every verified issue from a real audit gets baked back in. It only gets harder. The agents only get better.
Consistency: The unsexy secret
Nobody writes about this because it’s boring. But consistency is what separates a demo from a product.
Three things that make output consistent:
Templates: Every agent has an output template in templates/output.md: Exact fields, structure, and severity scale. If the output looks different every run, you don’t need a better prompt. You need a template file.
Run logs: After every execution, the agent appends a summary to memory/runs.log. Timestamp, site, pages crawled, issues found, duration. The next run reads this log. It knows what happened last time. It can compare and provide outputs like, “Found 14 issues last run. Found 16 this run. 2 new issues identified.”
Schema enforcement: Field names are locked: “severity” not “priority,” “url” not “page_url,” “description” not “summary.” When you let field names drift, downstream tooling breaks. Templates solve this permanently.
If your agent output looks different every run, you need a template file, not a better prompt. I cannot stress this enough. The single fastest way to improve quality for any agent is a strict output template.
The stack that makes it work
A quick note on infrastructure, because the tools matter.
Our agents run on OpenClaw. It’s the runtime that handles wake-ups, sessions, memory, and tool routing. Think of it as the operating system the agents run on. When an agent finishes one task and needs to pick up the next, OpenClaw handles that transition. When an agent needs to remember what it did last session, OpenClaw provides that memory.
Paperclip is the company OS. Org charts, goals, issue tracking, task assignments. It’s where agents coordinate. When the crawler finishes mapping a site and needs to hand off to the specialist agents, Paperclip manages that handoff through its issue system. Agents create tasks for each other. Auto-wake on assignment.
Claude Code is the builder. Every script, every agent instruction file, every tool was built with Claude Code running Opus 4.6. I’m a vibe coder with 20 years of SEO expertise and zero traditional programming training. Claude Code turns domain knowledge into working software.
The combination: OpenClaw runs the agents. Paperclip coordinates them. Claude Code builds everything.
This process resulted in 14+ audits completed with 12 to 20 developer-ready tickets per audit, including exact URLs and fix instructions. All produced in hours, not weeks.
We have a 99.6% approval rate on internal linking recommendations on 270 links across two sites, verified by a dedicated review process.
We completed more than 80 SEO checks mapped across seven specialist agents. Each check has expected outcomes, evidence requirements, and false positive rules. Every finding is specific (i.e., “the main app JavaScript bundle is 78% unused. Here are the exact files to fix”).
That level of specificity comes from the skill architecture. The folder structure. The tools. The references. The templates. The review layer. Not the prompt.
If you want to build SEO agent skills that actually work, stop writing prompts and start building workspaces. Give your agents tools, not instructions. Test on sandboxes, not clients.
Build the reviewer first. Enforce templates. Log everything. The first version will fail. The fifth version will surprise you.
This is how you turn agent output into something repeatable. The same system produces the same quality — whether it’s the first audit or the 14th — because every step is structured, verified, and encoded.
Not because the AI is smarter. Because the architecture is.
Over the past few years, Performance Max has gone from an opaque experiment to a more capable — though still imperfect — campaign type for B2B marketers.
The fundamentals haven’t changed: skepticism still matters, first-party data is critical, experimentation is non-negotiable, and actionable reporting drives optimization. What has changed is how much better Google has gotten at operationalizing those inputs.
That means your Performance Max strategy needs to adapt. Here are five best practices for running more effective PMax campaigns for B2B today.
1. Guide AI with the right inputs
In 2022, given the automated nature of PMax campaigns and the aggressive way Google reps were pushing them, I predicted we’d see an accelerated move toward AI integration. That’s certainly played out, probably in part because of competitive pressures introduced by ChatGPT and the like.
AI Max for Search (launched in 2025) and PMax are both being prioritized by Google, and that’s not necessarily a bad thing since Google hasn’t deprecated standard Search campaign for B2B and has provided a slew of helpful updates that make PMax more viable for B2B.
Three updates worth using include:
Search themes, which are useful for more precise targeting.
Brand exclusions, which help minimize CPC inflation and over-investment on less-incremental queries.
Account-level channel reporting, which gives you a single dashboard look at performance across campaigns. For this feature, segment by conversion metrics to drill down on ROI by channel. You’ll quickly see overperformers where you can increase investment and underperformers that cry out for further optimization or reduced budget.
B2B lead quality in search campaigns has always been a challenge, and PMax’s relative lack of advertiser control makes that challenge tougher. I’ve pushed offline conversion tracking (OCT) since we’ve had that capability, but it’s an absolute non-negotiable for B2B campaigns.
Citing the phase-out of third-party cookies that still hasn’t happened (!), Google officially sunsetted Similar Audiences in 2023, which — well, it was a big loss for advertisers.
To compensate, understand and adapt according to the nature of PMax targeting, which is based on audience signals. Feed the AI high-quality first-party data (CRM lists) and let the algorithm find “lookalikes” through its own internal signals.
CRM lists for B2B are obviously critical, and this should give you even more incentive to clean up and segment CRM data, with audience lists closest to the point of revenue (e.g., SQLs or revenue if you don’t have enough closed-won data to send strong signals), especially valuable for finding high-value new users.
Creative is an important part of the puzzle for PMax. Good creative can prompt the right audience to engage, and great creative can deter the wrong audience from engaging.
Because YouTube is now a massive part of PMax campaigns, video — which has never been a B2B strength — should be prioritized more than ever for performance marketing.
Google has made this easier by adding the ability to build AI-generated assets right in the Google Ads interface. Just recently, they launched an important complementary feature in beta: PMax A/B creative testing to help advertisers understand which creatives are actually driving performance, and to use test-and-control structures to surface winning (and losing) elements.
A major source of frustration with PMax has been a lack of transparency into results. Over the last few years, Google has introduced reporting updates to address some of those concerns.
Search term insights and auction insights in the Insights tab provide more visibility into performance. Search term insights show how your ads perform for the queries users actually type, including how those ads are being matched and served. This added nuance makes optimization more precise.
Auction insights add competitive context, showing how your campaigns perform against others in the same auctions through metrics like impression share and outranking share.
Finally, asset-level reporting brings visibility to creative performance, with data on impressions, clicks, cost, and conversions for each asset.
Together, these updates give you a clearer view into what’s driving performance — and where to focus optimization efforts.
Taken together, recent updates make PMax more viable for B2B marketers than it used to be, especially for those with strong first-party data to train bidding algorithms and a need to find new customer pockets.
After more than 10 years in marketing, I still prefer having controllable levers — and I’m not willing to fully trust Google to act more in my (or my clients’) best interests than its own. Use everything at your disposal to make PMax campaigns work for you, and keep an eye out for new features Google releases that can give you more visibility and control over your account performance.
Programmatic SEO (pSEO) has been viewed with suspicion by the market. For many SEOs, the term is synonymous with low-quality pages, duplicate content, and the old tactic of “find and replace” city names in static templates.
Google’s spam policies on scaled content abuse are clear: generating vast amounts of unoriginal content primarily to manipulate search rankings is a violation.
Modern pSEO replaces mass page generation with an infrastructure that answers thousands of specific search intents with local nuance and semantic depth at a scale that isn’t possible manually.
This blueprint shows how to evolve from syntax-based pSEO (swapping keywords) to semantics-based pSEO (meaning and context), using a methodology we’ve applied to major players in Brazil.
The fallacy of the static template vs. semantic granularity
The most common mistake when starting a pSEO project is starting with the template, not the data. The old mindset said: “I have a template for ‘Best Hotel in [City].’ I’ll replicate this for 500 cities.”
The problem? The search intent for “Best Hotel in [Las Vegas]” (focused on nightlife, casinos, and luxury) can be radically different from the intent for “Best Hotel in [Orlando]” (focused on family suites, park shuttles, and pools). The user priorities, amenities sought, and decision-making criteria change completely.
The semantic approach requires us to use AI to granularize content. Instead of just swapping the {{City}} variable, we use LLMs to rewrite entire sections of the page based on the specific travel intent of that destination.
We don’t want to create 1,000 pages that say the same thing. We want 1,000 pages that answer 1,000 unique travel needs while maintaining a scalable technical structure.
Before writing a single line of content, you must answer a critical question: Where do I have permission to rank?
Many pSEO projects fail because they try to cover topics where the domain lacks historical authority. The solution we developed involves a deep analysis of topic clusters based on real Google Search Console (GSC) data, not just third-party search volume.
The authority map methodology works in three stages:
Cluster audit: Identify which topics the domain already dominates, which are opportunities, and where semantic gaps exist.
Priority definition: pSEO should be used surgically to fill these gaps and strengthen topical authority, not to shoot in all directions.
Connection with the calendar: The pSEO strategy must be born from this data. If GSC shows you have growing authority in a topic like “Mortgage Credit,” that is where scale should be applied first.
From there, AI suggests themes and direction, taking into account seasonality and brand guide specifications. This approach transforms pSEO from a “gamble” into a tactic of territorial defense and expansion based on proprietary data.
Solving ‘brand hallucination’: Context governance
The biggest barrier to AI adoption in enterprise companies is brand consistency. How do you ensure that 500 AI-generated articles don’t sound generic or, even worse, hallucinate information outside the company’s tone of voice?
The answer lies in context governance. Instead of relying on isolated prompts, the pSEO architecture must include a brand guidelines layer that acts as a guardian before text generation. This means systematically injecting:
Brand persona: (e.g., “We are technical, but accessible”).
Negative constraints: (e.g., “Never use the word ‘cheap,’ use ‘affordable’”).
Proprietary data: Institutional information that AI doesn’t have in its training data.
By centralizing these guidelines in a digital brand guide that feeds all AI agents, we ensure that multiple sites within the same corporate group (such as a retail conglomerate) maintain their distinct verbal identities, even when producing content on the same topic (like Black Friday) simultaneously.
The AI stops being a “junior copywriter” and starts acting as a specialist trained in the company’s culture.
The architecture: The semantic mesh (internal linking)
You’ve created 1,000 excellent pages. How do you ensure Google finds and values all of them? The answer isn’t using “related posts” plugins that only look for matching tags. You need to create a strategy based on real data.
The end of the ‘dead end’
You don’t want the user to land on a page and leave. You want to offer the next logical step. Cross-reference search intent with the destination:
The practical example: If a user lands on the site searching for “What is a CRM,” they are in the discovery phase. If that page doesn’t link semantically to “Advantages of [your company’s] CRM,” the user journey “dies” there. The semantic mesh connects the question to the solution.
Strategic reasoning in practice
Instead of randomness, our analysis works based on semantic meaning. The AI identifies:
“I noticed you are about to write about ‘customer retention.’ We have an older article about ‘churn rate’ that complements this topic perfectly. Insert a link to it.”
The tool suggests links between these pages because the context is relevant, strengthening the site’s Topical Mesh.
In programmatic SEO projects, where site depth can grow rapidly, this automation via vectors is the only way to ensure no good page gets forgotten at the bottom of the index.
This closes the loop of topical authority, ensuring no page generated at scale becomes an orphan page.
Case study: Regionalization and seasonality at scale
Theory is nice, but seeing it in practice is even better. Let’s analyze the case of Ânima Educação, one of the largest private education players in Brazil, with about 310,000 students and 18 higher education institutions.
The challenge
The National High School Exam (ENEM) is the “Black Friday” of Brazilian education. Search volume explodes in a short period, competition is brutal, and search intents shift rapidly (from “how to study” to “what is my score good for”). Furthermore, Brazil has continental dimensions; the questions of a student in the Northeast are different from those of a student in the extreme South.
The execution
Using the semantic pSEO methodology and the brand governance mentioned above, it was possible to structure complete coverage of the candidate journey — from exam preparation to the release of grades.
We ensured that all 18 brands were positioned to answer student questions at the exact moment of the search, respecting local nuances.
The results
Scale with precision: During five months, hundreds of undergraduate course pages and articles were optimized or created with granular local relevance.
Business impact: Surpassed the organic revenue target by 110% during the critical ENEM season.
Omnichannel dominance: Visibility across Google Search, Google Discover, and AI Overviews, and LLMs like Gemini and ChatGPT.
Strategic shift: The SEO team transitioned from repetitive manual tasks to high-level strategic oversight.
The technical guardian: Conversational monitoring
Scaling content without scaling technical monitoring is a recipe for disaster. Publishing 500 pages that result in 404 errors, redirect loops, or poor Core Web Vitals (CWV) can destroy the site’s crawl budget.
Modern pSEO requires a layer of real-time technical SEO. It isn’t enough to wait for the monthly report. You need to connect data to the workflow.
The trend now is the use of technical SEO agents — conversational interfaces that allow the professional to ask the data: “Of the 200 pages published today, which ones have indexing issues?” or “Which clusters are suffering from high LCP?”
This closes the cycle:
Planning (authority map).
Execution (pSEO with brand governance and semantic linking).
Programmatic SEO has ceased to be about volume to become about relevance. Success won’t come from publishing 10,000 pages tomorrow, but from building an infrastructure that delivers genuine value at scale.
You can use this semantic pSEO roadmap to start your transformation:
Start with data, not templates: Use your authority map (GSC) to identify where you already have permission to grow. Don’t waste resources attacking territories where your brand has no history.
Implement context governance: Before scaling, create the “rules of the game.” Inject your brand guidelines and proprietary data into prompts to avoid generic content and hallucinations. The AI should sound like your best expert.
Build bridges, not islands: Ensure every new page is integrated into a robust semantic mesh. Use internal linking to transfer authority and guide the user toward conversion, avoiding dead ends.
Monitor with AI: Abandon sporadic manual audits. Adopt technical agents that monitor your site’s health in real time as you scale.
The future of SEO isn’t about who creates the most content. It’s about who can unite the scale of the machine with the sensitivity of the human to deliver the best answer, at the right moment, for each individual user.
The trial is live, limited to the U.S. for now, and moving faster than you likely expected. ChatGPT ads launched Feb. 9 for logged-in users on Free and Go tiers, with 600+ advertisers already in.
With 800 million weekly active users, a global rollout of ChatGPT ads is a matter of when, not if.
OpenAI has confirmed the next expansion to Australia, New Zealand, and Canada. The latest update from Adthena trialists suggests the UK could see ads as early as mid-May.
We’ve tracked ChatGPT ad placements since rollout. With an index of 50,000+ daily placements across B2B software, ecommerce, fintech, and consumer verticals, we’ve had a front-row view of how this format is evolving. Here’s what we’ve found.
What ChatGPT ads actually look like
ChatGPT ads appear inline within conversation responses. When you ask something with commercial intent like “best weekend getaway” or “top running shoes under $100,” a sponsored result can appear alongside the AI’s answer, clearly labeled “Sponsored.”
This isn’t a search bar. It’s a conversation. Users arrive already engaged, already researching, often close to a decision.
The format is tighter than traditional search: no sitelinks or extensions — just a headline, short body copy, and a destination.
But here’s what we didn’t expect. Our data shows what we’re calling the Adthena “Double Parked” phenomenon: a single brand appearing twice in the same response.
We spotted New Balance with two separate sponsored placements in one ChatGPT answer. This raises a key question around visibility, frequency, and what it means to own a conversation on this platform.
10 things we’ve learned from 50,000+ daily placements
If you move fast, this is a rare moment: a new format, an uncontested landscape, and data most competitors don’t have yet. Here’s what it shows.
Headlines follow a “Brand: Benefit” formula. A name, a colon, a value claim. Think “Betterment: 5.25% APY Cash Account.” Dominant across top performers.
Almost every ad leads with the brand name. Awareness thinking for a format where users are already deep in a conversation, not just entering a search bar.
Headlines average just 30 characters, with a ceiling around 36. The constraint forces hyper-concise messaging and every word earns its place.
Body copy runs around 19 words, structured as two tight sentences. One lead proof point, one offer or nudge. One reason to click.
Context mirroring is a defining feature. The strongest ads echo the user’s query directly. A running shoe ad referencing “the transition from 5k to 21.1k” isn’t a coincidence.
The $ symbol drives conversion. Specific dollar figures, precise APY rates, credit amounts. Concrete claims consistently outperform vague promises in intent-heavy environments.
Numbers dominate body copy. Specs, trial lengths, rates. Hard numbers feel more native and trustworthy than soft superlatives in a research-led environment.
“Free” is the most common conversion lever. It removes friction for users already in research mode and close to a decision.
CTAs are action-specific and generic “Learn More” is virtually absent. “Open Account,” “Shop Cell Phones,” “Claim Credits.” Every CTA names the brand, offer, or next step.
Tone is confident and measured. Exclamation marks are rare. The best ads mirror ChatGPT’s calm register—hype punctuation kills trust here.
What this means for your paid search strategy
Top-performing brands in ChatGPT don’t repurpose Google ad copy and hope for the best. They write for a conversational, intent-rich environment where users are already halfway through a decision before the ad appears.
Lead with your brand name. Anchor value in specifics. Make low-friction offers central to your creative. If you’re not thinking about context mirroring, you’re leaving performance on the table.
The bigger question is visibility. If your competitors show up in ChatGPT conversations and you don’t, you’re not just missing clicks — you’re missing the conversation.
See exactly what’s happening with Adthena’s ChatGPT Ads Intelligence
Knowing the trends is one thing. Knowing what your competitors are doing on your exact prompts is another. That’s the problem we set out to solve.
Right now, ChatGPT ads give you impressions and clicks — nothing more. No competitive context, no prompt-level visibility, no insight into who else appears in the same conversations or where you’re missing coverage. You’re optimizing blind.
Adthena’s ChatGPT Ads Intelligence changes that. Here’s what you get.
Your performance, in context
The Ads Performance tab gives you a live snapshot of your ChatGPT activity: ad presence rate, top-performing intent group, total impressions, average CTR, and unique competitors detected. The trend chart shows your presence over time so you can clearly see whether you’re gaining or losing momentum.
Know which topics you’re winning and where to close the gap
The Topics and Keywords Analysis view breaks down performance by intent group, showing your ad presence rate against the competitor average. Each group includes a built-in tactical recommendation, so you always know your next move.
See your own ads as users see them
The Ads Sampling tab shows all your ChatGPT creatives with their headline, description, image, and format. The insight panel highlights your top-performing creative and surfaces optimization opportunities, like pairing a price anchor with a time-limited offer.
Understand exactly what competitors are running
The Competitor Creative Analysis panel breaks down rival ads across your tracked prompts: the images they use, the dominant copy themes, and their format mix. No more guessing what your competition is doing.
Never miss a shift in the competitive landscape
The Ads Benchmarking tab shows who’s advertising on your prompts and how their presence changes week to week. The “What changed this week?” feed flags new entrants and share shifts in plain language before your next campaign review.
Find the gaps before your competitors do
The Competitor Gap Analysis table shows every prompt where competitors have presence and you don’t, flagged by intent group and competitor count. A clear, prioritized view of where to expand your ChatGPT coverage.
The first prompt is the new first click
We’re tracking early-stage data from a platform still in limited rollout. As OpenAI expands to new countries and the advertiser base grows, the competitive landscape will shift fast. Brands building their ChatGPT presence now — learning the format, testing creative, mapping competitive gaps — will have a meaningful head start over those who wait.
Don’t let competitors win the first prompt. Join the product waitlist to uncover your ChatGPT ads landscape.
In the meantime, get your ads ready with Adthena’s free ChatGPT AdBridge. Connect your Google Ads account and we’ll build your ChatGPT ads setup with AI-enriched campaigns and smarter negative keywords — delivered to your inbox, ready to import.
Google is trying to simplify one of its most complex products, helping advertisers and analysts get more value from Google Analytics without deep technical expertise.
What’s new. Google Analytics is rolling out Task Assistant, a guided workflow tool that surfaces tailored recommendations to improve property setup, data collection and reporting.
How it works. Available in the left-hand navigation, Task Assistant organizes recommendations into clear categories like connecting accounts, enhancing reporting and fixing data issues. Users can mark tasks as complete as they go or skip items that don’t align with their business goals, creating a more flexible setup experience.
Why we care. Google is making it easier to identify gaps in tracking and fix them quickly, which leads to more reliable data and better decision-making. Task Assistant helps ensure Analytics is properly configured without requiring deep expertise, reducing the risk of missed insights or inaccurate reporting. Ultimately, better data setup means more confident optimization of campaigns and budgets.
Between the lines. Analytics platforms are powerful but often underutilized due to poor configuration. Task Assistant is Google’s attempt to reduce that friction by turning setup into a step-by-step process rather than a manual audit.
The bottom line.Task Assistant aims to make Google Analytics more actionable, guiding users toward better data quality and more effective measurement with less guesswork.
Google is filling a key measurement gap between awareness and consideration, giving advertisers a clearer view of how their brand is actually perceived — not just remembered.
What’s new. Google Ads has introduced a new “Association” metric within Brand Lift Studies. Advertisers can define a concept, category or attribute, and Google will ask users a survey-style question: which brands they associate with that specific idea.
How it works. Instead of measuring simple recall, the metric evaluates whether audiences connect your brand to a desired positioning. That could mean “premium,” “sustainable,” or even a product category — offering a more nuanced read on brand perception.
Why we care. Google is giving you a way to measure brand positioning, not just awareness or recall. The new Association metric helps determine whether campaigns are actually shaping how consumers perceive a brand — a critical step between being known and being chosen. It also enables more strategic optimization of creative and messaging, especially for brands trying to own specific attributes or categories.
Between the lines. Brand Lift has traditionally focused on awareness, recall and consideration. Association sits in between, helping advertisers understand whether their messaging is shaping how people think about the brand, not just whether they recognize it.
The catch. There’s still a constraint: advertisers can only select three Brand Lift metrics per study, so adding Association means making trade-offs with existing KPIs.
The bottom line. Association gives advertisers a more strategic lens on brand building — measuring not just visibility, but whether campaigns are landing the intended message.
First seen. This update was first spotted by Google Ads expert, Thomas Eccel who shared the update on LinkedIn.
Reddit is quickly becoming a powerful platform shaping how people discover and perceive brands. As AI search engines increasingly surface Reddit threads and comments, these conversations now influence visibility.
To understand this shift, I analyzed 117 SaaS brands on Reddit. People reveal what they really think there, which doesn’t always match polished marketing.
As communities shape brand perception, Reddit is no longer optional.
Here’s my analysis, plus how you can use Reddit to your advantage.
How I analyzed 117 SaaS brands: The methodology
My analysis of 117 brands across the SaaS industry started with identifying the verticals to address:
Development and software development and IT operations (DevOps) (12 brands)
AI (12 brands)
Customer support and engagement (10 brands)
Analytics and data (10 brands)
Sales and revenue (8 brands)
Collaboration and communication (10 brands)
From there, I created a Google Spreadsheet with the brand names for each vertical. Then, I mapped out the following details for each brand:
Link: A direct link to the brand’s subreddit.
Brand subreddit: When the brand’s subreddit was created, the number of weekly visitors and the number of weekly contributors.
Subreddit features: The number of moderators and whether they were branded moderators.
Topics: Common topics in the subreddit, including tips, use cases, compliments, criticisms, and subscription cost.
Across all 117 brands, I analyzed over 300 Reddit threads, including brand mentions, sentiment, community engagement, and brand participation.
Let’s dive into the key findings.
1. Reddit rewards authentic brands
One thing became clear early on: people respond to people, not corporate brands.
Brands run by moderators who were helpful, honest, and non-promotional were received more favorably than those using a polished, corporate tone. Redditors tended to ignore or downvote obvious marketing copy.
In general, redditors don’t want to be marketed to. They want real opinions and real experiences.
As a result, peer recommendations felt more credible than brand messaging. When redditors asked questions or shared frustrations, the most authentic answers came from other users.
When brands stepped in with scripted or promotional responses, they often struggled to gain traction.
However, when brands answered directly, acknowledged limitations, and used conversational language, responses improved. In some cases, brand moderators even earned upvotes and thanks.
2. Brands not on Reddit are missing out
Redditors talk about brands, whether or not they’re present on the platform. In many cases, brands simply aren’t there.
Thirty of the 117 brands I analyzed have no Reddit presence. Another 23 are on Reddit, but their subreddits are abandoned.
In several instances, users asked direct questions like:
“Anyone here used this?”
“What should I use instead of X?
“Best alternative to X?”
They received responses from other redditors sharing experiences, opinions, recommendations, and problems.
When brands aren’t there, the conversation continues without them. Over time, their reputation on Reddit exists outside the brand’s control.
Other negative outcomes can follow. When brands aren’t present, others can take their place.
In one instance, I found a community using a popular brand name that had nothing to do with the brand. This shows how easily brand presence can be shaped or misrepresented.
Redditors are already discussing your brand. The only question is whether you’re part of that conversation.
3. Reddit is a customer research goldmine
Reddit is an incredible source of unfiltered customer insights.
If you want to know what drives people away, what people value, and how people compare tools, you’ll find the answers on Reddit.
Here are some ways Reddit helps with customer research.
Reddit captures feedback that traditional methods miss
On Reddit, you’ll find people asking questions and sharing:
Onboarding struggles.
Integration challenges.
Complaints about mobile usability.
Frustrations with AI features.
Confusion around updates.
Users building alternative tools.
Reddit users tend to say exactly what they think. This kind of honesty is hard to find anywhere else.
These insights are critical for improving SaaS products. Traditional feedback methods don’t always capture these comments — but Reddit does.
Reddit supports brand advocates
Your Reddit community is a good place for happy customers to advocate for your brand. For example, this Reddit post by Monday shares a brand ambassador program.
In the comments, some brand advocates share insights into their experience, helping elevate the post.
Some brands have self-sustaining Reddit communities
When discussing some community-led brands, redditors often highlight solutions to problems and help fill brand gaps. For example, I noticed users helped each other with troubleshooting, sharing fixes, and recommending integrations.
In some cases, these communities were almost fully self-sustaining, requiring little brand involvement.
Redditors highlight preferred competitor features and pricing frustrations
Across the topics I reviewed, redditors often expressed negative sentiment about pricing and suggested alternatives, especially for enterprise SaaS tools.
As a result, SaaS brands are often associated with soaring costs and limited pricing transparency, which can hurt perception. When users highlight competitor features, they surface gaps and alternative tools to consider.
Redditors share their actual use cases
Reddit attracts people who discuss how they use software. In my analysis, I observed that users shared:
Workflows
Screenshots and builds
Tutorials and guides
These posts and comments give brands insight into real use cases they can use to improve products.
Reddit is essential for brand visibility and perception
Reddit is no longer a side conversation. It’s where brand perception is shaped in real time.
Across the 117 brands I analyzed, conversations are happening on Reddit — even when the brand isn’t present. Increasingly, those conversations feed into AI search, influencing what people see, trust, and choose.
Smart brands shouldn’t ignore Reddit. They should track mentions, listen closely, show up where it matters, and treat Reddit as both a reputation channel and a product insight engine.
Google’s Preferred Sources now supports all languages, not just the English language. “Preferred Sources is now rolling out globally in all supported languages,” Google wrote on its blog this morning.
“This feature gives you more control over the news you see on Search by letting you choose the outlets and sites you want to appear more often in Top Stories,” Google added.
In December, Google rolled out preferred sources globally but it only supported English. Now it supports all languages globally as well.
Stats. Google added some interesting data including:
“Readers are twice as likely to click through to a site after marking it as a Preferred Source”
“People have already selected over 200,000 unique sites — from niche local blogs to global news desks”
Preferred Sources. Preferred Sources let searchers star publications in the Top Stories section of Google Search, and Google uses that signal to show more stories from those starred outlets. The feature entered beta in June, rolled out in the U.S. and India in August, and is now expanding globally.
How it works. You click the star icon to the right of the Top Stories header in search results. After that, you can choose your preferred sources – assuming the site is publishing fresh content.
Google will then start to show you more of the latest updates from your selected sites in Top Stories “when they have new articles or posts that are relevant to your search,” Google added.
Why we care. Traffic from Google Search is hard and if you can get your readers, loyal readers, to make your site a preferred source, that can help. Google said those users are twice as likely to click, which can help drive more traffic.
So add the preferred source icon to your site and encourage users to sign up. You can make Search Engine Land a preferred source by clicking here.
The difference between a 2% margin and a 20% margin increasingly comes down to whether you’re renting attention or owning the answer.
For years, search rewarded the ability to buy visibility. That model is weakening.
As AI systems increasingly resolve queries without a click, the value shifts from traffic acquisition to answer formation.
When you move from buying clicks to engineering answers (i.e., structuring content so it can be surfaced, cited, and trusted by AI systems), you change what you own. Instead of renting placement, you build answer equity: durable inclusion in the outputs that shape decisions.
The goal isn’t to turn off paid search. It’s to stop relying on it as your primary source of demand. Over time, this can lower acquisition costs and reduce volatility, because you’re not competing for every impression.
An atomic sandwich
To operationalize this shift, you need a content structure that maximizes what AI systems can extract. Think of it as an “atomic sandwich.”
An atomic sandwich content structure shifts the focus from chasing traffic to maximizing intent density. Here’s how:
The atomic fact (top bun)
Most organizations treat their search budget like a high-interest payday loan.
You keep pouring cash into the paid bucket for that immediate hit of traffic, and it feels like you’re winning.
But the moment you stop feeding the meter, your brand disappears.
The forensic proof (the meat)
For many organizations, this isn’t just marketing inefficiency — it’s an organizational risk.
In the emerging Answer Economy, your rented audience is evaporating. Data from Seer Interactive (Sept 2025) shows paid CTR on informational queries has dropped 68% when Google’s AI Overviews are present.
You’re not just paying for clicks. In many cases, your paid traffic contributes to awareness that AI systems can later satisfy without requiring a click.
The structural directive (bottom bun)
The “box” has changed.
Here’s the structural leak in your balance sheet: to survive 2026, you must stop buying a crowd and start engineering the answer.
If your brand isn’t among the trusted sources behind the machine’s answer, your visibility — and influence — shrinks significantly.
The new “box”: From librarian to forensic auditor
We’ve moved from a search engine that directs users to a generative engine that validates information. Every dollar you spend on ads to cover a lack of E-E-A-T is money you’re burning.
The data is clear: appearing in search results is no longer a viable model on its own.
The organic collapse: A SISTRIX (March 2026) analysis found that when an AI Overview is present, position 1 CTR drops from 27% to 11% — a 59% decline.
The global impact:Ahrefs (Dec 2025) found AI Overviews correlate with a 58% lower average CTR for the top-ranking page.
The goal is no longer just to rank in search, but to be consistently included among the sources AI systems rely on.
Without trust, you’re paying for ghost impressions.
In the old box, you could survive by being loud. In the new box, you survive by being certain.
The search addiction cycle (why your org can’t quit)
Most companies are in organizational denial.
You see the cost of rented clicks rising and quality falling, but you’re too afraid to stop because you’ve neglected your information architecture and have no foundation. That’s a balance sheet liability.
Stage 1 — the vanity hit: early paid search wins made you feel like a genius. You mistook traffic volume for business health.
Stage 2 — tolerance building: As the Answer Economy evolved, keywords got more expensive. Instead of fixing structural integrity, you upped the dose.
Stage 3 — the context-debt overdose: You’re paying for zombie facts — content an AI can summarize in seconds. Zero-click searches have surged to 69%. Your expensive awareness is consumed for free by AI.
Stage 4 — total dependency: Your marketing manager becomes a budget operator rather than a builder of durable demand. They aren’t building answer equity; they’re managing cash transfer to Google.
The forensic intervention: The 7-point organizational health check
Use this checklist in your next review to find where your Answer Equity is leaking.
The Information Gain test: Ask Gemini to summarize your page. If it adds nothing beyond common results, you’re in violation of Google’s Information Gain patent. You have a zombie fact with zero value.
The entity audit: Does your brand have a verified Google Knowledge Graph ID? Without it, you’re not an asset — you’re just text.
Source of ground truth: Are you cited in AI Overviews? BrightEdge (Sept 2025) shows that without a citation, your visibility is effectively zero.
The faucet test: If you cut PPC spend by 20%, does lead volume drop 20%? If so, you have no foundation — you’re renting revenue.
Schema and provenance: Are you using Schema.org/Person to link experts to your brand? Unverified content is untrusted noise to a retriever.
The “meat” ratio: Review your top 10 posts. Do they include primary research? If not, they’re fodder for the AI’s top bun with no reason to click.
Machine-readable graph adoption: Is your team moving toward W3C RDF-star (RDF 1.2) or ISO/IEC GQL standards? These are the 2026 blueprints for verifying Answer Equity.
The recovery plan: From rented clicks to owned authority
1. Purge the zombie facts (the information gain protocol)
Stop rewarding word count. Every piece of content must deliver a “meat” layer — information gain a retriever can’t synthesize from the rest of the web. That’s how you reclaim your margins.
2. Build your “E-E-A-T engine” (the trust infrastructure)
Stop treating schema as a technical extra. It’s your trust score on the digital exchange. Ensure your authors have strong provenance so AI retrievers can instantly crawl and confirm your expertise.
3. Measure ‘intent density’ (the scoreboard shift)
If your traffic drops but lead quality holds, you’re winning. Focus on users who bypass the summary because they need the deep, forensic expertise only you provide.
The shift from renting an audience to owning the answer is the most significant strategic pivot your organization will make this decade. It moves you from a marketing expense to a balance sheet asset.
The paid trap offers a temporary high but leads to a fiscal dead end. Every dollar spent there is consumable — used once and gone when the auction ends.
When you move that capital into your information infrastructure, you stop paying for the privilege of being ignored. You start building a digital entity that owns its facts, earns trust, and controls its future in the Answer Economy.
Your first step: don’t boil the ocean.
Take your top-performing paid landing page and run the seven-point health check. If it’s a “zombie fact” environment, engineer information gain back into the page.
Stop asking for a ranking report; start asking for an entity audit.
The 2026 organization isn’t defined by how much it spends to rent an audience, but by how much it proves it owns the answer.
You have the blueprints. You have the data. Now stop funding the payday loan and start building answer equity.
Across 90 prompts we tested in ChatGPT, commercial prompts triggered web searches 78.3% of the time. Informational prompts did so just 3.1%.
That gap changes what you should write if you want to appear in a ChatGPT answer.
ChatGPT doesn’t pull every response from the same place. Some answers come from training data; others use live web search — a behavior called query fan-out. The model expands your prompt into multiple background searches, then retrieves and synthesizes across those subtopics. If your page isn’t on those branches, it won’t be pulled in.
So the question is no longer just how to rank. It’s which pages open the fan-out door in the first place.
In our sample, informational pages didn’t. Read on to discover where the system went instead.
We tested 90 prompts across three industries: beauty, legaltech/regtech, and IT. We analyzed prompt intent, downstream query expansion, and the intent those expansions reflected.
Here’s the breakdown and the core finding: most queries aligned with commercial intent, not purely informational prompts.
Why this question matters now and how query fan-outs come into play
Query fan-outs change the content game because the system isn’t limited to the literal prompt.
It expands the request into multiple background searches, then retrieves and synthesizes across those subtopics.
Fan-outs trigger parallel web searches tied to the initial prompt, creating opportunities for retrieval, mention, and link citation.
Multi-query expansion is a core design pattern in modern generative search systems. Google describes AI Mode this way: it breaks a question into subtopics, searches them in parallel across multiple sources, then combines the results into a single response.
That raises a strategic SEO question: should you invest more in top-of-funnel educational content, or in lower-funnel comparison, shortlist, and recommendation content?
This experiment framed that problem.
The objective was to test, across selected industries, where fan-out appears by intent category: informational, commercial, transactional, or branded.
The initial hypothesis was direct: informational prompts wouldn’t trigger fan-out, while commercial prompts would, and those fan-outs would stay at the same funnel level or move lower.
We found that ChatGPT-generated fan-outs are overwhelmingly associated with commercial intent.
Disclaimer: This experiment measures observed prompt expansion behavior in ChatGPT. Google AI Mode is cited only as context to show multi-query expansion as a broader pattern in generative search, not as proof of ChatGPT’s internal architecture.
The setup: what we tested
The core sample includes 90 numbered prompts, heavily weighted toward informational intent.
Prompt intent
Prompts
Share of sample
Prompts with fan-out
Fan-out rate
Informational
65
72.2%
2
3.1%
Commercial
23
25.6%
18
78.3%
Branded
1
1.1%
0
0.0%
Transactional
1
1.1%
0
0.0%
The sample skews heavily toward informational prompts, with some commercial ones and minimal branded and transactional queries.
We structured the experiment around the sectors in the brief: beauty/personal care, legaltech/regtech, and IT/tech.
The result: commercial prompts triggered almost everything
The main finding is clear.
Out of 90 prompts, 20 triggered fan-out. Of those, 18 were commercial and 2 informational.
Informational prompts made up about 10% of fan-out triggers (2 of 20). When they did trigger expansion, they were rewritten into more evaluative, solution-seeking subqueries.
In other words, 90% of fan-out-triggering prompts in the core sample came from commercial intent.
The contrast is stronger than the raw totals suggest. Commercial prompts triggered fan-out 78.3% of the time; informational prompts did so just 3.1%.
This supports the working hypothesis: in this sample, fan-out was overwhelmingly a commercial phenomenon.
Those 20 prompts produced 42 fan-out queries — an average of 2.1 per triggered prompt.
Of those 42 fan-out queries:
39 were commercial.
2 were branded.
1 was informational.
Even when a prompt triggered expansion, the system usually shifted toward comparison, product evaluation, feature filtering, shortlist creation, or brand-specific exploration — not broad educational discovery.
Methodology: how we performed the analysis
The experiment used 90 prompts across three industries, mostly informational, with a smaller set of commercial prompts and minimal branded and transactional queries.
In the analysis, we have:
Selected a representative battery of prompts.
Identified the fan-outs.
Classified each fan-out by intent.
Observed distribution by prompt metadata.
The analysis then followed three steps:
Each prompt was classified according to prompt-intent labels.
We counted the prompts triggering fan-out (at least one).
We inspected the observed expansion queries and their assigned fan-out intent labels.
That produced two distinct but complementary views:
A prompt-level view, asking whether a given prompt triggered fan-out at all.
A fan-out-query view, asking what kind of intent the downstream expansion actually took.
That distinction matters: the first shows which prompts open the fan-out path, while the second shows where the system goes once it opens.
Interpreting the results: fan-out tends to move down-funnel
The cleanest interpretation is that, in this sample, fan-outs behave less like open-ended topic expansion and more like assisted decision support.
Commercial prompts almost always opened the door.
Once they did, fan-outs usually stayed commercial.
The system expanded into comparisons, feature-based filtering, product lists, pricing-adjacent queries, and brand-specific evaluations.
A few examples make that concrete.
“Suggest the best accounting software for small business and explain why” expanded into a commercial comparison query around features.
“What are the top AI document management systems for lawyers?” expanded into multiple product-oriented legaltech queries.
“What are the best products for skin care?” expanded into a shortlist-style query around product categories and reviews.
The two informational exceptions are even more revealing than the rule.
“I need an open-source document management system. What can you suggest?” was labeled informational at prompt level, but the resulting fan-out moved into solution recommendation.
“AI tools for legal research and document automation” also moved into a clearly commercial/evaluative downstream query.
So, even when the prompt starts broad, fan-out often translates that breadth into a lower-funnel retrieval path.
What this means for content strategy
The takeaway isn’t to stop writing informational content.
It’s this: informational content alone is unlikely to align consistently with fan-out expansion, at least in this dataset.
If your goal is visibility in AI answers tied to product selection, vendor discovery, or option narrowing, you need stronger coverage of pages and passages that match those downstream commercial branches.
In practical terms, your content model shouldn’t be just ToFU or BoFU, but ToFU with commercial bridges.
A broad article can still help, but it should include passages the system can easily reformulate into decision-support subqueries.
A purely educational piece that explains a category without naming products, tradeoffs, features, use cases, pricing logic, or selection criteria is much less likely to align with the fan-out paths seen here.
Put simply: Don’t just answer the obvious question — anticipate the next evaluative step the system is likely to generate in the background.
Limitations
This result is directional, not universal.
90 prompts reveal a pattern, but not a stable law of AI retrieval behavior.
The prompt mix is uneven. Informational prompts dominate the sample, while branded and transactional prompts are barely represented. That means those findings aren’t proof of absence.
The dataset spans industries but isn’t normalized by brand, wording style, or use case. Some sectors may be easier to express in product-discovery language.
This is an observational analysis of recorded fan-outs, not a controlled platform-level test. It shows what happened in this prompt set, not how ChatGPT always behaves.
Google’s description of fan-out provides context, but this isn’t a Google AI Mode test. It’s a ChatGPT-focused prompt and fan-out dataset. The takeaway is strategic, not architectural.
What to test next
The next version of this experiment should isolate the question more aggressively and expand the dataset.
A follow-up should map triggered fan-outs back to specific content formats.
The goal isn’t just to confirm that commercial intent wins. It’s to identify which page templates and passage structures best cover the fan-out branches AI systems prefer.
I keep hearing people say AI understands their brand. It doesn’t. Let’s get that out of the way first.
What it does is pattern-match at scale. It compresses your positioning, product, proof, and tone into a bundle of signals it can retrieve and remix at speed.
Those patterns come from two places:
Training: What the model absorbed historically.
Retrieval: What it can fetch at answer time from the live web and other sources.
So “AI SEO” isn’t a new channel. It’s a new representation problem: which version of your brand gets encoded, retrieved, and repeated.
Most brands are already in the game. They’re just not playing with purpose.
The internet is no longer a library
Classic SEO was a library problem. You publish a URL. Google indexed it. A human searched and found it.
AI search is a conversation that stretches out the demand curve. Head terms still drive the majority of visibility, but, ever so slowly, more volume is moving into context-heavy prompts.
“With these constraints”
“Like this competitor but cheaper”
“Which tool fits a team like mine with these requirements?”
“Given what you know about me, recommend…”
Your job is to be the most relevant match inside a model’s memory and retrieval pipeline.
Not by being ranked. But by being represented.
AI doesn’t run on opinions. It runs on associations.
From keywords to entities to embeddings
Classic SEO competed for keywords. Then it shifted to entities. AI systems go one layer deeper. They turn entities into vectors.
Your brand becomes a coordinate in dimensional space. Close to some concepts. Distant from others. Pulled by whatever your content and mentions repeatedly associate you to.
If your brand is consistently associated with “enterprise analytics”, “real-time dashboards” and “data governance”, your vector lives near those clusters.
If your messaging sprawls into adjacent territory because someone got bored of writing about the same things, the vector spreads. Precision drops. The model still has a position for you. It’s just fuzzier, less confident, and easier to swap for a competitor with cleaner signals.
Three layers of AI brand visibility
Before you “fix AI SEO,” identify which layer your brand is failing on. The same tactics don’t work everywhere.
Training layer
Your historical footprint. Press, blogs, documentation, reviews, every old thread on a forum you forgot existed.
You can’t fully control it.
But you can reduce fragmentation by finding and editing all possible past mentions (social profiles, directory listings, wikis, etc) to create a consistent identity across the internet.
Understand the training layer by asking an AI chatbot to describe your brand with web search turned off.
Retrieval layer
Your live surface area. Indexed pages, product feeds, APIs. This is where traditional technical SEO of crawling, indexing and rendering matter most. It defines what the AI system can access for citations.
Understand the retrieval layer by running branded intent and market category intents prompts daily using a LLM tracker and reviewing which sources are consistently cited.
Generation layer
That is the output seen in AI Overviews, AI Mode, ChatGPT or whatever your brand gets reassembled in front of an actual customer. Your brand will be written into the answer only if it’s a must.
So ask yourself, what unique, quotable, additive content forces the LLM to mention you?
Understand the generation layer by using the same LLM tracker data, but reviewing brand mentions within responses and their semantic associations.
Four mechanics that decide what AI says
Think of these as the forces quietly shaping your representation across the layers.
1. Consolidation (identity resolution)
AI systems merge different references to the same brand if it’s obvious they belong together.
Most brands don’t have one clear identity. They often have:
A brand name (spaced or cased inconsistently).
A legal name.
A domain name.
An abbreviation.
A legacy name.
Humans merge that automatically. Models don’t. They consolidate by pattern, not intent. Every inconsistent self-reference is a vote for fragmentation.
Allow your brand to be written five different ways and split your visibility signals five times.
2. Co-occurrence (association formation)
Models learn what appears together:
Brand + category
Brand + use case
Brand + audience
Brand + competitor
Repeat the right pairings, and the association strengthens. Be inconsistent, and it weakens. It’s genuinely that simple.
3. Attribution (who says it, where)
Models track who is being described, by whom, in what context.
Your own site is one layer. Third-party mentions are another. High-trust sources carry more weight.
Not because of “authority” in the classic SEO sense, but because they appear frequently inside reliable contexts in the training data and retrieval corpora. Similar outcome. Different mechanisms.
4. Retrieval weighting (what gets used in AI answers)
When generating answers, AI systems decide which information to use. That decision depends on clarity, relevance, uniqueness, and ease of extraction.
If key facts are buried in narrative copy, implied through metaphor, scattered across sections, the model will simply pull from somewhere else.
On the other hand, if you repeat them, structure them, and make them explicit, you are more likely to be chosen by the model.
You’re not writing poetry, you’re building a graph
In your content, on-page and off-page, make the core entities unmissable. Your brand. Your products. Your categories. Your audience. Your differentiators.
Craft a clear, consistent, canonical positioning that the machine can’t misread by creating a canonical brand bio:
[Brand] is a [market category] for [audience] who need [use case], differentiated by [proof].
Then, honestly ask yourself if your answer could also describe your competition. Or better, ask AI that question. If the answer is yes, rewrite it’s unmistakably you.
Then roll out that positioning everywhere. On-page with “retrieval-ready” chunks, in structured data, in “sameAs” references, industry publications, partner sites, user reviews, community discussions, social posts.
Repeat key associations deliberately across pages until it feels excessive. Reduce unnecessary variation in terminology. Then the associations strengthen. Are reinforced. Compound.
Beware brand drift,where inconsistencies allow misrepresentations, and a lack of information allows hallucination to creep in. Police all the edges. Consolidate or kill the pages that introduce conflicting descriptions of your brand.
This is not about gaming AI. It is about reducing entropy.
If that sounds boring, good. The brands that win the AI era are not going to win it with cleverness. They are going to win it with discipline.
Because if answers are inconsistent across sources, your brand won’t be cleanly encoded. And the version of you that AI systems are quietly passing along to customers won’t be the one you intended.
First 5 steps to AI brand visibility
Write your canonical brand bio: Lock-in spacing, casing, abbreviation rules for the brand name, and clear positioning.
Implement graph-based schema: Define relationships between your brand (consolidated by sameAs) and other key entities.
Make proof easy to quote: Ensure awards, benchmarks, customer numbers, policies, all notable brand information is explicit and extractable.
Fix historical identity fragmentation: Clean up past mentions and enforce canonical positioning everywhere possible.
Repeat key associations with intention: Brand + category, use case, audience, vs competitor. Not only on your own site, but also build coverage on high-trust third parties.
It’s not about you
If AI systems can’t confidently represent your brand, they will default to a safer option. Usually, it’s a competitor with cleaner signals. Not because that competitor is “better”. Because that competitor is easier for the machine to use.
AI doesn’t need to understand your brand perfectly. It needs to approximate it well enough to recommend you. Your job is to control that approximation through consistency, structure, and distribution.
Not by publishing more. By making your brand impossible to misunderstand.
Google is doubling down on AI-driven ads just as search behavior shifts toward conversational queries, giving advertisers more automation while trying to preserve control.
What’s new.
AI Max expands beyond Search: Now rolling out to Shopping campaigns and travel-specific formats, broadening reach across more advertiser types.
AI Brief (powered by Gemini): A new interface that lets advertisers steer AI using natural language inputs.
Text disclaimers + URL automation: Compliance-friendly updates to pair with automated landing page selection.
Why we care. Google is making AI Max a core layer across Search, Shopping and Travel, meaning automation will increasingly determine how ads are matched to user intent. This update expands reach into more conversational, high-intent queries that traditional keyword strategies miss, helping brands capture demand earlier in the journey.
At the same time, tools like AI Brief and new compliance features give advertisers more control over messaging and targeting, reducing the risk of fully automated campaigns feeling like a “black box.”
Shopping gets smarter. For retailers, AI Max for Shopping uses Merchant Center data to generate more adaptive ads that can respond to long-tail and exploratory queries, helping brands appear earlier in the discovery phase rather than only at the point of purchase. The rollout is positioned as a simple upgrade for existing Shopping campaigns, suggesting Google wants rapid adoption.
Travel gets consolidated. Travel advertisers get a consolidation play. Search Campaigns for Travel bring previously fragmented formats into a single interface with unified reporting and integrated AI Max capabilities. The move reduces operational complexity while reinforcing Google’s push toward centralized, AI-driven campaign management.
More control with AI Brief. The most notable addition is AI Brief, which attempts to solve a long-standing advertiser concern: lack of compliance control in automated systems. Advertisers can define messaging rules, specify which queries to prioritize or avoid, and shape how different audiences are addressed. The system then generates previews, allowing feedback before campaigns go live.
Automation meets compliance. Google is refining how traffic is directed to websites. Final URL expansion uses AI to select the most relevant landing page for each query, and the new text disclaimer feature ensures required legal messaging remains intact even when automation is active. This signals a push to make AI usable in more regulated industries without sacrificing compliance.
AI may not see your brand the way you think it does, according to Scott Stouffer, co-founder and CTO at Market Brew.
Brands still publish content, optimize pages, build authority, and follow SEO best practices. But that may not be enough anymore.
Search has moved away from a simple battle over keywords, links, and page-level signals. It’s now shaped by meaning, intent, embeddings, and retrieval, Stouffer said during his SEO Week presentation.
In legacy SEO, a page could rank lower and still exist in the search results. In AI-driven systems, the first question isn’t whether you rank. It’s whether you’re ever retrieved.
“If you’re not retrieved, you do not exist to AI,” Stouffer said.
Your brand already exists inside AI systems as a mathematical object. You may call yourself one thing. Your homepage may say another. Your brand guidelines may promise a clear position. But AI systems build their own view of your brand from the content you have published.
That computed version of your brand may be different from the one you intended to build.
Retrieval now matters before ranking
AI visibility begins before ranking, Stouffer said.
In traditional SEO, marketers focus on positions — first, third, or tenth. But AI systems apply a filter earlier. Before anything is ranked, the system determines which content is eligible for consideration.
That is retrieval.
When a user asks a question, the system pulls a limited set of passages or chunks that best match the query. Those passages define the answer space.
If your content isn’t included, you get no impressions, no clicks, and no visibility at all, Stouffer said.
The real shift is moving from exclusion to inclusion.
“You don’t lose. You just never entered the game,” Stouffer said.
AI does not see pages the way SEOs do
AI systems don’t treat a webpage as one clean unit, Stouffer said. They don’t evaluate pages as whole objects or prioritize layout, structure, or formatting.
Content is broken apart. A page becomes chunks: passages, sections, and individual ideas.
Each chunk is evaluated independently. A paragraph deep in a guide can compete on its own. A single sentence can be selected if it aligns closely with the query.
This shifts competition from page versus page to passage versus passage.
Most of a page may never be considered. Only the most aligned chunks are evaluated.
Meaning becomes math
Each chunk is converted into a vector, Stouffer explained.
This vector represents meaning as a position in a high-dimensional space. It captures context and intent rather than exact wording.
Two pieces of content can use different words but sit close together if they express the same idea. Others can share keywords, but sit far apart if they represent different meanings.
“It’s comparing meaning, not wording, measuring distance, not keyword overlap,” Stouffer said.
Relevance is determined by proximity. The closer a chunk is to a query in this space, the more likely it is to be retrieved.
Your content forms clusters
As chunks are mapped into this space, they group together.
Content with similar meaning forms clusters, even across different pages. These clusters reflect how AI systems understand topics.
This understanding comes from how content naturally groups by meaning, not by site structure or labels, Stouffer said.
If content is consistent, clusters become dense and clear. If content is scattered, clusters become fragmented.
What matters is not what a brand intends to say, but what its content actually communicates.
The centroid is your brand to AI
Within these clusters, there is a center point — the centroid, Stouffer said.
The centroid represents the average position of all related content. It reflects the site’s core meaning.
Every page and paragraph influences that position. Consistent content creates a clear, stable centroid. Inconsistent content dilutes it.
That centroid is how AI understands your brand.
Not your homepage. Not your messaging. Not your brand guidelines.
Your centroid is the combined signal of everything you have published, Stouffer said.
“Your centroid doesn’t care about intent. It reflects the math of everything you’ve ever published,” Stouffer said.
Alignment beats isolated optimization
This changes how content should be evaluated.
The key question isn’t whether a page is optimized in isolation. It’s whether it aligns with the rest of the site.
Each page either strengthens the centroid or pulls it in a different direction.
“Optimization without alignment creates drift, and drift is what breaks consistency,” Stouffer said.
As drift increases, the site becomes harder for AI systems to interpret and retrieve.
“You don’t write pages, you project meaning,” Stouffer said.
Retrieval starts with proximity
When a query is entered, the system converts it into a vector, Stouffer said.
It then searches for the closest matches in meaning space.
This includes both individual chunks and the centroids that represent broader content clusters.
If your content is close enough, it enters the candidate set. If it is too far away, it is excluded.
Only after this stage do traditional ranking signals apply.
Content quality, links, and structure matter — but only if the content is first retrieved.
If not, those signals are never evaluated, he said.
Most brands look too similar to AI
Many brands follow similar strategies, use the same sources, and produce similar content.
As a result, their centroids converge in the same region, Stouffer said.
He described this as cluster collision.
When multiple brands occupy the same space, AI systems don’t select all of them. They choose a few and ignore the rest.
“They’re not failing best practices. They’re colliding with everyone else using them,” Stouffer said.
Distinct meaning is the new advantage
Producing more content or improving existing content isn’t enough. If content remains similar in meaning, it remains in the same space.
“You need a distinct centroid,” Stouffer said.
A clear, separate position in meaning space reduces competition and increases the likelihood of retrieval.
SEO becomes a control loop
This is not a one-time adjustment.
Every piece of content shifts the centroid.
That requires an ongoing process of measurement and adjustment, Stouffer said.
Teams need to monitor alignment continuously and correct drift as it occurs.
Over time, this creates a more stable system where new content reinforces the existing structure.
The visibility problem is really an observability problem
Most teams can’t see how their content exists in this system.
They can’t see clusters, centroids, or distances — or why content is excluded.
So they rely on trial and error, Stouffer said.
They publish, optimize, and wait for results. When nothing changes, they try something else.
Without visibility into the system, they react to outcomes rather than understanding causes.
Is AI seeing the brand you think you’ve built?
Your brand already exists as a mathematical object inside AI systems, Stouffer said.
You do not get to choose that.
You only choose whether to measure and control it or let it drift.
AI does not see your brand the way you describe it. It sees the aggregate meaning of your content.
“If you control your centroid, you control your visibility,” Stouffer said.
For more than two decades (nearly as long as I’ve been in SEO), backlinks have been core to SEO. Google’s PageRank changed search by using backlinks as a proxy for trust.
A link wasn’t just a pathway; it was a vote. The more votes you had and the more authoritative the voters were, the higher you ranked.
But as Google and AI systems matured, entity-based understanding emerged. AI models became better at understanding content, context, and credibility without always needing a hyperlink as a crutch.
Today, visibility isn’t driven solely by links. It’s strengthened by the broader signals your brand has earned: how often it’s mentioned, cited, and trusted across authoritative sources.
Search engines and AI platforms now prioritize these signals.
AI’s role in reducing reliance on links alone
Modern AI systems can evaluate trust and expertise in ways that were impossible a decade ago. AI has changed how authority, trust, and expertise are measured. It can now assess authority through signals once approximated mainly by backlinks.
AI can:
Identify entities and map their relationships across the web.
Interpret sentiment and contextual relevance.
Detect manufactured link patterns with near-perfect accuracy.
Understand brand prominence without a single hyperlink.
Evaluate reputation signals from reviews, mentions, and citations.
Cross-reference information across multimodal sources.
A brand mention in a reputable publication—even without a link—reinforces entity authority. Consistent expert citations validate expertise. These signals can’t be faked.
The result is a new era where links still matter, but they’re no longer the only star. Authority is now a network of signals.
The rise of entity‑first SEO
As Google relies less on raw link signals, something else has increased: entities — the people, brands, organizations, and concepts behind the content. Google increasingly showcases brands based on who they are and how they’re discussed across the web, alongside their backlink profile.
At its core, entity-first SEO means Google and LLMs are mapping relationships: identifying brands, understanding what they’re known for, and evaluating how they’re referenced in trusted sources.
For example, an outdoor gear company with a modest backlink profile began appearing in AI Overviews for “best hiking backpacks” after repeated mentions in Reddit threads, YouTube reviews, and a few expert roundups. Only some mentions included links, but the brand appeared consistently in trusted, topic-relevant conversations. Google interpreted those unlinked mentions as proof of real-world relevance.
If your brand consistently appears in a positive light in topic-related conversations, AI sees that as proof you’re relevant and trusted. The brands that win now have the strongest entity presence.
PR‑style links + editorial = off-page powerhouse
PR-style links and editorial coverage are earned mentions in reputable publications — the kind that signal real-world authority, not algorithmic manipulation.
Why editorially earned links outperform volume-based link building
Old-school, volume-based link building is less effective as AI improves at detecting manufactured patterns. But high-quality, relevance-driven link building—especially when paired with PR signals—is more valuable than ever.
Editorial PR links from journalists, analysts, and industry voices who choose to reference a brand because it’s newsworthy or authoritative reflect genuine credibility. They’re the digital equivalent of a trusted expert saying, “This brand matters.”
Authority-Based Link Building
Volume-Based Link Building
Strong editorial context
Thin or generic content
High topical relevance
Limited relevance
Natural language anchors
Over‑optimized anchors
Trusted authors and publications
Sites with weak editorial oversight
Clear entity associations
Obvious link‑selling footprints
AI doesn’t just look at the presence of a link; it evaluates the context around it. Models are trained to reward authenticity. Search aims to reward the most authoritative entities.
Creating multi‑signal authority
The real power comes from a combination of signals. As search has evolved, quality has become more powerful than quantity.
Now AI is driving another shift. You can grow traditional, relevance-focused links alongside new brand signals.
Secondary coverage as other sites pick up the story.
This is multi-signal authority — holistic credibility that AI systems are designed to reward. It tells Google and LLMs: you’re known, trusted, and relevant. You need to be part of the conversation.
As powerful as PR signals are, they’re only one part of a larger authority ecosystem. AI evaluates brands through a multi-signal trust profile that determines visibility.
Breaking down the new authority stack
Authority is now defined by the breadth and consistency of signals that validate who your brand is across the web. It’s evaluated as humans do: reputation, recognition, expertise, and prominence.
Authority is no longer a single metric tied to links. It’s a network of signals, including:
Brand strength: Rising branded search volume, navigational queries, and direct traffic patterns that signal real-world recognition.
Entity validation: Consistent NAP details, schema markup, and unified profiles help confirm your brand and connect references back to the same entity.
Topical authority: Depth of content, subject-matter experts, and external collaboration to show your brand is genuinely knowledgeable about the topics you discuss.
Reputation signals: Reviews, citations, third-party mentions, and sentiment patterns that reflect trustworthiness.
PR signals: News coverage, interviews, podcast appearances, and industry mentions that reinforce your brand’s relevance.
Together, these signals create a holistic authority profile that AI can interpret. The brands that win have the strongest multi-signal authority footprint.
Brand strength is the silent factor
Brand strength quietly outweighs other signals. The data shows it: brands in the top 25% for web mentions average 169 AI Overview citations, while the next quartile averages just 14.
That’s not a small gap.
This aligns with Ahrefs’ analysis of ~75,000 brands. The strongest correlations with appearing in AI Overviews were branded web mentions, branded anchors, and branded search volume—all signals of real-world brand presence.
Consider two competing fitness apps. One has thousands of backlinks from generic listicles. The other is frequently mentioned in Reddit threads, YouTube reviews, and TikTok “day in the life” videos. The second app appears consistently in AI Overviews because AI sees it as part of the real-world fitness conversation, not just the link graph.
The brands dominating AI Overviews have the strongest brand presence, supported by consistent links, mentions, citations, and contextual relevance.
Predictions for 2027 and beyond
By 2027, link building will undergo radical change. The shift from a numbers game to a confidence game will become the norm, and Share of Authority or Voice will be the new metric.
Here are my top three predictions for what’s next.
Prediction 1: Visibility will be measured by a “Share of Model” metric. AI rewards signal density, not link density.
Link building will expand to include “seeding” information in AI training hubs. Instead of mass outreach to low-tier blogs, strategies will target user-preferred sources like Reddit, LinkedIn, Substack, and GitHub, which LLMs use for high-quality, human-led data.
Brands that appear most often in training data, trusted sources, and high-authority conversations will earn visibility. This is the next step in a world where signals determine authority.
Traditional Metric
Predicted Metric
Why the Change
Backlink Count
Entity Citation Frequency
AI values brand mentions as much as links
Domain Authority (DA)
Source Reliability Score
Focus on the trustworthiness of the source
Anchor Text
Semantic Context
AI reads the intent around the link, not just the text
PageRank
Share of Model (SoM)
Success is being the AI’s preferred answer
Prediction 2: Brands will act as primary newsrooms as proprietary data generates the strongest authority signals.
As AI systems rely more on multi-signal authority, proprietary data becomes one of the most powerful assets a brand can produce. Data isn’t just content — it’s a signal engine. It naturally earns the signals AI trusts most:
PR coverage.
Citations.
Mentions.
Social discussion.
Co‑occurrence with authoritative entities.
Long‑tail references in future content.
Traditional link building still provides foundational authority, but data-driven assets are the accelerant. They create high-trust, high-context signals that AI models weigh heavily.
On a platform where visibility depends on how often your brand appears in authoritative contexts, proprietary data is the most scalable way to increase your Share of Authority.
Prediction 3: Unlinked brand mentions will become one of the most valuable authority signals
Traditional contextual links will continue to build the foundation. But beyond that, search engines will track every time your brand appears alongside specific topics. Links will need “semantic context.”
Every mention of your brand in news, podcasts, reviews, forums, social posts, and roundups becomes a signal that strengthens your entity.
AI isn’t replacing link building — it’s expanding it
The future of off-page SEO isn’t a battle between traditional link building and AI-driven signals. It’s the realization that links were always just one signal. Now search engines can understand dozens more.
Traditional link building still matters. It provides the foundational authority, crawl paths, and topical relevance every site needs.
AI has widened the field. It can read context, interpret sentiment, understand entities, and evaluate brand presence.
These signals don’t replace links — they amplify them.
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