The AEO Trends To Help You Gain More AI Citations & Execute A Budget-Smart Strategy
Shannon Vize, Sr. Content Marketing Manager at Conductor, and Pat Reinhart, VP of Services & Thought Leadership at Conductor, shared field-tested strategies to help you operationalize AEO and build brand authority across fragmented AI search experiences.
You’ll Learn:
Which AEO Trends & Content Types Generate The Highest Chance Of AI Citations: A prioritized breakdown of the content marketing and AEO trends that will drive search visibility and performance.
Agentic Workflows That Scale AI Visibility: Specific tactics for using agentic tools to produce authority-building content formats at scale.
Walk away with a prioritized framework for shifting your content investment toward visibility-first tactics, covering agentic workflows, authority-building formats, and the metrics that actually reflect performance in AI search.
Whether you’re leading digital strategy or driving day-to-day execution, this on-demand session will give you the clarity and direction needed to evolve your approach for an AI-first search landscape.
Register above to watch the full recording and get the actionable AEO framework and trend analysis your team needs to drive visibility and performance in AI-first search.
Google’s John Mueller says markdown pages serve a specific purpose for developer documentation sites but won’t help most websites, even as search becomes more agentic.
Mueller laid out his reasoning in a Bluesky thread. He was responding to a question from Lily Ray about why Google publishes LLMs.txt files and markdown pages, even though they aren’t needed for search performance.
His response focused mainly on markdown versions of developer documentation, not llms.txt as a standalone file.
Mueller wrote:
“The short answer is that it’s not done for search. There’s more to websites than just SEO :-).”
Mueller’s Discovery Vs. Functionality Framework
His reasoning focuses on two different website goals.
He called the first “discovery,” or being found via a search engine, and the second “functionality,” which helps users complete tasks on the page.
Mueller acknowledged the term wasn’t precise. “There’s probably a more accurate term for this,” he wrote in the thread.
He compared the distinction to calls to action on traditional pages, stating:
“You don’t ‘do them’ for SEO (to be found), but if you’re responsible for the website overall, ensuring a high ‘discovery rate’ (SEO) together with a high conversion rate is useful to justify your work.”
Why Developer Docs Are Different
On developers.google.com, he noted, markdown versions make sense.
Mueller said;
“AI coding has gotten very popular, and these coding systems can be (I think) efficient and accurate with the code they produce if they can easily read / parse reference material, such as developer documentation.”
He added that markdown can help AI systems “understand the context of the documentation they’re looking at, as well as a simplified version of the reference page.”
Mueller called this a workaround rather than a long-term need, adding:
“OF COURSE they can read HTML just fine, so this is imo more of a temporary crutch, perhaps to save some tokens.”
Non-Developer Sites Should Skip It
For everyone else, Mueller was direct, stating:
“For non-developer sites, I don’t think this makes much sense, even with more agentic traffic in the future. Making a markdown version of a shoe’s specs is not going to get you more sales (competitors appreciate it tho).”
He went further in a follow-up post, pushing back on the idea that sites should prepare for a future where agents drive more traffic.
Mueller added:
“And (I know, nobody reads this far), if you think this is important to prepare for when agents are everywhere: your site (all sites) have much more important things to do for SEO than to prepare for a potential future situation that may or may not come. Prioritize needs before dreams.”
Why This Matters
Mueller’s comments show a more detailed position than his earlier statements on the topic.
In February, Mueller called the idea of serving markdown pages to bots “a stupid idea.” His Bluesky comments carve out an exception for developer documentation while holding the line for every other type of site.
The thread also arrived on the same day we reported that Google’s guidance on llms.txt now depends on which product you ask. Google’s generative AI optimization guide says to skip llms.txt, while Lighthouse 13.3 added an experimental audit that checks for the file as part of agentic browsing readiness.
Looking Ahead
Mueller’s distinction between discovery and on-page functionality can help you evaluate whether agentic optimization is worth their time. The test is whether building for agents right now produces measurable results for a specific site.
The “prioritize needs before dreams” line captures a broader tension in the industry right now. Vendors have been promoting llms.txt and markdown optimization as emerging practices, but neither Google’s search documentation nor independent data support investing in these for non-developer sites.
After weeks of delay, WordPress 7.0, named Armstrong, is finally released. The centerpiece feature was supposed to be real-time collaboration (RTC) but what is shipping is bigger: Native AI integration, a watershed moment in the content management system’s history. Native AI integration is what will carry WordPress into the future and put more distance between it and competitors.
Four Building Blocks Form The Foundation Of WordPress AI
WordPress 7.0 introduces four foundational building blocks that together form its native AI architecture. The larger story is that WordPress is building the infrastructure for a future where AI becomes part of how the CMS itself operates.
The Four WordPress 7.0 AI Building Blocks
WP AI Client
Client-Side Abilities API
AI Connectors Screen
Connectors API
These four features form the pillars that support a radical transformation of how information will be published and websites are designed. What makes this especially powerful is the massive community of developers around the world who can now create new ways of using themes, dream new ways of building websites, analyzing data, and making it easier to build a business online. No other CMS has that people-power behind it.
WordPress explains it like this:
“WordPress 7.0 unlocks AI capabilities right in your website. The new WP AI client adds a central interface that lets plugins communicate with generative AI models while remaining provider-agnostic. WordPress Core handles request routing for you. Managed in the Settings > Connectors screen with API keys funneled through the Connectors API, you can start with some preset models and add your favorites.
As a bonus, the Abilities API is integrated directly into the WP AI Client, delivering new and expansive AI abilities that can be built into workflows that run abilities fluidly, one after another.”
WP AI Client Enables AI Provider Integration
WordPress Core enables users to bring their own AI providers and easily integrate them into the CMS. The WP AI Client makes that possible by giving plugins a central, provider-agnostic interface for sending prompts to AI models and receiving responses through WordPress.
Plugin developers do not have to build separate AI integrations for every provider. They can integrate with the WP AI Client interface instead.
A plugin can describe what it needs, WordPress can route the request to a suitable configured model, and site owners can control which AI providers are available inside WordPress.
The release also introduces model preference ordering, feature detection, advanced configuration controls, and a Prompt Builder class for interacting with models. WordPress says developers can prioritize models based on capabilities, cost, and processing efficiency.
Client-Side Abilities API Extends AI Into WordPress Actions
WordPress 7.0 gives AI and automation tools a way to interact with WordPress from inside the browser. That means AI can be connected to actions such as navigating the admin, inserting blocks, running commands, and participating in workflows instead of simply generating text outside the CMS.
This is where the AI story becomes bigger than content creation. WordPress is creating a layer where AI agents, plugins, and automation tools can act on the same set of WordPress capabilities through a shared interface.
The practical effect is that WordPress can become an environment that AI tools operate within, not just a place where AI-generated content is pasted.
AI Connectors Centralize External AI Services
The new Connectors screen gives site owners one place to manage connections to outside AI services. Instead of scattering API keys and provider settings across individual plugins, WordPress is creating a central location for managing those services.
The Connectors API is the technical layer behind that screen. It handles the provider registry, authentication details, metadata, and future connection types, which gives WordPress a standardized way to recognize and manage external AI services.
That matters because AI will not be limited to one provider or one kind of integration. WordPress is preparing for a future where multiple AI services can be connected, managed, and used across the CMS.
WordPress explains how the Connectors API works behind the scenes:
“The Connectors API is the backbone of the Connectors screen; an extensibility API that facilitates and supports the inclusion of agents.
The API supports two authentication methods (api_key and none) based on provider metadata, and is designed to facilitate additional connector types in future releases. The Connectors API uses the WP AI Client’s default registry to automatically discover providers, and corresponding metadata to generate connectors, while connectors authenticated via other methods are stored in the PHP registry.
You can use the wp_connectors_init action to override connectors metadata, which will be the key for registering new connector types in future releases. The API includes three public functions for querying the registry, and the frontend UI can be customized using client-side JavaScript registration.”
WordPress Is Building Beyond AI Features
The release is not just about adding AI to WordPress. It is about giving WordPress the internal structure needed for AI-workflows like publishing, SEO automation, site design, site building, and agent-based workflows.
The four building blocks built into WordPress 7.0 make it all happen:
The WP AI Client connects WordPress to models.
The Abilities API gives AI a way to take action.
The Connectors screen gives users control over providers.
The Connectors API gives developers a standard foundation for future integrations.
Real-time collaboration was expected to define WordPress 7.0. Native AI integration may prove to be the feature that defines what WordPress becomes next.
Google announced two new ad formats for AI Mode during Google Marketing Live: Conversational Discovery ads and Highlighted Answers.
Both formats are powered by Gemini and designed to place ads more directly inside AI-generated responses and recommendation flows.
According to Google, the formats will include an independent AI explainer that synthesizes information about a product or service alongside the advertiser’s creative. Ads will continue to carry sponsored labels.
Read on to learn more about the new ad formats and when you can expect to start seeing them.
Conversational Discovery Ads Respond To Nuanced Prompts
Conversational Discovery ads are designed to respond to detailed or exploratory prompts inside AI Mode.
Google’s example showed someone asking how to make their home smell like “fancy spas or a rainy forest” using low-maintenance solutions.
Instead of relying primarily on keyword targeting, Gemini generates tailored creative and surfaces product features tied to the context of the conversation.
That creates a different type of Search interaction than advertisers are used to optimizing for today.
These ads appear built for longer, conversational prompts where users may refine what they want throughout the interaction rather than searching with a single high-intent query.
Google has been steadily moving in this direction through AI Overviews, AI Mode testing, and earlier sponsored placements appearing inside AI-generated experiences.
Highlighted Answers Insert Ads Into Recommendation Lists
The second format, Highlighted Answers, places ads directly inside recommendation lists generated by AI Mode.
Google used the example of someone researching language learning apps before a trip. Advertisers with highly relevant ads may appear directly within those recommendations.
This moves ads closer to the recommendation itself instead of alongside traditional Search results.
For advertisers, that could create visibility earlier in the research process before users narrow down to a final decision.
Google also said these experiences will remain clearly labeled as sponsored and include AI-generated explainers alongside the ad.
Why This Matters For Advertisers
These updates suggest Google is pushing ads deeper into conversational Search experiences.
For advertisers, that may increase the importance of creative quality, landing page content, structured product data, and first-party conversion signals.
Gemini is evaluating more than a simple keyword query. It’s interpreting the broader context of the conversation before surfacing ads.
It also creates new reporting and measurement questions.
Conversational searches are far less structured than traditional keyword searches. That may make it harder for advertisers to understand which prompts, themes, or interactions actually influenced performance over time.
Google made it clear that AI Mode is becoming a larger part of Google’s Search strategy.
Conversational Discovery ads and Highlighted Answers also provide a clearer picture of how Google plans to monetize those experiences.
Measurement and optimization may become far more complicated as searches become longer, more conversational, and less tied to traditional keyword behavior.
Both formats are expected to be tested within AI Mode, with no confirmation yet on when they are expected to start surfacing.
Google released a report detailing how people use AI Mode in the U.S., drawing on internal Search data and Google Trends to map search behavior one year after launch.
The report, published alongside Google I/O 2026 announcements, said that AI Mode has surpassed 1 billion monthly active users globally. Queries have more than doubled every quarter since launch.
How Query Behavior Is Changing
The report states that the average AI Mode search is three times longer than a traditional search. Both short and long queries are increasing in AI Mode, with users having conversations and asking longer questions.
Follow-up queries in AI Mode rose over 40% monthly in the U.S. More than one in six AI Mode searches are multimodal, using voice, images, or video. Image-based searches are up over 40% month-over-month since launch.
Top keywords include “information,” “identify,” “find,” “explain,” and “summarize.” Common first words are “what,” “how,” “I,” “is,” and “can,” with “I” especially notable, which may suggest people treat AI Mode more like a conversation than a traditional search.
What People Search For
Google grouped AI Mode search topics into five categories: Explore, Decide, Learn, Create, and Do. The top 10 topics include creative content, media, education, fashion, food, health, tech, travel, productivity, and development.
Brainstorming queries increased 30% faster than overall AI Mode queries since launch, with searches for “where to,” “where should I,” and “ideas for” also rising, per Google Trends.
Planning-related queries grew 80% faster over six months, with decision questions starting with “which” increasing 40%, especially “which of” and “which one.”
Shopping And Local Behavior
Shoppers start with traditional search, then move to AI Mode for deeper inquiry, especially in electronics, books, apparel, health and beauty, and automotive.
In AI Mode, store-related questions focus on “near me,” replacement parts, financing-related dealership searches, online options, and stock.
Top retail concerns include price, location, color, brand, and availability. For restaurants, users seek kid-friendly options, views, bars, vegan or vegetarian choices, and outdoor seating.
Creative And Educational Use
AI Mode’s image creation queries have more than tripled since early 2026, with users mainly requesting photos, quizzes, logos, stories, and code, as well as editing photos, documents, videos, messages, and code.
For education, top subjects include math, Spanish, history, English, and biology, while professional development searches focus on Security+, black belt, Network+, bar exam, and real estate license.
Why This Matters
The data shows AI Mode users are searching in ways that don’t map cleanly to traditional keyword patterns. Queries are longer, conversational, and increasingly multimodal. Follow-up conversations are growing, and planning and decision queries are among the strongest growth signals in the report.
If query length and follow-ups keep growing, that means thin content faces a different competition than conversational answers to multi-part questions.
The keyword and query data covers May 2025 to April 2026 and comes from a random, unbiased sample of Google searches. The Trends data measures search interest as a share of AI Mode searches, not total query volume. AI Mode Trends data is not publicly available on trends.google.com.
“There is now ample evidence, collected over the last few years, that AI systems are unpredictable and difficult to control.” That’s Dario Amodei in January, writing about the technology his company sells.
Compare with what’s on your LinkedIn timeline this week. Here’s the script: Schema markup ensures AI engines parse your content. The first sentence of every section must be the answer. Optimize for chunk-level retrieval. There’s a 13% citation lift available if you do X, a 2.8x conversion improvement if you do Y.
It’s one of the cleanest patterns going right now, and the industry has elected not to notice. The people closest to these systems are increasingly cautious about claims of control. The people furthest from it are increasingly certain they know how it works … they’ve cracked it. That gradient runs the wrong way.
“We mostly treat AI models as a black box: something goes in and a response comes out, and it’s not clear why the model gave that particular response instead of another.”
Anthropic, writing about its own model, two years ago.
Things haven’t gotten more confident since. Neel Nanda, who runs Google DeepMind’s mechanistic interpretability team, gave an interview to 80,000 Hours in September 2025 in which the headline finding was that the most ambitious version of mech interp is probably dead. He doesn’t see a realistic world where the discipline delivers “the kind of robust guarantees that some people want from interpretability.” Worth re-reading.
The person whose job is to read AI minds is publicly conceding that the project, as originally conceived, won’t get there.
At NeurIPS 2024, Ilya Sutskever, co-founder of Safe Superintelligence and formerly chief scientist at OpenAI, accepted his Test of Time award and used the platform to say something the room wasn’t expecting from him:
Sutskever’s career is essentially the scaling hypothesis with a face on it. Hearing him say the next phase produces less predictable outputs is itself an admission.
Now scroll back to your timeline. The gradient is Dunning-Kruger redrawn at an industry scale: Mt. Stupid with a pricing page, and the valley of calibration where the actual work happens.
Image Credit: Pedro Dias
What The People Selling It Actually Say
A practitioner posts a four-pillar framework for “Technical GEO.” A consultant guarantees inclusion in AI Overviews. An agency markets a 13% lift in citation likelihood, derived from data the agency itself produced about the agency’s own prescriptions. A widely shared post promises that maintaining a 300-character paragraph limit dictates how a vector database chunks your content. A vendor claims a 78% “share of model.” A senior figure in your inbox describes a 2.8x improvement in conversion from being cited in SGE.
The vocabulary is deterministic: “ensures,” “guarantees,” “dictates,” percentages precise to the decimal, frameworks confidently named. None of it sounds anything like the language the people who built these systems use when describing how the systems behave.
This is the part I keep getting stuck on. The consultants are confident about the tactics they’ve measured against themselves. Run the same playbook on a few clients, watch some metric move, call it evidence. No control groups, no pre-registered hypotheses, no measurement of what the tactic is actually claimed to change. That’s the bar a real test has to clear; everything else has been confirmation in costume. The problem is the confidence level, which is wrong by an order of magnitude regardless of whether the underlying tactic does anything. The same model that Anthropic publicly says it cannot fully account for is being optimized against by people who confidently claim to know exactly what they’re doing.
Either Anthropic has been suspiciously modest in public, or somebody else is suspiciously certain.
When Somebody Tests
On Monday, last week, Ahrefs published a study by Louise Linehan and Xibeijia Guan with a title that should ideally be impossible: We Tracked 1,885 Pages Adding Schema. AI Citations Barely Moved.
The methodology is the kind of work you would expect to be standard, if the discipline cared about standards. 1,885 pages that added JSON-LD schema between August 2025 and March 2026. 4,000 matched control pages. Citation changes measured 30 days before and 30 days after the schema was added, across Google AI Overviews, Google AI Mode, and ChatGPT. Difference-in-differences on the matched groups.
The finding: No meaningful uplift in citations on any platform. AI Overviews actually showed a small but statistically significant decline. The report notes the odds of a gap that large being chance are roughly 1 in 2,500. The schema-makes-LLMs-understand-your-content thesis, tested at scale against a controlled baseline, did not survive the test.
This is the empirical confirmation of the technical case I made a week ago in The Whole Point Was the Mess: that LLMs read unstructured language, and that schema-and-chunking prescriptions are reasoning about an architecture that doesn’t exist. From first principles, two weeks ago. From controlled measurement, last Monday.
It is worth sitting with that. The dominant prescriptive category in the entire GEO playbook has been empirically falsified under controlled conditions, by a vendor with a substantial audience, in the open. And the frameworks keep selling.
“Many suggested ‘hacks’ aren’t effective or supported by how Google Search actually works.”
Google names Answer Engine Optimization and Generative Engine Optimization by their full terms and rejects the playbook outright.
Image Credit: Pedro Dias
That is the search engine the consultants claim to be optimizing for, telling its own developer audience that the optimizations don’t work. From first principles, two weeks ago. From controlled measurement, last Monday. From Google itself, last Friday. Three independent sources of the same answer, all within a fortnight. All ignored by the people selling the opposite.
The Cost Of Asking
This is where the diagnosis stops being polite.
Confident claims compound on these platforms in a way that skeptical corrections don’t. The difference is in who pays. Posting a confident claim costs you nothing. It gets engagement, builds an audience, generates inbound, makes the slide deck look forward-looking. If it turns out to be wrong, nothing happens. By the time anyone notices, everyone’s moved on to the next acronym.
Posting the correction costs you. It picks a fight. It marks you as a contrarian, or worse, as somebody who doesn’t get it. On LinkedIn, where most of this happens, it works against your professional brand. The algorithm will not reward it. The original poster owns the comment section and can ignore your methodology question while engaging with the congratulatory replies. Your reply lives in a collapsed thread.
There’s a specific move worth naming here. Ask a GEO consultant to explain, in plain terms, what their methodology actually does, what mechanism it acts on, what would count as evidence, what would falsify it. The response escalates into jargon. “Vector-space alignment.” “T1 query optimisation.” “Chunk-level semantic retrieval.” Real terms from machine-learning research, glued into combinations that sound rigorous and resist plain-language verification. The pattern works because it can. Asking “what does that actually mean” looks naive, and observers without the specific technical knowledge can’t tell which combinations are real and which are improvised on the spot.
Read the comments on any high-engagement GEO post. Fifteen replies in, 12 are agreements or “here’s another skill to add to your list.” Two or three offer diplomatically-framed skepticisms: “I would love to see more data,” or “the list is right, but…” The author engages substantively with the philosophical objection because pushing back against “this is too technical” is easy. The methodological objection, that the prescribed skills produce confident speculation without a measurement layer underneath, gets the politest burial.
What this adds up to is gaslighting at industry scale. The people reading the technology correctly get positioned as the ones who haven’t caught up; the prescriptions that controlled tests just falsified get sold as forward-looking. GEO has worked out how to make calibration look like the deficiency.
A recent X experiment captured the dynamic outside SEO. Someone posted a Monet painting and claimed it was AI-generated, asking the replies to explain its inferiority to a real Monet. Hundreds responded, confidently cataloging the “AI tells.” Flat brushwork, soulless composition, no cohesion, no soul. They were analyzing a Monet. The frame determined what they saw.
It’s the same trick. Vocabulary substitutes for substance; framing activates confirmation bias before any examination begins; the performance of analysis becomes what’s purchased rather than the analysis itself; “this is X” arrives before anyone checks whether it is. Once the frame is set, the analysis follows.
So the people most equipped to push back, the practitioners who’ve actually tried to test things, the technical SEOs who know what schema does and doesn’t do, the ones who can spot a fabricated lift number from across the room, stay quiet.
The result, on the timelines the C-suite reads, is a one-sided market.
The cost falls on the people who buy the claim. Clients pay for schema audits the Ahrefs study just falsified. Junior practitioners build careers on methodologies that won’t survive a controlled test. And the discipline burns credibility it will need later, when traditional search displaces further, and SEOs are expected to sit in rooms with engineering teams who’ve just spent two years watching the field confidently mis-call the technology.
Knowledge advances by trying to disprove your hypothesis, not confirm it. GEO does the opposite, runs studies designed to validate what it’s already selling. If the professionals claiming this expertise won’t even try to falsify themselves, who do we expect to believe us?
The Absence Is The Data
Strip the discourse, and what remains is the absence.
A serious technical field watches a controlled test contradict its dominant prescriptions, and the prescriptions keep selling. At that point, asking whether the prescriptions are wrong stops being the interesting question. That has been answered. The harder question is what’s wrong with a field that watches and doesn’t correct.
Same with the gradient. When the people who built the systems hedge and the people optimizing for those systems guarantee, asking who’s right stops being interesting. The researches and builders are right. Nobody who has worked on inference attribution thinks otherwise. The harder question is why the field lets the guarantees travel unchallenged.
The honest answer is that the incentives don’t pull toward correction. Confidence sells in ways caution can’t. The reportable framework wins the budget; the sensible assessment loses. And hedged language doesn’t fit on a pricing page where a guarantee fits perfectly.
None of this needs villains. The market for attention rewards confidence over calibration, every time.
You can keep watching the gradient run the wrong way. Or you can read what it actually is: an industry standing on Mt. Stupid, charging for the view.
More Resources:
This post was originally published on The Inference.
Google’s Search and Chrome documentation now point in different directions on llms.txt, depending on whether the goal is Search visibility or agentic browser readiness.
Google Search recently published a new optimization guide that lists llms.txt among the tactics you don’t need for generative AI features. The guide groups it with content chunking, AI-specific rewriting, and special schema.
Days earlier, Google’s Lighthouse tool shipped version 13.3, which added a new Agentic Browsing category. The update includes an llms.txt audit that checks whether a site provides the file and flags server errors when retrieving it.
The Lighthouse documentation describes llms.txt as a way to provide “a machine-readable summary of a website’s content, specifically designed for LLMs and AI agents.” It adds that without the file, “agents may spend more time crawling the site to understand its high-level structure and primary content.”
What Google Search Has Said
Google’s Search team has maintained for over a year that llms.txt is not a Google initiative or something Google plans to adopt.
Google’s optimization guide explicitly states llms.txt should be skipped, providing the most recent direct statement from the Search team.
What Chrome’s Lighthouse Now Does
Lighthouse 13.3 ships with the Agentic Browsing category by default, checking WebMCP integration, agent accessibility, layout stability, and llms.txt.
The llms.txt audit only marks sites as “Not Applicable” if they return a 404; errors flag the audit. The Lighthouse docs describe llms.txt as an “emerging convention” at llmstxt.org, advising site owners to create and place it in their root directory.
This category is separate from SEO audits and indicates that llms.txt helps browser-based agents understand site structure, not improve search rankings or AI citations.
Google Has Been Here Before
Google’s internal teams have sent mixed signals on llms.txt before.
In December, Lidia Infante spotted an llms.txt file on Google’s Search Central developer documentation. Mueller responded on Bluesky with “hmmn :-/” and didn’t clarify further.
Dave Smart noted that the file appeared on multiple Google developer properties, including developer.chrome.com and web.dev. The pattern suggested an internal CMS platform update that automatically deploys llms.txt files, not a Search team decision.
The Search Central file was removed within hours, but files on other Google properties remained.
Why This Matters
Google’s answer on llms.txt varies by use case.
For Google Search, llms.txt isn’t needed for AI Overviews, AI Mode, or other generative AI Search features.
For browser-based agents, Lighthouse considers llms.txt optional in an experimental machine interaction category.
Guidance is split between different Google developer sites, which can lead to conflicting instructions when comparing Lighthouse or its llms.txt documentation with Google’s Search docs.
Looking Ahead
Google hasn’t commented on the documentation gap between the two product teams.
For many sites, creating a basic llms.txt file is simple, but maintaining it is questionable, given that Google Search states it’s unnecessary for AI Search visibility.
This post was sponsored by WP Engine. The opinions expressed in this article are the sponsor’s own.
Why are we missing the SERP window on breaking stories we should be winning? How are smaller outlets ranking faster than us on the same news? Why is our ad stack tanking Core Web Vitals on our highest-traffic pages?
In most large newsrooms, the answer traces back to the same culprit: a fragile, patchwork legacy CMS held together with ad-hoc plugins. For SEO and growth teams, that’s a direct hit to organic search traffic and ad revenue. Below are four publishing workflow fixes that move both metrics in the same direction.
The 4 Publishing Pillars That Improve SEO & Monetization
To stop paying this tax, media organizations are moving away from treating their workflows as a collection of disparate parts. Instead, they are adopting a unified system that eliminates the friction between engineering, editorial, and growth.
A modern publishing standard addresses these marketing hurdles through four key operational pillars:
Pillar 1: Automated Governance (Built-In SEO & Tracking Integrity)
Marketing integrity relies on consistency.
In a fragmented system, SEO metadata, tracking pixels, and brand standards are often managed manually, leading to human error.
A unified approach embeds governance directly into the workflow.
By using automated checklists, organizations ensure that no article goes live until it meets defined standards, protecting the brand and ensuring every piece of content is optimized for discovery from the moment of publication.
Pillar 2: Fearless Iteration (Continuous SEO & CRO Optimization Without Risk)
High-traffic articles are a marketer’s most valuable asset. However, in a legacy stack, updating a live story to include, for instance, a Call-to-Action (CTA), is often a high-risk maneuver that could break site layouts.
A modern unified approach allows for “staged” edits, enabling teams to draft and review iterations on live content without forcing those changes live immediately. This allows for a continuous improvement cycle that protects the user experience and site uptime.
Pillar 3: Cross-Functional Collaboration (Reducing Workflow Bottlenecks Between Editorial, SEO & Engineering)
Any type of technology disruption requires a team to collaborate in real-time. The “Sticky-taped” approach often forces teams to work in separate tools, creating bottlenecks.
A modern unified standard utilizes collaborative editing, separating editorial functions into distinct areas for text, media, and metadata. This allows an SEO specialist or a growth marketer to optimize a story simultaneously with the journalist, ensuring the content is “market-ready” the instant it’s finished.
Late-breaking or real-time events, such as global geopolitical shifts or live sports, require in-the-moment storytelling to keep audiences informed, engaged, and on-site. Traditionally, “Live Blogs” relied on clunky third-party embeds that fragmented user data and slowed page loads.
A unified standard treats breaking news as a native capability, enabling rapid-fire updates that keep the audience glued to the brand’s own domain, maximizing ad impressions and subscription opportunities.
If those are things you’ve explored changing, it may be time to examine your own Fragmentation Tax, and why a new publishing standard is required to reclaim growth.
Stop Paying The Fragmentation Tax: How A Siloed CMS, Disconnected Data & Tech Debt Are Costing You Growth
The Fragmentation Tax is the hidden cost of operational inefficiency. It drains budgets, burns out teams, and stunts the ability to scale. For digital marketing and growth leads, this tax is paid in three distinct “currencies”:
1. Siloed Data & Strategic Blindness.
When your ad server, subscriber database, and content tools exist as siloed work streams, you lose the ability to see the full picture of the reader’s journey.
Without integrated attribution, marketers are forced to make strategic pivots based on vanity metrics like generic pageviews rather than true business intelligence, such as conversion funnels or long-term reader retention.
2. The Editorial Velocity Gap.
In the era of breaking news, being second is often the same as being last. If an editorial team is forced into complex, manual workflows because of a fragmented tech stack, content reaches the market too late to capture peak search volume or social trends. This friction creates a culture of caution precisely when marketing needs a culture of velocity to capture organic traffic.
3. Tech Debt vs. Innovation.
Tech debt is the future cost of rework created by choosing “quick-and-dirty” solutions. This is a silent killer of marketing budgets. Every hour an engineering team spends fixing plugin conflicts or managing security fires caused by a cobbled-together infrastructure is an hour stolen from innovation.
Conclusion: Trading Toil for Agility
Ultimately, shifting to a unified standard is about reducing inefficiencies caused by “fighting the tools.” By removing the technical toil that typically hides insights in siloed tools, media organizations can finally trade operational friction for strategic agility.
When your site’s foundation is solid and fast, editors can hit “publish” without worrying about things breaking. At the same time, marketers can test new ways to grow the audience without waiting weeks for developers to update code. This setup clears the way for everyone to move faster and focus on what actually matters: telling great stories and connecting with readers.
The era of stitching software together with “sticky tape” is over. For modern media companies to thrive amid constant digital disruption, infrastructure must be a launchpad, not a hindrance. By eliminating the Fragmentation Tax, marketing leaders can finally stop surviving and start growing.
Jason Konen is director of product management at WP Engine, a global web enablement company that empowers companies and agencies of all sizes to build, power, manage, and optimize their WordPressⓇ websites and applications with confidence.
When Unilever CEO Fernando Fernández stood before investors and declared that the era of expensive corporate brand advertising was over, calling traditional TV-heavy campaigns “lazy marketing,” the shockwave through the agency world was immediate. Half of Unilever’s massive global advertising budget would shift to a “social-first” strategy. Creator collaborations would scale by 20 times. The target would be an army of over 300,000 influencers, including a micro-influencer in every postal code in key markets like India.
Traditional advertising agencies that had spent decades building relationships around six-figure production budgets and a handful of celebrity partnerships suddenly faced a client with an operationally impossible mandate. Manual sourcing, onboarding, and content approval at 300,000-creator scale simply does not exist as a human workflow. Specialized creator agencies picked up business that legacy agency-of-record relationships had assumed were locked in.
The panic was understandable. It was also aimed at the wrong target.
The More Important Question
A March 2026 Adobe Express study surveyed video creators across YouTube, TikTok, and Instagram and found that 71% have now adopted AI video generation or editing tools. Of those, 41% deploy them on a weekly basis. 56% of creators using AI tools report saving over 30 minutes per video on average, with 10% shaving more than four hours off their production time. On the performance side, they’re seeing a 19% average increase in audience watch time and a 17% boost in community engagement. Half plan to increase their AI tool spending over the next year.
So, Unilever is building an army of 300,000 creators, and 71% of creators are now using AI to produce their content. The math is straightforward, and what Unilever is actually building is a massive distributed network for the production and distribution of AI-assisted content at a scale the marketing industry has never seen.
The question that hasn’t been answered yet is whether any of it will work.
Unilever’s 300,000-creator network is generating content at a scale that makes traditional test-and-learn frameworks difficult to apply cleanly. When hyper-local micro-influencers are producing AI-assisted videos for niche audiences across hundreds of markets simultaneously, the signal-to-noise problem becomes acute. Individual pieces of content may perform well in isolation while the overall brand narrative diffuses into incoherence. Or the personalization may be exactly what audiences want, and the aggregate effect may be stronger than anything a single high-production campaign could achieve. Right now, the honest answer is that nobody knows with confidence.
Where DAIVID And ADIN.AI Come In
On April 27, 2026, two companies that many SEO professionals and digital marketers haven’t heard of yet announced a partnership that addresses the exact problem Unilever’s strategy creates.
DAIVID is a creative intelligence platform whose AI models, trained on tens of millions of human responses to ads, predict in seconds how any piece of ad creative will perform – measuring attention, 39 distinct emotions, memory encoding, brand recall, and likely next-step actions – without requiring human panels. ADIN.AI is an AI-native operating system for enterprise marketing that sits above an organization’s existing tools and provides a unified intelligence layer across channels, budgets, and decisions.
The partnership embeds DAIVID’s creative effectiveness models directly into ADIN.AI’s platform, creating what they describe as a live loop between creative intelligence and media execution. Before a campaign launches, marketers can identify which creative is most likely to succeed and allocate budget accordingly. While campaigns run, they can scale high-performing assets and pause underperformers in real time. After campaigns end, the historical performance data becomes benchmarks that guide future creative and media planning.
Ian Forrester, CEO of DAIVID, described the core problem the partnership solves: “Creative is a key driver of advertising outcomes, but for too long it has been measured in isolation, disconnected from media results.” The first live client is Ajinomoto, the global food and nutrition company.
Why This Matters For SEO And Digital Marketing Professionals
The traditional advertising agency’s anxiety about Unilever’s creator pivot was understandable but slightly misdirected. The real disruption isn’t that Unilever is working with 300,000 influencers instead of three ad agencies. The real disruption is that when 71% of those creators are using AI tools to produce content at speed, and that content is being distributed across dozens of platforms in hundreds of markets simultaneously, the evaluation infrastructure that used to separate good creative decisions from bad ones stops working.
Human panels are too slow. A/B testing individual pieces of content across a 300,000-creator network is logistically impossible. Traditional brand-tracking surveys capture what happened last quarter, not what’s working right now.
What DAIVID and ADIN.AI are building is the kind of infrastructure that makes the Unilever model actually governable – a system that can score creative at scale, link those scores to media performance in real time, and surface the signal from the noise before the budget has already been allocated to the wrong places.
Shelley Walsh made the point in her recent Search Engine Journal article on AI content scaling that enterprise brands face a specific trap: They know what they want to do (scale content production) but not how to do it without sacrificing the quality signals that make the content worth producing. The DAIVID and ADIN.AI partnership doesn’t solve the content quality problem. But it does solve the evaluation problem – which is arguably more urgent when you’re managing 300,000 creators rather than three.
For SEO professionals and content marketers, the practical implication is familiar. The distribution channels are changing, the production tools are changing, and the volume is increasing. What stays constant is the need to measure what’s actually working and make decisions based on that measurement rather than assumptions. That’s true whether you’re optimizing for search citations or creator content performance. Ground truth it, as always.
Every advertiser, from small businesses to enterprises, can struggle with knowing if their budget is allocated for the best results. Budget allocation used to be more straightforward, but campaign spend has shifted, and a lot of accounts could use a second look.
Performance Max has disrupted how budget flows through accounts in new ways over the past few years. Advertisers who set up their campaign structure without considering PMax are running budgets against a different landscape than what they originally designed for.
Drawing from patterns I see consistently across accounts, here are three ways Google Ads budget gets misallocated across campaign types and how to diagnose what’s happening in your own account.
Reason 1: Low Budgets Restrict Smart Bidding
Smart Bidding is basically an exercise in pattern recognition. When a campaign has low conversion volume, the algorithm is forced to make decisions based on a small data set rather than meaningful trends. This leads to unpredictable performance swings and bid-shunting, where the system pulls back spend because it lacks the information to enter competitive auctions.
Starting and optimizing are not the same thing, though. While a cold start is possible, the algorithm still requires a steady stream of ongoing data to calculate its bids against real-world performance. Without this, the campaign stays in a perpetual learning state, and the ad manager has problems scaling.
2. The Campaign Vs. Account Threshold
A common mistake for ad managers is evaluating conversion volume at the account level. Google’s internal recommendations emphasize that thresholds for stability apply at the campaign level. According to official best practices:
For Target CPA: A campaign should ideally see at least 30 conversions in the last 30 days.
For Target ROAS: A minimum of 50 conversions in the last 30 days is recommended for the algorithm to accurately predict future conversion value.
Dividing a budget across three campaigns, each generating 15 conversions, is not mathematically the same as one campaign generating 45. In that fragmented scenario, the machine learning operates within three isolated silos, each struggling to reach a statistical significance high enough to make aggressive bidding decisions. This often results in budget throttling, where a campaign fails to spend its daily budget because the algorithm is holding back on serving.
What To Prioritize: Strategic Consolidation And Bid Floor Alignment
To optimize a low-volume account, ad managers should restructure smaller campaigns to consolidate into fewer, larger campaigns, for modern bidding success:
Consolidate for Conversion History: Combine smaller campaigns into larger campaigns. This is the fastest way to push a campaign forward. By pooling data, you can give the algorithm enough conversion history it needs to identify winning signals and exit the learning phase faster. Google’s own stance on campaign consolidation reinforces this approach, noting that consolidation is now a core recommendation for stable Smart Bidding performance.
Change to Maximize Strategies: If volume is consistently low, switch from Target bidding (tCPA/tROAS) to Maximize Conversions or Maximize Conversion Value. These strategies are more forgiving because they prioritize spending the budget to find the best available opportunities rather than restricting spend to hit a rigid efficiency metric the algorithm doesn’t yet have the data to guarantee.
The 10x Rule for Stability: To keep the algorithm from restricting delivery, ensure your daily budget is at least 10x your Target CPA. As explored in this breakdown of why budgets overspend even with a Target ROAS or CPA in place, setting a budget too close to your target, such as a $50 tCPA on a $60 daily budget, limits the algorithm’s ability to enter auctions, leading to stagnant spend and missed targets.
Reason 2: Performance Max Overspending Budget
The core problem with PMax is that it’s basically a black box for incrementality. In PPC, incrementality measures true lift, meaning the conversions that happened because of your ad and wouldn’t have occurred otherwise. Because PMax is built to maximize conversion value, it often can’t tell the difference between a net-new customer and someone who was already going to buy from you.
1. The Brand Traffic Problem
Branded queries have the highest intent and the lowest CPA in most accounts. PMax tends to go after them aggressively because they’re easy wins that help hit ROAS targets. From the dashboard, the campaign looks like it’s crushing it. What’s actually happening is that PMax is intercepting traffic that a lower-cost branded search campaign or your organic listing would have captured anyway.
That’s not incremental revenue. You’re paying a premium for a customer who was already knocking on your door, and it inflates CPCs on terms you already own.
Google recognizes the overlap between PMax and Branded Search, recommending Brand Exclusions as the primary tool for advertisers to maintain control over brand-specific traffic and avoid redundant costs.
2. The Zombie Logic (Underperforming Offers)
PMax funnels budget toward products with strong conversion history and largely ignores everything else. New launches and niche SKUs with limited data get almost no impressions. Ad managers who think they’re running a full-catalog campaign often find, after auditing the Listing Groups, that PMax has been directing the majority of spend toward a small slice of top performers the whole time.
While the industry uses the term “Zombie Products,” Google addresses this directly in its Retailer Best Practices. Google advises managers to monitor the Product Issues column for underperforming offers. To ensure full-catalog coverage, Google suggests using Custom Labels to segment high-priority or low-velocity products into separate campaigns, preventing the algorithm from starving niche inventory of budget.
3. The 2024 Auction Shift: From Priority To Ad Rank
Historically, PMax held absolute priority over Standard Shopping. If a product existed in both campaign types, PMax won the auction automatically. As of October 2024, that rule is gone. Google Ads Liaison Ginny Marvin confirmed that normal auction dynamics now apply: the campaign with the highest Ad Rank serves.
Google’s second-price auction means you won’t directly bid against yourself in a way that inflates your own CPC, but running overlapping campaigns can still create budget unpredictability and complicate attribution. Without the PMax priority rule, you can no longer guarantee which campaign type will win the auction for a specific product. That makes it very hard to run clean tests because both campaign types are now competing for the same user intent.
What To Prioritize: Taking Back Budget Control
The fix here is moving beyond a set-it-and-forget-it PMax setup:
Implement Brand Exclusions: Use Brand Settings at the campaign level, or account-level negative keyword lists, to block PMax from bidding on your brand terms. As I covered previously in my analysis of AI-driven budget rebalancing, branded queries carry the highest intent but the lowest incremental value. Brand exclusions push the algorithm toward true prospecting, where AI actually adds value.
Activate New Customer Acquisition Goals: The new customer acquisition goal setting tells PMax to bid more aggressively for new users. This shifts the focus from total attributed ROAS to incremental growth, so the budget is working to find people who haven’t bought from you before.
Segment by Product Volume: Move low-data products out of your main PMax campaign and into a separate PMax campaign or a Standard Shopping campaign with manual bids. This keeps budget from concentrating on your top 5% of SKUs while everything else gets ignored.
Clean Up Campaign Structure: With PMax priority gone, use Negative Keyword Themes and Product Filters to explicitly separate PMax and Standard Shopping. Letting Ad Rank sort traffic between the two leads to unpredictable and messy reporting. Clean segmentation is the only way to get reliable data.
Reason 3: Why Your Budget Is Sitting In Non-Converters
One critical mistake an ad manager can make is cutting budget from campaigns that show zero or low conversion value. On a standard last-click dashboard, this is a smart optimization. In reality, this can lead to account-wide performance decline.
Data-Driven Attribution uses AI to assign fractional credit across the entire customer journey. A campaign that shows zero conversions on a last-click basis might have influenced a final sale on a different traffic source. Cut that budget and you’re cutting the assist that your top-performing campaigns rely on to close the conversion.
2. The Signal Loss Chain Reaction
Smart Bidding requires a constant stream of signals to identify who to bid on. Upper-funnel and discovery campaigns often provide the first touchpoint that qualifies a user.
When you pause an underperforming campaign, you create a signal gap. Because of conversion lag, the time it takes for a user to convert after their first interaction, you may not see the impact of this budget cut for 7 to 14 days. As outlined in this guide to PPC budget strategies across campaign stages, pausing campaigns for extended periods can damage algorithm performance upon restart, potentially taking weeks to recover historical context. By the time your best campaigns start to decline, you’ve likely forgotten the budget decision that caused it.
What To Prioritize: Audit The Assists Before You Cut
Before you reallocate budget from a low-conversion campaign, verify its true hidden value using these two diagnostic checks:
The Google Ads Attribution Report: Navigate to Goals > Measurement > Attribution. Use the Model Comparison tool to compare Last Click against Data-Driven. If the campaign shows a significantly higher conversion value under the Data-Driven model, it is an essential part of your funnel and should not be paused.
The GA4 Advertising Report: Access the Google Analytics 4 Model Comparison report to see how your campaigns interact across channels. GA4’s Conversion Paths visualization lets you see exactly where a low-converting campaign sits in the early or mid-stages of the journey.
The rule of thumb: If a campaign has high assisted conversions but low direct conversions, treat it as a feeder campaign. Instead of pausing it, move it to a lower maintenance budget to keep the data signals flowing to your PMax and Search campaigns.
Before You Move Budget, Run These 3 Checks
Before you shift any spend, run through three quick checks.
Does each campaign have enough conversion volume to support its current bidding strategy?
Is PMax running Brand Exclusions and a New Customer Acquisition goal?
Before pausing anything for low conversion value, have you checked the GA4 Model Comparison report?
If you can answer yes to all three, your budget is likely in the right place.
The accounts I see perform best aren’t necessarily top-tier spenders. They’re better structured, and designed with a specific purpose for each campaign.
