Welcome to the week’s Pulse: updates affect how deep links appear in your snippets, how your robots.txt gets parsed, how agentic features work in Search, and how the EU’s data-sharing rules apply to AI chatbots.

Here’s what matters for you and your work.

Google Lists Best Practices For Read More Deep Links

Google updated its snippet documentation with a new section on “Read more” deep links in Search results. The documentation lists three best practices that can increase the likelihood of these links appearing.

Key facts: Content must be immediately visible to a human on page load, and content hidden behind expandable sections or tabbed interfaces can reduce the likelihood of these links appearing. Sections should use H2 or H3 headings. The snippet text needs to match the content that appears on the page, and pages with content loaded after scrolling or interaction may further reduce the likelihood.

Why This Matters

The three practices are the first specific guidance Google has published on this feature. Sites using expandable FAQ sections, tabbed product detail areas, or scroll-triggered content for core information may see fewer deep links in their snippets compared with sites that render the same content on page load.

The guidance matches a pattern Google has applied to other Search features. Content that renders without user interaction is more likely to appear in enhanced display.

Slobodan Manić, founder of No Hacks, made a related observation on LinkedIn:

“The documentation is framed around one snippet behavior (read more deep links in search results), but the language Google chose reads as a general preference. ‘Content immediately visible to a human’ is the structural instruction, not a read-more-specific tip.”

Manić’s point extends his April 16 IMHO interview with Managing Editor Shelley Walsh, where he argued that most websites are structurally broken for AI agents. He argues that search crawlers and AI agents now face the same structural problem, and the audit is the same for both.

For existing pages, the audit question is whether key information is contained within a click-to-expand element. If a page already has a “Read more” deep link for one section, that section’s structure serves as a guide to what works. For other sections on the same page, replicating that structure may also improve their chances.

Google describes the guidance as best practices that can “increase the likelihood” of deep links appearing. That hedging matters because this is not a list of requirements, and following all three may not guarantee the links appear.

Read our full coverage: Google Lists Best Practices For Read More Deep Links

Google May Expand Its Robots.txt Unsupported Rules List

Google may add rules to its robots.txt documentation based on analysis of real-world data collected through HTTP Archive. Gary Illyes and Martin Splitt described the project on the latest Search Off the Record podcast.

Key facts: Google’s team analyzed the most frequently unsupported rules in robots.txt files across millions of URLs indexed by the HTTP Archive. Illyes said the team plans to document the top 10 to 15 most-used unsupported rules beyond user-agent, allow, disallow, and sitemap. He also said the parser may expand the typos it accepts for disallow, though he did not commit to a timeline or name specific typos.

Why This Matters

If Google documents more unsupported directives, sites using custom or third-party rules will have clearer guidance on what Google ignores.

Anyone maintaining a robots.txt file with rules beyond user-agent, allow, disallow, and sitemap should audit for directives that have never worked for Google. The HTTP Archive data is publicly queryable on BigQuery, so the same distribution Google used is available to anyone who wants to examine it.

The typo tolerance is the more speculative part. Illyes’ phrasing implies that the parser already accepts some misspellings of “disallow,” and more may be honored over time. Audit any spelling variants now and correct them, rather than assuming they will be ignored.

Read our full coverage: Google May Expand Unsupported Robots.txt Rules List

EU Proposes Google Share Search Data With Rivals And AI Chatbots

The European Commission sent preliminary findings proposing that Google share search data with rival search engines across the EU and EEA, including AI chatbots that qualify as online search engines under the DMA. The measures are not yet binding, with a public consultation open until May 1 and a final decision due by July 27.

Key facts: The proposal covers four data categories shared on fair, reasonable, and non-discriminatory terms. The categories are ranking, query, click, and view data. Eligibility extends to AI chatbot providers that meet the DMA’s definition of online search engines. If the Commission maintains eligibility through the final decision, qualifying providers could gain access to anonymized Google Search data under the Commission’s proposed terms.

Why This Matters

This proposal explicitly extends search-engine data-sharing eligibility to AI chatbots under the DMA. If the eligibility survives the consultation, the regulatory category of “search engine” now includes products that most search marketing work has treated as a separate category.

The consequences vary depending on where you operate. For sites optimizing for EU/EEA visibility, the change could broaden the scope of where anonymized search signals flow. AI products competing with Google in that market could use the data to improve their retrieval and ranking systems, which could, in turn, affect which content they cite.

Outside the EU, the direct regulatory effect is zero. The category definition is a different matter. How the Commission draws the line between “AI chatbot” and “AI chatbot that qualifies as a search engine” is likely to be cited in future proceedings.

The eligibility question is the story to watch through May 1. If the Commission narrows the AI chatbot criteria in response to consultation feedback, the implications stay regulatory. If it holds the line, that would set a material precedent for how AI search is classified.

Read our full coverage: Google May Have To Share Search Data With Rivals

Google Adds New Task-Based Search Features

Google introduced new Search features that continue its evolution toward task completion. Users can now track individual hotel price drops via a new toggle in Search, and Google is adding the ability to launch AI agents directly from AI Mode.

Key facts: Hotel price tracking is available globally through a toggle in the search bar. When prices drop for a tracked hotel, Google sends an email alert. The AI agent launched from AI Mode allows users to initiate tasks handled by AI within the search interface. Rose Yao, a Google Search product leader, posted about the features on X.

Why This Matters

Each task-based feature moves a process that previously started on another site into Google’s own surface. Hotel price tracking has existed at the city level for months. Expansion to individual hotels adds a new signal that users can set inside Google rather than on hotel or aggregator sites.

Direct-booking visibility depends on being inside Google’s ecosystem. Sites relying on price-drop alerts as a return-trigger for users may see some of that engagement reallocated to Google’s tracking UI. For hotel brands, this raises the stakes for ensuring individual hotel pages are fully populated in Google Business Profile and hotel feeds.

On LinkedIn, Daniel Foley Carter connected the feature to a broader pattern:

“Google’s AI overviews, AI mode and now in-frame functionality for SERP + SITE is just Google eating more and more into traffic opportunities. Everything Google told US not to do its doing itself. SPAM / LOW VALUE CONTENT – don’t resummarise other peoples content – Google does it.”

The AI agent launch is more speculative. Google has not published detailed documentation explaining what kinds of tasks users can delegate or how sources get cited. The feature confirms that agentic search, described by Sundar Pichai as “search as an agent manager,” is appearing incrementally in Search rather than as a single launch.

Read Roger Montti’s full coverage: Google Adds New Tasked-Based Search Features

Theme Of The Week: The Rules Are Getting Written

Each story this week spells out something that was previously implicit or underway.

Google signaled plans to expand what its robots.txt documentation covers. The company listed specific practices that can increase the likelihood of “Read more” deep links appearing. The European Commission proposed measures that extend search-engine data-sharing eligibility to AI chatbots under the DMA. And task-based features that Sundar Pichai described in interviews are rolling out as toggles in the search bar.

For your day-to-day, the ground gets firmer. Fewer questions are judgment calls. What does and doesn’t qualify, what Google supports, and what counts as a search engine to a regulator are all getting written down. That works to your advantage when it means clearer audit criteria, and against you when “we weren’t sure” is no longer a defensible answer.

Top Stories Of The Week:

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Microsoft announced a wave of AI updates this week, and most of the coverage will likely focus on the individual launches. New targeting options, diagnostics, commerce tools, Copilot enhancements, and campaign features will naturally get the headlines.

What stood out to me was the broader vision behind them.

Microsoft is not just talking about better ads. They’re talking about a different internet, where businesses need to be relevant to both people and AI systems helping shape decisions.

In their announcement this week, AI agents are becoming the fastest-growing audience. The company says automated traffic is growing 8x faster than human traffic, AI-driven sessions nearly tripled in 2025, and agentic browser traffic is up roughly 8,000% year over year. Those visitors don’t browse the way people do. They evaluate, select, and act. If a brand’s data is weak, incomplete, or untrusted, they move on.

That changes what modern performance marketing may require. Visibility inside AI answers, stronger product data, better measurement, faster diagnostics, audience precision, and clearer control over automation all start to matter more in that environment.

Google is pushing many of these same themes in its own way, especially around product feeds, automation, and AI-assisted search experiences. But Microsoft’s recent announcements offer a distinct perspective on where advertiser value may come from as discovery and buying behavior continue to shift.

Because underneath the product updates is a bigger question for PPC teams: how do you compete when the next valuable audience may not always be human?

Microsoft Is Selling A Different AI Future

Most platform announcements focus on what a new feature does. Microsoft spent more time explaining why advertiser behavior may need to change.

Their framework centered on three parallel realities:

• People still searching on their own (the Human web)
• People using AI to compare options (the LLM web)
• AI systems taking action on behalf of users (the Agentic web)

What they’re saying beyond these parallels is that customer journeys are less linear and are finally being recognized as such.

For years, many PPC teams optimized around the click because the click was the clearest measurable moment. Someone searched, clicked, landed, and converted. That model still matters, but it no longer explains every influence that leads to a sale.

If an AI assistant narrows the shortlist before a search happens, the brand has already won or lost ground. If a shopping assistant compares shipping speed, loyalty perks, and product availability in seconds, the decision may be shaped before the landing page visit. If an agent eventually completes more transactions directly, structured data and transaction readiness become part of media performance.

That is why this announcement deserves more attention than a standard product roundup. Microsoft is describing a future where paid media performance depends on more than media settings.

Why This Matters For PPC Managers

Many advertisers are still operating with a channel mindset. Additionally, these channels likely sit within different teams in an organization (Search, SEO, CRM data, Analytics, etc.)

That separation becomes harder to sustain and sustains friction if buying journeys are influenced by connected systems rather than isolated clicks.

This is where the role of PPC teams can start to expand and/or evolve.

Strong practitioners still need campaign skills – that’s never going to change. They also need to spot when the real constraint sits outside the account, bring the right teams together, and push improvements that create better inputs for the platform.

Having these skills become your advantage as a PPC marketer down the road when campaign management and optimization become automated, but that’s a subject for another day.

How Microsoft’s AI Vision Takes A Different Approach

Google remains the largest force in paid search. It also continues to launch strong AI updates across bidding, creative, search experiences, and campaign management. This is not about Google falling behind.

What stood out to me was where Microsoft placed its focus.

A lot of AI discussion still centers on better ads, faster automation, or the next big interface. Microsoft spent more time talking about how buying behavior is changing and what advertisers may need to do differently.

Their view suggests the audience is no longer only the customer.

It can also be the AI system helping compare products, narrow options, recommend brands, or complete tasks on someone’s behalf.

That is where I think Microsoft’s message becomes more interesting than a standard product launch. They are pushing marketers to think beyond clicks and impressions and pay closer attention to how decisions are being shaped before a traditional ad interaction ever happens.

If that shift continues, many teams will realize they were optimizing the final step of the journey while missing the earlier moments that influenced the outcome.

AI Visibility In Microsoft Clarity Is Their Competitive Advantage

If I had to choose the most useful announcement for marketers, I would put AI Visibility in Microsoft Clarity near the top of the list.

Why? Because it speaks to a blind spot many businesses may already have.

A lot of performance reporting has been built around clicks, visits, and conversions that happen in trackable sessions. As AI tools start summarizing answers, citing brands, and influencing decisions before someone reaches a site, that model becomes less complete.

Some brands may already be winning attention in those moments. Others may be losing ground. Many likely cannot see either clearly today.

That is what makes this update so interesting.

Microsoft is giving businesses a way to understand how AI systems discover, cite, and surface their content. You do not need to advertise on Microsoft for that to matter. SEO teams, content teams, e-commerce leaders, and paid media teams all have a reason to care about how their brand appears in AI-driven experiences.

My bigger view is that tools like this will eventually become normal. Right now, Microsoft is one of the first major platforms speaking clearly about the problem and trying to give marketers something actionable to measure.

Audience Generation Could Be More Useful Than It Sounds

Audience Generation may sound like another setup feature, but I think it deserves more attention than that.

Microsoft describes it as an AI-powered audience assistant where advertisers can describe an ideal customer in natural language and receive recommended targeting settings. That can include demographics, locations, in-market signals, and dynamically generated audiences.

What interests me most is how this could improve strategic thinking, not just save time during campaign creation.

Many advertisers already know their obvious audience. But strong audience strategy often depends on ideas a team does not think to test.

For example, an advertiser may know they want “young professionals interested in fitness.” They may not think about adjacent areas where those consumers spend time, neighborhoods with stronger purchase intent, seasonal behaviors tied to events, or combinations of signals that reveal higher-value segments.

That is where a tool like this can become valuable.

Used thoughtfully, it can help marketers find new angles to test, challenge stale audience assumptions, and build stronger targeting plans than they may have created manually.

How Microsoft Is Turning That AI Vision Into Practical Tools

A broader vision only matters if it shows up in tools advertisers can actually use.

That is where Microsoft’s recent updates become more interesting.

Explainability Is Part Of The Product

One of the more useful launches was performance shift root-cause analysis inside the Microsoft Advertising Platform.

When results move sharply, most marketers don’t need another dashboard. They need to know what changed and clear “why”. Without the why, marketers can’t identify how to improve campaigns or pivot strategy.

Getting to that answer faster can save hours of manual work. It can also help teams act with more confidence instead of making reactive changes.

Google is thinking in a similar direction. Its Ads Advisor experience is also designed to help advertisers ask questions, surface insights, and understand account performance faster.

The opportunity for marketers is not choosing one assistant over another. It is using these tools to reduce analysis time and spend more time on better decisions.

Guardrails Still Matter

Microsoft also emphasized brand exclusions, term exclusions, and messaging constraints tied to AI-powered products like AI Max.

It mimics where Google has gone with their AI Max direction and broader advertiser controls across automated products.

That matters because many advertisers are not operating in a world where they can simply turn everything on and hope for the best. Legal review, brand standards, regulated categories, stakeholder approvals, and internal risk tolerance all shape how new tools get adopted.

That is why control features deserve more attention than they usually get. They are often what make adoption possible in the first place.

Product Data Continues To Be Bigger Than Shopping Campaigns

One of the clearest signals from both Microsoft and Google right now is that product data is starting to matter far beyond traditional Shopping campaigns.

Clean titles, accurate availability, pricing consistency, strong attributes, shipping details, and trustworthy structured data can now influence how products are surfaced across search experiences, AI recommendations, comparison journeys, and agent-assisted buying flows.

That is exactly why I wrote last week that Google’s product feed strategy points to the future of retail discovery. Product data is no longer just supporting Shopping campaigns. It is becoming part of how platforms understand inventory, evaluate relevance, and decide what gets shown in newer discovery environments.

Microsoft’s recent announcements point to the same shift through a different lens. Google is emphasizing Merchant Center and commerce surfaces. Microsoft is emphasizing agentic commerce, Copilot experiences, and AI visibility.

Feed health is becoming a growth issue, not just an operations issue – something that both Google and Microsoft are telling the industry.

What Advertisers Are Saying

Navah Hopkins, the Microsoft Ads Liaison, took to LinkedIn to share her thoughts on these updates. She highlighted diagnostics, clearer explanations, and the idea that marketers should decide what they own, what they share with AI, and what they delegate. That framing reflects how adoption actually happens inside businesses. Teams rarely hand over everything at once. They test where trust has been earned.

She also pointed to Microsoft Clarity as an increasingly valuable source of behavioral insight as AI-driven experiences grow, which I completely agree with.

Mark Creusen added his thoughts to her post:

The owning and sharing bit always pops for me. Way easier to chill about AI when you just mark out what’s “yours” and what you’re happy to throw to the bots instead of trying to wrangle it all. Otherwise teams just end up dragging each other to burnout mountain.

Frederick Vallaeys focused on another risk: invisibility. In his write-up after Microsoft’s partner event, he argued that many businesses may be unprepared for AI-driven discovery and cited Microsoft’s discussion around sites still blocking AI agents through robots.txt. He also highlighted strong early commerce statistics shared at the event, including higher purchase likelihood after Copilot interactions and conversion lifts tied to Brand Agents.

What This Means For Your Campaigns

The bigger lesson from Microsoft’s updates is that campaign performance may increasingly be shaped by factors that sit outside the traditional campaign build. That includes how your products are structured, how clean your measurement setup is, how well your audiences reflect real buying behavior, and whether your brand is visible in AI-assisted discovery moments before a search click ever happens.

Below are a few areas worth reviewing that can help shape a broader operating mindset:

• Product data quality: If your feeds are incomplete, outdated, or inconsistent, the risk may extend beyond Shopping campaigns. Product titles, availability, pricing, shipping details, and attributes can influence how platforms understand and surface your inventory across emerging discovery experiences.
• Measurement health: Now is a good time to audit conversion actions, tag coverage, offline imports, and attribution settings. As journeys become less direct, weak measurement creates larger blind spots and poorer optimization inputs.
• Audience strategy: Many accounts still rely on narrow audience assumptions or static segments. Revisit whether your current targeting reflects how customers actually behave today. There may be untapped value in layered signals, geographic nuance, seasonal behaviors, or adjacent intent patterns.
• Search term coverage: If AI tools help users refine decisions earlier, the searches that remain may become more specific, comparative, or action-oriented. Review whether your keyword strategy and ad copy are aligned to that shift in intent.
• Platform diversification: Secondary channels can become valuable learning environments before they become major budget lines. Even modest investment in Microsoft Ads can help teams test new audience models, automation controls, and reporting approaches that may influence broader strategy later.

Looking Ahead

Microsoft’s biggest advantage may not be trying to out-Google Google.

It may be continuing to invest where it already has a credible edge: advertiser workflow tools, B2B audience intelligence through LinkedIn, clearer visibility into AI-driven discovery, and commerce experiences built for a world where assistants help shape decisions.

That is a different lane, and it could be a valuable one for marketers if Microsoft keeps executing.

The next year will likely tell us whether these announcements were a strong signal of where the platform is headed or simply another round of product updates.

Which of Microsoft’s new AI features, if any, would you seriously consider testing in your own campaigns?

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Last year, I taught a module on content marketing around the PESO model (Paid, Earned, Shared, and Owned media). Matt Bailey asked me to include more content about influencers in this year’s module; I joked that it might take me all morning to come up with a new acronym. He shot back, “Can you adapt it to a DIRHAM model instead of PESO?”

That’s when I had an epiphany: Buried beneath our banter was a strategic insight.

Publishing great content used to be enough. Write something valuable, post it, and trust that search engines, social feeds, and your audience will handle the rest. For most of the past decade, that assumption held. It no longer does.

Between your content and your audience now stand three powerful gatekeepers, and none of them are human. AI summarization systems like Google’s AI Overviews surface answers without delivering clicks. Social feed algorithms pre-select what users ever encounter, often before those users have articulated what they want. Private messaging networks carry enormous volumes of content sharing through channels that are invisible to any analytics tool. If your content isn’t built to pass through all three of these filters, quality becomes irrelevant. It simply won’t be found.

In response to this challenge, I created the DIRHAM framework.

Why The Old Frameworks No Longer Work

Content marketers generally have organized their thinking around PESO: Paid, Earned, Shared, and Owned media. The model served its purpose well as a categorization tool, helping teams allocate budgets and map campaigns across channels. The problem is that PESO was built to answer a distribution question that no longer captures the real strategic challenge. It told you where to place content. It said nothing about how to make content visible in a world where algorithms, not humans, decide what gets surfaced.

DIRHAM is a visibility system rather than a categorization scheme. It is behavior-driven and AI-aware, designed around how content is actually discovered today rather than how it traveled through digital channels a decade ago. The distinction matters because discovery itself has fragmented across three systems that operate on entirely different logic. Search has become an AI answer engine that returns summaries instead of links. Social platforms use recommendation algorithms that predict what users want before those users have searched for anything. And messaging apps carry significant content sharing through what marketers call dark social, private exchanges that leave no traceable footprint in your analytics dashboard.

Each of these systems decides relevance differently, which means a single distribution strategy cannot serve all three. That, in turn, exposes the deeper problem with channel-first thinking. Asking “where should we post?” is no longer the right starting point. The more productive question is how this particular audience actually discovers things, and what each system needs to see before it will serve your content to them.

The Six Pillars Of DIRHAM

D: Digital Advertising

The role of paid media has changed in ways that most campaign budgets haven’t caught up with yet. The old model treated paid advertising as a direct delivery mechanism: You bought impressions, people clicked, some of them converted. In the AI era, that logic is incomplete. Paid media’s primary strategic function now is to generate the early engagement signals that algorithms need before you should invest in distributing your content organically. Paid doesn’t deliver to the audience anymore. It earns the algorithmic attention that makes organic delivery possible.

This reframing has real implications for how budgets should be structured and how creative should be evaluated before spend. Rather than committing to a single campaign execution, the more effective approach is a three-stage cycle: Run small tests across multiple creative variations, use AI performance tools to identify which executions are generating genuine signal, then scale selectively into what’s actually working. Small bets, fast reads, concentrated fuel.

Targeting has matured in a parallel direction. Legacy demographic segmentation worked from surface assumptions about who a person was based on age, gender, and location. AI-powered clustering works from behavioral reality, tracking what people actually do, what they read past, what they share, what they ignore. Content that mirrors real behavioral patterns gets amplified. Content that shouts without matching those patterns gets filtered out, regardless of budget. And creative that looks like advertising at a glance will fail to generate the engagement signals that trigger wider distribution in the first place. Native creative, content that looks and feels like organic content in each platform’s environment, is not just aesthetically preferable. It is structurally necessary.

I: Influencer Partnerships

In an environment where AI-generated content floods every platform, human credibility has become the most effective filter against noise. Audiences, consciously or not, are calibrating their attention toward sources that have demonstrated genuine expertise or authentic experience, and away from the polished but anonymous brand voice that could have been written by anyone or anything. This is why influencer strategy in the DIRHAM model is not primarily about reach. It is about borrowed trust.

The distinction matters because it changes who you look for and what you ask them to do. A creator with 200,000 engaged followers who have followed them for three years because they trust their judgment is more valuable in this environment than a creator with 2 million followers and a transactional relationship with branded content. The former has built the authenticity, consistency, and credibility that together produce real trust. The latter has reach without the authority that makes recommendations land.

The operational implication is a move away from one-off campaign sponsorships toward integrated, ongoing relationships. When influencer programs feel bought rather than believed, they fail on two levels. They fail to generate the authentic engagement that algorithms reward, and they fail to produce the kind of trust transfer that makes the partnership valuable in the first place. The most effective influencer programs are built around shared narratives and long-term creative collaboration, which produces compounding community value that a single sponsored post cannot. This also means that creator selection has to account for context. In government and public sector campaigns, credibility and safety are the primary criteria, with success measured through sentiment and public awareness. In commercial campaigns, fit and demonstrated performance matter most, and success gets measured through conversion and sales velocity. Reach alone is never sufficient justification for a partnership.

R: Regional And Local Context

AI systems are not passive distributors. They actively parse content to determine who it is for, and generic content sends signals that are simply too ambiguous for the system to act on confidently. Without specific geographic or cultural markers, content can get deprioritized, not necessarily because it’s of poor quality, but because the algorithm cannot reliably categorize it or identify the right audience to serve it to. The counterintuitive result is that narrowing your focus tends to increase your reach. Anchoring content in regional or local specificity gives the system exactly the classification signal it needs to serve the content to people who will engage with it.

One of the most common mistakes brands make when addressing multilingual markets is treating bilingual content as a translation problem. It is not. Arabic and English audiences in the UAE, for example, engage with content on the same platforms through fundamentally different cultural frames. English-language content in that market tends to perform around adventure, exploration, and discovery. Arabic-language content, produced by creators with genuine cultural proximity, centers on heritage, family, and values that are better expressed in local dialect than in formal translated language. The difference is not vocabulary. It is intent and tone, and no translation process produces it reliably. What local creators bring to content distribution is something that should be understood as shared context: an intuitive grasp of reference, nuance, and community expectation that outside brands cannot replicate and cannot purchase directly. They can only access it by working genuinely with people who hold it.

H: Hybrid Content

Hybrid content is what happens when passive consumption and active involvement are designed into the same piece of content. The reason it matters so much in the current environment is that engagement is not merely a metric for how interesting your content was. It is the distribution mechanism itself. When users comment, complete a challenge, share to their own network, or otherwise participate in content, they are not just expressing interest. They are distributing the content on your behalf. Without that participation, reach is bounded by budget. With it, reach compounds through the network in ways that no paid campaign can replicate in isolation.

This changes the design question for content. Broad content, built for a generic audience and a generic platform, tends to produce passive consumption. People scroll past it, or watch it to completion, and move on. Specific content, anchored in a particular cultural reality or a particular community’s concerns, provokes a response. It invites people to add themselves to the story, to disagree or affirm, to share with someone they know, because it lands with enough specificity to feel personal. Gamification, photography challenges, and community incentives work in this context not as marketing gimmicks but as structural mechanisms for turning audience members into distributors. AI tools can accelerate the production of hybrid content significantly, handling drafting, formatting, and initial translation at volume. But the human editorial layer remains essential. Resonance, cultural accuracy, and the kind of tonal authenticity that makes people want to participate cannot be automated. The goal is not automated publishing; it is automated drafting with rigorous human curation.

A: AI Visibility

Becoming visible to AI answer engines requires a different optimization logic than traditional SEO. The governing rule is that AI systems reward reliability and structural clarity above creativity and cleverness. A headline that works brilliantly for a human reader because it is unexpected or witty may work against you in an LLM context, because the machine cannot confidently categorize content whose purpose is obscured by figurative language. Clear, consistent, authoritative content builds the kind of signal that answer engines recognize and cite over time.

Structure is the mechanism. AI models parse structural elements before they interpret meaning, which means clear headers function as navigation signals, declarative sentences enable clean fact extraction, and credibility markers such as named sources, cited research, and identified authorship communicate authority to the system in ways that stylistic sophistication simply does not. If the architecture of the content is unclear, the quality of what’s inside it goes unread.

There is also a significant measurement gap that most organizations have not addressed. AI and LLM conversations represent the fastest-growing discovery channel in most content categories, but they are almost entirely invisible to conventional SEO tools. Tools like Cairrot have emerged specifically to track brand citations inside AI models, showing where and how organizations appear when users ask ChatGPT, Perplexity, or Gemini a relevant question. The new SEO is not optimizing for a position on a search results page. It is optimizing to become the source an AI system trusts enough to cite.

M: Measuring Outcomes

The final pillar of DIRHAM is still where most organizations’ discipline breaks down, and where the gap between doing DIRHAM and doing it well tends to be widest. The standard that should govern every measurement decision is straightforward: If a metric doesn’t change what you do next, it doesn’t matter. Impressions, follower counts, and raw reach have always been easier to report than to act on, and in an era of infinite AI-generated content production, they have become almost entirely disconnected from influence or impact.

The hierarchy that actually serves strategic decisions looks different. Impressions and vanity metrics get ignored. Engagement signals get observed carefully because they reveal which content is generating the algorithmic response and community participation that the other pillars depend on. Behavioral change and decisions get optimized toward relentlessly, because those are the outcomes the content exists to produce. Every campaign run this way becomes the prototype for the next one. The data from this cycle funds better decisions in the next.

For organizations with “trust” instead of “cash” as a strategic objective, particularly in government and public sector contexts, the Hon and Grunig Trust Scorecard provides a quantifiable measurement approach. It assesses trust through three dimensions: Integrity, measured through whether stakeholders believe the organization treats people fairly and considers them in decisions; Dependability, measured through whether stakeholders believe the organization keeps its commitments; and Competence, measured through whether stakeholders believe the organization can deliver what it promises. Stakeholders rate these dimensions on a Likert scale, producing a quantifiable trust score that can be tracked over time and correlated with content and campaign activity.

DIRHAM In Action: The World’s Coolest Winter Campaign

Abstract frameworks earn their place by explaining real results. The UAE’s World’s Coolest Winter campaign, which concluded on Feb. 2, 2026, is an unusually clean example of the DIRHAM model operating at full scale, because the framework wasn’t applied after the fact. Distribution was the blueprint from the beginning.

The campaign’s paid media strategy used TikTok and Snapchat as the primary channels, with short-form cinematic video built specifically for scrolling behavior rather than for broadcast viewing. Instant-experience formats connected directly to destination booking, collapsing the distance between discovery and action. Critically, paid spend was deployed to generate algorithmic ignition rather than to deliver impressions. The goal was to earn enough early engagement signal that organic sharing would carry the campaign forward, which is exactly what happened. Paid lit the fire. Organic kept it burning.

On the influencer side, the campaign avoided the trap of centralizing its voice. Instead of a single spokesperson, it deployed influencer missions structured around distinct audience segments. Lifestyle creators on TikTok highlighted adventure and entertainment experiences, reaching audiences looking for something unexpected to do. Professional voices on LinkedIn surfaced the UAE as a destination for remote work and family travel, reaching audiences whose priorities are entirely different. The strategic logic was that diversity of influence produces diversity of reach. Trust is built through credible local voices, not through a polished corporate message broadcast at scale.

The regional dimension of the campaign revealed something that straightforward localization would have missed. English-language content was built around adventure, hidden gems, and the kind of active discovery that appeals to visitors approaching the country as travelers. Arabic-language content was built around heritage, privacy, and family, using local dialect and family-centric themes that resonated with residents and regional visitors through a completely different cultural logic. The same destination, communicated through entirely different frames. That specificity did two things simultaneously: It made the content more resonant for human audiences, and it gave AI discovery systems the clear categorical signals they need to serve content to the right people. The regional strategy wasn’t just a localization effort. It was an authority signal.

The hybrid content mechanism at the center of the campaign was a gamified digital passport system that invited visitors to earn stamps by experiencing all seven Emirates, with photography challenges and completion incentives that rewarded actual behavior rather than passive attention. This bridged digital content discovery with physical travel behavior, and it recruited participants as content creators in the process. Every visitor who shared a photograph or completed a challenge was generating authentic user content that no brand team could have produced centrally. The campaign’s AI visibility strategy depended on exactly this kind of volume: thousands of UAE residents posting under shared hashtags simultaneously created what the campaign called a Signal Storm. That mass of authentic, organic, contextually rich content fed AI discovery systems with the consistent high-volume signal that establishes topical authority at scale. Social proof of this kind cannot be manufactured. It must be engineered through genuine participation.

The outcomes validated the model. The campaign generated AED 12.5 billion in hotel revenues, attracted 5 million guests, representing a 5% increase over the prior period, and achieved an 84% nationwide hotel occupancy rate. These are behavioral outcomes, not impression counts. They are the direct result of distribution strategies built around how people actually discover, evaluate, and act on content. When distribution aligns with behavior, visibility compounds.

The Integrated Workflow

Understanding each pillar individually is necessary but insufficient. What makes DIRHAM work as a system is the way the pillars interact, and where the interaction breaks down.

Digital advertising without content relevance generates clicks that produce no signal worth amplifying. Influencer reach without genuine trust is wasted on an audience that has already learned to filter branded content. Regional specificity without hybrid participation anchors the content in place without recruiting the network to carry it further. AI visibility without structural clarity leaves authoritative content invisible to the systems that would otherwise surface it. Measurement that reports on impressions rather than behavioral change tells you what happened last quarter without informing you about what you should do this one. Each element depends on the others. Weakness in one area suppresses results across the whole system.

The workflow that holds this together operates as a continuous loop. It begins with paid signals to earn algorithmic attention, moves through influencer validation to establish human trust, anchors in local context to signal relevance to both algorithms and audiences, amplifies through participation by designing for users to become distributors, optimizes for machine readability, so AI systems can parse and cite the content, and closes with measurement of behavioral impact. That measurement then determines the budget, targeting, and creative decisions that ignite the next cycle. Measurement connects directly back to the D. The loop is continuous rather than linear, and the information flowing from the M back to the D is what makes the system improve over time.

Key Takeaways

After creating a rough draft of my updated online course on content marketing, I sent it to Bailey for his review. He quipped, “Great framework. Is it copyrighted?”

You can adopt the DIRHAM Framework with just as much confidence. Why? Because William Gibson, a speculative fiction writer, was strangely prescient when he observed, “The future has arrived – it’s just not evenly distributed yet.”

The World’s Coolest Winter campaign demonstrated four principles that hold across contexts far beyond UAE tourism.

• Visibility is engineered. In the AI era, reach is not accidental. It is designed, and the design has to account for the three gatekeepers that now stand between content and audience. Distribution can no longer be treated as the final step in a content process. It must be the architecture around which the content is built.
• Visibility beats volume. Strategic placement outperforms mass production. A smaller amount of content built for the specific behavioral context of each discovery system and each regional audience will consistently outperform a larger volume of generic content scattered across channels without strategic intent.
• Trust over polish. Authentic local voices outperform corporate narration, and the gap is widening as AI content floods every platform. Human credibility is the scarcest resource in the current information environment, which means influencer strategy should be evaluated on the depth of trust the creator has built, not the size of the audience they have accumulated.
• Measurement changes behavior. Metrics that don’t alter the decisions made in the next cycle are not measuring anything useful. The only numbers worth tracking are the ones that tell you what to do differently.

The DIRHAM model is systemic, scalable, and built to adapt as platforms and algorithms evolve, because it is grounded in human discovery behavior rather than in the specific mechanics of any particular platform. Content competes on distribution first. That has always been true to some degree, but it has never been as consequential as it is now.

More Resources:

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Featured Image: Tetiana Yurchenko/Shutterstock

https://www.searchenginejournal.com/localized-distribution-in-the-ai-era-the-dirham-framework/569851/

Google updated their spam reporting documentation to make it clearer that spam reports are not wholly confidential and that it’s possible for personal identifiable information to be shared with the sites receiving a manual action.

Change In Response To Feedback

Google’s changelog noted that they were updating the spam reporting form based on feedback they’d received about personal information contained in the spam report that is shared with spammy sites that receive a manual action (formerly known as a penalty).

The update contains a new notice that spam reports containing personal information will not be processed.

The changelog noted:

“Clarifying when and why we may take manual action based on spam reports
What: Further clarified when and why we may take manual action based on spam reports.
Why: To address feedback we received about the change on using spam reports to take manual action.”

Google removed the following from their documentation:

“If we issue a manual action, we send whatever you write in the submission report verbatim to the site owner to help them understand the context of the manual action. We don’t include any other identifying information when we notify the site owner; as long as you avoid including personal information in the open text field, the report remains anonymous.”

The above wording was replaced with the following:

“Don’t include any personally identifying information in your submission. To comply with regulations, we must send the submission text to the site owner to help them understand the context of a manual action, if one is issued.

Because of this, we won’t process your submission if we determine it contains personally identifying information to protect privacy. Not including such information fully ensures your information is safe and prevents your submission from being discarded.”

Action Moving Forward

On the one hand it’s good that Google won’t proceed with a manual action if the report contains personal information. This means that if you’re submitting spam reports to Google, don’t name your site, business name, personal name or anything else that you don’t want the affected spammer to know.

Read the updated documentation here:

Report spam, phishing, or malware

Learn more about Google’s spam reporting tool: Google Just Made It Easy For SEOs To Kick Out Spammy Sites

Featured Image by Shutterstock/andre_dechapelle

https://www.searchenginejournal.com/google-wont-act-on-spam-reports-if-they-contain-personal-information/572929/

Google may expand the list of unsupported robots.txt rules in its documentation based on analysis of real-world robots.txt data collected through HTTP Archive.

Gary Illyes and Martin Splitt described the project on the latest episode of Search Off the Record. The work started after a community member submitted a pull request to Google’s robots.txt repository proposing two new tags be added to the unsupported list.

Illyes explained why the team broadened the scope beyond the two tags in the PR:

“We tried to not do things arbitrarily, but rather collect data.”

Rather than add only the two tags proposed, the team decided to look at the top 10 or 15 most-used unsupported rules. Illyes said the goal was “a decent starting point, a decent baseline” for documenting the most common unsupported tags in the wild.

How The Research Worked

The team used HTTP Archive to study what rules websites use in their robots.txt files. HTTP Archive runs monthly crawls across millions of URLs using WebPageTest and stores the results in Google BigQuery.

The first attempt hit a wall. The team “quickly figured out that no one is actually requesting robots.txt files” during the default crawl, meaning the HTTP Archive datasets don’t typically include robots.txt content.

After consulting with Barry Pollard and the HTTP Archive community, the team wrote a custom JavaScript parser that extracts robots.txt rules line by line. The custom metric was merged before the February crawl, and the resulting data is now available in the custom_metrics dataset in BigQuery.

What The Data Shows

The parser extracted every line that matched a field-colon-value pattern. Illyes described the resulting distribution:

“After allow and disallow and user agent, the drop is extremely drastic.”

Beyond those three fields, rule usage falls into a long tail of less common directives, plus junk data from broken files that return HTML instead of plain text.

Google currently supports four fields in robots.txt. Those fields are user-agent, allow, disallow, and sitemap. The documentation says other fields “aren’t supported” without listing which unsupported fields are most common in the wild.

Google has clarified that unsupported fields are ignored. The current project extends that work by identifying specific rules Google plans to document.

The top 10 to 15 most-used rules beyond the four supported fields are expected to be added to Google’s unsupported rules list. Illyes did not name specific rules that would be included.

Typo Tolerance May Expand

Illyes said the analysis also surfaced common misspellings of the disallow rule:

“I’m probably going to expand the typos that we accept.”

His phrasing implies the parser already accepts some misspellings. Illyes didn’t commit to a timeline or name specific typos.

Why This Matters

Search Console already surfaces some unrecognized robots.txt tags. If Google documents more unsupported directives, that could make its public documentation more closely reflect the unrecognized tags people already see surfaced in Search Console.

Looking Ahead

The planned update would affect Google’s public documentation and how disallow typos are handled. Anyone maintaining a robots.txt file with rules beyond user-agent, allow, disallow, and sitemap should audit for directives that have never worked for Google.

The HTTP Archive data is publicly queryable on BigQuery for anyone who wants to examine the distribution directly.

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Featured Image: Screenshot from: YouTube.com/GoogleSearchCentral, April 2026. 

https://www.searchenginejournal.com/google-may-expand-unsupported-robots-txt-rules-list/572866/

OpenAI’s public crawler documentation now lists OAI-AdsBot, a bot that may visit pages submitted as ChatGPT ads to check policy compliance and help determine ad relevance.

The entry sits alongside OAI-SearchBot, GPTBot, and ChatGPT-User on OpenAI’s crawler docs page, bringing the documented bot count to four.

OpenAI states that OAI-AdsBot only visits pages submitted as ads and that the data it collects isn’t used to train its generative AI foundation models.

What The Bot Does

Per OpenAI’s docs, OAI-AdsBot may visit an ad’s landing page after the ad gets submitted. The bot checks whether the page complies with OpenAI’s ad policies. It may also use content from the landing page to help decide when to show the ad to ChatGPT users.

The bot identifies itself with the user-agent string Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko); compatible; OAI-AdsBot/1.0; +https://openai.com/adsbot.

OAI-SearchBot and GPTBot are both at version 1.3, per OpenAI’s docs. The crawler only visits pages submitted as ad landing pages, not the wider web.

What The Bot Doesn’t Do

Data collected by OAI-AdsBot isn’t used to train generative AI foundation models. That keeps OAI-AdsBot out of GPTBot’s territory, which handles training data collection.

It also keeps OAI-AdsBot separate from OpenAI’s other bots. OAI-SearchBot surfaces content in ChatGPT search, while ChatGPT-User fetches pages during user-initiated browsing, and OAI-AdsBot is limited to ad validation.

OAI-SearchBot and GPTBot can be controlled independently through robots.txt. ChatGPT-User is user-initiated, and the company notes that robots.txt rules may not apply to it. The OAI-AdsBot entry doesn’t say how the bot treats robots.txt.

No Public IP List Yet

OpenAI publishes IP range files for its three earlier bots at openai.com/searchbot.json, openai.com/gptbot.json, and openai.com/chatgpt-user.json. At the time of publication, no equivalent openai.com/adsbot.json file appears in OpenAI’s docs.

Without a published list, verifying a real OAI-AdsBot visit becomes harder. User-agent strings can be spoofed, and the IP lists give you a way to cross-check for the other three OpenAI bots. For OAI-AdsBot, that cross-check isn’t available.

Why This Matters

OAI-AdsBot has two audiences. Advertisers buying placements on ChatGPT need the bot to reach their landing pages; otherwise, the ad may not validate. Anyone tracking AI bot activity in server logs gets a new user-agent to watch, one tied to paid inventory rather than search or training.

Aggressive bot protection through Cloudflare, Akamai, or similar tools may block OAI-AdsBot before it reaches the page. That could create validation friction for advertisers who use strict bot-mitigation tools.

Looking Ahead

ChatGPT’s ad program has moved fast since OpenAI started testing ads on Feb. 9. As access opens up to more advertisers, OAI-AdsBot traffic will start showing up in more server logs. Watch for an eventual IP range file at openai.com/adsbot.json if OpenAI chooses to publish one. For now, the user-agent string is what you have to work with.

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Featured Image: Blossom Stock Studio/Shutterstock

https://www.searchenginejournal.com/openais-crawler-docs-now-list-oai-adsbot-for-chatgpt-ads/572861/

Google announced two updates to Demand Gen ahead of Google Marketing Live.

View-through conversion (VTC) optimization is now available for Demand Gen campaigns in Google Ads. This setting lets campaigns optimize toward view-through conversions on YouTube.

Google is also expanding Commerce Media Suite to support Demand Gen inventory in Google Ads. This adds Google Ads to existing Commerce Media Suite support in Display & Video 360 and Search Ads 360.

What’s New

VTC Optimization

When enabled, VTC optimization lets Demand Gen campaigns optimize toward view-through conversions on YouTube. A view-through conversion happens when a user sees an ad, doesn’t click, but later converts.

Commerce Media Suite

With the Google Ads expansion, advertisers can use retailers’ first-party catalog and conversion data to reach shoppers. Inventory covers YouTube, Discover, and Gmail.

The Performance Claim

In the announcement, Google cited Fospha’s Demand Gen and YouTube Playbook, a third-party vendor report. Fospha attributes an 18% higher share of new-customer conversions to Demand Gen versus the paid media average. Coverage spans 127 retail brands across fashion, cosmetics, and consumer goods from 2024 to 2025.

Fospha is a marketing attribution vendor with a commercial interest in measurement across advertising platforms. Google didn’t publish its own performance data alongside the announcement.

Why This Matters

VTC optimization brings Demand Gen closer to the capabilities advertisers already use on other ad platforms. For teams running Demand Gen alongside video campaigns on those platforms, the optimization setup no longer has to differ by channel.

The Commerce Media Suite expansion gives Google Ads advertisers access to retailer first-party catalog and conversion data. This adds Google Ads to existing Commerce Media Suite support in Display & Video 360 and Search Ads 360.

Since last year, Google has added Demand Gen optimization levers, including in-store sales optimization and shoppable CTV. VTC optimization and Commerce Media Suite support continue that pattern.

Looking Ahead

This announcement lands ahead of Google Marketing Live, where Google says more Demand Gen solutions will follow.

https://www.searchenginejournal.com/google-adds-view-through-conversion-optimization-to-demand-gen/572840/

This series has spent five articles mapping what the AI search transition requires of your team, your content, your technical infrastructure, and your strategic framing. This piece addresses the question those five articles don’t answer: How do you actually make the organizational shift happen?

Most teams won’t fail here because they lack vision. The failure mode is execution, specifically the gap between knowing change is necessary and building the structure that makes it real.

The Transition Problem Is A People Problem, Not A Technology Problem

Only about 30% of enterprise SEO teams have restructured roles and responsibilities as a result of AI implementation. That means roughly 70% of teams who understand the shift intellectually haven’t made a structural move yet. The tools exist. The research is available. The urgency is visible in the data. And most teams are still running the same org chart they had three years ago.

This isn’t a strategic failure. It’s a change management failure, and it has a predictable shape. Three stall patterns show up consistently.

Analysis paralysis is the team that has attended every conference session, read every report, and built a compelling internal case, but can’t commit to a starting point because the landscape keeps shifting. The logic feels defensible: Why restructure when the platform behavior might change next quarter? The answer is that waiting for stability in an unstable environment isn’t patience. It’s avoidance dressed up as diligence.

Pilot purgatory is more widespread than most leaders want to admit. A survey of 200 U.S. marketing leaders found that 82% of teams using AI for campaigns are still operating in pilot or experimental mode, with 61% using AI only at the individual level rather than building it into collaborative team workflows. The pilot never fails cleanly; it just never graduates to production.

Reorg fatigue is the subtlest of the three. Teams that have been through digital transformation cycles carry scar tissue. They’ve watched priority initiatives get announced, resourced, and quietly abandoned when the next priority arrived. When a VP announces a pivot to AI visibility, the team’s first internal question often isn’t how to do it; it’s how long until this one goes away, too. Credibility for this transition requires demonstrating that it’s structurally different from the previous three, which means visible commitment in budget, headcount, and KPI design, not just slide decks.

The Resistance Map

Not all resistance is the same, and treating it as a uniform problem produces uniform failure. Four distinct patterns appear in SEO and marketing teams, each requiring a different response.

Seniority-based resistance sounds like: I’ve been doing this for 15 years, and I know what works. This is often the hardest pattern to address because it’s partly legitimate. Senior practitioners have real pattern recognition that junior team members lack, and they’ve watched enough vendor-driven hype cycles to be appropriately skeptical of any new essential framework. The correct response isn’t to dismiss the experience; it’s to reframe the transition as an addition to what they know, not a replacement of it. As established in the context moat piece earlier in this series, the fundamentals of relevance and trust don’t disappear in an AI search environment. They compound. Senior practitioners who make that conceptual bridge become accelerants, not obstacles.

Skills-based anxiety is a different problem entirely. This person isn’t resisting because they distrust the framework; they’re resisting because they don’t know how to operate inside it. The language of vector indexes, structured data expansion, and retrieval architecture is genuinely foreign to someone who built their career on keyword clustering and link building. A useful diagnostic lens here comes from the ADKAR model, a change management framework developed by Prosci that identifies five sequential conditions an individual needs to reach for change to stick: Awareness, Desire, Knowledge, Ability, and Reinforcement. Skills-based anxiety is almost always a Knowledge or Ability gap, not a motivation problem. Treating it as motivation resistance wastes time and confirms the team member’s fear that leadership doesn’t understand what they’re actually being asked to do.

Political resistance is structural, not personal. If AI visibility expands SEO scope to include retrieval architecture, machine-facing content design, and cross-functional data coordination, someone’s budget conversation changes. Marketing ops, IT, and content teams all have a plausible claim on parts of that expanded scope. This resistance rarely surfaces as direct opposition; it shows up as slow approvals, ambiguous priorities, and repeated requests to align with stakeholders before anything moves. The response requires making budget and ownership decisions explicitly, not hoping that clarity emerges from collaboration.

Legitimate skepticism deserves its own category because it’s the resistance pattern most leaders mishandle. When someone asks to see the revenue connection, that isn’t obstruction; it’s the right question. The answer needs to be honest, which means acknowledging that the measurement infrastructure for AI visibility is still developing. Trying to manufacture certainty in response to legitimate skepticism destroys credibility faster than admitting the gap. Acknowledging where the data is incomplete while demonstrating directional progress is more durable.

Running Both Operations At Once

Most teams can’t switch from traditional SEO to AI visibility operations in a single reorg cycle, and the honest answer is that most won’t need to. The practical reality is a period of parallel operation, where traditional work continues while AI visibility capabilities are built alongside it, and for the majority of organizations, that parallel period won’t resolve into a clean new structure. It will simply become how the team operates. The most common near-term pattern is already visible: The existing SEO gets handed AEO responsibilities alongside their current work, budgets don’t expand to match the expanded scope, and the team figures it out. That state will persist for years in most organizations, and in many it will persist indefinitely. New dedicated roles will emerge at larger organizations and in more competitive verticals, but that’s the exception rather than the rule.

Ultimately, the right allocation isn’t a fixed ratio dropped in from outside your organization; it’s a function of where your current traffic and business value are coming from, and how fast that’s shifting. What research on enterprise AI adoption does confirm is a consistent structural principle: Organizations that successfully scale AI spend the majority of their transition effort on people and process, not on the technology layer itself. That inversion, most attention on tools and least on people, is the primary driver of the pilot purgatory pattern described above. Your capacity allocation decisions need to reflect that. Building a new AI visibility capability on inadequate team development produces a capability that exists on paper and stalls in practice.

Two operational principles matter during the parallel period. First, not all traditional SEO activities need equal intensity to maintain. Technical hygiene, crawl accessibility, and core structured data work protect your existing position and directly support AI retrieval; they aren’t legacy activities to deprioritize. High-volume tactical content production, by contrast, is where capacity can be reallocated toward AI-era work without meaningful risk to current performance. Second, the AI visibility workstream needs dedicated ownership, not shared bandwidth. Work that lives in everyone’s job description at the margin of their other responsibilities doesn’t graduate from pilot mode. Someone needs to own the new work as a primary accountability.

Sequencing The Role Transitions

Not all roles change at the same time, and trying to restructure everything simultaneously is how reorg fatigue gets manufactured. A phased sequence reduces disruption while building the internal momentum that carries later phases.

Phase one starts with content strategists, because the conceptual bridge is shortest. The move from “what does my audience search for” to “what context does a retrieval model need to surface my content accurately” is an extension of existing thinking, not a departure from it. As covered in the roles series, this is the capability layer with the most upskilling potential and the least new-hire dependency. Start here, build early wins, and let the internal success story carry credibility into subsequent phases.

Phase two moves to technical SEOs, who face a more demanding knowledge transition. Vector index hygiene, structured data expansion beyond standard schema implementations, and crawl accessibility for AI bots require genuine new technical literacy, and not every existing practitioner will choose to develop it. This is where the upskill-versus-hire question starts to get real, and more on that in the next section. The technical SEO role isn’t disappearing, but its scope is expanding in directions that require deliberate investment.

Phase three introduces roles that may not yet exist on your team: an AI visibility analyst responsible for monitoring retrieval inclusion and brand representation, and someone focused on machine-facing content architecture. These may start as partial responsibilities before they justify dedicated headcount, but they need to exist as named functions with owners before the measurement conversation in phase four can work.

Phase four restructures reporting lines and performance metrics to reflect the new operating model. Teams held accountable to AI visibility outcomes, while their performance reviews are built entirely around traditional organic traffic metrics, produce the behavior you’d expect: compliance theater. This phase shouldn’t wait until phase three is complete; it should be designed in phase one and communicated clearly so the team understands what the finish line looks like from the start.

The Training Investment Decision

Whether to upskill existing team members or hire new ones is often framed as a budget decision. It’s actually a knowledge gap assessment.

If the gap is conceptual, covering how retrieval works, how AI models use structured data, how community signals feed into model training as discussed in the community signals piece, invest in training. These are learnable frameworks, and experienced practitioners who understand the underlying logic of traditional SEO have strong transfer potential. Analysis of more than 10,000 SEO job postings shows a 21% year-over-year increase in AI-related skill requirements, which reflects real employer demand but also signals that the market expects existing practitioners to develop these capabilities, not that companies are replacing their teams wholesale.

If the gap is technical execution, building APIs, working directly with embedding architectures, constructing systems that require software engineering background, the calculus shifts toward hiring or contracting. This is specialized enough that the training timeline to bring an existing practitioner to production competency may exceed the cost and speed of hiring someone who already has it.

A practical diagnostic for each capability gap: ask whether a competent practitioner with your team’s existing background could reach working proficiency in 90 days with focused investment. If yes, train. If the honest answer is longer, or if the gap requires a completely different mental model of how software systems work, consider hiring. The important discipline here is answering honestly rather than answering in the direction of what’s cheaper.

Measuring The Transition Itself

The transition needs its own measurement framework, separate from the visibility metrics the transition is designed to improve. Without it, leadership has no way to distinguish between a team that is genuinely progressing and a team that is performing progress.

Leading indicators tell you whether the structural shift is actually happening: team fluency with retrieval concepts verified through practical exercises rather than self-reporting, the number of AI visibility experiments in active testing rather than sitting in a backlog, and cross-functional collaboration frequency between SEO, content, and technical teams on AI-era work.

Lagging indicators connect to the outcomes the transition is meant to produce: Brand citation share in AI-generated responses, retrieval inclusion rates across major platforms, and the accuracy of brand representation when your content is surfaced. The framework for approaching these metrics was laid out in the GenAI KPIs piece, and the methodology there applies directly to the lagging indicators here.

The honest acknowledgment is that standardized measurement infrastructure for AI visibility is still developing. The industry hasn’t produced the equivalent of what organic search has in terms of agreed-upon tracking methodology. That isn’t a reason to defer the transition; it’s a reason to document your own methodology consistently from the start, so you’re building a proprietary baseline as standards eventually emerge. Companies that begin measuring now, even imperfectly, will have comparative data that teams starting eighteen months from now won’t be able to reconstruct.

A 90-day scorecard for the transition itself should include: at least one role with formal AI visibility responsibilities assigned, a named owner for the dual operating model, at least two active retrieval experiments generating learning data, and a completed skills gap assessment for every team member against the phase three role definitions. None of those are visibility metrics. They’re execution metrics, and execution is where most transitions fail.

Who Wins?

The organizations that navigate this transition successfully won’t be the ones with the clearest vision of what AI search requires. They’ll be the ones that converted that vision into structure: named owners, phased timelines, honest skills assessments, and measurement that tracks the work before it tracks the outcomes. Vision is table stakes, and every team reading this already has it. The ones that pull ahead will be the ones that open Mondays with a plan.

More Resources:

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This post was originally published on Duane Forrester Decodes.

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Featured Image: GaudiLab/Shutterstock; Paulo Bobita/Search Engine Journal

https://www.searchenginejournal.com/the-real-reason-your-seo-team-hasnt-made-the-ai-transition-yet/572445/

Money, obviously. But it’s deeper than that.

Google’s market share has broadly held firm in the wake of everything AI. By held firm, I mean its share price has gone through the roof, and its AI offering is growing ever stronger.

But I don’t think all is as rosy as it seems.

Google’s search product isn’t addictive – as much as they’re trying to change that. Nobody hangs out there except saddos like us. And audiences – particularly younger ones – have options.

They’re turning away from more traditional methods of information retrieval, and that’s a big problem. Even for Google.

Even the search engine giant isn’t immune.

Older audiences – those already ingrained in the system – are taking up a larger percentage of their audience. The younger ones have more exciting and addictive options, and best believe they’re using them to find stuff.

Across every engagement metric, 18-24-year-olds have deteriorated faster than 65+ users over the same period. Shorter visit duration, fewer pages per visit, and a worse bounce rate. And it’s declining more rapidly with younger audiences.

Evolution for Google and the wider web is a necessity.

Although interesting to note that the 18-24 year old audience share has only suffered a small decline according to Similarweb data. The real losses were in the 25-34 cohort.

TL;DR

1. The publishing industry and Google have more in common than perhaps either of us cares to admit.
2. The changes Google has made are a very deliberate effort to engage with – and retain – younger audiences. Audiences who behave differently.
3. Engagement data on news websites (pages per visit, bounce rate, and time on site) declines with audience age. Exactly the same is true of Google.
4. AI Mode is Google’s attempt to create a “sticky” product. One aimed at younger audiences.

What’s Changed?

Well, the obvious:

Just look at the SERP for almost any term, particularly middle-of-the-funnel comparison ones.

What people apparently want is not very publisher, or legacy-search-friendly. What they want is video.

Particularly the youth.

Right now, it’s feasible children spend almost four hours per day watching video on YouTube and TikTok. Four hours. That same group spends just four minutes on publisher websites.

The younger you are, the more time you spend watching, the less you spend reading. So the obvious counter (from a company that primarily organizes written content) is to saturate the market with video content.

Obviously, it’s very helpful if you own the market.

And this doesn’t just affect organic search. Adverts are more expensive to run because AIOs have destroyed the entire search ecosystem’s click-through rates. So for almost all businesses, customer acquisition is more expensive.

You could say that’s Google’s way of paying for AIOs – a far more expensive SERP to generate – due to the massive computational power and energy needed to run large language models (LLMs).

But I am not going to insinuate anything of the sort. It would be incomprehensible to me that the guys who earn the entire ad and search market would make the ad side of the business more expensive to run to pay for their search experiments.

Wait a minute…

Why Now?

I think this is a direct response to two things;

1. The 2023 Code Red Google sent out in response to OpenAI.
2. Younger audiences shifting information retrieval methods.

One is obvious.

OpenAI forced Google to move quicker than they would’ve liked. Hence, all the absolute trash in AI Overviews in the beginning. Well, and sort of now. It smacked of a product that hadn’t gone through the required amount of rigorous testing.

Two is more nuanced.

This data correlates almost perfectly with the Similarweb data I pulled. In isolation, this may not be a problem. Could be as simple as saying younger audiences will grow into it.

But I don’t think that argument works. We see it in news and publishing. We are living through it, and we’re watching the decline in real time.

Younger audiences have the highest recorded screen time on record (globally, 7 hours 22 minutes), but are spending less and less time reading. More on far more visually engaging, stimulating, and addictive technologies.

Based on screen time alone, younger audiences should spend the most time on Google. But they don’t. I’m sure that is blatantly obvious to the Googlers.

While content consumption is at an all-time high, the way a person consumes content is not conducive to more traditional publishing practices.

Just 4 minutes a day on news websites for younger audiences vs. 18 minutes for the over 55s. A 350% increase.

The same principle is true of more traditional search.

At the risk of sounding a bit too AI-y, this is a really seismic shift. Ironically, not one driven by AI. Not entirely. One driven by a combination of big tech’s insatiable appetite for money, a lack of trust in more traditional brands, and the rise of the creator ecosystem.

And AI, obviously.

As someone in the comments said, Google is Unc. Maybe a little like news websites. Their ability to attract younger audiences has diminished.

I think we can clearly correlate the changes Google has made to the reduction in the younger audience share for publishers. A generation less inclined to click.

One could argue that the traffic losses so many seem to have suffered are almost exclusively from younger audiences. I certainly am.

Audiences more likely to adopt new technologies – particularly flashy ones.

There Are Clear Parallels Between News And Search

Google has gotten richer, as has the AI bubble. All that money has to come from somewhere.

It’s everyone else who struggles.

These changes are designed to counter a younger generation’s shift toward people and ultra-engaging platforms that encourage passive or more incidental methods of information retrieval.

Since 2015, interest in news has declined – more significantly (43%) in 18-24-year-olds than in any other age group. And just 64% of 18-24-year-olds consume news on a daily basis, compared with 87% of people 55 and over.

Historically, news has been sought out.

Either you browsed a news website (a real paper if you felt fancy) or you searched for it. But the discovery layer changed, and search – the engine that powered the volume-driven publishing model for two decades – is responding.

Responding to younger audiences’ shifting consumption habits. Just like publishers and websites will have to.

Passive consumption is just the norm now with younger audiences. This is why 44% of 18-24-year-olds see social media as the main source of news, compared to just 15% of 55+.

They expect you to just appear. Algorithmic consumption has reduced the need, want, and desire to actively seek something out. If what you serve isn’t delivered directly to their feed, you don’t exist.

Combine this with diminishing trust in more traditional brands, zero-click searches, and the rise of the creator, and you can see why publishers and Google are having to change.

There have been alternatives to Google when it comes to accessing and retrieving information – Instagram, Amazon, YouTube, et al., for years.

Really, this is, or has been, Search Everywhere Optimization. It has been around for a decade. It is also, IMO, why reframing SEO as GEO or some other BS because of LLMs is so moronic.

And now the individual has become the competition. The creator economy – soon to be worth $480 billion – has produced a new class of competitor: individuals with direct audience relationships, authentic voices, and none of the structural cost of a legacy newsroom.

51% of 18-24-year-olds pay attention to creators and personalities, compared to 39% who pay attention to traditional media and journalists – a 12 PP inversion.

And this is a problem for Google, too. People used to use their organizational skills to satisfy all of their needs. Now, it is so heavily navigational that it’s hard to know how much “new” stuff people really use it for.

Outside of news, at least, ironically.

Will This Work?

If it’s anything like news publishers, their primary concern is to continually generate new and engaged audiences with habitual products. AI Mode could absolutely be that product. Discover is their version of a social network. They are, in their own way, engaging products.

Although the low intent nature of Discover makes the advertising rubbish, and Google not really care about it. Sad, but true.

Like Google, the engagement data for publishers tells a pretty bleak story.

If we isolate this to the youngest and oldest audience, it’s pretty clear what is going on.

Younger audiences:

• Are far less engaged with the traditional news offering than older audiences.
• Use these (and any) websites differently.

There’s no denying that younger audiences have more diverse and engaging options. This means they use websites like news publishers differently. To fact-check. To confirm something isn’t just spurious BS. To scan and skim.

The same is true of Google. Less of a discovery journey. More one of fact-checking and navigational searching.

Now, I’m not insinuating that older audiences get stuck with adverts and can’t use a menu. That can’t account for an extra 14 minutes of time spent on news websites.

But having watched my mother with a computer, it’s not impossible.

So, What’s The Answer?

To lean into what the new generation likes. Adapt and evolve.

The same is true for search (internally and externally) and publishers. If you work for Google, it makes complete sense you would try to expand your video presence in the SERP and prioritize “quality” UGC.

The quality part is lacking as most of the internet – as we’re finding out – is a stinking pile of garbage.

But notoriously, the tide is tricky to swim against.

For publishers, it means working with creators, leveraging their audiences and ability to deliver things quickly. Differently. And creators can benefit from the trust associated with proper news organizations.

Is it that unreasonable to think Google should do the same?

Instead of abusing their position, they could start by giving people an idea of the impact of AIOs and AI Mode. I’m not a financial guru, but I reckon Google has enough money to build and foster creator and publisher programs that are not one-sided. That brings genuine value to people and the wider information retrieval ecosystem.

In this scenario, everyone benefits. When AI companies refuse to pay for publisher content, everyone loses.

• LLMs lose because they have less unique, human-created, quality content to train on.
• Publishers lose because they are forced to suppress their visibility and don’t get any money.
• Users lose because the end output isn’t as good.

Model collapse is on the horizon. AI learning on AI falsehoods. A repetitive cycle of garbage. Joyous.

These companies should invest in the ecosystems that built them. Particularly Google.

For publishers:

1. Build owned channels. Get away from relying on big tech.
2. Create brilliant, unique journalism.
3. Supplement it with habit-forming products – puzzles being the obvious example.
4. Build and sponsor audio and video programs that reach your intended audience.
5. Implement channel-specific strategies.

Even the New York Times doesn’t rely solely on subscriptions from written content. Not by a long shot. It isn’t enough.

Final Thoughts

Unfortunately, I think the recent spate of job losses in the publishing industry is just the beginning. Bauer, the BBC, The Washington Post. It’s not UK or SEO-specific. 100,000 roles are becoming 70,000 ones. Teams are shrinking. And there are real-world ramifications.

We are not in a good moment. Some of this can be attributed to AI. But I think more of it is due to longer-term economic difficulties, audiences switching off from traditional news, and things like the Site Reputation Abuse update destroying much-needed revenue lines overnight.

It is hard to make these businesses profitable. Google doesn’t have that problem. But they’re not immune to changing behaviors and becoming yesterday’s news either.

Should you be enough of a psychopath, you can follow the job cuts via this updated Press Gazette article.

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https://www.searchenginejournal.com/why-google-has-changed-whos-really-paying-for-it/572796/

The assumption has been that producing something more detailed, more original, and more useful would naturally lead to stronger results, since that approach worked in a search ecosystem where discovery (and success) depended on rankings, clicks, and users actively choosing what to read.

That ecosystem rewarded the most compelling, scannable, or comprehensive option on the page, which made craftsmanship feel like the primary lever for success.

It is no longer the ecosystem we are working in, and continuing to apply that same logic without adjusting is exactly where many teams are starting to fall behind. We’ve seen this with the gamification of listicles already, and how large language models (and Google) are having to “patch” exploits as they’re found.

AI has not reduced the importance of content, but it has shifted where value is created and how that value is realized, which now revolves around who gets surfaced, cited, and reused within systems that sit between users and the web.

Content quality still matters, but it is no longer the deciding factor, and treating it as such creates a blind spot that is becoming increasingly difficult to ignore.

The Shift From Authorship To Retrieval

In traditional search, authorship carried clear weight because you created a page, earned visibility through rankings, and relied on users to click through and engage directly with what you had produced.

Success was closely tied to ownership and placement within a list of results, which made the relationship between effort and outcome feel transactional, and easily reportable to stakeholders.

Authorship still matters, and it still influences whether content is trusted, referenced, and reused, but its role has shifted toward how it supports retrieval rather than how it drives direct consumption.

Content now needs to function not only as a complete piece for human readers but also as a collection of ideas that can be extracted and reused across different contexts. This creates pressure on structure, clarity, and alignment with recognizable entities, since an author is no longer just a name attached to a page but an entity that exists across a broader ecosystem of signals, references, and mentions.

When those connections are strong, authorship reinforces retrieval and increases the likelihood that content will be selected and reused. When they are weak or absent, even high-quality content can struggle to gain traction.

AI systems don’t ignore authorship, but the way that we’ve thought about Google and authorship vectors is adapting. LLMs compress it by relying on signals of credibility and consistency, then expressing that trust through what they retrieve and include in generated responses.

This changes the unit of competition from pages to fragments and shifts the focus from ownership to accessibility, while still anchoring value in who created the content and how that creator is understood elsewhere. Strong writing and clear expertise improve the chances of being retrieved, but they do not guarantee it, which means success depends on combining credible authorship with high retrievability.

Does Being Cited Matter More Than Being Read?

For the past two decades, content strategies have been built around generating clicks, with teams refining headlines, descriptions, and formats to encourage users to visit their pages and engage directly with their work.

The visit itself served as the primary measure of success, which made traffic a reliable proxy for impact. In AI-driven experiences, that step is often removed because answers are formed within the interface before a user considers visiting a website, which fundamentally changes what visibility looks like.

Being read becomes less important than being cited, since citations now act as the mechanism through which influence is established. When content is consistently used to construct answers, it shapes user decisions even without a measurable visit, which makes its impact harder to track but no less significant.

Content that is not used in this way becomes effectively invisible, regardless of how much effort was invested in creating it.

This shift disrupts the feedback loop that marketers have relied on for years, since traffic is no longer a reliable indicator of presence or influence, even though many teams continue to optimize for it.

Distribution Wins

Challenging the idea that better work leads to better outcomes is uncomfortable because it runs counter to a belief that has been widely accepted for a long time. The ability to write excellent content still plays a role, but it is no longer the primary driver of success, and overinvesting in it while neglecting other factors is becoming a strategic risk (depending on how strong your brand and distribution mechanisms are).

Distribution has taken on a more important role, although it needs to be understood in a broader sense than traditional concepts like social reach or link building. In an AI-driven search ecosystem, distribution refers to how information exists across a network of sources that inform and validate what systems retrieve and use.

This includes being referenced across multiple trusted platforms, appearing in formats that are easy for machines to interpret, reinforcing consistent narratives about your brand, and showing up in places where systems look for confirmation.

The goal is to create alignment between what you publish and how systems evaluate credibility, relevance, and usefulness. It is entirely possible to produce an exceptional piece of content and still underperform if it exists in isolation, while a network of average content that is widely distributed and consistently reinforced can outperform it.

Content Needs To Do More Than ‘Be Read’

Great content that is not surfaced has no meaningful impact, which highlights a shift that many teams are still coming to terms with.

Quality continues to matter because weak content cannot sustain visibility over time, but the threshold for what qualifies as good enough is lower than many assume, especially when compared to the level of effort being invested.

Once that threshold is met, positioning becomes the factor that determines whether content is retrieved, cited, and embedded into answers or ignored entirely.

This reflects a broader change in how outcomes are determined, since effort no longer has a clear or direct relationship with results.

Alignment with systems on the platforms where content exists now plays a larger role, which requires a different way of thinking about strategy.

What This Means In Practice

A strategy that focuses only on improving content quality addresses only part of the challenge and leaves a significant opportunity untapped, particularly as AI continues to shape more of the user journey.

It becomes essential to consider how easily content can be extracted and reused, where ideas are reinforced outside of owned platforms, whether structure supports both human understanding and machine interpretation, and how consistently narratives appear across the broader ecosystem.

This shift also requires rethinking how success is measured, since influence can increase without a corresponding rise in traffic, which can feel uncomfortable for teams that are used to clear attribution models.

The goal is not to abandon quality but to recognize that it is no longer sufficient on its own, and that positioning needs to be treated as a core component of strategy.

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Featured Image: Roman Samborskyi/Shutterstock

https://www.searchenginejournal.com/why-great-content-is-no-longer-enough-what-beats-it-in-ai-search/572001/