Web Design and Development San Diego

LinkedIn: AI-powered search cut traffic by up to 60%

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AEO playbook

AI-powered search gutted LinkedIn’s B2B awareness traffic. Across a subset of topics, non-brand organic visits fell by as much as 60% even while rankings stayed stable, the company said.

  • LinkedIn is moving past the old “search, click, website” model and adopting a new framework: “Be seen, be mentioned, be considered, be chosen.”

By the numbers. In a new article, LinkedIn said its B2B organic growth team started researching Google’s Search Generative Experience (SGE) in early 2024. By early 2025, when SGE evolved into AI Overviews, the impact became significant.

  • Non-brand, awareness-driven traffic declined by up to 60% across a subset of B2B topics.
  • Rankings stayed stable, but click-through rates fell (by an undisclosed amount).

Yes, but. LinkedIn’s “new learnings” are more like a rehash of established SEO/AEO best practices. Here’s what LinkedIn’s content-level guidance consists of:

  • Use strong headings and a clear information hierarchy.
  • Improve semantic structure and content accessibility.
  • Publish authoritative, fresh content written by experts.
  • Move fast, because early movers get an edge.

Why we care. These tactics should all sound familiar. These are technical SEO and content-quality fundamentals. LinkedIn’s article offers little new in terms of tactics. It’s just updated packaging for modern SEO/AEO and AI visibility.

Dig deeper. How to optimize for AI search: 12 proven LLM visibility tactics

Measurement is broken. LinkedIn said its big challenge is the “dark” funnel. It can’t quantify how visibility in LLM answers impacts the bottom line, especially when discovery happens without a click.

  • LinkedIn’s B2B marketing websites saw triple-digit growth in LLM-driven traffic and that it can track conversion from those visits.
    • Yes, but: Many websites are also seeing triple-digit (or more) growth in LLM-driven traffic. Because it’s an emerging channel. That said, this is still a tiny amount of overall traffic right now (1% or less for most sites).

What LinkedIn is doing. LinkedIn created an AI Search Taskforce spanning SEO, PR, editorial, product marketing, product, paid media, social, and brand. Key actions included:

  • Correcting misinformation that showed up in AI responses.
  • Publishing new owned content optimized for generative visibility.
  • Testing LinkedIn (social) content to validate its strength in AI discovery.

Is it working? LinkedIn said early tests produced a meaningful lift in visibility and citations, especially from owned content. At least one external datapoint (Semrush, Nov. 10, 2025) suggested that LinkedIn has a structural advantage in AI search:

  • Google AI Mode cited LinkedIn in roughly 15% of responses.
  • LinkedIn was the #2 most-cited domain in that dataset, behind YouTube.

Incomplete story. LinkedIn’s article is an interesting read, but it’s light on specifics. Missing details include:

  • The exact topic set behind the “up to 60%” decline.
  • Exactly how much click-through rates “softened.”
  • Sample size and timeframe.
  • How “industry-wide” comparisons were calculated.
  • What tests were run, what moved citation share, and by how much.

Bottom line. LinkedIn is right that visibility is the new currency. However, it hasn’t shown enough detail to prove its new playbook is meaningfully different from doing some SEO (yes, SEO) fundamentals.

LinkedIn’s article. How LinkedIn Marketing Is Adapting to AI-Led Discovery

Read more at Read More

Web Design and Development San Diego

Are we ready for the agentic web?

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Are we ready for the agentic web?

Innovations are coming at marketers and consumers faster than before, raising the question: Are we actually ready for the agentic web?

To answer that question, it’s important to unpack a few supporting ones:

  • What’s the agentic web?
  • How can the agentic web be used?
  • What are the pros and cons of the agentic web?

It’s important to note that this article isn’t a mandate for AI skeptics to abandon the rational questions they have about the agentic web. 

Nor is it intended to place any judgment on how you, as a consumer or professional, engage with the agentic web.

LinkedIn poll on Copilot Checkout

With thoughts and feelings so divided on the agentic web, this article aims to provide clear insight into how to think about it in earnest, without the branding or marketing fluff.

Disclosure: I am a Microsoft employee and believe in the path Microsoft’s taking with the agentic web. However, this article will attempt to be as platform-agnostic as possible.

What’s the agentic web? 

The agentic web refers to sophisticated tools, or agents, trained on our preferences that act with our consent to accomplish time-consuming tasks.

In simple terms, when I use one-click checkout, I allow my saved payment information to be passed to the merchant’s accounts receivable systems. 

Neither the merchant nor I must write down all the details or be involved beyond consenting to send and receive payment.

For fun, I put this question to four different AI models, and the responses are telling: 

  • Copilot: “The agentic web is an emerging layer of the internet where AI agents can reason and take actions on behalf of people or organizations, turning expressed intent into outcomes while preserving user choice, transparency, and control.” 
  • Gemini: “The agentic web is an evolution of the internet where autonomous AI agents – rather than just human users – interact with websites, APIs, and each other to perform complex tasks and make decisions on our behalf. “
  • Perplexity: “The agentic web is an emerging phase of the internet where autonomous AI agents act as the primary interface, navigating, interpreting, and taking actions across websites and services on behalf of users with minimal human involvement.” 
  • Claude: “The agentic web is an evolution of the internet where AI agents autonomously navigate, interact with, and complete tasks across websites and services on behalf of users.” 

I begin with how different models answer the question because it’s important to understand that each one is trained on different information, and outcomes will inevitably vary.

It’s worth noting that with the same prompt, defining the agentic web in one sentence, three out of four models focus on diminishing the human role in navigating the web, while one makes a point to emphasize the significance of human involvement, preserving user choice, transparency, and control.

Two out of four refer to the agentic web as a layer or phase rather than an outright evolution of the web. 

This is likely where the sentiment divide on the agentic web stems from.

Some see it as a consent-driven layer designed to make life easier, while others see it as a behemoth that consumes content, critical thinking, and choice.

It’s noteworthy that one model, Gemini, calls out APIs as a means of communication in the agentic web. APIs are essentially libraries of information that can be referenced, or called, based on the task you are attempting to accomplish. 

This matters because APIs will become increasingly relevant in the agentic web, as saved preferences must be organized in ways that are easily understood and acted upon.

Defining the agentic web requires spending some time digging into two important protocols – ACP and UCP.

Dig deeper: AI agents in SEO: What you need to know

Agentic Commerce Protocol: Optimized for action inside conversational AI 

The Agentic Commerce Protocol, or ACP, is designed around a specific moment: when a user has already expressed intent and wants the AI to act.

The core idea behind ACP is simple. If a user tells an AI assistant to buy something, the assistant should be able to do so safely, transparently, and without forcing the user to leave the conversation to complete the transaction.

ACP enables this by standardizing how an AI agent can:

  • Access merchant product data.
  • Confirm availability and price.
  • Initiate checkout using delegated, revocable payment authorization.

The experience is intentionally streamlined. The user stays in the conversation. The AI handles the mechanics. The merchant still fulfills the order.

This approach is tightly aligned with conversational AI platforms, particularly environments where users are already asking questions, refining preferences, and making decisions in real time. It prioritizes speed, clarity, and minimal friction.

Universal Commerce Protocol: Built for discovery, comparison, and lifecycle commerce 

The Universal Commerce Protocol, or UCP, takes a broader view of agentic commerce.

Rather than focusing solely on checkout, UCP is designed to support the entire shopping journey on the agentic web, from discovery through post-purchase interactions. It provides a common language that allows AI agents to interact with commerce systems across different platforms, surfaces, and payment providers. 

That includes: 

  • Product discovery and comparison.
  • Cart creation and updates.
  • Checkout and payment handling.
  • Order tracking and support workflows.

UCP is designed with scale and interoperability in mind. It assumes users will encounter agentic shopping experiences in many places, not just within a single assistant, and that merchants will want to participate without locking themselves into a single AI platform.

It’s tempting to frame ACP and UCP as competing solutions. In practice, they address different moments of the same user journey.

ACP is typically strongest when intent is explicit and the user wants something done now. UCP is generally strongest when intent is still forming and discovery, comparison, and context matter.

So what’s the agentic web? Is it an army of autonomous bots acting on past preferences to shape future needs? Is it the web as we know it, with fewer steps driven by consent-based signals? Or is it something else entirely?

The frustrating answer is that the agentic web is still being defined by human behavior, so there’s no clear answer yet. However, we have the power to determine what form the agentic web takes. To better understand how to participate, we now move to how the agentic web can be used, along with the pros and cons.

Dig deeper: The Great Decoupling of search and the birth of the agentic web

How can the agentic web be used? 

Working from the common theme across all definitions, autonomous action, we can move to applications.

Elmer Boutin has written a thoughtful technical view on how schema will impact agentic web compatibility. Benjamin Wenner has explored how PPC management might evolve in a fully agentic web. Both are worth reading.

Here, I want to focus on consumer-facing applications of the agentic web and how to think about them in relation to the tasks you already perform today.

Here are five applications of the agentic web that are live today or in active development.

1. Intent-driven commerce  

A user states a goal, such as “Find me the best running shoes under $150,” and an agent handles discovery, comparison, and checkout without requiring the user to manually browse multiple sites. 

How it works 

Rather than returning a list of links, the agent interprets user intent, including budget, category, and preferences. 

It pulls structured product information from participating merchants, applies reasoning logic to compare options, and moves toward checkout only after explicit user confirmation. 

The agent operates on approved product data and defined rules, with clear handoffs that keep the user in control. 

Implications for consumers and professionals 

Reducing decision fatigue without removing choice is a clear benefit for consumers. For brands, this turns discovery into high-intent engagement rather than anonymous clicks with unclear attribution. 

Strategically, it shifts competition away from who shouts the loudest toward who provides the clearest and most trusted product signals to agents. These agents can act as trusted guides, offering consumers third-party verification that a merchant is as reliable as it claims to be.

2. Brand-owned AI assistants 

A brand deploys its own AI agent to answer questions, recommend products, and support customers using the brand’s data, tone, and business rules.

How it works 

The agent uses first-party information, such as product catalogs, policies, and FAQs. 

Guardrails define what it can say or do, preventing inferences that could lead to hallucinations. 

Responses are generated by retrieving and reasoning over approved context within the prompt.

Implications for consumers and professionals 

Customers get faster and more consistent responses. Brands retain voice, accountability, and ownership of the experience. 

Strategically, this allows companies to participate in the agentic web without ceding their identity to a platform or intermediary. It also enables participation in global commerce without relying on native speakers to verify language.

3. Autonomous task completion 

Users delegate outcomes rather than steps, such as “Prepare a weekly performance summary” or “Reorder inventory when stock is low.” 

How it works 

The agent breaks the goal into subtasks, determines which systems or tools are needed, and executes actions sequentially. It pauses when permissions or human approvals are required. 

These can be provided in bulk upfront or step by step. How this works ultimately depends on how the agent is built. 

Implications for consumers and marketers 

We’re used to treating AI like interns, relying on micromanaged task lists and detailed prompts. As agents become more sophisticated, it becomes possible to treat them more like senior employees, oriented around outcomes and process improvement. 

That makes it reasonable to ask an agent to identify action items in email or send templates in your voice when active engagement isn’t required. Human choice comes down to how much you delegate to agents versus how much you ask them to assist.

Dig deeper: The future of search visibility: What 6 SEO leaders predict for 2026

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4. Agent-to-agent coordination and negotiation 

Agents communicate with other agents on behalf of people or organizations, such as a buyer agent comparing offers with multiple seller agents. 

How it works 

Agents exchange structured information, including pricing, availability, and constraints. 

They apply predefined rules, such as budgets or policies, and surface recommended outcomes for human approval. 

Implications for consumers and marketers 

Consumers may see faster and more transparent comparisons without needing to manually negotiate or cross-check options. 

For professionals, this introduces new efficiencies in areas like procurement, media buying, or logistics, where structured negotiation can occur at scale while humans retain oversight.

5. Continuous optimization over time 

Agents don’t just act once. They improve as they observe outcomes.

How it works 

After each action, the agent evaluates what happened, such as engagement, conversion, or satisfaction. It updates its internal weighting and applies those learnings to future decisions.

Why people should care 

Consumers experience increasingly relevant interactions over time without repeatedly restating preferences. 

Professionals gain systems that improve continuously, shifting optimization from one-off efforts to long-term, adaptive performance. 

What are the pros and cons of the agentic web? 

Life is a series of choices, and leaning into or away from the agentic web comes with clear pros and cons.

Pros of leaning into the agentic web 

The strongest argument for leaning into the agentic web is behavioral. People have already been trained to prioritize convenience over process. 

Saved payment methods, password managers, autofill, and one-click checkout normalized the idea that software can complete tasks on your behalf once trust is established.

Agentic experiences follow the same trajectory. Rather than requiring users to manually navigate systems, they interpret intent and reduce the number of steps needed to reach an outcome. 

Cons of leaning into the agentic web 

Many brands will need to rethink how their content, data, and experiences are structured so they can be interpreted by automated systems and humans. What works for visual scanning or brand storytelling doesn’t always map cleanly to machine-readable signals.

There’s also a legitimate risk of overoptimization. Designing primarily for AI ingestion can unintentionally degrade human usability or accessibility if not handled carefully. 

Dig deeper: The enterprise blueprint for winning visibility in AI search

Pros of leaning away from the agentic web 

Choosing to lean away from the agentic web can offer clarity of stance. There’s a visible segment of users skeptical of AI-mediated experiences, whether due to privacy concerns, automation fatigue, or a loss of human control. 

Aligning with that perspective can strengthen trust with audiences who value deliberate, hands-on interaction.

Cons of leaning away from the agentic web 

If agentic interfaces become a primary way people discover information, compare options, or complete tasks, opting out entirely may limit visibility or participation. 

The longer an organization waits to adapt, the more expensive and disruptive that transition can become.

What’s notable across the ecosystem is that agentic systems are increasingly designed to sit on top of existing infrastructure rather than replace it outright. 

Avoiding engagement with these patterns may not be sustainable over time. If interaction norms shift and systems aren’t prepared, the combination of technical debt and lost opportunity may be harder to overcome later.

Where the agentic web stands today

The agentic web is still taking form, shaped largely by how people choose to use it. Some organizations are already applying agentic systems to reduce friction and improve outcomes. Others are waiting for stronger trust signals and clearer consent models.

Either approach is valid. What matters is understanding how agentic systems work, where they add value, and how emerging protocols are shaping participation. That understanding is the foundation for deciding when, where, and how to engage with the agentic web.

Read more at Read More

Web Design and Development San Diego

7 digital PR secrets behind strong SEO performance

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7 digital PR secrets behind strong SEO performance

Digital PR is about to matter more than ever. Not because it’s fashionable, or because agencies have rebranded link building with a shinier label, but because the mechanics of search and discovery are changing. 

Brand mentions, earned media, and the wider PR ecosystem are now shaping how both search engines and large language models understand brands. That shift has serious implications for how SEO professionals should think about visibility, authority, and revenue.

At the same time, informational search traffic is shrinking. Fewer people are clicking through long blog posts written to target top-of-funnel keywords. 

The commercial value in search is consolidating around high-intent queries and the pages that serve them: product pages, category pages, and service pages. Digital PR sits right at the intersection of these changes.

What follows are seven practical, experience-led secrets that explain how digital PR actually works when it’s done well, and why it’s becoming one of the most important tools in SEOs’ toolkit.

Secret 1: Digital PR can be a direct sales activation channel

Digital PR is usually described as a link tactic, a brand play or, more recently, as a way to influence generative search and AI outputs.

All of that’s true. What’s often overlooked is that digital PR can also drive revenue directly.

When a brand appears in a relevant media publication, it’s effectively placing itself in front of buyers while they are already consuming related information.

This is not passive awareness. It’s targeted exposure during a moment of consideration.

Platforms like Google are exceptionally good at understanding user intent, interests and recency. Anyone who has looked at their Discover feed after researching a product category has seen this in action. 

Digital PR taps into the same behavioral reality. You are not broadcasting randomly. You are appearing where buyers already are.

Two things tend to happen when this is executed well.

  • If your site already ranks for a range of relevant queries, your brand gains additional recognition in nontransactional contexts. Readers see your name attached to a credible story or insight. That familiarity matters.
  • More importantly, that exposure drives brand search and direct clicks. Some readers click straight through from the article. Others search for your brand shortly after. In both cases, they enter your marketing funnel with a level of trust that generic search traffic rarely has.

This effect is driven by basic behavioral principles such as recency and familiarity. While it’s difficult to attribute cleanly in analytics, the commercial impact is very real. 

We see this most clearly in direct-to-consumer, finance, and health markets, where purchase cycles are active and intent is high.

Digital PR is not just about supporting sales. In the right conditions, it’s part of the sales engine.

Dig deeper: Discoverability in 2026: How digital PR and social search work together

Secret 2: The mere exposure effect is one of digital PR’s biggest advantages

One of the most consistent patterns in successful digital PR campaigns is repetition.

When a brand appears again and again in relevant media coverage, tied to the same themes, categories, or areas of expertise, it builds familiarity. 

That familiarity turns into trust, and trust turns into preference. This is known as the mere exposure effect, and it’s fundamental to how brands grow.

In practice, this often happens through syndicated coverage. A strong story picked up by regional or vertical publications can lead to dozens of mentions across different outlets. 

Historically, many SEOs undervalued this type of coverage because the links were not always unique or powerful on their own.

That misses the point.

What this repetition creates is a dense web of co-occurrences. Your brand name repeatedly appears alongside specific topics, products, or problems. This influences how people perceive you, but it also influences how machines understand you.

For search engines and large language models alike, frequency and consistency of association matter. 

An always-on digital PR approach, rather than sporadic big hits, is one of the fastest ways to increase both human and algorithmic familiarity with a brand.

Secret 3: Big campaigns come with big risk, so diversification matters

Large, creative digital PR campaigns are attractive. They are impressive, they generate internal excitement, and they often win industry praise. The problem is that they also concentrate risk.

A single large campaign can succeed spectacularly, or it can fail quietly. From an SEO perspective, many widely celebrated campaigns underperform because they do not generate the links or mentions that actually move rankings.

This happens for a simple reason. What marketers like is not always what journalists need.

Journalists are under pressure to publish quickly, attract attention, and stay relevant to their audience. 

If a campaign is clever but difficult to translate into a story, it will struggle. If all your budget’s tied up in one idea, you have no fallback.

A diversified digital PR strategy spreads investment across multiple smaller campaigns, reactive opportunities, and steady background activity. 

This increases the likelihood of consistent coverage and reduces dependence on any single idea working perfectly.

In digital PR, reliability often beats brilliance.

Dig deeper: How to build search visibility before demand exists

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Secret 4: The journalist’s the customer

One of the most common mistakes in digital PR is forgetting who the gatekeeper is.

From a brand’s perspective, the goal might be links, mentions, or authority. 

From a journalist’s perspective, the goal is to write a story that interests readers and performs well. These goals overlap, but they are not the same.

The journalist decides whether your pitch lives or dies. In that sense, they are the customer.

Effective digital PR starts by understanding what makes a journalist’s job easier. 

That means providing clear angles, credible data, timely insights, and fast responses. Think about relevance before thinking about links.

When you help journalists do their job well, they reward you with exposure. 

That exposure carries weight in search engines and in the training data that informs AI systems. The exchange is simple: value for value.

Treat journalists as partners, not as distribution channels.

Secret 5: Product and category page links are where SEO value is created

Not all links are equal.

From an SEO standpoint, links to product, category, and core service pages are often far more valuable than links to blog content. Unfortunately, they are also the hardest links to acquire through traditional outreach.

This is where digital PR excels.

Because PR coverage is contextual and editorial, it allows links to be placed naturally within discussions of products, services, or markets. When done correctly, this directs authority to the pages that actually generate revenue.

As informational content becomes less central to organic traffic growth, this matters even more.

Ranking improvements on high-intent pages can have a disproportionate commercial impact.

A relatively small number of high-quality, relevant links can outperform a much larger volume of generic links pointed at top-of-funnel content.

Digital PR should be planned with these target pages in mind from the outset.

Dig deeper: How to make ecommerce product pages work in an AI-first world

Secret 6: Entity lifting is now a core outcome of digital PR

Search engines have long made it clear that context matters. The text surrounding a link, and the way a brand is described, help define what that brand represents.

This has become even more important with the rise of large language models. These systems process information in chunks, extracting meaning from surrounding text rather than relying solely on links.

When your brand is mentioned repeatedly in connection with specific topics, products, or expertise, it strengthens your position as an entity in that space. This is what’s often referred to as entity lifting.

The effect goes beyond individual pages. Brands see ranking improvements for terms and categories that were not directly targeted, simply because their overall authority has increased. 

At the same time, AI systems are more likely to reference and summarize brands that are consistently described as relevant sources.

Digital PR is one of the most scalable ways to build this kind of contextual understanding around a brand.

Secret 7: Authority comes from relevant sources and relevant sections

Former Google engineer Jun Wu discusses this in his book “The Beauty of Mathematics in Computer Science,” explaining that authority emerges from being recognized as a source within specific informational hubs. 

In practical terms, this means that where you are mentioned matters as much as how big the site is.

A link or mention from a highly relevant section of a large publication can be more valuable than a generic mention on the homepage. For example, a targeted subfolder on a major media site can carry strong authority, even if the domain as a whole covers many subjects.

Effective digital PR focuses on two things: 

  • Publications that are closely aligned with your industry and sections.
  • Subfolders that are tightly connected to the topic you want to be known for.

This is how authority is built in a way that search engines and AI systems both recognize.

Dig deeper: The new SEO imperative: Building your brand

Where digital PR now fits in SEO

Digital PR is no longer a supporting act to SEO. It’s becoming central to how brands are discovered, understood, and trusted.

As informational traffic declines and high-intent competition intensifies, the brands that win will be those that combine relevance, repetition, and authority across earned media. 

Digital PR, done properly, delivers all three.

Read more at Read More

Web Design and Development San Diego

Google: 75% of crawling issues come from two common URL mistakes

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Google discussed its 2025 year-end report on crawling and indexing challenges for Google Search. The biggest issues were faceted navigation and action parameters, which accounted for about 75% of the problems, according to Google’s Gary Illyes. He shared this on the latest Search Off the Record podcast, published this morning.

What is the issue. Crawling issues can slow your site to a crawl, overload your server, and make your website unusable or inaccessible. If a bot gets stuck in an infinite crawling loop, recovery can take time.

  • “Once it discovers a set of URLs, it cannot make a decision about whether that URL space is good or not unless it crawled a large chunk of that URL space,” Illyes said. By then it is too late and your site has slowed to a halt.

The biggest crawling challenges. Based on the report, these are the main issues Google sees:

  • 50% come from faceted navigation. This is common on ecommerce sites, where endless filters for size, color, price, and similar options create near-infinite URL combinations.
  • 25% come from action parameters. These are URL parameters that trigger actions instead of meaningfully changing page content.
  • 10% come from irrelevant parameters. This includes session IDs, UTM tags, and other tracking parameters added to URLs.
  • 5% come from plugins or widgets. Some plugins and widgets generate problematic URLs that confuse crawlers.
  • 2% come from other “weird stuff.” This catch-all category includes issues such as double-encoded URLs and related edge cases.

Why we care. A clean URL structure without bot traps is essential to keep your server healthy, ensure fast page loads, and prevent search engines from getting confused about your canonical URLs.

The episode. Crawling Challenges: What the 2025 Year-End Report Tells Us.

Read more at Read More

Web Design and Development San Diego

Microsoft rolls out multi-turn search in Bing

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Microsoft today rolled out multi-turn search globally in Bing. As you scroll down the search results page, a Copilot search box now dynamically appears at the bottom.

About multi-turn search. This type of search experience lets a user continue the conversation from the Bing search results page. Instead of starting over, the searcher types a follow-up question into the Copilot search box at the bottom of the results, allowing the search to build on the previous query. Here’s a screenshot of this feature:

Here’s a video of it in action:

What Microsoft said. Jordi Ribas, CVP, Head of Search at Microsoft, posted this news on X:

  • “After shipping in the US last year, multi-turn search in Bing is now available worldwide.
  • “Bing users don’t need to scroll up to do the next query, and the next turn will keep context when appropriate. We have seen gains in engagement and sessions per user in our online metrics, which reflect the positive user value of this approach.”

Why we care. Search engines like Google and Bing are pushing harder to move users into their AI experiences. Google is blending AI Overviews more deeply into AI Mode, even as many publishers object to how it handles their content. Bing has now followed suit, fully rolling out the Copilot search box at the bottom of search results after several months of testing.

Read more at Read More

Web Design and Development San Diego

Why most SEO failures are organizational, not technical

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Why most SEO failures are organizational, not technical

I’ve spent over 20 years in companies where SEO sat in different corners of the organization – sometimes as a full-time role, other times as a consultant called in to “find what’s wrong.” Across those roles, the same pattern kept showing up.

The technical fix was rarely what unlocked performance. It revealed symptoms, but it almost never explained why progress stalled.

No governance

The real constraints showed up earlier, long before anyone read my weekly SEO reports. They lived in reporting lines, decision rights, hiring choices, and in what teams were allowed to change without asking permission. 

When SEO struggled, it was usually because nobody rightfully owned the CMS templates, priorities conflicted across departments, or changes were made without anyone considering how they affected discoverability.

I did not have a word for the core problem at the time, but now I do – it’s governance, usually manifested by its absence.

Two workplaces in my career had the conditions that allowed SEO to work as intended. Ownership was clear.

Release pathways were predictable. Leaders understood that visibility was something you managed deliberately, not something you reacted to when traffic dipped.

Everywhere else, metadata and schema were not the limiting factor. Organizational behavior was.

Dig deeper: How to build an SEO-forward culture in enterprise organizations

Beware of drift

Once sales pressures dominate each quarter, even technically strong sites undergo small, reasonable changes:

  • Navigation renamed by a new UX hire.
  • Wording adjusted by a new hire on the content team.
  • Templates adjusted for a marketing campaign.
  • Titles “cleaned up” by someone outside the SEO loop.

None of these changes look dangerous in isolation – if you know before they occur.

Over time, they add up. Performance slides, and nobody can point to a single release or decision where things went wrong.

This is the part of SEO most industry commentary skips. Technical fixes are tangible and teachable. Organizational friction is not. Yet that friction is where SEO outcomes are decided, usually months before any visible decline.

SEO loses power when it lives in the wrong place

I’ve seen this drift hurt rankings, with SEO taking the blame. In one workplace, leadership brought in an agency to “fix” the problem, only for it to confirm what I’d already found: a lack of governance caused the decline.

Where SEO sits on the org chart determines whether you see decisions early or discover them after launch. It dictates whether changes ship in weeks or sit in the backlog for quarters.

I have worked with SEO embedded under marketing, product, IT, and broader omnichannel teams. Each placement created a different set of constraints.

When SEO sits too low, decisions that reshape visibility ship first and get reviewed later — if they are reviewed at all.

  • Engineering adjusted components to support a new security feature. In one workplace, a new firewall meant to stop scraping also blocked our own SEO crawling tools.
  • Product reorganized navigation to “simplify” the user journey. No one asked SEO how it would affect internal PageRank.
  • Marketing “refreshed” content to match a campaign. Each change shifted page purpose, internal linking, and consistency — the exact signals search engines and AI systems use to understand what a site is about.

Dig deeper: SEO stakeholders: Align teams and prove ROI like a pro

Positioning the SEO function

Without a seat at the right table, SEO becomes a cleanup function.

When one operational unit owns SEO, the work starts to reflect that unit’s incentives.

  • Under marketing, it becomes campaign-driven and short-term.
  • Under IT, it competes with infrastructure work and release stability.
  • Under product, it gets squeezed into roadmaps that prioritize features over discoverability.

The healthiest performance I’ve seen came from environments where SEO sat close enough to leadership to see decisions early, yet broad enough to coordinate with content, engineering, analytics, UX, and legal.

In one case, I was a high-priced consultant, and every recommendation was implemented. I haven’t repeated that experience since, but it made one thing clear: VP-level endorsement was critical. That client doubled organic traffic in eight months and tripled it over three years.

Unfortunately, the in-house SEO team is just another team that might not get the chance to excel. Placement is not everything, but it is the difference between influencing the decision and fixing the outcome.

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Hiring mistakes

The second pattern that keeps showing up is hiring – and it surfaces long before any technical review.

Many SEO programs fail because organizations staff strategically important roles for execution, when what they really need is judgment and influence. This isn’t a talent shortage. It’s a screening problem

The SEO manager often wears multiple hats, with SEO as a minor one. When they don’t understand SEO requirements, they become a liability, and the C-suite rarely sees it.

Across many engagements, I watched seasoned professionals passed over for younger candidates who interviewed well, knew the tool names, and sounded confident.

HR teams defaulted to “team fit” because it was easier to assess than a candidate’s ability to handle ambiguity, challenge bad decisions, or influence work across departments.

SEO excellence depends on lived experience. Not years on a résumé, but having seen the failure modes up close:

  • Migrations that wiped out templates.
  • Restructures that deleted category pages.
  • “Small” navigation changes that collapsed internal linking.

Those experiences build judgment. Judgment is what prevents repeat mistakes. Often, that expertise is hard to put in a résumé.

Without SEO domain literacy, hiring becomes theater. But we can’t blame HR, which has to hire people for all parts of the business. Its only expertise is HR.

Governance needs to step in.

One of the most reliable ways to improve recruitment outcomes is simple: let the SEO leader control the shortlist.

Fit still matters. Competence matters first. When the person accountable for results shapes the hiring funnel, the best candidates are chosen.

SEO roles require the ability to change decisions, not just diagnose problems. That skill does not show up in a résumé keyword scan.

Dig deeper: The top 5 strategic SEO mistakes enterprises make (and how to avoid them)

When priorities pull in different directions

Every department in a large organization has legitimate goals.

  • Product wants momentum.
  • Engineering wants predictable releases.
  • Marketing wants campaign impact.
  • Legal wants risk reduction.

Each team can justify its decisions – and SEO still absorbs the cost.

I have seen simple structural improvements delayed because engineering was focused on a different initiative.

At one workplace, I was asked how much sales would increase if my changes were implemented.

I have seen content refreshed for branding reasons that weakened high-converting pages. Each decision made sense locally. Collectively, they reshaped the site in ways nobody fully anticipated.

Today, we face an added risk: AI systems now evaluate content for synthesis. When content changes materially, an LLM may stop citing us as an authority on that topic.

Strong visibility governance can prevent that.

The organizations that struggled most weren’t the ones with conflict. They were the ones that failed to make trade-offs explicit.

What are we giving up in visibility to gain speed, consistency, or safety? When that question is never asked, SEO degrades quietly.

What improved outcomes was not a tool. It was governance: shared expectations and decision rights.

When teams understood how their work affected discoverability, alignment followed naturally. SEO stopped being the team that said “no” and became the function that clarified consequences.

International SEO improves when teams stop shipping locally good changes that are globally damaging. Local SEO improves when there is a single source of location truth.

Ownership gaps

Many SEO problems trace back to ownership gaps that only become visible once performance declines.

  • Who owns the CMS templates?
  • Who defines metadata standards?
  • Who maintains structured data? Who approves content changes?

When these questions have no clear answer, decisions stall or happen inconsistently. The site evolves through convenience rather than intent.

In contrast, the healthiest organizations I worked with shared one trait: clarity.

People knew which decisions they owned and which ones required coordination. They did not rely on committees or heavy documentation because escalation paths were already understood.

When ownership is clear, decisions move. When ownership is fragmented, even straightforward SEO work becomes difficult.

Dig deeper: How to win SEO allies and influence the brand guardians

Healthy environments for SEO to succeed

Across my career, the strongest results came from environments where SEO had:

  • Early involvement in upcoming changes.
  • Predictable collaboration with engineering.
  • Visibility into product goals.
  • Clear authority over content standards.
  • Stable templates and definitions.
  • A reliable escalation path when priorities conflicted.
  • Leaders who understood visibility as a long-term asset.

These organizations were not perfect. They were coherent.

People understood why consistency mattered. SEO was not a reactive service. It was part of the infrastructure.

What leaders can do now

If you lead SEO inside a complex organization, the most effective improvements come from small, deliberate shifts in how decisions get made:

  • Place SEO where it can see and influence decisions early.
  • Let SEO leaders – not HR – shape candidate shortlists.
  • Hire for judgment and influence, not presentation.
  • Create predictable access to product, engineering, content, analytics, and legal.
  • Stabilize page purpose and structural definitions.
  • Make the impact of changes visible before they ship.

These shifts do not require new software. They require decision clarity, discipline, and follow-through.

Visibility is an organizational outcome

SEO succeeds when an organization can make and enforce consistent decisions about how it presents itself. Technical work matters, but it can’t offset structures pulling in different directions.

The strongest SEO results I’ve seen came from teams that focused less on isolated optimizations and more on creating conditions where good decisions could survive change. That’s visibility governance.

When SEO performance falters, the most durable fixes usually start inside the organization.

Dig deeper: What 15 years in enterprise SEO taught me about people, power, and progress

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Web Design and Development San Diego

Google Ads API update cracks open Performance Max by channel

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Is your account ready for Google AI Max? A pre-test checklist

As part of the v23 Ads API launch, Performance Max campaigns can now be reported by channel, including Search, YouTube, Display, Discover, Gmail, Maps, and Search Partners. Previously, performance data was largely grouped into a single mixed category.

The change under the hood. Earlier API versions typically returned a MIXED value for the ad_network_type segment in Performance Max campaigns. With v23, those responses now break out into specific channel enums — a meaningful shift for reporting and optimization.

Why we care. Google Ads API v23 doesn’t just add features — it changes how advertisers understand Performance Max. The update introduces channel-level reporting, giving marketers long-requested visibility into where PMax ads actually run.

How advertisers can use it. Channel-level data is available at the campaign, asset group, and asset level, allowing teams to see how individual creatives perform across Google properties. When combined with v22 segments like ad_using_video and ad_using_product_data, advertisers can isolate results such as video performance on YouTube or Shopping ads on Search.

For developers. Upgrading to v23 will surface more detailed reporting than before. Reporting systems that relied on the legacy MIXED value will need to be updated to handle the new channel enums.

What to watch:

  • Channel data is only available for dates starting June 1, 2025.
  • Asset group–level channel reporting remains API-only and won’t appear in the Google Ads UI.

Bottom line. The latest Google Ads API release quietly delivers one of the biggest Performance Max updates yet — turning a black-box campaign type into something advertisers can finally analyze by channel.

Read more at Read More

Web Design and Development San Diego

How to build a modern Google Ads targeting strategy like a pro

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Search marketing is still as powerful as ever. Google recently surpassed $100 billion in ad revenue in a single quarter, with more than half coming from search. But search alone can no longer deliver the same results most businesses expect.

As Google Ads Coach Jyll Saskin Gales showed at SMX Next, real performance now comes from going beyond traditional search and using it to strengthen a broader PPC strategy.

The challenge with traditional Search Marketing

As search marketers, we’re great at reaching people who are actively searching for what we sell. But we often miss people who fit our ideal audience and aren’t searching yet.

The real opportunity sits at the intersection of intent and audience fit.

Take the search [vacation packages]. That query could come from a family with young kids, a honeymooning couple, or a group of retirees. The keyword is the same, but each audience needs a different message and a different offer.

Understanding targeting capabilities in Google Ads

There are two main types of targeting:

  • Content targeting shows ads in specific places.
  • Audience targeting shows ads to specific types of people.

For example, targeting [flights to Paris] is content targeting. Targeting people who are “in-market for trips to Paris” is audience targeting. Google builds in-market audiences by analyzing behavior across multiple signals, including searches, browsing activity, and location.

The three types of content targeting

  • Keyword targeting: Reach people when they search on Google, including through dynamic ad groups and Performance Max.
  • Topic targeting: Show ads alongside content related to specific topics in display and video campaigns.
  • Placement targeting: Put ads on specific websites, apps, YouTube channels, or videos where your ideal customers already spend time.

The four types of audience targeting

  • Google’s data: Prebuilt segments include detailed demographics (such as parents of toddlers vs. teens), affinity segments (interests like vegetarianism), in-market segments (people actively researching purchases), and life events (graduating or retiring). Any advertiser can use these across most campaign types.
  • Your data: Target website visitors, app users, people who engaged with your Google content (YouTube viewers or search clickers), and customer lists through Customer Match. Note that remarketing is restricted for sensitive interest categories.
  • Custom segments: Turn content targeting into audience targeting by building segments based on what people search for, their interests, and the websites or apps they use. These go by different names depending on campaign type—“custom segments” in most campaigns and “custom search terms” in video campaigns.
  • Automated targeting: This includes optimized targeting (finding people similar to your converters), audience expansion in video campaigns, audience signals and search themes in Performance Max, and lookalike segments that model new users from your seed lists.

Building your targeting strategy

To build a modern targeting strategy, you need to answer two questions:

  • How can I sell my offer with Google Ads?
  • How can I reach a specific kind of person with Google Ads?

For example, to reach Google Ads practitioners for lead gen software, you could build custom segments that target people who use the Google Ads app, visit industry sites like searchengineland.com, or search for Google Ads–specific terms such as “Performance Max” or “Smart Bidding.”

You can also layer in content targeting, like YouTube placements on industry educator channels and topic targeting around search marketing.

Strategies for sensitive interest categories

If you work in a restricted category such as legal or healthcare and can’t use custom segments or remarketing, use non-linear targeting. Ignore the offer and focus on the audience. Choose any Google data audience with potential overlap, even if it’s imperfect, and let your creative do the filtering.

Use industry-specific jargon, abbreviations, and imagery that only your target audience will recognize and value. Everyone else will scroll past.

Remember: High CPCs aren’t the enemy

Low-quality traffic is the real problem. You’re better off paying $10 per click with a 10% conversion rate than $1 per click with a 0.02% conversion rate.

When evaluating targeting strategies, focus on conversion rate and cost per acquisition, not just cost per click.

Search alone can’t deliver the results you’re used to

By expanding beyond traditional search keywords and using content and audience targeting, you can reach the right people and keep driving strong results.

Watch: How to build a modern targeting strategy like a pro + Live Q&A

Read more at Read More

Bing Webmaster Tools testing new AI Performance report

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Microsoft has been promising to give data on the performance of websites mentioned in AI results within Bing and Copilot since February 2023 and then again in April 2023. But then decided to let us down and only lump the data together with web queries, not giving us a clear view of how our sites perform within Bing’s AI experiences.

Now Bing is reportedly testing showing a new report within Bing Webmaster Tools named AI Performance report.

AI Performance report. This report is currently in a super limited beta – Microsoft has not announced anything about this publicly. But a source told us this report shows citation data from both Microsoft Copilot and partners. It shows the number of citations and the number of cited pages by day.

You can see how many times Copilot cited your website and across how many pages. It does not show you how many people clicked from those citations on Copilot to your site.

It does also let you see the data listed by “grounding queries” and “pages.” Grounding queries is likely not the full query entered into the search box on Copilot but how Bing interprets that query. Plus, it will show you the “intent” behind the query, whether it is a navigational, informational, or other form of query.

The report also shows you the specific pages cited by Copilot.

ETA. Again, Microsoft has not announced this report yet but some are seeing it go live within Bing Webmaster Tools under the Search Performance report named “AI Performance.” I do not know when you or I will gain access to the report.

Why we care. It is great to see more AI performance reporting coming from Bing Webmaster Tools, but I really do wish for click data. Every publisher, content creator, and site owner wants to know how the click-through rate from AI experiences compares to web search.

It just feels like all the search engines are deliberately hiding this data from us.

Read more at Read More

Google AI Overviews follow up questions jump you directly to AI Mode

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Google will now jump you directly into AI Mode when you do a follow-up question from AI Overviews within Google Search. This makes the “transition to a conversation even more seamless,” Robby Stein, VP of Product, Google Search wrote.

Plus, Google AI Overviews are powered by Gemini 3 by default, globally.

AI Overviews jumping to AI Mode. We covered when Google was officially testing this back in December and also before Google confirmed the test in October 2025. The ask a follow-up question within the Google Search AI Overviews will jump you into a conversation directly in AI Mode.

Google said this is about “making the transition to a conversation even more seamless,” within Google Search.

Why is Google doing this? Google said that during its testing, it “found that people prefer an experience that flows naturally into a conversation – and that asking follow-up questions while keeping the context from AI Overviews makes Search more helpful.”

Here is how it works:

When you click on “Show more,” Google will overlay AI Mode directly over the search results. You can to click the X at the top right of the screen to go back to the search results. And all the sources are removed from this view, so much for sending more traffic to publishers and content creators…

Note, this is live on mobile only right now.

Gemini powering AI Overviews. Google also said that it is rolling out Gemini 3 as the default model for AI Overviews globally. Robby Stein said, “we’re making Gemini 3 the new default model for AI Overviews globally, so you get a best-in-class AI response right on the search results page, for questions where it’s helpful.”

This is different from his previous announcement about a week ago, where Gemini 3 Pro would power AI Overviews for complex queries for English globally for Google AI Pro & Ultra subs.

Now, Gemini 3 is the default model uses for AI Overviews globally.

Why we care. While Gemini 3 may provide better quality responses for AI Overviews, the bigger news is that Google officially rolling out that follow up questions go to AI Mode from Google Search’s AI Overviews.

This is a big deal, because this will likely result in even fewer clicks from Google Search to publishers and instead will drive more searchers into AI Mode.

AI Overviews show up at the top of the search results for many queries. It is hard enough to get clicks from those citation cards now, and it will be even harder as this new follow-up experience rolls out. Google is actively pushing those searchers from Search into AI Mode and not to your website.

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