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.
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.
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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.
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.
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.
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.
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.
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.
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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.
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.
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.
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.
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.
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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.
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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.
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.
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.
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.
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.
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.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-02-03 13:00:002026-02-03 13:00:00Why most SEO failures are organizational, not technical
Is traditional SEO is dead? Not exactly. But definitely evolving. Google still controlled a whopping 89% of all U.S. web traffic in 2025. It’s still a search powerhouse, no doubt, but it isn’t the only show in town anymore.
SEO as we know it is no more. The way people find information is changing dramatically.
Google’s rolling out 12-plus algorithm changes per day. At the same time, platforms like TikTok, Amazon, and generative AI tools like ChatGPT and Claude are becoming major players in the search game.
Let’s face it. Traditional SEO tactics aren’t always the best option.
Let’s dig into the data for a pulse check on SEO in 2026.
Key Takeaways
SEO isn’t dead, but traditional tactics alone won’t cut it. To stay visible, your strategy must account for AI Overviews, zero-click searches, and shifting user behavior across platforms.
AI Overviews and SERP features now dominate page one. If your content isn’t cited or structured for AI, you risk being invisible—no matter your ranking.
Brand signals like search volume, authority, and trust matter for AI visbility. Google favors entities, not just pages. Build real-world credibility if you want to rank.
Optimize for LLMs and SEO at the same time. Clear formatting, concise answers, and fact-rich content help you rank and get quoted in generative results.
Search is no longer just on Google. Users discover content through social media, marketplaces, and AI engines—your optimization strategy needs to reach beyond traditional search.
Is SEO Dead?
Google doesn’t share its search volume data. However, approximations place it in the tens of billions, somewhere over 15 billion per day.
This shows that SEO still holds weight, but AI and LLM searches are growing. Currently, these platforms account for about 6% of global search volume, which doesn’t seem like much. But when you consider that the number is about triple what it was a year ago, it makes marketers start to take notice.
According to SmartInsights, the top 3 positions carry double-digit click-through rates, but these drop drastically for positions lower down the page. Just look at the chart below:
This drastic drop highlights how Google’s been steadily moving toward a “zero-click” search experience.
Does this mean AI Overviews are surely going to kill SEO? Well, no, but they’re definitely shaking things up. In fact, Google’s been moving toward its “answer engine” model and its new AI mode for a while now.
Features like featured snippets and answer boxes already provide concise information directly on the search results page, reducing the need for users to click through to websites.
This trend is driven by the rise of “zero-click content”—content that’s so comprehensive and informative that it satisfies user intent right on the search engine results page (SERP).
Essentially, users can find their answers without visiting a website.
AI Overviews take the zero-click approach to a whole new level, providing even more content directly in the search results.
So, how do we come to grips with both truths—that zero-click search directly results in less engagement with SEO results and that organic search is still a significant driver of traffic?
A common concern for marketers is that emerging AI engines, like ChatGPT, will kill the industry as we know it. But consider this: AI search engines still rely on Google and other algorithm-driven engines for information.
Instead of assuming SEO is dead, we should consider how SEO works today in conjunction with these trends.
The Face of the New SEO Campaign
To understand what success looks like in the new world of search, let’s look at a successful campaign of one of our NP Digital clients, RefiJet.
RefiJet has quickly become a leader in the motorcycle and auto loan refinancing space over the last decade. But to grow further, they needed to differentiate themselves from competitors and grow their digital footprint, all while AI search was changing the very way the game is played. Their company also faced macroeconomic challenges as high interest rates pushed many borrowers to the sidelines.
Our strategy for them blended new AI search principles with traditional SEO best practices. We focused on traditional technical SEO aspects such as crawlability, site speed, and structured data optimization. These moves boosted RefiJet’s inclusion in AI overviews.
Next, we launched on-page optimization tactics. These were aimed at catching traditional long-tail, high-intent search queries. We also leveraged retrieval-augmented generation (RAG) to showcase RefiJet’s authority in its space and boost citations across the web.
This blended approach helped RefiJet achieve some pretty eye-catching results:
Their SERP features increased 30,800% (that’s not a typo) since May 2024
Their rankings in the highly coveted 1-3 slots in Google increased 522% year-over-year
Traffic from LLMs is up 2012% and site-wide page views from LLMs are up 7144% year over year.
Most importantly, RefiJet’s funded loans from organic search and LLMs are up 178% year over year.
So, no. Traditional SEO is not dead. The “new” strategy just takes a modern, blended approach to modern search problems.
SEO Isn’t Dying (It’s Just Changing)
So, is SEO dead? At this point, I think you know my answer.
That would be a resounding no.
SEO isn’t going anywhere. However, for brands to find success with SEO strategies, there are specific things to keep in mind when developing campaigns.
We know Google functions more as a discovery engine but here is what else you need to know to dominate SERPs.
AI Is Taking Up A Larger Portion of the SERPs
If you’ve searched for anything on Google lately, you’ve probably seen it. That big, AI-generated box right at the top—pushing organic results further down the page.
Google’s AI Overviews are live, and they’re eating up prime SERP real estate. For certain keywords, especially broad informational ones, they dominate. And if your content doesn’t get cited in those summaries? You might not even show above the fold.
But it’s not just AI Overviews. Google has been quietly expanding other SERP features too, like interactive knowledge panels, visual product listings, “Discussions and Forums,” and even its experimental AI mode inside Search Labs. The days of ten blue links are long gone.
This shift doesn’t mean SEO is over. It means we have to rethink how we optimize. Your content still needs to be the best answer, but now it also needs to be the kind of content Google’s AI is willing to quote.
If you haven’t yet, start digging into how AI Overviews work. See which types of pages Google is pulling from. Understand the patterns.
SEO isn’t dying. But the way we earn visibility is shifting. Fast.
Technical Fundamentals Still Matter
Google’s focus isn’t backlinks, keyword density, or a specific SEO metric. Instead, the focus is on a seamless and enjoyable user experience.
What metrics does Google use to gauge user experience?
Using a clear navigation structure is a good place to start. If you want people to spend a lot of time on your site, you need to understand how users navigate. This includes using a clear URL structure, enabling breadcrumbs, and linking internally.
Core Web Vitals—a set of standardized metrics Google uses to measure real-world page performance—is another good launchpad. These include:
Largest Contentful Paint (LCP): The time from when a user starts loading a page until the largest image or text block is visible in the viewport. Goal: 2.5 seconds or less.
Interaction to Next Paint (INP): The time between a user action, like a click or key press, and the next time it takes for the page to respond. Goal: 200 milliseconds or less.
Cumulative Layout Shift (CLS): How much a webpage’s layout unexpectedly shifts during loading. Goal: A CLS score of less than 0.1.
Other important user experience metrics include dwell time, time spent on page, bounce rate, and exit rate. You can find these metrics in Google Analytics.
So, how can you improve user experience? There are a few steps you can take that will positively impact the metrics mentioned above:
Improve site speed: The faster your site loads, the better experience the user will have (We saw the impact this can have in our RefiJet example). You can gauge site speed with tools like PageSpeed Insights and Pingdom.
Optimize for mobile: You can’t afford to not optimize for mobile, as it accounts for more than 50% of web traffic. Tools like PageSpeed Insights can give you the information you need to start, like eliminating render-blocking resources or reducing unused code. You will also want to consider a responsive design if you’re not already using one.
Social Search Is Taking A Larger Share
Google remains a powerful tool, but, as we’ve established, it’s no longer the sole player in search and discovery.
Platforms like TikTok, Reddit, and even voice search engines—such as Alexa and Siri—are reshaping SEO. The question is: Are you reshaping your strategies to match them?
When Google is deciding what to rank and where to rank it, it looks past its own dataset toward other spots online, like the platforms mentioned above.
All these platforms have one thing in common: They cater to users who prefer quick, conversational, or visual content.
So, what does optimizing your content strategy to leverage these platforms look like in practice? Each app has its own wrinkles you need to consider to maximize your performance across channels:
Reddit: Participate in relevant subreddits and provide value without overtly promoting.
YouTube: Create a combination of long-form and short-form videos, targeting different users on the platform.
Voice search: Focus on conversational keywords and provide clear answers to common questions.
You may be asking, why don’t users just use those platforms to find what they need?
They do, but before you say “SEO does not matter,” remember while Google is a search engine, it can provide results from other platforms, making them relevant. We’ve seen this recently with Reddit results surging to the top of the SERPs, and showing up in a whopping 97.5 percent of Google search queries for product reviews.
As younger audiences use social media or videos more for discovery, Google will continue to update and adapt to meet user needs. And since Google pulls from so many different spaces (not just social), it still offers more reliable results on topics people want to find.
Take Reddit, for example. It shows up in a whopping 97.5 percent of Google search queries for product reviews.
Google Loves Brands
As your brand grows, you’ll find your rankings climb because Google takes authority, trustworthiness, and relevance into account. Typically, well-established brands have a higher authority and level of trustworthiness. Branded search volume is the number of searches for keywords containing your brand name on a search engine. This is one of the metrics for tracking growth because it reflects user’s interest and awareness of your brand.
Let’s consider what happens when you type “men’s running shoes” into Google’s search bar. Here is an example of what you might get:
Brands, brands, and more brands.
If you search my name, Google assumes you want to look at my website, businesses, and information about me or my social accounts.
Often, Google assumes that people searching for these terms already know what they want (and likely plan to make a purchase). This is especially the case if a customer is searching for an already well-established brand.
So, how do you establish your brand?
Aligning with the E-E-A-T framework is a good start. When your brand exudes Expertise, Experience, Authoritativeness, and Trustworthiness, Google will notice (and so will users).
To build those signals:
Create content that shows off your real-world experience and authority. Think in-depth tutorials or original research. Customer stories help, too.
Earn mentions and backlinks from reputable sources. Digital PR matters here.
Foster community. Social proof, like reviews, forum engagement, or user-generated content, tells Google and users that your brand is alive and active.
While some argue SEO is dead, building brand authority proves otherwise. In the age of AI and zero-click searches, it’s your ticket to higher rankings and increased visibility.
To be clear, E-E-A-T doesn’t only help for branded terms. Be sure to optimize for branded and non-branded terms to get in front of the most users.
Intent Is More Important Than Ever
Google is getting better at understanding intent, and users expect results that feel tailored to what they actually want, not just what they typed. If someone searches “best running shoes,” are they looking to buy now, compare options, or read reviews? If your page doesn’t match that intent, it’s not going to rank or convert.
It’s not just about categories like “informational” or “transactional” anymore, either. Google’s updates and AI enhancements have made search more personalized. Things like location and device type all influence which results appear and in what format.
That means one-size-fits-all content just won’t cut it. You need to build pages that solve specific problems for specific searchers, and make it obvious within the first few seconds that your content delivers the answer.
Look at the top results for your target terms. What kind of experience is Google rewarding? Long guides? Product roundups? Local directories?
When you align with intent, you’re not just improving your SEO, you’re giving users what they came for. And that’s how you win in the long run.
Create Content That’s Friendly For LLMs and SEO (there’s crossover)
Your niche is where your product or services fit in the market. What do you offer, and LLM tools like ChatGPT and Perplexity are changing how people search. Users have the ability to ask a question and get an instant summary. That means your content has to be referenced in this zero-click section of the search.
This is where LLMO (large language model optimization) comes in. It overlaps with SEO in a lot of ways. Clear structure and concise answers are good for both. But there are key differences.
LLMs don’t care about keyword density; they care about relevance and clarity. They’re more likely to pull from well-organized content (read as “easy to parse”) and rich in facts. Formatting matters. Use short copy blocks and bulleted lists to increase readability, and, on the technical side, clean HTML and schema markup help machines understand your content even more.
When it comes to backlinks, they still matter for SEO, but LLMs are more influenced by how well your content explains a topic.
If you want to future-proof your content, think about both: ranking high in search and being the source that LLMs pull from when people skip the SERPs entirely. For example below, well-known and reviewed medical sources pop up for this medical question.
Smart content creators are already optimizing for both worlds. Don’t get left behind.
User-Generated Content/Original Content Matters
Have you noticed that when you search on Google, your results are different than those Google is focusing on rewarding content that actually shows experience.
That’s why original content, especially from real users, is more valuable than ever. Some good examples of this type of content are:
Customer reviews
Community Q&As
Case studies
Proprietary research
Photos from your team.
Here’s an example of a successful UGC campaign from GoPro:
These types of content act as trust signals that feed directly into Google’s E-E-A-T framework (experience, expertise, authoritativeness, and trustworthiness).
If you haven’t already, get familiar with E-E-A-T. It’s the lens Google uses to figure out if your content deserves to rank. And in a world where LLMs are regurgitating the same surface-level info, showing firsthand knowledge is how you stand out.
User-generated content helps with that. So does publishing original insights—whether that’s internal data, lessons learned, or your unique take on industry trends. This is the kind of material Google can’t find anywhere else. It’s also what LLMs prefer to cite when pulling answers.
If you’re just rephrasing what’s already out there, you’re invisible. But if you create something worth referencing, both humans and machines will take notice.
Start building a content library that’s not just SEO-optimized, but undeniably your brand.
Focus Metrics Are Changing
Clicks and rankings used to be the gold standard in SEO. Not anymore.
Today, traditional SEO numbers like clicks and rankings only tell part of the story. With AI Overviews and zero-click searches taking over the SERPs, it’s possible to “rank” without getting any traffic. In this new environment, the way we measure success needs to evolve.
Instead of obsessing over position one, look at visibility across AI and SERP features. Are you showing up in AI summaries? In featured snippets? In the “People Also Ask” box? These touchpoints matter more now because they shape user behavior before a click even happens.
Engagement metrics are shifting, too. Scroll depth, dwell time, and interaction with on-page elements can reveal more about content quality than bounce rate ever did. The same goes for branded search volume and return visits—both strong signs that your content is resonating.
Search Everywhere Optimization Has Taken Center Stage
Search engines no longer corner the market on search. Non-search platforms, like social media and generative AI engines, are increasingly being used for search and discovery, disrupting traditional SEO norms.
This is what search everywhere optimization is all about.
You can no longer assume that users are only using search engines to find services and products that they need. They’re also using marketplaces (e.g., Amazon, Walmart), social media (e.g., TikTok, Pinterest), and generative AI (e.g., ChatGPT).
This means you need to expand your search optimization efforts, well, everywhere! Here’s how:
Social media: Platforms like TikTok and Instagram prioritize engaging, visual content. Optimize by using trending hashtags, creating shareable posts, and collaborating with influencers. Forums like Reddit are also highly cited in LLM results.
Generative AI engines: Tools like ChatGPT are shaping search behavior by delivering conversational and context-aware responses. Businesses should focus on producing concise, relevant, and authoritative content to rank within these engines.
Marketplaces: Amazon and similar sites act as search engines for product discovery. Ensuring optimized product titles, descriptions, and reviews is crucial.
We’ve seen this trend of the expanded search surface for a while now, but in 2026, your audience can be found across more platforms than ever. That’s why finding where your audience hangs out, and spending time to see how they’re interacting within the community, is such an important part of modern marketing strategy.
The traditional direct path of top-of-funnel to mid-funnel to bottom-of-funnel doesn’t play anymore. Your audience can convert from virtually anywhere in today’s market. Understand where they are, understand how they’re interacting, and understand the nuances of marketing on each platform, and you’ll be good to go.
FAQs
Is local SEO dead?
Not even close. Local SEO remains essential for businesses that rely on local customers. In fact, features like the local pack, Google Business Profiles, and map results are highly influential, especially on mobile. What’s changing is how users find you. Optimize for reviews and local content to stay competitive.
How long will SEO exist?
SEO is here to stay, but it continues to evolve. As long as people use search engines, social platforms, and AI tools to discover information, SEO will exist, even if tactics evolve.
Conclusion
SEO isn’t dead; it’s adapting to how people search today.
With AI reshaping the SERPs and user behavior shifting fast, what worked five years ago won’t cut it now. But the fundamentals still matter: create useful content, match search intent, and build trust with your audience.
If you’re unsure where to start, look at your content strategy. Are you prioritizing originality and structure? That’s what both Google and LLMs are rewarding.
Now’s also the time to rethink how you measure success. Traffic is great—but brand signals like engagement and trust are carrying more weight, and will only grow in importance in the future.
Want more tactical advice? Check out our guides on search engine trends and how to improve your SEO rankings.
Modern platforms like AI didn’t kill SEO; they just made it smarter. And we all need to follow suit.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-02-02 20:00:002026-02-02 20:00:00Is SEO Dead in 2026?
Natural language is quickly becoming the default way people interact with online tools. Instead of typing a few keywords, users now ask full questions, give detailed instructions, and are starting to expect clear, conversational answers. So, how can you make sure your content provides the answer to their question? Or better yet, how can you make it possible for them to interact with your website in a similar way? That’s where Microsoft’s NLWeb comes in.
Meet NLWeb, Microsoft’s new open project
NLWeb, short for Natural Language Web, is an open project recently launched by Microsoft. The aim of this project is to bring conversational interfaces directly to websites, rather than users having to use an external chatbot that’s in control of what’s shown. Instead of relying on traditional navigation or search bars, NLWeb is designed to allow users to ask questions and explore content in a more personal, conversational way.
At its core, NLWeb connects website content to AI-powered tools. It enables AI to understand what a website is about, what information it contains, and how that information should be interpreted for the purpose of returning personalized results. With this project, Microsoft is moving toward a more interoperable, standards-based, and open web that allows everyone to prepare their website for the future of search.
This project was initiated and realized by R.V. Guha, CVP and Technical Fellow at Microsoft. Guha is one of the creators of widely used web standards such as RSS and Schema.org.
How NLWeb works
NLWeb works by combining structured data, standardized APIs and AI models capable of understanding natural language. Every NLWeb instance acts as a Model Context Protocol (MCP) server, which makes your content discoverable for all the agents operating in the MCP ecosystem. This makes it easy for these agents to find your website.
Using structured data, website owners then present their content in a machine-readable way. AI applications can then consume this data and answer user questions accurately by matching them to the most relevant information. The result is a conversational experience powered by existing content, either directly on a website or through using an online search tool. A conversational interface for both human users and AI agents collecting information.
An important thing to note is that NLWeb is an open project. It’s not a closed ecosystem, meaning that Microsoft wants to make it accessible to everyone. The idea is to make it easy for any website owner to create an intelligent, natural language experience for their site, while also preparing their content to interact with and be discovered by other online agents, such as AI tools and search engines.
How does natural language work?
Natural language simply refers to the way we speak and write. This means using full sentences that allow room for intent, context and nuance. More than keywords or short commands, natural language reflects how people think and what they are looking for exactly.
To give you an example: a focus keyphrase might be running shoes trail. But using natural language, the request would look more like this: What are the best running shoes for trail running in wet conditions?
Natural language in AI tools
Modern AI tools are designed to understand this kind of input. The large language models behind these tools can analyze intent and context to generate responses that fulfill the given request. This is why conversational interfaces feel more intuitive than traditional search or forms.
Tools like AI chat assistants, voice search, and even traditional search engines rely heavily on natural language understanding and users have quickly adapted to it.
The current state of search
The way people find information online is changing fast. A change that is heavily influenced by the use of AI-powered tools. We now expect personalized answers instead of a list of results to sort through ourselves. AI chatbots also give us the option to follow up on our original search query, which turns search into a conversation instead of a series of clicks.
Research from McKinsey & Company shows that AI adoption and natural language interfaces are becoming mainstream, with 50% of consumers already using AI-driven tools for information discovery. The majority even say it’s the top digital source they use to make buying decisions. As these habits continue to grow, websites that aren’t optimized for natural language risk becoming invisible in AI-generated answers.
Why this is interesting for you
The shift to natural language isn’t just a technical trend. As discussed above, it directly impacts your online visibility and competitive position.
If users ask an AI system for information, only a handful of sources will be referenced in the response. This is because, like search engines, AI platforms also need to be able to read the information on your website. Being one of those sources can be the difference between being discovered or being overlooked.
NLWeb collaborates with Yoast
With NLWeb, you are communicating your website’s content clearly and in a standardized way. That means your brand, products, or expertise can appear in AI-powered answers instead of your competitors. To help as many website owners as possible benefit from this shift, Yoast is collaborating with NLWeb.
The best part? If you’re a user of any of our Yoast plans designed for WordPress, you’re well ahead here. Yoast’s integration with NLWeb will roll out in phases, starting with functionality that helps our users using WordPress express their content in ways AI systems can interpret accurately, without any additional setup required. So sit tight and let us help you prepare your website for the new world of search!
NLWeb aims to make your content understandable not just for people, but for the AI systems that are increasingly relevant to your website’s discovery.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-02-02 14:57:232026-02-02 14:57:23What is NLWeb (Natural Language Web)?
Google’s been quietly upgrading Search Console and Analytics with AI. No fanfare. Just better data filtering. They sit quietly inside platforms you already use, like Search Console and Google Analytics, and they change how data is surfaced, filtered, and interpreted.
These updates don’t power AI Overviews or conversational search. They work behind the scenes in platforms you already use. Google is using AI to reduce manual analysis, surface issues faster, and help marketers understand complex datasets without exporting everything to spreadsheets.
Indexing patterns and performance trends are easier to spot, even if the underlying work still requires human judgment. Google’s automating the diagnostics. You still handle the strategy.
Key Takeaways
Google’s embedding AI into Search Console and Analytics 4 to cut down on manual data analysis. The AI handles filtering and pattern detection—you still make the decisions.
AI-powered features focus on filtering, pattern detection, and prioritization rather than execution.
Google Search Console AI helps surface performance insights faster.
Google Analytics 4 uses AI for anomaly detection, predictive metrics, and guided analysis.
Predictive metrics in GA4 (like churn probability) give you directional guidance, not guarantees. Use them to build hypotheses, not to replace analysis.
Why Google Is Embedding AI in SEO Tools
Google’s SEO tools have always produced more data than most teams can realistically analyze. As sites grow, so do performance reports and behavioral metrics. AI helps Google address that scale problem.
The main shift is from reactive analysis to proactive surfacing of insights. Instead of expecting marketers to manually filter reports, compare date ranges, and segment data, Google is using AI to highlight patterns and outliers automatically.
Search Console now groups issues more intelligently, with clearer prioritization, and more context around what matters. Analytics delivers automated insights, anomaly detection, and predictive metrics.
The most practical benefit is time savings. AI-powered filtering lets you type what you want to see instead of clicking through multiple dropdowns. You can ask for specific trends, segments, or anomalies and let the system do the slicing for you. That alone removes a lot of friction from daily SEO work.
Your SEO expertise still matters. AI just handles the mechanical steps that used to slow you down. Google’s goal is to help marketers spend less time finding the signal and more time deciding what to do with it. For teams managing complex sites, this automation is table stakes.
If you want to understand how AI fits into broader SEO workflows, check out our guide on AI SEO.
AI Features in Google Search Console
Google Search Console has gradually introduced AI-assisted functionality that focuses on diagnostics and data interpretation rather than automation.
As a start, Search Console’s performance reporting benefits from smarter analysis. The platform highlights notable changes in clicks, impressions, and rankings without requiring manual comparison. This helps teams catch traffic drops or unexpected gains earlier, before they become larger problems.
Conversational-style filtering saves even more time. Instead of manually applying multiple filters, marketers can describe what they want to see, and Search Console narrows the data automatically. This reduces the time spent digging through reports just to answer basic questions.
Here’s how it works in practice: Instead of clicking Performance > Filters > Query > Contains > ‘product name’ > Apply, you type ‘show me queries for product pages with declining CTR.’ The AI interprets your request, applies the right filters, and shows you the data. That’s the time savings—going from five clicks to one typed question.
Note: Conversational filtering is rolling out gradually and may not be available in all Search Console accounts yet.”
AI won’t fix your indexing issues or update your site. It finds problems faster so you can fix them yourself. The value comes from speed and clarity, not automation. For SEO teams, this shortens the path between detection and action without removing human oversight.
AI Features in Google Analytics 4
This is partly because GA4 handles more complex event-based data and cross-device behavior.
Analytics Advisor is the most visible AI feature. Currently in Beta and not available for everyone yet, It automatically flags unusual patterns, such as sudden traffic spikes, drops, or changes in engagement. These insights appear without manual configuration and are designed to draw attention to potential issues or opportunities.
To access Analytics Advisor, click the lightbulb icon in the top right corner of any GA4 property. The insights refresh daily and highlight metrics that deviate from your baseline. You might see ‘Pageviews from organic search increased 47% compared to last week’ with a link to explore the affected pages. That’s faster than manually comparing week-over-week reports.
Predictive metrics add another layer. Examples include purchase probability, churn probability, and revenue prediction for eligible properties. These metrics help teams forecast outcomes based on historical behavior rather than relying purely on past performance.
Predictive metrics require at least 1,000 positive and 1,000 negative examples of the target event over 28 days. If your site doesn’t meet that threshold, you won’t see predictions for purchase probability or churn. This makes the feature more useful for high-traffic e-commerce sites than small content publishers.
Another important use of AI in GA4 is automated anomaly detection. The platform monitors metrics continuously and alerts users when behavior deviates from expected patterns. This can surface tracking issues, campaign impacts, or site problems more quickly than manual review.
GA4’s AI points you toward what matters. You still handle the investigation. Teams still need to validate data quality, understand context, and decide how insights should influence strategy.
Other Google Tools Getting Smarter With AI
Beyond Search Console and GA4, other Google tools now have AI-supported features. Several other Google tools marketers use regularly now rely on machine learning to guide decisions and reduce manual work.
Google Analytics 4’s predictive metrics extend beyond reporting. They influence how audiences are built and activated, especially when connected to Google Ads. This allows marketers to target users based on likely future behavior rather than past actions alone.
Google Ads leans on machine learning to suggest budget shifts, adjust bids automatically, and test creative variations. You can accept or reject these suggestions, the control stays with you. These systems focus on optimization suggestions rather than forced changes, leaving final control with advertisers.
Here’s what matters: diagnostic AI explains what’s happening now. Predictive AI estimates what comes next. Diagnostic AI explains what is happening now and why. Predictive AI estimates what might happen next. Both influence how marketers act, but they serve different purposes. Understanding which type of insight a tool provides helps teams decide how much weight to give its recommendations.
This changes your daily workflow. Instead of checking reports manually and looking for problems, you respond to flagged issues. Instead of building audience segments from scratch, you refine AI-generated segments. The shift is from ‘find the problem’ to ‘validate the finding.’ That’s faster, but it requires trust in the system’s baseline accuracy.
Should You Trust AI to Support Your Reporting?
Google’s using AI to decide what you see first in your reports. That raises control questions. These tools influence what you see first, what gets flagged, and what feels urgent.
Trust the insights. Verify the recommendations. AI supports reporting by prioritizing information, not by defining truth. Understanding its role helps teams use it effectively without losing oversight.
Is AI Taking Too Much Control?
One concern is that AI-driven data points could push marketers into autopilot mode. When tools highlight issues automatically, it’s tempting to assume they reflect the full picture.
AI helps you see more. It surfaces technical problems and data anomalies that teams often miss because they’re buried in reports or obscured by volume. AI helps surface data anomalies that teams might miss due to scale or limited time. It reduces the chance that important issues stay hidden in reports.
Don’t follow every data point blindly. AI recommendations are based on models and thresholds that may not reflect business context. Treat insights as starting points, not final answers. Validation still matters.
Who Really Gets the Advantage?
People assume big brands with more data get better AI insights. Not true. Everyone has access to the same tools.
The advantage goes to teams that actually use the insights. A local contractor who spots a data anomaly flagged by Search Console and acts on it outranks a national franchise that ignores the same alert.
AI lowers the barrier to analysis, but it doesn’t guarantee better outcomes. Interpretation and execution still determine results.
FAQs
Does AI in GA4 replace manual analysis?
No. AI highlights anomalies and predictions, but analysts still need to validate findings and decide how to act.
Are predictive metrics in GA4 always accurate?
Predictive metrics are estimates based on historical data. They provide directional guidance, not certainty.
Conclusion
AI makes Google’s SEO tools more efficient. It doesn’t replace the need for strategy. You still need to validate insights, understand your business context, and decide how to act on recommendations. The teams winning with these tools treat AI as an assistant, not an autopilot.
They use automated insights to find problems faster, then apply their own expertise to fix them. That combination (AI-powered detection plus human strategy) is what drives results. Start by exploring the AI features already available in your Search Console and GA4 accounts. Check what Analytics Advisor has flagged. Look at how Search Console groups your indexing issues.
See if the insights align with what you’re already tracking manually. Then decide where automation saves you real time.
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The January 2026 SEO Update by Yoast is part of our monthly webinar series covering the latest developments in search and AI. In each session, we review the most important news from the past month and explore what it means for your search strategy. Hosted by Carolyn Shelby and Alex Moss, this month’s update looks at key industry shifts and practical takeaways for staying competitive. Below is a recap of the topics discussed and what they mean for your strategy.
Here’s the recap video on YouTube
Watch the full recap on YouTube to hear Carolyn and Alex dive deeper into these topics, answer audience questions, and provide additional examples of how these changes could affect your work.
SEO and AI news from January 2026
SEO is shifting from rankings to selection
Microsoft’s recent guide on AEO (Agentic Engine Optimization) and GEO (Generative Engine Optimization) highlights a major change: the goal isn’t just to rank, but to be chosen by AI and users. Tools like Gemini and ChatGPT don’t just match keywords; they evaluate brand authority, structured data, and real-world mentions. If your content isn’t clear, well-organized, or trustworthy, AI may overlook it, even if it performs well in traditional search. To stay competitive, focus on structured data, fast-loading pages, and strong brand signals.
Agentic commerce is on the rise
Google’s Universal Commerce Protocol (UCP) is an open-source framework designed to help AI handle purchases. This means AI won’t just recommend products, but could also buy them for users. For businesses, optimizing for AI “selection” is now as important as ranking. If you sell products, prioritize product schema, fast load times, and a strong brand presence to ensure AI picks you.
Google’s core updates continue to reshape publishing
The December 2025 core update hit news publishers hard, particularly those relying on prediction-based content (like “2026 Oscar predictions”). Google is favoring original, authoritative reporting over speculative or AI-generated content. If you’re in publishing, EEAT (Experience, Expertise, Authoritativeness, Trustworthiness) remains critical.
YouTube is a growing force in AI search
Gemini is now pulling YouTube videos into its responses, even for non-video queries. If you’re not repurposing content for YouTube, you’re missing an opportunity. Optimize video titles, descriptions, and transcripts so AI can find and cite your work.
New tools are changing how we work
Anthropic’s Claude CoWork can organize files and automate tasks, while open-source tools like Moltbot (formerly Clawdbot) let you run AI agents locally. These tools aren’t just novelties, but signs of how quickly AI is integrating into workflows. For SEO, staying adaptable and testing new tools will be key.
Yoast is helping AI work for everyone
Yoast is building on Microsoft’s NLWeb framework to help AI systems better understand web content. The goal is to ensure small publishers and businesses aren’t left behind as AI-driven discovery grows. If you’re using WordPress, Yoast SEO’s existing tools—like schema markup and readability checks—already support this effort. We’ve also added Gemini and Perplexity to our AI Brand Insights tool, so you can track how AI models perceive your brand.
What to focus on in 2026
Structure your content so AI can parse it easily (schema markup helps)
Build brand authority across channels—social media, PR, email, and YouTube all send signals AI notices
Understand agentic commerce if you sell products. Fast, well-structured pages will help AI “select” you
Avoid AI-generated slop. AI can help draft content, but human insight and expertise are irreplaceable
Sign up for the next SEO Update by Yoast
The next SEO Update by Yoast is on February 24, 2026, at 4 PM CET (10 AM EST). Sign up to join the live discussion or get the recording. Don’t miss it!
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The open web is the part of the internet built on open standards that anyone can use. This concept creates a democratic digital space where people can build on each other’s work without restrictions, just like how WordPress.org is built. For website owners, understanding and leveraging the open web is increasingly crucial. Especially with the rise of AI-powered systems and the general direction that online search is taking. So, let’s explore what the open web is and what it means for your website.
What is the open web?
The open web refers to the part of the internet built on open, shared standards that are available to everyone. It’s powered by technologies like HTTP, HTML, RSS, and Schema.org, which make it easy for websites and online systems to interact with each other. But it is more than just technical protocols. It also includes open‑source code, public APIs, and the free flow of data and content across sites, services, and devices. Creating a democratic digital space where people can build on each other’s work without heavy restrictions.
Because these standards are not owned or patented, the open web remains largely decentralized. This allows content to be accessed, understood, and reused across devices and platforms. This not only encourages innovation but also ensures that information is discoverable without being locked behind proprietary ecosystems.
The benefits of an open web
The open web is built on publicly available protocols that enable access, collaboration, and innovation at a global scale.
The most important benefits include:
Collaboration and innovation: Open protocols enable developers to build on each other’s work without proprietary restrictions.
Accessibility: Users and AI agents alike can access and interact with web content regardless of device, platform, or underlying technology.
Democratization: No single company controls access to information, giving publishers greater autonomy.
Inclusion: The open web creates a more level playing field, where everyone gets a chance to participate in the digital economy.
The open web vs the deep web
To give you a better idea of what the open web is, it helps to know about the “deep web” and closed or “walled garden” platforms. The deep web covers content not indexed by search engines, while closed systems or walled gardens restrict access and keep data siloed.
On the open web, anyone can access information freely. A good example of that is Wikipedia. Accessible to anyone looking for information on a topic and anyone who wants to contribute to its content. Closed-off platforms, like proprietary apps or social media ecosystems, create places where content is only available if you pay or use a specific service. Well-known examples of this are social media platforms such as Facebook and Instagram. Another example is a news website that requires a paid subscription to get access.
In essence, the open web keeps information discoverable, accessible, and interoperable – instead of locked inside a handful of platforms.
AI and the open web
The popularity of AI-powered search makes open web principles more important than ever. Decentralized and accessible information allows AI tools to interact with content directly and use it freely to generate an answer for a user.
“We believe the future of AI is grounded in the open web.”
Ramanathan Guha, CVP and Technical Fellow at Microsoft.
Microsoft’s open project NLWeb is a prime example. It provides a standardized layer that enables AI agents to discover, understand, and interact with websites efficiently, without needing separate integrations for every platform.
What this means for website owners
For website owners, including small business owners, embracing the open web means making your content freely available in ways that AI can interpret. By using structured data standards like Schema.org, your website becomes discoverable to AI tools. Increasing your reach and ensuring that your content remains part of the future of search.
Yoast and Microsoft: collaborating towards a more open web
Yoast is proud to collaborate with NLWeb, a Microsoft project that makes your content easier to understand for AI agents without extra effort from website owners. Allowing your content to remain discoverable, reach a wider audience with and show up in AI-powered search results.
The open web strives towards an accessible web where content is available for everyone. A web where it doesn’t matter how big your website or marketing budget is. Giving everyone the chance to be found and represented in AI-powered search. NLWeb helps turn this vision into reality by connecting today’s open web with tomorrow’s AI-driven search ecosystem
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