Tag: AI

Unlocking AI Visibility: Why Ranking Content Falls Short

I’ve been contemplating how even when content ranks well on search engines, it can still falter when it comes to AI retrieval. These AI systems assess pages very differently, based not just on their rank, but also on how information is extracted, embedded, and structured.

There’s an intriguing disconnect between traditional ranking and being successfully parsed by AI. A webpage can comply with excellent SEO guidelines and still miss the mark with AI-generated responses and citations.

In many situations, content quality isn’t the issue. It’s about whether the information can be reliably extracted after being segmented and embedded by AI systems.

This challenge is becoming increasingly common as search engines view pages as complete entities, but AI systems dive into the raw HTML to extract meaning from fragments rather than entire pages.

Crucial insights can get lost if they’re not appropriately structured or if they rely too heavily on visual rendering or inference.

This leads to a divergence between what’s visible in search and what’s accessible via AI, where content might exist in an index but lacks substantial meaning for AI retrieval.

The visibility gap is something I’ve been grappling with: Understanding the difference between ranking versus retrieval is key.

As search winds its processes around rankings, AI systems engage with fragments operated within a different representation of similar information. It’s here the visibility gap takes shape.

A page might rank high, but if its embedded content is incomplete or poorly organized, then the AI retrieval process becomes unreliable.

Treat retrieval as an entirely unique visibility factor. It doesn’t override SEO, but increasingly defines whether content can be effectively surfaced, summarized, or cited when AI filters come into play.
Dig deeper: What is GEO (generative engine optimization)?

Another structural issue arises when content never even becomes accessible to AI. Many AI crawlers only parse raw HTML without executing JavaScript or client-side rendering. This creates blind spots, especially for JavaScript-heavy sites where the core content may appear in Google’s index but remains invisible to AI.

Testing if your content appears in initial HTML is quite straightforward. Simply inspect the HTML response at fetch time rather than the version rendered in a browser.

Running requests with AI user agents like “GPTBot” reveals if your site returns blank HTML even if it appears fully populated to users, highlighting its absence in initial responses.

Tools like Screaming Frog can validate this at scale. Disabling JavaScript rendering can reveal what AI systems see—if your essential content only displays with JavaScript, it can be indexed by Google’s search but not by AI retrieval systems.

Keep in mind that even with content returned, excessive code and scripts can hinder extraction by AI systems. Cleaner HTML results in more reliable embeddings, enhancing AI visibility.

To tackle this, deliver fully rendered HTML when AI systems fetch your content. Pre-rendering can often fix these retrieval issues, ensuring content is present in initial responses.

Delivery can be managed effectively at the edge layer, providing AI crawlers with complete pages instantly. Human users receive a dynamic version while AI sees what it needs to extract meaning.

If pre-rendering isn’t viable, focus on ensuring primary content is accessible in a clean initial HTML response, even without script execution.

Columns laden with excessive markup can interfere with proper extraction, diminishing the content’s value.

The next structural failure to consider is when content is optimized for keywords rather than the entities AI seeks. Traditional SEO applies keyword relevance, but AI retrieves based on entity relationships.

Without clear definition, entity signals can weaken, causing pages to underperform in retrieval even if they rank well for queries.

AI evaluates sections independently once extracted, making the consistency of header tags essential to maintaining coherence.

Ensuring sections have a single, defined purpose allows for better embedding when isolated from larger context.

Finally, conflicting signals or metadata can dilute the semantics retrieved by AI, creating noise and ambiguity.

SEO doesn’t have to mean choosing between ranking and retrieval anymore. Both must be prioritized to succeed in today’s landscape.

Inspired by this post on Search Engine Land.

February 6, 2026
Why Your Local Search Rankings Hold but Calls Vanish
I’ve noticed something puzzling in my local business performance lately. Despite high rankings, the number of calls and website visits from Google Business Profiles seems to be dropping at an alarming rate.

This disconnect is becoming increasingly common in local search. Rankings are stable, but visibility and customer engagement are not keeping pace.

The alligator of local SEO, if you will, has made its presence known.

The visibility crisis behind stable rankings

I’ve observed that across various U.S. industries, the familiar local 3-packs are often getting replaced or supplemented by AI-run local packs. These new formats differ significantly from the traditional map results many of us are used to optimizing.

According to Sterling Sky’s analysis of Google Business Profiles, a startling pattern emerges. Clicks-to-call are taking a nosedive, particularly for law firms managed by Jepto.

When AI-powered packs take over, the landscape changes notably in four key areas:
- Shrinking real estate: AI packs frequently display only two businesses instead of the usual three.
- Missing call buttons: The summaries generated by AI often omit the instant click-to-call functionality, complicating the customer’s journey.
- Different businesses appear: Companies featured in AI packs do not necessarily align with those in the traditional 3-pack.
- Accelerated monetization of local search: The presence of paid ads increasingly results in the loss of direct call and website buttons in traditional 3-packs, thereby reducing opportunities for organic conversion.
There’s an additional challenge compounding this issue:
- Measurement blind spots: Most rank trackers have yet to account for AI local packs. A business may hold a top spot in a traditional 3-pack that users rarely encounter.
In 2026, AI local packs surfaced only 32% as many unique businesses as traditional map packs, according to Sterling Sky. Astonishingly, in 88% of the 322 markets examined, the total number of visible businesses plummeted.

Meanwhile, paid ads are steadily claiming the space that once belonged to organic results, marking a clear transition toward a pay-to-play environment in local search.

What Google Business Profile data shows

This trend is echoed in the U.S., where Google is proactively testing new local formats, as indicated by data from GMBapi.com. Increased impressions from traditional 3-packs are being nudged out by:
- AI-powered local packs.
- Paid placements inside traditional map packs: Sponsored listings now appear adjacent to or within the map pack, relegating organic results and removing essential call and website buttons. This interrupts organic customer interactions.
- Expanded Google Ads units: Even Local Services Ads are consuming space that once granted organic visibility.
Impression trends continue to vary due to seasonal factors, market disparities, and occasional API glitches. Nevertheless, a clearer picture emerges by focusing on GBP actions rather than mere impressions.

Mentions within AI-generated results still count as impressions, even if they no longer convert into calls, clicks, or visits.

External factors, such as known Google API issues in June, also contribute to these fluctuations. Additionally, the spike in Google Ads investment by significant advertisers towards year-end heavily affects Mobile Maps impressions.

Currently, there’s no method to differentiate these impressions by Google Ads, organic results, or AI Mode.

Despite these challenges, user behavior is undeniably shifting. Interaction rates are dwindling, with fewer direct actions taken from local listings.

Year-on-year data from the U.S. indicates that while impression losses remain moderate and somewhat seasonal, GBP actions are disproportionately affected.

In contrast, data from the Dutch market, where SERP experiments are limited, shows far more stable action trends.

The evidence is clear. AI-driven SERP alterations, increasing Google Ads, and the removal of call and website buttons from the Map Pack are eroding organic real estate. Despite appearances, businesses have fewer opportunities to convert visibility into actual user actions.

Local SEO is becoming an eligibility problem

Traditionally, local optimization focused on key ranking factors like proximity, relevance, prominence, reviews, citations, and engagement.

There’s now an additional layer to consider: eligibility.

Some businesses find themselves absent in AI-powered local results not due to a lack of authority, but because Google’s systems deem them inadequate for the specific query context. Research from Yext and experiences shared by experts like Claudia Tomina emphasize the importance of aligning three core signals:
- Business name
- Primary category
- Real-world services and positioning
Misalignment in these areas can prevent businesses from appearing in certain result types, regardless of how well their Google Business Profile is optimized.

How to future-proof local visibility

Navigating today’s zero-click reality involves moving beyond reliance solely on a well-optimized Google Business Profile. Here’s a new playbook for local SEO.

The eligibility gatekeeper

Inclusion in local packs is now influenced more by perceived relevance and classification than by links or review quantity.

Hyper-local entity authority

AI systems rely on platforms like Reddit, social media, forums, and local directories to evaluate if a business is legitimate and active. Inconsistencies across these platforms can erode visibility without any obvious signs.

Visual trust signals

High-quality and frequently updated photos, along with video, are critical. Google’s AI evaluates visual content to gauge services, intent, and categorization.

Embrace the pay-to-play reality

The hard truth is that Google Ads, particularly Local Services Ads, is now essential to retaining prominent call buttons that organic listings are steadily losing. Adopting a hybrid strategy that merges local SEO with paid search is no longer optional but necessary.

What this means for local search now

Local SEO has evolved beyond a simple directory exercise. Google Business Profiles remain central to local discoverability but now exist within a broader ecosystem informed by AI validation, constant SERP changes, and Google’s pursuit of local search monetization.

Visibility no longer depends solely on where your GBP ranks against local rivals. Search engines, including AI-infused SERP features and advanced models like ChatGPT and Gemini, are increasingly focused on understanding a business’s genuine purpose, not merely its listing position.

Success lies in being widely verified, consistently active, and contextually relevant within the AI-visible ecosystem.

Our findings reveal that there is little correlation between businesses ranking well in traditional Map Packs and those prioritized in Google’s AI-generated local answers. This discrepancy offers a real opportunity for businesses willing to adapt.

In essence, this entails blending local input with central management.

Authentic engagement across multiple channels, locally tailored content, and actual community signals are necessary alongside brand governance, data consistency, and operational scale. Businesses deeply ingrained in their community, discussed, recommended, and referenced, both online and offline, find themselves halfway there.

For agencies and brands with multiple locations, the challenge is balancing control with local nuances and ensuring trusted signals extend beyond Google, encompassing Apple Maps, Tripadvisor, Yelp, Reddit, and other pertinent review ecosystems. Producing locally relevant content and citations at scale without losing authenticity is the real test.

Even if rankings appear stable, true performance is occurring elsewhere.

The full data. Local SEO in 2026: Why Your Rankings are Steady but Your Calls are Vanishing

Inspired by this post on Search Engine Land.
February 5, 2026
How AI is Transforming Google Search Engagement
Google Search is currently experiencing what I see as an ‘expansionary moment,’ powered by the dynamics of AI technology. The search experience I rely on has transformed through longer queries, follow-up questions, and the increasing use of voice and images. This was highlighted during Alphabet’s recent earnings call, where executives shared these evolving trends.

In other words: Google’s search interface is becoming increasingly AI-driven, facilitating interactions within its system. This isn’t about replacing old queries—instead, we’re witnessing a new era of digital exploration.

Why we care. The integration of AI into Google Search is not just a trial. For me, it’s a structural transformation altering how we discover, interact with, and navigate the web.

By the numbers: Alphabet’s Q4 advertising revenue reached $82.284 billion, marking a 13.5% increase from $72.461 billion in 2024.
- Google Search & other: $63.073 billion (up 16.7%)
- YouTube: $11.383 billion (up 8.7%)
- Google Network: $7.828 billion (down 1.5%)
For the 2025 fiscal year, Alphabet’s advertising revenue climbed to $294.691 billion, a growth of 11.4% from the previous year.
- Google Search & other: $224.532 billion (up 13.4%)
- YouTube: $40.367 billion (up 11.7%)
- Google Network: $29.792 billion (down 1.9%)
AI Overviews and AI Mode are now core to Search. Sundar Pichai, Alphabet/Google’s CEO, emphasized how central AI has become to Google’s search products, with over 250 AI-related product launches in just the last quarter.

Google has recently upgraded its AI Overviews to the Gemini 3 model, a move that connects AI Overviews more seamlessly with conversational search experiences.
- “We have also made the search experience more cohesive, ensuring the transition from an AI overview to a conversation in AI mode is completely seamless,” Pichai noted.
AI is driving more Google Search usage. As Google puts it, AI-driven search is expanding the ways people use search rather than replacing traditional searches.
- “Search saw more usage in Q4 than ever before, as AI continues to drive an expansionary moment,” Pichai emphasized.
- “Once people start using these new experiences, they use them more,” he added.
Changing search behavior. AI Mode is making searches longer, more conversational, and multimodal. “Queries in AI mode are three times longer than traditional searches,” said Pichai.

Not only are queries longer, but sessions are also becoming more conversational, often leading to follow-up questions.
- “We are also seeing sessions become more conversational, with a significant portion of queries in AI Mode now leading to a follow-up question,” he said.
- “Nearly one in six AI mode queries are now non-text, using voice or images,” Pichai shared.
Google’s visual search capabilities continue expanding with “Circle to Search” available on over 580 million Android devices.
- “We haven’t seen any evidence of cannibalization,” Pichai said about the coexistence of Google Search and the Gemini app.
- “The combination of all of that, I think, creates an expansionary moment,” he concluded.
Inspired by this post on Search Engine Land.
February 5, 2026
Harnessing First-Party Data for AI-Enhanced Ad Success

I recently discovered how crucial first-party data has become in the evolving landscape of AI-powered advertising. It’s fascinating to see how it shapes the optimization and measurement of automated ad campaigns.

During a chat with Search Engine Land, I learned from Julie Warneke, CEO of Found Search Marketing, about the profound impact first-party data has on profitable advertising, regardless of potential changes to Google’s third-party cookie policies.

Embracing first-party data means tapping into customer information that I own, typically stored in a CRM, like lead details, purchase history, revenue, and customer value collected from various touchpoints.

This type of data is distinct from platform-owned or browser-based data, over which I have limited control.

Digital advertising has evolved over the years. The shift from focusing on impressions and clicks to outcomes emphasizes profitable conversions, according to Warneke. Advertisers who provide AI systems with quality customer data gain a significant edge.

Although rising cost-per-clicks (CPCs) are inevitable in paid media, first-party data enhances conversion quality, revenue, and return on ad spend, making higher costs justifiable with better results.

By leveraging first-party data tied to revenue and customer value, AI bidding systems can target users resembling high-value customers, even beyond usual demographic or geographic signals, leading to better conversions.

Among campaign types, Performance Max (PMax) thrives with first-party data activation. It performs best when I shift from manual optimizations to feeding it accurate data, allowing the system to learn, as Warneke highlighted.

Even small and mid-sized businesses can leverage first-party data, as seen in Warneke’s examples of success with small customer lists. The challenge lies in setting up proper infrastructure for tracking, consent management, and data flow.

Common mistakes include weak data capture, where brands rely on browser-side tracking that falters on platforms like iOS, and broken feedback loops from sporadic CRM data uploads. Continuous data streams are crucial.

Warneke advises taking a step back to audit how data is captured, stored, and relayed to platforms. Incremental improvements can pave the way for significant long-term gains, even starting with a small portion of a budget as a test.

Ultimately, AI optimization reflects the quality of signals received. By refining first-party data, I can influence outcomes favorably, avoiding inefficiency risks.

Inspired by this post on Search Engine Land.

February 5, 2026
AI Chat Ads: Why Claude Stands Firm and ChatGPT Changes

I recently learned that Anthropic has made a firm decision regarding the inclusion of ads in AI chatbots. They’ve announced that Claude will remain ad-free, even as other AI platforms start experimenting with sponsored messages and branded placements during chats.
Anthropic argues that placing ads in AI chats would undermine user trust, distort incentives, and conflict with how people use assistants like Claude—for work, problem-solving, and sensitive topics. In their latest blog post, they clearly lay out their stance.
Why this matters to us. Anthropic’s decision effectively removes Claude and its 30 million users from the potential AI advertising market. So, brands shouldn’t count on having sponsored links, conversations, or responses inside Claude. Meanwhile, ChatGPT opens up a new frontier for brands to potentially connect with an estimated 800 million weekly users.
Here’s the situation. According to Anthropic, AI conversations are quite unlike search results or social feeds where users might expect a combination of organic and paid content. They emphasize that many interactions with Claude involve personal inquiries, complex technical tasks, or high-stakes decisions, where inserting ads would seem intrusive and could subtly sway responses beyond user awareness.
Incentives matter. This is more than a product preference; it’s a strategic business model decision for Anthropic:
An ad-free assistant can concentrate fully on user benefits—even if that means a brief interaction or no follow-up. On the flip side, an ad-supported model might create pressure to identify monetizable moments or keep users engaged longer than necessary, potentially making users question whether suggestions are genuinely helpful or commercially driven.
Anthropic embraces commerce without ads. While Claude will assist users in researching, comparing, and purchasing products upon request, the commerce is user-initiated, not advertiser-driven. Likewise, third-party integrations with platforms like Figma or Asana will be user-directed and free from sponsor influence.
Super Bowl declaration. Anthropic took their message to a wider audience with a bold Super Bowl ad campaign. They critiqued intrusive AI advertising by placing mock product pitches into personal conversations. The ad concluded robustly: “Ads are coming to AI. But not to Claude.”
This campaign is likely a direct response to OpenAI’s announcement about introducing ads in ChatGPT.
Check out the ad:
Claude’s recent blog post explains further. Feel free to check it out here: Claude is a space to think

Inspired by this post on Search Engine Land.

February 4, 2026
Is Google Targeting Self-Promotional ‘Best of’ Listicles?

I might be witnessing a significant shift as Google seems to be tightening its grip on self-promotional ‘best of’ listicles. This trend was highlighted by Lily Ray, who leads SEO strategy and research at Amsive.
Recently, many SaaS brands experienced a sharp decline in visibility, ranging from 30% to 50%. These companies often featured content that ranked their own products as ‘Number 1’ in their fields, frequently updating with the latest year to capitalize on recency signals.
Understanding the Trend. Following the December 2025 core update, there was noticeable volatility in Google search results throughout January, as reported by Barry Schwartz. Although Google hasn’t confirmed any updates for this year, the timing matches the visibility drops experienced by major SaaS and B2B brands. Lily Ray observes:
• In several situations, organic visibility dropped by as much as 50% within weeks. The losses were primarily in subfolders containing blogs, guides, and tutorials.
• These sections often housed numerous self-promotional listicles for ‘best’ queries, with the publishers typically ranking themselves first. Most articles were minimally refreshed with the addition of ‘2026’ to their titles, without substantial updates.
• “It seems likely that these declines in Google organic rankings might also affect visibility across other search engines and AI platforms that utilize Google’s results, like Gemini and ChatGPT,” Ray explained.
Why This Matters. There has been a longstanding practice of using self-promotional listicles to sway search rankings and AI-generated responses. If Google is reconsidering this kind of content, any strategies focusing on ‘best’ queries might face substantial challenges.
The Controversy. Ranking oneself as ‘the best’ without independent verification or third-party endorsement is often seen as a dubious SEO move. While not outright banned, it conflicts with Google’s guidelines on reviews and trustworthiness.
• Google maintains that quality reviews should display firsthand experience, originality, and clear evaluation. Self-serving listicles frequently fall short, particularly when bias isn’t disclosed.
However. Self-promotional listicles may only be one of several factors affecting organic visibility. Affected sites often showed signs of fast content expansion, automation, aggressive year-based updates, and other risky tactics.
• Nevertheless, the prevalence of self-promoting ‘best’ content among the most impacted sites suggests that this signal might now be more influential, especially when used extensively.
What’s Next. The outcome for self-promotional listicles in terms of gaining recognition and organic visibility is still uncertain, as Google seldom implements changes uniformly or immediately.
• If this volatility is linked to updates in Google’s review system, the trend is evident: Content aimed mainly at influencing rankings, rather than offering credible evaluations, poses growing risks.
• The enduring lesson for brands seeking online visibility is clear: SEO shortcuts may yield effective results, but only until they don’t.
Further Insights. You can read more about this development in Lily Ray’s analysis titled Is Google Finally Cracking Down on Self-Promotional Listicles?

Inspired by this post on Search Engine Land.

February 4, 2026
Microsoft Launches Game-Changing AI Content Marketplace

I’m thrilled to share that Microsoft Advertising has just unveiled the Publisher Content Marketplace (PCM). This innovative system allows publishers like us to license premium content to AI products and earn revenue based on its usage.

How It Works. At its core, PCM creates a direct value exchange. As a publisher, I have the freedom to set my own licensing and usage terms. Meanwhile, AI developers can discover and license this content for grounding their algorithms in real-world scenarios. The marketplace also offers detailed usage reports, providing insights into how our content performs and where it contributes the most value.

Designed to Scale. The PCM is a scalable solution designed to eliminate the need for one-off licensing deals. Participation is entirely voluntary, and ownership and editorial independence remain with the publishers. It’s a platform inclusive of everyone from large global publishers to smaller niche outlets like ours.

Why We Care. As AI technology progresses from merely answering questions to making impactful decisions, the quality of content is becoming increasingly crucial. Whether it’s about influencing purchases, finance, or healthcare, AI systems need to tap into premium content, elevating the importance of credibility and trust in our brands.

Early Traction. Microsoft Advertising has partnered with notable U.S. publishers such as Business Insider, Condé Nast, and Hearst to co-design PCM. Initial pilot projects anchored Microsoft Copilot responses to licensed content, with companies like Yahoo as early adopters.

What’s Next. Looking ahead, Microsoft plans to extend the pilot program to more publishers and AI developers who share the belief that as the AI web evolves, the value and governance of high-quality content should be recognized and rewarded.

The Big Picture. In the evolving landscape of AI-driven web interactions, tools are now summarizing, reasoning, and making recommendations through conversation. The effectiveness of these tools hinges on access to trusted and authoritative sources, many of which are under paywalls or in secured archives.

The Tension. The traditional model where publishers provide content in exchange for traffic from platforms is changing. AI is increasingly delivering answers directly, which reduces clicks but still relies on high-quality content.

Bottom Line. For AI to make better decisions, it must have access to superior inputs. Microsoft’s PCM is a strategic move towards establishing a sustainable content economy that supports the next wave of AI innovation.

Microsoft’s Announcement. Learn more about this initiative in Microsoft’s blog post on Building Toward a Sustainable Content Economy for the Agentic Web.

Inspired by this post on Search Engine Land.

February 3, 2026
Exploring the Agentic Web: Are We Prepared?

The rapid emergence of the agentic web has left many of us pondering: Are we genuinely ready for this new frontier?

To get to the bottom of this, let’s start by addressing some foundational questions:

What’s the agentic web? How can it be utilized? What benefits and drawbacks does it present?

This piece is not aimed at pressuring AI skeptics to dismiss their valid queries regarding the agentic web.

Additionally, it doesn’t pass judgment on how you, as an individual or professional, engage with this digital landscape.

With diverse opinions swirling around the agentic web, I hope to offer some clarity, devoid of any marketing varnish.

Disclosure: While I am employed by Microsoft and have faith in their direction with the agentic web, my aim is to maintain a platform-neutral perspective.

Now let’s delve deeper into what exactly the agentic web entails.

The agentic web consists of advanced tools or agents, tailored to our preferences, that perform time-consuming tasks with our permission.

For instance, using one-click checkout allows me to transmit my payment details to a merchant seamlessly.

Neither the merchant nor I need to manage the minutiae; instead, I just consent to the transaction.

Curious about how AI defines the agentic web, I posed this question to four AI models, discovering intriguing variations in their answers.

Copilot describes it as a layer of the internet where AI agents turn human intentions into outcomes, keeping user choice intact. Gemini, Perplexity, and Claude offer similar but varyingly nuanced definitions.

Understanding these varying views helps underscore the fact that AI models are trained on differing data, resulting in diverse responses.

The clear sentiment divide arises from whether we view the agentic web as a user-friendly layer or an overwhelming digital entity.

Gemini mentions APIs, crucial for communicating within this space, emphasizing how saved preferences will increasingly play a role.

To truly grasp the agentic web, we must explore two protocols, ACP and UCP, that underpin its operations.

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

The Agentic Commerce Protocol (ACP) focuses on actions initiated by express user intent, streamlining transactions via standardized AI interactions.

Meanwhile, the Universal Commerce Protocol (UCP) encompasses the whole shopping experience, facilitating interactions across platforms and payment systems.

ACP and UCP are not competitors; rather, they enhance different stages of the user journey, supporting precise or exploratory shopping needs.

So, what is the agentic web? Truthfully, it’s still evolving alongside user behavior, placing us in a position to shape its destiny.

Next, let’s explore how we can harness the agentic web, along with its potential benefits and pitfalls.

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

Based on the unified theme of autonomous actions in its definitions, here are practical applications of the agentic web.

Consider delving into Elmer Boutin’s technical exploration of how schema will affect agentic web compatibility. Benjamin Wenner offers insights into PPC management in a full agentic ecosystem.

Let’s now focus on some consumer-centric applications, presented through tasks you’re already familiar with.

Five consumer applications of the agentic web are currently operational or in development.

1. Intent-driven Commerce

For example, stating “Find me the best running shoes under $150” allows an agent to handle the entire commerce process, optimizing around user intent.

By processing user intent, these agents provide a streamlined shopping experience, balancing guidance and user control.

Consumers enjoy reduced decision fatigue, while brands benefit from enhanced engagement and more direct consumer interactions.

2. Brand-owned AI Assistants

Brands can deploy their own AI agents, maintaining control over their customer interactions and providing consistently reliable support.

By utilizing first-party data, these assistants deliver faster responses while upholding the brand’s voice and accountability.

They enable global participation without sacrificing brand identity to larger platforms.

3. Autonomous Task Completion

Users can assign outcomes like “Prepare a weekly performance summary,” with agents autonomously executing the necessary tasks.

As agents mature, they evolve from task managers to independent analysts, optimizing results based on initial input.

This shift enables professionals to focus on strategic insights rather than logistics.

4. Agent-to-Agent Coordination

Agents can negotiate and coordinate with each other, streamlining processes like procurement or media buying.

This could revolutionize consumer and professional interactions, making them quicker and more efficient.

5. Continuous Optimization

Agents continuously learn from every action they take, improving their performance over time.

As they learn, interactions become increasingly personalized, and systems naturally enhance their effectiveness.

Understanding the pros and cons of the agentic web helps us make informed decisions about its integration into our lives.

Pros of Embracing the Agentic Web

We’ve already been conditioned to appreciate convenience. The agentic web proposes a future that embraces this concept fully.

Simplified interactions, characterized by interpreted user intent, represent the epitome of this streamlined process.

Cons of Embracing the Agentic Web

Brands may face challenges in adapting their content to align with AI systems while preserving human accessibility.

There’s the hazard of designing primarily for machines at the risk of losing the human touch.

Pros of Resisting the Agentic Web

Some users value human-centric experiences, suspicious of automated systems—leaning away can help build trust with this audience.

Cons of Resisting the Agentic Web

A complete detachment could impact visibility and limit access to emerging opportunities as the digital landscape evolves.

Many existing systems are built to integrate, preserving fundamental infrastructure rather than replacing it completely.

The agentic web is still evolving based on how we engage with it. Our understanding of it will guide us in determining its role in our lives.

Inspired by this post on Search Engine Land.

February 3, 2026
Unlocking Digital PR: 7 Secrets to Boost Your SEO Success
I’ve noticed a shift as informational searches decline. Digital PR is now fueling authority, relevance, and high-intent outcomes in SEO. Here’s what I’ve discovered about its impact.

Digital PR is becoming more crucial than ever—not just a trend or a rebranded link-building tactic. The dynamics of search and discovery are evolving, and here’s why that’s important to me.

Brand mentions, earned media, and the broader PR ecosystem are redefining how search engines and large language models perceive brands. This shift influences how I approach visibility, authority, and revenue in SEO.

Informational search traffic is dwindling. Fewer people are clicking on expansive blog posts for top-of-funnel keywords.

Now, the commercial value is centered around high-intent queries, like product and category pages. Digital PR perfectly aligns with these changes.

Here are seven practical, experience-driven secrets showcasing how effective digital PR is transforming SEO in my view.

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

While digital PR is often seen as a link-building or branding strategy—or recently, a way to influence AI and generative search outputs—its direct impact on revenue is sometimes overlooked.

Being featured in the right media puts your brand before buyers, not during passive awareness but in a critical moment of consideration.

Platforms like Google excel at understanding user intent. Digital PR leverages this by positioning you where your audience is already searching.

Executed well, two outcomes arise:
- Your brand gains recognition even if your site already ranks well. Familiarity grows as your name associates with credible insights.
- Exposure boosts brand searches and direct clicks. Some readers click through immediately, while others search your brand later, entering your funnel with trust that generic searches lack.
This effect, rooted in recency and familiarity, is hard to single out in analytics but significantly impacts commerce.

I’ve observed this mostly within direct-to-consumer, finance, and health markets, where purchasing is highly active and intent-driven.

In the right environment, digital PR is not just aiding sales—it’s part of the sales engine.

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

Repeated exposure in relevant media helps build familiarity, turning it into trust and then preference. This is the mere exposure effect—vital for brand growth.

This often happens through syndicated coverage, where a powerful story in regional or sector-specific media results in widespread mentions.

Historically, some SEOs undervalue such coverage due to non-unique links, missing the point.

Repetition builds a network of associations, positioning your brand alongside relevant subjects, influencing public and machine perception alike.

An ongoing digital PR strategy, rather than sporadic major hits, accelerates familiarity growth in both human and algorithmic terms.

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

Large digital PR campaigns are tempting, generating excitement and industry approval. However, they also consolidate risk.

Giant campaigns might succeed tremendously or flounder silently, often failing to yield the desired links or mentions.

The reason is simple: what marketers enjoy may not meet journalists’ needs.

Journalists need prompt, engaging stories. If a campaign doesn’t translate well, it will flounder. Overinvestment in one idea leaves no backup.

A diversified digital PR approach spreads resources across smaller efforts and reactive opportunities, ensuring consistent coverage.

In the realm of digital PR, consistent reliability often trumps one-off brilliance.

How to Build Search Visibility Before Demand Exists

Secret 4: The journalist’s the customer

It’s easy to forget the gatekeeper in digital PR: the journalist.

From a brand’s angle, goals include links and authority. For journalists, it’s about stories that capture reader interest.

They determine if your pitch succeeds, so they are, quite literally, the customer.

Effective digital PR should first simplify journalists’ tasks by offering clear angles, credible data, timely insights, and quick responses.

Helping journalists enriches your exposure, affecting search engines and AI systems favorably.

Treat journalists as partners instead of mere distribution channels.

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

Not every link is created equal in SEO. Links to product, category, and core service pages are usually more valuable than those to blogs. Yet, securing these links via traditional outreach can be challenging.

Digital PR shines here. It places links naturally within product or service contexts, steering authority to revenue-driving pages.

As informational content’s traffic-generating role diminishes, ranking high-intent pages grows in economic significance.

Sometimes, a few high-quality, relevant links are more valuable than numerous superficial ones directed at top-of-funnel content.

Digital PR planning should prioritize these target pages from the start.

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 emphasized context, underscoring the importance of surrounding text and brand descriptions for defining brand identity.

This has escalated with large language models, which interpret data chunks, drawing meaning from context rather than isolating links.

Repeatedly mentioning your brand with specific topics enhances your standing in those arenas, a process known as entity lifting.

This approach extends beyond individual pages, enhancing term and category rankings by bolstering overall authority.

AI systems also increasingly reference consistently relevant brands.

Digital PR offers a scalable method to spread this contextual insight.

Secret 7: Authority comes from relevant sources and relevant sections

Ex-Google engineer Jun Wu explains in “The Beauty of Mathematics in Computer Science” that authority stems from recognition within specific knowledge clusters.

In simpler terms, where you’re mentioned and the context matters as much as the site’s size.

A relevant mention within a prestigious publication’s specific section often surpasses a home-page mention in terms of value.

Effective digital PR should focus on publications and sections aligned closely with your industry and target topics.

That’s how authority is constructed for both search engines and AI systems.

The New SEO Imperative: Building Your Brand

Where Digital PR fits in SEO

Digital PR is emerging not just as a support tool for SEO but central to how brands are discovered, understood, and trusted.

As informational traffic wanes and high-intent competition surges, successful brands will blend relevance, repetition, and authority across earned media.

When done right, digital PR offers all three.

Inspired by this post on Search Engine Land.
February 3, 2026
Discover Bing’s New Multi-Turn Search Feature Now Live Worldwide

I’m excited to share that Microsoft has introduced a game-changing update to Bing with the global rollout of multi-turn search. As I scroll through Bing’s search results, I now see a Copilot search box conveniently positioned at the bottom, waiting to assist with follow-up queries.

What is multi-turn search? In essence, this feature enables me to continue my search seamlessly. Imagine typing a follow-up question in the Copilot search box right at the bottom of the results page without any need to scroll back up. It feels so intuitive and user-friendly!

Here’s a vivid screenshot that perfectly captures this experience:

And here’s a video that brings it to life, showcasing the seamless functionality:

Here’s what Microsoft had to say. Jordi Ribas, the CVP and Head of Search at Microsoft, took to X to share this exciting update, revealing that “After shipping in the US last year, multi-turn search in Bing is now available worldwide.”

Ribas went on to explain that “Bing users don’t need to scroll up to do the next query, and the next turn will keep context when appropriate,” indicating a significant enhancement in user experience.

He further noted, “We’ve seen gains in engagement and sessions per user in our online metrics, highlighting the positive user value of this approach.”

Why it’s important for us. With many search engines, including giants like Google, trying to push for more AI integration, Bing’s new feature is a step in that direction. Google’s AI Overviews, although not entirely without controversy, are pushing users deeper into AI interfaces. Meanwhile, Bing’s Copilot box, after rigorous testing over several months, is now fully available, underscoring Microsoft’s commitment to user-centered innovation.

Inspired by this post on Search Engine Land.

February 3, 2026