Tag: SEO

AI Search Impact: Revealing Attribution and Buying Decisions

AI search has a subtle impact on trust, sales velocity, and potential client shortlists, which often isn’t reflected in analytics data. These insights came to light through a series of revealing experiments I’ve been involved in.

It was a chance encounter with a new prospect who mentioned, “I actually found you via Grok.” That was a pivotal moment for me. Not only had we not attempted to rank on Grok, but we also weren’t monitoring it. Yet, here it was, influencing potential buyers’ search and evaluation processes.

This realization permeated conversations with other clients; fascination with AI search was rampant, but there was skepticism regarding data credibility. Many wanted visibility on platforms like ChatGPT but hesitated due to unclear attribution.

So, I embarked on structured testing using resources I could control entirely—our agency website, personal experiments, e-commerce ventures, and select domains for testing purposes. The goal wasn’t to attain AI rankings but to decode which elements remain crucial once AI integrates into buying decisions.

These inquiries involved figuring out if AI search altered purchasing preferences or merely the ranking of brands. Additionally, I wanted to understand if revenue metrics could be influenced by AI visibility without hitting the analytics tracking radar and how AI-driven recommendations might affect performance across other channels.

I realized early conversations around AI search revolved around visibility metrics—think brand citations, visibility screenshots from AI tracking platforms, and more. I believed that the primary role of search remains to aid decision-making. My experiments aimed to determine if AI search retained this capability and transformed business outcomes.

Focusing on measurement was crucial. Instead of just relying on API data—which often diverges from user interactions—I observed live interfaces of ChatGPT, Perplexity, Gemini, and Google AI Overviews. Prompt tracking aided in identifying patterns but was not a definitive gauge of success.

During my first experiment, the creation of self-promotional ‘best of’ lists on my own website revealed fascinating insights. Agencies frequently leveraged a tactic where they placed themselves atop ‘best X’ lists, allowing AI systems to inadvertently amplify their prominence.

Inspired by Glen Allsopp’s extensive research, which highlighted how ‘best’ lists were frequently cited by ChatGPT, I tested the findings on my brand webpage. I was intrigued by the rapid visibility of my site, LawrenceHitches.com, across AI platforms for queries like “best SEO agency Sydney.”

However, ranking visibility alone lacked significance. Similarly, when I fabricated a landscaping site to further test self-promotional tactics, it also appeared swiftly in AI responses, reaffirming visibility alone’s limited value.

Through these experiments, it became evident that while AI simplifies appearing on search radars, building and sustaining trust remains pivotal—a sentiment ringing true from the likes of Wil Reynolds. Self-lauding across one’s platform may catalyze skepticism rather than assurance.

I’ve also seen how prompt tracking tools became popular, with demand from clients ever-increasing. Yet, reliability remained a challenge. Surfer SEO research suggested brands often appeared differently in API outputs versus real user sessions. With overlap sometimes as low as 24%, discrepancies remind us that prompt appearances could be misleading.

This is where the narrative eases away from where brands show up and involves questioning efficacy: How did AI influence sales velocity? Did consultations eliminate the need for education? Was buying speedily initiated?

I discovered that signals—where leads factored AI tools into decision-making without prompting—started appearing, shaking traditional attribution’s foundation. A telling instance was Kadi, an e-commerce brand we support, encountering a buyer who, influenced by AI, engaged in a thorough purchasing journey yet showed attribution through Instagram.

For Kadi, digital PR efforts garnered visibility spurt, but gaps in fundamentals meant traditional SEO foundation work was essential to move past quick traction and truly compete. AI played a silent role in buyer decisions, even when attribution data failed to capture its essence.

My journey with StudioHawk provided another layer of understanding. Post a rebranding and digital migration, SEO emerged as a potent channel, complemented by AI leads that became more recurrent.

Sales processes further illustrated the transformation, where AI-affected leads saw reduced education requirements and minimized objections, closing deals notably faster than traditional SEO leads. The blend of ChatGPT, Perplexity, and Grok-influenced conversions stood testament to AI’s influence, even as traditional paths remained evasive in attribution reporting.

Throughout these endeavors, I’ve realized that while AI doesn’t redefine discovery, it compresses consideration significantly. The buyer’s journey is evolving beyond static funnels. AI provides succinct answer summaries, reshaping the ‘messy middle’ where amenities like risk reduction, vendor shortlisting, and trust assurance occur.

It’s evident AI aids decision-making once foundational trust is laid. Traditional SEO confirms search engines recognize your entity, but its real value is now within supporting thoughtful content that pre-sells your services.

So, as I reflect, brands need to realign focuses. Record where AI’s footprints actually land beyond mere appearances. Prioritize intelligibility over creativeness in content. Opt for consistency in entity-driven narratives and prioritize content resonating with comparison and risk evaluations.

Inspired by this post on Search Engine Land.

February 11, 2026
Unlock AI Insights: New Bing Webmaster Tools Feature

Today, I stumbled upon some exciting news from Microsoft. They have officially launched the AI Performance feature in Bing Webmaster Tools, albeit in beta. Now, I have a tool that lets me see where and how often my content is cited in AI-generated answers across platforms like Microsoft Copilot and Bing’s AI summaries.

What I find particularly useful is how AI Performance details exactly which URLs from my website are cited, the queries that trigger those citations, and how this activity evolves over time. It feels like a game-changer for understanding my content’s footprint in the AI domain.

Initially, Search Engine Land reported on January 27 that Microsoft was testing the AI Performance report. Today, I can tell you firsthand that this new dashboard in Bing Webmaster Tools is a treasure trove for tracking citation visibility across AI interfaces.

What’s new? I now have access to a specific dashboard dedicated to AI Performance. Unlike typical SEO tools that measure clicks or rankings, this one reveals if my content is grounding AI-generated answers. Microsoft describes it as an early step toward Generative Engine Optimization (GEO), helping me comprehend how my work appears in AI-oriented discovery.

What it looks like? Thanks to Microsoft, I’ve seen an image of the AI Performance feature in action. It’s sleek and provides clear insights into how my content is performing across AI experiences.

Insights from the dashboard? The AI Performance dashboard offers several new metrics, which include:

Total citations: This tells me how many times my site is used as a source for AI-generated answers over a set period.

Average cited pages: This metric gives me the average number of unique URLs from my site that AI systems reference daily.

Grounding queries: These are sample query phrases that AI systems utilize to retrieve and cite my content.

Page-level citation activity: Showing citation counts by URL, it highlights which pages of mine are popular in AI responses.

Visibility trends over time: I can see a timeline view that shows how citation activity changes throughout different AI platforms.

Though these metrics are informative, they only reflect citation frequency. They don’t give insights into my content’s ranking, prominence, or its specific contribution to AI answers. That’s something I’d have to explore further.

Why I care? Knowing where and how my content is cited is fantastic, yet Bing Webmaster Tools doesn’t yet show how these citations convert into clicks, traffic, or concrete business results. Without click data, it’s still an open question whether AI visibility provides actual value.

How can I use this? Microsoft suggests I utilize this data to:

– Verify which pages of mine already appear in AI answers.

– Spot topics that frequently show up across AI-generated responses.

– Enhance clarity, structure, and completeness on less frequently cited pages.

The advice echoes familiar best practices: maintaining clear headings, evidence-backed claims, up-to-date information, and consistent entity representation.

What comes next? Microsoft has promised improvements in inclusion, attribution, and visibility across both search results and AI experiences, and to keep evolving these capabilities moving forward.

Microsoft’s announcement. For more details, you can check out their announcement here: Introducing AI Performance in Bing Webmaster Tools Public Preview

Inspired by this post on Search Engine Land.

February 10, 2026
Unveiling Agentic AI: Guiding E-commerce Execs with Clarity
Agentic AI is now a hot topic among executives. I’m here to break down precisely what’s happening, what remains unchanged, and how e-commerce brands should adapt.

As an SEO leader working with e-commerce brands, I’m often in the position of clarifying the realities behind buzzwords like ‘agentic AI’. Executives frequently inquire about its implications for growth, risk, and competition.

Executives crave facts over hype. They seek concise explanations, grounded insights, and actionable advice.

My role as an SEO leader becomes essential here, not in predicting the future, but in enlightening leadership about the changes, the constants, and how to proceed pragmatically. Here’s my roadmap.

Start with Defining ‘Agentic’

First, I focus on demystifying the term. Agentic systems don’t replace customers; they work on their behalf. While the intent and preferences originate from individuals, the execution is taken over by the software.

The working dynamics shift, where tasks like discovery, comparison, and even execution are now managed by software, processing data faster than any human.

In discussions with executive teams, I emphasize simple illustrations:
- “We’re not losing customers; instead, we’re incorporating a new decision-maker, which is the software acting as a customer proxy.”
Understanding this calms the conversation and steers focus away from fear towards preparation.

Manage Expectations to Avoid Hype

Another key role I play is in tempering expectations. Agentic AI won’t sweep over all at once. Its effects will be gradual and varied across different categories.

Some industries, with standardized products and organized data, will adapt faster. Others will face more challenges due to complexities and regulatory hurdles.

I often see leadership teams falling into two detrimental traps:
1. Panic: Hastily altering strategies and budgets without clarity.
2. Dismissal: Ignoring changes until it impacts performance, leading to rushed responses.
I offer a steady perspective, noting that agentic AI merely accelerates existing trends. It’s not about chasing new features but reinforcing strong fundamentals.

Dig deeper: Are we ready for the agentic web?

Shift Focus from Rankings to Eligibility

I encourage conversations to evolve beyond search rankings. When agents lead the journey, the critical question becomes, “Are we eligible to be chosen?”

Eligibility hinges on clear, consistent, and trustworthy data. Agents must grasp your offerings, target audience, pricing, availability, and risk factors associated with choosing your brand.

Raising thoughts about data consistency, pricing reliability, and whether policies add or reduce uncertainty positions SEO as a practical bridge between strategy and execution.

SEO Beyond Marketing

There’s a misconception that SEO is confined to marketing. Agentic behavior challenges this notion.

Selection by an agent involves variables beyond marketing, like data accuracy, technical integrity, inventory management, and payment reliability.

My explanations revolve around broadening SEO’s scope—it’s about ensuring the business is machines-readable, trustworthy, and consistent.

SEO becomes vital in helping leaders identify system or data gaps that could hinder the brand’s selection, highlighting its connection to both risk management and operational resilience.

Dig deeper: How to integrate SEO into your broader marketing strategy

Discovery’s Evolution

In most e-commerce brands, agentic systems affect the top of the funnel first. Discovery shifts towards more personalized, conversational interactions.

Instead of brief search phrases, users convey needs, constraints, and preferences, which the agent then transforms into actions.

This decreases the significance of owning category head terms. If an agent has comprehensive user data, it acts like a knowledgeable repeat customer.

This presents a new reporting challenge. Not all SEO work will appear as direct demand creation, yet it still impacts outcomes. Leaders need to anticipate this shift.

Rethink Consideration

The consideration phase evolves too. Traditionally, it involves hosting reviews, comparisons, and reassurances.

With agentic intervention, consideration morphs into a filtering process, retaining only the options that align with user preferences.

This necessitates a quality over quantity strategy in content, emphasizing structural trust signals and consistent, verifiable information.

Brands might be selected without user awareness. While this could boost conversions, it also poses a risk to brand recognition if not addressed elsewhere.

Dig deeper: Align your SEO strategy with buyer intent stages

Establish Honest Measurement Expectations

Measurement often concerns executives, and agentic AI complicates this. With more processes happening inside AI, fewer interactions leave traceable or clear data.

I address this early by stressing that while this isn’t a failure of optimization, it merely highlights the analytics limits in a complex digital landscape.

The focus should shift to directional indicators and blended performance over precise attribution, acknowledging the new decision-making landscape.

Advocate Proactive, Low-risk Responses

The crux of leadership dialogue is next steps. Fortunately, most appropriate responses to agentic AI carry low risk.

Enhancing product information, eliminating inconsistencies, strengthening reliability signals, and addressing technical vulnerabilities benefit the business now and pave the way for the future.

Building brand trust outside search also plays a critical role. Trusted brands are more likely to be selected by agents performing comparisons.

This strategy reassures leaders that success doesn’t require radical change but calls for focused improvement.

Agentic AI: Focus Shifts, Fundamentals Persist

For us SEO leaders, agentic AI modifies our focus. Instead of solely optimizing for visibility, we aim to protect eligibility, reduce ambiguities, and illustrate influence.

This demands confidence and clear articulation, challenging hype with grounded perspectives. Agentic AI renders SEO more strategic and no less crucial.

Agentic AI isn’t an imminent threat or foolproof advantage. It’s a transformation in decision-making approaches.

For e-commerce brands, the winners are those who stay composed, communicate effectively, and transition their SEO approach from driving clicks to securing selections.

This transition forms the backbone of the current SEO leadership discussions.

Dig deeper: SEO Predictions for 2026: Insights from Leaders

Inspired by this post on Search Engine Land.
February 10, 2026
Unveiling ChatGPT’s Brand Bias: An Insightful Analysis

I recently embarked on a fascinating exploration of ChatGPT’s brand recommendation patterns, and let me tell you, the findings offer a lot to chew on!

We all know that AI responses are a roll of the dice – ask the same question ten times, and you’re bound to get ten different answers. But I couldn’t help but wonder, just how varied are these responses?

Rand Fishkin’s intriguing research dives into this very question. His findings have significant repercussions for how we approach AI visibility tracking for brands.

Fishkin experimented with prompts ranging from recommendations for chef’s knives to cancer care hospitals, as well as Volvo dealerships in Los Angeles.

His results showed that AI systems like ChatGPT almost never recommend the same set of brands in the same order twice.

Moreover, when asking about something specific like running shoes, certain brands tend to appear more frequently than others.

Building on this research, I zeroed in on B2B scenarios, adding some of my own twists: does the complexity of the prompt or the competitiveness of the category make a difference to AI’s consistency?

To investigate, I crafted twelve varied prompts, half of which addressed highly competitive B2B software categories, like accounting, and the rest focused on niche categories, such as user entity behavior analytics (UEBA) software.

Further, I examined simple prompts against nuanced ones that included specific personas and use cases.

Each prompt was fed into ChatGPT 100 times using different IP addresses to mimic 1,200 unique users.

Now onto the juicy part: the findings.

Submitting a single prompt to ChatGPT 100 times revealed that, on average, 44 different brands got mentioned. However, some response sets listed as many as 95 brands, heavily dependent on the category.

Notably, competitive categories yield twice as many brand mentions per 100 responses compared to niche ones.

Simple vs. nuanced prompts? ChatGPT typically mentions fewer brands in response to nuanced requests, but this isn’t a hard and fast rule.

When diving deeper into ChatGPT’s brand consistency, I found that in a set of 100 B2B software recommendations, only about five brands (11% of the total) were mentioned 80% or more of the time.

Dominant brands in a category like accounting software were names we all recognize: QuickBooks, Xero, Wave, and the like.

If you’re not among the big guns, working within a niche offers a strategic advantage given the increased chance to be consistently recognized by AI.

For marketers, this study underscores the necessity of standing out and perhaps carving a niche if dominance in a broad category seems out of reach.

Moreover, most AI visibility tools might not give you the full picture if they’re conducting only a single spot-check. For more reliable data, multiple runs per prompt are essential.

So, if you’re tracking pivotal prompts, run each a handful of times to get a better sense of your brand’s visibility.

I’m excited to share that future reports will explore ChatGPT’s understanding of brands and whether consistent recommendations reflect deeper brand awareness.

This article was originally published on Visible and republished with permission.

Inspired by this post on Search Engine Land.

February 10, 2026
Measure PR Success: SEO, PPC, and GEO Strategies Unveiled

As I reflect on the challenges of PR measurement, it becomes clear that many hurdles exist. Limited budgets and siloed teams often make it tough to connect our media efforts with tangible results.

That’s why I’m convinced that collaboration with SEO, PPC, and digital marketing teams is key. Together, we can achieve what feels impossible on our own:

Specifically, by linking media outreach with customer actions, integrating SEO and GEO into our measurement, and choosing the right tools, we can truly measure impact.

This piece offers a practical roadmap for achieving this without needing an enterprise budget or specialized analytics team.

Our digital age of communication isn’t linear. Audiences often engage with content across various channels before taking action, if they do at all. Understanding this loop is essential for measurement.

I’m reminded of how SEO and PPC professionals focus on actions like searches, clicks, and conversions. We in PR should adopt this action-oriented mindset to enhance our measurement strategies.

First, we need to prove the link between media outreach and customer actions. This often requires cross-departmental collaboration to access valuable data currently scattered across different systems.

By incorporating PR touchpoints into analytics tools like Google Analytics 4, I can see our earned media’s influence on downstream behavior, turning PR from a cost center into a demand-creation channel.

Second, while SEO is widely accepted, understanding its measurement in PR is less clear. Traditional metrics like coverage volume or sentiment don’t fully capture SEO’s impact.

GEO presents a new frontier, focusing on whether our content is a source for AI-generated answers. Tools like Profound and Semrush’s AI Visibility Toolkit offer insights into this new layer of measurement.

Lastly, it’s crucial that we select tools based on strategic goals, not just what’s trendy. This involves working backward from the desired audience actions to choose the right measurement tools.

In collaboration, PR, SEO, and PPC teams can integrate their strategies, avoid duplication, and create comprehensive insights that inform and improve future campaigns.

Ultimately, this collaborative approach gives us the edge, allowing us to adapt swiftly to evolving measurement tactics and strengthen our collective impact.

Inspired by this post on Search Engine Land.

February 6, 2026
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
Google & Bing Advise Against Separate LLM Markdown Pages
I’ve been following the lively debate around creating separate markdown pages for LLMs, and it appears that both Google and Bing are advising against this approach.

Recently, I noticed that representatives from Google Search and Bing Search have specifically recommended not to create separate markdown (.md) pages designed exclusively for LLMs. This practice involves presenting different content to the LLMs compared to what users see, which can be considered a form of cloaking—a direct violation of Google’s policies.

The question arose when Lily Ray inquired on Bluesky about the prevalence of creating markdown or JSON pages targeted at bots.
- “Not sure if you can answer, but starting to hear a lot about creating separate markdown / JSON pages for LLMs and serving those URLs to bots.”
Google’s stance, as explained by John Mueller, is clear. He replied to Lily’s query saying that LLMs have always interacted with standard web pages and don’t require separate markdown pages.
- “I’m not aware of anything in that regard. In my POV, LLMs have trained on—read & parsed—normal web pages since the beginning, it seems a given that they have no problems dealing with HTML. Why would they want to see a page that no user sees? And, if they check for equivalence, why not use HTML?”
John Mueller even criticized the whole idea, stating:
- “Converting pages to markdown is such a stupid idea. Did you know LLMs can read images? WHY NOT TURN YOUR WHOLE SITE INTO AN IMAGE?” Of course, converting your entire site to a markdown format is an extreme measure.
I’ve collected many of John Mueller’s remarks on this topic, which you can find here.

Bing’s perspective is shared by Fabrice Canel from Microsoft Bing, who suggested that creating duplicate, non-user content isn’t effective.
- “Lily: really want to double crawl load? We’ll crawl anyway to check similarity. Non-user versions (crawlable AJAX and like) are often neglected, broken. Humans eyes help fixing people and bot-viewed content. We like Schema in pages. AI makes us great at understanding web pages. Less is more in SEO!”
Why this matters to us: Many of us are tempted by shortcuts to improve search engine performance. Yet, these shortcuts often backfire or yield short-lived benefits. As Lily Ray remarked on LinkedIn, managing duplicate and differing content for bots violates established search engine policies.

Lily Ray’s thoughts on this are clear:
- “I’ve had concerns the entire time about managing duplicate content and serving different content to crawlers than to humans, which I understand might be useful for AI search but directly violates search engines’ longstanding policies about this (basically cloaking).”
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
Google’s February 2026 Discover Update Enhances Local Content
Exciting news! I’ve just learned about Google’s February 2026 Discover core update, a major advancement in how content is showcased in Google Discover. According to Google’s announcement, this is a broad update aimed at improving the overall Discover experience.

This update initially targets English-speaking users in the US. However, Google plans to roll it out globally across various languages over the next few months. The complete process should take about two weeks, as was stated by Google here.

What can we expect? This Discover update is set to enhance the experience in several important ways:
- Delivering more locally relevant content based on users’ locations
- Cutting down on sensationalism and clickbait
- Elevating in-depth, original content from expert sites
Since the focus is on local content, websites publishing for a specific country might see changes in traffic patterns. However, once the update is live globally, any shifts in traffic should even out.

Additional insights: Google’s systems are fine-tuned to recognize expertise across different subjects. Whether a website specializes in a single topic or covers multiple, it can gain visibility in Discover. To illustrate, a local news outlet with a gardening section can still be seen as an authority, even if it covers other themes. Conversely, a site primarily about movies wouldn’t be recognized as authoritative in gardening from a single post.

Moreover, Google continues to tailor content recommendations based on individual preferences, ensuring a more personalized user experience.

Prepare for changes: As this update unfolds, expect to see some fluctuation in your Google Discover traffic. Google has noted that while some sites may experience increased or decreased visibility, many will not notice drastic changes.

Progressive rollout: Currently, the update is being released to English users in the US, but the plan is to go international and multilingual in the upcoming months.

Why this matters to us: Changes in Discover traffic could impact your site’s engagement. If you need assistance navigating this update, Google provides core update guidance and resources like the Get on Discover page.

Ultimately, Google’s testing suggests that this update has made the Discover feature more useful and valuable for users.

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