I recently came across some exciting updates from Google that are designed to enhance the way we search for and interact with content. Google is introducing new features to its AI experiences, including AI Mode and AI Overviews, by incorporating preferred sources along with a perspectives carousel and highly cited labels.
Preferred Sources in AI Mode and AI Overviews. One of the updates brings preferred sources to AI search results. According to Duncan Osborn, Product Manager at Google Search, users will now be able to easily identify links in AI responses from sources they have selected. I find this particularly beneficial as it helps me quickly access content from sources I trust.
I saw Google testing this feature recently, and now we have the final version that’s rolling out. There will be a label highlighting preferred sources within AI results, making it noticeable to us. It’s fascinating how this is now available globally and in all languages. Google mentions that users have selected over 345,000 unique sources, and these sources receive double the click-through rate. For those interested in trying it out, you can find more details in Google’s documentation.
Perspectives Carousel. Another interesting addition is the perspectives carousel. Google will present a new carousel for certain searches, tailored to help us dive deeper into specific topics, especially when they’re rapidly evolving. The carousel will prominently feature our preferred sources, making recent articles more accessible across various search queries.
In addition to this, there’s also a carousel that shows helpful perspectives from online discussions, forums, and social media. This is a wonderful way for us to tap into diverse viewpoints, broadening our understanding of topics that interest us. These features are being rolled out in AI Mode and AI Overviews.
Highly Cited Label. Finally, Google is expanding the highly cited label to more web article links within search results. This feature makes it easier to find articles that many other stories refer to. It’s a fantastic tool for me to trace a story back to its primary reporting, ensuring that I am viewing the original source of information. This feature will be available across Google Search, beyond just AI-specific functions.
I find it fascinating that users interact differently when faced with AI Overviews compared to AI Mode. New clickstream data reveals that AI Overviews significantly alter user behavior—from reverse scrolling to extended evaluation of search results across various intents.
Take Netflix, for example. The average user spends about 18 minutes just browsing. They skim through tiles, watch trailers, and often circle back. It turns out, searching isn’t much different these days, thanks to new insights.
This week, I’m diving into:
Four notable behavioral shifts observed with AI Overviews, gathered from over 846,000 Google sessions.
The evolving role of brand-name searches and why they no longer offer the same shortcuts.
An insight that might change how you craft title tags and meta descriptions this quarter.
Eric Van Buskirk from Clickstream Solutions mined anonymized clickstream data supplied by Surfer SEO. The study analyzed around 846,000 U.S.-based Google searches from February and March of 2026.
This marks the fifth study on user behavior with Google’s AI features over the past year. Earlier, a UX study on 70 users in May 2025 utilized think-aloud and screen recording methods, while a study from October 2025 examined AI Mode specifically. This research trades depth for scale, uncovering patterns too subtle for smaller studies.
For a bit of context, previous SERP mouse-tracking studies involved only a handful of people—this one, however, evaluates queries from tens of thousands of users.
A fascinating contrast surfaces: User behavior in AI Overviews starkly opposes that in AI Mode, where AI Mode is akin to autoplay, while AI Overviews replicate the browsing experience.
This article outlines four major findings from this recent study and how they might influence your title tags and meta descriptions in 2026. Full methodology available here.
With groundbreaking insights, like how nearly half of AI Overview interactions involve reverse scrolling and how search types no longer reliably predict behavior, this data is invaluable. It challenges traditional assumptions and has meaningful implications for e-commerce and decision-heavy categories.
Surprising findings include brand searches losing their shortcut advantage, implying even users searching specifically for brands might pause to consider adjacent content on the SERP.
Read more intriguing insights on how the AI landscape shifts user engagement and strategy in SEO.
I recently followed an intriguing conversation with Google’s CEO, Sundar Pichai, where he explored the transformative journey that awaits Google’s AI, Search, and digital tools. The path forward envisions these elements coalescing into a unified powerhouse capable of executing tasks seamlessly.
In a detailed exchange with Nilay Patel from The Verge, Pichai addressed concerns about an evolving Search landscape. He firmly reiterated Google’s commitment to connecting users with the open web, assuaging publisher concerns about potential traffic declines.
Pichai assured, “Through it all, we are very committed to both meeting user expectations and also connecting them to what’s out on the web.” Yet, it’s clear why some fears persist as Google steers towards an AI-driven future where Search evolves to include conversational agents and task-oriented tools, reducing the need for traditional clicks.
Why we care. It’s important to recognize the emerging landscape, one where Google’s Search, Gemini, and agent technologies blend into a singular AI layer. This shift points toward a revamped approach to discovering information, creating content, and handling tasks.
Agents are the future. These AI agents are poised to drive the next evolution on the web. According to Pichai, “I look at agents, and that is the next evolution of the web. I think it will evolve the web pretty profoundly.”
In the background, Google’s efforts in developing agentic tools across Search, Gemini, Spark, and Antigravity aim to bring these innovations together for a more cohesive user experience. Acknowledging this unified trajectory, Pichai envisions Google’s ecosystem as evolving into an ‘agent manager’ model.
One product. When asked if Google’s suite of AI search and app-building tools might eventually merge into one, Pichai affirmed, “It will.” This convergence means Google agents will quietly assist users in planning and executing tasks, a vision for which Google is diligently assembling essential building blocks.
Pichai elaborated, “We are laying a lot of the primitives of what we need for agents to work end to end, and more importantly, for AI to work.”
Dig deeper. Explore perspectives on how Google’s Search and Gemini might converge or continue to diverge in the discussion led by Google’s Liz Reid.
Google rejects Google Zero. In the face of concerns about Google’s evolving role in web traffic, Pichai illustrated his view of an expansive information ecosystem, far broader than Google alone.
Addressing Condé Nast’s apprehension about declining search traffic, he highlighted the dynamism of the current landscape, where publishers adapt continually to shifts in user behaviors and new digital formats.
“It’s exceptionally dynamic, and so it makes sense to me every publisher is adapting to this new world,” he observed.
Google says some clicks are going away. While Pichai refrained from advising publishers on business planning, he emphasized that as technology improves, low-quality clicks naturally dwindle, alongside metrics reflecting a decline in bounce clicks.
Google points to subscriptions. By highlighting Google’s adjustments to support subscription models, Pichai acknowledged this as a key adaptation amid evolving publisher strategies.
“We are adapting to the fact that publishers are increasingly turning to subscription offerings, too,” he stated, promoting Google’s efforts to highlight subscribed content as preferred sources for users.
It’s worth noting that the drive towards subscriptions was, in part, a response to diminishing reliance on search traffic.
Search had to move faster. The decision to reorganize Google Search was a strategic move to enhance agility in the rapidly advancing AI era, positioning the platform for rapid decision-making and innovation under new leadership.
For more insights into Sundar Pichai’s thoughts on AI, search, and the future of the web, consider listening to the full interview here.
When I think about the future of AI in search engines, I’m reminded of a statement by Nick Fox, Google’s senior vice president of Knowledge & Information. He believes that as AI begins handling simpler search queries, we need to focus on crafting content that’s richer with human perspectives—something AI summaries simply cannot replicate.
As I ponder how our content can remain relevant in the age of AI, I remember Fox’s advice shared during the Google Marketing Live 2026 interview with Ben Smith of Semafor. Here, he emphasized that quality content must transcend surface-level answers to truly shine.
Consistency is key. Fox noted that our approach to ranking in AI search remains similar to traditional methods. It’s all about crafting exceptional content.
“The way to optimize for AI search is the same way to optimize for search. Create great content.”
He advised, though, that moving beyond basic summaries is crucial.
“The additional piece of advice we give is go beyond the surface level.”
According to Fox, while AI summaries might address initial queries, the content that truly excels goes further, answering deeper layers of questions.
“If you assume that the AI will provide sort of a first-level response, high-level framing, the best content that will do the best within AI is one that goes one level deeper, two levels deeper, and is really helpful there.”
It got me thinking—how does Google distinguish “deeper” content from just longer pages?
The human touch AI can’t duplicate. I find it intriguing that Google’s new AI search guidelines emphasize the value of content AI can’t easily reproduce. These guidelines caution against creating “commodity” content that merely echoes others or is readily generated by AI models.
Producing content that offers little in unique insight is discouraged, whereas content rich with expert or personal experience goes far beyond the ordinary, and that stays with me during content creation.
During the interview, Fox highlighted the web’s future role, emphasizing the need for human perspective in AI-driven search results.
“If you’re looking to buy something, you don’t just want to hear what the AI says. You want to hear from someone who’s used it. What did they think? What did they experience? What was amazing about it? That kind of rich human content is invaluable.”
“As humans, we want to hear from other humans. We crave human perspectives and experiences.”
Addressing traffic concerns. I’m aware that Google’s focus on human experience underscores the web’s value, even as AI summaries cut down on organic search traffic clicks that traditionally supported such enriching content.
Unfortunately, the interview didn’t touch upon how AI summaries might shrink organic search traffic or counteract these drops.
Changing search habits. Observing people has shown me that search behavior is evolving, influenced by conversational AI tools. As Fox pointed out, queries are becoming more intricate and detailed.
“The questions that people are asking now are these two-, three-, four-sentence queries.”
He highlighted how natural-language searches now include more context, offering intricate prompts rather than short keyword phrases. Google didn’t accompany this with specific data, but I’ve noticed the change in my own search habits.
Why this matters to us. In our pursuit of creating content that stands out, AI-generated responses with basic summaries mean we must offer original reporting, share firsthand experiences, or deliver valuable analyses not available in generic AI answers.
The interview. For those interested, you can watch the complete interview with Nick Fox on the future of AI and search.
Have you ever noticed how ads are transforming from simple clicks to engaging conversations? Google’s latest AI advancements have unveiled an incredible shift in how we interact with advertising, challenging our perceptions of visibility, trust, and the role of marketers.
Google Ads Liaison Ginny Marvin recently penned a detailed piece on over 40 new innovations spanning Google Ads, Analytics, AI, and more. While these updates cover everything from conversational AI to predictive attribution, the underlying narrative reveals a more profound transformation.
I see Google consciously reshaping the advertising landscape to focus on intent prediction, AI-driven decision-making, and automation that qualifies users even before they become customers.
These innovations are poised as solutions to a familiar marketer’s challenge: bridging the gap between generating leads and generating valuable leads.
Marvin notes that prospective customers will now be able to ask specific questions about services or pricing directly within the ad. This shift deeply impacts the role of ads by embedding interaction and qualification into the experience itself.
Historically, lead generation was straightforward: click, land on a page, and fill a form. Now, AI is enhancing the process by embedding layers of qualification and assurance right in the ad experience.
For businesses in trust-critical sectors like finance or healthcare, this evolution could significantly reshape lead quality dynamics.
Intent over Volume
Marvin’s updates steer towards optimizing predicted business results rather than merely conversion volumes.
With new tools like lead intent scores and journey-aware bidding, Google aims at reducing ineffective leads within the pipeline.
The approach solves the industry’s pain point of focusing solely on cheap conversions that add little to the client base.
However, with more aspects of qualification and forecasting handled by Google, advertisers might lose transparency in decision-making processes, an important consideration in the AI-driven era.
AI Max: Evolving Performance
AI Max signifies how Google’s AI-driven optimization is sweeping through Search. It applies extended algorithmic exploration to campaigns, broadening targeting and uncovering new opportunities beyond traditional pathways.
While ecommerce players with strong data may find new scale opportunities, lead generation marketers without robust offline conversion data might face higher risks.
This phase of rollout, echoing early Performance Max challenges, underlines the need for advertisers to back automation with rich, business-quality signals.
Rich data integration is critical as AI systems only optimize based on received data, highlighting why offline conversion tracking and CRM integration are now pivotal in Google Ads strategy.
Predictive Measurement at the Core
An understated yet crucial change is Google’s pivot to predictive measurement models, linking ad exposure to future behaviors.
Such foresight promises insights into long buying journeys but also fosters reliance on opaque AI forecasts.
The strategic debate looms over the trade-off between automation efficiency and advertiser visibility.
Revolutionizing Creative Production
Marvin’s insights suggest Asset Studio’s rise as an AI-driven creative production powerhouse. Google aspires to unify creative development, analysis, optimization, and testing into a single workflow.
This can alleviate bottlenecks for lean teams, but as AI democratizes creativity, real differentiation will hinge on brand strategy and deep audience insights over sheer production prowess.
The Bigger Picture
While some of these enhancements might appear incremental, collectively, they mark a substantial evolution within Google Ads. Google’s crafting itself into the backbone of contemporary advertising decision-making.
Ultimately, the task for advertisers is finding the right balance between embracing automation and retaining strategic insight.
Though AI promise advancements and opportunities, understanding key signals, genuine business outcomes, and when to rely on human insight will define long-term success.
From February to May 2026, I dove deep into the fascinating world of agentic AI adoption. I explored how it’s being embraced by enterprises, mid-market players, and SMBs across the U.S. and worldwide. By gathering insights from top consulting firms like McKinsey, Gartner, and IDC, as well as academic institutions and AI leaders, I pieced together a comprehensive overview of agentic AI’s current landscape.
This report fuses insights from over 30 research efforts and industry surveys, covering 15,000+ businesses. It provides a granular look into how businesses are integrating autonomous AI agents this year, breaking it down by company size, industry, deployment stage, primary use cases, and adoption and abandonment patterns.
*Statistics are based on data up to May 14, 2026, unless indicated otherwise.
While generative AI generates immediate outputs, agentic AI shifts the way systems function entirely. This piece zeroes in on agentic AI’s adoption, defined as follows:
Agentic AI revolves around AI systems autonomously planning, deciding, and executing complex tasks from beginning to end.
The term adoption signifies any case where an organization uses at least one agentic AI system at any stage, from initial trials to full-scale implementation.
Meanwhile, abandonment involves halting an agentic AI program or specific projects. This doesn’t always mean closing an organization’s entire AI operations, as they might continue other initiatives.
Agentic AI adoption significantly varies by organization size. A breakdown of recent adoption rates across different segments unveils fascinating trends.
As I dug into the data, I discovered enterprises are leading the way with 25% adoption, thanks to their resources and AI budgets. However, smaller sectors, like mid-market firms and SMBs, are catching up fast. Their year-on-year growth rates are even outpacing those of enterprises!
I predict that SMBs and mid-markets will continue adopting agentic AI faster than their larger counterparts. This trend is partly driven by accessible solutions such as Salesforce Agentforce and Microsoft Copilot Studio, which empower companies with tighter budgets. In contrast, enterprises face challenges due to their intricate systems and diverse data environments.
Agentic AI deployment spans various maturity stages, presenting unique challenges depending on available resources. For SMBs, scaling can be costly, making it particularly challenging.
The table showcases deployment stages among adopters, revealing that 62% of enterprises, despite higher resources, linger in the experimentation phase. Notably, only 13% achieve full deployment.
A few patterns stand out from the data:
Firstly, experimentation predominates across sizes, with a 56% average gap to partial deployment. This highlights caution across sectors in deploying agentic AI.
Despite enterprises’ resources, mid-market companies are seeing greater partial deployment rates, likely due to fewer approval bottlenecks and more budgetary leeway compared to SMBs.
Also, scaling correlates with resources. Enterprises, despite early-stage phases, manage full-scale deployment at rates double those of mid-markets.
These patterns reveal that most organizations are still exploring, with few transitioning to production deployment.
It’s not all smooth sailing. According to Gartner, around 40% of agentic AI projects might be canceled by 2027, due to challenges encountered during deployment.
Although abandonment rates generally decline over time, mid-markets still see higher rates due to their broader range of obstacles and fewer resources compared to large enterprises.
Summarizing the common reasons for project failures:
Data quality matters. Without quality data, agents struggle, highlighting a universal need for centralized and uniform data pre-deployment.
Clear expectations are vital. Projects without well-defined success criteria often fail to demonstrate value, risking cuts in resources when results are inconspicuous.
Costs weigh heavily on SMBs. For SMBs, financial constraints dominate abandonment reasons, overshadowing other factors. Mid-market firms display more varied primary drivers.
Such insights explain why full implementation is elusive for many, despite significant investments. Companies need to address multiple challenges concurrently to progress beyond experimentation.
On an industry level, exploring adoption across sectors shows where agentic AI thrives and lags. Regulatory factors, data readiness, and competitive dynamics result in differing adoption levels.
Industries like education, construction, and real estate lag, owing to budget constraints, less advanced data infrastructures, and fewer automation opportunities. Nonetheless, even these sectors demonstrate notable enterprise adoption, signaling a broader reach beyond tech and financial services.
Finally, examining use cases underscores where agentic AI is making headway. Customer service and supply chain coordination rank high due to their structured processes. On the other hand, finance sees lower adoption due to stringent regulatory scrutiny.
If you fancy obtaining a PDF copy of this insightful report or learning more about our work, feel free to reach out here.
For further exploration into agentic AI and its surrounding trends, consider delving into the following reads:
I’ve been to numerous AI conferences and training sessions over the years. I’ve witnessed inspiring innovations, and I’ve also seen many people getting nowhere fast.
Having hands-on experience with AI automation across different businesses, I’ve found myself in both those positions. Here, I want to share my insights so you can save time, energy, and resources—while strategically using AI to boost revenue and cut costs.
Many AI Projects Miss the Mark on Value
All too often, I see entrepreneurs trying to reinvent the wheel. I’ve lost count of people touting their new AI-driven CRMs when there are already hundreds of excellent platforms available. Building a new CRM from scratch is unnecessary when existing ones provide every conceivable feature with teams dedicated to keeping them updated and functional.
The same logic applies to apps and software mimicking existing tools. I’ve been guilty of this too, but the truth is, we don’t need another version of an already oversaturated tool.
On rare occasions, creating new software is justified, mainly if it launches quickly and offers something proprietary—a novel formula, a distinct process, or exclusive data access. It has to be core to your business model.
Otherwise, you risk squandering time and money on tech that’s irrelevant to your business improvement.
Strategic AI is Your Real Competitive Edge
The businesses achieving significant AI success are solving measurable operational challenges with it.
The key to success is deploying AI in ways that tangibly enhance revenue and efficiency.
How AI Can Directly Increase Revenue
Consider using AI to develop a highly targeted prospect list and automate outreach, seamlessly leading prospects into your marketing funnel. Some companies even use AI for parts—or the entirety—of their sales process. This approach is drawing in fresh, targeted leads on auto-pilot daily.
This strategy provides a cost-effective, scalable way to grow revenue without the expense of additional hiring. However, you must ensure your business can manage the increased demand. While scaling is beneficial, any slip-ups can quickly tarnish your reputation.
Proper implementation is crucial; it demands oversight, testing, and operational discipline. Poorly executed AI can spawn as many problems as it fixes.
AI Can Reduce Time and Operational Costs
AI can streamline workloads efficiently, cutting both time and costs. I’ve used it to swiftly analyze market conditions, enabling me to make more precise pricing decisions when dealing in property transactions.
AI excels in rapidly compiling, analyzing, and extracting insights from vast datasets, revealing patterns and opportunities a human might miss.
By leveraging AI, I can quickly identify the most promising deals and make offers faster than competitors, a critical advantage in winning business.
One Simple AI Workflow that Saves Hours
A PR firm I collaborate with employs AI to oversee their clients’ media interview schedules. Post-interview, the system promptly locates the Zoom recording, transcribes it, and prepares an email with the video and transcript for journalists.
This process saves about 30 minutes per interview, delivering everything rapidly, as opposed to waiting for human intervention. Apart from time and cost savings, it offers journalists greater value by streamlining their workflow.
Other High-Impact AI Utilizations
There are numerous strategic ways AI can significantly bolster revenue and productivity. Some methods I’ve applied include:
AI virtual phone assistants offering 24/7 service.
Intelligent website chatbots specifically tailored to your business.
Efficient appointment scheduling.
Recovering missed calls efficiently.
Implementations focusing on better response times and improved customer experiences.
AI’s Effectiveness Lies in Strategic Use
Currently, a significant opportunity exists in helping service businesses recapture revenue lost from overlooked prospects.
Most small enterprises don’t need intricate platforms or custom AI apps. They need systems that respond faster than manual efforts can. This might be an AI-powered phone assistant handling calls and scheduling appointments around the clock, or a web assistant trained to address inquiries and capture leads on the spot. Strategically applied, AI isn’t about displacing workers but preventing missed opportunities.
Businesses integrating AI effectively are likely to surpass competitors that lag in enhancing operational efficiency and response speed.
The most impactful AI setups aren’t flashy. They address specific operational issues: lowering missed calls, improving response times, hastening analysis, qualifying leads swiftly, or automating repetitive tasks.
If an AI system doesn’t noticeably enhance revenue, efficiency, customer experience, or decision-making, it’s worth questioning its necessity.
Utilizing AI in this pragmatic manner provides a substantial edge over competitors less willing to compete efficiently.
So the question remains: will you allocate time to employ AI strategically?
I often find myself explaining Reddit’s role in AI search. It’s frequently underestimated, yet its influence extends well beyond training data.
Clients frequently ask how AI training, licensed access, and retrieval systems can affect SEOs and AI strategies, particularly concerning Reddit.
Here are the typical questions I receive:
Should I engage with Reddit to boost my brand visibility?
Is advertising on Reddit beneficial if AI uses Reddit for training?
Our CEO suggests creating a subreddit for each product. Is that wise?
Why does Google’s AI reference a Reddit thread criticizing my product?
These inquiries often conflate three separate but interrelated concepts:
Training data.
Licensed or real-time access.
Citation and retrieval systems.
Although connected, they serve different purposes. Understanding these distinctions impacts how we approach SEO and AI citations, especially as Reddit increasingly appears in AI-driven results.
Let’s demystify AI training, access, and citation. You might think, “ChatGPT was trained on Reddit,” means every post is directly stored in its memory—an incorrect assumption.
Training AI is akin to education. Kids learn concepts like using the Pythagorean theorem without remembering specific textbook answers. Similarly, AI learns conversational patterns, not individual Reddit posts.
AI doesn’t remember specific threads but discerns key discussion points from Reddit, like consumer preferences on r/RockTumbling.
Reddit partnerships with Google and OpenAI in 2024 enabled a transition from static datasets to ongoing access, allowing AI to stay updated on Reddit dialogs.
If AI training is like schooling, licensed access is a continuous flow of information akin to subscribing to a newspaper.
AI can cite Reddit, not because it’s preferential part of the training, but finds it useful for real-time querying, just like humans might refer to yesterday’s conversation.
Reddit’s prominence in AI results impacts my SEO strategy, yet it’s not only due to formal partnerships. Reddit’s depth in human experiences enhances its informational value.
Reddit offers what many websites lack: practical user insights and diverse opinions. Where official sites provide features, Reddit adds authentic experiences and user narratives.
Rather than mimicking Reddit, I focus on fostering authentic discussion by leveraging user insights from reviews, interviews, or forums, enhancing the context around my content.
I’ve realized that prioritizing nuanced details and showing reasoning can increase credibility, making my content more relatable in subjective decision-making scenarios.
Ultimately, integrating firsthand experiences and transparency can elevate content strategy, aiding systems that synthesize human input into AI insights.
When I think about AI search, I realize it’s more than just translating or localizing results. It’s about deciding which sources, narratives, and realities emerge on top. This complex system is incredibly fascinating to me, especially when I consider how multilingual regions like Catalonia challenge these AI search systems.
The unique geography of Catalonia, where Catalan and Spanish languages coexist, serves as an excellent stress test for AI technology. It’s intriguing to see the underlying patterns unfold when the same queries are entered in both languages across platforms like Google AI Overviews and ChatGPT.
In Catalonia, a query like Tradicions de Sant Jordi shows how AI systems can sometimes misidentify the language, often tagging Catalan as Occitan. This discovery was both surprising and revealing, shedding light on broader problems that transcend multilingual spaces.
Consider this: an AI system operating out of Barcelona with a local IP may choose the less prevalent language of Occitan over Catalan, a decision that feels bizarre given Catalonia’s linguistic and geographical context.
This issue isn’t isolated. In January 2023, Google acknowledged downgrading Catalan results in favor of Spanish, which sparked dissatisfaction among users. The subsequent updates improved things somewhat, but the root language-identification errors persist, affecting how AI synthesizes information today.
My journey into this topic has involved documenting AI search variations across Hispanic markets, observing how it often treats diverse Spanish-speaking regions as uniform, ignoring their unique contexts. However, in Catalonia, where geography remains constant, the retrieval patterns unfold in more distinct and educational ways.
For me, multilingual regions expose the foundational defaults in retrieval systems. Here, users can switch languages and observe firsthand how the system reallocates meaning, authority, and even the language of an answer.
The reality is, the same issues will likely emerge in seemingly monolingual markets, manifesting in different ways as AI technology advances.
Recently, I’ve been exploring the fascinating divergence in AI adoption between professional circles and general consumers. According to Datos and SparkToro’s latest data, this trend is becoming increasingly apparent.
It was intriguing to see how AI usage is starting to plateau among consumers while remaining on the rise in professional environments. Tools like Claude, ChatGPT, and Gemini are seemingly more popular in the B2B landscape.
Why we care. As I delve deeper into AI’s impact, it’s becoming clear that a universal AI strategy won’t work for everyone. It’s essential to identify whether my audience aligns with these broader trends or if their AI engagement habits are entirely different.
ChatGPT desktop growth slowed. From Fishkin’s analysis, it appears that ChatGPT’s usage in the U.S. has stagnated over recent months while Claude and Gemini continue their growth trajectories. It seems that professionals are continually finding value in these tools.
At its zenith, 37% of U.S. desktop users engaged with OpenAI or ChatGPT back in September 2025. This number dipped slightly to 34% by March, a trend mirrored, albeit with higher numbers, in the EU and U.K.
Claude gained with professionals. I noticed Claude is particularly gaining traction among professional users. Fishkin’s data suggests a significant rise in usage among business professionals, resonating with the notion that AI adoption is stronger in B2B contexts.
The analysis even revealed that Claude’s use among B2B professionals was 373% higher than the U.S. average, reinforcing the tool’s growing popularity in business circles.
Consumer audiences look different. Interestingly, when it comes to the retail-shopping consumer audience, ChatGPT isn’t as prevalent, being 15% less likely to be used compared to the typical American consumer. For this group, Claude isn’t even in the top four AI tools.
This might explain why AI seems so prevalent in professional networks like LinkedIn, while its visibility is not as pronounced among general consumers.
The research. You can view Rand Fishkin’s detailed insights on LinkedIn by watching his video here.
