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How AI Transforms American Search Habits: Insights From Pew

I’ve noticed something remarkable about how we, as Americans, are searching for information these days. Pew Research Center recently reported that 60% of us are now reading AI-generated summaries at the top of our search results, while approximately 40% have turned to chatbots for finding information.

It’s fascinating to see that AI-generated answers are appearing more and more, whether in traditional search results or dedicated chatbot platforms like ChatGPT, Gemini, and Copilot, as Pew discovered.

AI summaries reach most searchers. According to Pew, six out of ten American adults have read AI summaries at the top of search results. Surprisingly, three out of ten haven’t, which suggests room for growth.

Interestingly, another 10% are unsure if they’ve read AI summaries. It seems some of us may not clearly recognize them when they pop up in our search results. The research also found that men are slightly more inclined than women to read these summaries, with 63% versus 57%. Those of us aged 65 and older are less likely to engage with them.

Chatbots are search tools. Chatbots are increasingly becoming popular search tools. About half of American adults have used AI chatbots, which is a jump from one-third back in 2024. What’s more, about one in four of us make use of them daily.

The most common reason we use chatbots? Searching for information. Around 40% of adults turn to chatbots for this purpose, more than for entertainment, media creation, or even advice on fitness and medical matters. Interestingly, work-related tasks follow closely behind, with 38% of employed adults utilizing chatbots at their jobs.

ChatGPT dominates. ChatGPT remains the most popular chatbot by a significant margin. Pew indicates that 44% of U.S. adults have now engaged with ChatGPT, which is up from 34% last year and over twice the number reported in 2023.

Gemini takes second place, with about a quarter of us using it, followed by Copilot and Meta AI. Tools like Grok, Claude, and Character.ai have a much smaller audience, with only about one in ten of us using them, if at all.

Why we care. In today’s world, finding information doesn’t just mean looking at traditional search results. We now also find answers through AI summaries and chatbot responses, which is a fact worth noting, especially when it comes to understanding where people are sourcing their information.

Dig deeper. For more insights on AI search adoption and consumer trust, check out the study.

About the data. Pew Research Center gathered this data by surveying 5,119 American adults from February 17-23, 2026, via its American Trends Panel. The margin of error for this study is plus or minus 1.6 percentage points.

The report. For a more detailed look at the survey, visit Americans and AI 2026: Chatbots, Smart Devices and Views on Impact.

Inspired by this post on Search Engine Land.

June 18, 2026
Unlock Enhanced Ad Performance with Google’s New Conversion Beta
In a significant move, Google Ads has launched a beta feature that allows advertisers like me to connect additional data sources directly to website conversion actions. This innovative step gives us a chance to enhance tag-based measurements using our backend conversion data.

The new feature equips advertisers to merge conversion signals gathered through Google tags with transactional data from various platforms, such as CRMs, order databases, and e-commerce systems.

What’s new. Now, I can append an additional data source to an existing website conversion action via Google Ads Data Manager or through the Data Manager API.

Designed to enhance—not replace—website tagging, this beta allows us to send conversion data from backend systems into the same conversion action utilized for campaign measurement and optimization.

Why we care. This beta is crucial for filling conversion measurement gaps by fusing Google tag data with our first-party data from backend structures like CRMs. It helps us capture conversions that might be overlooked due to browser limits, privacy settings, or ad blockers, providing a fuller view of campaign performance.

Why Google launched it. Google indicates that combining tag-based measurement with backend conversion data allows advertisers to construct a more comprehensive picture of conversions, subsequently boosting campaign performance.

Here’s what this feature helps achieve:
- Recover conversions that may escape website tags.
- Enhance measurement resilience.
- Deliver more exhaustive data for automated bidding.
- Simplify data integration through the Data Manager.
How it works. The system combines website conversion data captured by Google tags with conversion records uploaded from an advertiser’s backend systems.

To avoid duplicate reporting, Google utilizes transaction IDs to identify and de-duplicate conversions between the tag and the supplementary data source within the same conversion action.

What advertisers need to know. The beta is currently restricted to website conversion actions that implement Google tags or Google Tag Manager.

It’s not available for:
- Google Analytics imported conversions.
- URL-based conversion actions.
Google advises attaching an additional data source to an existing conversion action rather than initiating a new one to eschew potential double-counting across campaign goals.

Data requirements. Each upload must encompass:
- Transaction ID.
- Conversion date and time.
Advertisers need to supply at least one attribution identifier, like hashed customer data or a Google click identifier.

Google suggests that I upload conversion data as swiftly as possible and ensure the conversion values match the currency format utilized by website tags.

Bottom line. This beta signifies Google’s ongoing effort to bolster conversion measurement by integrating backend transaction data directly into Google Ads. As we seek more comprehensive performance insights, this feature provides a streamlined means to enhance website measurement using first-party business data.

Inspired by this post on Search Engine Land.
June 18, 2026
Mastering Domain Moves: Utilize Google’s Change of Address Tool

I recently explored Google’s updated guidelines for site moves, specifically about handling all domain variants using their Change of Address tool. This update aims to clarify the process of moving your site from one domain to another, ensuring a smooth transition for all domain variations.

Google’s advice is straightforward: enter every domain variant in their Change of Address tool during a site migration. They emphasize this in their documentation to prevent potential indexing issues.

Google’s Note: They encourage submitting requests for each subdomain and the www and non-www variants of your previous domain. For instance, ensure you submit en.example.com, www.example.com, and example.com if you’re moving to new-example.net, even if these variants aren’t actively used. It’s crucial to have them verified in the Search Console for a seamless migration.

Understanding domain variants is key. These include subdomains and different TLDs, allowing for a comprehensive transition from your old site to the new one without hiccups.

Why It Matters: Proper domain migration ensures that all site variants migrate without issues, which Google confirms as the best practice for SEO. Following Google’s guidelines can significantly mitigate the stress associated with site migrations.

For any SEO practitioner or site owner, site moves can be daunting. However, adhering to these detailed steps can make the transition less overwhelming. The Change of Address tool is designed to expedite this process, so making the most of it is essential.

Inspired by this post on Search Engine Land.

June 18, 2026
Unlocking AI Search Success with Adobe’s New Tool

I’m excited to share how Adobe’s latest tool is changing the game for businesses eager to boost their brand visibility in AI-driven searches.

With the backing of 300 million AI prompts and the comprehensive data of Semrush, this platform is adept at tracking mentions, gauging share of voice, and identifying content gaps across prominent AI platforms.

Adobe introduced a pioneering solution for brands aiming to bolster their visibility and trustworthiness across AI interfaces. As part of the Adobe CX Enterprise, this tool offers an agentic AI system to streamline customer lifecycle management, covering everything from initial acquisition to fostering long-term loyalty.

AI traffic is skyrocketing. The way LLMs are utilized for product and service research represents a major pivot for both marketers and consumers. Recently, Adobe revealed data underlining this massive surge in AI traffic to U.S. retail sites—up by an impressive 1,324% from October 2024 to May 2026. The travel industry saw an even greater increase of 2,215% in the same timeframe.

As Vice President of strategy and product, Loni Stark, remarked to MarTech, “We used to get back the same thing—a SERP page with links. Now results seem random, but aren’t when scaled, and companies lack tools for this.”

Understanding brand visibility in AI search. Adobe Brand Visibility marks Adobe’s first venture into generative engine optimization (GEO), following its acquisition of Semrush. By integrating Adobe LLM Optimizer with Semrush’s AI Optimization tool, it provides unmatched insights.

Drawing from a staggering database of 300 million real-world AI search prompts, Adobe Brand Visibility helps teams pinpoint which prompts lead to brand exposure or loss.

Additionally, utilizing Adobe’s first-party data from owned channels, marketers gain a holistic view of how their brands appear on platforms like ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. Metrics encompass mention frequency, reach, competitive share of voice, and content gaps, allowing AI agents to offer prioritized recommendations that teams can rapidly implement and evaluate results.

Competitive intelligence unleashed. Adobe Brand Visibility offers tools for competitive brand analysis, comparison, and trend tracking, enabling marketers to effectively benchmark against competitors.

Featuring advanced SEO intelligence driven by Semrush’s extensive data of 28.5 billion keywords and 43 trillion backlinks, this platform underscores the continued importance of SEO fundamentals for AI search visibility. It shows the potential for existing search authority to yield AI citations and identifies opportunities for content investments across channels.

While there’s still much to learn about leveraging LLMs for brand visibility, Stark is confident in Adobe’s leadership position in this emerging space.

As Stark stated, “Adobe had proprietary data while Semrush offered data and trends. Though we may not have all answers, we possess unrivaled data.”

Inspired by this post on Search Engine Land.

June 18, 2026
How Google AI Prefers Competitors in ‘Best’ Listicles

Recently, I’ve been delving into an intriguing study by Lily Ray, which reveals some unexpected findings about Google’s AI Overviews. Apparently, these Overviews frequently reference brands’ own listicles but tend to recommend their competitors.
The study highlighted that Google AI Overviews cited these self-promotional listicles in a whopping 69% of B2B software-related queries. Yet, they favored rival brands in their recommendations. This got me thinking about the strategies brands employ to influence AI search outcomes.
Detailed Findings. I discovered that the analysis was quite comprehensive. Ray reviewed 100 B2B queries spanning categories like “best [category] software.” She gathered data across three specific periods: April 15, May 15, and June 8.
The study found that out of 80 queries that triggered an AI Overview, self-serving listicles were referenced 323 times, yet in 224 instances, Google didn’t actually recommend those brands. This mismatch intrigued me.
Analysis of Recommendations. While examining specific cases, it became evident that Google sometimes cited a brand’s listicle but opted to recommend more renowned competitors instead. For instance, in the search for “best LMS for selling courses,” Oasis LMS was mentioned, yet Kajabi and others were pushed forward as the preferred options.
This pattern wasn’t just isolated to LMS software; it appeared in multiple domains like help desk tools, task management, and more. It made me ponder over the dominance of stronger brands in recommendations.
Observing Organic Declines. An interesting trend noted was a drop in organic visibility for websites heavily leaning on self-promotional listicles. I noticed beginnings of these declines back in January and observed further drops post-Google’s May 2026 core update.
Interestingly, these sites also seemed to have expanded into AI-generated content and other “best” pages prominently featuring their own brands.
Rise of Third-party Citations. Ray’s analysis also showed an upsurge in Google comprising third-party content for “best” queries. Platforms like Reddit, Forbes, and YouTube gained traction in citations.
Understanding Impact. I believe it’s crucial to realize that merely having your content cited doesn’t equate to a recommendation. This situation offers competitors the chance to snag attention and, ultimately, valuable visibility.
Keeping Up with Changes. Previously, Search Engine Land shared insights on how some SaaS and B2B businesses witnessed visibility losses after banking on self-ranked “best” lists. The risks are significant when company-driven content doesn’t transparently disclose material relationships as mandated by the FTC’s Consumer Review Rule.
About Ray’s Data. To reach her conclusions, Ray employed Ahrefs Brand Radar to examine numerous AI Overview responses. Her analysis spanned 100 B2B software queries, focusing on citations versus actual recommendations.
The full report is available on Ray’s Substack, titled Why Calling Yourself the Best Could Be Helping Your Competitors Win in AI Search.

Inspired by this post on Search Engine Land.

June 18, 2026
How UK Authorities Are Challenging Google’s Search Practices
I recently came across an intriguing development regarding Google and its operations in the UK. The UK’s Competition and Markets Authority (CMA) has taken a proactive stance, requiring Google to not only allow site owners a way to opt out of AI Overviews but also to clarify how they rank search results.

In addition, Google is required to enable users to port their search data to specific third-party services, a move aimed at increasing data portability.

Transparency on search rankings. The CMA’s demand for Google is to enhance transparency and fairness in ranking search results, with an implementation deadline of six months.

Many UK businesses have voiced concerns to the CMA, claiming that Google’s ranking practices lack fairness and transparency. They argue that changes are implemented without sufficient notice, impacting their operations without providing them with adequate avenues to express their concerns.

Yes, we cover Google search updates frequently, and it’s evident that Google is constantly refining its algorithms to make search results more relevant and to deter manipulation attempts.

According to the CMA, Google must:
- Establish clear processes for businesses to voice concerns about Google’s ranking methods, ensuring these concerns are addressed effectively.
- Use objective and non-discriminatory criteria to rank ‘organic’ search results, which includes AI Overviews but excludes sponsored results.
- Offer businesses greater transparency on ranking mechanics and provide advance notice of significant changes.
Data portability. The CMA also seeks Google’s cooperation to “Allow users to port their search data to authorized third parties, such as rewards platforms or businesses offering personalized offers or discount codes”, aiming for this within three months.

The potential for third-party companies to access Google’s search data could open new avenues for personalized services, such as tailored travel suggestions and more relevant shopping deals, enhancing consumer experiences.

Why we care. Despite these orders, I’m skeptical that Google will comply, as doing so might compromise its highly valued search ranking algorithm, risking exposure to competitors and potential manipulation.

This isn’t the first time such demands have been made and undoubtedly won’t be the last. Google is likely to resist these orders firmly.

Inspired by this post on Search Engine Land.
June 17, 2026
Unlocking Google’s Auto-Classification for Conversion Lists
Starting in August 2026, Google will begin to automatically categorize customer types in conversion-based lists, removing some of the control we advertisers once had. I must now provide Google’s systems with clearer signals on where audiences are in their customer journey.

As someone deeply involved in advertising, I know the importance of precise audience targeting. With these changes, I’m urged to review and update my classifications in the Google Audience Manager before they kick in.

What’s Changing? From August 2026, Google Ads will automatically classify customer lists into categories like:
- Existing customers
- New customers
- Other customer segments
Why Google’s Making This Shift. It appears that Google aims to enhance audience consistency across its tools for customer acquisition and retention. This standardization allows for better optimization decisions in Google’s automated bidding and targeting systems by clearly defining prospecting from retention audiences.

Why This Matters to Us. As an advertiser utilizing customer acquisition strategies, the precise classification of these lists is crucial. Any misclassification could impact Google’s optimization of users throughout their lifecycle, affecting campaign performance.

What We Should Do. It’s vital for us to audit our Customer Match lists—based on conversion data—before August. Consider these questions:
- Are my customer lists categorized correctly?
- Do they represent existing customers versus acquisition targets?
- Will Google’s automatic classification align with my internal definitions?
Reviewing these settings now could prevent unexpected changes when Google enforces these classifications.

The Bottom Line. Google is taking an active role in managing audiences, further streamlining the signals powering their automated advertising systems by assigning lifecycle labels to conversion-based lists.

First Spotted. This update was noticed by Google Ads expert Bia Camargo, who shared the alert on LinkedIn.

Inspired by this post on Search Engine Land.
June 17, 2026
AI Referrals Dramatically Boost Travel Site Engagement
I’ve noticed a fascinating trend recently: AI referrals to U.S. travel sites have surged significantly in May. According to Adobe, travelers coming from AI sources tend to spend more time on these sites and are less likely to leave immediately compared to those from traditional referral sources.

By the numbers: This remarkable growth is backed by data showing a 194% increase in AI-driven traffic year-over-year for May 2026. Since Adobe started monitoring AI traffic in October 2024, there’s been an astounding 2,215% rise.
- AI-assisted travel planning has moved beyond initial stages. Now, it’s common for travelers to utilize large language models for comparing destinations, examining hotel features, creating itineraries, discovering promotions, and making bookings.
AI visitors showed stronger engagement: Although AI-referred visitors currently convert 28% less than non-AI visitors, the gap is closing. Adobe reports that the difference has narrowed by nearly 70% since October 2024.
- Engagement metrics reveal that AI-referred travelers are 21% more engaged than their non-AI counterparts, spending 70% more time per visit and having a 41% lower bounce rate.
- Adobe suggests that such patterns indicate more deliberate and high-intent behavior, even though AI-referred traffic still lags slightly in conversion rates.
Travel pages and AI readability: Adobe has also been assessing the readability of travel websites by AI systems. They developed an AI Content Visibility Checker to evaluate how much page content AI can process.
- Within the travel sector, hotels and car rentals are ahead. Hotel homepages scored 63% readability, while car rental homepages reached 59%. Individual product pages performed even better, with hotels at 73% and car rentals at 71%.
- Nonetheless, Adobe reports that over a third of content on leading travel pages is still unreadable by AI systems.
Where travel sites scored best: Hotels seem to excel in several page categories, including destination guides, activity pages, search results, customer service, and promotions.
- Car rentals excelled on FAQ pages, while cruises led in blogs and news content. Conversely, airlines lagged behind other major travel sectors across all page types analyzed by Adobe.
- This trend illustrates how well-structured, information-rich pages allow AI systems to better interpret content, thanks to detailed property descriptions, amenities, and core offerings.
Retail’s conversion advantage: AI-driven traffic to U.S. retail sites also set a new record in May, surging 138% year-over-year and an impressive 1,324% since October 2024.
- Unlike in the travel sector, AI-referred retail visitors had a 54% higher conversion rate than non-AI traffic, overturning last year’s trend where AI conversion rates were nearly half.
- Cosmetics and electronics shine in retail readability due to detailed content like ingredient lists, tutorials, product specs, and how-to guides, while grocery and furniture lagged.
Why we care: Adobe’s insights suggest AI referrals are increasingly valuable commercially, particularly in retail. However, many sites miss the mark by having significant content inaccessible to AI systems. If key content is hidden, poorly structured, or blocked, you could lose visibility before users reach your site.

About the data: Adobe’s research draws on over 8 million visits to U.S. travel sites, over 1 trillion visits to U.S. retail sites, and more than 100 million SKUs. Additionally, they surveyed more than 5,000 U.S. consumers in March regarding their use of AI in shopping and travel planning.

Inspired by this post on Search Engine Land.
June 17, 2026
Discover How Search Transforms ChatGPT’s Recommendations

Recently, I delved into an intriguing study exploring how enabling search impacts ChatGPT’s product recommendations. Remarkably, these changes affect a vast 80.2% of responses, as observed from an extensive analysis of 20,000 interactions conducted by Jeff Oxford, the founder and CEO of Visibility Labs.

In Oxford’s experiment, he executed 1,000 product-recommendation prompts, running each ten times with search enabled and ten times with it disabled.

Surprisingly, a mere 19.8% of products recommended without search were repeated in the results with search activated.

Search reshapes top suggestions. Even the products that ChatGPT frequently recommended without search seldom appeared once search was turned on. Among those consistently recommended in search-disabled responses, only 15.8% showed up when search was activated.

Oxford anticipated that highly recommended products would still dominate with search, but they turned out to have the least overlap.

Source mentions and visibility. This study also scrutinized whether products cited in ChatGPT’s sources appeared more frequently in recommendations, showing a modest correlation of 0.4 Pearson between source mentions and recommendation frequency.

Products mentioned more often in cited sources had higher Visibility Scores, based on the percentage of instances a product appeared for a given prompt.

The analysis didn’t prove that source mentions directly caused these recommendations.

Search refines the list. With search enabled, ChatGPT’s responses averaged 5.2 products compared to 6.2 without search.

On average, across ten runs for each prompt, there were 19 unique products returned with search enabled, versus 21.8 with it disabled.

Why it matters to us. These findings are crucial because they show how search significantly changes ChatGPT’s product recommendations, even for staple products. Also, products cited in sources may achieve greater visibility when search is enabled, though this study doesn’t conclusively show that source visibility is more influential than web visibility as a whole.

About the study. The analysis covered 1,000 product-recommendation prompts, with each run ten times with search enabled and ten times without. Product names were standardized for consistency. As an observational study, it didn’t establish a direct cause between source mentions and recommendation frequency.

The detailed report. For more insights, see the full study here.

Explore more. AI recommendation lists repeat less than 1% of the time: Study

Inspired by this post on Search Engine Land.

June 17, 2026
Discover How Meta’s New AI Mode Transforms Facebook Search

I am excited to share that Meta has rolled out the revolutionary AI Mode in Facebook Search, designed to enhance the user experience by providing AI-generated answers directly gleaned from public Facebook content such as Groups, Reels, and other Meta platforms.

Instead of the usual list of search results, Facebook now offers direct responses crafted by Meta AI. These answers are rooted in actual conversations and experiences shared publicly across Facebook’s apps, providing real-life advice and insights.

AI answers in search. With AI Mode, I can explore both broad topics and specific queries. As I navigate Facebook, the Meta AI surfaces relevant public content right in my feed, transforming how information is discovered and shared.

For instance, engaging with Groups and Reels offers a novel method to gather information about products, places, hobbies, and everyday tips.

Source selection is unclear. Although Meta promises “real answers from real people,” how AI Mode selects which public posts, Groups, or Reels get featured remains a mystery. Additionally, it’s not yet clear if brands, creators, or publishers will be informed when their content is utilized.

Why we care. This evolution signals a shift in Facebook’s search landscape, relying heavily on AI-generated responses from public social interactions. Consequently, the discovery process for recommendations, local news, and brand discussions is undergoing transformation within Meta’s universe.

A familiar name. Interestingly, Meta’s new feature shares its name with Google’s AI Mode, which raises some eyebrows about creativity.

What Meta is saying. This new AI Mode harnesses the power of both Meta AI and Muse Spark. However, Meta hasn’t divulged how Muse Spark affects search rankings, or the selection and generation of answers.

This search enhancement is just a piece of a larger Facebook AI update introducing new creative features for Photos, Videos, Profile Pictures, and Stories.

The announcement. Discover more in Meta’s official statement here: New AI Tools to Help You Make Things Happen on Facebook

Inspired by this post on Search Engine Land.

June 16, 2026

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