I’m watching Google add a new layer of AI transparency to ads across Search, YouTube, and Discover. The company said its new How this ad was made section will appear inside My Ad Center, giving people a clearer view of whether AI played a role in the ad creative they see.
The panel will show whether an ad was created or modified with AI. I see this as a meaningful expansion of Google’s ad transparency tools, especially as more advertisers rely on generative AI to produce images, copy, and other campaign assets at scale.
What it looks like. I’ll be able to access the disclosure from the three-dot menu or the info icon on an ad. In the screenshot Google shared with Search Engine Land, the My Ad Center panel includes a dedicated section explaining how the ad was made.
Google will handle some disclosures. When advertisers use Google’s own generative AI ad tools, Google will automatically add the disclosure inside My Ad Center.
Google’s My Ad Center adds a clear AI disclosure, helping users see when ad creative may have been created or edited with generative AI.
For advertisers using third-party AI tools, Google said they will have control over whether to disclose AI use. Depending on local requirements, an AI label may also appear directly on the ad, either automatically or after the advertiser uses that control.
Why I care. AI-generated ads are getting easier and faster to create, so disclosure matters more than ever. I want to know when creative was made or changed with AI because requirements can vary by market, platform, and ad format.
Existing ad rules still apply. Google said its ad policies still prohibit misleading or deceptive advertising, whether AI was involved or not. This update adds more visibility into how an ad was made, but it does not change the requirement that advertisers clearly identify who they are and what they are promoting.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Earlier AI safeguards. Google already embeds imperceptible signals, including SynthID, into content created with its generative AI tools. Election advertisers are also required to disclose synthetic or digitally altered content in political ads, under a policy Google introduced in 2023.
Let me be blunt: SEO advice can sound completely made up to people who do not live in search every day.
When I say things like “change this canonical,”“don’t block that resource,” or “we need this content exposed in the rendered HTML,” I understand why someone outside SEO might hear it and wonder whether I am inventing rules on the spot.
That is one reason SEO still gets treated like black magic inside many organizations.
I have been pushing the idea of “un-nerding SEO” for years, but this is about something very practical: I use Google’s own documentation to earn approval, build trust, and help SEO work get prioritized.
Not because Google tells us everything. Not because every sentence in its documentation should be treated as gospel. I use it because documented evidence is much harder to dismiss than personal opinion.
When I need buy-in, the strongest argument is rarely “trust me.”
It is usually something closer to: “Google has already documented how this should be approached.”
The buy-in problem is usually not the recommendation itself
In my experience, most SEO recommendations do not die because they are wrong. They die because they are competing with everything else happening inside the business.
Dev sprints, product timelines, CMS limitations, legal concerns, brand standards, executive assumptions, and the classic “we’ve always done it this way” all have a seat at the table. SEO is rarely the only priority in the room, even when the recommendation is technically correct.
That is why I do not rely on “best practice says” or “from an SEO perspective” when I am trying to move work forward. Those phrases sound optional, especially to teams already balancing risk, deadlines, and competing requests.
But “Google has official documentation that supports this recommendation” lands differently.
It may not automatically win the argument, and it definitely does not mean the work will be prioritized tomorrow. But it changes the conversation from “the SEO person said so” to “we have official Google documentation explaining why this matters.”
Google documentation is not gospel
I know the objection already: “Are we really pretending Google tells us the full truth about how search works?”
Absolutely not.
Google’s documentation is not the complete truth of search. It has omissions. It simplifies complex systems. Sometimes it explains how Google wants site owners to behave, not every technical factor that influences organic visibility.
Google also writes for a broad audience, which means nuance gets smoothed out, edge cases get skipped, and the answer can be technically true without being the entire story.
So no, I am not treating every Google statement as if it were carved into stone and carried down from Mountain View.
But that does not make the documentation useless.
It makes it a starting point. A receipt. An official reference point.
It moves the discussion away from “I think this matters” and toward “Google has explicitly documented why this matters.” That distinction matters when I am asking someone else to approve and prioritize the work.
Documentation is especially useful with developers
This is where Google documentation often earns its keep the fastest. SEOs need developers, and I have learned that the quickest way to lose developer support is to treat every recommendation like a command instead of a requirement that needs to be implemented thoughtfully.
And yes, just in case it ever works, I still wish I could run this:
google.exe /disable-ai-overviews /please
Bummer. No dice.
Developers are not wrong just because they disagree with an SEO recommendation. Most of the time, they are optimizing for completely valid priorities: performance, code quality, technical debt, security, and avoiding the kind of production mistake that can take a whole site down.
But sometimes developers are wrong about how Google discovers, crawls, renders, indexes, or interprets content.
And telling a developer “you’re wrong” is a great way to make sure my ticket never sees the light of day.
This is where documentation helps. It removes some of the subjectivity and shifts the discussion toward how to implement the requirement inside the existing technical environment.
The point is that I now have an external source of truth to discuss. That is a much better conversation than two teams arguing from preference.
Documentation is also a client management tool
For client-facing SEO work, documentation helps me separate serious recommendations from “trust me, bro, I have a contact at Google” consulting.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
That matters even more when a client has been burned by bad SEO advice before.
Instead of saying, “We need to change this because it’s better for SEO,” I can frame the recommendation with evidence.
“Here’s what Google documents. Here’s where your current setup conflicts with that. Here’s the risk. Here’s the recommendation. Here is the estimated reward.”
That framing builds trust because it shows the recommendation is not relying on blind faith.
It also makes the SEO look less like a magician and more like an interpreter.
That is how I see the real role of SEO: translating Google’s documented needs into business and technical decisions that a team can actually act on.
Less black magic, more receipts
SEO has a reputation problem, and some of it is earned.
Too much SEO work is still explained with vague phrases and shaky confidence. I hear people say things like “Google likes this” or “this needs to exist for the bots” when the stronger version is: “Google documents this behavior here, and here is how it applies to our situation.”
That does not mean documentation alone creates buy-in.
Dropping a Google link into a ticket or Slack thread is not a strategy. I still have to translate what it means, explain the risk, connect it to business outcomes, and help the team understand why the recommendation deserves attention.
Google documentation will never replace experience, testing, or judgment. It will not tell me everything, and I should not treat it like the final answer to every SEO debate.
But it can make SEO easier to defend, easier to prioritize, and much harder for leaders to dismiss.
The best SEOs are not just the ones who know what to recommend. They are the ones who can prove why the recommendation deserves to be taken seriously.
Less black magic. More receipts. More results.
Google documentation may not be the whole truth, but I would rather show up to a buy-in conversation with official references than with “my buddy from Google told me.” Suuuure they did.
This post first appeared on the author’s website and is republished here with permission.
When I’m running Google Ads in 2026, one setting I always check carefully is “Search Partners.” It often appears in campaign settings as a simple way to extend reach beyond Google Search, and on the surface, that sounds useful.
But more reach does not automatically mean better reach. In my experience, Search Partners can bring traffic, but the quality of that traffic is usually the problem.
For most advertisers, I would not leave Search Partners enabled by default. I’d rather start with the main Google Search results page, prove the campaign can convert, and only then consider whether extra volume is worth testing.
What are Google Search Partners?
Google Search Partners are third-party sites that use Google-powered search results. When someone searches on those sites, your ad may be eligible to show there. This network can include YouTube, directories, other search experiences, and even parked domains.
That sounds like a broader opportunity, but I usually see a familiar pattern: lots of impressions, plenty of clicks, and cheaper CPCs than Google Search. The issue is that cheaper clicks are not always useful clicks. Real conversions and meaningful business value from these placements are often limited.
If I’m using conversion-focused Smart Bidding, I often see Search Partner spend fall naturally over time. The bidding system eventually learns that those placements are not producing the conversions it wants, so it stops pushing budget there.
How Search Partners differ from the Google Display Network
I see advertisers confuse Search Partners with the Google Display Network all the time. Some websites can be involved in both, but the intent and placement logic are different.
The Google Display Network is made up of websites and apps that use AdSense, where ads can appear while people browse content. It can show up as a placement option in Demand Gen, Video campaigns where it is called “Video Partners,” and Performance Max campaigns.
Search Partners are tied to search-based activity. That is why they apply to Search, Shopping, and Performance Max campaigns rather than standard Display placements.
How I audit Search Partner performance
I do not recommend taking anyone’s word for it, including mine. The better move is to check what Search Partners are actually doing inside your own Google Ads account.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
For Search or Shopping campaigns
In Google Ads, I go to the campaign view, select Segment, and choose Network (with search partners). This splits performance into separate rows for Google Search and Search Partners, which makes the difference much easier to see.
What I usually find is a lot of Search Partner impressions and clicks, often at lower CPCs than Google Search. But when I look for true conversions, the results are usually weak unless the account is tracking something shallow or easy to manipulate, such as a page view or a low-friction form fill.
For Performance Max campaigns
Performance Max works differently. Search Partners are required for this campaign type, so I cannot simply opt out. What I can do is monitor the activity through the Channel Performance report.
If I see heavy Search Partner spend in a Performance Max campaign, I treat it as a signal to review conversion tracking, bid strategy settings, and the quality of the conversion actions being used for optimization.
Check the Content Suitability report
For more transparency, I also check the Content Suitability report under Insights and reports. This report can show the actual websites or YouTube channels where ads appeared on the Search Partner network.
That list is often enough to make the decision clear. Once I see where the ads have been running, I usually find many placements that look low quality, irrelevant, or simply not worth the spend.
In Google Ads, many decisions really do depend on the account, the market, and the goal. This is one of the few areas where my starting recommendation is straightforward.
If I’m building a new Search or Shopping campaign, I leave Search Partners unchecked. After the campaign is performing well and has strong conversion data, I may test Search Partners for added volume. Until then, I keep the budget focused on the main Google SERP.
This article is part of the ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature and explains what advertisers need to know to get better results in a quick 3-minute read.
I do not see search demand disappearing. I see it moving. In this analysis, 29% of high-volume search demand is declining, while nearly the same amount is growing somewhere else. Overall demand is essentially flat because people are redistributing how and where they search instead of abandoning search altogether.
That changes how I think about SEO strategy. I would not start by panicking over shrinking keywords. I would start by identifying which queries are losing volume, which ones are gaining momentum, and where a brand can build enough authority to appear in both traditional search results and AI-generated answers.
This study looks at where search demand is shifting, which industries are seeing the sharpest changes, and what those patterns mean for SEO teams trying to adapt to AI-driven discovery.
How I studied AI’s impact on search
In 2024, Gartner predicted that traditional search engine volume would fall 25% by 2026 as consumers shifted to AI chatbots and virtual agents. Fractl and Search Engine Land set out to test that prediction. (Disclosure: I’m the co-founder of Fractl.)
I worked from Semrush data covering 1,010,848 high-volume keywords, each with at least 10,000 monthly searches, across 379 brands in eight verticals. I also looked at survey responses from 1,004 U.S. consumers to better understand how AI is changing the way people search.
The analysis measured which keywords gained or lost search volume over the past year, how those shifts differed by industry, and how consumer behavior is evolving as AI tools become part of everyday discovery.
The 29% search decline is real, but it depends on the vertical
Across more than 1 million high-volume keywords, I found that 29% of search volume is in measurable decline. That is 4 percentage points above Gartner’s forecast. In a dataset representing 35.4 billion monthly searches, that difference represents a meaningful amount of search activity.
The impact is not evenly distributed. FinTech showed the largest decline at -37.7%, while Lifestyle saw the smallest decline at -15.2%. Only three of the eight verticals, Insurance, SaaS, and Lifestyle, came in below Gartner’s 25% threshold. FinTech, HealthTech, and Wellness were well above it.
The pattern makes sense when I look at how information-heavy each category is. When a chatbot can answer the question completely, such as summarizing drug interactions, explaining deductibles, or giving a quick overview of a fund, search volume is more likely to fall. When people need to compare prices, complete a transaction, or navigate to a specific site, search demand tends to hold up better.
That is why transactional verticals such as SaaS, Lifestyle, Insurance, and Travel are growing or staying close to flat. Information-heavy verticals such as HealthTech, FinTech, and Wellness are seeing the largest declines.
Before reacting to broad claims about AI-driven search decline, I would benchmark these findings against the specific vertical in question. HealthTech and FinTech teams should expect more exposure than the overall 29% decline suggests. SaaS and Lifestyle teams have more reason to challenge the idea that search demand is simply collapsing.
Search demand is being redistributed
The headline number gets attention, but the offset is just as important. Demand did not vanish. It moved to a different set of words, and those are the terms I would want to understand first.
Among the high-volume keywords tracked, 40.7% are in measurable decline, meaning they lost more than 15% of their volume over the past year. Within that group, the average decline is -41%, and 112,378 keywords lost more than 40% of their volume. For brands that depend on those terms, the impact is significant.
At the same time, 20.1% of keywords are growing by more than 15%. When I add up the volume on both sides, the decline and growth almost cancel each other out.
The 285,489 declining keywords represent roughly 10.29 billion monthly searches. The 140,835 growing keywords represent roughly 10.31 billion monthly searches. Across the entire dataset, the net change is +16.8 million searches per month.
Fewer keywords are growing than declining, but the growing keywords carry more volume each. That is why the totals balance out. In practical terms, I see demand relocating more than shrinking.
The vertical-level growth-to-decline ratios show where that new demand is landing. Lifestyle leads at 2.6x, with 40% of keywords growing versus 15% declining. SaaS follows closely at 2.5x, with 48% growing versus 19% declining. HealthTech sits at the other end with an inverted ratio of 0.4x, making it the most disrupted vertical in the set.
The first audit I would run is simple: pull the tracked keyword set, filter it by year-over-year volume change, and see which side of the ledger the portfolio sits on.
Non-branded queries are the most vulnerable
I see non-branded queries as the easiest ones for AI chatbots to replace. When a search term does not include a brand name, the user is not necessarily trying to reach a specific site or source. The full exchange can happen inside the chat window.
Across the dataset, 90% of all tracked search volume is non-branded. HealthTech, at 99.6%, and Wellness, at 98.5%, are the most exposed. Insurance, at 73.8%, and SaaS, at 82.0%, are less exposed, and both are growing overall. SaaS volume is up 48% year over year, while Lifestyle is up 40%.
If I wanted to identify the content most at risk, I would start with keyword patterns. They offer one of the clearest signals in the study.
The reason SaaS and Lifestyle can be heavily touched by AI and still grow comes down to what happens after the AI answer. If AI recommends a project management platform or a couch, many people still search for the specific brand, retailer, review, or product page before buying. The AI answer creates a downstream search.
HealthTech and FinTech often behave differently. A drug-interaction question or a “what is a deductible” query can be answered completely inside the chat window. There may be no next step that sends the user back to Google.
If a category produces complete AI answers with no natural next click, I would treat AI visibility as a core strategy, not an SEO side project. In those cases, showing up in the answer may be the entire opportunity.
70% of consumers use AI more, but only 17% use search less
The keyword data shows what is happening in the index. The survey data shows what is happening in the minds of the people doing the searching.
Search behavior is spreading across more platforms. Many people are adding AI to their routines without giving up Google.
Social platforms are also acting like search engines in a way they did not a few years ago. YouTube leads at 68%, followed by Reddit at 57%, Instagram at 42%, Facebook at 40%, and TikTok at 33%.
If I had not already prioritized YouTube and Reddit, I would move them up the list. Both rank ahead of TikTok, Instagram, and Facebook as search destinations, and both can also surface in Google results, which gives visibility there a compounding effect.
What has actually moved from Google to AI
More than a third of respondents, 35%, say they have not replaced traditional search with AI for anything yet. Among those who have, how-to guides and tutorials have taken the biggest hit.
For purchase research, 47% of consumers start with a traditional search engine, tied with online retailers at 47%. Only 13% start with an AI chatbot, and shoppers check an average of three online sources before making a purchase.
The number I would bring to a strategy meeting is this: nearly one in five consumers, 18%, have bought something based on an AI recommendation without checking it against a separate search.
That creates a different kind of buyer journey. In that path, the brand may never receive a search-driven touchpoint. To be considered, the brand has to be one of the names the chatbot returns.
Gen Z and millennials are 2.5x more likely than baby boomers to buy based on an unverified AI recommendation, at 20% versus 7%. Across all consumers, 59% say they are likely to visit a brand’s website after an AI chatbot mentions or recommends it.
That is the emerging conversion funnel I am watching closely. Brand mentions in AI answers are starting to function like rankings. Visits to a brand’s website after an AI mention are starting to look like the new click-throughs.
Trust is still mixed. In the survey, 33% of consumers trust AI and traditional search equally, 46% trust search more, and 20% trust AI more.
More than half of consumers, 56%, are at least somewhat skeptical of AI product recommendations. I read that as a sign that people are willing to let AI filter and shortlist options, but many still want to verify before they buy.
The 5-year outlook: Google is not going away, but the shift matters
When asked whether Google will still be their primary search tool in five years, 52% of consumers say yes, including 17% who say definitely and 35% who say probably. Another 27% are unsure, while 20% say probably or definitely not.
The top reasons people prefer AI over traditional search are better summaries across sources, at 21%; faster and more direct answers, at 20%; and the ability to ask conversational follow-up questions, at 19%. More personalized results and avoiding website click-throughs were much lower, at 6% and 4%.
When asked what would bring them back to traditional search, the top answer was AI giving unreliable answers, at 35%. That means much of this shift depends on whether AI maintains trust as adoption scales. More accurate search results followed at 29%, then a preference for multiple source links at 22%, and privacy concerns at 20%.
The 20% who expect to leave Google are not the majority, but I would not dismiss them. A strategy does not need to be rebuilt entirely around them today, but brands do need to appear where those users are already moving.
What this means for content and SEO strategy
I see Gartner’s 25% prediction as a useful directional warning. The real shift may be steeper, but calling it only a decline misses the more important story. Total search volume is basically flat. What has changed is which searches carry the demand.
AI visibility is not just a threat to manage. I see it as a distribution channel. With 59% of consumers saying they are likely to visit a brand’s website after an AI mention, GEO has become a meaningful part of brand discovery.
Earned media, credible third-party coverage, and strong entity signals all help brands appear in chatbot answers. That is why digital PR and GEO are becoming more closely connected.
Search is moving, not disappearing.
The brands that lose will be the ones still optimizing mainly for queries that AI now answers better. The brands that win will be the ones building enough authority to become the answer, whether that answer appears in Google or inside a chatbot.
Methodology
This study combined two data sources to test Gartner’s 2024 prediction that traditional search engine volume would fall 25% by 2026.
Fractl and Search Engine Land analyzed Semrush search volume data for 1,010,848 high-volume keywords with 10,000 or more monthly searches each, covering 379 brands across eight verticals: FinTech, HealthTech, Wellness, Travel, Education, Insurance, SaaS, and Lifestyle. The dataset represented 35.4 billion in aggregate monthly search volume. Keyword-level year-over-year volume change was measured as of April 2026 and classified as declining, meaning more than 15% loss; stable, meaning within 15%; or growing, meaning more than 15% gain. Query pattern groupings, including “What is X,” “Best X for Y,” “X vs. Y,” and “How to X,” were applied at the keyword level.
Fractl and Search Engine Land also surveyed 1,004 U.S. consumers about their search habits, AI tool adoption, and purchase research behavior. The sample was 52% women, 46% men, and 1% nonbinary, with 49% millennials, 26% Gen X, 16% Gen Z, and 9% boomers. The median respondent age was 41, with a range of 18 to 82.
I’m watching a new Google Search ad test that could change how people understand sponsored results. Google appears to be experimenting with AI-generated summaries beneath paid search ads, giving its own AI more influence over how advertiser messaging is framed.
What’s happening. Some advertisers are seeing AI-generated summaries appear directly below Google Ads descriptions in Search results. These summaries include a warning from Google that says: “Google AI responses are generated independently and can make mistakes, so double-check responses.”
I first saw this test surface through digital marketer Darcy Burk, who shared a screenshot of the experience on X. The placement is notable because the AI-generated text appears close enough to the ad that users may treat it as part of the paid result, even though Google says the response is generated independently.
Why I care. If Google expands this more broadly, these summaries could shape how users interpret ads by emphasizing the details Google considers most relevant, not necessarily the exact message the advertiser intended to highlight. That raises real questions about accuracy, brand control, and whether click-through rates could be helped or hurt by AI-written context.
Between the lines. Google has already tested AI-generated summaries for organic search listings, so seeing similar functionality move into paid ads feels like another step in bringing generative AI deeper into the Search experience. What I still do not know is how these summaries are created, what sources they rely on, or whether advertisers will get any say in the copy.
What I’m watching. Google has not publicly announced this feature or responded to requests for comment, so it is unclear whether this is a small experiment or the beginning of a wider rollout. Until Google explains the mechanics, advertisers are left guessing how much control they may have over AI-generated text attached to their ads.
The bottom line. Google is testing AI-generated summaries inside Search ads, and I see that as a sign that generative AI could soon play a larger role in paid search presentation, even when advertisers are not writing that extra copy themselves.
First spotted. Darcy Burk, understandably, was not pleased with this update.
I’m tracking an important AMP update from Google Search: users who tap AMP results will now be sent directly to publisher-hosted AMP pages instead of cached AMP pages shown inside Google’s AMP viewer.
A Google spokesperson told Search Engine Land, “Starting today, we are updating how we connect users to AMP pages from Search, taking them directly to the AMP host pages.”
Google also made it clear that this is not a ranking change. AMP content will continue to rank like any other webpage, and Google said the serving and ranking of AMP content in Google Search and Google Discover will remain the same.
From my perspective, the practical value here is mostly on the publisher side. By sending searchers straight to the AMP host page, Google says publishers should have simpler analytics management and tracking, along with less maintenance work when creating and supporting AMP content.
Google told us it will continue to support the open-source AMPhtml format, and it also posted the update in its Search documentation.
I also think it’s worth noting how much AMP’s role has changed over time. AMP has not received preferential treatment in Google’s Top Stories for a while, and AMP pages are much less common to encounter than they once were. Search Engine Land even turned off AMP in 2021.
It has been a long time since I’ve had much reason to cover AMP closely, but this change matters because it shifts the user journey back to publisher-hosted pages while keeping AMP’s ranking treatment unchanged.
LLMs have changed how people search and how Google responds. The SERP has not been limited to 10 blue links for a long time, but traditional search has usually centered on one core intent: the thing someone is trying to find.
Now, AI Overviews can create a full answer directly in the SERP. They do more than respond to the original query. They also bring in related terms, contextual refinements, and supporting information that help searchers make better decisions.
That is why I pay close attention to Google query expansion. When I understand how Google connects related searches, I can find visibility opportunities that competitors may miss.
What is Google query expansion?
I think of Google query expansion as Google broadening a searcher’s query so it can return more accurate results, especially for long-tail searches that might otherwise produce weak or limited results.
This can happen through synonyms. For example, Google may connect “budget” with “affordable” when the intent is similar.
It can also happen through intent expansion. Google may understand what my audience means even when they do not type the exact words I expected.
Related topic expansion matters too. Google can use similar searches and connected topics to surface content that supports the searcher’s broader need.
I do not use this as an excuse to stuff keywords into a page. Instead, I use query expansion as a research signal. When I see related searches that make sense, I can add useful supporting information and help my content rank for a wider range of relevant queries.
Here is a simple example. If I have an article about backyard chicken care and someone searches “What’s the average lifespan of a chicken?”, my page might appear even if I never used the word “lifespan.”
In that case, Google has decided the article is semantically relevant. Once I know Google has made that connection, I can add a helpful section about chicken lifespan. That gives the page a stronger chance to rank for the term and attract more traffic.
It can also improve the odds that my content appears in relevant AI Overviews.
The difference between Google query expansion and query fan-outs
Google query expansion and query fan-outs are related, but I do not treat them as the same thing.
Query expansion is part of traditional search. Google broadens a query with synonyms, related terms, and intent signals before results are generated. Because of that, my content can rank for searches I did not directly target.
Query fan-outs are part of AI Mode. They break a query into multiple related subqueries while the AI response is being generated. Because of that, my content can be retrieved as a source for an AI-generated answer.
So why does traditional query expansion still matter in a search world shaped by LLMs and AI Overviews?
Because the same semantic relationships that help Google expand a query can also influence which content AI systems retrieve during query fan-outs.
How I find query expansion opportunities
The first place I look is Google Search Console. It is one of the clearest ways to confirm whether query expansion is already happening for my site and my content.
My workflow is straightforward. I go to Performance > Search results, filter by a specific page, pull the full query list, and sort by impressions.
From there, I look for queries I never intentionally targeted. I pay attention to synonyms with meaningful impressions, question-based searches that may be especially useful for AI visibility, and broader keywords that are not currently addressed on the page.
I do not assume every discovered query deserves a content update. Sometimes a page appears for terms that are not truly relevant. When that happens, I audit the page and make sure the content is not drifting into unrelated topics that fail to match the promise of the SERP result.
How I plan better content with query expansion
Once I understand which expanded queries Google is connecting to my content, I use that data to strengthen the page instead of chasing isolated keywords.
I write for topic coverage
For a long time, strong SEO has been less about exact keywords and more about semantic relevance. I try to build coverage around subtopics, related questions, and adjacent ideas because that gives Google more context than a page built around one keyword alone.
I answer questions adjacent to the main topic
For example, if I am working on content for a company that sells chicken feed, I would not only explain the feed itself. I would also consider why the right balance matters and how the right feed can support chicken health.
I can find those adjacent questions by reviewing query expansion data in Google Search Console, checking tools like Ahrefs, and studying the SERP to see what supporting information Google is already surfacing for the topic.
I use expansion data to find content gaps
If Google Search Console shows that Google is pulling my page for a query I have not planned for, and that query is genuinely relevant, I treat it as a signal that the page may need more complete coverage.
Sometimes query expansion data includes odd or unrelated searches. I ignore those. But when I find adjacent queries that clearly strengthen the topic, I add them to the page in a useful and natural way.
I also revisit content regularly, usually at least once a quarter. New queries can appear, while others fade away. Since I am already keeping content fresh for the SERP, query expansion gives me another practical way to make each topic stronger.
How I use query expansion to improve AI Overviews visibility
AI Overviews often pull from ranking pages on a topic to build a more complete answer. Those answers can include semantic connections and supporting subtopics, not just the exact phrase someone searched.
That is why I cross-reference my query expansion data with the main keyword in the SERP. If an AI Overview includes supporting topics that are relevant to my page, I consider adding those topics to the content.
For example, I followed this process for a blog post titled “Tandem vs. Spread Axles in Trucking.” After filtering by impressions, I found that the page appeared for “tandem truck meaning,” even though that exact phrase was not specifically included in the content.
The page ranked first, but it was not included in the AI Overview for that specific query. That told me there was an opportunity.
Because the page already ranked well, I could use the expanded query and the supporting information in the SERP to create a section that better addressed both the query expansion term and the query fan-out patterns behind the AI Overview.
That is the value of this process. Query expansions can reveal supporting topics that strengthen traditional search visibility and improve the chances of being included in AI-driven results.
How query expansion helps my SEO strategy evolve
I use query expansion as a practical way to identify supporting topics and expand content coverage across search experiences.
As clicks become harder to earn, I want my content to appear across more relevant search moments. Broader visibility can strengthen brand awareness, support AI visibility, and keep my content in front of the people most likely to need it.
I’m noting that Google has confirmed its June 2026 spam update is now fully rolled out. The update started on Wednesday, June 24, around noon ET, and finished on June 26 at 2 p.m. ET.
Google’s official status update was brief and direct: “The rollout was complete as of June 26, 2026.”
What stands out to me is that this was the second Google spam update announced in 2026. It appeared to feel somewhat bigger than the March 2026 spam update, but as with most updates, if my site was not affected, I would treat that as a good sign for now.
That said, I always keep in mind that spam updates can sometimes affect sites that are not intentionally trying to spam Google. Hopefully, that is not the case for your site, but it is still worth watching traffic, rankings, and Search Console data closely after a rollout like this.
As for the type of update, Google originally described it as a normal spam update that would roll out across all languages and locations, with completion expected to take a few days.
If I wanted more context on how these updates work, I would review Google’s official documentation on spam updates in this Google help document.
A year ago, I saw 82% of consumers say AI-powered search was more helpful than traditional search. By 2026, that number had fallen to 54%, a 28-point drop in sentiment in just 12 months.
That does not mean people are abandoning AI search. In fact, 70% of consumers say they are using AI tools for search more than they did last year. The tension is clear: adoption is rising, but trust is slipping.
That is the core issue I believe search marketers need to solve in 2026. It is no longer enough to appear in AI answers. I need my brand, and the brands I work with, to be visible, accurate, credible, and trusted when AI systems surface information.
To understand the shift, Fractl partnered with Search Engine Land to expand our 2025 research. We surveyed 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are changing in the AI search era. Disclosure: I am the co-founder of Fractl.
Here is what I believe the data means for 2026 search strategy.
Consumers are using AI more, but trusting it less
AI search adoption is no longer the main story. Seventy percent of consumers report increased use of AI tools for search over the past year, while only 3% say their use has decreased. The bigger question is whether people trust what those tools return.
One surprising finding is that baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically embrace AI while older users lag behind. What I see instead is a more complicated market where trust has to be earned across every generation.
In 2025, only 3% of consumers said AI was less helpful than traditional search. By 2026, that skeptic group had grown to 17%, nearly six times larger than the year before. Even among the 54% who still find AI helpful, enthusiasm is softer: 37% say it is only somewhat more helpful, while 17% say it is much more helpful.
I think hallucinations and low-quality AI content are changing how people evaluate the entire channel. Consumers may use AI because it is convenient, but convenience does not automatically create confidence.
AI content volume has become a brand trust risk
In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%. For me, that makes AI content scale a reputational issue, not just an operational decision.
If I publish AI-assisted content at scale without disclosure, strong editorial standards, or obvious quality signals, I am asking my audience to trust a process they are increasingly skeptical of. That is a risk more brands need to take seriously.
Gen Z is especially strict. Fifty-four percent of Gen Z consumers say heavy AI use in a brand’s marketing would decrease their trust, compared with 32% of baby boomers and 33% of Gen X. Women are also more likely than men to penalize brands for heavy AI use, 44% vs. 34%.
That matters because Gen Z is often the audience most likely to engage deeply, share content, shape online conversations, and influence long-term organic visibility. If that audience matters to a brand, AI-generated filler is not a harmless shortcut.
Disclosure is now a consumer expectation
Across every major content format, more than 80% of consumers want AI-generated content labeled. Video leads at 91%, followed by images at 90%, audio at 87%, and written content at 84%. More than half of respondents strongly agree with labeling in every category.
I do not read that as a mild preference. I read it as a near-universal expectation. The brands that treat AI disclosure as optional are creating a gap between how they operate and what their audiences want.
Consumers still believe AI will shape the future of search. Sixty-four percent agree that AI will replace traditional search engines within five years, nearly unchanged from 66% in 2025. The channel is not going away. But being present in AI results and being trusted in AI results are now two different challenges.
Google still leads on trust, especially for buying decisions
When consumers are making purchase decisions, 39% turn to Google first. Reddit follows at 15%, AI tools at 14%, and review sites and friends or family each at 11%. The trust people have built with Google has not automatically transferred to AI tools.
Platform preference also changes by query type. Google dominates five of six major search categories. It is the first stop for local businesses, product research, travel planning, and health questions. YouTube overtakes Google for how-to content, while ChatGPT is now the second-most-used destination for health questions and ranks strongly for product research, travel planning, and how-to content.
That tells me there is no single AI search platform to optimize for. I need to map content strategy to actual user behavior: where people search, what they are trying to decide, and which platforms influence confidence at each stage.
Before making a purchase decision, the average consumer checks 2.4 platforms. Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2. This behavior is consistent enough that I now think of search optimization as a multi-platform visibility strategy, not a rankings-only discipline.
A brand that appears in Google results but nowhere else can lose to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has strong third-party review content. Visibility now has to travel with the buyer.
AI is changing marketing operations quickly
AI now touches 53% of marketing work on average, up from 38% in 2025. In practical terms, the equivalent of one full workday per week has shifted to AI-assisted workflows in just 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it is involved in three-quarters or more.
For SEO and content teams, this means competitors are moving faster. But speed alone is becoming commoditized. Accuracy, original insight, expert judgment, and brand credibility are much harder to copy.
Marketers are also feeling pressure to adopt AI. Fifty-five percent of marketing roles report a 7-out-of-10 level of pressure to use it. SEO and analytics teams feel that pressure most, while PR is not far behind. As AI makes generic content easier to produce, the advantage shifts toward what AI cannot automate well: judgment, relationships, trust, and reputation.
The quality tradeoff is real. Only 26% of marketers say AI made their work both faster and better. Nearly half say it made their work faster but more generic, and 7% report an outright quality decline.
That is where I see a major competitive opening. If other teams are scaling generic AI content while I invest in original data, expert quotes, third-party validation, and earned brand mentions, I am building assets that are more visible, credible, and retrievable across search engines, social platforms, and LLMs.
AI governance is still too weak
About three in four organizations conduct human editorial review before publishing AI-generated content. Sixty-two percent check for brand voice, 54% check facts, and 42% conduct legal or compliance review. Only 27% evaluate content for bias.
That means nearly half of AI-generated content may enter the market without fact-checking, legal review, or plagiarism checks. Too many teams are still relying on surface-level review: Does it sound right? Is the tone appropriate? Are there typos?
In a year when consumers are already prepared to distrust generic AI content, I see governance as one of the cheapest gaps to close and one of the most expensive to ignore.
The disclosure gap is just as serious. Heavy, generic AI use is now a brand-trust liability, yet only 20% of organizations always disclose AI use to their audiences. Compare that with the 84% average consumer demand for labeling written content, and the disconnect is obvious.
The takeaway is not to abandon AI. It is to stop treating governance as optional. Every AI workflow needs accuracy checks, transparency standards, bias review, and human accountability before content reaches an audience.
AI hallucinations are already a brand problem
A year ago, about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved to 24%. At the same time, 27% of brands have already been misrepresented in AI-generated responses, and 14% say an AI inaccuracy has affected a customer relationship, sale, or PR situation.
More brands have been misrepresented by AI than have a formal monitoring process. That should concern every search and communications team.
If AI is summarizing my category, comparing my product, or explaining my brand incorrectly, that is not only an SEO issue. It is a reputation risk, a revenue risk, and a PR issue waiting to escalate.
When AI misrepresents a brand, I believe fixing the source matters more than arguing with the output. That can mean reaching out to publishers for updates, correcting owned profiles, improving brand pages, and publishing clear correction content tied to the entity.
Organic traffic is under pressure, not in freefall
Half of the marketers surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI. That is meaningful, but it is not the whole story.
The larger shift is not simply from Google to ChatGPT. It is from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across platforms, communities, assistants, and review environments.
The same marketers reporting organic losses are often finding visibility elsewhere. Fifty-seven percent report growth from social platforms such as TikTok, Reddit, and YouTube. Forty percent see growth from AI assistants such as ChatGPT, Gemini, and Perplexity. Thirty-one percent see growth in direct or branded traffic, while only 10% report no visibility growth anywhere.
That is why I think 2026 brand visibility depends on brand mentions and entity authority across the web, not just individual page rankings in Google.
Marketers are prioritizing the easiest tactics
Many teams are moving in the right general direction: community building, earned authority, owned audiences, expert content, and traffic diversification. The most prioritized strategies include building brand presence on social platforms at 59%, GEO and AEO optimization at 54%, and creating authoritative expert content at 44%.
Half of surveyed marketers say organic traffic has fallen since AI Overviews arrived, but the data points to pressure rather than collapse, with 30% reporting no change.
But the least prioritized strategy is original research and data, at only 15%. I see that as a strategic inversion.
Original, proprietary research is one of the hardest content assets for AI to replicate or commoditize. It earns citations, attracts links, builds topical authority, and gives journalists, communities, search engines, and AI systems something distinctive to reference.
In GEO, the same pattern appears. Many marketers are using content-led tactics that AI can easily replicate. Long-tail FAQs can help with AI Overviews, and schema can support structure, but neither one builds credibility by itself.
As organic search pressure grows, marketers are finding brand visibility gains across social platforms, AI assistants, direct traffic and Google AI features, according to Fractl and Search Engine Land.
The stronger moat is entity authority: proprietary data, expert perspectives, topical depth, and third-party validation. These are the assets that make a brand worth citing.
GEO measurement is lagging behind execution
Only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results. That is understandable for a newer channel, but GEO is becoming too important to manage casually.
Marketers are leaning into practical GEO tactics, with FAQ optimization leading the pack, while entity authority, original research and citations trail behind.
I believe visibility tracking, citation monitoring, branded search lift, and AI-assisted conversion analysis all need more attention. Teams that can prove GEO ROI will be able to defend and grow investment while others are still guessing.
The main barrier to deeper AI integration is not leadership buy-in. Only 2% cite that as the obstacle. The top barrier is team training and skill gaps at 26%, followed by tool fragmentation at 20%, budget constraints at 19%, unclear ROI at 12%, and legal or compliance concerns at 12%.
For search teams, that means AI literacy, prompt strategy, content quality control, and GEO measurement skills may be more valuable right now than adding another tool to the stack.
Most marketers see early signs their GEO strategy is working, but only 12% report measurable results, highlighting a major gap in AI search measurement.
What I would do for a 2026 search strategy
First, I would audit the brand’s AI footprint. I would query the brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews, then document what is accurate, what is missing, and what is wrong. Waiting until an AI error becomes a PR issue is too late.
Second, I would invest in entity authority and original research. AI cannot invent legitimate proprietary survey data, named expert perspectives, verified brand facts, or original market analysis. Those assets become more valuable as AI systems get better at rewarding genuine authority.
Third, I would distribute visibility across multiple platforms. Google organic remains necessary, but it is no longer sufficient. A brand needs a consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media.
Fourth, I would build AI content governance, not just AI content workflows. Consumer demand for AI disclosure ranges from 84% to 91% across formats, while only 20% of brands always disclose. That gap is a reputational liability and may become a legal and regulatory one.
Fifth, I would close the GEO measurement gap. If I can connect AI search mentions to traffic, lead quality, and revenue, I can prove ROI at a time when most teams cannot. That creates a budget and strategy advantage that compounds.
Finally, I would double down on what AI cannot easily replicate: proprietary data, named experts, human-verified claims, transparent sourcing, and a consistent high-quality brand voice. In 2026, the brands that treat quality as a strategic differentiator are the ones most likely to be surfaced, cited, and trusted.
Methodology
Fractl and Search Engine Land surveyed 1,008 U.S. consumers and 150 marketers in Q2 2026. The consumer sample was nationally representative across age, gender, and region. The marketer sample included companies ranging from fewer than 10 employees to more than 5,000 and covered roles in SEO, content, social, analytics, paid media, PR, and marketing leadership.
Where noted, findings are compared year over year against the same questions asked in Fractl’s 2025 consumer study conducted with Search Engine Land.
What Google said. Google wrote, “Released the June 2026 spam update, which applies globally and to all languages. The rollout may take a few days to complete.”
Timing. I expect this update to move fairly quickly, since Google said the rollout may take only a few days to finish.
Why I care. Google releases search ranking updates several times each year, and spam updates are meant to target sites that use manipulative tactics to abuse the ranking system. If a site is not relying on those kinds of practices, I would not expect it to be the main target of this update.
More on spam updates. Google’s documentation explains that its automated systems are always working to detect search spam, but the company occasionally makes notable improvements to those systems and labels them as spam updates.
Google also points to SpamBrain, its AI-based spam-prevention system, as one example of how it improves its ability to identify spam and catch new types of abuse.
If I saw a ranking change after a spam update, my first step would be to review Google’s spam policies and make sure the site is complying with them. Sites that violate those policies may rank lower or disappear from results, while improvements can help over time if Google’s automated systems recognize that the site is now compliant.
For link spam updates specifically, Google says recovery can work differently. If Google removes the value of spammy links, any ranking benefit those links once created is lost, and that benefit cannot be regained simply by cleaning up the links later.
