As someone who’s been closely observing AI advancements, I found Google’s AI Overviews to have improved significantly. By February, they correctly answered standard factual benchmarks 91% of the time, a notable rise from 85% back in October. This assessment came from a rigorous analysis conducted by The New York Times in collaboration with the AI startup, Oumi.
Yet, considering Google processes more than 5 trillion searches annually, this still implies that millions of answers could be incorrect every hour. In essence, there’s much room for improvement.
Why it matters to me. My interactions with Google have evolved from just link clicks to encountering AI-generated summaries. This evolution suggests that while AI Overviews have gotten better, they still mix accurate responses with poor sourcing and blatant errors, potentially misleading searchers and affecting visibility for many publishers.
The nitty-gritty details. Oumi put 4,326 Google searches to the test using SimpleQA, a benchmark known for measuring factual precision in AI systems. AI Overviews hit a 91% accuracy rate post-upgrade to Gemini 3 from Gemini 2’s 85%.
The more pressing issue for me is the sourcing. Oumi discovered that more than half of February’s correct responses were ‘ungrounded,’ meaning the linked references didn’t fully back the answers.
This lack of grounding makes verification a challenge. Even if the answer is correct, the linked pages might not sufficiently illustrate the reasoning.
What shifted. While the accuracy saw improvements from October to February, grounding declined. In October, 37% of accurate answers were ungrounded; by February, this figure increased to 56%.
Real-world examples. The Times pointed out several inaccuracies: For instance, Google incorrectly dated when Bob Marley’s home became a museum. Google’s answer was 1987, but the actual year was 1986, and the cited sources conflicted. A search about Yo-Yo Ma and the Classical Music Hall of Fame yielded a link to the Hall’s site, yet Google stated he wasn’t inducted. Moreover, while Google got Dick Drago’s age at death right, it flubbed his date of death.
Google’s standpoint: Google contested the Times’ findings, arguing that the benchmark used in the study was flawed and didn’t mirror actual search behavior. Google spokesperson Ned Adriance mentioned that the study had some ‘serious holes.’
Furthermore, Google asserted that its AI Overviews utilize search ranking and safety measures to minimize spam and has consistently cautioned that AI responses might contain errors.
I remember the days when a Google search was akin to embarking on a quest for information. It was an adventure of navigating various links and forming my own opinions.
Nowadays, tools like AI Overviews, ChatGPT, and Perplexity condense all that information into a single, simplified answer. This transformation often strips away the finer details while amplifying certain perspectives.
This shift has redefined online reputation management. Now, search engines not only present information but shape the underlying narratives. This raises the stakes for brands, as even a top-ranking status doesn’t guarantee influence if AI stories tell a different tale.
For brands, the game has changed. Being number one doesn’t ensure visibility and influence anymore. The underlying narrative holds far greater power.
AI Narrative Formation: Crafting User Answers
AI platforms now utilize what I like to call ‘AI narrative formation.’ This process crafts the responses we receive from various search engines. Let me walk you through how this system works.
Source Pooling
These systems pull content from numerous sources. Contrary to expected reliance on peer-reviewed articles, they gather data from Reddit, YouTube, and social platforms like Instagram and TikTok.
Signal Weighting
Not all sources are equal. Often, a popular yet low-quality source can outweigh a singular, credible entry. A bustling Reddit thread with negative feedback might overshadow a well-researched Wikipedia page.
Narrative Compression
The summarization process compresses diverse inputs, often losing nuance along the way. Complex reputations are simplified into general statements like, ‘Users find this company untrustworthy.’
Continued Reinforcement
These summaries transcend their original context, getting shared and re-shared across social media. As these echoes return as new data, they further entrench the narratives in AI responses.
Unraveling a Finance Company’s Reputation in AI Search
To illustrate AI narrative formation, consider a recent case I worked on involving a financial company, which we’ll call Company X.
Company X’s reputation remained strong on traditional SERPs. High Trustpilot ratings and reputable endorsements were the norm until Google AI Overview threads surfaced a forgotten Reddit forum rife with grievances against them.
The AI Overview skewed the narrative, suggesting Company X had unresolved customer service issues, even though these concerns had been addressed years prior. This created a skewed perception that was hard to counteract.
The Amplified Risk from AI Searches
AI dramatically increases reputational risk through several mechanisms:
The Spread of Negative Narratives: Negative content surfaces faster and more prominently than before.
AI Hallucinations: Despite growing awareness, AI inaccuracies continue to deceive.
The Snowball Effect: Repeated narratives gain momentum, complicating reputation management efforts.
It has become evident that in ORM, repetition often overrides accuracy.
Auditing AI-Generated Narratives: A Step-by-Step Approach
Let’s consider a situation involving an AI-generated narrative challenge faced by CEO X of a well-known SaaS company.
After an out-of-context quote from CEO X’s podcast appearance went viral, AI summarized him unfavorably. Quickly, his reputation transformed negatively across major platforms.
Step 1: Mapping Queries
I initiated a process to understand what queries AI outputs were generating about CEO X. This helped identify the underlying issues.
Step 2: Capturing Outputs
Identifying repeated claims revealed how CEO X was perceived. Narratives from Google AI and ChatGPT were consistently portraying him negatively.
Step 3: Delving Through Sources
The next step involved examining the quality of sources contributing to these narratives, often outdated or lacking accuracy.
Step 4: Analyzing the Narrative Gap
This involved assessing discrepancies between AI narratives and his actual reputation, contextualizing the initial quote, and examining the long-standing perception of CEO X.
Step 5: Correcting and Replacing Sources
Finally, I focused on directly addressing, correcting, and replacing those negative narratives. This involved engaging directly with platforms that contributed to the misinformation and reinforcing positive content elsewhere.
A New Perspective: From SEO to Narrative Management
The focus has shifted from merely achieving top SEO rankings to understanding and adapting to narrative shifts. We must rethink our strategy from content engagement to managing the narratives AI disseminates.
To succeed, it’s important to reinforce AI systems with quality inputs, including crafting high-quality content, pursuing credible mentions, disseminating structured data, and managing misinformation directly.
I was surprised when despite all the right moves—maintaining a fast website, creating comprehensive content, and achieving a top 10 ranking—my site didn’t show up in Google’s AI Overview. It turns out that high rankings don’t guarantee AI Overview visibility.
This issue isn’t about how well my content ranks, but rather how it’s retrieved. Understanding this distinction is vital for anyone involved in SEO today.
AI Overviews prioritize content that offers the clearest, most usable answers, rather than just relying on high-ranking signals.
If my content doesn’t meet this standard, my search ranking becomes irrelevant. I realized I needed to understand where things were going wrong to make sure my content appeared in more AI Overviews.
The ranking-citation gap is real — and growing
The overlap between AI Overview citations and organic rankings increased from 32.3% to 54.5% between May 2024 and September 2025, according to BrightEdge. Although positive, this means that many AI Overview citations still come from pages not ranked at the top. Google often chooses pages that better suit the AI Overview format.
This trend varies by industry. In ecommerce, the overlap stayed almost flat over time, while in YMYL categories like healthcare, insurance, and education, it remained between 68%-75%.
High ranking and visibility don’t always align. I’ve seen scenarios where I rank second but remain invisible, while sometimes ranking on the second page gets more visibility in an AI Overview.
1. Your content answers the wrong version of the question
AI Overviews are often triggered by long-tail, conversational searches. These drive 57% of AI Overviews, whereas commercial queries less so, according to Semrush.
Google’s AI looks for content matching user intent, not just the keywords. For instance, a query about managing remote teams may overlook my page if it primarily discusses “project management software.”
2. You’ve buried the answer
If I start with too much context and not enough answer, search systems move on. They extract clean, immediate information. If my response isn’t close to the top, it gets skipped.
3. Your structure is opaque to AI systems
AI systems need clear, self-contained answers with concise paragraph structure and heading hierarchies. Overly complex narratives confuse AI, even if the content is accurate.
4. Your E-E-A-T signals aren’t visible at the content level
Google emphasizes E-E-A-T signals for quality. These need to be explicit in the content, beyond domain authority. Each page needs to establish credibility independently.
Who wrote it?
Where did the data come from?
Does it demonstrate field expertise?
Such signals are crucial in YMYL content where misinformation risks are high.
5. You’re targeting queries that don’t trigger AI Overviews
Before optimizing for AI, I check if my queries trigger Overviews. As of late 2025, they appeared in 16% of searches, but not evenly across types.
Transactional queries, navigational searches, and local searches trigger fewer Overviews. If my traffic is commercial, the lack of a citation might not reflect my content quality but the nature of the query.
What the data tells us about the impact of this shift
The stakes are high. Seer Interactive found AI Overviews reduced CTRs for informational queries by 61% between June 2024 and September 2025. Brands featured in Overviews, however, experienced a 35% increase in CTR.
As Pew Research noted, only 8% of users clicked a traditional result when AI Overviews were present. Without being cited, I could miss not just the Overview visibility but also clicks from organic listings.
How to optimize for retrieval, not just rankings
Rewrite introductions: Provide a direct answer immediately. Context can follow later.
Restructure headings: Make them specific and complete. Each section should operate independently.
Add explicit expertise signals: Use author details, original insights, and reliable sources to enhance credibility.
Audit query triggers: Check if queries trigger AI Overviews and study cited source structures.
Expand topical coverage: Don’t focus excessively on a single page. Deliver comprehensive knowledge across your topic.
AI Overviews show the split between content quality and ranking signals. High rankings used to equal quality, but now they don’t guarantee AI compatibility.
Ranking still matters, but understanding AI identification and retrieval processes is critical for visibility today. We can no longer rely solely on top rankings to bring visibility.
To improve AI Overview inclusion, I focus on understanding how AI systems extract information, making content adjustments accordingly.
As someone who closely follows advancements in technology, I was thrilled to learn about Google’s latest breakthrough with the TurboQuant algorithm. It’s designed to enhance the speed of vector searches, fundamentally changing the way we interact with AI-powered data searches.
If you’re like me and value precision in data retrieval, this algorithm is exciting news. A tiny error-correction signal maintains compressed vectors’ accuracy, enabling AI systems to retrieve data more broadly and precisely than ever before.
Google’s TurboQuant is a compression algorithm that can shrink and organize large AI datasets with nearly zero indexing time. This technology might just obliterate one of the major speed bottlenecks in modern search engines.
What TurboQuant Is. For me, TurboQuant represents a monumental way of handling the data behind AI and search by keeping it compact without losing precision. It significantly reduces memory usage and cuts down the time to build searchable AI indexes almost to zero, according to Google’s research paper.
How It Works. Modern search systems, which convert content into vectors, can be resource-heavy. These numeric representations cluster based on similarity, allowing searches to match the closest ideas. But let’s face it, these vectors are massive and expensive to store. That’s where TurboQuant steps in, using efficiently compressed data that mirrors the original extremely well through:
Smart Compression. It rotates data mathematically, organizing it like neatly packed boxes, an image that resonates with how I like to visualize innovative data solutions.
Error Correction. By introducing a 1-bit signal, it corrects minor compression mistakes, ensuring the data remains accurate, which is quite a comforting thought for anyone concerned about data integrity.
What This Means. For those of us deeply engaged with AI, TurboQuant signifies a shift. Vector search systems, the backbone of semantic search and AI-driven answers, have traditionally been slow and costly. Google claims TurboQuant makes these operations quicker and more cost-effective, enabling faster similarity searching, lower memory consumption, and real-time processing of colossal datasets.
Why It Matters to Us. Imagine Google being able to analyze far greater volumes of documents per query, not just a limited subset. Should Google implement this into its Search, AI Overviews could access a wider, more accurate range of sources, making instant summaries from large data sets far more accessible.
I’ve noticed that the cost-per-click (CPC) is increasing across most industries, and I’m sure you’re observing the same. Let’s dive into what’s causing this trend and explore strategies to safeguard your profit margins.
According to WordStream by LocaliQ’s 2025 benchmarks, nearly 87% of industries saw their CPCs rise year-over-year. The average CPC for Google Ads across sectors is now at $5.26 per click. In high-intent verticals, such as legal services, the average is $8.58, with some competitive B2B segments reaching $8 to $9 per click.
These increases reflect significant shifts in the design of search results pages, the optimization of auctions, and inefficiencies that accumulate across paid search accounts. Often, these issues remain hidden until a detailed PPC audit brings them to light. To begin reclaiming your budget, especially your branded terms, you need to understand the current landscape.
Here are the five trends every advertiser needs to grasp at this moment.
What’s Driving Your CPC?
More Advertisers Are Chasing the Same Limited Inventory
At its core, search advertising is an auction. As more advertisers target the same keywords, prices naturally increase. While global PPC spending continues to rise (Quantumrun Research), the number of available click slots on search results pages hasn’t expanded at the same pace. This results in higher CPCs, as more money competes for limited inventory.
The pandemic has had a permanent effect on this shift. Brands that previously didn’t invest in paid search have now joined Google’s auction and have stayed active.
Google’s AI Overviews Are Taking Over
Over the past decade, one of the most significant changes in paid search is happening right within the Search Engine Results Page (SERP). Google’s AI Overviews now dominate the space for informational and exploratory questions. As they grow into 2024 and 2025, they diminish the number of organic and paid listings visible above the fold.
A late-2025 analysis by Seer Interactive, reviewing 3,119 search terms across 42 organizations, revealed that the paid click-through rate (CTR) on queries with AI Overviews declined by 68%—from 19.7% to 6.34%.
The straightforward mechanism is that AI Overviews take more real estate (Skai), reducing the number of visible paid placements above the fold. As a result, impression share tightens, and automated bidding becomes more aggressive, driving up prices.
The important detail here is that users who navigate beyond an AI Overview tend to be further in their purchasing journey. WordStream data indicates approximately 65% of industries experienced higher conversion rates despite the increase in CPCs. This suggests the need to shift budgets toward high-intent transactional queries where AI Overviews are less likely to dominate, and away from informational queries where they are prevalent.
Smart Bidding Is Raising Auction Costs
Modern Google Ads campaigns more heavily rely on automated bidding strategies like maximizing conversions or targeting CPA. According to Google’s Smart Bidding documentation, the system precisely sets bids for each auction based on predicted conversion chances, prioritizing performance over cost control.
As almost every competitor utilizes the same logic, there’s a self-reinforcing loop of rising bid pressure, a market-wide dynamic that you need to adapt to rather than reverse.
Unauthorized Brand Bidding Is Inflating Costs Internally
Although platform algorithms and macroeconomics are beyond your control, one significant driver of CPC inflation is something you can manage.
When affiliates, partners, or competitors bid on your trademarked keywords, they enter an auction that should have minimal competition. Each additional bidder elevates your branded CPC, making you pay twice: once to create the demand, and again when third parties capture that same searcher at the bottom of the funnel.
The impacts accumulate. AI Overviews have already condensed available click inventory; unauthorized brand bidding further inflates the inventory cost you actually secure.
Detecting violations goes beyond manual SERP checks. Unauthorized bidders frequently use cloaking—geotargeting away from your headquarters or dayparting outside business hours—to evade detection. With a platform like Bluepear, you can implement automated 24/7 monitoring across search engines, geographies, and devices, capturing ad copy and landing page evidence to contest invalid affiliate commissions and enforce trademark guidelines at scale. Fewer bidders on your branded terms mean less auction pressure and lower CPCs for traffic you rightfully own. It’s one of the few paid search levers that doesn’t need a comprehensive strategic overhaul to be effective.
What To Do About It: Three Priorities for Advertisers
The gathered data indicates three clear priorities as you navigate this environment:
Protect your branded baseline. Your branded keywords represent demand you’ve already generated. Rigorously monitor competitors in those auctions and eliminate unauthorized bidders with automated brand protection tools—an essential high-leverage action at present.
Anchor optimization to cost per acquisition. Based on WordStream’s 2025 benchmarks, higher CPCs can bring a higher-quality, further-down-funnel user, leading to a lower CPA. The headline CPC figure is becoming an unreliable measure for campaign health.
Build first-party data infrastructure. The best defense against continued CPC inflation is leveraging high-quality, proprietary conversion signals for your bidding algorithms, thus minimizing reliance on the platform’s broad audience approximations.
Average CPCs are reaching new heights and this trend is unlikely to reverse. Advertisers who effectively manage costs have already adjusted their strategies in response.
Unsure how many unauthorized bidders are in your branded auction at the moment?Register with the promo code BRANDAUDIT to receive a personalized audit of your branded search landscape from the Bluepear team within 48 hours!
For continuous insights into branded search and paid search protection, follow Bluepear on LinkedIn.
Seeing the shifts in Google’s search traffic firsthand, I’ve noticed publishers losing organic search traffic, yet there’s a silver lining with breaking news traffic soaring by 103%, and Google Discover clicks surging.
Google’s AI Overviews might be cutting into traditional search clicks, but I believe publishers can still find significant growth through breaking news and Google Discover according to recent insights from Define Media Group.
Organic search clicks have dropped 42% since AI Overviews began expanding in Google Search, based on Define Media Group’s analysis of Google Search Console data from 64 sites. It’s quite revealing!
Why we care. AI-generated answers are dramatically reshaping how search traffic is distributed. While evergreen content loses clicks, real-time news coverage and Discover distribution are becoming more potent channels for us publishers.
By the numbers. In Google Search, Discover, and Google News, breaking news traffic has grown 103% from November 2024 to early 2026 within the company’s dataset. However, losses have mainly hit informational and evergreen content.
Here are some figures to consider: organic search traffic averaged 1.7 billion clicks per quarter from Q1 2023 through Q1 2024. Post AI Overviews launch, traffic took a 16% plunge immediately and couldn’t recover. As Google expanded AI Overviews in May 2025, these declines accelerated. By Q4 2025, search traffic had fallen 42% from the pre-AI Overviews baseline.
Discover’s role: Google Discover, which has grown by 30% across the portfolio, is becoming a primary growth engine for breaking news distribution, rising steadily even as web search traffic dips. It’s the first time Discover and web search have driven almost equal traffic.
Interestingly, the report highlights a significant increase in Discover traffic following the December 2025 Google core update, although some gains eased after the February 2026 Discover core update. Yet, according to Chartbeat data, Discover was the main driver of Google referrals to news sites last summer.
Why is this happening? AI Overviews appear less frequently for news queries compared to other topics. Reports show that AI Overviews appeared for only about 15% of news queries, which is nearly three times less often than in categories like health and science.
It seems news queries frequently trigger the Top Stories carousel, linking directly to publisher articles, especially for major events such as international conflicts. Define Media Group suggests that Google may avoid AI summaries for breaking news due to rapid changes and high accuracy needs.
AI Overviews are reshaping the landscape of paid search by lowering click-through rates, increasing cost-per-click, and compressing the buyer journey. As I’ve seen in my own campaigns, adapting to these changes is crucial for maintaining performance and staying competitive.
I’ve noticed Google’s AI Overviews appear across search results with varying frequency. However, in some categories, they take over completely. According to Adthena:
Finance queries with five or more words see AI Overviews on 79% of searches.
Retail shows an 84% visibility for comparison and product discovery queries in the 9-10 word range.
Healthcare keywords, even short ones (1-3 words), trigger high AI Overview penetration.
I realize that organic traffic faces obvious challenges, yet the downstream impact on paid search is more severe than I thought. Here’s how that manifests in practice.
AI Overviews systematically alter paid search by affecting click volume, auction dynamics, and user behavior during conversion. They speed up structural trends that reshape search, such as SERP saturation, automated bidding, and Performance Max adoption.
The speed at which Google rolled out AI Overviews is staggering. Many verticals have seen transitions that typically spanned years compressed into months. To understand how this impacts my paid search, I must consider how AI Overviews have reshaped each component of campaign performance.
So now, how much have the response rates been affected by AI Overviews? Recent data from Seer Interactive shows the decline’s scale. Paid CTR on queries featuring AI Overviews plummeted by 68%, dropping from 19.7% to 6.34% between June 2024 and September 2025.
At the same time, organic CTR fell 61% on the same queries, but the steeper decline in paid traffic suggests AI Overviews reshape where paid ads appear and who clicks them, not simply their overall presence.
The drop accelerated sharply in July 2025, when paid CTR collapsed from approximately 11% to 3% within a month due to Google aggressively expanding AI Overviews.
Non-branded informational queries saw the most severe declines. But it’s not all bad news. Branded searches and high-intent queries exhibited greater resilience, and many advertisers noticed minimal impact on key conversion terms.
There’s a direct link between AI Overviews and rising campaign costs. As response rates decline, CPC inflation occurs due to supply and demand mechanics. Google Search spending grew 9% YoY in Q1 2025, but click growth was just 4%. The 5% gap reflects more money chasing fewer clicks.
AI Overviews boost CPC inflation via several mechanisms, including ad positioning. Research on ad positioning reveals that ads performing well above an AI Overview see a performance dip for those below, reducing impression share and CTR.
AI Overviews also accelerate the consideration phase of the buyer’s journey. Activities that once took days are now compressed into minutes, facilitating research and comparisons across sessions.
For instance, what used to be a multi-day process in 2023, like looking for the [best project management software for remote teams], can now convert users in a single session with the help of AI Overviews.
This shift affects campaigns in three ways: smaller retargeting pools, diminished brand awareness, and AI Overviews mentions being a must for visibility.
The compression of the buyer journey results in a surprising economic outcome. While click volume shrinks, conversion rates improve. An analysis of 16,446 campaigns showed enhanced conversion rates in 65% of industries despite reduced click volume.
Enhanced conversion rates signify that AI Overviews are filtering out casual inquiries, leaving high-intent prospects to convert. While this could offset CPC inflation, the need for strategic adaptation in campaigns remains vital.
Therefore, let’s discuss the four strategic pivots I find essential in today’s AI-driven search environment.
First, monitor and optimize informational intent performance. Given AI Overviews’ impact, systematic observation and adaptation are necessary to identify profitable versus draining keywords.
Second, prioritize feed quality. AI can summarize but not invent details like price and inventory. Robust product feeds offer a competitive advantage here.
Third, craft creative that stands out. Ads need to answer why customers should choose your service over others and why now.
Fourth, leverage audience data over keyword targeting. Audience lists built from first-party data allow targeting based on customer relationships.
In conclusion, AI Overviews are reshaping paid search, leaving advertisers at a crossroads. Personalized strategies that embrace new realities will help navigate these challenges effectively.
When I first heard about Google’s AI Overviews, I realized they weren’t just going to affect visibility; they had the potential to hit revenue hard. Adthena’s latest data analysis sheds light on just how significant this impact could be on CTR and CPC.
Adthena dove into a detailed study from late December 2025 through January 2026, involving a comprehensive look across six major industries. This involved tracking performance metrics from millions of ads.
While on the surface, aggregate data seemed stable, a closer inspection revealed a more complex reality. For advertisers like myself, these automated summaries don’t just pose visibility issues; they’re a direct threat to PPC revenue.
What AI Overviews Mean for Paid Search Revenue
AI-generated summaries are altering the very structure of successful campaigns. When a Google AI Overview pushes ads below the page fold, it sets off a sequence of events impacting my profitability:
Lower CTR = fewer clicks: With diminished visibility, there’s a noticeable drop in visits to landing pages, diminishing the traffic flow.
Fewer clicks = fewer conversions: A decrease in traffic inevitably means fewer leads or sales.
Higher CPC = reduced profitability: In industries where AI summaries appear on competitive terms, maintaining relevance costs more, squeezing margins and lowering ROAS.
AI Overviews Impact Across Six Industries
Adthena’s study tracked AI Overview frequency, content themes, and CPC/CTR performance across devices. The results paint a complex picture, with impacts varying by industry, device, query type, and content intent.
Content Themes: The Battle for Mid-Funnel Intent
Adthena pinpoints a shift where Google moves deeper into comparison and instructional content spaces, directly targeting high-converting paid search areas.
The comparison conflict: In Telecom, Technology, and Retail, AI Overviews frequently deliver comparison content, which could satisfy user curiosity prematurely, preventing a further click on my ads.
The informational buffer: In Healthcare and Financial Services, themes like news and FAQs can act as intent barriers, potentially safeguarding ad spend by meeting low-intent signals before a user clicks on a paid ad.
The opportunity gap: Problem-solving content remains mostly unaffected at 0-2%. This creates a safe harbor for advertisers, with minimal AI interference in these areas.
CPC Trends: The Premium for Visibility
By tracking CPC fluctuations, I can identify where the cost of visibility is increasing due to the presence of AI Overviews.
Technology: AI Overview-related queries consistently yield higher CPCs, signaling increased costs for visibility.
Automotive & Retail: Across these sectors, costs remain similar regardless of AI Overviews, signifying less immediate impact.
Financial Services: Even modest CPC spikes can significantly impact profitability in industries with already high CPCs.
Device Splits Expose Desktop Saturation
Breaking down data by device reveals notable differences, showing more nuance than initially apparent.
Desktop dominance: Queries in Technology and Education are heavily populated by AI Overviews, making ad competition unavoidable.
Mobile opportunity: While AI Overviews appear less frequently on mobile, they more aggressively displace ads due to limited screen space, unlike desktop where multiple ads can sit below the overview.
CTR Trends Provide Evidence of Traffic Erosion
Examining CTR trends reveals ongoing discrepancies between influenced and standard search outcomes.
Persistent gaps: In Telecom and Technology, lower CTRs with AI Overviews highlight the direct impact on traffic flow.
Consumer resilience: Financial Services and Retail show narrower CTR gaps, indicating ad preference despite AI Overviews.
Late month volatility: Spikes in Healthcare showcase rapid performance fluctuations as Google refines its AI deployment.
Distribution Data Reveals the Zero Click Reality
This data layer exposes a winner-take-all dynamic often obscured by average metrics.
The baseline gap: In the absence of AI Overviews, CTR remains strong across sectors, particularly Retail. However, where AI Overviews are rampant, the gap reveals the complete story.
High AI Overviews frequency, low CTR: Ubiquitous AI Overviews mean reduced CTR across sectors, including Technology. As frequency climbs, ad traffic capture decreases.
Resilience in Automotive: Automotive maintains a relatively diverse spread in mid-frequency ranges, suggesting users bypass summaries for brand information.
Three Immediate Steps to Adapt Your Paid Search Strategy
To protect my margins, here’s what I can do:
Monitor Click Through Rates (CTR) and Cost Per Click (CPC) changes: Although they don’t provide the complete picture, shifts in CTR or CPC can warn of AI Overview effects.
Segment performance by device: By separating desktop and mobile data, I can discover hidden trends that might be blurred in combined reporting.
Use Adthena’s free Market Share reports: These reports allow me to understand AI Overview frequency in my category and recognize at-risk areas for visibility.
Gaining Visibility with Adthena’s AI Overview Solution
To grasp AI Overview effects, continuous and detailed query-level intelligence is crucial. Adthena’s AI Overview solution regularly indexes search results, providing advertisers with insights into:
AI Overviews frequency patterns by query, industry, and device.
Content themes and citation sources.
Performance metrics including impact on CPC and CTR.
Ad position vs AI Overviews.
These insights help advertisers like me detect and address disruptions to revenue before they impact performance.
Coming soon: Adthena’s enhancement to the AI Overviews solution will include visibility into ads within AI Overviews, offering a comprehensive assessment of ad performance throughout the SERP.
The SERP Has Changed: Adapt or Fall Behind
While Google’s AI Overviews are here to stay, their effect isn’t uniform nor unsurmountable. Successful advertisers, like those who are vigilant, understand precisely where and how AI Overviews appear, what content they promote, and how their audience reacts.
Precision is vital. Assumptions lead to downfall.
Book a demo to see exactly how AI Overviews are impacting your campaigns.
I’ve realized that many of us, myself included, might be tracking the wrong SEO metrics lately. We need to shake things up, especially with 2026 approaching.
Picture this: I present an impressive chart depicting a 47% increase in site traffic. But instead of excitement, I’m met with puzzled looks from the CMO, wondering why revenue remains stagnant. Or, I celebrate a top-three ranking for a keyword nobody searches for.
The SEO metrics that boosted my confidence back in 2019 might just be steering me wrong in 2026. With AI Overviews taking over search results and zero-click searches becoming the new standard, clinging to outdated metrics might jeopardize my strategy and budget.
I’m ready to take you through the precise metrics that our SEO team should retire and which new, revenue-focused metrics to prioritize instead.
Traffic Metrics
1. Organic Traffic
Organic traffic has been my go-to KPI in SEO reports ever since I started. But relying solely on it doesn’t provide enough context.
Not all traffic is equally valuable. A thousand visitors who bounce instantly are not beneficial. However, a hundred visitors converting at an 8% rate? That’s a success story.
I witnessed a local HVAC company whose traffic dropped by 22%, year on year. Panic, right? Yet, organic revenue increased by 31%. We focused on enriching high-intent service pages, pruning low-intent content. Fewer visitors, but better ones.
Before panicking over traffic drops, I always reassess where traffic is declining. If losses involve informational articles and customer login pages, it’s not a revenue issue. That’s just noise exiting my dashboard.
2. Total Impressions Without Intent Segmentation
This metric can mislead. A million impressions from merely informational queries like “what is SEO” might build some awareness, but they contribute zero revenue. Meanwhile, ten thousand impressions from business-driven queries like “best enterprise SEO agency” could significantly boost my pipeline.
Google Search Console offers this data, but many teams, myself included, often fail to segment it intelligently.
3. Traffic Growth Without Revenue Correlation
This is a risky trap for SEO teams. Bringing a 35% increase in organic traffic to a quarterly review sounds impressive, right until the CFO asks, “And how does this translate to revenue?” If I can’t answer that, I’m just reporting noise.
Ranking Metrics
4. Average Keyword Position
This metric might look compelling in a dashboard, but it doesn’t hold up under scrutiny. If I rank first for a keyword with ten monthly searches and fiftieth for one with 50,000, my average position might seem okay, but I’m losing where it matters most.
The average position treats all keywords as identical when they aren’t. With personalized search results, an “average position” can vary greatly by user and location.
5. Isolated Keyword Tracking
Searchers these days don’t typically use isolated keywords. They pose questions, explore themes, and adjust their queries. Google’s focus has shifted toward semantic search and topic modeling.
Tracking a solitary keyword like “lawyer” is pointless without understanding intent — are searchers interested in criminal defense, divorce services, or merely looking up what lawyers do?
6. Share of Top 10 Rankings
This metric sounds clever until it’s clear that 80% of my top-10 rankings might involve low-intent, low-volume queries. Meanwhile, competitors claim the top-three spots for crucial commercial queries in my niche.
Achieving a No. 1 ranking for a high-converting transactional keyword is more valuable than holding 50 top-10 positions for low-value informational queries.
Authority and Engagement Metrics
7. Domain Authority and Domain Rating
DA and DR might not align with Google’s metrics. They’re proprietary scores from SEO tool companies. Yet, teams often set misguided goals like boosting DA from 42 to 50 by Q3.
I’ve seen how backlink volume is often overrated. Google’s algorithm prioritizes link quality, relevance, and context over sheer volume.
A single link from a high-quality, relevant site outweighs hundreds of low-grade directory links. I’ve seen sites with 100,000+ backlinks struggle to rank for meaningful terms because most links lacked quality.
9. Bounce Rate
I’ve found bounce rate misunderstood for years. If someone searches for my company’s business hours, finds them on the contact page, and leaves, that’s a success with a 100% bounce rate.
Google replaced bounce rate with “engagement rate” in GA4 for a reason. Similarly, session duration and pages per session need context. A high pages-per-session score on my pricing page may indicate confusion, not engagement.
Why These SEO Metrics Are Failing Now
I’ve noticed the search landscape shifting quite a bit. Up to 58.5% of U.S. and 59.7% of EU Google searches now conclude without a click, as per SparkToro’s zero-click study. This means, for every 1,000 searches, only 360 result in a visit to a site.
AI technologies are capturing and synthesizing information, bypassing the need for a click. My content can gain visibility and influence without contributing to sessions in Google Analytics.
Wynter’s latest B2B buyer research indicates nearly 24% of CMOs now utilize AI tools like ChatGPT for research, a significant rise from last year.
Buyers discover brands via AI tools and use Google to validate those discoveries. This alters my SEO focus from merely driving traffic to ensuring my brand is visible during pivotal decision-making stages.
Modern customer journeys can be erratic. Often, users who initially find us through organic search might return through paid ads or direct links. If we use last-click attribution, the true value of SEO is obscured, although this organic start was critical for conversion.
For ecommerce, I aim to track revenue from organic sessions by product category and landing pages. For lead-generation, I’ll track how many leads convert to customers. Integrating with a CRM helps in connecting those dots.
No one’s interested in your DA if you can demonstrate $1.2 million in revenue attributed to organic channels.
Conversion-weighted Visibility
I’ll focus on visibility for high-value terms that lead to conversions.
A franchise client noticed they dominated low-intent queries but were invisible for crucial local terms. We adjusted priorities, and their qualified leads doubled in four months.
Topic Cluster Performance
This metric supersedes individual keyword rankings. Monitoring how I rank across full topic clusters, and the aggregate visibility and conversions from these clusters, gives a comprehensive view of topic authority.
SERP Real Estate Ownership
By gauging control over the entirety of search pages, not just listings, including snippets and local packs, I can effectively keep competitors at bay for crucial queries.
AI Platform Visibility and Brand Mentions
My focus will also be on how frequently my brand is mentioned in AI responses. Mentions are becoming as crucial as click-through rates.
For instance, if I secure a favorable recommendation rate across multiple AI platforms for vital topics, it’s a win, even if website traffic appears unchanged.
While tools are emerging to monitor this, manual spot checks can reveal valuable insights, enhancing authority and awareness, eventually leading to brand searches and conversions.
Branded Search and Direct Traffic as AI Visibility Proxies
I notice when buyers find out about my brand through zero-click searches, they often search the brand name directly instead of clicking through. This reflects in my branded and direct traffic rather than organic metrics.
If I see no change in nonbranded organic traffic but an increase in branded search and direct visits, it usually indicates that my content gains attention in AI Overviews.
How to Transition My Reporting
Revamping reporting around new metrics might feel daunting. Stakeholders are comfortable with old metrics.
I start by evaluating my current dashboard, ensuring relevant metrics face business outcomes directly rather than just tallying activities.
Transition by gradually omitting vanity metrics. If organic traffic was my focal KPI, I now introduce it segmented by intent and accompany it with organic-attributed revenue. Gradually, I pivot focus and phase out the dated metrics.
When I introduce new metrics, I frame them in relatable terms. Avoid using “conversion-weighted visibility.” Opt for “visibility metrics for top-converting terms.”
The Metrics That Prove SEO’s Value
The metrics we’ve relied upon — organic traffic, average keyword position, domain authority, bounce rate — aren’t inherently harmful. They’re just incomplete, providing a potentially false sense of security while others prioritize revenue-generating metrics.
Newly adopted metrics — revenue contributions, conversion-oriented visibility, topic authority, SERP dominance, AI platform mentions — directly relate SEO to tangible business outcomes. They prove ROI, justify budgets, and align strategies with business growth.
Consider which metrics in your dashboard lend false impressions of activity over effectiveness. Retire them. Replace them.
Ultimately, no one’s concerned with traffic numbers or DA scores. They want to know if SEO drives growth. Make sure your metrics affirm it.
I’ve recently come across an interesting study highlighting a significant shift in search click dynamics. It turns out that text ad clicks have dramatically increased year over year, while the traditional organic clicks in major verticals have taken a sharp decline.
This transformation isn’t solely due to AI Overviews for sure. Google’s expansion of paid search real estate is playing a pivotal role here. In the U.S., data reveals a steep drop in classic organic click share across product categories like headphones, jeans, greeting cards, and online games between January 2025 and January 2026.
The numbers are quite telling. Classic organic click share fell significantly across these categories, making way for text ads, which emerged as the biggest beneficiaries, gaining a notable share of clicks.
Why does this shift matter to us? As digital marketers, it’s no longer just AI-powered features that we’re contending with. Text ads have won substantial ground, capturing about one-third of the clicks in several product categories. For brands seeing a dip in organic visibility, increasing paid efforts seems to be a necessary strategy.
Numbers tell the story. When diving into four main verticals, text ads showed consistent click-share increases. Classic organic lost between 11 to 23 percentage points, while text ads gained anywhere from 7 to 13 percentage points across the board. Paid click share has doubled in several key product categories.
Comprehensive breakdown: Classic organic click shares have seen a year-over-year decline across all verticals. For instance, headphones lost dramatically, shrinking from 73% to 50%, and even organic-heavy areas like online games dropped by double digits. Such declines emphasize the urgent need for many brands to reassess their search strategies.
Data shows that text ads inched forward share-wise in every industry examined. For instance:
Headphones: Rose from 3% to 16%
Online games: Up from 3% to 13%
Jeans: Climbed from 7% to 16%
Greeting cards: Up from 9% to 16%
Moreover, Product Listing Ads (PLAs) are further supporting this change in product sectors:
Headphones: Increased from 16% to 36%
Jeans: Went from 18% to 34%
Greeting Cards: Rose from 10% to 19%
AI Overviews have seen a diverse impact. While the presence of Google AI Overviews on SERPs has certainly increased, the extent varies significantly across sectors:
Headphones: 2.28% → 32.76%
Online games: 0.38% → 29.80%
Greeting cards: 0.94% → 21.97%
Jeans: 2.28% → 12.06%
Zero-click searches remain significant but stable. Even though the overall zero-click rates haven’t seen dramatic changes, online games have witnessed a noticeable uptick:
Headphones: 63% (unchanged)
Jeans: Down from 65% to 61%
Online games: Up from 43% to 50%
Greeting cards: Increased from 51% to 53%
Brands adapt by increasing paid presence. In the headphones market, for example, companies like Amazon boosted paid clicks by 35% despite losing organic traffic, while Walmart increased theirs nearly sixfold.
In the jeans sector, Gap saw a 137% growth in paid clicks, rising to become the leading paid player.
For online games, CrazyGames quadrupled its paid clicks, and Arkadium entered the paid scene after a significant drop in organic clicks.
These shifts have led to a self-reinforcing cycle, as pointed out by Aleyda Solis, the study’s author. Organic share declines, competition increases, and brands continuously boost their paid-search budgets.
Study insights. This study was conducted using Similarweb data, thoroughly examining the SERP composition and click patterns for the top 5,000 U.S. queries in the areas of headphones, jeans, and online games, alongside the top 956 greeting card-related queries. Over time, it has highlighted a marked shift in click distribution among classic organic results, text ads, PLAs, zero-click searches, and AI Overviews.
If you’re curious about deeper insights, you can check out the full study by Aleyda Solis.
