Last updated: February 10, 2026
Large language models are not just changing how people find information. They are changing how people make buying decisions. And that changes everything about how you should run Google Ads.
Google's AI Overviews now reach 1.5 billion users monthly across 200+ countries. ChatGPT processes over 800 million weekly users. Perplexity, Claude, Gemini, and a growing list of other AI platforms are becoming the first place people turn when they want to research a purchase, compare services, or find a provider. These platforms do not send users to ten blue links. They synthesize answers, recommend specific products and brands by name, and sometimes satisfy the entire query without a single click reaching your website.
For advertisers, this creates a compounding problem. Organic traffic is declining, which means more businesses are competing for paid clicks. But paid clicks are also declining, while their cost is rising. The average Google Ads CPC hit $5.26 in 2025, up 12.9% year over year, with 87% of industries seeing increases. You are paying more per click for fewer clicks in a search environment that is fundamentally different from the one you optimized for even twelve months ago.
This is not a temporary disruption. It is a structural transformation of how discovery, research, and purchase decisions happen. The advertisers who understand this and adapt will thrive. The ones who keep running their 2023 playbook will watch their costs climb and their results deteriorate.
Here is what is actually happening, what it means for your Google Ads strategy, and what the smartest advertisers are doing about it.
The most comprehensive study of AI Overview impact comes from Seer Interactive, which analyzed 3,119 informational queries across 42 organizations spanning 25.1 million organic impressions and 1.1 million paid impressions between June 2024 and September 2025.
The numbers are stark. Organic CTR dropped 61%, from 1.76% to 0.61%, for queries where AI Overviews appear. Paid CTR dropped 68%, from 19.7% to 6.34%. The decline was not gradual. July 2025 saw paid CTR crash from roughly 11% to 3% in a single month.
Other studies confirm the directional trend. Ahrefs found a 34.5% CTR reduction for the top organic result when AI Overviews are present, based on an analysis of 300,000 keywords. Pew Research Center tracked actual browsing behavior of 900 adults and found that only 8% of users clicked a traditional result when an AI summary appeared, compared to 15% without one. Perhaps most telling: 26% of users ended their browsing session entirely after seeing an AI summary, versus 16% without one. People are not just clicking less. They are stopping their search altogether because the AI answered their question.
Zero-click searches have climbed from 56% in 2024 to approximately 69% in 2025. AI Overviews now appear on roughly 15-25% of all searches globally, with significantly higher prevalence on informational and commercial queries. And Google is actively expanding these features. AI Mode, currently in testing in the US, represents an even more AI-native search experience where traditional results are secondary to conversational AI answers.
The implication for paid search is direct: the pool of available clicks is shrinking while the number of advertisers competing for those clicks continues to grow. Skai's Q3 2025 report confirmed that average CPCs reached their highest level in six years. BrightEdge found search impressions up nearly 50% year over year, yet paid CTRs down roughly one-third. You can see your ads more, but people click on them less, and each click costs more.
The counterintuitive truth is that declining organic traffic makes Google Ads more valuable, not less. Here is the logic.
When organic CTR drops 61% on queries with AI Overviews, businesses that relied on organic traffic for lead generation and sales are losing a significant share of their inbound pipeline. SEO is not dead, but its ceiling has been materially lowered for many query categories. The businesses affected must replace that lost traffic somehow, and for most, paid search is the most direct replacement.
At the same time, ads are being integrated into AI Overviews themselves. By October 2025, ads appeared in 25.56% of AI Overview-containing SERPs, up from 5.17% in March, a 394% increase in eight months. Google is also testing ads in AI Mode. As Google introduces more ad placements within AI-generated responses, paid search becomes the primary mechanism for reaching users who never scroll past the AI answer.
This creates a flywheel: less organic traffic pushes more businesses into paid search, which increases auction competition, which drives up CPCs, which demands better campaign management to maintain profitability. The businesses that win are not the ones spending the most. They are the ones extracting the most value from every click in an increasingly expensive environment.
The second major shift goes beyond Google entirely. ChatGPT, Perplexity, Gemini, Claude, and other LLMs are now functioning as recommendation engines. When someone asks "what is the best CRM for a small law firm" or "recommend a project management tool for remote teams," these platforms do not show ads or organic results. They name specific brands, explain why they recommend them, and sometimes provide comparisons.
This creates an entirely new competitive landscape that most advertisers are not thinking about. Your competitor might not outbid you on Google. They might simply be the brand that ChatGPT recommends when your potential customer asks for advice.
The data on LLM brand visibility is revealing. Research shows that ChatGPT and Google AI disagree on brand recommendations 61.9% of the time. Only 17% of queries produce the same brands across platforms. ChatGPT offers no brand mentions at all in 43.4% of queries, while Google AI Mode stays silent 46.8% of the time. This means the brand landscape users see depends heavily on which AI platform they use, and most businesses have zero strategy for influencing any of them.
Traffic from AI platforms grew 527% year over year in 2025, while traditional organic traffic grew less than 4%. The volume is still small relative to Google, but the trajectory is unmistakable. And unlike organic search traffic, where the user has already decided to visit your site, LLM recommendations happen upstream in the decision process. By the time someone searches for your brand on Google after an AI recommended a competitor, you have already lost.
For paid search specifically, this changes the economics. If your brand is being recommended by LLMs, users who then search on Google are more likely to click your ad, more likely to convert, and more likely to become repeat customers. Seer Interactive's data shows that brands cited in AI Overviews earn 35% more organic clicks and 91% more paid clicks compared to brands that are not cited. LLM visibility does not replace Google Ads. It makes your Google Ads dramatically more effective.
The Google Ads playbook needs updating for this new environment. Here is what smart advertisers are doing differently.
AI Overviews predominantly appear on informational queries. "How does solar panel installation work" is likely to get an AI Overview that satisfies the user without a click. "Solar panel installation quote near me" is more likely to generate a traditional SERP with ads that get clicked. The actionable shift: move budget away from broad informational queries (where AI Overviews are eating clicks) and toward high-intent, bottom-of-funnel queries where users need to interact with a business to accomplish their goal.
This does not mean abandoning top-of-funnel entirely. It means recognizing that the top of the funnel is increasingly owned by AI-generated content and adjusting your paid investment accordingly. Put your heaviest ad spend where intent is highest and click probability is greatest.
If a user has already read an AI Overview explaining the general landscape, your ad should not repeat that same information. Your ad should offer the next step: a specific offer, a unique differentiator, or a clear reason to click through rather than stay on the SERP. Think "Get Your Free Solar Quote in 60 Seconds" rather than "Everything You Need to Know About Solar Panels."
AI Overviews are doing the educational heavy lifting. Your ads should capitalize on the educated user and give them a reason to take action. Test ad copy that assumes awareness and leads with specificity, urgency, and clear value propositions.
Users who click through an AI Overview are different from users who click a traditional search result. They have already absorbed a summary of the topic. They do not need another explanation of the basics. If your landing page opens with three paragraphs of introductory context, the post-AI user will bounce because you are repeating what they already know.
Redesign landing pages for users who arrive pre-educated. Lead with your differentiator. Lead with your offer. Lead with social proof, case studies, and specifics that the AI Overview could not provide. Make the next step (contact form, quote request, purchase) immediately visible and frictionless.
As ads increasingly appear within AI-generated experiences, campaigns enabled for AI Max and Performance Max are eligible for this new inventory. AI Max-enabled Search campaigns can serve ads in AI Overviews on both desktop and mobile. Google is testing ads in AI Mode. Performance Max covers all Google surfaces including these emerging placements.
The advertisers who have these campaign types properly configured (with strong negative keyword lists, accurate conversion tracking, and comprehensive creative assets) will capture impressions in AI surfaces that competitors miss. The ones who are still running only traditional exact-match Search campaigns may find their available inventory shrinking as AI results consume more of the SERP.
LLM recommendations and AI Overviews mean that the path from awareness to your website is longer and more fragmented. A user might first hear about your competitor from ChatGPT, then see them in a Google AI Overview, and then search for them by name. At that point, your brand ad is competing against a user who has already been pre-sold on someone else.
Aggressive brand bidding protects against this. When someone searches your brand name after encountering a competitor recommendation, your brand ad needs to be there with compelling messaging. When someone searches a generic category term after an AI Overview, your brand ad needs to reinforce whatever awareness they already have of you. Treat brand campaigns not as a formality but as the defensive infrastructure that prevents LLM-influenced users from being captured by competitors.
Research has identified a new user behavior pattern emerging from AI search: skim, then verify. Users read the AI Overview for the quick answer, then click through to a specific source to verify or go deeper. In healthcare, Skai's analysis found that organic CTR actually improved in some subcategories because users wanted expert confirmation of what the AI told them.
This two-step behavior has implications for ad strategy. Your ads should acknowledge that the user likely already has context. Messaging that offers verification, deeper expertise, or a specific next step (rather than general awareness) aligns with where the user is in their decision process. Test messaging frameworks like "See why [specific number] businesses chose [your solution]" rather than "Learn about [your category]." The AI already handled the learning. Your ad should trigger the doing.
As your ads appear in AI Overviews and eventually AI Mode, the conversion behavior of users from these placements may differ significantly from traditional SERP clicks. Users who click an ad within an AI Overview have consumed more context before clicking, which could mean higher conversion rates (better educated) or lower conversion rates (the AI already answered their question and they are casually browsing).
Segment your reporting to isolate performance from AI-generated placements versus traditional placements. This data should inform your bidding strategy, landing page design, and budget allocation. If AI placement clicks convert at a different rate, your target CPA and ROAS calculations need to account for this.
Getting recommended by ChatGPT, Perplexity, Google AI Overviews, and other LLMs is the new SEO. It does not replace traditional SEO or paid search. It amplifies both. Brands cited in AI responses see measurably better performance across every channel.
The signals that LLMs use to decide which brands to recommend are different from traditional search ranking factors. Research shows that classic SEO metrics have weak correlations with AI citations. Instead, LLMs weigh factors like third-party mentions, entity consistency, domain trust, content readability, and authoritative list placements.
Here is what the evidence shows works.
Authoritative list mentions represent the most influential factor in AI brand recommendations, accounting for roughly 41% of the signal. When Perplexity or ChatGPT recommends a "best CRM," they are heavily drawing from industry roundups, expert rankings, and "best of" compilations that already exist on trusted sites. Getting your brand into G2, Capterra, industry-specific ranking sites, expert roundups, and credible comparison lists directly increases the probability of LLM recommendation.
This is not about gaming a system. It is about ensuring your brand is accurately represented in the information ecosystem that LLMs draw from.
LLMs prefer content that provides clear, factual, structured answers to specific questions. Content with original research, specific data points, case study results, and expert commentary gets cited more than generic overviews that rehash what everyone else is saying.
Structure key pages with direct question-and-answer formatting. Include specific statistics, benchmarks, and outcomes. Use clear entity markup and structured data (schema) to help AI systems understand what your content is about and who created it. The content that gets cited by AI is not necessarily the content that ranks first on Google. It is the content that provides the clearest, most authoritative answer to a specific question.
LLMs build their understanding of brands from information scattered across the web: your website, LinkedIn profiles, press mentions, review sites, directory listings, social media, podcasts, and more. When this information is inconsistent (different descriptions, different founding dates, different capability claims), LLMs either recommend you less or represent you inaccurately.
Audit your brand's presence across all platforms. Ensure your company description, product capabilities, leadership, and key differentiators are consistent everywhere. This is the digital equivalent of controlling your brand narrative, except the audience is now an AI model building a representation of your business from every source it can find.
Research indicates that 71% of AI citations come from content published between 2023 and 2025. Freshness matters. LLMs appear to weight recent content more heavily, which means a single great article from 2022 may lose its citation status to a decent article from 2025 on the same topic.
Publish regular, substantive content that addresses the questions your audience asks. Update existing content with current data. This is where content strategy and paid search strategy converge: the content that gets your brand cited by LLMs is the same content that builds the topical authority that improves your Google Ads Quality Score and the landing page quality that improves your conversion rate.
A growing category of tools (Semrush AI Visibility Toolkit, Profound, Peec AI, Otterly, and others) now track how often your brand appears in AI-generated answers across ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude. At minimum, manually test your top 30-50 queries monthly across these platforms to understand where your brand appears and where it does not.
You cannot improve what you do not measure. AI visibility is now a marketing metric that belongs alongside organic rankings, paid CTR, and conversion rate.
In the pre-LLM era, content marketing and paid search were managed as separate functions with separate teams and separate metrics. Content built organic traffic over months. Ads bought traffic immediately. The two strategies informed each other loosely but operated independently.
That separation no longer works. In the LLM era, your content directly affects your paid performance, and your paid strategy should directly inform your content priorities.
When your content earns citations in AI Overviews and LLM responses, your brand enters the user's awareness before they ever see your ad. Seer Interactive's data shows that brands cited in AI Overviews see 91% more paid clicks. Your content is literally pre-selling users on your brand before your ad appears, making every ad dollar more efficient.
Conversely, your paid search data reveals exactly which queries are still generating clicks (and which are being consumed by AI Overviews). This intelligence should drive your content strategy. If a high-value informational query is now dominated by AI Overviews and generates minimal clicks, creating the authoritative content that gets cited in that AI Overview may be more valuable than bidding on the keyword directly. If a transactional query still generates strong paid clicks, that is where your ad budget belongs.
The practical implication: your content team and your paid search team (or your autonomous management system) need to operate from shared intelligence. The queries where AI Overviews appear but your brand is not cited are content opportunities. The queries where your brand is cited and ads still appear are high-priority paid opportunities. The queries where AI Overviews fully satisfy the user and no ads appear are candidates for budget reallocation.
This convergence is one more reason why the operational complexity of modern Google Ads management has exceeded what most human teams can handle. Coordinating content strategy, paid strategy, AI visibility monitoring, landing page optimization, and cross-platform brand management as an integrated system requires continuous data processing and cross-functional decision-making that simply does not happen when these functions operate in silos with weekly meetings.
The AI-driven search landscape changes faster than any human or semi-autonomous tool can keep up with. AI Overviews appeared on 5.17% of SERPs in March 2025. By October 2025, that number was 25.56%. The queries that trigger AI Overviews shift constantly. Authoritas found that about 70% of pages cited in AI Overviews change over a 2-3 month period. The cost per click moves daily. New ad placements emerge inside AI experiences with no announcement. Smart Bidding algorithms adjust their behavior as user patterns change.
This is an environment defined by continuous, rapid change across every dimension simultaneously. Consider what a human manager or a semi-autonomous tool has to do now that they did not have to do eighteen months ago. Monitor which queries trigger AI Overviews and adjust bidding accordingly. Track whether their ads appear in AI Overview placements. Analyze whether clicks from AI placements convert differently than traditional SERP clicks. Manage AI Max settings to ensure eligibility for new AI inventory. Adjust negative keywords as AI Max's expanded matching interacts with new query patterns from AI-influenced search behavior. Reallocate budget between informational queries (where AI Overviews crush CTR) and transactional queries (where clicks still happen). Monitor landing page performance for post-AI users who arrive pre-educated. Track brand visibility across multiple AI platforms and adjust content strategy accordingly.
Each of these tasks requires daily attention. Together, they create a management burden that exceeds what any human can sustain across all dimensions simultaneously. A media buyer might catch the CPC spike on Monday but miss the query-level shift until Wednesday. They might optimize ad copy for post-AI users on Thursday but not realize their landing page bounce rate changed until the following week. The gaps between observations are where money gets wasted.
Autonomous management systems like groas are architecturally designed for exactly this kind of environment. They process every data signal continuously and simultaneously. When a query category shifts from traditional SERP to AI Overview-dominated, groas detects the CTR drop and adjusts bidding within hours, not days. When AI Max surfaces new query patterns from AI-influenced searches, groas evaluates conversion quality and adds negatives before waste accumulates. When a landing page shows higher bounce rates from AI Overview traffic, groas identifies the pattern and adjusts campaign signals to prioritize better-performing pages.
The critical advantage is not speed alone. It is the ability to optimize across all twelve campaign levers simultaneously while tracking the interactions between them. When AI Overviews change the query landscape, that affects bidding, which affects budget allocation, which affects which ad copy performs best, which affects which landing pages receive traffic, which affects conversion rates, which feeds back into bidding decisions. A human manages these as separate problems on separate days. An autonomous system manages them as one interconnected system in real time.
Google's own AI (Smart Bidding, AI Max, Performance Max) is designed to work within this environment, but it is optimized for Google's interests (maximizing ad revenue) rather than your interests (maximizing profitable conversions). groas sits between you and Google's AI, working with it where it adds value and counteracting it where it does not, continuously and across every lever.
Putting it all together, here is the strategic framework for running Google Ads effectively in a world shaped by LLMs.
Your paid search strategy and your LLM visibility strategy are no longer separate. They are two sides of the same coin. Brands that are visible in AI responses see 35% more organic clicks and 91% more paid clicks. Content that gets cited by LLMs builds the authority that improves ad quality scores. Landing pages designed for post-AI users convert better, which improves ROAS, which lets you bid more aggressively.
The businesses winning in 2026 are the ones that have recognized this convergence and organized around it. They invest in content that earns AI citations while simultaneously running paid campaigns optimized for the high-intent queries that AI Overviews do not fully satisfy. They monitor brand visibility across AI platforms and use that intelligence to inform their ad messaging. They redesign landing pages for users who arrive already educated by AI. And they use autonomous campaign management to handle the operational complexity this creates, because no human team can monitor, analyze, and adjust across all these dimensions at the speed this environment demands.
The LLM revolution has not made Google Ads obsolete. It has made excellent Google Ads management more valuable than ever. Every click is more expensive, which means every click matters more. Every conversion must work harder, which means every landing page, every ad, every negative keyword, every bidding decision matters more. The margin for error has narrowed, and the pace of change has accelerated.
That is exactly the environment where autonomous management delivers its greatest advantage.
AI Overviews significantly reduce click-through rates for both organic and paid results. Research shows organic CTR drops 61% and paid CTR drops 68% on queries where AI Overviews appear. This leads to fewer available clicks, increased competition for remaining clicks, and higher CPCs. The average Google Ads CPC reached $5.26 in 2025, up 12.9% year over year. However, ads are now appearing within AI Overviews themselves, creating new inventory for properly configured campaigns.
For most businesses, the answer is strategic reallocation rather than a blanket increase or decrease. Shift budget away from informational queries where AI Overviews satisfy the user without a click and toward high-intent, transactional queries where clicks still happen. Ensure campaigns are configured for AI Max and Performance Max to access AI-generated placements. The total budget needed depends on your industry, but expect to pay more per click for fewer, higher-quality clicks.
Focus on earning placements on authoritative "best of" and ranking lists (the strongest signal at roughly 41% of influence). Create content with original research, specific data, and clear answers to common questions. Maintain consistent brand information across all platforms. Publish fresh, citation-worthy content regularly. LLMs draw from a broad information ecosystem, so your presence on review sites, industry directories, press mentions, and expert roundups all contribute to whether AI platforms recommend your brand.
Not replacing, but restructuring. LLM recommendations influence the research phase of buying decisions, shaping which brands users consider before they ever search on Google. Brands cited by LLMs see 91% more paid clicks when users do search on Google. The most effective strategy combines LLM visibility (earning AI recommendations) with Google Ads (capturing high-intent search traffic). They amplify each other rather than competing.
Design landing pages for users who arrive already educated by AI summaries. Lead with your differentiator, offer, or social proof rather than introductory explanations. Make conversion actions immediately visible. Include specific data points, case studies, and unique information that AI Overviews cannot provide. Test separate landing page experiences for users coming from AI-generated placements versus traditional search results.
GEO is the practice of optimizing your content and brand presence to be cited, referenced, and recommended by AI-generated search experiences including Google AI Overviews, ChatGPT, Perplexity, Gemini, and Claude. Unlike traditional SEO which focuses on ranking in search results, GEO focuses on being selected as a source by AI systems that synthesize answers from multiple sources. GEO and traditional SEO overlap significantly (good content and strong authority help both), but GEO introduces additional considerations like entity consistency, structured data, citation-worthy formatting, and freshness signals.
groas continuously monitors and adapts to the rapid changes in the AI-driven search environment. When query categories shift from traditional SERPs to AI Overview-dominated results, groas detects CTR changes and adjusts bidding within hours. It manages the interaction between AI Max's expanded matching and the new query patterns created by AI-influenced search behavior. It evaluates landing page performance for post-AI users and adjusts campaign signals to prioritize better-converting pages. And it does all of this across all twelve optimization levers simultaneously, which is the only way to keep pace with an environment that changes faster than any human team can track.
Track AI citation frequency (how often your brand appears in AI-generated answers), share of voice (your mentions relative to competitors), citation sentiment (how positively AI platforms describe your brand), and citation consistency (whether AI platforms accurately represent your capabilities). Tools like Semrush AI Visibility Toolkit, Profound, Peec AI, and Otterly track these metrics across multiple AI platforms. At minimum, manually test your top 30-50 queries monthly across ChatGPT, Perplexity, and Google AI Overviews.
The evidence suggests it is structural, not temporary. Seer Interactive tracked 15 months of consistent decline with no sign of reversal. As AI Overviews expand to more query types and more countries, and as AI Mode rolls out more broadly, the trend is likely to intensify. Google's business model depends on keeping users on Google's properties, and AI-generated answers achieve this more effectively than traditional results. Advertisers should plan for a permanently lower organic CTR baseline and adjust their paid and content strategies accordingly.
The impact varies significantly by industry and query type. Informational queries see the largest CTR declines (up to 89% in some publisher-focused studies). Health and educational content is particularly affected because AI Overviews can synthesize comprehensive answers. Transactional and local queries are less impacted because users still need to interact with a business to complete their goal. B2B industries with complex purchase decisions may see less direct impact on bottom-funnel queries but significant impact on research-phase queries that previously drove awareness traffic.
