The digital environment has permanently and fundamentally changed. Google’s share of the search engine market has already dropped from 93.37% in 2023 to 89.34% in 2025. A similar trend is quite evident in how Gen Z users are bypassing the traditional search engines and using social media platforms for discovery. Hence, we are assured that online research and search behavior have changed since the outbreak of AI tools.
In this new era, marketers are facing a new landscape of AI-enhanced search. Where an AI bot is used to discover products and assess newer brands. This shift, as predicted by Gartner, could mean traditional search engines losing almost 25% of web traffic by 2026. So now, if you want to promote your products using advertisements, the new playground for displaying those ads will be AI search engines. This is exactly why marketers need to switch their focus from search ads or display ads and focus more on creating PPC ads for AI search engines.
This is not a gradual update, but rather a structural rewriting of search, with serious ramifications for paid advertising. AI search engine advertising is changing the consumer experience and the practice of marketing, undermining the historical dominance of Google and other traditional search methods. Success in this new environment involves strategic agility, a lot of experimentation, and investment in specific AI-native strategies.
Decline of Traditional Dominance and Rise of AI-Based Results
The traditional search engine landscape is undergoing a dramatic period of change. With the rise of high-capacity, AI-powered search engines, businesses are forced to reexamine their marketing strategies. For most queries, users are increasingly drawn to platforms like Chat GPT, Gemini, Copilot, Perplexity, and Claude, which offer more personalized, direct answers than traditional search engines.
Trends widely suggest that roughly 50% of Google searches already include AI summaries, and this share is supposed to reach 75% by 2028. Customers have begun to intentionally seek out AI-powered search engines, with a majority placing it as their top digital source for making buying decisions.
This adoption is well-spanned across generations, but the most evident change can be witnessed in Gen Z’s who are favoring social media hubspots like TikTok and Instagram for discovery. This shift in consumer behavior means that traditional search engines have already experienced a 9% decline in initial queries. This data can also be validated by Google’s decline in the search engine market and its attempt to counter this trend with its own AI-powered overviews, which now reach over a billion users monthly.
This ensures we are witnessing a foundational change in digital advertising. AI search engine advertising must now account for systems where ads are seamlessly pulled from existing campaigns (Search, Shopping, Performance Max, and App campaigns) and woven directly into AI answers and conversational journeys. The traditional search engine results pages (SERPs) we have optimized for years are now morphing into new surfaces that look and act differently.
The AI-Powered Consumer Decision Journey
AI-driven search is often leveraged at virtually all stages of the consumer decision journey. Over 70 percent of users of AI-powered search started by asking questions early on in the funnel to learn about the category, brand, product, or service.
If a consumer wanted to know what the best cross-training shoes were, an AI-powered search helps the consumer immediately make sense of what is important in terms of price, style, and functionality, which brands excel at different features, and to identify models with notable reviews. The consumer can even personalize the search based on their own parameters, such as budget or training goals. In comparison, getting to a similar conclusion using regular search would require the consumer to browse discussion threads online, then sift through several review sites and product pages, summarizing insight from three or more sources.
The downstream impact of this efficiency is clear, as 44 percent of AI-powered search users report that AI-powered search is their primary method and preferred source of insights, significantly more than traditional search (31 percent), retailer/brand websites (9 percent), or review sites (6 percent).
Generative AI Engine Optimization (GEO): The New Mandate
As consumers increasingly make purchasing decisions within AI platforms, brands looking to win must all act to enhance visibility and positive perceptions not just on AI summaries, but also on the AI platform itself. This is why the evolution of our concept of General AI Engine Optimization (GEO) is necessary to the strategy. Just a few years ago, Search Engine Optimization (SEO) was the centerpiece of marketing strategy, and now brands need to find a way to also be a component of any integrated digital and marketing strategy that ensures coverage at consumer decision touchpoints.
Being a strong brand with historical performance is no longer an indicator that a brand can compete in this new world; visibility is not guaranteed. In fact, market leaders are not guaranteed visibility in AI search.
Why Brands are Missing in AI Search Results
The main difference between SEO and GEO lies in the underlying source material. SEO has always focused on content associated with the brand’s own website; however, the brand’s own website now accounts for only 5-10% of the sources AI search will reference. AI search references from the broadest and most diverse repository of sources that it uses to generate search results, which will include affiliates and UGC materials. Those sources of material will vary considerably amongst LLMs, hard-coded elements by geography, search category, and question type, and certainly will change as LLMs improve.
In major categories like credit cards, apparel, electronics, and hotels, some leading brands can be missing from some responses in platforms for AI-powered search, such as Google AI Overview. That means some brands can have a lower share on AI-powered search than traditional search, and their overall market share would suggest.
Brands need to take two things into account to embolden this landscape of sources: the questions consumers are asking and the sources that are answering. Also, brands need to stay updated about the evolution of LLMs. Only 16% of brands currently track performance on AI search in any systematic way.
Invest in GEO: a Four-Step Roadmap.
To win in the age of AI search, brands need to rethink how they structure, produce, and amplify their content. Four things can help organizations move toward GEO successfully:
- Conduct a Comprehensive Diagnostic: Companies need to evaluate existing GEO performance in order to measure the potential value at risk. Based on past experience, it is not uncommon for even the leading companies in an industry to be trailing the account in SEO performance by 20 to 50 percent in GEO. A comprehensive diagnostic evaluates sentiment and visibility across AI-based search engines, and identifies the drivers of performance originating from individual sources of content.
- Shift Content Strategies and Investments: This step is focused on the wide array of content types that amount to answering AI-powered searches: owned content marketing services, community discussions, and third-party content. For example, in both financial services as well as consumer-packaged goods, 65+%+ of the sources used by AI-powered search engines originate from publisher sites (microsites and magazines), affiliate sites, and user-generated content.
- Optimize Content for AI-Powered Search: Optimizing means increasing credibility and relevance related to unique information and topical coverage, as well as optimizing the structure of content to be clear and use headings. It is important to ensure that the owned and newspaper content is LLM-optimized.
- Invest in GEO as a Capable Core: Investment means developing a strategic priority for AI and for developing cross-functional capability spanning marketing, SEO, and the customer experience.
The Collision: AI Search Engine Advertising and AI Mode
The primary and urgent difficulty for marketers is how they will place advertising using Google’s AI Overviews and AI Mode. Google does not only use AI to create responses to user queries; it leverages AI to guide users through multi-step, conversational experiences. This is intended to “reduce the time from discovery to decision.”
Google has been very clear with its vision; it is reported that the AI Overviews were one of the most successful launches in Search over the last 10 years. Google also mentioned that there was a significant increase in commercial queries in multiple major markets. Ads will be pulled directly into these AI surfaces, again from existing ad campaigns: Search, Shopping, Performance Max, and App campaigns. Sometimes the ads will show above or below the AI summaries; sometimes they may be embedded within an AI summary.
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The Opaque Reality of Blended Inventory
For advertisers, the issue is transparency. Google thinks of the ad as “just part of the journey” for the user. For the advertiser, there is no opt-out, no new campaign type, and no reporting that differentiates the impressions or clicks from AI Mode versus the traditional search. The inventory is blended. Essentially, marketers will be bidding blindly on whether an ad has appeared in a regular SERP or in an AI-generated SERP.
This structural compression favors Google. The organization has consistently steered the pendulum towards monetization, turning traffic from Shopping ads to displacing text-based ads, and now replacing Performance Max with non-disclosure regarding where ads will be shown. AI Mode is the utmost evolution of where the ad appears directly within the answer set. As Rand Fishkin so eloquently noted, it’s obvious – “zero click is taking over everything. Google is trying to answer searches without a click.”
The Supply-and-Demand Problem: Rising CPCs
User behavioral changes that are generated via AI Overviews create a classic supply-and-demand scenario for paid search. Data points to the fact that when an AI Overview is present:
- Only 8 % of visits included a click on the traditional results, while that number was 15% when no overview appeared.
- Only 1% of visits on AI included a click on one of the links in the AI box.
- Similarweb saw a huge increase in zero-click search trends to almost 70% of all queries by mid-2025.
- In news queries, traffic to a top-ranking result saw nearly an 80% decrease when an AI Overview showed above it.
If fewer people garner clicks through to websites, demand and competition for clicks will be greater. Ad real estate will be even more congested, as CPC increases. As the organic counterpoint shrinks, more pressure will be put on paid budgets. This CPC inflation is becoming a structural reality of AI-powered search, forcing advertisers to compete harder and pay more.
Larger brands utilizing broad match strategies, with strong budget capabilities and comprehensive product feeds, are naturally positioned to have the advantage in this AI-driven system. Here, smaller or niche advertisers risk being squeezed out because the system inherently privileges scale.
The Measurement Gap and Creative Evolution
The most glaring problem of the AI mode is the lack of transparency. Without specific reporting columns in Google Ads or Search Console, marketers cannot definitively measure the value delivered by these new AI surfaces. Boards and CFOs require this visibility to defend budget allocations.
To navigate through this challenge, marketers are creating their own measurement frameworks, using marketing mix models to estimate AI’s contribution, and connecting CRM systems to campaign spend tightly. Tracking mid-funnel signals, such as downloads or demos, is critical because these signals often tell you if AI-driven impressions are contributing to the conversion paths. Until Google is able to give us AI-specific performance reporting, marketers need to be wary of performance claims and invest in these proprietary measurement systems, not to operate blindly.
In addition, creative expectations are changing. Conversational journeys require conversational advertisements. A blunt call-to-action (CTA) may make an impression jarring in a multi-step process. Rather, something that appears to flow like “Find the right plan for your family” and “See how much you could save in a few minutes” is more aligned with the AI experience.
Broader AI Applications in Marketing
AI and Machine Learning (ML) have evolved beyond buzzwords to become the engine of modern marketing. They enable brands to handle massive data sets, predict consumer behavior, and personalize experiences in real time.
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1. Hyper-Personalization and Real-Time Engagement
AI analyzes vast amounts of behavioral data to deliver personalized content and offers.
It powers:
- Dynamic website experiences that adapt to each visitor
- Tailored email campaigns based on browsing and purchase history
This level of customization enhances user engagement and conversion rates, turning generic interactions into meaningful, contextual experiences.
2. Predictive Targeting and Forecasting
By analyzing billions of data points, AI identifies consumer intent patterns and optimal engagement moments.
It enables marketers to:
- Forecast trends for smarter budget allocation
- Target audiences with higher purchase probability
- Optimize campaign timing for maximum impact.
AI turns reactive marketing into a proactive strategy, guiding decisions before market shifts happen.
3. Automation and Operational Efficiency
AI-driven automation streamlines repetitive and data-heavy tasks such as:
- Programmatic media buying
- Audience segmentation
- Performance tracking
This efficiency frees marketing teams to focus on strategic goals rather than manual execution, improving both accuracy and speed.
4. Predictive Intelligence for ROI Maximization
Beyond automation, AI brings predictive precision to marketing performance. Key applications include:
- Predictive lead scoring: Helps prioritize high-value prospects.
- Dynamic pricing: Determines optimal prices in real time for competitiveness and profitability.
- Churn prediction: Uses ML algorithms to identify users likely to disengage, enabling proactive re-engagement through targeted content and offers.
These predictive tools ensure resources are invested where ROI is highest, reducing waste and improving retention.
5. The Strategic Impact
AI’s integration is reshaping how brands interact with consumers.
It enhances efficiency, drives personalization at scale, and fosters deeper, more data-driven connections.
Marketers who master these AI capabilities gain not only a performance edge but also a sustainable customer relationship advantage in a competitive digital landscape.
Navigating the Future of AI Search Engine Advertising
The future of paid search is incredibly complicated. It overturns the three pillars of paid search that we have relied on for decades now: predictable intent, transparency, and measurability; with the entirety of the industry, including Microsoft Copilot and OpenAI’s sponsored answers, ALL baiting the path to “AI-assisted journeys,” adaptation is mandatory.
To survive this, marketers should re-prioritize:
1.) Journey Strategy: Think in terms of journeys, not keywords. Campaigns that assume multi-staged customer journeys (for an insurance company, this requires thinking beyond “compare rates” to even anticipating “how to switch providers.”)
2.) Automate with Guardrails: Use the engines of eligibility for AI surfaces, such as Performance Max and broad match, with guardrails. You need to use negative keywords and ensure you have clean product feeds, along with audience signals, to minimize wasted ad budget without missing out on the audience.
3.) Data Quality Matters: In the attribution era, your asset quality matters more than ever. Your structured data, schema markup, and entity-rich product feeds determine your eligibility to appear inside the AI responses. Poor data ultimately means poor visibility.
4.) Measure the Market: Move beyond last-click Cost-Per-Acquisition (CPA): Marketing leaders must begin measuring mid-funnel signals, including speed of sales cycle, prospect lead quality, and assisted revenue, in order to determine if AI impressions are really moving customers forward.
Brands that successfully integrate GEO as a core capability and proactively adjust their advertising strategies to the realities of AI Overviews will gain a competitive advantage. Because when ads become the answer, the brands that prepare early will be the ones that still get found. The transition may be disruptive, but the opportunity to define how consumers discover brands tomorrow lies within the mastery of AI search engine advertising today.
