2025 rewrote the rules of search. 2026 is rewriting them again.
As generative AI reshapes the user experience on search giants like Google and Bing and AI-powered chatbots like ChatGPT, Claude, and Perplexity gain traction with consumers, advertisers are adjusting to a search landscape whose present and future look markedly different from its past. More recently, agentic AI tools—which are capable of autonomously completing multi-step research and purchasing decisions on a consumer's behalf—have emerged and stand to further disrupt how and where search happens.
The emergence of AI has resulted in an increasingly fragmented search landscape. While Google still dominates the market, its market share dropped below 90% for the first time since 2015 in 2024, and has largely remained there ever since. While much of that volume went to established competitors like Bing, Yandex, and Yahoo, newer AI search agents are gaining ground as well: ChatGPT, for example, has grown far beyond the 1% search market share threshold that was once considered a milestone. By the end of Q4 2025, ChatGPT commanded an estimated 17% of digital queries (vs. Google’s 78%).
Fragmentation aside, the shift towards conversational interfaces on traditional search engines is already impacting organic traffic and advertising opportunities, forcing marketers to quickly adapt to a still-shifting environment. To succeed in the future of search engine marketing, agency and brand leaders must understand how AI is reshaping user behavior and take proactive measures to help their search teams evolve in kind.
AI is ushering in a fundamental change in how consumers search online. Historically, people have used keywords to search (ex. “Miami beachside hotel.”) But AI is spurring a shift from keyword searching to natural language conversations (ex. "Can you find me a beachside hotel in Miami with vacancy on May 23rd?”) This can be seen with the growing popularity of AI chatbots like ChatGPT, Gemini, and Perplexity, as well as in traditional search engines with features like Google’s AI overviews (AIOs). Already, AI-powered search is the preferred source of information (over traditional search engines, review sites, social platforms, and more) among those who rely on these tools.
The shift towards conversational interactions is also leading to a larger focus on voice search. Google has leaned into voice with its Gemini Live feature, enabling users to have back-and-forth voice conversations with the tool in real time. OpenAI has also continued to evolve its voice capabilities, rolling out a significant upgrade to its Advanced Voice Mode in mid-2025 that made ChatGPT’s voice responses notably more natural and fluid. Perplexity has also made voice a priority, expanding its voice capabilities across iOS, Android, and desktop throughout 2025. Conversational voice interaction is becoming table stakes across the AI search landscape.
In addition to generative AI, agentic AI is poised to further transform search behavior. With agentic AI, consumers can offload the research they would typically do manually with traditional or gen AI search engines onto agentic AI tools, which can complete multi-step processes. For example, consumers can use agentic AI to plan and book an entire vacation by researching destinations, comparing prices, reading reviews, and completing the transaction—all without them ever opening a browser.
Tools like Manus (a leading AI agent recently acquired by Meta) offer an early look at where consumer-facing agentic AI is heading. Meanwhile, on the business side, platforms like Claude Code and Agentforce are already enabling businesses to execute complex, multi-step workflows at scale—a signal of where broader adoption is likely to follow. Rather than issuing a single query and sifting through results, both individual users and businesses are expected to increasingly delegate entire decision-making journeys to these tools.
As consumer adoption of generative AI and agentic AI increases, and competition among companies providing AI-powered chatbots and agents rises, the overall search market will likely grow increasingly fragmented. While we’ll eventually see a decline in the use of traditional search engines, we’ll likely also see a net positive engagement with generative AI-powered search engine-like queries.
AI is also fueling the rise of zero-click search, or searches where a user’s query is answered directly on the search results page, thereby removing any need to click through to a website. Currently, the biggest AI-related change that marketers are seeing with their search performance as a result of zero-click search is a drop in organic traffic from Google and other traditional search engines. An April 2025 study found that search results featuring an AI Overview were associated with a 34.5% lower average clickthrough rate (CTR), while a February 2026 follow-up study found they were associated with a 58% lower average CTR, suggesting that the impact of AI Overviews on site traffic is only growing more significant.
While Google could work to mitigate the drop in organic traffic with future updates, it has made no real effort to do so thus far, and the current outlook has advertisers and businesses concerned. In 2025, education technology company Chegg filed a lawsuit against Google, claiming that AIOs have negatively impacted the company’s traffic and revenue.
In my conversations with brand and agency leaders, I’ve heard an equal amount of fear and excitement around how AI will change both search and digital advertising as a whole. Ensuring teams grow their AI expertise and increase their familiarity with these new tools is one way organizations can prepare for—and adapt to—the coming changes.
AI-powered targeting is quickly becoming the standard for how marketing campaigns are run. As such, marketing teams should be using AI-powered targeting to continuously test and learn what resonates with target audiences in today’s evolving search environment.
This tactic has grown even more important in the context of signal loss, offering a privacy-friendly way to reach target audiences on search platforms like Google and Bing, while simultaneously giving media teams hands-on experience with the machine learning-based systems that are growing increasingly entrenched in search advertising. By nurturing proficiency in these tools now, teams can build the agility and expertise they’ll need to stay competitive as search becomes even more AI-driven.
95% of marketing and advertising professionals are using generative or agentic AI in their work at least once a month, and a third use it every day. To make the most of the technology, marketing teams should actively experiment with various generative AI tools to better understand how and where they can make the campaign process more efficient and data driven.
At the same time, AI comes with risks such as inaccuracies and bias, and leaders must put the proper guardrails in place to minimize those risks—particularly when it comes to generating creative content and analyzing consumer data.
Google’s Performance Max (PMax) is one of the most prominent examples of how AI is shaping the future of advertising, particularly when it comes to using generative AI to create ads. For instance, within PMax, an advertiser can upload a picture of their product and tell PMax to generate an image of that product on a beach at sunset. PMax will then generate four variations of that basic image for use in an ensuing campaign. There are some enormous time- and cost-efficiency benefits to this: Advertisers can cut thousands of dollars that would typically be spent on production and go to market much more quickly. They can even download that asset and use it on other channels for greater creative continuity.
While advertisers may not love the levels of control and transparency offered by PMax, the campaign type is becoming a mainstay, especially for conversion-driven campaigns. AI Max is a newer, search-focused campaign type that expands keyword reach using AI to match ads to relevant queries beyond an advertiser's existing keyword list, while also enabling more dynamic, personalized ad copy. For leaders navigating an increasingly AI-driven search landscape, leaning into both campaign types is key. PMax is fast becoming the baseline for conversion-driven campaigns, while AI Max represents an early opportunity to test and learn before it becomes equally ubiquitous (making now the right time to nurture internal expertise in both).
The shift to AI overviews and resulting decline in organic traffic doesn’t mean that brands should deprioritize their SEO efforts. Brands that continue to invest in SEO will be better positioned to have their content featured as a source in Google’s AI overviews, which often include clickable links that drive traffic back to a brand’s site.
However, knowing that Google’s AIOs are driving a drop in clickthrough rate, as well as allowing more relevant—but often lesser ranked—listings to drive answers, marketing teams should also develop a separate strategy for appearing in AIOs. This strategy should focus on optimizing content to appear as a direct answer while also addressing potential follow-up questions and offering context, rationale, and detailed information about the products or services being promoted.
Beyond AIOs, teams should also be thinking about optimization for Google's AI Mode, which takes the conversational AI experience a step further by allowing users to ask multi-part questions in a chatbot-style interface. Because Google's own guidance emphasizes that success in AI Mode, as with AI Overviews, comes down to providing unique, valuable content that satisfies user needs and anticipates follow-up questions, the optimization principles for both experiences are largely the same. And brands that follow this guidance will be better positioned for success across a range of AI-powered search platforms—from Google’s AIOs and AI Mode to ChatGPT, Perplexity, Claude, and others.
As consumers increasingly adopt AI-powered platforms like ChatGPT, Claude, Perplexity, and Gemini for search, brands need to think beyond traditional SEO. GEO, or the practice of optimizing content to be cited in AI-generated responses, is critical to maintaining brand visibility on these AI chatbots.
Unlike SEO, where success is measured in rankings and clicks, GEO prioritizes citation authority and AI visibility. An effective GEO strategy rests on a few core principles:
As consumers increasingly turn to agentic AI tools to conduct research on their behalf, brands will also need to start thinking about how their websites communicate with AI bots in addition to human visitors. This means preparing digital ecosystems for machine readability by ensuring that structured data, clean APIs, and metadata-rich content are in place so that agentic AI tools can easily find and accurately interpret information about your brand.
On the technical side, preparing for AI agents includes configuring your robots.txt file to confirm you aren't inadvertently blocking AI crawlers from accessing your content, implementing agent-responsive design to make it easy for AI agents to interpret and interact with your site, and maintaining an up-to-date llms.txt file.
One of the greatest benefits that AI offers advertisers is its ability to quickly process and analyze huge amounts of data. As the technology develops, data-related insights will become more widely available, and businesses will need the infrastructure and the know-how to use those insights effectively.
Data-driven cultures prioritize using data to guide decision-making—and invest time, energy, and money into the people, processes, and tools that make it possible. For leaders, this might mean improving data quality and consolidation workflows, conducting audits of all existing data sources (e.g., social media, website analytics, customer surveys, etc.), or investing in a CDP to better capitalize on first-party data. Ultimately, the organizations best positioned to take advantage of AI-powered tools will be those that have already built a unified cross-channel data foundation that can sit at the heart of their tech stack and provide the infrastructure needed to turn AI-generated insights into action.
By investing in AI-powered tools, data-facing teams will be able to generate new insights, improve accuracy, and automate tasks. That hands-on experience will also make it easier for organizations to adopt additional AI-powered solutions as they emerge.
With 68% of marketing and business professionals reporting that they hadn’t received any AI training from their companies as of April 2025, and 62% reporting that a lack of education and training is a top barrier to AI adoption, leaders should prioritize continuing AI education to further empower their teams in this new era. This is especially true given how rapidly AI is evolving and how fast new tools are emerging. By partnering with vendors or consultants for tailored workshops, creating AI-focused knowledge-sharing forums, and investing in training and education platforms, advertising leaders can grow teams whose AI expertise gives them an edge over their competitors.
Lastly, marketers should aim to stay on top of news related to how search engines are changing, monitor what new AI-driven advertising opportunities are available, and pay attention to what successes and failures their peers are having with artificial intelligence tools.
In particular, marketers should continue to stay attuned to the potential challenges and pitfalls posed by artificial intelligence. 100% of marketers agree that generative AI presents a brand safety and misinformation risk. A hallucinating AI chatbot, for example, can make up fake “facts” and generate misinformation that can be difficult for content moderation tools to spot, and the resulting content can represent a threat to brand safety.
There are also many unanswered questions related to AI-generated content and copyright infringement—from the legality of chatbots being trained on unlicensed content, to questions around who owns AI-generated media. Courts have begun to weigh in: In June 2025, for example, federal judges ruled in both Bartz v. Anthropic and Kadrey v. Meta that using copyrighted books to train AI models was protected by fair use (a legal doctrine that permits the use of copyrighted material without permission when the use is sufficiently transformative and doesn't substitute for the original). Much remains unresolved, however, so for now the best approach is to stay informed as the legal landscape continues to develop.
The quickly evolving search landscape asks a lot of marketing and advertising leaders. Advertisers will need to get comfortable with being uncomfortable in the coming years as artificial intelligence moves the industry towards an uncertain future. Teams that use AI-powered targeting, adopt generative and agentic AI tools, optimize for AI Overviews and AI Mode, invest in GEO, prepare their digital ecosystems for agentic AI, nurture data-driven cultures, and commit to continuing AI education will have a leg up on those who are less proactive about adapting to how AI is changing search.
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Want to learn more about how your peers are leveraging AI? We surveyed marketing professionals across brands, agencies, and publishers to find out what tasks marketing teams are using AI for, how AI tools are impacting efficiency, how they predict AI will transform the future of marketing, and more. Check out AI and the Future of Marketing for all the findings.
