Yousef Kattan, Founder and CEO of Truth Marketing, joins this episode of Adtech Unfiltered for a candid conversation about the state of measurement and attribution. From the myth of last-click to the limits of a so-called “single source of truth,” Yousef explores how the industry is evolving—and what it will take to get measurement right.
Together with host Noor Naseer, Kattan unpacks privacy regulation, AI-driven modeling, MTA’s future, and the growing responsibility agencies have to educate clients. As imperfect data, platform discrepancies, and CFO scrutiny intensify, this episode offers a timely conversation on strengthening measurement strategy.
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.
Key Takeaways:
AI’s role in media isn’t new. However, the rise of generative AI presents powerful new opportunities for advertisers to harness the technology to drive impact for their brand or clients.
While almost all marketing and advertising professionals report using generative or agentic AI at work at least once a month, only a third use it every day. This reflects the barriers limiting wider adoption, including insufficient data readiness as well as inadequate skills and training, unclear strategy, and concerns about reliability.
Overcoming these hurdles creates real separation from competitors, with media strategy emerging as the biggest unlock due to the speed and performance the technology enables. Successful adoption hinges on understanding where AI can provide the most value to media strategists. To that end, this article explores key use cases for AI in media strategy as well as recommendations for successful implementation.
Three use cases stand out when it comes to AI’s applications on the media strategy side:
AI has long been used in media via machine learning algorithms that analyze large datasets to optimize programmatic ad buying, predict audience behavior, and automate bidding strategies. Today’s AI tools create new opportunities to activate advanced data analysis—helping media strategists quickly structure and clean inputs like historical performance, first-party data, brand health studies, and MMM outputs, which often come in the form of massive unwieldy Excel sheets.
Similarly, AI tools can help media strategists quickly gather and synthesize information around audience, competition, and trends in the marketplace to ground themselves in the context of their business or their client’s business. These tasks, which have historically taken media strategists days and weeks to complete, can now be consolidated down with the help of AI.
AI can also serve as a powerful brainstorming partner, helping media strategists generate thought starters and new ideas that they wouldn’t have thought of on their own. This application resonates widely: Nearly 80% of marketers report using AI for brainstorming, making it the most common AI use case, according to one survey.
Which tools and platforms are advertisers using for data analysis, information synthesis, and brainstorming? ChatGPT appears to be far and away the most commonly used tool, with 88.6% of marketers reporting that either they or their organization use it. Gemini (45%) and Copilot (41%) round out the top three most-used platforms.
While AI’s applications in media strategy will continue to evolve, these three use cases—data analysis, information synthesis, and brainstorming—have already proven their value and should be part of every media strategist's toolkit.
Of course, to make the most of AI’s expanding media strategy capabilities, it takes more than just having the right tools: The way marketers and marketing teams harness AI in service of these use cases has an enormous impact on the technology’s effectiveness. To realize this potential, individual marketers need to refine their approach to AI, and leaders must establish the right infrastructure and practices across their teams.
One of the most critical considerations for advertisers to keep in mind when using AI to assist in any marketing function is the technology’s tendency to hallucinate. 35% of brand marketers cite reliability concerns, especially those around hallucinations, as the most significant hurdle for marketing AI implementation. One study found that close to half of marketers experience AI inaccuracies multiple times a week, and over 70% say they dedicate multiple hours per week to fact-checking as a result. Recent tests of six major LLMs found that ChatGPT tended to hallucinate the least and produced the highest percentage (59.7%) of fully correct answers, while Grok had the highest error rate (21.8%) and the lowest proportion of fully correct answers (39.6%). Because of this tendency to hallucinate, media strategists must validate all AI outputs to ensure accuracy, demonstrating how human expertise and critical thinking will continue to be indispensable to the media buying process.
Media strategists will also be essential to ensuring that any AI-assisted work produces differentiated, compelling recommendations rather than generic outputs. It’s easy to look at AI tools as a shortcut to which we can outsource work. But they’re most impactful as collaborative partners, blending AI’s computational power with human creativity. Since AI tools like Claude and Gemini train on similar data, their outputs tend toward the generic—and the last thing any marketer wants is a media plan that mirrors their competitor’s. Turning standard AI outputs into expert-level recommendations requires that strategists embed their brand knowledge, category perspective, competitive insight, and planning approach into the process. It’s also what ensures marketers can defend their recommendations and confidently explain the strategy behind them, rather than presenting AI outputs they don't fully understand.
Finally, the way media strategists go about prompting the AI tools they work with is a critical differentiator. This includes everything from incorporating guardrails around hallucinations to instructing tools on how they’re expected to “think” and communicate with the prompter. For example, LLMs are trained to be polite, so they’re rarely going to give negative feedback. This is something marketers must actively work around in their prompting as well as in their evaluation of AI outputs. Equally important is anchoring prompts in brand context—clear audiences, KPIs, and competitive dynamics—so outputs optimize for your goals, not generic playbooks. The closer the input reflects your real business nuances, the more differentiated the recommendations.
While human oversight, subject-matter expertise, and thoughtful prompting are critical, they’re not the sole drivers of distinctive AI-powered strategy. The use of proprietary, brand-specific data to augment the broader LLM datasets is foundational to unlocking AI's full strategic potential, enabling it to generate recommendations that reflect a brand's unique audiences, competitive positioning, and historical performance rather than broad, generalized patterns. Without proprietary data, even the most sophisticated AI tools will produce strategies that could belong to any brand in any category.
Leaders play a critical role in ensuring their media strategy teams are implementing AI effectively. This includes regularly discussing AI applications with their employees and actively encouraging its use. It also means establishing expectations around how teams leverage the technology and setting guardrails around utilization, so that employees don’t default to thinking of it as an “easy button” and risk losing the human skills that are so important to successful AI use.
Leaders can also empower their teams by working to operationalize AI. This might mean investing in differentiated or custom tools for media strategy purposes to complement their team’s use of tools like ChatGPT and Claude. Specialized tools can offer capabilities tailored to media planning workflows, proprietary data integrations, and industry-specific insights that general-purpose AI tools lack—all of which will help to further differentiate and strengthen AI outputs.
One key aspect of operationalizing AI effectively—and making use of custom AI tools—is data readiness. Because AI outputs are only as good as their inputs, advertisers need large volumes of high-quality data to fuel high-quality media channel recommendations, audience insights, budget allocation strategies, and any other tasks AI tools are used for. And, to make those outputs as differentiated and brand-specific as possible, organizations need ready access to clean, comprehensive data across channels and campaigns.
Currently, most organizations lack the data readiness to fuel their AI use in this way: Only a fifth of marketers call first-party data “foundational” to their organizations’ AI initiatives, and one-third report that first-party-data plays little or no role in their organizations’ AI initiatives. To truly make the most of AI, leaders must treat data infrastructure as a strategic priority, investing in systems that collect, organize, and make first-party data accessible for AI applications.
All in all, leaders who establish clear usage guidelines, invest in the right tools, and build robust data infrastructure will position their teams to extract maximum value from AI in media strategy.
The trajectory of AI in media strategy points toward increasing automation. However, humans will continue to play a key role in managing AI tools and ensuring their organizations are maximizing their AI outputs.
As AI models improve and prove their capabilities through consistent results, marketers may become more comfortable reducing human intervention. For now, however, the winning approach balances AI’s computational power with human strategic judgment. Media strategists who master this collaboration—validating outputs, injecting expertise, and continuously refining their AI tools—will lead the field as the technology matures.
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Looking for more insights on AI’s impact across marketing? We surveyed professionals at leading brands and agencies to uncover adoption patterns, performance gains, and roadblocks to implementation. Check out AI and the Future of Marketing for comprehensive findings on the forces driving—and hindering—AI integration in marketing.
Agentic AI is rapidly emerging as the next frontier in marketing technology. The global market is forecast to surge from $7.55 billion in 2025 to a whopping $199.05 billion by 2034—a compound annual growth rate (CAGR) of 43.84%.
While interest and momentum among marketers is building, adoption remains tentative, with only 20% currently deploying AI agents for marketing work.
Understanding what’s changing, what’s coming, and how to prepare for an agentic AI-powered future is essential for marketers looking to navigate these shifts effectively.
Key Takeaways:
Agentic AI represents a fundamental shift for marketers. These autonomous systems differ from the assistive AI many teams use today by handling complete workflows without constant human intervention. This evolution to proactive execution is poised to transform how marketers operate in three major ways: driving significant efficiency and ROI gains, reshaping marketing roles from execution to oversight, and creating an entirely new agentic web where AI systems interact alongside humans.
One of agentic AI’s biggest selling points for marketers is its ability to drive significant efficiency and effectiveness gains. Through capabilities like autonomous campaign optimization across channels, real-time personalization at scale, and intelligent lead qualification, agentic AI can help marketers achieve better results with less manual effort.
Use of the technology across marketing and sales is forecast to fuel over 60% of agentic AI’s overall value across the enterprise. Early deployments for task automation and human assistance can boost company-wide productivity by 3 to 5% annually, with gains potentially reaching 10% or higher as systems evolve to handle more sophisticated workflows.
Today, most marketers primarily use AI as assistive tools—systems that respond to prompts but need human guidance for each task.
Agentic AI is poised to change how marketing teams work due to its ability to autonomously complete multi-stage processes in service of key goals. Rather than simply generating a single ad variant or analyzing one dataset, AI agents will be able to handle entire workflows end to end. For example, within the realm of programmatic advertising, agentic AI will eventually streamline entire campaign processes from start to finish. Beyond media buying, AI agents are expected to take on specialized roles like strategy assistants that generate and test campaign ideas, data analysts that surface actionable insights, and customer engagement coordinators that manage personalized communications across channels.
As agentic AI expands across marketing functions, the marketer’s role will evolve from hands-on execution to managing AI agents that complete tasks autonomously.
As agentic AI transforms how marketers work, it will also alter consumer behavior.
Consumers are expected to increasingly adopt AI agents to handle complex spending decisions such as booking vacations, planning weddings, and comparing financial products. These behavioral shifts will further disrupt the search landscape, with experts saying that many consumers will eventually delegate entire research and decision-making processes to AI agents.
This shift signals a new era for the internet, where the web will be made up not only of the traditional human-facing web, but also an agentic web where AI systems communicate, evaluate, and transact autonomously. Marketers will need to evolve to ensure discoverability across both layers, optimizing for algorithmic systems while maintaining the creative storytelling that resonates with humans.
While agentic AI promises significant transformation, most marketing teams are still in the early stages of adoption. Successfully integrating this technology requires four key steps: identifying high-impact opportunities for AI agents, preparing data infrastructure, managing organizational change effectively, and optimizing for both human and agentic web discovery.
To harness the efficiency and effectiveness benefits of agentic AI, marketing teams must start by identifying the marketing functions where the technology can make the most impact. This involves auditing workflows for repetitive tasks and processes that can be automated by AI agents.
Key opportunity areas include media and performance optimization, creative operations, account management, and strategy and intelligence. More specifically, agentic AI tools can minimize errors and speed up execution across tasks like budget management, creative testing, ad placement verification, and cross-platform data analysis.
Beyond these foundational areas, marketers should explore creative use cases specific to their team’s unique needs.
Agentic AI’s effectiveness hinges on data quality. However, most marketing teams don’t have the data readiness to make the most of the technology: Only 17.9% of marketers say their first-party data is extensive and well-structured, and one-third report that first-party data plays a minimal role in their current AI initiatives.
To get ahead, teams must work to unify their data within centralized platforms and databases and establish clear governance protocols for its usage. Without unified data that AI agents can access and act on, autonomous decision-making proceeds on a fragmented foundation. AI agents may still be able to execute workflows, but their decisions will be based on incomplete datasets that produce generic or flawed outputs rather than unique, differentiated benefits suited to an organization and its specific audience(s). Marketing teams should also invest in measurement tools that provide real-time, cross-channel insights, as this powers AI agents’ ability to learn and optimize continuously.
Thoughtful change management is another critical component of integrating agentic AI effectively. This means earning employee buy-in, providing comprehensive training to build AI fluency across teams, embedding AI tools into standard workflows, and finding ways to measure how successfully teams are using the technology. Leadership should also establish clear governance frameworks that define how AI agents operate and make decisions. These frameworks should clarify the critical role that human employees play in monitoring and managing those processes.
As consumers increasingly delegate research and purchasing decisions to AI agents, brands must ensure they’re discoverable to both autonomous systems and human audiences. This means creating structured data, clean APIs, and metadata-rich content that AI systems can easily ingest and interpret. Marketing teams should audit their websites, product catalogs, and digital assets to verify they’re optimized for both human visitors as well as AI agents.
The rise of agentic AI represents both a challenge and an opportunity for marketing teams. Those who prepare now—by identifying opportunities, building data readiness, proactively managing organizational change, and optimizing for machine discoverability—will be best positioned to capitalize on the technology’s transformative potential.
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Looking for information on how your peers are approaching and thinking about AI? Our report, AI and the Future of Marketing, synthesizes unique insights from a proprietary survey of marketing and advertising professionals across leading agencies and brands.
YouTube has grown into the largest political video platform in the US. In this session of Basis' 2026 Political Advertising Bootcamp, our experienced Candidates & Causes team breaks down exactly how to win with YouTube advertising in the 2026 election cycle—from ad formats and targeting strategy to compliance and cost considerations. It’s your practical playbook for running political, advocacy, ballot initiative, or issue-based campaigns.
Watch now to learn how to build smarter, more efficient YouTube campaigns.
You'll Learn:
The Basis Political Advertising Bootcamp series continues series next month with: Winning with Audio.
Where clicks used to play a key role in the customer journey of discovering, evaluating, and eventually purchasing a product, zero-click search is changing how consumers discover and interact with brands. Learn how advertisers can adapt attribution models and media strategies for AI-driven search.
Key Takeaways:
With the emergence of zero-click search environments, how consumers discover and evaluate brands is fundamentally changing. AI-driven summaries and chatbot responses now deliver near-instant answers, curated recommendations appear without clicks, and purchase decisions often happen quickly.
Today, users who see AI summaries in Google only click a link in the search results 8% of the time, compared to 15% when those summaries don’t appear. And about 80% of consumers now rely on zero-click results in at least 40% of their searches. For advertisers who have built discovery strategies around driving measurable traffic through organic and paid search, that foundation is shifting.
Thanks to zero-click search, today’s customer journey is often shorter and faster. Where users may have previously conducted multiple searches, clicked through various links, and synthesized information themselves, many are now choosing rapidly based on AI-generated results—often without clicking through to any of the sources cited. For advertisers, this means rethinking measurement frameworks, adjusting channel strategies, and optimizing content for AI consumption rather than just human readers.
Zero-click search environments fundamentally change how audiences encounter brands. In traditional search, advertisers compete for clicks through SEO-optimized content and paid placements. In AI-driven search, whether within browsers or large language models (LLMs), the algorithm decides which sources to surface, synthesize, and recommend.
Consider someone searching for vegan protein powder. Upper-funnel queries like “Are there non-dairy protein powders?” now yield AI-generated summaries pulling from reviews, blogs, and retail sites. Middle-funnel searches for “best” recommendations now often surface lists scraped from affiliate content rather than brand pages. And lower-funnel branded searches often show retail listings, not the manufacturer’s site.
As a result, AI serves as a sort of intermediary between brands and consumers, contextualizing information in ways advertisers and brands can’t control. This shift is accelerating: As of late 2025, about 50% of Google searches included AI summaries, and this figure is expected to surpass 75% by 2028. This means approximately half of searches now include AI-generated content that may reduce click-through rates or obscure brand messaging.
This new search environment creates winners and losers. Small publishers and content creators, for instance, report traffic falling by as much as 70% after the introduction of AI Overviews. And brands relying on broad upper-funnel queries, rather than product-specific information, have faced similar drops. Take, for example, HubSpot. The company previously drove pipeline through broad searches like “famous sales quotes”—content that attracted traffic but wasn’t tightly aligned to their product. When AI Overviews launched, their traffic dropped between 70% and 80%. AI systems prioritize content that directly addresses what a product does or solves, so teams must optimize for relevance over volume.
This trend extends beyond traditional search engines. Between about 40% and 70% of LLM users use these platforms for traditional search engine use cases: conducting research and summarizing information, understanding the latest news and weather, and asking for shopping recommendations. The shift from click-based to zero-click discovery is becoming the norm across multiple interfaces.
The attribution challenges that follow zero-click are fairly predictable: If users don’t click, traditional tracking breaks down.
Historically, clicks on non-brand search terms (i.e., generic category searches like “vegan protein powder”) signaled awareness-stage engagement. Teams tracked users gathering information, then returning later to convert through branded search. In zero-click environments, that initial touchpoint disappears. Users may not enter attribution models until (or if) they appear in brand searches, making it difficult to measure which campaigns drove discovery.
For advertisers, this means top-of-funnel visibility now often gets cut off. Someone researching products may not appear in measurement systems until they search for a specific brand name. This measurement gap makes it even harder to understand the already complex “messy middle” consumer journey that connects awareness to conversion.
Yet drops in click-through rates don’t necessarily signal poor performance. In fact, conversion rates may increase even as fewer people click, as the users who do take the time to click through may have already completed their initial research and be closer to making a decision. Users may see ads alongside AI summaries without clicking, and those impressions still influence purchase decisions. In some ways, zero-click makes non-brand paid search campaigns even more valuable. Ads appearing in category searches deliver messages that brands and advertisers can control, versus AI-generated results that they cannot. The challenge is that impression-based influence is harder to measure and easier to overlook. And higher conversion rates from fewer clicks can mask the total impact of a campaign. As such, pulling back on search investments because CTRs are declining could mean abandoning a channel that’s still driving conversions.
To adapt, advertisers should adopt broader measurement frameworks that account for both the quality of clicks and the reach of impressions. Statistical modeling across platforms, on-site surveys that ask “How did you hear about us?,” and brand lift studies can help replace lost upper-funnel indicators. As zero-click behavior further fragments the customer journey, understanding campaign impact across channels becomes more complex. Tools like automated advertising platforms that consolidate and unify data from search, social, programmatic, and video into clear dashboards help teams see patterns that single-channel reporting can miss.
Zero-click search demands strategic shifts across channels:
As a growing share of searches conclude within the results pages themselves, the brands that adapt most effectively will diversify their channel strategies, invest in measurement tools that capture impression-based influence, and optimize content for AI consumption. Success in a zero-click search environment requires expanding beyond reliance on search traffic alone and maintaining presence across channels where messaging remains controllable.
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Looking for more insights on how search and programmatic advertising are evolving? Our 2026 Programmatic Trends report explores the shifts reshaping digital advertising in the year ahead.
Key Takeaways:
That video advertising playbook you relied on last year? It’s already outdated. Between shifting viewer behavior, emerging platforms, and evolving quality concerns, the video ecosystem is changing fast.
By 2030, connected TV will capture more than 40% of global TV ad investment, reflecting a fundamental shift in how audiences consume video content. Today, however, linear TV still offers mass reach—which means advertisers must strategize around extracting maximum value now while planning for continued viewership declines. Meanwhile, the rise of social video, growing concerns about inventory quality, and complexities around addressability are reshaping what it means to run a successful video campaigns.
For advertisers building video strategies this year, understanding the nuances of this changing landscape is critical. Read on for six insights to guide your TV and CTV planning in 2026:
With nearly seven in 10 advertisers planning to increase their CTV spend in 2026, the industry’s commitment to CTV is continuing to accelerate. However, there's still an increasingly wide gap between ad spend and viewership: In 2027, there will be a 14-point gap between the percent of time US adults spend with CTV per day and the percent CTV ad spend makes up of total US ad spending.
CTV's advantages make this gap notable. The channel combines television's high-impact storytelling with digital precision targeting, superior completion rates, and direct attribution to consumer actions. In fact, three-quarters of American CTV owners prefer targeted ads to enhance their viewing experience, and more than one in five viewers have used their CTV devices to complete a purchase after seeing an ad. Add to this the emergence of new CTV ad formats and expanding inventory options, and it's clear that advertisers who match their spend to viewership now stand to gain significant competitive advantage before the market catches up.
But don’t write off linear TV just yet: While connected TV continues its steady climb, traditional television still delivers the kind of mass reach that many campaigns need, particularly those focused on driving brand awareness.
And no, this isn’t just your grandparents watching Jeopardy (or you watching Jeopardy, if Jeopardy is your thing!). Audiences across all age groups still tune in to traditional broadcasts, though younger generations are doing so less frequently than their older counterparts.
However, linear TV’s reach is eroding as more viewers shift to streaming alternatives: In 2026, CTV offers access to 15% more of the US population than linear. Advertisers must ensure their linear and CTV strategies complement each other in the short term, while planning for linear’s decline and the continued rise of CTV and social video.
This might look like running broad awareness campaigns on linear TV during high-profile events like live sports, then using CTV to extend reach to cord-cutters and younger viewers who don't watch traditional TV. Or, it could look like managing CTV campaigns with a platform that offers Open Addressable Ready (OAR) capabilities, which enable consistent, addressable campaigns across both linear and streaming using unified audience data and measurement. Advertisers should also allocate budget dynamically—shifting spend to CTV as linear reach declines, while maintaining traditional TV presence where it remains cost-effective.
Ultimately, success lies in treating linear and CTV as complementary tools in a holistic video strategy that evolves alongside viewer behavior.
The boundaries between TV and social media are dissolving faster than audiences can scroll to the next video in their TikTok feed. This is especially true among younger audiences, with nearly 80% of young people aged 10-24 reporting that they watch movies or TV shows on social platforms.
Sports content, in particular, is driving social video engagement (as well as live CTV engagement): Between 2020 and 2024, the percentage of Americans who reported they watched live sports games on social media platforms in the last month grew by 34%.
Following these viewership trends, US advertisers invested more than $10 billion more in social video than in linear TV in 2025.
Recent spending trends show social video and CTV budgets are climbing while linear investments decline: In 2025, CTV and social video dominated the priority list for video advertising budgets, setting the stage for continued growth in 2026.
The living room TV? Still relevant. But today, it’s far from the only screen that matters for reaching video-watching audiences.
In 2026, buying inventory from a recognizable streaming service doesn’t guarantee your ads will appear in brand-safe, high-quality environments. As the streaming ecosystem has matured, the term “premium” has been applied so broadly that it’s lost much of its meaning. Spoiler alert: Slapping a well-known logo on an ad buy doesn’t automatically make it premium.
Much of what advertisers purchase on major platforms runs within long-tail apps, user-generated content channels, or bundled placements that offer minimal transparency. As a result, 15% of streaming TV ad spend is wasted in low-quality environments rather than premium streaming content.
Considering this, it’s critical that advertisers demand transparency from their CTV providers. Just as important is implementing quality controls when buying CTV inventory to ensure spend isn’t wasted on low-quality placements—for example, by checking in on campaigns midstream to make sure ads are showing up where intended. Overlaying ACR (automatic content recognition) data into CTV buys can also help by offering more precise visibility into placements, verifying exactly what content appeared on-screen when your ad ran.
Ultimately, premium placement in streaming comes from verification and control, not brand recognition alone.
While video strategies of the past focused on specific channels and distribution methods, the video strategies of the future will focus on following engaged audiences wherever they’re consuming content, regardless of the screen.
This means treating CTV, social video, and linear TV as complementary tools in a unified strategy rather than competing channels. Advertisers must align their content objectives with inventory-specific tactics across platforms. Broad awareness campaigns might justify run-of-content purchases, but performance-focused initiatives demand curated placements and app-level visibility.
The shift from channel-first to audience-first planning is showing up in how advertisers choose their partners: 66% of media buyers cite audience personalization capabilities as the most important factor when choosing video ad partners.
Marketing teams are implementing this audience-first approach by leveraging CTV targeting parameters including behavioral and demographic segmentation as well as content-level contextual targeting to connect with viewers in high-quality environments—then extending those learnings across social video and other channels. AI-powered, all-channel platforms can help with this by automatically applying these learnings to optimize buys across channels.
Streaming TV’s promise of precise, addressable advertising faces a critical data quality challenge. Many platforms claim they can offer addressability, but the accuracy varies significantly based on the kind (and quality) of data powering it.
IP-based targeting, which many platforms rely on, suffers from significant accuracy problems. IP-to-postal linkages are correct just 13% of the time on average, while IP-to-email connections hit the mark only 16% of the time. Yes, that means they’re wrong a whopping 87% and 84% of the time, respectively! These error rates undermine the targeting precision that makes addressable TV attractive in the first place.
As the addressability landscape develops, advertisers must understand what kind of addressability the platforms they invest in offer. To accomplish this, teams should ask their CTV partners specific questions: What data sources power your targeting capabilities? How accurate is your addressability, and how do you measure it? Do you primarily use deterministic data or probabilistic data? Platforms that can’t answer these questions transparently may not offer the precision they promise. And to enhance precision, advertisers should layer first-party and deterministic data—which offer the highest level of targeting accuracy—into their buys.
Today’s streaming landscape offers tremendous opportunities for advertisers willing to navigate its complexities. Success requires moving beyond assumptions about premium inventory, channel effectiveness, and targeting precision, and crafting strategies grounded in audience behavior, content quality, and data transparency.
Six Key Takeaways:
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Want to dive deeper into what’s shaping the future of advertising? Check out Rewinding to Fast Forward: The 2026 Digital Advertising Trends Report for more insights into how the media landscape is evolving and what it means for your marketing strategy.
Innovation gets thrown around a lot in advertising—but what does it actually mean inside an agency?
In this episode of AdTech Unfiltered, we sit down with Savannah Westbrock, Head of Agency Solutions at Radar Analytics, to unpack how agencies can spot real opportunities for change, avoid hype-driven distractions, and build innovation that truly helps clients. From "desire paths" to pilot programs, Savannah shares practical frameworks for testing new ideas, improving workflows, and using AI to accelerate creative and media performance.
Welcome to our recurring blog series, “The Breakdown.” Each post breaks down a complex marketing concept and explores its applications—helping you make smarter decisions that drive meaningful results.
Marketers face a pivotal choice in 2026: to frame their use of AI around driving business growth, or around cutting costs.
While AI can accelerate efficiency and reduce costs by speeding production and scaling content, its real power lies in the effectiveness it can deliver in the form of optimized creative, personalization, and predictive insights. Marketing leaders should guide their teams toward strategies that use AI to strengthen brand equity and long-term enterprise value, rather than focusing solely on its ability to drive efficiency gains and short-term margin improvements.
Data readiness is critical to achieving this goal. Without a strong foundation of unified first-party data and custom AI solutions trained on that data, organizations will struggle to create the differentiated outputs that drive brand and business growth.
How marketing leaders approach and position their use of AI in 2026 will define the role of marketing within their organizations for years to come.
While teams should absolutely use AI for efficiency-related gains like faster campaign execution and lower production costs, emphasizing its impact on efficiency alone can inadvertently play into the notion that marketing is merely a cost center that should be trimmed to the bone, rather than a strategic asset that can fuel effectiveness and multiply revenues. This, in turn, can reduce departmental resources and weaken marketing’s role in the larger organization.
Instead, marketers should embrace AI’s ability to drive sustainable growth through smarter targeting, better creative, and real-time optimization. This can look like implementing enterprise AI tools to augment or automate the media planning process, leveraging AI-powered software to improve and scale creative ideation and testing, exploring AI-powered optimization for real-time performance enhancement across campaigns, or creating AI synthetic audiences—AI-generated, data-driven models of real people—that simulate how specific customer groups might respond to creative variations.
This value-driven approach can have transformative impacts: When AI is used to enhance marketing effectiveness rather than just to improve efficiency, companies can more than double marketing-driven profitability.
Critically, this work goes beyond using AI to drive long-term growth. Marketing teams must also demonstrate how their teams’ use of AI impacts business outcomes, and earn executive buy-in for reinvesting productivity gains into long-term growth rather than settling for short-term wins.
Differentiated AI results require proprietary solutions fueled by large volumes of clean, unified first-party data. And with 58% of industry professionals citing data quality and accessibility issues as critical challenges to adopting AI, marketers must prioritize data readiness to realize these maximal business-level gains.
A lack of data readiness appears to be the norm among marketing teams: Just 17.9% of marketing and advertising professionals describe their first-party data as extensive and well-structured, and a whopping one-third of those professionals say that first-party data plays little-to-no role in their businesses’ current AI initiatives. Even more, nearly half of marketers and advertisers report that their organizations don’t currently use any custom AI solutions or have any imminent plans to develop them.
Personalized, company-specific instances of AI solutions are critical to driving the business-level outcomes that can position marketing as an indispensable revenue driver, but they require quality data for training and rigorous planning to ensure that any desired use cases align with specific, measurable outcomes. As such, organizations that prioritize data readiness—and use that infrastructure to power custom AI solutions—stand to substantially outperform their competitors.
Marketers who frame AI as a driver of business growth rather than solely operational efficiency will be better positioned to secure ongoing investment and strategic influence.
This starts with building business cases that connect specific AI capabilities directly to revenue and brand outcomes. Demonstrating marketing's contribution to enterprise value creates the foundation for budget expansion rather than contraction.
Realizing these outcomes requires prioritizing first-party data infrastructure. Quality, accessible data differentiates AI outputs—without it, even sophisticated tools produce generic results competitors can replicate. Once that data infrastructure is in place, it should fuel custom AI solutions built to amplify growth. Proprietary (and/or semi-custom) models trained on brand-specific data reflect unique market positioning and audience understanding, enabling organizations to establish competitive advantages that strengthen marketing's strategic position over time.
Ultimately, the choices marketing leaders make about AI positioning today will determine whether their teams are properly perceived as growth drivers—or unfairly maligned as cost centers—for years to come.
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Want more insights into how AI is reshaping marketing strategy? We surveyed marketing and advertising professionals from top agencies and brands to gather data on AI adoption trends, performance improvements, and implementation challenges. Check out AI and the Future of Marketing for a deeper dive into the opportunities and obstacles shaping marketing’s AI future.
