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Political advertising has never been a simple undertaking. It demands strategic precision, creative discipline, and the ability to move quickly in an environment where the rules, the platforms, and the electorate itself seem to shift by the day. The 2026 US midterm elections promise all of that and more, and they’re arriving at a moment when the political advertising landscape has been fundamentally reshaped by the rapid evolution of connected TV, the maturation of streaming audio, the rise of AI-assisted creative and optimization tools, and deep uncertainty around redistricting, candidate turnover, and an increasingly fragmented media environment.
This guide is designed to help political advertisers—including campaigns, causes, consultants, and agencies—navigate what lies ahead. Whether you're managing a statewide senate race, running a closely contested congressional district, or promoting a ballot measure campaign, the insights here are designed to be actionable, practical, and grounded in the realities of the 2026 election cycle.
Key Takeaways for Political Advertising in 2026
With control of both the House and the Senate up for grabs, the 2026 elections are expected to be the most expensive midterm cycle in US history. Political ad spending for the year is projected to hit $10.8 billion, a 20%+ increase over the last midterms held in 2022.
The backdrop for this fall’s elections is unusually complex. Beyond the anticipated referendum on an unpopular president, a wave of congressional retirements—early estimates suggest approximately 10% of sitting members may not seek reelection—is creating an unusually large number of open-seat races where neither candidate enters with the name recognition and voter relationships of an established incumbent. At the same time, redistricting proceedings are underway in several key states, including California, Missouri, North Carolina, Ohio, Texas, and Utah, with the potential to redraw competitive boundaries heading into the cycle.
The implications for political advertisers are significant. Open-seat races typically require heavier investment in early awareness and fundraising, since candidates cannot rely on existing voter relationships to carry their message. And in races where redistricting has created entirely new districts, voters may find themselves represented by a candidate they have never heard of, making name recognition advertising even more critical in the early campaign phase.
"I think everything's up for grabs," says Jackie Huelbig McLaughlin, VP of Candidates & Causes at Basis.
Watch List: Redistricting is underway in California, Missouri, North Carolina, Ohio, Texas, and Utah. Campaigns in these states should plan for a shifting competitive landscape and prioritize early awareness investment.
The political benefits of redistricting will ultimately be state-dependent, and predictions around competitiveness in those districts will be somewhat difficult to make until primaries clarify the field. Either way, campaigns in affected states should not assume that historical spending patterns or targeting approaches will translate to the new map. Early investment in audience research and media planning will pay dividends later.
Additionally, the retirement wave, in particular, is likely to make primaries more competitive across the board. Candidates who might have had a clear path in a race against a known incumbent now face crowded fields where early advertising, grassroots organizing, and digital presence can be decisive. For campaigns with limited budgets, this means making hard choices about where to allocate early dollars—and recognizing that earned media, organic social, and community presence may play an outsized role in building momentum before paid media becomes the primary vehicle.
Every election cycle teaches the industry something new, and 2024 was no exception. Among the most instructive contrasts of the last presidential race was the divergence in how the Trump and Harris campaigns approached their paid digital advertising in the campaign's final stretch. The Trump campaign, hamstrung by budgetary restrictions, was highly selective with their media buys: If they knew a voter was already in their corner, that voter simply wasn't seeing their ads. The Harris campaign, meanwhile, was “throwing spaghetti against the wall,” says Huelbig McLaughlin—having entered the race late and operating under time pressure, they took a broader approach, running significantly more volume across more audiences in the hopes that something would connect.
In 2026, knowing your audience before you spend will be key to maximizing political advertising budgets. Heading into a midterm cycle—where the margin for error is increasingly narrow—campaigns cannot afford to waste impressions on voters who are already committed. “The goal,” says Huelbig McLaughlin, “should be to stretch the dollars in a very mindful way" through disciplined targeting and a thoughtful media mix.
This philosophy will define the most competitive 2026 races. Campaigns that invest early in understanding their voter base—who needs persuasion, who needs activation, and who is already a lost cause—will be the ones best positioned to allocate their media dollars efficiently when spending peaks in October and November.
The fundamental rhythm of the political advertising spending cycle has not changed: awareness and fundraising in the early months, a building cadence through the summer, and then an all-out sprint from Labor Day through Election Day. Basis data from the 2024 election found that 48% of digital ad budgets were spent in the final 30 days before Election Day, with nearly half of that concentrated in the final 10 days of the campaign.
Early in the cycle—through primaries and into the summer—the goal is awareness: getting the candidate's name and core message in front of as broad an audience as possible. Then, as Election Day approaches, the objective shifts to precision: Identifying the specific persuadable voters who haven't committed, reaching them with the right frequency, and getting out the vote. These two objectives require different channel strategies, different targeting approaches, and different creative executions.
In 2026, the dynamics of that late-cycle sprint will be shaped by two intersecting factors: the aforementioned volume of competitive races created by redistricting and retirements, and the growing supply of premium CTV inventory. In races where multiple well-funded campaigns are all racing to reach the same undecided voters in the final weeks, the premium on reserving inventory early cannot be overstated.
Budget Strategy and Timing Tips:
Political advertisers have long operated in a more constrained targeting environment than their commercial counterparts. But some restrictions on audience targeting for election ads on platforms like Google and Meta, coupled with state-by-state regulatory variations, can make audience strategy an increasingly complex, jurisdiction-specific exercise.
In this environment, geopolitical targeting has become an increasingly important tool. Basis data from the 2022 midterms showed that nearly 20% of political programmatic ads used geopolitical targeting to reach voters in specific districts—with 51% using congressional district targeting and 32% using state senate district targeting. With third-party cookie deprecation having largely reshaped the programmatic targeting landscape, these geography-based approaches have only grown more prominent in the last four years.
Beyond geotargeting, identifying ways to more precisely reach undecided voters remains the political targeting holy grail: Who are they, where do they live, what do they watch, what are their passions? In districts where redistricting has created new competitive dynamics—and where an anticipated referendum on an increasingly-unpopular president has loomed over many races—this kind of audience intelligence work is especially valuable, as the old assumptions about which precincts are reliably red or blue may no longer apply.
Automatic Content Recognition (ACR) data from smart TV manufacturers, combined with programmatic targeting capabilities on CTV platforms, offers one of the most compelling answers to this challenge. Campaigns can now reach households based on their actual viewing behavior—not inferred demographic proxies—and serve political messages in contextually appropriate environments at the moment those households are most engaged.
Speaking of which…
Connected television (CTV) has been the hottest digital channel of the past several election cycles, and 2026 will be no different. While broadcast television will remain the single largest channel for political ad spend at $5.3 billion, CTV is projected to rank second at $2.48 billion—and, tellingly, is the only segment expected to show meaningful growth. For political advertisers who have already internalized the case for CTV, the challenge is now to invest more effectively given an increasingly crowded and complex inventory landscape.
The fundamental appeal of CTV has always been its ability to deliver the emotional and persuasive power of television-style video with the precision targeting capabilities of digital. And with the number of cord-cutters and “cord-nevers” growing by the year, campaigns must approach CTV as a distinct and valuable channel to complement their linear efforts, as the audiences for each TV-consumption method are increasingly divergent.
There is now a sizable subset of undecided and swing voters who are only reachable via connected TV. This audience of individuals who are not watching broadcast or cable skews younger, and is generally associated with urban and higher-income audiences. But audiences of all kinds are making the switch from cable to connected TV, making it increasingly critical to an array of midterm races in competitive districts.
Case in point: “My parents and in-laws in their 70s all cut the cord this year and are relying entirely on YouTube TV or Hulu TV,” says Huelbig McLaughlin. “The cord-cutting age is going up."
Key Data Point: $2.48 billion: Forecast CTV political ad spend in 2026—the only digital channel projected to grow this year. (Source: AdImpact)
In recent election cycles, the gap between demand for political CTV advertising and the available supply constrained campaigns' ability to fully exploit the channel—particularly in premium environments.
Fortunately for CTV-hungry political advertisers, that gap is narrowing rapidly, and 2026 is poised to be the first US election where CTV inventory meets the market’s needs.
More and more streaming platforms and publishers are now accepting (if not outright welcoming) political and advocacy advertising. The shift is fueled, in part, by the proliferation of smart TV apps that come pre-loaded on devices from manufacturers like LG, Samsung, and Vizio. These OEM (original equipment manufacturer) platforms operate their own owned-and-operated channels—including free ad-supported streaming TV (FAST) channels—and are opening up significant new inventory.
Additionally, companies like Disney have opened up valuable inventory across many of its platforms, such as Hulu and ESPN—a particularly notable development for campaigns targeting sports-adjacent audiences—to go along with growing opportunities across DIRECTV, HBO Max, Paramount, and other premium providers.
Live sports inventory, in particular, have increasingly migrated to streaming platforms and offer particularly high value, with the FIFA World Cup, NFL, college football, MLB, and other major sports events providing exactly the kind of broad, engaged audience that campaigns are always eager to reach.
But the streaming story goes beyond mere supply: ACR data generated by smart TVs is making targeting dramatically more sophisticated, with tens of millions of Americans having opted in to sharing data about what is displayed on their screens. This creates rich household-level viewing data that advertisers can use to reach audiences based on the content they actually consume—whether that’s live sports, their favorite shows, or even specific streaming services.
Campaigns that want to maximize their CTV presence in 2026 should plan to use a mix of buying approaches rather than relying on any single method. Programmatic open exchange, private marketplace deals, and programmatic guaranteed each offer different tradeoffs in terms of pricing, inventory access, and targeting precision. As demand surges in October and inventory tightens dramatically in competitive markets, campaigns that have locked in favorable PMP or programmatic guaranteed pricing in advance will have a significant edge over those relying solely on the open exchange.
In most congressional districts, sufficient inventory exists—but only if campaigns plan strategically and secure access before peak season.
Key Takeaways
Campaigns looking to make the most of CTV advertising opportunities should:
If CTV has been the marquee channel story of recent cycles, streaming audio is shaping up to be one of the defining new opportunities of 2026. Audio represented just 1% of political ad spend in the 2022 midterms and 3% during the 2024 election season (most of which was radio) despite the average voter spending 21.2% of their total media time consuming audio, representing a significant underexplored opportunity.
With workers having increasingly returned to offices (and their accompanying commutes) in recent years, audio consumption is rising in kind. The medium is uniquely well-positioned to reach voters during screen-free moments: In the car, on a commute, during a workout, while cleaning the house. Podcast listenership, in particular, has demonstrated itself increasingly resonant across key voter demographics, delivering outsized political impact. And with programmatic buying now available even for broadcast radio inventory, the barriers that once made audio difficult to activate at scale have largely been removed.
While targeted podcast advertising at scale remains somewhat nascent, the streaming audio opportunity through platforms like Spotify and iHeart is very real—and growing. Basis’ Jaime Vasil (Group VP, Candidates & Causes) anticipates meaningful growth in streaming audio political spend this cycle, and campaigns that are early movers in the channel stand to benefit from both lower CPMs and less competitive inventory than they will find in CTV.
Beyond inventory, the emergence of streaming audio reflects a key shift in the ways media consumption has changed in the past several years. Voters have moved from passively consuming programming at a specific, scheduled time (and on a specific, dedicated channel) to actively constructing their own media environments across an array of mediums—combining on-demand streaming services with live sports, podcasts, social video, and background audio throughout the day. Effective political advertising in 2026 will need to follow voters into all of those environments rather than waiting for them to find you.
“People are now curating and consuming their own personalized entertainment menus across different channels and different timeframes,” says Huelbig McLaughlin. “And as political advertisers, we’re going to have to crack that.”
One of the more nuanced conversations in political advertising heading into 2026 concerns the relationship between paid media and organic digital content—and, specifically, whether the viral social media success stories of recent cycles are broadly replicable for candidates in smaller or less urban markets.
The impetus for the discussion, of course, is newly elected New York City mayor Zohran Mamdani, whose authentic, personality-driven TikTok and Instagram content generated outsized organic reach among younger voters. The appeal is obvious: Organic content costs relatively little, can generate significant reach, and communicates a kind of authenticity that traditional political advertising often struggles to replicate.
The question, then, is whether the “Mamdani effect” will grow and influence campaigns this midterm cycle?
While the content itself is undeniably fresh and engaging, Vasil offers an important caution: "It's sometimes more about the candidate than the tactics." Mamdani was a young candidate living in Queens, genuinely relatable to the digital-native voters he was trying to reach. The same approach deployed by a candidate in a rural congressional district—or by any candidate who lacks the natural charisma or cultural fluency to pull it off—is unlikely to produce the same results. Trump's social media dominance, for example, was built on 25+ years of name recognition accumulated through television and business ventures—something that’s not exactly replicable for most candidates.
That said, authentic organic social content will likely become increasingly important for candidates seeking to connect with voters under 55. The key word there is "authentic," as voters—particularly younger ones—are highly attuned to content that feels manufactured or performative. The opportunity is real, but campaigns should resist the temptation to force organic content that doesn't fit the candidate's actual personality and voice.
The practical strategic implication is for campaigns to use organic social to establish their voice, generate earned media, and raise early awareness and funds—particularly for less-known candidates in the primary phase—while investing those raised funds into disciplined paid media as the general election approaches.
When it comes to creative, far too many campaigns still embrace an outdated mindset: Developing a polished 30-second television spot, then repurposing it across digital channels without thinking through adaptation, context, or modern attention spans.
But in the current media environment, that approach is no longer fit for purpose. Or, as Huelbig McLaughlin puts it succinctly: "People need to stop creating ads for linear TV and thinking that they translate directly to digital."
The fundamental challenge is attention. Whether a voter is watching CTV, scrolling through social content, or listening to a podcast, they are in an inherently different cognitive mode than a traditional broadcast television viewer. Attention spans are shorter, and the threshold for capturing interest is higher. A 30-second ad that might work perfectly in the context of an evening news broadcast may fail to register on a streaming platform where the viewer can skip, mute, or simply look away.
Effective creative in 2026 will require campaigns to develop platform-adapted versions of their messaging: the 30-second anchor spot for broadcast and premium CTV, a 15-second cut optimized for digital video, and a six-second bumper designed to work as a pure impression. The good news is that AI-assisted creative tools are making this kind of versioning more accessible and less expensive than it has ever been.
The greatest looming constraint here, of course, is budget. A campaign with $50,000 in media budget, for instance, is going to approach creative testing very differently than one with $500,000 a month. But even resource-constrained campaigns can benefit from a few guiding principles: Lead with the most important message in the first three seconds, design for sound-off viewing on social, and ensure that every creative asset has a clear and singular call to action.
Political Advertising Creative Best Practices:
Artificial intelligence has become impossible to ignore in any conversation about marketing, and political advertising is no exception. But there is a significant gap between the hype around AI and the practical reality of what it can actually do well for political campaigns, and understanding that distinction is essential for campaigns trying to make smart decisions about where to invest in AI capabilities.
The areas where AI creates the most tangible value for political advertisers in 2026 generally fall into two broad categories: campaign optimization and creative development.
On the optimization side, AI-powered bidding and campaign management tools offer real efficiency and ROAS gains for campaigns managing complex multi-channel buys. AI can process far more data signals than any human media buyer, adjusting bids and allocations in near-real-time based on performance data. For campaigns running programmatic CTV, audio, display, native, search, and/or social simultaneously, this kind of automated optimization can meaningfully improve performance without requiring additional headcount.
On the creative side, AI is becoming a reliable tool for creative testing and iteration. The ability to quickly generate multiple versions of ad copy, test different message framings with focus groups or panel research, and identify which creative elements are resonating—at a speed and cost that would have been prohibitive even a few years ago—is a real competitive advantage. "Using AI for creative testing is going to add meaningful efficiency and effectiveness gains," says Vasil.
Of course, the risks of AI in political advertising are also quite real, and campaigns should approach the technology with appropriate care.
Perhaps the most significant concern is AI-generated content that misleads voters—whether through deepfakes, manipulated audio, or fabricated quotes attributed to real candidates and officials. This concern, sadly, is no longer hypothetical: The technology required to produce convincing deepfake content is now widely accessible, and bad actors will almost certainly use it in the 2026 cycle.
Meanwhile, on the compliance side of the equation, platform policies around AI disclosure are moving quickly. Google already requires political advertisers to disclose any use of AI in their ads, and similar requirements are likely to proliferate across platforms. Meta does the same, albeit in only certain circumstances. And a UChicago Harris/AP-NORC Poll found that voters strongly prefer that political ads disclose any use of AI, a finding that should help curb any temptations to omit disclosures even where they are not yet required.
The regulatory approach to AI usage in political advertising remains somewhat of a moving target, but the directional trend points toward greater transparency, more required disclosures, and tighter platform enforcement. Campaigns that build disclosure compliance into their workflow now will be better positioned as requirements evolve.
Misinformation and disinformation have had a prominent presence in every recent election cycle, but AI is changing its character.
The same tools that make it easier to test creative messages and generate ad variations can also be used to produce misleading content at scale and speed that would have been impossible just a few years ago. With trust in news sources and political information already eroding, the 2026 cycle will require campaigns to actively work to build and maintain credibility with voters who are increasingly skeptical of everything they see.
In an environment where voters are increasingly questioning what is real and making personal decisions about what constitutes “fact” vs. “opinion,” campaigns need to demonstrate their authenticity through more than just advertising. Being visible in communities, having candidates with verifiable records and clear commitments, and using paid media to amplify genuine moments rather than manufactured ones can help create foundations of trust that advertising can subsequently reinforce and build upon.
Additionally, brand safety tools from verified partners remain essential for ensuring that campaign ads are not serving alongside misinformation and disinformation, or appearing in contexts that could damage the campaign's credibility. Tools from providers like Comscore and Peer39 offer contextual controls that should be standard practice for any political media buy.
But importantly, campaigns cannot count on technology alone to handle the disinformation problem on their behalf. Trust and safety teams at major tech companies have been substantially reduced in recent years, and while platforms continue to invest in automated content moderation, the volume and sophistication of AI-generated misinformation and disinformation is growing faster than defensive capabilities. Political advertisers need their own mitigation strategies—including clear messaging frameworks, rapid response protocols, and media buys designed to maintain a commanding share of voice in competitive markets—to better protect themselves against disinformation’s most prevalent risks.
Historically, dating back to 2016 and the Cambridge Analytica scandal that rocked Facebook, the regulatory environment for political advertising has consistently trended toward increasing restrictions, with platforms tightening rules around what creative they will accept, narrowing targeting options, and expanding disclosure requirements. In 2026, however, the picture is decidedly more mixed, with some meaningful new inventory openings debuting alongside continued compliance complexity.
On the positive side for political advertisers: Publishers are opening up more and more inventory to political and advocacy advertising, drawn to the category’s astronomic ad spending and sensing an opportunity to tap into a lucrative new revenue stream—particularly during this projected record-setting midterm cycle.
At the same time, many digital publishers have added newly-rigorous vetting and disclosure requirements. Advertiser verification, signed disclaimer forms, and detailed record-keeping are becoming standard, following the model that has long been standard practice across linear television.
Political advertisers will want to seek out partners with built-in compliance infrastructure and workflows, allowing platforms to capture the information needed for regulatory disclosure in a systematic, automated way that alleviates compliance concerns and streamlines media buys. Otherwise, campaigns that cannot demonstrate clean, documented compliance with platform policies and applicable state regulations risk having their ads pulled at the worst possible moment.
The 2026 midterms will be contested on a fundamentally different media landscape than any previous cycle. The combination of cord-cutting acceleration, CTV inventory expansion, streaming audio's growing political presence, AI-driven creative and optimization tools, redistricting uncertainty, and an unusually large cohort of first-time and open-seat candidates creates both significant challenges and significant opportunities for political advertisers.
The campaigns that succeed will be those that approach the cycle with intellectual humility, strategic discipline, and a willingness to learn from the latest data. And don't make assumptions about what worked in 2022 or 2024, either: The audience has changed, the platforms have changed, and the competitive map has changed along with them. Start early, invest in better understanding your voter universe, lock in premium inventory before the crunch, build for the screens and devices your voters are actually using, treat creative as a strategic asset rather than an afterthought, and seek out experienced advertising and technology partners to support you in your goals.
Explore how Latcha + Associates integrated Basis DSP into their platform as their programmatic buying arm for a Fortune 500 automotive leader, deploying 40,000+ campaigns with 56,000 tags across 385+ dealerships in less than a week.
Latcha + Associates is a Michigan-based, full-service digital agency that specializes in brand strategy, creative development, UX, and relationship management.
While they had pitched and built a marketing platform for dealerships, Latcha + Associates realized it needed a DSP that offers spend control, data-driven automation, and scalability. After securing a leading luxury automotive client, they faced a new hurdle: managing complex media execution alongside 385+ dealerships while maintaining spend control, leveraging individual first-party data sets, and automating processes—all without adding complexity to their existing system.
In 2024, Latcha + Associates partnered with Basis to integrate Basis DSP via API into their proprietary platform and optimize its design for their new Fortune 500 client’s dealership needs. Basis’ Platform Ops team provided technical consulting, referred partner contacts, and clear documentation to ensure a seamless build.
Basis’ automation flexibilities enabled Latcha + Associates to automate the campaign development process across the dealerships, reducing manual and repetitive work for 40,000+ campaigns. The client can customize and personalize how they’d like to set up the DSP within their platform, giving them more control over what each dealer can access. In addition, Basis’ two-way integration with Google Campaign Manager (GCM) allowed them to seamlessly use and push information from GCM to Basis DSP.
Alongside the platform build project, Basis’ Customer Success team provided educational resources, including a presentation to Latcha + Associates’ client, to highlight what was possible within Basis DSP. To support faster adoption, Basis delivered tailored education—including a Programmatic 101 session for Latcha’s client—and supplied dealer-friendly resources.
In early 2025, Latcha + Associates launched a pilot run of 7,500 display campaigns across 25 dealerships, followed by a rapid rollout to 370+ dealers in which Basis expedited QA for more than 56,000 tags during this phase. To date, Latcha + Associates has successfully launched and deployed 40,000+ campaigns and plans to expand into streaming audio, connected TV, and digital video for their leading automotive client.
Since onboarding Basis DSP, Latcha + Associates' clients have seen greater success across their multi-channel campaigns, including:
“Basis DSP has been a breeze to use. Given the number of campaigns we’re building, it could get overwhelming, but its ease of use has made running these campaigns much more manageable."
- Senior Digital Media Planner, Latcha + Associates
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.
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.
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 and advertisers 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-powered marketing'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.