The rise of AI has put marketers under some serious pressure: Adopt the technology quickly, demonstrate growth and efficiency gains, and stay ahead of a landscape that’s evolving by the day. Easy peasy, right?
Of course, the upside more than justifies the growing pains, with close to two-thirds of marketers reporting that AI has made them moderately-to-significantly more effective over the last year. Still, with new solutions entering the market every day and marketers constantly discovering creative new applications, it’s easy to feel like everyone else is one step ahead.
To that end, here’s a look at where the industry actually stands, and how marketers are finding real value in the technology.
Recent research offers a useful baseline for understanding how AI has taken hold across the marketing industry. Nearly all marketing and advertising professionals (95%) report using generative or agentic AI at least once a month, and a third use it every day. When it comes to what marketers are actually doing with AI tools, the top use cases are ideation and brainstorming, research, and drafting content and creative. ChatGPT dominates as the go-to platform, with Gemini and Copilot rounding out the top three.
On the organizational side, nearly two-thirds of marketers (65.7%) say their companies pay for or subscribe to premium AI tools. However, adoption of more sophisticated, custom in-house solutions remains limited, with nearly half (47.1%) reporting that their organizations don't use any at this time.
Beyond adopting custom tools and achieving the data readiness necessary to fuel those tools, the creative use of AI is increasingly what separates high-performing marketing teams from the rest. Below, Basis experts share a variety of ways they're finding value in the technology across their roles.
“I’ve found AI very useful as a partner to expedite tasks related to writing and communication. A few examples:
Robert Kurtz | Business Outcomes Partner
“I used Copilot to run a basic regression model for performance forecasting based on historical spending and online conversion data. Copilot helped organize and clean the data set, ran the analysis, and summarized findings so that I could interpret and contextualize the data outputs. This was a cool way to expedite data analysis so that I could focus on interpreting the numbers.”
Lauren Johnson | Effectiveness Lead
“One of our clients was receiving some negative responses online after running new, bold creative executions. They asked us for support in what we could see online from a brand sentiment perspective, but we couldn’t find much using our social listening platform. So, we took a handful of social media posts and asked Copilot to summarize sentiment and themes. The tool helped us to quickly understand audience sentiment on social media based on 15 social posts with 600+ comments.”
Emily Zelenz | VP, Integrated Client Solutions
“I’ve found AI particularly useful for keeping up with what competitors are doing in market. One of our clients has a long list of competitors, so I built a prompt that can look for new competitor creative launched within specific time periods. It makes competitive updates in the QBR much more streamlined—previous to this, I’d have to look at each brand one by one in an ad tracking tool and see if any looked new.
On a similar note, I've also given Copilot a list of competitors in a category and asked for a SWOT analysis of each. Individually curated research would be a lot more in-depth and likely more interesting—but leveraging Copilot gave a quick overview in just 2 minutes. That quick intel is a great starting point, and then it's up to us to identify where we need to dive deeper.”
Molly Marshall | Strategic Business Outcomes Partner
“I work with a lot of pharma clients, and the drugs and diseases we look at are often very complicated. I use a custom GPT for disease and drug research that:
I use the output as a starting point for my own understanding, then ask targeted follow-up questions and cross-check key points until I’m confident in the fundamentals and feel comfortable having an educated conversation with my clients.”
Ryan Sperry | VP, Integrated Client Solutions
“One of our clients partnered with a vendor that develops synthetic personas representing key target audiences. The initial output included detailed audience profiles covering demographics, psychographics, media consumption behaviors, and trust metrics.
We are currently in the process of validating those findings against syndicated sources such as GWI and IPSOS, and the early review indicates strong directional alignment.
One area we are exploring as a next step is using these personas to inform creative message testing, in addition to broader audience strategy and planning. We are still in the early stages of evaluating how this could be applied most effectively, but the results so far are both promising and exciting.”
Jackie Etter-Krause | Sr Integrated Client Solutions Director
My team played around with AI to help us determine the best radius targeting for one of our multi-location clients. Copilot analyzed factors like population density, traffic patterns, bridge/toll road barriers, etc., and helped us determine the appropriate radius as well as some exclusions to implement within Meta & Google.”
Marykate Dougherty | VP, Integrated Client Solutions
These use cases are just a snapshot of the ways marketers are finding value in AI, and the list is only growing. Whether pressure testing a media strategy, navigating a PR crisis, or getting up to speed in an unfamiliar vertical, AI has proven itself as a capable and versatile partner across a variety of marketing functions.
Not every application needs to be groundbreaking—some of the most impactful uses are the ones that simply save time, surface better insights, or help advertisers show up more prepared for a client conversation. As the technology continues to evolve, marketers who are actively experimenting today will be best positioned to scale what works tomorrow.
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For more insights on how marketing teams are using AI, we asked professionals from leading brands and agencies about which tools they’re using, where the technology is driving results, and where the challenges still lie. Check out the 2025 AI and the Future of Marketing Report for an inside look at what's actually working.
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
Humans are wired to search.
With billions of search queries per day, marketers have long known where and how to find their audiences. Now with the rise of generative engines and AI, the search landscape is facing its biggest upheaval in decades. Traditional search, long the backbone of digital strategy, is being rapidly replaced and reshaped by generative engine optimization (GEO), fragmented user journeys, and new discovery ecosystems.
In this webinar, Robert Kurtz, Basis’ Strategic Business Outcomes Partner, joins host Noor Naseer, VP of Media Innovations + Technology, to discuss the past, present, and future of search. They will highlight immediate threats to legacy search approaches and inspire strategies for brands, marketers, and advertisers to reclaim visibility and influence.
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 Campaigns & 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.
