• Commerce media has evolved well beyond retail, expanding into travel, payments, and other transaction-rich sectors.
• Because commerce media is powered by first-party data, it gives advertisers access to highly targeted, relevant audiences at scale.
• Offsite and omnichannel activation are accelerating, making commerce media an increasingly full-funnel solution that supports brand-building efforts alongside performance marketing.
• Fragmentation and measurement inconsistency remain the channel's biggest operational challenges, making it critical for teams to evaluate partners carefully, unify data across platforms, and invest in tools that reduce complexity.
• The rise of agentic AI is poised to reshape how products are discovered and purchased—and while full disruption of the lower funnel remains a longer-term story, marketers should start auditing their digital presence for AI discoverability now.

Commerce media has all the characteristics of a digital advertising channel that every brand is searching for.

What began with retailers has expanded into travel, payments, and other sectors rich in transactional data—empowering advertisers to connect with targeted audiences in relevant moments with personalized messages.

Because commerce media is powered by first-party data, those audiences, moments, and messages are as targeted, relevant, and personalized as possible. A growing number of commerce media platforms also enable a sophisticated omnichannel approach, allowing advertisers to capture audience attention in saturated digital environments.

While non-retail commerce media is still nascent, its swift evolution is opening up new, less-saturated channels for marketers to reach high-intent audiences. In fact, non-retail commerce media is growing even faster than retail media, with ad spend forecast to surpass $100 billion by 2028. It’s a space where innovation—particularly AI-related innovation—is driving rapid expansion.

Considering all this, now is the time for advertising leaders in every sector to get ahead of the curve by understanding the forces driving commerce media’s growth as well as how to leverage the channel strategically.

A Channel That Meets the Challenges of Today and Tomorrow

Marketing teams today are struggling with signal loss, a lack of data readiness, and media fragmentation—all of which make it harder to deliver personalized, timely messages and craft the coordinated omnichannel media strategies that are key to connecting with consumers in today’s crowded digital environment. At the same time, economic volatility is constraining marketing budgets and putting marketing leaders under increased pressure to prove out the value of their investments.

In this environment, commerce media is well-positioned to take on a larger role in marketing budgets. “Commerce media is growing more accessible and attractive to brands outside of CPG and retail as the supply side continues to grow through the collection, organization, and availability of first-party data,” says Jane Frye, VP of Integrated Client Solutions at Basis. And the reliability, accuracy, and abundance of this data make it possible for advertisers to use commerce media for personalization at scale.

There are also increasing opportunities for advertisers to use that high-quality data for audience targeting beyond a commerce media entity’s site or app to create highly personalized omnichannel advertising experiences. Offsite retail media ad spend, which enables advertisers to activate commerce media audiences beyond a retailer’s site or app, is forecast to grow at double the rate of onsite media spend through 2026, and off-platform placements will account for over 63% of display ad spend by 2029.

Even more, commerce media is well-suited for times of economic uncertainty. As a highly measurable channel, it allows marketers to clearly demonstrate ROI. And while commerce media’s early growth was largely driven by performance marketing, it has since evolved into a full-funnel solution that supports brand-building efforts as well.

Ultimately, these strengths make commerce media not just a timely solution for today’s challenges, but also one that’s aligned with the trajectory of digital advertising.

Harnessing the Commerce Media Opportunity

To capitalize on the commerce media opportunity—whether a team is testing and learning on the channel or executing a large-scale strategy—marketing teams must strategize around fragmentation and a lack of measurement standardization, while also prioritizing data readiness and preparing for how the rise of agentic commerce will transform the channel.

Strategize Around Fragmentation

The commerce media marketplace is growing increasingly crowded. One way to simplify the space is to limit the number of networks in play. As such, advertisers should carefully evaluate which networks can deliver the most value.

Many new entrants offer access to first-party data but lack the infrastructure to support meaningful advertising outcomes. Marketers should strike a balance between testing emerging platforms with low barriers to entry and investing in more established players with comprehensive ad solutions.

At the same time, there are some meaningful advantages to diversifying spend across a variety of platforms. A diversified strategy is the most effective approach for brand building, says Frye, as channels thrive on the cumulative effect of many small exposures. It also ensures advertisers are not over-relying on a single platform or data source, which mitigates risk and gives teams the flexibility to optimize based on performance.

To help their teams succeed in an increasingly fragmented landscape—both within commerce media and across the broader digital ecosystem—advertising leaders must take steps to reduce the complexity it creates. Tools that reduce manual labor, streamline parts of the campaign process, and unify often-disjointed systems like reporting and billing can help teams operate more efficiently and adapt to new opportunities.

Prioritize Data Readiness

High-quality data is one of commerce media’s most compelling advantages. As the space evolves, marketing teams are finding smart ways to maximize and extend that data. Tools like data clean rooms are opening up new opportunities for data collaboration, with advertisers combining data from commerce media networks, advertising platforms, and publishers with their own first-party data to drive more sophisticated strategies.

To fully capitalize on these opportunities, teams must refine how they collect, organize, store, and activate their data. This is closely tied to the broader challenge of digital advertising fragmentation and resulting tech stack sprawl: When data is siloed across a variety of tools, it becomes difficult to extract meaningful value. With over 50% of agency marketers using eight or more tools to manage client campaigns and 40% using 10 or more tools, unifying data across platforms should be a top priority for marketing teams aiming to succeed not only in commerce media, but across their broader marketing efforts. This is especially true when it comes to harnessing and operationalizing AI: Since AI outputs are only as good as their inputs, marketing teams need access to large volumes of unified, high-quality data to get reliable and differentiated outputs from their AI tools.

Prepare for the Rise of Agentic Commerce

Another key consideration for advertisers investing in commerce media is the rise of agentic AI and agentic commerce. Agentic AI’s ability to independently research, compare, and execute purchases on behalf of consumers is poised to change how products are discovered and bought—and, by extension, how commerce media functions as a channel. Marketers who get ahead of this shift will have a meaningful advantage over their competitors.

Consumer interest in AI-assisted shopping is already significant: In late 2024, 71% of US shoppers—and 84% of Gen Z shoppers—expressed interest in using an AI agent to make purchases on their behalf once items hit a target price. Meanwhile, major AI platforms are actively building commerce capabilities that could eventually compete with what retail media has long owned at the lower funnel: Both OpenAI and Perplexity, for example, are building out fully agentic commerce infrastructure. However, OpenAI’s recent scaling back of their plans to integrate checkout directly into ChatGPT suggests that even the most well-resourced AI companies are finding it difficult to own the full purchase journey. Retail media giants, meanwhile, are leaning in on their own agentic bets: Walmart, for example, has introduced Marty, a new agentic capability built to help advertisers manage billing and bidding for their sponsored search campaigns.

As consumers increasingly rely on AI agents to research and buy on their behalf, brands and retailers will need to ensure their products are discoverable and purchasable by those agents in addition to human shoppers. This means auditing websites, product catalogs, and digital assets to verify they’re optimized for AI systems as well as human visitors, including structured data, clean APIs, and metadata-rich content that AI agents can easily parse and act on. Equally important is keeping a pulse on how this space develops, as the agentic commerce space is evolving quickly and the strategies that work today may need to be revisited as new platforms, capabilities, and consumer behaviors emerge.

Commerce Media and the Future of Marketing Strategy

Commerce media addresses many of the challenges advertisers are grappling with today, offering precision, scale, and omnichannel activation. At the same time, its measurability is a significant advantage at a time when marketers are under increasing pressure to prove performance.

To fully realize the channel’s potential, marketing leaders must take care to invest in the right partners, unify fragmented systems, build robust and streamlined data ecosystems, and prepare for how agentic AI will change the landscape. By taking these steps, teams can unlock the full value of commerce media—not only to meet current challenges, but to set the stage for sustainable growth in the years to come.

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Commerce media isn’t the only space that AI is set to transform: The technology is also poised to reshape how marketers work on a fundamental level. To find out how teams are actually navigating that shift, we surveyed professionals from leading brands and agencies about the tools they're using, where the technology is delivering, and where the gaps remain. Check out AI and the Future of Marketing for all the findings.

The post Beyond Retail: The Rise of Commerce Media appeared first on Basis.

For advertisers, this means navigating fragmented platforms, limited reporting, and new intent dynamics—all while direct inventory access remains restricted for most. Here’s a look at the current state of AI media advertising, what makes these environments distinct, and where advertisers should focus their attention.

Key Takeaways:

• AI search ad spending, including both chatbots and AI-powered summaries, is projected to grow by more than 1146% between 2026 and 2029.
• Google, Microsoft, and OpenAI have all begun testing ads within AI-powered search and chat environments. Google’s AI Overviews and AI Mode, Microsoft Copilot, and ChatGPT now support paid placements (or are testing such placements), with Gemini ad inventory expected to follow this year.
• AI media environments are fragmenting. Platforms vary by format, access, audience, and business model, and not all are moving toward advertising.
• Measurement and attribution remain significant open questions. Neither ChatGPT nor Google AI Overviews currently provide full reporting, and standard attribution models weren’t built for these environments.
• Direct access to AI ad inventory is limited for most advertisers today, as many platforms are still testing these placements. Advertisers can still act now by optimizing for LLM discoverability and monitoring how ads are triggered in AI environments, building readiness before access opens more broadly.

A New AI Ad Environment Is Taking Shape

After a slow start (during which OpenAI CEO Sam Altman famously described ads on ChatGPT as a “last resort”), advertising in AI-powered environments has begun to debut across different platforms. And while the timelines have varied across the broader AI ecosystem, the pace has picked up considerably over the past year.

Advertising in Google AI Overviews and AI Mode

At Google Marketing Live 2025, the company confirmed that paid ad placements would begin appearing within AI Overviews and AI Mode, its AI-powered search experience. Both text-based Search ads and image-forward Shopping ads are now available inside AI-generated summaries on desktop, integrated directly into answers rather than sitting alongside them as separate units.

Advertising in Microsoft Copilot

Microsoft began testing ad formats within Copilot in March 2025, making it one of the earlier chatbot platforms to introduce sponsored placements. And, notably, early insights show that ad relevance metrics in the interface are 25% better than what is seen in traditional search.

Advertising in ChatGPT

In early 2026, OpenAI announced it would begin testing ads within ChatGPT. Placements appear at the bottom of responses—clearly labeled and distinct from the LLM’s organic answer—when a sponsored product or service is deemed “relevant” to the conversation (though there is still uncertainty around what determines this relevance). Early analysis shows ads triggering most frequently on high-intent queries containing modifiers like “best” and “new,” with brands including Best Buy, AT&T, and Expedia among the early participants. The pilot remains restricted to select global brands, with a reported minimum spend commitment of $200,000.

Advertising in Google Gemini

Google has signaled to advertising clients that it is targeting a 2026 rollout for ad placements within Gemini (which is, importantly, a separate initiative from AI Mode and AI Overviews). No formats, pricing structures, or testing timelines have been shared publicly, but with Gemini surpassing 750 million monthly users, the platform represents meaningful scale. And the direct outreach to buyers signals that Google is actively moving to monetize its chatbot, even as the specifics remain undefined.

Advertising in Anthropic Claude

Anthropic has taken a notably different position on advertising. In February 2026, the company reaffirmed its commitment to Claude remaining ad-free, stating, “There are many good places for advertising. A conversation with Claude is not one of them.” Their reasoning centers on trust: Anthropic’s view is that users shouldn’t have to second-guess whether an AI is genuinely helping them or steering them toward something monetizable. The company says it plans to sustain that model through enterprise contracts and paid subscriptions rather than ad revenue.

Ads in Other AI Media Environments

Claude aside, the broader picture is a market that is quickly fragmenting. Some AI platforms are moving quickly to monetize through advertising. Others are positioning ad-free experiences as a trust differentiator (at least for now). What’s clear is that AI media environments are no longer a single category. They vary by platform, format, access, audience, and business model. For advertisers, understanding which platforms are ad-supported, which are better suited to organic reach, and how each one fits into a broader media strategy is becoming critical.

What Makes AI Advertising Different

Why User Intent Is Different in AI Environments

Advertising in AI environments introduces a different set of dynamics than traditional search or display, and those distinctions can carry real implications for strategy.

Perhaps the most significant change is the quality of intent. In traditional search, a query signals interest. But in a conversational AI environment, users have often already moved further down the decision path—articulating specific needs and narrowing their options before they ever see an ad.

“I believe that AI-driven answers, regardless of platform, will shorten the research cycle and turn intent into conversion more quickly,” says Lindsay Martin, Group VP of Search Media Investment at Basis. “As a result, click-throughs are more likely to be pre-qualified.”

That pre-qualification impacts landing page strategy. Martin notes that intent-specific landing pages will be critical in these environments to reflect the nuance of the prompt. This is a meaningful departure from the broader match approach that has traditionally anchored search. The prompts that trigger AI ads carry more context than a traditional keyword, and creative built for broad audiences isn’t designed to meet that specificity. Messaging will need to prioritize usefulness, clarity, and context.

How AI Platforms Are Addressing Trust

Trust is another variable that sets AI environments apart. Recent research finds that 66% of US adults are highly worried about getting inaccurate information from AI tools, and introducing advertising into these environments only deepens that concern, layering a commercial motive onto platforms that users are already approaching with caution.

How platforms plan to handle that transparency gap varies. OpenAI has outlined principles governing how ads will function in ChatGPT, including that ads will not influence responses and that user conversation data will not be shared with advertisers. Google has published documentation on how ads in AI Overviews are triggered: Both the user query and the content of the AIO are considered when serving ads, and placements are currently excluded from sensitive verticals including finance, healthcare, and politics. What that documentation doesn’t address is how users are informed about why a particular ad appeared, or what guardrails exist to ensure the presence of ads doesn’t shape how AIOs are generated in the first place. Whether users accept any of these approaches—or notice the difference—remains to be seen.

“I’m interested in understanding how the different AI platforms’ algorithms determine relevance for advertising and maintain users’ trust,” Martin says. “There isn’t much transparency on that topic yet.”

The Measurement Gap in AI Advertising

Lastly, tracking and measurement questions compound both of these challenges. And to date, neither ChatGPT nor Google AI Overviews has provided advertisers with detailed metrics on the success of advertising in these AI-powered environments.

Martin anticipates that attribution capabilities will become more robust over time—showing how AI conversation functions as a conversion assist earlier in the customer journey. For now, reporting remains limited, and the infrastructure to meaningfully capture that influence has yet to become a standard for continued ad optimization.

“If I were a brand,” says Martin, “I would not be comfortable with the high price tag for early advertising without a strong measurement framework in place.”

How Advertisers Can Build Readiness for AI Advertising

For most advertisers today, direct access to AI ad environments remains limited. However, there’s still plenty that teams can do to prepare for when inventory becomes more accessible.

On the paid side, the immediate priority should be awareness and readiness. Monitoring how ads are triggered in chatbots (e.g., query types and intent signals) provides a useful foundation before access opens more broadly. And maintaining paid search presence also remains important: Ads appearing alongside AI-generated summaries can deliver brand impressions even without a click, and those impressions still influence decisions.

The more immediate opportunity, however, is organic. Large language models decide which brands to surface, cite, and recommend in their responses, and that process is already happening now, independent of any paid placement. Optimizing for that inclusion, increasingly referred to as generative engine optimization (GEO), means prioritizing structured, factual content, clear product and FAQ pages, and content formatted in ways AI systems can parse and surface reliably.

“If an LLM has no reason to include you in results, you will be left behind,” Martin says.

The intersection of organic and paid strategy in these environments also warrants attention, with Martin noting that GEO is “muddying the waters with how we’ve traditionally differentiated SEO and SEM.” Teams that have treated organic and paid search as separate disciplines will need to think about how those strategies align in environments where the same AI system may determine what it recommends organically and what it surfaces as a sponsored result.

And as the channel continues to mature, AI media advertising will also add new complexity to an already fragmented landscape. Each platform operates on different formats, measurement frameworks, and reporting standards, meaning that teams managing presence across ChatGPT, Google AIOs, and other emerging inventory will be navigating multiple systems simultaneously. That complexity will make it all the more critical for advertisers to seek out platforms that allow them to unify budget, performance, and strategy in one central location—making it easier to respond quickly as inventory expands and the rules continue to evolve.

Looking Ahead: The Future of AI Media Advertising

For advertisers building toward long-term readiness and resilience, staying updated on the latest AI media advertising developments, diversifying measurement frameworks, prioritizing content that AI systems can find and use, and maintaining presence across channels where messaging remains controllable is key. AI search ad spending—spanning both generative AI platforms and AI-powered search summaries—is projected to grow from $2.08 billion in 2026 to $25.93 billion in 2029.

Though the environments driving that growth are still taking shape, advertisers who invest in understanding them now will be better positioned to compete as access widens and the rules become clearer.

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For a deeper look at how AI is reshaping the future of search and what it means for media strategy, check out AI and the Future of Search Engine Marketing.

The post What Do Marketers Need to Know About Advertising in AI Environments? appeared first on Basis.

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.

The Data on How Marketers are Using AI At Work

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.

AI Use Cases from Basis Experts

Accelerating Writing and Communication Tasks

“I’ve found AI very useful as a partner to expedite tasks related to writing and communication. A few examples:

• I’ve used Copilot to summarize long internal documents, decks, and meeting notes into clear POVs or talking points. This has been especially useful for turning messy thinking into something I can react to and refine.
• I’ve used AI as a 'blank page killer' when drafting strategy documents, internal POVs, or thought leadership. I don’t expect final copy—I just use it to get momentum, and then I heavily edit.
• I’ve used AI to rewrite complex thinking into simpler language (ex: 'explain this like I’m talking to a CMO' or 'make this less jargon‑heavy').”

Robert Kurtz | Business Outcomes Partner

Bringing Analytical Horsepower to Performance Forecasting

“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

Uncovering Brand Sentiment When Listening Tools Fall Short

“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

Keeping Tabs on the Competition

“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

Accelerating Disease and Drug Research for Pharma Clients

“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:

1. Describes how the drug and disease work using medical terminology.
2. Breaks down how the drug and disease work in simpler, more readable language.
3. Provides a market landscape including standard of care, competitors, other relevant therapies, and notable pipeline treatments by phase
4. Suggests relevant diagnosis and procedure codes that can help inform targeting

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

Pressure Testing Ideas and Strategies

“I’ve experimented with using generative AI to pressure test ideas. I'll ask it to poke holes in an argument or tell me why a certain idea is 'bad.' While I don't always agree with what it comes up with, it's a great way to stress-test my thinking. This is a particularly good use case for leveraging AI as a brainstorming partner on the media strategy side.”

Kelly Boyle | SVP, Strategic Business Outcomes

Building Synthetic Audience Personas

“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

Determining Radius Targeting for Multi-Location Clients

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

Putting AI to Work

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.

The post How Marketers Are Really Using AI Today appeared first on Basis.

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

• CTV is still the dominant digital channel—and the only one expected to grow. Invest accordingly and lock in inventory early.
• Streaming audio is a breakout opportunity. Programmatic audio reaches voters in screen-free moments that CTV and display miss entirely.
• Redistricting and retirements are creating more open-seat and newly competitive races than any recent cycle. Awareness investment must start earlier.
• AI offers real value in optimization and creative testing, but platform compliance requirements are evolving rapidly. Build disclosure workflows now.
• Creative must be adapted for each platform. Stop repurposing broadcast spots for digital without cutting them for the attention environment of the channel.
• Authentic organic social content can be a powerful differentiator, but only when it genuinely fits the candidate. Embrace it, but don't force it.
• Precision targeting in the final weeks requires early audience intelligence work. Know who your persuadables are and where to reach them before the sprint begins.
• Misinformation and disinformation will be more sophisticated in 2026. Campaigns need rapid response strategies and strong brand safety controls, not just platform trust.

Record Spending, Redistricting, Retirements, and the New Competitive Map

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.

The Lessons of 2024: Precision Over Volume

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.

Budget Strategy and Timing: Saving for the Sprint

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: 

• Allocate sufficient early-cycle budget for primary awareness and fundraising, especially in open-seat or redistricted races.
• Secure CTV and audio inventory through PMP deals and programmatic guaranteed well before the October/November crunch.
• Shift targeting from broad awareness to precision persuasion as Election Day approaches by embracing "mobisuasion" (aka mobilization + persuasion) tactics, targeting voters with messaging designed to both persuade and boost turnout.
• Plan for a heavy final-30-days sprint, but don't sacrifice awareness investment to fund it.

Targeting Voter Audiences: Finding Precision in a Signal-Constrained Environment

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…

The CTV Landscape in 2026: More Inventory, More Reach, More Complexity

Why CTV Remains the Dominant Digital Channel

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)

The Expanding CTV Inventory Ecosystem

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.

How to Buy CTV in 2026

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: 

• Use a mix of programmatic open exchange, PMP deals, and programmatic guaranteed.
• Prioritize premium network partnerships (such as Hulu/ESPN, HBO Max, Paramount, and DIRECTV) for high-value audiences.
• Leverage ACR data targeting to reach audiences based on actual viewing behavior.
• Lock in pricing in advance to avoid being priced out as Election Day approaches.
• Use platforms with built-in measurement tools to track incremental reach beyond linear buys.

Audio's Breakout Moment

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.”

Organic Social, Authentic Storytelling, and the “Mamdani Effect”

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.

Creative Strategy: Building for the Screen People Are Actually Watching

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: 

• Develop platform-adapted creative assets for every channel: 30-second spots for broadcast/CTV, 15-second cuts for digital video, and 6-second bumpers for awareness.
• Lead with your most compelling message in the first three seconds.
• Design for sound-off viewing on social.
• Have a single, clear call to action.

AI in Political Advertising: Promise, Pitfalls, and Platform Rules

Where AI Creates Real Value

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.

Navigating the Risks

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.

Disinformation, Trust, and the Voter Relationship

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.

The Publisher Landscape: Inventory and Accountability

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.

Wrapping Up: What Every Political Advertiser Needs to Know for 2026

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.

The post The Ultimate Guide to Political Advertising in 2026 appeared first on Basis.

The Challenge

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.

The Solution: Basis & Latcha + Associates

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.

The Results

Since onboarding Basis DSP, Latcha + Associates' clients have seen greater success across their multi-channel campaigns, including: 

• 41% higher service response rate for dealers opted into digital
• 28% higher sales rate with multi-channel campaigns (compared to e-mail)
• 29% higher purchase rate for dealers opted into all channels
• 200% ROAS using digital media with traditional channels vs. e-mail and direct mail only

Why It Worked

1. Tailoring Effective API Connection: Basis DSP’s API offered customization options and clear documentation, allowing Latcha + Associates to customize the DSP to their platform and dealership needs without added complexity.
2. Ongoing Education and Industry Resources: Basis equipped the client and their stakeholders with industry knowledge through AdTech Academy, including a customized Programmatic 101 presentation, empowering their team and providing dealer-facing materials to simplify adoption.
3. Strategic First-Party Data Tools: Basis supported Latcha + Associates’ priority to leverage first-party data for creating models and targeting, ensuring campaigns were highly relevant and aligned with dealer preferences and expectations.
4. Comprehensive Raving Fan Support: The Basis team provided end-to-end support throughout the process, from consulting on the client’s overall tech platform build to the ongoing customer success support to discuss strategies and partner recommendations as needed.

Hear it from Latcha + Associates

“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

The post How Latcha + Associates Drove a 29% Higher Purchase Rate appeared first on Basis.

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. 

The post Is Search Totally F**ked? What Do We Do Now?  appeared first on Basis.

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.

The post The Attribution Reset appeared first on Basis.

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.

How AI Is Changing Search Behavior

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.

Voice Search

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.

Agentic AI

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.

How Zero-Click Search is Impacting Organic Traffic

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.

How Leaders Can Prepare

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.

Nurture teams’ proficiency in AI-powered targeting

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.

Support teams in adopting generative and agentic AI

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.

Ensure teams are making the most of Performance Max

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).

Make optimization for AI Overviews and AI Mode a priority

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.

Make generative engine optimization (GEO) a priority

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:

• Entity clarity, aka ensuring AI systems can clearly identify and understand your brand as a distinct, well-defined subject. This means using consistent brand name, descriptors, and positioning language across your website, press coverage, and third-party mentions so AI tools can confidently identify and surface you.
• Creating content that is easy for AI systems to extract and reassemble. Lead with direct, plainly worded answers to common questions—think structured product pages and FAQs.
• Building a multi-platform presence across the forums, social platforms, and third-party sites that AI tools draw from when generating responses. Earning mentions on sites like G2, Trustpilot, LinkedIn, and relevant Reddit communities helps establish the broad, corroborating presence that signals credibility to AI systems.

Prepare for the agentic web

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.

Nurture a data-driven culture

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.

Provide continuing AI education

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.

Keep learning and stay up to date on developments

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 Future of Search Engine Marketing

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.

The post AI and the Future of Search Engine Marketing appeared first on Basis.

• AI excels at three core aspects of media strategy: analyzing large datasets, synthesizing information, and serving as a brainstorming partner for creative ideation.
• Human media strategists are still essential for validating all AI outputs due to hallucination risks, and can help maintain the human touch by injecting brand expertise, competitive insights, and strategic thinking into AI-assisted work.
• Effective prompting is a critical differentiator—incorporating guardrails against hallucinations and instructing AI on how to think and communicate can dramatically improve output quality.
• Leaders should establish clear usage guidelines, invest in specialized or custom AI tools tailored to media workflows, and prioritize data infrastructure to fuel high-quality AI outputs.

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.

How Can Media Strategists Use AI?

Top 3 AI Applications for Media Strategy

Three use cases stand out when it comes to AI’s applications on the media strategy side:

1. Analyzing large data sets
2. Synthetizing information and gathering insights on audience, competition, and market trends
3. Serving as a collaborative partner for brainstorming

Data Analysis

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.

Synthesizing Information

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.

Brainstorming

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.

Best Practices for Implementing AI in Media Strategy

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.

AI Best Practices for Media Strategists

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.

Leadership Strategies for AI Implementation

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 Future of 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.

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