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The programmatic advertising trends shaping 2026 mark a shift from volume-driven growth to a more disciplined, accountable phase of strategy. Rising scrutiny around media quality, shifting consumer discovery patterns, and the deepening role of AI are pushing advertising leaders to rethink how programmatic delivers true value that moves beyond scale.
Key Takeaways:
These trends underscore a broader shift in programmatic advertising, and success in 2026 will depend less on maximizing volume and more on managing complexity with intention, structure, and accountability.
Programmatic Advertising in 2026, By the Numbers:
As global ad spending is set to surpass $1 trillion for the first time, programmatic advertising continues to play a central role.
In 2025, programmatic digital display ad spending in the US grew 16.6%, surpassing $187 billion and accounting for nearly 95% of all digital display ad spend. That momentum shows no signs of slowing. In 2026, US programmatic display spending is expected to exceed $220 billion, representing year-over-year growth of 17.4%. Globally, programmatic is projected to account for roughly 90% of display ad budgets and nearly all incremental growth in display for the foreseeable future. With major events like the Winter Olympics, FIFA World Cup, and key midterm elections fueling the media landscape in 2026, the scale and stakes of advertisers’ programmatic investments will be especially high.
This growth is occurring against a backdrop of both challenge and opportunity. Privacy expectations continue to rise, data signals remain inconsistent, and long-standing assumptions about addressability and measurement are being tested.
While Google reversed its plans to fully deprecate third-party cookies in Chrome, the decision did little to resolve the broader challenges associated with signal loss. For many advertisers, declining data quality and shrinking addressable audiences remain daily realities, regardless of browser policy changes. Yet these same pressures are driving innovation in inventory curation, first-party data strategies, and privacy-conscious targeting approaches that promise more effective, sustainable advertising.
Artificial intelligence is further reshaping the programmatic ecosystem. AI is now embedded across the advertising workflow—from campaign planning and optimization to creative development and analytics—but its most alarming impact may be the flood of AI-generated content entering the programmatic supply chain. The rapid proliferation of generative AI is raising the stakes for brand safety and media quality, as low-quality synthetic content becomes harder to distinguish from legitimate inventory. While AI is also powering many of the tools to combat these challenges, the race between synthetic content creation and detection will be a defining feature of programmatic quality in 2026.
Meanwhile, consumer attention continues to splinter across channels and formats. Retail media networks are expanding quickly, short-form video is commanding a growing share of budgets, and ad-supported streaming is evolving with new formats and placements. At the same time, discovery itself is changing, as zero-click search experiences and AI-generated summaries reduce traditional paths to traffic and engagement (not to mention attribution).
Taken together, these forces have made programmatic advertising all the more powerful…and all the more demanding. The year ahead will test how well advertisers can manage AI-driven risks and opportunities, consolidate data foundations, adapt to new discovery patterns, and execute across evolving video and commerce environments.
Simply put, AI is reshaping the programmatic landscape.
While the technology has improved efficiency, the rapid spread of AI-generated content has increased the volume of low-quality inventory that can slip into campaigns, making it harder to separate genuine human interest from surface-level impressions.
AI-generated sites and content farms can deliver high impression counts and engagement signals that look legitimate in reporting but don’t translate to brand recall or purchase intent. This dynamic has elevated brand safety from a reputational concern to a performance issue. With 54% of advertisers believing generative AI has contributed to a decline in overall media quality, teams must rethink how they evaluate inventory and interpret campaign results to ensure optimization is grounded in authentic engagement.
Advertising leaders are responding by layering smarter controls into their buying strategies. Pre-bid protections, contextual intelligence, curated supply, and ongoing delivery analysis can help advertisers identify patterns associated with low-value or synthetic content before spend accumulates. These approaches reduce waste and ensure optimization decisions are based on reliable signals rather than volume that masks low-quality environments.
In 2026, advertisers must find ways to reliably and consistently apply these guardrails systemically across their programmatic strategies, leveraging AI for routine monitoring while reserving human expertise for strategic oversight and decision-making.
As consumer concerns around data privacy remain high and signal loss continues to reshape addressability, first-party data has become central to programmatic strategy.
In 2025, 40% of US marketers relied on first-party data as their primary privacy-centric targeting approach. Meanwhile, the usefulness of third-party cookies has continued to decline, this despite Google’s U-turn on deprecation in Chrome. As audiences move fluidly across platforms and formats, the signals cookies provide are increasingly partial, limiting their effectiveness as a foundation for long-term planning.
Yet as powerful a tool as first-party can be, data alone is not enough. Without strong organization and consolidation, even high-quality data struggles to deliver impact, particularly as AI becomes more deeply embedded in planning, activation, and optimization.
Fragmented data remains a major barrier for marketers: Fewer than one in five industry professionals say their first-party data is extensive and well-structured, while 34% describe it as limited or disconnected. Much of this stems from tech stack sprawl, with more than half of agency professionals reported to use eight or more tools to manage campaigns and 40% juggling 10 or more. These gaps make it harder to respect consumer choice, apply consistent governance, and generate reliable insights, while simultaneously undermining AI performance by limiting data-powered optimizations and increasing the likelihood of flawed recommendations driven by incomplete or inconsistent inputs.
Without data consolidation, these challenges compound. Unifying data across systems enables privacy-conscious targeting, clearer measurement, and more responsible use of AI—allowing advertisers to do more with less signal while maintaining trust and performance.
With the emergence of zero-click search environments, advertisers must also adapt to how consumers discover and evaluate brands.
As AI-generated summaries, AI Overviews, and AI agents increasingly resolve queries directly within a search or chatbot interface, fewer users are clicking through to websites. Recent research shows that only about 8% of users click links from Google’s AI summaries, signaling a meaningful shift in how discovery happens. More broadly, a growing share of searches conclude within the results pages themselves, with many users finding the information they need without clicking through to another destination.
This evolution challenges long-standing assumptions about search performance and attribution. When answers are delivered without a click, traditional KPIs like CTR and last-touch conversions become less reliable indicators of impact.
In this new context, brand exposure, contextual relevance, and repeated presence across channels increasingly shape consideration throughout the customer journey, with audiences visiting websites later and later in the process…if they visit at all. In parallel, brands must also account for how they appear within AI-driven search results and large language model (LLM) outputs, where summaries, recommendations, and cited sources can shape perception without any direct interaction. Visibility now extends beyond links and placements to include how—and whether—a brand is represented in these emerging information environments.
For programmatic advertisers, this shift elevates the role of display, video, and contextual placements in the awareness and consideration process. These channels help establish familiarity and preference in moments when consumers are forming opinions, even if they never leave the search or AI interface. Measurement frameworks and media strategies must evolve to reflect a world where visibility still drives value—just not always traffic—and where influence is distributed across a broader, more decentralized ecosystem.
As measurement and attribution models evolve to account for zero-click influence, programmatic budgets continue flowing toward environments that connect media to transaction data.
Commerce media has been one of the fastest-growing areas within programmatic advertising, reshaping how brands connect media exposure to purchase behavior. In 2025, retail media programmatic display spending grew more than twice as fast as total programmatic display, reflecting advertisers’ appetite for environments tied closely to transaction data and retail signals. WPP’s end-of-year forecast highlighted just how quickly the category has scaled, projecting that commerce media would surpass television in total spend by the end of 2025.
As the category matures, however, the focus is shifting from rapid expansion to execution and integration. Growth may not continue at the same pace, and leaders are increasingly grappling with inconsistency across retail media networks, misaligned measurement frameworks, and rising operational demands. At the same time, early signs of agent-driven commerce are beginning to influence how value is created within these environments. Industry forecasts suggest that by 2028, a meaningful share of digital storefront interactions could be handled by automated “machine customers.” Early examples are already visible in agent-driven shopping experiences, where AI assistants compare products, surface promotions, and complete purchases on a shopper’s behalf—raising the stakes for clean product feeds and accurate pricing data. As these systems play a larger role in product discovery, comparison, and purchase decisions, the quality and consistency of product data, pricing signals, and promotions are becoming just as important as media placement itself.
Today, commerce media demands integration rather than investment alone. Advertisers that treat commerce media as a core component of their programmatic strategies—supported by disciplined measurement, strong data foundations, and intelligent automation—will be better positioned as retail media evolves from a breakout growth channel into a more machine-mediated, long-term pillar of the media mix.
Programmatic video is increasingly concentrating around short-form, mobile-first formats. As audiences turn to quick, scroll-based video for entertainment, inspiration, and product research, ad budgets are following in kind.
In 2025, social video accounted for 53.7% of programmatic video ad spending, reflecting how budgets have followed audience behavior. Much of that demand sits with platforms built expressly for the format—TikTok, Instagram Reels, and YouTube Shorts—where vertical, scroll-based video shapes both entertainment and product discovery. Engagement patterns reinforce this shift, with a majority of consumers interacting with short-form video multiple times per day and using it as a source for finding new products as well as recommendations.
This dynamic is especially pronounced among younger audiences: Gen Z—whose spending power is projected to reach $12 trillion by 2030—engages with short-form video at a higher frequency than older cohorts and relies on it as a primary source of entertainment, inspiration, and discovery. As a result, short-form video ads are influencing consideration earlier and compressing the path from exposure to action faster than traditional video formats.
In 2026, the challenge for advertisers will be less about whether to invest in short-form video and more about how deliberately they do so. Creative must be built for the format and paired with strong brand safety guardrails and deliberate KPIs that account for how quickly short-form video can drive movement from awareness to action. As short-form video continues to absorb both attention and budgets, advertisers that integrate it thoughtfully within broader omnichannel strategies will be better positioned to convert fleeting moments into durable impact.
Subscription fatigue is driving viewers toward ad-supported streaming—and turning CTV into a laboratory for format innovation. The experimentation is pushing CTV beyond standard pre-roll and mid-roll placements toward formats designed to complement the viewing experience rather than interrupt it. Pause ads, interactive units, content hubs, and contextual sponsorships are gaining traction as advertisers look for ways to capture attention without increasing ad load.
Audience behavior is reinforcing this shift, with viewers increasingly multitasking while streaming, thereby creating demand for formats that invite engagement rather than passive exposure. Interactive CTV ads are emerging as one response: More than 40% of US marketers already use interactive features across social and CTV, and over half expect interactive elements to account for at least a quarter of their ads. Early performance signals suggest these formats can deliver meaningful lift, including higher unaided recall and stronger brand affinity, when aligned with content and context.
As CTV inventory continues to fragment across platforms and environments, new formats have in turn created new operational challenges. The absence of universal CTV standards has created significant variability in how ads are served, measured, and experienced, increasing the need for structure and visibility as experimentation accelerates. Programmatic activation—supported by a unified, omnichannel platform—provides a framework for testing emerging formats with guardrails, allowing advertisers to compare performance, manage frequency, and maintain consistency across placements. In 2026, advertisers that pair creative experimentation with programmatic discipline will be better positioned as CTV shifts from a reach-first channel to a more interactive, performance-aware component of the media mix.
Across all these channels and formats, a common thread emerges: the need for greater control over where ads appear and how budgets are deployed. Programmatic curation pairs the scale of the open exchange with vetted, curated supply paths, giving advertisers more control over inventory quality, transparency, and working spend.
Programmatic advertising has long been synonymous with scale. Yet as programmatic investment continues to climb, so does scrutiny around media quality and working spend. Leaders are increasingly expected to defend not just performance, but also the transparency and quality of the media supply chain. In 2025, inefficiencies and waste in programmatic spend were estimated to total about $26.8 billion globally, underscoring the gap between dollars invested and working media outcomes.
Curated inventory packages give advertisers more visibility into where ads appear and how supply paths function, helping reduce inefficiencies and improve confidence in campaign outcomes. Curation has gained traction as marketers seek stronger alignment between media quality and performance, with 41% citing curated deals as a path to higher ROI. DSPs and SSPs are responding by expanding tools that support flexible deal structures and clearer supply chain insight.
The open exchange remains an important source of reach, but in 2026, it is increasingly paired with curated strategies that bring added control. Together, they enable advertisers to pursue growth without compromising trust.
The trends shaping programmatic advertising in 2026 reflect a steady recalibration (rather than a dramatic reset). As budgets continue to grow and scrutiny intensifies, advertisers are moving away from volume-driven approaches and toward strategies that prioritize quality, accountability, and adaptability. The shift is visible across the programmatic ecosystem—from consolidated data foundations and stronger media quality guardrails to evolving commerce media, changing discovery and video environments, and curated supply paths.
What connects these shifts is the rising importance of integration. Disconnected channels, formats, and signals require systems and operating models that support consistent decision-making across planning, activation, and measurement. As automation becomes more embedded in programmatic workflows, human oversight will become less focused on manual execution and more centered on governance, interpretation, and long-term value creation.
This year, advertisers that pair thoughtful experimentation with clear guardrails, maintain transparency as strategies evolve, and combine automation with human expertise will be best positioned to evaluate performance, defend investment decisions, and sustain growth amidst ongoing change. Programmatic success in 2026 will be, in large part, measured by how well advertisers manage complexity, turning fragmented tools, signals, and channels into a unified system that supports accountable, AI-ready media execution.
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Looking for more insights into the major innovations and opportunities to watch this year? Our 2026 Trends Report: Rewinding to Fast Forward provides real perspective on the key trends that are poised to shape the year ahead.
Key Takeaways:
Few technologies have reshaped marketing as quickly as AI.
In the less than four years since ChatGPT’s public debut, AI has become increasingly embedded in many marketers’ workflows, influencing everything from media buying to creative development. Now, its role is expanding.
Agentic AI—autonomous systems that both generate outputs and execute decisions, often with less human review at each step—is moving deeper into advertising workflows. In fact, 77.7% of agency leaders plan to increase their AI investment over the next 12 months and 90.7% of industry professionals believe that AI will radically transform the industry within the next three to five years. Yet amidst this enthusiasm, teams also need to consider whether they have the data infrastructure needed to support this rapid pace of adoption.
When AI runs on fragmented or low-quality inputs, results are unreliable. Insights get blurred, personalization misses the mark, and recommendations fall flat. Such missteps have a direct cost, quickly compounding into wasted spend and weakened consumer trust. The organizations that thrive in the AI era will be those that invest in clean, unified, privacy-compliant first-party data—the foundation AI needs to deliver accurate, differentiated value.
Because AI systems can only produce outputs as reliable as the inputs they run on, data quality is a primary predictor of AI advertising performance. Without a high quality data foundation, AI tools can produce unreliable targeting, mistargeted personalization, and wasted spend.
Three main data factors determine whether an AI advertising solution performs:
- Clean, unified first-party data: Inputs should be accurate, de-duplicated, and consolidated across platforms into a single source of truth.
- Privacy-compliant data collection: Data collected with consent and stored securely reduces legal risk and supports sustainable targeting as signal loss continues.
- Human oversight at key checkpoints: As agentic AI executes decisions with less review, errors in the underlying data compound faster. Human oversight helps to ensure that agents don't act on flawed inputs.
Marketers recognize the importance of data when it comes to AI, with nearly half (45%) anticipating that data quality or accessibility issues will pose significant or critical challenges to their AI efforts in the next one to two years.
From faster analysis to smarter targeting to more relevant creative and beyond, the potential for AI in marketing is significant. But when the data fueling AI tools is inaccurate, siloed, or inaccessible, the risk of error increases significantly. Poor data muddies results and raises the odds of hallucinations, where AI generates outputs that appear credible but are instead fabricated. Because the technology mimics the information it’s trained on, gaps or inaccuracies in the data increase the likelihood of such mistakes. This is a significant problem, with a recent study finding that nearly half of marketers encounter AI inaccuracies several times a week.
Those hallucinations can look like real insights: an optimization tool shifting spend toward audiences built on incomplete signals, a personalization engine delivering irrelevant product recommendations with full confidence, or a dashboard surfacing “top-performing” keywords that don’t exist. These are the kinds of costly missteps weak data foundations can produce. Even small inaccuracies can snowball, feeding back into models and negatively shaping future decisions. And as AI moves from assisting to acting, the stakes grow even higher: An agent executing decisions autonomously removes the human checkpoint that might otherwise catch these errors before they compound into a much larger issue.
Agentic AI is top-of-mind for US ad buyers. Two-thirds say agentic AI ad buying and execution is an increased focus this year, and 84% cite media planning and buying recommendations as a current or likely use case. Yet, despite this interest, many are also hesitant: 40% of buyers report that understanding agentic ad buying and campaign execution is one of their greatest concerns or challenges at present.
Data is often a meaningful part of that hesitation. Understanding how an AI agent functions means understanding the data it acts on. Generative AI and agentic AI can both produce errors that are difficult to catch—such as biased outputs, fabricated insights, or recommendations built on incomplete signals—but the workflows around them are inherently different. Generative AI typically sits inside a process with regular human review, whereas agentic AI often makes multiple sequential decisions before a human is involved. When an agent acts on flawed data, that single error can compound across each decision—a budget shift, a paused campaign, or a reallocated bid—all before anyone notices. Which is why data readiness, rather than enthusiasm, is what separates teams that benefit from agentic AI from those it puts at risk.
For all the focus on AI (whether generative or agentic), most organizations are still lagging behind when it comes to data readiness. A patchwork of state-level privacy regulations and increasing signal loss have already made first-party data critical, and AI adoption further raises the stakes.
Clean, consented, and unified data is what ultimately powers accurate insights and effective personalization. Yet few organizations are currently treating it that way: Just 21.4% of industry professionals say first-party data is foundational to their AI efforts, while more than a third admit it plays little or no role.
That gap has proven to be a significant impediment to agentic AI adoption, with eight in ten companies currently citing data limitations as a barrier to scaling agentic AI. And this challenge only grows as advertisers add more tools to their tech stacks, as each new source adds yet another potential point of fragmentation.
Even when teams recognize the importance of first-party data, the data they have often isn’t ready to deliver. Roughly 34% of industry professionals say their first-party data is limited and fragmented, and less than one in five describe it as extensive and well-structured.
Some of the key challenges that hinder marketers’ ability to effectively leverage data are data accuracy and quality, as well as scale and volume of data. While most teams have access to large amounts of data, fragmentation prevents them from forming a reliable single view of the customer. For example, a travel company might struggle to connect loyalty profiles with search behavior and purchase history. Despite having plenty of data, it’s difficult to use and more prone to errors because it’s siloed across platforms and systems. AI trained on only one slice of that picture could misread intent, recommending irrelevant offers or overvaluing the wrong audiences. Multiply that problem across numerous systems and campaigns, and marketers lose both efficiency and accuracy to data problems.
AI delivers its strongest results when it runs on a solid data foundation. Advertisers that prioritize clean, consolidated, and accessible data systems see stronger AI-powered targeting, personalization, and optimizations. For agencies, reliable data ecosystems fuel creative and strategic outputs that capture the nuances of their clients’ audiences.
This groundwork starts with first-party data itself: ensuring it is collected with consent, stored securely, and structured in ways that make it easy to analyze and share across teams. Data hygiene practices, such as regularly auditing for accuracy, de-duplicating records, and unifying customer identifiers, are also essential for maintaining quality at scale. Leaders that embed these practices into ongoing workflows, rather than treating them as one-off clean-up projects, see compounding benefits over time.
Equally important is making data actionable. Tools that unify disparate data sources and streamline reporting consolidate scattered signals into one source of truth, giving AI tools consistent inputs and reducing the errors that come from siloed systems. This also creates a shared foundation across marketing, sales, and finance, making it easier to align strategy and measure impact.
The stakes climb higher with agentic AI. Some platforms now build a unified data foundation directly into the system, so that agentic capabilities draw on consolidated, accurate inputs from the start. Even then, the inputs are only as good as the underlying data. Those foundations matter more, not less, as execution becomes more autonomous.
AI has quickly become embedded in marketing workflows, but its value depends largely on the data that fuels it. Too often, that foundation is fragmented or incomplete.
Organizations that invest in systems to ensure clean, compliant, and unified first-party data will be positioned to capture AI’s full value. The payoffs are significant: stronger ROI, personalization that resonates, and long-term differentiation. In the years ahead, as AI shifts from assisting to acting, those who have built the strongest data foundations are likely to come out ahead.
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Looking for even more insights into the state of AI in marketing? We surveyed marketing and advertising professionals from leading agencies and brands for our third annual AI and the Future of Marketing report. It’s filled with insights to help industry leaders evaluate how to use AI responsibly, strategically, and with urgency.
Media buying has never run on a single tool, and for most teams, that fragmentation is a daily operational reality. Campaigns span display, video, connected TV, audio, and more—each with its own levers, its own data, and its own reporting format to reconcile at the end of the week. As programmatic has matured and channel options have multiplied, the DSP at the center of that programmatic stack has become one of the most consequential platform decisions an advertising team can make. Choose well, and you consolidate complexity into a unified, optimizable workflow. Choose poorly, and you add another silo to an already fragmented stack.
Choosing the right omnichannel DSP for your campaigns comes down to evaluating inventory breadth across channels, cross-channel reporting and attribution capabilities, cost structure transparency, optimization logic, and integration with AI-driven planning workflows. The best DSP for your team is one that consolidates fragmented media buying into a single platform, giving you unified control over campaign activation, measurement, and optimization across display, video, connected TV, audio, DOOH, and more. This guide covers the criteria, pitfalls, and comparison framework agency teams need to make the call.
An omnichannel DSP is a demand-side platform that enables advertisers to plan, buy, and optimize programmatic media across multiple channels—such as display, video, native, connected TV, audio, and mobile—from a single interface. Rather than managing separate tools or logins for each channel, an omnichannel DSP centralizes the entire media buying workflow so that audience targeting, budget allocation, and performance reporting happen in one place.
A single-channel or specialized DSP, by contrast, focuses on one specific media type or environment. A CTV-only DSP, for example, may offer deep inventory access within streaming environments but lacks the ability to coordinate messaging across display, audio, or mobile simultaneously. Similarly, a mobile-focused DSP may excel at in-app targeting but leaves you managing separate platforms for every other channel in your media plan.
The core difference is operational scope. An omnichannel DSP treats your campaign as a unified effort across touchpoints, while a specialized DSP treats each channel as a silo. For teams running cross-channel campaigns, this distinction directly impacts efficiency, data consistency, and the ability to optimize holistically rather than channel by channel.
If you're newer to programmatic advertising and want a foundational overview of how demand-side platforms work before diving into the omnichannel comparison, this DSP explainer covers the basics.
Consolidating media buying into a single omnichannel DSP matters because it eliminates the fragmentation that slows down campaign execution, muddies reporting, and inflates operational costs. The scale of the problem is significant—and accelerating. More than one-third of full-service and media agencies are now managing 10 or more tools across their adtech stack, more than twice as many as in 2024, according to Basis' 2026 Advertising Agency Report. And only 10% of marketers say their ad tech stacks are fully connected across channels like CTV, social, display, and retail media. When your team runs campaigns across three or four separate platforms, every additional tool introduces its own data taxonomy, reporting cadence, and optimization logic, making it nearly impossible to get a clean, unified view of performance.
The direction of buyer investment reinforces the urgency. The IAB's 2026 Outlook Study found that two-thirds of buyers are now focused on agentic AI for ad buying and campaign execution, with five of the top six buyer priorities tied to AI. Agentic AI doesn't work on top of fragmented systems—it needs connected data and unified workflows to make autonomous decisions worth trusting. The agencies preparing for that shift are the ones consolidating their stacks now, not later.
A consolidated approach means your audience data flows across channels without manual reconciliation. You can see how a connected TV impression influenced a display click or how an audio ad contributed to a conversion—all within the same reporting environment. This cross-channel visibility is what allows media buyers to make smarter allocation decisions in real time rather than waiting for post-campaign analysis to reveal what worked.
There's also a practical efficiency argument. Managing fewer vendor relationships, fewer invoices, and fewer platform-specific training requirements frees up time that campaign managers can redirect toward strategy and optimization. For agencies managing multiple clients, this consolidation compounds, reducing overhead across every account.
Omnichannel DSPs matter because today's consumers don't move through neat, single-channel journeys, and the programmatic campaigns that reach them effectively can't be planned as if they do. A typical consumer might encounter a brand through a CTV ad during their evening stream, see a related display ad the next morning, and hear a programmatic audio spot during their commute. Each of those touchpoints is part of one buyer journey, but in a fragmented programmatic stack, they're activated, optimized, and reported on as if they were separate campaigns.
That disconnect is where strategic value gets lost. When programmatic channels operate as silos, agencies lose the ability to manage frequency across the full programmatic plan, leading to overexposure on some channels and underexposure on others. Budget allocation becomes a guessing game because performance data isn't comparable across DSPs. And full-funnel measurement within programmatic—the ability to attribute conversions back to upper-funnel awareness touchpoints across CTV, display, audio, and more—becomes nearly impossible without manual data stitching.
Omnichannel DSPs solve these problems by treating the programmatic campaign as the unit of measurement, and not merely the channel. As a result, audience targeting flows across channels, rather than being rebuilt in each one. Frequency caps apply to the consumer, not the platform. And performance reporting reflects the full programmatic journey, which means optimization decisions can shift budget toward the channels and tactics actually driving outcomes, and not just the ones that look strongest in isolation.
This shift carries strategic weight beyond any single campaign. Agencies operating on omnichannel DSPs build cleaner, more unified historical programmatic performance data over time. That data becomes a strategic asset: the foundation for AI-driven planning, the evidence base for client recommendations, and the proof points that justify budget increases. Agencies and brands still running programmatic across siloed, channel-specific DSPs accumulate fragmented data that's harder to learn from and harder to act on.
That said, programmatic is only one part of the modern media mix. Even the most capable omnichannel DSP leaves search, social, and direct buys running in separate tools—and the same logic that argues for unifying programmatic channels argues for unifying programmatic with the rest of the workflow. But we'll talk more about that in just a bit. (For a deeper look at how omnichannel advertising platforms fit into broader agency strategy, this overview of omnichannel advertising platforms covers the category in more depth.)
The best DSP for cross-channel campaigns is one that scores well across five core evaluation criteria: inventory breadth, cross-channel measurement, cost transparency, optimization capabilities, and channel coverage depth. Each of these areas directly impacts whether a platform can serve as your team's single source of truth for omnichannel media buying.
Inventory breadth and quality: Evaluate how many supply-side platforms (SSPs) and exchanges the DSP integrates with, and whether it provides access to premium inventory across the channels you care about. A platform with limited supply partnerships will constrain your reach and force you back into channel-specific tools for certain buys.
Cross-channel reporting and attribution: The DSP should offer unified reporting that connects impressions, clicks, and conversions across channels in a single dashboard. Look for platforms that support multi-touch attribution models rather than last-click-only measurement, so you can understand the full path to conversion.
Cost structure transparency: Understand how the DSP charges—whether through a percentage of media spend, a flat platform fee, or a hybrid model. Hidden fees, opaque auction mechanics, or bundled data costs can erode your effective CPMs and make it difficult to compare true cost efficiency across platforms.
Optimization logic: Assess whether the platform offers algorithmic optimization that works across channels, not just within them. The best DSP will automatically shift budget toward the highest-performing channel-audience combinations based on your campaign goals, whether that's reach, engagement, or conversions.
Channel coverage depth: True omnichannel capability means more than checking boxes. With US CTV ad spend projected to reach $37.95 billion in 2026 at nearly 15% year-over-year growth per eMarketer, a DSP that treats emerging channels as afterthoughts will limit your ability to reach audiences where they're increasingly spending time. Evaluate whether the DSP offers robust activation across high-priority channels like connected TV and audio, not just display and video.
To see how these criteria map to a specific platform's capabilities, explore Basis DSP and compare its feature set against the framework above.
AI-driven planning workflows are fundamentally changing how teams evaluate omnichannel DSPs because they compress the time between campaign brief and activation from days to minutes. This capability—where AI converts a campaign brief into a complete, ready-to-activate omnichannel media plan—is emerging as a key differentiator that no third-party review site or analyst report currently covers in depth.
Traditionally, building a cross-channel media plan required manual research into audience segments, channel mix recommendations, budget allocations, and bid strategies. A media planner might spend hours or days assembling these components before a single impression is served. AI-native DSP features automate this process by taking in campaign objectives, audience parameters, and budget constraints, then generating a recommended plan that spans channels and tactics.
This matters for DSP selection because it shifts the evaluation conversation from "Which platform has the most features?" to "Which platform makes my team faster and smarter?" An omnichannel DSP with agentic AI capabilities—such as Basis' Compass, which converts campaign briefs into optimized omnichannel media plans—can go beyond merely executing buys by accelerating the strategic planning process that precedes them.
And that execution gap is real. Mediaocean's 2026 Advertising Outlook Report found that 41% of marketers cite difficulty connecting AI insights across systems as one of the top barriers to scaling AI effectively, second only to data quality and access. AI in the planning layer is far more valuable when it sits inside a platform that can also activate, optimize, and report against the resulting plan. The question to add to vendor assessments is whether the DSP integrates AI into the planning layer or only the optimization layer. As crucial as AI-powered optimization is to campaign performance, a platform that only uses machine learning for bid adjustments after a campaign launches is solving a different problem than one that uses AI to architect the entire campaign from the start.
An omnichannel DSP solves part of the fragmentation problem...but only when it comes to programmatic. Most agency teams still run paid search through one interface, paid social through another, direct buys through a third, and reconcile billing in a fourth. Each handoff between systems is a point where data gets lost, plans get duplicated, and margin gets quietly eaten by manual work.
A truly omnichannel advertising platform consolidates the full digital media workflow—programmatic, site-direct, search, social, and CTV—into a single system. Planning, activation, optimization, reporting, and billing share one data layer, which means a media planner, a buyer, and a finance lead all work from the same source of truth.
This matters for three operational reasons:
This is also where the broader market is heading: 39% of marketers are prioritizing cross-platform orchestration this year, signaling a shift away from siloed point solutions toward more connected infrastructures.
For agencies and brands evaluating an omnichannel DSP as a standalone purchase, it's worth considering whether the addition of another point solution could wind up recreating the same fragmentation problem one layer down—and whether a unified advertising platform would solve it once.
Truly omnichannel advertising platforms answer that question by closing the gap structurally rather than asking agencies to bridge it with workflow. Basis, for instance, integrates its DSP with search, social, direct, and CTV, layers in agentic AI media planning through Compass, and connects financial workflows through its partnership with Mediaocean.
Simply put, an omnichannel advertising platform is better than single-channel tools because it unifies planning, activation, optimization, reporting, and billing across every digital advertising channel—programmatic, search, social, direct, and CTV—within one system, while single-channel tools handle only one piece of that workflow in isolation. The difference shows up in five operational areas:
Data and reporting: An omnichannel platform produces a single, unified view of campaign performance across channels. Single-channel tools produce channel-specific reports that have to be manually reconciled, which delays optimization decisions and introduces measurement inconsistencies.
Cross-channel optimization: An omnichannel platform can shift budget, frequency, and audience targeting across channels in response to live performance data. Single-channel tools optimize within their own channel only, which means cross-channel budget allocation decisions happen offline, in spreadsheets, after the fact.
Workflow continuity: Planning, buying, reporting, and billing live in one environment on an omnichannel platform. With single-channel tools, every workflow stage requires a separate login, a separate data export, and a separate reconciliation step—each one a point where errors are introduced.
AI and automation readiness: AI-driven planning and agentic optimization depend on connected, unified data. An omnichannel platform feeds AI tools with the full picture of campaign performance. Single-channel tools produce fragmented datasets that limit what AI can actually do, even when each tool offers its own AI features.
Team coordination: Media planners, buyers, and finance teams work from the same data on an omnichannel platform. With single-channel tools, every team works from its own version of the truth, which slows decisions and creates version-control problems on media plans and budgets.
The perceived trade-off is depth vs. breadth—essentially, that a single-channel tool will outperform on its specific channel because that's all it does. But in practice, that trade-off is overstated. Modern omnichannel platforms can offer the same channel-level depth as single-channel tools while also delivering the cross-channel data, optimization, and workflow advantages above. The best omnichannel advertising platforms provide granular streaming inventory access, full programmatic capabilities, paid search and social functionality, and direct buying without forcing users to choose between specialization and consolidation.
The most common pitfall when choosing a demand-side platform is evaluating it based on feature lists alone rather than testing how those features perform in your actual workflow. A DSP can claim omnichannel support across dozens of channels, but if the user experience for activating campaigns on those channels is clunky, siloed, or requires workarounds, the feature list becomes meaningless. After all, inefficient processes (44.1%) and siloed systems (40.4%) top the list of obstacles agencies face today—and the wrong DSP (in the wrong environment) can add to both.
Pitfall: Choosing based on brand name alone: The largest DSPs in the market command significant share, but size doesn't guarantee fit. It's critical to match the platform to your team's actual operational reality.
Pitfall: Ignoring the onboarding and support model: Switching DSPs is a high-friction decision. If the vendor doesn't offer structured onboarding, dedicated account support, and training resources, your team will spend months underperforming on the new platform. Ask about implementation timelines, support SLAs, and whether you'll have access to platform specialists, not just a help center.
Pitfall: Overlooking data portability and integration: Your DSP doesn't operate in isolation. It needs to integrate cleanly with your other media buying workflows—such as search, social, and site-direct—as well as your DMP, CDP, analytics stack, and creative management tools. If the platform locks you into proprietary data formats or doesn't support standard API integrations, you'll create new silos even as you try to eliminate old ones.
Pitfall: Conflating optimization with transparency: Some DSPs offer strong algorithmic optimization but provide limited visibility into how decisions are made. If you can't see why the platform shifted budget from one channel to another, you lose the ability to learn from your campaigns and refine your strategy over time. Look for a platform that does both.
For practitioner-level perspective on how experienced programmatic professionals navigate these selection mistakes, this conversation on thriving as a programmatic consultant offers insights from experts who've evaluated and switched platforms multiple times.
Use this checklist as a structured framework when your team is shortlisting and comparing omnichannel DSP vendors. It translates the evaluation criteria covered above into a practical scoring tool you can bring into vendor meetings and RFP processes.
| Evaluation Criteria | Questions to Ask | What to Look For |
|---|---|---|
| Channel coverage | Which channels can I activate from a single platform? | Support for display, video, native, CTV, audio, mobile, and DOOH without requiring separate tools |
| Inventory access | How many SSPs and exchanges does the DSP integrate with? | Broad supply partnerships with premium and open exchange inventory across all supported channels |
| Cross-channel reporting | Can I see unified performance data across channels in one dashboard? | Single reporting environment with multi-touch attribution, not channel-by-channel exports |
| Cost transparency | How is pricing structured, and are there hidden fees? | Clear fee model (percentage of spend, flat fee, or hybrid) with no opaque markups on data or inventory |
| Optimization logic | Does the platform optimize across channels or only within them? | Cross-channel algorithmic optimization that reallocates budget based on holistic campaign goals |
| AI planning capabilities | Does the DSP use AI at the planning stage, not just the execution stage? | Agentic AI features that generate media plans from campaign briefs, reducing manual planning time |
| Data integration | Does the platform integrate with my existing tech stack? | Open APIs, standard data formats, and native integrations with major DMPs, CDPs, and analytics tools |
| Platform breadth | Does the DSP live inside a broader platform that also handles search, social, and direct? | Unified workflow across programmatic, search, social, and direct buying, with planning and billing connected |
| Onboarding and support | What does the implementation process look like? | Structured onboarding timeline, dedicated account support, and ongoing training resources |
| Scalability | Can the platform grow with my team's needs? | Flexible seat licensing, multi-client management for agencies, and enterprise-grade infrastructure |
This checklist is designed to be used alongside competitive research. For a complementary view of how the leading agency platforms compare on media buying capabilities, this comparison of the top advertising agency platforms provides additional context for your evaluation.
Choosing the right omnichannel DSP is ultimately about aligning platform capabilities with your team's operational needs, campaign goals, and growth trajectory. The evaluation isn't just technical—it's strategic. The platform you select will shape how your team plans, activates, measures, and optimizes media across every channel.
Start by mapping your current pain points. If fragmented reporting is your biggest challenge, prioritize cross-channel measurement capabilities. If manual planning is consuming too much of your team's time, weight AI-driven planning workflows more heavily. If you're an agency managing multiple clients, scalability and multi-account management should move to the top of your checklist.
Then pressure-test your shortlist against the criteria and pitfalls outlined in this guide. Request demos that walk through your actual use cases. Ask for references from teams with similar campaign structures and channel mixes. And evaluate the vendor's roadmap: A DSP that's investing in AI-native planning, expanded channel coverage, and transparent reporting is one that's building for where the industry is heading, and not just where it's been.
The most important question to ask, though, is the one most evaluations skip: Does this DSP live inside a platform that also handles the rest of your digital media workflow? An omnichannel DSP that solves programmatic fragmentation but leaves search, social, and direct in separate tools has only moved the problem. Teams that select for full-platform unification—not just DSP-level omnichannel—are the ones positioned to operate faster, measure more accurately, and build the connected data assets that AI and agentic systems will increasingly depend on.
What is an omnichannel DSP and how does it work?
An omnichannel DSP is a demand-side platform that allows advertisers to buy and manage programmatic media across multiple channels—including display, video, native, connected TV, audio, and mobile—from a single interface. It works by connecting to multiple supply-side platforms and ad exchanges, enabling real-time bidding on inventory across channels while centralizing audience targeting, budget management, and performance reporting in one place.
How is an omnichannel DSP different from a single-channel or specialized DSP?
An omnichannel DSP supports campaign activation across multiple media channels within one platform, while a single-channel or specialized DSP focuses on one specific environment, such as CTV or mobile. The key difference is that an omnichannel DSP enables unified audience targeting, cross-channel optimization, and consolidated reporting, whereas specialized DSPs require separate tools and workflows for each channel, creating data silos and operational fragmentation.
What channels does an omnichannel DSP support?
A true omnichannel DSP supports display, video, native, connected TV, audio, mobile, and digital out-of-home. The specific channel coverage varies by platform, so it's important to verify that the DSP offers robust activation capabilities—not just nominal support—for the channels that matter most to your media plan.
What should I look for when choosing the best DSP for omnichannel campaigns?
When choosing the best DSP for omnichannel campaigns, evaluate five core areas: inventory breadth across channels, cross-channel reporting and attribution capabilities, cost structure transparency, algorithmic optimization that works across channels rather than within them, and AI-driven planning features that accelerate campaign activation. Additionally, assess the platform's integration with your existing tech stack and the quality of its onboarding and support model.
How does an omnichannel DSP improve cross-channel measurement and attribution?
An omnichannel DSP improves cross-channel measurement by consolidating performance data from all channels into a single reporting environment. This enables multi-touch attribution models that show how impressions across display, CTV, audio, and other channels collectively contribute to conversions, rather than measuring each channel in isolation with last-click-only models. Unified measurement gives teams the visibility needed to make smarter budget allocation decisions in real time.
Why does an omnichannel DSP need to live inside a unified advertising platform?
An omnichannel DSP addresses programmatic fragmentation, but most agency teams also run search, social, and direct buys through separate tools. When the DSP lives inside a broader platform that handles all of these channels plus planning, reporting, and billing, the agency operates from a single data layer rather than reconciling outputs from multiple systems. This unification is what enables true cross-channel pacing, frequency management, and the connected historical data that AI and agentic systems need to optimize outcomes.
What makes an omnichannel advertising platform better than single-channel tools?
An omnichannel advertising platform unifies planning, activation, optimization, reporting, and billing across programmatic, search, social, direct, and CTV in one system, while single-channel tools handle only one piece of that workflow in isolation. The platform model delivers unified reporting, cross-channel optimization, connected data for AI workflows, and shared visibility across media planning, buying, and finance teams. Single-channel tools may offer deeper functionality within their specific channel, but they require manual reconciliation across the rest of the workflow.
What are the main benefits of running campaigns on an omnichannel DSP?
The main benefits of running campaigns on an omnichannel DSP include:
Is an omnichannel DSP suitable for both brand awareness and performance campaigns?
Yes, an omnichannel DSP is suitable for both brand awareness and performance campaigns. Upper-funnel channels like connected TV and audio drive reach and awareness, while display, native, and mobile support mid- and lower-funnel performance goals like clicks and conversions. Running both campaign types within a single DSP allows teams to measure the full-funnel impact of their media spend and optimize across objectives rather than treating awareness and performance as separate efforts.
Wheelhouse DMG leveraged Basis’ tech and programmatic expertise to increase ad spend by 41% and deliver privacy-friendly performance for a leading healthcare brand.
Wheelhouse DMG is a performance marketing agency built for highly regulated industries, with deep expertise in medical device and healthcare.
Wheelhouse DMG was engaged by an industry-leading healthcare tech client to promote a specialized medical device to a highly specific US healthcare audience of emergency medicine physicians, physicians and doctors' offices, medical doctors, and hospitals and medical offices.
To do so, they turned to Basis.
Prior to the campaign’s launch, Basis’ Programmatic Strategy team audited the 2024 setup and provided recommendations for scaling success in 2025.
This January–November 2025 programmatic campaign with the goals of driving:
Through the partnership and combined efforts between Wheelhouse DMG and Basis, programmatic is consistently one of their top performing channels.
The Results:
Key Takeaways:
It seems to happen every year, like clockwork: There’s a damning new report on ad fraud, MFA site proliferation, or brand safety failures, and suddenly everyone wants to talk about supply path optimization (SPO). But then, of course, the fervor dies down, the urgency fades away, and SPO moves from “front of mind” to “on the backburner.”
But the truth about SPO is that failing to sustain it comes with a price tag in the form of wasted spend, degraded inventory, and performance left on the table.
Understanding these stakes requires clarity around what SPO seeks to accomplish. At its core, SPO is about answering a deceptively simple question: What’s the best path for an ad dollar to travel between a brand and its target audience, and how much value gets lost along the way? The answer has significant implications for efficiency, quality, and business outcomes.
The benefits of getting SPO right are striking: In Q4 2025, advertisers implementing rigorous quality governance directed 56.7% of their programmatic spend into impressions that were viewable, measurable, and fraud- and MFA-free, while lower-performing advertisers directed just 37.5%.
That performance gap adds up. Across the industry, an estimated $21.6 billion in programmatic spend is lost to supply chain inefficiency every year. For advertisers serious about recapturing that value, SPO must be treated as a non-negotiable strategic priority rather than a periodic rallying cry.
Programmatic advertising involves more intermediaries than most advertisers account for. Before an impression reaches a publisher's ad server, it may pass through a chain of buying platforms, selling platforms, exchanges, and resellers, each of which takes a cut, slows delivery, and adds another potential point where things can go wrong.
Reducing the number of hops in that chain means that more of each dollar functions as working media rather than disappearing into intermediary fees. Because each additional intermediary introduces another opportunity for arbitrage and low-quality inventory to slip through, shorter paths also reduce opportunities for fraud or invalid traffic.
In recent years, several converging forces have come together to make SPO increasingly consequential:
The payoff for managing that complexity well is measurable. Even as overall programmatic market performance softened in Q4 2025, advertisers who optimized their supply chains around viewable, fraud-free, and MFA-free impressions—rather than CPM alone—still made quarter-over-quarter gains. Even more, those advertisers saw cost per conversion drop by nearly 40%, even as their CPMs went up.
That data makes the case for SPO clear. Disciplined supply path management drives stronger ROAS, lower cost per conversion, and higher quality inventory. As such, brands that treat SPO as a standing priority will continue to pull ahead of competitors who only prioritize it periodically.
While advertisers agree that the industry needs more transparency, how teams frame SPO internally shapes the effectiveness of their efforts. Teams that treat SPO primarily as a cost-cutting exercise often end up optimizing for the lowest CPM rather than the best outcome. That approach tends to push spend toward lower-quality inventory, undermine publisher relationships, and produce results that look efficient on paper but underperform in practice.
The most successful advertisers flip that logic. They evaluate each supply path by what it delivers—verified inventory, real audience reach, measurable conversions—and route spend accordingly, even when the cleaner path costs more per impression.
Research from the 614 Group identified eight questions advertisers can use to evaluate and compare platforms on their SPO capabilities, giving buyers a concrete way to make more informed investment decisions. Together, they form a practical test of whether a platform is meaningfully equipped to support SPO as a core business discipline.
Before committing to, or continuing with, an advertising platform, teams should pressure-test its SPO capabilities against these questions:
A platform's ability to answer these questions clearly is a direct signal of its SPO maturity. Vague or incomplete answers usually mean the visibility isn’t there.
In general, advertisers should prioritize platforms that offer neutral buy-side transparency, or the ability to see clearly across the supply chain without the platform's own conflicts coloring the picture. Strong platforms will offer visibility across multiple SSPs, publisher-level reporting, deal-type comparisons, and brand safety controls built into the buying process rather than layered on afterward. Platforms without a stake in the sell side are better positioned to give buyers an objective view of the full supply chain—including retail media networks, where conflicts of interest are especially common and supply path norms are still being established.
Ultimately, advertisers who approach SPO with the right framing, the right questions, and the right partners will be best positioned to use it as a real business advantage.
SPO will keep resurfacing in industry conversations. Each new report on ad fraud, MFA inventory, and brand safety will bring another wave of urgency—and another round of advertisers scrambling to respond. Those who only move when the headlines do will continue to lose out on the value rigorous SPO can provide.
The advertisers pulling ahead are asking harder questions, demanding more from their platforms, and managing their supply paths with the same rigor they bring to audience targeting or creative performance. That rigor is what turns SPO from a recurring industry fire drill into a sustained competitive advantage.
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The best advertising platform for political and advocacy campaigns is an omnichannel platform with a built-in demand-side platform (DSP): one system that handles planning, buying, compliance, and optimization across every channel a campaign runs, fast enough to keep pace with a deadline that doesn't move.
Political advertisers, advocacy groups, and nonprofits evaluating platforms for the 2026 midterms should prioritize six capabilities: omnichannel activation across CTV, programmatic, audio, social, and search; CTV inventory access with pre-negotiated deals; geopolitical targeting at the congressional district and state legislative level; rapid activation workflows that compress planning-to-launch timelines; built-in compliance and brand safety tools; and AI-powered planning and optimization that preserves human strategic control.
Political ad spending for the 2026 midterms is projected to reach $10.8 billion, making this the most expensive midterm cycle in US history. That total represents a 20%-plus increase over the 2022 midterms and comes within striking distance of the $11.2 billion spent during the 2024 presidential cycle.
The forces driving that spending are significant. Control of both chambers of Congress is up for grabs. A record wave of congressional retirements is creating open-seat races where neither candidate enters with the advantages of incumbency. Redistricting is underway in several key states, including California, Missouri, North Carolina, Ohio, Texas, and Utah. With more competitive races, more open seats, and more uncertainty than any recent midterm, campaigns that cannot move quickly from planning to activation risk falling behind before the general election even begins.
For political advertisers managing campaigns across CTV, programmatic display, streaming audio, social, and search, the advertising platform you choose determines whether your team can move at the speed this cycle demands or gets buried in manual execution during the final sprint. The strongest option is a system that unifies activation with compliance, so candidates, campaigns, and advocacy groups can plan, buy, and optimize across every channel from one workflow. Basis is an omnichannel advertising platform built for political and advocacy advertising, combining all-channel activation, a built-in DSP, pre-negotiated political inventory, and compliance tooling in a single system. This guide covers what political advertisers should prioritize when evaluating a platform for the 2026 midterms.
Key Takeaways
Political advertising operates under constraints that intensify every platform decision. The timeline is fixed, as Election Day does not move. The spending cadence is compressed: Basis data from the 2024 election cycle shows 48% of digital ad budgets ran in the final 30 days, with nearly half of that concentrated in the last 10 days. As such, the margin for error is narrow. A pacing miscalculation or delayed campaign launch in late October cannot be corrected in November.
These constraints shape the platform evaluation criteria that follow. Each section addresses a capability that matters in any advertising context but becomes especially high-stakes when the deadline is immovable, the budget is front-loaded, and there is no second attempt.
Political advertising in 2026 spans a wide channel list. CTV is projected to account for 23% of total political ad spend, the only channel forecast to experience growth during this cycle. Streaming audio is emerging as a breakout opportunity, reaching voters during screen-free moments that CTV and display miss entirely. Social platforms remain essential for fundraising and persuasion. And programmatic display and video continue to serve awareness and retargeting roles across the funnel.
The operational question is whether your platform can activate across all of these channels from a single workflow or whether your team needs to rebuild campaigns in separate interfaces for each.
Platforms that handle programmatic, direct publisher buying, search, social, and CTV in one system eliminate the manual handoffs that slow campaign launches and create inconsistency across channels.
For political campaigns specifically, channel coverage should include access to CTV inventory across platforms that accept political ads (Hulu, ESPN, Roku, YouTube, DIRECTV), programmatic audio through streaming and podcast platforms, and social activation through Meta, Google, TikTok, and Snapchat. Platforms that also support programmatic terrestrial radio through partnerships like iHeart expand reach into a format that political advertisers have historically underinvested in. Digital out-of-home (DOOH) is another channel worth evaluating, particularly for reaching voters in transit, at events, and in public spaces where screen-based media doesn't reach.
What to ask:
The spending cadence of political advertising creates unique operational demands. Basis data from the 2024 election found that 48% of digital ad budgets ran in the final 30 days, with nearly half of that concentrated in the last 10 days.
That compression means platforms need to support rapid campaign activation, real-time budget pacing, and mid-flight optimization under sustained time pressure.
Specific capabilities to evaluate:
Political targeting operates under platform-specific restrictions and state-by-state regulatory variations. Restrictions on audience targeting for election ads on platforms like Google and Meta, combined with state-by-state regulatory variations, make audience strategy a jurisdiction-specific exercise.
Geopolitical targeting has become an increasingly important tool in this environment. Basis data from the 2022 midterms showed that nearly 20% of political programmatic ads used geopolitical targeting, with 51% targeting by congressional district and 32% by state senate district. With redistricting underway in multiple states, the ability to target by updated district boundaries is essential.
Beyond geography, platforms should support integrations with major voter data providers, including L2, TargetSmart, i360, Tunnl, and Aristotle. Direct integrations with identity resolution providers like LiveRamp allow campaigns to upload, match, and activate voter file data quickly rather than waiting days for manual onboarding. Platforms with additional data partnerships—such as Foursquare for location intelligence, CivicScience for attitudinal data, and Adastra for identity resolution—give campaigns more options for building precise audience segments.
Automatic Content Recognition (ACR) data from smart TV manufacturers adds another layer of targeting precision. Campaigns can reach households based on actual viewing behavior, not inferred demographic proxies, and serve political messages in contextually appropriate streaming environments. Cross-device and household-level targeting further extends this capability, allowing campaigns to expand reach beyond individual devices to entire households.
What to ask:
CTV is the only media channel projected to see increased political spending over the 2024 presidential cycle, with $2.5 billion in CTV political ad spend expected in 2026. For political advertisers, CTV access is as much about securing inventory at favorable pricing as it is about reach: CPMs surge as Election Day approaches, and campaigns that wait until October pay a steep premium.
The CTV inventory ecosystem for political advertising has expanded significantly. Disney has opened inventory across Hulu and ESPN. OEM platforms from LG, Samsung, and Vizio operate their own FAST channels with growing political inventory. DIRECTV, HBO Max, Paramount, and other premium providers are accepting political and advocacy advertising in increasing volumes. Peacock, Tubi, Sling, Discovery+, Pluto TV, and fuboTV have also entered the political CTV market, giving campaigns a broader set of inventory sources than any previous cycle.
But not all CTV inventory is created equal. Platforms that accept political ads (Hulu, Roku, YouTube, ESPN) will see the sharpest CPM increases as Election Day approaches. Basis data from 2024 showed that programmatic video CPMs nearly doubled the election cycle average in November and had already increased 50%+ above average in October.
The platform evaluation question is whether your advertising platform gives you the inventory relationships and buying mechanisms to lock in favorable pricing before that surge hits.
Buying mechanisms to evaluate:
Measurement capabilities matter here too. Platforms that integrate cross-channel reach measurement—comparing digital campaign performance to linear TV—help campaigns quantify incremental reach and optimize budget allocation between CTV and traditional broadcast. Basis integrates Comscore Campaign Ratings for Advanced Reach Measurement, enabling campaigns to measure cross-channel reach, frequency, and incrementality from a single platform.
Live sports inventory deserves special attention in 2026. The FIFA World Cup (June-July), NFL, and college football season overlap directly with general election spending. Political advertisers increasingly use live sports as a targeting proxy, reaching voters in specific states and DMAs based on the games they watch. Platforms with strong sports-adjacent CTV inventory access will have a meaningful advantage. BasisTV+ provides access to CTV/OTT, addressable, and live sports and events inventory, reaching 93% of US smart TV households with 1,000+ advanced TV targeting parameters and 80+ trackable metrics.
AI-generated political content, including deepfakes, adds a new layer of brand safety complexity in 2026. Deepfake political ads are already running in midterm campaigns. There is no federal law governing AI use in political advertising, and while 26 states have enacted some form of deepfake disclosure legislation, enforcement remains limited.
At the same time, platform policies around AI disclosure are evolving quickly. Google requires political advertisers to disclose any use of AI in their ads. Meta requires disclosure in certain circumstances. The directional trend points toward greater transparency requirements.
Political advertisers need platforms that build compliance into the workflow rather than bolting it on as a separate step. Specific capabilities to look for:
Political advertising clients range from well-funded Senate campaigns with experienced media buyers to first-time candidates and ballot measure committees running their first digital campaign. The platform should accommodate both.
For experienced political media teams, self-serve access with full control over targeting, bidding, and optimization is essential. For campaigns that need additional support, managed service options that handle execution while preserving strategic control offer a middle ground.
The Basis Candidates & Causes practice has served political advertisers for two decades, offering service tiers from fully self-serve to fully managed execution. The team includes more than 40 political subject matter experts who understand the compliance, timing, and operational nuances that distinguish political media buying from commercial campaigns. In the 2024 cycle, the team supported more than 1,400 state and local campaigns, managing over $130 million in political ad spend across video, display, native, audio, and text ads.
This flexibility matters because political campaigns don't all operate the same way. A statewide Senate race and a state legislative campaign have very different team structures, budgets, and execution needs. The right platform adapts to those differences rather than forcing a one-size-fits-all approach.
Audio represented just 3% of political ad spend during the 2024 election season, despite the average voter spending 21.2% of their total media time consuming audio content. That gap is a meaningful opportunity for political campaigns willing to move early.
Streaming audio reaches voters in screen-free moments: during commutes, workouts, and household tasks. With workers returning to offices and commutes increasing, audio consumption is rising. Podcast listenership has demonstrated increasing resonance across key voter demographics. And with programmatic buying now available even for broadcast radio inventory, the barriers that once made audio difficult to activate at scale have been largely removed.
Campaigns that are early movers in streaming audio stand to benefit from lower CPMs and less competitive inventory than they will find in CTV, particularly during the October-November sprint when CTV pricing spikes.
Platforms should support programmatic audio activation through streaming services like Spotify and iHeart, ideally within the same workflow used for CTV, display, and social. Campaigns that can add audio to their channel mix without logging into a separate tool or rebuilding targeting from scratch will capture this opportunity more efficiently. Exclusive inventory partnerships add further value. Basis provides access to DAX (Digital Ad Exchange) inventory reaching over 20% of total US audio consumers across leading streaming and podcast platforms, with 60,000+ podcasts and 350+ terrestrial US radio stations, plus a multicultural network for reaching Spanish-speaking voters.
The operational demands of political advertising compress the workflow challenges agencies already know well into a much shorter timeline. Basis data shows that agencies using its automation platform save an average of 51 hours per campaign—equivalent to roughly 6.5 work days—and approximately $8,925 per campaign in efficiency gains. In political advertising, where the window for peak spending narrows to weeks, those saved hours translate directly into competitive advantage.
Automation delivers measurable time savings across five areas that consistently drain political campaign time:
Campaign setup and trafficking. Building campaigns across CTV, display, audio, social, and search typically requires logging into separate platforms and manually configuring each. Automated platforms let planners build campaigns once and traffic them to all channels simultaneously, eliminating redundant data entry and the consistency errors that come from rebuilding campaigns in interfaces that were never designed to talk to each other.
Budget pacing. When 48% of your budget runs in the final 30 days, real-time pacing visibility isn't a convenience feature — it's a necessity. Automated pacing tracks spend across all channels from a single view and adjusts delivery rates to hit flight date targets without daily manual check-ins across disconnected tools.
Performance monitoring. Cross-channel dashboards that consolidate CTV, display, audio, social, and search metrics into a single view let teams identify optimization opportunities faster. In the final sprint, the ability to spot an underperforming tactic and reallocate budget in hours rather than days can determine whether you reach persuadable voters before Election Day.
Reporting. Pulling final reporting from multiple platforms and consolidating it into client-ready formats is one of the most time-consuming tasks in the campaign lifecycle. Automated reporting with customizable templates eliminates manual exports and spreadsheet merging.
Billing reconciliation. Managing billing across multiple delivery sources creates additional operational burden, particularly for campaigns with FEC reporting requirements. Platforms with billing integrations and automated reconciliation streamline a task that otherwise consumes hours per campaign.
AI adds measurable value to political advertising in two areas: pre-activation planning and campaign optimization.
On the optimization side, AI-powered bidding 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 CTV, audio, display, and social simultaneously, automated optimization improves performance without requiring additional headcount. Basis' SmartBid technology processes dozens of performance signals throughout the day to make continuous, goal-aligned adjustments across the full media mix. In testing, SmartBid has delivered a 133% increase in return on ad spend (ROAS).
On the planning side, tools like Compass by Basis represent a shift in how campaigns can approach pre-activation workflows. Compass generates media strategies by synthesizing uploaded campaign briefs, analyzing market data, and utilizing Basis' proprietary omnichannel IMPACT framework. The output includes a proposed media strategy, channel mix recommendations, targeting recommendations with named audience personas, and a presentation deck. Agency teams can then refine the strategy through a back-and-forth conversation with the Compass agent before plans flow directly into campaign activation. Every recommendation includes clear reasoning, so teams can see what's driving each allocation and adjust the strategy as needed.
For political advertisers managing multiple races simultaneously, or for consultancies handling campaigns across several states, this kind of planning acceleration can be the difference between launching on time and falling behind.
That said, AI should augment strategic judgment, not replace it. The strongest platforms automate mechanical tasks and surface recommendations while preserving full human control over strategic decisions. Political campaigns that differentiate through audience insight, creative strategy, and local market expertise need platforms that enhance those capabilities rather than attempting to automate them away.
The following checklist summarizes the platform capabilities political advertisers should evaluate for the 2026 midterm cycle.
Channel coverage. Does the platform support CTV, programmatic display and video, streaming audio, DOOH, social (Meta, Google, TikTok, Snapchat), search, and direct publisher buying from a single workflow?
CTV inventory access. Which premium publishers are accessible? How many PMPs are pre-approved for political? Does the platform support programmatic guaranteed buying? Does it offer cross-channel reach measurement against linear TV?
Political targeting. Does the platform support congressional district, state legislative district, DMA, and hyperlocal targeting? Which voter data providers integrate directly? How fast is voter file activation? Does the platform support cross-device and household-level targeting?
Activation speed. Can the platform move from brief to live campaign across channels in hours? Does planning connect directly to activation without manual reformatting?
Pacing and optimization. Does the platform provide real-time cross-channel pacing? Can budget be shifted between channels mid-flight from a single interface? Does the platform offer AI-powered bidding that optimizes across the full media mix?
Brand safety and compliance. Does the platform include DAA Political Ad Icon support, rapid creative review, contextual controls, and verification partner integrations? Does it include fraud prevention tools?
Streaming audio. Does the platform support programmatic audio alongside CTV and display within the same workflow?
AI capabilities. Does the platform offer AI-powered optimization and planning tools? Do those tools preserve human control over strategic decisions? Do AI recommendations include transparent reasoning?
Service flexibility. Can the platform scale from self-serve to fully managed execution based on campaign needs?
Political expertise. Does the platform provider have a dedicated political advertising practice with experience across election cycles?
Basis is an omnichannel advertising platform with a built-in DSP, built for political and advocacy advertising. It has served political advertisers for two decades, supporting thousands of campaigns, independent expenditure committees, and issue advocacy advertisers since 2006. In the 2024 cycle alone, the Candidates & Causes team supported more than 1,400 state and local campaigns, managing over $130 million in political ad spend. Basis brings all necessary capabilities together in one platform built for the operational demands of political advertising.
The 2026 midterms will test every political advertiser's ability to move fast, spend efficiently, and reach the right voters across a fragmented media environment. The platform you choose determines whether your team spends the final 30 days executing strategy or wrestling with disconnected tools.
What makes political advertising platform requirements different from commercial media buying?
Political advertising operates on a fixed deadline with no flexibility. Election Day does not move, and the spending cadence compresses heavily into the final weeks. Platforms need to support rapid activation, real-time pacing under extreme time pressure, political-specific targeting (congressional districts, voter file data), compliance tools for political ad disclosure, and inventory relationships with publishers that accept political advertising.
What platforms help nonprofits and advocacy groups run digital advertising?
Nonprofits and advocacy groups are best served by an omnichannel platform with a built-in demand-side platform (DSP) that specializes in political and issue advocacy advertising, with built-in compliance and flexible service tiers. Basis is one such platform: its Candidates & Causes practice has served political and advocacy advertisers since 2006, including candidates, campaigns, PACs, independent expenditure committees, issue advocacy groups, nonprofits, and ballot measure committees. The practice pairs omnichannel activation and pre-negotiated political inventory with disclosure and brand safety tools, geopolitical and issue-based targeting, and a team of 40+ political subject matter experts—so organizations of any size can run compliant digital campaigns across CTV, programmatic, audio, and search from a single workflow.
How early should political campaigns lock in CTV inventory?
Early—ideally before September. Programmatic video CPMs in 2024 nearly doubled the election cycle average in November and increased 50%+ in October. Campaigns that secure PMP deals and programmatic guaranteed pricing before September will have a significant cost advantage over those relying solely on open exchange buying during peak season.
Can smaller campaigns benefit from the same platforms used by major races?
Yes. Platforms with flexible service tiers accommodate campaigns at different scales. Self-serve access gives experienced political media buyers full control, while managed service options support campaigns with smaller teams or less programmatic experience. The operational efficiency gains from automation — faster campaign setup, unified reporting, automated pacing — benefit campaigns regardless of budget size.
How should political advertisers think about streaming audio in their media mix?
Audio represented just 3% of political ad spend in 2024 despite voters spending over 21% of their media time with audio content. That gap means lower CPMs and less competitive inventory compared to CTV, particularly during the October-November sprint. Campaigns that activate streaming audio through the same platform used for CTV and display can add the channel without additional operational overhead.
What role does AI play in political advertising platforms?
AI delivers value in two areas: campaign optimization (automated bidding, pacing, and budget allocation based on real-time performance data) and pre-activation planning (generating media strategies and channel mix recommendations from campaign briefs). The strongest platforms use AI to accelerate mechanical tasks while preserving human control over strategic decisions like audience definition, budget allocation, and creative messaging.
What is geopolitical targeting in political advertising?
Geopolitical targeting allows political advertisers to serve ads based on political boundaries—congressional districts, state legislative districts, DMAs, ZIP codes, and other jurisdiction-level geographies—rather than relying solely on demographic or behavioral audience segments. This capability is especially important during redistricting cycles, when district boundaries shift and campaigns need to reach voters within updated lines. During the 2022 midterms, Basis data showed that nearly 20% of political programmatic ads used geopolitical targeting, with 51% of those targeting by congressional district and 32% by state senate district. For the 2026 cycle, with redistricting underway in states including California, Missouri, North Carolina, Ohio, Texas, and Utah, platforms that support targeting by updated district boundaries give campaigns a structural advantage in reaching the right voters.
What is issue advocacy advertising?
Issue advocacy advertising is paid messaging that promotes a policy position, cause, or ballot measure rather than a specific candidate for office. It is run by advocacy organizations, nonprofits, trade associations, PACs, and ballot measure committees to shape public opinion, mobilize supporters, and influence legislation. Like candidate advertising, issue advocacy is subject to platform disclosure policies and, in many cases, state and federal reporting requirements, so advocacy advertisers benefit from platforms with built-in compliance and disclosure tools. Basis supports issue advocacy advertisers through its Candidates & Causes practice, pairing compliant omnichannel activation with geopolitical and issue-based targeting and dedicated political expertise.
Key Takeaways
Picture this: The weekend is finally here, it’s game time, and you’ve got your homemade nachos all set to go (your secret ingredient: home-pickled jalapenos!). You plop down on the couch, crack open your first beer, turn on the big screen and...shoot. Where’s the game? Didn’t you read something about Amazon securing the rights for this season? No wait, that was Peacock...or was it Apple TV+? ESPN+? Maybe TBS? Or TNT? One of the Ts? Fox? CBS? Hulu? YouTube TV? Is this one of the games on Netflix? Why can’t you find it?! Was there ever even a game today? THE NACHOS ARE GETTING COLD!
Tuning in to live sports used to be so simple. And we’re not even talking about 50+ years ago, when that meant “going to the game” or “turning on the radio” for 95% of your live sports consumption. As recently as the 2000s, when it came to sports broadcasts, there were the major networks, ESPN, an occasional game on one of the Turner channels, and that pretty much was it.
Today, sports leagues are scattering their broadcast rights around like digital Johnny Appleseeds, adding to an already-complex CTV and streaming video environment and creating new challenges for advertisers and consumers alike. This fragmentation is reshaping sports advertising as we know it—forcing brands to rethink where (and how) they run sports ads to reach today’s fans. The clear reason for this shift? Money—big money. US sports TV and streaming rights are forecast to reach $32.8 billion in 2026 and are forecast to surpass $36 billion in 2030. All this has taken place in the face of (or, perhaps, helped fuel) cord cutting that drives essential revenue away from traditional broadcasters and into the pockets of streaming services.
In light of these dramatic shifts, how can digital advertisers effectively reach and connect with sports fans? And is navigating the disparate live sports landscape worth all the trouble? (Spoiler alert: yes, yes it is!) Read on to learn all about it.
The biggest shift in sports advertising over the past several years is where games actually air, and that change is reshaping every advertiser’s media plan.
First off, let’s look at some of those new (or, at least, new-ish) sports broadcast partnerships, an area that’s seen some significant departures from the “old normal” in recent years:
The fact that so many major American sports entities have granted exclusive broadcast rights to streaming platforms marks a significant shift in the industry. And with digital live sports viewership surpassing linear TV in 2023—a gap that’s only widened since—it’s clear that the streaming-first revolution in sports broadcasting has arrived.
Just as meaningful is the price those companies paid for their live sports streaming rights: $200 million per year from Disney, Amazon Prime Video, and NBCUniversal for WNBA games; $5 billion over the next ten years from Netflix for the WWE “Raw” programming; $1 billion per year from Amazon for their weekly regular season NFL matchup; a reported $2+ billion per year from Google for Sunday Ticket; and $150 million from Netflix for its two Christmas day NFL games in 2024.
To make up for these kinds of skyrocketing costs, linear broadcasters and streaming video platforms alike are turning to two main revenue sources: subscription price hikes and—you guessed it!—advertising. So, without further ado, let’s take a look at how (and why) advertisers can make the most of this evolving landscape.
Live sports advertising offers brands a rare combination of guaranteed reach, deeply engaged audiences, and rich targeting opportunities—making sports ads some of the most valuable placements in any media plan.
Roughly two-thirds of Americans are sports fans. And people who watch sports aren’t going to catch a replay of the game once it hits Netflix in a few months—they’re going to watch it live. This is a valuable “guaranteed” audience upon which platforms and advertisers alike can place outsized value compared to other broadcasts (no wonder sports tend to dominate lists of the most-watched US broadcasts year after year). When brands want to ensure they are meeting a large, built-in audience all at once, there are few opportunities quite like live sports.
Which is not to say that brands can’t benefit from advertising against other sports content, such as highlights, clips, and replays (more on this in a bit!). Those often represent prime contextual advertising opportunities, whether via contextual partners like Comscore and DoubleVerify, or with specific publishers such as the AP, Gannett, or (of course) ESPN.
On the more local level, no matter what embarrassment, scandal, or years-long losing streak might afflict their favorite team, fans tend to “root root root for the home team” through thick and thin. For advertisers that want to geotarget, sporting events often post remarkable ratings in specific markets—and fan loyalty can translate to brand loyalty. No wonder organizations of all kinds pay out top dollars to be the official beer, official pizza, official bank, official cryptocurrency platform, or even official HR/payroll provider of your hometown team.
Another key factor that’s fueling sports viewership? Sports betting, which has gone from being largely prohibited under federal law (except in Nevada) before 2018 to being all over American sports coverage today. After the Supreme Court struck down the federal ban that year, individual states began legalizing it, and total consumer spending on sports betting has since skyrocketed—surpassing $100 billion for the first time in 2023, and expected to exceed $235 billion in 2028.
This growing excitement around sports betting is delivering new, passionate audiences to live sports, with over a fifth of US adults reporting that they have personally engaged with sports betting within the last year. And if someone is spending money on the game, they’re a whole lot more likely to tune in, with 85% of sports bettors saying it makes them more interested in watching the games.
As for where and how they’re watching...
Nearly 165 million Americans regularly watch live sports—almost 50% of the total population. Perhaps even more notably, more than 122 million of those viewers currently tune in on digital devices, and that number is projected to rise to 141 million by 2029. Yep: Just like the rest of the video world, the future of live sports advertising is digital.
Of course, as is the case with that larger digital video environment, the increasingly disparate nature of sports broadcast agreements (even in light of new offerings like ESPN’s new direct-to-consumer streaming service) is only adding to the complexity and fragmentation that mark the digital video and CTV space. Among avid sports fans, 69% feel it’s a hassle to navigate multiple providers to watch the same sport and 59% feel it’s gotten more difficult to find what they want to watch, and you can only imagine how frustrating that must be when the start of the game is rapidly approaching (and your nachos are getting cold...) And for advertisers, the evolution from a few reliable live sports hubs to numerous broadcasters across multiple channels can mean added complexity in campaigns targeting these audiences. So as streaming becomes the norm for live sports, advertisers and viewers alike are adapting to some growing pains.
That said, to their credit, the big tech companies that have waded into the live sports streaming wars are taking crucial steps toward optimizing benefits for advertisers. Prime Video, now a major home for NBA coverage, has rolled out interactive tools like “key moments,” advanced stats, and personalized bet tracking for NBA broadcasts. Meanwhile, NBCUniversal and Walmart launched integrated shoppable experiences across both linear and streaming sports inventory in late 2024, bringing retail media into live broadcasts. On the measurement front, Nielsen’s Big Data + Panel tool now includes live sports as a core category, and leagues are pushing for streaming platforms to share first-party metrics to support accurate ad pricing.
Even the more traditional homes of live sports have readily embraced the potential of streaming those events for maximum impact. NBC's presentation of Super Bowl LX in February 2026 drew 125.6 million viewers across NBC, Peacock, Telemundo, NBC Sports Digital, and NFL+—making it the second most-watched Super Bowl ever, behind only the prior year's Super Bowl LIX (127.7 million viewers). The fact that both records now factor in streaming and digital audiences alongside linear underscores how integral those platforms have become to live sports measurement. And annual events like the Masters golf championship and NCAA men’s basketball tournament have long had authorized (and ad-filled) streams as part of their overall broadcast packages. As viewers increasingly flock to OTT and CTV for their live sports consumption, brands will have new ways to personalize and target these consumers as part of their cross-channel marketing strategies.
Digital marketing in sports now extends well beyond traditional in-game commercials. Brands are building sports-focused campaigns that span social media, athlete partnerships, branded content within sports podcasts, contextual ads around highlights and recaps, and programmatic placements on sports news sites and apps.
Sports fans spend significant time on digital platforms throughout the week, not just during games—and they respond well to content that speaks to their fandom. For advertisers, that creates year-round opportunities to stay top-of-mind with a passionate audience, well beyond the few hours a game is live.
Speaking of which...
The digital evolution of live sports broadcasts goes beyond individual devices.
More and more fans are watching the game on digital platforms, particularly bigger screens like CTV. Sports viewership on YouTube’s connected TV app grew by 30% in 2024—a clear signal that connected TV (CTV) has become a major player in sports advertising.
But the real secret weapon for advertisers may be resting in your pocket (or your hand) right now: smartphones. Sports broadcasts present a unique cross-platform marketing opportunity, with viewers often using second screens to look up players and team statistics, use social media to engage with others, watch other games on a separate device, place bets, and more—all while watching live sports at home.
This multi-device behavior creates valuable targeting opportunities that extend well beyond game time. Sports fans are a widely targetable audience segment through private marketplaces (PMPs) like Tapjoy, and they can be further segmented via top data providers like Alliant (golf), eXelate (NBA), and Cuebiq (NHL). These tools help advertisers continue to market to viewers even after the game clock hits 0:00. Put it all together, and sports programming offers a powerful way to consistently reach and remarket to specific target audiences across multiple devices.
Beyond retargeting viewers across devices during and immediately after games, there’s also a significant opportunity to build upon the momentum of live sports and continue the conversation long after the final whistle. Sports fans are often ideal audiences for marketers as they are deeply engaged when it comes to their favorite teams. Considering the fact that 41% of fans are already locked into their favorite pro sports team by the age of 12 (and 62% by the age of 17), it’s clear that sports fans are an active, impassioned audience ripe for engagement. By leaning into this passion and connecting with these fans within relevant content related to their favorite teams, marketers can further deepen brand loyalty and drive meaningful engagement.
Whether by running ads alongside clips and replays, within sports shows, or even alongside social media content, advertisers have ever-expanding opportunities to engage with sports fans beyond live events themselves—and perhaps even to persuade more viewers to tune into live games. The power of such placements is underscored by deals like the 2024 NBA and Warner Bros. Discovery agreement which includes the studio show Inside the NBA, as well as other NBA content like Bleacher Report and House of Highlights, a social media network that distributes sports clips and content.
In addition to using ad placements within gameday-adjacent content, brands and marketers can also harness the power of sports by working with athlete influencers. Take, for instance, rugby star and Olympian Ilona Maher. She rose to fame not only for her powerful presence and performance on the field, but also for her active presence on social media—where she now has over five million followers on Instagram and works with major brands like Barbie and Maybelline. Additionally, many athletes have also started their own podcasts (like Angel Reese’s “Unapologetically Angel” and the Kelce brothers’ “New Heights”), offering brands the opportunity to place high-impact ads that connect with fans within their shows.
Sports offer brands a unique opportunity to connect with deeply passionate and engaged audiences, both during and outside of the game. By leveraging gameday-adjacent content and collaborating with athlete influencers, brands and marketers can tap into the enthusiasm of sports fans to build stronger connections, deepen brand loyalty, and drive meaningful engagement across a variety of platforms.
Sporting events are a fixture of American culture. From Super Bowl Sunday every winter to the WNBA Finals every summer, live sports are a reliable way to bring people together in front of their TVs, laptops, and other streaming devices to catch the action (and, of course, the commercials). And even as the way fans consume their sports continues to evolve hand-in-hand with the rest of the video realm, advertisers will look to live sports as a pillar of their omnichannel marketing strategies. In short: It’s a home run opportunity for brands to hit their goals, assist in the revenue-driving process, and score some big wins.
(And yes, there were seven sports puns in that last sentence. Touchdown.)
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The role of CTV in live sports advertising is expected to dramatically increase in the years ahead. Check out our CTV advertising guide for tips on everything from CTV campaign best practices, to safeguards against CTV ad fraud, to effective targeting tactics, and much, much more.
Key Takeaways:
Marketing teams have moved quickly to adopt AI. But when it comes to delivering measurable returns on those investments, most organizations are still finding their footing.
For the second consecutive year, agency leaders named AI as their top investment priority, with more than three-quarters planning to increase their AI spend over the next 12 months. However, only about 29% of organizations across sectors say they can dependably measure ROI on their AI initiatives, and CEOs report that just 25% of their AI initiatives have delivered expected ROI.
Right now, the gap between investment and demonstrated impact is significant. Closing it requires a deliberate approach to how tools are selected, how a strong foundation is built before implementation, and how their impact gets translated into stories that resonate with stakeholders.
Getting real returns on AI starts well before a tool is ever deployed. Key steps include:
With a proliferation of AI solutions crowding the market, tool selection has become an increasingly consequential decision. According to Lauren Johnson, Effectiveness Lead at Basis, a deep understanding of how an AI tool works is the starting point for evaluating whether it will deliver meaningful results.
"If we are going to ask AI to help us evaluate datasets, we need to understand how it's executing that task," says Johnson. "What are its outputs based on? What historical data is it drawing from? Marketers need to understand how their tools work in order to assess whether they'll be able to drive the impact they're looking for."
Given the state of the market, deep evaluation is critical: Gartner has warned that many vendors are engaging in “agent washing,” or positioning existing products as agentic AI without adding genuine agentic functionality. Of the thousands of vendors pitching themselves as agentic AI providers, Gartner estimates only around 130 actually deliver on that promise.
A deeper understanding of how AI tools work also makes the case for seeking out specialized solutions over generic ones. Out-of-the-box AI solutions have strengths when used strategically, but specialized tools trained on large volumes of relevant industry data tend to produce more precise and differentiated outputs. An AI media planning tool trained specifically on advertising performance data, for example, can generate cross-channel recommendations grounded in real campaign outcomes—something a general-purpose model isn't equipped to do.
Data quality is a make-or-break factor for AI performance, but most marketing organizations aren't yet where they need to be. Only 21.4% of industry professionals describe first-party data as “foundational” to their organization's AI initiatives, and roughly one-third say first-party data plays little-to-no role in their current AI use at all. Half of leaders also say their businesses don’t have the technical or data stack readiness to support AI agent deployment.
Without clean, unified, accessible data, AI tools produce outputs that are generic at best and misleading at worst—neither of which supports the kind of differentiated results that justify continued investment. Leaders must treat data readiness as a strategic prerequisite. That involves:
Finally, understanding the impact of AI investments requires knowing exactly what success looks like before deployment. “Just like with anything in our world,” says Johnson, “assessing effectiveness starts with setting a goal for what you want the tool to do for you.” If saving time on a specific workflow is the goal, for instance, measure how long that workflow takes today and define a clear target for how much AI should reduce it.
Currently, only 40% of marketing professionals are using or planning to use defined KPIs specifically for their AI solutions. Crafting a phased roadmap for AI adoption, with concrete milestones and tool-specific performance targets, gives teams both a framework for evaluating impact and the foundation for communicating that impact to stakeholders.
How marketing teams use and communicate about their AI investments is just as important as laying the groundwork for them to succeed. The teams best positioned to demonstrate impact tend to:
To achieve maximum value from AI tools, humans must consistently scrutinize what they produce. The technology can provide weak or inaccurate outputs if trained on low-quality data, and of course, there’s the matter of hallucinations: One study found that close to half of marketers spot inaccuracies in AI outputs several times a week.
“AI tools are not going to tell you when they're wrong,” notes Johnson. “Teams need to continually push back against and stress-test AI outputs in order to get real value.”
Leaders should nurture a team culture where pushing back on AI outputs is both expected and encouraged. When that standard is set by leadership and integrated into how teams operate on a daily basis, it drives higher-quality AI usage across the board. Teams that operationalize this approach are better positioned to extract demonstrable value from their AI investments over time.
Proving AI’s value to stakeholders is a significant challenge for marketing teams in 2026. In fact, only 41% of marketers can confidently prove out the ROI of their AI investments, down from 49% in 2025.
This is where the goals and KPIs set during the planning phase become essential. Tracking performance against those benchmarks over time—whether that’s hours saved on a specific task, improvement in campaign performance, or lift in a business outcome—gives teams the evidence they need to make a credible case for AI’s impact.
Beyond quantifying AI’s impact, marketing leaders must strategize around using that evidence to craft compelling narratives for stakeholders. For one thing, while AI can (and should) drive meaningful improvements in speed and cost, leaders should focus more on how their AI investments contribute to business outcomes. AI efficiency gains can be significant and are worth including in these stories, but positioning AI’s value primarily around efficiency risks reinforcing the perception of marketing as a cost center rather than a strategic growth driver.
Connecting investments to business outcomes is also what tends to land with senior decision-makers. The most resonant executive narratives tie major investment asks to concrete business drivers that connect marketing’s AI investments to the revenue and business outcomes that sales and finance leaders care about.
Another key to making those narratives credible is grounding them in a deep understanding of the tools themselves. “Executives want to hear that we're using AI, but they also want to know that it's grounded in real data,” says Johnson. “They want to know what’s powering these tools and that they can trust the outputs.”
That deep understanding is part of what makes an AI narrative credible at the executive level. Leaders who build expertise in how their tools work, what data powers them, and how that functionality is advancing business goals will be poised to earn sustained buy-in.
The ability to drive and demonstrate ROI on AI tools is quickly becoming one of the clearest dividing lines between marketing teams that lead and those that fall behind. Getting there requires a deliberate approach at every stage: selecting tools with a deep understanding of how they work, fueling them with the data infrastructure they need to perform, and translating their impact into narratives that resonate with stakeholders.
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Want more insights on how marketing teams are approaching AI? Our AI and the Future of Marketing report synthesizes findings from a proprietary survey of professionals across leading brands and agencies, covering adoption trends, workforce shifts, and the biggest barriers teams are still working through.
As 2026 progresses, brands are taking a harder look at their agency relationships.
A full 85% of US B2C marketing executives plan to review their media agencies this year. That level of reevaluation reflects a broader shift in what brands want from their agency partners, and what they’re no longer willing to accept.
Agencies are feeling this pressure. Some 65.3% of agency professionals have had clients move work in-house within the last year, and 87.3% say the traditional agency model is broken or heading in that direction. This leaves brands in a difficult position. As the industry transforms, they’re evaluating agency partners against rising expectations—sharper AI judgment, leaner operations, clearer business storytelling—while agencies are still evolving to meet them. In this context, asking the right questions can separate partners built for where the industry is going from those still operating on where it’s been.
Below are five questions worth asking before signing a contract, renewing one, or kicking off a review:
First, brands should start with the structural questions nobody wants to open a pitch with: Who owns the data? Who owns the tech stack? What gets packed up and sent with the brand if the relationship ends, and what stays behind?
This matters more in 2026 than it did even two years ago. The share of full-service and media agencies managing eight or more tools has more than doubled since 2024 (from 22.1% to 46.7%), with more than a third now juggling 10 or more tools. Every additional tool is another place where a brand’s data, historical insights, and optimization learnings can live with the agency rather than with the brand itself. When the agency changes, the brand often has to start from scratch.
The agencies worth partnering with in 2026 are comfortable with portability and clear about ownership. They can articulate exactly what a brand owns, where that data sits, and how it would transfer in a transition. Better still, they’re open to operating inside a brand’s owned media infrastructure rather than requiring the brand to operate inside theirs. When the tools, the data, and the vendor relationships belong to the brand, a roster change doesn’t mean starting over.
As a practical first step, brands can ask for a consultative audit of how an agency’s stack maps to their own. They can explore where it complements, where it duplicates, and what a brand should own directly to protect long-term flexibility, regardless of who handles execution. That distinction signals whether an agency is confident competing on the quality of its strategy and service rather than the stickiness of its tooling.
Asking whether an agency uses AI no longer yields much signal—it’s used at more than 99% of agencies today, with nearly 60% of professionals using it daily. The sharper questions get at how agencies are using the technology, and where they are deciding not to apply it.
Strong answers to these questions will be specific. An agency should be able to name the workflows where AI is adding real value, those where it’s being tested with clear guardrails, and the places where the team has consciously held back. Brands should also expect a clear answer on how an agency protects sensitive data inside AI tools. A vague answer—or worse, a reflexive “AI everything”—is a red flag. AI tools can produce strategies that sound authoritative but aren’t rooted in reality: predictions built on extrapolated data, synthesized “case studies” that don’t reflect actual market outcomes, audience insights the tool inferred rather than verified, etc. When an agency builds recommendations on that kind of output without pressure-testing it, the brand ends up paying the cost.
A stronger approach sounds thoughtful and curious. Take AI-powered search as an example. Some platforms are monetizing chat-based search in ways that could eventually open powerful new audience and targeting opportunities. But right now, the data is a black box and the measurement is thin. An agency that says, “We’re watching this closely. Here’s what we want to see before we recommend it, and here’s how we’ll know when it’s ready,” is showing exactly the kind of judgment a brand is paying for. An agency that says, “Let’s just run ads there and see what happens,” is not.
The same principle applies as agencies adopt agentic AI. A partner that’s using agents to streamline planning cycles or improve reporting, for example, should be able to walk through what the agent is doing, where the underlying logic is coming from, what the human is still doing, and why that split makes sense.
Similar questions apply to how AI is used for ad creation. The IAB acknowledged the industry’s concerns around this use case in early 2026 with its first AI Transparency and Disclosure Framework, citing a widening gap between how advertising executives think consumers feel about AI-generated ads and how those consumers actually feel. Brands should expect their agency partners to have a specific, current point of view on that gap, and for them to be able to explain the strategy behind their approach to using AI for creative generation. Clarity on these questions can help brands discern a meaningful AI strategy from something any agency could offer.
Transparency is a word nearly every agency uses. What it actually delivers in practice is where brands often find a gap. Some 88.3% of agency professionals say the digital advertising industry needs more of it, which is both encouraging and telling: The people closest to the work know there’s a gap worth closing.
Transparency doesn’t just mean a brand gets a dashboard. Dashboards show a brand what happened—real transparency explains the “why” behind what happened. Why was budget shifted between channels last week? Why was a particular audience deprioritized? Why did the team recommend pausing a tactic that was still performing? The “why” is where trust gets built, and it’s what brands should expect from their partners.
Transparency also shows up in how agencies handle pricing. The traditional structures—commission, FTE, billable hours—were built around the time-intensive, often-manual labor AI is now compressing or automating. The agencies adapting fastest are moving toward pricing tied to outputs and outcomes rather than hours, an evolution brands evaluating partners should welcome. Brands should expect the upside of that shift: clear fee structures, visibility into what’s a pass-through cost versus a markup, and flexibility on the pricing model itself, whether that’s project-based, IO-based, a clean percentage of media, or something custom to the engagement. Agencies that treat their pricing as a black box tend to treat a lot of other things that way too.
Over half of agency professionals (54.0%) say their client relationships are more strained today than they were two years ago. Much of that friction traces back to a storytelling gap. Agencies often speak in impressions, clicks, and conversion rates. Brands, on the other hand, answer to CFOs, boards, and a CMO role that has been reshaped in recent years to tie every dollar back to measurable business return.
The capability gap brands should explore with agency partners is the ability to connect campaign performance to business outcomes. In other words, they should look for agencies who can move from “We drove a 12% lift in CTR” to “We drove a 12% lift in CTR, and here’s how that shaped pipeline. Here’s what it suggests about audience intent, and here’s what we’d recommend next based on the business context you’ve shared with us.” That kind of translation and storytelling is the work agencies increasingly have to own, as connecting the dots between media activity and business impact is where real value gets created.
The agencies positioned to do this work well tend to have one thing in common: They’ve invested in connected infrastructure that pulls their work together across channels, so their teams aren’t spending the majority of their time stitching together data from disconnected tools. When the foundation is sound, human attention can shift to interpretation and strategy, which is where brands are actually trying to buy value in the first place.
Though a simple question, it’s a meaningful one. The top performing agencies in 2026 are proactive partners. They bring POVs on industry shifts before the brand has to chase them down. They flag emerging channels with a clear stance on whether they’re worth testing and why. They raise risks early, even if those risks might reduce their own scope of work.
A reactive relationship is one a brand has to manage, whereas a proactive one is one a brand can lean on. The difference shows up in small moments, like whether a weekly check-in brings new ideas to the table or just rehashes last week’s performance. But those small moments add up. Over a year, they can be the difference between an agency acting as a vendor and one acting as an extension of the brand’s own team.
The strongest version of this dynamic feels less like a brand managing an agency and more like a brand working with a trusted partner who’s actively looking out for its bottom line.
Brands are investing in digital advertising to drive business impact, and agencies too often deliver activity instead. Closing that gap, between what’s happening in a campaign and what it means for the business, is the real work of partnership in 2026.
The industry is in the middle of a structural reset. Revenue models are under pressure, AI is redefining what agency labor actually looks like, and brands have more options for how to get the work done. The brands that come out of this period with the strongest partnerships will be the ones asking the sharpest questions and holding out for agencies whose answers line up with how their business runs.
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Interested in a deeper look at what’s reshaping the agency business in 2026, including how agencies are thinking about AI, tech stack consolidation, and the future of their revenue models? Explore the 2026 Advertising Agency Report.