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Ad fraud is no longer a back-office concern. It’s a line item in every media director’s risk calculus, and it’s growing faster than the budgets used to fight it. Global advertisers lost an estimated $63 billion to invalid traffic in 2025, with roughly 8.5% of all paid digital traffic flagged as invalid—bots, automated scrapers, malicious competitor clicks, and synthetic engagement that drains budgets and corrupts the optimization signals AI-driven campaigns depend on.
That figure is on a steep upward curve. Global ad fraud losses are expected to reach $172 billion by 2028 as bot networks adopt generative AI and agentic automation. For agencies managing multi-million-dollar client portfolios, fraud protection has grown from a checkbox feature into a critical vendor-selection criterion. The DSP you choose either defends ad spend against this growing threat or quietly funnels a share of every campaign budget into the fraud economy.
This guide compares six leading demand-side platforms on the dimensions that matter most for brand safety and ad fraud protection: certification posture, IVT filtering methodology, verification partner integrations, and how each platform handles emerging fraud vectors like signal loss and agentic bot traffic.
| Platform | Core fraud protection approach | Best for |
| Basis | AI-powered inventory cleansing combined with human monitoring; integrated verification across Comscore, DoubleVerify, Peer39, and Protected by Mediaocean | Agencies and brands that want unified ad fraud and brand safety controls integrated across programmatic, social, search, and direct |
| The Trade Desk | Pre-bid filtering with IAS, DoubleVerify, and HUMAN Security; supply path optimization through OpenPath | Enterprise programmatic teams with dedicated ad ops resources |
| DV360 | Google-owned verification, Active View viewability, and third-party integration with major verification vendors | Performance teams operating primarily within the Google ecosystem |
| Amazon DSP | Proprietary fraud detection on Amazon-owned inventory; third-party verification on off-Amazon supply | Retail and e-commerce advertisers buying primarily within Amazon’s ecosystem |
| StackAdapt | Pre-bid and post-bid filtering through DoubleVerify, IAS, and Peer39 integrations | Mid-sized agencies running programmatic on the open web |
| Viant | Household ID-based verification, AI Lattice Brain anomaly detection, third-party verification partners | CTV-heavy buyers prioritizing identity-based measurement |
Ad fraud protection is the set of technologies, certifications, and operational controls that detect and block invalid traffic, fraudulent inventory, and brand-unsafe placements before, during, and after a campaign runs. It spans pre-bid filtering, post-bid analysis, supply path optimization, and third-party verification, and it determines how much of an advertiser’s media spend actually reaches real human audiences.
The financial stakes have changed the conversation. When fraud losses sat at 5% to 8% of media spend, many advertisers still treated brand safety as a compliance step. At today’s projected loss rates, fraud exposure can rival the margin on a mid-sized account. Industry research from Fraudlogix, based on analysis of 105.7 billion impressions, found a global invalid traffic rate of 20.64%— in other words, roughly one in five impressions across the open programmatic ecosystem showed signals consistent with fraudulent or non-human activity.
Agencies that run campaigns on a DSP without robust fraud controls are paying a fraud tax measured against client revenue, reporting decks that omit IVT filtering data are overstating performance, and contract renewal conversations now include increasingly pointed client scrutiny on how each platform protects budget against fraud exposure.
Ad fraud is the deliberate practice of generating fake impressions, clicks, or conversions to extract payment from advertisers without delivering real human engagement. The Media Rating Council (MRC) defines its detection scope using two categories of invalid traffic:
Within those categories, the fraud techniques most relevant to programmatic buyers include:
Domain spoofing: Fraudulent supply sources misrepresent the inventory they’re selling, claiming impressions from a premium publisher when ads actually serve on a low-quality or fraudulent site. The ads.txt and sellers.json standards from IAB Tech Lab were created specifically to verify which intermediaries are authorized to sell a given publisher’s inventory.
Ad stacking: Multiple ads are layered on top of each other within a single placement, with only the top ad visible. Advertisers pay for impressions that no human ever sees.
Pixel stuffing: Ads are served into a 1x1 pixel space—technically loaded and counted as impressions, but invisible to users.
Click farms and bot networks: Coordinated networks generate clicks and engagement signals at scale, often from real devices manipulated through malware or paid human operators.
Made-for-advertising (MFA) sites: Low-quality websites built primarily to harvest ad revenue rather than serve users. These sites often pass basic fraud filters while delivering near-zero campaign value.
The newest fraud vector is agentic AI bot traffic: Autonomous systems that mimic human browsing patterns, including scrolling, hesitation, and form interaction. These bots are designed specifically to defeat traditional pattern-based detection, and they’re already showing up in CTV and mobile environments, where verification infrastructure is less mature. To cite just one example, DoubleVerify detected 140% more CTV fraud schemes in Q1 2026 than Q1 2025, identifying more than 50 distinct bot attacks and variants in 2025 alone.
Programmatic fraud protection operates at three points in the campaign lifecycle, and you can measure a DSP’s strength by how it handles all three.
Pre-bid filtering: Before a bid is placed, the DSP screens the inventory request against blocklists, IVT signal libraries, ads.txt/sellers.json verification, and supply path metadata. Inventory that fails the screen is filtered out, and no bid is placed. Pre-bid filtering is widely considered the most effective fraud defense because it prevents wasted spend at the source.
In-flight monitoring: During campaign delivery, the platform continuously analyzes impression-level signals—device fingerprints, behavioral patterns, supply path consistency, viewability data—and dynamically adjusts buying behavior. Suspicious supply sources are throttled or suspended, and campaign budgets are reallocated to verified inventory.
Post-bid analysis and reporting: After delivery, the platform reconciles served impressions against fraud verification data, identifies invalid traffic that slipped through pre-bid filters, and generates reporting that quantifies the IVT rate. Strong post-bid analysis enables agencies to recoup wasted spend through make-good negotiations and to refine future supply path decisions.
Each layer requires both proprietary technology and third-party verification. No DSP can credibly verify its own fraud filtering rates without independent measurement, which is why integration with established verification vendors should be a baseline requirement when considering any programmatic platform.
The decline of third-party cookies and traditional identifiers has reshaped the fraud landscape in ways many media teams underestimate. As deterministic signals erode, fraud detection systems have less data to work with—and fraudsters have more room to operate.
Traditional fraud detection relies heavily on cross-site behavioral signals, device graphs, and identity-based pattern recognition to distinguish human users from sophisticated bots. When those signals weaken, detection accuracy weakens with them, and bots that previously failed cross-site consistency checks now operate in environments where those checks no longer apply.
The shift to alternative identity frameworks has introduced its own exposure. Some publisher-side IDs and probabilistic graphs are easier to spoof than legacy device IDs, particularly when verification infrastructure hasn’t caught up. Platforms that have invested in privacy-resilient measurement, contextual targeting infrastructure, and AI-based behavioral detection will hold up better as signal loss accelerates, but DSPs that depend heavily on legacy identity signals are exposed on two fronts, as addressability continues to shrink and fraud detection degrades simultaneously.
Evaluating a DSP for fraud protection means going beyond marketing claims and asking for documentation of three things: certifications, verification partnerships, and operational controls.
MRC accreditation: The Media Rating Council audits and accredits measurement methodologies, including impression counting, viewability, and invalid traffic filtration. A DSP or verification vendor with current MRC accreditation has been independently audited against published standards. Note that accreditation is product-specific—for example, a vendor may be accredited for desktop display IVT filtration, but not for CTV—so be sure to ask which specific measurements are accredited, and which are not.
IAB Tech Lab standards: Ads.txt and sellers.json are the industry’s authoritative supply chain transparency standards. Ads.txt files allow publishers to specify which sellers are authorized to represent their inventory. Sellers.json lets buyers verify the identity of every intermediary in the supply path. DSPs that crawl and enforce these standards as part of pre-bid filtering can identify and block unauthorized resellers and many forms of domain spoofing before a bid is placed.
SOC 2 Compliance: SOC 2 reports cover a platform’s security, availability, processing integrity, confidentiality, and privacy controls. While SOC 2 doesn’t directly measure fraud filtering effectiveness, it’s a baseline indicator of operational maturity, and is particularly relevant for compliance teams evaluating data handling, access controls, and incident response.
Independent verification vendors—such as DoubleVerify, Integral Ad Science (IAS), HUMAN Security, Peer39, Comscore, and Protected by Mediaocean—provide the third-party measurement that turns a DSP’s fraud claims into auditable data. The strongest DSPs offer:
Beyond certifications and partner logos, the operational details that separate strong fraud defense from weak fraud defense come down to a handful of practical questions worth asking every vendor:
That last item matters more than most agencies realize. Some platforms charge separately for pre-bid fraud blocking, which means the baseline product includes meaningfully less protection than the platform’s marketing suggests.
The platforms below represent the leading options agencies and brands evaluate when fraud protection is a primary buying criterion. Each entry covers the platform’s fraud detection methodology, brand safety controls, certification posture, and verification partnerships.
Basis is an AI-powered advertising platform built for how agencies operate, consolidating campaign planning, programmatic, social, search, direct deals, reporting, and billing into a single platform. That consolidation matters for fraud protection because brand safety controls and verification data flow through the same workflow as campaign activation—rather than living in separate dashboards that require manual reconciliation.
Fraud detection methodology: Basis combines AI-powered automated inventory cleansing with human monitoring to filter fraudulent and questionable traffic before a bid is placed. The platform’s pre-bid blocking incorporates IAB/ABC Spiders and Bots User Agent Lists, Pixalate data sets, ads.txt crawling, and proprietary fraud signal data. Suspicious inventory is filtered at the bid request layer, and ongoing monitoring continues throughout campaign delivery.
Brand safety controls: Basis applies pre-bid brand protection layers and filters ahead of campaign launches, with post-bid analysis and block-list application as a second layer of defense. Advertisers can configure controls at the advertiser, campaign, and placement level.
Verification partnerships: Basis integrates with Comscore, DoubleVerify, Peer39, and Protected by Mediaocean for third-party brand safety and verification. The Protected by Mediaocean integration brings AI-driven media quality, brand safety, and attention signals directly into Basis’ campaign activation workflows, enabling real-time verification inside the same interface used to plan, buy, and optimize campaigns. The integration eliminates the operational gap that exists when verification data lives outside the activation environment, as media quality controls become part of the buying decision in flight (rather than a post-campaign audit.)
Compliance posture: Basis is SOC 2 compliant, with documented controls across security, availability, and confidentiality. The platform’s commitment to supply chain transparency includes active enforcement of ads.txt and sellers.json standards.
Basis is strongest for: Agencies and brands that want fraud protection and brand safety controls integrated into a unified workflow that spans programmatic, social, search, and direct, with verification data and campaign management in the same platform.
The Trade Desk is one of the most technically capable programmatic DSPs on the market, with broad CTV inventory access and a long-standing focus on supply path transparency. Its fraud protection posture reflects that enterprise orientation.
Fraud detection methodology: The Trade Desk applies pre-bid filtering across its supply, with proprietary detection augmented by integrations with major verification vendors. The platform’s OpenPath initiative emphasizes direct publisher integrations and supply path simplification as a structural defense against fraudulent intermediaries.
Brand safety controls: Pre-bid brand suitability segments are available through IAS, DoubleVerify, and HUMAN Security. Custom blocklists and category-level controls are configurable at the campaign level.
Verification partnerships: Integrations with IAS, DoubleVerify, and HUMAN Security cover pre-bid filtering and post-bid measurement.
Limitations to weigh: The Trade Desk is a programmatic-only platform. Fraud protection within The Trade Desk only covers the programmatic portion of an agency’s media mix—search, social, and direct buys run through separate systems with separate brand safety controls. The Kokai interface and the platform’s overall complexity require dedicated ad ops resources to configure and manage fraud settings effectively, and some advanced features trigger additional fees that can compound across campaigns.
The Trade Desk is strongest for: Large enterprise agencies running high-volume programmatic programs with dedicated ad ops resources and clients whose media mix is weighted toward programmatic channels.
DV360 is Google’s enterprise programmatic platform, part of the Google Marketing Platform. Its fraud and brand safety posture benefits from Google’s scale and infrastructure—and inherits the constraints of operating inside the Google ecosystem.
Fraud detection methodology: Google’s proprietary invalid traffic filtration operates across DV360 inventory, with Active View viewability measurement integrated natively. Fraudulent and non-human traffic is filtered pre-bid and reconciled post-bid through Google’s own measurement systems.
Brand safety controls: DV360 offers content category targeting and exclusion, keyword blocklists, and inventory-level filtering. Integration with IAS, Scope3, DoubleVerify, and HUMAN Security is confirmed for pre-bid fraud filtration and brand safety verification.
Limitations to weigh: DV360 prioritizes Google-owned environments, and verification flexibility on YouTube and other Google properties is more constrained than on the open programmatic ecosystem. Access requires a Google Marketing Platform contract with practical spend thresholds that exclude smaller agencies. DV360 is also not a full agency workflow platform—paid social, direct buys, and billing run on separate systems.
DV360 is strongest for: Performance advertisers managing Google-heavy campaigns who need YouTube inventory access and have the team to manage the platform’s technical complexity.
Amazon DSP offers exclusive access to Amazon’s shopping and streaming data, with fraud protection structured around that closed ecosystem.
Fraud detection methodology: Inventory served on Amazon-owned properties—such as Prime Video, Twitch, and Fire TV—benefits from Amazon’s first-party fraud detection across logged-in user environments. Logged-in identity reduces certain fraud vectors significantly. Off-Amazon programmatic inventory bought through Amazon DSP uses third-party verification and pre-bid filtering through standard verification vendors.
Brand safety controls: Pre-bid brand suitability targeting is available, with controls configurable at the campaign level. Inventory-level filtering and exclusion lists are supported.
Verification partnerships: Amazon DSP supports integration with major third-party verification vendors for off-Amazon inventory measurement.
Limitations to weigh: Amazon DSP operates as a walled garden. Data generated within Amazon’s ecosystem stays within it, which limits cross-platform measurement and audit options. The platform is built for commerce verticals, and agencies with clients outside retail and CPG may find the platform’s core data advantage less applicable. Self-service carries flexibility, while managed service requires a $50,000 monthly minimum.
Amazon DSP is strongest for: Retail and e-commerce advertisers buying primarily within Amazon’s ecosystem who benefit from logged-in user verification and Amazon’s commerce data.
StackAdapt is a self-serve programmatic DSP with a footprint across CTV, DOOH, display, native, audio, and in-game.
Fraud detection methodology: StackAdapt applies pre-bid filtering through integrations with leading verification vendors, supplemented by proprietary supply path controls.
Brand safety controls: Pre-bid and post-bid brand safety segments are available through DoubleVerify and IAS integrations. Inventory and category-level exclusions are configurable. Unlike many DSPs that operate proprietary IVT detection layers, StackAdapt relies primarily on third-party verification vendors for fraud filtering, which can limit detection depth on emerging fraud patterns not yet covered by integrated partners.
Limitations to weigh: StackAdapt is programmatic-only—no search or social campaign management—so brand safety controls cover one portion of an agency’s media mix. The platform also lacks the agency workflow layer (billing, reconciliation, financial operations) needed to manage fraud exposure as part of a unified operational picture across all client buys.
StackAdapt is strongest for: Mid-sized agencies prioritizing programmatic execution with select third-party verification on the open web.
Viant is a CTV-focused programmatic DSP with a deterministic identity infrastructure built around its Household ID system. The platform’s fraud protection approach is tied closely to its identity-based measurement model.
Fraud detection methodology: Viant’s AI Lattice Brain analyzes campaign data for anomalies and fraudulent traffic patterns, with verification supplemented by DoubleVerify and IAS integrations across CTV and display. Its Household ID system links household-level identity to connected devices within that database, which may help strengthen verification signal density in CTV environments.
Brand safety controls: Pre-bid brand safety filtering and verification integrations are available across CTV, display, and other supported channels.
Limitations to weigh: Viant is programmatic-only, with no search, social, or direct buying capabilities. Additionally, the platform’s autonomous “Outcomes” product reduces trader control over optimization decisions, which can be a concern for agencies that want hands-on visibility into how fraud signals influence buying behavior. Viant’s smaller scale relative to larger DSPs may also raise platform-stability questions for agencies underwriting long-term enterprise commitments.
Viant is strongest for: CTV-focused advertisers prioritizing identity-based measurement and AI-driven optimization within the connected TV ecosystem.
Three patterns separate DSPs with mature fraud protection from those with marketing claims:
That last point is where the broader operational picture intersects with fraud defense. Agencies running fragmented stacks across multiple DSPs, separate social and search tools, and disconnected verification dashboards lose visibility at the seams. Fraud signals that surface in one system may never reach the team making buying decisions in another. The agencies with the cleanest fraud protection postures are typically the ones operating from unified platforms, not the ones stacking up the most verification vendor logos.
For agencies and brands evaluating DSPs on fraud protection, a structured audit framework helps separate marketing claims from operational reality. The following questions are worth asking every vendor under consideration before contract renewal:
The answers separate platforms that have built fraud protection into their operational fabric from platforms that bolt verification onto baseline product capabilities. For agencies with multi-million-dollar client portfolios, the difference is measured directly against client trust.
Basis is built for unified fraud protection across every channel an agency runs, with verification data and campaign management in the same platform.
What is ad fraud protection and how does it work in programmatic advertising? Ad fraud protection is the set of technologies and operational controls that detect and block invalid traffic, fraudulent inventory, and brand-unsafe placements across the campaign lifecycle. In programmatic environments, protection operates pre-bid (filtering bid requests against fraud signal libraries before bidding), in-flight (continuous monitoring during delivery), and post-bid (reconciling served impressions against verification data after delivery). The strongest protection combines proprietary platform detection with third-party verification from vendors like DoubleVerify, IAS, Peer39, and Protected by Mediaocean.
Which DSPs offer the strongest ad fraud protection and brand safety features in 2026? The leading DSPs for fraud protection in 2026 include Basis and The Trade Desk, as well as DV360, Amazon DSP, StackAdapt, and Viant. Basis differentiates by integrating fraud protection and brand safety controls across programmatic, social, search, and direct from a single platform—including verification partnerships with Comscore, DoubleVerify, Peer39, and Protected by Mediaocean. The Trade Desk, DV360, and Viant focus on programmatic; Amazon DSP focuses on its closed commerce ecosystem; StackAdapt focuses on self-serve programmatic on the open web.
What is the difference between ad fraud protection and brand safety? Ad fraud protection focuses on filtering invalid traffic, fraudulent inventory, and non-human engagement to ensure ads reach real audiences. Brand safety focuses on controlling the content environment ads appear in, keeping campaigns away from inappropriate, controversial or off-brand contexts. The two functions overlap operationally (most verification vendors offer both) but address different risks: fraud protection defends spend, while brand safety defends reputation.
How do MRC accreditation and IAB Tech Lab standards protect against ad fraud? MRC accreditation independently audits a platform’s measurement methodologies—including impression counting, viewability, and invalid traffic filtration—against published standards. A DSP or verification vendor with current MRC accreditation has had its specific measurements independently validated. IAB Tech Lab standards like ads.txt and sellers.json create supply chain transparency by letting publishers declare authorized sellers and letting buyers verify every intermediary in the supply path. Together, MRC accreditation and IAB Tech Lab enforcement provide independent validation that a platform’s fraud filtering does what it claims to do.
What types of invalid traffic does ad fraud protection software detect and block? Ad fraud protection detects two categories of invalid traffic. General Invalid Traffic (GIVT) includes known bots, spiders, data center traffic, and pre-listed non-human user agents identifiable through routine filtration. Sophisticated Invalid Traffic (SIVT) includes hijacked devices, falsified location signals, manipulated measurement, and bots designed to mimic human behavior—including the newest generation of agentic AI bots that simulate scrolling, hesitation, and form interaction. Detecting SIVT requires advanced behavioral analytics, machine learning, and human review.
How can advertisers measure whether their DSP’s ad fraud protection is actually working? Effective measurement requires independent third-party verification rather than relying on the DSP’s self-reported data. Verification vendors—DoubleVerify, IAS, HUMAN, Peer39, Protected by Mediaocean—measure IVT rates, viewability, and brand safety performance independently of the DSP, providing an audit layer advertisers can use to validate platform claims. Strong fraud protection postures publish IVT rates, support pre-bid integration with multiple verification vendors, and surface verification data inside the same interface used for campaign management.
How does third-party cookie deprecation increase ad fraud exposure? Cookie deprecation weakens the deterministic signals fraud detection systems rely on to distinguish human users from sophisticated bots—for example, cross-site behavioral patterns, device graphs, and identity-based pattern recognition. As those signals erode, detection accuracy degrades. Bots that previously failed cross-site consistency checks now operate in environments where those checks don’t apply, and CTV and mobile in-app environments show higher IVT rates as a result. Platforms that have invested in privacy-resilient measurement and AI-based behavioral detection are better positioned to maintain detection accuracy as signal loss accelerates.
Can a DSP’s built-in fraud detection replace dedicated verification tools? No DSP can credibly verify its own fraud filtering rates without independent measurement. Built-in fraud detection is the first line of defense, but third-party verification from vendors like DoubleVerify, IAS, HUMAN, Peer39, and Protected by Mediaocean provides the independent audit layer that lets advertisers validate platform claims. The strongest fraud protection postures combine robust DSP-native detection with deeply integrated third-party verification, not just one or the other.
What questions should agencies ask DSP vendors about fraud protection before contract renewal? The questions that matter most include: Which specific measurements have current MRC accreditation? Which verification vendors are integrated pre-bid versus post-bid? Are pre-bid blocking controls included in base pricing or do they trigger additional fees? Does the platform actively crawl and enforce ads.txt and sellers.json? Are blocklists configurable at the advertiser and campaign levels? Is the platform SOC 2 compliant? These questions separate platforms with operational fraud protection from platforms with marketing claims.
On this episode of AdTech Unfiltered, host Noor Naseer sits down with Frank-Lamont Wade, Director of Performance Marketing at the University of Phoenix, to discuss what it takes to run a high-performing, accountable performance marketing operation.
Frank shares insights on balancing lead quality with volume, navigating long customer acquisition timelines, refining measurement and attribution models, and building collaborative testing cultures across media channels. The conversation also explores the critical thinking skills modern performance marketers need to thrive in an increasingly complex advertising environment. Frank draws on years of experience in higher education, but the strategies and insights he shares apply across industries and verticals.
Ronk Communications used Basis to expand Colorado State University Global's programmatic presence across display, audio, and CTV, uncovering new audiences to reach and increasing spend to 63% YoY while maintaining a $6.18 eCPA.
Ronk Communications is a media buying agency for high-performing digital, social and traditional ad campaigns across events, venues, government and higher education. Their client, Colorado State University Global (CSU Global), is the nation's first fully accredited, 100% online state university, dedicated to providing top-ranked, affordable Bachelor’s and Master’s degree programs with the flexibility students need to succeed
Colorado State University Global engaged Ronk Communications to drive prospective students into its enrollment funnel through paid media. A 2024 test delivered strong results to justify a larger 2025 investment, with a goal of scaling spend while finding new audience signals.
With a 63% budget increase and more markets to crack, Ronk Communications needed tighter campaign control, smarter optimization, and a partner that could support an evolving strategy in real time.
Ronk Communications used Basis to expand CSU Global's targeting beyond Denver into California and Colorado Springs, layering in new audience strategies, PMPs, and SmartBid optimization throughout the flight.
The results:
"Ronk Communications is armed to service a major advertiser like Colorado State University Global and exceed their expectations with a multichannel, high-performing ad campaign thanks to our trusted partnership with Basis. We are supported every step of the way whether it comes to high-quality inventory, brand protection, targeted audiences, pixels, reports, data providers or vendor collaboration. Basis is a part of your team, making you compete with any sized ad agency." - Ronk Communications
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.
AI-powered strategic media planning tools are software platforms that analyze client briefs and automatically generate structured media plan drafts, including channel recommendations, budget allocations, and strategic guidance.
These tools address one of the biggest challenges facing agencies today: inefficient planning processes that drain time, create inconsistency across teams, and prevent planners from focusing on strategic work. As AI adoption accelerates across the industry, understanding how these tools work and who benefits most from them matters for teams looking to stay competitive.
Key Takeaways
An AI-powered strategic media planning tool is software that analyzes client briefs and generates a structured media plan draft, including channel recommendations, budget allocations, and tactical suggestions.
This technology works by interpreting natural language from client briefs and matching campaign goals with industry benchmarks and channel-specific insights. Rather than starting from a blank page, planners receive a structured plan they can refine and customize based on client needs as well as their own strategic and creative judgment.
For example, a tool like Compass by Basis reads a client brief, identifies campaign objectives and target audiences, and generates a complete omnichannel media strategy—including prioritized audience segments, competitive context, channel-by-channel budget allocations with visual breakdowns, and campaign flighting—all within a conversational interface inside the platform where campaigns get activated. Planners can refine the strategy through follow-up prompts before building it into client deliverables or moving into activation.
Though powered by many of the same technologies, these tools differ from more general AI assistants or chatbots because they're purpose-built for media planning workflows. They understand advertising terminology, channel dynamics, and how to structure plans that translate directly into campaign execution. Compass, for instance, is built on Basis’ proprietary IMPACT omnichannel framework—a methodology used across thousands of successful media campaigns—rather than relying on generic AI reasoning alone.
According to the IAB State of Data 2025 report:
And, data from Basis’ 2026 Advertising Agency report finds:
This gap highlights where AI adoption has lagged most: operational planning, not creative execution.
Agencies who begin to implement AI toward operational inefficiencies now can gain a strategic edge, freeing up their teams to focus on strategy rather than manual tasks.
AI planning tools follow a structured process to convert briefs into actionable media strategies:
1. Brief Upload and Extraction
The planner uploads a client brief—whether a structured planning document or a simple prompt—and the tool extracts and summarizes key information. This includes campaign objectives, target audiences, budget parameters, KPIs, geographic focus, and timing constraints. In Compass, this extraction step is visible in the interface, so planners can confirm the tool understood the brief correctly before strategy generation begins.
2. Audience Strategy and Prioritization
The tool builds prioritized audience segments based on the brief, going beyond basic demographics. Each segment includes targeting rationale, recommended channels for reaching that audience, and messaging direction. This creates a strategic foundation where audience strategy, channel selection, and messaging are connected from the start.
3. Strategic Framework and Competitive Context
The tool generates a broader strategic framework that includes competitive context, key challenges, and a recommended approach, rather than just a channel list. This strategic layer guides the channel and budget recommendations that follow, grounding them in campaign-specific logic rather than general best practices. The framework also documents the channels the tool evaluated but chose not to recommend, with the reasoning behind each decision. This gives planners a defensible rationale to share with clients, showing that the recommended mix reflects deliberate trade-offs rather than default choices.
4. Channel Mix and Budget Allocation
The tool recommends a channel mix with specific budget allocations, including dollar amounts and percentage breakdowns with rationale for each channel. Visual outputs like budget allocation charts make it easy to see how spend is distributed and share recommendations with stakeholders. The tool can also generate multiple budget scenarios at different investment levels, each with its own channel allocation and rationale. When a client adjusts the budget or asks to see options, planners have ready-made tiers to work from instead of rebuilding the plan each time.
5. Campaign Plan and Flighting
AI maps out campaign flighting with budget allocation by phase, accounting for seasonal moments, tentpole events, and how different channels should ramp up or down throughout the flight. Channel-specific timing guidance ensures the plan reflects real-world campaign dynamics, not just even budget distribution.
6. Measurement and KPI Framework
The tool builds a measurement framework that maps KPIs to each channel’s role in the funnel, complete with relevant benchmarks. Planners get primary and secondary metrics for each channel, along with the business outcome each metric ladders up to. Having these benchmarks built in saves planners from researching performance standards across channels and gives clients a clear view of how success will be measured from the start.
7. Refinement and Strategy Delivery
The strategy generates within a conversational interface where planners can ask follow-up questions, request deeper analysis on specific sections, or adjust recommendations through natural prompts. The result is a complete, structured strategy that planners can refine and use to build client-ready deliverables. Because Compass lives inside the Basis platform, the strategy and eventual campaign activation share the same system, reducing the manual handoffs and reformatting that typically separate planning from execution.
When orchestrated by agentic AI media planning tools, this entire process can happen in minutes. What traditionally required multiple planning sessions, spreadsheet modeling, and cross-referencing past campaigns now generates automatically, giving agency talent more time to focus on strategic refinement and client-specific nuances.
AI media planning tools deliver several concrete benefits that address some of agencies’ most pressing operational challenges:
The time savings from AI planning tools come from automating specific tasks that eat up planners' days.
Take benchmark research. Manually researching industry benchmarks for CPMs, CTRs, and conversion rates across different channels takes significant time. AI tools have this data built in and automatically apply relevant benchmarks based on campaign parameters.
Budget modeling works similarly. Testing different budget scenarios manually requires rebuilding spreadsheets for each variation. AI tools can generate multiple budget allocation models instantly, letting planners compare approaches without manual calculation work.
Channel analysis is another time sink. Evaluating which channels make sense for a specific audience and campaign goal requires cross-referencing multiple data sources. AI planning tools synthesize this analysis automatically, presenting channel recommendations with supporting rationale.
Then there’s plan documentation—formatting decks, documenting strategic rationale, and creating presentation-ready outputs. AI-powered planning tools produce formatted plans that planners can review and refine rather than building from scratch.
With so many manual tasks wrapped up in drafting media plans, time savings derived from using AI-powered planning tools can add up fast. For instance, teams can create media plans 50% faster when using Compass by Basis, and that time savings can then shift to strategic consultation, client communication, or campaign optimization.
Consistency in media planning creates several advantages for agencies:
Standardized Strategic Approach: AI tools encode best practices into their planning logic. Every plan starts from the same strategic foundation: proven frameworks for audience targeting, channel selection, and budget allocation. This doesn't mean every plan looks identical, but it ensures no planner misses critical strategic considerations.
Quality Baseline for Junior Planners: Junior team members can often struggle without senior guidance. AI tools give them access to senior-level strategic thinking, helping them develop better plans while learning. The tool serves as a training resource that improves plan quality across experience levels.
Reduced Errors: Manual planning risks introducing errors such as calculation mistakes, overlooked channels, and misallocated budgets. AI tools eliminate these mechanical errors, catching issues before plans reach clients. This improves client trust and reduces the costly back-and-forth of fixing mistakes.
Scalable Quality Control: As agencies grow, maintaining consistent plan quality becomes harder. AI tools scale that quality automatically—the hundredth plan generated gets the same strategic rigor as the first.
These consistency benefits matter even more when you consider the tech stack complexity most agencies face. More than one-third (36.8%) of agencies now juggle 10+ tools in their tech stack—up dramatically from 17.3% in 2024—and managing that many disconnected systems can create inconsistency. When AI planning tools integrate into unified platforms where planning connects directly to activation, consistency extends beyond plan creation into execution. The fewer handoffs between systems, the fewer opportunities for plans to get lost in translation.
One concern about AI tools is the "black box" problem, i.e., a lack of visibility or understanding around how the AI reaches its recommendations. But well-designed AI planning tools address this through transparency features, providing rationale into their reasoning as well as ample opportunities for human interaction, iteration, and oversight.
IAB research finds that 51% of brands worry they don't have enough transparency about how agency partners use AI. Transparent AI tools that clearly show their work help address this concern: Agencies can demonstrate their value and provide visibility into their strategic process.
The key difference between manual and AI-powered media planning is how planner time is allocated: manual planning prioritizes mechanics, while AI planning prioritizes strategy.
| Aspect | Manual Media Planning | AI-Powered Media Planning |
| Speed of Drafting Initial Plan | Hours to days per campaign | Minutes per campaign |
| Benchmark Research | Manual lookup across multiple sources | Automatic application of relevant benchmarks |
| Consistency | Varies by planner experience and approach | Standardized strategic framework across all plans |
| Budget Modeling | Manual spreadsheet work for each scenario | Instant generation of multiple allocation models |
| Junior Planner Support | Depends on senior availability for guidance | Built-in access to senior-level strategic thinking |
| Measurement Setup | Research benchmarks and build KPI framework manually | KPI framework with channel-level benchmarks generated automatically |
| Error Rate | Higher risk of calculation and oversight errors | Reduced mechanical errors |
| Time Allocation | More time on mechanics, less on strategy | More time on strategy, less on mechanics |
| Scalability | Requires adding planners to handle more volume | Same team handles increased planning volume |
| Knowledge Transfer | Lost when team members leave | Captured in the tool |
| Planning-to-Activation Handoff | Manual export, reformatting, and rebuilding in activation platform | Strategy built inside the same platform where campaigns get activated |
Using AI-powered media planning tools doesn’t mean replacing planners. Rather, it allows planners more time to scale their work effectively and efficiently, while simultaneously providing them with more time to focus on the deep, strategic work best completed by humans. Manual planning forces planners to focus on mechanical tasks. AI planning shifts that time to strategic consultation, creative collaboration, and client relationship building.
This shift matters because 54.0% of agencies report more strained client relationships compared to two years ago. When planners spend less time on administrative work, they have more capacity for the client-facing strategic work that strengthens relationships.
AI-powered media planning tools are best suited for agencies and brands managing planning complexity, scale, or constrained resources.
Agencies managing multiple clients across various industries handle significant planning volume. AI tools help these agencies scale planning operations without proportionally scaling headcount, improving profitability while maintaining quality. They're particularly valuable when agencies need to pitch new business quickly or accommodate compressed timelines.
Organizations adding junior planners benefit from AI tools that give newer team members strategic scaffolding. Instead of requiring constant senior oversight, junior planners can produce quality work more independently while learning planning fundamentals.
Any agency where inefficient processes or disconnected systems create operational friction will benefit from AI planning tools. Given that 48.9% of agency leaders cite inefficient processes as their top challenge, this includes a significant portion of the industry.
AI planning tools deliver the most value when integrated into platforms where planning connects directly to activation. When the same system that generates the plan also executes it, data flows seamlessly—no manual transfers, no disconnected spreadsheets, no reconciliation work. This integration addresses the silos/disconnected systems problem that 40.4% of agencies identify as a major challenge.
The ideal scenario combines AI-powered planning with all-channel activation capabilities and AI that extends across the entire media buying process, from brief to activation to optimization. When these capabilities exist within a single platform rather than requiring multiple point solutions, agencies avoid the tech stack bloat that creates new inefficiencies (or accentuates existing ones).
Agencies looking to adopt AI-powered media planning tools should follow a structured approach:
Document how much time planning currently takes and where bottlenecks exist. Identify which parts of the planning process consume the most time and which would benefit most from automation. This assessment creates a baseline for measuring improvement.
Don't add another disconnected tool to an already complex tech stack. Look for AI planning capabilities that integrate with existing systems or exist within unified platforms. The planning tool should connect seamlessly to wherever campaigns get activated—whether that's programmatic buying, publisher-direct placements, or search and social platforms (or ideally, a platform that combines all of these in one).
Test AI planning on a small set of campaigns before rolling out across all clients. Choose campaigns that represent typical planning challenges, such as finding the right mix of channels, moderating complexity, and meeting realistic timelines. This pilot phase helps teams learn the tool and build confidence before scaling.
Invest in training so planners understand what the tool can do and how to refine its outputs effectively. Focus on explaining the logic behind recommendations so planners can make informed decisions about when to accept, modify, or override AI suggestions.
Create clear workflows for how AI-generated plans get reviewed and approved. Define who validates outputs, what criteria determine plan quality, and how feedback gets incorporated to improve future plans. This process maintains quality control while scaling efficiency.
Track metrics that matter: planning time per campaign, error rates, client feedback on plan quality, and planner satisfaction. These measurements justify the investment and identify areas for continued optimization.
Once the pilot proves successful, expand AI planning to additional teams and client accounts. Gradual rollout allows for learning and refinement without disrupting operations.
The investment priority is clear in the data: 77.7% of agency leaders plan to increase AI investment in the next 12 months, with automation tools tied for the second priority at 44.7%. Agencies moving quickly on AI planning implementation gain competitive advantage while others wait.
Basis Compass is purpose-built to solve the operational challenges agencies cite most: inefficient processes and disconnected systems. Here’s what sets it apart:
Compass gives agency employees back the time they need to do the strategic and creative work that clients are seeking, while automating the spreadsheet juggling that drains valuable hours from every week.
The shift to AI-powered media planning represents an opportunity to amplify human judgment, while reducing the manual tasks that slow teams down. These tools handle the mechanical work that drains time and creates inconsistency, giving planners capacity to focus on strategy, creativity, and client relationships. For agencies facing increasing complexity, tighter timelines, and pressure to do more with the same resources, AI planning tools offer a practical path forward.
The agencies that integrate these capabilities thoughtfully, particularly within unified platforms that connect planning directly to activation, will differentiate themselves through both efficiency and quality. They'll respond to briefs faster, produce more consistent work, and give planners more time for the strategic thinking that clients value most.
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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