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Key Takeaways:
AI is coming for the traditional agency model.
The technology is rewriting the way agencies do business in real time, driving efficiencies, transforming workflows, and opening up new strategic and creative opportunities. AI represents a powerful opportunity for agencies to evolve and improve the way they operate, which is why agency leaders have named it their top investment priority for the second year in a row. At the same time, as these tools automate the time-intensive work that once justified traditional billing structures, 87.3% of agency professionals and 91% of senior agency leaders believe that the traditional agency model is either broken or quickly heading in that direction.
To come out ahead as AI transforms that model, agency leaders must use this critical time to strategize around how the tech can amplify their unique differentiators, adapt their pricing models to account for how AI is changing advertising work, and build the AI-fluent teams that the next era of advertising demands.
In this new era of digital advertising, agencies win by using AI to amplify what makes them unique. Embracing AI is table stakes: It's how agencies adopt it that will separate the frontrunners from the stragglers.
Amplifying those differentiators with AI depends on getting the fundamentals of implementation right. Providing in-depth AI education to employees is critical, as teams that understand AI’s strengths and weaknesses are better equipped to apply it effectively. Thoughtfully integrating AI into existing workflows, rather than layering on disconnected point solutions, ensures efficiency gains aren't undermined by the creation of new silos. Investing in custom or specialized AI tools, meanwhile, empowers agencies with advantages that are harder for competitors to replicate. Data readiness—clean, comprehensive data consolidated across channels and accessible to AI systems—is also critical, as AI outputs are only as good as their inputs. Agencies that fuel their AI solutions with large volumes of high quality, unified data will generate outcomes that are more precise, personalized, and tailored to drive maximum impact for their clients.
These operational moves are the foundation for effective AI usage. However, the agencies that stand out in this new era will be those that go further by using AI to amplify what sets them apart. Agencies have long distinguished themselves with unique offerings—access to unique audiences, strategy planning processes, attribution processes, and more. This remains critical today: With 99% of agencies now using AI, those that simply incorporate it into their processes without combining it with genuine differentiators will risk irrelevancy. To provide brands with the value they expect, agencies must offer something inherently unique that they’ve amplified through AI.
AI-driven efficiency is pushing agencies away from commission, FTE, and billable-hour pricing, and toward output- and performance-based models.
That's because as agencies realize efficiency gains from AI, brands expect to realize them too. Even back in late 2024, three-quarters of brands wanted to change their agency compensation model.
As agencies know all too well, getting the agency compensation model right was a longstanding challenge even before the rise of AI. Part of that challenge has been that brands want to hire agencies for high-level strategic thinking, not to fund the mundane tasks typically handled by junior staff. For many years, however, that kind of manual busywork was unavoidable.
That started to change with the rise of digital and programmatic advertising, and AI is poised to further automate many of the manual tasks that took up a significant portion of agencies’ billable hours. Tasks like pulling reports, putting decks together, billing, and reconciliation—which were once very manual—can now be largely automated with AI. Those time savings undermine the logic of hourly billing: When AI collapses a task that once took days into minutes, hours-based pricing no longer captures the value delivered.
What does the path forward look like? Savvy agencies are already sunsetting pricing models based on commission, FTEs, or billable hours. And as AI compresses timelines, brands are looking for models that are output-based rather than time-based. For example, some agencies are exploring hybrid pricing models, which mix a percent of media with incentive tiers based on innovation or performance. However, performance-based models are complicated by the industry’s continued struggles to actually prove out performance effectively.
While most agencies are still in the early stages of figuring out these evolutions, those best positioned for what’s ahead are already having these conversations with clients. Doing so proactively signals the kind of forward-thinking partnership that brands expect from their agencies in this new era.
AI is shifting the most valuable agency skills toward AI fluency, requiring leaders to automate routine work, protect the expertise AI can't replicate, and upskill their teams.
With over a quarter of marketers reporting that their organizations have replaced human tasks with AI solutions in the past year, auditing workflows to find places where AI can drive efficiency is important work for agency leaders right now.
Reduced headcount may be one outcome for some agencies, but even more, AI will fundamentally change how agencies work, from team structure to how time is spent. Agencies have been through these kinds of shifts before: Before the rise of programmatic advertising, for example, high-impact advertising skills centered around RFPs and negotiations; advertisers had to be great negotiators if they wanted to get the best rates for their clients. Then, once programmatic advertising took hold, the most valuable skills shifted to how effectively advertisers could place bids, test and learn, and optimize accordingly. As the industry shifts again, it’s the individuals with expertise in AI who will drive the most profit for agencies.
Agency leaders should structure their teams accordingly, and work to upskill their current workforce in the technology as well. With 80% of CMOs concerned about an “AI skills gap,” agencies can increase the value they provide their clients by upskilling current employees and growing the AI expertise that brands are seeking.
As AI changes the industry, leading agencies will amplify their unique offerings with AI, price around delivered value, build AI-fluent teams, and fuel their AI tools with clean, unified data.
The traditional agency model may be under pressure, but the opportunity to build something better in its place is real. The agencies taking full advantage of that opportunity now are the ones that will lead the industry in the coming years.
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Looking for more insights around how agencies are approaching this moment? Our 2026 Advertising Agency report synthesizes insights from advertisers working across leading agencies, exploring how they feel about their jobs, their organizations, and the future of the agency work.
Every programmatic impression travels through a chain of intermediaries before it reaches a person, and most advertisers can't see what happens along the way. That blind spot carries a measurable cost. Only 43.3% of programmatic ad spend reached a quality impression—viewable, measurable, fraud-free, and clear of made-for-advertising inventory—in Q1 2026. For the lower-performing half of advertisers in the ANA's benchmark, the figure drops to 32.1%, meaning more than two-thirds of every dollar was wasted.
That gap isn't random. It separates advertisers who actively manage supply quality, measurement coverage, and inventory curation from those who don't. Transparency is what makes that management possible. Yet most advertisers still can't see exactly where their money goes once a bid is placed, which intermediaries extracted fees along the way, or whether their ads ran in environments that match their brand standards.
This guide covers what programmatic transparency means in 2026, how independent DSPs and walled garden platforms compare on the dimensions that matter most, what a layered brand safety and suitability approach looks like in practice, and how to evaluate any platform's fraud protection claims with appropriate skepticism.
Key Takeaways
A programmatic advertising platform is transparent when buyers can see and verify the full path their money takes from a bid to a publisher's page: which intermediaries handled the impression, what each one charged, and what the publisher actually received. That visibility is called supply path transparency: the ability to trace every step an ad impression takes from publisher to DSP and verify each intermediary's legitimacy.
Programmatic advertising, of course, is the automated buying and selling of digital ad inventory through real-time bidding across display, video, audio, native, CTV, and DOOH. A typical transaction routes every impression through multiple intermediaries, each extracting fees and introducing a potential point of failure along the way. The quality of that supply chain determines the quality of the campaigns it supports.
IAB Tech Lab standards (ads.txt, app-ads.txt, and sellers.json) establish the baseline, letting publishers declare which sellers are authorized to represent their inventory and letting buyers verify every intermediary in the supply chain. Transparency goes beyond compliance with those standards. A transparent programmatic platform gives buyers four things:
Independent DSPs deliver more supply-path transparency. Walled gardens deliver more closed-loop reach. Each model serves a different purpose, and the transparency profile of each reflects that. Most advertisers run both.
| Evaluation Criteria | Independent DSPs (ex. Basis) | Walled Garden DSPs (ex. DV360, Amazon DSP) |
| Supply-path visibility | Publisher-level pricing, hop counts, deal IDs, multi-SSP reporting | Limited; platform controls most visibility into inventory sourcing |
| Domain-level reporting | Standard | Often aggregated or restricted to platform-defined metrics |
| Data ownership | First-party data exportable and portable across buys | Data generally retained within the platform |
| Cross-platform measurement | Native integrations with third-party verification and cross-channel measurement | Measurement primarily constrained to platform ecosystem |
| Third-party verification support | Native integrations with multiple vendors; open-systems reporting | Supports select vendors, often with platform-mediated reporting |
| Buy-side neutrality | No media ownership; no inventory bias | Own and sell media; inherent revenue-vs.-transparency conflict |
| PMP access | Broad open-web PMP inventory and DSP-agnostic deal access | PMP access may be limited to platform relationships |
| Fraud protection scope | Covers programmatic and open-web channels natively | Strong within platform; fragmented across other channels |
| Log-level data access | Impression-level log data available in leading independent DSPs | Limited or inconsistent; walled gardens generally restrict LLD access or provide it selectively |
| Minimum spend | Varies; accessible at agency and enterprise scales | Some managed services require high minimums (ex. Amazon DSP managed service: $50K/month) |
Each model solves a different problem. Walled gardens deliver strong in-platform reach and performance for search, social, and commerce outcomes. Independent DSPs provide transparent, hands-on control over data, optimization, and cross-channel execution, along with supply path efficiency and access to high-quality open-web inventory where visibility, accountability, and brand safety are critical. For agencies and brands running campaigns across audio, CTV, display, DOOH, native, and video, a platform like Basis—which unifies those channels in a single workflow with consistent brand safety enforcement—can close the gap that fragmented stacks leave open.
Best for: Independent DSPs are strongest for transparent, cross-channel open-web buying with brand safety mandates; walled gardens are strongest for intent-driven search, social, and commerce outcomes. For agencies prioritizing supply-path transparency across channels, Basis is a strong option among independent omnichannel platforms.
Brand safety is the practice of ensuring ads are delivered in environments that align with an advertiser's standards for content, quality, and legitimacy across all channels. It combines content adjacency controls, supply-path validation, inventory curation, and pre- and post-bid verification to protect brand reputation and keep media investments in trusted environments.
No single control is sufficient. The platforms with the strongest brand safety and suitability offerings layer six mechanisms:
A practical note on trade-offs: tighter brand safety controls reduce available inventory and can increase CPMs. Higher-quality placements carry higher costs, and well-calibrated controls account for that trade-off.
Programmatic platforms prevent ad fraud primarily by blocking invalid impressions before a bid is placed, then verifying delivery after. Ad fraud is any deliberate activity that prevents proper delivery of ads to real human audiences, including bot traffic, domain spoofing, click injection, and ad stacking. Fraud protection encompasses the tools and processes used to identify and block these threats across the buying lifecycle, with a strong emphasis on pre-bid controls and supply-path validation to prevent invalid impressions before a bid is placed, alongside post-bid detection and remediation.
The most common fraud techniques in programmatic environments include:
Fraud protection operates at three points across the buying lifecycle:
A private marketplace (PMP) is an invite-only programmatic auction where select advertisers access premium publisher inventory through pre-negotiated deal terms. PMPs give advertisers a more direct, controlled path to premium inventory, reducing the supply hops, fraud exposure, and brand safety variability that come with open-exchange buying.
PMPs don't eliminate fraud entirely, and layered verification remains necessary regardless of buying method. But they reduce the attack surface significantly, and spending trends reflect that shift. PMP spending grew nearly 13% in 2025 against roughly 3% for the open exchange, per eMarketer—a gap that reflects advertisers' growing prioritization of inventory quality, brand safety, and supply chain accountability over bid-price savings.
When evaluating a platform's PMP offering, the size of the pre-negotiated deal library matters, but so does how it's organized. Platforms that maintain curated deal groups—by vertical, channel, content category, or audience type—save media buyers the time of evaluating individual deals from scratch. Basis maintains 2,000+ pre-negotiated deals organized in a browsable library that buyers can activate within the same workflow used for open exchange buying. Troubleshooting tools that surface setup issues before campaigns launch catch problems before they cost impressions.
Programmatic guaranteed (PG) is a deal type that combines the fixed pricing, guaranteed impressions, and direct publisher relationships of an insertion-order buy with the automation and flexibility of programmatic media. Inventory control is complete: the buyer always knows exactly where ads are appearing, and all PG buys consolidate into the DSP invoice rather than generating separate publisher invoices. Platforms with established PG relationships—Basis' partners include Equativ, Tubi, Beachfront, Google, Connatix, Magnite, OpenX, and FreeWheel—can accelerate deal setup considerably compared to negotiating publisher relationships from scratch.
Supply path optimization (SPO) is the strategic process of selecting the most efficient, transparent, and high-performing route for digital advertising transactions to flow from advertiser to publisher. Its goal is to find the best path to the target audience while maximizing value and minimizing waste. The demand for that visibility is broad-based: 88.3% of agency professionals say digital advertising needs more transparency, per Basis' 2026 Advertising Agency Report.
SPO has historically been framed as a cost-cutting exercise: fewer hops, lower CPMs. The 614 Group's study pushes back on that framing directly. When SPO is treated as a race to the bottom, it harms publishers, degrades inventory quality, and ultimately undermines advertiser outcomes. The more durable frame is optimization toward outcomes—using supply path visibility to improve ROAS, inventory quality, and brand safety at the same time, not just to shave a few basis points off CPMs.
The ANA Q1 2026 Benchmark puts numbers to what that difference looks like. The ANA splits advertisers into two halves based on how much of their spend converts into quality impressions. The gap between the top half and the bottom half breaks down as follows:
Tighter supply curation and better measurement coverage drive that difference. Rate negotiation doesn't.
That reframe has practical implications for platform selection. The 614 Group study found that most buyers don't want more data; they want insights they can act on. Platforms that surface supply path intelligence as actionable reporting, rather than as data exports that require engineering to interpret, are the ones that make SPO a repeatable operational practice rather than a periodic project.
To identify where a platform stands on supply path transparency, the 614 Group's SPO research synthesized feedback from senior marketers and agency leaders into eight questions every buyer should ask their DSP.
Three tensions show up consistently when agencies and brands evaluate programmatic platforms on brand safety and transparency.
Transparency vs. ease of execution: Independent DSPs provide supply-path visibility and cross-channel control, and the best ones are built to minimize the operational overhead that complexity can create. Basis is designed to consolidate omnichannel campaign management, reporting, and brand safety controls in a single workflow, reducing the expertise barrier without sacrificing transparency. Walled gardens simplify execution within their own ecosystems but limit cross-platform visibility and data portability in ways that compound over time.
Safety vs. scale: PMPs offer strong open-web control through pre-approved publisher relationships but constrain available impressions compared to the open exchange. That trade is deliberate: PMPs exchange raw scale for quality and control. The optimal allocation depends on campaign objectives, not a fixed formula.
Cost vs. control: Higher brand safety, verification, and managed service support increase costs through higher CPMs, platform fees, or minimum spend requirements. Consolidating channels and controls within a unified platform can reduce the total cost of that control by eliminating tool fragmentation and operational overhead.
A portfolio approach works best. Walled gardens for intent-driven search, social, and commerce outcomes. Independent DSPs and curated PMPs for open-web reach, supply-path transparency, and brand safety mandates. The two models are complementary, not competitive.
Applying these practices rarely requires switching platforms, but it does require active management.
What is the difference between brand safety and fraud protection?
Brand safety ensures ads appear in appropriate, high-quality environments by controlling content adjacency and publisher context. Fraud protection prevents invalid or deceptive traffic, including bots, domain spoofing, and click injection. The two are distinct but complementary, and strong platforms address both through integrated controls rather than treating them as separate programs.
What percentage of programmatic ad spend reaches a quality impression? [NEW]
Only 43.3% of programmatic ad spend reached a quality impression—viewable, measurable, fraud-free, and clear of made-for-advertising inventory—in Q1 2026, according to the ANA’s programmatic transparency benchmark (published May 2026). For the lower-performing half of advertisers, the figure fell to 32.1%, meaning more than two-thirds of every dollar was wasted.
Which programmatic advertising platforms provide the most transparency?
Independent DSPs provide the most supply-path transparency, including domain-level reporting, publisher-level pricing, and cross-platform auditability. Walled garden platforms like DV360 and Amazon DSP offer strong in-platform measurement but limit visibility into supply chain economics and restrict data portability. Basis is an example of an independent omnichannel platform with a built-in DSP that provides publisher-level pricing, deal-type comparison reporting, and multi-SSP visibility without sell-side conflicts.
Which DSPs have the best brand safety and fraud protection?
The DSPs with the strongest brand safety and fraud protection combine pre-bid filtering, post-bid verification, native integrations with multiple verification vendors, and human monitoring. Basis integrates with DoubleVerify, Comscore, Peer39, and Protected by Mediaocean for third-party verification, with pre-bid brand safety layers and a dedicated RTB Operations team for ongoing inventory quality monitoring.
How do advertising platforms prevent ad fraud?
Fraud protection operates at three points: pre-bid filtering (screening inventory before a bid is placed), in-flight monitoring (continuous analysis during delivery), and post-bid analysis (reconciling impressions against verification data). The strongest platforms, including Basis, combine proprietary detection with independent third-party verification, ads.txt and sellers.json enforcement, and human review for sophisticated fraud patterns that automated systems miss.
Are private marketplace deals safer than open auction inventory?
Yes. PMPs offer more control, direct publisher relationships, and higher-quality inventory than the open exchange. But they are not completely immune to fraud, so layered pre-bid and post-bid verification is still recommended regardless of buying method.
Which programmatic platforms offer the best pre-negotiated private marketplace deals?
Platforms that maintain large curated PMP libraries with deal-group organization by vertical, channel, or content category give media buyers the fastest path to brand-safe premium inventory. Basis maintains 2,000+ pre-negotiated PMP deals, including programmatic guaranteed relationships with publishers across CTV, display, audio, and video. Deal management, troubleshooting, and activation happen within the same workflow as open exchange buying.
How do independent DSPs compare to walled garden platforms for transparency?
Independent DSPs provide greater supply-path visibility, cross-platform auditability, and first-party data portability. Walled garden platforms offer strong in-platform targeting and measurement but constrain visibility and data ownership to their ecosystems. The TAG TrustNet LLD Register illustrates this gap: Basis provides full log-level data with all required data fields; Amazon Advertising provides no LLD support; major social walled gardens—Meta, TikTok, X—are listed as unknown. The two models work best in combination: walled gardens for intent-driven in-platform outcomes, independent DSPs for open-web reach with supply-path transparency.
What is supply path optimization, and how does it relate to brand safety?
Supply path optimization (SPO) is the practice of evaluating and refining the routes through which inventory is purchased to prioritize high-quality, transparent, and efficient supply. Cleaner supply paths—fewer hops, more curated deals, tighter domain footprints—directly reduce non-measurable and non-viewable inventory, which is where most programmatic waste occurs.
Do we still need third-party verification if we buy through a PMP or walled garden?
Yes. Layered verification is industry best practice regardless of buying method. PMPs reduce fraud risk through vetted inventory, but they don't eliminate it. Walled gardens measure within their own ecosystems, which creates both coverage gaps and an inherent conflict of interest. Independent verification from accredited vendors provides the audit layer that makes fraud protection claims auditable.
When does PMP buying make more sense than open auction buying?
When brand safety, viewability, inventory quality, and supply-path transparency matter more than maximum scale or the lowest CPMs. PMPs are the appropriate primary channel for campaigns with explicit brand safety mandates, for advertisers in sensitive categories, and for any program where inventory context is as important as audience targeting.
When does PMP buying make more sense than open auction buying?
When brand safety, viewability, inventory quality, and supply-path transparency matter more than maximum scale or the lowest CPMs. PMPs are the appropriate primary channel for campaigns with explicit brand safety mandates, for advertisers in sensitive categories, and for any program where inventory context is as important as audience targeting.
Key Takeaways
In 2026, consumer trust is anything but a given. In fact, for many audience segments, distrust is the norm.
A recent Edelman Trust Barometer framed today’s climate as a global “Crisis of Grievance,” the result of events over the past 25 years—from the Iraq War to the 2008 financial crisis to the COVID-19 pandemic—that have chipped away at trust in leaders and institutions. Evidence of this crisis includes an unprecedented decline in employees’ trust in their employers to do what’s right, record-high levels of concern that leaders lie to the public, and four in 10 global respondents reporting that they view hostile activism (including threats or even violence) as a viable means for driving change. That ratio rose to 1 in 2 among people between the ages of 18 and 34.
Among major institutions, brands still hold a relatively advantageous position—ranking as more trusted than government, NGO, and media entities—but this climate of growing distrust is impacting them as well. The rise of AI usage among brands has opened up fresh concerns: A stark 79% of Americans don’t trust businesses to leverage AI responsibly. AI has also amplified consumers’ data privacy concerns, with fewer than half (48%) of US consumers saying the benefits of online services outweigh the privacy risks.
Even more, consumers have grown increasingly willing to voice their displeasure by “voting with their wallets.” This shift reflects the rise of conscious consumerism, a movement in which shoppers actively align their spending with their values. Recent years have seen a variety of high-profile consumer boycotts—on companies ranging from Bud Light and Cracker Barrel to Target to Apple and Netflix—driven by consumers unwilling to support brands whose actions conflicted with their values. Nearly a third of Americans have boycotted a business, and 45% say they research a company's values or stance before buying at least some of the time—a figure that climbs to 59% among Gen Z. To top it all off, US consumer sentiment dropped to a record low in May, adding economic anxiety to an already fragile trust environment.
Brands today cannot take consumer trust for granted. This guide covers how to build brand trust and loyalty in 2026, with a focus on four key strategies: brand authenticity, consistency, data privacy, and brand safety.
Brand authenticity is one of the most powerful drivers of consumer trust and brand loyalty in 2026—and one of the hardest to maintain. Over the past decade or so, the rise of conscious consumerism has led to brands increasingly taking social and environmental stands. But consumers and stakeholders demand authenticity and expect a coherent alignment between brands’ words and their actions: Those who try to talk the talk without walking the walk will quickly garner backlash and lose consumer trust—and dollars—as a result (see: “greenwashing”; “rainbow washing”; “woke-washing”).
But a key aspect of effective authenticity that’s less discussed (though equally important) is consistency. “Brands need to be loud and proud about what they stand for,” says Molly Marshall, Client Strategy and Insights Partner at Basis. “But they also need to do that consistently. When brands try to please everyone or shift their values according to the cultural or political climate, that’s when they receive backlash.”
Bud Light offers one of the most notorious recent examples of how inconsistency can generate backlash from multiple directions at once. In 2023, the brand partnered with influencer Dylan Mulvaney on a promotional campaign. The campaign received backlash from conservatives, with critics accusing Bud Light of “going woke” for featuring a transgender woman in its campaign. That response drew its own backlash, with calls for a "buycott" encouraging people to buy Bud Light in support of the campaign. Anheuser-Busch released a statement that further fanned dissent on both sides by neither standing by its partnership with Mulvaney nor directly addressing the controversy at all. Added together, the financial and brand damage was sustained and significant: Bud Light sales and purchase incidence were roughly 28% lower than the same period in prior years in the three months following the boycott, a decline that persisted for close to eight months.
Another cautionary tale around consistency (or lack thereof) has been playing out in the industry in recent years in relation to Target. In 2023, the brand received blowback for the Pride month-themed merchandise it featured, with conservative consumers and social media creators encouraging a boycott. Target, which at that point had celebrated Pride month with Pride-themed merchandise for over a decade, then released a statement that it would remove some of the items from that year’s Pride collection in response to the backlash. This, in turn, garnered even more negative reactions: The company received bomb threats accusing it of betraying LGBTQIA+ people, and a coalition of 15 state attorneys general came together to encourage the brand to stand by the LGBTQIA+ community. Panelists discussing LGBTQ brand advocacy at SXSW the following year agreed that Target’s decision to walk back its stance in the face of backlash ultimately made things worse for the brand. Still, the controversy continues in 2026: The State Board of Administration of Florida has an ongoing class-action lawsuit against Target alleging that the brand misled shareholders about the risks associated with its 2023 Pride Month campaign, resulting in billions of dollars in investor losses.
A similar controversy began in early 2025, when Target—known at the time for its strong support of diversity, equity, and inclusion (DEI) initiatives and Black-owned businesses after the 2020 George Floyd protests—announced plans to scale back several DEI programs. This move aligned with a broader trend among major brands and came as the Trump Administration took steps to end government DEI programs. The retail giant received swift blowback from consumers, including boycotts that appeared to compound Target’s existing financial and operational struggles, with Target executives admitting in May 2025 that it had contributed to a decrease in sales. The company’s CEO stepped down in August of that year as the company reported its third consecutive quarter of declining sales—a slump that continued through 2025 and is only now beginning to lift.
“When consumers see a brand like Target—which had previously committed to DEI—pull those commitments back, they’re going to wonder if they can trust them to authentically act out their brand values or if they’re just going to react based on what’s happening politically,” says Kate Diehl, Group VP of Integrated Client Solutions at Basis.
For brands, the combination of today’s crisis of trust with the rise of conscious consumerism and a polarized political climate means that taking a stand on social and political issues comes with real risk. Brands should only take a stand when they can back it up with authentic action and are prepared to weather criticism. And, when pushback comes, the best response is often to stay the course and maintain their initial stance to demonstrate consistency.
That said, for many brands, the best move may be to simply not take a position on such polarizing issues. US adults are split on whether or not businesses should take a public stance on current events, with 51% saying they should and 49% saying they shouldn’t. For brands whose products or services aren’t related to social or political issues, that split may be reason enough not to engage.
Data privacy in marketing has shifted from a compliance checkbox to a core driver of consumer trust. Data privacy concerns have increased dramatically in recent years, with the share of US consumers worried about data privacy and security increasing by 10 percentage points between 2024 and 2025, from 60% to 70%. Advertisers recognize how these concerns impact trust, with 46% naming transparent communication about data practices as the best way to grow customer confidence. “Data privacy is considered table stakes by consumers at this point,” says Marshall. “Still, brands and advertisers are struggling to implement it consistently.”
In addition to consumer concern, signal loss has also pushed advertisers towards more privacy-first approaches in recent years. “Even beyond building trust with consumers by respecting their data privacy, advertisers need to be able to rely on privacy-friendly solutions like first-party data to successfully target and measure their campaigns as signals drop off,” says Diehl. At the same time, first-party data comes with an ethical responsibility for advertisers—to gather, organize, store, and leverage that data in ways that preserve consumers’ privacy.
The rise of AI has further amplified privacy considerations. With 95% of digital advertisers reporting that they use generative or agentic AI in their work at least once a month, marketing teams must take even more care to protect customer data.
Some AI-driven tools, particularly those used in collecting and analyzing data, present serious data privacy risks. Agentic AI raises the stakes even further, as AI agents can access data across systems and act on it autonomously, meaning a privacy misstep can compound before a human ever reviews it. As advertisers increasingly adopt these tools, brands risk garnering significant distrust if they don’t take the proper precautions.
To adapt AI responsibly, advertisers must establish clear data governance protocols and place guardrails around what data feeds AI tools. AI partner selection is equally important: Marketers should audit AI vendors to ensure their commitment to data privacy, prioritizing solutions that offer transparency and demonstrate the reasoning behind their outputs rather than operating as a black box. Brands that treat privacy as a core component of their AI strategy will be best positioned to earn and maintain consumer trust.
Brand safety has become one of the most visible ways brands can demonstrate their commitment to consumer trust. Advertisers looking to build more trust with consumers should work to prioritize brand safety across their campaigns, with recent industry developments making this focus all the more critical.
Advertisers have felt increasing concern around brand safety for years now—indeed, close to half of media experts name brand suitability as their top priority when it comes to media quality. The rise of generative and agentic AI has only amplified these concerns, with 100% of marketers agreeing in a recent survey that AI presents a brand safety and misinformation risk, and 88.6% describing that risk as moderate to significant. The proliferation of MFAs and AI-generated content online has made rigorous supply path optimization (SPO) even more critical for programmatic advertisers—without it, ad spend will continue to flow toward low-quality, AI-generated inventory that both compromises brand safety and inflates impression counts while delivering little real value.
Social media carries the highest brand risk of all digital media channels, according to just over half of advertisers. In fact, two-thirds of global marketing and advertising decision makers feel concerned about the suitability of ads placed on social platforms. Recent years have given advertisers plenty of reasons for that unease. In one high-profile 2025 incident, Meta apologized after Instagram users reported seeing extreme violence in their Reels feeds, including videos of people being murdered.
“This is an example of a brand safety concern that’s really hard for brands and agencies to get ahead of,” says Marshall. “I do think it brings up larger questions around what platforms are safe and effective for advertisers, and what consumers expect from brands who run on those platforms.” Even more, content moderation rollbacks at social media platforms like Facebook, Instagram, and X have aggravated the riskiness of social media environments.
Beyond social media, brand safety made headlines just last year as a result of an Adalytics report that found that multiple adtech companies have placed ads for major brands on websites hosting CSAM. (Note: The report cites Basis as an adtech vendor who did not serve ads on any of the sites in question.) This extraordinary brand safety crisis underscores how critical it is for brands to have robust and multi-layered systems to ensure their advertising content is only shown in safe and suitable environments—to safeguard consumer trust as well as to avoid ethical catastrophes such as these.
Of course, there’s a case to be made that consumers are now savvy enough to know that brands aren’t choosing to serve ads next to disturbing content, hate speech, or misinformation, particularly on social media. Still, 82% say it’s important to them that the content around online ads is appropriate, and three-quarters say they would feel less favorable towards brands that serve ads on sites that contain misinformation. Considering this majority opinion as well as the broader culture of consumer distrust, brands who prioritize brand safety likely stand to gain a competitive advantage over their peers who take a laxer approach.
Leading advertisers are responding by investing in brand safety tools that go beyond surface-level filtering. For example, solutions such as Protected by MediaOcean are using semantic intelligence to evaluate content in context rather than relying on keyword blocking alone, and can incorporate real-time signals to stop waste more efficiently. And as ad fraud and brand safety concerns rise in the realm of CTV, vendors like Peer39 offer content-level contextual segments that give advertisers more control over where their CTV ads are served. Teams who stay at the forefront of these technological evolutions stand to gain concrete performance advantages over competitors.
In the face of deepening consumer distrust, heightened social tensions, and growing scrutiny around corporate behavior, earning and maintaining trust from target audiences will be a defining priority for today’s most successful brands. And authenticity, consistency, data privacy, and brand safety will be foundational elements of their strategies.
For marketing and advertising leaders, now is the time to double down on trust as a core metric of success. Failing to do so may carry financial consequences in a world where consumers are spending more intentionally and brand loyalty is increasingly difficult to earn and maintain.
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Looking for more insights on how AI is changing advertising? Our AI and the Future of Marketing report explores how marketers are using the technology, navigating its risks, how it’s reshaping advertising jobs and teams, and more.
Media reconciliation is the final step of an ad campaign: matching delivered impressions against what was contracted, chasing down discrepancies, and squaring vendor invoices. It’s also where agency teams lose some of their most valuable hours. For teams still doing it by hand, every close cycle means exporting delivery data, checking it line by line against insertion orders, and resolving the mismatches manually.
Most advertising technology stops short of automating that work. Platforms plan the buy, activate it, and report on performance, then stop at the point the campaign becomes a financial transaction. This leaves reconciliation to spreadsheets and manual review. Automating campaign billing and reconciliation closes that gap, so teams spend the end of every flight strategizing on the next campaign instead of wrapping up the last one.
This guide covers what causes reconciliation breakdowns, how automated media reconciliation works, what agencies should look for in a solution, and how to measure the payoff.
Media reconciliation takes so much agency time because the data needed to close a campaign often lives in different systems from the data used to run it: Delivery sits in the DSP, ad server, or platform dashboards, contracted terms in insertion orders (IOs), and vendor invoices in a third system, often arriving weeks after a flight ends.
Squaring the three often means moving numbers between systems by hand, one placement at a time. That manual movement is the kind of work agencies say slows them down considerably. In Basis’ 2026 Advertising Agency Report, agencies ranked inefficient processes and siloed systems as their top two operational challenges—both hallmarks of reconciliation that’s still done manually.
The cost of manual media reconciliation compounds past the hours themselves. Delivery often doesn’t match the IO to the line, so makegoods, credits, and rate adjustments have to be tracked and applied correctly. Vendors invoice in different formats and tax treatments, and contract versions change mid-flight. Each is easy to miss on its own, and each one left unresolved becomes a billing dispute, a delayed close, or a write-off later.
Agencies automate billing and reconciliation by consolidating delivery, contract, and invoice data in one system, then comparing delivered performance against the plan, so the team’s attention goes to resolving genuine exceptions instead of reconciling line items by hand.
A mature automated media reconciliation workflow moves through six connected stages:
Platforms that automate reconciliation connect these steps instead of treating finance as an afterthought. Basis, for instance, links planning, buying, optimization, reporting, and automated billing in one system, connecting media contracts and campaign actuals to ERP systems and flagging discrepancies in real time, so that reconciliation runs as a byproduct of the workflow rather than a separate month-end project. And it integrates with the ERP and billing systems teams already run rather than replacing them, connecting media execution to financial close.
A platform can technically handle reconciliation and still leave agency teams doing the hard part manually. When comparing options, four differences decide whether reconciliation actually gets easier: channel coverage, ERP fit, governance, and how well a platform connects to the systems you already run.
Documentation is an oft-overlooked prerequisite here. Automated matching only works when the supporting materials—IOs, amendments, proof-of-performance—are stored where the system and your finance team can both reach them. Basis Document Storage, for example, centralizes those assets against the campaigns they belong to.
The clearest signals that automated campaign reconciliation is working are a faster time to close, fewer disputes, and less manual effort per invoice. Tracking a handful of metrics before and after can give agency leadership a concrete read on the return and turn an operational change into a business case.
When done effectively, reconciliation stops being a monthly scramble and becomes an automated process, and the hours it used to consume move to campaign work.
Basis measures its billing automation against exactly these outcomes: Basis users report a 15% average reduction in time to collect, and a Forrester Total Economic Impact study found a 40% reduction in manual steps across media operations.
Reconciliation automation delivers a return teams can feel. Closes shrink from weeks to days, fewer disputes reach clients, and finance hours shift from chasing discrepancies toward forecasting and controls. Getting there means treating reconciliation as part of the campaign workflow rather than a task that starts once the campaign ends.
Basis brings reconciliation into the same system agencies use to plan, buy, optimize, and report, so financial close is connected to the work that produced it rather than run as a separate project downstream.
Reconciliation is one piece of a larger media buying platform decision. For how agency platforms compare across the full campaign workflow, see The Top 5 Advertising Agency Platforms for Media Buying; for how leading DSPs handle billing and reconciliation specifically, see Best DSP for Agencies in 2026.
How can agencies automate client billing and reconciliation?
Agencies automate billing and reconciliation by centralizing delivery, contract, and invoice data in one platform, comparing delivered performance against planned rates and contracted terms, and routing only flagged discrepancies for review. Reconciled data then feeds invoicing and exports to finance systems. The practical starting point is consolidating the data sources a close depends on, so matching no longer requires manual exports between tools.
What advertising tools reduce time spent on campaign reconciliation?
The tools that cut reconciliation time match delivery against contracted terms automatically and connect to downstream finance systems, rather than leaving that work in spreadsheets. Platforms that unify planning, buying, reporting, and billing—like Basis—reduce it most, because the data never has to be moved or re-keyed between systems to close a campaign.
What is the best media buying platform with integrated billing and reconciliation?
The best fit is a platform that reconciles across every channel you buy and connects to your existing finance systems, so billing isn't a separate manual project. Basis is a strong option for agencies because it unifies programmatic, direct, search, social, and advanced TV in one platform and pushes reconciled data into ERP systems.
Which DSPs offer built-in billing and reconciliation?
Most DSPs focus on activation and leave billing and reconciliation to separate finance tools. Basis, an omnichannel advertising platform that includes a DSP, is built differently, with billing and reconciliation connected to the same platform used to plan, buy, and report.
What causes media billing discrepancies?
Discrepancies happen when delivery doesn't match the plan: Makegoods, credits, rate changes, and mid-flight contract edits all open gaps between what was contracted and what ran. They multiply when delivery data, contracts, and invoices live in separate systems or spreadsheets, because every mismatch has to be caught and resolved by hand. Automating the comparison surfaces them early, before they turn into disputes or write-offs.
Agencies today manage campaigns across an average of eight or more separate tools for planning, buying, reporting, and billing. Each tool creates its own data silo, its own login, its own reporting format, and its own set of manual handoffs that slow campaigns down and introduce error. That fragmentation is a growing competitive liability at a time when global ad spend is projected to surpass $1 trillion and agency teams are under pressure to do more with fewer resources.
An AI advertising platform is specialized software that uses machine learning, predictive analytics, and automation to plan, execute, and optimize digital ad campaigns with minimal manual intervention. Unlike basic automation tools that follow static rules, a true AI advertising platform continuously learns from campaign data—adjusting bids, reallocating budgets, refining audience targeting, and testing creative in real time. The defining characteristic is adaptive intelligence: the system improves over time without requiring a human to manually update its logic.
The category has matured. What separates the leading platforms in 2026 is not whether they use AI, but how deeply AI is integrated across the full campaign lifecycle, and whether that integration helps agencies consolidate fragmented workflows or simply adds another tool to the stack.
The core difference between an AI advertising platform and a traditional demand-side platform (DSP) is the degree of autonomous decision-making. A traditional DSP executes programmatic media buys based on rules and parameters set by a human operator. An AI advertising platform layers predictive models and real-time optimization on top of that execution, making campaign adjustments that would be impossible for a human to perform at the same speed or scale.
Instead of waiting for a buyer to analyze yesterday's data and adjust bids manually, an AI platform processes live signals—shifting spend toward higher-performing placements, pausing underperforming creative, and expanding into audience segments the model identifies as high-probability converters.
| Capability | Traditional DSP | AI Advertising Platform |
|---|---|---|
| Bid optimization | Rule-based, manually adjusted | Real-time, model-driven, self-adjusting |
| Audience targeting | Predefined segments set by buyer | Dynamic segmentation with predictive modeling |
| Creative management | Manual A/B testing | Automated multivariate testing and generation |
| Budget allocation | Set at campaign launch, periodically reviewed | Continuously reallocated based on live performance |
| Cross-channel coordination | Typically siloed by channel | Unified optimization across channels |
| Reporting | Retrospective dashboards | Predictive insights with recommended actions |
For agency teams running campaigns through a legacy DSP, the need now is to evaluate whether a platform can integrate AI into existing workflows without creating disruption—and whether it can extend that intelligence beyond a single channel.
The most reliable way to evaluate AI advertising platforms is to assess them across five dimensions: automation depth, real-time optimization, cross-channel integration, creative testing, and provable ROAS impact.
Automation depth refers to how much of the campaign workflow the platform handles without manual input. Can it autonomously launch campaigns, adjust targeting, and reallocate budgets? Or does it surface recommendations that a human still needs to act on? 77.7% of agency leaders plan to increase their AI investment in the next 12 months—but the gap between investing in AI and operationalizing it remains wide. Platforms that automate end-to-end workflows, and not just individual tasks, close that gap fastest.
Real-time bid optimization is the engine behind campaign efficiency. Platforms that adjust bids in milliseconds based on live auction data, audience behavior, and conversion probability consistently outperform those relying on hourly or daily batch updates. When evaluating vendors, ask how frequently their models retrain and how granular their bid adjustments are.
Cross-channel integration determines whether you can manage programmatic, search, social, CTV, and direct buys from a single platform. While 86% of marketers say cross-channel orchestration is important, only 10% report having fully unified ad tech systems in place. That gap—between the ambition for unified media buying and the reality of fragmented tools—is where platform selection has the greatest impact.
Creative testing capabilities have become a key differentiator. Platforms that generate creative variations and automatically test them against live audiences accelerate the optimization cycle significantly. Look for platforms that go beyond A/B testing to run multivariate experiments at scale.
Provable ROAS impact is the ultimate measure. Any platform can claim improved performance, but few can provide transparent attribution, clear before-and-after benchmarks, and reporting that you can confidently present to clients. The IAB's AI Transparency and Disclosure Framework, released in January 2026, underscores the growing industry expectation that AI-driven decisions should be explainable—not opaque.
The leading AI advertising platforms span a range of approaches, from full-stack omnichannel solutions to specialized programmatic execution engines. The right choice depends on your agency's operational needs, client portfolio, and the degree of workflow consolidation you need.
| Platform | Primary Strength | AI Capabilities | Channel Coverage | Strongest For |
|---|---|---|---|---|
| Basis | Omnichannel unification | Agentic AI planning (Compass), AI-driven optimization (SmartBid) | Programmatic, search, social, direct, CTV | Agencies needing planning-through-billing in one platform |
| The Trade Desk | Programmatic execution | Kokai AI (deep learning bid optimization) | Programmatic (display, video, CTV, audio, DOOH) | Agencies running large-scale programmatic with full transparency |
| DV360 | Google ecosystem integration | Google AI/ML bidding, audience modeling | Programmatic, YouTube (exclusive), display, video, CTV | Agencies prioritizing YouTube inventory and Google stack integration |
| Amazon DSP | Commerce and shopper data | Purchase-based audience targeting, full-funnel automation | Programmatic, Prime Video, Twitch, Fire TV | Agencies with retail, CPG, and e-commerce clients |
| Mediaocean | Financial infrastructure | AI-driven ad serving (Innovid), orchestration | Planning, billing, reconciliation, ad serving | Large agencies needing financial workflow and ad operations at scale |
| StackAdapt | Accessible multi-channel programmatic | AI-powered optimization, contextual targeting | Programmatic (display, native, CTV, DOOH, audio, in-game) | Mid-sized agencies prioritizing ease of use and pricing transparency |
Basis is an AI-powered advertising platform built specifically for how agencies operate. It consolidates campaign planning, programmatic media buying, paid social, search, direct deals, reporting, and billing into a single platform—eliminating the fragmentation that drives up cost and manual effort across agency teams.
What distinguishes Basis from other platforms in this comparison is that it addresses the full campaign lifecycle, not just a single buying channel. Most platforms on this list are programmatic execution engines; Basis connects programmatic with search, social, and direct buys in one interface, with planning through billing unified end to end. Compass, the platform's agentic AI media planning tool, takes a campaign brief and produces a complete, ready-to-activate omnichannel media plan—the first independent platform to connect brief-to-activation across major channels spanning the open web and walled gardens. SmartBid, Basis's AI-driven bidding engine, continuously optimizes bids across programmatic campaigns in real time—adjusting to live auction signals, audience behavior, and conversion probability to improve performance throughout the campaign flight. Agencies that use SmartBid have reported up to 5x improvement in advertising performance.
Basis also partners with Mediaocean on financial workflows, connecting media planning data with downstream billing and reconciliation systems—making it compatible with agencies already using Mediaocean for back-office operations.
Strongest for: Agencies managing complex, multi-channel campaigns that need planning, media buying, reporting, and billing unified in one platform.
The Trade Desk is widely regarded as one of the most technically advanced independent DSPs on the market. Its Kokai platform integrates deep learning across every stage of the programmatic buying process, processing millions of ad impression opportunities per second to optimize bid decisions in real time.
Key differentiators include Unified ID 2.0, an open-source identity framework for post-cookie targeting, and access to a massive third-party data marketplace. The Trade Desk has strong CTV positioning, and is a preferred DSP for many premium streaming services.
The Trade Desk is programmatic-only. Agencies using the platform still need separate tools for paid search, paid social, and direct buys, plus additional platforms for billing and reconciliation. User reviews consistently note the platform's complexity, particularly with the Kokai interface, and tech fees can accumulate quickly.
Strongest for: Agencies running large-scale programmatic campaigns that prioritize bidding transparency, open-internet inventory, and advanced identity solutions.
DV360 is Google's enterprise DSP, part of the broader Google Marketing Platform. Its primary competitive advantage is deep integration with Google-owned properties—most notably exclusive access to YouTube inventory, the Google Display Network, and seamless interoperability with Campaign Manager 360 and Google Analytics 4.
The platform connects to over 70 ad exchanges and supports programmatic buying across display, video, CTV, audio, and DOOH. Recent developments include biddable access to NBCUniversal's live sports CTV inventory and expanded premium streaming partnerships. Google's AI and machine learning power the platform's bidding and audience modeling capabilities.
DV360 does not handle paid social, direct media buys, billing, or financial reconciliation. It is a programmatic activation and measurement tool within Google's ecosystem—not a full agency operational platform. Agencies prioritizing platform independence may find the Google-ecosystem dependency limiting.
Strongest for: Agencies that need exclusive YouTube programmatic access and deep Google stack integration for large-scale campaigns.
Amazon DSP is Amazon's demand-side platform for programmatic display, video, and audio advertising on and off Amazon. The core differentiator is exclusive access to Amazon's first-party shopping and streaming data—a reported 300 million+ active customer accounts globally—which powers audience targeting based on actual purchase behavior rather than inferred intent.
The platform provides access to premium inventory including Prime Video, Twitch, Thursday Night Football, and Fire TV, alongside thousands of third-party publishers. Amazon Marketing Cloud offers clean-room analytics for deeper measurement and attribution. Amazon recommends a $10,000 campaign minimum for some self-service formats to generate sufficient data for optimization, and managed-service campaigns require a $50,000 monthly minimum.
Amazon DSP operates as a walled garden: data generated within Amazon's ecosystem stays within it, limiting portability and cross-platform measurement. The platform's strongest value is for retail, CPG, and e-commerce advertisers. Agencies with diverse client portfolios spanning non-commerce verticals will find the core data advantage less relevant. Amazon DSP does not handle search (outside Amazon's own sponsored ads), paid social, media planning workflows, billing, or financial reconciliation.
Strongest for: Agencies with retail, CPG, and e-commerce clients who need purchase-based audience targeting and premium streaming inventory.
Mediaocean is one of the advertising industry's foundational financial and workflow platforms, processing over $200 billion in annualized ad spend across more than 100,000 users globally. Its product suite includes Prisma (the industry-standard system of record for media management and finance), Innovid (ad serving and measurement), Flashtalking (dynamic creative optimization), and Protected (brand safety and ad verification).
Mediaocean's own 2026 Advertising Outlook Report acknowledged the orchestration problem directly: only 10% of marketers say their ad tech stacks are fully connected across channels, with 42% citing data quality issues and 41% citing difficulty connecting AI insights across systems as barriers to scaling AI effectively.
Mediaocean's strength is financial infrastructure and ad serving—not campaign activation, optimization, or performance buying. The product portfolio is assembled through acquisitions rather than built as a natively unified system, which can create integration gaps. For agencies that use Mediaocean for billing and finance, Basis is a good fit to serve as the execution engine that sits in front of it. For agencies that do not need holding-company-scale financial infrastructure, Basis can serve as the unified platform for both execution and back-office operations.
Strongest for: Large agencies and holding companies that need financial workflow infrastructure, ad serving, and billing at scale.
StackAdapt is a self-serve DSP with programmatic capabilities across CTV, DOOH, display, native, audio, and in-game.
StackAdapt is programmatic-focused and does not offer search or social campaign management within the platform. It lacks the full agency workflow layer—billing, reconciliation, financial operations—that agencies managing multiple clients need. The DSP is a strong execution tool, but agencies using StackAdapt still need additional tools for non-programmatic channels and back-office operations.
Strongest for: Mid-sized agencies that prioritize ease of use, pricing transparency, and strong support for programmatic campaigns.
The platforms that deliver the greatest value for agencies share three characteristics: they reduce tool count, they connect data across channels, and they embed AI into operational workflows rather than bolting it on as an add-on feature.
A recent report found that 87% of agency professionals believe the traditional agency model is either broken or will need to fundamentally change within three to five years. Inefficient processes were the top challenge agencies reported, ahead of rising costs and shrinking margins. That finding tracks with what Dentsu's global forecast describes as the arrival of the "algorithmic era"—a market where 71.6% of ad spend is projected to be algorithm-driven by 2026, rising to 76% by 2028.
For agencies, this means the operational cost of fragmentation—aka the time spent reconciling data across platforms, the errors introduced by manual handoffs, the inability to optimize holistically across channels—is now a strategic vulnerability. The agencies building the cleanest, most unified data infrastructure today are the ones positioning themselves to compete effectively as agentic AI reshapes how campaigns are planned, bought, and optimized.
The key to adopting the right platform for your agency is understanding and appreciating which platform's strengths align with your agency's operational reality, and which gaps in your current stack are costing you the most.
Measuring ROI from an AI advertising platform requires tracking both direct performance improvements and operational efficiency gains. The most meaningful metrics are ROAS lift, cost-per-acquisition reduction, time saved on manual optimization, and speed to campaign launch.
For direct performance, compare ROAS, CPA, and conversion rates before and after implementation. Control for external variables—seasonality, budget changes, audience shifts—to isolate the platform's impact. Platforms that provide built-in benchmarking and before-and-after reporting make this significantly easier.
Operational efficiency is the metric that often gets overlooked but delivers substantial value. If an AI platform reduces the time your team spends on manual bid adjustments, campaign setup, and reporting by several hours per week, that time can be redirected toward strategy, creative development, and client management. For agencies managing dozens of accounts, this efficiency gain compounds fast.
The platforms that deliver the clearest ROI combine AI-driven automation with transparent reporting—showing not just what changed, but why the AI made the decisions it did. Opacity in optimization logic may deliver short-term results, but it makes it difficult to justify continued investment or troubleshoot performance dips.
An AI advertising platform is software that uses machine learning and automation to plan, execute, and optimize digital ad campaigns with minimal manual intervention. Unlike traditional tools that rely on static rules, these platforms continuously learn from campaign data to adjust bids, reallocate budgets, refine targeting, and test creative in real time. The defining characteristic is adaptive intelligence—the platform improves its own performance over time without requiring manual updates.
The best platform depends on the agency's operational needs. Basis is purpose-built for agencies managing campaigns across multiple channels and clients, with planning through billing unified in one platform. The Trade Desk is a leading independent programmatic DSP. DV360 offers exclusive YouTube inventory access. Amazon DSP provides unique commerce data for retail-focused clients. The right choice depends on channel mix, client portfolio, and how much workflow consolidation the agency needs.
A traditional DSP executes programmatic buys based on rules set by a human operator. An AI advertising platform autonomously optimizes those decisions using machine learning and real-time data—adjusting bids in milliseconds, dynamically reallocating budgets, and predicting which audience segments will convert. AI platforms augment the media buyer's capabilities rather than simply executing their instructions.
Evaluate platforms across five criteria: automation depth (how much workflow the platform handles end-to-end), real-time bid optimization (how fast and granular the models are), cross-channel integration (whether the platform consolidates or fragments your tool stack), creative testing (automated multivariate testing at scale), and provable ROAS impact (transparent attribution and before-and-after benchmarks).
Impact varies by platform and use case, but the gains can be substantial. Agencies using Basis's SmartBid AI campaign optimization engine have reported up to a 5x improvement in advertising performance. Beyond direct performance, AI platforms also deliver operational efficiency gains—reducing hours spent on manual bid adjustments, campaign setup, and reporting—which compounds across dozens of accounts. Dentsu projects that 71.6% of global ad spend will be algorithm-driven by 2026, rising to 76% by 2028.
Compass is Basis's agentic AI media planning tool. It takes a campaign brief and produces a complete, customizable, ready-to-activate omnichannel media plan spanning programmatic, direct, paid search, and paid social. Compass uses Basis' proprietary IMPACT planning framework to synthesize brief inputs into strategy recommendations, audience segments, channel mix allocations, and budget plans—reducing planning time from hours to minutes.
No. AI advertising platforms automate repetitive, data-intensive tasks like bid adjustments, budget reallocation, and performance monitoring, freeing media buyers to focus on strategy, client relationships, and creative direction. The most effective agency teams use AI to handle execution at scale while humans provide strategic judgment and contextual understanding.
Track both direct performance improvements (ROAS lift, CPA reduction, conversion rate increases) and operational efficiency gains (time saved on manual optimization, faster campaign launch, reduced reporting overhead). Control for external variables to isolate the platform's impact, and prioritize platforms that provide transparent, explainable reporting on how AI decisions were made.
Only 10%. While 86% of marketers say cross-channel orchestration is important, the vast majority still operate with partially unified or fully fragmented systems—creating friction in scaling AI and coordinating campaigns across channels.
The Trade Desk is a leading independent programmatic DSP focused on open-internet inventory, advanced identity solutions, and AI-driven bid optimization. Basis is an omnichannel advertising platform that handles programmatic, search, social, direct, and CTV in one platform—with planning, buying, reporting, and billing connected end to end. Agencies using The Trade Desk still need separate tools for non-programmatic channels and back-office operations; Basis consolidates those workflows into a single system.
The holiday season is won months before checkout
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Whether you plan media at an agency or lead marketing for a brand, this report gives you the consumer evidence to brief creative, set budgets, and time your flighting with confidence.
See how US shoppers will discover, decide, and buy this holiday season, and find out what it means for your 2026 campaigns, in this new proprietary research report Basis.
The report features:
The report's findings come from a proprietary Basis ImpactIQ and GWI survey of 2,006 US consumers, fielded in May 2026 and weighted to be nationally representative. The study captures how people celebrated in 2025 and how they plan to shop and celebrate in 2026.
Select insights include:
Ready to uncover all the details and start building more successful campaigns? Download your free copy of the 2026 Winter Holidays Shopping Trends report today.
Evaluating whether or not your brand should in-house its media buying often involves a critical realization: In-housing media buying is not a single decision. Rather, it is a series of them, with multiple valid answers depending on your specific brand and context. Which channels do you bring in-house first? What stays with the agency? Who operates the platform? Who holds the contract? And what happens to your data if any of those answers change?
This guide covers what in-house advertising actually involves, exploring the three operating models—fully outsourced, hybrid, and fully in-housed—that brands choose among, and the practical questions of data ownership, transparency, capability, and agency dynamics that should drive the decision.
In-house advertising is the practice of a brand taking direct ownership of its media buying operations—channels, platforms, data, and reporting—that agencies have traditionally handled on its behalf. Rather than briefing an agency and receiving results, a fully in-housed brand operates the buying platform itself and keeps the resulting data inside its own ecosystem.
Programmatic is where in-house media buying gets the most attention, and for good reason: It is often the largest line item in a brand's digital budget, the most operationally complex channel to manage through a third party, and the one where fee structures and supply-path economics are hardest to see into. It is also where a brand's most valuable assets—its audience segments, rate intelligence, and performance history—are most likely to live inside a platform the agency controls, which makes the ownership question particularly acute.
Though programmatic garners significant attention when it comes to in-house advertising, the same ownership and transparency questions apply to other digital channels as well. What unites brands evaluating some level of in-house media buying is a desire for more control over spend visibility, optimization speed, and first-party data, three things that get harder to guarantee when a third party sits between the brand and its media execution, whatever the channel.
In-house advertising exists on a spectrum. Some brands in-house everything; many in-house one or two channels and keep agency support for the rest; others stay fully outsourced but insist on owning the underlying technology contract and data.
The right operating model is the one that matches your media spend, your in-house talent, and how much operational overhead you are willing to carry, which is why three brands of different sizes can make three different, equally correct choices.
The brand briefs an agency, which then plans, buys, and reports on its behalf. This model demands the least internal capability and offers the most immediate access to specialized talent and cross-client scale. The cost is distance: The brand often sees results rather than the levers behind them, and fee structures and the gap between what the brand pays and what reaches the publisher are not always fully visible.
The brand handles some channels or functions internally and retains agency support for others, often keeping paid search or social in-house while leaning on the agency for complex programmatic, or owning the platform and data while the agency provides the activation muscle. For most brands, this is a pragmatic destination. It captures transparency where it matters most while preserving agency expertise where the internal team is still ramping. Brands can split media operations between in-house teams and agency partners when the underlying platform supports shared access and clean handoffs. Basis supports this shared setup through Unify, giving in-house advertising teams and agency partners access to the same campaigns, data, and reporting so work can move between them easily.
The brand runs planning, buying, optimization, and reporting end to end. This model delivers maximum transparency and typically complete data ownership, but it also concentrates the operational burden (including hiring, training, technology, and process design) entirely on the brand. It rewards organizations with sufficient spend, existing media operations talent, and executive patience, and can be challenging for those that underestimate any of the three.
The table below summarizes how the three models compare across the factors brands weigh most. Data ownership is deliberately absent. It is the one dimension that tracks the platform contract rather than the operating model—whether a brand in-houses its media, fully outsources, or runs a hybrid.
| Dimension | Fully Outsourced | Hybrid | Fully In-Housed |
| Transparency | Can be limited into fees and supply path | High where brand operates directly | Full cost and supply visibility |
| Internal capability needed | Minimal | Moderate and growing | Substantial |
| Optimization speed | Bound by agency cycles | Fast on owned channels | Fastest; no external approval layer |
| Best fit for | Lower spend, lean teams | Most brands building capability | High spend, mature media teams |
A sound in-house advertising decision starts with four questions. The first question—whether you own your media data and can see what is happening with your spend—is one brands often underweight. The rest center on internal capability, cost, and the agency relationship, and are the practical considerations that determine if, how, and when to move.
This is the question many brands discover too late, and it has two halves: custody and visibility.
Custody typically depends on who holds the platform contract, not who runs the campaigns. When an agency manages buying on a platform it controls, the brand's campaign history, audience segments, rate intelligence, and performance benchmarks live inside the agency's environment—and can walk out the door when the relationship ends. That is a big reason agency transitions are so disruptive and expensive, and a dimension brands often overlook going in. The fix is contractual, and whether the right contract is available to you depends on the platform you build on.
Visibility is the other half. When buying runs through an agency on a platform the brand cannot see into, the gap between what the brand pays and what reaches the publisher—sometimes referred to as a “tech tax”—is not always disclosed, and neither are the data and platform fees layered on top. Real transparency means seeing performance, budgets, and spend across every channel in real time, rather than waiting on a monthly report assembled by someone else. That visibility is what lets a marketing leader defend the budget internally, catch waste early, and optimize on evidence rather than on an agency's summary.
Both halves are achievable without going fully in-house. The deciding factor is whether the platform surfaces the underlying data and spend to the brand, regardless of who operates the campaigns. Solutions like Unify by Basis are designed to allow brands to own their media data and technology, treating that ownership and line of sight as the default rather than a concession.
Capability is the dimension that most often decides whether in-house media buying improves performance or quietly degrades it. Brands that bring buying in-house without the right people and processes can end up worse off than they were with their agency. A functioning in-house operation generally needs several key roles, which smaller teams may choose to consolidate across fewer senior hires early on:
Technology is the other half of capability, and the two have to scale together. A skilled buyer limited by manual processes is as much a bottleneck as a powerful platform with no one to run it. The practical move for most brands is to consolidate buying and reporting into a single workflow through an omnichannel advertising platform, rather than stitching together point tools. A unified platform lowers the technical bar for a new in-house team and shortens the ramp.
The cost of in-house advertising depends on which model you choose and how fast you are able to move. A fully in-housed media buying operation carries fixed costs, including salaries for a buyer, planner, ad ops lead, and analyst, plus the platform fees and data subscriptions that come with running media directly. For brands with smaller budgets, these costs can be prohibitive and are part of the reason hybrid models are so common: They let a brand absorb the fixed costs gradually as internal capability grows, rather than front-loading them on day one.
The less visible cost is often the transition itself. Ramp time, parallel operations while the agency hands off, and recruiting and competing for experienced hires are real and worth budgeting for. As such, brands that plan a transition with overlap tend to come out cleaner than those that try to flip a switch.
Brand-agency relationships are under strain: In the 2026 Basis Advertising Agency Report, 54% of agency professionals said client tensions have increased over the past two years. That strain is often part of what puts evaluating in-house media buying on the table, and part of why the move is so often framed as “firing” the agency. The more durable framing, however, is renegotiating the division of labor. The strongest brand-agency relationships are built on trust and continuity, and a brand that owns its platform and data can restructure the relationship—shifting some channels in-house, keeping the agency on others—without the steep cost of a full review and re-onboarding.
That said, owning your platform and data is not simply a hedge against a messy agency breakup. Brands with strong agency relationships benefit too, as increased visibility can improve collaboration.
The platforms best suited to in-house and hybrid media buying models share one defining trait: The brand holds the technology contract and the data, with direct visibility into spend and performance. That combination, ownership plus transparency, is what lets a brand run its own media, see what every dollar actually buys, and shift work between internal teams and agencies as its needs change.
Unify by Basis is a solution specifically for brand-side control and closer brand-agency collaboration. It allows brands to retain control of their media stack and data, unify performance across channels, and partner more effectively with agencies. Powered by the Basis platform—which unifies programmatic, direct, search, social, and CTV— its capabilities include:
The practical effect is that a brand does not have to choose its operating model once and live with it. It can sit anywhere on the outsourced-to-in-housed spectrum, and move along it over time, while keeping ownership of the asset that matters most: its data. The result is faster optimization, full spend visibility, and audience and performance data that compounds inside the brand rather than walking out with the next agency change.
Bringing advertising in-house is a significant decision, but it does not have to be an overwhelming one, and it does not have to be all-or-nothing. Start with the questions that matter most—for example, “Do you own your data, and can you see your spend?”—then weigh the practical considerations of cost, capability, and the agency relationship against them. For most brands, the answer is some form of hybrid rather than a full leap, and the right model is the one that keeps the brand in control no matter who runs the campaigns.
The technology you choose shapes how well any of these models works, because the platform is what determines whether your data stays yours.
Basis keeps brands in control across programmatic, direct, search, social, and CTV. Unify, the Basis solution for brand-side control and brand-agency collaboration, combines the automation, transparency, and flexible services that make in-house and hybrid media buying models manageable from day one—so brands can move faster on optimization, defend budget with real-time spend visibility, and protect the audience and performance data that compounds over time. Contact us to talk through the model that fits your brand.
What is in-house advertising?
In-house advertising is when a brand takes direct ownership of the media buying that agencies have traditionally handled, including the demand-side platform, audience data, campaign workflow, and reporting. Instead of briefing an agency and receiving results, an in-housed brand operates the buying platform itself and keeps the resulting data inside its own ecosystem. In practice, brands adopt this on a spectrum, from moving a single channel in-house to running the entire operation internally.
What is programmatic in-housing?
Programmatic in-housing is when a brand takes direct control specifically of its programmatic media buying, typically including the demand-side platform, the audience targeting, the real-time bidding, and the performance data those campaigns generate. It is often a starting point for in-housing because programmatic tends to be the largest digital spend category, the most operationally complex, and the channel where fee and supply-path transparency really matter.
What are the benefits of in-house advertising?
The primary benefits are speed (optimization without agency approval cycles), transparency (full visibility into fees, data costs, and what actually reaches the publisher), and control over first-party data (audience segments and performance history stay inside the brand's ecosystem). In-house advertising teams also build institutional knowledge of the brand's audiences and what works over time.
How do brands bring media buying in-house?
Brands bring media buying in-house by assessing readiness across spend, talent, data strategy and executive support; securing access to an omnichannel media buying platform (ideally one that keeps data and the contract with the brand); hiring or developing core roles; and phasing the transition channel by channel rather than all at once. Many brands use a flexible services model during the transition, drawing on outside expertise while the internal team builds capacity.
Can brands split media operations between in-house media buying teams and agency partners?
Yes. A hybrid model runs some channels in-house while the agency handles others. It works when the platform supports shared access, role-based permissions, and clean handoffs so both sides operate in one system. Basis supports this shared brand-and-agency setup through Unify.
Which advertising platforms let brands keep their data when switching agencies?
Platforms that offer solutions designed for brand-side control keep the technology contract and first-party data with the brand rather than the agency, so campaign history, audience segments, rate intelligence, and performance benchmarks stay portable through an agency change. Unify by Basis is one such solution: A brand's first-party data always belongs to it and can be moved or shared when switching partners or working with several at once.
Which platforms help brands own their media data and technology?
Platforms that help brands own their media data and technology keep both the platform contract and first-party data with the brand rather than the agency. Unify by Basis is one such solution. With Unify, a brand's first-party data always belongs to it and stays movable, and the brand keeps direct visibility into spend and performance across every channel. That ownership holds whether the brand runs a fully in-housed, hybrid, or fully outsourced model, and it means audience and performance data compounds inside the brand instead of walking out with the next agency change.
Which platforms support programmatic in-housing?
The platforms best suited to programmatic in-housing keep the technology contract and the data with the brand, give the brand direct visibility into spend and performance, and support all-channel activation from one interface. Unify by Basis specifically supports this, supporting fully in-housed, hybrid, and outsourced models on the same underlying platform.
What advertising platforms are designed for brand-side media teams?
Platforms built for brand-side teams prioritize data ownership and portability, cross-channel transparency, all-channel activation from one interface, and flexible expert services a brand can scale up or down. Unify by Basis is a solution that allows brands to retain control of their media stack and data, unify performance across channels, and partner more effectively with agencies, supporting fully in-housed, hybrid, and outsourced models on the same underlying platform.
Key Takeaways:
In 2026, advertising leaders are grappling with a defining question: Where does AI drive better results, and where is the human touch a competitive differentiator?
AI use among marketers has skyrocketed in a short period of time. Just a few years ago, almost a full third of marketing organizations weren’t using generative or agentic AI at all. Today, over 60% of agency professionals say they use AI tools daily. But leaders are still figuring out how to implement and scale AI effectively: Driving and demonstrating ROI from AI tools remains a challenge, with fragmented and low-quality data serving as a major barrier.
At the same time, as AI is integrated into advertising platforms, it’s shifting how advertisers work. Within walled gardens, advertisers have gained some visibility into how targeting and placement decisions get made, but it remains limited. Transparent programmatic environments still offer visibility and control, though optimization within them is becoming more algorithmic and less deterministic as well. These shifts align with one of AI’s core strengths: processing massive data sets to make faster, more precise decisions than manual optimization ever could.
In this new era of AI-led digital advertising, creative has become a critical point of human control and ingenuity. With so much of the campaign process automated with AI, strong creative allows advertisers to capture attention and build trust with consumers, while also positioning AI tools to achieve better outcomes.
One of the biggest AI-driven shifts advertisers are adapting to in 2026 is reduced control over campaign builds and targeting. For years, advertisers spent much of their time tweaking intricate pieces of their media strategies, from targeting parameters to placements, in pursuit of better results. As AI tools have evolved, advertisers have had to give up some of that visibility and control. Walled garden advertising environments like Meta operate as black boxes, giving advertisers little insight into who they’re actually reaching. This evolution extends to other programmatic channels as well, where AI increasingly automates bidding, optimization, and audience discovery.
Planners and buyers have voiced real concern over this loss of manual control. It’s a big adjustment to not be able to say, “I want this specific message going to this specific person.” But as AI takes on more of that targeting work, planners and buyers can put more of their time into the broader campaign strategy and performance decisions that drive results. However uncomfortable this adjustment feels, it reflects where advertising is headed, and the advertisers who adapt fastest are learning to work strategically within these new systems.
Placing more of an emphasis on strong creative is a key piece of that adaptation. With audience identification shifting from fixed definitions to signal-based discovery, platforms increasingly rely on creative performance itself to learn who to reach, rather than predefined segments. Providing AI advertising tools with strong creative assets designed to land with a variety of different target audiences, then, helps AI algorithms learn more and perform better.
The industry has needed this course correction for a while. With the rise of programmatic advertising, advertisers’ focus shifted to targeting precision, and creative quality took a back seat. But creative is a major driver of advertising effectiveness: When campaigns are awarded for creativity, their likelihood of also winning effectiveness awards more than doubles, from 20% to 42%.
Creative is also an impactful point to integrate the human touch into advertising. AI is increasingly able to help humans create assets more efficiently and at scale, but I believe consumers react better to human-led creative. One recent study found that while AI can efficiently create credible assets, human work consistently performs better in terms of emotional engagement and driving business outcomes. And while AI can accelerate creative execution, it can't do the work of creative strategy: deciding what a brand should stand for, which consumer tensions are worth speaking to, which cultural moments to respond to, and so on. That strategic thinking is where humans deliver value that AI models can’t match.
Ultimately, as AI continues to evolve how advertisers work, success depends on reevaluating the role of creative and pairing a strong creative strategy with the right AI-driven advertising tools.
Two steps are key for leaders looking to successfully adapt to the creative opportunity in AI-led advertising:
In the traditional media planning model, creative was thought of as an asset to be placed. Today, leaders should think about it as a lever to be pulled. That's because in AI-led advertising, creative helps determine the audience, supplying the engagement data that guides where the algorithm delivers a campaign.
In practice, using creative as a lever looks like developing a variety of strong creative iterations crafted to resonate with target audiences, then allowing AI advertising platforms to deliver them to the right people. That variety should be strategic: Each iteration should speak to a distinct motivation, tension, value proposition, or proof point, giving the platform different messages to match with different people. Most audience segments contain micro-communities that advertisers can't fully define in advance—enough creative variety allows platforms to discover them, reaching segments that manual targeting would have missed. Then, once a campaign wraps, advertisers can assess how their creative performed and identify how to improve on the messaging itself.
This approach also represents a powerful way for brands to differentiate themselves. When advertisers all use the same AI-powered tools and platforms, outputs can tend toward the generic. The creative inputs and signals advertisers feed into those tools are what set the results apart.
This mindset shift may lead some advertisers to invest more in creative than they have in the past. For teams that are used to leaning on targeting parameters to do the heavy lifting, this is a real adjustment. But the teams making that adjustment now will be better positioned as AI takes on more of the work that manual targeting used to handle.
The relationship between creative and AI in advertising is reciprocal. Strong, varied creative gives AI tools richer signals to learn from, and in return, those tools deliver each message to the audiences most likely to respond, including segments advertisers couldn't have defined on their own.
That reciprocity is why advertisers’ choice of AI tools matters so much. The stronger the platform, the more value flows back to the creative. In particular, platforms that offer transparency and omnichannel activation give AI the best conditions to perform:
While transparency into AI-led advertising functions within black boxes such as social media and retail media platforms is limited, advertisers should work to create transparency wherever possible. Transparent programmatic platforms, for example, show advertisers not just what performs, but where and why. That feedback loop fuels strong creative strategy.
The best AI advertising tools provide transparency as well. Advertisers benefit from working with tools that provide visibility into the data and reasoning behind each recommendation, as they can assess those recommendations alongside their own judgment.
One of the biggest challenges caused by the complexity of today’s digital media environment is that campaign insights often sit siloed across platforms and channels. When performance data lives in separate systems, advertisers risk missing patterns that only show up when there’s cross-channel visibility. Advertisers working within a unified platform, meanwhile, can ground their strategies in one consistent data set instead of piecing together fragmented reports.
When multiple channels sit within the same system, advertisers can identify creative patterns across platforms, turning creative into an even sharper lever for performance. Even more, a unified data foundation gives AI tools a more holistic set of inputs to learn from, strengthening optimization across every channel.
Success in this new era of digital advertising depends on strong creative paired with the innovative use of AI. AI-led advertising capitalizes on the technology's ability to make decisions and adjustments based on massive data sets, while strong creative provides consumers with the human touch that drives authority and trust.
When the two work together, each strengthens the other, driving better outcomes for brands and advertisers.
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Curious how marketers across brands and agencies are operationalizing AI? We surveyed advertising professionals on adoption trends, performance gains, and where implementation still falls short. Check out AI and the Future of Marketing for the full findings.
Ad curation is the practice of pre-selecting and bundling brand-safe, high-quality ad inventory, often paired with first-party data, into deals that advertisers buy programmatically. This helps them reduce waste, improve transparency, and reach target audiences in trusted environments.
Ad curation has evolved from a niche programmatic tactic to a strategic necessity for many advertisers navigating quality concerns, budget scrutiny, and supply chain opacity. As spending on curated deals climbs and transparency becomes non-negotiable, marketing leaders need to understand what's driving this shift and how to leverage curation effectively.
Key Takeaways:
Ad curation isn’t a new concept, but in 2026, advertisers are embracing it with renewed urgency.
From widespread bot traffic and heightened brand safety concerns to the proliferation of made-for-advertising (MFA) sites, industry challenges are creating considerable stress for marketing teams. CMOs are under pressure to do more with less budget. Agency leaders are caught between rising operational costs and shrinking profit margins while trying to prove value to clients. Meanwhile, media buyers must deliver results in a digital ecosystem that’s seemingly growing more complex and fragmented by the day.
In this context, it’s no wonder ad curation is gaining momentum. As advertisers seek control, efficiency, and transparency, spending on curated inventory is rising fast, with programmatic advertisers expected to spend more than twice as much on private marketplaces (PMPs)—a common format for curated deals—as on the open exchange this year. By enabling advertisers to access pre-vetted, curated inventory enriched with data signals like first-party data, strategic curation allows teams to invest in quality, avoid waste, and connect with target audiences in key moments of impact. And for leaders, it can offer a way to bring clarity to complexity: streamlining media strategies, prioritizing brand safety, and delivering performance that’s easier to measure and defend.
By understanding what’s driving the current momentum behind ad curation and applying it intentionally, advertisers can drive stronger outcomes, reach people in trusted environments, and prove the ROI of their media investments to key stakeholders.
Intentional media buying has become a strategic necessity. In a digital landscape plagued by quality concerns and growing performance pressure, advertisers are becoming far more discerning in where they invest.
Concerns around waste, brand safety, and personalization are at the forefront for most marketers. One recent report found that roughly $701 million—representing 10% of global open programmatic ad spend on websites—went to MFA sites in Q1 of 2025 alone. Bot traffic is on the rise, now accounting for 37% of all internet traffic. For the first time in a decade, automated traffic has overtaken human traffic, a trend largely driven by the explosion of low-quality, AI-generated content. At the same time, signal loss and rising privacy expectations are making it harder to reach target audiences using traditional identifiers.
Economic volatility only intensifies these pressures. As budgets tighten, scrutiny on media investments increases. Every dollar must work harder, and leaders are expected to prove ROI faster and more rigorously than before.
Curation can offer a powerful response to these many challenges. When implemented strategically, curated deals, often powered by first-party data activation and contextual targeting, can enable advertisers to deliver more relevant messaging in trusted environments—while also respecting user privacy. By bundling premium, brand-safe inventory with privacy-friendly data signals, well-executed curated deals can help advertisers reach audiences in the right moments, minimize waste, and drive measurable performance.
The growing emphasis on investment reflects a deeper recognition: In today’s media landscape, quality and performance are no longer separate considerations.
Low-cost media placements might require less investment, but they often underperform. Campaigns run on unvetted sites can suffer from low viewability, weak engagement, or poor conversion rates. These hidden costs—from wasted impressions to brand safety risks—can easily outweigh any initial savings.
In contrast, curated deals can allow advertisers to focus on inventory that’s prepackaged for relevance and engagement. For example, a baby and toddler brand running holiday promotions might use a curated deal built around parenting lifestyle content with high engagement from millennial parents. Or, a financial services company could activate a curated PMP featuring premium business publications, reaching high-intent readers in trusted, high-attention environments.
These kinds of curated activations allow advertisers to align messaging with contextually relevant content, avoid fraudulent or wasted impressions, and reach audiences in environments that drive stronger outcomes. In an economic environment where every dollar is under increased scrutiny, this kind of precision is critical. While curated media may not always offer the lowest upfront cost, it can often deliver stronger value by reducing waste and supporting more focused, results-oriented investment.
In addition to quality concerns, the conversation around programmatic transparency has reached a tipping point. For years, advertisers accepted opacity in the supply chain as an unavoidable cost of doing business. But in 2026, that indifference carries a price few organizations can afford.
Analysis by the ANA shows that only 36 cents of every programmatic dollar actually reaches publishers. The remaining funds are absorbed by intermediaries, platform fees, and layers of technical infrastructure that often provide little visibility or tangible value to advertisers. The scale of this inefficiency makes it difficult to ignore: In 2025, waste in programmatic spend was estimated to total about $26.8 billion globally.
Such inefficiencies directly erode campaign performance, weaken publisher sustainability, and undermine the business case for media investments. When nearly two-thirds of every dollar spent vanishes before reaching working media, leaders face an uphill battle defending budgets.
Curation can help close that gap. By providing supply path optimization (SPO) for tighter, verifiable supply paths, curated deals can give advertisers clearer visibility into where ads appear and how budgets are deployed. While curation does add another layer to the supply chain, it can also streamline the path between advertiser and publisher by consolidating spend with trusted partners and delivering greater transparency. And when used in conjunction with open inventory, curation can enhance programmatic scale, allowing advertisers to balance broad reach with verified quality and maintain flexibility across different campaign objectives.
The urgency around transparency reflects a practical challenge: Proving ROI is incredibly difficult when most of the budget disappears into the nebulous unknown. Without verified, contextually aligned impressions served to real people, performance metrics themselves become unreliable. Advertisers who treat transparency as a strategic function (rather than an afterthought) gain a measurable advantage: 41% of marketers see curated deals as a path to higher ROI, driven by reduced waste and stronger performance in trusted environments.
As media buying becomes more complex, efficiency and precision are growing increasingly important. For advertisers facing quality and transparency challenges, curated deals can support both goals—not by replacing DSPs, but by working in tandem with them.
By filtering inventory based on performance criteria, brand safety, and contextual relevance, curation simplifies the early stages of the media buying process. By the time this inventory enters a DSP, it is already optimized for different campaign goals, allowing buyers to focus on optimizing bids and managing campaign performance in real time.
This can help improve operations across the board. When incorporating curation within a holistic media strategy, teams spend less time fixing inventory issues and more time refining strategy. And when curated inventory is available within a unified platform that also includes search, social, and open marketplace programmatic, the benefits are even greater. Rather than managing multiple point solutions and reconciling data across different dashboards, teams gain a more streamlined workflow, cross-channel optimization, and unified reporting.
Together, these capabilities help advertisers to reduce waste, improve outcomes, and respond more confidently to performance expectations. And for CMOs and agency leaders, they provide better visibility into campaign performance, which is key for building compelling business cases for media investments.
To get the most from curated media, advertisers need to take a thoughtful approach. Teams should look for partners and integrations that are clear about how inventory is curated—including the data used, the filtering logic applied, and the deal structures or fee arrangements behind it. Transparency is essential, especially as more solutions in the market begin labeling themselves as “curated” without meaningful differentiation. As curation gains momentum, advertisers should be wary of vendors repackaging standard programmatic inventory under the “curated” label without implementing rigorous quality controls or supply path optimization. This risks teams paying premium prices for deals that don’t deliver measurably better performance or transparency than open exchange alternatives.
Additionally, given that curation adds another layer to the supply chain, advertisers should evaluate whether the performance lift justifies both the additional cost and complexity. Structured testing that measures incremental value against these trade-offs can help determine when curation delivers meaningful returns.
Taking a holistic approach is also critical. Curated media shouldn’t operate in isolation. The most effective strategies embed curation into broader omnichannel campaigns—and ideally within platforms that support unified workflows across multiple channels. This ensures that curated media supports larger business goals and works alongside other channels and strategies.
Finally, performance measurement is key. Advertisers should track how curated media performs compared to non-curated environments, with attention to both engagement and cost efficiency. Premium pricing may be justified if the results are there, but it takes intentional measurement to know when and why that’s true.
The momentum behind ad curation reflects a fundamental shift in how advertisers approach programmatic investment. It signals that advertisers are moving away from purely volume-driven tactics and instead prioritizing inventory that delivers measurable impact while maintaining brand safety standards.
For agency and brand leaders, this shift presents both an opportunity and a strategic imperative. The open exchange remains an important source of reach, but in 2026, it’s increasingly paired with curated strategies that bring added control. Together, they enable advertisers to pursue growth without compromising accountability.
As generative AI and advanced data signals continue to evolve, curation will grow even more sophisticated, enabling more precise contextual targeting while maintaining privacy standards. Those who integrate curated approaches in a thoughtful way now—combining them with data-driven strategies and cross-channel optimization—will be better positioned to demonstrate clear ROI, defend investments to key stakeholders, and gain a competitive advantage in the increasingly complex digital media landscape.
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Want deeper insights into the trends shaping advertising this year? Our 2026 Trends Report: Rewinding to Fast Forward explores the forces shaping media quality, transparency, and performance—including how to turn the industry’s quality quandary into a strategic advantage.