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The holiday season is won months before checkout
The 2026 season is longer, more digital, and more deliberate than the one before it. Shoppers now start earlier, compare across more channels, and lean on search, social, and AI long before a brand enters the picture. This report shows you where consumers actually form their decisions, so you can plan campaigns around how people shop instead of how we assume they do.
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.
What does growth marketing look like at one of the world's most iconic brands? In this episode, Ashley Travis, Head of Growth Marketing at Pizza Hut, shares the strategies shaping the QSR giant's future.
Together with host Noor Naseer, Ashley covers loyalty, martech, CRM, digital experiences, and the challenge of competing in a crowded category. She also shares lessons from her past life at Starbucks, explains why nostalgia is a competitive advantage, and reveals the metrics her team focuses on to drive sustainable growth.
Key Statistics:
Key Takeaways:
Marketing teams have moved quickly to adopt AI. But when it comes to delivering measurable returns on those investments, most organizations are still finding their footing.
For the second consecutive year, agency leaders named AI as their top investment priority, with more than three-quarters planning to increase their AI spend over the next 12 months. However, only about 29% of organizations across sectors say they can dependably measure ROI on their AI initiatives, and CEOs report that just 25% of their AI initiatives have delivered expected ROI.
Right now, the gap between investment and demonstrated impact is significant. Closing it requires a deliberate approach to how tools are selected, how a strong foundation is built before implementation, and how their impact gets translated into stories that resonate with stakeholders.
Getting real returns on AI starts well before a tool is ever deployed. Key steps include:
With a proliferation of AI solutions crowding the market, tool selection has become an increasingly consequential decision. According to Lauren Johnson, Effectiveness Lead at Basis, a deep understanding of how an AI tool works is the starting point for evaluating whether it will deliver meaningful results.
"If we are going to ask AI to help us evaluate datasets, we need to understand how it's executing that task," says Johnson. "What are its outputs based on? What historical data is it drawing from? Marketers need to understand how their tools work in order to assess whether they'll be able to drive the impact they're looking for."
Given the state of the market, deep evaluation is critical: Gartner has warned that many vendors are engaging in “agent washing,” or positioning existing products as agentic AI without adding genuine agentic functionality. Of the thousands of vendors pitching themselves as agentic AI providers, Gartner estimates only around 130 actually deliver on that promise.
A deeper understanding of how AI tools work also makes the case for seeking out specialized solutions over generic ones. Out-of-the-box AI solutions have strengths when used strategically, but specialized tools trained on large volumes of relevant industry data tend to produce more precise and differentiated outputs. An AI media planning tool trained specifically on advertising performance data, for example, can generate cross-channel recommendations grounded in real campaign outcomes—something a general-purpose model isn't equipped to do.
Data quality is a make-or-break factor for AI performance, but most marketing organizations aren't yet where they need to be. Only 21.4% of industry professionals describe first-party data as “foundational” to their organization's AI initiatives, and roughly one-third say first-party data plays little-to-no role in their current AI use at all. Half of leaders also say their businesses don’t have the technical or data stack readiness to support AI agent deployment.
Without clean, unified, accessible data, AI tools produce outputs that are generic at best and misleading at worst—neither of which supports the kind of differentiated results that justify continued investment. Leaders must treat data readiness as a strategic prerequisite. That involves:
Finally, understanding the impact of AI investments requires knowing exactly what success looks like before deployment. “Just like with anything in our world,” says Johnson, “assessing effectiveness starts with setting a goal for what you want the tool to do for you.” If saving time on a specific workflow is the goal, for instance, measure how long that workflow takes today and define a clear target for how much AI should reduce it.
Currently, only 40% of marketing professionals are using or planning to use defined KPIs specifically for their AI solutions. Crafting a phased roadmap for AI adoption, with concrete milestones and tool-specific performance targets, gives teams both a framework for evaluating impact and the foundation for communicating that impact to stakeholders.
How marketing teams use and communicate about their AI investments is just as important as laying the groundwork for them to succeed. The teams best positioned to demonstrate impact tend to:
To achieve maximum value from AI tools, humans must consistently scrutinize what they produce. The technology can provide weak or inaccurate outputs if trained on low-quality data, and of course, there’s the matter of hallucinations: One study found that close to half of marketers spot inaccuracies in AI outputs several times a week.
“AI tools are not going to tell you when they're wrong,” notes Johnson. “Teams need to continually push back against and stress-test AI outputs in order to get real value.”
Leaders should nurture a team culture where pushing back on AI outputs is both expected and encouraged. When that standard is set by leadership and integrated into how teams operate on a daily basis, it drives higher-quality AI usage across the board. Teams that operationalize this approach are better positioned to extract demonstrable value from their AI investments over time.
Proving AI’s value to stakeholders is a significant challenge for marketing teams in 2026. In fact, only 41% of marketers can confidently prove out the ROI of their AI investments, down from 49% in 2025.
This is where the goals and KPIs set during the planning phase become essential. Tracking performance against those benchmarks over time—whether that’s hours saved on a specific task, improvement in campaign performance, or lift in a business outcome—gives teams the evidence they need to make a credible case for AI’s impact.
Beyond quantifying AI’s impact, marketing leaders must strategize around using that evidence to craft compelling narratives for stakeholders. For one thing, while AI can (and should) drive meaningful improvements in speed and cost, leaders should focus more on how their AI investments contribute to business outcomes. AI efficiency gains can be significant and are worth including in these stories, but positioning AI’s value primarily around efficiency risks reinforcing the perception of marketing as a cost center rather than a strategic growth driver.
Connecting investments to business outcomes is also what tends to land with senior decision-makers. The most resonant executive narratives tie major investment asks to concrete business drivers that connect marketing’s AI investments to the revenue and business outcomes that sales and finance leaders care about.
Another key to making those narratives credible is grounding them in a deep understanding of the tools themselves. “Executives want to hear that we're using AI, but they also want to know that it's grounded in real data,” says Johnson. “They want to know what’s powering these tools and that they can trust the outputs.”
That deep understanding is part of what makes an AI narrative credible at the executive level. Leaders who build expertise in how their tools work, what data powers them, and how that functionality is advancing business goals will be poised to earn sustained buy-in.
The ability to drive and demonstrate ROI on AI tools is quickly becoming one of the clearest dividing lines between marketing teams that lead and those that fall behind.
Ultimately, three factors determine whether AI delivers ROI in marketing: Selecting tools with a deep understanding of how they work, fueling them with the data infrastructure they need to perform, and translating their impact into narratives that resonate with stakeholders.
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Want more insights on how marketing teams are approaching AI? Our AI and the Future of Marketing report synthesizes findings from a proprietary survey of professionals across leading brands and agencies, covering adoption trends, workforce shifts, and the biggest barriers teams are still working through.
Advertising automation is the use of software and AI to plan, activate, optimize, and reconcile ad campaigns with reduced manual intervention. An AI advertising platform brings these capabilities into a single system, spanning media planning, buying, optimization, measurement, and billing across programmatic, search, social, and CTV, rather than a stack of single-purpose point tools. This guide explains what advertising automation delivers in 2026, where vendor claims outrun reality, and how to choose an AI advertising platform.
Key Takeaways:
The best tools for automating media operations are systems that unify campaign planning, activation, optimization, reporting, and reconciliation across programmatic, direct, search, social, and CTV. But while these systems are optimal, many advertisers in 2026 are operating from a far different place, piecing together a stack of single-purpose point solutions.
According to Basis’ 2026 Advertising Agency Report, 36.8% of full-service and media agencies now manage ten or more tools to run their clients’ campaigns, more than double the 2024 share. In the same Basis research, agencies named inefficient processes (44.1%) and siloed, disconnected systems (40.4%) as their top operational challenges.
Today’s advertisers face a market full of vendors promising automation, and a buying environment where the line between genuine operational lift and AI-flavored marketing copy is increasingly blurry. This guide breaks down what advertising automation can actually deliver in 2026, where marketing promises run ahead of platform reality, and how media teams should evaluate platforms for the next stage of automated advertising: the autonomous era.
Advertising automation maturity has four tiers: rule-based automation, semi-automated workflows, algorithmic optimization, and autonomous advertising. Advertising automation lives on this spectrum, and knowing where a platform actually sits is the first filter for any serious evaluation.
At one end: rule-based automations, such as “if-then” logic that pauses a campaign at a spend cap or fires a pacing alert when delivery falls behind. At the other end: autonomous advertising, wherein AI systems plan, execute, and optimize campaigns end-to-end with humans in an oversight role rather than an execution role.
Most of what the industry markets as “automation” today sits in the middle of that spectrum. Rule-based and semi-automated workflows deliver reliable, measurable time savings—and for many teams, they are an impactful capability a platform can offer.
The teams getting the most out of automation today are the ones evaluating across all four tiers. They also scrutinize every ‘autonomous’ claim, checking where a platform truly lands on this spectrum rather than where its marketing says it does.
A practical way to categorize where a platform’s automation actually operates:
| Automation Tier | What it does | Maturity in 2026 |
| Rule-based automation | Predefined if-then triggers and actions (ex. pause at a spend cap, fire off a pacing alert) | Reliable, generally transparent, widely available |
| Semi-automated workflows | Templates, bulk operations, and guided processes that cut manual steps but still need human initiation | Common today |
| Algorithmic optimization | Machine learning adjusts bids, budgets, and targeting within human-set parameters | Maturing, in production |
| Autonomous advertising | AI plans, executes, and optimizes independently, with humans in oversight rather than execution | Emerging, evaluate on a platform-by-platform basis for real capability |
In 2026, four categories of advertising automation are delivering genuine, measurable time savings for media teams right now: automated planning, automated performance, automated measurement, and automated billing. Each of these maps to a stage of the campaign lifecycle where manual work has historically consumed the most hours.
| Automation category | What it automates | Measured result with Basis |
| Automated planning | Turning briefs into omnichannel media plans | Compass by Basis builds media plans 50% faster |
| Automated performance | Bid optimization and mid-flight budget reallocation | SmartBid drives 36% lower CPA and 35% higher CTR |
| Automated measurement | Normalizing cross-channel data into client-ready reporting | Unified dashboards across programmatic, search, social, and CTV |
| Automated billing | Reconciliation and invoicing from plan to payment | Basis delivers a 15% average reduction in time to collect |
Automated planning: AI-driven media planning translates briefs into omnichannel strategies in minutes, pulls in past performance to sharpen recommendations, and exports presentation-ready plans without the manual build—all within the same platform where campaigns are eventually activated. The 2026 Advertising Agency Report found that only 29.1% of agencies currently use AI for media planning and 22.1% for media buying strategy—among the lowest adoption rates of any AI use case, despite both being among the highest-leverage. AI advertising platforms that handle planning natively, like Compass by Basis, allow teams to create media plans 50% faster.
Automated performance: This is where many teams already feel the impact: algorithmic bid optimization, real-time budget reallocation, and continuous mid-flight optimization that’s difficult for human teams to replicate at scale. For instance, Basis’ SmartBid AI optimization tool drives a 36% decrease in cost per acquisition and 35% increase in CTR for clients deploying it across their programmatic campaigns. This is also a tier where ‘AI-powered’ claims can be easy to test. Rather than asking whether a platform optimizes bids and budgets (since nearly all of them do), ask how much it moved CPA and CTR and how consistently.
Automated measurement: Pulling performance data from a fragmented stack, normalizing metrics across channels, and assembling client-ready reports is one of the most time-intensive jobs in media operations. Unified dashboards that aggregate programmatic, direct, search, social, and CTV data into a single view eliminate hours of weekly spreadsheet work with live reporting teams can actually trust, and they connect into the tools agencies already run on—such as Looker, Datorama, TapClicks, and Ninjacat—rather than forcing a rebuild.
Automated billing: Reconciliation and invoicing remain among the least glamorous, most time-consuming, and most error-prone work in media operations. Spend has to be matched, contracts have to tie out, and invoices can’t go out until the numbers agree. Platforms that offer automated billing, like Basis, treat this as a closed loop from first media plan to final invoice, keeping planning, activation, reporting, and reconciliation in one system rather than handing data between disconnected tools and finance teams. With Basis, this results in a 15% average reduction in time to collect. Many ‘automation’ pitches skip this stage entirely, so it’s worth asking a vendor how much of the plan-to-payment cycle actually runs without manual intervention.
Not every automation claim holds up under operational scrutiny (especially when AI is involved), and the gap between marketing and infrastructure is where many evaluations break down. That said, the right kind of skepticism is not anti-AI. Rather, it’s a demand for specifics: What the AI actually does, where it operates in the workflow, what data is powering it, and what it measurably changes.
Separating real automation from marketing comes down to recognizing a few recurrent patterns:
Red flag: “AI-powered” with no specifics
What model powers each capability? The “AI” label has become so broadly applied that it has lost meaning without further elaboration. A platform using a rules engine to automate a workflow is not the same as one using machine learning to optimize in real time, yet both often market themselves as AI-driven. When evaluating platforms, ask what specific model powers each capability, what data it trains on, how often it retrains, and how its recommendations get validated.
Red flag: black-box optimization
Can you inspect, override, and audit the AI’s decisions? Some platforms surface AI recommendations that the buyer cannot inspect, override, or audit. That works fine in a demo. It is a serious problem in a campaign where questions like “Why did we spend $10,000 on this placement?” need an answer the team can actually defend. Look for platforms that expose the logic, show the inputs, and let humans intervene at any step.
Red flag: optimization biased toward the platform’s own inventory
Does the AI favor its inventory or your outcome? Walled-garden AI tools—particularly the ones bundled with single-channel ad products—often optimize toward their own inventory by default. When evaluating an omnichannel platform, the question to ask is whether a platform’s optimization logic is built to favor inventory it controls or built to favor the marketer’s outcome. Some platforms own inventory and bias toward it. Some own inventory and choose not to. Some have no inventory stake at all. Basis, for instance, owns Basis DSP but does not optimize toward it; media decisions in Compass and SmartBid are made against marketer outcomes, not internal inventory preference. That distinction becomes even more important as autonomous systems take on a larger share of budget allocation decisions.
Red flag: instant, zero-effort onboarding
What setup does meaningful automation actually require? Vendors sometimes imply that automation works out of the box with no configuration, training, or workflow adjustment. In practice, meaningful automation requires setup: defining rules, mapping workflows, integrating reliable data sources, and training teams.
When choosing an AI advertising platform, evaluate it against six criteria that map to real operational lift: workflow coverage, integration depth, honest maturity-tier assessment, human-in-the-loop control, time-to-value, and total cost of ownership.
These criteria typically sort platforms into four broad groups. Where a platform lands shapes both its automation ceiling and whether its optimization works toward the marketer’s outcome or its own inventory:
| Platform type | Best for | Automation ceiling | Risk of inventory bias |
| Unified omnichannel platforms | Unified media planning through reconciliation across all channels | Algorithmic optimization across channels, with a foundation built toward autonomous workflows | Depends on platform |
| Legacy DSPs with AI add-ons | Programmatic buying with bolted-on optimization | Algorithmic optimization | Depends on vendor |
| Walled-garden / single-channel tools | Deep automation within one channel | Channel-specific optimization and automation | High (often optimizes toward own inventory) |
| Point solutions | A single workflow (ex. planning, reporting, or reconciliation) | Task-level automation | Depends on vendor |
Autonomous advertising runs on infrastructure, data, and interconnectivity. This is also the foundation for agentic advertising, where AI agents take on planning and optimization tasks independently while humans set strategy and guardrails. Whether described as autonomous or agentic, the operational prerequisites are the same. The teams ready for the next several years of AI evolution are the ones with the operational layer already in place: a connected platform that automates the campaign lifecycle, AI applied transparently against unified data, and human judgment looped in intentionally.
That is the foundation Basis is built around. Compass for AI-powered planning. SmartBid for performance optimization. Unified dashboards and automated billing reconciliation closing the loop from plan to payment. All inside a single omnichannel platform—connecting programmatic, search, social, site direct, and CTV—with the auditability and oversight media teams need to actually trust the AI underneath it.
What is the best AI advertising platform for agencies in 2026?
The strongest AI advertising platforms unify planning, activation, optimization, reporting, and reconciliation across programmatic, search, social, direct, and CTV in one system, rather than bolting AI onto a single channel. Evaluate them on workflow coverage, integration depth, and transparent, unbiased optimization. Basis is one omnichannel example built around this model.
What is advertising automation, and how does it work?
Advertising automation is the use of technology—including rule-based logic, algorithmic optimization, and AI-driven decisioning—to streamline tasks across the paid media campaign lifecycle: planning, trafficking, bid optimization, cross-channel reporting, and financial reconciliation. It reduces operational hours, minimizes errors, and frees talent up to focus on strategy.
How is advertising automation different from marketing automation software?
Advertising automation focuses on paid media operations across programmatic, search, social, direct, and CTV. Marketing automation software manages email workflows, lead nurturing, and CRM sequences. The two categories solve different problems and serve different teams within an organization.
Which platforms automate campaign planning and activation?
Platforms that automate planning and activation natively, rather than offering planning as a standalone module, are the ones delivering the most lift today. Look for AI-driven media planning that translates briefs into omnichannel strategies, exports presentation-ready plans, and activates campaigns with one click against live line items. Compass by Basis is one example, and teams that use Compass build media plans 50% faster.
Can advertising automation work across programmatic, search, social, direct, and CTV in one platform?
Yes. The key distinction is whether the platform offers true workflow integration—where data flows automatically between planning, activation, and reporting—or simply provides multi-channel access through separate modules. A unified omnichannel platform like Basis eliminates the manual data transfers and context-switching that fragment cross-channel campaign management.
What advertising tools reduce time spent on campaign reconciliation?
Reconciliation tools that match delivered spend against contracted terms, flag discrepancies automatically, and push actuals into ERP systems eliminate hours of spreadsheet work and reduce billing errors. Platforms like Basis handle reconciliation natively rather than requiring exports to a separate finance system.
What is autonomous advertising, and which vendors lead the category?
Autonomous advertising is AI-driven planning, activation, optimization, and reconciliation across the full campaign lifecycle, with humans in an oversight role. The leaders in the category are omnichannel platforms with AI built natively into the campaign lifecycle, unbiased regardless of inventory ownership, and transparent in how their optimization logic works.
How do I measure whether advertising automation is delivering real ROI?
Track operational metrics before and after implementation: hours spent per week on campaign setup, reporting, and reconciliation; error rates in trafficking and reconciliation; time from campaign brief to activation; campaigns or accounts each team member can manage; reduction in billing discrepancies. Compare those savings against the total cost of the platform.
Media is being rebuilt from the ground up. In this episode of Adtech Unfiltered, Axios media correspondent and CNN media analyst Sara Fischer joins Noor Naseer to unpack how AI, consolidation, creator-led businesses, and shifting consumer behavior are reshaping media.
They discuss why audience attention is more fragmented than ever, what publishers are getting right (and wrong), how AI is changing the economics of journalism, and why trusted brands still matter. It's an inside look at the trends shaping the future of media, advertising, and the business models that will determine who thrives next.
AI-powered strategic media planning tools are software platforms that analyze client briefs and automatically generate structured media plan drafts, including channel recommendations, budget allocations, and strategic guidance.
These tools address one of the biggest challenges facing agencies today: inefficient planning processes that drain time, create inconsistency across teams, and prevent planners from focusing on strategic work. As AI adoption accelerates across the industry, understanding how these tools work and who benefits most from them matters for teams looking to stay competitive.
Key Takeaways
An AI-powered strategic media planning tool is software that analyzes client briefs and generates a structured media plan draft, including channel recommendations, budget allocations, and tactical suggestions.
This technology works by interpreting natural language from client briefs and matching campaign goals with industry benchmarks and channel-specific insights. Rather than starting from a blank page, planners receive a structured plan they can refine and customize based on client needs as well as their own strategic and creative judgment.
For example, a tool like Compass by Basis reads a client brief, identifies campaign objectives and target audiences, and generates a complete omnichannel media strategy—including prioritized audience segments, competitive context, channel-by-channel budget allocations with visual breakdowns, and campaign flighting—all within a conversational interface inside the advertising platform where campaigns are activated. Planners can refine the strategy through follow-up prompts before building it into client deliverables or moving into activation.
Though powered by many of the same technologies, these tools differ from more general AI assistants or chatbots because they're purpose-built for media planning workflows. They understand advertising terminology, channel dynamics, and how to structure plans that translate directly into campaign execution. Compass, for instance, is built on Basis’ proprietary IMPACT omnichannel framework—a methodology used across thousands of successful media campaigns—rather than relying on generic AI reasoning alone.
According to the IAB State of Data 2025 report:
And, data from Basis’ 2026 Advertising Agency report finds:
This gap highlights where AI adoption has lagged most: operational planning, not creative execution.
Agencies who begin to implement AI toward operational inefficiencies now can gain a strategic edge, freeing up their teams to focus on strategy rather than manual tasks.
AI planning tools follow a structured process to convert briefs into actionable media strategies:
1. Brief Upload and Extraction
The planner uploads a client brief—whether a structured planning document or a simple prompt—and the tool extracts and summarizes key information. This includes campaign objectives, target audiences, budget parameters, KPIs, geographic focus, and timing constraints. In Compass, this extraction step is visible in the interface, so planners can confirm the tool understood the brief correctly before strategy generation begins.
2. Audience Strategy and Prioritization
The tool builds prioritized audience segments based on the brief, going beyond basic demographics. Each segment includes targeting rationale, recommended channels for reaching that audience, and messaging direction. This creates a strategic foundation where audience strategy, channel selection, and messaging are connected from the start.
3. Strategic Framework and Competitive Context
The tool generates a broader strategic framework that includes competitive context, key challenges, and a recommended approach, rather than just a channel list. This strategic layer guides the channel and budget recommendations that follow, grounding them in campaign-specific logic rather than general best practices. The framework also documents the channels the tool evaluated but chose not to recommend, with the reasoning behind each decision. This gives planners a defensible rationale to share with clients, showing that the recommended mix reflects deliberate trade-offs rather than default choices.
4. Channel Mix and Budget Allocation
The tool recommends a channel mix with specific budget allocations, including dollar amounts and percentage breakdowns with rationale for each channel. Visual outputs like budget allocation charts make it easy to see how spend is distributed and share recommendations with stakeholders. The tool can also generate multiple budget scenarios at different investment levels, each with its own channel allocation and rationale. When a client adjusts the budget or asks to see options, planners have ready-made tiers to work from instead of rebuilding the plan each time.
5. Campaign Plan and Flighting
AI maps out campaign flighting with budget allocation by phase, accounting for seasonal moments, tentpole events, and how different channels should ramp up or down throughout the flight. Channel-specific timing guidance ensures the plan reflects real-world campaign dynamics, not just even budget distribution.
6. Measurement and KPI Framework
The tool builds a measurement framework that maps KPIs to each channel’s role in the funnel, complete with relevant benchmarks. Planners get primary and secondary metrics for each channel, along with the business outcome each metric ladders up to. Having these benchmarks built in saves planners from researching performance standards across channels and gives clients a clear view of how success will be measured from the start.
7. Refinement and Strategy Delivery
The strategy generates within a conversational interface where planners can ask follow-up questions, request deeper analysis on specific sections, or adjust recommendations through natural prompts. The result is a complete, structured strategy that planners can refine and use to build client-ready deliverables. Because Compass lives inside the Basis platform, the strategy and eventual campaign activation share the same system, reducing the manual handoffs and reformatting that typically separate planning from execution.
When orchestrated by agentic AI media planning tools, this entire process can happen in minutes. What traditionally required multiple planning sessions, spreadsheet modeling, and cross-referencing past campaigns now generates automatically, giving agency talent more time to focus on strategic refinement and client-specific nuances.
AI media planning tools deliver several concrete benefits that address some of agencies’ most pressing operational challenges:
The time savings from AI planning tools come from automating specific tasks that eat up planners' days.
Take benchmark research. Manually researching industry benchmarks for CPMs, CTRs, and conversion rates across different channels takes significant time. AI tools have this data built in and automatically apply relevant benchmarks based on campaign parameters.
Budget modeling works similarly. Testing different budget scenarios manually requires rebuilding spreadsheets for each variation. AI tools can generate multiple budget allocation models instantly, letting planners compare approaches without manual calculation work.
Channel analysis is another time sink. Evaluating which channels make sense for a specific audience and campaign goal requires cross-referencing multiple data sources. AI planning tools synthesize this analysis automatically, presenting channel recommendations with supporting rationale.
Then there’s plan documentation—formatting decks, documenting strategic rationale, and creating presentation-ready outputs. AI-powered planning tools produce formatted plans that planners can review and refine rather than building from scratch.
With so many manual tasks wrapped up in drafting media plans, time savings derived from using AI-powered planning tools can add up fast. For instance, teams can create media plans 50% faster when using Compass by Basis, and that time savings can then shift to strategic consultation, client communication, or campaign optimization.
Consistency in media planning creates several advantages for agencies:
Standardized Strategic Approach: AI tools encode best practices into their planning logic. Every plan starts from the same strategic foundation: proven frameworks for audience targeting, channel selection, and budget allocation. This doesn't mean every plan looks identical, but it ensures no planner misses critical strategic considerations.
Quality Baseline for Junior Planners: Junior team members can often struggle without senior guidance. AI tools give them access to senior-level strategic thinking, helping them develop better plans while learning. The tool serves as a training resource that improves plan quality across experience levels.
Reduced Errors: Manual planning risks introducing errors such as calculation mistakes, overlooked channels, and misallocated budgets. AI tools eliminate these mechanical errors, catching issues before plans reach clients. This improves client trust and reduces the costly back-and-forth of fixing mistakes.
Scalable Quality Control: As agencies grow, maintaining consistent plan quality becomes harder. AI tools scale that quality automatically—the hundredth plan generated gets the same strategic rigor as the first.
These consistency benefits matter even more when you consider the tech stack complexity most agencies face. More than one-third (36.8%) of agencies now juggle 10+ tools in their tech stack—up dramatically from 17.3% in 2024—and managing that many disconnected systems can create inconsistency. When AI planning tools integrate into unified platforms where planning connects directly to activation, consistency extends beyond plan creation into execution. The fewer handoffs between systems, the fewer opportunities for plans to get lost in translation.
One concern about AI tools is the "black box" problem, i.e., a lack of visibility or understanding around how the AI reaches its recommendations. But well-designed AI planning tools address this through transparency features, providing rationale into their reasoning as well as ample opportunities for human interaction, iteration, and oversight.
IAB research finds that 51% of brands worry they don't have enough transparency about how agency partners use AI. Transparent AI tools that clearly show their work help address this concern: Agencies can demonstrate their value and provide visibility into their strategic process.
The key difference between manual and AI-powered media planning is how planner time is allocated: manual planning prioritizes mechanics, while AI planning prioritizes strategy.
| Aspect | Manual Media Planning | AI-Powered Media Planning |
| Speed of Drafting Initial Plan | Hours to days per campaign | Minutes per campaign |
| Benchmark Research | Manual lookup across multiple sources | Automatic application of relevant benchmarks |
| Consistency | Varies by planner experience and approach | Standardized strategic framework across all plans |
| Budget Modeling | Manual spreadsheet work for each scenario | Instant generation of multiple allocation models |
| Junior Planner Support | Depends on senior availability for guidance | Built-in access to senior-level strategic thinking |
| Measurement Setup | Research benchmarks and build KPI framework manually | KPI framework with channel-level benchmarks generated automatically |
| Error Rate | Higher risk of calculation and oversight errors | Reduced mechanical errors |
| Time Allocation | More time on mechanics, less on strategy | More time on strategy, less on mechanics |
| Scalability | Requires adding planners to handle more volume | Same team handles increased planning volume |
| Knowledge Transfer | Lost when team members leave | Captured in the tool |
| Planning-to-Activation Handoff | Manual export, reformatting, and rebuilding in activation platform | Strategy built inside the same platform where campaigns get activated |
Using AI-powered media planning tools doesn’t mean replacing planners. Rather, it allows planners more time to scale their work effectively and efficiently, while simultaneously providing them with more time to focus on the deep, strategic work best completed by humans. Manual planning forces planners to focus on mechanical tasks. AI planning shifts that time to strategic consultation, creative collaboration, and client relationship building.
This shift matters because 54.0% of agencies report more strained client relationships compared to two years ago. When planners spend less time on administrative work, they have more capacity for the client-facing strategic work that strengthens relationships.
AI-powered media planning tools are best suited for agencies and brands managing planning complexity, scale, or constrained resources.
Agencies managing multiple clients across various industries handle significant planning volume. AI tools help these agencies scale planning operations without proportionally scaling headcount, improving profitability while maintaining quality. They're particularly valuable when agencies need to pitch new business quickly or accommodate compressed timelines.
Organizations adding junior planners benefit from AI tools that give newer team members strategic scaffolding. Instead of requiring constant senior oversight, junior planners can produce quality work more independently while learning planning fundamentals.
Any agency where inefficient processes or disconnected systems create operational friction will benefit from AI planning tools. Given that 48.9% of agency leaders cite inefficient processes as their top challenge, this includes a significant portion of the industry.
AI planning tools deliver the most value when integrated into platforms where planning connects directly to activation. When the same system that generates the plan also executes it, data flows seamlessly—no manual transfers, no disconnected spreadsheets, no reconciliation work. This integration addresses the silos/disconnected systems problem that 40.4% of agencies identify as a major challenge.
The ideal scenario combines AI-powered planning with all-channel activation capabilities and AI that extends across the entire media buying process, from brief to activation to optimization. When these capabilities exist within a single platform rather than requiring multiple point solutions, agencies avoid the tech stack bloat that creates new inefficiencies (or accentuates existing ones).
Agencies looking to adopt AI-powered media planning tools should follow a structured approach:
Document how much time planning currently takes and where bottlenecks exist. Identify which parts of the planning process consume the most time and which would benefit most from automation. This assessment creates a baseline for measuring improvement.
Don't add another disconnected tool to an already complex tech stack. Look for AI planning capabilities that integrate with existing systems or exist within unified platforms. The planning tool should connect seamlessly to wherever campaigns get activated—whether that's programmatic buying, publisher-direct placements, or search and social platforms (or ideally, a platform that combines all of these in one).
Test AI planning on a small set of campaigns before rolling out across all clients. Choose campaigns that represent typical planning challenges, such as finding the right mix of channels, moderating complexity, and meeting realistic timelines. This pilot phase helps teams learn the tool and build confidence before scaling.
Invest in training so planners understand what the tool can do and how to refine its outputs effectively. Focus on explaining the logic behind recommendations so planners can make informed decisions about when to accept, modify, or override AI suggestions.
Create clear workflows for how AI-generated plans get reviewed and approved. Define who validates outputs, what criteria determine plan quality, and how feedback gets incorporated to improve future plans. This process maintains quality control while scaling efficiency.
Track metrics that matter: planning time per campaign, error rates, client feedback on plan quality, and planner satisfaction. These measurements justify the investment and identify areas for continued optimization.
Once the pilot proves successful, expand AI planning to additional teams and client accounts. Gradual rollout allows for learning and refinement without disrupting operations.
The investment priority is clear in the data: 77.7% of agency leaders plan to increase AI investment in the next 12 months, with automation tools tied for the second priority at 44.7%. Agencies moving quickly on AI planning implementation gain competitive advantage while others wait.
Basis Compass is purpose-built to solve the operational challenges agencies cite most: inefficient processes and disconnected systems. Here’s what sets it apart:
Compass gives agency employees back the time they need to do the strategic and creative work that clients are seeking, while automating the spreadsheet juggling that drains valuable hours from every week.
The shift to AI-powered media planning represents an opportunity to amplify human judgment, while reducing the manual tasks that slow teams down. These tools handle the mechanical work that drains time and creates inconsistency, giving planners capacity to focus on strategy, creativity, and client relationships. For agencies facing increasing complexity, tighter timelines, and pressure to do more with the same resources, AI planning tools offer a practical path forward.
The agencies that integrate these capabilities thoughtfully, particularly within unified platforms that connect planning directly to activation, will differentiate themselves through both efficiency and quality. They'll respond to briefs faster, produce more consistent work, and give planners more time for the strategic thinking that clients value most.
What is an AI-powered strategic media planning tool?
An AI-powered media planning tool is software that analyzes client briefs and automatically generates a structured media plan draft, including channel recommendations, budget allocations, and tactical suggestions. Planners can then refine and customize this draft based on client needs and their own strategic judgment.
Do AI media planning tools replace human media planners?
No, human oversight remains essential throughout the process. The typical workflow is AI generates a draft, then the planner reviews, refines, and approves it, keeping strategic decision-making with the planner while automating mechanical tasks.
How much time can AI planning tools save agencies?
Manual media planning can take hours or days, while AI tools can reduce this to minutes. Teams using Compass by Basis, for example, create media plans 50% faster than with manual processes.
Are AI-generated media plans transparent?
Well-designed tools avoid functioning as a black box by showing their reasoning behind each recommendation. Platforms like Compass explain the rationale for channel and budget decisions and let planners see which data sources informed them.
Can AI tools generate multiple budget scenarios?
Yes, AI planning tools can instantly generate multiple budget allocation models at different investment levels, each with its own rationale. This lets planners present clients with options without rebuilding the plan from scratch each time.
What data do AI media planning tools use to build strategies?
They combine campaign objectives and parameters from a client brief with industry benchmarks, performance data, and proprietary frameworks. Compass, for instance, is built on Basis's IMPACT omnichannel campaign framework, a methodology used across thousands of successful media campaigns.
How does AI media planning improve consistency across agency teams?
AI tools encode best practices into their planning logic, so every plan starts from the same strategic foundation regardless of which planner builds it. This gives junior planners access to senior-level strategic thinking while reducing calculation and oversight errors.
Why does connecting media planning to activation matter?
When planning tools integrate into the same platform used for activation, data flows seamlessly without manual transfers or reformatting. Compass lives inside the Basis platform, allowing planners to move from a brief to an active campaign without switching systems.
Who benefits most from AI-powered media planning tools?
Mid-to-large agencies managing high planning volume, teams with growing numbers of junior planners, and organizations facing inefficient processes or disconnected tech stacks see the most benefit. These tools are most valuable for teams using unified advertising platforms where planning connects directly to activation.
How is Compass by Basis different from a general AI chatbot?
Compass is purpose-built for media planning workflows rather than relying on generic AI reasoning, using Basis's proprietary IMPACT framework alongside industry benchmarks. It operates within the same platform where agencies activate and manage campaigns, so plans move directly into execution without manual handoffs.