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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.
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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 platform where campaigns get activated. Planners can refine the strategy through follow-up prompts before building it into client deliverables or moving into activation.
Though powered by many of the same technologies, these tools differ from more general AI assistants or chatbots because they're purpose-built for media planning workflows. They understand advertising terminology, channel dynamics, and how to structure plans that translate directly into campaign execution. Compass, for instance, is built on Basis’ proprietary IMPACT omnichannel framework—a methodology used across thousands of successful media campaigns—rather than relying on generic AI reasoning alone.
According to the IAB State of Data 2025 report:
And, data from Basis’ 2026 Advertising Agency report finds:
This gap highlights where AI adoption has lagged most: operational planning, not creative execution.
Agencies who begin to implement AI toward operational inefficiencies now can gain a strategic edge, freeing up their teams to focus on strategy rather than manual tasks.
AI planning tools follow a structured process to convert briefs into actionable media strategies:
1. Brief Upload and Extraction
The planner uploads a client brief—whether a structured planning document or a simple prompt—and the tool extracts and summarizes key information. This includes campaign objectives, target audiences, budget parameters, KPIs, geographic focus, and timing constraints. In Compass, this extraction step is visible in the interface, so planners can confirm the tool understood the brief correctly before strategy generation begins.
2. Audience Strategy and Prioritization
The tool builds prioritized audience segments based on the brief, going beyond basic demographics. Each segment includes targeting rationale, recommended channels for reaching that audience, and messaging direction. This creates a strategic foundation where audience strategy, channel selection, and messaging are connected from the start.
3. Strategic Framework and Competitive Context
The tool generates a broader strategic framework that includes competitive context, key challenges, and a recommended approach, rather than just a channel list. This strategic layer guides the channel and budget recommendations that follow, grounding them in campaign-specific logic rather than general best practices. The framework also documents the channels the tool evaluated but chose not to recommend, with the reasoning behind each decision. This gives planners a defensible rationale to share with clients, showing that the recommended mix reflects deliberate trade-offs rather than default choices.
4. Channel Mix and Budget Allocation
The tool recommends a channel mix with specific budget allocations, including dollar amounts and percentage breakdowns with rationale for each channel. Visual outputs like budget allocation charts make it easy to see how spend is distributed and share recommendations with stakeholders. The tool can also generate multiple budget scenarios at different investment levels, each with its own channel allocation and rationale. When a client adjusts the budget or asks to see options, planners have ready-made tiers to work from instead of rebuilding the plan each time.
5. Campaign Plan and Flighting
AI maps out campaign flighting with budget allocation by phase, accounting for seasonal moments, tentpole events, and how different channels should ramp up or down throughout the flight. Channel-specific timing guidance ensures the plan reflects real-world campaign dynamics, not just even budget distribution.
6. Measurement and KPI Framework
The tool builds a measurement framework that maps KPIs to each channel’s role in the funnel, complete with relevant benchmarks. Planners get primary and secondary metrics for each channel, along with the business outcome each metric ladders up to. Having these benchmarks built in saves planners from researching performance standards across channels and gives clients a clear view of how success will be measured from the start.
7. Refinement and Strategy Delivery
The strategy generates within a conversational interface where planners can ask follow-up questions, request deeper analysis on specific sections, or adjust recommendations through natural prompts. The result is a complete, structured strategy that planners can refine and use to build client-ready deliverables. Because Compass lives inside the Basis platform, the strategy and eventual campaign activation share the same system, reducing the manual handoffs and reformatting that typically separate planning from execution.
When orchestrated by agentic AI media planning tools, this entire process can happen in minutes. What traditionally required multiple planning sessions, spreadsheet modeling, and cross-referencing past campaigns now generates automatically, giving agency talent more time to focus on strategic refinement and client-specific nuances.
AI media planning tools deliver several concrete benefits that address some of agencies’ most pressing operational challenges:
The time savings from AI planning tools come from automating specific tasks that eat up planners' days.
Take benchmark research. Manually researching industry benchmarks for CPMs, CTRs, and conversion rates across different channels takes significant time. AI tools have this data built in and automatically apply relevant benchmarks based on campaign parameters.
Budget modeling works similarly. Testing different budget scenarios manually requires rebuilding spreadsheets for each variation. AI tools can generate multiple budget allocation models instantly, letting planners compare approaches without manual calculation work.
Channel analysis is another time sink. Evaluating which channels make sense for a specific audience and campaign goal requires cross-referencing multiple data sources. AI planning tools synthesize this analysis automatically, presenting channel recommendations with supporting rationale.
Then there’s plan documentation—formatting decks, documenting strategic rationale, and creating presentation-ready outputs. AI-powered planning tools produce formatted plans that planners can review and refine rather than building from scratch.
With so many manual tasks wrapped up in drafting media plans, time savings derived from using AI-powered planning tools can add up fast. For instance, teams can create media plans 50% faster when using Compass by Basis, and that time savings can then shift to strategic consultation, client communication, or campaign optimization.
Consistency in media planning creates several advantages for agencies:
Standardized Strategic Approach: AI tools encode best practices into their planning logic. Every plan starts from the same strategic foundation: proven frameworks for audience targeting, channel selection, and budget allocation. This doesn't mean every plan looks identical, but it ensures no planner misses critical strategic considerations.
Quality Baseline for Junior Planners: Junior team members can often struggle without senior guidance. AI tools give them access to senior-level strategic thinking, helping them develop better plans while learning. The tool serves as a training resource that improves plan quality across experience levels.
Reduced Errors: Manual planning risks introducing errors such as calculation mistakes, overlooked channels, and misallocated budgets. AI tools eliminate these mechanical errors, catching issues before plans reach clients. This improves client trust and reduces the costly back-and-forth of fixing mistakes.
Scalable Quality Control: As agencies grow, maintaining consistent plan quality becomes harder. AI tools scale that quality automatically—the hundredth plan generated gets the same strategic rigor as the first.
These consistency benefits matter even more when you consider the tech stack complexity most agencies face. More than one-third (36.8%) of agencies now juggle 10+ tools in their tech stack—up dramatically from 17.3% in 2024—and managing that many disconnected systems can create inconsistency. When AI planning tools integrate into unified platforms where planning connects directly to activation, consistency extends beyond plan creation into execution. The fewer handoffs between systems, the fewer opportunities for plans to get lost in translation.
One concern about AI tools is the "black box" problem, i.e., a lack of visibility or understanding around how the AI reaches its recommendations. But well-designed AI planning tools address this through transparency features, providing rationale into their reasoning as well as ample opportunities for human interaction, iteration, and oversight.
IAB research finds that 51% of brands worry they don't have enough transparency about how agency partners use AI. Transparent AI tools that clearly show their work help address this concern: Agencies can demonstrate their value and provide visibility into their strategic process.
The key difference between manual and AI-powered media planning is how planner time is allocated: manual planning prioritizes mechanics, while AI planning prioritizes strategy.
| Aspect | Manual Media Planning | AI-Powered Media Planning |
| Speed of Drafting Initial Plan | Hours to days per campaign | Minutes per campaign |
| Benchmark Research | Manual lookup across multiple sources | Automatic application of relevant benchmarks |
| Consistency | Varies by planner experience and approach | Standardized strategic framework across all plans |
| Budget Modeling | Manual spreadsheet work for each scenario | Instant generation of multiple allocation models |
| Junior Planner Support | Depends on senior availability for guidance | Built-in access to senior-level strategic thinking |
| Measurement Setup | Research benchmarks and build KPI framework manually | KPI framework with channel-level benchmarks generated automatically |
| Error Rate | Higher risk of calculation and oversight errors | Reduced mechanical errors |
| Time Allocation | More time on mechanics, less on strategy | More time on strategy, less on mechanics |
| Scalability | Requires adding planners to handle more volume | Same team handles increased planning volume |
| Knowledge Transfer | Lost when team members leave | Captured in the tool |
| Planning-to-Activation Handoff | Manual export, reformatting, and rebuilding in activation platform | Strategy built inside the same platform where campaigns get activated |
Using AI-powered media planning tools doesn’t mean replacing planners. Rather, it allows planners more time to scale their work effectively and efficiently, while simultaneously providing them with more time to focus on the deep, strategic work best completed by humans. Manual planning forces planners to focus on mechanical tasks. AI planning shifts that time to strategic consultation, creative collaboration, and client relationship building.
This shift matters because 54.0% of agencies report more strained client relationships compared to two years ago. When planners spend less time on administrative work, they have more capacity for the client-facing strategic work that strengthens relationships.
AI-powered media planning tools are best suited for agencies and brands managing planning complexity, scale, or constrained resources.
Agencies managing multiple clients across various industries handle significant planning volume. AI tools help these agencies scale planning operations without proportionally scaling headcount, improving profitability while maintaining quality. They're particularly valuable when agencies need to pitch new business quickly or accommodate compressed timelines.
Organizations adding junior planners benefit from AI tools that give newer team members strategic scaffolding. Instead of requiring constant senior oversight, junior planners can produce quality work more independently while learning planning fundamentals.
Any agency where inefficient processes or disconnected systems create operational friction will benefit from AI planning tools. Given that 48.9% of agency leaders cite inefficient processes as their top challenge, this includes a significant portion of the industry.
AI planning tools deliver the most value when integrated into platforms where planning connects directly to activation. When the same system that generates the plan also executes it, data flows seamlessly—no manual transfers, no disconnected spreadsheets, no reconciliation work. This integration addresses the silos/disconnected systems problem that 40.4% of agencies identify as a major challenge.
The ideal scenario combines AI-powered planning with all-channel activation capabilities and AI that extends across the entire media buying process, from brief to activation to optimization. When these capabilities exist within a single platform rather than requiring multiple point solutions, agencies avoid the tech stack bloat that creates new inefficiencies (or accentuates existing ones).
Agencies looking to adopt AI-powered media planning tools should follow a structured approach:
Document how much time planning currently takes and where bottlenecks exist. Identify which parts of the planning process consume the most time and which would benefit most from automation. This assessment creates a baseline for measuring improvement.
Don't add another disconnected tool to an already complex tech stack. Look for AI planning capabilities that integrate with existing systems or exist within unified platforms. The planning tool should connect seamlessly to wherever campaigns get activated—whether that's programmatic buying, publisher-direct placements, or search and social platforms (or ideally, a platform that combines all of these in one).
Test AI planning on a small set of campaigns before rolling out across all clients. Choose campaigns that represent typical planning challenges, such as finding the right mix of channels, moderating complexity, and meeting realistic timelines. This pilot phase helps teams learn the tool and build confidence before scaling.
Invest in training so planners understand what the tool can do and how to refine its outputs effectively. Focus on explaining the logic behind recommendations so planners can make informed decisions about when to accept, modify, or override AI suggestions.
Create clear workflows for how AI-generated plans get reviewed and approved. Define who validates outputs, what criteria determine plan quality, and how feedback gets incorporated to improve future plans. This process maintains quality control while scaling efficiency.
Track metrics that matter: planning time per campaign, error rates, client feedback on plan quality, and planner satisfaction. These measurements justify the investment and identify areas for continued optimization.
Once the pilot proves successful, expand AI planning to additional teams and client accounts. Gradual rollout allows for learning and refinement without disrupting operations.
The investment priority is clear in the data: 77.7% of agency leaders plan to increase AI investment in the next 12 months, with automation tools tied for the second priority at 44.7%. Agencies moving quickly on AI planning implementation gain competitive advantage while others wait.
Basis Compass is purpose-built to solve the operational challenges agencies cite most: inefficient processes and disconnected systems. Here’s what sets it apart:
Compass gives agency employees back the time they need to do the strategic and creative work that clients are seeking, while automating the spreadsheet juggling that drains valuable hours from every week.
The shift to AI-powered media planning represents an opportunity to amplify human judgment, while reducing the manual tasks that slow teams down. These tools handle the mechanical work that drains time and creates inconsistency, giving planners capacity to focus on strategy, creativity, and client relationships. For agencies facing increasing complexity, tighter timelines, and pressure to do more with the same resources, AI planning tools offer a practical path forward.
The agencies that integrate these capabilities thoughtfully, particularly within unified platforms that connect planning directly to activation, will differentiate themselves through both efficiency and quality. They'll respond to briefs faster, produce more consistent work, and give planners more time for the strategic thinking that clients value most.
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.
Key Takeaways:
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Remember when researching a purchase meant toggling between about 20 different tabs on your laptop? You’d run a string of keyword searches on Google, scroll through the results, open tabs for all the promising-sounding options, and review. Your collection of tabs might exist for days, weeks, or even months, growing and shrinking along with your research until you finally felt informed enough to pull the trigger (or until your browser decided to stage an intervention and crash).
Now think about the last item you bought. Maybe you asked ChatGPT for product recommendations and made a purchase after reviewing them. Or perhaps an AI summary at the top of a Google search compared three options for you, and you picked one without having to do any additional research. Sound familiar?
The path that moves a person from “I might need this” to “I bought it” looks almost nothing like it did a decade ago, or even three years ago. It’s fragmented, nonlinear, and increasingly shaped by algorithms and AI. For advertisers, that shift changes what it looks like—and the underlying technology it requires—to reach consumers effectively in key moments of influence.
Not long ago, the customer journey was relatively straightforward. A customer became aware of a product, considered and evaluated it, and finally made their decision and completed the purchase. Advertising mapped neatly onto that path. A billboard or TV spot built awareness, while a well-placed search or display ad nudged a shopper toward a decision. Advertisers could reasonably predict where a customer was headed and meet them there.
Today’s journey looks quite different: It bends, loops, scatters across channels, and rarely starts or ends where advertisers expect.
Discovery now happens everywhere, all the time. Most shoppers say they discover new products at least once a week, and that discovery is spread across TikTok FYPs, Instagram feeds, AI summaries, retail apps, and beyond. This discovery also often happens across multiple devices at the same time, with the majority of media consumers across every generation saying they now browse the internet or use apps on their phones while watching TV. With content so readily available, advertisers are competing for attention that is splintered across screens and digital spaces. That makes showing up intentionally and consistently across channels even more important.
The research phase has changed as well, evolving into a multi-touch, multi-channel endeavor. Consumers now research a product three or more times before buying, and nearly a quarter research five times or more. They also turn to a variety of sources for their research: online reviews and listicles, social media, recommendations from family and friends, in-store visits, search engines, AI, and beyond. For advertisers, that scatter makes presence across channels less of a “nice-to-have” and more of a requirement, since there’s no longer a single place where decisions get made.
Purchase has also grown more unpredictable. More than 30% of shoppers say they research online but buy in-store, a pattern that makes attribution especially difficult. When someone discovers a product through a TikTok creator but buys it at Walmart, connecting that sale to the original touchpoint—or any other touchpoints along the way—is a real challenge for advertisers trying to understand what’s working. Without a connected view of those touchpoints, advertisers risk crediting the wrong channel and misallocating their next dollar.
In addition to the rising complexity of digital media, AI is also playing a major role in the evolution of the customer journey. Among people who use AI to shop, it now ranks as the second most influential shopping source—trailing only behind search engines and outranking retailer sites, apps, and recommendations from family and friends.
And adoption is climbing quickly. AI now plays a role in 86% of shoppers’ retail journeys. Nearly half of AI shoppers use it most or every time they shop, with 80% saying they anticipate relying on it more moving forward. People who use AI for shopping are also finding real value in the tool: 81% say AI makes the job easier, 77% say it makes them more confident in their decisions, and nearly 90% report it helps them find products they wouldn’t have known about otherwise.
AI also tends to expand the path to purchase rather than shortening it. After an AI interaction, shoppers tend to add more steps to their customer journey, often in an effort to validate their choice before buying. Though AI certainly does streamline some stages of the path to purchase, it also adds steps that weren’t there before. And each of those new steps is another opportunity for advertisers to connect with shoppers on their way to making a decision.
Zero-click search is reshaping the journey further. As AI summaries and chatbot responses answer questions directly in the results, fewer users click through to a brand’s site at all. That doesn’t mean those impressions stop mattering, however: Ads appearing alongside AI-generated summaries still influence decisions, even without a click. It does mean advertisers have to rethink how they measure influence and where they show up, since a growing share of discovery and decision-making now happens inside environments where AI shapes what consumers see, hear, and trust about a brand.
Adapting to how the customer journey has evolved starts with recognizing and accepting the complexity of it. CTV, retail media, short-form video, AI chatbots, AI search summaries, and more are all live, simultaneous touchpoints, each with its own signals and rules. Advertisers who try to manage each in isolation will likely struggle to keep up. The teams adapting best treat these channels as one connected system, planning and buying across them together rather than each in isolation.
Accomplishing this depends on a few capabilities. One is real-time visibility and reporting. When AI tools can compress discovery, evaluation, and purchase into minutes, advertisers need to see what’s resonating as it happens (not days later in a reconciled report) so they can move budget toward what’s working during key moments of impact.
That kind of visibility is hard to come by when data stays fragmented. Nearly half of agency marketers use eight or more tools to manage campaigns, and more than a third manage 10 or more. Even more, fewer than one in five industry professionals describe their first-party data as extensive and well-structured. This leaves teams to piece together the path to purchase from incomplete inputs across systems that weren’t necessarily built to talk to each other.
Speed is another key capability, in both execution and planning. Shoppers today move through different steps quickly and across channels, which means bid strategies, creative, budget allocation, and the media plans behind them all need to keep pace. Automated, AI-powered optimization that makes continuous, goal-aligned adjustments, powered by live performance signals, can be the difference between capitalizing on the channels where target audiences are spending time and missing those opportunities entirely. That same speed matters earlier in the campaign process, too. Considering how dynamic the customer journey is today, teams that can build and adjust media plans quickly—rather than rebuilding them manually each quarter—stay aligned with how consumers actually behave. AI-powered tools are increasingly helping compress that planning work so agency talent can focus on strategy over manual setup.
Taken together, these capabilities underscore what adapting to the modern customer journey requires: A strategy built around how consumers behave today, and the infrastructure to execute it.
In a journey this fragmented and fast-moving, the infrastructure beneath a team’s advertising workflows matters as much as the strategy on top of it. But not all infrastructure is created equal, and “unified” can mean different things in practice.
Real visibility across channels means little if teams must continually switch between tools to access data, billing, and reconciliation systems. Real-time optimization falls short if the platform powering it can’t handle the complexity of true omnichannel work. For example, a platform that unifies programmatic but treats search, social, and site direct buys as afterthoughts isn’t unified in the way that advertisers need to adapt to the complexity of the 2026 customer journey.
The advertisers best positioned for navigating it are the ones working from a single, unified platform that connects programmatic, search, social, and CTV, supported by infrastructure stable enough to make agile, cross-channel activation reliable at scale.
In 2026, the customer journey is fragmented, nonlinear, and shaped by AI at every turn. To reach people in moments of meaningful impact, advertisers need visibility across channels, the speed to act on what they see, and the connected infrastructure to make both possible.
The days of the tidy linear funnel and the slow, self-directed path to purchase aren’t coming back. Today’s customer journey calls for a different kind of toolkit, one well-suited for media fragmentation, AI, and the speed at which today’s consumers move. The advertisers who invest now in unified, real-time infrastructure—the kind that brings every channel into a single view and acts on customer signals as they happen—will be the ones who keep pace as the journey keeps changing.
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Looking for more information on how to adapt your media planning for the modern customer journey? Check out Beyond the Funnel: A Better Way to Plan Media.
Key Takeaways:
Brand safety and suitability look very different today than they did just a few years ago, and most advertisers’ strategies haven’t kept up with the new pace.
Online spaces increasingly characterized by harmful and polarizing content, the proliferation of AI-generated media, reduced platform moderation, and the growing complexity of digital advertising have combined to raise the brand risk profile for advertisers.
Closing that gap requires a mindset shift from leaders. Instead of treating brand safety as a box to check once campaigns are live, brand safety and suitability must be approached as a strategic consideration built into media planning from the start.
The brand safety and suitability environment has evolved considerably in recent years. The open web has grown more volatile, with offensive language, controversial content, and hate speech on the rise. Just between 2024 and 2025, the share of offensive content online rose by 72%. Considering that 64% of global consumers say the genre of content surrounding an ad influences how they perceive it, the increasing hostility of online spaces creates significant content adjacency issues for advertisers.
The emergence of generative AI and subsequent proliferation of AI-generated content online has exacerbated such concerns. A 2025 Basis study found that a full 100% of marketers and advertisers agree that AI presents a brand safety and misinformation risk, and 53% of media experts in the US cite advertisements’ proximity to gen AI content as a top media challenge this year.
Social media has grown particularly contentious, especially as major social platforms have rolled back their content moderation policies in recent years. Close to two-thirds of marketers running campaigns on social feel concerned about the brand suitability of those ad placements.
April Weeks, Chief Media Officer at Basis, says the combination of these and other factors has raised the stakes for brand safety and suitability. “The risk has increased,” says Weeks, “and to adapt, advertisers must treat brand safety and suitability as brand-specific governance issues that are integrated into the media plan.”
Beyond content adjacency issues, wasted spend is a major concern when it comes to programmatic investments.
The ANA’s latest Programmatic Transparency Benchmark found a considerable gap in how effectively advertisers convert their spend into working media. Higher-performing advertisers directed 54% of their programmatic investments toward impressions that were measurable, viewable, and free of invalid traffic and made-for-advertising (MFA) content. Lower-performing advertisers converted just 32.1%—in other words, more than two-thirds of their spend was wasted.
The platforms marketing teams use for programmatic advertising have a considerable impact on how effectively they’re able to direct their spend. For example, platforms that prioritize supply path optimization (SPO)—offering supply chain visibility, neutral buy-side transparency, and brand safety controls built into the buying process—help advertisers convert more spend into quality placements.
“The best DSPs clean up the supply chain before an advertiser even bids—vetting publishers, filtering out bots, and removing invalid traffic up front,” notes Lindsey Freed, SVP of Media Investment at Basis.
Successfully addressing brand suitability, brand safety, and programmatic waste in today’s media environment requires marketing leaders to think about these issues differently than they have in the past.
“Historically, brand safety meant not showing up next to negative content,” says Dan Wilson, GVP of Integrated Client Solutions at Basis. “Today, it's about safeguarding your brand's integrity: considering where your ads are placed, the quality of the surrounding content, and what's suitable for your brand, audience, message, and moment in the customer journey.”
Legacy brand safety approaches were characterized by post-campaign verification, a reliance on platforms to manage risk, and blunt controls like broad keyword blocks or genre-level content blocking. As the complexity of the digital media environment has grown, Weeks says that advertisers must take on more responsibility, taking the time to craft nuanced brand safety and suitability strategies that are engrained into the planning process.
Leading advertisers are now incorporating pre-bid tools alongside post-bid verification, adding solutions to block MFAs and other low-quality websites, and accounting for channel- and platform-specific risks. Social listening, for example, has become essential given the polarization of content on social platforms.
Content adjacency approaches are also becoming more nuanced. The most successful advertisers are moving away from binary “safe vs. unsafe” thinking, and towards more granular, context-specific approaches. Rather than applying a blanket block on all news content, for instance, advertisers can use inclusion lists of trusted publishers paired with contextual targeting to ensure ads appear alongside news the brand is comfortable with, and within trusted editorial environments. “It’s about approaching it from a lens that isn’t black and white,” says Wilson.
Technology is evolving to support advertisers in these more granular approaches. For example, newer solutions can go beyond keyword matching, using contextual and semantic analysis to assess whether content is actually suitable and incorporating real-time signals to reduce waste.
Ultimately, success depends on leaders shifting their mindset, considering brand safety and suitability in the planning phase, and addressing them through a nuanced, multi-pronged approach.
Crafting a brand safety strategy suited to the complexity of today's media landscape takes real investment. Auditing legacy approaches, building channel-specific controls, and evolving workflows and tech stacks all take time. For leaders willing to invest that time, however, the potential returns are significant.
"The opportunity amongst the complexity is there," says Wilson. "The question is, will advertisers take the time to find it?"
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For a deeper look at how supply path optimization supports stronger brand safety outcomes, check out The Case for Supply Path Optimization as Strategic Priority.
The local branch of a marketing & advertising agency holding company based in San José, Costa Rica wanted to compare Basis’ campaign efficiency—specifically with time and cost savings—against working with separate media owners.
To do so, they used Basis to boost brand awareness and visibility for two leading client brands in the beverage industry to their target consumers.
Basis implemented a strategic, data-driven digital out-of-home (DOOH) campaign that included:
Basis delivered a data-driven DOOH campaign in Costa Rica that simplified execution, reached 665k users, and proved the platform’s power to reduce costs and maximize efficiency.
The results:
Strategic Virtual Roadmap
Basis designed a journey roadmap of high-traffic zones and pinpointed 13 key screens that aligned with target audience’s mobility patterns, maximizing reach with the right audience at the right time.
Precise Activation with PMPs
Basis used three distinct private marketplace deals for precise targeting and scheduling across a combination of indoor and outdoor screens.
Centralized Execution Across Media Vendors
Basis enabled seamless coordination and exact time scheduling across multiple vendors, reducing complexity and boosting efficiency.
Integrated Post-Campaign Reporting
Paired with a measurement provider, Basis provided a detailed report that covered campaign performance metrics and qualitative audience data like consumer demographics and device type.
For most agencies, the DSP is where media plans turn into live campaigns, which makes it one of the more consequential platform decisions a team can make. Choose well, and planning, buying, and reporting move faster across every account. Choose poorly, and the platform adds friction to campaigns it was supposed to accelerate.
The best DSP for agencies depends on your agency's size, client volume, and channel mix. Platforms like Basis, The Trade Desk, DV360, Amazon DSP, Viant, and Simpli.fi each serve different agency profiles, from independent boutiques managing a handful of accounts to mid-market teams running programmatic, search, social, and direct buys across dozens of verticals. The right demand side platform reduces operational complexity, improves reporting transparency, and scales with your client roster rather than against it.
That decision carries more weight every year. Programmatic now accounts for roughly 91% of US digital display ad spend, so the platform an agency uses to access it shapes a growing share of the work. At the same time, tool sprawl is climbing: Basis' 2026 Advertising Agency Report found that more than one-third of full-service and media agencies now manage 10 or more adtech tools, more than double the share two years ago, with inefficient processes (44.1%) and siloed systems (40.4%) ranking as their top operational challenges. The DSP you choose either adds to that burden or helps remove it.
This guide compares leading demand side platforms by agency type, walks through the evaluation criteria that matter most, and helps you build a case for the right investment.
The right DSP for your agency is the one that matches your team's workflow, your clients' channel requirements, and your growth trajectory. There is no universal "best" platform. A platform that excels for a holding-company network may create friction for an independent shop, and vice versa.
Start by identifying where you fall across three dimensions:
Once you map your agency against these dimensions, the field narrows quickly. A boutique with five clients and a display-heavy media mix has different requirements than a mid-market shop running full-funnel campaigns across 30 accounts. For a deeper look at the criteria that should guide your evaluation, this guide to choosing the right omnichannel DSP covers the decision in more detail.
Agencies should evaluate DSPs across six core dimensions: inventory breadth, multi-client account management, campaign optimization, reporting transparency, onboarding support, and pricing model clarity.
Inventory breadth determines where your ads can run. The strongest programmatic platforms for agencies offer access to premium display, video, native, audio, CTV, and digital out-of-home inventory through direct publisher integrations, private marketplaces, and the open exchange. This matters more as budgets shift to streaming: US CTV ad spend is on pace to reach about $38 billion in 2026, up nearly 15% year over year, so inventory access increasingly means streaming reach. Brand safety and ad fraud protection are also part of this evaluation. (For a detailed look at how leading DSPs handle both, see this comparison of DSPs for ad fraud protection and brand safety.)
Multi-client account management is where many DSPs fall short for agencies. You need hierarchical account structures that let you manage budgets, audiences, and creative assets at the client level without cross-contamination. Platforms designed for single-advertiser use often require workarounds that slow your team down.
Campaign optimization should go beyond basic bid adjustments. Look for algorithmic optimization across KPIs, automated budget pacing, and the ability to shift spend across tactics in real time. With AI now used at more than 99% of agencies, the question is no longer whether a platform applies AI but how much routine optimization it removes from your team's plate.
Reporting transparency matters for both internal decisions and client communication. Your DSP should provide granular, exportable reporting with clear visibility into costs, margins, and performance by tactic, channel, and audience segment. If you need to rebuild reports outside the platform, that is a red flag.
Onboarding support is especially critical for agencies switching platforms or adopting programmatic for the first time. Structured onboarding, dedicated success managers, and ongoing training reduce time-to-value and protect campaign performance during the transition.
Pricing model clarity separates platforms you can trust from those that obscure costs. Understand whether a DSP charges on a CPM basis, a percentage of spend, a flat SaaS fee, or some combination. Hidden fees can erode your margins and make it harder to forecast profitability for your clients.
If you want a refresher on how these platforms work, this DSP fundamentals guide covers the essentials.
The leading demand side platforms popular with agencies in 2026 each have distinct strengths. The right fit depends on your agency's profile, so the comparison below is organized by use case rather than a single ranking.
| Platform | Core strength | Best for |
| Basis | Unified planning, buying, optimization, reporting, and billing across programmatic, search, social, and direct | Agencies that want to consolidate their full workflow in one platform, plus expansive programmatic inventory, supply path transparency, and award-winning service |
| The Trade Desk | Enterprise-grade programmatic scale | Large, tech-savvy programmatic teams with substantial budgets |
| DV360 | Deep Google ecosystem integration and YouTube access | Google-centric campaigns that lean on GA4 and CM360 |
| Amazon DSP | Purchase-intent targeting built on Amazon shopping data | Commerce and retail clients with high spend |
| Viant | CTV reach and people-based, cookieless identity | Programmatic teams prioritizing streaming and AI-driven execution |
| Simpli.fi | Localized and multi-location programmatic at scale | Agencies with franchise, local, or multi-location clients |
| Platform | Programmatic | Paid search | Paid social | Direct buys | Billing & reconciliation | CTV | Entry point |
| Basis | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | Lower threshold |
| The Trade Desk | ✓ | ✗ | ✗ | ✗ | ✗ | ✓ | ~$10K+/mo |
| DV360 | ✓ | Limited¹ | ✗ | ✗ | ✗ | ✓ | GMP contract (~$50K+/mo) |
| Amazon DSP | ✓ | ✗² | ✗ | ✗ | ✗ | ✓ | $10K rec. self-serve / $50K managed |
| Viant | ✓ | ✗ | ✗ | ✗ | ✗ | ✓ | Self-serve & managed |
| Simpli.fi | ✓ | ✗ | Partial³ | ✗ | Separate⁴ | ✓ | Self-serve & managed |
¹ DV360's search functionality is limited; agencies typically run paid search through Google Ads separately. ² Amazon's search ads run through its own sponsored-ads console, separate from Amazon DSP. ³ Simpli.fi reaches social inventory programmatically but is not a paid-social campaign-management tool. ⁴ Simpli.fi offers agency workflow and billing through its separate Advantage and Core Media product line, not a natively unified platform.
The best DSPs for agencies in 2026 include Basis, The Trade Desk, DV360, Amazon DSP, Viant, and Simpli.fi, and the right choice depends less on raw programmatic horsepower than on how much of the agency workflow a platform can absorb. A team running programmatic across a handful of large accounts, for instance, has different needs than a shop reconciling dozens of clients across channels. With that in mind, the sections below will evaluate each platform on channel coverage, workflow depth, and the agency profile it fits best.
Basis is an AI-powered advertising platform built specifically for how agencies operate. It consolidates campaign planning, programmatic buying, paid social, search, direct deals, reporting, and billing into a single platform, eliminating the tool fragmentation that drives up operational cost and manual effort across agency teams.
Here is how a typical agency campaign flows through Basis:
Basis' partnership with Mediaocean extends its financial workflow capabilities, connecting media planning data with downstream billing and reconciliation systems. For agencies that use Mediaocean for billing and finance, Basis functions as the execution engine that sits in front of it. The platform's AI optimization has produced measurable gains, with some agencies reporting up to a 5x improvement in advertising performance. For agencies managing high client volume across verticals, that combination of consolidation and performance is what separates Basis from point solutions that address only one stage of the campaign lifecycle.
Basis is strongest for: Full-service and media agencies that need one platform to handle every stage of the campaign lifecycle—from planning to activating to optimizing to reporting to billing—across both the open web and walled gardens.
The Trade Desk has built a strong reputation for enterprise-grade programmatic buying. Its bidding capabilities, access to a large third-party data marketplace, and strong connected TV inventory make it a credible choice for sophisticated, large-scale programmatic programs.
That sophistication, however, comes with a steep learning curve, and its formidable monthly minimums make it best suited to agencies with dedicated programmatic expertise and clients with substantial budgets. The Trade Desk is also programmatic-only. It does not handle paid search, paid social, or direct buys, so agencies still need separate tools for non-programmatic channels and a separate system for billing and reconciliation.
The Trade Desk is strongest for: Large agencies running high-volume programmatic programs with dedicated ad tech resources and clients whose media mix is weighted toward programmatic.
DV360 (Google Display & Video 360), part of the broader Google Marketing Platform, offers programmatic buying with detailed attribution and tight integration across Google's ecosystem, including exclusive YouTube inventory, the Google Display Network, Campaign Manager 360, and Google Analytics 4. For advertisers running Google-heavy campaigns, that interoperability is hard to match.
The tradeoffs, however, are significant. DV360 is not available as a self-serve product: Access requires a Google Marketing Platform contract with practical minimum spend thresholds, and its utility diminishes outside Google-owned environments. Search functionality is limited, and the platform does not handle paid social, direct buys, billing, or financial reconciliation. For agencies whose clients need omnichannel reach beyond Google, DV360 addresses only part of the buying workflow.
DV360 is strongest for: Agencies running Google-heavy, attribution-focused campaigns, particularly teams with in-house analytics expertise already operating within the Google stack.
Amazon DSP gives agencies access to something few platforms can replicate: targeting built on Amazon's proprietary shopping, browsing, and streaming data. Purchase-intent signals derived from Amazon's retail ecosystem offer uniquely powerful audience targeting based on actual purchase behavior rather than inferred intent, alongside premium inventory across Prime Video, Twitch, Thursday Night Football, and Fire TV.
The tradeoff is cost and scope. Self-service access carries no hard minimum, though Amazon recommends roughly a $10,000 campaign budget for some formats to generate enough data for optimization, while managed service requires a minimum commitment of around $50,000 per month. The platform's core advantage is strongest for retail, CPG, and e-commerce clients; agencies serving other verticals will find it less relevant. Amazon DSP also operates as a walled garden and does not handle paid search, paid social, direct buys, media planning workflows, billing, or reconciliation.
Amazon DSP is strongest for: Agencies with commerce-focused clients that can put Amazon's shopper data and premium streaming inventory to work.
Viant is an AI-powered, CTV-focused programmatic DSP whose central differentiator is its Household ID, a deterministic, people-based identity solution built for cookieless, cross-device targeting and measurement. The platform pairs solid connected TV and video inventory with autonomous campaign features, including a product that handles setup, optimization, and management with limited manual intervention.
For agencies, the constraints mirror other programmatic-only platforms. Viant does not offer search, social, or direct buying, and it carries no agency workflow, billing, or financial operations layer. Its interface and advanced features can present a learning curve, and the autonomous approach, while innovative, reduces hands-on trader control, which some teams prefer to keep.
Viant is strongest for: Programmatic teams that prioritize CTV reach and people-based identity and are comfortable leaning on automated execution.
Simpli.fi is a programmatic platform with a clear specialty in localized and hyperlocal advertising, including geo-fencing, geo-conversion tracking, and multi-location campaign management at scale across CTV, display, video, native, and audio.
However, users flag a complex interface and a learning curve for new teams. Additionally, Simpli.fi's strength is concentrated in local and multi-location use cases, so agencies with national or non-local clients may find its core value proposition less applicable.
Simpli.fi is strongest for: Smaller agencies serving local, multi-location clients that need hyperlocal targeting at scale.
The strongest platforms automate routine optimization and surface reporting that scales across clients, so teams spend their time on strategy rather than manual adjustments.
Campaign optimization in modern DSPs goes well beyond setting a bid and walking away. Leading platforms use machine learning to adjust bids in real time, shift budget across tactics and channels as results come in, and pace spend to avoid over- or under-delivery, all aimed at the outcomes clients care about. Reporting is where that quality becomes visible to clients: The best platforms offer dashboards configurable per client, scheduled report delivery, transparent cost breakdowns that separate media from platform fees, and cross-channel views that show how programmatic, search, social, and direct buys perform together.
The deeper divide is structural. Most of the platforms above handle one slice of the workflow well. A programmatic DSP executes programmatic buys, but with most demand side platforms, agencies still need to run separate tools for search, social, direct deals, and the back-office work of billing and reconciliation. Each added tool is another login, another data silo, and another manual handoff, which is why tool sprawl tracks so closely with the inefficiency and siloed-system challenges agencies report. The platforms that stand apart are the ones that reduce the number of systems an agency has to operate, not add to it. That unified model is where Basis, in particular, is built to compete—and it is increasingly what cross-channel, AI-driven optimization depends on, since connected data is the input those systems need to work.
Independent DSPs operate without ties to a specific holding company or media conglomerate, giving agencies more flexibility in how they buy media and where they allocate spend. Holding-company-affiliated platforms may offer preferential pricing or bundled services, but they can also limit access to competitive inventory or lock teams into a single ecosystem.
For independent and boutique agencies, this distinction matters. If your agency is not part of a major network, you need a DSP that offers transparent pricing, open marketplace access, and support that does not assume you have a 50-person ad ops team. The best independent DSPs provide the same caliber of technology, inventory access, and optimization available to large networks, without enterprise-level minimums.
Third-party agencies evaluating DSPs should pay close attention to contract terms. Some platforms require long-term commitments or minimum monthly spend that can be prohibitive for smaller shops; others offer flexible models that scale with the business. The broader question is how the DSP fits the full tech stack alongside your ad server, data tools, and campaign management. For a wider view, see how top advertising agency platforms for media buying compare.
Building a business case for a new DSP requires framing the investment in terms leadership cares about: operational efficiency, margin improvement, and client retention.
Basis unifies programmatic, direct, search, social, and connected TV buying in a single platform, giving agencies one place to plan, execute, optimize, and report across every digital channel.
For agencies managing high client volume, Basis provides the multi-client account structures, automated reporting, and cross-channel visibility that reduce operational complexity. For independent agencies competing with larger networks, it offers enterprise-grade technology paired with dedicated onboarding, training through AdTech Academy, and ongoing support from a dedicated Success Manager.
Explore Basis DSP to see how it fits your agency's needs.
What is the best DSP for agencies? The best DSP for agencies depends on client volume, channel mix, and team experience. Agencies that want planning, programmatic, search, social, direct buys, and billing in one place tend to favor a unified platform like Basis, while teams focused purely on programmatic scale may prefer The Trade Desk or DV360. Map your requirements first, then match them to the platform that removes the most friction across your full operation.
What is a DSP and how do agencies use it for programmatic advertising? A DSP, or demand side platform, is software that lets advertisers and agencies buy digital ad inventory programmatically through automated, real-time auctions. Agencies use DSPs to plan, execute, and optimize campaigns across display, video, native, CTV, and audio from a single interface. The DSP automates bidding, applies audience targeting, and reports on performance, letting agencies manage media buying at scale across multiple clients.
Which leading demand side platforms are popular with agencies in 2026? Leading demand side platforms used by agencies in 2026 include Basis, The Trade Desk, DV360, Amazon DSP, Viant, and Simpli.fi. Each serves a different profile: Basis unifies programmatic, search, social, CTV and direct buys with planning, billing; The Trade Desk and Viant focus on programmatic scale and CTV; DV360 integrates with the Google ecosystem; Amazon DSP brings shopper-data targeting; and Simpli.fi specializes in localized, multi-location campaigns.
What is the best DSP for independent agencies? The best DSP for an independent agency offers transparent pricing, open marketplace access, flexible contract terms, and strong onboarding support without enterprise-level minimums. Independent shops should prioritize platforms like Basis that deliver the same technology and inventory access as large networks while providing hands-on support, since they rarely have large in-house ad ops teams.
Which DSP is best for agencies managing high client volume across verticals? Agencies managing high client volume across verticals need hierarchical multi-client account structures, per-client reporting, and broad channel coverage in one system. A unified platform that handles programmatic, search, social, and direct buys, along with billing and reconciliation, reduces the manual handoffs and data silos that multiply as account counts grow.
What DSPs offer the best customer support and onboarding? Agencies adopting or switching platforms should prioritize structured onboarding, dedicated success managers, and ongoing training, all of which reduce time-to-value and protect performance during a transition. Basis pairs its platform with a services organization that provides consulting, onboarding, and training through AdTech Academy, along with a dedicated Success Manager for ongoing support.
What is the difference between a managed-service DSP and a self-serve DSP for agencies? A self-serve DSP gives your team direct control over setup, optimization, and reporting, which suits agencies with experienced programmatic traders. A managed-service DSP provides hands-on support from the platform's team, handling some or all execution on your behalf. Many platforms offer both, letting agencies choose the level of support that matches their team's capabilities and workload.
Can small or independent agencies access the same DSP technology as large agency networks? Yes. Many leading DSPs offer independent agencies the same technology, inventory access, and optimization they provide to large networks. The key is to evaluate pricing minimums, contract flexibility, and onboarding support, since some platforms require enterprise-level spend commitments while others offer flexible models built for independent teams.
Where can I compare trusted DSP platforms for digital campaigns? You can compare DSPs using the at-a-glance and channel-coverage tables in this guide, which evaluate Basis, The Trade Desk, DV360, Amazon DSP, Viant, and Simpli.fi across channel support, billing, CTV access, and entry point. Score each platform against your own client volume, channel mix, and team experience to identify the strongest fit.
How much does it cost to run a DSP campaign through an agency? DSP campaign costs vary by pricing model, media spend, and the channels you activate. Common structures include a percentage of media spend, CPM-based fees, or a flat SaaS subscription, and some platforms combine them. Beyond platform cost, factor in creative production, data fees for audience targeting, and any managed-service charges. Request a transparent fee breakdown from each DSP you evaluate so you can forecast client margins accurately.