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For years, market research and advertising operated in separate worlds. But as media grows more fragmented and marketers face greater pressure to prove every dollar, those worlds are beginning to merge.
On this episode of AdTech Unfiltered, Noor Naseer talks with Laura Manning, SVP of Measurement at Cint, about how advertising research is evolving. They explore how consumer insights can inform campaigns while they are still live, why first-party research can be more valuable than modeled assumptions, what AI and synthetic data mean for the future of measurement, and why the post-campaign report may finally be on its way out.
Noor Naseer: Hey, this is Noor Naseer for Adtech Unfiltered. For years, market research and adtech lived in pretty separate worlds. Research told you what consumers thought, and adtech told you what they did. And by the time the research showed up, the campaign was usually over. But that model is starting to change. Today, consumer insights are moving closer to the media decision itself, with advertisers looking for faster signals, better first-party data, and a much clearer connection between media exposure, brand impact, and business outcomes. So what happens when research becomes something you can actually act on in real time? I'm talking about that with Laura Manning, SVP of Measurement at Cint. We get into brand lift, AI, synthetic data, data quality, and why the pressure to prove every media dollar is changing what marketers expect from research. Let's get into the episode with Laura now.
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NN: Laura, I find myself not having enough conversations about research and ad tech, so I'm excited to chat with you and learn a little bit more about Cint today.
Laura Manning: Great, thanks for having me.
NN: I'll kick things off with this question. For so many years, market research and adtech, they really lived in separate realms. Today, they're converging in new ways and you guys are playing a part in that. What is it that has allowed for this change? And why are advertisers suddenly treating consumer insights as a core part of their media strategy?
LM: Yeah, absolutely. So Cint actually is really interesting because we're a market research study company who started doing media measurement. So I started at Lucid, actually on the market research side of our business. That's kind of where I got my feet wet in this industry. And then as we build our ad effectiveness products, moved over. So I think what really is powerful about Cint is that we're able to kind of combine the power of that market research, huge set of consumer data with advertising data that we get from our partners like Basis. So we're able to see kind of like exactly how ads are moving these consumer opinions by harnessing kind of that market research backbone of the company. So it's a little bit different than somehow others in ad tech play in the space. We kind of have the power of all those consumers, but we also are able to sort of layer in advertising data, other media partners data, sales data, conversion data, whatever it might be to tell like a bigger story. I think for advertisers and for brands, it's really important those functions have been very separate at those companies. You'll have a consumer insights team who's researching the brand name Coca-Cola, and if it has 98% or 99% penetration, and then they'll have a totally different division focused on media, and we're seeing those converge as well. It really is very similar. It's just like measuring a different experience for those brands. So I think we'll see that kind of continue to shift, and I think we kind of stride against that at Cint as well.
NN: You hinted at some market signals as far as what's happening inside of organizations, but I'm curious, what has the receptivity been to offerings such as your own? And also, has there been a demand and an interest for this that signaled to you all that this is a necessary offering?
LM: The reason that we, and we started in this space in sort of ad effectiveness and brand lift probably 10 years ago now, but really what we'd heard was we went to pitch kind of this consumer insights product, the market research side of the business to a big ad agency in New York. And they're like, no, no, no, no, actually what you need to do is build a pixel and start doing brand lift. So it actually was consumer driven or customer driven rather from the start. And it was so interesting because their whole positioning was this side of the industry is very legacy and very dated. Everyone was kind of like doing research after the fact, you were getting insights about your campaign six weeks after it ended. So you're already into the next campaign, you haven't learned anything. And so we've been trying to tighten that window to sort of make it easier for customers to make a decision in real time about, is this publisher working, is this ad play working, is this creative working, to hopefully better use their media dollars.
NN: Yeah. I think back to my agency days, and sometimes we look at these post reports.
LM: And 50 pages, years later.
NN: This sounds so terrible to say, I guess I'm going to say it. This belongs in one place.
LM: Yeah.
NN: The trash can. Where am I going to use this? And there really wasn't an application opportunity. So I do love the momentum and the capacity and opportunity to optimize, actually optimize in a way where we're leveraging this type of information. So since it's in a unique position between brands, agencies, researchers and media platforms, as we've just discussed, what does that vantage point allow you to see that most companies in advertising cannot?
LM: It's so interesting because we work across such a swath of the industry, we start to see trends a lot faster. So I think one thing that was really interesting is in the pandemic, a lot of the billboards in New York City, for example, became digital. All of a sudden, everyone's asking us about measuring digital out of home. We had probably never received that question until like one minute, it's everywhere. I think it's easy for us to pick up on those industry trends because we see the full scope of the industry. We're working with the big DSPs, we're working with SSPs, we're working with premium CTV platforms, we're working with big agencies, we're working with brands, and we're able to piece together that pattern recognition of, oh, we're shifting this way. I think probably a year or so ago, we started hearing from all customers across the swath of, we need to be able to justify our media dollars up to that CFO level, at a much higher rate than we have in the past. Brand isn't enough, we're running a brand campaign, but they also want to know, did it do anything else? And so we're able to kind of pick up on those trends because we sit across such a swath of the industry and decide, oh, we should add this to our product or we should do this. And it's a very cool spot to be in, I would say.
NN: Yeah. Laura, it also feels important for me to ask you about first party data. Everybody's obsessed with first party data, but not all first party data is equally useful or we can apply it in different ways. What makes consumer insights data uniquely valuable compared to transactional or behavioral data?
LM: I think what's interesting is we have so much first party data at Cint. So things like age or gender, household income, we're collecting every time people are coming into our platform. And we'll get on calls with media platforms and they'll be like, well, where are you getting this data? And we're like, we asked them. And they're like, what? I'm like, this is real. This isn't like modeled, this isn't assumed. This is actually what people are telling us about their real lives. And I think that's really powerful because so much of the industry, even if you think you're buying a hot moms who love yoga and live in San Francisco segment, a lot of that is just assumptions made about a person that have then been modeled out to create an audience, whatever it might be. I think it's so important to hear from people themselves that you can see where those trends are, what things are happening in their real lives, and how that's driving how they react to ads. We do find a lot of data, behavioral data, purchased data, that's all very valuable too. And what we have the power of at Cint is being able to connect our identity graph and our real people into those datasets to be able to say, okay, this person told us they live in New York, they told us they love yoga, they told us that they live in an apartment, but next year they want to have a home, and they saw this ad. And then they took another action. And I think being able to tie that story together is really powerful for marketers. And that's something that I think having like a strong first party do is that allows you to do.
NN: Adtech in general is a lot of the data is based on extrapolation, right? Even cookie data, there's a predictive element to it. We just make so much presumption. I'd like to ask you, obviously, you all fall on the other side of things where you're asking, but even with this asked data, I'm curious how you're marrying that with this world of AI that's happening out there. Is there some sort of superpower data where you're leveraging AI? What does AI look like when you're marrying it with your consumer insights and data?
LM: So on the measurement side of the business, the main places we're looking to use AI are to make our customers lives easier. Instead of having to download seven different PowerPoint reports, can they speak with our chatbot Lucy to get a better export that delivers exactly what they need for their client presentation in an hour? I think those are the use cases we see immediately. We do have a division at Cent working on synthetic data which is the new world of taking a big training set of known data and then trying to use AI to create new respondents that are modeled. That's probably where the market research on the business is going, is like digital twins, like being able to see, okay, we had this core data set, we want to extrapolate that out, but we haven't heard as much about synthetic play and measurement, because I think people are so interested in making sure they are true actions that were monitored, observed, and deliver brand outcomes.
NN: I just imagine there's a lot of curiosity in that area, because everyone hears about synthetic data, but if your synthetic data is being pulled off of this high-quality first-party data, which you all are in the business of, and again, we have so much proxy data or presumption-oriented data that there's inevitably people knocking down the door wanting to know what your synthetic products are. Even if there's still a work in progress today.
LM: Totally. And there are a lot of AI companies that are also using our data to train models. So even things that aren't for research, other types of use cases, other interests, there's a lot of companies popping up who need tons of real human answers to kind of guide what their products are going to look like. So there's really a lot of interesting plays in the AI space. And I think we're early, but we're participating, would be my description.
NN: I want to ask you some more questions about application. How are brands using research data earlier in campaign planning, rather than simply measuring effect in this after the fact?
LM: Yeah. So I'd say like a year or so ago, we kind of kept getting the question of like, what's next? A partner I was meeting within New York was like, brand lift is cool, but then what? This kind of got our heads jogging a little bit. I think there's two different ways we're kind of striving to tackle the like, and then what happened piece. One is our new outcomes product, which is pulling in purchase data to be able to say deterministically this brand campaign, maybe it drove ad recall 8 percent, it drove favorability 2 percent, and then it also drove conversions and average order value increases. So being able to kind of like a full funnel journey for retailers is one way we're looking at that. The other way is optimization piece, and I think the power of our data set is that it is in real time. So we're giving daily stat tested weighted data all available via API, and we're working with platforms who can take that API and say, oh, we're running dynamic creative optimization. There's a hundred different creatives. A week into the campaign, these four are driving brand awareness. And that's something that's not really existed before. So I think that's a really powerful tool to be able to optimize and choose based on something other than a click or acquisition. Really still focused on those upper funnel metrics, but more actionable and more able to be chosen and adjusted. I think all of that is happening in this big push toward what's next is because people are getting tighter on media budgets. Everyone's answering to the CFO. You have to prove that what you're doing, even if it is a brand campaign, is helping the full funnel of your business. We're trying to fit into that as well.
NN: Are there any particular verticals or industries that have really leaned into leveraging some of the solutions that you bring to the table?
LM: The great thing is we work across the industries. So we really can do anything from like pharma, it's a big CPG, financial services. It really isn't a limitation for us. I think one use case that's really interesting, especially like in the US this year, is the political play. The political campaigns are making decisions so fast. Like your average carbonated beverage campaign doesn't need to decide in the next hour if it should spend another 100 grand or not, whereas political campaigns are like tweaking minutiae every day, all day. Some of those clients have even asked us if we could go faster than daily data. Like they want it powerfully, minutely, you know. But I think the political space is so interesting because those campaigns really need that data to help guide them. They're trying to move the message on. We worked with ad council during the pandemic on a big vaccine campaign. They're trying to decide right now, how do we reach this person and shift their perception? So there's so many interesting things you can do with that data and I think that's kind of like the next step.
NN: It's interesting to hear you say that because so often someone being a service provider or a solution provider will talk about real time optimization or will just talk about things being available real time or quickly. And then when we really start to ask questions, the speed with which we can actually access that and apply it isn't quite what we were actually hoping or wanting and it sounds like you guys are really putting a lot of time and energy into making it accessible and then applicable as fast as possible.
LM: Absolutely. I mean, we built our API with a DSP partner probably five years ago, six years ago now at this point. And so from the jump, we've had that mindset of like it needs to be fast enough for them to make actions. And at that time, it was very much like the trader is going in and making little tweaks manually. The future is obviously that's going to be done by AI, that's going to be done by agents. And so we're meeting that moment, making sure our tools can kind of plug in to the next steps, too.
NN: Laura, I want to ask you a question about trust around data. Sure. What separates high quality, trustworthy data like yours from the flood of lower quality or synthetic data that's currently entering the marketplace?
LM: Yeah, I think one thing that if you're not involved in the research space, you wouldn't know about is how much fraud targets that industry and how, I mean, we have a dedicated entire team called Trust and Safety, that their entire job is to make sure that we are catching fraudulent behaviors in our safe, secure environment before it happens. So the idea that you're then asking a question in an ad or asking a question, who knows where, and just taking that as fact is tricky. There's definitely a lot more bad actors in this space, and it's usually people who are like a bot farm in the Philippines. It's not like a person who wants to give bad data or ruin your brand study. It's like a more targeted effort. But Cint, we have a really large team that's kind of entire focus is like, A, stopping bad actors before they even get into the platform, but B, if they do, tweaking our tools to make sure that going forward, these things can't happen. The AI front, of course, poses even more tricks there. If you have an open ends like Chad GPT could write that for you, you could probably create a cloud bot that can go through and take every survey. It could dream up. So it's really always a moving target. It's something that requires constant vigilance from companies to make sure it's accurate. If you think about when I first got into the space like 12 plus years ago, it was like, okay, we're checking their IP, we're checking that their cookies say they haven't taken this. It's so much more advanced than that now. And that's something that I think as a brand or as a marketer you have to think about. You have to think like where is this person getting this data? And are they doing all these other steps to make sure that this isn't actually just full of someone's cloud bot, chat agents taking surveys or taking or answering questions or just providing data?
NN: I also have to ask you about price. I don't usually ask and jump to price, but when I think of research and for an advertiser who doesn't traditionally invest in research, the immediate thought is, that's not a line item on the budget. I don't have money for this. What's your response in this case? When we're looking at and thinking about pricing, especially when you have offerings that are available in real-time biddable environments, how should a consumer who doesn't otherwise know, and by consumer, I mean media buyer, how should they be thinking about your offering?
LM: Totally. I think one of the great things about what we've built is we've also enabled the ability to buy on a CPM. So we are able to buy in the currency that media buyers are used to, and that way they can plan it in with everything else they're doing. With Basis, for example, we are rolling out an API integration where buyers, hands-on keys can just add us to their campaign, just like they'd add viewability or anything else in a data segment. So that I think has helped us in a lot of ways. It helps make things a lot more planable in the future. Beyond that, though, I'd say that it's really hard to make decisions without any data. So if you're running big brand campaigns, you're just trying to raise general awareness, but you're not figuring out what's working or not. You're wasting a lot of money that could have been used in a better way. Yes, research has a cost, but it also helps you make smarter decisions and really use those dollars in the best way.
NN: Laura, I'm going to end our conversation asking you about the future. Here's my question for you. If you were advising a CMO today, where would you tell them they're under-investing when it comes to consumer intelligence?
LM: Where I'd see the people under-investing is that they're getting maybe an added value study from their social media platform. They're getting something else from this other platform. They're getting something else here, and then they're not able to merge those together. And I think if you're a big brand and you're running these large campaigns, you really need to be able to tell a holistic story. It is very tempting when a publisher is like, oh, we'll do this. YouTube gives you something, somebody else gives you something else. And you look at them and then you're straight to the trash, as you said. There is a lot of value if you are sitting on that big campaign to be able to see which parts of it are working or not holistically. And I think not enough people are investing that way. That's just like a trend in the industry. We of course work with tons of folks who are doing an added value study because a brand asked them to. But it's like, I think they're missing the mark when they're not able to see a holistic journey. So whether that's running measurement off their DSP or other places they're buying media, I think being able to see that full story is really important, and especially important when the CMO is answering to the CFO. I'm like, okay, you spent $10 million, now what? You need to be able to have that data to be able to help guide that story in the future.
NN: I sense there's a lot of momentum in the research hybrid ad tech space and that many more buyers are going to be looking for solutions such as what Cint is bringing to the table. So I'm going to be very curious to follow up with you, hear more about Simplotic's audiences. So I'll have to keep my eyes open on what's happening in the trades, but maybe we'll have a secondary conversation.
LM: Love it, would love to. No, this is great. I think it's so interesting. People ask me all the time why I'm still here. I was at Lucid, then we were acquired by Cint. It's been 12 plus years, and I think my answer is that it's always changing. So like this space seems so, you know, research, oh, that's existed forever, but it's changing constantly, and it keeps us busy, it keeps us building new things, and I think it, for me at least, it's very exciting. So always happy to chat.
NN: Yeah, I'd say this side of research is me. There's a lot of sides.
LM: Excited to talk about it. Our research is changing all the time.
NN: Well, thanks for the time, Laura, and we'll be in touch.
LM: Thanks for having me.
NN: An interesting takeaway from this conversation with Laura is that the future of measurement may be less about generating more data and more about making all of that data tell one coherent story. Because marketers are getting plenty of reports, including those from platform studies, brand lift, behavioral data, purchase data, and more. The harder problem is connecting those signals to something that can actually guide the next decision. Laura made the case for moving beyond measurement that simply tells you what happened towards intelligence that can influence what happens next. And as AI accelerates the shift, the questions around data quality, trust in what counts as real consumer signals only gets more important. Thanks again to Laura Manning. I'm Noor Naseer, another episode of Adtech Unfiltered, out real soon.
Choosing a CTV advertising platform is harder than it used to be. CTV budgets are climbing—advertisers plan to increase CTV spend by 17.5% in 2026, according to eMarketer—and the money is arriving faster than the infrastructure to spend it well. The buying landscape has splintered into walled gardens, open-web programmatic, and standalone CTV point solutions that rarely share data. The IAB has called fragmentation one of the biggest barriers to digital video growth, warning that the pace of new platforms and formats is outrunning the systems used to buy and measure them.
Agencies feel that fragmentation twice over, because CTV is only one channel in the mix. Basis’ 2026 Advertising Agency Report found that more than one-third of full-service and media agencies now manage 10 or more adtech tools, with inefficient processes and siloed systems ranking as their top operational challenges. The advertising platform an agency picks for CTV either adds to that pile or helps consolidate it.
The connected TV advertising platforms most popular with agencies in 2026 are Basis, The Trade Desk, Amazon DSP, Google DV360, Viant, Simpli.fi, and Roku. Each combines premium streaming inventory with the targeting and measurement client campaigns require. The platform decision comes down to fit: how you buy, how much you want to run in-house, and whether CTV stands alone or connects to the rest of your media.
Connected TV advertising delivers ads to smart TVs, streaming sticks, and gaming consoles. Those ads run inside streaming content like the shows, movies, and live events people watch, and increasingly on home screens, menus, and other interface placements as publishers open up new inventory. The most familiar format is the in-stream spot, when a viewer starts watching content and a targeted ad is chosen for that household—the same programmatic mechanics behind display advertising applied to full-screen, TV-quality video. (For the fundamentals, including devices, targeting, measurement, and how CTV differs from OTT and linear, check out our complete guide to connected TV advertising.)
How advertisers buy that inventory varies, and it can shape which platforms are worth your time. Some supply trades through open real-time auctions. Much of the premium inventory moves through private marketplaces (PMPs) and programmatic guaranteed (PG) deals, where publishers offer specific placements at negotiated terms, and some is still bought directly from the publisher. A platform’s value depends heavily on which of these buying models it opens and how much premium, brand-safe supply it can reach.
Most CTV platforms claim strong targeting, measurement, and premium inventory. The differences show up in a handful of details that decide whether one fits your accounts: inventory access and quality, targeting depth, measurement and attribution, brand safety and fraud protection, service model, and minimum spend. These weigh differently by client: a national brand advertiser would likely prioritize premium inventory and measurement depth, while a local or multi-location shop might weigh geo-targeting and low minimums. You can walk through these considerations in depth in this framework for choosing a CTV advertising platform.
The top CTV advertising platforms include a range of options, each of which has unique strengths and weaknesses. The list below details the best picks for agencies in 2026, each suited to a different kind of agency, client mix, and way of buying.
Basis is an AI-powered omnichannel advertising platform that unifies CTV, programmatic, search, social, and site direct in one system that is built specifically around how agencies operate. CTV lives inside Basis right alongside those other channels, so all of an agency’s planning, buying, optimization, reporting, data, and billing stay connected in the same platform instead of being scattered across an array of point solutions.
When it comes to CTV specifically, Basis reaches 93% of US smart TV households through open exchanges and 700+ private marketplaces and PG deals, with premium supply from publishers including Hulu, ESPN, and Disney+. Media planners get access to 1,000+ advanced TV targeting parameters and 80+ trackable metrics, plus brand safety and verification coverage through partnerships such as DoubleVerify and Protected by Mediaocean.
Its AI capabilities provide benefits across the campaign lifecycle: Compass builds activation-ready omnichannel media plans that weigh CTV against every other channel rather than in isolation, and SmartBid optimizes bids and budgets in real time against live performance signals. Basis also offers automation features that generate 30-40% productivity gains for its users.
Basis’ workflow advantages compound because CTV connects to everything else. For instance, an agency running streaming on a standalone DSP must still assemble separate tools for search, social, direct buys, and billing reconciliation, each one a handoff where budgets and reporting can drift. Basis unifies agencies’ walled-garden and open-web investments into a single view, so cross-channel frequency, pacing, and measurement run on connected data. And for teams that would rather not run everything in-house, Basis also offers expert managed services.
Best for: Agencies that want CTV inside a unified omnichannel system, with the targeting depth and measurement to prove results and without the tool fragmentation that drives up cost and manual work.
The Trade Desk is an enterprise programmatic DSP with real-time bidding across CTV and other digital channels. It has relationships with major networks and streaming publishers, giving agencies access to a large marketplace of premium CTV inventory. Buyers can target with first- and third-party data across screens, control frequency across networks and devices, and run optimization through its AI, Koa.
That said, it comes with a steep learning curve and sizable spend commitments, making it best suited to large agencies with dedicated programmatic traders and clients with substantial budgets. It is also programmatic-only, so agencies must run search, social, direct, and billing on separate systems.
Best for: Large, programmatic-first agencies with in-house trading expertise and clients whose budgets and media mix are weighted heavily toward programmatic.
Amazon DSP's advantage is its first-party data: shopping, browsing, and streaming signals from Amazon's own ecosystem that let advertisers target on real purchase behavior rather than inferred intent. On CTV, its owned inventory spans Prime Video, Thursday Night Football, Fire TV, and Twitch, and it can also reach third-party streaming supply through publisher deals and exchanges.
The tradeoff is scope. Its real benefit—its shopper data and owned premium inventory—is concentrated in Amazon’s own properties, so its strength clusters in retail, CPG, and commerce use cases. Additionally, its managed service tier carries a high enough entry point to favor advertisers with substantial budgets.
Best for: Retail, CPG, and commerce advertisers whose campaigns live inside Amazon's ecosystem and can put its shopper data and owned streaming inventory to work.
Google Display & Video 360 (DV360) is Google’s enterprise DSP within the Google Marketing Platform, buying across CTV, display, video, audio, and DOOH. Its defining advantage is the Google ecosystem: exclusive YouTube inventory and native interoperability with Campaign Manager 360 and Google Analytics 4 make it especially powerful for Google-centric campaigns. It also buys third-party CTV and open-web supply, but its distinct edge is concentrated in that Google-native stack.
DV360 is not self-serve—access runs through a Google Marketing Platform contract that puts it out of reach for smaller accounts—and it does not handle paid social, direct buys, or billing, so it covers just part of an agency's workflow rather than the whole.
Best for: Large agencies running Google-centric campaigns that lean on YouTube inventory and already operate within the Google stack at scale.
Viant is a programmatic DSP spanning CTV, display, mobile, audio, and DOOH. It offers self-service, co-managed, and managed models. It targets and measures at the household level through its Household ID technology, layering demographic, behavioral, contextual, and geographic targeting on top. It offers access to quality CTV inventory as well as partnerships with fraud-protection solutions.
What it does not handle is the rest of the stack: Search, social, direct buys, and billing run on separate systems.
Best for: CTV-centric advertisers that value household-level targeting and measurement and don't need a cross-channel workflow that spans the entire campaign lifecycle.
Simpli.fi is a programmatic DSP that markets itself as specialized for local and multi-location advertising, with tooling built around trade-area, ZIP-code, and address-level campaigns across CTV and other channels. Its targeting leans on localized, geo-based audience building, and it offers both self-serve and managed models so agencies can run campaigns in-house or lean on support.
Its specialization also defines its fit. Agencies running franchise or multi-location campaigns get a platform organized around their use case, while national or single-brand advertisers get less benefit from the local orientation. Simpli.fi also focuses on programmatic buying and does not natively cover search, direct buys, or unified billing, so those sit within separate systems.
Best for: Agencies running local, multi-location, franchise, or political CTV campaigns.
Roku is a streaming platform rather than a standalone DSP, which makes it a different kind of entry on this list. It owns the operating system, the devices, and The Roku Channel, giving agencies direct access to a large base of logged-in streaming households. Through its platform, Roku sells both its owned inventory—home-screen placements and The Roku Channel—and aggregated in-stream video across 100+ ad-supported streaming apps, bought through Roku Ads Manager, a self-serve platform with built-for-CTV optimization and a low entry point. Its inventory can also be reached programmatically through third-party DSPs. Targeting and measurement draw on Roku's first-party, logged-in account data at the household level, and the inventory it sells is curated, premium streaming supply rather than open-web placement.
Because Roku is a publisher platform, it does not handle other digital channels or the complete campaign lifecycle.
Best for: Agencies that want direct access to Roku’s owned streaming audience and home-screen inventory through an accessible, self-serve CTV platform.
| Platform | Best For |
| Basis | Agencies buying CTV at scale who want it unified with programmatic, search, social, and direct media in one system that automates planning through reconciliation |
| The Trade Desk | Large, programmatic-first agencies with substantial client budgets |
| Amazon DSP | Retail, CPG, and commerce advertisers activating shopper data across Fire TV and Prime Video |
| Google DV360 | Large agencies operating within the Google ecosystem at scale |
| Viant | CTV-centric advertisers that don’t need a cross-channel workflow that spans the entire campaign lifecycle |
| Simpli.fi | Agencies running local, multi-location, or franchise CTV campaigns |
| Roku | Agencies wanting direct, self-serve access to Roku's streaming audience and home-screen inventory |
| Platform | Type | Inventory Access | Beyond CTV | Entry Point |
| Basis | Omnichannel advertising platform | Premium and open exchange inventory, 700+ curated and exclusive PMPs, programmatic guaranteed and direct buying options | Programmatic, paid search, paid social, direct buys, natively unified billing & reconciliation | Lower threshold |
| The Trade Desk | Enterprise DSP | Open exchange, PG, and PMPs | Programmatic-only; no paid search, no paid social, no direct buys; no natively unified billing & reconciliation | ~$10K+/mo |
| Amazon DSP | Walled garden/DSP hybrid | Owned inventory, PG, and PMPs | Programmatic-only; no paid social, paid search runs separate from Amazon DSP, no direct buys; no natively unified billing & reconciliation | $10K rec. self-serve / $50K managed |
| Google DV360 | Walled garden/DSP hybrid | Open exchange, PG, and PMPs | Programmatic-focused; limited paid search, no paid social, no direct buys; no natively unified billing & reconciliation | GMP contract (~$50K+/mo) |
| Viant | DSP | Open exchange, PG, and PMPs | Programmatic-only; no paid search, no paid social, no direct buys; no natively unified billing & reconciliation | Self-serve & managed |
| Simpli.fi | DSP | Open exchange, PG, and PMPs | Programmatic-focused; no paid search, partial paid social, no direct buys; no natively unified billing & reconciliation | Self-serve & managed |
| Roku | Streaming platform/publisher | Owned Roku inventory plus aggregated in-stream supply across 100+ streaming apps; also buyable via third-party DSPs | CTV-only; no paid search, no paid social, no natively unified billing & reconciliation | Low self-serve minimum |
The strongest CTV platform for your agency is the one that fits how you work, versus the one with the longest feature list. Many of the top CTV advertising platforms run CTV as one programmatic channel, which leaves media and full-service agencies assembling their tech stacks by cobbling together point solutions, using one tool for search, another one (or several) for social, another still for direct deals, and yet more for the back-office work of billing and reconciliation. Each additional tool is another login, another data silo, and another manual handoff between planning and payment—the fragmentation the IAB now names as a leading barrier to video ad growth and a major source of errors and makegoods.
The sharpest question, then, is how much of that fragmentation a platform removes. For a single-channel buyer, a specialist DSP or publisher platform might be plenty. For a multi-client agency running the full lifecycle across digital channels, platforms that close the loop from planning through reconciliation could be worth far more than those that stop at the buy. Which of those tradeoffs matters most depends on your agency’s client mix, buying model, and how much you want to run in-house.
What is a connected TV advertising platform?
A connected TV advertising platform is software that lets advertisers plan, buy, target, and measure video ads delivered to streaming audiences on smart TVs, streaming devices, and connected apps. These platforms use programmatic buying to deliver ads in real time based on the viewer’s profile and the advertiser’s targeting criteria. They give advertisers precision targeting and measurable results that traditional TV cannot match.
How is CTV different from linear TV and OTT?
CTV refers to ads delivered to internet-connected televisions and streaming devices, while linear TV refers to traditional broadcast and cable viewing. CTV offers household-level targeting and detailed performance measurement, whereas linear TV reaches broad demographic panels with limited attribution. OTT (over-the-top) describes the streaming content delivered over the internet; CTV describes the connected device that content plays on, and the two terms are often used interchangeably.
What features should agencies look for in a CTV advertising platform?
Agencies should look for deep targeting parameters, robust measurement and attribution, a service model that fits their team, minimum spend that matches client budgets, and strong brand safety and fraud protection. Targeting depth controls audience precision, while measurement depth lets you prove value and justify budget shifts. Agencies running multiple channels also benefit from platforms that connect CTV to search, social, direct, and reporting rather than operating in isolation.
How do advertisers measure CTV campaign performance?
Advertisers measure CTV campaign performance using metrics like impressions, video completion rates, reach and frequency, conversions, and incrementality. Unlike linear TV, CTV platforms report household-level delivery and outcome data, so advertisers can tie streaming ad exposure to measurable results. Platforms vary in measurement depth: Basis, for example, supports 80+ trackable metrics, giving planners the reporting needed to build data-backed recommendations.
What does connected TV advertising with Basis look like?
With Basis, CTV runs as one channel inside a single omnichannel platform rather than a standalone tool. Teams reach 93% of US smart TV households through open exchanges and 700+ private marketplaces, target with 1,000+ advanced TV parameters, and measure across 80+ metrics—then plan, activate, report, and reconcile that CTV spend alongside programmatic, search, social, and direct media in one place. That connected setup lets agencies grow CTV investment without adding another tool or another round of manual reporting.
Key Takeaways:
As adoption of AI search tools rises, the open web is feeling a little less, well, open.
ChatGPT reached 1 billion global monthly active app users in May, just three years after its launch. Perplexity counts 45 million. Google’s AI Mode crossed 1 billion monthly active users just a year after it was introduced, and its AI Overviews are now surfaced on 40% of all US Google searches.
As consumers increasingly use these tools to search and research, they’re spending less time on the open web. Already, searchers are staying in AI Mode between one and nine minutes longer than on a traditional Google search. And a 2025 Growth Memo study found that in about 75% of sessions, users never ventured out of AI Mode at all. The effect on open web traffic has been significant: One study of 218 websites across 12 industries found that organic search has lost close to a quarter of its traffic volume since the start of 2023.
This shift in consumer behavior has significant implications for advertisers, who have spent years building strategies around how consumers move from site to site, and channel to channel. As more of the customer journey takes place within AI environments, the cookie trail advertisers once relied on to track them is thinning out, and audiences are scattering across a growing set of walled gardens. The result is a more fragmented, complex environment that will force marketing teams to rethink how they reach and influence target audiences.
AI search will intensify fragmentation and complexity both in terms of tracking the customer journey and in managing campaigns across channels and platforms.
Consider a consumer who has a conversation in AI Mode that surfaces a particular brand. That interaction is essentially invisible to the advertiser—there’s no click or site visit to signal that it happened at all. The first sign of the consumer may be the purchase itself, leaving the brand with no way to trace what shaped their decision. This is a major shift for marketing teams accustomed to using cookies to follow consumers across the open web. And while Google may come out with features that allow advertisers to track consumers within AI Mode and AI Overviews, as of right now, those capabilities don’t exist.
Evolving consumer behavior is also likely to result in additional fragmentation, spread across an array of walled gardens—from other AI search tools like ChatGPT and Gemini, to social platforms like TikTok, Instagram, YouTube, and Reddit, all of which are rolling out similar AI features (Meta, for instance, recently launched their new AI assistant). This means marketing teams must confront a familiar problem—the media fragmentation that’s been making advertising work increasingly difficult, with over a third of full-service and media agencies already using 10 or more tools across their workflows—at an even greater scale.
Advertisers who want to stay competitive will need to adapt their strategies accordingly, trading the tactics built for a trackable open web for ones designed to reach and measure audiences across a scattered digital landscape.
In order to succeed as AI search changes the customer journey, brands will need to prioritize three things:
Let’s take a quick look at each of them:
Today, media diversification helps advertisers reach audiences across the growing number of channels and platforms where they spend time. This diversification will only grow more important as AI search changes the customer journey. As people shift away from the open web and into AI environments and other walled gardens, the path to purchase will become more fragmented and less observable. A diversified media mix will help teams capture more audience signals and stay present wherever consumers choose to discover, research, and make decisions.
Programmatic channels like video and CTV are well-suited for that diversification, particularly when advertisers layer on premium content and private marketplaces (PMPs) to keep honing in on target audiences.
Of course, spreading spend across more platforms compounds an operational problem that already plagues marketing teams: tech stack sprawl and disconnected systems. One study found that 40% of agencies cite siloed systems as a top challenge, second only to the inefficient processes those disconnected tools create. Finding ways to unify advertising workflows across channels will be key to meeting this moment, bringing scattered channels into a single view so teams can diversify without losing the holistic perspective they need to plan, buy, and measure effectively.
AI is likely going to exacerbate a model where consumers are only in market for a very small amount of time. The hours they once spent researching on the open web will compress as AI tools compile that research for them, which means that the touchpoints where advertisers can reach consumers mid-research will grow scarcer.
AI gives consumers the CliffsNotes, so brands will want to amp up the awareness and salience they’re driving outside of AI environments to stay top of mind for the moment a consumer does enter the market. Inside those environments, generative engine optimization (GEO) (also known as answer engine optimization or AEO) will be just as important, helping brands show up the way they intend when AI tools compile their summaries. Together, these efforts can help build the kind of familiarity that tips a brand into the consideration set when it comes time to decide.
At the same time, advertisers will need to expand their measurement approaches to get a more holistic view of performance. As more of the customer journey moves inside AI environments, the breadcrumbs (i.e., third-party cookies) advertisers once relied on to track them won’t exist in the same way. That could change if Google rolls out tracking within AI Overviews and AI Mode, or if OpenAI does the same for ChatGPT, but given the tools at hand, advertisers will need to lean more into sophisticated measurement tools to prove out success.
Layering on consideration metrics, incrementality studies, and modern modeling approaches like MMM and scenario planning will help advertisers capture a more complete picture of how the growing number of platforms and channels that influence their audience work together to drive impact.
Resetting expectations will also be key, as attribution won’t be as immediate as brands are used to. Teams accustomed to putting a dollar in and getting three dollars out almost instantly on platforms like Facebook and Instagram will find that pace is no longer the norm as advertising influence plays out over longer, less visible timelines. Proving the value of that spend will depend less on instant, click-based returns and more on measurement built to capture impact across the entire journey.
As AI changes how people discover, research, and make purchase decisions, it will change how advertisers approach their work just as fundamentally. Teams will need to adapt to more fragmentation and complexity, while simultaneously dealing with less visibility into their audience’s buyer journey.
In this new era, brands who diversify their media spend, lean into brand building, and evolve how they measure success will be better positioned to reach and influence audiences wherever they spend their time. And the sooner teams begin to adapt, the more they’ll be able to keep pace as AI continues to influence the customer journey.
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Looking to learn more about how AI is changing advertising work? We interviewed marketers across leading agencies and brands to find out how they’re using the technology, how it’s changing jobs and teams, how they see it transforming the industry in the coming years, and more. Download AI and the Future of Marketing for all our top takeaways.
Key Takeaways:
Marketing and advertising teams are increasingly turning to AI to overcome a wide range of constraints, including limited budgets, resources, and time.
To date, the technology appears to be delivering on at least some of its potential, with nearly three-quarters of marketing and advertising professionals saying it has made them moderately to significantly more efficient at their jobs. But while adoption is accelerating, many organizations are still figuring out where AI can deliver the most meaningful impact.
In 2026, marketers and advertisers are using AI across five key areas: creative and content generation, process streamlining, media buying and optimization, media strategy, and campaign measurement.
Tapping into AI for ideation and to generate content and creative assets is one of the most widely adopted use cases in marketing today. A Basis survey found ideation and brainstorming to be the most popular use of AI among advertising and marketing professionals, while drafting content and creative assets ranked third. Teams are applying AI across a range of tasks, from streamlining internal communications to drafting marketing content to producing digital ads.
When it comes to generating ad creative, AI is creating powerful new opportunities to scale personalized creative more efficiently. “Marketers should evaluate their messaging and creative strategies to find where AI can unlock scalable personalization and variation that was previously limited by time and cost,” says April Weeks, Chief Investment and Media Officer at Basis.
That being said, the involvement of human employees remains an important step in developing effective creative and content, with three-quarters of industry professionals believing that AI-generated content does not yet match the standard of human-generated work. AI is best used as a complement to human-led work, helping teams produce more content in less time while enabling more tailored outputs at scale.
Marketing and advertising teams are also widely leveraging AI to streamline complex processes: According to one survey, it’s the fourth most popular use case of generative AI among industry professionals today. As teams look for ways to reduce manual work and improve operational efficiency, close to two-thirds of marketers and advertisers say their organizations have invested in technologies that automate or streamline processes within the past year.
This is a critical piece of an effective AI strategy, as solutions that are tacked on to existing workflows rather than thoughtfully integrated into them will likely exacerbate existing tech stack sprawl. High-performing teams are nearly three times as likely as others are to report that their organizations have completely restructured individual workflows with AI in mind. The shift from tacking on point solutions to rethinking entire workflows is what enables marketing teams to achieve efficiency gains with AI.
Another key consideration is data governance and unification, as fragmented data can be a major barrier to both workflow efficiency and effective AI adoption. Unified, well-governed data not only streamlines the workflows that feed AI tools, but also improves the quality of their outputs. To move towards data readiness, marketers should audit their tech stacks with data unification in mind, seeking out platforms and tools that centralize data sources, enforce consistent governance standards, and create a single source of truth that both their teams and their AI tools can reliably draw from.
Marketers and advertisers use AI in media buying and optimization to process real-time signals, adjust bids, shift budgets across channels, and target audiences more precisely.
AI has long played a role in media buying and optimization, but recent advances are enabling marketers to improve how they allocate and adjust their investments. “We’re moving toward a place where buyers will be able to move much more quickly, because AI will be surfacing data signals and insights faster and accelerating tasks like predictive optimization and forecasting,” says Weeks.
Already, AI-powered tools can process dozens of real-time signals to inform bidding decisions and shift budget across channels as performance evolves. This allows teams to respond more quickly and drive ROI by making more informed adjustments over the course of a campaign. AI also enables more advanced audience targeting, helping marketers surface overlooked segments and uncover new audiences via approaches like building synthetic audience personas.
Personalization at scale—a persistent challenge in media strategy—is another area where AI is opening new doors. Studies show that 71% of customers expect personalized interactions, and 76% get frustrated when they don’t happen. And when applied effectively, AI-driven personalization has been shown to improve customer satisfaction by 15% to 20%, drive revenue increases of 5% to 8%, and lower the cost to serve by up to 30%.
Adoption, however, has not kept pace. Only one-third of marketers are using predictive AI for media buying and planning, while roughly a quarter are applying generative AI in this area. That gap between potential and adoption represents a clear competitive opportunity. To capitalize, advertisers should ensure they’re using advertising platforms that offer AI-enhanced media buying and optimization, prioritizing those that have these tools integrated into their core workflows.
Marketers and advertisers use AI in media strategy to synthesize large volumes of audience, competitor, and market data, then move from planning to activation more quickly. That speed is increasingly valuable as audiences evolve.
Over half of businesses cite shifting consumer preferences as their top challenge. Yet only around one quarter of marketers say they are using AI as part of their media strategy development process. AI can help marketers better keep up with these shifts by enabling deeper, faster insights.
“AI is creating new opportunities for media strategists to synthesize large volumes of data and uncover insights more efficiently and comprehensively,” says Weeks. “It can take tasks that were once highly manual for advertisers and execute them faster, using a broader set of data.”
Most current use cases for AI across media strategy fall into three core areas:
Together, these capabilities can help marketers better understand their customers and inform a more effective media strategy. Emerging tools are also beginning to extend beyond insights into execution. For example, there are now AI solutions that can turn media briefs into full omnichannel media strategies spanning both the open web and walled gardens, helping agencies and in-house teams reduce manual planning work and move more quickly from strategy to activation.
Campaign measurement remains an under-utilized AI use case poised to become central in the years ahead.
AI excels at parsing, organizing, and analyzing large data sets, and many marketing and advertising teams today are swimming in more campaign data than they can effectively use. Because of the fragmentation of that data, marketers are often weighed down by the manual processes necessary to unify data across platforms, structure it for analysis, and extract meaningful insights in a timely manner. AI can help streamline that workflow, making it faster and more scalable.
AI can also assist with tasks like running regression analyses for performance forecasting based on historical spend and conversion data, helping teams move from raw data to actionable insight more efficiently.
“As AI becomes more embedded in marketers’ workflows, it will increasingly shape how teams approach reporting, insights, and analytics,” says Weeks. “That shift is already underway, but adoption and maturity vary widely across organizations.”
What will separate the agencies and brands who effectively adopt AI from those who don’t? According to Weeks, the companies that succeed will be those who lean into AI and understand the importance of data governance and data hygiene.
In addition to prioritizing data readiness, marketing and advertising teams that thoughtfully implement AI across content generation, process streamlining, media buying, strategy, and measurement will be better positioned to move faster, make smarter decisions, and focus their human talent on the work that matters most.
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Want more insights into how AI is reshaping digital advertising strategy? We surveyed marketing and advertising professionals from top agencies and brands to understand how they are adopting AI, where they are seeing results, and more. Check out AI and the Future of Marketing for all the top takeaways.
Much like the arrival of digital advertising several decades ago, agentic advertising has gone from emerging concept to leadership priority remarkably fast.
Two-thirds of ad buyers say agentic AI ad buying and execution is an increased focus this year, and 84% name media planning and buying recommendations as a current or likely use case, according to IAB’s 2026 Outlook Study. For many, the first instinct is to ask which AI tool to buy. But what actually determines success is whether an agency’s data infrastructure can support an agent that plans, decides, and executes on its own.
Agentic advertising is advertising run by an AI system that executes multiple steps toward a goal with less human intervention at each step. In agentic advertising workflows, a person sets the goal and reviews the output rather than approving every action, while AI typically moves between the intermediate steps on its own. Taking a media brief from strategy through planning, activation, optimization, and reporting is one example, but the same pattern applies wherever an agent carries a multi-step task to completion. It sits a step beyond rule-based automation and a step short of fully autonomous advertising, and it’s a tier agencies are already putting to work or actively exploring.
Agentic advertising can be powerful, effective, and efficient…but it can also be unforgiving. When an AI system shifts budgets, adjusts targeting, and executes media buys with less human review at each step, the quality of the data underneath it decides almost everything. An agent pointed at fragmented, stale, or un-auditable data won’t recognize anything is wrong, and it will still act, executing the errors at machine speed—faster than anyone can review and correct them.
An agency’s readiness for agentic advertising, then, is a question of data infrastructure. This guide walks through the four dimensions that determine whether agentic workflows will deliver for an agency: data centralization, workflow integration, system-of-record integrity, and governance and auditability.
Though automation, agentic, and autonomous often get used interchangeably and there is some overlap, they do not mean precisely the same thing. Understanding each is an important first step to determining whether an agency is ready for agentic advertising.
Advertising automation uses AI and software to plan, activate, optimize, and reconcile campaign tasks with reduced manual intervention. It spans four maturity tiers:
Knowing where a platform actually sits on that spectrum—and what role(s) it plays in the process—is the first filter for any serious evaluation. To learn more about how to choose a platform, check out AI and Advertising Automation in 2026.
Agentic advertising is a form of advertising automation (and a tier below fully autonomous advertising) where an AI agent executes multiple steps toward a goal with less human intervention at each step. Compass, Basis’ AI for omnichannel media planning and activation, is a good example of agentic advertising. It turns briefs into complete, insight-driven media strategies, allocating spend across channels and audiences spanning both the open web and walled gardens. And every recommendation from Compass is accompanied by the reasoning behind it, so a media planner can review the logic, refine the plan, and activate on their own terms.
Autonomous advertising builds on agentic advertising and takes it a step further, with multiple agents orchestrating together simultaneously, each handling part of the workflow and coordinating without a human directing the handoffs. Few organizations run true autonomous advertising yet, but it is where the industry is heading. Getting there depends on a unified operating system that connects planning, activation, and reporting across channels, a foundation many agencies don’t yet have in place.
| Advertising Automation | Agentic Advertising | Autonomous Advertising | |
| How it works | A spectrum, from if-then rules to independent optimization | One agent, multiple steps, less intervention at each step | Multiple agents orchestrating simultaneously |
| Human role | Sets the rules or parameters | Sets the goal, reviews the output | Sets the objective and guardrails; oversees and reviews |
| Maturity | Widespread | In use now, but limited adoption | Emerging |
A strong, unified data foundation becomes more important the higher a platform sits on the automation spectrum. A single agentic system is only as sound as the data it acts on. Coordinate several agents autonomously, and any weakness in that shared foundation compounds across all of them at once. Readiness for agentic advertising, and eventually autonomous advertising, is an infrastructure question before it is an AI question.
Much of the industry debate over agentic tools fixates on the underlying models (ex. proprietary methodologies versus generic LLMs) and on which is best suited to the task at hand.
Yet even the right model for the job is governed primarily by the system and the data it acts on. A generic large language model working from clean, unified, owned performance data will likely make better media decisions than a proprietary or task-specific one working from fragmented, siloed inputs. That is why data quality, rather than simply AI adoption, is a primary predictor of whether AI advertising delivers differentiated results for an agency.
Give an agentic system only a partial view of performance and it will optimize against it with full confidence, moving spend toward what looks efficient in isolation, losing sight of how channels perform together—and with no human reviewing each step to catch it. The problem compounds because the infrastructure most agencies run wasn’t built for autonomous decision-making.
Basis’ 2026 Advertising Agency Report found that 36.8% 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, disconnected systems (40.4%) ranking as their top operational challenges. An AI agent inherits that fragmentation. As such, readiness for agentic advertising means ensuring a strong data foundation first.
An agency’s readiness for agentic advertising comes down to four dimensions of data infrastructure. Each one determines whether an agent can act on a complete, trustworthy, accountable picture, or a broken one. These four dimensions also align with how to evaluate any platform’s automation: workflow coverage, integration depth, maturity-tier honesty, and human-in-the-loop control.
| Dimension | Not Ready | Partially Ready | Ready |
| Data Centralization | Data lives in separate channel tools and spreadsheets | Some sources integrated; gaps remain across channels | All channels and historical performance unified in one view |
| Workflow Integration | Planning, buying, and reporting happen in disconnected systems | Some stages connected; manual handoffs persist | An agent can act across the full workflow in one unified platform |
| System-of-Record Integrity | No single source of truth; duplicated, conflicting records | One source of truth exists but isn’t consistently maintained | Clean, owned, continuously maintained record agents can trust |
| Governance & Auditability | No way to trace what an automated system did or why | Some logging; not decision-level or client-ready | Every automated decision is logged, explainable, and auditable |
What it is: All of an agency’s campaign data, across every channel and reaching back through historical performance, accessible in one place.
Why agentic workflows fail without it: An agent optimizes against the data it can see. When display, CTV, search, and social live in separate tools, the agent makes confident decisions on a fragment, shifting budget toward what looks efficient in one silo while missing how channels perform together. The gap between what the agent sees and what is actually happening becomes the gap between its decisions and good ones.
How to assess: Could an agent see every channel and historical results without a person assembling the data first?
What ready looks like: A unified data foundation where cross-channel and historical performance are already consolidated, so an agent acts on the whole picture rather than a slice of it.
What it is: Planning, activation, optimization, reporting, and billing connected in one continuous workflow, rather than stitched together across separate systems via human intervention or manual work.
Why agentic workflows fail without it: An agent can recommend a budget shift, but if a person still has to re-key that shift into a separate buying tool, the speed advantage evaporates and errors enter at every handoff. A Forrester Total Economic Impact study of the Basis platform found that consolidating those workflows reduced manual steps by 40% and media operations time by 43%, the exact overhead that keeps agents from acting end to end.
How to assess: Once an agent makes a decision, can it act on that decision across workflows without a human moving it between systems?
What ready looks like: A connected workflow where an agent’s decision flows through planning, buying, reporting, and billing without manual re-entry.
What it is: A single, clean, continuously maintained source of truth for what happened in every campaign, owned by the agency rather than scattered across vendor exports.
Why agentic workflows fail without it: An agent treats its system of record as reality. If that record holds duplicated, stale, or conflicting numbers, the agent doesn’t hesitate over the discrepancy the way a person might. Weak data quality is already the constraint most teams name, with 45% of marketers saying they expect data quality or accessibility to pose significant challenges to their AI efforts over the next one to two years. Owning that record, rather than renting fragments of it from each channel, is what makes it trustworthy enough to hand to an agent.
How to assess: When two systems disagree on a number today, does the agency have one record that serves as the source of truth?
What ready looks like: One owned, deduplicated, continuously updated record that both the agency’s team and an agent can act on without reconciling exports first.
What it is: The ability to trace what an agentic advertising tool did, why it did it, and to intervene, for every decision it makes.
Why agentic workflows fail without it: Agencies must stay accountable to their clients, and any decision an agent cannot explain can turn into a decision the agency cannot defend. As human oversight moves out of each individual step, the ability to reconstruct what happened afterward becomes the safeguard. This is where agentic AI has to be built for transparency rather than treated as a black box. Compass, for example, pairs every recommendation with the reasoning behind it, so a team can see what drove each allocation, adjust it, and activate with confidence. Auditability is also what makes governance real across brand safety and privacy obligations, where an agency has to show both the outcome and the decision path that led there.
How to assess: If a client asked why an agent moved their budget last week, could the agency show them the reasoning and the trail behind it?
What ready looks like: Every automated decision is logged, explainable in plain terms, and open to human override, so autonomy never comes at the cost of accountability.
Agencies adopting agentic workflows need a complete, trustworthy, accountable foundation for their agents to act on. That is what the Basis platform is built to be, the operating system for autonomous advertising, with AI like Compass woven directly inside it.
Basis consolidates the full advertising workflow, unifying planning, activation, optimization, reporting, and reconciliation across programmatic, search, social, and CTV in one platform, rather than a stack of single-purpose point tools. That consolidation is what turns the four readiness dimensions from aspirations into defaults. Data centralization comes from unifying every channel and campaign into one dataset. Workflow integration comes from connecting planning through reporting so decisions flow without re-keying. System-of-record integrity comes from owning that dataset rather than reconciling vendor exports. Governance comes from the controls Basis applies to how that data is accessed, used, and tracked across workflows. And because it runs inside that same governance, Compass brings transparency to the decisions themselves, pairing every recommendation with the reasoning behind it.
The four dimensions are what separate agencies that can act on agentic workflows now from those that need to build readiness for them. Basis is built so they come as defaults rather than a project an agency has to assemble on its own.
What is agentic advertising?
Agentic advertising is advertising run by an AI system that executes multiple steps toward a goal with less human intervention at each step. A person sets the goal and reviews the output rather than approving every action, while the system moves between the intermediate steps on its own. Taking a media brief from strategy through planning, activation, and optimization is one example, though the same pattern applies wherever an agent carries a multi-step task to completion. It sits between rule-based automation, which follows if-then rules a person defines, and autonomous advertising, where multiple agents coordinate simultaneously. Agencies are using agentic advertising today, including through Compass, Basis’ agentic AI for omnichannel media planning and activation.
What is autonomous advertising?
Autonomous advertising is advertising run by multiple AI agents that orchestrate together simultaneously, each handling part of the workflow and coordinating without a person directing the handoffs. It sits one tier beyond agentic advertising, where a single agent executes multiple steps toward a goal on its own. Few organizations run true autonomous advertising today, but it is the direction the advertising industry is heading.
Which advertising platforms have launched agentic tools?
Basis offers agentic capabilities today through Compass, its AI for omnichannel media planning and activation, and SmartBid, its AI for real-time campaign optimization. Several adtech and point-solution vendors have launched narrower agentic features as well, though most operate within a single channel or workflow stage rather than across the full campaign lifecycle.
Which AI advertising tools are built on proprietary methodologies versus generic LLMs?
Compass is trained on Basis’ proprietary IMPACT campaign framework, a planning methodology tested across thousands of media campaigns, rather than reasoning from general-purpose training data alone. A model grounded in proprietary, campaign-tested data and unified performance history has an edge—but that edge only holds when the data underneath it is clean and unified, which makes the advantage an infrastructure question more than a model question.
Which advertising platforms reduce manual campaign operations?
Platforms that consolidate the campaign workflow reduce manual operations most. A Forrester Total Economic Impact study of the Basis platform found consolidation reduced manual steps by 40% and media operations time by 43%. SmartBid further cuts manual work by handling bid and budget adjustments automatically against live performance signals, freeing teams to focus on strategy.
How can an agency tell if it is ready for agentic advertising?
An agency can assess readiness across four infrastructure dimensions: data centralization, workflow integration, system-of-record integrity, and governance and auditability. If its data is unified, its workflow is connected end to end, its system of record is clean and owned, and every automated decision is auditable, agentic workflows can deliver. Gaps in any dimension are what to resolve first.
Key Takeaways:
One might expect that the more time consumers spend with a certain channel, the more money advertisers would want to invest in it. In reality, however, the two rarely match.
Some channels, like CTV, command far more consumer time than their share of ad dollars suggest. Others, like social media, attract spend that far outpaces the time consumers give them.
These gaps are, in part, a result of the pressure to prove how every dollar spent contributes to business outcomes. This pressure can lead teams to overinvest in channels with advanced performance offerings, thereby undermining the long-term demand and brand equity built by upper-funnel marketing. Understanding these dynamics is key to crafting effective media plans in 2026, especially as economic anxiety makes consumers more discerning with their spending.
Divergences between the percent of daily time spent by US consumers and the percent of total US media ad spend show up across many (if not most) advertising channels.
The divide is particularly pronounced on subscription OTT, where ad spend accounts for 12.5 percentage points less than consumer time spent, and social media, where ad spend accounts for 15.2 percentage points more than consumer time spent.
Adding to the disconnect, social engagement is growing by only a few minutes annually, but ad spend will grow by over 20% in 2026. This phenomenon is largely driven by Meta platforms: US adults spend 3.9% of their daily time with digital media on Facebook and 3.4% on Instagram, but those channels receive 11.9% and 12.8% of total US digital ad spending, respectively.
There are a variety of reasons behind the gaps between consumer time spent and ad spend. CTV inventory, for example, is expensive to buy, and producing broadcast-quality video adds another layer of cost, keeping the channel out of reach for many advertisers despite its heavy viewership. Social platforms sit at the other end of the spectrum, with comparatively low CPMs and low barriers to entry that make them approachable for smaller-budget teams. On the other hand, audio and radio draw significant daily time but tend to be consumed more passively, which may be part of why they attract less spend despite the hours consumers listen.
These factors explain part of the picture, but they don't account for the full size of the gap. The imbalances are also rooted in advertisers’ efforts to demonstrate the impact of every dollar spent. CMOs are increasingly under pressure to deliver measurable business outcomes, which understandably leads marketing teams to invest more in channels whose performance advertising offerings make ROI easy to show.
Meta is a prime example, offering granular targeting, massive scale, and conversion tracking that ties spend directly to outcomes—likely one of the main factors driving advertisers to invest so heavily in Facebook and Instagram. In contrast, CTV has a reputation for being more difficult to measure, which is part of why ad spend drags so heavily behind consumer time spent.
Channels that make ROI easy to prove have real value. At the same time, attribution-based measurement can overstate how much performance marketing actually contributes. These methods tend to over-credit the channels closest to the point of purchase, skewing the picture of how channels work together to drive results. For example, one study found that 30% of paid search clicks are attributable to other advertising channels (mostly video). And the channels with the biggest imbalances between time spent and ad spend are where this dynamic plays out most, with a recent ad effectiveness analysis showing that large brands tend to heavily overspend on social.
These skewed understandings of channel contribution are a problem because overinvesting in performance marketing can backfire. Reports have framed this trend as defining the industry’s “performance era,” finding that these overinvestments can decrease revenue returns by 20% to 50%, while moving from a more performance-focused investment profile to one that balances performance with brand can increase overall revenue returns by a median of 90%.
“You can’t just chase performance,” says Kelly Boyle, SVP of Strategic Business Outcomes at Basis. “If you’re not building future demand, your performance marketing will eventually lose steam.”
Finding the optimal media mix for each brand and campaign depends on marketing teams’ approach to measurement. Attribution-based measurement is often platform-specific, which leaves advertisers without a clear view of how investments across different channels work together. Modern modeling approaches like MMM, regression modeling, and scenario planning, on the other hand, show teams how investments across channels and platforms can optimally work together to drive business outcomes. Platform-specific attribution should remain a core function, but modern modeling is essential to seeing the full picture.
Of course, consumer time spent is just one consideration to factor into a media strategy. “Every brand’s audience and their consumer journey is different,” says Boyle. “It’s not just about showing up in the places where people are spending time. It’s also important to consider, ‘Where is my audience being influenced? Where are they making decisions? Where are they in the right mindset for a certain message?’”
Ultimately, the most successful advertisers use a granular understanding of the brand's customer journey—informed by platform-specific measurement and modern modeling as well as additional consumer signals and market research—to allocate budget where it will actually move the business.
Channels that make ROI easy to demonstrate have earned their place in the media mix. At the same time, it’s important for advertisers to understand how last-click attribution can lead teams to overinvest in them—and that it does so at the expense of the brand-building that sustains performance over time.
In response, leading marketing teams are evolving their measurement approaches to get a clearer picture of how their investments work together and allocating accordingly. Ultimately, the quality of a team's measurement approach is what separates spending that looks good from spending that actually grows the business.
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Want to go deeper on what better measurement looks like in 2026? In Bringing Momentum to Measurement: What Marketers Are Missing in the Pursuit of Effectiveness, Basis experts break down where most teams get stuck and what measurement approaches actually drive effectiveness.
The way people consume media has splintered across platforms. But many of the brand safety signals used to determine where ads should and shouldn't appear haven't kept pace. As a result, advertisers are excluding valuable inventory, missing relevant audiences, and paying for impressions that meet technical safety standards but fail to deliver real performance.
In this webinar, Sara Maskivish, SVP of Market Enablement - Verification at Protected by Mediaocean, joins host Noor Naseer to unpack what modern brand safety and suitability strategies require. They’ll explore how a smarter approach to brand safety and suitability can help advertisers balance scale, relevance, and risk while improving the effectiveness of every media dollar.
What you'll learn:
Innocean needed to scale campaign execution, improve spend efficiency, and maintain performance without increasing operational overhead.
The California-based full-service agency manages media for Wienerschnitzel, a franchise brand with 300+ stores, co-op funded budgets, and frequent promotional cycles, such as "Wiener Wednesdays." The franchise structure centered by equitable budget distribution across all stores, but was challenged by:
To overcome the challenges, Innocean turned to Basis to bring structure, automation, and data-driven decisioning to their media operation. Basis and Innocean focused on eliminating inefficiencies in targeting, planning, and execution while building a foundation that could support continued growth. With Basis, Innocean was able to achieve:
As the Weiner Wednesday promotion campaign gained traction, media investments shifted from launch-level support to reminder-focused messaging, demonstrating the team's ability to sustain business impact more efficiently over time.
Basis provided a data-driven approach that helped Innocean scale franchise media more efficiently while driving measurable in-store impact, with resulting including:
“Basis has been a transformative partner to the Wienerschnitzel business, elevating our media approach through innovative strategy, measurable impact, and exceptional collaboration.”
– Leo Hernandez | Media Director, Innocean
Key Takeaways:
The economy is weighing on consumers.
Amidst an uncertain job market, persistent inflation, and ongoing geopolitical tensions, consumer sentiment has taken a hit. In the second quarter of 2026, the share of US consumers feeling optimistic about the economy fell to its lowest point in two years.
Despite the hardship, market conditions have not translated to a recession economy—at least as of yet—as consumer spending is forecast to dip only about 1% in the second half of the year. The US ad market also remains strong, with ad spend projected to grow by 9.5% in 2026 (and those gains aren't just a byproduct of political, Olympics, and World Cup spending).
All together, today’s market looks more like a value economy: Consumers are spending more cautiously and taking the time to hunt for the best deals. For advertisers, understanding how to adjust campaign strategies in response is key to success.
While consumer financial wellbeing remains resilient, economic concerns are impacting discretionary purchases, with a larger share of consumers planning to spend less on nonessential items and experiences over the next few months than last quarter.
That caution runs deepest among lower-income households. However, higher- and middle-income consumers reported the steepest declines in optimism in Q2, with many reconsidering their "nice-to-haves." Given that groceries cost about a third more than they did in 2019, with housing and family health insurance up by even greater proportions, the rising cost of “must-haves” is putting pressure on the budget consumers otherwise reserve for wants.
That shift is already showing up in consumer spending, with retail sales slipping in July for the first time in nine months.
Increased caution around discretionary spending will likely play out in a few different ways. Some consumers will postpone purchases, weighing whether their old phone or car can last another six months. Others will make trade-offs rather than cut spending, hunting for the best value they can find across categories like travel, retail, and clothing. And some will forego certain purchases entirely, deciding a nonessential isn't worth the money right now.
The common thread is that consumers are scrutinizing discretionary purchases and looking harder for reasons to justify their spending—which means it's on advertisers to supply that justification.
Economy-related shifts in consumer sentiment and behavior should inform brands' messaging more than their media strategies. For most categories, the audience and media mix don't need to change when consumers grow cautious. By adjusting messaging, however, brands can give discerning consumers the justification they need to make a purchase.
Considering this, value-based messaging is key. As consumers evaluate cheaper alternatives, brands should make the case for their premium—for example, by showing how their product outperforms or outlasts competitors’. And as consumers consider postponing purchases, brands should give them clear reasons to buy now, such as a limited-time offer.
As consumers have grown more cautious, many brands have done the same. I’ve seen some begin gravitating more towards performance marketing, seeking measurable returns that make investments more easily defensible.
Leaning harder into performance makes sense when budgets are under scrutiny and marketers need to demonstrate ROI. But pulling back on brand marketing for too long tends to backfire. When brands don’t continuously generate demand at the top of the funnel, they eventually run out of prospects to convert lower down. History bears this out: Brands that pull back on spending during economic downturns tend to fare worse, while brands that keep investing tend to come out ahead. Even outside of a downturn, one study found that shifting from a performance-focused strategy to a more balanced mix of performance and brand delivered a median revenue ROI increase of 90%.
The risks that come with cutting brand investment are particularly acute for premium brands. A brand that stops making the case for its premium leaves consumers to make purchase decisions based on cost alone—a risky position when value is top of mind. Sustained brand investment is what keeps consumers perceiving a certain brand as worth the premium, even with cheaper alternatives available.
Inflation, geopolitical unrest, and job market uncertainty are changing how consumers spend in 2026. The brands that navigate this period most successfully will be those that meet a more cautious consumer with value-focused messaging and factor in the risks of pulling back on brand marketing for too long.
As economic and geopolitical conditions continue to shift, the brands that keep a pulse on what’s top of mind for consumers will be best positioned to earn their dollars.
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