In this episode of Adtech Unfiltered, Malorie Benjamin, Chief Transformation Officer at Dixon Schwabl + Company, joins host Noor Naseer to unpack how the agency pitch process is evolving.
She shares how data, AI, and predictive modeling are reshaping the way agencies approach new business, why many clients are still stuck on last-click attribution, and how transparency can build trust in programmatic buying. Malorie also explores the growing pressure on CMOs to adopt AI, how agencies can simplify complex tech for clients, and what truly sets agency pitches apart today. The discussion offers a candid look at what it takes to win modern agency pitches.
Humans are wired to search.
With billions of search queries per day, marketers have long known where and how to find their audiences. Now with the rise of generative engines and AI, the search landscape is facing its biggest upheaval in decades. Traditional search, long the backbone of digital strategy, is being rapidly replaced and reshaped by generative engine optimization (GEO), fragmented user journeys, and new discovery ecosystems.
In this webinar, Robert Kurtz, Basis’ Strategic Business Outcomes Partner, joins host Noor Naseer, VP of Media Innovations + Technology, to discuss the past, present, and future of search. They highlight immediate threats to legacy search approaches and inspire strategies for brands, marketers, and advertisers to reclaim visibility and influence.
Key Takeaways:
After years of uncertainty, TikTok's sale to a group of US investors has finally given TikTok advertisers something they haven't had in a while: stability.
For years, regulatory volatility kept many advertisers from making long-term commitments on the platform. After all, why build a strategy around an app that might eventually cease to exist in the US? Now that the threat of a ban is off the table and the sale is finalized, advertisers can plan with more confidence than they’ve had in years.
Still, this new era of TikTok comes with unanswered questions that advertisers would do well to keep tabs on. Potential changes to the app’s famed algorithm, new privacy concerns, the evolution of TikTok commerce, and the development of AI-led advertising features stand out as the most important areas to watch in the coming months.
TikTok's algorithm is, in many ways, the true heart of TikTok itself. It's what fueled the app's rise to prominence and what keeps users so deeply engaged (see: “I built this algorithm brick by brick”). As such, whether the algorithm will change under new ownership has become one of the biggest questions for users and advertisers alike.
The good news? The owners are incentivized to keep things as similar as possible to not rock the boat with users and advertisers. Still, even subtle changes could alter the user experience in meaningful ways—and some algorithm changes are essentially baked into the deal itself. The app’s new owners (a group called TikTok USDS Joint Venture LLC) have licensed the algorithm to Oracle, and stated that they will “retrain, test, and update the content recommendation algorithm on US user data.”
Major content changes seem unlikely in the short term, but significant questions remain as to how this group of investors will influence what people see in their TikTok feeds. This concern isn’t lost on users: Shortly after the sale, many reported signs of content suppression on the app. Perception matters too: Whether changes are happening or not, if users feel like their feeds are shifting, that could affect usership and engagement. User sentiment is already in flux, with one recent survey finding that one-third of Gen Z users now feel they must actively train their TikTok algorithms because they’re not as personalized as they once were.
Considering this, advertisers should monitor TikTok usership and engagement over the coming months to stay abreast of any changes that may occur as a result of the algorithm’s evolution.
Algorithm uncertainty isn’t the only perception risk advertisers should track. TikTok’s new privacy policy presents another layer for advertisers to consider. The app is now collecting more user data, which has garnered privacy concerns from both users and human rights experts. Even Gen Z—the generation that drove TikTok's rise—is taking notice: 64% of Gen Z TikTok users say the sale made them more aware of their data.
This presents a potential risk for advertisers, as these privacy concerns could impact usership and engagement. As with the algorithm, the key for advertisers is to keep a close eye on how these concerns play out in the numbers.
How the commerce side of TikTok will develop under new ownership is another open question worth watching. Oracle—now a key player in TikTok's new ownership structure—has deep roots in retail commerce and supply chain management, which could have notable implications for TikTok Shop. If Oracle further integrates TikTok with new commerce solutions or introduces an Oracle commerce solution for the app, that could be a significant selling point for advertisers looking for closed-loop measurement on retail and commerce campaigns.
The flip side, though, is balance. If the platform leans too heavily into commerce at the expense of entertainment content, the user experience could suffer. Already, 79% of Gen Z TikTok users miss the early days of the app, before the surge in brand partnerships and the launch of TikTok Shop. Advertisers should monitor how that balance plays out, and whether engagement holds steady as the commerce side of the app continues to evolve.
Oracle's involvement also raises questions about where TikTok's AI-driven advertising capabilities are headed. Meta has set a high bar with campaigns that leverage AI for campaign builds, optimization, and audience targeting. Before the sale, TikTok was just scratching the surface in this area. With Oracle now in the mix, it's reasonable to expect TikTok's AI-led advertising offerings to accelerate.
If that is how things play out, creative is going to be an even bigger lever on TikTok. With AI taking on more of the campaign building and optimization work, the brands investing in UGC, influencer content, and platform-native creative will have a meaningful edge. Those that aren't will find it increasingly difficult to perform.
Overall, while there are some big question marks in the TikTok advertising space post-sale, the outlook is mostly positive. The app’s future is stable, advertising performance appears to have remained consistent, and TikTok continues to be cost-efficient relative to other social platforms, with strong conversions and CPA.
That said, the advertisers who keep a close eye on the questions outlined above—around the algorithm, privacy, commerce, and AI-led advertising—will be best positioned to navigate the platform successfully as this new chapter continues to unfold.
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AI is poised to transform more than just TikTok advertising. To see how marketing teams at leading brands and agencies are already putting it to work (and where the challenges still lie), check out AI and the Future of Marketing.
Key Takeaways:
You need to get started using programmatic buying tools, but you’ve never done it before, and you don’t know where to begin. Join the club. We get it. It’s the same reason we haven’t learned to cook for ourselves yet.
Here’s the good news: We’ve got experts at Basis who know the ins and outs of programmatic advertising. All you have to do is ask the right questions. Lucky for you, we’ve asked the basic questions and we’ve come equipped with answers—and, lucky for us, we've been assured repeatedly that there is no such thing as a dumb question.
To start: Programmatic is a very broad term. Simply put, it’s technology that automates digital media buying. This can include automating anything from rate negotiation and campaign set up to optimizations and actualizations. One of the primary buying tools you have at your disposal is a DSP.
A demand side platform (DSP) is an automated ad buying platform, where advertisers and agencies go to purchase digital ad inventory. Examples of ad inventory include banner ads on websites, mobile ads on apps and the mobile web, and in-stream video. DSPs are integrated into multiple ad exchanges.
Popular examples of DSPs include The Trade Desk, Google DV360, Amazon DSP, and Basis DSP.
DSPs use real-time bidding to purchase ad impressions automatically. Here's how the process works:
An SSP is not the same thing as a DSP, but it is similar in concept.
Supply-side platforms, or sell-side platforms (SSPs), facilitate the sale of publisher inventory through an ad exchange. SSPs offer services such as minimum bid requirements in order for the publisher to maximize how much their ad space sells for. The difference is that DSPs are for marketers and SSPs are for publishers. SSPs, like DSPs, are plugged into multiple ad exchanges.
Think of the ad exchange as the "go-between" in the automated buying world. An ad exchange is a digital marketplace that enables advertisers and publishers to buy and sell advertising space via real-time bidding (RTB). Real-time bidding (RTB) is the process of buying and selling ad impressions through instantaneous auctions that occur in milliseconds. The ad exchange announces each impression—with the inventory flowing through DSPs and SSPs—in real time and asks buyers if they are interested in buying said impression and at which price
| Platform | Definition | Who Uses It |
|---|---|---|
| DSP (Demand-Side Platform) | Automated platform for buying digital ad inventory | Advertisers and agencies |
| SSP (Supply-Side Platform) | Platform that helps publishers sell their ad inventory | Publishers |
| Ad Exchange | Digital marketplace where DSPs and SSPs connect to buy and sell ads via real-time bidding | Both buyers and sellers |
In order to understand why DSPs matter, it's important to remember where the need came from and how the ad industry operated before automated buying. Historically, if you were a media buyer at an ad agency, the buying process was facilitated through human beings—it was you (the advertisers), the publishers (website where ad will appear), an audience (the viewer of the ad), and a bunch of spreadsheets and emails going back and forth negotiating prices. This process was complicated, time-consuming, and often error-prone. DSPs allow advertisers and agencies to buy across a lot of sites at the same time—and all of this is done instantly and efficiently, usually before the webpage loads.
There are many DSPs in the programmatic world to choose from. Choosing the right DSP for you depends on a number of factors.
DSP Evaluation Checklist:
Some DSPs come with a full team of experts, offering you everything from full-service to self-service and everything in between. With Basis DSP, you'll start with a three-month platform training program, offering you an overview of programmatic, a walk-through of the interface, and best practices for campaign creation and optimization. Ongoing support is available in the form of a customer success manager and resources to keep you informed—like new feature webinars, best practice guides, and industry-leading research reports.
Learn more about programmatic advertising with Basis.
Sean Duggan and Kristin Battersby from the Basis Candidates & Causes Team host a deep dive into the political audio advertising marketplace heading into the 2026 midterm elections. They share insights and best practices for how to effectively leverage audio—including streaming, podcasting and more—to reach voters and win this November.
Key takeaways:
Commerce media has all the characteristics of a digital advertising channel that every brand is searching for.
What began with retailers has expanded into travel, payments, and other sectors rich in transactional data—empowering advertisers to connect with targeted audiences in relevant moments with personalized messages.
Because commerce media is powered by first-party data, those audiences, moments, and messages are as targeted, relevant, and personalized as possible. A growing number of commerce media platforms also enable a sophisticated omnichannel approach, allowing advertisers to capture audience attention in saturated digital environments.
While non-retail commerce media is still nascent, its swift evolution is opening up new, less-saturated channels for marketers to reach high-intent audiences. In fact, non-retail commerce media is growing even faster than retail media, with ad spend forecast to surpass $100 billion by 2028. It’s a space where innovation—particularly AI-related innovation—is driving rapid expansion.
Considering all this, now is the time for advertising leaders in every sector to get ahead of the curve by understanding the forces driving commerce media’s growth as well as how to leverage the channel strategically.
Marketing teams today are struggling with signal loss, a lack of data readiness, and media fragmentation—all of which make it harder to deliver personalized, timely messages and craft the coordinated omnichannel media strategies that are key to connecting with consumers in today’s crowded digital environment. At the same time, economic volatility is constraining marketing budgets and putting marketing leaders under increased pressure to prove out the value of their investments.
In this environment, commerce media is well-positioned to take on a larger role in marketing budgets. “Commerce media is growing more accessible and attractive to brands outside of CPG and retail as the supply side continues to grow through the collection, organization, and availability of first-party data,” says Jane Frye, VP of Integrated Client Solutions at Basis. And the reliability, accuracy, and abundance of this data make it possible for advertisers to use commerce media for personalization at scale.
There are also increasing opportunities for advertisers to use that high-quality data for audience targeting beyond a commerce media entity’s site or app to create highly personalized omnichannel advertising experiences. Offsite retail media ad spend, which enables advertisers to activate commerce media audiences beyond a retailer’s site or app, is forecast to grow at double the rate of onsite media spend through 2026, and off-platform placements will account for over 63% of display ad spend by 2029.
Even more, commerce media is well-suited for times of economic uncertainty. As a highly measurable channel, it allows marketers to clearly demonstrate ROI. And while commerce media’s early growth was largely driven by performance marketing, it has since evolved into a full-funnel solution that supports brand-building efforts as well.
Ultimately, these strengths make commerce media not just a timely solution for today’s challenges, but also one that’s aligned with the trajectory of digital advertising.
To capitalize on the commerce media opportunity—whether a team is testing and learning on the channel or executing a large-scale strategy—marketing teams must strategize around fragmentation and a lack of measurement standardization, while also prioritizing data readiness and preparing for how the rise of agentic commerce will transform the channel.
The commerce media marketplace is growing increasingly crowded. One way to simplify the space is to limit the number of networks in play. As such, advertisers should carefully evaluate which networks can deliver the most value.
Many new entrants offer access to first-party data but lack the infrastructure to support meaningful advertising outcomes. Marketers should strike a balance between testing emerging platforms with low barriers to entry and investing in more established players with comprehensive ad solutions.
At the same time, there are some meaningful advantages to diversifying spend across a variety of platforms. A diversified strategy is the most effective approach for brand building, says Frye, as channels thrive on the cumulative effect of many small exposures. It also ensures advertisers are not over-relying on a single platform or data source, which mitigates risk and gives teams the flexibility to optimize based on performance.
To help their teams succeed in an increasingly fragmented landscape—both within commerce media and across the broader digital ecosystem—advertising leaders must take steps to reduce the complexity it creates. Tools that reduce manual labor, streamline parts of the campaign process, and unify often-disjointed systems like reporting and billing can help teams operate more efficiently and adapt to new opportunities.
High-quality data is one of commerce media’s most compelling advantages. As the space evolves, marketing teams are finding smart ways to maximize and extend that data. Tools like data clean rooms are opening up new opportunities for data collaboration, with advertisers combining data from commerce media networks, advertising platforms, and publishers with their own first-party data to drive more sophisticated strategies.
To fully capitalize on these opportunities, teams must refine how they collect, organize, store, and activate their data. This is closely tied to the broader challenge of digital advertising fragmentation and resulting tech stack sprawl: When data is siloed across a variety of tools, it becomes difficult to extract meaningful value. With over 50% of agency marketers using eight or more tools to manage client campaigns and 40% using 10 or more tools, unifying data across platforms should be a top priority for marketing teams aiming to succeed not only in commerce media, but across their broader marketing efforts. This is especially true when it comes to harnessing and operationalizing AI: Since AI outputs are only as good as their inputs, marketing teams need access to large volumes of unified, high-quality data to get reliable and differentiated outputs from their AI tools.
Another key consideration for advertisers investing in commerce media is the rise of agentic AI and agentic commerce. Agentic AI’s ability to independently research, compare, and execute purchases on behalf of consumers is poised to change how products are discovered and bought—and, by extension, how commerce media functions as a channel. Marketers who get ahead of this shift will have a meaningful advantage over their competitors.
Consumer interest in AI-assisted shopping is already significant: In late 2024, 71% of US shoppers—and 84% of Gen Z shoppers—expressed interest in using an AI agent to make purchases on their behalf once items hit a target price. Meanwhile, major AI platforms are actively building commerce capabilities that could eventually compete with what retail media has long owned at the lower funnel: Both OpenAI and Perplexity, for example, are building out fully agentic commerce infrastructure. However, OpenAI’s recent scaling back of their plans to integrate checkout directly into ChatGPT suggests that even the most well-resourced AI companies are finding it difficult to own the full purchase journey. Retail media giants, meanwhile, are leaning in on their own agentic bets: Walmart, for example, has introduced Marty, a new agentic capability built to help advertisers manage billing and bidding for their sponsored search campaigns.
As consumers increasingly rely on AI agents to research and buy on their behalf, brands and retailers will need to ensure their products are discoverable and purchasable by those agents in addition to human shoppers. This means auditing websites, product catalogs, and digital assets to verify they’re optimized for AI systems as well as human visitors, including structured data, clean APIs, and metadata-rich content that AI agents can easily parse and act on. Equally important is keeping a pulse on how this space develops, as the agentic commerce space is evolving quickly and the strategies that work today may need to be revisited as new platforms, capabilities, and consumer behaviors emerge.
Commerce media addresses many of the challenges advertisers are grappling with today, offering precision, scale, and omnichannel activation. At the same time, its measurability is a significant advantage at a time when marketers are under increasing pressure to prove performance.
To fully realize the channel’s potential, marketing leaders must take care to invest in the right partners, unify fragmented systems, build robust and streamlined data ecosystems, and prepare for how agentic AI will change the landscape. By taking these steps, teams can unlock the full value of commerce media—not only to meet current challenges, but to set the stage for sustainable growth in the years to come.
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Commerce media isn’t the only space that AI is set to transform: The technology is also poised to reshape how marketers work on a fundamental level. To find out how teams are actually navigating that shift, we surveyed professionals from leading brands and agencies about the tools they're using, where the technology is delivering, and where the gaps remain. Check out AI and the Future of Marketing for all the findings.
For years, advertising in AI-powered environments was limited if not largely nonexistent. But after a flurry of activity over the past few months, that’s no longer the case: Many chatbots and AI-generated search results are now ad-supported (or will be soon), and the pace of change is accelerating.
For advertisers, this means navigating fragmented platforms, limited reporting, and new intent dynamics—all while direct inventory access remains restricted for most. Here’s a look at the current state of AI media advertising, what makes these environments distinct, and where advertisers should focus their attention.
Key Takeaways:
After a slow start (during which OpenAI CEO Sam Altman famously described ads on ChatGPT as a “last resort”), advertising in AI-powered environments has begun to debut across different platforms. And while the timelines have varied across the broader AI ecosystem, the pace has picked up considerably over the past year.
At Google Marketing Live 2025, the company confirmed that paid ad placements would begin appearing within AI Overviews and AI Mode, its AI-powered search experience. Both text-based Search ads and image-forward Shopping ads are now available inside AI-generated summaries on desktop, integrated directly into answers rather than sitting alongside them as separate units.
Microsoft began testing ad formats within Copilot in March 2025, making it one of the earlier chatbot platforms to introduce sponsored placements. And, notably, early insights show that ad relevance metrics in the interface are 25% better than what is seen in traditional search.
In early 2026, OpenAI announced it would begin testing ads within ChatGPT. Placements appear at the bottom of responses—clearly labeled and distinct from the LLM’s organic answer—when a sponsored product or service is deemed “relevant” to the conversation (though there is still uncertainty around what determines this relevance). Early analysis shows ads triggering most frequently on high-intent queries containing modifiers like “best” and “new,” with brands including Best Buy, AT&T, and Expedia among the early participants. The pilot remains restricted to select global brands, with a reported minimum spend commitment of $200,000.
Google has signaled to advertising clients that it is targeting a 2026 rollout for ad placements within Gemini (which is, importantly, a separate initiative from AI Mode and AI Overviews). No formats, pricing structures, or testing timelines have been shared publicly, but with Gemini surpassing 750 million monthly users, the platform represents meaningful scale. And the direct outreach to buyers signals that Google is actively moving to monetize its chatbot, even as the specifics remain undefined.
Anthropic has taken a notably different position on advertising. In February 2026, the company reaffirmed its commitment to Claude remaining ad-free, stating, “There are many good places for advertising. A conversation with Claude is not one of them.” Their reasoning centers on trust: Anthropic’s view is that users shouldn’t have to second-guess whether an AI is genuinely helping them or steering them toward something monetizable. The company says it plans to sustain that model through enterprise contracts and paid subscriptions rather than ad revenue.
Claude aside, the broader picture is a market that is quickly fragmenting. Some AI platforms are moving quickly to monetize through advertising. Others are positioning ad-free experiences as a trust differentiator (at least for now). What’s clear is that AI media environments are no longer a single category. They vary by platform, format, access, audience, and business model. For advertisers, understanding which platforms are ad-supported, which are better suited to organic reach, and how each one fits into a broader media strategy is becoming critical.
Advertising in AI environments introduces a different set of dynamics than traditional search or display, and those distinctions can carry real implications for strategy.
Perhaps the most significant change is the quality of intent. In traditional search, a query signals interest. But in a conversational AI environment, users have often already moved further down the decision path—articulating specific needs and narrowing their options before they ever see an ad.
“I believe that AI-driven answers, regardless of platform, will shorten the research cycle and turn intent into conversion more quickly,” says Lindsay Martin, Group VP of Search Media Investment at Basis. “As a result, click-throughs are more likely to be pre-qualified.”
That pre-qualification impacts landing page strategy. Martin notes that intent-specific landing pages will be critical in these environments to reflect the nuance of the prompt. This is a meaningful departure from the broader match approach that has traditionally anchored search. The prompts that trigger AI ads carry more context than a traditional keyword, and creative built for broad audiences isn’t designed to meet that specificity. Messaging will need to prioritize usefulness, clarity, and context.
Trust is another variable that sets AI environments apart. Recent research finds that 66% of US adults are highly worried about getting inaccurate information from AI tools, and introducing advertising into these environments only deepens that concern, layering a commercial motive onto platforms that users are already approaching with caution.
How platforms plan to handle that transparency gap varies. OpenAI has outlined principles governing how ads will function in ChatGPT, including that ads will not influence responses and that user conversation data will not be shared with advertisers. Google has published documentation on how ads in AI Overviews are triggered: Both the user query and the content of the AIO are considered when serving ads, and placements are currently excluded from sensitive verticals including finance, healthcare, and politics. What that documentation doesn’t address is how users are informed about why a particular ad appeared, or what guardrails exist to ensure the presence of ads doesn’t shape how AIOs are generated in the first place. Whether users accept any of these approaches—or notice the difference—remains to be seen.
“I’m interested in understanding how the different AI platforms’ algorithms determine relevance for advertising and maintain users’ trust,” Martin says. “There isn’t much transparency on that topic yet.”
Lastly, tracking and measurement questions compound both of these challenges. And to date, neither ChatGPT nor Google AI Overviews has provided advertisers with detailed metrics on the success of advertising in these AI-powered environments.
Martin anticipates that attribution capabilities will become more robust over time—showing how AI conversation functions as a conversion assist earlier in the customer journey. For now, reporting remains limited, and the infrastructure to meaningfully capture that influence has yet to become a standard for continued ad optimization.
“If I were a brand,” says Martin, “I would not be comfortable with the high price tag for early advertising without a strong measurement framework in place.”
For most advertisers today, direct access to AI ad environments remains limited. However, there’s still plenty that teams can do to prepare for when inventory becomes more accessible.
On the paid side, the immediate priority should be awareness and readiness. Monitoring how ads are triggered in chatbots (e.g., query types and intent signals) provides a useful foundation before access opens more broadly. And maintaining paid search presence also remains important: Ads appearing alongside AI-generated summaries can deliver brand impressions even without a click, and those impressions still influence decisions.
The more immediate opportunity, however, is organic. Large language models decide which brands to surface, cite, and recommend in their responses, and that process is already happening now, independent of any paid placement. Optimizing for that inclusion, increasingly referred to as generative engine optimization (GEO), means prioritizing structured, factual content, clear product and FAQ pages, and content formatted in ways AI systems can parse and surface reliably.
“If an LLM has no reason to include you in results, you will be left behind,” Martin says.
The intersection of organic and paid strategy in these environments also warrants attention, with Martin noting that GEO is “muddying the waters with how we’ve traditionally differentiated SEO and SEM.” Teams that have treated organic and paid search as separate disciplines will need to think about how those strategies align in environments where the same AI system may determine what it recommends organically and what it surfaces as a sponsored result.
And as the channel continues to mature, AI media advertising will also add new complexity to an already fragmented landscape. Each platform operates on different formats, measurement frameworks, and reporting standards, meaning that teams managing presence across ChatGPT, Google AIOs, and other emerging inventory will be navigating multiple systems simultaneously. That complexity will make it all the more critical for advertisers to seek out platforms that allow them to unify budget, performance, and strategy in one central location—making it easier to respond quickly as inventory expands and the rules continue to evolve.
For advertisers building toward long-term readiness and resilience, staying updated on the latest AI media advertising developments, diversifying measurement frameworks, prioritizing content that AI systems can find and use, and maintaining presence across channels where messaging remains controllable is key. AI search ad spending—spanning both generative AI platforms and AI-powered search summaries—is projected to grow from $2.08 billion in 2026 to $25.93 billion in 2029.
Though the environments driving that growth are still taking shape, advertisers who invest in understanding them now will be better positioned to compete as access widens and the rules become clearer.
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For a deeper look at how AI is reshaping the future of search and what it means for media strategy, check out AI and the Future of Search Engine Marketing.
Key Takeaways:
An omnichannel advertising platform is a centralized technology that connects programmatic, site direct, search, social, and connected TV channels in one interface for unified campaign management and execution. This new breed of technology empowers marketers by simplifying the complexities of modern digital advertising, ultimately consigning the days of multiple single-point solution logins to the past.
Key Benefits of Omnichannel Advertising Platforms:
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Frequent reporting, quick experimentation, effective optimization, flawless execution.
This is the basic blueprint for success for any digital advertising campaign. Of course, there are great nuances to each stage, but this is essentially what a successful advertising strategy in action looks like.
Two decades ago, this cycle was fairly simple to manage. Marketers only had to concern themselves with building a couple of creative iterations, reaching one device, measuring a handful of cost types (for example, CPM or PPC), and utilizing a few tracking methods. However, today’s reality couldn’t be starker. The explosion of digital intermediaries, coupled with the continued emergence of new channels, has irrevocably changed the media landscape—enabling a proliferation of touchpoints and breeding an environment rife with fragmentation.
To combat these challenges, many marketing organizations have simply added more and more single-point solutions to their technology stacks—the theory being that separate, bespoke systems built to master individual channels or specific devices will ultimately drive the best results.
This tactic was once powerful enough to satisfy consumers who wanted nothing more than the basics, like quality service and competitive pricing. But meeting the needs of today’s consumer is a far more demanding proposition. They assume the promotional code they see on Hulu will be valid both on a website and in-store. They believe if they fill up their basket online and close the desktop window, said basket will automatically transfer to the mobile app. They presume an online chat agent will have full knowledge of the complaint they lodged days ago on social media. As such, unified cross-channel experiences and cross-platform mobility are now overtly baseline requirements for brands.
These evolving consumer expectations—driven by tech-savvy millennials and Gen Z—are perennially pushing marketers to improve the seamlessness of their advertising operation. Those that fail to do so will fall behind the curve.
Consider these statistics:
Together, these numbers highlight just how important it is for brands to embrace an omnichannel marketing strategy that works by breaking down silos and putting audiences at the very center of the advertising experience. By doing this, media buyers can get hyper-granular and more precise with their execution, delivering relevant messages that meet individuals right in their moment. Examples of this in practice might include:
The benefits of this approach are both profound and wide-ranging—heightened operational efficiency, improved customer retention, and increased customer lifetime value, to name but a few.
However, these sizable rewards don’t come easily, and the path leading to them is riddled with complexity. Many companies have attempted to implement an omnichannel strategy, yet few have truly succeeded in constructing a sweeping, unified experience for the consumer. Some don’t even bother initiating the process due to the perception that the effort would demand an unreasonable amount of time and personnel resources, while those that have done so often stumble because of immature implementation methods, competing priorities, slow progress, and spiraling costs. The most significant hurdle, though, is technology: legacy systems designed to run basic single-channel consumer interactions lack the necessary interconnectivity inherent to a true omnichannel experience, which can create massive holes in the ambition for single, holistic campaigns.
The good news for media buyers is that novel solutions are coming onto the market equipped with everything required to nullify all these barriers simultaneously: Omnichannel advertising platforms.
The technology is a game-changer for advertisers.
The omnichannel platform empowers marketers by connecting all the major digital channels—programmatic, site direct, search, social, and connected TV—in one centralized ecosystem, thereby simplifying campaign management and execution. It ultimately consigns the days of multiple single-point solution logins to the past: with omnichannel platforms, marketers can buy programmatic audio inventory, negotiate guaranteed direct page takeovers, and run a series of dynamic carousel ads on Facebook from one interface.
Examples of omnichannel advertising platforms include comprehensive media buying solutions like Basis, as well as enterprise marketing suites from vendors such as Adobe, Salesforce, and MediaMath, which offer varying degrees of unified cross-channel campaign management.
To be clear, this technology should not be confused with omnichannel programmatic platforms that deal solely in the programmatic sphere. An omnichannel advertising platform can do everything those systems can and then some, pulling together all the core components of an omnichannel campaign and adding on top a combination of solutions like workflow automation, integrated reporting, and other AI-powered functionalities built to enhance collaboration and partnership both internally and externally.
| Feature | Omnichannel Advertising Platform | Omnichannel Programmatic Platform |
|---|---|---|
| Channel Coverage | Programmatic, site direct, search, social, and connected TV | Programmatic channels only |
| Buying Methods | Automated and direct/guaranteed buys | Automated/programmatic buys only |
| Additional Features | Workflow automation, integrated reporting, AI-powered functionalities | Primarily focused on programmatic execution |
| Use Case | Full omnichannel campaign management | Programmatic-specific campaign management |
By leveraging this type of all-in-one digital media technology, marketers can gain instant access to every tool they need to power data-based growth and unlock massive time-saving benefits that can transform the way they communicate and engage with consumers.
For marketing organizations large and small, the practice of data consolidation has never been more important. It is the key to building the kind of data-driven culture that’s fundamental to success in a severely fractured industry. Data itself is a dynamic asset, and marketers rely heavily on its accuracy to run effective programs that drive overarching business goals. Unfortunately, many still find themselves perpetually handicapped by incomplete analytics, where siloed data lies scattered in a mix of disparate systems, email chains, Excel spreadsheets, and other communication channels such as Slack or Teams. These limitations are time-sapping and inefficient and make it nearly impossible for marketing organizations to maximize ROI.
With the implementation of an omnichannel platform, marketers can remove such hindrances from their day-to-day by activating a system of record that automatically consolidates, aggregates, and stores campaign data in one centralized hub.
It is a move with two major advantages:
Key Takeaway: Consolidated data eliminates silos, frees teams from manual processes, and enables better collaboration across marketing organizations.
Creative type A resonated well in California but performed poorly in Michigan. Customer Y browses online but always moves to in-app before purchasing. Prospect Z was in a campaign targeting utility buyers but actually purchased a luxury product. Creative type B converts 75% more from Forbes over Yahoo Finance.
When organizations adopt an omnichannel platform equipped with a robust reporting engine, they can give their marketing and media buying teams unprecedented granular visibility at scale. In simple terms, this analytical architecture can surface real-time correlations and customer behaviors that were previously invisible to the naked eye. It automates the process of merging disconnected reports and displays them in dynamic, interactive dashboards that break down media performance on both micro and macro levels.
This type of omnichannel platform can radically change the day-to-day working lives of media buyers and analysts. No more manual data stitching. No more missing spreadsheets. No more guesswork required. Everything automatically unified. With this technology in their arsenal, organizations can essentially do six hugely important things:
The actionable intelligence this reporting engine can provide is startling. It enables campaign optimization on levels that are virtually impossible to reach without it.
Key Takeaway: On-demand multi-dimensional reporting provides real-time, granular visibility that enables unprecedented campaign optimization and data-driven decision making.
A 2021 survey revealed the majority of advertisers use seven platforms in a typical day and nine platforms over the course of a typical ad campaign. The findings are evidence that for all the incredible progress the industry has seen around digital processes and the development of AI-powered support—particularly over the last 10 years—there has been a stark lack of any proportionate upgrade to the technology and procedures designed to help marketers manage it all.
Omnichannel platforms remedy this growing imbalance. By deploying campaigns across all programmatic, site direct, search, social, and connected TV channels from one singular interface, media buyers no longer need to bounce around between multiple disparate systems in order to simply do their job. The real-world implications of these efficiency gains are massive—shortened workflow processes allow marketers to be nimbler with their movement of media weight and can help them adapt digital spend around unplanned events more quickly to improve profit margins. Whether it is uploading new media to a specific channel, iterating on creative within existing assets, altering the segmentation of targeting parameters, or pulling entire omnichannel campaigns from the market, procedures that once took days—or even weeks—now take mere hours.
All of this increases a brand’s ability to keep pace with the forever-changing demands of consumers and deliver impactful advertising that flows freely across channels.
Key Takeaway: Enhanced campaign execution through a single interface dramatically reduces workflow time and enables marketers to respond quickly to market changes.
Today, success with omnichannel marketing is not the daunting, seemingly unachievable aspiration it was. An omnichannel platform makes it all possible by providing marketing organizations with all the functionalities they need to create a customer-centric, journey-focused approach that is now so vital in the fight to win hearts and wallets.
The data shows that the demand for seamless experiences is high and utilizing multiple disconnected single-point solutions simply won’t meet them. It is now up to marketers to evolve with the times and deal with these expectations head on. This all-encompassing technology is the starting point.
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What is an omnichannel advertising platform? An omnichannel advertising platform is a centralized technology that connects programmatic, site direct, search, social, and connected TV channels in one interface for unified campaign management and execution.
What channels do omnichannel advertising platforms support? Omnichannel advertising platforms support programmatic, site direct, search, social, and connected TV channels, allowing marketers to manage all digital advertising from a single interface.
How is an omnichannel advertising platform different from an omnichannel programmatic platform? An omnichannel advertising platform covers all major digital channels including direct buys, while an omnichannel programmatic platform deals solely in programmatic channels and automated buying.
What are the main benefits for media buyers? The main benefits include consolidated data in one hub, on-demand multi-dimensional reporting, enhanced campaign execution from a single interface, and significant time savings through workflow automation.
The rise of AI has put marketers under some serious pressure: Adopt the technology quickly, demonstrate growth and efficiency gains, and stay ahead of a landscape that’s evolving by the day. Easy peasy, right?
Of course, the upside more than justifies the growing pains, with close to two-thirds of marketers reporting that AI has made them moderately-to-significantly more effective over the last year. Still, with new solutions entering the market every day and marketers constantly discovering creative new applications, it’s easy to feel like everyone else is one step ahead.
To that end, here’s a look at where the industry actually stands, and how marketers are finding real value in the technology.
Recent research offers a useful baseline for understanding how AI has taken hold across the marketing industry. Nearly all marketing and advertising professionals (95%) report using generative or agentic AI at least once a month, and a third use it every day. When it comes to what marketers are actually doing with AI tools, the top use cases are ideation and brainstorming, research, and drafting content and creative. ChatGPT dominates as the go-to platform, with Gemini and Copilot rounding out the top three.
On the organizational side, nearly two-thirds of marketers (65.7%) say their companies pay for or subscribe to premium AI tools. However, adoption of more sophisticated, custom in-house solutions remains limited, with nearly half (47.1%) reporting that their organizations don't use any at this time.
Beyond adopting custom tools and achieving the data readiness necessary to fuel those tools, the creative use of AI is increasingly what separates high-performing marketing teams from the rest. Below, Basis experts share a variety of ways they're finding value in the technology across their roles.
“I’ve found AI very useful as a partner to expedite tasks related to writing and communication. A few examples:
Robert Kurtz | Business Outcomes Partner
“I used Copilot to run a basic regression model for performance forecasting based on historical spending and online conversion data. Copilot helped organize and clean the data set, ran the analysis, and summarized findings so that I could interpret and contextualize the data outputs. This was a cool way to expedite data analysis so that I could focus on interpreting the numbers.”
Lauren Johnson | Effectiveness Lead
“One of our clients was receiving some negative responses online after running new, bold creative executions. They asked us for support in what we could see online from a brand sentiment perspective, but we couldn’t find much using our social listening platform. So, we took a handful of social media posts and asked Copilot to summarize sentiment and themes. The tool helped us to quickly understand audience sentiment on social media based on 15 social posts with 600+ comments.”
Emily Zelenz | VP, Integrated Client Solutions
“I’ve found AI particularly useful for keeping up with what competitors are doing in market. One of our clients has a long list of competitors, so I built a prompt that can look for new competitor creative launched within specific time periods. It makes competitive updates in the QBR much more streamlined—previous to this, I’d have to look at each brand one by one in an ad tracking tool and see if any looked new.
On a similar note, I've also given Copilot a list of competitors in a category and asked for a SWOT analysis of each. Individually curated research would be a lot more in-depth and likely more interesting—but leveraging Copilot gave a quick overview in just 2 minutes. That quick intel is a great starting point, and then it's up to us to identify where we need to dive deeper.”
Molly Marshall | Strategic Business Outcomes Partner
“I work with a lot of pharma clients, and the drugs and diseases we look at are often very complicated. I use a custom GPT for disease and drug research that:
I use the output as a starting point for my own understanding, then ask targeted follow-up questions and cross-check key points until I’m confident in the fundamentals and feel comfortable having an educated conversation with my clients.”
Ryan Sperry | VP, Integrated Client Solutions
“I’ve experimented with using generative AI to pressure test ideas. I'll ask it to poke holes in an argument or tell me why a certain idea is 'bad.' While I don't always agree with what it comes up with, it's a great way to stress-test my thinking. This is a particularly good use case for leveraging AI as a brainstorming partner on the media strategy side.”
Kelly Boyle | SVP, Strategic Business Outcomes
“One of our clients partnered with a vendor that develops synthetic personas representing key target audiences. The initial output included detailed audience profiles covering demographics, psychographics, media consumption behaviors, and trust metrics.
We are currently in the process of validating those findings against syndicated sources such as GWI and IPSOS, and the early review indicates strong directional alignment.
One area we are exploring as a next step is using these personas to inform creative message testing, in addition to broader audience strategy and planning. We are still in the early stages of evaluating how this could be applied most effectively, but the results so far are both promising and exciting.”
Jackie Etter-Krause | Sr Integrated Client Solutions Director
My team played around with AI to help us determine the best radius targeting for one of our multi-location clients. Copilot analyzed factors like population density, traffic patterns, bridge/toll road barriers, etc., and helped us determine the appropriate radius as well as some exclusions to implement within Meta & Google.”
Marykate Dougherty | VP, Integrated Client Solutions
These use cases are just a snapshot of the ways marketers are finding value in AI, and the list is only growing. Whether pressure testing a media strategy, navigating a PR crisis, or getting up to speed in an unfamiliar vertical, AI has proven itself as a capable and versatile partner across a variety of marketing functions.
Not every application needs to be groundbreaking—some of the most impactful uses are the ones that simply save time, surface better insights, or help advertisers show up more prepared for a client conversation. As the technology continues to evolve, marketers who are actively experimenting today will be best positioned to scale what works tomorrow.
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For more insights on how marketing teams are using AI, we asked professionals from leading brands and agencies about which tools they’re using, where the technology is driving results, and where the challenges still lie. Check out the 2025 AI and the Future of Marketing Report for an inside look at what's actually working.
