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Key Takeaways:
AI is transforming how consumers search online and what paid media can accomplish within those environments.
This transformation is resulting in three key shifts that marketers must understand to invest effectively in paid search:
For advertisers, understanding how to strategize around each of these shifts is essential to building a paid search strategy that holds up in an LLM-powered environment.
The search results page has always been a place where advertisers vie for attention, but AI is changing the terms of that competition. AI Overviews now dominate above-the-fold real estate on Google for about half of all queries, pushing paid and organic listings further down the page. As users opt to scan AI-generated summaries first and scroll less as a result, there are fewer moments for them to see and act upon your ads.
This attention compression directly affects paid search performance, with recent research finding that the presence of an AIO correlates with a 58% lower CTR for the top-ranking page. As advertisers compete for fewer available clicks, these changes could drive up CPCs as well.
For agencies and internal marketing teams, it's critical to proactively communicate the context of these changes to stakeholders, so that declining CTRs and rising CPCs are understood in context rather than treated as performance failures.
Not all search intent is affected equally by AI. AIOs are showing up predominantly for the upper-funnel, research-driven queries that users rely on to learn, compare, and evaluate before making a final decision. In fact, 99.2% of the keywords that trigger AIOs are informational in intent.
This means that it has become more difficult for paid search to deliver the upper-funnel discoverability and efficient clicks it is known for. There are also emerging implications for programmatic, as fewer impressions become available from key publishers whose organic traffic is being absorbed by AI-powered search experiences.
Action- and decision-oriented queries, however, remain largely unaffected—which presents a clear opportunity for search advertisers. Lower-funnel, intent-driven keywords should be prioritized accordingly.
Perhaps the most important mindset shift for search advertisers right now is reframing the value paid search provides. As AI search increasingly resolves questions without site visits, paid media will drive fewer clicks. But that doesn’t necessarily mean those investments are driving less value.
Clicks have long been advertisers' primary focus because they’re measurable, but paid search has always contributed to outcomes beyond that immediate action. Paid search-driven brand exposure drives recall and brand trust, influences later decisions and return visits, and impacts whether a brand makes it into a consumer’s consideration set at all.
Of course, measuring the impact of paid search in this way is more complex than traditional paid search attribution. As the causality between paid search exposure and action becomes harder to track, teams will need to lean more heavily on marketing mix modeling and multi-touch attribution to connect paid media investment to business outcomes.
AI’s impact on paid media performance is already significant: Attention is more compressed, different intents are leading to different outcomes, and the click-based model of demonstrating paid media value is under pressure.
Advertisers who understand these dynamics and adjust their strategies, benchmarks, and measurement approaches accordingly will be far better positioned to drive results from paid search investments as the search landscape continues to evolve.
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Looking for more insights on the future of search engine marketing? Explore everything marketers need to know about the shifting landscape by watching our recent webinar: Is Search Totally F**ked? What Do We Do Now?.
In this episode, former Discover media and audience strategy leader Clare Liston joins host Noor Naseer to break down how top marketers actually evaluate adtech and martech partners.
From avoiding “shiny object” syndrome to pressure-testing vendors with direct questions, Clare shares a practical framework for choosing tech that drives real business outcomes. She unpacks why most adoption is reactive, how to think about ROI within working media, why human credibility matters more than jargon, and more.
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, the clearest opportunities for AI-driven impact in digital advertising stem from 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.
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.
For advertisers eager to achieve meaningful differentiation with AI, media strategy presents significant opportunities.
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.
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.