AI didn’t just speed everything up. It pushed advertising into a new operating reality. At CES 2026, that acceleration showed up in the most human way possible: rooms packed with marketers, agency leaders, and technologists all focused on a more pragmatic question than “What can AI do?” They came to understand how to extract real value from the AI investments already made, and how to redesign processes that were built for slower cycles and less complex stacks.
The headline takeaway was not tool proliferation. It was a structural change. The way business gets done is shifting all at once, across people, process, and the tech stack. That reality requires new systems: champions who drive adoption, interoperable platforms, strong data foundations with governance, and intelligence that is connected and accessible enough to improve decisions, not just increase output.
What is striking is how quickly the conversation has evolved in just a few months. In our October Advertising Week recap, we noted that the rules of advertising were being rewritten. Only a few months later, the technology is better, usage is broader, and the cultural resistance has softened. AI has moved from experimentation to expectation. Now comes the existential moment: leaders realize they are moving faster than legacy workflows and structures. That is why the conversation has leapt from “Which tools should we use?” to “What systems will make this work at scale?”
Build the operating system, not just the outputs
One of the clearest signals from CES came out of a Monks session framed around transforming the economics of advertising: There is no playbook in this moment.
The reason is operational, not philosophical. On a panel featuring leaders from Monks, Adobe, and The Drum, speakers pointed to a striking reset in production timelines. Work that clients once planned for over eight to nine months can now be executed in as little as two weeks. That kind of compression changes the brand and agency relationship at its core, because it demands new operating rhythms, faster approval cycles, and a clearer shared definition of what “done” actually means.
AI makes it easier to produce, but it does not automatically make organizations faster at decision making. The teams that win are the ones who treat speed as an operating model problem and build the infrastructure around it to address it head on: clearer briefing, streamlined governance, and internal champions who can turn experimentation into repeatable practice.
This is where MediaRadar becomes more valuable as AI accelerates everything around it. When cycles compress, the edge shifts to decision velocity and decision quality, which depends on market clarity. Brands and agencies need to know what is changing in the category, what competitors are doing, and where channel mix and messaging are moving so they can make better, trusted decisions at higher speed.
Trust is the growth strategy
At CES, the most mature AI conversations were not about novelty. They were about deployment at scale and acknowledging that trust will sit at the center of it. Hannah Elsakr, VP of GenAI New Business Ventures at Adobe, put it plainly: “AI won’t replace vision, craft, or narrative. It is a tool to accelerate you.” But acceleration without trust does not scale. This is why Adobe’s approach to AI has been centered around being commercially safe, training AI on licensed creative to deliver “clean pixels, in and out.”
What matters for advertisers is what that stance implies. As AI becomes embedded in planning, creative development, and optimization, the inputs and governance behind those systems become strategic. Risk does not disappear, it compounds. This is one of the reasons MediaRadar’s core promise, trusted marketing intelligence that teams can use with confidence, grows in importance in an AI era. When leaders rely on faster systems to make faster calls, they need an intelligence layer that is transparent, connected, and consistent across channels, not fragmented signals that encourage siloed decisions.
Parallel workflows raise the bar for learning speed
CES also reinforced a fundamental workflow shift: production has moved from a linear, waterfall process to a more agile model where work can run in parallel.
AI makes this inevitable because it lowers the cost of iteration and increases the volume of variants that teams can produce. That sounds positive until the real bottleneck shows up. When everyone can produce more, volume stops being the differentiator. The differentiator becomes learning speed. Teams that can strategically measure and connect what they are shipping to what is working, and then adapt in-market faster than competitors, are the ones who will convert AI-powered output into business advantage.
This is exactly where MediaRadar’s Creative Intelligence and Competitive Intelligence focus areas map to the moment. If AI enables a content supply chain, teams need a faster way to understand what is happening in the market right now: what messages are proliferating, what channels are heating up or cooling down, how quickly competitors are refreshing creative, and which narratives are gaining momentum. The goal is not to copy competitors. The goal is to reduce guesswork and help creative and media teams move with sharper strategic context.
Outcomes, not proxies, determine whether AI creates real value
Another CES theme that kept surfacing is that AI will optimize whatever you reward. That puts pressure on marketers to define success clearly and measure it responsibly. In a session on creative effectiveness, the speakers called out the need to train toward outcomes and avoid vanity metrics that do not build brand equity.
Danny Medico, Director of Media Analytics at T-Mobile, described the operating rhythm behind it: “We run a lot of tests to see how the solutions hit our goals. It’s a feedback loop that helps us to optimize and forecast.”
The implication is that the real AI advantage is not a model, it is a culture of experimentation paired with a measurement system that can see across the ad ecosystem. In addition, speakers also emphasized unified measurement and the need to look beyond individual walled gardens for a true read of what’s occurring in real time.
MediaRadar complements internal analytics by adding market-facing context. We help teams pressure-test performance decisions against competitive reality, including share of voice movement, channel mix shifts, and messaging trends. The result is smarter optimization that reflects what is happening across the category, not just what is happening inside your internal dashboard.
The next era is agents and AI needs contextual intelligence
CES 2026 also hinted at a deeper structural change: marketing will not only be brand-to-human, it will increasingly be mediated by agents. In a session on architecting the new media stack for commerce and engagement, Ed Soohoo, WW CTO Global Accounts at Lenovo, captured the shift with a line that sticks: “There’s an old phrase in Hollywood and it’s ‘have your agent call my agent.’”
That idea reframes what brands are building for. It is no longer enough to create great experiences for people. Brands also need experiences that agents can understand, evaluate, and act on. The industry has been focused on the shift from SEO to AEO, but the bigger change is that connections now have two endpoints: humans and machines. To compete in that world, brands need more than content. They need contextual intelligence that can travel across systems, stay consistent across channels, and remain accessible wherever decisions get made.
In an agent-mediated ecosystem, advantage comes from clarity, consistency, and flow. Agents summarize, compare, and recommend based on what they can parse and validate, which raises the importance of coherent messaging, verifiable claims, and signals that match across touch points. That requires interoperability between the systems to create content, deliver media, measure outcomes, and govern truth. When context is locked in silos, agents fill the gaps with assumptions. When context flows across the stack, brands show up with precision.
This is where MediaRadar becomes strategically relevant. Our Data Cloud connects the dots across the advertising ecosystem, unifying the signals teams need so competitive context is available at the moment of decision, not trapped in separate tools or disconnected reports. With visibility into spend and flighting, brands can spot first movers as categories shift, understand where budgets are flowing, and anticipate when competitors are turning campaigns on and off. And with Creative Intelligence, teams can see which partnerships, co-op plays, and joint narratives are shaping the market, then use those inputs to fuel sharper strategy for both human audiences and agent-mediated discovery.
The unglamorous foundations matter more than ever
If CES had a quiet consensus, it was this: for systems to win, systems must run on strong data foundations, with governance and accessibility. Sepi Motamedi, Global Head of Sports and Live Media Marketing at NVIDIA, captured the dependency clearly: “AI requires context to do its work.” Denise Colella, VP Global Digital Strategy Group at Adobe, echoed the operational prescription: “It’s important to have data normalized and in one place. The first thing we do is recommend having your data in order.”
Those are not technical footnotes. They are the prerequisites for scale. Champions drive adoption, interoperability enables flow, governance keeps speed from turning into risk, and accessible intelligence keeps decisions aligned across teams. That is the world MediaRadar is built for. As AI raises the pace of marketing, trusted and connected marketing intelligence becomes the stabilizer that helps teams move faster without guessing.
Closing
CES 2026 made the next chapter of advertising feel less like a technology upgrade and more like a redefinition of how work flows. AI will increasingly machine the tactical layer of marketing, from execution to iteration, and that is exactly what creates the opportunity. It frees teams to spend more time on the decisions only humans can make: defining the narrative, protecting trust, and designing a coherent experience across all activations. The winners will be the ones who use strategy to choreograph that flow and use real market context to keep it grounded. That is why intelligence matters more as AI accelerates. With MediaRadar, teams can anchor decisions in competitive signals and the joint stories shaping categories, so speed becomes an advantage and not just noise.
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ABOUT THE AUTHOR | Karisa Schroeder
Karisa Schroeder is the Director of Product Marketing at MediaRadar, where she leads go-to-market strategies to launch and scale new products and solutions. With expertise in marketing intelligence, data marketplaces, DaaS, and AI-powered insights, she helps brands and agencies deliver connected customer experiences, focusing on creative, competitive, and sports advertising intelligence to drive measurable growth.
