Retail Media’s AI Moment at CES 2026: When the Store Became the Operating System

Retail Media : sponsored products to autonomous commerce decisioning

At CES 2026, the most important story in advertising didn’t play out on a TV screen or a social feed. It unfolded in the retail aisles—physical and digital—where artificial intelligence quietly started running the shelves, the search results, and increasingly, the media plans. Retail media stopped behaving like a bolt‑on performance channel and started to look like core infrastructure for brand building, commerce, and measurement.

AI in the Aisles and On‑Site

The shift is being driven by AI systems within retail media networks that operate more like agents than traditional ad tools. These systems are not just tweaking bids; they are orchestrating how brands show up across search, on‑site placements, in‑app surfaces, and even in‑store screens, all in near real time. The question for marketers is no longer whether retail media “deserves” a line on the plan. The question is how comfortable they are letting AI shape the path to purchase inside environments that now rival TV for budget.

Consider what’s happening on the advertiser interface itself. At several leading retailers, campaign setup is evolving; the process is turning into a dialogue box, marketers can ask, “Where am I wasting budget?” or “Which products should I promote this weekend?” and receive recommendations that the system is ready to implement. Early data from these assistants reveals something: once advertisers can speak naturally, they expose a much broader set of questions and needs than traditional UX designers ever anticipated. The front-end changes, but the bigger story lies beneath—AI agents learning from every query, building a richer model of how marketers think about trade-offs between margin, share, and incrementality.

On the back end, AI is being let loose on some of the industry’s messiest problems: pricing, assortment, and incremental sales attribution across walled‑garden retail ecosystems. Instead of isolated sponsored‑product campaigns, AI‑driven retail media stacks are beginning to look at the whole basket. Which ads drove true incremental units, not just subsidised organic demand? How should budgets move between on‑site search, off‑site display, and social extensions as inventory and competition fluctuate hour by hour? These are questions humans can frame but struggle to answer at the speed and scale modern commerce demands. AI, fed with rich first‑party data and transaction logs, is starting to answer them.

Then there is the “off‑site expansion” Retail media is aggressively moving beyond its own dot‑coms and apps, following logged‑in shoppers out into the broader web and into connected TV. AI is the connective tissue that turns disparate placements into a coherent system. Creative, audiences, bids, and frequency can now be tuned across channels based on downstream signals that only the retailer can see: basket value, category switching, and repeat purchase behaviour. For national brands, that changes the calculus.

Retailers are no longer pitching just “sponsored search,” but end‑to‑end systems that claim to understand not only who saw an ad, but what made it into the cart.

Accountability and Measurement Tensions

This is where accountability comes crashing in. The more budget retail media siphons from TV and open‑web display, the louder the questions about measurement methodology become. Marketers are increasingly discovering that reported ROAS can swing wildly from one network to another, not because the reality is so different, but because the rules of the game are. How incrementality is defined, which sales are counted, and how lookback windows are set—all of these factors can dramatically affect performance numbers. AI doesn’t magically solve that problem, but it raises the stakes. When optimisation loops run continuously, any baked‑in bias or methodological inflation is amplified.

The smarter retailers know this and are leaning into transparency as a differentiator. That means clearer disclosure of methodology. It means giving brands access to clean‑room environments where they can interrogate the data with their own models. And increasingly, it means treating measurement as a product, not a PDF. AI can help here too—surfacing anomalies, flagging when lift looks suspiciously high, and explaining which levers actually moved outcomes. The winners won’t just be the retailers with the most data, but those willing to let partners see how their AI thinks.

Then there is the physical store. For years, in‑store media was discussed as the “third shelf” or the “third screen” and then largely left as an execution headache. Now, AI is turning that headache into a frontier. Camera feeds, sensors, loyalty apps, and point‑of‑sale data are being fused to understand how shoppers move, dwell, and decide. That intelligence feeds dynamic endcaps, digital shelf tags, and smart screens whose content shifts by time of day, local events, or inventory levels.

Instead of static planograms locked months in advance, retailers are experimenting with planograms that AI can adjust on the fly—and media that follows.

New Org Charts and Budget Politics

The organisational implications for brands and agencies are enormous. If retail media becomes a living system, governed by AI but shaped by commercial strategy, who owns it? Is it shopper marketing, ecommerce, media, or a new hybrid commerce team? When an AI agent can decide which SKU to push, in which format, at which margin, the line between “advertising” and “trade” blurs. The most progressive marketers at CES were not those dabbling in one more test, but those rethinking internal structures: combining retail and media budgets, building dedicated retail media strategy pods, and investing in people who can interrogate and govern AI recommendations rather than manually pull levers.

Writing the Rules for the Shelf

For all the enthusiasm, the questions hanging over AI‑driven retail media are as sharp as the opportunity. Who is accountable when an AI-optimised campaign raises short‑term ROAS but damages brand equity or trains consumers to buy only on promotion? How do brands audit algorithms that suggest prioritising private label over national brands in pursuit of retailer margin? What happens when a retailer’s AI agent optimising for category profitability meets a brand’s AI agent optimising for share and lifetime value? Those agent‑to‑agent negotiations may be invisible in the UI, but their consequences will be felt in P&Ls and category dynamics.

What stands out most in this moment is that retail media is no longer “just another channel.” Powered by AI, it is becoming the operating system for how consumers discover, compare, and decide in environments where intent is at its peak. For marketers, that raises a simple but uncomfortable truth: sitting it out is no longer neutral. As more of the shelf—physical and digital—is managed by AI, brands that don’t engage will find themselves competing in a store where the rules are being written by someone else’s algorithm.

The opportunity, then, is not to hand over the wheel, but to learn to write the driving instructions. That means getting fluent in how retail media AI systems reason, demanding transparency in how they measure, and setting clear red lines about what should never be optimised away: brand building, fair category competition, and the long‑term value of consumer trust. Retail media’s AI moment is here. The question is whether brands choose to merely ride it, or to help shape the code that will quietly run the shelf for years to come.