AI is bringing retail media closer to the sale, and marketers want more accountability
Better shopper data and AI optimization are pushing retail media toward measurable business outcomes
Retail media networks are no longer just another media channel for brands. They are becoming performance engines tied directly to shopper behavior, conversion signals, and measurable business outcomes. But as retail media grows, so does the operational complexity behind it. Marketers are now dealing with fragmented customer journeys, overlapping channels, and mounting pressure to prove incremental return on ad spend.
At an ADWEEK House Possible panel co-hosted with CVS Media Exchange (CMX), executives from CMX and Kenvue outlined how AI is reshaping retail media operations. The discussion focused less on futuristic AI hype and more on practical execution: better audience management, faster optimization, and using shopper data to make campaigns more accountable.
For marketers, the bigger story is this: retail media is evolving into an AI-assisted outcomes business where optimization happens continuously, but human oversight still matters.
Table of contents
Jump to each section:
- Why AI is becoming essential for retail media operations
- How CMX is using AI to optimize retail media campaigns
- Why retail media performance is shifting toward business outcomes
- What marketers should know about AI-powered retail media
- Why human oversight still matters in AI-driven advertising
Why AI is becoming essential for retail media operations
Retail media has exploded over the past few years, but scale has created a new problem: complexity. Brands now have access to more shopper data, more retail media networks, and more signals than ever before. At the same time, consumers move fluidly between online and offline environments, making attribution and personalization harder to manage manually.
During the panel discussion, Kelly Shu, director of omni shopper and customer marketing at Kenvue, pointed to personalization as one of the clearest opportunities for AI adoption.
According to Shu, marketers have talked about personalization for years, but AI may finally make large-scale personalization operationally realistic.
Parbinder Dhariwal, vp and general manager at CVS Media Exchange, framed the challenge around signal fragmentation. Retail media teams are now trying to interpret huge volumes of behavioral data across physical stores, ecommerce platforms, loyalty programs, and media channels.
This is exactly the kind of operational problem AI is designed to solve: turning fragmented data into faster decisions.
How CMX is using AI to optimize retail media campaigns
CMX’s retail media strategy is heavily tied to its loyalty ecosystem and physical retail footprint.
Dhariwal shared that CVS Media Exchange operates with access to 90 million ExtraCare records, approximately 9,000 CVS locations, and around 5 million daily in-store shoppers. That creates a massive amount of behavioral and transactional data.
The challenge is not collecting the data. The challenge is making it usable in real time.
CMX is approaching that through CorIQ, its AI-powered marketing platform designed to support:
- Media planning
- Audience segmentation
- Real-time optimization
- Campaign measurement
In practice, the AI layer can automatically remove shoppers from a targeting segment after a purchase, replenish audience pools dynamically, or reduce ad exposure for shoppers unlikely to convert.
That matters because retail media fatigue is becoming a real issue. Consumers increasingly expect relevance, not repetitive ad bombardment.
For marketers, this points toward a larger industry shift: retail media platforms are moving from static audience buying toward adaptive audience management.
Why retail media performance is shifting toward business outcomes
One of the most important points from the discussion was how retail media measurement is evolving.
Traditional digital advertising metrics like impressions, clicks, and engagement still matter, but retailers increasingly want campaigns tied directly to sales outcomes.
Dhariwal stated that CMX has already seen measurable performance gains from AI-assisted optimization, including:
- 16% greater efficiency in reach
- 14% increase in attributed sales
- 15% increase in incremental ROAS
Kenvue also reported more than 30% lift alongside improvements in buyer conversion and basket size.
This reflects a broader industry trend: marketers are under pressure to justify spend with measurable business impact, not just media efficiency.
Retail media networks are in a strong position here because they sit close to actual purchase behavior. Unlike many traditional ad platforms, retailers can connect media exposure directly to transaction data.
That proximity to the sale is becoming one of retail media’s biggest competitive advantages.
What marketers should know about AI-powered retail media
Retail media teams should pay close attention to how AI is changing operational workflows, not just targeting capabilities.
Here are several strategic shifts marketers should prepare for:
- Audience management will become increasingly dynamic: Static segmentation is losing value. AI-driven systems can continuously refresh audiences based on live shopper behavior.
- Measurement expectations are rising: Retailers and brands increasingly expect closed-loop attribution tied directly to sales outcomes.
- Operational speed matters more: Faster optimization cycles mean teams can react to campaign performance while campaigns are still active, not after reporting periods close.
- Retail data is becoming a competitive moat: Loyalty programs and first-party transaction data are now critical strategic assets in advertising.
- AI will pressure media efficiency: As optimization improves, brands may expect stronger incremental returns from the same budgets.
This also raises an important challenge for marketers: not every AI recommendation should be accepted automatically.
Optimization without strategic oversight can create short-term efficiency while weakening long-term brand positioning or customer trust.
Why human oversight still matters in AI-driven advertising
One of the more grounded parts of the panel discussion was the acknowledgment that AI still requires human supervision.
Shu emphasized that while AI can surface optimization opportunities, marketers still need to oversee strategic decisions and ensure campaigns align with broader business goals.
Dhariwal echoed that view, suggesting that even as retail media platforms become more automated, humans will remain involved in critical decision-making.
That balance is becoming increasingly important across AI-powered marketing systems.
Retail media platforms now have the ability to automate targeting, bidding, segmentation, and optimization at scale. But marketers still need to manage:
- Brand safety
- Consumer trust
- Messaging quality
- Ethical personalization boundaries
- Long-term customer relationships
The brands that benefit most from AI-powered retail media will likely be the ones that combine automation with disciplined human judgment.
Celi Anindia
Retail media is entering a new phase where AI is not just improving campaign efficiency but reshaping how marketers approach audience management, measurement, and business accountability.
The conversation between CMX and Kenvue highlights a larger industry reality: brands no longer want media platforms that only deliver impressions. They want systems tied directly to sales outcomes and shopper behavior.
AI is helping retail media networks move closer to that goal by accelerating optimization, improving personalization, and making attribution more actionable.
But the winning strategy is unlikely to be fully autonomous advertising. The real advantage may come from combining AI-driven operational speed with experienced human oversight that keeps campaigns aligned with brand goals and customer trust.
