Machine learning infrastructure enables retailers to increase ad load and efficiency without degrading shopper experience through real-time personalization technology.

Drew Cashmore, Managing Director, Adaptive Retail Group

The application of artificial intelligence in retail media extends beyond theoretical discussions of agentic commerce into practical monetization strategies that retailers can implement today, according to Andreas Preuer, Senior Director of Business Development for EMEA at Moloco. Speaking during a retail media roundtable discussion released on November 3, 2025, Preuer outlined how machine learning technology developed by major platforms can help retailers optimize their on-site advertising inventory.

“If you break it down, right, like essentially transform models are changing like consumer behavior, changing how shopping works, how commerce works,” said Preuer during the conversation with Drew Cashmore, Managing Director at Adaptive Retail Group and Chief Strategist at Vantage. The discussion, published by the Interactive Advertising Bureau Europe, focused on immediate applications rather than speculative future scenarios.

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According to Preuer, most retail media companies currently operate using technology that is “years old,” creating limitations in how they monetize their most valuable assets. Moloco, which employs predominantly machine learning engineers, approaches retail media challenges from what Preuer characterized as a “tech first” perspective informed by practices at major technology companies.

“I think a lot of like the tech the big tech players that exist currently and essentially win in retail media, they um have already built tech infrastructure that is machine learning based that is AI based that allows them to outperform everyone else,” Preuer said. “And I think the rest of the industry only has a chance to win to monetize their assets by using the latest technology.”

Real-time personalization drives efficiency improvements

The core differentiator for on-site retail media involves personalizing advertisements in real time using transformer models similar to those powering conversational AI applications. “We have the technology that can take the the word uh turn it into turn it into numbers predict ultimately what’s going to happen next,” Preuer explained. “And uh for advertising not everyone’s using this.”

This personalization capability combined with outcome-based bidding produces what Preuer described as “dramatic efficiency improvements.” The technology enables retailers to increase ad load—the number of advertisements displayed to shoppers—without degrading the customer experience. “Once you combine what I just mentioned in terms of like personalization with outcome based bidding uh you get dramatic efficiency improvements and that allows in addition to those efficiency improvements you can also increase ad load without changing the experience for consumers,” Preuer said.

The assertion challenges prevailing assumptions in retail media that retailers have reached maximum capacity on their owned and operated properties. Many retail media networks have expanded into offsite advertising after concluding their on-site inventory cannot support additional revenue growth. Cashmore noted that the industry typically assumes retailers must “look outwards to find new spaces to monetize” once they reach maximum capacity.

However, Preuer maintained that significant untapped value remains in owned and operated inventory through better optimization. “You also got to you have to have a look at like the base and where the growth is happening,” he said. “You should use the latest technology and optimize and create the best experience possible for any shopper uh for the advertisers, for the brands and just create like the best possible uh experience for everyone involved.”

Technology architecture remains fragmented

Cashmore identified fractured technology architecture as a fundamental challenge facing retail media networks. “One of the core challenges in our space is that we have created a very fractured technology architecture that that the ecosystem for retail media is built in a silo at a tactic level,” he said during the discussion.

Advertisers working with individual retailers often must navigate five, six, or ten different tools or technologies to buy media across various retail media inventory types. When multiplied across hundreds of retail media businesses globally, this fragmentation creates substantial management complexity. “The thing that I’m fixated on right now at a true barebones level is making it easier and more appealing for advertisers to buy and make it easier and more uh efficient for retailers to operate,” Cashmore said.

His solution involves creating a unification layer connecting disparate pieces into a single buying workflow. “You’re going to get best-in-class solutions for on-site. You’re going to get best-in-class solutions for a CMS in store. You’re going to get best-in-class solutions for a CDP purpose-built for that thing,” Cashmore explained. “And then bringing it all together I think is what is going to make to your point this industry competitive with the top.”

Preuer emphasized that buying processes must become as automated as those operated by Meta and Google. “Have like campaign creation that is fully automated and don’t like build teams uh managing campaigns. Build teams that can generate revenue and automate campaign creation, right?” he said. “That’s what like the big ones do. That’s what like Meta does. That’s what Google does.”

The retail media sector reached €13.7 billion in European spending during 2024, representing 21.1% growth according to IAB Europe data. This growth trajectory substantially exceeded overall European advertising market expansion. Omdia research projects retail media networks will exceed $300 billion by 2030, capturing approximately 20% of total global advertising revenue.

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Behavioral change management poses implementation challenge

The transition from spreadsheet-based retail operations to AI-driven automation systems requires substantial organizational change management, both executives acknowledged. Retail organizations have built expertise around spreadsheet-based analysis and manual campaign management over decades.

“Retail is very comfortable in a spreadsheetbased world,” Cashmore observed. “And behaviorally, the transition from that to letting um an LLM or or letting a an agent run everything end to end is to me a pretty significant leap.”

Preuer confirmed this assessment based on Moloco’s market experience. “I do see and hear um that change management within organization. So like behavior that exists within organization is actually one of the biggest topics at every company,” he said. “Because essentially what you just described like that change within a company and that behavior change within the company um takes time and is actually one of the biggest tasks.”

The implementation challenge extends beyond deploying new technology. “Technology can be implemented but ultimately the biggest job is actually change management and transformation within the organization,” Preuer emphasized.

An MIT study referenced during the conversation examined United States adoption of AI within organizations. The research identified a phenomenon called “work slop”—situations where organizations use AI primarily for tasks like email composition or presentation formatting rather than leveraging its full efficiency potential. “The true um utilization and and uh platform in in finding efficiencies and and um using it to its full extent has not truly been realized yet,” Cashmore said.

Internal debates drive optimal inventory allocation

Preuer described how leading technology companies including Amazon and Google have historically managed internal conflicts between teams focused on organic experiences and those responsible for advertising revenue. These debates, he suggested, represent a productive framework for optimizing inventory allocation.

“For example one of the battles that is fought in every company is how much inventory do you make available for ads,” Preuer said. The question involves whether AI systems should determine ad placement or whether human oversight remains necessary to ensure quality experiences.

“Obviously there’s always like a an an error rate but you got to make it so that uh that it’s highly relevant,” he continued. “If you use the technology available the ad experience for everyone will increase and improve and for that debate to happen between how how much ad inventory do you make available you got to define like some um KPIs.”

According to Preuer, organizations should establish key performance indicators that enable different teams to compete based on measurable outcomes. “That’s exactly what should happen. That’s exactly what happened at Amazon. That’s exactly what happened at at Google,” he said. “I I remember those folks like the there’s like the search folks and then there are the ads folks. Those were the fiercest battles within a company.”

Moloco employs leaders who previously worked at Amazon and Google and fought similar internal battles at those organizations. “It’s amazing because like it turns out like these battles are the team in all these companies,” Preuer noted.

This framework drives organizations toward continuous improvement while requiring everyone to use technology. “That also requires everyone to use technology because otherwise you can’t actually like you can’t win this race,” Preuer said.

Future commerce experiences will require new monetization approaches

Looking beyond immediate implementation challenges, both executives expressed interest in how emerging consumer experiences will reshape retail media. “If I look into the future like one, two, three, four years, I’m excited about the future consumer experiences that are about to come and how we monetize those,” Preuer said. “I truly believe like there’s going to be completely new experience and we will start to monetize those too.”

Preuer cited agentic commerce—where AI agents handle transactions on behalf of consumers—as one potential future scenario. “I love to talk about like how fun it is to book my travel through an AI agent, an agentic type of commerce and that’s really exciting,” he said. However, he characterized such developments as requiring “some time” before broad implementation.

“I do think though a lot of those things will take some time and will require some re rework of and we talked earlier about this like will take some time to rework some of the pipes,” Preuer explained. He predicted this wave of innovation would generate numerous new companies comparable to previous waves around web and mobile technologies.

Cashmore emphasized returning to retail media’s fundamental value proposition. “What I’m most excited about is this premise or idea that we can get back to the basics in this thing,” he said. Retail media should remain “ingrained in the overall retail proposition” as a new business model explored holistically rather than becoming overly focused on performance marketing mechanics.

“The value proposition of retail media, if you if you strip it down to what it was originally intended to do, is getting advertisers in front of the consumer when they’re reaching for their wallet,” Cashmore said. “And we’re getting back to that place now and recognizing that true value.”

Recent partnerships demonstrate industry consolidation around unified platforms. Topsort partnered with Skai on September 23, 2025, becoming the first API integration delivering access to retail media networks across 40 countries through unified platforms. Criteo became Google’s first onsite retail media partner on September 10, 2025, enabling advertisers to create campaigns across Criteo’s network of over 200 retailers directly within Google Search Ads 360.

UK digital advertising expenditure is set to reach £45 billion by 2026, with retail media cited as a primary growth driver. Online retail media hit £1.5 billion in the first half of 2025 in the UK market alone.

Timeline

• November 3, 2025: Interactive Advertising Bureau Europe publishes retail media roundtable discussion featuring Andreas Preuer from Moloco and Drew Cashmore from Vantage
• October 23, 2025: LiveRamp expands measurement capabilities for retail media networks through Clean Room platform
• October 1, 2025: Mastercard launches commerce media network with $100 billion market potential by 2028
• September 28, 2025: Topsort partners with Skai to expand global retail media reach across 40 countries
• September 26, 2025: UK retail media spending drives digital ad growth with £45 billion forecast by 2026
• September 10, 2025: Criteo becomes first onsite retail media partner for Google Search Ads 360
• September 5, 2025: Omdia projects retail media to capture 20% of global ad revenue by 2030

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Summary

Who: Andreas Preuer, Senior Director of Business Development for EMEA at Moloco, and Drew Cashmore, Managing Director at Adaptive Retail Group and Chief Strategist at Vantage, participated in the roundtable discussion published by the Interactive Advertising Bureau Europe.

What: The discussion examined practical applications of artificial intelligence and machine learning in retail media, focusing on real-time personalization technology, outcome-based bidding, and infrastructure challenges facing retailers seeking to optimize on-site advertising inventory.

When: The roundtable discussion was published on November 3, 2025, as part of the Interactive Advertising Bureau Europe’s Retail Media Roundtable Podcast series.

Where: The conversation addressed global retail media markets with particular emphasis on European operations, where retail media spending reached €13.7 billion in 2024 representing 21.1% growth.

Why: Retailers face pressure to maximize revenue from owned and operated properties while maintaining customer experience standards. Machine learning infrastructure developed by major technology platforms offers practical solutions for increasing ad load and efficiency without degrading shopper experience, but implementation requires substantial organizational change management to transition from spreadsheet-based operations to AI-driven automation systems.