How AI Can Solve Retail Media's Growing Pains

Forbes

24-04-2025

Retail media has emerged as one of the fastest-growing advertising channels in recent years, but new data suggests that this growth is beginning to slow. According to the IAB's 2025 State Of Data Report, while retail media is projected to grow by 15.6% in 2025, this represents a significant deceleration from the 25.1% growth seen in 2024—a drop of nearly 10 percentage points year-over-year.
This slowdown comes at a critical moment for the retail media ecosystem. Despite continuing to outpace overall advertising growth (projected at 7.3% for 2025), retail media networks face mounting challenges that threaten their momentum. As brands demand greater standardization, more sophisticated measurement capabilities, and better cross-platform integration, artificial intelligence has emerged as perhaps the most promising solution to these growing pains.
But implementation won't necessarily be easy.
The IAB data confirms what many industry insiders have been observing: retail media is entering a more mature phase where growth is becoming harder to sustain. This isn't surprising given the channel's explosive expansion over the past five years, with hundreds of retail media networks now competing for advertiser budgets.
Several factors are contributing to this deceleration:
As a result, while advertisers still value retail media's proximity to the point of purchase and its closed-loop measurement capabilities, many are becoming more selective about where they allocate their retail media dollars.
The IAB's State of Data 2025 report offers a potential path forward through artificial intelligence. According to the report, 80% of buyers are already using or exploring generative AI tools for media planning and activation, with agencies leading adoption at 83% compared to brands at 71%.
For retail media specifically, AI applications are being deployed across the entire campaign lifecycle to address key pain points:
The IAB identifies AI-driven scenario planning as a key opportunity for brands and agencies. Using AI, buyers can simulate different budget allocations across retail media networks and forecast outcomes before launch. This helps optimize spending across the fragmented ecosystem by identifying which networks deliver the best performance for specific campaign objectives.
Alex Arnott, a Director at NewStream Media, which consults with retailers on their media offering, says that the industry is in the early stages of understanding how AI can be wielded to automate and augment retail media strategy. He calls out buy-side ad buying platforms like Skai and Xnurta which are now utilizing AI to derive omnichannel insights and automate the development of omnichannel media plans.
For activation, the IAB recommends AI-powered automation that dynamically adjusts bid strategies, pacing, and creative rotation across multiple retail media networks in real time. The IAB's State of Data 2025 report highlights how AI can enable "AI-Driven Campaign Orchestration & Content Optimization," allowing brands to "leverage AI tools to coordinate and launch campaigns across paid, owned, and earned channels" while monitoring "real-time performance and automatically adjust tactics" to improve results.
Vince Crimaldi, EVP, Retail Market Unit Leader at Capgemini says that AI algorithms can analyze real-time sales data within a specific network to automatically shift budget towards higher-converting products or ad placements.
Perhaps most importantly, AI is helping address the measurement challenges that have plagued retail media since its inception. The IAB highlights advanced measurement frameworks that integrate multi-touch attribution (MTA) and market mix modeling (MMM) to provide a holistic view of performance across channels.
One of the IAB's recommendations is that brands should "integrate Market Mix Modeling (MMM) with multi-touch attribution, adapting to client-specific data and KPIs. Leverage synthetic or proxy data to fill measurement gaps while maintaining privacy compliance."
Capgemini's Vince Crimaldi agrees that successfully integrating AI will be key not just for sustaining growth, but for proving the long-term ROI that brands increasingly demand. 'Ultimately this will impact retailer revenue and ad tech valuations,' he says.
One of the most early to market and easily adopted use cases for AI is in developing written, visual, and video creative for advertising. New Stream Media's Alex Arnott says that many Retail Media Networks are utilizing GenAI to provide brands with a self-service creative offering. This allows brands to quickly and efficiently develop their own creative assets and or AI-powered landing pages (as opposed to incurring added creative production fees for the RMN to develop). 'RMN ad platforms are also jumping on the bandwagon as they are building out-of-the-box GenAI creative functionality into their platforms to incentivize long-tail brand investment,' Arnott says.
This capability is particularly powerful when integrated with the automated campaign optimization strategies discussed earlier, creating a more responsive and intelligent advertising ecosystem.
While AI holds promise, retail media faces distinct hurdles that make implementation more difficult than in other digital advertising channels:
Technical Fragmentation Challenges: Unlike other digital advertising channels, retail media suffers from a fundamental infrastructure problem. Each retailer maintains a unique data ecosystem with different taxonomies and measurement standards, creating significant barriers to cross-network AI implementation. While large players like Amazon offer robust APIs, many smaller networks lack the technical infrastructure needed for comprehensive AI-driven optimization. This fragmentation makes it extremely difficult to develop AI models that can provide meaningful insights across multiple retail media platforms.
Clean, organized datasets: Neal Sheridan, an RMN expert who has held leadership roles at Macy's and Belk's RMNs, says that access to first-party data can enhance personalized advertising and targeting, but AI's effectiveness hinges on clean, organized datasets.
Retailers often struggle with data silos, inconsistent quality, and integration issues, Sheridan says, which complicate accurate analysis in addition to concerns around consumer privacy. 'Companies must also invest in robust technology infrastructure and rethink how they measure campaign effectiveness,' Sheridan says.
Data Privacy and Competitive Concerns: Retailers face a critical challenge: balancing data privacy with AI's need for comprehensive insights. The segregated nature of retail media's data environments creates a paradox where protecting shopper information limits AI's potential. As Sheridan notes, "collaboration among stakeholders becomes essential for effective data sharing while maintaining consumer trust."
For retail media networks to maintain their growth trajectory while addressing these challenges, several priorities emerge:
Embrace Standardization Where Possible: While full integration across networks remains challenging, retail media players must invest in baseline measurement standards and technical capabilities that sophisticated CPGs now demand. Ram Krishnan, PepsiCo CEO of North America beverages, said last year that in order to continue winning the CPG giant's ad business, retail media networks must "meet a certain threshold at other media bought, like the media that we do with Meta or Google."
PepsiCo evaluates networks based on several factors including targeting capabilities, measurement clarity, creative flexibility, and interestingly – API availability. His statement that "not all retailers should be media companies" underscores the reality that smaller networks must make significant infrastructure investments to remain competitive.
Establish Core Data Infrastructure: Before introducing buzzy bells and whistles that get media attention, retailers need a solid data infrastructure. Lori Johnshoy, Head of Global Retail, Media Network and CPG Industry Strategy at LiveRamp, agrees with the IAB's highlight on fragmented data ecosystems, which she says can limit AI's effectiveness.
Johnshoy, who formerly helped to launch Target's Roundel media platform, says that this is why businesses need a trusted data collaboration partner — one who can help establish a cohesive data framework, bringing all data points into a single, accessible location. 'Doing so enables businesses to unlock the full potential of their AI investments, which are becoming increasingly essential in today's competitive retail media network landscape where demonstrating performance is crucial," she says.
Explore Real-Time Bidding (RTB): RTB represents a promising technical solution to address fragmentation challenges while meeting brands' increasing performance demands. As I explored in my recent post for Forbes, RTB enables automated, impression-level bidding across multiple networks while providing a standardized flow of event-level data. While full RTB integration faces challenges from retailers' walled gardens, even partial implementation could help address rising costs through more efficient allocation and provide the technical infrastructure needed for true cross-channel AI applications.
Improving ad relevancy. Beyond automation and measurement, serving highly relevant ads remains the single most important success factor for retail media. Andreas Reiffen, CEO of ad server tech company Pentaleap, says that sponsored Products account for roughly 80% of retail media revenues, but that CTRs (Click Through Rates) for these ad types can be just a third those of organic product results. This means retailers aren't currently delivering relevant search ads to consumers, limiting scale for the retailer and ad inventory for brands. With more relevant paid and organic search results, the Home Depot is able to surface 25 Sponsored Products per page, while many others show fewer than five. (Note, Pentaleap is a client of mine.)
As retail media's rapid growth begins to decelerate, artificial intelligence emerges not as a silver bullet, but as a strategic tool to address the industry's most pressing challenges. From fragmented ecosystems and measurement difficulties to rising costs and integration complexities, AI offers targeted solutions that can help retail media networks remain competitive.
However, successful implementation requires more than technological enthusiasm. It demands a pragmatic approach that acknowledges the sector's unique obstacles: limited data standardization, privacy concerns, and the need for robust technical infrastructure. The networks that will thrive are those that view AI as a strategic capability to be carefully developed, not a quick fix to be rashly deployed.
The future of retail media belongs to those who can balance technological innovation with a deep understanding of advertiser needs—using AI to create more relevant, measurable, and efficient advertising experiences that deliver genuine value to brands and consumers alike.