The Impact of Artificial Intelligence on Retail Media Campaigns
As artificial intelligence (AI) continues to evolve, it is revolutionizing the way media campaigns are planned and executed across various platforms. The Interactive Advertising Bureau (IAB) has noted significant shifts in audience segmentation, media buying, real-time optimization, and performance measurement within the industry.
Retail media, in particular, has seen tremendous growth in recent years, establishing itself as a key player in the advertising landscape. However, the latest data from the IAB’s 2025 State Of Data Report suggests that this growth is starting to slow down. While retail media is projected to grow by 15.6% in 2025, this marks a notable decrease from the 25.1% growth seen in 2024—an almost 10 percentage point drop year-over-year.
The Challenges of Retail Media Ecosystem
The deceleration in growth for retail media networks comes at a crucial juncture where the industry is facing mounting challenges that threaten its momentum. Brands are now demanding greater standardization, advanced measurement capabilities, and seamless cross-platform integration. In this scenario, artificial intelligence emerges as a promising solution to address these pressing issues.
Despite the promising possibilities AI offers, its implementation in retail media is not without its challenges.
The Perfect Storm: Why Retail Media Growth Is Slowing
The retail media industry is entering a more mature phase where sustaining growth becomes increasingly challenging. The explosive expansion of the channel over the past five years has led to a crowded market with numerous retail media networks vying for advertiser budgets.
Factors Contributing to Deceleration
- Ecosystem Fragmentation: The presence of over 70 retail media networks in North America alone has created a fragmented landscape, making it difficult for brands to manage relationships across multiple platforms.
- Rising Costs: As competition intensifies, the costs of retail ad units have risen significantly, prompting brands to reevaluate their ROI. Some brands are experiencing stagnant sales growth despite increased retail media spending, leading to questions about the effectiveness of these investments.
- Measurement Challenges: The lack of standardized measurement practices across networks hinders advertisers’ ability to compare performance and justify their investments.
- Integration Complexity: Brands seek integrated on-site and off-site inventory solutions, but many networks still treat these as separate channels, creating integration challenges.
Despite the challenges, advertisers still recognize the benefits of retail media, such as its proximity to the point of purchase and closed-loop measurement capabilities. However, there is a growing trend of brands becoming more selective in allocating their retail media budgets.
Addressing Challenges with Artificial Intelligence
Recognizing the industry’s growing pains, the IAB’s State of Data 2025 report highlights the potential of artificial intelligence to address key pain points in retail media. According to the report, a significant percentage of buyers are already utilizing or exploring AI tools for media planning and activation.
1. Smarter Planning and Audience Development
AI-driven scenario planning allows buyers to simulate different budget allocations across retail media networks and forecast outcomes before launching campaigns. By optimizing spending across the fragmented ecosystem, AI can identify networks that deliver the best performance for specific campaign objectives.
Industry experts like Alex Arnott emphasize the early stages of leveraging AI to automate and enhance retail media strategies. Buy-side ad buying platforms are integrating AI to derive omnichannel insights and automate media plan development.
2. Automated Campaign Optimization
AI-powered automation enables dynamic adjustments to bid strategies, pacing, and creative rotation across multiple retail media networks in real time. The IAB’s report highlights how AI can facilitate campaign orchestration and content optimization, allowing brands to coordinate campaigns across paid, owned, and earned channels while monitoring performance and adjusting tactics to optimize results.
Experts like Vince Crimaldi suggest that AI algorithms can analyze real-time sales data within networks to allocate budgets toward higher-converting products or ad placements.
3. Unified Measurement and Attribution
AI plays a crucial role in addressing measurement challenges in retail media by integrating multi-touch attribution and market mix modeling. These advanced measurement frameworks provide a comprehensive view of performance across channels.
Recommendations from the IAB emphasize the integration of Market Mix Modeling with multi-touch attribution and the use of synthetic or proxy data to fill measurement gaps while ensuring privacy compliance.
4. AI-Driven Creative
AI is also being utilized to develop written, visual, and video creative content for advertising. Retail Media Networks are leveraging AI to offer self-service creative solutions to brands, allowing for efficient development of creative assets and AI-powered landing pages.
Integrating AI-driven creative capabilities with automated campaign optimization strategies enhances the responsiveness and intelligence of the advertising ecosystem.
Challenges in Implementing AI in Retail Media
While AI shows significant promise in addressing retail media challenges, the industry faces distinct hurdles that complicate implementation:
Technical Fragmentation Challenges: Retail media’s fundamental infrastructure problem stems from the diverse data ecosystems maintained by each retailer, creating barriers to cross-network AI implementation. Limited technical infrastructure in smaller networks further complicates AI-driven optimization.
Clean, Organized Datasets: The effectiveness of AI in retail media depends on access to clean and organized datasets. Data silos, inconsistent data quality, and integration issues hinder accurate analysis and campaign effectiveness.
Data Privacy and Competitive Concerns: Retailers must navigate the delicate balance between data privacy and AI-driven insights. Segregated data environments impede comprehensive insights, requiring collaboration among stakeholders to share data effectively while safeguarding consumer trust.
Strategies for Overcoming Challenges and Harnessing AI in Retail Media
As retail media networks strive to maintain growth amidst these challenges, certain priorities emerge:
Embrace Standardization Where Possible: Retail media players must invest in baseline measurement standards and technical capabilities that align with the demands of sophisticated CPGs. Meeting specific criteria for targeting capabilities, measurement clarity, creative flexibility, and API availability can help networks differentiate themselves.
Establish Core Data Infrastructure: Building a robust data infrastructure is essential before implementing advanced AI solutions. Partnerships with data collaboration experts can streamline data processes, unlocking the full potential of AI investments in a competitive landscape.
Explore Real-Time Bidding (RTB): Real-time bidding offers a promising solution to address fragmentation challenges and meet brands’ performance expectations. Implementing RTB enables automated bidding across networks and provides standardized event-level data flow, enhancing efficiency in budget allocation and technical infrastructure for cross-channel AI applications.
Improving Ad Relevancy: Ensuring relevance in advertising content is key to success in retail media. By focusing on delivering highly relevant ads, retailers can enhance user engagement and drive better results for brands.
The Future of Retail Media with AI
As the growth of retail media begins to stabilize, artificial intelligence emerges as a strategic tool to tackle industry challenges. However, successful implementation of AI requires a pragmatic approach that acknowledges the unique obstacles in the retail media landscape, including data standardization, privacy concerns, and technical infrastructure requirements.
Networks that view AI as a strategic capability to be carefully developed—rather than a quick fix—will thrive in the evolving retail media landscape. Balancing technological innovation with a deep understanding of advertiser needs will pave the way for more relevant, measurable, and efficient advertising experiences that deliver genuine value to brands and consumers alike.