This article first appeared on Marketing Interactive on 11 June 2025.
We are at a cusp in digital advertising as Retail Media Networks (“RMN”) are gaining rapid interest from both brands and retailers. Globally, the retail media market is projected to hit US$179.5 billion in 20251, while its growth is projected to climb 13.5%2 and 8% respectively in APAC and in Southeast by 20303.
Fuelled by a convergence of e-commerce growth, advancements in data analytics, demand for highly targeted, data-rich advertising solutions and omni-channel integration by brands, RMNs are a strategic solution offered to advertisers by retailers, that is also bringing a highly valuable and profitable revenue stream to them.
Though the digitally native retailers had launched their RMN solutions earlier, the rush towards eCommerce during Covid was an inflection point that drove brands to pay attention and experiment with them more seriously. The traditional retailers have followed suit, giving advertisers a plethora of choices in the different types of retail media solutions to utilise for achieving their marketing and sales goals.
Even as RMNs are developing and stabilizing, AI has come into the picture in the last 12-18 months, adding more power to the medium.
The reality of working in media buying today is that one is no longer just setting rules and hoping that the campaign performs. Particularly as RMNs accelerate, AI will be the key differentiator in making sharp, real-time decisions and output to help retail advertisers scale efficiently and meaningfully. Instead of reacting after the fact, AI can optimise bids, placements, and targeting as the data comes in.
In retail media especially, there are incredibly rich datasets available, such as shopper behavior, product availability, margin data, and AI can bring all of that together to make smarter investment decisions across onsite and offsite channels. It is not just about automation anymore; it is about unlocking better performance through intelligent, always-on systems that adapt as the customer is engaging with the campaign.
In an increasingly competitive retail landscape, AI’s role is not just operational but also strategic, enhancing every part of the retail media value chain—from targeting to attribution. It offers the ability to deliver more relevant, efficient, and measurable campaigns at scale.
The real magic of AI turns data into context-aware recommendations
As speed and efficiency take centre-stage, for data to be meaningful, it must work for retailers. Today, having data alone is no longer a challenge as there is no lack of dashboards full of data. The real magic happens when AI takes that data and actually tells us what to do next.
AI excels at identifying patterns in vast, complex data sets that humans cannot parse quickly. It takes structured and unstructured data, such as transaction logs, browsing behavior, product metadata, and campaign performance - all the diverse functions in retail media - and applies machine learning to surface correlations, predict trends, and recommend actions.
For example, in a retail media context, AI might detect that shoppers who interact with product videos are three times more likely to convert, and dynamically prioritise creative formats accordingly. Or it may learn that certain customer segments respond better to bundling strategies and automatically optimise product placements and promotions.
AI also supports inventory-aware media buying, connecting ad placements with real-time product availability and pricing. When a product is out of stock, ads are paused or rerouted, and on the contrary, promotions are automatically highlighted when available, resulting in better customer experience and efficient spends.
The key value is not just reporting data, but turning it into context-aware recommendations, in terms of what product to promote, to whom, when, and on which surface. Therefore, marketers are able to reach high-intent shoppers with uncanny precision, maximising relevance and ROI.
GenAI as a collaborative creative assistant
If retail advertisers ever felt bottlenecked by creative production timelines, the introduction of GenAI is a game changer for retail media networks, as it can supercharge scalable creative production. This is where retailers can benefit from high volumes of high-quality, personalised content—at speed, at scale, while ensuring efficient customisation across multiple formats and channels, with ease.
Imagine targeting multiple customer segments or running a multi-product campaign — a big studio team will not be needed to scale it. GenAI helps tailor creative assets to context, audience, and even mood. It becomes a collaborative creative assistant, helping one stay relevant without compromising speed or quality. Think fresh creatives adapted for different ad variations and placements, across apps, websites, in-store, email, and more, in mere minutes. Or reimagine hyper-personalised copies, visuals, and product selections that can excite shoppers with dynamic ads tailored based on their lifestyle choices.
Dynamic Creative Optimisation, for example, generates dozens of ad variations, whether that is copy, imagery or video, serving the best-performing creative to each user segment, and learning which formats convert best over time, resulting in less manual A/B testing, faster iteration, and far more personalisation.
A more autonomous, insight-rich, and ROI-focused environment to power shopper intelligence
As marketers stepping into an exciting new era, one where precision, performance, and personalisation come together, with AI, there is no longer guesswork about what works.
Machine learning models predict seasonal demand patterns, category trends, and optimal times and channels to activate campaigns. Campaigns are optimised in real time, using real shopper data, in a closed-loop system that shows exactly how media spend drives sales.
Moreover, with GenAI, there is no limit on bandwidth when it comes to content. Marketers can personalise messaging at scale, adapt creative on the fly, and focus more on the strategy while the tools handle the complexity of execution.
In retail media, this can be effective, for example, when AI can predict shopper purchase cycles, such as for grocery items, and serves ads at just the right time. In another example, AI can identify which products benefit most from ads, and in turn reduce investments on low-performing SKUs. With AI, systems can predict ideal audiences based on behavioural and purchase history.
The result? Marketers gain a more autonomous, insight-rich, and ROI-focused environment. It also elevates their role, from managing execution to orchestrating strategy, supported by intelligent systems that handle tedious complexity.
AI-powered RMNs are not simply enhancements, but rather, a strategic enabler for retailers and brands looking to stay competitive at speed and scale through precise targeting, personalised creative, real-time optimisation, and measurable ROIs. We can expect next-generation RMNs to take that to unprecedented levels as smart, adaptive ecosystems evolve to blur the lines between commerce and media by blending cutting-edge AI and automation to power shopper-first experiences of the future.
References:
1 Retail Media Forecast Report Update, eMarketer, 29 January 2025
2 Future-Proofing APAC Advertising: Key Ad Tech Trends for 2025, DoubleVerify, 25 April 2025
3 SEA retail media ad spend projected to grow 11% by 2030, WARC, 29 April 2024