Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
HIGHLIGHTS
- Traditional retail media networks (RMNs) struggle with fragmented data, rule-based media planning, and static creative execution, leading to inefficiencies in audience targeting and budget allocation.
- Vertical AI agents are rewriting the retail media playbook unlocking higher ad revenue, stronger shopper engagement, and better outcomes for brands and retailers alike.
- Businesses implementing agentic AI-driven RMNs have reported a 1.5x higher engagement rate, 1.3x increase in return on ad spend (ROAS), and a double-digit boost in ad performance through autonomous budget reallocation and dynamic and personalized advertisements.
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Game changer
Retail media networks (RMNs) are influencing shoppers’ buying decisions more than ever before.
'Strongest coffee for a snowed-in Fargo.' Generating such hyperlocal and contextual creatives on the fly driven by real-time environments are now a reality. With vertical AI agents feeding second - and third-party data into dynamic creative optimization engines, retailers can now overlay both the local weather and pin code specifics for establishing deeper and lasting connections with customers.
By seamlessly integrating advertising into the shopping journey and directly reaching out to high-intent consumers, RMNs help brands drive customer engagement and conversion. However, several retailers fail to capitalize on it, due to a lack of seamless and integrated workflow, and data with little or zero overlay of intelligence or insights. Fragmented data, rule-based systems, and manual optimization compound the problem. This outdated approach leads to data silos, inefficient budget allocations, and ad strategies that fail to adapt to real-time consumer behavior. Consequently, advertisers miss personalization opportunities and retailers struggle to synchronize campaign execution and supply chains to shifting consumer demands.
While traditional AI is often perceived as a tool assisting humans in decision-making, vertical AI agents act as strategic partners and can autonomously execute workflows with minimal human intervention. Machine agents inbuilt with predictive and generative AI can autonomously segment the audience, allocate budget to campaigns, generate creatives based on live market signals, optimize ad placements, personalize content, and adapt media planning at scale. Retailers leveraging AI agents have realized measurable improvements in efficiency, engagement, and ROI.
Cross-channel marketing synchronization
Seamless coordination of cross-channel marketing reduces ad spend and ensures consistent brand experience.
Traditionally, marketing teams have managed digital, in-store, and loyalty-driven promotions separately. This siloed approach results in inconsistent messaging, inefficient media spend, and fragmented customer experiences. Vertical AI agents integrate and synchronize consumer data across all marketing touch points—digital ads, in-store promotions, and loyalty programs—creating a real-time, unified customer profile. This ensures consistent messaging across multiple platforms, real-time updates to promotions and offers, and higher ad relevance by aligning placement with consumer intent across channels. Getting back to the coffee example, the retailer can customize price promotions and potentially replace the stock keeping unit (SKU) with a larger SKU, considering increased consumption of coffee on a snowy day and bundle other offerings like cookies and crisps by changing the in-store layout to capture demand from the coffee aisle.
By harmonizing cross-channel marketing with agentic AI, retailers are poised to realize a substantial increase in return on ad spend (ROAS) and ad performance. Further, incrementality measurement powered by agentic AI enables brands to assess the true impact of their ads by distinguishing between organic and paid conversions, improving campaign attribution and investment decisions.
Dynamic personalization of creatives
Timely and contextually relevant creatives drive higher audience engagement and reduce ad-fatigue.
But many RMNs rely on generic and static creatives designed for a broad audience. They cannot adapt in real time to consumer intent or retail dynamics such as competitor moves. This leads to lower engagement rates, message fatigue, and missed revenue opportunities.
With their ability to be highly context-aware, vertical AI agents can leverage multimodal data such as text (search queries, reviews), images (product photos, user-generated content (UGC), behavior (browsing, purchase history), and real-time signals (inventory, pricing, weather) to create dynamic shopper profiles and deliver hyper-personalized creatives. They can autonomously execute workflows to dynamically adapt ad elements—such as messaging, images, and call-to-actions—based on contextual triggers such as location, weather, time of day, traffic, and trending topics.
Imagine a user searches for ‘comfortable running shoes’ on a retailer’s app. Vertical AI agents detect wet season based on location data and dynamically update the ad with a strong CTA: "Rain or shine, run your best—get 20% off on all-weather running shoes now!". With autonomous A/B testing, machine agents generate intelligent choices for ad variations and experiment with creative variations, instantly identifying and scaling the most effective one. This ensures that the messages are fresh and highly relevant throughout the campaign life cycle.
Retailers adopting dynamic creative personalization of ads with vertical AI agents have experienced a 30% improvement in ad performance, 25% reduction in costs, and 1.5x higher engagement rates.
Autonomous budget optimization with human oversight
Traditional RMNs often allocate ad-spend based on historical performance data without factoring real-time shifts.
This rigidity results in overspending on underperforming placements and underinvestment in high-performing segments. Vertical AI agents can help optimize budgets with autonomous operations and human oversight at critical points. Imagine a beauty brand running ads on a retailer’s media network. As the temperature climbs, a vertical AI agent detects an increase in the search for summer skincare. It autonomously recommends shifting ad-spend to promote lightweight moisturizers and sunscreens while reducing budgets on promoting heavier winter creams. The marketing team receives a summary explaining the shift, with intelligent choices, and options to adjust or approve, ensuring retailers can maximize ROI while staying aligned with the brand’s seasonal strategy.
With autonomous budget optimization, retailers can achieve a 1.3x increase in ROAS, as machine agents dynamically shift investments based on live campaign data. AI-powered budget management also ensures resources are distributed efficiently for achieving campaign goals.
Opening new frontiers
Until now, most RMN strategies remained confined to retailer-owned websites, limiting audience reach.
With agentic AI, retailers can expand beyond the enterprise boundaries to target high-intent shoppers across social media, connected TV (CTV), programmatic channels, as well as external platforms like Meta and The Trade Desk, increasing ad reach while maintaining high relevancy. Agentic AI-powered shopping assistants leverage real-time consumer intent to offer highly relevant intelligent choices at the moment of engagement. For example, a leading US retailer is developing AI-powered shopping assistants capable of recommending and purchasing items on behalf of customers, reinforcing AI’s role in decision automation and media planning.
The big shift
In an increasingly competitive landscape, vertical AI agents are rewriting retail media the playbook.
They are transforming retail media networks from static ad platforms into self-optimizing ecosystems, continuously analyzing data, adjusting bids, and reallocating budgets in real time to maximize performance. This ensures campaigns stay agile, relevant, and profitable without constant manual intervention.
