6 Myths About AI Personalization Limiting Your Retail Media Growth

Retail media has reached a performance inflection point. Advertising budgets are flowing – over $140 billion globally each year, accounting for one in five digital dollars and a quarter of search spend. As the channel matures and advertiser options multiply, performance has become the number one reason commerce media advertisers shift spend—nearly double the importance of omnichannel buying or reaching new audiences. 

To meet those high expectations, retailers must address the gap around customer experience (CX) across their owned sites and apps. While 92% of retailers believe they deliver a personalized customer experience, only 48% of their shoppers agree. This disconnect directly impacts advertiser results. When shoppers feel frustrated by irrelevant recommendations, they are less likely to engage with ads or convert into new customers.

To truly deliver on ads personalization at scale, retailers and marketplaces must tap into artificial intelligence (AI) – and this journey doesn’t have to be as complex or time-consuming as you might think. Those thriving in commerce media’s rapidly evolving ecosystem don’t necessarily have the biggest budgets or cleanest data, but the courage to act and partner for success. Let’s look at some of the most common myths and misconceptions holding back retail media growth.

Myth #1: "Our data isn't ready for personalization."

The myth: Many retailers assume they need pristine, perfectly structured datasets before implementing AI-powered personalization. This mindset stems from traditional analytics approaches where data quality directly determined output accuracy.

The reality: Modern AI models are designed to handle imperfect data and improve through real-world implementation. Advanced transformer models can process incomplete datasets, learning patterns and filling gaps through contextual understanding.

Consider Wayfair's journey: serving over 14 million home goods products to 22 million customers, their catalog complexity initially seemed like a barrier to AI implementation. Instead of waiting for perfect data organization, they partnered with an AI-native onsite ads solution that can understand implied product relationships and user intent at scale. The result? 30% higher click-through-rates (CTRs) with dynamic recommendations that adapt to real-time shopping patterns.

The business impact: Every month spent waiting for "perfect" data represents lost revenue opportunity. Moloco’s AI-driven retail partners reach 2% of ad revenue to gross merchandise value roughly 24 months faster than even Amazon achieved the same threshold.

Myth #2: "Personalization is too manual and resource-intensive."

The myth: AI often brings to mind massive engineering teams and complex management that prevents retailers from getting started. This misconception is reinforced by legacy ad-tech vendors that require extensive manual tuning. 

The reality: Modern AI solutions automate the heavy lifting, replacing time-intensive keyword bidding and manual targeting with simple, outcomes-based approaches. 

Traditional campaign management requires teams to constantly monitor performance, adjust bids, pause low performers, and manually scale successful campaigns – an approach that consumes significant resources and introduces human delays and error risks. AI-powered personalization flips this model entirely. Teams set business objectives—target ROAS, maximum cost-per-order, or revenue goals—and AI handles the tactical execution.

The business impact: AI-powered campaigns reduce management time by up to 80%, enabling teams to focus on strategy over execution and scaling to levels impossible with human management.

Myth #3: "Personalization is risky for consumer privacy."

The myth: Concerns over compliance and cookie deprecation often stop retailers from investing in personalization. Many assume that effective strategies require invasive tracking of personally identifiable information (PII) or other potential privacy violations.

The reality: AI-powered personalization actually requires fewer personal identifiers than legacy solutions. Recommendations do not require PII or persistent cookies, but rather leverage contextual intelligence, real-time session behavior, and retailer’s privacy-safe IDs to deliver highly relevant and effective experiences.

The business impact: By swapping invasive cookies for in-session context data, retailers can future-proof their personalization strategies while driving sales and building consumer trust. 

Myth #4: "We already use AI for personalization."

The myth: Many retailers believe their current "AI-powered" solutions provide true personalization, when they're actually using basic relevancy scores, rules engines, and offline predictions.

The reality: True AI personalization requires real-time inference—making predictions within 60-80 milliseconds based on current session data, not yesterday's batch processing. These models process live signals—what the user just searched for, which products they're currently viewing, how they're navigating the site—and generate unique predictions for each impression, understanding that a customer's intent at 9 AM on Monday differs from their intent at 8 PM on Friday, even if they're the same person looking at similar products.

The business impact: Real AI performance often exceeds legacy systems by 100% or more in engagement and conversion, while adapting instantly to user behavior changes, seasonal trends, or new product launches.

Myth #5: "We've hit our ads growth ceiling."

The myth: With stiff competition and stagnant traffic, many retailers think they’ve reached the limits of growth. They fear that increasing ad loads will inevitably hurt the user experience, or that they've maximized their onsite ads inventory.

The reality: Real-time AI enables 3-5x incremental growth even for mature networks by making ads more relevant rather than intrusive. AI-powered personalization expands inventory beyond search keywords and across the customer journey—from homepage to checkout—while improving user engagement metrics. Higher relevancy means higher click rates, which unlocks more biddable impressions and ad revenue from existing traffic. 

The difference is dramatic. Yogiyo, South Korea's leading food delivery platform, thought their growth was tapped with their existing time-based ad model. Advertiser participation was low, and niche inventory went unfilled. After implementing AI that processes user events, restaurant locations, and delivery features in real time, they onboarded over 25,000 advertisers in one month and grew ad-driven GMV by 2.7x while increasing revenue by 94%.

The business impact: Higher-performing ads have a compounding effect: proven business outcomes attract more advertiser investment, which fuels greater supplier diversity and ad relevancy, which drives even greater performance and revenue growth. 

Myth #6: "More ads always negatively impact sales."

The myth: A common worry in retail is the "Faustian bargain" of sacrificing customer experience for ad monetization. Retailers worry sponsored content will cannibalize organic sales or reduce customer satisfaction.

The reality: Properly personalized ads actually improve overall sales and shopper engagement by enhancing product discovery and the overall shopping journey. As retail media analyst Andrew Lipsman notes: "The more indistinguishable sponsored ads are from organic results, the better the onsite CX and the higher the conversion rates—a win-win-win for shoppers, merchants, and brands."

The business impact: Highly relevant ads help customers discover products they might have missed through organic browsing alone, boosting revenue and sales alike. 

Moving from myth to momentum

These myths and misconceptions share a common thread: they reflect outdated assumptions about how AI personalization works in practice. The retailers breaking through growth ceilings aren't those with perfect data or unlimited resources—they started with AI-powered personalization using their existing assets and learned through implementation.

The path forward is clear: now is the time for retail media leaders to take action and start building the AI capabilities they need to drive performance, scale, and growth. 

Ready to level up your retail media strategy with the power of AI personalization? Download our retail media leader’s guide or contact us today for a free consultation.