Brands know that personalisation drives performance, but there is a growing gap between the experiences they think they offer and what shoppers actually encounter. This is most visible on mobile and in online marketplaces, where attention is scarce, choice is abundant and relevance determines whether a shopper engages or moves on.
To close this personalisation gap, many brands are investing in retail media – the ads and sponsored product placements that sit inside a retailer’s own digital properties. Retail media has quickly become a vital revenue stream for retailers and a major performance driver for brands, but its success hinges on personalisation at scale. This is where misconceptions about AI continue to undermine results. Too often, brands either overestimate the complexity involved or underestimate what modern systems can already do.
The reality is that, when used correctly, AI can help any retailer deliver more relevant shopping experiences, foster brand loyalty and drive incremental growth. There are three common myths standing in the way of this realisation that savvy retailers should leave behind as they look to unlock a new competitive edge in 2026.
Myth #1: AI personalisation is reserved for big, data-rich organisations
A persistent belief is that only the largest brands stand to benefit from AI-driven personalisation. Smaller and mid-sized retailers often assume that, without millions of shoppers or a giant product catalogue, AI cannot generate meaningful results for them. This leads retailers to delay investment.
In reality, modern AI systems do not depend on sheer data volume. They learn from in-session behaviour and contextual signals rather than static datasets. That means that even with modest traffic, models can infer shopper intent in real time and refine predictions continuously as new interactions occur.
The biggest advantage for retailers is speed. Instead of building large data science teams or overhauling existing infrastructure, they can deploy AI in live environments and improve through iteration. This lowers the barrier to entry and gives retailers of all sizes the autonomy to compete on experience. In 2026, AI personalisation will reward those who act early, not just those who operate at scale.
Myth #2: AI personalisation adds complexity, not clarity
Many brands assume that running AI-driven personalisation at scale is labour-intensive and requires large teams to constantly optimise campaigns. Legacy ad-tech solutions – which do require extensive manual fine-tuning – reinforce this perception, making commerce media feel complicated and costly.
Modern AI solutions are designed to handle imperfect data, learn patterns autonomously and fill gaps through contextual understanding. This directly benefits teams that are typically tasked with constant performance monitoring and manual adjustments. In fact, AI-powered campaigns have been shown to reduce management time by up to 80 percent, allowing teams to focus on strategy rather than execution.
Moreover, by outsourcing execution to AI, brands can scale personalised campaigns to a level that would be impossible to reach through human management alone. For example, when Wayfair set out to elevate its personalisation strategy, its vast catalogue – 14 million home goods marketed to 22 million customers – seemed like a barrier to AI implementation. In practice, the retailer found that AI could understand implied product relationships and shopper intent at scale, resulting in more effective recommendations and 30 percent higher click-through rates. In 2026, AI turns complexity into capability – enabling brands to deliver personalised experiences at scale with greater ease.
Myth #3: Personalisation presents a privacy risk
Concerns over compliance and the decline of third-party cookie tracking often make brands hesitant to adopt personalisation. The misconception is that delivering relevant experiences requires invasive tracking of personally identifiable information (PII). That assumption has left some retailers missing out on the performance benefits of commerce media.
Modern AI platforms require far fewer personal identifiers than legacy solutions. Recommendations can be driven by real-time session behaviour, contextual intelligence and privacy-safe IDs, rather than static profiles or PII. By analysing signals as shoppers interact with products, the system can rank what is most relevant for the next action without exposing private data. These models are fast: the most effective AI systems can make predictions within 60-80 milliseconds based on current session data and contextual signals.
By swapping invasive tracking for in-session context, brands can build trust while scaling personalised campaigns. In 2026, privacy and personalisation do not have to be in conflict. They can coexist, enabling retailers to drive revenue growth, foster loyalty and future-proof their strategies.
The 2026 playbook for retail media
Success in retail media does not depend on vast datasets or risk-forward behaviour, but on the willingness to act. Done well, personalisation turns data into context, and context into relevance – helping shoppers find what they want faster and giving every interaction genuine value.
For years, retail media has focused primarily on driving advertiser performance and incremental revenue. In 2026, retailers will increasingly elevate the shopper experience to the same level of priority, applying AI where it matters most: improving prediction relevance, creating clusters of shoppable content and enabling total page optimisation beyond what manual processes can achieve.
Retailers that start small, learn fast and refine through live testing can unlock meaningful impact long before those chasing data perfection. In commerce media, it is execution and adaptability that will separate leaders from laggards.
