Connecting data across teams is also critical. Nikolett Vilmos, head of marketing and AI transformation at Christie’s International Real Estate for Dubai, described bringing together previously siloed government transaction data, product information, and performance analytics. Campaign dashboards only reflect choices teams have already made, she said, so combining them with market and product data helps identify opportunities those choices may have missed.
When AI comes between brands and buyers
These shifts also require new education, Magnuson noted. For example, her team has had to explain declining website traffic in a zero-click environment to stakeholders outside marketing. She explained that consumers still research and arrive with greater purchase intent, even if that research no longer happens on the brand’s site. That changes how teams across the enterprise interpret demand and model conversions.
There’s a related change in affiliate marketing. Joe McCambley, CMO at Saatva, said large language models (LLMs) now recommend products directly, so shoppers skip affiliates. To adapt, his team has focused on schema, which lets them share unlimited product, pricing, FAQ, and review details. He also stressed that consistency across platforms matters. If schema pricing doesn’t match the page, or an affiliate describes a product differently, LLMs may distrust the brand.
Manny Flores, head of performance and AI at Ipsos, added that brands are losing control over distribution, which makes relevance more important than reach. Algorithms now build audiences from real-time behavior, so insights, media, and distribution cycles that once ran separately now overlap.
