Full Answer

AI tools don't have judgement; they have patterns. They extrapolate from the events you give them, which means the quality ceiling of any prediction is set by the quality of the input data, not the cleverness of the model. Feed a churn or lookalike model a dataset where a third of conversions never arrived, and it will faithfully optimise for a distorted reality.

Client-side tracking is exactly that distorting filter. Between ad blockers, Safari and Brave privacy defences, and cookie-consent rejection, somewhere around 30 to 40 percent of events go missing, and they don't go missing at random, they skew toward privacy-conscious and mobile users. The model then learns that those people don't convert, and bidding follows the bias. Gartner's 2025 research found only about 12 percent of organisations have data of sufficient quality to support AI, which is why so many tools underdeliver despite capable algorithms.

Clean first-party data fixes the input. Captured server-side, it records the events client-side tracking loses; deduplicated and consistently structured, it stops double-counting and schema drift from poisoning the training set. The model then sees all your customers, not a self-selected slice. Before adding AI to a store, the honest first step is auditing whether the underlying data can actually be trusted.