Full Answer

Strip away the marketing and an AI tool needs four things from your data. First, completeness: it must see all the relevant events, not a self-selected slice. Client-side tracking typically delivers only 60 to 70 percent of conversions after ad blockers, ITP, and consent loss, and the missing portion is biased, so a model trained on it learns a skewed reality. Second, consistency: stable identifiers and a fixed schema so a purchase always carries the same fields in the same shape, otherwise the model spends its capacity on noise.

Third, history. Predictions about lifetime value, churn, or seasonality require enough past to contain the pattern; weeks of data can't teach a model what a year of behaviour reveals. Fourth, structure it can actually query, raw events in a warehouse with clear event names and product identifiers, rather than a pre-aggregated dashboard view. Gartner's finding that only about 12 percent of organisations have AI-ready data is mostly a verdict on these four points.

Notice none of this is about the model. The same capable tool gives sharp answers on complete, consistent, historical first-party data and vague ones on partial, inconsistent, shallow data. That's why AI readiness for a WooCommerce store is really data readiness: capture events server-side so they're complete, give them a stable schema, keep the history, and store it where it can be queried. Do that and almost any decent tool performs; skip it and none of them will.