Full Answer

AI readiness has a fast part and a slow part, and people underestimate the slow part. The fast part is infrastructure: implementing server-side tracking, defining a consistent event schema, and routing everything into a warehouse you own can be done in weeks. That work creates the container. It does not fill it.

The slow part is accumulation. Seasonality, repeat-purchase cycles, churn, and campaign effects only become legible to a model once you have many months of clean, joined data covering them. A year of history teaches a model more than a clever algorithm can extract from a thin three-week sample. Gartner's own figure that prototype-to-production averages around eight months sits on top of that data-gathering period, not instead of it, so the model work begins only once the history is already deep enough to be worth modelling at all.

This is why the timing advice is counterintuitive: the best moment to start is before you need AI at all. The store that began capturing clean data two years ago can deploy useful models today; the one that starts now will be useful in a couple of years. Time is an ingredient, not an obstacle to engineer around, so the cheapest way to shorten the wait is to start the clock early.