Classically, AI as a field was all about how to encode human knowledge in a form accessible to logical inference techniques. This involved a lot of manual entering of rules, logical propositions, fact databases, etc.
The rise of statistical learning, which can benefit from a lot more un-preprocessed data, is comparatively new.
It's not that AI didn't use data before, just that the classical models relied on a lot of data being meticulously encoded by hand. As you can imagine, this was fine for academic pursuits, but real applications quickly hit the limits of what was feasible to tackle this way due the amount of (expert) labour needed. The success of the machine learning approach is largely due to the massive scaling afforded by not requiring human intervention in every individual data point.