EDA is a search problem, not unlike medical diagnosis and chess. After each action, your state of knowledge changes. But even if local criteria are heuristically good, the search space in the vicinity of a decision can be enormous. The average branching factor of a search is the average number of actions feasible at each decision. EDA has a much larger branching factor.
Any statistics package permits dozens of operations on dozens or hundreds of subsets of variables.
An analyst might run any of hundreds of operations at each juncture, from simple transformations, to descriptive statistics, to model specification and analyses of residuals, and so on. The search space for EDA, even for a small dataset, is effectively infinite. AI tames intractable search spaces two ways, by reformulating a problem to have a smaller search space, and by using expert knowledge to focus attention on small pieces of the space. My student Robert St. Amant has used both techniques in a program called AIDE, for Automated Intelligent Data Exploration. It's clear that statisticians do not think of EDA as sequences of independent operations selected from menus of statistics packages.