A mid-size industrial equipment manufacturer's CEO has publicly committed to "AI-first" operations and secured a three-year budget. Plant supervisors, however, still record inspection results on paper forms that are transcribed by hand each week, and no one owns the resulting spreadsheets. The company wants to launch an AI-based defect-prediction pilot. Which readiness dimension is the most urgent gap to close before starting the pilot?

A defect-prediction model can only be as good as the data it learns from. Hand-transcribed, unowned spreadsheets mean inconsistent, delayed, and error-prone inputs, which will undermine the pilot regardless of what else is in place. That makes data quality and accessibility the most urgent gap to close first. Pointing to leadership alignment is tempting because leadership matters in any transformation, but the stem already shows strong executive commitment and a secured budget, so reinforcing something already present doesn't address the actual bottleneck. Framing this as cultural preparedness assumes pushback that the stem never establishes; the supervisors' paper-based workflow reflects a process and tooling gap, not documented resistance to change. Blaming technical infrastructure misdiagnoses the problem as insufficient computing capacity, but the scenario gives no evidence of an infrastructure shortfall, and even unlimited compute cannot fix unreliable, hand-transcribed source data. The general principle: when assessing readiness, prioritize the dimension the evidence in the scenario actually points to, not the one that sounds most technically impressive.