Meridian Regional Airlines' predictive maintenance lead tells the steering committee that the AI initiative's success depends far more on the sensor and maintenance-log data than on which vendor's algorithm the team ultimately picks. Which CPMAI principle does this statement reflect?

CPMAI is explicitly a data-centric methodology: the outline treats data quality and availability as the primary driver of AI project outcomes, not algorithm selection. The statement reflects the principle that AI projects are data-centric, so the quality and availability of data determine success more than the specific model chosen. A predictive-maintenance model trained on incomplete or mislabeled sensor logs will underperform regardless of which vendor's engine is behind it. The model-centric claim inverts the methodology's core premise — model-centric thinking is exactly the trap CPMAI warns against, since swapping algorithms rarely fixes a data problem. The vendor-roadmap scoping claim is a distractor about vendor hype, a named AI-project-failure mode in Domain II, but it isn't what the statement describes here. The project-manager-certification claim is irrelevant scope creep; PM certification has no bearing on data quality. The exam-worthy distinction: when a PM diagnoses a struggling AI project, the first place to look is the data pipeline and data quality, not the model architecture — this is one of the most frequently tested judgment calls across both Domain II and Domain IV.