Poor data quality currently blocks most enterprises from scaling AI adoption. While consumer tools excel, MIT Technology Review notes that corporate leaders face fragmented legacy stacks. Companies must now prioritize data cleaning and restructuring over model selection. This shift moves the bottleneck from algorithmic capability to basic data engineering for enterprise AI practitioners.