The AI data dilemma every CIO must address

The excerpt argues that IT data analysts must be retrained and data management practices revised to produce the right kind of (often messy) data for AI, and highlights the ‘AI data quality paradox’ — that production-ready clean data is rare while valuable data exists in noisy logs, sensor glitches, and inconsistent categories, requiring organizational investment and budget justification.