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AI adoption often stalls not because of model quality, but because organizations lack data readiness. Nearly half of employers report they can’t move forward with AI due to fragmented, inaccurate, or inaccessible data. Employees echo this concern, citing low trust in data and limited access as major blockers. Data readiness means centralized access, consistent definitions, governed permissions, and workforce data literacy. Without these foundations, AI outputs feel unreliable and adoption fails. Fixing data readiness is the prerequisite for making AI usable, trusted, and valuable at scale.

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