Designing a Data Operating Model for Enterprise AI Scale

AI programs often stall not because of model capability, but because data ownership, governance, delivery, security, and measurement are fragmented. The article explains how a Data Target Operating Model aligns business strategy, organizational roles, governance, technology architecture, engineering practices, compliance controls, and performance metrics. It also outlines a five-stage path from maturity assessment to incremental implementation, with AI readiness as the target state. Technology and data leaders should use the model to turn isolated data initiatives into repeatable operating practices that can support analytics and AI at enterprise scale.